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Evaluation of bias in HIV seroprevalence estimates from national household surveys

Identifieur interne : 000B99 ( Main/Exploration ); précédent : 000B98; suivant : 000C00

Evaluation of bias in HIV seroprevalence estimates from national household surveys

Auteurs : V. Mishra [États-Unis] ; B. Barrere [États-Unis] ; R. Hong [États-Unis] ; S. Khan [États-Unis]

Source :

Sexually Transmitted Infections [ 1368-4973 ] ; 2008-08.

RBID : ISTEX:5E3F50F0C60003DCBC1A292CFE52585BD9DAE61E

Descripteurs français

Wicri :
- geographic : Cambodge, Lesotho, Malawi, Rwanda, Ouganda, Zimbabwe.

English descriptors

Abstract

Objectives: To evaluate HIV seroprevalence estimates from demographic and health surveys (DHS) and AIDS indicator surveys (AIS) for potential bias because of non-response and exclusion of non-household population groups. Methods: Data are from 14 DHS/AIS surveys with HIV testing, conducted during 2003–6. Blood samples were collected and analysed for HIV using standard laboratory and quality control procedures. HIV prevalence among non-tested adults was predicted based on multivariate statistical models of HIV for those who were interviewed and tested, using a common set of predictor variables. Estimates of the size of non-household populations in national censuses were used to assess potential bias because of their exclusion in the household surveys under different assumptions about proportion of adults and HIV prevalence in non-household populations. Results: Non-tested men had significantly higher predicted HIV prevalence than those tested in eight of the 14 countries, while non-tested women had significantly higher predicted prevalence than those tested in seven of the 14 countries. Effects of non-response were somewhat stronger in lower-prevalence countries. The overall effect of non-response on observed national HIV estimates was small and insignificant in all countries. Estimated effects of exclusion of non-household population groups were generally small, even in concentrated epidemics in India and Cambodia under the scenario that 75% of the non-household population was adults having 20 times greater HIV prevalence than adults in household surveys. Conclusions: Non-response and the exclusion of non-household population groups tend to have small, insignificant effects on national HIV seroprevalence estimates obtained from household surveys.

Url:

https://api.istex.fr/document/5E3F50F0C60003DCBC1A292CFE52585BD9DAE61E/fulltext/pdf

DOI: 10.1136/sti.2008.030411

Affiliations:

