Estimating Sizes of Key Populations at the National Level: Considerations for Study Design and Analysis

Citation

Edwards, Jessie K.; Hileman, Sarah Bassett; Donastorg, Yeycy A.; Zadrozny, Sabrina; Baral, Stefan D.; Hargreaves, James R.; Fearon, Elizabeth; Zhao, Jinkou; Datta, Abhirup; & Weir, Sharon S. (2018). Estimating Sizes of Key Populations at the National Level: Considerations for Study Design and Analysis. Epidemiology, 29(6), 795-803. PMCID: PMC6542351

Abstract

BACKGROUND: National 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.
METHODS: Available 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.
RESULTS: Using 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.
CONCLUSIONS: Viewing 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.

URL

http://dx.doi.org/10.1097/ede.0000000000000906

Reference Type

Journal Article

Year Published

2018

Journal Title

Epidemiology

Author(s)

Edwards, Jessie K.
Hileman, Sarah Bassett
Donastorg, Yeycy A.
Zadrozny, Sabrina
Baral, Stefan D.
Hargreaves, James R.
Fearon, Elizabeth
Zhao, Jinkou
Datta, Abhirup
Weir, Sharon S.

PMCID

PMC6542351

ORCiD

Weir - 0000-0001-6014-1460
Zadrozny - 0000-0002-8811-8868
Edwards, J -0000-0002-1741-335X