Refusal Bias in HIV Data from the Demographic and Health Surveys: Evaluation, Critique and Recommendations

SMU Economics & Statistic Working Paper No. 06-2019

26 Pages Posted: 23 Apr 2019

See all articles by Oyelola Adegboye

Oyelola Adegboye

James Cook University - Australian Institute of Tropical Health & Medicine

Tomoki Fujii

Singapore Management University - School of Economics

Denis Leung

Singapore Management University - School of Economics

Date Written: February 25, 2019

Abstract

Non-response is a commonly encountered problem in many population-based surveys. Broadly speaking, non-response can be due to refusal or failure to contact the sample units. Although both types of non-response may lead to bias, there is much evidence to indicate that it is much easier to reduce the proportion of non-contacts than to do the same with refusals. In this article, we use data collected from a nationally-representative survey under the Demographic and Health Surveys program to study non-response due to refusals to HIV testing in Malawi. We review existing estimation methods and propose novel approaches to the estimation of HIV prevalence that adjust for refusal behaviour. We then explain the data requirement and practical implications of the conventional and proposed approaches. Finally, we provide some general recommendations for handling non-response due to refusals and we highlight the challenges in working with Demographic and Health Surveys and explore different approaches to statistical estimation in the presence of refusals. Our results show that variation in the estimated HIV prevalence across different estimators is due largely to those who already know their HIV test results. In the case of Malawi, variations in the prevalence estimates due to refusals for women are larger than those for men.

Keywords: Bias, Demographic and Health Surveys, Missing Data, Non-Response, Refusals, Malawi

JEL Classification: C18, I15

Suggested Citation

Adegboye, Oyelola and Fujii, Tomoki and Leung, Denis, Refusal Bias in HIV Data from the Demographic and Health Surveys: Evaluation, Critique and Recommendations (February 25, 2019). SMU Economics & Statistic Working Paper No. 06-2019. Available at SSRN: https://ssrn.com/abstract=3232931 or http://dx.doi.org/10.2139/ssrn.3232931

Oyelola Adegboye (Contact Author)

James Cook University - Australian Institute of Tropical Health & Medicine ( email )

Australia

Tomoki Fujii

Singapore Management University - School of Economics ( email )

90 Stamford Road
178903
Singapore
+6568280279 (Phone)
+6568280833 (Fax)

HOME PAGE: http://www.mysmu.edu/faculty/tfujii/

Denis Leung

Singapore Management University - School of Economics ( email )

90 Stamford Road
178903
Singapore

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