UVic Thesis Template - Department of Geography - University of ...

Constructing a Regional Adolescent Health and Wellness Index for British Columbia, 

Canada 

Gina Chrissy Martin 

BSc, University of Victoria, 2008 

A Thesis Submitted in Partial Fulfillment 

of the Requirements for the Degree of 

MASTER OF SCIENCE 

in the Department of Geography 

© Gina Chrissy Martin, 2010 

University of Victoria 

All rights reserved. This thesis may not be reproduced in whole or in part, by photocopy 

or other means, without the permission of the author.

Supervisory Committee 

Constructing a Regional Adolescent Health and Wellness Index for British Columbia, 

Canada 


by 

Gina Chrissy Martin 

BSc, University of Victoria, 2008 

Dr. Peter Keller, (Dean of Social Science, Department of Geography) 

Co-Supervisor 

Dr. Les Foster, (Department of Geography, and School of Child and Youth Care) 

Co-Supervisor 

ii

Abstract 


Dr. Peter Keller, (Dean of Social Science, Department of Geography) 

Co-Supervisor 

Dr. Les Foster, (Department of Geography, and School of Child and Youth Care) 

Co-Supervisor 

The purpose of this thesis is to construct an index of adolescent health and wellness for 

British Columbia, Canada, using the most recent available data. A three- round Delphi 

study is used in order to decide on what indicators to include in the index and each 

indicator’s relative weight. Spatial multi-criteria analysis (MCA) is utilized to combine 

the indicators into a single measure. The spatial MCA, technique for order preference by 

similarity to an ideal solution (TOPSIS) method was applied to the adolescent population 

as a whole and to examine male and female variation. This revealed that adolescent 

health and wellness is not experienced equally across the province. The Health Service 

Delivery Areas (HSDAs) Fraser South and Fraser North proved to have the greatest 

levels of adolescent health and wellness while the Northwest has the least. A rural/ urban 

gradient in adolescent health and wellness was revealed at the HSDA level. Male and 

female adolescents also experience health and wellness differently, with females 

achieving higher health and wellness across all HSDAs in the Province when directly 

comparing the two genders. The findings of this research are useful in informing 

discussions of resource allocation for reducing inequalities and inequities and in order to 

target future research. 

iii

Table of Contents 

Supervisory Committee.................................................................................................... ii 

Abstract.............................................................................................................................iii 

Table of Contents ............................................................................................................. iv 

List of Tables ................................................................................................................... vii 

List of Figures................................................................................................................... ix 

List of Abbreviations ....................................................................................................... xi 

Acknowledgments ..........................................................................................................xiii 

1. STUDY RATIONALE AND RESEARCH FRAMEWORK .................................... 1 

1.1 Introduction............................................................................................................... 1 

1.2 Study Area and Demography.................................................................................... 4 

1.3 Data Availability and Limitations............................................................................. 7 

1.3.1 The McCreary Centre Society Adolescent Health Survey, 2008 .................. 7 

1.3.2 The Canadian Community Health Survey 2007/2008................................... 9 

1.3.3 BC Ministry of Education School Satisfaction Survey 2007/2008 ............. 10 

1.3.4 BC Stats ....................................................................................................... 11 

1.3.5 Other Data Sources ...................................................................................... 12 

1.4 Research Goals and Questions................................................................................ 12 

1.5 Literature Review: .................................................................................................. 13 

1.5.1 Defining Adolescence.................................................................................. 13 

1.5.2 Adolescent Health and Wellness ................................................................. 14 

1.5.3 Health Indices: An Introduction................................................................... 15 

1.6 Research Framework and Structure of the Thesis .................................................. 17 

2. TOWARDS A REGIONAL ADOLESCENT HEALTH AND WELLNESS 

INDEX USING AVAILABLE DATA IN BRITISH COLUMBIA, CANADA......... 19 

2.1 Abstract................................................................................................................... 19 

2.2 Introduction............................................................................................................. 19 

2.3 Background............................................................................................................. 20 

2.3.1 Building Composite Indices......................................................................... 20 

2.3.2 Limitations of Current Indices..................................................................... 25 

2.3.3 The Delphi Technique.................................................................................. 26 

2.4 Methods................................................................................................................... 28 

2.4.1 Delphi Study Participant Sample................................................................. 28 

2.4.2 Delphi Study Design.................................................................................... 31 

2.4.3 Inter-Rater Reliability .................................................................................. 34 

2.4.4 Secondary Data Acquisition ........................................................................ 34 

2.5 Analysis................................................................................................................... 34 

2.5.1 Round One ................................................................................................... 35 

2.5.2 Round Two................................................................................................... 42 

2.5.3 Preliminary Review of Available Data........................................................ 43 

2.5.4 Round Three................................................................................................. 45 

iv

2.5.5 Inter-Rater Agreement Analysis .................................................................. 49 

2.5.5.1 Krippendorff’s Alpha-Reliability Analysis................................... 49 

2.5.5.2 Intraclass Correlation Coefficient (ICC)....................................... 50 

2.5.6 Data Selection .............................................................................................. 52 

2.5.7 Data Acquisition .......................................................................................... 61 

2.5.7.1 Computing New Variables............................................................ 64 

2.5.8 Testing For Geographic Variation ............................................................... 65 

2.5.9 Summary of Indicators................................................................................. 67 

2.6 Results..................................................................................................................... 79 

2.7 Discussion and Conclusions ................................................................................... 80 

2.8 Limitations .............................................................................................................. 81 

3. ANALYSIS OF GEOGRAPHICAL INEQUALITIES IN ADOLESCENT 

HEALTH AND WELLNESS: A SPATIAL MULTI-CRITERIA ANALYSIS 

APPROACH.................................................................................................................... 82 

3.1 Abstract................................................................................................................... 82 

3.2 Introduction............................................................................................................. 82 

3.3 Methods................................................................................................................... 84 

3.3.1 Delphi Technique for Indicator Selection and Weighting ........................... 85 

3.3.2 Weighting Criteria ....................................................................................... 86 

3.3.3 Combination Rules....................................................................................... 93 

3.3.4 TOPSIS Method......................................................................................... 103 

3.3.5 Accounting for Uncertainty ....................................................................... 105 

3.3.5.1 Accounting for Error in Data ...................................................... 106 

3.3.5.2 Accounting for Uncertainty in Weight Values ........................... 108 

3.3.6 Cluster Analysis......................................................................................... 110 

3.3.7 Male/Female Variation .............................................................................. 110 

3.4 Results................................................................................................................... 112 

3.4.1 Results of Index ......................................................................................... 113 

3.4.2 Results of Sensitivity Analysis .................................................................. 116 

3.4.3 Results of Cluster Analysis........................................................................ 119 

3.4.4 Results of Male/ Female Variation ............................................................ 120 

3.5 Discussion and Conclusions ................................................................................. 123 

3.6 Limitations and Considerations ............................................................................ 134 

4. Discussion and Conclusions ..................................................................................... 137 

4.1 Summary............................................................................................................... 137 

4.2 Study Contributions .............................................................................................. 137 

4.3 Recommendations for Future Research................................................................ 138 

References...................................................................................................................... 139 

Appendix A Ethics Approval ....................................................................................... 156 

Appendix B Email Script.............................................................................................. 157 

Appendix C Consent Form .......................................................................................... 158 

Appendix D Study Initiation Email Script ................................................................. 160 

v

Appendix E Round One Survey .................................................................................. 161 

Appendix F Round One Results Report ..................................................................... 167 

Appendix G Round Two Survey.................................................................................. 174 

Appendix H Round Three Survey............................................................................... 175 

Appendix I Matrix of Absolute Difference from the Mean....................................... 184 

Appendix J Individual Indicator Maps ...................................................................... 186 

Appendix K Cluster Analysis Dendrogram................................................................ 210 

vi

List of Tables 

Table 1: Adolescent population (12-19), 2008 ................................................................... 6 

Table 2: Summary of identified indicators, consolidated responses and number of 

participants who identified the indicator........................................................................... 39 

Table 3: Breakdown of panel participation....................................................................... 43 

Table 4: Results of round three Delphi survey ................................................................. 47 

Table 5: Results of Krippendorff's α analysis................................................................... 50 

Table 6: Results of ICC..................................................................................................... 52 

Table 7: Summary of adolescent health and wellness indicators and data sources.......... 55 

Table 8: CV of indicators under consideration for the index, n=14 ................................. 66 

Table 9: Criteria weighting methods (n=number of criteria/ indicators).......................... 87 

Table 10: Weighting of indicators .................................................................................... 90 

Table 11: Input statistical data for index........................................................................... 92 

Table 12: Combination rules for spatial multi-criteria analysis........................................ 95 

Table 13: Pearson’s correlation matrix of indicators in BCAHWI, N= 14 ...................... 98 

Table 14: Data definitions and function in the index...................................................... 101 

Table 15: Design of sensitivity analysis applied to both rank sum and rank reciprocal 

weights ............................................................................................................................ 110 

Table 16: Comparison of BC adolescent health and wellness index scores by three 

different weights ............................................................................................................. 113 

Table 17: Pearson’s correlation coefficient of the three BCAHWI score using various 

weighting schemes.......................................................................................................... 114 

Table 18: Results of sensitivity analysis for rank sum (light grey shading indicates a 

change in ranking from the original) .............................................................................. 117 

Table 19: Results of sensitivity analysis for rank reciprocal (light grey shading indicates a 

change in ranking from the original) .............................................................................. 117 

Table 20: Patterns of clusters using Ward's method of hierarchical clustering (rank sum 

weighting) ....................................................................................................................... 119 

Table 21: External factors related to BCAHWI, n =14 .................................................. 129 

vii

Table 22: Indicators related to BCAHWI, n =14............................................................ 130 

Table 23: Indicators not significantly related to BCAHWI, n =14................................. 130 

viii

List of Figures 

Figure 1: Health Service Delivery Area boundaries........................................................... 5 

Figure 2 Adolescent percent of total population 12-19 years of age, 2008 ........................ 6 

Figure 3: Map showing School District boundaries and Health Service Delivery Areas. 11 

Figure 4: Delphi study administration process (adapted from Okoli & Pawlowski, 2004) 

........................................................................................................................................... 31 

Figure 5: Participant panel by self identified position...................................................... 36 

Figure 6: Steps taken in data analysis Questionnaire 1, Section 2 (round one)................ 37 

Figure 7: Screenshot of round three surveys..................................................................... 46 

Figure 8: Screenshot of TOPSIS selection method......................................................... 105 

Figure 9: Results of BCAHWI using rank sum .............................................................. 115 

Figure 10: Results of BCAHWI using rank reciprocal................................................... 115 

Figure 11: Results of BCAHWI using equal weights..................................................... 115 

Figure 12: Results of BCAHWI using three different weighting schemes..................... 116 

Figure 13: Map of 4 clusters produced by hierarchical cluster analysis......................... 120 

Figure 14: Results of female BCAHWI, calculated using rank sum weights................. 121 

Figure 15: Results of male BCAHWI, calculated using rank sum weights.................... 122 

Figure 16: Index score for females and males when the maximum and minimum values of 

both genders are applied ................................................................................................. 123 

Figure 17: Relationship between BCAHWI (using rank sum weights) and the percent of 

the total population ......................................................................................................... 126 

Figure 18: Relationship between BCAHWI (using rank sum weights) and the population 

density rate...................................................................................................................... 126 

Figure 19: Relationship between BCAHWI (using rank sum weights) and the average 

family income, 2006 ....................................................................................................... 127 

Figure 20: Relationship between BCAHWI (using rank sum weights) and the incidence 

of low income in economic families, 2006..................................................................... 127 

Figure 21: Relationship between BCAHWI (using rank sum weights) and percent of 

students classified as rural/small town............................................................................ 129 

ix

Figure 22: Relationship between BCAHWI and % of adolescents with their own room 

......................................................................................................................................... 132 

x

List of Abbreviations 

AHP Analytical Hierarchy Process 

AHS Adolescent Health Survey 

BC British Columbia 

BCAHWI British Columbia Adolescent Health and Wellness Index 

BMI Body Mass Index 

CA Census Agglomeration 

CCHS Canadian Community Health Survey 

CMA Census Metropolitan Area 

CV Coefficient of Variation 

CWI Child Wellbeing Index 

E Expert 

GIS Geographic Information Systems 

HSDA Health Service Delivery Area 

I 1 Family Connectedness 

I 2 Freedom from Abuse 

I 3 Physical Activity 

I 4 Healthy Diet 

I 5 Freedom from Chronic Conditions (including mental health conditions) 

I 6 School Connectedness 

I 7 Good Mental Health 

I 8 Positive Peer Influences 

I 9 Positive Adult Mentors 

I 10 Adolescents Feeling They Are Good at Something 

I 11 Tobacco/ Alcohol Use of Teen Mothers 

I 12 Healthy Weight 

I 13 Literacy 

I 14 Suicide 

I 15 Illicit Drug Use 

I 16 Adolescent Pregnancies 

I 17 Community/Cultural Connectedness 

I 18 Residing Outside the Parental Home 

I 19 Educational Achievement 

I 20 Adolescent Crime 

I 21 Self Rated Health 

I 22 Tobacco Use 

I 23 Housing and Neighbourhood 

I 24 Child Welfare Contacts 

ICC Intraclass Correlation Coefficient 

MAUP Modifiable Areal Unit Problem 

MCA Multi-criteria Analysis 

MCS McCreary Centre Society 

OWA Order Weighted Average 

PHSA Provincial Health Services Authority 

xi

RAND Research and Development Corporation 

RST Rural Small Town 

SAW Simple Additive Weighting 

SD School District 

SE Standard Error 

SHIS Salutogenic Health Indicator Scale 

TOPSIS Technique for Order Preference by Similarity to an Ideal Solution 

WHO World Health Organization 

WLC Weighted Linear Combination 

xii

Acknowledgments 

First and foremost, I would like to thank my supervisors, Dr. Peter Keller and Dr. 

Les Foster, for providing advice, support and encouragement throughout the research 

process. Their experience and guidance have been invaluable to me during these past two 

years. 

I would also like to express my gratitude to the anonymous members who served 

on the panel of this study. Their time and participation formed the backbone of this 

research and their insights were crucial in the success of this project. 

Special thanks to the data providers of this research- BC Stats, BC Perinatal 

Database, those involved in the wellness mapping project at UVIC and the McCreary 

Centre Society. In particular, I would like to express my extreme gratitude to Dr. 

Weihong Chen, Dr. Colleen Poon and Dr. Elizabeth Saewyc from the McCreary Centre 

Society, for their support and advice during this process and for presenting opportunities 

for students to share their research with each other. 

Last but certainly not least I would like to thank my friends and family. I am 

particularly thankful to my best friends, Amy Lanthier, Coura Niang, and Stephanie 

Welters, for being the brightest part of my life and my partner Jed Long for his ongoing 

support. Importantly, thank you to my parents, Joseph Martin and Joanne MacDougall, 

for their unconditional love no matter what endeavour I am undertaking. I would also like 

to acknowledge my grandmother, Janet Currie, who served as a role model of kindness, 

caring and independence throughout my life. 

xiii

1. STUDY RATIONALE AND RESEARCH FRAMEWORK 

1.1 Introduction 

Up until the last decade, adolescent health inequalities have received less attention than 

those of adults and young children (Currie et al., 2008). In Canada there has been 

relatively limited research into the health of the adolescent population (Geddes et al., 

2005). During the transitional time from childhood to adult status the adolescent is 

expected to move from requiring adult monitoring to exercising self control and behaving 

in a socially responsible way (Dahl, 2008; Gaudet, 2007; Tonkin, 2005). Due to the 

distinctive health behaviours and concerns that affect adolescents, it is evident that this 

faction of the population deserves specific attention. This research seeks to create a 

composite index of adolescent health and wellness in British Columbia (BC) and to 

examine its geographical variation. 

The ecological/population approach to health asserts that the environment in which one 

lives is linked with people’s health and wellness (Hills & Carroll, 2009). It recognizes 

that individuals are embedded within social, political and economic systems that shape 

behaviours and access to resources and has been the subject of renewed attention in 

health studies (Etches et al., 2006). The focus of this approach is on population level 

change, measured with numeric indicators of health and wellness, rather than on 

individual change and interventions (Roussos & Fawcett, 2000). Health and wellness 

indicators quantify behaviours or situations of a population over time, across groups, or 

across geographical units (Corbett, 2008). These indicators are used with the goal in mind

of reducing health and wellness inequalities and improving the health and wellness of the 

population under study (Etches et al., 2006). A goal of this research is to consider the 

unique issues that contribute to adolescent health and wellness when examining 

population health. 

Furthermore, focus in population health studies has increasingly taken a wellness 

perspective rather than an illness perspective. It has been established that health research 

should strive not only to examine negative but also positive indicators of health (Bringsen 

et al., 2009). This follows from the World Health Organization (WHO) definition of 

health as “a state of complete physical, mental and social well-being and not merely the 

absence of disease or infirmity” (WHO, 1948), beginning a societal shift that moved to 

looking at health from a holistic viewpoint rather than a purely physiological one. 

Wellness definitions have varied within the literature, but commonly measure states of 

positive health on a continuum and from a holistic viewpoint. Taking a wellness 

perspective can be done by drawing on definitions of wellness to promote the generation 

and maintenance of population health (Miller & Foster, 2010). 

This study seeks to draw on the WHO’s holistic definition of health and use a wellness 

perspective in order to examine adolescent health and wellness. This perspective is 

supported by growing research that has found that, rather than an emphasis on youth 

problems, focus should be given to elements of youth that help adolescents grow up 

healthy (He et al., 2004). The media often stereotype youth as at risk. This trend of 

examining negative aspects of adolescence leads policy and decision makers to focus on 

2

problems that affect adolescents rather than supporting factors that make adolescents 

healthy and well (Cicognani et al., 2008). Positive elements of health and development 

are protective against risk taking behaviour and facilitate a healthy transition to adulthood 

(Smith & Barker, 2009). Looking at key factors of those who choose healthy lifestyles 

has been found to be important in understanding and fostering healthy populations (He et 

al., 2004; Moore et al., 2004). Although it is difficult to pinpoint what makes an 

adolescent population healthy, it is evident that looking at what makes the population 

well in addition to what makes it unwell is important in getting a complete and accurate 

picture into the lives of adolescents (Moore et al., 2004). 

It is the goal of this work to utilize geographical analysis, including visualization, to 

explore geographic variation of adolescent health and wellness using key indicators that 

are tailored to adolescents in the Province of British Columbia (BC), Canada, in order to 

create an index of adolescent health and wellness. Maps of health and disease have a long 

history of presenting data for visualization of complex geographical information for 

purposes including: hypotheses generation, surveillance, to highlight areas that appear at 

risk and to aid policy formation (Elliot et al., 2000). The power of using a population 

approach is that it lends to visualization of the similarities and differences between areas. 

Patterns can be seen and further interpreted. It has been noted that few studies examining 

adolescents have looked at multiple positive factors (Vesely et al., 2004). Indices can be 

used as a tool for combining multiple indicators of health and wellness and for ranking 

geographic areas. These composite indices have more explanatory power than single 

indicators (Boyle & Torrance, 1984; Bringsen et al., 2009; Frohlich & Mustard, 1996). 

3

The results of the analysis presented in this thesis are valuable to inform policy makers in 

regards to adolescent health and wellness in BC for use in decision making (Etches et al., 

2006; HCC, 2007). By learning from the areas with high levels of adolescent health and 

wellness, it is hoped that geographical health inequalities can be reduced and the 

Province itself can become healthier (HCC, 2007). 

1.2 Study Area and Demography 

The Province of BC is the area under study. The data are examined at the Health Service 

Delivery Area (HSDA) for which data are readily available. The disadvantage underlying 

HSDAs is that they are relatively large geographic units. Further, two of the HSDAs 

(North Vancouver Island and North Shore/ Coast Garibaldi) correspond to non- 

contiguous polygons. There are 16 HSDAs in BC. The HSDAs are administrative areas 

defined and legislated by the provincial ministries of health. They represent geographic 

areas of regional health authorities and are subject to change (Statistics Canada, 2009a). 

Figure 1 displays the area boundaries. 

4

Figure 1: Health Service Delivery Area boundaries 

In 2008, there were an estimated 442,663 people who fell into the age category of 12 – 19 

living in BC. This accounts for approximately 1 in every 10 British Columbians (10% of 

the population of BC). Table 1 shows the proportion of adolescents (age 12 – 19) in each 

HSDA in order of the proportion of the population that adolescents make up (highest to 

lowest). It is possible to see that the regions in Northern BC have the highest proportion 

of adolescents while Vancouver has the lowest. Figure 2 illustrates this same information 

visually. 

5

Table 1: Adolescent population (12-19), 2008 

HSDA # HSDA Name Population 12 - 19 years of age % of Total Population 

51 Northwest 9715 13.0 

53 Northeast 8073 12.1 

52 Northern Interior 16785 11.8 

21 Fraser East 31673 11.5 

23 Fraser South 75954 11.2 

43 North Vancouver Island 13024 11.0 

14 Thompson Cariboo 23384 10.6 

33 North Shore/ Coast Garibaldi 28795 10.5 

22 Fraser North 60885 10.4 

12 Kootenay Boundary 8122 10.4 

11 East Kootenay 8126 10.3 

42 Central Vancouver Island 26241 10.2 

13 Okanagan 34479 10.0 

31 Richmond 18606 9.8 

41 South Vancouver Island 32578 8.9 

32 Vancouver 46223 7.3 

British Columbia 442663 10.1 

(Data source: BC Stats) 

Figure 2 Adolescent percent of total population 12-19 years of age, 2008 

6

1.3 Data Availability and Limitations 

In an ideal world it would be desirable to have individual data for each adolescent in a 

study area obtained through interview, self-reporting and administrative data. Access to 

such primary data (or releasing such confidential data), clearly is not feasible or realistic. 

In reality researchers usually work with secondary data consisting of existing sources of 

data collected by agencies and other researchers as part of other and usually larger 

studies. Collections of primary data are beyond the scope of this study. 

Data sources that track and link adolescent health are limited. It has been identified that 

the availability of data sources is a challenge when examining child and adolescent health 

(Currie et al., 2008) and so without collecting primary data it is only possible to deduct 

information from a range of aggregated data sources. Boundary selection is forced by 

limitations in the data (Staines & Jarup, 2000). The following reports on secondary data 

available and accessible to incorporate into an index of adolescent health and wellness for 

BC. 

1.3.1 The McCreary Centre Society Adolescent Health Survey, 2008 

The McCreary Centre Society (MCS) Adolescent Health Survey (AHS) was undertaken 

in 1993, 1998, 2003 and 2008. These surveys of approximately 30,000 students in grades 

7 to 12 in BC schools are collected to produce statistically reliable estimates at the HSDA 

level for each grade surveyed (Saewyc & Green, 2009). The AHSs are a rich dataset that 

cover a range of topics. 

7

There are a number of considerations when using the AHS data. First, data are collected 

in schools and in the 2008 survey it was left up to each school district (SD) to decide on 

the consent procedure used prior to the survey (active or passive consent by the parents). 

Active (parental signed) consent vs. passive (parental notification) consent is argued by 

the MCS to have a significant effect on response rates to some questions in the survey 

which may impact statistical significance of subsequent analyses (Saewyc & Green, 

2009). Moreover, the MCS uses a cluster -stratified sampling design in that the data are 

collected in randomly selected classes. Consequently individual responses are clustered at 

the classroom level. Using SPSS Complex Samples software (www.spss.com), accounts 

for the cluster stratification and differential population sizes and scales estimates to the 

enrollment of each school district. 

Not all SDs agreed to participate in the 2008 AHS. Of the school districts 50 out of 59 or 

approximately 85% participated. This implies incomplete data across BC (Provincial 

Health Officer, 2008). The 50 school districts that did participate contained 92% of all 

students in grades 7 to 12 enrolled in BC public schools at the time of the survey 

administration (Saewyc & Green, 2009). Non-participation of school districts occurred in 

two HSDAs, leaving two geographic gaps in the dataset. No SDs in the Northeast 

participated. Fraser East only had two small SDs that took part, leaving this area under 

represented. In order to account for this the MCS reports combines estimates of Fraser 

South with Fraser East when reporting the estimates at the HSDA level. This is not useful 

when combining various data sources into an index, consequently Fraser East is not used. 

8

Fraser South on its own has a large enough sample size to report. Data from the AHS are 

available by gender. 

1.3.2 The Canadian Community Health Survey 2007/2008 

The Canadian Community Health Survey (CCHS) is a nationwide cross sectional survey 

undertaken by Statistics Canada that is designed to examine health outcomes at the 

HSDA level (Bell et al., 2007a; Provincial Health Officer, 2008). The CCHS targets 

persons aged 12 or older who live in a private dwelling (Statistics Canada, 2009b). 

Persons not surveyed are: those living on Indian Reserves or Crown lands, those in 

institutions, fulltime members of the Canadian Forces and residents of very small remote 

regions. Statistic Canada states that the CCHS covers approximately 98% of the 

Canadian population age 12 and over (Statistics Canada, 2009b). 

In BC, the sample size for the 2007/2008 CCHS was 14,651 for those who agreed to 

share their responses with the provinces (McKee et al., 2009). The sampling was 

designed to ensure an over-representation of those aged 12-19; if the interviewee was 12- 

15 then verbal permission had to be granted by the parents/guardians. In order to ensure 

privacy during the personal interviews or on the phone, if privacy was suspected to be 

breached by the interviewer (i.e. suspecting another person listening in) then the survey 

was coded as refusal. All items, for this age group, regarding income and food security 

were answered by parents/ guardians at the end of the survey. Approximately half of the 

surveys were conducted in person and the other half were conducted over the phone 

(Statistics Canada, 2009b). Data from the CCHS are available by gender. 

9

1.3.3 BC Ministry of Education School Satisfaction Survey 2007/2008 

Beginning in the 2000/2001 school year, the BC Ministry of Education has been 

undertaking a School Satisfaction Survey of students at the grade levels of 3/4 (grade 3 

students are only surveyed if there are no grade 4 students within the school),7, 10 and 

12. These surveys are carried out annually and are provided to all students, but 

participation is optional. Still there is consistency in the results over time, suggesting that 

there is little bias that results from participation rates. For the 2006/2007 survey there was 

a 98.9 percent participation rate of eligible public schools (Provincial Health Officer, 

2008). 

The purpose of this survey is to measure various issues relating to the school 

environment, including health and safety. These data are deemed to be a complete 

population dataset (Provincial Health Officer, 2008). Data are available at the SD level; 

there are 59 districts in BC. The districts, in some places, nest logically inside HSDAs 

while in other areas there is considerable discrepancy in boundary overlap. Figure 3 

illustrates this. 

