Department of Global Public Health and Primary Care, University of Bergen, N5020, Bergen, Norway
Chr. Michelsen Institute, N5892, Bergen, Norway
Abstract
Background and objectives
Most studies on health inequalities use average measures, but describing the distribution of health can also provide valuable knowledge. In this paper, we estimate and compare withingroup and betweengroup inequalities in length of life for population groups in Ethiopia in 2000 and 2011.
Methods
We used data from the 2011 and 2000 Ethiopia Demographic and Health Survey and the Global Burden of Disease study 2010, and the MODMATCH modified logit life table system developed by the World Health Organization to model mortality rates, life expectancy, and length of life for Ethiopian population groups stratified by wealth quintiles, gender and residence. We then estimated and compared withingroup and betweengroup inequality in length of life using the Gini index and absolute length of life inequality.
Results
Length of life inequality has decreased and life expectancy has increased for all population groups between 2000 and 2011. Length of life inequality within wealth quintiles is about three times larger than the betweengroup inequality of 9 years. Total length of life inequality in Ethiopia was 27.6 years in 2011.
Conclusion
Longevity has increased and the distribution of health in Ethiopia is more equal in 2011 than 2000, with length of life inequality reduced for all population groups. Still there is considerable potential for further improvement. In the Ethiopian context with a poor and highly rural population, inequality in length of life within wealth quintiles is considerably larger than between them. This suggests that other factors than wealth substantially contribute to total health inequality in Ethiopia and that identification and quantification of these factors will be important for identifying proper measures to further reduce length of life inequality.
Introduction
The need to measure and document health inequality is well established
In this study, we looked at length of life inequality in different population groups in Ethiopia. Measuring length of life inequality is one among several ways of capturing overall health inequality. This was first done by Julian Le Grand
Ethiopia
SubSaharan Africa
*2010
Total population (000)
84 734
853 931
Urban population (% of total)
17.0
36.5
Life Expectancy at birth (years)
59.2
54.2*
GNI per capita (constant 2000 US$)
229
571
Poverty headcount ratio at 2$ a day (% of population)
66.0
69.9
Health expenditure per capita (current USD)
16.6
95
Physicians per 1 000 population
0.02*
0.16*
Total fertility rate (births per woman)
4.0
4.9
Maternal mortality rate
350*
500*
Under5 mortality rate
77
108
The report from the Commission on Social Determinants of Health in 2008 acknowledges that specific national and local contexts have to be taken into consideration in order to reduce health inequities
We aim to model life expectancy and length of life inequality across gender, urban–rural residence and a national total for the years 2000 and 2011, and for wealth quintiles for 2011. Then we compare these within and between group inequalities. As no good quality vital registration data exists, we believe that modeling health distribution using available summary measures will be of great value. Estimates of life expectancy and length of life inequality between and within population groups will provide a novel understanding of health distribution in Ethiopia, and it could serve as an important baseline for both theoretical work and for concrete policy making and priority setting.
Methods
We modeled life expectancy and length of life inequality for Ethiopian population groups using a model life table system. We used available underfive and adult mortality rates as input to generate populationgroup specific life tables with estimates of life expectancy and agespecific mortality for different agegroups. This is used to estimate length of life inequality both within and between groups, calculated as Gini health scores (Gini_{H}), absolute length of life inequality (ALI), concentration indexes (CI) and absolute differences.
Life table modeling
To produce groupspecific abridged life tables we used the MODMATCH modified logit life table system (MODMATCH)
To model life expectancy for groups, the modified logit life table system requires both underfive and adult mortality input. While underfive mortality data by gender, urban–rural residence and wealth quintiles are available from the 2011 EDHS
Measuring inequality
We used the Gini index to measure inequality in length of life. Originally developed for measuring economic inequality
We calculate the Gini health index (Gini_{H}) using Wagstaff’s formula
where
Smits and Monden have calculated relative length of life inequality (RLI) based on the Gini_{H} index
with
To calculate betweengroup inequalities, we used estimates of life expectancy to calculate concentration indices and absolute differences between the groups. The concentration index (CI) derives from the concentration curve, where individuals are ranked according to their relative socioeconomic position on the xaxis and with the yaxis presenting the cumulative proportion of health in these individuals. When this curve is plotted, its deviation from the diagonal Line of Equality (LoE) can be estimated, and the CI is defined as twice the area between the curve and the line of equality
Data selection
We used underfive mortality data from the 2011 and 2000 Ethiopia Demographic and Health Survey (EDHS)
Wealth quintiles are used as proxies of socioeconomic position. They are measured in a standardized way in each DHS by an asset index. The index is constructed from household asset data and dwelling characteristics such as ownership of a television, a bicycle or car, source of drinking water, sanitation facilities and type of material used for flooring
Data used on adult mortality and life expectancy is from the Global Burden of Disease study 2010 (GBD 2010)
Results
Figure
Mortality distribution for highest and lowest wealth quintile 2011
Mortality distribution for highest and lowest wealth quintile 2011. Mortality given as deaths per 1000 (yaxis) plotted against fiveyear age groups (xaxis).
