Measuring Health Gaps between the Rich and the Poor: A Review of the Literature and its Implications for Health Research in Africa
Identifieur interne : 001835 ( Pmc/Checkpoint ); précédent : 001834; suivant : 001836Measuring Health Gaps between the Rich and the Poor: A Review of the Literature and its Implications for Health Research in Africa
Auteurs : Amson Sibanda ; Henry V. Doctor [États-Unis]Source :
- Journal of Public Health in Africa [ 2038-9922 ] ; 2013.
Abstract
Measuring variations and gaps in health and wellbeing across individuals, social groups and societies is a critical issue confronting social scientists in their quest to explain why gaps in health between the rich and the poor persist within and across societies. This article provides a systematic review of the measurement of inequalities and their implications on rural and remote health. A comprehensive literature review was conducted using online databases and other collections of published research on measuring health gaps between the rich and the poor in order to trace the development of this field of inquiry. Despite the enormous information on the subject area, it is not always easy to disentangle the
Url:
DOI: 10.4081/jphia.2013.e3
PubMed: 28299092
PubMed Central: 5345422
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Measuring Health Gaps between the Rich and the Poor: A Review of the Literature and its Implications for Health Research in Africa</title>
<author><name sortKey="Sibanda, Amson" sort="Sibanda, Amson" uniqKey="Sibanda A" first="Amson" last="Sibanda">Amson Sibanda</name>
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<series><title level="j">Journal of Public Health in Africa</title>
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<front><div type="abstract" xml:lang="en"><p>Measuring variations and gaps in health and wellbeing across individuals, social groups and societies is a critical issue confronting social scientists in their quest to explain why gaps in health between the rich and the poor persist within and across societies. This article provides a systematic review of the measurement of inequalities and their implications on rural and remote health. A comprehensive literature review was conducted using online databases and other collections of published research on measuring health gaps between the rich and the poor in order to trace the development of this field of inquiry. Despite the enormous information on the subject area, it is not always easy to disentangle the <italic>independent</italic>
effects of social class or socio-economic status (SES) on health inequalities from genetic or biological differences when analyzing racial/ethnic, gender or age gaps in mortality and morbidity. The meaning of SES or social class also varies from one culture to the other. Despite decades of work in this field, it is not clear what it is about SES or social class that is associated with inequalities in health. Is it simply a question of access to resources? And on the issue of measurement, studies from various disciplines have shown that it is important to employ a raft of measures in order to measure and present the distributions fully from various angles and value judgments. In the rural African context, tackling vertical and horizontal inequalities in health requires tackling the root causes of poverty and promoting social policies that empower individuals and communities. Hence, the review discusses recent methodological developments that hold promise for addressing the knowledge gap that remain. We hope that researchers will reflect on the dynamics in measures of inequalities discussed in this paper as they continue to assess the status of health in Africa’s contemporary and largely dominated rural population.</p>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">J Public Health Africa</journal-id>
<journal-id journal-id-type="iso-abbrev">J Public Health Africa</journal-id>
<journal-id journal-id-type="publisher-id">JPHIA</journal-id>
<journal-title-group><journal-title>Journal of Public Health in Africa</journal-title>
</journal-title-group>
<issn pub-type="ppub">2038-9922</issn>
<issn pub-type="epub">2038-9930</issn>
<publisher><publisher-name>PAGEPress Publications, Pavia, Italy</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">28299092</article-id>
<article-id pub-id-type="pmc">5345422</article-id>
<article-id pub-id-type="doi">10.4081/jphia.2013.e3</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Measuring Health Gaps between the Rich and the Poor: A Review of the Literature and its Implications for Health Research in Africa</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Sibanda</surname>
