Estimating sizes of key populations at the national level: considerations for study design and analysis


ja-18-257

Author(s): Edwards, Jessie K; Hileman, Sarah; Donastorg, Yeycy; Zadrozny, Sabrina; Baral, Stefan; Hargreaves, James R; Fearon, Elizabeth; Zhao, Jinkou; Datta, Abhirup; Weir, Sharon S

Year: 2018


Epidemiology:  August 16, 2018 - Volume Publish Ahead of Print.  DOI: 10.1097/EDE.0000000000000906
Estimating sizes of key populations at the national level: considerations for study design and analysis Abstract:

BackgroundNational estimates of the sizes of key populations, including female sex workers, men who have sex with men, and transgender women are critical to inform national and international responses to the human immunodeficiency virus (HIV) pandemic. However, epidemiologic studies typically provide size estimates for only limited high priority geographic areas. This paper illustrates a two-stage approach to obtain a national key population size estimate in the Dominican Republic using available estimates and publicly available contextual information.

MethodsAvailable estimates of key population size in priority areas were augmented with targeted additional data collection in other areas. To combine information from data collected at each stage, we used statistical methods for handling missing data, including inverse probability weights, multiple imputation, and augmented inverse probability weights.

ResultsUsing the augmented inverse probability weighting approach, which provides some protection against parametric model misspecification, we estimated that 3.7% (95% CI: 2.9, 4.7) of the total population of women in the Dominican Republic between the ages of 15 and 49 were engaged in sex work, 1.2% (95% CI: 1.1, 1.3) of men ages 15 – 49 had sex with other men, and 0.19% (95% CI: 0.17, 0.21) of people assigned the male sex at birth were transgender.

ConclusionsViewing the size estimation of key populations as a missing data problem provides a framework for articulating and evaluating the assumptions necessary to obtain a national size estimate. In addition, this paradigm allows use of methods for missing data familiar to epidemiologists.

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