LE
Gini _{ H }
ALI (years)
CI
Abs.diff (years)
(years)
* calculated between highest and lowest wealth quintile.
Wealth quintile
Lowest
53.4
0.29
30.6
Second
56.2
0.26
29.4
Middle
60.6
0.22
27.0
0.030
9.0*
Fourth
59.9
0.23
27.5
Highest
62.5
0.21
25.9
We see a clear socioeconomic gradient in Ethiopia, with a life expectancy ranging from 53.4 years in the lowest wealth quintile to 62.5 years in the highest quintile  an absolute difference of 9 years. There is also a correspondingly decrease in length of life inequality from the lowest to the highest quintile: a Gini_{H} score of 0.29 in the lowest wealth quintile and 0.21 in the highest quintile.
The absolute difference in life expectancy between the highest and lowest quintile was 9 years, and the CI was estimated to 0.030. As we can see in Table
Withingroup and betweengroup inequality among wealth quintiles 2011
Withingroup and betweengroup inequality among wealth quintiles 2011. Life expectancy (central dots) and absolute length of life inequality (high and low bar) for wealth quintiles indicates larger within than betweengroup inequality.
In Table
LE
Gini _{ H }
ALI (years)
CI
Abs.diff (years)
(years)
Gender
2000
Male
48.9
0.33
32.6
0.005
1.7
Female
50.6
0.33
33.4
2011
Male
56.7
0.25
28.6
0.014
3.2
Female
59.9
0.23
28.0
Residence
2000
Urban
55.1
0.28
30.9
0.008
6.2
Rural
48.9
0.34
33.3
2011
Urban
63.1
0.20
25.5
0.013
5.6
Rural
57.5
0.25
28.8
National
2000
Total
49.7
0.33
33.1


2011
Total
60.9
0.23
27.6


Life expectancy has increased and withingroup length of life inequality has decreased for all groups between 2000 and 2011, and there are greater length of life inequality among males and rural residents compared to females and urban residents. In 2011 males and females had a Gini_{H} score of 0.25 and 0.23 respectively, decreasing from 0.33 in 2000. In terms of absolute length of life inequality, males had 28.6 and females 28.0 years in 2011, compared to 32.6 and 33.4 years in 2000. The absolute difference in life expectancy between males and females was 3.2 years in 2011 and 1.7 years in 2000, corresponding to a CI of 0.014 and 0.005 respectively.
There is greater length of life inequality among rural than urban residents, with Gini_{H} scores of 0.25 and 0.20 in 2011, compared to 0.34 and 0.28 in 2000. Absolute length of life inequality has been reduced from 33.3 and 30.9 years for rural and urban residents in 2000, to 28.8 and 25.5 years in 2011. The absolute difference in life expectancy was 5.6 years in 2011 and 6.2 years in 2000, with CI of 0.013 and 0.008. The total length of life inequality in Ethiopia has also decreased; from a Gini_{H} score of 0.33 in 2000 to 0.23 in 2011. In the same period, life expectancy has increased from 49.7 years to 60.9 years.
Discussion
Our findings show that length of life inequality has decreased and life expectancy increased in Ethiopia from 2000 to 2011 and that withingroup inequality are substantially larger than betweengroup inequality. Inequality between wealth quintiles only account for about one third of total health inequality. We find larger length of life inequality between males, rural residents, and the less wealthy, compared to women, urban residents and the wealthier. Estimates of life expectancy follow the same pattern. By estimating length of life inequality and life expectancy for females and males, urban and rural residents, and for wealth quintiles, we offer a new and more comprehensive picture of population level health in Ethiopia. This is important, as it can provide a baseline for priority setting and resource allocation in Ethiopia.
