Session 8 – Triangulation of Results Training Course: Planning for Developing Population Size Estimates for Key Populations at Increased Risk of HIV August, Tehran, Iran

Recap of the process (1) 1.Conduct initial assessment 2.Make a plan for doing DIRECT size estimation data collection in selected locations (Note: we will work on this later today and tomorrow) – This will provide you with data for “precise local estimates” to use for local program purposes – These local estimates will also be used as the “building blocks” for extrapolation Within strata Eventually to the national level

Recap of the process (2) 3. Conduct DIRECT size estimation data collection activities in selected locations 4. Develop local size estimates – Adjust the results of your DIRECT estimates – Triangulate data with other available estimates 5. Reach consensus on a plausible local estimate

Recap of the process (3) 3. Conduct DIRECT size estimation data collection activities in selected locations 4. Develop local size estimates – Adjust the results of your DIRECT estimates – Triangulate data with other available estimates 5. Reach consensus on a plausible local estimate

Recap of the process (4) 1.Conduct initial assessment 2.Make a plan for doing DIRECT size estimation data collection in selected locations (Note: we will work on this later today and tomorrow) – This will provide you with data for “precise local estimates” to use for local program purposes – These local estimates will also be used as the “building blocks” for extrapolation Within strata Eventually to the national level

Triangulation – what does it mean? The process used to “transform” the output of your “direct” size estimation data collection into a local size estimates Two parts: 1.Adjust the result of your direct estimate for known biases 2.Compare with other data to develop a plausible estimate or range – Here you will be making judgments about “suspected” biases

Triangulation – Part 1 Adjust the result of your direct estimate for known biases Mostly occurs with mapping Examples of “known” biases related to mapping 1.Double-counting due to mobility 2.Frequency of attending sites 3.Misclassification of identify of key population 4.Missing portion of population

Adjust for double-counting Suppose you do mapping of MSM and you count 50 of them across all the sites If you have data telling you that 30% of them visit two sites in a week (on average), you need to reduce the number to account for double-counting 50-(30%*50%*50) = 42.5

Adjust for frequency of attending sites Suppose you do mapping of sex workers and you count 100 across the sites If you have data telling you the frequency of attending sites is as follows: – 50% frequent sites daily – 25% frequent sites 2-3 times a week – 25% visit sites once a week You can inflate the using this formula: ( 100*50%*7) + (100*25%*2.5 days a week) + (100*25% * 7 days a week) (50) + (62.5) + (175)= 287 So the adjusted estimate is

Sources of “unknown” bias with Capture Recapture All eligible members should be tagged (counted) only once during each sweep. Duplicates (same individual being tagged or counted multiple times) and omissions (eligible people not being tagged or counted) can lead to overestimates or underestimates of population size and should be avoided. There is also a potential for underestimating when the less visible portion of the population is missed

Sources of “unknown” bias with Multiplier Method Situations that may lead to overestimationSituations that may lead to underestimation Program-based multiplier -People not eligible for the survey get included in the count -Duplicates get included in the count Unique Object -Not all objects are distributed but the calculation is made as if they were -Some objects are distributed to people who are not members of the KP -Survey respondents fail to report accessing the services or receiving the object even though they did receive it -Survey not random - Respondents more likely to have received services or object than people not included in survey -Survey respondents report accessing the service or receiving the object although they did not

Sources of “unknown” bias with Network Scale-Up Transmission Bias: Respondent not knowing that people they know engage in the behaviors of interest Response Bias: Respondent not being willing to reveal information Barrier Effect: Social or physical barriers that make it less likely that some people will “know” people in the population of interest Poorly defined populations