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Le document en format XML

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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Additional variables</term>
<term>Adults increases</term>
<term>Aids indicator surveys</term>
<term>Behavioural characteristics</term>
<term>Burkina faso</term>
<term>Cambodia</term>
<term>Clear patterns</term>
<term>Different assumptions</term>
<term>Eligible respondents</term>
<term>Female kenya</term>
<term>Generalised epidemics</term>
<term>Health surveys</term>
<term>Homeless population</term>
<term>Household adults</term>
<term>Household members</term>
<term>Household questionnaire</term>
<term>Household samples</term>
<term>Household survey</term>
<term>Household surveys</term>
<term>Households population</term>
<term>Individual interview</term>
<term>Individual interview response rates</term>
<term>Lesotho</term>
<term>Lower prevalence countries</term>
<term>Major challenge</term>
<term>Malawi</term>
<term>Minimal effect</term>
<term>National estimates</term>
<term>National household surveys</term>
<term>National population</term>
<term>National seroprevalence estimates</term>
<term>National surveys</term>
<term>Nonhousehold</term>
<term>Nonhousehold adults</term>
<term>Nonhousehold population</term>
<term>Nonhousehold population groups</term>
<term>Nonresponse</term>
<term>Nonresponse rates</term>
<term>Percentage points</term>
<term>Population groups</term>
<term>Population prevalence</term>
<term>Potential bias</term>
<term>Potential impact</term>
<term>Predictor variables</term>
<term>Prevalence</term>
<term>Prevalence estimate</term>
<term>Prevalence estimates</term>
<term>Prevalence levels</term>
<term>Protective factors</term>
<term>Response rate</term>
<term>Response rates</term>
<term>Royal government</term>
<term>Rural areas</term>
<term>Rwanda</term>
<term>Sampling weights</term>
<term>Scenario</term>
<term>Seroprevalence</term>
<term>Seroprevalence estimates</term>
<term>Significant effects</term>
<term>Standard laboratory</term>
<term>Supplement table</term>
<term>Survey estimates</term>
<term>Survey year</term>
<term>Total population</term>
<term>Transm</term>
<term>Uganda</term>
<term>Uganda bureau</term>
<term>Various risk</term>
<term>Wealthier respondents</term>
<term>Zimbabwe</term>
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<term>Adults increases</term>
<term>Aids indicator surveys</term>
<term>Behavioural characteristics</term>
<term>Burkina faso</term>
<term>Cambodia</term>
<term>Clear patterns</term>
<term>Different assumptions</term>
<term>Eligible respondents</term>
<term>Female kenya</term>
<term>Generalised epidemics</term>
<term>Health surveys</term>
<term>Homeless population</term>
<term>Household adults</term>
<term>Household members</term>
<term>Household questionnaire</term>
<term>Household samples</term>
<term>Household survey</term>
<term>Household surveys</term>
<term>Households population</term>
<term>Individual interview</term>
<term>Individual interview response rates</term>
<term>Lesotho</term>
<term>Lower prevalence countries</term>
<term>Major challenge</term>
<term>Malawi</term>
<term>Minimal effect</term>
<term>National estimates</term>
<term>National household surveys</term>
<term>National population</term>
<term>National seroprevalence estimates</term>
<term>National surveys</term>
<term>Nonhousehold</term>
<term>Nonhousehold adults</term>
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<term>Nonhousehold population groups</term>
<term>Nonresponse</term>
<term>Nonresponse rates</term>
<term>Percentage points</term>
<term>Population groups</term>
<term>Population prevalence</term>
<term>Potential bias</term>
<term>Potential impact</term>
<term>Predictor variables</term>
<term>Prevalence</term>
<term>Prevalence estimate</term>
<term>Prevalence estimates</term>
<term>Prevalence levels</term>
<term>Protective factors</term>
<term>Response rate</term>
<term>Response rates</term>
<term>Royal government</term>
<term>Rural areas</term>
<term>Rwanda</term>
<term>Sampling weights</term>
<term>Scenario</term>
<term>Seroprevalence</term>
<term>Seroprevalence estimates</term>
<term>Significant effects</term>
<term>Standard laboratory</term>
<term>Supplement table</term>
<term>Survey estimates</term>
<term>Survey year</term>
<term>Total population</term>
<term>Transm</term>
<term>Uganda</term>
<term>Uganda bureau</term>
<term>Various risk</term>
<term>Wealthier respondents</term>
<term>Zimbabwe</term>
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<term>Lesotho</term>
<term>Malawi</term>
<term>Rwanda</term>
<term>Ouganda</term>
<term>Zimbabwe</term>
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<front><div type="abstract">Objectives: To evaluate HIV seroprevalence estimates from demographic and health surveys (DHS) and AIDS indicator surveys (AIS) for potential bias because of non-response and exclusion of non-household population groups. Methods: Data are from 14 DHS/AIS surveys with HIV testing, conducted during 2003–6. Blood samples were collected and analysed for HIV using standard laboratory and quality control procedures. HIV prevalence among non-tested adults was predicted based on multivariate statistical models of HIV for those who were interviewed and tested, using a common set of predictor variables. Estimates of the size of non-household populations in national censuses were used to assess potential bias because of their exclusion in the household surveys under different assumptions about proportion of adults and HIV prevalence in non-household populations. Results: Non-tested men had significantly higher predicted HIV prevalence than those tested in eight of the 14 countries, while non-tested women had significantly higher predicted prevalence than those tested in seven of the 14 countries. Effects of non-response were somewhat stronger in lower-prevalence countries. The overall effect of non-response on observed national HIV estimates was small and insignificant in all countries. Estimated effects of exclusion of non-household population groups were generally small, even in concentrated epidemics in India and Cambodia under the scenario that 75% of the non-household population was adults having 20 times greater HIV prevalence than adults in household surveys. Conclusions: Non-response and the exclusion of non-household population groups tend to have small, insignificant effects on national HIV seroprevalence estimates obtained from household surveys.</div>
</front>
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<name sortKey="Hong, R" sort="Hong, R" uniqKey="Hong R" first="R" last="Hong">R. Hong</name>
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Evaluation of bias in HIV seroprevalence estimates from national household surveys

Evaluation of bias in HIV seroprevalence estimates from national household surveys

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

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