areas <strong>of</strong> MCOs and contains information on health plan type, pr<strong>of</strong>it status, membership size, andmodel type. Organizational characteristics <strong>of</strong> the universe <strong>of</strong> MCOs are provided in Exhibit 1.Exhibit 1ORGANIZATIONAL STRUCTURE OF MCOS IN THEAAHP 2001 DIRECTORY OF HEALTH PLANSUnavailable2% (n=16)Health Plan TypeHMO29% (n = 263)Mix45% (n=403)PPO23% (n = 210)POS1% (n=12)IPA31% (n = 280)Model TypeNetwork15%(n = 133)Unavailable18% (n=14)Group8% (n=6)Group4% (n=36)Staff2% (n=12)Mix7% (n=68)Pr<strong>of</strong>it StatusNon -pr<strong>of</strong>itFor Pr<strong>of</strong>it32% (n =293)68% (n =611)Membership Size*Small Large32% (n = 290) 25% (n = 232)Medium38% (n=340)Unavailable5% (n=42)* • Smalall = 500-49,999• Medium = 50,000000 -199,999• Lararge = 200,000+Disadvantages associated with using an association’s directory as a study sampling frameare shared by any similar directory. First, directory listings typically have a lag <strong>of</strong> about a yearbetween information collection and publication, which could be especially problematic for thisstudy considering the level <strong>of</strong> consolidation occurring in the health care system at that time.Second, because directories <strong>of</strong>ten serve as a sort <strong>of</strong> yellow pages where purchasers can seek outorganizations in certain geographic locales, listings are <strong>of</strong>ten too detailed and repetitious. Suchrepetition may require a fair amount <strong>of</strong> file manipulation to remove duplicate listings, at somecost.During the study design phase, several characteristics and conditions were explored andconsidered for developing an appropriate sampling strategy. The most important component wasto ensure that the study sample would reflect a nationally representative group <strong>of</strong> MCOs. Asecond consideration was that the MCOs selected for study participation should includeorganizational, membership, and geographic variation.Discussions were held among the research team, OMH staff, and the project advisorygroups to determine the appropriate strategy. Initially, the research team proposed severalvariables for stratifying the sample <strong>of</strong> MCOs, including the metropolitan statistical areas (MSAs)in which the organizations reside; organizational characteristics such as health plan type,pr<strong>of</strong>it/non-pr<strong>of</strong>it status, or membership size <strong>of</strong> each MCO; and demographic characteristics (e.g.,racial, ethnic, cultural, and linguistic composition) <strong>of</strong> the population served by each MCO.Following extensive deliberations, members <strong>of</strong> the expert panel advised that the rapidlychanginglandscape <strong>of</strong> health plans—at both the service and organizational levels—was tooCOSMOS Corporation, December 2003 G-3
complex and uncertain at the time to employ a stratified sampling strategy <strong>of</strong> any sort. Becausethe health care system was in a fluid and evolutionary state, many MCOs were merging withother organizations, and many were going out <strong>of</strong> business. As such, characteristics or conditionsthat would normally be used to stratify a sample <strong>of</strong> MCOs were transient during the time <strong>of</strong> thisstudy. Therefore, the group concluded that the most appropriate strategy for selecting the studysample was the most parsimonious one, i.e., a national random selection <strong>of</strong> MCOs.To ensure that the sample drawn would be representative <strong>of</strong> all MCOs, calculations wereperformed to determine the degree <strong>of</strong> precision with which generalizations could be made to thetarget national population. The most typical measure <strong>of</strong> survey precision is the widths <strong>of</strong>confidence intervals for simple univariate estimates and for estimates within analyticalsubgroups. An analysis <strong>of</strong> confidence intervals for this study revealed that a sample <strong>of</strong> 240MCOs (from the universe <strong>of</strong> approximately 1,100 at the time) would allow 95 percentconfidence intervals for categorical variables that have widths <strong>of</strong> approximately 4-7 percent. Fortypical subgroup analyses utilizing six subgroups, a sample <strong>of</strong> 240 MCOs would yieldconfidence intervals that range in width from 6-15 percent, depending on the size <strong>of</strong> thesubgroup sample and the particular variable.2. DEVELOPING THE DATA COLLECTION PLANOnce the three respondent types (per MCO) were identified, the next step was to develop adata collection plan which would most likely render a high response rate. Here too, however, therapidly-changing structure <strong>of</strong> MCOs at the time—i.e., the evolution <strong>of</strong> the organizationsthemselves, as well as changes in the staff within the organizations—influenced the decisionsmade during the data collection planning phase.The first point <strong>of</strong> contact with each MCO would be an introductory letter mailed to theorganization’s senior executive, as listed in the AAHP directory, with a follow-up telephone callconducted to confirm receipt <strong>of</strong> the letter and determine the senior executive’s availability forstudy participation. The contact information in the directory provided a starting point forpenetrating the MCOs and for determining the appropriate senior executive to complete thetelephone interview.An interview is the most appropriate method for gathering information on an organization’spolicies, histories, and future plans; because the types <strong>of</strong> information to be collected typicallyrequire the participation <strong>of</strong> senior-level executives who have busy schedules, these respondentsare more likely to agree to a telephone interview than to a face-to-face interview (Marshall andRossman 2000). The telephone interviews for this study would be conducted using ComputerAssisted Telephone Interviewing (CATI) by experienced interviewers who would receivetraining specifically for this data collection effort. The use <strong>of</strong> CATI provides a number <strong>of</strong> timesavingadvantages in the data collection process including: electronic recording <strong>of</strong> aparticipant’s responses (eliminating the manual data entry step); electronic guidance through theinterview (e.g., making appropriate skips automatically based on the respondent’s answers); andlogic checking capability to ensure consistent responses.COSMOS Corporation, December 2003 G-4
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Developing a Self-Assessment Toolfo
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ContentsChaptersPage1. Introduction
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Chapter 1Introduction
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While many LPHAs currently provide
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LPHAs include: adult and child immu
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Professions Education Partnership A
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Chapter 2Methodology for Developing
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Public Health Practice Program Offi
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assess their own beliefs and have k
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communicate with, and clearly under
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2.1.6 Identifying Key Components of
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Given these persistent disparities
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(Exhibit 2-2, Continued)DOMAIN / KE
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limited to the actual clinical enco
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Chapter 3Project Results and Recomm
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local board of health. PEP and PAG
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numerous promising CLAS practices a
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References
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Becker, M.H., and L.A. Maiman, “S
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Frye, B., “Health Care Decision M
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Marin, G., “Defining Culturally A
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Perkins, Jane, “Overcoming Langua
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U.S. Bureau of the Census, “Censu
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Appendix AMembership Lists of the P
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Appendix A-1Developing a Self-Asses
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Appendix A-2Developing a Self-Asses
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Appendix B-1Overview of Eight CLAS
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Domain 3: Culturally Inclusive Heal
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understanding of diversity issues i
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Appendix B-2Conceptual Framework fo
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Appendix CLPHA Director or Designee
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