Adjusting HIV prevalence for survey non-response using mortality rates: an application of the method using surveillance data from rural South Africa
Adjusting HIV prevalence for survey non-response using mortality rates: an application of the method using surveillance data from rural South Africa
Background: the main source of HIV prevalence estimates are household and population-based surveys; however, high refusal rates may hinder the interpretation of such estimates. The study objective was to evaluate whether population HIV prevalence estimates can be adjusted for survey non-response using mortality rates.
Methodology/principal findings: data come from the longitudinal Africa Centre Demographic Information System (ACDIS), in rural South Africa. Mortality rates for persons tested and not tested in the 2005 HIV surveillance were available from routine household surveillance. Assuming HIV status among individuals contacted but who refused to test (non-response) is missing at random and mortality among non-testers can be related to mortality of those tested a mathematical model was developed. Non-parametric bootstrapping was used to estimate the 95% confidence intervals around the estimates. Mortality rates were higher among untested (16.9 per thousand person-years) than tested population (11.6 per thousand person-years), suggesting higher HIV prevalence in the former. Adjusted HIV prevalence for females (15–49 years) was 31.6% (95% CI 26.1–37.1) compared to observed 25.2% (95% CI 24.0–26.4). For males (15–49 years) adjusted HIV prevalence was 19.8% (95% CI 14.8–24.8), compared to observed 13.2% (95% CI 12.1–14.3). For both sexes (15–49 years) combined, adjusted prevalence was 27.5% (95% CI 23.6–31.3), and observed prevalence was 19.7% (95% CI 19.6–21.3). Overall, observed prevalence underestimates the adjusted prevalence by around 7 percentage points (37% relative difference).
Conclusions/significance: we developed a simple approach to adjust HIV prevalence estimates for survey non-response. The approach has three features that make it easy to implement and effective in adjusting for selection bias than other approaches. Further research is needed to assess this approach in populations with widely available HIV treatment (ART)
e12370
Nyirenda, Makandwe
b273a917-750f-4125-8cb8-599a432e6168
Zaba, Basia
e5d3b7e2-e51a-4b2d-a6cd-c90d152623f0
Barnighausen, Till
f99001d2-60f1-4447-b554-b01dca4b4c5e
Hosegood, Victoria
c59a89d5-5edc-42dd-b282-f44458fd2993
Newell, Marie-Louise
87caf679-14d9-405d-b5b3-e00ae33728e3
August 2010
Nyirenda, Makandwe
b273a917-750f-4125-8cb8-599a432e6168
Zaba, Basia
e5d3b7e2-e51a-4b2d-a6cd-c90d152623f0
Barnighausen, Till
f99001d2-60f1-4447-b554-b01dca4b4c5e
Hosegood, Victoria
c59a89d5-5edc-42dd-b282-f44458fd2993
Newell, Marie-Louise
87caf679-14d9-405d-b5b3-e00ae33728e3
Nyirenda, Makandwe, Zaba, Basia, Barnighausen, Till, Hosegood, Victoria and Newell, Marie-Louise
(2010)
Adjusting HIV prevalence for survey non-response using mortality rates: an application of the method using surveillance data from rural South Africa.
PLoS ONE, 5 (8), .
(doi:10.1371/journal.pone.0012370).
(PMID:20811499)
Abstract
Background: the main source of HIV prevalence estimates are household and population-based surveys; however, high refusal rates may hinder the interpretation of such estimates. The study objective was to evaluate whether population HIV prevalence estimates can be adjusted for survey non-response using mortality rates.
Methodology/principal findings: data come from the longitudinal Africa Centre Demographic Information System (ACDIS), in rural South Africa. Mortality rates for persons tested and not tested in the 2005 HIV surveillance were available from routine household surveillance. Assuming HIV status among individuals contacted but who refused to test (non-response) is missing at random and mortality among non-testers can be related to mortality of those tested a mathematical model was developed. Non-parametric bootstrapping was used to estimate the 95% confidence intervals around the estimates. Mortality rates were higher among untested (16.9 per thousand person-years) than tested population (11.6 per thousand person-years), suggesting higher HIV prevalence in the former. Adjusted HIV prevalence for females (15–49 years) was 31.6% (95% CI 26.1–37.1) compared to observed 25.2% (95% CI 24.0–26.4). For males (15–49 years) adjusted HIV prevalence was 19.8% (95% CI 14.8–24.8), compared to observed 13.2% (95% CI 12.1–14.3). For both sexes (15–49 years) combined, adjusted prevalence was 27.5% (95% CI 23.6–31.3), and observed prevalence was 19.7% (95% CI 19.6–21.3). Overall, observed prevalence underestimates the adjusted prevalence by around 7 percentage points (37% relative difference).
Conclusions/significance: we developed a simple approach to adjust HIV prevalence estimates for survey non-response. The approach has three features that make it easy to implement and effective in adjusting for selection bias than other approaches. Further research is needed to assess this approach in populations with widely available HIV treatment (ART)
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Published date: August 2010
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Local EPrints ID: 181363
URI: http://eprints.soton.ac.uk/id/eprint/181363
ISSN: 1932-6203
PURE UUID: e301bc0f-4ef9-436a-95d3-fd9078cc35d6
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Date deposited: 15 Apr 2011 14:22
Last modified: 14 Mar 2024 02:56
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Author:
Makandwe Nyirenda
Author:
Basia Zaba
Author:
Till Barnighausen
Author:
Marie-Louise Newell
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