For example, the stunting prevalence for the poorest quintile was The mean difference between Q5 and Q1 also remained large and did not improve over time, moving from We observed that the coverage rates of all nine interventions increased between round 1 and round 3 in each wealth quintile for countries with available data in round 1 and round 3 Figure 1 b.
Consistent with results presented in Table 1 , we found that access to improved sanitation had the largest difference between Q5 and Q1, Other indicators that rank highly for mean inequality include supply of improved water, SBA, and four or more antenatal care visits: the differences between Q5 and Q1 for these three indicators were Except for access to improved sanitation and care seeking for suspected pneumonia, seven out of nine selected indicators showed reductions in inequalities between round 1 and round 3.
Country-level change in inequality status in child health outcomes and child health interventions: round 1 vs. Figure 2 shows the changes in absolute inequalities in three indicators that have data available in all three rounds in more than 30 countries: stunting prevalence, access to improved sanitation, and SBA. Figure A2 further shows the change in three more indicators with data available in more than 30 countries, including access to improved water, coverage of full immunization, and ORT for diarrhea. Tables A8 and A9 present the inequality status of the indicators in each round for each country.
In Figure 2 a , we observed that among the 34 countries with valid data on child stunting status in all three rounds, five of them experienced a significant reduction in the difference between Q5 and Q1 during survey rounds 1 and 3, including Dominican Republic, Egypt, Ghana, Mongolia, and Peru marked in orange in Figure 2 a ; three of them were Countdown countries. Two countries Congo, Dem.
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Sixteen of the 18 countries were Countdown countries. However, 14 countries experienced significant increases in the absolute difference, marked in green in Figure 2 b with 10 of them representing Sub-Saharan African countries.
Regional health-care inequity in children’s survival in Zhejiang Province, China | SpringerLink
Thirteen of them were Countdown countries. Figure 2 c shows that, among the 39 countries with available data on delivery with SBA in all three rounds, 14 experienced a significant reduction in the difference between Q5 and Q1 between round 1 and round 3, including seven countries in Sub-Saharan African Benin, Congo, Dem. Eleven of the 14 are Countdown countries. All of them were Countdown countries. The estimates of relative inequality Tables A10 — A13 were basically aligned with the estimates of absolute inequality. This study has two salient findings.
Firstly, remarkable improvements in child health and coverage of interventions have been observed between and in both Q1 and Q5, yet large inequalities remain in these indicators. Except for ITN, the mean differences between Q5 and Q1 for all other indicators were significantly different from zero in the most recent survey round.
Though analyzing determinants of inequality is beyond the scope of this study, we speculate that measuring the progress of reaching MDGs by focusing on national means could lead to less policy attention paid to inequalities [ 36 ]. As the Countdown equity analysis indicates, child health interventions tend to reach the wealthiest children first in the absence of policy instruments for addressing inequality [ 14 ]. The second salient finding is that the progress made in reducing child health inequalities differs greatly by country, with some countries making significant improvements between and , some significantly deteriorating, and others remaining statistically unchanged.
For example, we found that 18 countries significantly reduced the difference between Q5 and Q1 in access to improved sanitation over time, with nine of them reducing the difference by more than 20 percentage points, indicating a remarkable improvement in equality of access to improved sanitation.
Meanwhile, we observed a significant growth in absolute difference in 14 countries, implying a deteriorating level of equality in access to improved sanitation. Although significant reductions or increases in child health inequalities have occurred in both Countdown and non-Countdown countries, we found that the deterioration in inequality was heavily concentrated in Countdown countries.
All countries experiencing deteriorating inequality in child health outcomes were Countdown countries. The following may be plausible explanations for this situation: firstly, out-of-pocket medical payments for receiving care could be too high for the poorest households in Countdown countries, thus deterring their access to the related care.
Consequently, poor families may choose not to use SBA during deliveries [ 38 ]. Secondly, travel costs and foregone earnings are important opportunity costs in consuming health services for the poorest households, especially those living in remote rural areas. In Uganda, for example, Thirdly, poor quality of care could prevent poor rural patients in countries such as Rwanda from using the services [ 40 ]. The number of patients was found to be negatively correlated with absentee rate [ 41 ].
This paper provides two distinct contributions to the study of child health inequalities. Firstly, taking advantage of available data, we extended the previous analyses by expanding the scope of considered countries 88 countries with 57 Countdown and 31 non-Countdown countries and time between and Secondly, we expanded upon the inequalities studied by including three child health outcomes and five interventions, such as access to improved water and sanitation. There are several limitations to this study.
Firstly, the classification of wealth quintiles is country-specific and time-sensitive. The poorest quintile in an upper-middle-income country could be better off than the richer quintiles in a less developed country. Secondly, due to the varying availability of variables in the DHS and MICS by country and year, we were only able to conduct a trend analysis for a subset of countries, which limited our knowledge on the progress of reducing inequality in all 88 countries.
Moving forward, updating assessments on child health inequality, both within and across countries, is essential as more data become available. More research needs to be conducted to identify the facilitators or barriers to reducing inequality in child health. For example, similar to Barros et al. Interventions that could be delivered at the community level, such as vitamin A supplements, tend toward greater equitability [ 45 ].