10

Figure 3: Map showing School District boundaries and Health Service Delivery Areas 

1.3.4 BC Stats 

BC Stats has census data and BC ministry data, from various years, much of which has 

been aggregated to the HSDA level. Crime and educational information is included in 

this. These data are not available by gender. All area boundary shape files used for 

mapping in this thesis were provided by BC Stats. 

11

1.3.5 Other Data Sources 

The BC Ministries of: Healthy Living and Sport, Children and Family Development, and 

Education as well as the BC Perinatal Database have relevant data available on 

adolescents throughout the Province. 

1.4 Research Goals and Questions 

There are three main goals that guide this project: 

1. to use geographical analysis and visualization capitalizing on geographic 

information systems (GIS) capacity and other statistical analysis to examine 

adolescent health and wellness across the Province; 

2. to establish an adolescent health and wellness index; and 

3. to show geographic variations of adolescent health and wellness to inform policy 

and decision making. 

There are four major research questions that guide this project: 

1. What are the key indicators of adolescent health and wellness in BC, Canada? 

2. Can a spatial multi-criteria analysis (MCA) contribute to the understanding of the 

geographic variation of adolescent health and wellness in BC, Canada? 

3. What is the pattern of adolescent health and wellness in the Province, as 

established through a BC Adolescent Health and Wellness Index (BCAHWI)? 

4. Can this approach be applied to examine male and female patterns in adolescent 

health and wellness? 

12

1.5 Literature Review: 

A review of relevant literature is undertaken to provide a foundation for this study. 

Definitions of adolescence are briefly explored in Section 1.5.1 to support that 

adolescence is a unique timeframe in the human lifecycle and that it requires indicators 

that reflect its unique nature. An overview of contemporary adolescent health and 

wellness issues will be presented in Section 1.5.2. Section 1.5.3 will examine the use of 

indices in health research. The literature review provides a foundation to guide and 

develop the methodological and theoretical techniques used in this study. 

1.5.1 Defining Adolescence 

Clear age boundaries are helpful to provide starting points and cut offs for policy and 

research. Defining adolescence becomes particularly challenging because it is marked by 

dynamic development and has many biological and social influences (Beaujot & Kerr, 

2007; Dahl, 2008; Gaudet, 2007). There is a strong case in the literature that adolescence 

begins with the onset of puberty (CPS, 2008; Dahl, 2008; Tonkin, 2005). Typically 

puberty occurs at 12 years of age in females and 13 in males. Puberty is a period of 

significant transformation marked by rapid physical growth, onset of sexual maturity and 

sexual interests and may commence mid-childhood, sometimes as early as age 9 in girls. 

During puberty strong emotional influences affect the capacity for self regulation and 

impact decision making (Dahl, 2008; Tonkin, 2005). 

The WHO defines the years of adolescence as ages 10-19 (WHO, 2008). In BC, as set out 

by the Child, Family and Community Service Act (1996), once an individual turns the 

age of 19 they are no longer considered a child; this corresponds with BC’s legal drinking 

13

age and the age at which one can buy cigarettes (Tonkin, 2005). Of course it is 

recognized that many adolescence experiment with drinking and smoking at a much 

earlier age. The Canadian Paediatric Society (CPS) uses a definition that is less age 

focused. It defines adolescence as beginning at the onset of physiologically normal 

puberty, and ends when adult identity and behaviour are established. According to the 

CPS this period of development corresponds roughly to the ages of 10 to 19 years, which 

is consistent with the WHO’s definition. In addition, for administrative and research 

purposes, it is useful to define adolescence by middle and high school years as this group 

faces many similar challenges and issues within society (CPS, 2008). At 19 most people 

have graduated from high school. 

Due to data constraints a lower limit of age 12 or grade 7 and an upper limit of 19 or 

grade 12 were used in this study. In certain cases data did not meet this range in its 

entirety and so only a subset of this group is represented. For example the juvenile crime 

rate is only available from BC Stats for ages 12-17. 

1.5.2 Adolescent Health and Wellness 

Adolescence is recognized as a period of increased desire for independence, 

experimentation, and an aspiration to discover the world and is characterized by change, 

growth and risk (Stangler & Zweig, 2008). Many adolescents will experiment with risky 

behaviours, including unsafe substance use and sexual experimentation exposing 

themselves to health risks, while some continue on such a path of high risk behaviour 

well into adulthood thereby incrementally increasing their exposure to health risks 

(CCSD, 2008). 

14

Adolescents are often considered vulnerable or at risk of poor health and wellness 

outcomes due to increased independence from parents and social protectors along with 

increased peer influences (Chassin et al., 1988). Contemporary focus of much North 

American research has pointed out the negative elements of adolescence, such as 

unhappiness, anxiety, depression and harmful behaviours. This creates a view of 

adolescents as placing a strain on society (Moore et al., 2004; Scales, 2001). It is 

important that policy and decision makers know what types of risky health behaviours are 

prevalent, but research should also examine positive elements of adolescent health and 

wellness (Moore et al., 2004). Positive health outcomes are associated with a beneficial 

transition into adulthood as well as an enhancement of the present health and wellness of 

adolescents (Stagner & Zweig, 2008). 

1.5.3 Health Indices: An Introduction 

Indices are created by combining indicators. In order to understand how adolescent health 

and wellness varies by region it is possible to construct an index using techniques that 

draw on past studies of deprivation and health index construction. Over the last 20 years 

there have been a large number of studies that focus on indices of material deprivation 

and how they vary by area. Socio-economic deprivation indices have become a common 

tool in developed nations and are frequently used in policy making (Gatrell, 2002). These 

indices rank geographic areas using composite indices made of multiple indicators of 

socio-economic characteristics (i.e. Bell et al., 2007a; Frohlich & Mustard, 1996). Such 

indices have been shown to be a useful tool in studies of population health (Gatrell, 

2002). Also, there have been a number of health, well-being and wellness indices 

15

produced in order to examine the health of populations (i.e. Bobbit et al., 2005; Bradshaw 

and Richardson, 2009; Bringsen et al., 2009; Bradshaw et al., 2009; Foster & Keller, 

2007). 

Indicators reflect the domains (dimensions or categories) of health defined by the study. 

Each domain contains component indicators (Bringsen et al., 2009; Bradshaw et al., 

2009). In choosing appropriate indicators, domains are used to provide a framework for 

health and vary by the population group under study. The principles that should be 

aspired to for each indicator is that the indicator should: relate to the domain, measure a 

major feature in health and wellness, be up to date, be capable of being updated on a 

regular basis, be statistically robust and be available at the level under study (Bradshaw et 

al., 2009; Pencheon, 2008). Keeping with these principles, it is clear that in order to gain 

a comprehensive representation of adolescent health and wellness appropriate indicators 

must be selected (Moore et al., 2004). 

Combing the indicators can be done in many ways from a simple additive way to using 

more complex multivariate statistical methods. A score for an area is then calculated 

(Bell et al., 2007a; Bringsen et al., 2008; Gatrell, 2002). A spatial method of multi- 

criteria analysis embedded in GIS has been implemented to construct a socio-economic 

deprivation index (Bell et al., 2007a). No matter the method used in index construction, 

there are two fundamental questions that must be addressed. The first is selecting which 

of all possible indicators should be included in the index. The second is deciding on what 

weights should be assigned to each indicator (Frohlich & Mustard, 1996). 

16

The approach I am using is novel in that it: 1) incorporates a positive health and wellness 

perspective when examining adolescent health and wellness, and 2) utilizes a spatial 

MCA in the creation of the index and to observe and analyze the findings. 

1.6 Research Framework and Structure of the Thesis 

This study asks stakeholders, employed in the public sector, with expertise in adolescence 

and/or health and wellness to consider what they feel are the most important indicators of 

adolescent health and wellness and to identify each indicator’s relative importance. A 

spatial MCA then is carried out in order to create a BC Adolescent Health and Wellness 

Index (BCAHWI). 

This thesis has been structured into two distinct phases. The goal of the first phase is to 

identify the key indicators of adolescent health and wellness in BC and their relative 

weights of importance. This is done by conducting a three-round Delphi study of a panel 

of people with expertise in the field of adolescence and/or health and wellness. This 

phase is addressed in Chapter 2 of this thesis. 

The second phase of this research uses the indicators selected from the former study and 

their associated weights in order to construct a BCAHWI using the spatial MCA 

technique for order preference to the ideal solution (TOPSIS) method. This phase is 

addressed in Chapter 3 of this thesis. 

17

A summary of the research, conclusions and recommendations for future studies is 

presented in Chapter 4. 

18

2. TOWARDS A REGIONAL ADOLESCENT HEALTH AND 

WELLNESS INDEX USING AVAILABLE DATA IN BRITISH 

COLUMBIA, CANADA 

2.1 Abstract 

This research seeks to identify key indicators of adolescent health and wellness in British 

Columbia (BC) available from secondary sources. This information will then be used as a 

basis for construction of an index of adolescent health and wellness for the Province. A 

three-round Delphi study was utilized to examine what a panel of expertise feel are the 

most influential indicators of adolescent health and wellness. A review of available data 

was undertaken to identify what data could represent the indicators identified. The Delphi 

study identified 27 indicators as most influential in measuring adolescent health and 

wellness in BC. After reviewing the available data from secondary sources, 24 indicators 

were considered appropriate for inclusion in an adolescent health and wellness index. 


In order to measure adolescent health and wellness in BC it is important to identify 

indicators that are relevant to adolescents living in the Province, since what defines this 

period of human growth can vary regionally, culturally and individually. To date it has 

not been established what indicators are of most importance to researchers, service 

providers and decision and policy makers within BC. One objective of this study is to 

identify a set of potential indicators of adolescent health and wellness and determine each 

indicator’s relative importance by collaborating with a mixture of researchers, service 

providers, and decision and policy makers, in order to create a composite index of 

adolescent health and wellness for BC. The indicators and index values can then be 

19

mapped using GIS as a visualization tool to examine the pattern of adolescent health and 

wellness in BC (Jankowski & Nyerges, 2001). 

By using collaboration with stakeholders with expertise in the index construction, local 

knowledge can be utilized to identify issues that are specific to the Province (Bell et al., 

2007a; Boyle & Torrance, 1984); thus, enhancing the validity of research and joining 

together people with “diverse skills, knowledge, expertise and sensitivities” (Israel et al., 

1998). Together with indicator value weights, derived from a panel of expertise’s 

opinions, the selected indicators are used to compute a ranking of the HSDAs in BC. 

2.3 Background 

In order to establish a composite index of adolescent health and wellness it is important 

to pick the most influential indicators to populate the index and to choose appropriate 

weights for them. In order to examine these issues the appropriate literature on building 

composite indices was reviewed. The findings of this are summarized in Section 2.3.1. 

Some limitations of current indices are addressed in Section 2.3.2. Finally an introduction 

to the Delphi technique is presented in Section 2.3.3 as a method of selecting and 

weighting the relative importance of each indicator. 

2.3.1 Building Composite Indices 

The health and wellness of a population is made up of many components. Measuring it is 

a multi-attribute and evaluative issue. Multiple indicators are needed to address questions 

of spatial patterns of adolescent health and wellness. Creating an index becomes part of a 

decision process that includes the following questions: 1) which indicators should be 

20

selected for evaluation criteria, 2) who should select the indicators (Jankowski & 

Nyerges, 2001), and, 3) what is the relative importance that each indicator should have in 

the index (Frohlich & Mustard, 1996; Malczewski, 1999)? Selecting indicators and then 

combining them into a single scale are two important steps; it has been established that 

when building health indices, the indicators used must fall within different levels of 

human health and the procedure must then produce an index of cardinal values (Boyle & 

Torrance, 1984). It is the position of this study that deriving weights from a panel of 

expertise has information that can aid in an exploratory analysis of adolescent health and 

wellness in BC. 

There have been many composite indices developed to study health and wellness of the 

human population at various regions and scales. A selection of these is examined below. 

The Foundation for Child Development created a Child and Youth Well-being Index, for 

the United States. It uses equal weights for seven domains (each comprised of an unequal 

number of indicators); this influences the meaning of the index because each of the items 

in a domain with a lower number of indicators has more impact on the resulting index 

score than a domain with a higher number of indicators (Moore et al., 2008). More data 

are included for adolescents than other age groups. As a result the index more accurately 

reflects adolescence than infancy, preschool or childhood (Mitic & Leadbeater, 2009). 

This index is used to track changes in child and youth well-being over time. 

21

In a Swedish study, Bringsen et al. (2008) has developed a Salutogenic (positive factors 

that affect human health and wellness) Health Indicator Scale (SHIS) that is based on 

measuring health from a holistic view point. This scale was developed using primary 

data. The indicators fall into ten dimensions [domains] of human health (perceived stress, 

illness, energy, physical function, state of morale, psychosomatic function, expression of 

feelings, cognitive ability, social capacity and self-realization). The questions asked of 

participants had both positive and negative wording. An example of this is the indicator 

of sleep, which falls under the dimension of psychosomatic function, which is associated 

with positive wording, “slept well,” and negative wording, “had sleeping problems.” 

Both positive and negative wording reflect health as a continuum. A written 

questionnaire was delivered in which participants were asked to rate how much they 

agreed with either of the opposing statements. The index was validated by examining the 

correlation of the indicators with self-rated health and self-rated sick leave. The level of 

measurement was the individual (Bringsen et al., 2008). 

An index of wellness in BC was derived utilizing data from the CCHS 2005 in the BC 

Atlas of Wellness. The method used in developing the index was establishing areas that 

were statistically significantly higher or lower than the provincial average for 26 CCHS 

wellness indicators. The amounts of positive and negative scores were then summed to 

derive a net wellness score (Foster & Keller, 2007). The level of measurement is at the 

HSDA level. 

22

A Child Wellbeing Index (CWI) (2009) was created exclusively for children in England 

(Bradshaw et al., 2009). This index was created for the small area level (the 32,482 

Lower Super Output Areas). Of note is that it was restricted because data on children are 

largely collected by surveys which lack the robustness to be broken down to the small 

area level. It was undertaken due to the fact that during public consultation the British 

Government was called to produce separate indices for different groups of the population. 

This index includes the following seven domains: material wellbeing, health, education, 

crime, housing, environment, and children in need. Indicators were limited to data 

available at the small area units. The indicators reflect both a positive and negative 

impact on child well-being; examples are burglary rate and percentage of green space 

(Bradshaw et al., 2009). 

GIS based Order Weighted Average (OWA) Multi-criteria Analysis (MCA) was used by 

Bell et al. (2007a) to construct a socio-economic deprivation index for the Vancouver 

Census Metropolitan Area. Both OWA and MCA are well known in spatial analysis but 

have been used little in social epidemiology (Bell et al., 2007a). Municipal Health 

Officers were used in indicator selection and weighting. It was concluded from this study 

that local knowledge can play an important role within quantitative analysis in public 

health research (Bell et al., 2007a). 

Bradshaw and Richardson (2009) created an Index of Child Well-being for comparison of 

the 27 countries of the European Union, Norway and Iceland. It uses 43 indicators 

derived from administrative and survey data around the year 2006. Seven domains were 

23

then used to classify the indicators (health, subjective well-being, personal relationships, 

material resources, education, behaviour and risks, and housing and the environment). In 

this index the child rather than the parent, family or household was used as the focus of 

analysis. Indicators of present well-being are given priority on the grounds that the 

present life stage is valuable in its own terms. The inclusion of indicators that represent 

what the children think and feel reflects accordance with the United Nations Convention 

on the Rights of the Child (1990) determination, that “the primary consideration in all 

actions concerning children must be their best interest and that their views must be taken 

into account.” Average z scores were used to compute the index score. Bradshaw and 

Richardson (2009) used equal weights citing that there is no theoretical justification for 

unequal weights but also stated that there is no theoretical justification for equal weights. 

Bobbit et al. (2005) developed a County Level Index of Well-being for Larimer County, 

Colorado. This index attempts to include both “strength and problem” indicators. In 

order to weight the indicators they used stanine (standard nine) scores (data are 

standardized and then the z-scores are used to create stanine scores which range from 1 – 

9). They found that data availability, reliability and validation were the greatest 

challenges to constructing an index of this type (Bobbit et al., 2005). It was stated that the 

reliability of the index was high as they used credible sources to populate it. 

24

2.3.2 Limitations of Current Indices 

There are three limitations that are present in the majority of current health indices: 

1) Most widely used indices created to date measure negative outcomes and behaviours. 

The widespread use of socio-economic deprivation indices illustrates that current health 

measurement tends to measure aspects of ill or poor health rather than health in general 

(Bringsen et al., 2008). These measurements are needed and useful but only provide part 

of the picture. In order to complement the measurement tools that examine negative 

impacts on health, health and wellness indices can be created in order to meet both a 

health and wellness perspective thereby using indicators that measure health status and 

considers subjective experience (Bringsen et al., 2008). 

2) Different groups within the population face varying health issues which are not 

reflected in many indices that measure the population as a whole. Adolescents are part of 

a unique cohort that has many distinct health behaviours. Creating a composite index that 

combines adolescents with the population as a whole, or younger populations, therefore 

may not reflect the true health and wellness of this group. 

3) Expert opinion is often overlooked in index construction. This can leave out local 

knowledge that maximizes the opinions and expertise of key stakeholders and public 

health agents (Bell et al., 2007a). Although not common, there are examples of 

stakeholder based index construction created by Bell et al. (2007a) and Jarman (1984); 

these are both used as deprivation indices. 

25

2.3.3 The Delphi Technique 

In order to address the questions of: 1) what indicators should be included in the index 

and, 2) what are the relative weights of each indicator, the Delphi technique was used. 

The Delphi technique is considered a useful tool when evidence is limited, where 

subjective evidence can be of benefit (Vernon, 2009) and when little factual data exist or 

there is uncertainty surrounding the topic being investigated (Malczewski, 1999; Syed et 

al., 2009; Vernon, 2009). This technique is fitting to the questions at hand as it has been 

established that measuring health and wellness of humans is an “inexact and changing 

science” (Millar & Hull, 1997). It has been deemed a strong methodology for answering 

questions based on expertise from a panel of selected participants (Okoli & Pawlowski, 

2004; Malczewski, 1999). 

The Delphi technique was originally developed by the RAND (Research and 

Development) Corporation in order to help structure communication and decision making 

around complex issues (Beech 1999, Okoli & Pawlowski, 2004; RAND, 2009; Syed et 

al., 2009; Vernon, 2009). It uses a series of questionnaire rounds to illicit and distribute 

information to and from a panel of members with expertise on the topic under study 

(Beech, 1999; Hanafin & Brooks, 2005). The technique has the advantage of being a 

relatively quick and inexpensive way of gathering expert opinions thereby bringing 

together a wide range of experience, with the goal to reach consensus (Beech, 1999; 

Hanafin & Brooks, 2005; Normand et al., 1998; Okoli & Pawlowski, 2004; Syed et al., 

2009; Vernon, 2009). 

26

Although there is flexibility and variation in how the Delphi technique is implemented 

there are four characteristics that typify a Delphi study: 1) a panel of expertise (defined 

by the context of the study), 2) anonymity, 3) iterations with controlled feedback 

(continuation of surveys past one round that are aggregated and analyzed with the 

feedback controlled), and 4) statistical group response (statistically summarizing each 

item under consideration), which allows panel members to compare their responses to 

that of the group collective in anonymity (Normand et al., 1998; Ospina et al., 2007; 

Vernon, 2009). Several variations of the Delphi technique exist; one type is a ranking 

Delphi which elicits a weight based on the comparison of variables (Normand et al., 

1998; Okoli & Pawloski, 2004). This lends itself to answering the question of what 

relative importance each indicator should have in the index (Frohlich & Mustard, 1996; 

Malczewski, 1999). 

There are many benefits to using the Delphi technique: 1) it is able to bring together the 

opinions of a collection of expertise, 2) it is flexible in its design, 3) the anonymity of this 

technique can minimize the dominance of one or more participants or group pressure 

allowing freedom of expression and equality of opinions (Dalkey, 1969; Normand et al., 

1998; Vernon, 2009), and 4) the outcomes of the study are derived from information 

obtained from participants in a collaborative process (Jankowski & Nyerges, 2001). 

27

2.4 Methods 

A three round Delphi study was conducted from July, 2009 to December, 2009. The 

study was reviewed and approved by the Human Research Ethics Board at the University 

of Victoria (Certificate # 09-194) (Appendix A). An outline of the participant sample of 

the Delphi study is presented in Section 2.4.1. The methods used in this study are 

presented in Section 2.4.2. Section 2.4.3 summarizes the means to which reliability of the 

results were investigated. Section 2.4.4 discusses acquiring secondary data to populate 

the index. 

2.4.1 Delphi Study Participant Sample 

The panel was selected with the goal of utilizing local knowledge to obtain age 

appropriate indicators that are place-specific to issues pertaining to adolescents in BC. 

The Delphi technique does not require a statistical sample but rather that the panel must 

be qualified in that they have a deep understanding of the issue under investigation (Okoli 

& Pawlowski, 2004). The foremost criterion is that participants are qualified and 

knowledgeable of issues pertaining to adolescence and/or health and wellness. It is 

therefore deemed that the individual is able to identify factors that are indicative of 

adolescent health and wellness based on this experience and expertise. A broad definition 

of a person with expertise was utilized in order to obtain a range of experience and 

background within the panel. The definition of a person with expertise for the purpose of 

this study is: An individual who is experienced and qualified in the field of youth and/or 

health and employed in the public sector within the Province of BC and has been 

employed within more than one public sector organization over their career. Their 

28

expertise may be drawn from practice as a decision maker, researcher or service provider 

(Hanafin & Brooks, 2005). This study uses the rationale that professional knowledge, 

which may also include privileged information, places one in an expert position (Vernon, 

2009). This position is utilized to identify indicators of adolescent health and wellness 

specific to BC. 

In order to get a large breadth of knowledge it was decided to obtain participants with 

various experiences. A heterogeneous panel (employed in several different areas of the 

public sector) was considered advantageous in order to gain a wider understanding of 

adolescent health and wellness than could be addressed with a homogeneous panel 

(Vernon, 2009). In group decision making, heterogeneous groups have been found to be 

more creative than homogeneous ones (Okoli & Pawlowski, 2004). There is no 

prescribed guideline to the number of members in a panel when using the Delphi 

technique (Vernon, 2009). From the experience of past studies 10 – 18 participants on a 

panel is the recommended number (Okoli & Pawlowski, 2004) and this range was 

decided as the goal for this study. 

Twenty-one people were asked to participate in the study. Those asked to participate 

were from the Provincial Government of BC ministries of: education, children and family 

development, health services, healthy living and sport, aboriginal relations and 

reconciliation, labour and citizen’s services and the Office of the Representative for 

Children and Youth. All deal with health and/ or adolescent issues. There were also 

participants working in the non-government public sector in the field of adolescent 

29

community services or health promotion. Public sector employees are exposed to both 

professional and privileged knowledge and are employed to work towards the public 

good, and so were reasoned to be the most appropriate panel for this study. 

Each participant was contacted by email and invited to participate in the study (Appendix 

B). Email was chosen as the medium because it is quick, cost effective and responses are 

sure to be legible (Hanafin & Brooks, 2005; Okoli & Pawlowski, 2004; Syed ey al., 

2009). Access to email may be seen as a biasing factor (Okoli & Pawlowski, 2004), but 

due to the fact that all participants are employed at agencies requiring access to email, it 

is not a source of bias. At this time the invited participants were also encouraged to 

clarify any questions or issues they had pertaining to the study. Of the 21 invited 

participants 19 accepted the invitation to participate and signed a Letter of Consent 

(Appendix C) (90% response rate). This exceeded the criterion of obtaining between 10 

to18 participants but it was decided better to overshoot the participant population to 

account for possible attrition through subsequent rounds of surveys. Reminders were also 

sent out to those who had not responded by a certain date at various stages in the study. 

Reminders have been shown to aid in achieving a high response rate in a Delphi study 

(Syed et al., 2009). 

30

2.4.2 Delphi Study Design 

This study followed the steps of a ranking type Delphi (Okoli & Pawlowski, 2004). 

Generally these steps are: 1) brainstorming the important indicators, 2) narrowing down 

the list of indicators, and 3) providing a rank for the indicators (Okoli & Pawlowski, 

2004). Figure 4 summarizes the steps taken in this research. 

Figure 4: Delphi study administration process (adapted from Okoli & Pawlowski, 2004) 

Fitting with the traditional Delphi technique it was deemed that an open-ended question 

was best to begin the brainstorming process of the Delphi study in round one (Vernon, 

2009). Although some studies begin with a list of indicators upon which each panel 

member is asked to remark (Vernon, 2009), this was ruled out as the possibilities of 

31

indicators are large and could be overwhelming to the study participants. For instance, in 

a 2008 report entitled “Population and Public Health Indicators for British Columbia” 

(2008), prepared by The Population and Public Health Evidence and Data Expert Group 

on behalf of The Provincial Health Services Authority (PHSA), there were 246 public 

health indicators identified for the general population of BC (PHSA, 2008). 

When developing an index to be used in general applications it is up to the researcher to 

formulate the definition of health and wellness so that the indicators are selected in 

accordance with this definition (Boyle & Torrance, 1984). The following short 

definitions of adolescence and health and wellness were sent out via email (Appendix D) 

to ensure that all participants considered the same definitions when selecting their 

indicators and that a health and wellness perspective rather than an illness or ill health 

perspective would be considered in the indicator selection: 

Definition of Adolescence: The Canadian Paediatric Society (CPS) defines adolescence as 

beginning at the onset of physiologically normal puberty, and ending when adult identity 

and behaviour are established. According to the CPS this period of development 

corresponds roughly to the period between the ages of 10 and 19 years, which is 

consistent with the World Health Organization’s definition (WHO, 2008). For 

administrative and research purposes, it can be useful to define adolescence by middle 

and high school years as this group faces many similar challenges and issues within 

society (CPS, 2008). 

32

Definition of Health and Wellness: Increasingly, focus in population health studies has 

taken a wellness perspective rather than an illness perspective (Bringsen et al., 2009; 

Foster & Keller, 2007; PHAC, 2008). In 1948, the WHO defined health as “a state of 

complete physical, mental and social well-being and not merely the absence of disease or 

infirmity” (WHO, 1948). This began a societal shift to look at health from a holistic view 

point rather than a purely physiological one. Wellness definitions have varied within the 

literature but they tend to measure states of positive health on a continuum and from a 

holistic view point (Miller & Foster, 2010). It is the goal of this study to draw on the 

WHO’s holistic definition of health and use a wellness perspective. 

