Behavioural Surveillance Surveys - The Wisdom of Whores

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The following is one example of how to calculate the 95 percent confidence interval from the above data. Male STD patients: n = 435, p = 15. 95% CI = 15 ± 1.96 * √(15*85) = 15 ± 3.36 95% CI = 11.6 - 18.4 √435 From these data, it appears that condom use during the most recent commercial sex act is relatively low among male STI patients in Tamil Nadu, and since the sample size is large, this can be asserted with some degree of confidence. It is also entirely plausible: recent unprotected sex with a sex worker may well be the source of the STI which led to the respondent’s inclusion in the survey sample. At the other end of the spectrum lie male students, 80 percent of whom used a condom the last time they had sex with a sex worker, according to the survey findings. However because only 18 students reported sex with a sex worker in the last year, the denominator for this indicator is very small and the confidence interval is very wide. The range of the proportion of students using condoms the last time they had sex with a sex worker, according to these calculations, lies anywhere between 62 and 99 percent. In other words, anywhere from under two thirds of students to almost all students used condoms the last time they had sex with a sex worker. However, this very large range makes it difficult for program planners to respond with appropriate prevention programs for this group and to gauge how successful previous efforts have been. If the true value lies closer to 99 percent, then condom use is very high and the battle has already been partially won. In this case, prevention strategies (and corresponding funding) could be best geared for the maintenance of desired behaviors. But since there is a large confidence interval, the true value may just as likely lie near 62 percent, which indicates that a significant proportion of the population is still putting themselves at risk for HIV infection. This illustrates the difficulty of interpreting data that have wide confidence intervals. Bivariate analysis Bivariate analysis is used to investigate the relationship between two different variables (in the case of BSS usually categorical variables) that may be associated. Variables are associated if the value of one tells you something about the value of another. For example, level of education is generally associated with income levels. If you know that a person has a university education, you could guess that they earn more money than a person who did not finish primary school. Obviously there will be exceptions. The role of statistical tests in bivariate analysis is to determine the extent to which the association is a real one at a population level, and the extent to which it may have occurred just by chance. The most common test used in this context is known as the Chi-square test, usually written using the annotation: χ 2 77 B EHAV I OR A L S U R V EI L L A NC E SURV EY S CHAPTER 7
Table 2 : Reported number of non-regular sex partners in the last 12 months among truck drivers, by age of driver. Ivory Coast, 1998 Age Number of sexual partners, past 12 months 0 1 2 3+ Mean Total 38.7% 12.9% 19.4% 29.0% 2.1 100%
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BEHAVIORAL SURVEILLANCE SURVEYS GUI
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Family Health International (FHI) H
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Chapter 7 Analysis and interpretati
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3. Choosing populations for behavio
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G UIDELINES FOR REPEATED BEHAVIORAL
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Recognizing that HIV surveillance d
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Different approaches to collecting
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The principal rationale for focusin
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It is not possible to validate data
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GUIDELINES FOR REPEATED BEHAVIORAL
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The process of building partnership
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Funders of HIV prevention activitie
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What is to be measured ? Indicators
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Step 7 : Constructing a sampling fr
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Step 12 : Data collection and super
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choosing population groups GUIDELIN
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the spread of HIV. For this reason,
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In some circumstances, public healt
Page 37 and 38: GUIDELINES FOR REPEATED BEHAVIORAL
Page 39 and 40: The majority of sub-populations of
Page 41 and 42: In the end, the issue boils down to
Page 43 and 44: In looking at behaviors of hard-to-
Page 45 and 46: Maps derived from program planning
Page 47 and 48: Selecting primary sampling units (c
Page 49 and 50: ...when measures of size are not av
Page 51 and 52: If there is no reason to believe th
Page 53 and 54: Figure 3 : Decision tree for first-
Page 55 and 56: Implications of alternative samplin
Page 57 and 58: Table 5 : Values of Z 1-α and Z 1-
Page 59 and 60: Table 6 : Sample size requirements
Page 61 and 62: equired. In this case, the pros and
Page 63 and 64: Should one- or two-tailed z-score v
Page 65 and 66: Other measurement issues for BSS Th
Page 67 and 68: A “low-tech” solution to the pr
Page 69 and 70: 5 Weighting in multi-stage sampling
Page 71 and 72: Figure 6 : Procedures for calculati
Page 73 and 74: Calculating weights from sampling p
Page 75 and 76: Calculating standard errors with mu
Page 77 and 78: 6 Adapting and using questionnaires
Page 79 and 80: Some attempts have in the past been
Page 81 and 82: Informed consent Confidentiality an
Page 83 and 84: GUIDELINES FOR REPEATED BEHAVIORAL
Page 85 and 86: Recommended Methods of Statistical
Page 87: where n is the sample size in the d
Page 91 and 92: False conclusions: the danger of co
Page 93 and 94: Analysis of trends in behavior over
Page 95 and 96: Changes in sampling methodology ove
Page 97 and 98: 8 Using the data collected to impro
Page 99 and 100: Improving prevention programs As a
Page 101 and 102: Finally, information about HIV and
Page 103 and 104: Figure 9 : HIV and STD prevalence,
Page 105 and 106: 9 Indicators This guide identifies
Page 107 and 108: The standard questionnaires are acc
