Income-related health inequalities associated with the coronavirus pandemic in South Africa: A decomposition analysis.
Identifieur interne : 000335 ( Main/Exploration ); précédent : 000334; suivant : 000336Income-related health inequalities associated with the coronavirus pandemic in South Africa: A decomposition analysis.
Auteurs : Chijioke O. Nwosu [Afrique du Sud] ; Adeola Oyenubi [Afrique du Sud]Source :
- International journal for equity in health [ 1475-9276 ] ; 2021.
Descripteurs français
- KwdFr :
- MESH :
- statistiques et données numériques : Revenu.
- épidémiologie : République d'Afrique du Sud.
- Adulte, Disparités de l'état de santé, Femelle, Humains, Mâle.
- Wicri :
- geographic : Afrique du Sud.
English descriptors
- KwdEn :
- MESH :
- geographic , epidemiology : South Africa.
- statistics & numerical data : Income.
- Adult, COVID-19, Female, Health Status Disparities, Humans, Male.
Abstract
BACKGROUND
The coronavirus disease 2019 (COVID-19) has resulted in an enormous dislocation of society especially in South Africa. The South African government has imposed a number of measures aimed at controlling the pandemic, chief being a nationwide lockdown. This has resulted in income loss for individuals and firms, with vulnerable populations (low earners, those in informal and precarious employment, etc.) more likely to be adversely affected through job losses and the resulting income loss. Income loss will likely result in reduced ability to access healthcare and a nutritious diet, thus adversely affecting health outcomes. Given the foregoing, we hypothesize that the economic dislocation caused by the coronavirus will disproportionately affect the health of the poor.
METHODS
Using the fifth wave of the National Income Dynamics Study (NIDS) dataset conducted in 2017 and the first wave of the NIDS-Coronavirus Rapid Mobile Survey (NIDS-CRAM) dataset conducted in May/June 2020, this paper estimated income-related health inequalities in South Africa before and during the COVID-19 pandemic. Health was a dichotomized self-assessed health measure, with fair and poor health categorized as "poor" health, while excellent, very good and good health were categorized as "better" health. Household per capita income was used as the ranking variable. Concentration curves and indices were used to depict the income-related health inequalities. Furthermore, we decomposed the COVID-19 era income-related health inequality in order to ascertain the significant predictors of such inequality.
RESULTS
The results indicate that poor health was pro-poor in the pre-COVID-19 and COVID-19 periods, with the latter six times the value of the former. Being African (relative to white), per capita household income and household experience of hunger significantly predicted income-related health inequalities in the COVID-19 era (contributing 130%, 46% and 9% respectively to the inequalities), while being in paid employment had a nontrivial but statistically insignificant contribution (13%) to health inequality.
CONCLUSIONS
Given the significance and magnitude of race, hunger, income and employment in determining socioeconomic inequalities in poor health, addressing racial disparities and hunger, income inequality and unemployment will likely mitigate income-related health inequalities in South Africa during the COVID-19 pandemic.
DOI: 10.1186/s12939-020-01361-7
PubMed: 33413442
PubMed Central: PMC7790046
Affiliations:
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Nwosu</name><affiliation wicri:level="1"><nlm:affiliation>The Impact Centre, Human Sciences Research Council, Cape Town, South Africa. cnwosu@hsrc.ac.za.</nlm:affiliation><country xml:lang="fr">Afrique du Sud</country><wicri:regionArea>The Impact Centre, Human Sciences Research Council, Cape Town</wicri:regionArea><wicri:noRegion>Cape Town</wicri:noRegion></affiliation></author><author><name sortKey="Oyenubi, Adeola" sort="Oyenubi, Adeola" uniqKey="Oyenubi A" first="Adeola" last="Oyenubi">Adeola Oyenubi</name><affiliation wicri:level="4"><nlm:affiliation>School of Economics and Finance, University of the Witwatersrand, Pretoria, South Africa.</nlm:affiliation><country xml:lang="fr">Afrique du Sud</country><wicri:regionArea>School of Economics and Finance, University of the Witwatersrand, Pretoria</wicri:regionArea><orgName type="university">Université du Witwatersrand</orgName><placeName><settlement type="city">Johannesbourg</settlement><region type="region" nuts="2">Gauteng</region></placeName></affiliation></author></analytic><series><title level="j">International journal for equity in health</title><idno type="eISSN">1475-9276</idno><imprint><date when="2021" type="published">2021</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult (MeSH)</term><term>COVID-19 (MeSH)</term><term>Female (MeSH)</term><term>Health Status Disparities (MeSH)</term><term>Humans (MeSH)</term><term>Income (statistics & numerical data)</term><term>Male (MeSH)</term><term>South Africa (epidemiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte (MeSH)</term><term>Disparités de l'état de santé (MeSH)</term><term>Femelle (MeSH)</term><term>Humains (MeSH)</term><term>Mâle (MeSH)</term><term>Revenu (statistiques et données numériques)</term><term>République d'Afrique du Sud (épidémiologie)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>South Africa</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Income</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Revenu</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>République d'Afrique du Sud</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>COVID-19</term><term>Female</term><term>Health Status Disparities</term><term>Humans</term><term>Male</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte</term><term>Disparités de l'état de santé</term><term>Femelle</term><term>Humains</term><term>Mâle</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Afrique du Sud</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>The coronavirus disease 2019 (COVID-19) has resulted in an enormous dislocation of society especially in South Africa. The South African government has imposed a number of measures aimed at controlling the pandemic, chief being a nationwide lockdown. This has resulted in income loss for individuals and firms, with vulnerable populations (low earners, those in informal and precarious employment, etc.) more likely to be adversely affected through job losses and the resulting income loss. Income loss will likely result in reduced ability to access healthcare and a nutritious diet, thus adversely affecting health outcomes. Given the foregoing, we hypothesize that the economic dislocation caused by the coronavirus will disproportionately affect the health of the poor.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>Using the fifth wave of the National Income Dynamics Study (NIDS) dataset conducted in 2017 and the first wave of the NIDS-Coronavirus Rapid Mobile Survey (NIDS-CRAM) dataset conducted in May/June 2020, this paper estimated income-related health inequalities in South Africa before and during the COVID-19 pandemic. Health was a dichotomized self-assessed health measure, with fair and poor health categorized as "poor" health, while excellent, very good and good health were categorized as "better" health. Household per capita income was used as the ranking variable. Concentration curves and indices were used to depict the income-related health inequalities. Furthermore, we decomposed the COVID-19 era income-related health inequality in order to ascertain the significant predictors of such inequality.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>The results indicate that poor health was pro-poor in the pre-COVID-19 and COVID-19 periods, with the latter six times the value of the former. Being African (relative to white), per capita household income and household experience of hunger significantly predicted income-related health inequalities in the COVID-19 era (contributing 130%, 46% and 9% respectively to the inequalities), while being in paid employment had a nontrivial but statistically insignificant contribution (13%) to health inequality.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>Given the significance and magnitude of race, hunger, income and employment in determining socioeconomic inequalities in poor health, addressing racial disparities and hunger, income inequality and unemployment will likely mitigate income-related health inequalities in South Africa during the COVID-19 pandemic.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">33413442</PMID><DateCompleted><Year>2021</Year><Month>01</Month><Day>15</Day></DateCompleted><DateRevised><Year>2021</Year><Month>01</Month><Day>15</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1475-9276</ISSN><JournalIssue CitedMedium="Internet"><Volume>20</Volume><Issue>1</Issue><PubDate><Year>2021</Year><Month>01</Month><Day>07</Day></PubDate></JournalIssue><Title>International journal for equity in health</Title><ISOAbbreviation>Int J Equity Health</ISOAbbreviation></Journal><ArticleTitle>Income-related health inequalities associated with the coronavirus pandemic in South Africa: A decomposition analysis.</ArticleTitle><Pagination><MedlinePgn>21</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12939-020-01361-7</ELocationID><Abstract><AbstractText Label="BACKGROUND">The coronavirus disease 2019 (COVID-19) has resulted in an enormous dislocation of society especially in South Africa. The South African government has imposed a number of measures aimed at controlling the pandemic, chief being a nationwide lockdown. This has resulted in income loss for individuals and firms, with vulnerable populations (low earners, those in informal and precarious employment, etc.) more likely to be adversely affected through job losses and the resulting income loss. Income loss will likely result in reduced ability to access healthcare and a nutritious diet, thus adversely affecting health outcomes. Given the foregoing, we hypothesize that the economic dislocation caused by the coronavirus will disproportionately affect the health of the poor.