There are some limitations to our findings. Length of life inequality does not fully capture the overall health inequality in a population, and we do not claim that it should be the only indicator used to describe health. We do think it provides important and supplementary information to other wellknown measures, like life expectancy, DALYs, and mortality rates, as it describes the
The wealth index is only a proxy of socioeconomic position, and although it is commonly used, it does not capture the full impact of other socioeconomic determinants like income and education. Measuring only the differences between the highest and lowest group obviously neglects the middle groups. Still, absolute differences in health between groups are among the most commonly used measures of health inequality between socioeconomic groups and we therefore think its use is justified. By comparing it to ALI, an individual measure of inequality, we want to illustrate the need for individual health measures as a supplement to the average measures between population groups.
Traditionally, betweengroup inequalities have received more attention from researchers, in addition to claims that differences between predefined socioeconomic groups are what we should be morally concerned with
From our findings we can also see that wealth only gives a limited contribution to total health inequality. This is indicated by comparing the total inequality in 2011 of 27.6 years with the absolute difference between the wealth quintiles: if we randomly select two individuals, one from the highest and one from the lowest wealth quintile, their average difference in life expectancy would equal the absolute difference in life expectancy between the highest and lowest quintile of 9 years. If we then randomly select two individuals from the whole population, their expected difference in life expectancy would be 27.6 years. A full decomposition of factors associated with inequality in age at death could reveal how much of total inequality can be explained, and this calls for further analysis.
These findings demonstrate how wealth alone provides an insufficient explanation of health inequalities in Ethiopia. Wagstaff and van Doorslaer have estimated socioeconomic inequality to be about 25% of total inequality
Both the general wealth level and the method of assessing wealth in Ethiopia can partly explain our findings. According to the World Bank, in 2005 77.6% of the population lived on less than 2 USD per day
As in most low income countries, Ethiopia has a rural–urban migration pattern, with an increase of the urban population ratio from 14.7% to 17.0% from 2000 to 2011. This corresponds to an absolute growth in urban population ratio of 2.3%. For the SubSaharan region as a whole, the absolute growth in urban population ratio was 4.5%, as the urban population ratio increased from 32.0% to 36.5%
The positive development from 2000 to 2011, with a decrease in length of life inequality and an increase in life expectancy, can be seen as a part of the positive general development in Ethiopia. Efforts like the Health Sector Development Program
Our findings suggest that other factors than wealth contributes to length of life inequality in Ethiopia. We do not claim that an unequal distribution of wealth is acceptable, but we ask if health inequalities in Ethiopia can be reduced by also addressing other factors. With coverage rates for many important interventions still being low
Conclusion
Our findings support the observed positive trend in Ethiopian population health: life expectancy has increased and the distribution of health is more equal, with length of life inequality reduced for all population groups. Still there is a large potential for further improvement. In the Ethiopian context, with a poor and rural population, inequality in length of life within wealth quintiles is considerably larger than between them, implying that factors other than wealth make a substantial contribution to total health inequality. If this unequal distribution of health is of concern, measures must be taken to reduce inequality, including further work to identify and quantify contributing factors.
Appendix
A thorough description of the MODMATCH modified logit life table system is available in Murray et al’s 2003 paper
where
with
A unique x was calculated for wealth quintiles in 2010 and urban–rural residence in 2010 and 2000, and group specific adult mortality rates was estimated. There was no need to adjust input when modeling life tables based on gender, as specific underfive and adult mortality data was available. Underfive mortality by wealth quintiles were not available in the EDHS 2000, and therefore are only gender and residence groups compared over time. For urban–rural and wealth groups, male and female agespecific mortality rates was summarized using gender ratios from the World Bank Database
Abbreviations
0q5: Underfive mortality rate probability of dying between 0 and 5 years; 45q15: Adult mortality rate probability of dying between 15 and 60 years; ALI: Absolute length of life inequality; CI: Concentration index; DALY: Disabilityadjusted life year; DHS: Demographic and health survey; EDHS: Ethiopia demographic and health survey; GBD: 2010 global burden of disease study 2010; LE: Life expectancy; RLI: Relative length of life inequality; WHO: World health organization; USD: United States dollar.
Competing interests
The authors declare that they have no competing interest.
Authors’ contributions
EJT and OFN designed the study, preformed the analysis, interpreted the result and wrote the paper. MA assisted in data analysis, interpretation of the data and writing of the paper. All authors read and approved the final manuscript.
Acknowledgements
We thank the members of the research group Global Health: Ethics, Economics and Culture at the University of Bergen for valuable feedback and general support. A special thanks to Yukiko Asada and Trygve Ottersen for helpful comments on an earlier draft, and to the participants at the University of Oslo PhD course ‘The Political Determinants of Health’ for inspirational feedback.