In the second round participants were asked to validate the results of the first round 

(Appendix E). Then in the third (final) round each participant was asked to identify, using 

a Likert scale, which indicators they feel are “very influential” (5) to “not very 

influential” (1). Participants could also answer “unsure” if they were uncertain of the 

importance of an indicator. (Appendix H). Indicators that had a group mean of 4 or more 

were deemed an influential indicator and retained for use in the index. This value was 

decided as the cut off a priori. The aggregate response scores are used in the next phase 

of this study (Chapter 3) to weight the indicators. 

There has not been an established method of what value indicates consensus or the 

number of rounds that should be used in a Delphi study (Ospina et al., 2007; Vernon, 

2009). The study ended after three rounds in order to keep the length of the study 

manageable and to reduce possibility of attrition. 

33

2.4.3 Inter-Rater Reliability 

Statistical techniques can help increase confidence in stakeholder responses by 

addressing inter-rater agreement (Bell et al., 2007b). In a past study Cohen’s kappa 

statistic was used to increase the confidence in the level of agreement of responses (Bell 

et al., 2007b). However, Cohen’s kappa is best for a sample of only 2 raters. Fleiss’ 

kappa can be used when the sample size is greater than 2 but Fleiss’ kappa is limited to 

nominal data. Cronbach’s alpha is another commonly used statistic except it is not 

appropriate for measures of rater agreement but rather it is in measuring the reliability of 

aggregate scales (Hayes & Krippendorff, 2007). In order to examine the agreement of the 

panel of expertise with ordinal data with over 2 raters, Krippendorff’s alpha and 

Intraclass correlation (ICC) were employed (Field, 2005; Hayes & Krippendorff, 2007). 

2.4.4 Secondary Data Acquisition 

After indicators have been selected by the participants, available data were then acquired 

from various data sources in BC in order to assemble the index. A review of various BC 

data sources was undertaken in order to ensure data were available and met certain 

criteria. This is addressed further below. 

2.5 Analysis 

Analysis of the three round Delphi study is presented in the following three Sections: 

2.5.1, 2.5.2 and 2.5.4. The inter-rater reliability analysis conducted at the conclusion of 

round three is presented in Section 2.5.5. Furthermore, reviews of available data are 

presented in Section 2.5.6; a preliminary review is also presented in Section 2.5.3. 

Acquiring the data is discussed in Section 2.5.7 and analysis of the geographic variation 

34

of each indicator is presented in Section 2.5.8. A brief summary of each indicator is 

included in Section 2.5.9. 

2.5.1 Round One 

In early July, 2009 (with the exception of one later acceptance) the participants were sent 

the first round of questionnaires (Appendix E). The first round questionnaire was divided 

into two sections. Section 1 presented a short series of questions pertaining to the 

participant’s background and experience. Section 2 asked the panellists to identify up to 

12 indicators that they felt are influential in measuring adolescent health and wellness in 

BC. A follow-up question asked for a brief reason why they felt that the indicator should 

be included in the list. This explanation was used in understanding and consolidating the 

various indicators and aided in classifying the indicators into domains 

[categories/themes] (Okoli & Pawlowski, 2004). 

Of the 19 participants who completed the Letter of Consent, 14 participants completed 

the round one questionnaire by September, 2009. The attrition rate was 26%. This was 

deemed acceptable as the 14 participants fell within the desired 10-18 Delphi participant 

sample size. The mean number of years each participant worked in their capacity in the 

field of adolescence and/or health was 15.6, with a maximum of 32 and a minimum of 6. 

There was a standard deviation of 8.5 years. Figure 5 shows the breakdown of the self 

identified positions of the participant panel. 

35

7% 

14% 

50% 

Figure 5: Participant panel by self identified position 

Schmidt’s (1997) guidelines for analysis of a Delphi study were utilized in analyzing the 

second section of round one. The first step is that the responses are consolidated into a 

single list. When several different terms were used for what appears to be the same issue, 

the terms were listed together and one consolidated description of the indicator was used 

(Hasson et al., 2000; Schmidt, 1997). 

To reduce the complexity of the total indicator list, the indicators were divided into 

natural groupings by the researcher based on information derived from participants 

(Boyle & Torrance, 1984). Results were consolidated by the reseacher, duplicates were 

removed and the terminology was unified (Okoli & Pawlowski, 2004). The list of 

indicators was compacted by aggregating items that appear to have high similarity and 

then categorized based on knowledge from literature and thematic analysis (Aronson, 

1994; Malczewski, 1999). These groupings are commonly referred to as domains in 

health literature. Domains vary by the population group under study. 

29% 

Decision Maker 

Researcher 

Service Provider 

Other 

36

Thematic analysis refers to the iterative process of recovering identifiable themes within 

data through the process of reading and summarization. It is an inductive process in that 

themes emerge from the data (Aronson, 1994; Fereday & Muir-Cochrane, 2006; van 

Manen, 1990). Related literature is then referred to in order to validate the themes. The 

analysis was undertaken manually by the author with consultation from the supervisors of 

this research. No software was utilized as the sample of participants was small and 

produced a manageable amount of text, this outweighed the time and cost that is 

associated with using a software to do the analysis (Bryne, 2001). Figure 6 summarizes 

the process undertaken in the analysis of the second section of the round one 

questionnaire. Of note is that one questionnaire did not contribute any indicators due to 

the fact that it did not list any specific indicators 1 ; but, by acknowledging a response the 

participant was retained in the panel. 

Step 1: Compile a list of all Questionnaire 1, Section 2 indicators with attached explanation 

Step 2: Group each indicator into a broad theme that emerges from the data inductively 

(iteration 1) and repeat (iteration 2 & 3) 

Step 3: Create sub-themes, refine themes at each of the iterations, which establish each 

indicator based on indicator title and associated explanation (iteration 4-9). After 3 

subsequent iterations yield the same result then it is deemed that nothing new will be learned 

from subsequent rounds. 

Step 4: Send results back to participants back for validation (Round 2) 

Figure 6: Steps taken in data analysis Questionnaire 1, Section 2 (round one) 

1 This participant offered many thoughts on indicators and their pros and cons as well as examples in the form 

of past experience rather than a list of indicators 

37

After nine iterations of thematic analysis 62 indicators were identified, with 19 being 

identified by 3 or more participants. To minimize researcher intervention all indicators 

were kept in the list even if they occurred only once (Hasson et al., 2000). At this point a 

results report was sent back to the participants for validation (Schmidt, 1997). Table 2 

presents the results of round one. 

38

Table 2: Summary of identified indicators, consolidated responses and number of 

participants who identified the indicator 

Domain Indicator Consolidated Responses 

General Health 

Physical Activity physical activity/exercise amount/ involvement in 

recreation or sporting activities/ physical development 

Healthy Weight healthy weight/obesity/ appropriate weight & height 

for age/ overweight rate of grade 10/12 students/ BMI 

or waist circumference 

Healthy Diet healthy diet/ healthy eating/ fruit and vegetable 

consumption/food choices/ nutrition/ food security 

Freedom From Chronic 

Conditions 

free of chronic disease/ chronic conditions 

Self Rated Health self rated health 

Screen time time spent on computers or alone/ in front of display 

terminals; TV, computer or games 

Time for leisure extra curricular activities/ leisure time 

Freedom From Allostatic 

Load 

freedom from allostatic load 

Good Nutritional Knowledge good nutritional knowledge 

Health Literacy health literacy 

Oral/ Dental Care oral/ dental care 

Physical and Mental Health 

Admission Contacts 

physical and mental health admission contacts 

Relationships Family Connectedness family connectedness/ loving supportive family/ 

family functioning/ relationship with parents, guardian 

& siblings/ social development and sense of belonging 

(family/extended) 

Positive Peer Influence positive peer relations/ influence of peer pressure 

/positive and supportive peer groups/ social 

development and sense of belonging (peers) 

Residing Outside Of Parental number of children living out of parental home/ youth 

Home 

custody rates 

Single Parent Families single parent families 

Number of Children Living 

With Lone Female Parents 

number of children living with lone female parent 

Child Welfare Contacts child welfare contacts 

Supportive Relationships supportive relationships 

Positive Adults Mentors positive adult mentors/ adults to talk to 

Collaboration Between Peers, 

Family, Schools and 

Communities 

strong links between peers, family, schools and 

communities 

39 

Number of 

Participants 

who 

Identified the 

Indicator 

(out of 14) 

8 

7 

6 

2 

2 

2 

2 

1 

1 

1 

1 

1 

9 

4 

2 

1 

1 

1 

1 

1 

1


Community Community/Cultural 

Connectedness 

community/cultural connectedness/ family culture and 

religious experience/ social development and sense of 

belonging (work and broader community) 

Civic Engagement civic engagement/ community involvement/ social 

supports/ meaningful youth participation 

Safe Place To Be safe places to be/ safe environment/ youth safety/ 

emotional and physical safety 

Available Resources and community resources available/opportunities for 

Opportunities for 

Engagement 

engagement 

Housing and Neighbourhood physical environment (housing & neighborhood) 

Rural/Urban Index rural/urban index 

Education Educational Achievement education/ educational achievement/ high school 

graduation rate/ school completion rates/School 

achievement/ success 

School Connectedness school connectedness/ social development and sense of 

belonging (school) 

Literacy English 10 provincial exam pass rate/literacy 

Appropriate Age at School appropriate age at school 

Diverse Educational 

Foundation and Opportunities 

diverse educational foundation & opportunities 

Intellectual Development intellectual development 

Foundation Skills 

Assessment Scores 

foundation skills assessment scores 

Substance Use Tobacco Use tobacco/smoking/rates including tried in the last 12 

months 

Alcohol Use alcohol (amount and frequency)/use/ later debut for 

alcohol/personal health and wellbeing (trying alcohol) 

Illicit Drug Use amount & frequency of drug use/ illicit drug use, 

including have tried marijuana/ substance use( drug use 

rates)/personal health and wellbeing (trying drugs) 

Tobacco/Alcohol Use of 

Teen Mothers 

tobacco/alcohol use of teen mothers 

Free of Substance Misuse free of substance misuse 

Prescription Medication Use prescription med use 

40 


Participants 

Who 


Indicator 


8 

4 

4 

2 

1 

1 

7 

5 

2 

1 

1 

1 

1 

7 

6 

5 

1 

1 

1


Behaviour & 

Safety 

Injury Rates injury rates/child injuries/free of avoidable 

hospitalization through injury/ preventable injury rates 

Adolescent Pregnancies number of teen pregnancies/teen non-pregnancy 

rate/birth rate/healthy birth outcomes 

Adolescent Crime youth crime rate/ no interaction with the justice 

system/conflicts with the law 

Freedom from Abuse domestic violence/rate & incidence of child 

maltreatment 

Later Sexual Debut later sexual debut/early sexual activity 

Hospitalization Rates of 

Children/ Youth 

hospitalization rates of children/ youth 

Material Material Wellness material wellbeing/average family income/ family 

income level and economic status/ percent of families 

on income assistance/ number of children in low 

income families/ family poverty/neighbourhood 

location and median income level 

Capacity to Earn or Access 

Income 

capacity to earn or access income 

Mental Health Adolescent Suicide youth suicide rates/ freedom from suicide ideation 

Self Esteem self esteem/ opportunities to build self esteem 

Good Mental Health Good mental health/ psychological and emotional 

development 

Self Efficacy self efficacy/ skill building and mastery 

Feeling Good at Something feeling good at something 

Free of Mental Health 

Conditions 

mental health problems/chronic conditions 

Have Communication Skills have communication skills 

Other First Nation's Population first nations/ proportion of aboriginal population 

Early Childhood 

Development 

early childhood development 

Infant Mortality Rate infant mortality rate 

Life Expectancy at Birth life expectancy at birth 

Low Birth Rate low birth rate 

Proportion of Childhood 

Attendance in Early 

Childhood Education 

Programs 

proportion of childhood attendance in early childhood 

education programs 

41 


Participants 

Who 


Indicator 


Healthy Birth Outcomes healthy birth outcomes 1 

5 

5 

3 

3 

2 

1 

6 

1 

3 

2 

2 

2 

1 

1 

1 

2 

1 

1 

1 

1 

1

2.5.2 Round Two 

In the second questionnaire participants were asked to verify the results from round one. 

Each participant was sent a summary report (see Appendix F) and asked to verify the first 

round findings (Schmidt, 1997). This was done using survey monkey online survey 

software (www.surveymonkey.com). Each participant was asked whether they “verify the 

results of the round one analysis” If they replied “no” they were asked to comment either 

in the comment box on the survey or to request the summary report document in word 

format so that they could make changes on the document. 

Two weeks were given for this round. Of the 14 individuals 12 responded by the end of 

the two weeks, two participants responded late and one participant made comments, via 

email. These comments mainly had to do with the naming of the domains 2 . However, as 

these comments only were made by one participant, the theme names identified in the 

round one analysis were left for round three. These domains were only used to keep the 

number of indicators under consideration manageable. It can realistically be expected that 

in human health an indicator could fall into more than one domain, because of this each 

individual indicator in the composite index is weighted individually. One item needed to 

be changed to reflect a participant’s opinion; “low birth rate” was changed to “low birth 

weight rate.” This was identified by contacting the individual participant who identified 

2 In addition to that already discussed, the feedback from this participant addressed the possibility of using 

software to analyze the data from the first round. This was not done as the amount of text was manageable 

manually (Section 2.5.1 expands on this). Additionally, there was mention of indicators that this participant 

was surprised were not identified by the panel. However, it was not expressed that these indicators should 

be added to this participants list. Other comments reflected the importance of considering “social- 

ecological” influences. 

42

this indicator. The ability to ask for clarification from participants is one of the benefits of 

the Delphi technique. The attrition rate was 0% at this round (Table 3). 

Asked to 

participate 

Table 3: Breakdown of panel participation 

Agreed to 

participate 

Completed 

Round One 

(attrition rate) 

Completed 

Round Two 


43 

Completed 

Round Three 


21 19 14* (26%) 14 (0%) 14 (0%) 

* One of the responses was unable to be included for analysis as it did not follow the questionnaire format and was not specific to 

British Columbia. The participant was included in subsequent rounds 

2.5.3 Preliminary Review of Available Data 

In order to ensure the feasibility of the creation and mapping of an index that looks at 

geographic variation of adolescent health and wellness within BC, a preliminary review 

of available data was undertaken prior to sending out the last questionnaire. The CCHS 

2007/2008, AHS 2008, Ministry of Education School Satisfaction Survey 2007/2008, BC 

Stats website and the BC Perinatal Database were reviewed to identify measures of the 

indicators identified by the questionnaire participants. After careful and extensive 

searches, if an indicator did not have representative data available at the HSDA or the SD 

level, it was not included in the next round; this was done in conjunction with the 

supervisors of this research. The following 8 indicators lacked available data at the 

HSDA or SD level: 

1) good nutritional knowledge, 

2) collaboration between peers, family, schools and community, 

3) available resources and opportunities for engagement, 

4) rural/ urban index (this is an indicator that is currently being developed but was 

not ready at the time of the analysis), 

5) diverse educational foundation and opportunities,

6) intellectual development, 

7) having communication skills, and 

8) safe places to be 

All of the indicators with no data were identified by 2 or less of the panel participants, 

except safe places to be, which was identified by 4 members of the panel. There is, 

information on “feeling safe at school” but this is included in the school connectedness 

scale reported by the MCS. It came as a surprise that there were no data available to 

measure safe places to be outside of the school environment, at the HSDA or SD level. 

This should be addressed in future surveys undertaken in BC. 

Some indicators were aggregated, in order to avoid redundancy and double counting, as 

the data available included some indicators nested within others. These included: 

1) Free of substance misuse became a domain as it is covered by the indicators: 

tobacco use, alcohol use & illicit drug use 

2) Supportive relationships is covered by the indicators: family connectedness, 

positive peer influence and positive adults mentors 

3) Chronic mental health conditions is included in questions about chronic 

conditions 

4) Prescription medication use without prescription is included in illicit drug use 

5) Lone single female parent families is included in single parent families 

6) Healthy birth outcomes and low birth weight rate where combined into healthy 

birth weight 

With the removal and aggregation of 14 indicators a total of 48 indicators still remained. 

44

2.5.4 Round Three 

In the round three questionnaire (Appendix H) the participants were asked to identify 

how influential they felt each of the 48 indicators are for evaluating adolescent health and 

wellness in BC with the goal to derive weights for each indicator (Okoli & Pawlowski, 

2004). A 5 point Likert scale of “very influential” to “not very influential” was used to 

derive the indicator weights and eliminate indicators that are deemed of little influence 

(Boyle & Torrance, 1984). Participants could also choose “Unsure” as an option if they 

felt unsure of the influence of a particular indicator. Survey Monkey online survey 

software (www.surveymonkey.com) was again used. Weighting was assigned based on 

the results of the Likert scale. The Likert-type rating scale has been found in past studies 

to be comfortable psychologically for participants to perform when tested against simple 

ranking and paired comparisons (Turoff, 1975). Rating procedures are limited by the 

ability of participants to process information. Psychologically, the higher the number of 

indicators, the more difficult it is for participants to assign relative weights to them. To 

reduce the complexity of high numbers of indicators the indicators were classified into 

groups (Jankowski & Nyerges, 2001). This was done by using the domains that were 

identified in round one and verified in the round two analyses. Each domain was a screen 

in the survey (see Figure 7). 

45

Figure 7: Screenshot of round three surveys 

Reminders were sent at two week intervals to non- respondents. As of December 17 th , 

2009, all 14 participants had responded. Results of round three are shown in Table 4. 

Of the 48 indicators that participants were asked to report on 27 had a mean score of 4 or 

greater (Table 4). One participant sent comments via email 3 . 

3 Comments were given explicitly to the use of specific indicators. However, in this phase of the study a 

statistical group response was used in line with the theory of the Delphi technique. If time were permitting, 

further rounds that explored these comments would be useful in exploring, in more depth, the group 

response. 

46

Criteria 

Table 4: Results of round three Delphi survey 

Experts E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 Avg. 

Family Connectedness 5 5 5 4 5 5 5 4 5 4 5 5 5 5 4.79 

Freedom from Abuse 5 5 5 5 5 5 5 4 5 5 5 5 3 5 4.79 

Physical Activity 4 4 4 5 5 5 5 4 4 5 5 4 5 5 4.57 

Healthy Diet 

Freedom from Chronic Conditions 

(including mental health 

4 5 4 5 5 5 5 5 4 4 4 4 5 5 4.57 

conditions) 5 4 4 5 5 5 4 4 4 5 5 5 4 5 4.57 

School Connectedness 4 3 4 5 5 5 5 4 5 4 5 5 5 5 4.57 

Good Mental Health 5 5 5 5 5 5 5 3 4 4 5 5 4 4 4.57 

Self Efficacy 5 5 5 4 5 5 5 2 5 5 4 5 4 5 4.57 

Positive Peer Influences 4 5 5 4 5 5 5 4 4 4 5 3 5 5 4.50 

Positive Adult Mentors 4 5 5 5 5 3 5 4 5 4 5 4 4 5 4.50 

Adolescents Feeling They Are 

Good at Something 

Tobacco/ Alcohol Use of Teen 

5 5 5 4 5 4 5 3 5 4 4 4 5 5 4.50 

Mothers 5 4 5 5 5 5 5 4 4 4 4 Unsure 4 4 4.46 

Healthy Weight 5 4 4 5 5 5 5 4 3 5 5 4 5 3 4.43 

Literacy 5 5 4 4 5 5 4 4 4 4 4 5 4 5 4.43 

Suicide 5 5 5 5 5 5 4 4 4 5 5 1 3 5 4.36 

Self Esteem 

Illicit Drug Use (including 

prescription medication use 

5 4 5 5 5 5 4 2 5 5 4 2 5 5 4.36 

without a prescription) 4 4 5 5 5 5 4 4 4 5 4 Unsure 4 3 4.31 

Adolescent Pregnancies 

Community/Cultural 

4 4 5 5 5 5 4 4 3 4 4 3 5 5 4.29 

Connectedness 

Residing Outside of Parental 

4 4 3 5 4 5 5 4 4 4 5 3 5 4 4.21 

Home 5 3 5 4 4 4 4 4 4 4 4 4 4 5 4.14 

Educational Achievement 4 5 4 4 5 4 4 3 4 3 4 5 4 5 4.14 

Early Childhood Development 4 3 4 4 5 4 5 3 4 3 4 5 5 5 4.14 

Adolescent Crime 4 4 5 5 5 5 3 4 3 4 4 Unsure 3 4 4.08 

Self Rated Health 4 5 4 4 4 3 4 4 3 3 5 5 4 4 4.00 

Child Welfare Contacts 5 4 5 5 3 3 4 3 3 4 5 4 3 5 4.00 

47

Criteria 

Experts E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 Avg. 

Tobacco Use 2 3 5 5 5 5 5 4 4 3 4 Unsure 4 3 4.00 

Housing and Neighbourhood 4 4 4 5 4 4 5 3 3 4 4 4 5 3 4.00 

Alcohol Use 

Hospitalization Rates of Children/ 

3 3 5 5 4 5 4 4 4 4 3 Unsure 4 3 3.92 

Youth 

Capacity to Earn or Access 

3 3 4 5 5 3 4 4 3 4 4 Unsure 4 5 3.92 

Income 3 4 3 4 5 5 5 3 3 4 4 3 5 3 3.86 

Injury Rates 4 3 4 5 5 4 4 4 3 3 4 Unsure 3 4 3.85 

Oral/ Dental Care 

Foundation Skills Assessment 

5 2 4 4 5 5 3 4 3 2 4 4 3 5 3.79 

Scores 3 3 4 5 5 5 4 4 3 3 Unsure 3 4 2 3.69 

Aboriginal Population 3 1 Unsure 5 5 3 5 2 4 4 5 Unsure 5 2 3.67 

Civic Engagement 4 3 3 5 4 3 4 4 4 3 5 3 3 3 3.64 

Healthy Birth Weight 

Physical and Mental Health 

4 1 4 5 5 2 4 4 4 3 3 5 4 3 3.64 

Admission Contacts 4 4 4 4 4 4 3 4 3 4 3 2 3 3 3.50 

Time for Leisure 3 4 4 4 4 3 3 3 3 4 4 3 4 3 3.50 

Health Literacy 3 3 3 4 5 4 4 4 2 2 3 5 3 3 3.43 

Material Wellness 5 3 3 4 3 3 4 2 3 3 3 4 4 4 3.43 

Single Parent Families 2 2 4 5 4 5 4 2 3 3 2 3 5 3 3.36 

Appropriate Age at School 

Attendance in Early Childhood 

4 3 4 3 5 3 5 4 2 4 2 2 4 2 3.36 

Education Programs 3 1 4 4 4 4 5 2 4 3 3 Unsure 5 1 3.31 

Later Sexual Debut 3 3 4 4 5 3 2 3 3 3 5 3 3 2 3.29 

Life Expectancy at Birth 3 1 2 4 5 2 4 4 3 4 4 Unsure 2 4 3.23 

Hours of Screen Time 4 3 3 3 5 3 4 3 2 3 4 2 3 3 3.21 

Infant Mortality Rate 4 1 2 4 5 2 4 4 3 4 4 1 2 4 3.14 

Freedom from Allostatic Load 5 Unsure Unsure 3 4 3 2 3 3 4 Unsure 1 3 3 3.09 

48

2.5.5 Inter-Rater Agreement Analysis 

The panel of experts’ responses to the rating of the indicators is analyzed to evaluate the 

level of agreement between the participants. Both Krippendorff’s alpha and ICC are used 

to evaluate the final survey results. This is presented in the following Sections. A 

preliminary analysis of each participant’s absolute deviation from the mean showed that 

the average absolute deviation from the mean was less than 1 for all participants, 

computed using Cran’s R statistical software (http://cran.r-project.org) (Appendix I). 

2.5.5.1 Krippendorff’s Alpha-Reliability Analysis 

The results of the Delphi technique were analyzed in SPSS v. 17 (www.spss.com) using a 

macro designed specifically to derive Krippendorff’s alpha (α) (Hayes & Krippendorf, 

2007). Kripendorff’s α is a reliability coefficient used to measure the amount of 

agreement among raters (Krippendorff, 2007). The benefits of this statistical method is 

that it can be used for ordinal data, whenever there are two or more raters and in the 

presence of missing data (Hayes & Krippendorff, 2007). When α = 1 it indicates perfect 

agreement among raters and when α = 0 it indicates no reliability, equivalent to raters 

recording responses by throwing of a dice or other method of chance (Krippendorf, 

2007). 

The Krippendorff’s α result was .2163, indicating a fairly low level of agreement. 

Bootstrapping (number of bootstrapped samples = 5000) was applied to the dataset in 

order to provide a sampling distribution of α. Bootstrapping empirically generates a 

sampling distribution by taking a random sample from the available pairs of judgements, 

49

weighted by how many participants rated a given indicator. The α is calculated for each 

“resample” and the process is repeated a defined number of times to give the upper and 

lower confidence intervals (Hayes & Krippendorff, 2007). In this case the upper and 

lower confidence intervals were .1851 and .2476 at the 95% level. Krippendorff’s α does 

not provide whether observed agreement is sufficiently above chance as this null 

hypothesis is not appropriate in questions of rater reliability; rather the question is 

whether the observed unreliability is tolerable in subsequent analysis (Hayes & 

Krippendorff, 2007). Thus, although there is a low level of agreement it is greater than 

expected by chance. Table 5 shows that when treating the data other than ordinal the 

Krippendorff’s α declines. This demonstrates that although consensus was not met the 

participants’ ordinal perceptions is confirmed (Krippendorff, 2007). The analysis was run 

on the 14 rater’s evaluation of the 48 indicators presented in the round three surveys. 

Table 5: Results of Krippendorff's α analysis 

Data Treated as: Krippendorff’s α 

Nominal .0863 

Ordinal .2163 

Interval .2090 

Ratio .1805 

n=48 

2.5.5.2 Intraclass Correlation Coefficient (ICC) 

The ICC can be used in reliability studies of several raters on a set of objects [indicators] 

(Field, 2009; Shrout and Fleiss, 1979). Similar to Krippendorff’s α, ICC will approach 

1.0 when there is no variance within each indicator being rated (each rater gave the same 

rating to all indicators) and no residual variance to explain (Garcon, 2010). Before 

calculating the ICC it is important to determine the appropriate model for the data at 

50

hand; all models are based on one way repeated measures Analysis of Variance (Field, 

2005) and are outlined as follows [taken from Field (2005) and Shrout & Fleiss (1979)]: 

a) One-way random effects model is used when the order of the items is irrelevant. 

b) Two-way random model is most common when there are various raters of items 

[indicators]. 

c) In two way mixed effects model the value does not change from the above 

though the interpretation does (Field, 2005) in that it applies only to a sample of a 

study of all possible raters. 