</AbstractText><AbstractText Label="METHODS">Using the fifth wave of the National Income Dynamics Study (NIDS) dataset conducted in 2017 and the first wave of the NIDS-Coronavirus Rapid Mobile Survey (NIDS-CRAM) dataset conducted in May/June 2020, this paper estimated income-related health inequalities in South Africa before and during the COVID-19 pandemic. Health was a dichotomized self-assessed health measure, with fair and poor health categorized as "poor" health, while excellent, very good and good health were categorized as "better" health. Household per capita income was used as the ranking variable. Concentration curves and indices were used to depict the income-related health inequalities. Furthermore, we decomposed the COVID-19 era income-related health inequality in order to ascertain the significant predictors of such inequality.</AbstractText><AbstractText Label="RESULTS">The results indicate that poor health was pro-poor in the pre-COVID-19 and COVID-19 periods, with the latter six times the value of the former. Being African (relative to white), per capita household income and household experience of hunger significantly predicted income-related health inequalities in the COVID-19 era (contributing 130%, 46% and 9% respectively to the inequalities), while being in paid employment had a nontrivial but statistically insignificant contribution (13%) to health inequality.</AbstractText><AbstractText Label="CONCLUSIONS">Given the significance and magnitude of race, hunger, income and employment in determining socioeconomic inequalities in poor health, addressing racial disparities and hunger, income inequality and unemployment will likely mitigate income-related health inequalities in South Africa during the COVID-19 pandemic.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Nwosu</LastName><ForeName>Chijioke O</ForeName><Initials>CO</Initials><Identifier Source="ORCID">0000-0001-9739-5448</Identifier><AffiliationInfo><Affiliation>The Impact Centre, Human Sciences Research Council, Cape Town, South Africa. cnwosu@hsrc.ac.za.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Oyenubi</LastName><ForeName>Adeola</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>School of Economics and Finance, University of the Witwatersrand, Pretoria, South Africa.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2021</Year><Month>01</Month><Day>07</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Int J Equity Health</MedlineTA><NlmUniqueID>101147692</NlmUniqueID><ISSNLinking>1475-9276</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086382" 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03;12:327</Citation><ArticleIdList><ArticleId IdType="pubmed">22554095</ArticleId></ArticleIdList></Reference><Reference><Citation>Health Policy Plan. 2012 Mar;27 Suppl 1:i35-45</Citation><ArticleIdList><ArticleId IdType="pubmed">22388498</ArticleId></ArticleIdList></Reference><Reference><Citation>J Health Econ. 2009 Mar;28(2):516-20, author reply 521-4</Citation><ArticleIdList><ArticleId IdType="pubmed">19167117</ArticleId></ArticleIdList></Reference><Reference><Citation>Soc Sci Med. 2005 Mar;60(6):1359-70</Citation><ArticleIdList><ArticleId IdType="pubmed">15626530</ArticleId></ArticleIdList></Reference><Reference><Citation>JAMA. 2020 May 19;323(19):1891-1892</Citation><ArticleIdList><ArticleId IdType="pubmed">32293639</ArticleId></ArticleIdList></Reference><Reference><Citation>Soc Sci Med. 2002 Aug;55(4):673-9</Citation><ArticleIdList><ArticleId IdType="pubmed">12188471</ArticleId></ArticleIdList></Reference><Reference><Citation>J Epidemiol Community Health. 2016 Mar;70(3):271-7</Citation><ArticleIdList><ArticleId IdType="pubmed">26438188</ArticleId></ArticleIdList></Reference><Reference><Citation>Health Policy Plan. 2015 Oct;30(8):1078-92</Citation><ArticleIdList><ArticleId IdType="pubmed">25288515</ArticleId></ArticleIdList></Reference><Reference><Citation>Fisc Stud. 2020 Jun 03;:</Citation><ArticleIdList><ArticleId IdType="pubmed">32836538</ArticleId></ArticleIdList></Reference><Reference><Citation>J Public Econ. 2020 Sep;189:104238</Citation><ArticleIdList><ArticleId IdType="pubmed">32834178</ArticleId></ArticleIdList></Reference><Reference><Citation>J Health Econ. 2009 Mar;28(2):504-15</Citation><ArticleIdList><ArticleId IdType="pubmed">18367273</ArticleId></ArticleIdList></Reference><Reference><Citation>Soc Sci Med. 2015 Mar;128:316-26</Citation><ArticleIdList><ArticleId IdType="pubmed">25577953</ArticleId></ArticleIdList></Reference><Reference><Citation>Pediatrics. 2002 Oct;110(4):e41</Citation><ArticleIdList><ArticleId IdType="pubmed">12359814</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Afrique du Sud</li></country><region><li>Gauteng</li></region><settlement><li>Johannesbourg</li></settlement><orgName><li>Université du Witwatersrand</li></orgName></list><tree><country name="Afrique du Sud"><noRegion><name sortKey="Nwosu, Chijioke O" sort="Nwosu, Chijioke O" uniqKey="Nwosu C" first="Chijioke O" last="Nwosu">Chijioke O. Nwosu</name></noRegion><name sortKey="Oyenubi, Adeola" sort="Oyenubi, Adeola" uniqKey="Oyenubi A" first="Adeola" last="Oyenubi">Adeola Oyenubi</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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