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

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...

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Main Authors: ADEGBOYE, Oyelola A., FUJII, Tomoki, LEUNG, Denis H. Y.
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語言:English
出版: Institutional Knowledge at Singapore Management University 2019
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在線閱讀:https://ink.library.smu.edu.sg/soe_research/2249
https://ink.library.smu.edu.sg/context/soe_research/article/3248/viewcontent/adegboye_et_al_SMMR_WP_.pdf
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spelling sg-smu-ink.soe_research-32482019-09-30T06:48:04Z Refusal bias in HIV data from the Demographic and Health Surveys: Evaluation, critique and recommendations ADEGBOYE, Oyelola A. FUJII, Tomoki LEUNG, Denis H. Y. 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 dierent approaches to statistical estimation in the presence of refusals. Our results show that variation in the estimated HIV prevalence across dierent 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. 2019-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2249 https://ink.library.smu.edu.sg/context/soe_research/article/3248/viewcontent/adegboye_et_al_SMMR_WP_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Bias Demographic and Health Surveys Missing data Non-response Refusals Malawi Econometrics Health Economics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Bias
Demographic and Health Surveys
Missing data
Non-response
Refusals
Malawi
Econometrics
Health Economics
spellingShingle Bias
Demographic and Health Surveys
Missing data
Non-response
Refusals
Malawi
Econometrics
Health Economics
ADEGBOYE, Oyelola A.
FUJII, Tomoki
LEUNG, Denis H. Y.
Refusal bias in HIV data from the Demographic and Health Surveys: Evaluation, critique and recommendations
description 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 dierent approaches to statistical estimation in the presence of refusals. Our results show that variation in the estimated HIV prevalence across dierent 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.
format text
author ADEGBOYE, Oyelola A.
FUJII, Tomoki
LEUNG, Denis H. Y.
author_facet ADEGBOYE, Oyelola A.
FUJII, Tomoki
LEUNG, Denis H. Y.
author_sort ADEGBOYE, Oyelola A.
title Refusal bias in HIV data from the Demographic and Health Surveys: Evaluation, critique and recommendations
title_short Refusal bias in HIV data from the Demographic and Health Surveys: Evaluation, critique and recommendations
title_full Refusal bias in HIV data from the Demographic and Health Surveys: Evaluation, critique and recommendations
title_fullStr Refusal bias in HIV data from the Demographic and Health Surveys: Evaluation, critique and recommendations
title_full_unstemmed Refusal bias in HIV data from the Demographic and Health Surveys: Evaluation, critique and recommendations
title_sort refusal bias in hiv data from the demographic and health surveys: evaluation, critique and recommendations
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/soe_research/2249
https://ink.library.smu.edu.sg/context/soe_research/article/3248/viewcontent/adegboye_et_al_SMMR_WP_.pdf
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