Additionally, there are two things that are measured by ICC: 

i. Consistency examines whether the agreement is high without anchoring 

the values (i.e. if the rankings are the same but one is consistently lower 

than another). 

ii. Agreement observes differences in judgments as an important source of 

disagreement and between raters variability is seen as an important source 

of variation. 

In this study each indicator is rated by each participant and between rater disagreements 

is important in measuring the level of agreement, therefore a two-way agreement model 

was used. The ICC analysis was run in SPSS v.17 (www.spss.com). The data 

corresponded exactly to that used for Krippendorff’s α analysis. A limitation of ICC is 

that it cannot process items with missing data and therefore all indicators in which a 

participant selected “unsure” [n=12 (25%)] were not included; only 36 (75%) of the 

indicators were used in calculating the ICC. 

51

ICC can be measured in two ways: single measures and average measures. Single 

measure reliability is used to assess if the ratings of one participant are apt to be the same 

as another participant (Garson, 2010). In this study, single measure ICC is .188 

(significant at the .000 level when .000 is the test level), indicating a low level of inter- 

rater consistency on the 36 indicators. Given the low single measure ICC, it is concluded 

that different participants rate the indicators differently. The average measure uses the 

mean of all ratings as the unit of analysis. This is used “if the research design involves 

averaging multiple ratings for each item across all raters, perhaps because the researcher 

judges that using an individual rating would involve too much uncertainty” (Garson, 

2010). In this case the average measure ICC is .759, (significant at the .000 level when 

.000 is the test level) which indicates an acceptable level of inter-rater consistency on the 

average of all 36 ratings. When the average measure ICC is high it means that when 

items (in this case the indicators) “are averaged across all raters, the mean ratings are 

very stable” (Garson, 2010). It does not mean that raters agree in their ratings. Table 6 

gives the results of the ICC analysis. 

Table 6: Results of ICC 

95% Confidence 

ICC 

Intervals 

(lower, upper) 

Single Measures .184 .108, .304 .000 

Average Measures .759 .630, .859 .000 

n=36 (indicators that had 1or more “Unsure” responses were not included in this analysis) 

2.5.6 Data Selection 

Beyond the list of panel selected indicators, seven criteria used by Bobbit et al. (2005) 

adapted from Sustainable Measures (2002), was used as a guideline in the data selection 

to populate the index. These were that each indicator: 1) contribute logically to the index 

52 

Sig.

concept, 2) are understandable to the general public, 3) has not been identified as a poor 

indicator, 4) have agreement about the general direction (e.g., up is good or bad), 5) come 

from credible sources, 6) has the same measurement methodology across comparison 

sites and 7) show variability and frequency adequate to be reflective of change. 

Criteria 1 through 4 were addressed by removing indicators that did not make theoretical 

sense and subsequently doing a summary of each indicator (Section 2.5.9). Criteria 5 and 

6 were met by using data from credible sources and using the same data source for each 

individual indicator across all HSDAs within BC. Criterion 7 was met by measuring the 

variability of each indicator across the HSDAs using the coefficient of variation (CV) to 

measure dispersion around the mean and subsequently not including indicators that did 

not show variation within the Province (Section 2.5.8). 

Using these criteria, the indicator of early childhood development was removed from the 

index because although data are available, in the form of an early childhood development 

index, these do not reflect the state of adolescence in the present. Longitudinal data 

would be needed to address current adolescent’s early childhood development. These 

data are available from the National Longitudinal Survey of Children and Youth 

(Statistics Canada, 2010a) but these data are only available publicly at the provincial 

level. The decision to remove this indicator was made in consultation with the 

supervisors of this research. 

53

The remaining data for the index were acquired through the following sources: BC Stats, 

the AHS 2008, the CCHS 2007/2008 and the BC Perinatal Database. Where two 

indicators have exactly the same or highly similar data available from different sources, 

the source with the greatest geographic coverage was used. Where there were similar data 

available, the data that were most appropriate to adolescents were utilized. Where 

administrative data were available these were used above all other sources as these data 

are more robust in that sampling did not occur, although it must be noted that 

administrative data are not flawless. Table 7 gives the definition of the indicator and the 

data source along with the rationale of why the primary data source was selected rather 

than the secondary source (where applicable). 

54

Table 7: Summary of adolescent health and wellness indicators and data sources 

Domain Indicator Definition Age(s) 

General Health Physical Activity % who scored active or 

age 

moderately active on the leisure 

time Physical Activity Index 

12-19 

Healthy Weight % who have a healthy weight 

based on self reported height and 

weight 

Healthy Diet % who eat fruits and vegetables 

five or more times or servings a 

Freedom From 

Chronic Conditions 

(including chronic 

mental conditions) 

day 

% who do not have a health 

condition or disability that keeps 

them from doing some things 

other kids their age do 

Self Rated Health % who report good to excellent 

health 

grades 

7- 12 

age 

12-19 

grades 

7- 12 

age 

12-19 

Primary 

Data 

Source Year(s) 

CCHS 2007/2008 

AHS 

2008 

Secondary 

Data Source 

McCreary 

CCHS 

55 

Rationale for use of 

primary data source 

over secondary 

Complete geographic 

coverage 

Derived BMI tailored to 

different age groups 

using Cole et al.’s 

(2000) international 

standards 

CCHS 2007/2008 McCreary Complete geographic 

coverage 

AHS 2008 CCHS Question tailored to 

adolescents 

CCHS 2007/2008 McCreary Complete geographic 

coverage

Relationships 

Family Connectedness Average score on Family Connectedness 

Scale 

Positive Peer Influence % whose friends would be upset if they 

got arrested, beat someone up, carried a 

weapon for protection, got pregnant or 

got someone pregnant, dropped out of 

school, got drunk or used marijuana 

Residing Outside of 

the Parental Home 

Child Welfare 

Contacts 

Positive Adults 

Mentors 

% who reported they do not live with a 

parent or step parent most of the time 

but rather live with another adult 

(related or unrelated) or no adults 

% who reported that they had been in 

group homes, foster homes or youth 

agreements at some point 

% who felt there was an adult in their 

family who they could talk to, or an 

adult not in their family they could talk 

to if they had a serious problem 

grades 

7- 12 

grades 

7- 12 

grades 

7- 12 

grades 

7- 12 

grades 

7- 12 

AHS 2008 - - 

AHS 2008 

- - 

AHS 2008 - - 

AHS 2008 - - 

AHS 2008 - - 

56

Community 

Education 


Connectedness 

Housing and 

Neighbourhood 

Educational 

Achievement 

% who have a strong sense of belonging 

to local community 

% who answered “yes” to the question 

“do you have your own bedroom?” 

% who did NOT graduate 

This is calculated as the population 18 

years old minus the number of high 

school graduates as a % of 18 year olds. 

It is used as an indicator of the high 

school drop out rate. 

School Connectedness Average score on a School 

Connectedness Scale 

Literacy 

Average % of students who did NOT 

pass or write grade 10 English exam 

Grade 10 Provincial Exam Non- 

Completion Rate for English, is the 

percent of students enrolled in Grade 10 

who did not take or did not pass the 

provincial examination. Data are threeyear 

averages. 

age 

12-19 

grades 

7- 12 

18 

year 

olds 

grades 

7- 12 

grade 

10 

CCHS 2007/2008 McCreary Question tailored 

to general 

community rather 

than an ethnic 

group 

AHS 2008 - - 

BC Stats- 

Source: 

Ministry of 

Education 

2007/08 

- - 

AHS 2008 - - 

BC Stats- 

Source: 

Ministry of 

Education 

2005/2006- 

2007/2008 

- 

- 

57

Substance Use 

Behaviour & Safety 

Tobacco Use % presently a non-smoker age 

12-19 

Illicit Drug Use % who have never tried marijuana, 

prescription pills without a doctor's 

consent, cocaine (coke, crack), 

hallucinogens (LSD, acid, PCP, dust, 

mescaline, salvia), ecstasy, mushrooms 

(shrooms, magic mushrooms), inhalants 

(glue, gas, nitrous, whippits, aerosols), 

amphetamines (speed), crystal meth, 

heroin, injected an illegal drug (shot up 

with a needle), or steroids without a 

doctor's prescription 

Tobacco/Alcohol 

Use of Teen 

Mothers 

Adolescent 

Pregnancies 

% of mothers that did NOT have 

smoking OR alcohol identified as a risk 

factor during the current pregnancy 

grades 

7- 12 

Mental Health 

Adolescent 

Suicide 

% who have not considered suicide in the 

last twelve months 

Self Esteem % who agree or mostly agree to the 

statement “I usually feel good about 

myself 

Good Mental 

Health 

% who report good to excellent Self 

Perceived Mental health 

Self Efficacy % who agree or mostly agree to the 

statement “I am able to do things as well 

as most other people” 

Feeling Good at 

Something 

% who can think of some things they are 

really good at? 

grades 

7- 12 

grades 

7- 12 

age 

12-19 

grades 

7- 12 

grades 

7- 12 

59 

AHS 2008 CCHS Small ample 

size from 

CCHS at this 

level 

AHS 2008 - Removed - 

low levels of 

geographic 

variation 

CCHS 2007/200 

8 

- - 

AHS 2008 - Removed - 

low levels of 

geographic 

variation 

AHS 2008 - -

There were some indicators where it was more difficult to decide on what data to use. 

These are outline below: 

Cultural/community connectedness could be reflected by either AHS 2008 cultural 

connectedness scale or CCHS 2007/2008 sense of belonging. The CCHS data of percent 

of people age 12-19 who have a strong sense of belonging to the local community were 

used as these data best reflected a broader sense of community while the AHS 2008 

cultural connectedness scale asked questions that were more specific to feeling connected 

to an ethnic group. This decision was made in consultation with the supervisors of this 

research. 

Literacy was measured by the percent “provincial English grade 10 did not pass scores or 

did not write the exam.” Grade 10 was used rather than the grade 12 as there is a greater 

enrolment of students in grade 10 public schools than in grade 12, from 2005/2006 – 

2007/2008 and therefore a greater population size is used to derive the estimates (MoEd, 

2009). 

The most recent data for juvenile crime rates were not released at the time of this analysis 

so the 2004-2006 data were used. The new estimates are scheduled to be released by BC 

Stats sometime summer 2010. 

60

2.5.7 Data Acquisition 

Data were acquired from several sources. A detailed definition of each indicator is 

presented in Section 2.5.9. Below is a breakdown of where the data were acquired from 

and in some cases, how the indicators were calculated. 

BC Stats: 

• Educational Achievement 

• Literacy 

• Adolescent Pregnancies 

• Adolescent Crime 

Data from BC Stats is publicly available and was obtained from their website (with the 

exception of teen pregnancies). Data acquired on teenage pregnancies (obtained from BC 

Stats) was aggregated from Local Health Areas (LHA) to HSDAs; true rates were 

calculated. Data are provided to BC Stats from the British Columbia Vital Statistics 

Agency, Police Services, the Solicitor General and the BC Ministries of Health, 

Education and Public Safety. 

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CCHS: 

• Physical Activity 

• Healthy Diet 

• Self Rated Health 

• Community/ Cultural Connectedness 

• Tobacco Use 

• Good Mental Health 

Data for CCHS 2007/2008 were obtained from the BC Wellness Atlas Project derived 

from the share file (individual survey participants who agree to share their information 

with the provinces) data. 

BC Perinatal Database: 

• Tobacco/Alcohol Use of Teen Mothers 

Data from the BC Perinatal Database were processed by them and requested by the 

author. 

62

AHS: 

• Healthy Weight 

• Freedom From Chronic Conditions 

• Family Connectedness 

• Positive Peer Influence 

• Residing Outside of the Parental Home 

• Child Welfare Contacts 

• Positive Adult Mentors 

• Housing and Neighbourhood 

• School Connectedness 

• Illicit Drug Use 

• Freedom from Abuse 

• Adolescent Suicide 

• Self Esteem 

• Self Efficacy 

• Feeling Good at Something 

Data from the AHS 2008 survey were obtained by going directly to the MCS to gather 

data; this was done using SPSS v. 17 (www.spss.com). In order to account for the 

complex design of the sampling the Complex Sampling feature in SPSS was used. The 

Complex Sample file was already prepared by the MCS. In some cases new variables 

needed to be computed. 

63

2.5.7.1 Computing New Variables 

To measure positive peer influence a new variable was created that was a count of 

students who answered “yes” to all of the following questions: would your friends be 

upset with you if you a) got arrested, b) beat someone up, c) carried a weapon for 

protection, d) got pregnant or got someone else pregnant, e) dropped out of school, f) got 

drunk or g) used marijuana? If all of these questions were coded as missing then the new 

variable was coded as missing. A new value of “yes” was assigned to all cases where a 

student answered yes to all of these questions. 

Illicit drug use was calculated in the same way as above but included the variables of 

those who had answered “no” to having ever used marijuana, and responded “0 times” to 

having uses any of the following drugs: prescription pills without a doctor’s consent, 

cocaine (coke, crack), hallucinogens (LSD, acid, PCP, dust, mescaline, salvia), ecstasy, 

mushrooms (shrooms, magic mushrooms), inhalants (glue, gas, nitrous oxide, whippits, 

aerosol), amphetamines (speed), crystal meth, heroin, injected an illegal drug (shot up 

with a needle) and steroids without a doctor’s prescription. 

Freedom from abuse was calculated by assigning a label of “no” to those students who 

had reported “no” to having “ever been physically abused or mistreated by anyone in 

your family or by anyone else?” and “no” to having “ever been sexually abused?” In the 

survey this is defined as if “anyone (including a family member) touches you in a place 

you did not want to be touched, or does something to you sexually which you did not 

want.” This was done by taking counts of the “no” responses for these two questions and 

64

assigning a label of “no” to any variable that had a count of 2 (range 0-2). A missing 

value was assigned if both were coded as missing. 

The above is similar to how the indicator for positive adult mentors was calculated, 

except for this indicator, if a student replied “yes” to having an adult in their family they 

could talk to if they were having a serious problem or if a student replied “yes” to having 

an adult NOT in their family they could talk to if they were having a serious problem. 

This was done by taking counts of the “yes” responses for these two questions and 

assigning a label of “yes” to any variable that had a count greater than 1 (range 0-2). 

Again, a missing value was assigned if both were coded as missing. 

2.5.8 Testing For Geographic Variation 

Indicators that showed little to no variability were dropped from the general index 

aligning with Bobbit et al.’s (2005) criterion 7 that the comparison units show variability. 

The coefficient of variation (CV) (Equation 1) was used to examine and quantify the 

amount of variation across the 14 HSDAs included in the index. 

CV = σ / μ 

(1) 

Where: σ = the standard deviation and μ = the mean 

The CV is a measure of dispersion about the mean. If the dispersion is close to the mean 

then there is little variation between units. The purpose of the overall index is to compare 

the health and wellness of adolescents. If there is little to no variation between HSDAs 

then the indicator will not contribute in the index. For all 26 indicators the CV ranged 

65

from .009- .403 indicating that some of the indicators have a much greater variation than 

others. Table 8 gives the CV of each indicator displayed from highest to lowest. 

Table 8: CV of indicators under consideration for the index, n=14 

Indicator CV 

Adolescent Crime 0.403 

Teen Pregnancy 0.395 

Positive Peer Influences 0.346 

Child Welfare Contacts 0.332 

Tobacco/ Alcohol Use of Teen Moms 0.304 

Literacy 0.297 

Educational Achievement 0.248 

Residing Outside of the Parental Home 0.170 

Healthy Diet 0.161 

Illicit Drug Use 0.124 

Physical Activity 0.090 

Cultural/Community Connectedness 0.090 

Adolescents Feeling they are Good at Something 0.049 

Tobacco Use 0.047 

Housing and Environment 0.046 

Self Rated Health 0.041 

Freedom from Abuse 0.037 

Healthy Weight 0.032 

School Connectedness 0.029 

Good Mental Health 0.029 

Positive Adult Mentors 0.028 

Adolescent Suicide 0.021 

Chronic Conditions 0.020 

Family Connectedness 0.017 

Self Esteem 0.015 

Self Esteem Scale 0.014 

Self Efficacy 0.009 

A value of .015 was decided as the cutoff for indicators to not be included in the index. 

Testing showed that values at and below this CV corresponded with a range of .01 when 

the indicator values are standardized [which needs to occur when creating a composite 

index in order to make the varying units comparable (Chapter 3 addresses this further)]. It 

can be concluded that indicators with a low CV, when standardized show very little 

variation and the inclusion of these does not contribute to the index nor meet Bobbit et 

al.’s (2005) criterion that the units show variability. 

66

Two indicators that had a CV less than or equal to .015 were self efficacy and self 

esteem. From this it can be concluded that there is little geographic variation at the 

HSDA level. Self esteem and self efficacy are high in BC, ranging from 83.62-88.26% 

who agree or mostly agree with the statement “I usually feel good about myself” and 

89.94-92.44% who agree or mostly agree “they can do things as well as others”. In order 

to investigate this further, a self esteem scale created by MCS research associates that 

includes both positive and negative measures of both self esteem and self efficacy was 

examined. The scale was calculated by combining seven self esteem questions from the 

2008 AHS (including the two above). The scale produces a score that ranges from 1-4. 

The CV for this indicator is .014 and so also displays little variation across units. This 

further confirms that there is little geographic variation of high self esteem and efficacy 

within the province of BC at the HSDA level. Consequently, these two indicators were 

removed leaving 24 indicators remaining. 

A brief discussion of each of the 24 indicators that were included in the composite index 

is presented in the next section. A map displaying the variation of each indicator can be 

found in Appendix J. 

2.5.9 Summary of Indicators 


1. Physical Activity 

According to the WHO (2006), being physically active is essential to human health and 

wellness. The positive effects of physical activity also help counteract the risks associated 

with adolescence such as, tobacco, alcohol and drug use and violent behaviour. Some 

67

studies show that among adolescents, the more often they participate in physical activity, 

the less likely they are to use tobacco (WHO, 2006). Patterns of physical activity 

acquired during childhood and adolescence are more likely to be maintained throughout 

the lifespan (Kelder, 1994; WHO, 2006). Regular physical activity provides young 

people with physical, mental and social health benefits. These benefits are important as 

overweight and obese rates in developed nations are rising rapidly, especially among 

young people (WHO, 2006). 

To measure physical activity among adolescents the percent of those aged 12- 19 who 

scored active or moderately active in the Leisure Time Physical Activity Index was used. 

These data were available from the CCHS 2007/2008 survey. The CCHS classifies those 

who are active and moderately active from the derived daily energy expenditures, which 

in turn is derived from several questions about frequency, duration and intensity of 

participation in leisure time activities. Active is classified as an average daily energy 

expenditure of 3kcal/kg, moderately active is classified by an average daily expenditure 

between 1.5-2.9 kcal/kg. Below this cut off one is considered inactive (Foster & Keller, 

2007). 

2. Healthy Weight 

A person’s weight is influenced by a combination of nutrition and physical activity. Not 

being a healthy weight can have serious impacts on health which can affect adolescents in 

their youth and later in life (WHO, 2006). Obesity in Canadian children and youth has 

68

more than doubled in the last ten years, with an estimated 10 to 25 percent of teenagers 

now having weight problems (MoHS, 2008). 

This indicator is measured by the percent of student’s, grade 7-12, who have a healthy 

weight based on self reported height and weight. Body Mass Index (BMI), a value based 

on weight and height (kg/m 2 ), is a measurement widely used internationally. Although 

often criticized for not including body fat or fitness levels, BMI is still a useful tool to 

measure obesity (Smith et al., 2009). Special care needs to be taken in designating a BMI 

for young people below 18 years as it often changes with age (Cole et al., 2000). 

Cole et al. (2000) developed guidelines for classifying child and adolescent BMI based 

on six international surveys. The MCS research associates have developed a syntax file 

used to classify the BMI of the adolescent respondents using Cole et al.’s (2000) 

guidelines. These data are available from the 2008 AHS. 

3. Healthy Diet 

Healthy eating fosters optimal growth in children and youth and can help prevent 

nutrition related diseases (Kendall, 2003 in Foster and Keller, 2007). The percent of those 

aged 12- 19 who eat fruits and vegetables five or more “times or servings” a day were the 

data used to measure healthy diet in this study. These data were derived from CCHS 

2007/2008 data on self reported fruit and vegetable intake. Health Canada (2007) states 

that those age 9-13 should consume 6 servings while females 14-18 should consume 7 

and males in this age bracket should consume 8; the recommended amount again rises at 

the age of 19. 

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4. Freedom from Chronic Conditions 

Because adolescence is a time of physiological change and important social development, 

chronic conditions can be a major challenge both to the individual and to their family 

(Suris et al., 2004). Youth with chronic conditions have been shown to have more 

physical and emotional complaints, have higher abuse rates and be more likely to engage 

in substance use and unsafe sex (Tonkin & Murphy, 2002). Data were available from the 

AHS 2008. This indicator was measured by the percent of students, grade 7-12, who did 

not report that they have a health condition or disability that keeps them from doing some 

things other kids their age do. If the student answered “yes” they were asked to specify 

whether the disability be: a physical disability (deafness, cerebral palsy, uses a 

wheelchair, etc.); a long-term illness (diabetes, asthma, etc.); a mental or emotional 

condition (depression, eating disorder, etc.); or overweight or underweight. This 

information was not reported in the index; rather just the absence of these conditions was 

reported. For this indicator it is of particular importance to reiterate that this estimate is 

derived from a school based survey and it does not account for those with severe 

conditions who may be limited to the home or special institutions (Suris et al., 2004). 

5. Self Rated Health 

There is a general agreement that asking young people to rate their own health in surveys 

is a reliable and valid method of assessing overall health (Morgan et al., 2007). 

A longitudinal representative sample of youth in adolescence over a 6 year period (US 

National Longitudinal Survey of Adolescent Health) (1997 – 2003), showed that self- 

reported health is a valid measure of a variety of physical and emotional dimensions of 

adolescent health and wellness (Fosse & Haas, 2009). The percent of those 12-19 who 

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self reported good to excellent health are the data used for the indicator self rated health. 

These data are available from the CCHS 2007/2008. 

Relationships 

6. Family Connectedness 

Family connections have been used previously as an indicator of adolescent health and 

wellness (Barber & Schluterman, 2008; Search Institute, 2004). A positive relationship 

with parents and siblings is a positive indicator of adolescent health and well-being 

(Stagner & Zweig, 2008). Positive interactions with parents can better equip adolescents 

with the emotional resources they need to deal with stresses and problems (Ben- Zur, 

2003; Barnes et al., 2007). An adolescent family connectedness scale was developed by 

the MCS based on questions from the AHS. Eleven items were assessed in this index, 

including the extent to which students feel their family: understands them, pays attention 

to them, has fun together, how close the individual feels to their mother and father, how 

much they feel cared about by their mother and father, how much they feel their mother 

and father are warm and loving towards them, and how satisfied they are with their 

parental relationships. A higher score indicates more connectedness (Foster & Keller, 

2007). The average score (range 0-10) from the AHS 2008 for each HSDA on the Family 

Connectedness Scale for students, grade 7-12, was used as the data to quantify this 

indicator. 

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7. Positive peer influence 

During adolescents relatively less time is spent with family, than when a child, and more 

time is spent with peers (Padilla-Walker & Bean, 2009). Positive peer influence has been 

shown to deter adolescents from negative behaviour and to encourage positive behaviour 

(Brown et al., 1986). Data on positive peer influences are available from the AHS 2008. 

The percent of students, grades 7-12, whose friends would be upset if they: got arrested, 

beat someone up, carried a weapon for protection, got pregnant or got someone pregnant, 

dropped out of school, got drunk or used marijuana is used to represent this indicator. 

8. Child Welfare Contacts & 9. Residing Outside of Family Home 

In Canada, the number of children entering care has been increasing; the responsibility 

for child welfare falls to the provincial or territorial governments. Once leaving care there 

is greater risk of homelessness, substance abuse and single parenthood (Tweddle, 2007). 

Child welfare contacts are measured by the percent of students, grades 7-12, who 

reported that they had been in group homes, foster homes or youth agreements at some 

point. 

Residing outside of the family home was measured by the percent of students grades 7-12 

who reported they do NOT live with a parent or stepparent most of the time but rather 

live with another adult (related or unrelated) or no adults. 

These indicators are both minimized in the index. Data for both these indicators are 

available from the AHS 2008. 

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10. Positive Adult Mentors 

In BC, the majority of students report that they can seek support from an adult in their 

family or not in their family (Smith et al., 2009). Zimmerman et al. (2002) found that 

adolescents who self reported having mentors were less likely to smoke marijuana and 

were more positive about school. This indicator is measured by percent of students, grade 

7-12, who felt there was an adult in their family who they could talk to, or an adult not in 

their family they could talk to if they had a serious problem. These data are available 

from the AHS 2008. 

Community 

11. Community/ Cultural Connectedness 

Healthy development of youth is associated with a sense of connection to the community 

(Foster, 2005). Connectedness can refer to a feeling, such as, a sense of belonging, and 

has been correlated with positive indicators of health and negatively correlated with 

indicators of negative health (Barber & Schluterman, 2008). This indicator is measured 

by the percent age 12-19 who have a strong sense of belonging to local community. 

These data are available from the CCHS 2007/2008. 

12. Housing and Neighbourhood 

Housing and neighbourhood has been used as a domain in the Index of Child Well- being 

in Europe where several indicators were used to reflect this domain (Bradshaw & 

Richardson, 2009). The CCHS 2007/2008 survey presented a question asking for self 

satisfaction of the respondent to their housing and environment. This question was 

optional and not taken up by BC. These would have been the best data to use for this 

73

index as housing and environment are directly covered through self assessment but as 

they are unavailable for BC, the data used are percent of students in grade 7-12 that 

reported they had their own bedroom in the AHS 2008. This is a question that has been 

asked in the Health Behaviour in School-Aged Children (HBSC) Study, and was used to 

help gauge the family affluence of the respondents (Currie et al., 2008). This is similar to 

the indicator of overcrowding used in the Index of Child Well-being which is defined by 

rooms per persons in households with children. 