<given-names>Amson</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" corresp="yes"><name><surname>Doctor</surname>
<given-names>Henry V.</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
<xref ref-type="corresp" rid="cor1"></xref>
</contrib>
</contrib-group>
<aff id="aff001"><label>1</label>
<institution>United Nations Department of Economic and Social Affairs</institution>
, Division for Social and Policy Development,<addr-line>New York</addr-line>
</aff>
<aff id="aff002"><label>2</label>
<institution>Columbia University, Mailman School of Public Health, Department of Population and Family Health</institution>
,<addr-line>New York, USA</addr-line>
</aff>
<author-notes><corresp id="cor1">Columbia University, Mailman School of Public Health, Department of Population and Family Health, New York, NY 10032, USA. E-mail: <email>hvd2105@columbia.edu</email>
</corresp>
<fn><p>Dedication: this work is dedicated to Dr. Amadou Noumbissi who started the research but unfortunately passed away. Dr. Henry V. Doctor and Amson Sibanda concluded the project, which is published in his honor and memory.</p>
</fn>
</author-notes>
<pub-date pub-type="epub"><day>02</day>
<month>7</month>
<year>2013</year>
</pub-date>
<pub-date pub-type="collection"><day>25</day>
<month>6</month>
<year>2013</year>
</pub-date>
<volume>4</volume>
<issue>1</issue>
<elocation-id>e3</elocation-id>
<history><date date-type="received"><day>15</day>
<month>12</month>
<year>2011</year>
</date>
<date date-type="rev-recd"><day>19</day>
<month>6</month>
<year>2012</year>
</date>
<date date-type="accepted"><day>24</day>
<month>4</month>
<year>2013</year>
</date>
</history>
<permissions><copyright-statement>©Copyright A. Sibanda and H.V. Doctor</copyright-statement>
<copyright-year>2013</copyright-year>
<copyright-holder>Licensee PAGEPress, Italy</copyright-holder>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by-nc/3.0/"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (<uri xlink:href="http://creativecommons.org/licenses/by-nc/3.0/">http://creativecommons.org/licenses/by-nc/3.0/</uri>
) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
</license>
</permissions>
<abstract><p>Measuring variations and gaps in health and wellbeing across individuals, social groups and societies is a critical issue confronting social scientists in their quest to explain why gaps in health between the rich and the poor persist within and across societies. This article provides a systematic review of the measurement of inequalities and their implications on rural and remote health. A comprehensive literature review was conducted using online databases and other collections of published research on measuring health gaps between the rich and the poor in order to trace the development of this field of inquiry. Despite the enormous information on the subject area, it is not always easy to disentangle the <italic>independent</italic>
effects of social class or socio-economic status (SES) on health inequalities from genetic or biological differences when analyzing racial/ethnic, gender or age gaps in mortality and morbidity. The meaning of SES or social class also varies from one culture to the other. Despite decades of work in this field, it is not clear what it is about SES or social class that is associated with inequalities in health. Is it simply a question of access to resources? And on the issue of measurement, studies from various disciplines have shown that it is important to employ a raft of measures in order to measure and present the distributions fully from various angles and value judgments. In the rural African context, tackling vertical and horizontal inequalities in health requires tackling the root causes of poverty and promoting social policies that empower individuals and communities. Hence, the review discusses recent methodological developments that hold promise for addressing the knowledge gap that remain. We hope that researchers will reflect on the dynamics in measures of inequalities discussed in this paper as they continue to assess the status of health in Africa’s contemporary and largely dominated rural population.</p>
</abstract>
<kwd-group><title>Key words</title>
<kwd>Africa</kwd>
<kwd>health inequalities</kwd>
<kwd>life expectancy</kwd>
<kwd>socio-economic status</kwd>
<kwd>Millennium Development Goals</kwd>
<kwd>mortality</kwd>
</kwd-group>
<counts><fig-count count="0"></fig-count>
<table-count count="1"></table-count>
<equation-count count="0"></equation-count>
<ref-count count="66"></ref-count>
<page-count count="7"></page-count>
</counts>
</article-meta>
</front>
<floats-group><table-wrap id="table001" orientation="portrait" position="float"><label>Table 1.</label>
<caption><p>Summary measures of inequalities in health and selected attributes.</p>
