What explains gender inequalities in HIV/AIDS prevalence in sub-Saharan Africa? Evidence from the demographic and health surveys
Identifieur interne : 000111 ( Main/Exploration ); précédent : 000110; suivant : 000112What explains gender inequalities in HIV/AIDS prevalence in sub-Saharan Africa? Evidence from the demographic and health surveys
Auteurs : Drissa Sia ; Yentéma Onadja [Burkina Faso] ; Mohammad Hajizadeh ; S. Jody Heymann [États-Unis] ; Timothy F. Brewer [États-Unis] ; Arijit Nandi [Canada]Source :
- BMC Public Health [ 1471-2458 ] ; 2016.
Descripteurs français
- KwdFr :
- Adolescent, Adulte, Adulte d'âge moyen, Afrique subsaharienne (épidémiologie), Cameroun (épidémiologie), Comportement sexuel, Disparités de l'état de santé, Enquêtes de santé, Facteurs de risque, Facteurs sexuels, Femelle, Ghana (épidémiologie), Humains, Infections à VIH (épidémiologie), Infections à VIH (étiologie), Malawi (épidémiologie), Mâle, Ouganda (épidémiologie), Prévalence, Répartition par sexe, Swaziland (épidémiologie).
- MESH :
- épidémiologie : Afrique subsaharienne, Cameroun, Ghana, Infections à VIH, Malawi, Ouganda, Swaziland.
- étiologie : Infections à VIH.
- Adolescent, Adulte, Adulte d'âge moyen, Comportement sexuel, Disparités de l'état de santé, Enquêtes de santé, Facteurs de risque, Facteurs sexuels, Femelle, Humains, Mâle, Prévalence, Répartition par sexe.
English descriptors
- KwdEn :
- Adolescent, Adult, Africa South of the Sahara (epidemiology), Cameroon (epidemiology), Female, Ghana (epidemiology), HIV Infections (epidemiology), HIV Infections (etiology), Health Status Disparities, Health Surveys, Humans, Malawi (epidemiology), Male, Middle Aged, Prevalence, Risk Factors, Sex Distribution, Sex Factors, Sexual Behavior, Swaziland (epidemiology), Uganda (epidemiology).
- MESH :
- epidemiology : Africa South of the Sahara, Cameroon, Ghana, HIV Infections, Malawi, Swaziland, Uganda.
- etiology : HIV Infections.
- Adolescent, Adult, Female, Health Status Disparities, Health Surveys, Humans, Male, Middle Aged, Prevalence, Risk Factors, Sex Distribution, Sex Factors, Sexual Behavior.
Abstract
Women are disproportionally affected by human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) in sub-Saharan Africa (SSA). The determinants of gender inequality in HIV/AIDS may vary across countries and require country-specific interventions to address them. This study aimed to identify the socio-demographic and behavioral characteristics underlying gender inequalities in HIV/AIDS in 21 SSA countries.
We applied an extension of the Blinder-Oaxaca decomposition approach to data from Demographic and Health Surveys and AIDS Indicator Surveys to quantify the differences in HIV/AIDS prevalence between women and men attributable to socio-demographic factors, sexual behaviours, and awareness of HIV/AIDS. We decomposed gender inequalities into two components: the percentage attributable to different levels of the risk factors between women and men (the “composition effect”) and the percentage attributable to risk factors having differential effects on HIV/AIDS prevalence in women and men (the “response effect”).
Descriptive analyses showed that the difference between women and men in HIV/AIDS prevalence varied from a low of 0.68 % (
The factors that explain gender inequality in HIV/AIDS in SSA vary by country, suggesting that country-specific interventions are needed. Unmeasured factors also contributed substantially to the difference in HIV/AIDS prevalence between women and men, highlighting the need for further study.
