Refusal bias in HIV data from the Demographic and Health Surveys: evaluation, critique and recommendations

Adegboye, Oyelola, Tomoki, Fijjii, and Leung, Denis H.Y. (2020) Refusal bias in HIV data from the Demographic and Health Surveys: evaluation, critique and recommendations. Statistical Methods in Medical Research, 29 (3). pp. 811-826.

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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.

Item ID: 58263
Item Type: Article (Research - C1)
ISSN: 1477-0334
Keywords: bias, Demographic and Health Surveys, missing data, non-response, refusals, Malawi, HIV
Copyright Information: © The Author(s) 2019.
Date Deposited: 15 Jan 2020 21:24
FoR Codes: 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490502 Biostatistics @ 35%
49 MATHEMATICAL SCIENCES > 4905 Statistics > 490501 Applied statistics @ 30%
49 MATHEMATICAL SCIENCES > 4905 Statistics > 490506 Probability theory @ 35%
SEO Codes: 92 HEALTH > 9201 Clinical Health (Organs, Diseases and Abnormal Conditions) > 920109 Infectious Diseases @ 50%
97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 50%
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