Education 

13. Educational Achievement 

Health during adolescence is strongly associated with educational attainment, as 

adolescence is a critical period in the educational process. Health issues may lead to 

reduced educational participation and reduced expectations of the student (Jackson, 

2009). For the purposes of this study educational achievement is measured by the 

2007/08 percent of 18yr olds who did NOT graduate. These data are available publicly 

from BC Stats. This indicator is minimized in the index. 

14. School Connectedness 

School connectedness is usually defined by youth relationship to the school (Barber & 

Schluterman, 2008). To have a sense of connectedness to school can be indicative of a 

healthy community (PHSA, 2008). A caring school climate (overall perception of the 

school by the school community members) that provides encouragement has been 

identified as a developmental “asset” that supports healthy communities (Miller, 2007; 

Search Institute, 2004). An adolescent School Connectedness Scale was developed by 

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the MCS, based on questions in the AHS 2008. Seven self reported items were assessed 

in this index, including the extent to which: students feel teachers care about them, they 

are part of their school, they are happy to be at school, they feel safe in school, they get 

along with teachers, they get along with students and feel they are treated fairly by 

teachers. A higher score indicates more connectedness (Foster & Keller, 2007). The 

average School Connectedness Score (range 0-10) for students, grade 7-12, for each 

HSDA is used in this index. 

15. Literacy 

Literacy is important to adolescents as around grade four students begin to read to learn 

rather learning to read (Chall, 2000 in Ippolito et al., 2008). Literacy is a skill that is 

drawn on often in an adolescent’s daily life and helps them mature into adults (Ippolito et 

al., 2008). The measure of literacy used in this index is the average (2005/2006- 

2007/2008) percent of students who did NOT pass or write Grade 10 English Exam. Data 

are available publicly from BC Stats. This is an indicator that is minimized in the index. 

Substance Use 

16. Tobacco Use 

Negative impacts on health from tobacco use include diseases of the circulatory and 

respiratory system as well as cancer. Being a non-smoker is a wellness advantage that has 

been shown to have regional variation across the province (Foster & Keller, 2007). 

Youth who do not smoke, less commonly engage in risky behaviour than those that do 

(Tonkin & Murphy, 2002). This indicator was measured by the percent 12-19 who 

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eported to presently being a non-smoker. These data are available from the CCHS 

2007/2008. 

17. Illicit Drug Use 

This indicator is measured by the percent of students, grade 7-12, who reported that they 

have never tried marijuana, prescription pills without a doctor's consent, cocaine (coke, 

crack), hallucinogens (LSD, acid, PCP, dust, mescaline, salvia), ecstasy, mushrooms 

(shrooms, magic mushrooms), inhalants (glue, gas, nitrous, whippits, aerosols), 

amphetamines (speed), crystal meth, heroin, injected an illegal drug (shot up with a 

needle), or steroids without a doctor's prescription. In BC, the percent of students who 

have ever tried marijuana is 30%, while a smaller percent have tried the other drugs 

[ranging from 1% (heroin and injected an illegal drug) - 15% (prescription pills)] (Smith 

et al., 2009). Drug use has been linked to disengagement in school (Smith et al., 2009). 

These data are available from the AHS 2008. 

18. Tobacco/Alcohol Use of Teen Mothers 

Slightly more than 10% of all pregnant women in BC smoked during their current 

pregnancy in the fiscal year of 2006/2007 (BCPHP, 2007). In BC in the 2006/2007 fiscal 

year, the prevalence of smoking in pregnancy was 34.0% in teenage mothers compared to 

9.8% in non-teenage mothers (BCPHP, 2007). Data to measure this indicator were 

obtained from the BC Perinatal Database which consists of data collected from obstetrical 

facilities as well as births occurring at home, attended by BC Registered Midwives, for 

over 400,000 births currently in the provincial database (BCPHP, 2007). Data for this 

76

indicator were prepared by the BC Perinatal Database and are defined as the percent of 

mothers (age equal to or less then 19, at age of delivery) that did NOT have smoking or 

alcohol identified as a risk factor during the current pregnancy, from April 1, 2007 - 

March 31, 2008. 

Behaviour and Safety 

19. Adolescent Pregnancies 

Teenage motherhood has been associated with ill health behaviours such as smoking and 

repeat pregnancy (BCPHP, 2007). In the fiscal year of 2006/2007 less than 4% of births 

in the province were attributed to teenage women (BCPHP, 2007). However, these 

pregnancies are important to observe as those who become a teen mother can have an 

altered life trajectory and developmental issues may emerge for both mother and baby 

(Foster, 2005). This indicator is measured by the pregnancy rates/ 1,000 females (age 15- 

17 years) 2005 to 2007. Data for this indicator were provided by BC Stats. Pregnancies 

include live births, stillbirths, abortions, and miscarriages resulting in hospitalization. 

Multiple birth events are only counted as one pregnancy. 

20. Adolescent Crime 

Crime rate is an important indicator to measure as communities with lower crime rates 

are generally healthier (Foster & Keller, 2007). BC has a low rate of youth sentencing 

when compared to other provinces in Canada (Foster, 2005). In order to measure this 

indicator the juvenile crime rate was used. Juvenile crime rate is the number of offences 

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(charges for juveniles) per 1,000 of the population age 12-17. Data are 3 year averages 

from 2004-2006. These data are available publicly from BC Stats. 

21. Freedom from Abuse 

Adolescents can be affected by abuse emotionally and physically. Adolescent’s who have 

experienced abuse are more likely to report poor/fair health and consider suicide than 

those who have not. Troubling is that there has been a rise in the amount of abuse 

reported by BC students since 2003 (Smith et al., 2009). This indicator is measured by 

the percent of students, grade 7-12, who have reported that they never been physically or 

sexually abused. Data are available for this indicator from the AHS 2008. 

Mental Health 

22. Adolescent Suicide 

Suicide is the second leading cause of death of adolescents in BC (Smith et al., 2009). 

Thus, measuring those who have not considered suicide is important when examining the 

health and wellness of adolescents. This indicator is measured by the percent of students, 

grade 7-12, who have reported that they have not considered suicide in the last twelve 

months. Data for this indicator are available from the AHS 2008. 

23. Good Mental Health 

Many mental health problems may first appear during adolescence, a time of emotional 

and mental development (Smith et al., 2009). Positive emotional states, such as interest, 

alertness and joy, have been found to relate to social activity and life satisfaction. Positive 

emotions have been shown to have a positive affect on physical health (Ben-Zur, 2003; 

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Veenhoven, 2008). Moreover, negative emotional states, such as anger, fear, sadness and 

guilt have been shown to relate to stress and health complaints and increase risk of 

diseases (Ben-Zur, 2003; Prince, 2007; Veenhoven, 2008). This indicator is measured by 

the percent of those 12-19 who report good to excellent self perceived mental health. 

Data are available for this indicator from the CCHS 2007/2008. 

24. Feeling Good at Something 

Feeling good at something is measured by the percent of students, grade 7-12, who can 

think of some things they are really good at and is considered an indicator to maximize in 

the index. These data are available from the AHS 2008. 

2.6 Results 

The first round of the Delphi survey identified 62 indicators as influential when 

measuring adolescent health and wellness in BC. After the third round of Delphi surveys 

and a preliminary data review 27 indicators were identified as most influential. Of these 

27, 3 were deemed inappropriate for inclusion in the index either due to the lack of 

geographical variation or lack of appropriate longitudinal data. This left 24 indicators to 

be compiled into the overall index. 

There were some divergences from the first to the third Delphi rounds. Material wellness, 

civic engagement, alcohol use and injury rates were identified by 3 or more participants 

as an influential indicator in the first round but did not meet the cutoff value to stay 

included in the third round. Several indicators that were identified by only one participant 

in the first Delphi round did get identified as an influential indicator in the third round. 

79

These indicators are: positive adult mentors, housing and neighbourhood, tobacco and 

alcohol use of teen mothers, adolescents feeling they are good at something and child 

welfare contacts. Furthermore, although there was a low level of individual agreement 

(perfect consensus was not met) as evident by Krippendorff’s alpha and the ICC; there is 

evidence that the panel understood the ordinal quality of the round three survey and that 

the averages (which is the basis for the weights in the index) are stable. 

2.7 Discussion and Conclusions 

The fact that there were deviations between the results of the first and third Delphi rounds 

indicates that the Delphi surveys did give the participants an opportunity to re-evaluate 

their original responses when compared to the responses of the entire panel. The fact that 

the material well-being indicator was not deemed very influential (aggregate score of 4 or 

more) at the end of the third round corresponds with recent literature about measuring the 

material well-being of adolescents. Adolescents themselves have limited economic 

power, lack occupational social status, and maybe unaware or unwilling to disclose of 

their parents financial standings (Currie et al., 2008). Thus, asking the youth their 

material well-being may yield incorrect results. Only aggregate measures of material 

well-being taken from the census or adult’s reported earnings would be able to be used. 

These measures would not be reflective of the adolescent as the focus of the indicator and 

as Bradshaw and Richardson (2009) noted when choosing indicators for the construction 

of the Index of Child Well-being in Europe it is important to have the child/youth as the 

unit of analysis. 

80

The Delphi technique proved to be a useful tool for identifying what indicators should be 

used for measuring adolescent health and wellness that are place specific for BC, and 

what their relative importance are. It gave a forum to bring together people with different 

experiences and expertise where they could re-evaluate their opinions based on what 

others had stated. This technique led to a manageable number of indicators for which 

there are available data. 

2.8 Limitations 

Although the panel included numerous participants with expertise in fields related to 

adolescence and/or health and wellness, it did not include adolescents or those who are 

presently frontline staff (i.e. nurses, caregivers, teachers and social workers). Inclusion of 

these groups was beyond the scope of this project. Future studies that examine what 

adolescents feel are important indicators to them would be useful, as well as input from 

those currently working the frontline with adolescents. This would likely better guide 

studies that collect primary data rather than using available data. 

The nature of this master’s project left the data produced by the Delphi technique to be 

analyzed by the author with consultation by the supervisors of this research. Themes were 

identified by the author and the analysis was then discussed with the supervisory 

committee, allowing for consistency in the analysis but lacking a perspective beyond that 

of the researcher and supervisory team. 

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3. ANALYSIS OF GEOGRAPHICAL INEQUALITIES IN 

ADOLESCENT HEALTH AND WELLNESS: A SPATIAL 

MULTI-CRITERIA ANALYSIS APPROACH 

3.1 Abstract 

In order to examine the geographical patterns and potential inequalities in adolescent 

health and wellness in British Columbia (BC), Canada, a composite BC Adolescent 

Health and Wellness index (BCAHWI) was created utilizing a spatial multi-criteria 

analysis (MCA) approach. After reviewing various spatial MCA methods the technique 

for order preference by similarity to an ideal solution (TOPSIS) method was applied to 

the adolescent population as a whole and to examine male and female variation. This 

revealed that adolescent health and wellness is not experienced equally across the 

province. The Health Service Delivery Areas (HSDAs) Fraser South and Fraser North 

proved to have the greatest levels of adolescent health and wellness while the Northwest 

has the least. In addition, a rural/urban gradient in adolescent health and wellness was 

revealed at the HSDA level. Male and female adolescents also experience health and 

wellness differently with females achieving higher health and wellness across all HSDAs 

in the Province when directly comparing the two genders. 


In order to understand how adolescent health and wellness varies by region it is possible 

to construct an index using techniques that draw on past studies of deprivation, 

sustainability and health and well-being, and that utilizes spatial multi-criteria analysis 

(MCA). Over the last 20 years there have been a large number of studies that focus on 

indices of material deprivation and how they vary by area (i.e. Bell et al., 2007a; 

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Carstairs, 2000; Jarman, 1984). These indices have been shown to be a useful tool in 

studies of population health (Gatrell, 2002). Yet, until recently, these indices are typically 

used as a tool to examine measurements of negative health outcomes rather than positive 

health indicators in the population (Bringsen et al., 2008). It is a goal of this study to 

establish the BCAHWI, including both positive and negative adolescent health and 

wellness indicators. 

There are two fundamental questions that are asked prior to index construction. The first 

is selecting which of all possible indicators should be included in the index. The second is 

deciding on the weights that should be assigned to each indicator (Frohlich & Mustard, 

1996). Many indices simply apply equal weights citing that there is no justification for an 

alternative weighting system. However, there is also no general theoretical justification 

for equal weights (Bradshaw & Richardson, 2009). This research examined choosing 

indicators and applying weights based on local knowledge, utilizing a panel of experts all 

of whom are employed in the BC public sector in the fields of adolescence and/ or health 

and wellness. The details of this were addressed in Chapter 2. 

In order to construct a composite index a database must be created from the complete set 

of indicators at the HSDA level (Malczewski, 1999 & Chang, 2006). This makes 

computation of an indexed score from indicators possible using spatial MCA techniques. 

Area data are set at the HSDA level due to data constraints and sensitivity of health data. 

All individual indicators are then combined to result in a composite index map. This map 

will show the pattern of adolescent health and wellness in BC. 

83

For the purpose of this study only implicitly spatial data will be considered (indicators at 

the HSDA level) while the explicitly spatial characteristics are administrative boundaries. 

Data at the aggregated level were entered into ArcGIS 9.3 software (www.esri.com) to 

create maps of adolescent health and wellness for the Province. 

3.3 Methods 

The BCAHWI is constructed using a spatial MCA. “Spatial” MCA is the term used by 

this study as this is a loosely GIS based MCA. The literature drawn upon for this study 

refers to either spatial MCA or, most commoly, GIS based MCA. One of the major areas 

where GIS and MCA have advanced is spatial decision support (Malczewski, 2006a). “At 

the most rudimentary level, GIS-based multi-criteria decision analysis (GIS-MCDA) can 

be thought of as a procedure for combining geographical data and value judgments (the 

decision maker’s preferences) to obtain information for decision making” (Malczewski, 

2006b). It also supports map-centred exploratory analysis and hypothesis building in 

research. 

Malczewski (1999) states that part of the process of multi-criteria analysis (MCA) is 

determining the criteria [indicators] necessary to provide a useful assessment of the 

question at hand. Choosing and weighting influential indicators often involves 

professional judgments. Although commonly used in spatial analysis, MCA use has been 

limited in social health research (Bell et al., 2007a; Graymore el al, 2007). Spatial MCA 

methods have the ability to consider a number of indicators at one time and to allow 

visualization across areas to policy and decision makers (Graymore et al., 2007). 

84

Moreover, the process has the power to combine both objective and subjective 

information (Lamelas et al., 2006). The indicators chosen for use in the index were 

derived from a series of surveys undertaken by a panel of expertise using the Delphi 

technique (addressed in Section 3.3.1 and in more detail in Chapter 2). Another benefit of 

spatial MCA is that it allows various weighting schemes to be examined during 

sensitivity analysis (Graymore et al., 2007). 

3.3.1 Delphi Technique for Indicator Selection and Weighting 

A three-round Delphi study was conducted in order to determine a) what indicators (with 

available data at the scale of HSDA or SD) the panel of expertise (n=14) felt was 

influential in measuring adolescent health and wellness in BC and b) each indicator’s 

relative weight. In this case the panel of expertise is defined as a group in which 

individuals are employed in various areas of the public sector, in the fields of adolescence 

and/or health and wellness. In the first and second rounds of the Delphi study, a list of 48 

indicators was identified by the panel. In the third round of the Delphi study, the relative 

importance of each indicator was assessed to determine which indictors were deemed the 

most influential and to generate the weights that the most influential indicators hold in the 

index. Further research was conducted to ensure the indicators showed variation across 

the HSDAs and made theoretical sense in the index. At the end of this process, there were 

24 indicators were deemed suitable for inclusion in the index. This process was addressed 

in more detail in Chapter 2 of this thesis. 

85

3.3.2 Weighting Criteria 

Assigning the weights given to each indicator is an important part of the spatial MCA 

process. Different weighting techniques have been found to produce different outputs 

(Hobbs, 1980). Therefore, careful consideration was given to the method that was used in 

the weighting. Table 9 gives a summation of the advantages and considerations of the 

various weighting techniques. Pairwise comparisons are also addressed in more detail in 

Section 3.3.3. 

86

Table 9: Criteria weighting methods (n=number of criteria/ indicators) 

Weighting Description Number of Advantageous Considerations 

Technique 

Questions 

Rank Order Criteria are ranked i.e. from most n Easy to use Weights are ordinal level; low 

important = 1, to second most 

trustworthiness; larger the number of 

important= 2, etc. 

criteria the more difficult to 

comprehend. 

Categorization Categorization works similarly to 

ranking by sorting attributes into 

categories such as “very important”, 

“average importance”, etc. 

Rating Decision makers estimate weights on the 

basis of a predetermined scale. The 

simplest method is the point allocation 

Pairwise 

comparison 

Trade-off 

Analysis 

approach (usually 100 points) 

Takes pairwise comparisons of criteria 

to produces relative weights 

Makes use of trade-offs that the decision 

maker is willing to make between pairs 

of alternatives. The trade offs define a 

unique set of weights that will allow all 

of the equally preferred alternatives in 

the trade-off to get the same overall 

value/utility 

Sources: (Carver, 1991; Hobbs, 1980; Malczewski, 1999) 

n Easy to use Weights are ordinal level; as the ratio 

values are arbitrarily fixed the 

weights are unlikely to be 

theoretically valid; often 

underestimates ratios of weights 

relative to other methods; lack of 

theoretical foundation. 

n Easy to use Weights are interval; lack of 

theoretical foundation; larger the 

number of criteria the more difficult 

n(n-1)/2 Fairly easy to use; based on 

statistical/ heuristic 

underlying theory; estimates 

a consistency ratio that 

determines if comparisons 

are consistent 

After careful consideration, categorization was deemed the most acceptable method of 

weighting due to its ease of use for the Delphi study participants and the high number of 

indicators to be ranked at the onset of the third round (n = 48). Categorization can utilize 

a Likert scale to group the indicators into the levels of influence or importance they are 

believed to have. This corresponded with the third round of the Delphi study where 

weights were applied and only indicators that received an average score of 4 and up (out 

of a maximum of 5) were included in the index. This technique has been used in the past 

in spatial MCA (i.e. Bell et al., 2007a; Carver, 1991). 

The aggregate/ average value was used to rank the indicators given that several experts 

were “unsure” of some indicators and therefore did not answer questions regarding those 

indicators. Consequently, using summed values would rank these indicators as lower than 

ones that were responded to by all participants. To test the reliability of the averages, 

analysis utilizing the intraclass coefficient (ICC) statistic showed that the average values 

are stable (although only applied to the indicators without “unsure” values, n=36), as 

reported in Section 2.5.5.2. The indicators were ranked inversely to the original Likert 

scale with a rank of 1 being given to the highest average score, 2 to the next and so on. 

Ranked position was used to assign a weight that each indicator holds in the index. 

88

Each indicator’s average score from the final Delphi survey was used to derive its weight 

using the equation for ranked sum (Equation 2) and rank reciprocal (Equation 3). The 

equations are presented respectively as follows: 

n − rj 

+ 1 

w = 

(2) 

j ( n − r + 1) 

∑ 

k 

Where w j is the normalized weight for the jth indicator, n is the number of indicators (k = 

1, 2,…,n), and rj is the jth indicator’s ranked position (Malczewski, 1999). 

1/ 

rj 

w = 

(3) 

( 1/ 

) 

∑ 

j rk 

These are common weighting equations used when ranked position is available 

(Malczewski, 1999). Also, equal weights (Equation 4) were applied to examine 

differences in the three weightings. 

w j 

1 

= (4) 

n 

Table 10 shows the 24 indicators that had an average value of 4 or over, and met the 

criteria of contributing logically to the index and having variation between the HSDAs 

across the province (this is addressed in Chapter 2 of this thesis). It also shows the results 

of the three different weighting schemes that were applied. 

89

Indicator 

Abbreviation 

Table 10: Weighting of indicators 

Indicator Average Ranked 

Position 

Ranked 

Sum 

Ranked 

Reciprocal 

90 

Equal 

I 1 Family Connectedness 4.79 1.5 0.078 0.190 0.042 

I 2 Freedom from Abuse 

4.79 1.5 0.078 0.190 0.042 

I 3 Physical Activity 

4.57 5 0.067 0.057 0.042 

I 4 Healthy Diet 

4.57 5 0.067 0.057 0.042 

Freedom from Chronic 4.57 5 0.067 0.057 0.042 

I 5 Conditions (including mental 

health conditions) 

I 6 School Connectedness 4.57 5 0.067 0.057 0.042 

I 7 Good Mental Health 

4.57 5 0.067 0.057 0.042 

I 8 Positive Peer Influences 4.50 9 0.053 0.032 0.042 

I 9 Positive Adult Mentors 4.50 9 0.053 0.032 0.042 

I 10 

Adolescents Feeling They 

Are Good at Something 

4.50 9 0.053 0.032 0.042 

I 11 

Tobacco/ Alcohol Use of 

Teen Mothers 

4.46 11 0.047 0.026 0.042 

I 12 Healthy Weight 

4.43 12.5 0.042 0.023 0.042 

I 13 Literacy 

4.43 12.5 0.042 0.023 0.042 

I 14 Suicide 

4.36 14 0.037 0.020 0.042 

Illicit Drug Use (including 4.31 15 0.033 0.019 0.042 

I 15 prescription medication use 

without a prescription) 

I 16 Adolescent Pregnancies 4.29 16 0.030 0.018 0.042 

I 17 


Connectedness 

4.21 17 0.027 0.017 0.042 

I 18 

Residing Outside the Parental 

Home 

4.14 18.5 0.022 0.015 0.042 

I 19 Educational Achievement 4.14 18.5 0.022 0.015 0.042 

I 20 Adolescent Crime 

4.08 20 0.017 0.014 0.042 

I 21 Self Rated Health 

4.00 22.5 0.008 0.013 0.042 

I 22 Tobacco Use 

4.00 22.5 0.008 0.013 0.042 

I 23 Housing and Neighbourhood 4.00 22.5 0.008 0.013 0.042 

I 24 Child Welfare Contacts 4.00 22.5 0.008 0.013 0.042 

Sum 1 1 1

Data were reviewed from various data sources and the values were used to create an 

indicator matrix. The areas under consideration are HSDAs (N= 16). Two of the regions 

(Fraser East and Northeast) were missing data or had sample sizes too small to disclose 

from one or more of the data sources and so were not able to be included in the index, 

leaving 14 HSDAs. Table 11shows the data matrix that was used to create the index. 

91

Table 11: Input statistical data for index 

HSDA 

Indicator 11 12 13 14 22 23 31 32 33 41 42 43 51 52 

I 1 7.71 7.91 7.91 7.83 7.92 7.94 7.8 7.69 8.11 7.93 7.72 7.69 7.65 7.74 

I 2 72.75 76.84 79.61 78.24 81.15 80.4 82.16 79.76 84.03 78.67 77.63 76.57 75.35 77.5 

I 3 79.69 82.19 79.94 78.15 64.49 77.83 64.12 74.06 77.00 68.51 73.88 78.49 63.15 67.39 

I 4 55.02 35.83 48.64 45.80 56.35 43.87 44.91 50.16 53.38 51.89 48.06 63.44 34.66 43.71 

I 5 89.1 89.99 89.27 88.93 91.93 92.43 93.6 93.86 92.06 90.9 89.71 89.64 88.36 89.19 

I 6 6.39 6.72 6.83 6.67 6.87 7.01 6.87 6.84 6.97 7 6.62 6.64 6.53 6.52 

I 7 98.29 93.91 96.22 99.3 97.08 99.25 88.34 98.29 97.99 97.88 95.08 97.68 96.14 96.59 

I 8 12.97 9.91 16.39 13.51 22.85 22.72 26.71 29.47 16.35 15.09 13.45 13.76 13.22 12.92 

I 9 86.03 88.72 87.36 87.67 83.67 85.75 81.21 80.83 88.21 85.96 87.42 85.27 85.58 85.57 

I 10 87.28 88.32 87.21 86.52 84.96 86.74 75.52 75.28 87.12 86.74 85.81 86 86.75 85.82 

I 11 46 42.31 46.92 43.86 45.45 66.29 15.38 29.03 30.43 44.93 32.48 33.33 30.68 42.11 

I 12 78.37 76.47 79.18 75.94 78.59 77.9 79.17 82.11 81.74 79.81 76.85 77.04 72.24 76.26 

I 13 18.9 18 15.4 23.9 15.7 15.1 10.7 16.5 13 17.6 21.7 21.7 32.9 24 

I 14 84 88.9 87.9 86.8 89.6 89.3 88.9 90.1 90.2 87.9 85.9 86.6 85.5 87.2 

I 15 52.37 48.01 56.2 52.48 64.61 62.91 69.04 70.5 58.62 55.49 52.69 50.19 52.62 52.49 

I 16 13.26 13.96 16.12 19.72 14.18 14.02 7.7 13.11 10.69 16.02 22.87 29.57 30.48 25.5 

I 17 75.66 81.52 81.29 72.27 78.7 83.93 64.54 70.3 86.57 78.98 81.43 79.72 71.67 64.72 

I 18 4 5.07 3.77 5.42 4.39 3.39 5.21 4 3.7 3.36 4.86 4.78 5.53 5.13 

I 19 32.2 24 24.4 33.2 22.4 22.5 17.8 26.1 15 33.9 33.9 34.2 36.9 32.4 

I 20 7 6.5 3.8 5.8 4.3 4.8 3.1 3.9 3.8 4.4 6.1 11.2 9.6 6.5 

I 21 98.51 89.3 96.13 88.48 94.5 95.9 93.23 91.3 93.08 90.81 99.11 90.5 85.73 93.98 

I 22 91.67 86.82 82.43 84.47 90.67 92.65 90.19 94.44 94.1 89.27 85.67 93.33 82.02 89.19 

I 23 3.24 2.57 3.11 5.28 2.06 2.18 3.06 2.48 2.46 2.75 4.13 5.04 4.9 4.59 

I 24 92.78 93.73 93.55 92.81 87.79 88.2 85.73 79.07 91.31 93.31 92.29 92.47 92.89 93.01 

92

3.3.3 Combination Rules 

Combination rules define a relationship between the input (each indicator value for a 

given HSDA) and the output data (the BCAHWI score) by defining the algorithm(s) used 

to obtain the output (Lamelas et al., 2006). In spatial MCA, the combination rule used 

can greatly influence the outcome of the analysis. It has been established that when 

choosing the most appropriate combination rule there are several factors that should be 

considered: 1) characteristics of the decision problem size and complexity (i.e. number of 

criteria [indicators], alternatives [areas] and constraints as well as the amount of 

uncertainty) 2) characteristics of the participants (availability and desire to participate) 

and 3) characteristics of the combination rule (ease of use and cognitive burden on 

participants, time required to solve the problem, including interaction time with the 

participants and restrictions of the underlying assumptions) (Malczewski, 1999). These 

factors were considered when choosing the combination rule for this study. 