The online version of this article (doi:10.1186/s12889-016-3783-5) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12889-016-3783-5
PubMed: 27809824
PubMed Central: 5095963
Affiliations:
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Evidence from the demographic and health surveys</title><author><name sortKey="Sia, Drissa" sort="Sia, Drissa" uniqKey="Sia D" first="Drissa" last="Sia">Drissa Sia</name><affiliation><nlm:aff id="Aff1">Département des sciences infirmières, Campus de Saint-Jérôme, Université du Québec en Outaouais, 5, rue Saint-Joseph, bureau J-3226, Saint Jérôme, Québec J7Z 0B7 Canada</nlm:aff><wicri:noCountry code="subfield">Québec J7Z 0B7 Canada</wicri:noCountry></affiliation></author><author><name sortKey="Onadja, Yentema" sort="Onadja, Yentema" uniqKey="Onadja Y" first="Yentéma" last="Onadja">Yentéma Onadja</name><affiliation wicri:level="1"><nlm:aff id="Aff2">Institut Supérieur des Sciences de la Population (ISSP), Université Ouaga I Pr Joseph Ki-Zerbo, 03 BP 7118 Ouagadougou 03, Ouagadougou, Burkina Faso</nlm:aff><country xml:lang="fr">Burkina Faso</country><wicri:regionArea>Institut Supérieur des Sciences de la Population (ISSP), Université Ouaga I Pr Joseph Ki-Zerbo, 03 BP 7118 Ouagadougou 03, Ouagadougou</wicri:regionArea><wicri:noRegion>Ouagadougou</wicri:noRegion></affiliation></author><author><name sortKey="Hajizadeh, Mohammad" sort="Hajizadeh, Mohammad" uniqKey="Hajizadeh M" first="Mohammad" last="Hajizadeh">Mohammad Hajizadeh</name><affiliation><nlm:aff id="Aff3">School of Health Administration, Dalhousie University, Halifax, Nova Scotia Canada</nlm:aff><wicri:noCountry code="subfield">Nova Scotia Canada</wicri:noCountry></affiliation></author><author><name sortKey="Heymann, S Jody" sort="Heymann, S Jody" uniqKey="Heymann S" first="S. Jody" last="Heymann">S. Jody Heymann</name><affiliation wicri:level="2"><nlm:aff id="Aff4">Fielding School of Public Health, The University of California, Los Angeles (UCLA), Los Angeles, CA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Fielding School of Public Health, The University of California, Los Angeles (UCLA), Los Angeles</wicri:cityArea></affiliation></author><author><name sortKey="Brewer, Timothy F" sort="Brewer, Timothy F" uniqKey="Brewer T" first="Timothy F." last="Brewer">Timothy F. Brewer</name><affiliation wicri:level="2"><nlm:aff id="Aff5">David Geffen School of Medicine, The University of California, Los Angeles (UCLA), Los Angeles, CA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>David Geffen School of Medicine, The University of California, Los Angeles (UCLA), Los Angeles</wicri:cityArea></affiliation></author><author><name sortKey="Nandi, Arijit" sort="Nandi, Arijit" uniqKey="Nandi A" first="Arijit" last="Nandi">Arijit Nandi</name><affiliation wicri:level="4"><nlm:aff id="Aff6">Institute for Health and Social Policy and Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec Canada</nlm:aff><orgName type="university">Université McGill</orgName><country>Canada</country><placeName><settlement type="city">Montréal</settlement><region type="state">Québec</region></placeName></affiliation></author></analytic><series><title level="j">BMC Public Health</title><idno type="eISSN">1471-2458</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Africa South of the Sahara (epidemiology)</term><term>Cameroon (epidemiology)</term><term>Female</term><term>Ghana (epidemiology)</term><term>HIV Infections (epidemiology)</term><term>HIV Infections (etiology)</term><term>Health Status Disparities</term><term>Health Surveys</term><term>Humans</term><term>Malawi (epidemiology)</term><term>Male</term><term>Middle Aged</term><term>Prevalence</term><term>Risk Factors</term><term>Sex Distribution</term><term>Sex Factors</term><term>Sexual Behavior</term><term>Swaziland (epidemiology)</term><term>Uganda (epidemiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Afrique subsaharienne (épidémiologie)</term><term>Cameroun (épidémiologie)</term><term>Comportement sexuel</term><term>Disparités de l'état de santé</term><term>Enquêtes de santé</term><term>Facteurs de risque</term><term>Facteurs sexuels</term><term>Femelle</term><term>Ghana (épidémiologie)</term><term>Humains</term><term>Infections à VIH (épidémiologie)</term><term>Infections à VIH (étiologie)</term><term>Malawi (épidémiologie)</term><term>Mâle</term><term>Ouganda (épidémiologie)</term><term>Prévalence</term><term>Répartition par sexe</term><term>Swaziland (épidémiologie)</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Africa South of the Sahara</term><term>Cameroon</term><term>Ghana</term><term>HIV Infections</term><term>Malawi</term><term>Swaziland</term><term>Uganda</term></keywords><keywords