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Effects of Different Extrapolation Approaches on Estimating Female Sex Workers in Tanzania

This study examines the impact of different extrapolation approaches on estimating the number of female sex workers in Tanzania, highlighting the importance of accurate population size estimates for planning and funding allocation. Various methods of extrapolation are explored and their effects on the results are analyzed.

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Effects of Different Extrapolation Approaches on Estimating Female Sex Workers in Tanzania

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  1. Extrapolation of population size estimates for key populations Illustrating the effects of different extrapolation approaches on estimating the number of female sex workers in Tanzania Amrita Rao, Tobi Saidel, Virginia Loo, Abhirup Datta, Stefan Baral

  2. Gaps in consensus for extrapolation Population size estimates (PSE) play critical role in allocation of funding and planning In order to make decisions, stakeholders often extrapolate existingPSE from selected areas to obtain estimates at the national or subnational levels Multiple approaches for extrapolation have been applied, but with few considerations of how the approach selected may impact the results and the conclusions drawn

  3. Methods of extrapolation OBJECTIVE To predict the population size of FSW in 24 regions with no existing data using available data from other regions and explore the variation in results from applying different extrapolation approaches METHOD Allowed variation in how we modeled the: Outcome Estimate, proportion, proportion urban Method Simple/stratified imputation, regression Covariates Urbanicity, % delivery in health facility, HIV prevalence, literacy Misra, Kavita; Vu, Lung, 2014, "Tanzania (2013): HIV Biological and Behavioral Surveys among Female Sex Workers in Seven Regions. Round [1].", https://doi.org/10.7910/DVN/24174, Harvard Dataverse, V2, UNF:5:3V9JDRolaahOL62r53P4SQ== [fileUNF])

  4. Variation in predicted PSE by specification of extrapolation approach

  5. Variation in predictions when changing how the outcome is modeled

  6. Variation in predictions when changing the method used

  7. Variation in predictions when changing the covariate(s) used

  8. Conclusions Extrapolation can be used as an analytical tool to optimize use of existing data and provide data-driven program planning But choice of how the outcomes are modeled, choice of methods, and choice of covariates to include in models can have a dramatic impact on conclusions reached. This is where informative priors on estimate sizes, if available, can be utilized Next steps will include evaluating predictive value of the different methods using leave one out and other methods of validation

  9. Acknowledgements • Key Populations Program, JHU • Jess Edwards, Sabrina Zadrozny, Sharon Weir, UNC • Wolfgang Hladik, CDC • Le Bao, Maggie Niu, Penn State • MeSH consortium • SOURCES OF DATA • Direct size estimates: Tanzania (2013): HIV Biological and Behavioral Surveys among Female Sex Workers in Seven Regions • Auxiliary data • Urbanicity: Population distribution by Age and Sex, Tanzania 2012 Census, National Bureau of Statistics, Ministry of Finance Dar es Salaam, and Office of Chief Government Statistician President’s Office, Finance, Economy, and Development Planning Zanzibar • Literacy (15+): Literacy and Education Monograph, 2012 Population and Housing Census • % Delivery in HF: Routine Health Management Information System data, Ministry of Health, Community Development, Gender, Elderly, and Children (MoHCDGEC) • HIV Prevalence: 2011-2012 Tanzania HIV/AIDS and Malaria Indicator Survey

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