Page 109 and 110: INDICATORS Page INDICATORS FOR YOUT
Page 111 and 112: INDICATORS Page INDICATORS FOR MEN
Page 113 and 114: KEY INDICATORS What follows is a fu
Page 115 and 116: The indicator uses promoted data. O
Page 117 and 118: To be counted the numerator for thi
Page 119 and 120: Strengths and limitations Some meas
Page 121 and 122: Adult Indicator 5 Consistent condom
Page 123 and 124: Adult Indicator 7 (men only) Number
Page 125 and 126: Adult Indicator 9 Consistent condom
Page 127 and 128: Adult Indicator 11 Population seeki
Page 129 and 130: Adult Indicator 12 Exposure to inte
Page 131 and 132: Youth Indicator 2 No incorrect beli
Page 133 and 134: Youth Indicator 4 Youth sexually ac
Page 135 and 136: Youth Indicator 6 Number of sexual
Page 137 and 138: Youth Indicator 8 Consistent condom
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Youth Indicator 10 Commercial sex a
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Youth Indicator 14 Exposure to inte
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Female Sex Worker Indicator 2 No in
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Female Sex Worker Indicator 5 Sex w
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Indicators for men who have sex wit
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Men Who Have Sex With Men Indicator
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Men who have sex with men Indicator
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Indicators for injecting drug users
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Injecting Drug User Indicator 3 Inj
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Injecting Drug User Indicator 8 Dru
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Injecting Drug User Indicator 10 Dr
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Suggested Reading Brown T, Sittitra
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QUESTIONNAIRE For Adult Target Grou
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The ADULT questionnaire includes th
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FHI 1999 HIV/AIDS/STD BEHAVIORAL SU
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POSSIBLE ADDITIONAL QUESTIONS Commu
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STD Treatment seeking behaviors No.
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STD Treatment seeking behaviors (co
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QUESTIONNAIRE For Unmarried Male An
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The YOUTH questionnaire includes th
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No. Questions and filters Coding ca
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Stigma and Discrimination No. Quest
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FAMILY HEALTH INTERNATIONAL (FHI) H
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STD Treatment seeking behaviors No.
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STD Treatment seeking behaviors (co
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QUESTIONNAIRE For Men Who Have Sex
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The MSM questionnaire includes the
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STD Treatment seeking behaviours No
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No. Questions and filters Coding ca
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QUESTIONNAIRE For Injecting Drug Us
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This questionnaire includes the fol
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EXAMPLE OF SUPERVISORS’ AND INTER
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A “take-all” approach is to be
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• To probe for additional informa
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Part B: How to use the Long Distanc
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An example of a question for which
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Part C: Section-by-section review o
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Q109. If the respondent says he/she
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it is asking if the respondent has
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Section 5 : Non-regular partners Q5
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Q706 : This question determines the
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Q910 - Q912 : Knowledge of maternal
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in riskier behavior (i.e., not usin
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Section 5 : Non-paying partners Que
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3 APPENDIX WORKING EXAMPLES OF SAMP
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For the purposes of the BSS, the ke
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The second stage sampling procedure
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Notes: 1. It may not always be poss
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The information from the mapping ex
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In Figure 3, clusters were chosen b
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Fieldwork assignments when the “t
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Figure 4 : Example of targeted snow
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In some settings it may not be acce
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Figure 6 : Steps in using the segme
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Figure 9 : Map of hypothetical samp
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In-class sampling of school youth T
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Alternative to “in-class” sampl
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found at the various locations at d
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What should be done if, even after
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FIELDWORK FORMS The following set o
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Form I Cluster Selection Sheet (Pro
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Form 2 Cluster Selection Sheet (Equ
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Form 3 Assignment Information Sheet
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Form 4 Cluster Information Sheet Su
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TABLE OF CHI-SQUARE STATISTICS AND
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Step 2 : Calculate the expected num
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