scheme="MESH" qualifier="etiology" xml:lang="en"><term>HIV Infections</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Afrique subsaharienne</term><term>Cameroun</term><term>Ghana</term><term>Infections à VIH</term><term>Malawi</term><term>Ouganda</term><term>Swaziland</term></keywords><keywords scheme="MESH" qualifier="étiologie" xml:lang="fr"><term>Infections à VIH</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Female</term><term>Health Status Disparities</term><term>Health Surveys</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Prevalence</term><term>Risk Factors</term><term>Sex Distribution</term><term>Sex Factors</term><term>Sexual Behavior</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Comportement sexuel</term><term>Disparités de l'état de santé</term><term>Enquêtes de santé</term><term>Facteurs de risque</term><term>Facteurs sexuels</term><term>Femelle</term><term>Humains</term><term>Mâle</term><term>Prévalence</term><term>Répartition par sexe</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Women are disproportionally affected by human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) in sub-Saharan Africa (SSA). The determinants of gender inequality in HIV/AIDS may vary across countries and require country-specific interventions to address them. This study aimed to identify the socio-demographic and behavioral characteristics underlying gender inequalities in HIV/AIDS in 21 SSA countries.</p></sec><sec><title>Methods</title><p>We applied an extension of the Blinder-Oaxaca decomposition approach to data from Demographic and Health Surveys and AIDS Indicator Surveys to quantify the differences in HIV/AIDS prevalence between women and men attributable to socio-demographic factors, sexual behaviours, and awareness of HIV/AIDS. We decomposed gender inequalities into two components: the percentage attributable to different levels of the risk factors between women and men (the “composition effect”) and the percentage attributable to risk factors having differential effects on HIV/AIDS prevalence in women and men (the “response effect”).</p></sec><sec><title>Results</title><p>Descriptive analyses showed that the difference between women and men in HIV/AIDS prevalence varied from a low of 0.68 % (<italic>P</italic> = 0.008) in Liberia to a high of 11.5 % (<italic>P</italic> < 0.001) in Swaziland. The decomposition analysis showed that 84 % (<italic>P</italic> < 0.001) and 92 % (<italic>P</italic> < 0.001) of the higher prevalence of HIV/AIDS among women in Uganda and Ghana, respectively, was explained by the different distributions of HIV/AIDS risk factors, particularly age at first sex between women and men. In the majority of countries, however, observed gender inequalities in HIV/AIDS were chiefly explained by differences in the responses to risk factors; the differential effects of age, marital status and occupation on prevalence of HIV/AIDS for women and men were among the significant contributors to this component. In Cameroon, Guinea, Malawi and Swaziland, a combination of the composition and response effects explained gender inequalities in HIV/AIDS prevalence.</p></sec><sec><title>Conclusions</title><p>The factors that explain gender inequality in HIV/AIDS in SSA vary by country, suggesting that country-specific interventions are needed. Unmeasured factors also contributed substantially to the difference in HIV/AIDS prevalence between women and men, highlighting the need for further study.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s12889-016-3783-5) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Magadi, Ma" uniqKey="Magadi M">MA Magadi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pettifor, Ae" uniqKey="Pettifor A">AE Pettifor</name></author><author><name sortKey="Rees, Hv" uniqKey="Rees H">HV Rees</name></author><author><name sortKey="Kleinschmidt, I" uniqKey="Kleinschmidt I">I Kleinschmidt</name></author><author><name sortKey="Steffenson, Ae" uniqKey="Steffenson A">AE Steffenson</name></author><author><name sortKey="Macphail, 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sort="Heymann, S Jody" uniqKey="Heymann S" first="S. Jody" last="Heymann">S. Jody Heymann</name></region><name sortKey="Brewer, Timothy F" sort="Brewer, Timothy F" uniqKey="Brewer T" first="Timothy F." last="Brewer">Timothy F. Brewer</name></country><country name="Canada"><region name="Québec"><name sortKey="Nandi, Arijit" sort="Nandi, Arijit" uniqKey="Nandi A" first="Arijit" last="Nandi">Arijit Nandi</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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