There are many combination rules that may be applied. Malczewski (1999) subdivides 

combination rules into multiattribute or multiobjective. Multiattribute is data driven and 

assumes the number of alternatives (areas) is given, while multiobjective is based on 

mathematical programming and generates the alternatives (Lamelas et al., 2006). Due to 

the fact that the areas are given (n =14), a multiattribute approach is appropriate to use in 

this study. There are various types of multiattribute combination rules. These are 

discussed as the foundation for selection of the most appropriate combination rule for this 

study. 

93

Malczewski (2006) classifies spatial MCA rules for multiattribute decision analysis into 5 

types: 1) weighted linear summation, 2) analytical hierarchy process (AHP), 3) 

outranking (also known as, concordance) methods, 4) ideal/ reference point, and 5) other 

(which is often a blend of the other methods). Due to the fact that this step has such an 

impact on the results of studies, a review of the advantages and considerations of the 

different combination rules is presented and summary of the combination rules is 

presented in Table 12. 

94

Combination 

Rule 

Weighted 

Linear 

Combination 

(WLC) or 

Simple 

Additive 

Weighting 

(SAW) 

Outranking 

(aka 

Concordance) 

Methods 

Ideal/Reference 

Point 

Table 12: Combination rules for spatial multi-criteria analysis 

Variations Description Advantageous Considerations Output 

Simple WLC The weight of each map is multiplied to 

it’s standardized map and finally they 

are added together 

Order Weighted 

Average (OWA) 

Analytical 

Hierarchy Process 

(AHP) 

ELECTRE 

(elimination et 

choice translating): 

PROMETHEE 

TOPSIS 

(technique for 

order preference 

by similarity to an 

ideal situation) 

Combines Boolean overlay operations 

(ie. ANDness and ORness) and WLC 

Compares decisions on a pairwise basis 

to assign weights and then maps are 

combined based on WLC 

Based on pairwise comparisons of 

alternatives 

The best alternative is the one that is 

nearest to a theoretical ideal positive 

point and farthest from a theoretical 

ideal negative point 

Easy to implement and 

interpret 

Order weights allow for 

control over trade-off 

levels of criteria which 

can be used to explore 

evaluation criteria 

relationships 

Is flexible, relatively easy 

to interpret 

Does not require 

independence of 

indicators; Can consider 

objective and subjective 

criteria 

Does not require 

independence of 

indicators; gives cardinal 

rankings; rational and 

relatively simple 

Strict assumptions of 

linearity and additivity 

or may yield incorrect 

results 

Moderate restrictions; 

attention must be paid 

to how weights are 

ordered 

Moderate restrictions; 

requires high decision 

maker interaction; 

difficult to implement 

with many criteria 

Only expresses that one 

alternative is preferred 

to another but not by 

how much 

95 

Cardinal 

ranking 

Cardinal 

or 

ordinal 

ranking 

Cardinal 

ranking 

(ratio 

scale) 

Partial 

or 

ordinal 

ranking 

Cardinal 

Ranking 

Sources: (Carver, 1991; Hobbs, 1980; Lamelas et al., 2006; Malczewski, 1999, 2000, 2006a, 2006c; Morari et al., 2004; Opricovic & Tzeng, 2004; 

Roy, 1991; Srdjevic et al., 2004; Weistroffer et al., 2006)

Weighted linear combination (WLC), also referred to as simple additive weighting 

(SAW), is most often used in spatial MCA (Hobbs, 1980; Lamelas et al., 2006; 

Malczewski, 1999; Malczewski, 2000). It can be done quite simply by using map algebra 

and cartographic modeling and can be used in both a raster and vector environment in a 

GIS (Malczewski, 2000). Order Weighted Average (OWA) is based on WLC and 

Boolean overlay (Bell et al., 2007a; Malczewski, 2006c). Ordered weights allow for 

control over trade-off levels of criteria which can be used to explore evaluation criteria 

relationships (Bell et al., 2007; Malczewski, 1999). It uses the relative weight assigned by 

participants and the relative value of each indicator variable to assign the weight. 

A variant of WLC is Analytical Hierarchy Process (AHP) which compares criteria 

[indicators] on a pairwise basis within a hierarchy to assign weights and then the maps 

are combined based on an additive weighting model (Malczewski, 2006a; Opricovic & 

Tzeng, 2004). If there is a large number of criteria [indicators] the process will involve a 

large number of pairwise comparisons (Malczewski, 1999), with the size of these quickly 

getting unmanageable. 

In order for WLC and its variant methods to produce theoretically valid results, the 

weights must be proportional to the relative value of unit change of the attribute 

[indicator] value (Hobbs, 1980). The attribute [indicators] set must meet certain criteria: 

1) all attributes must be comprehensive, measurable, and complete (covering all relevant 

aspects of the decision problem and the degree to which it is achieved), 2) operational 

(can be meaningfully used in the analysis), 3) decomposable (the performance of one 

96

attribute can be evaluated independently), 4) non-redundant (not highly correlated) and 5) 

minimal. These criteria often are difficult to achieve in spatial MCA and therefore often 

are ignored, especially non-redundancy and decomposability (Malczewski, 2000). In a set 

of indicators, it is likely that some attributes will be correlated and therefore redundant. 

Beyond the above criteria, WLC methods have two strong assumptions that must be met: 

1) linearity, the desirability of an additional unit of an attribute [indicator] is constant and 

2) additivity which means that there is no correlation found between attributes 

(Malczweski, 1999). If these assumptions are not met, the analysis could yield incorrect 

results. In this study, as health and wellness is considered to be holistic and made of 

many different factors that interplay with each other, it is unlikely that the indicators are 

uncorrelated. A correlation matrix, shown in Table 13, confirms this. 

97

I1 1 

Table 13: Pearson’s correlation matrix of indicators in BCAHWI, N= 14 

I1 I2 I3 I4 I5 I6 I7 I8 I9 I10 I11 I12 I13 I14 I15 I16 I17 I18 I19 I20 I21 I22 I23 I24 

I2 .685 ** 1 

I3 .218 -.185 1 

I4 .064 .114 .164 1 

I5 .321 .730 ** -.199 .169 1 

I6 .739 ** .838 ** -.046 .112 .723 ** 1 

I7 .106 -.185 .374 .338 -.224 -.008 1 

I8 .050 .603 * -.324 .158 .889 ** .560 * -.174 1 

I9 .392 -.218 .556 * -.172 -.655 * -.158 .298 -.835 ** 1 

I10 .301 -.366 .382 -.045 -.726 ** -.222 .453 -.828 ** .889 ** 1 

I11 .325 -.168 .387 -.015 -.218 .131 .618 * -.195 .406 .605 * 1 

I12 .470 .600 * .192 .527 .725 ** .618 * .075 .577 * -.307 -.429 -.093 1 

I13 -.614 * -.675 ** -.195 -.345 -.751 ** -.690 ** .252 -.569 * .244 .365 -.006 -.837 ** 1 

I14 .639 * .858 ** -.050 .003 .794 ** .840 ** -.093 .625 * -.275 -.384 -.024 .615 * -.666 ** 1 

I15 .146 .669 ** -.378 .130 .900 ** .595 * -.198 .986 ** -.793 ** -.799 ** -.212 .611 * -.605 * .652 * 1 

I16 -.587 * -.549 * -.148 -.069 -.699 ** -.576 * .232 -.536 * .214 .365 -.019 -.737 ** .879 ** -.581 * -.604 * 1 

I17 .603 * .171 .563 * .268 -.033 .386 .384 -.249 .611 * .595 * .442 .219 -.264 .183 -.236 -.133 1 

I18 -.531 -.309 -.295 -.433 -.396 -.594 * -.453 -.291 -.019 -.098 -.474 -.712 ** .579 * -.353 -.319 .486 -.567 * 1 

I19 -.693 ** -.803 ** -.080 -.029 -.716 ** -.656 * .317 -.541 * .161 .309 .093 -.618 * .833 ** -.792 ** -.605 * .795 ** -.234 .335 1 

I20 -.596 * -.722 ** .087 .034 -.655 * -.677 ** .184 -.587 * .221 .393 -.035 -.702 ** .755 ** -.652 * -.676 ** .836 ** -.024 .466 .693 ** 1 

I21 .102 .062 .204 .360 .124 -.013 -.013 .131 .027 .032 .260 .371 -.459 -.101 .158 -.333 .248 -.440 -.217 -.351 1 

I22 .166 .312 .096 .582 * .664 ** .319 .202 .480 -.421 -.353 -.032 .653 * -.538 * .425 .461 -.392 .103 -.470 -.401 -.154 .224 

I23 .121 -.447 .237 -.112 -.849 ** -.387 .105 -.935 ** .836 ** .871 ** .284 -.538 * .430 -.572 * -.901 ** .448 .306 .176 .424 .450 -.036 -.537 * 1 

I24 -.614 * -.511 -.064 -.086 -.704 ** -.678 ** .057 -.528 .200 .194 -.230 -.690 ** .786 ** -.683 ** -.581 * .819 ** -.386 .692 ** .731 ** .703 ** -.342 -.452 .449 1 

** Correlation is significant at the 0.01 level (2-tailed) 

* Correlation is significant at the 0.05 level (2-tailed) 

98

The ease of the former combination rules within a GIS makes them popular; but GIS 

implementations of WLC are often employed without a full understanding of the 

assumptions (Malczewski, 2006). Some of the difficulties with the assumptions 

associated with the previous approaches can be avoided using outranking strategies or 

ideal/reference point (Malczewski, 2006). 

Both outranking and ideal/reference point methods are based on direct comparisons of 

alternatives [areas] by factors included in the evaluation (Carver, 1991). Outranking, also 

referred to as concordance methods, is based on pairwise comparisons of alternatives 

[areas under consideration] based on the concordance set (the subset of the criteria for 

which alternative [area] is not worse then the alternative [area] that it is being compared 

to). This is used to create a concordance matrix for each region (ie. regions=14; 14 x 14 

matrix). The rows are summed to get the rank values (Malczewski, 1999; 2000; Morari et 

al., 2004). In order to perform an outranking method, there needs to be a set of 

alternatives [areas] and a family of criteria [indicators] with associated weights (Roy, 

1991). Both of these criteria are met in this study of adolescent health and wellness 

which has a set of 24 indicators with associated weights with the areas under 

consideration being HSDAs. This method has many advantages: it can consider objective 

and subjective criteria and it has minimum participant requirements but there are some 

considerations. Outranking methods provides an ordinal ranking of the alternatives, and 

thus only expresses that alternative A is preferred to alternative B, but not by how much 

(Malczewski, 1999). This is not the goal of a composite index which seeks to compute 

cardinal values; therefore it is inappropriate for this study. 

99

The ideal/reference point method is based on distance from an ideal value point 

(Malczewski, 1999). The most common form is the technique for order preference by 

similarity to an ideal solution (TOPSIS) method; in this method the best alternative is the 

one that is the nearest Euclidean distance to an ideal (the best value of each indicator) and 

farthest Euclidean distance from the negative ideal (the worst value of each indicator) 

(Opricovic & Tzeng, 2004; Zanakis et al., 1998). The TOPSIS method is rational, 

intuitive and relatively simple to understand (Srdjevic et al., 2004; Zanakis et al., 1998). It 

has the same requirements as the outranking method, which are met by this study but 

produces a cardinal value. Steps for a spatial TOPSIS (adapted from Jakimavicius & 

Burinskiene, 2007) are: 

1. Standardize the indicator matrix 

2. Multiply the standardized indicator matrix by the vector of weight values 

3. Formulate an ideal positive value 

4. Formulate an ideal negative value 

5. Calculate the deviation from the ideal positive value 

6. Calculate the deviation from the ideal negative value 

7. Calculate the proportional value deviation from an ideal value 

The TOPSIS model allows indicators that are desirable to maximize and minimize to co- 

exist in the same model. This is an advantage in this analysis as there are both indicators 

to maximize and minimize as shown in Table14. 

100

Table 14: Data definitions and function in the index 

Indicator Data Definition Function 

I 1 Average Family Connectedness score for students grade 7-12, 2008 Maximize 

I 2 % of students, grade 7 -12, free from physical or sexual abuse, 2008 Maximize 

I 3 % age 12-19 who scored active or moderately active on the leisure time 

Physical Activity Index, 2007/2008 

Maximize 

I 4 % age 12- 19 who report eating fruit or vegetables 5 or more times a day, 

2007/2008 

Maximize 

I 5 % of students, grade 7-12, who report that they do not have a condition 

that keeps them from doing things other kids their age do, 2008 

Maximize 

I 6 Average School Connectedness score for students grade 7-12, 2008 Maximize 

I 7 % age 12- 19 who report good to excellent self perceived mental health, 

2008 

Maximize 

I 8 % of students, grade 7-12, whose friends would be upset if they got 

arrested, beat someone up, carried a weapon for protection, got pregnant 

or got someone pregnant, dropped out of school, got drunk or used 

marijuana, 2008 

Maximize 

I 9 % of students, grade 7 – 12, who felt there was an adult in heir family 

who they could talk to, or an adult not in their family they could talk to 

if they had a serious problem, 2008 

Maximize 

I 10 % of students, grade 7-12, who can think of some things they are really 

good at, 2008 

Maximize 

I 11 % of mothers (age equal to or less then 19 at age of delivery) that did 

NOT have smoking OR alcohol identified as a risk factor during their 

pregnancy, Apr. 2007- Mar. 2008 

Minimize 

I 12 % of students, grade 7-12, who have a healthy BMI based on self 

reported height and weight, 2008 

Maximize 

I 13 % of students who did NOT pass or write grade 10 English provincial 

exam, average 2005/2006-2007/2008 

Minimize 

I 14 % of students grade 7-12 who have not considered suicide in the last 

twelve months, 2008 

Maximize 

I 15 Percent of students grade 7-12 who have never tried marijuana, 

prescription pills without a doctor's consent, cocaine, hallucinogens, 

ecstasy, mushrooms, inhalants, amphetamines, crystal meth, herion, 

injected an illegal drug, or steroids without a doctor's prescription, 2008 

Maximize 

I 16 Pregnancy Rates/ 1000 females (ages 15-17 years), average 2005 - 2007 Minimize 

I 17 % age 12- 19 who have a strong sense of belonging to local community, 

2007/2008 

Maximize 

I 18 % of students grade 7-12 who reported they do not live with a parent or 

step parent most of the time but rather live with another adult (related or 

unrelated) or no adults, 2008 

Minimize 

I 19 % of 18yr olds who did NOT graduate, 2007/08 Minimize 

I 20 Juvenile Crime Rate/ 1000 of the population age 12-17, average 2004- 

2006 

Minimize 

I 21 % of those 12- 19 who report good to excellent health, 2007/2008 Maximize 

I 22 % age 12-19 presently a non-smoker, 2007/2008 Maximize 

I 23 % of students, grade 7-12 who have their own bedroom, 2008 Maximize 

I 24 % of students, grade 7-12, who reported that they had been in group 

homes, foster homes or youth agreements at some point, 2008 

Minimize 

101

To summarize, due to the holistic nature of human health and wellness, WLC and its 

variants are deemed to be inappropriate as the assumption of additivity is unmet. This is 

confirmed by presenting a correlation matrix (Table13) which shows that, as expected, 

many of the indicators are highly correlated. The TOPSIS method does not have the strict 

assumptions of the WLC methods and produces a cardinal value and therefore is judged 

to be the best method used in this analysis. 

102

3.3.4 TOPSIS Method 

The Statistical Design Institute Triptych v. 3.10.395 (www.sdi.com) software was used to 

perform the TOPSIS analysis. The use of a software tool in this study proved to be an 

efficient means that allowed various weighting schemes to be applied quickly and easily. 

In order to create a composite index, each indicator must be standardized to a common 

scale (Graymore et al., 2007; Lamelas et al., 2006; Malczewski, 2000). This step 

transforms dimensional indicators to non-dimensional so that they can be compared 

meaningfully to one another. The indicator matrix using the TOPSIS method is most 

commonly standardized by the following formula (Equation 5) as taken from several 

studies that utilized the TOPSIS method (Ashanti et al., 2009; Jakimavicius & 

Burinskiene, 2007) and is used by Triptych software tool when computing the 

standardized indicator matrix (Statistical Design Institute, 2008): 

rij 

= 

2 

1 kj 

x ij 

m 

∑ x 

k = 

Where r ij = the standardized value of the i-th measure of the j-th indicator, x ij = denotes 

the performance measure of the i-th measure of the j-th indicator, and m = all indicators. 

This results in a new matrix of standardized values. 

A weighted standardized matrix is then computed by multiplying the set of weights, 

where W= ( w 1, w 2, w 3, … , w 24) and the weights sum to 1, by the standardized matrix. 

(5) 

103

⎡ v 

⎢ 

Weighted Standardized Matrix = ⎢ 

. 

⎢ . 

⎢ 

⎣v 

11 

141 

v 

v 

12 

142 

... 

... 

v 

v 

124 

. 

. 

1424 

⎤ 

⎥ 

⎥ = 

⎥ 

⎥ 

⎦ 

⎡ w1r 

⎢ 

⎢ 

. 

⎢ . 

⎢ 

⎣w1r 

11 

141 

w r 

2 12 

w r 

1 142 

... 

... 

w 

w 

24 124 

. 

. 

r 

r 

24 1424 

The ideal positive and negative variants are formulated by taking the maximum and 

minimum values respectively for each indicator when the function is to maximize the 

indicator where i = 1, 2, 3,…, 24. If the function is to minimize the indicator, the ideal 

positive and negative values are the minimum and maximum values respectively where i 

= 1, 2, 3,…, 24. 

The Euclidian distance deviation from the ideal positive ( S j + ) (Equation 6) and the ideal 

negative solution ( S j − ) (Equation 7) (Statistical Design Institute, 2008) are calculated. 

This is done in Triptych v. 3.10.395 using the formulas: 

+ 

S j 

= 

− 

S j = 

n 

+ 

∑ ( vij − v 

j 

) 

2 

i= 

1 

n 

− 

∑ ( vij − v j ) 

2 

i= 

1 

The last step in the TOPSIS model is to calculate the relative closeness of each HSDA to 

the ideal solution ( 

C j ). This is defined by Equation 8, where 0 < C 

≤ 1 

j 

− 

S j 

C j = 

+ − 

S j 

+ S j 

(6) 

(7) 

(8) 

⎤ 

⎥ 

⎥ 

⎥ 

⎥ 

⎦ 

104

Figure 8 presents a screenshot of the Triptych v. 3.10.395 software that was used in this 

analysis. It shows the structure of the indicator matrix and presents the format in which 

the results were produced. 

3.3.5 Accounting for Uncertainty 

Figure 8: Screenshot of TOPSIS selection method 

In spatial MCA there are two types of uncertainty to be accounted for. The first is 

uncertainty in the data that populates the index and the second is uncertainty in the 

weights of the indicators. Spatial MCA is often applied without consideration of error in 

the source data (Malczewski, 1999). A brief discussion accounting for error in source 

data is provided in Section 3.3.5.1 and Section 3.3.5.2 discusses examining uncertainty in 

the weights. 

105

3.3.5.1 Accounting for Error in Data 

CCHS data were provided from the Atlas of Wellness project (Foster & Keller, 2007); 

estimates were derived from the share files and supplied to the research project with CV 

values calculated. In this case, Statistics Canada uses the CV to measure the standard 

deviation around an estimate (this differs from the application of the CV in Chapter 2, 

where the CV measures the standard deviation around the mean). In this case, the “CVs 

are derived using the variance formula for simple random sampling and incorporating a 

factor which reflects the multi-stage, clustered nature of the sample design” (Statistics 

Canada, 2009b). All estimates are weighted to the population which are the 

recommended estimates according to Statistics Canada (2009b). It is set out by Statistics 

Canada that if the respondents are less then 10 (for master file or share file) the estimate 

should not be released as the percentages are unreliable. Beyond that, if the CV is > 33.3 

for an estimate then it is unacceptable and should not be released; if it is between 16.6 - 

33.3 it is marginal and this must be made note of; anything below 16.6 is acceptable to be 

released (Statistics Canada, 2009b). 

For most of the data included in the index, the CV was less then 16.6. The exception is in 

HSDA 53 – the Northeast, which was not included in the index. Fruit and Vegetable 

consumption (the data that measures the indicator of healthy diet) fell within 16.6-33.3 in 

East Kootenay and Kootenay Boundary. When looking at the gender subpopulations, the 

CV increases beyond the releasable limit. Therefore, the indicator of healthy diet must be 

kept constant at the total population value and cannot be broken into gender when 

106

examining male and female variation. When the estimate is marginal, it is indicated on 

the individual indicator maps (see Appendix J). 

The MCS uses standard error (SE) to govern the release of estimates. The guidelines are 

as follows: estimates with SE < 4.99 are fully publishable, SE between 5 and 12.49 are 

marginal and a note should be made of it and SE over 12.5 is too high and the estimates 

are not releasable (Saewyc & Green, 2009). To reduce risk of deductive disclosure, if the 

un-weighted sample is less than 25 it is not released. AHS 2008 data estimates are 

derived using SPSS v.17 Complex Samples to account for the random clustered-stratified 

design of the survey and apply more accurate weighting to the estimates (Saewyc & 

Green, 2009). The Complex Samples takes into account the random clustered-stratified 

design in calculating the standard error, as the survey design makes it inappropriate to 

calculate the errors based on random sampling theory (Saewyc & Green, 2009). All the 

total population estimates met these criteria for the AHS 2008 data (SE

Other data were prepared by BC Stats and the BC Perinatal Database. These data sources 

are based on administrative data and although administrative data are not perfect it is 

difficult to quantify possible error. These are population data and so errors in the 

estimates are not measured. These are reliable data sources and so confidence is placed in 

these data. 

3.3.5.2 Accounting for Uncertainty in Weight Values 

Criteria [indicator] weights are often the greatest source of uncertainty (Chen et al., 

2009). Uncertainty may arise because participants may not be clearly able to state their 

preference. Also, not knowing what the underlying criteria [indicators] distribution is, 

creates uncertainty (Ozturk & Tsoukias, 2005). Addressing the uncertainty in weighting 

can be done by applying fuzzy weights into the decision theory or by performing a 

sensitivity analysis (Malczewski, 1999). Sensitivity analysis was deemed the most 

appropriate technique for this study because it is able to be performed in a relatively 

straightforward and timely fashion within the Triptych v. 3.10.395 (www.stat- 

design.com) software. Sensitivity analysis is a collection of methods that evaluates how 

sensitive a model output is to small changes to the input values. If the rankings remain 

unaffected as weights are varied, errors in weights can be considered insignificant. 

Sensitivity analysis is important to verify the stability of the results of the analysis 

(Delgado & Sendra, 2004). A full sensitivity analysis of all indicator weights is a difficult 

task when a large number of indicators are used in the analysis. It can be very time 

consuming and the results can be difficult to interpret. Consequently, it is common to do 

a sensitivity analysis just on the indicators that have the highest relative weights 

(Malczewski, 1999). It is important to note that as these weights have high influence 

108

even a small change is likely to have an impact on ranking of the alternatives [areas] 

(Malczewski, 1999). The sensitivity of weights is thought to be the most important error 

to consider when performing a spatial MCA because it is based on subjective values 

(Malczewski, 1999). 

According to Delgado and Sendra (2004), who reviewed 28 papers for review of 

sensitivity analysis in spatial MCA, only 17 included some kind of sensitivity analysis 

and they concluded that it is not as common as it ought to be. Of the 17 papers 14 used 

the technique of changing the input weights to test the robustness of the results (Delgado 

& Sendra, 2004). With sensitivity analysis if small changes in indicator weights have no 

influence on the ranking of HSDAs then more confidence can be had in the stability of 

the results (Jankowski & Nyerges, 2001). Comparing the three different weighting 

schemes also acts as a sensitivity analysis. Nonetheless, to compare the 2 weights that are 

based on the panel of expertise’s opinion (rank reciprocal and rank sum) additional 

sensitivity analysis was undertaken in order to determine how robust the two weighting 

schemes are. This technique examines the way the output changes under small 

modifications to the value of indicator weights (Malczewski, 1999). 

A simple change of inputs was used, as it is the simplest and most common way to test 

the robustness of the weights (Delgado & Sendra, 2004).The top ten weighted indicators 

were chosen to apply the sensitivity analysis. These were chosen as the three highest 

weights and therefore these ten indicators have the most influence on the index. 

Furthermore, because there are an equal number of indicators it is possible to apply a 

109

positive and negative weighting by splitting the criteria in half still maintaining a total 

value of one in the weights. The value of .005 was considered the threshold to which the 

sensitivity analysis would be run because beyond that the hierarchy of the indicator 

weights is altered. The iterations were run at .001 intervals to explore for change in the 

ranking. Table 15 shows the design of the sensitivity analysis. 

Table 15: Design of sensitivity analysis applied to both rank sum and rank reciprocal 

weights 

SA1a SA1b SA2a SA2b SA3a SA3b SA4a SA4b SA5a SA5b 

w1-5 

(+.005); 

w6-10 

(-.005) 

w1-5 

(-.005); 

w6-10 

(+.005) 

*w= weight of indicators 

w1-5 

(+.004); 

w6-10 

(-.004) 

3.3.6 Cluster Analysis 

w1-5 

(-.004); 

w6-10 

(+.004) 

w1-5 

(+.003); 

w6-10 

(-.003) 

w1-5 

(-.003); 

w6-10 

(+.003) 

w1-5 

(+.002); 

w6-10 

(-.002) 

w1-5 

(-.002); 

w6-10 

(+.002) 

w1-5 

(+.001); 

w6-10 

(-.001) 

A hierarchical cluster analysis was conducted in order to classify the HSDAs by their 

110 

w1-5 

(-.001); 

w6-10 

(+.001) 

statistical similarity of the BCAHWI. This was performed in SPSS v17 (www.spss.com). 

Ward’s method was used to measure the squared distance between the HSDA’s 

BCAHWI scores and the mean centre of the cluster. Ward’s method joins cases into 

clusters in a way that minimizes the variance within a cluster. The results of the cluster 

analysis are then mapped in order to examine any regional effects in the clusters 

(O’Sullivan & Unwin, 2003). This is presented in Section 3.4.3. 

