Size estimation of most at risk populations

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This Lecture was delivered in the First Sri Lanka National Consultation Meeting on MSM, HIV and Sexual Health, 18th – 21st November 2009
Organised and conducted by Companions on a Journey and Naz Foundation International.

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  • Dr. D. Ajith Karawita, RC (UNAIDS)
  • Dr. D. Ajith Karawita, RC (UNAIDS)
  • Dr. D. Ajith Karawita, RC (UNAIDS)
  • Dr. D. Ajith Karawita, RC (UNAIDS)
  • Dr. D. Ajith Karawita, RC (UNAIDS)
  • Dr. D. Ajith Karawita, RC (UNAIDS)
  • Dr. D. Ajith Karawita, RC (UNAIDS)
  • Dr. D. Ajith Karawita, RC (UNAIDS)
  • Dr. D. Ajith Karawita, RC (UNAIDS)
  • Dr. D. Ajith Karawita, RC (UNAIDS)
  • Dr. D. Ajith Karawita, RC (UNAIDS)
  • Dr. D. Ajith Karawita, RC (UNAIDS)
  • Dr. D. Ajith Karawita, RC (UNAIDS)
  • Dr. D. Ajith Karawita, RC (UNAIDS)
  • Dr. D. Ajith Karawita, RC (UNAIDS)
  • Size estimation of most at risk populations

    1. 1. Size estimation of most at-risk populations Dr. Don Ajith Karawita MBBS, PGD VEN, MD VenereologyNational STD/AIDS Control Programme, Sri Lanka
    2. 2. Overview of the presentation1. Overview of population size estimation methodologies2. Survey-based methods3. Mapping-based methods4. Mapping of MARPs in Sri Lanka 1. Objective 2. Specific objective 3. Methodology
    3. 3. Population size estimation overview
    4. 4. Introduction to size estimation of High Risk Groups (HRG)
    5. 5. (A). Survey-Based methods1. Study of individuals with high risk (Survey-Based methods) 1. Comparing two independent sources of data on high risk groups (multiplier method) 1. Program data compare with a probability survey data (Programmatic multiplier) 2. Distribution of unique object data compare with survey data (Unique object multiplier) 2. Capture-Recapture method 3. Network scale up
    6. 6. Multiplier method: Programmatic multiplier Source 1: Programme dataMSM population whose size is going to be estimated Use counts from a pre-existing programme listing during a specific time period e.g. No registered during last 3 m No attended for STI screening Source 2: probability survey data Probability survey of MSM Survey data should come from a MSM representative survey of the Registere population whose size is being d in NGO estimated X The survey area must encompass the program listing area i.e. people in the list have to be eligible for the survey
    7. 7. Example of how the programatic multiplier works If we want an estimate of the size of the MSM populationMSM population whose size is Source 1: Programme data going to be estimated NGO X reports that there were 80 Population size = ? MSM registered in their programme as of May 2007 Source 2: probability survey data Probability survey of MSM In the survey, conducted in May 2007, Proportion of MSM visited 40% of the respondents report NGO X = 40% receiving service from NGO X in the MSM past year. Registered in NGO X e.g. 80 Use of multiplier formula: Popu=NGO X No/ proportion of MSM found in the survey Population=80/40/100 = 200
    8. 8. Problems with field implementation (Bias)• Source 1: Programme data – Failure to record beneficiaries – Failure to remove inactive beneficiaries – Duplication of data – Failure to include appropriate beneficiaries (e.g. mixing up DU and IDU)• Source 2: probability survey data – Recall Bias - Failure to recall the service delivery point/service (Multiple NGOs providing services) – Respondents who are in contact with interventions more likely to be identified and sampled (survey is not representative).
    9. 9. Multiplier method: Unique object method Source 1: Unique object methodMSM population whose size is data going to be estimated A known number of “Unique objects” (T-shirt, Key tag, Purse etc.) are handed out to eligible individuals prior to the probability survey usually 1-2 wks before the survey. Probability survey of MSM Source 2: probability survey data No of Survey data should come from a unique representative survey of the objects population whose size is being distributed estimated The survey area must encompass distribution of unique object recipients
    10. 10. Example of how the unique object works If we want an estimate of the size of the MSM population Source 1: Unique object dataMSM population whose size is going to be estimated The survey team distribute 150 special key tags to MSW 2wks before the Population size = ? survey starts. Source 2: probability survey data Probability survey of MSM Proportion of MSM received In the survey, respondents are asked the unique object = 10% whether they received a key tag & are shown an example of the object. No of 10% of the survey respondents reported unique receiving the key tag objects distributed 150 Use of multiplier formula: Popu=No of objects/ proportion of MSM found received the object in the survey Population=150/10/100 = 1500
    11. 11. Multiplier method: Unique object method Source 1: Unique object methodMSM population whose size is data going to be estimated A known number of “Unique objects” (T-shirt, Key tag, Purse etc.) are handed out to eligible individuals prior to the probability survey usually 1-2 wks before the survey. Probability survey of MSM Source 2: probability survey data No of Survey data should come from a unique representative survey of the objects population whose size is being distributed estimated The survey area must encompass distribution of unique object recipients
    12. 12. Problems with field implementation (Bias)• Source 1: Unique object data – Object related – Object is not unique if commonly available in shops, not a memorable one . – Object distribution related - Giving to non-MSW, MSW outside the geographic area, non distribution without reporting or keeping by peer educators, giving more than one object, passing the object to others, objects are given to people who are more likely to participate in the survey or giving the object as they are recruited for the survey.• Source 2: probability survey data – Recall Bias - Failure to recall the object if recall period is long or object is not memorable, not showing the object during the survey. – Respondents who are known to peer educators are more likely to be given the object and identified and sampled for the survey (survey is not representative)
    13. 13. (B). Mapping-Based methods1. Study of spots with high risk activity (Mapping-Based methods) 1. Studying the whole list of spots (Census/Geographic mapping) 2. Studying hidden networks (Network mapping) 3. Studying a sample of spots (Enumeration)
    14. 14. Mapping Vs Survey-based methods Mapping MultiplierTypes of HRG Good for HRGs that are found in Listing or counts of HRG available. HRG accessible or public area. Areas can can be sampled be listedMain data Site specific sizes and site profiles Overall population estimates for largeapplications geographic areaResources Field workers familiar with HRG Existing service records and / orrequirment areas (NGO, Intervention teams etc) probability sample surveyKey challange Obtaining a comprehensive list of Achieving independence of the two sites sources of data
    15. 15. Thank you

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