</caption>
<table frame="box" rules="all"><thead><tr><th align="left" rowspan="1" colspan="1">Measure</th>
<th align="left" rowspan="1" colspan="1">Selected characteristics</th>
</tr>
</thead>
<tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">The Gini Coefficient (G) and Lorenz curve</td>
<td align="left" rowspan="1" colspan="1">Based on the Lorenz curve, an accumulated frequency that measures health inequalities among a specific population<break></break>
Most popular<sup><xref rid="ref32" ref-type="bibr">32</xref>
</sup>
and successful measure in economics and extensively applied in many studies on inequalities<sup><xref rid="ref48" ref-type="bibr">48</xref>
,<xref rid="ref49" ref-type="bibr">49</xref>
</sup>
</td>
</tr>
<tr><td align="left" valign="top" rowspan="1" colspan="1">Pseudo Lorenz curves</td>
<td align="left" rowspan="1" colspan="1">Based on grouped data. Population is distributed into social classes, which are ranked by health<break></break>
Because the classes are ranked by their health, the Pseudo Lorenz curve, just as the Lorenz curve fails to establish the association between the inequalities in health and SES<break></break>
Both Lorenz and Pseudo Lorenz curve are unable to verify if persons with the poorest health (the sickest) belong to the lower socioeconomic class or not<sup><xref rid="ref18" ref-type="bibr">18</xref>
</sup>
</td>
</tr>
<tr><td align="left" valign="top" rowspan="1" colspan="1">PCA</td>
<td align="left" rowspan="1" colspan="1">A statistical technique for data reduction; reduces the number of variables in an analysis by describing linear combinations of the variables that contain most of the information in the original variables (Stata Statistical Software: Release 7.0.; 2001. StataCorp., College Station, TX, USA)<sup><xref rid="ref50" ref-type="bibr">50</xref>
</sup>
<break></break>
PCA reduces <italic>n</italic>
dimensional system into fewer dimensions. For <italic>e.g.</italic>
, a set of <italic>n</italic>
SES indicators <italic>x<sub>1</sub>
, x<sub>2</sub>
,…x<sub>n</sub>
,</italic>
representing ownership of <italic>n</italic>
assets in each household, PCA can transform this <italic>n</italic>
dimensional random vector (<italic>x<sub>1</sub>
, x<sub>2</sub>
,…x<sub>n</sub>
</italic>
) into fewer dimensional SES variable <italic>y<sub>j</sub>
</italic>
. That is, <italic>y<sub>j</sub>
= a<sub>1</sub>
x<sub>1</sub>
+ a<sub>2</sub>
x<sub>2</sub>
+ … + a<sub>n</sub>
x<sub>n</sub>
</italic>
<break></break>
The variable <italic>y<sub>j</sub>
</italic>
is divided into quintiles of the asset index and the value of each indicator (health, nutrition, etc.) (Bawah AA, unpublished Ph.D. dissertation, 2002)<sup><xref rid="ref36" ref-type="bibr">36</xref>
</sup>
</td>
</tr>
<tr><td align="left" valign="top" rowspan="1" colspan="1">Poor-rich ratio indicator</td>
<td align="left" rowspan="1" colspan="1">Can be considered a by-product of PCA or any measure that produces ratios by socioeconomic group<sup><xref rid="ref32" ref-type="bibr">32</xref>
</sup>
<break></break>
If ratio > 1 it means that the poor are at a disadvantage. If <1 it means that the poor are at an advantage</td>
</tr>
<tr><td align="left" valign="top" rowspan="1" colspan="1">Concentration index (CI)</td>
<td align="left" rowspan="1" colspan="1">CI relates SES to health. Individuals in a population are ranked in ascending order of income or some other indicator of SES (beginning with the poorest and ending with the richest) and not by a health variable48<break></break>
Uses a concentration curve to express the cumulative proportion of ill health experienced by each cumulative proportion of the population ranked by SES21</td>
</tr>
<tr><td align="left" valign="top" rowspan="1" colspan="1">Health concentration index (C)</td>
<td align="left" rowspan="1" colspan="1">C is equal to twice the area between the concentration curve and the diagonal and provides a measure of the extent of inequalities in health that are systematically associated with SES<sup><xref rid="ref18" ref-type="bibr">18</xref>
,<xref rid="ref32" ref-type="bibr">32</xref>
</sup>
<break></break>
Lowest value, –1, occurs when all the population’s health is concentrated in the hands of the most disadvantaged person.<break></break>
Maximum value, 1, occurs when all the population’s health is concentrated in the hands of the least disadvantaged person<break></break>
Advantages: reflects the experiences of the entire population; sensitive to the distribution of the population across socio economic groups; also ensures that the socio-economic dimension to inequalities in health is taken into account because it ranks individuals by SES rather than by health</td>
</tr>