3.3.7 Male/Female Variation 

Pickle (2000) found in work on a mortality atlas of the US that one question that is asked 

of a map is: are there similar or different patterns for different groups, i.e. males and 

females? Gender is a determinant of youth health at the population level (Tonkin & 

Murphy, 2002) and the Public Health Agency of Canada has listed gender as one of the

key determinants of health (PHAC, 2003). In order to examine the possibility of male and 

female geographic variations in health and wellness levels, two indices that separated 

these two genders were also created using the same methods as for the total population 

BCAHWI. This changed the ideal positive and negative to that of the gender under study 

so the male/female results only show the difference within the groups but not between 

them. 

Additional analysis was conducted in order to compare between the two genders. To do 

this two theoretical regions were created. One that held the maximum value for either 

male and female and one that held the minimum value for either male or female when 

both genders are considered. This changed the maximum and minimum values to reflect 

both genders, so that comparisons between the two genders can be made. 

Indicators from BC Stats and the BC Perinatal Database were kept constant at the total 

value between the two groups because they were not available by gender. Ranking and 

weighting was done in the same fashion as with the total index. There were 16 indicators 

(all from self reported survey answers) that were able to be broken down by gender. 

Some of these self reported survey indicators had to be kept constant at the total 

population value because the sample size became too small or they did not meet the 

criteria of standard error or CV set forth by the MCS and Statistics Canada. Indicators 

held constant at the total from AHS were: residing outside of parental home and child 

welfare contacts. The only indicator held constant at the total population value for CCHS 

was fruit and vegetable consumption because females had one HSDA with a CV above 

111

33.3 and the majority HSDA estimates of the male and female for this indicator were to 

be used with caution (CV between 16.6 and 33.3). All other indicators from the CCHS 

had CVs that fell below 33.3 and met the criterion of having a sample size > 10. 

To summarize, examining gender differences was done by altering the total population 

values of 16 self reported survey indicators to reflect the values of female or male, while 

the other 8 indicators were constant for both groups, this was implemented in order to 

make BCAHWI to each gender and the total population BCAHWI more fluid. In order to 

compare the genders another analysis that used the ideal positives and negative from 

either female or male was completed. 

3.4 Results 

Section 3.4.1 presents the results of the TOPSIS analysis when applied to rank sum, rank 

reciprocal and equal weights. Comparisons of the three weighting schemes are done and 

act as a preliminary sensitivity analysis. Additionally the geographical patterns displayed 

by the analysis are outlined. Section 3.4.2 presents further sensitivity analysis of the two 

weights produced using local knowledge from a panel of expertise and outlines the 

findings. Drawing on the findings of Section 3.4.2 the cluster analysis results are 

presented in Section 3.4.3 using the most robust weighting scheme and Section 3.4.4 

presents the results of the examination of gender variation also using the most robust 

weighting scheme. 

112

3.4.1 Results of Index 

The three weighting schemes reveal that adolescent health is not experienced equally 

across the province. Fraser South ranked the highest on the rank sum and equal weights 

weighting schemes, followed by Fraser North. These two areas together both ranked first 

when using the rank reciprocal. The Northwest ranks the lowest with all weighting 

schemes. Table16 shows the complete rankings; this table is shaded in quintiles (all five 

groups containing, as much as possible, an equal number of cases), using the equal 

weights as reference. 

Table 16: Comparison of BC adolescent health and wellness index scores by three different 

weights 

113 

Equal weights Ranked sum Rank reciprocal 

Fraser South 0.76 Fraser South 0.71 Fraser South 0.67 

Fraser North 0.71 Fraser North 0.66 Fraser North 0.67 

Vancouver 0.66 Vancouver 0.62 Vancouver 0.62 

North Shore/ Coast 

Garibaldi 

0.66 Okanagan 0.57 


Garibaldi 

Okanagan 0.64 


Garibaldi 

0.56 Okanagan 0.58 

Richmond 0.63 Richmond 0.55 Richmond 0.56 

S. Vancouver Isl. 0.59 S. Vancouver Isl. 0.52 S. Vancouver Isl. 0.53 

East Kootenay 0.53 East Kootenay 0.51 East Kootenay 0.48 

Kootenay Boundary 0.52 Kootenay Boundary 0.43 N. Vancouver Isl. 0.44 

Thompson/Cariboo 0.42 Thompson/Cariboo 0.42 Thompson/Cariboo 0.42 

Central Vancouver Isl. 0.40 N. Vancouver Isl. 0.41 Kootenay Boundary 0.41 

N. Interior 0.37 Central Vancouver Isl. 0.37 Central Vancouver Isl. 0.39 

N. Vancouver Isl. 0.30 N. Interior 0.36 N. Interior 0.35 

Northwest 0.17 Northwest 0.18 Northwest 0.18 

The HSDA of North Vancouver Island shows a difference in ranking between the three 

weighting schemes. It is ranked lower using equal weights than with the two weighting 

schemes that utilized local knowledge. But, overall the different weights yield very 

0.62

similar results. This can be further concluded by the high correlation between all 

weighting schemes index values. Table 17 shows that ranked sum and equal weights have 

the highest correlation, rank sum and equal weights have the second highest correlation. 

Table 17: Pearson’s correlation coefficient of the three BCAHWI score using various 

weighting schemes 

Ranked Reciprocal Equal Weights 

Ranked Sum .984** .961** 

Ranked Reciprocal .947** 

**. Correlation is significant at the 0.01 level (2-tailed), n=14 

114

Figure 9: Results of BCAHWI using rank sum 

Figure 11: Results of BCAHWI using equal weights 

115 

Figure 10: Results of BCAHWI using rank 

reciprocal 

The results of the BCAHWI were mapped using 

the three different weighting schemes (Figures 9 

-11). The rank sum and rank reciprocal maps are 

identical. The equal weight map is similar to the 

other two with the exception of North Vancouver 

Island and Central Vancouver Island. All three 

maps reveal that the HSDAs Fraser South and 

Fraser North, located in the lower mainland, rank 

the highest. Vancouver ranks highly in the 

second quintile, although Richmond, its 

neighbour to the south, falls into the third 

quintile. The Northwest and the Northern 

Interior rank the lowest in all three maps.

Figure 12 shows the three BCAWI scores by the three weighting schemes. It is 

possible to see that generally equal weights yielded a higher score than the other two 

weighting schemes (with the exception of Thompson Cariboo Shuswap, North 

Vancouver Island and the Northwest). Rank sum and rank reciprocal weighting 

schemes showed similar rankings with the exception of the Okanagan, North 

Shore/Coast Garibaldi, Kootenay Boundary and North Vancouver Island. 

Index Score 

0.80 

0.70 

0.60 

0.50 

0.40 

0.30 

0.20 

0.10 

0.00 

Fraser South 

Fraser North 

Figure 12: Results of BCAHWI using three different weighting schemes 

3.4.2 Results of Sensitivity Analysis 

Vancouver 

Sensitivity analysis showed that rank sum is the more robust weighting scheme, when 

compared to rank reciprocal, as there is no deviation in rank at iteration 5a; this is 

shown in Tables 18 and 19. 

Okanagan 

Richmond 

N. Shore/ Coast 

Garibaldi 

S. Vancouver Isl. 

East Kootnenay 

HSDA 

Thompson Cariboo 

Shuswap 

N. Vancouver Isl. 

Kootenay Boundary 

Central Vancouver Isl. 

Ranked Sum 

Ranked Reciprocal 

Equal Weights 

Northern Interior 

Northwest 

116

Table 18: Results of sensitivity analysis for rank sum (light grey shading indicates a 

change in ranking from the original) 

HSDA Ranking Rank sum SA5a SA5b SA4a SA4b SA1a SA1b 

Fraser South 1.0 1.0 1.0 1.0 1.0 1.0 1.0 

Fraser North 2.0 2.0 2.0 2.0 2.0 2.0 2.0 

Vancouver 3.0 3.0 3.0 3.0 3.0 3.0 3.0 

Okanagan 4.0 4.0 4.0 4.0 4.0 4.0 4.5 

North Shore/ Coast Garibaldi 5.0 5.0 5.5 5.0 6.0 5.0 6.0 

Richmond 6.0 6.0 5.5 6.0 5.0 6.5 4.5 

S. Vancouver Isl. 7.0 7.0 7.0 7.0 7.0 6.5 7.0 

East Kootenay 8.0 8.0 8.0 8.0 8.0 8.0 8.0 

Kootenay Boundary 9.0 9.0 9.5 9.0 9.0 10.0 9.0 

Thompson/Cariboo 10.0 10.0 9.5 10.5 10.0 10.0 10.0 

N. Vancouver Isl. 11.0 11.0 11.0 10.5 11.0 10.0 11.0 

Central Vancouver Isl. 12.0 12.0 12.0 12.0 12.0 12.0 12.0 

N. Interior 13.0 13.0 13.0 13.0 13.0 13.0 13.0 

Northwest 14.0 14.0 14.0 14.0 14.0 14.0 14.0 

* Iterations 2 and 3 of SA are not included in the table as iterations 5 and 1 represent the two extremes. 

Iteration 4 is included to contrast iteration 5 where less deviation from the original occurs. 

Table 19: Results of sensitivity analysis for rank reciprocal (light grey shading indicates 

a change in ranking from the original) 

HSDA Ranking 

Rank 

reciprocal 

SA5a SA5b SA4a SA4b SA1a SA1b 

Fraser North 1.5 1.0 1.5 1.0 1.5 1.0 2.0 

Fraser South 1.5 2.0 1.5 2.0 1.5 2.0 1.0 

Vancouver 3.5 4.0 3.0 4.0 3.0 4.0 3.0 

North Shore/ Coast Garibaldi 3.5 3.0 4.0 3.0 4.0 3.0 4.0 

Okanagan 5.0 5.0 5.5 5.0 5.5 5.0 6.0 

Richmond 6.0 6.0 5.5 6.0 5.5 6.5 5.0 

S. Vancouver Isl. 7.0 7.0 7.0 7.0 7.0 6.5 7.0 

East Kootenay 8.0 8.0 8.0 8.0 8.0 8.0 8.0 

N. Vancouver Isl. 9.0 9.0 9.0 9.0 9.0 9.0 9.0 

Thompson/Cariboo 10.0 10.0 10.0 10.0 10.0 10.0 10.0 

Kootenay Boundary 11.0 11.0 11.0 11.0 11.0 11.0 11.0 

Central Vancouver Isl. 12.0 12.0 12.0 12.0 12.0 12.0 12.0 

N. Interior 13.0 13.0 13.0 13.0 13.0 13.0 13.0 

Northwest 14.0 14.0 14.0 14.0 14.0 14.0 14.0 

* Iterations 2 and 3 of SA are not included in the table as iterations 5 and 1 represent the two extremes. 

Iteration 4 is included to contrast iteration 5 where less deviation from the original occurs. 

Both showed deviations in the rankings until iteration 5 which only changed the 

original weight by .001. Rank sum is the most conservative estimate in that it has a 

smaller range in weighting values (.078 - .008) compared to rank reciprocal (.190 - 

117

.013) and in that it is more similar to equal weights. The rankings between ties and 

regions that had values close to each other were the most sensitive to changes in rank. 

When using the rank sum weighting (Table 18) it is possible to see that there is 

sensitivity in the rankings of North Shore/Coast Garibaldi and Richmond as well as 

Kootenay Boundary, Thompson/Cariboo/Shuswap and North Vancouver Island at the 

4 th and 5 th iteration of the SA. At the 1 st iteration, which represents the farthest 

deviation (.005) from the original weights, sensitivity can be found in the Okanagan, 

North Shore/ Coast Garibaldi, Richmond and South Vancouver Island as well as 

Kootenay Boundary, Thompson/Cariboo and North Vancouver Island. When 

examining the rank reciprocal weightings (Table 19) there is sensitivity in the top six 

ranked regions (Fraser North, Fraser South, Vancouver, North Shore/ Coast 

Garibaldi, Okanagan and Richmond) in the 5 th iteration of sensitivity analysis; this 

extends to the 7 th ranked position (South Vancouver Island) in the 1 st iteration. 

Therefore, when examining the cardinal values, regions that have BCAHWI scores 

close to each other are more sensitive to changes in rank when weights are altered. 

Due to the fact that rank sum has proved to be the most robust weighting scheme out 

of the two based on local knowledge, it was used in subsequent analysis of gender 

variation and examining possible correlations of factors with adolescent health and 

wellness in BC. 

118

3.4.3 Results of Cluster Analysis 

Cluster Analysis illustrates that there appears to be some regional effects on 

adolescent health and wellness throughout the province. Table 20 shows the 

corresponding results when the numbers of clusters are defined at 3, 4 and 5. The 

shading indicates a cluster. 

119 

Table 20: Patterns of clusters using Ward's method of hierarchical clustering (rank sum 

weighting) 

3 Clusters 4 Clusters 5 Clusters BCAHWI 

Score 

Fraser South Fraser South Fraser South 0.71 

Fraser North Fraser North Fraser North 0.66 

Vancouver Vancouver Vancouver 0.62 

Okanagan Okanagan Okanagan 

0.57 


Garibaldi 


Garibaldi 


Garibaldi 

0.56 

Richmond Richmond Richmond 0.55 

S. Vancouver Isl. S. Vancouver Isl. S. Vancouver Isl. 0.52 

East Kootenay East Kootenay East Kootenay 0.51 

Kootenay Boundary Kootenay Boundary Kootenay Boundary 

0.43 

Thompson/Cariboo Thompson/Cariboo Thompson/Cariboo 

0.42 

N. Vancouver Isl. N. Vancouver Isl. N. Vancouver Isl. 

0.41 

Central Vancouver Isl. Central Vancouver Isl. Central Vancouver Isl. 

0.37 

N. Interior N. Interior N. Interior 0.36 

Northwest Northwest Northwest 0.18 

It is possible to see that different number of clusters yield different results. Further 

examination reveals that adolescent health and wellness display some regional 

effects. There is a cluster of high adolescent health and wellness in the lower 

mainland region, with the exception of Richmond while the Northwest is a cluster of 

low levels of adolescent health and wellness unto itself. This confirms the patterns 

that were identified in Section 3.4.1. Figure 13 shows this pattern on a map.

Figure 13: Map of 4 clusters produced by hierarchical cluster analysis 

3.4.4 Results of Male/ Female Variation 

The following two maps (Figures 14 and 15) show the results of the TOPSIS 

technique when conducted using the ideal positives and negatives of each group 

under study. 

120

Figure 14: Results of female BCAHWI, calculated using rank sum weights 

Figure 14 illustrates that females in Fraser South and Fraser North have the highest 

ranking on the Female BCAHWI, while females in Central Vancouver Island and the 

Northern Interior rank quite low and the Northwest ranks the lowest. 

121

Figure 15: Results of male BCAHWI, calculated using rank sum weights 

When examining the Male BCAHWI scores, Figure 15 shows that the regions of 

Fraser South and Fraser North both rank high, followed by Vancouver, the North 

Shore/ Coast Garibaldi and the Okanagan. The regions of North Vancouver Island, 

Central Vancouver Island and Northern Interior rank fairly low followed by the 

Northwest. This is similar to the pattern found with the female BCAHWI score. 

122

However, when the ideal positive and negative values are altered so that comparisons can 

be made, females ranked higher than males in all HSDAs. The results of this are given in 

Figure 16, which shows that females have higher adolescent health and wellness scores in 

all health regions across the province. This difference is most pronounced in the Fraser 

South and the Okanagan. Also, the rankings alter and Vancouver ranks highest for males. 

Index Score 

0.80 

0.70 

0.60 

0.50 

0.40 

0.30 

0.20 

0.10 

0.00 

Fraser South 

Fraser North 

Vancouver 

Okanagan 


Garibaldi 

Figure 16: Index score for females and males when the maximum and minimum values of 

3.5 Discussion and Conclusions 

both genders are applied 

Adolescent health and wellness varies geographically and by gender. This is an important 

finding as it reveals the presence of health and wellness inequalities across the Province 

for those in adolescence. These findings are discussed in this section. 

S. Vancouver Isl. 

Richmond 

East Kootenay 

HSDA 

N. Vancouver Isl. 

Kootenay Boundary 

Thompson/Cariboo 

Central Vancouver Isl. 

Female 

Male 

N. Interior 

Northwest 

123

External and internal factors were examined for the BCAHWI (using rank sum) in order 

to understand adolescent health and wellness variation within the province. This was 

drawn from a study by Bradshaw and Richardson (2009) who applied a similar method to 

examine the variation in child well-being in Europe. 

External Factors 

Examining the patterns found in this research allows some insight into what external 

factors might be influencing adolescent health and wellness. Fraser South and North (the 

top two ranked HSDAs on the BCAHWI, regardless of weighting scheme) are comprised 

of mainly urban centres and have a low percentage of government transfer income, while 

Vancouver which also ranks high is an urban region. The Northern Interior and 

Northwest (the last 2 ranked HSDAs on the BCAHWI, using the rank sum and rank 

reciprocal weighting schemes) are rural regions with moderately high government 

transfer incomes (CIHI, 2003). Building on this and using census data available from BC 

Stats, possible factors associated with adolescent health and wellness are explored. The 

external factors considered are: economic status -measured by single variables rather than 

composite measures [2005 average income and 2005 incidence of economic families in 

low income as defined by Statistics Canada's Low Income Cut-Offs (LICO) to total 

economic families which varies by family size and by community] (Statistics Canada, 

2010b) and rural/urban demographics (measured by 2009 estimated percent of total 

population contained in each HSDA and 2006 estimated population density rate per 

1000km squared). Here the terms urban and rural are both used in their broadest and most 

124

common context of concentration of the population at high and low densities. It is 

important to note that urban and rural often operate on a continuum and different 

definitions can lead to different results (du Plessis et al., 2001; Puderer, 2009). 

Correlations were tested for using Spearman’s correlation coefficient as the relationships 

appeared non- linear between the variables and the rank sum BCAHWI scores (as shown 

in Figures 17-20). The association was highest for percent of the total population .798 

(significant at the .01 level), followed by the population density .763 (significant at the 

.01 level). Average family income was significant at .648 (at the .05 level). The incidence 

of low income in economic families was insignificant at .350. This indicates that there is 

an association between urban/rural measures and overall adolescent health and wellness 

while an association between economic measures and adolescent health is not confirmed 

as the results vary depending on the economic variable used. 

125

Figure 17: Relationship between BCAHWI (using rank sum weights) and the percent of the 

total population 

Figure 18: Relationship between BCAHWI (using rank sum weights) and the population 

density rate 

126

Figure 19: Relationship between BCAHWI (using rank sum weights) and the average 

family income, 2006 

Figure 20: Relationship between BCAHWI (using rank sum weights) and the incidence of 

low income in economic families, 2006 

The finding that measures of urban/rural are positively associated with overall adolescent 

health and wellness is supported in the health literature. In BC an established north-south 

gradient influences access to services, mortality and morbidity (Tonkin & Murphy, 

127

2002). Groft et al. (2005) stated that generally rural Canadians are less healthy than urban 

Canadians. Rural adolescents have higher rates of high risk behaviour (such as substance 

use) (Atav & Spencer, 2002) and rural areas often have less youth services and programs 

and have less access to transportation (Shepard, 2005; Tonkin & Murphy, 2002). 

To further investigate this relationship between adolescent health and wellness and 

urban/rural populations, another variable was examined for correlation with the 

BCAHWI using the MCS created dichotomous rural or small town (RST)/ or census 

metropolitan area (CMA) or census agglomeration (CA), based on Statistics Canada’s 

definitions. This variable classifies each student who took the AHS 2008 into one of 

these categories based on the postal code of the school he/she attends. Rural or small 

town is defined as populations living outside of the commuting zones of urban cores with 

a population of 10,000 or more (du Plessis et al., 2001; Statistics Canada 2010b). There 

are several sub-categories of rural or small town but these were not used by MCS as there 

were not enough students in each subcategory to make reliable comparisons. This 

variable became available to examine the relationship between it and the BCAHWI May 

2010. Figure 21 illustrates the relationship between the overall adolescent health and 

wellness and the percentage of students that are classified as rural or small town. Due to 

the non-linear association, again Spearman’s correlation coefficient is used. 

128

Figure 21: Relationship between BCAHWI (using rank sum weights) and percent of 

students classified as rural/small town 

There is a negative association, Spearman’s correlation coefficient =-.664, that is 

statistically significant at the .01 level. This confirms the positive correlations of the other 

measures of population density and the BCAHWI and also confirms that these 

relationships are not linear. Table 21 summarizes the above findings. 

Indicator 

Table 21: External factors related to BCAHWI, n =14 

Spearman’s Correlation with BCAHWI 

Percent of total population 

.798** 

Population density 

.763** 

Percent of students classified as rural/small town -.664** 

Average income 

.648* 

Incidence of low income in economic families .350 



Internal Factors 

In addition to examining external factors, analysis of the internal factors of the BCAHWI 

can tell what indicators are most closely associated to the overall adolescent health and 

129

wellness (Table 22) and what indicators are not associated (Table 23) (Bradshaw & 

Richardson, 2009). 

Table 22: Indicators related to BCAHWI, n =14 

130 

Indicator Pearson’s Correlation with BCAHWI 

% Not passing or completing grade 10 English 

provincials 

-.862 ** 

% of Child Welfare Contacts 

-.810 ** 

% Healthy Weight 

.781 ** 

Teen Pregnancy Rate 

-.778 ** 

% Not Reporting a Chronic Conditions 

.736 ** 

% Residing Outside of Parental Home 

-.731 ** 

Juvenile Crime Rate 

-.708 ** 

Average School Connectedness 

.707 ** 

% of 18 year olds not Graduating High School 

-.689 ** 

% Not Engaging in Illicit Drug Use 

.689 ** 

% Reporting Positive Peer Influences 

.680 ** 

% Who Have Not Considered Suicide in the Last Year .659 * 

% Reporting Freedom from Abuse 

.591 * 

% Reporting to be a Non-smoker 

.580 * 

Average Family Connectedness 

.555 * 

% Who Have Their Own Room -.546 * 



Table 23: Indicators not significantly related to BCAHWI, n =14 

Indicator Pearson’s Correlation with BCAHWI 

% Reporting Good to Excellent Health 

.450 

% Eating Fruit or Vegetables 5 or More Times a 

Day 

.400 

% of Teen Mothers Who Did Not Have Tobacco 

and Alcohol Identified as a Risk Factor 

.352 

% Who Report Having Positive Adult Mentors -.314 

% Reporting a Strong Sense of Belonging to the 

Local Community 

.306 

% Who Feel They Are Good at Something -.288 

% Who Score Active or Moderately Active on the 

Leisure Time Physical Activity Index 

.198 

% Who Report Good to Excellent Mental Health .145 

The majority of the indicators have a significant correlation with the BCAHWI scores. 

Table 22 presents the indicators with significant Pearson correlation coefficient to the

BCAHWI scores in rank order. Interestingly, having ones own room is statistically 

significantly negatively correlated with the overall index. Although this is a question used 

in national surveys outside of Canada (i.e. Currie et al., 2008) as a way to measure family 

affluence, it could be that in BC this is a more complex indicator that needs more 

exploration. Vancouver, Fraser South, Fraser North and Richmond all have relatively 

high BCAHWI scores. They also have the lowest proportions of students with their own 

bedroom. The Northwest which scores the lowest on the BCAHWI has a high proportion 

of adolescents who have their own room; this can be interpreted to mean the other factors 

in the urban regions of Vancouver, Fraser South, Fraser North and Richmond outweigh 

the fact that more adolescents share a bedroom (Figure 22). For instance the 2006 census 

found these areas to have the highest proportion of visible minorities, in BC, with the 

majority being Chinese or South Asian, perhaps pointing to a cultural factor influencing 

adolescents having their own room. 

131

Figure 22: Relationship between BCAHWI and % of adolescents with their own room 

Of the indicators not associated with overall adolescent health and wellness the majority 

are survey data, with the exception of tobacco and alcohol use of teenage mothers (this 

may point to the fact that this is a subset of the population that may not best represent the 

health and wellness of the adolescent population as a whole). The three indicators that 

have the least amount of association are explored below. 

• The variable that is associated with the BCAHWI the least is whether perceived 

mental health is good to excellent (although % not having thoughts of suicide in 

the last year does show statistically significant association). Adolescents in 

Richmond have a relatively low proportion of 12-19 year olds reporting good to 

excellent mental health while ranking 6 th in the BCAHWI scores. While those in 

the Northwest have a relatively high proportion of adolescents reporting good to 

excellent mental health and rank last in the BCAHWI scores. 

132

• The proportion of those who score active or moderately active on the leisure time 

Physical Activity Index is also not associated with general adolescent health and 

wellness. This suggests that physical activity only matters if it results in a healthy 

weight (which does show a statistically significant association) at the population 

level. Another possibility is that like the BMI this calculation is best suited for 

adults should be altered to reflect adolescents. 

• The proportion of adolescents who feel they are good at something is not 

Gender 

associated with overall adolescent health and wellness. This is attributed to the 

fact that Vancouver and Richmond (which rank moderately high on the 

BCAHWI) both have low proportions of adolescents who feel they are good at 

some things while the other HSDAs show little variation in this indicator (84.96- 

88.32%). 

The TOPSIS technique can be applied to males and females to examine gender 

differences in health and wellness. Some indicators could not be examined by gender due 

to unavailability of data. The sample size shrinks when teasing out the subsets of gender 

and thus less confidence can be had in these results. Also, the indicators deemed 

influential and their weights were selected for the general population. If this study were 

repeated asking the same panel to select the most influential indicators for adolescents in 

BC specifically to males or females, the results may vary. Still, the results are important 

as they show that male and female adolescents experience health and wellness differently 

133

across the province. When comparing males and females, females score higher in every 

HSDA. These gender differences are worth examining further in future studies. 

Final Discussion 

By utilizing local knowledge and spatial MCA it is possible to gain an understanding of 

the geographic inequalities of adolescent health and wellness in the Province of BC. The 

TOPSIS model allows for correlating indicators to be combined to create cardinal values 

for each HSDA. Using a composite index, the complexity of adolescent health and 

wellness can be visualized in order to gain a better understanding of the spatial patterns 

of adolescent health and wellness. Also of note, the HSDAs with the lowest BCAHWI 

scores (Northwest and Northern Interior) have a relatively high percent of adolescents in 

that region, thus, these areas should be a focus for policy and decision makers. 

3.6 Limitations and Considerations 

It is important to note that there are considerable demographic differences within the 

HSDA boundaries and interpretations can be misrepresented by the high level of 

aggregation (Frohlich & Mustard, 1996). HSDAs are an administrative boundary and 

thus rather arbitrary. Moreover the size and shape of the boundaries vary greatly. The 

modifiable aerial unit problem (MAUP) comes into play in spatial MCA as different 

levels of aggregation will produce different outcomes. Usually data at higher resolutions 

(disaggregate) are less biased and, on average, the larger the unit the higher the 

correlation between two variables (Malczewski, 1999 & 2000). This should be taken into 

account when using the BCAHWI data to look at correlations with external factors. The 

positive aspect of using highly aggregated units is that the areas are less likely to be 

134

influenced by the ‘small number problem’ (where the addition and subtraction of a single 

individual can alter the estimate drastically, thus yielding unstable estimates) (Gatrell, 

2002). As there are only 14 units, some of which are non-contiguous polygons, 

significant spatial clusters utilizing spatial analysis of autocorrelation cannot be detected. 

Using area data in this study opens up criticisms of ‘ecological fallacy’, being the 

implication that all individuals living in an area share in characteristics. This is clearly 

not the case (Carstairs, 2000; Frohlich & Mustard, 1998). It is important to note that in 

exploratory studies of spatial patterns, such as this, the inference remains at the 

aggregated level (Richardson and Monfort, 2000). Therefore, the conclusions of this 

study can only be applied to the HSDA level and not to lower levels of aggregation or 

individuals. 

Due to the high variability in area size, large areas in the North that are sparsely 

populated are given high “visual weight.” A cartogram may be a solution to this 

visualization issue (Gatrell, 2002) but this was not the focus of this study. 

Another limitation that is common in general indices is that they often do not address 

issues that pertain to “minority groups that are too small in number to be representative of 

the general samples of the population” (Bradshaw & Richardson, 2009). This is also true 

of this study. Data on various minority groups, such as different ethnicities, were not 

readily available by the majority of data sources. It is possible, however, to look at male 

135

and female variations but the indicators that are able to be reduced to these subsets are 

limited. 

Another consideration is that due to the nature of the survey data only students in public 

schools were assessed in the AHS 2008 and the CCHS 2007/2008 does not measure those 

on reserves or without residence; therefore, marginalized (i.e. those who have dropped 

out of school or who are living on the street) and “privileged” adolescents (i.e. those who 

are in high cost private schools) are not represented in these data sources. 

It is vital to emphasize that spatial MCA provides a greater understanding to a situation 

rather than providing a solution (Malczewski, 2000). Malczewski (2000) stated that often 

the process of the research will yield a better understanding than the output of the study. 

This is clearly a comment on the value of the process as a whole rather than examining 

just the yield. As such, the indicators identified by the panel of expertise may be relevant 

at the time of this study. As social climates change the indicators will, however, need to 

be reviewed for relevance (Carstair, 2000). 

136

4.1 Summary 

4. Discussion and Conclusions 

Measuring human health and wellness involves accounting for the holistic nature of 

people. There are many indicators that can be used to assess this. Adolescence is a unique 

timeframe in the life trajectory and should be gauged using indicators that are tailored to 

it. There is no consensus on what indicators are the best when examining adolescent 

health at the population level. This study utilized the knowledge of a panel of expertise to 

derive a set of 24 indicators that were deemed influential in measuring adolescent health 

and wellness and had data available from secondary sources to measure them. Weights 

were assigned to each indicator for use in index construction. 

The results of the BCAWHI reveal that there are geographic inequalities experienced in 

adolescent health and wellness. The pattern shows clustering of high adolescent health 

and wellness in the lower mainland (with the exception of Richmond). These areas can be 

used as a benchmark for policy and decision makers. Also addressed was geographic 

variation by gender, which showed similar patterns. Males show lower levels of 

adolescent health and wellness than females across the province when directly compared. 

4.2 Study Contributions 

The health inequalities outlined in this study have implications for those who plan the 

health services of the province. This study reveals inequalities in adolescent health and 

wellness that can be examined further to find ways to protect and promote health at the 

population level. One key finding is that geography does influence adolescent health and 

137

wellness in BC. The findings of this research could be used in informing discussions of 

resource allocation for reducing inequalities and inequities and in order to target future 

research. 

4.3 Recommendations for Future Research 

The findings of this research support views that the health of rural adolescents should be 

further researched in order to understand the perspectives of this portion of the 

population. Composition and contextual factors could be examined to develop a model of 

how the inequalities have arisen. An example would be facilitating community 

involvement in order to understand how levels of health can be raised in areas 

experiencing low levels of adolescent health and wellness. Indices that address the 

circumstances of minority groups, such as aboriginal and adolescents with disabilities, 

could be developed in order to complement the BCAHWI. Also, a study that includes 

adolescents in choosing the indicators included in the index would be of great value in 

better understanding the inequalities shown in this study. In addition, exploring the male/ 

female gender differences is something that warrants further study. 

Finally, it would valuable to repeat this at a lower level of aggregation. This would lend 

itself to revealing the variations within each HSDA and could further aid in policy and 

decision making. Considerable data access complications make this unlikely in the near 

future, but it would be a worthwhile project to undertake . 

138

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155

Appendix A 

Ethics Approval 

156

Dear _____: 

Appendix B 

Email Script 

You are being invited to participate in a study entitled “Geographic Variations and 

Contextual Effects on Adolescent Health in British Columbia, Canada”, conducted by 

myself, Gina Martin, a Masters student at the University of Victoria. This research is part 

of a Master’s Thesis conducted under the supervision of Dr. C. Peter Keller and Dr. Les 

Foster, who can be contacted at (250) 472- 5058 or by email at soscdean@uvic.ca and 

(250) 386- 5933 or by email at lfoster@uvic.ca respectively, and is funded in part by the 

Social Science and Humanities Research Council of Canada. 

The objective of this research is to examine patterns in adolescent health and wellness in 

British Columbia, Canada. A key component of this research is obtaining the opinions of 

a panel of experts in order to construct an index of adolescent health using available and 

up to date data sources. Your opinion would be of great value to this study. 

In order to accomplish this you are being asked to undertake a series of questionnaires. 

The questionnaires will be delivered by e-mail and can be completed at any location that 

is most convenient to you. 

If you have any interest in participating in this study please take the time to read the 

attached Letter of Consent. 

Please feel free to contact me with any question that you may have at (250) 472-4624 or 

by email at gcmartin@uvic.ca . 

Thank you 

Gina C. Martin 

MSc Candidate 

GIS Laboratory 

Department of Geography 


157

Appendix C 

Consent Form 

Letter of Consent 

Geographic Variations and Contextual Effects on Adolescent Health in British 

Columbia, Canada 

You are being invited to participate in a study entitled “Geographic Variations and 

Contextual Effects on Adolescent Health in British Columbia, Canada”, conducted by 

Gina Martin, a Masters student at the University of Victoria. You can contact her at (250) 

472- 4624 or by email at gmartin@uvic.ca. This research is part of a Master’s Thesis 

conducted under the supervision of Dr. C. Peter Keller and Dr. Les Foster, who can be 

contacted at (250) 472- 5058 or by email at soscdean@uvic.ca and (250) 386- 5933 or by 

email at lfoster@uvic.ca respectively. 

The objective of this research is to examine patterns in adolescent health and wellness in 

British Columbia, Canada. A key component of this research is obtaining the opinions of 

a panel of experts in order to construct an index of adolescent health using available and 

up to date data sources. In order to choose and weight the factors that make up a healthy 

and well population, your opinion would be of great value. 

An open ended survey will be initiated to identify a list of indicators of the health and 

wellness of adolescents in British Columbia, Canada. A second and third survey will 

follow to validate and weight the list acquired from the first survey. A fourth survey 

maybe implemented if deemed necessary by the research team. You will also be given 

the opportunity to comment on whether you feel that certain indicators are indicative of 

the same underlying domain of health or wellness. 

The surveys will be delivered by e-mail. A period of approximately one month is 

anticipated between surveys and it is anticipated that they will take approximately 30-60 

minutes to complete. 

Your participation in this research is completely voluntary and you may drop out of the 

study at anytime. If subsequent to the first survey you choose to drop out, the data may 

already be aggregated with the other results and then sent out in Questionnaire 2 or 3; this 

makes it difficult to remove individual participant data. Withdrawal will specify that 

further questionnaires will not be sent to you; however, data already given will remain in 

the study. Your confidentiality will be protected. All data will be stored and analyzed 

within the GIS Laboratory at the University of Victoria. Digital records will be destroyed 

upon project completion (spring 2010). Your anonymity will be protected, and results 

will not include any personal identifiers. Only the coordinators of this project will be 

aware of participant identities. However, there is a limit to the level of confidentiality 

based on the small sample size of people being asked to participate in this study. 

158

The results of this analysis will be valuable to inform policy makers for use in decision 

making. It is hoped that by learning from those regions with high levels of adolescent 

health and wellness, geographical health disparities can be reduced and the province itself 

can become healthier. 

It is anticipated that the aggregated results from this study will be shared with academics, 

policy makers and the general public. The results will form a foundation of a Masters 

Thesis and may be published and presented at research conferences. 

At the conclusion of this study, each participant will be sent a short executive summary 

(~ 2 pages) of the findings; including links where it is possible to access other results of 

the study. 

In addition to being able to contact the researcher and supervisors at the above phone 

numbers, you may verify the ethical approval of this study, or raise any concerns you 

might have, by contacting the Human Research Ethics Office at the University of 

Victoria at (250) 472-4545, or by email at ethics@uvic.ca. 

If you wish to participate in this study please fill out the fields below and send a digital 

copy to gcmartin@uvic.ca. This indicates that you understand the above conditions of 

participation in this study and that you have had the opportunity to have your questions 

answered by the researchers. Subsequent correspondence will be sent to the email address 

provided. 

Name of Participant Participant email 

Signature (may be typed) Date 

159

Appendix D 

Study Initiation Email Script 

Thank you for agreeing to take part in this Study, which is concerned with identifying a 

list of indicators of health and wellness for adolescents in British Columbia, Canada. 

The First Round Questionnaire is divided into two sections. Section 1 presents a short 

series of questions pertaining to your background and experience. Section 2 asks you to 

identify up to 12 indicators that you feel are influential in measuring adolescent health 

and wellness and asks for a brief reasoning of why you feel that indicator should be 

included in the list. 

For the purpose of this Study, please consider the following: 

The Canadian Pediatric Society (CPS) defines adolescence as beginning at the onset of 

physiologically normal puberty, and ending when adult identity and behavior are 

established. According to the CPS this period of development corresponds roughly to the 

period between the ages of 10 and 19 years, which is consistent with the World Health 

Organization’s definition (WHO, 2008). Additionally, for administrative and research 

purposes, it can be useful to define adolescence by middle and high school years as this 

group faces many similar challenges and issues within society (CPS, 2008). 

Increasingly, focus in population health studies has taken a wellness perspective rather 

than an illness perspective (Bringsen et al., 2009 & PHAC, 2008). In 1948, the WHO 

defined health as “a state of complete physical, mental and social well-being and not 

merely the absence of disease or infirmity” (WHO, 1948). This began a societal shift in 

moving to looking at health from a holistic view point rather than a purely physiological 

one (Miller and Foster, 2007). Wellness definitions have varied within the literature; 

however they tend to measure states of positive health on a continuum and from a holistic 

view point (Miller & Foster, 2007). It is the goal of this study to draw on the WHO’s 

holistic definition of health and use a wellness perspective. 

Therefore, it is hoped that age appropriate indicators are identified that assess the health 

and wellness (as outlined above) of the adolescent population in British Columbia, 

Canada. 

Thank you, 

Gina C. Martin 

MSc Candidate 

GIS Laboratory 



160

Appendix E 

Round One Survey 

Background Information: 

Name: 

Note: Only coordinators of this research project will have access to this information 

In what capacity are you responding to this questionnaire? 


Researcher 


Other: 

Please Specify: 

Please briefly highlight your experience in the field of youth and adolescents 

&/or health: 

How many years have you been working in this area? 

161

Questionnaire 1: 

Name: 

Date: 

Note: Only coordinators of this research project will have access to this information 

In the space provided below please identify up to 12 indicators that you feel 

are influential in measuring adolescent health and wellness within British 

Columbia, Canada: 

(These can also reflect an absence of a negative indicator i.e. abstaining from 

smoking) 

1. 

2. 

3. 

4. 

5. 

6. 

7. 

8. 

9. 

10. 

11. 

12. 

162

In the space provided below please provide a brief reasoning why you feel the 

corresponding indicator is influential in measuring adolescent health and 

wellness within British Columbia, Canada: 

1. 

2. 

3. 

4. 

163

5. 

6. 

7. 

164

8. 

9. 

10. 

165

11. 

12. 

166

Appendix F 

Round One Results Report 

Results of Round One Questionnaire on Adolescent Health and Wellness 

Geographic Variations and Contextual Effects on Adolescent Health in British 

Columbia, Canada 

Prepared by: 

Gina Martin 

MSc Candidate 



167

Round One Summarization and Results: 

First it is my pleasure to thank you for taking the time to complete the first round of 

questionnaires in this study. Your responses were of great value and your time is greatly 

appreciated. The response rate was slow as many participants were on holiday during the first 

round. However, we are pleased to say that the first round of analysis is complete. The following 

sections outline a summarization of the results. 

Section 1: 

Of 19 participants who completed the Letter of Consent 14 participants completed the 

Round One Questionnaire. The mean number of years each participant worked in their 

capacity in the field of youth and/or health was 15.6, with a maximum of 32 and a minimum 

of 6. There was a standard deviation of 8.5 years. Figure 1 shows the breakdown of the self 

identified positions of the participant panel. 

Figure 1: Participant population self identified position 


Researcher 


Other 

168

Section 2: 

The first round responses were analyzed and aggregated using thematic analysis. Thematic 

analysis refers to an iterative process of recovering identifiable themes within data through 

reading and summarization. It is inductive in that the themes emerge from the data. The 

responses were consolidated into a single list using your answers of Question 1 and 2. When 

several different terms were used for what appears to be the same issue, the terms were 

listed together and one description of the indicator was provided. 

After nine iterations of thematic analysis 62 indicators were identified, 19 of which were 

identified by 3 or more participants. The indicators were further broken down into 9 domains 

for ease of interpretation, although it must be noted that the domains are linked. Table 1 

shows the complete list of indicators identified. All the items on this list were brought 

forward by the participant panel; no item was added by the research team. 

169

Table 1 Summary of identified indicators, consolidated responses and number of participants who identified the indicator 



Physical Activity physical activity/exercise amount/ involvement in 

recreation or sporting activities/ physical 

development 

Healthy Weight healthy weight/obesity/ appropriate weight & height 

for age/ overweight rate of 10/12 students/ BMI or 

waist circumference 

Healthy Diet healthy diet/ healthy eating/ fruit and vegetable 

consumption/food choices/ nutrition/ food security 

Freedom From Chronic Conditions free of chronic disease/ chronic conditions 

Self Rated Health self rated health 

Screen time time spent on computers or alone/ in front of display 

terminals; TV, computer or games 

Time for leisure extra curricular activities/ leisure time 

Freedom From Allostatic Load freedom from allostatic load 

Good Nutritional Knowledge good nutritional knowledge 

Health Literacy health literacy 

Oral/ Dental Care oral/ dental care 

Physical and Mental Health Admission Contacts physical and mental health admission contacts 

Relationships Family Connectedness family connectedness/ loving supportive family/ 

family functioning/ relationship with parents, 

guardian & siblings/ social development and sense of 

belonging (family/extended) 

Positive Peer Influence positive peer relations/ influence of peer pressure 

/positive and supportive peer groups/ social 

development and sense of belonging (peers) 

Residing Outside Of Parental Home number of children living out of parental home/ youth 

custody rates 

Single Parent Families single parent families 

Number of Children Living With Lone Female Parents number of children living with lone female parent 

Child Welfare Contacts child welfare contacts 

Supportive Relationships supportive relationships 

Positive Adults Mentors positive adult mentors/ adults to talk to 

Collaboration Between Peers, Family, Schools and Communities strong links between peers, family, schools and 

communities 

170 


Participants 

Who Identified 

the Indicator 


8 

7 

6 

2 

2 

2 

2 

1 

1 

1 

1 

1 

9 

4 

2 

1 

1 

1 

1 

1 

1

Table 1 continued 


Community Community/Cultural Connectedness community/cultural connectedness/ family culture and 

religious experience/ social development and sense of 

belonging (work and broader community) 

Civic Engagement civic engagement/ community involvement/ social supports/ 

meaningful youth participation 

Safe Place To Be safe places to be/ safe environment/ youth safety/ emotional 

and physical safety 

Available Resources and Opportunities for Engagement community resources available/opportunities for engagement 

Housing and Neighbourhood physical environment (housing & neighborhood) 

Rural/Urban Index rural/urban index 

Education Educational Achievement education/ educational achievement/ high school graduation 

rate/ school completion rates/School achievement/ success 

School Connectedness school connectedness/ social development and sense of 

belonging (school) 

Literacy English 10 provincial exam pass rate/literacy 

Appropriate Age at School appropriate age at school 

Diverse Educational Foundation and Opportunities 

diverse educational foundation & opportunities 

Intellectual Development intellectual development 

Foundation Skills Assessment Scores foundation skills assessment scores 

Substance Use Tobacco Use tobacco/smoking/rates including tried in the last 12 months 

Alcohol Use alcohol (amount and frequency)/use/ later debut for 

alcohol/personal health and wellbeing (trying alcohol) 

Illicit Drug Use amount & frequency of drug use/ illicit drug use, including have 

tried marijuana/ substance use( drug use rates)/personal health 

and wellbeing (trying drugs) 

Tobacco/Alcohol Use of Teen Mothers tobacco/alcohol use of teen mothers 

Free of Substance Misuse free of substance misuse 

Prescription Medication Use prescription med use 

171 


Participants 


the Indicator 


8 

4 

4 

2 

1 

1 

7 

5 

2 

1 

1 

1 

1 

7 

6 

5 

1 

1 

1

Table 1 continued 


Behaviour & 

Safety 

Injury Rates injury rates/child injuries/free of avoidable hospitalization 

through injury/ preventable injury rates 

Adolescent Pregnancies number of teen pregnancies/teen non‐pregnancy rate/birth 

rate/healthy birth outcomes 

Adolescent Crime youth crime rate/ no interaction with the justice 

system/conflicts with the law 

Freedom from Abuse domestic violence/rate & incidence of child maltreatment 

Later Sexual Debut later sexual debut/early sexual activity 

172 


Participants 


the Indicator 


Hospitalization Rates of Children/ Youth hospitalization rates of children/ youth 1 

Material Material Wellness material wellbeing/average family income/ family income level 

and economic status/ percent of families on income assistance/ 

number of children in low income families/ family 

poverty/neighborhood location and median income level 

Capacity to Earn or Access Income capacity to earn or access income 1 

Mental Health Adolescent Suicide youth suicide rates/ freedom from suicide ideation 

Self Esteem self esteem/ opportunities to build self esteem 

Good Mental Health Good mental health/ psychological and emotional development 

Self Efficacy self efficacy/ skill building and mastery 

Feeling Good at Something feeling good at something 

Free of Mental Health Conditions mental health problems/chronic conditions 

Have Communication Skills have communication skills 1 

Other First Nation's Population first nations/ proportion of aboriginal population 

Early Childhood Development early childhood development 

Infant Mortality Rate infant mortality rate 

Life Expectancy at Birth life expectancy at birth 

Low Birth Rate low birth rate 

Proportion of Childhood Attendance in Early Childhood 

Education Programs 

Healthy Birth Outcomes healthy birth outcomes 

proportion of childhood attendance in early childhood 

education programs 

5 

5 

3 

3 

2 

6 

3 

2 

2 

2 

1 

1 

2 

1 

1 

1 

1 

1 

1

Thank you for taking the time to read this report. Please return to the e‐mail it 

accompanied for instructions or simply go to the following link to verify the results 

and/or make comments as you wish: 

http://www.surveymonkey.com/s.aspx?sm=dnXMUc_2fLMxEmKk1dCQKFmA_3d_3d 

173

Appendix G 

Round Two Survey 

174

Appendix H 

Round Three Survey 

175

176

177

178

179

180

181

182

183

Appendix I 

Matrix of Absolute Difference from the Mean 

E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 

0.21 0.21 0.21 0.79 0.21 0.21 0.21 0.79 0.21 0.79 0.21 0.21 0.21 0.21 

0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.79 0.21 0.21 0.21 0.21 1.79 0.21 

0.57 0.57 0.57 0.43 0.43 0.43 0.43 0.57 0.57 0.43 0.43 0.57 0.43 0.43 

0.57 0.43 0.57 0.43 0.43 0.43 0.43 0.43 0.57 0.57 0.57 0.57 0.43 0.43 

0.43 0.57 0.57 0.43 0.43 0.43 0.57 0.57 0.57 0.43 0.43 0.43 0.57 0.43 

0.57 1.57 0.57 0.43 0.43 0.43 0.43 0.57 0.43 0.57 0.43 0.43 0.43 0.43 

0.43 0.43 0.43 0.43 0.43 0.43 0.43 1.57 0.57 0.57 0.43 0.43 0.57 0.57 

0.43 0.43 0.43 0.57 0.43 0.43 0.43 2.57 0.43 0.43 0.57 0.43 0.57 0.43 

0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 1.50 0.50 0.50 

0.50 0.50 0.50 0.50 0.50 1.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 

0.50 0.50 0.50 0.50 0.50 0.50 0.50 1.50 0.50 0.50 0.50 0.50 0.50 0.50 

0.54 0.46 0.54 0.54 0.54 0.54 0.54 0.46 0.46 0.46 0.46 NA 0.46 0.46 

0.57 0.43 0.43 0.57 0.57 0.57 0.57 0.43 1.43 0.57 0.57 0.43 0.57 1.43 

0.57 0.57 0.43 0.43 0.57 0.57 0.43 0.43 0.43 0.43 0.43 0.57 0.43 0.57 

0.64 0.64 0.64 0.64 0.64 0.64 0.36 0.36 0.36 0.64 0.64 3.36 1.36 0.64 

0.64 0.36 0.64 0.64 0.64 0.64 0.36 2.36 0.64 0.64 0.36 2.36 0.64 0.64 

0.31 0.31 0.69 0.69 0.69 0.69 0.31 0.31 0.31 0.69 0.31 NA 0.31 1.31 

0.29 0.29 0.71 0.71 0.71 0.71 0.29 0.29 1.29 0.29 0.29 1.29 0.71 0.71 

0.21 0.21 1.21 0.79 0.21 0.79 0.79 0.21 0.21 0.21 0.79 1.21 0.79 0.21 

0.86 1.14 0.86 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.86 

0.14 0.86 0.14 0.14 0.86 0.14 0.14 1.14 0.14 1.14 0.14 0.86 0.14 0.86 

0.14 1.14 0.14 0.14 0.86 0.14 0.86 1.14 0.14 1.14 0.14 0.86 0.86 0.86 

0.08 0.08 0.92 0.92 0.92 0.92 1.08 0.08 1.08 0.08 0.08 NA 1.08 0.08 

0.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 1.00 1.00 1.00 1.00 0.00 0.00 

1.00 0.00 1.00 1.00 1.00 1.00 0.00 1.00 1.00 0.00 1.00 0.00 1.00 1.00 

2.00 1.00 1.00 1.00 1.00 1.00 1.00 0.00 0.00 1.00 0.00 NA 0.00 1.00 

0.00 0.00 0.00 1.00 0.00 0.00 1.00 1.00 1.00 0.00 0.00 0.00 1.00 1.00 

0.92 0.92 1.08 1.08 0.08 1.08 0.08 0.08 0.08 0.08 0.92 NA 0.08 0.92 

184

E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 

0.92 0.92 0.08 1.08 1.08 0.92 0.08 0.08 0.92 0.08 0.08 NA 0.08 1.08 

0.86 0.14 0.86 0.14 1.14 1.14 1.14 0.86 0.86 0.14 0.14 0.86 1.14 0.86 

0.15 0.85 0.15 1.15 1.15 0.15 0.15 0.15 0.85 0.85 0.15 NA 0.85 0.15 

1.21 1.79 0.21 0.21 1.21 1.21 0.79 0.21 0.79 1.79 0.21 0.21 0.79 1.21 

0.69 0.69 0.31 1.31 1.31 1.31 0.31 0.31 0.69 0.69 NA 0.69 0.31 1.69 

0.67 2.67 NA 1.33 1.33 0.67 1.33 1.67 0.33 0.33 1.33 NA 1.33 1.67 

0.36 0.64 0.64 1.36 0.36 0.64 0.36 0.36 0.36 0.64 1.36 0.64 0.64 0.64 

0.36 2.64 0.36 1.36 1.36 1.64 0.36 0.36 0.36 0.64 0.64 1.36 0.36 0.64 

0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 1.50 0.50 0.50 

0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 

0.43 0.43 0.43 0.57 1.57 0.57 0.57 0.57 1.43 1.43 0.43 1.57 0.43 0.43 

1.57 0.43 0.43 0.57 0.43 0.43 0.57 1.43 0.43 0.43 0.43 0.57 0.57 0.57 

1.36 1.36 0.64 1.64 0.64 1.64 0.64 1.36 0.36 0.36 1.36 0.36 1.64 0.36 

0.64 0.36 0.64 0.36 1.64 0.36 1.64 0.64 1.36 0.64 1.36 1.36 0.64 1.36 

0.31 2.31 0.69 0.69 0.69 0.69 1.69 1.31 0.69 0.31 0.31 NA 1.69 2.31 

0.29 0.29 0.71 0.71 1.71 0.29 1.29 0.29 0.29 0.29 1.71 0.29 0.29 1.29 

0.23 2.23 1.23 0.77 1.77 1.23 0.77 0.77 0.23 0.77 0.77 NA 1.23 0.77 

0.79 0.21 0.21 0.21 1.79 0.21 0.79 0.21 1.21 0.21 0.79 1.21 0.21 0.21 

0.86 2.14 1.14 0.86 1.86 1.14 0.86 0.86 0.14 0.86 0.86 2.14 1.14 0.86 

1.91 NA NA 0.09 0.91 0.09 1.09 0.09 0.09 0.91 NA 2.09 0.09 0.09 

Average 0.59 0.78 0.55 0.65 0.77 0.65 0.58 0.67 0.57 0.55 0.54 0.87 0.64 0.72 

185

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Appendix J 

Individual Indicator Maps 

Note: Indicators are classified so that dark green indicates higher levels of health and 

wellness and beige indicates lower levels of health and wellness. This may seem counter 

intuitive in examining the indicators that are to be minimized in the index. Care should be 

taken when interpreting the maps.

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Appendix K 

Cluster Analysis Dendrogram 

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