<tr><td align="left" valign="top" rowspan="1" colspan="1">Range measures</td>
<td align="left" rowspan="1" colspan="1">Measures such as rate differences and rate ratios are the most common. Generally used to compare the range in rates of morbidity or mortality between the top and the bottom socio-economic groups<sup><xref rid="ref21" ref-type="bibr">21</xref>
</sup>
<break></break>
For <italic>e.g.</italic>
, the <italic>Black Report</italic>
showed that in 1970-71 <italic>men and women in occupational class V had a 2.5 times greater chance of dying than their professional counterparts in class I</italic>
<sup><xref rid="ref10" ref-type="bibr">10</xref>
</sup>
<break></break>
Disadvantages: overlooks the dynamics <italic>n</italic>
the intermediate groups. The gap between the top and the bottom groups might, for <italic>e.g.,</italic>
remain unchanged, but the extent of inequality between the intermediate groups might be diminishing (or increasing). The range takes no account of the sizes of the groups being compared, which can lead to misleading results when comparisons are performed over time and across countries</td>
</tr>
<tr><td align="left" valign="top" rowspan="1" colspan="1">Index of dissimilarity (ID)</td>
<td align="left" rowspan="1" colspan="1">ID identifies the amount of ill health or deaths that would have to be redistributed across socioeconomic groups in order for all groups to have the same mortality or morbidity rate<sup><xref rid="ref21" ref-type="bibr">21</xref>
</sup>
<break></break>
ID assumes that socioeconomic inequalities in health arise as a result of the inequitable distribution of resources. To solve the distribution problem, societies can reduce the level of mortality or morbidity among the poor by taking away some of the mortality or morbidity gains enjoyed by higher socioeconomic groups<sup><xref rid="ref9" ref-type="bibr">9</xref>
</sup>
<break></break>
Disadvantages: ID is insensitive to the socio-economic dimension to inequalities in health; does not pay particular attention to where high morbidity or mortality rates are located in any one particular socioeconomic group</td>
</tr>
<tr><td align="left" valign="top" rowspan="1" colspan="1">Slope and relative indices of inequality (SII and RII)</td>
<td align="left" rowspan="1" colspan="1">Presented as a histogram, with the height of each bar indicating the level of ill health of the class in question and the width representing the relative size of the population in each class<sup><xref rid="ref18" ref-type="bibr">18</xref>
</sup>
<break></break>
The Slope Index then relates the rate of health problems to a measure of SES by means of regression analysis. The estimated slope is interpreted as the absolute difference in morbidity or mortality between successive socioeconomic groups</td>
</tr>
<tr><td align="left" valign="top" rowspan="1" colspan="1">Other measures of inequalities</td>
<td align="left" rowspan="1" colspan="1">Other measures have been used to determine the nature and magnitude of health inequalities and these include life expectancy, health expectancy, disability-free life expectancy, disability-adjusted life years, QALYs, SMR proportional mortality rates, rate ratios and odds ratios<sup><xref rid="ref33" ref-type="bibr">33</xref>
,<xref rid="ref51" ref-type="bibr">51</xref>
-<xref rid="ref54" ref-type="bibr">54</xref>
</sup>
<break></break>
With a few exceptions, these measures have not been used to monitor the patterns and sizes of health inequalities in Africa. Others<sup><xref rid="ref55" ref-type="bibr">55</xref>
</sup>
examined gender and provincial disparities in disability-free life expectancy in South Africa<break></break>
Other measures include a combination of individual- and family-level characteristics with the socioeconomic characteristics of communities. These studies have shown that the effects of place of residence or community on morbidity and mortality persist over and above the effects of individual-level attributes and household-level SES<sup><xref rid="ref39" ref-type="bibr">39</xref>
,<xref rid="ref56" ref-type="bibr">56</xref>
-<xref rid="ref58" ref-type="bibr">58</xref>
</sup>
</td>
</tr>
</tbody>
</table>
<table-wrap-foot><fn><p>SES, socio-economical status; PCA, principal components analysis; QALYs, quality-adjusted life years; SMR, standardized mortality rates.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</floats-group>
</pmc>
<affiliations><list><country><li>États-Unis</li>
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<country name="États-Unis"><noRegion><name sortKey="Doctor, Henry V" sort="Doctor, Henry V" uniqKey="Doctor H" first="Henry V." last="Doctor">Henry V. Doctor</name>
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