This may suggest that expanding community health programs could be positively linked to the reduction of inequality in intervention coverage. In addition, improving equality should be prioritized at the national and global health agendas. In the SDG era, it is critical to monitor progress in child health, to focus not only on population means but also on inequalities. Child health interventions included in previous studies are highlighted in light purple.
Even if the Q1 group improves at a slower rate than the Q5 group, the absolute difference may still shrink. In this case, the absolute difference between Q1 and Q5 groups decreases from to 90 per 1, live births, which indicates the Q1 and Q5 groups are being more equal. However, in fact, the Q5 group improves at a faster rate than the Q1 group, which appears to suggest the Q5 group did better than the Q1 group, and these two groups should become less equal.
This is counterintuitive. It will not reflect the overall status of a health indicator in a country. Yet intuitively, the latter one should be of higher concern. It has higher data requirements than the other equality measurements. There are usually two ways to obtain concentration index. One approach is using grouped data, which requires data on the health indicator and the number of individuals for each income group. The second approach uses micro-data, which requires health status data and wealth score for each individual.
This study adopted the first approach. It could be sensitive to the living standards measure, such as consumption, expenditure, and wealth index. The ratio will improve if the health status of the Q5 gets worse. Number of countries with available data on the three health outcome and 17 health intervention indicators. The full names and the definitions of the indicators are in appendix Table 1. The data for Tajikistan cannot be downloaded from Demographic Health Survey dataset, so we did not include it in our analysis. Analyzing health equity using household survey data: a guide to techniques and their implementation.
World Bank Publications, Second, we treated each country as a subject to calculate the mean values and weighted them according to their population size. Coverage of 17 child health interventions in each wealth quintile, round 1 vs. Various numbers of countries were involved in analyzing different indicators. Countries with significantly enlarged absolute difference between Q1 and Q5 in round 3 comparing to round 1 are shown in bold. Absolute difference between Q5 and Q1 in the nine selected child health interventions by survey round and country, 41 countries. Absolute difference between Q5 and Q1 in the nine selected child health interventions by survey round and country, 41 countries continued.
Countries with significantly enlarged absolute difference between Q1 and Q5 in round 3 compared with round 1 are shown in bold. Concentration index of three child health outcomes by survey round and country, 41 countries. Countries whose concentration index became significantly father away from zero between round 1 and round 3 are shown in bold.
Concentration index of three selected child health interventions by survey round and country, 41 countries. Ratio of Q5 to Q1 in the three child health outcomes by survey round and country, 41 countries. Countries whose ratio of Q5 to Q1 became significantly farther away from one between round 1 and round 3 are shown in bold. Ratio of Q5 to Q1 in the three selected child health interventions by survey round and country, 41 countries. For all three selected indicators, the poorest wealth quintile Q1 has a substantial proportion of values as zero, leading to infinite large ratio when calculating Q5 to Q1.
Tables 1 and A6 are at the aggregate level. World Bank Publications, Second, we treated each country as a subject to calculate the mean values. Table A7 is generated in the same way as Table A6 , yet instead of treating each country as an identical subject, we weighted the countries with their population sizes. While previous studies on child health inequalities exclusively focused on interventions in the health sector, we yielded a comprehensive evaluation of child health, tracking inequalities in three outcome indicators mortality and stunting and 17 interventions including access to improved water and sanitation.
We also extended the previous analyses by expanding the scope of the countries 88 countries , and time length — We call for greater investments toward the poorest to reduce child health inequalities. Supplemental files for this article can be accessed here. National Center for Biotechnology Information , U. Journal List Glob Health Action v. Glob Health Action.
Published online Dec Zhihui Li , Mingqiang Li , S. Subramanian , and Chunling Lu. Author information Article notes Copyright and License information Disclaimer. Received Aug 8; Accepted Nov This article has been cited by other articles in PMC. Associated Data Supplementary Materials Supplementary material. Table 1. Open in a separate window.
Selection of child health indicators Indicators on child health outcomes We selected three child health outcome indicators, infant mortality, under-five mortality, and stunting prevalence. Indicators on child health interventions We included 17 interventions that collectively account for all stages of the continuum of care for child health.
Inequality analysis When conducting trend analysis of an indicator, we consider it essential to compare the same group of countries over time.
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Results Mean level of inequality status in child health outcomes and interventions for 88 countries, most recent years Table 1 presents the mean level of absolute inequality in three child health outcomes and nine selected child health interventions, using data from the most recent survey of each country. Table A6 Trends of mean inequality status in child health outcomes and child health interventions: round 1 vs. Figure 1. Figure 2. Discussion and conclusion This study has two salient findings.
Supplementary Material Supplementary material: Click here for additional data file. Data sources of the countries involved in this study, surveys. Table A2.
Table A3. Definition of child health indicators. Table A4. Comparison of three inequality measurements. Inequality measurement Calculation Features Limitations Difference between Q5 and Q1 Absolute inequality measures the different in percentage points between the most advantaged group Q5 and the least advantaged group Q1 Improvement in this measurement implies Q1 group improve faster in terms of the absolute number in the health indicator comparing to the Q5 group, or Q5 group experienced faster absolute health deterioration than the Q1 group Even if the Q1 group improves at a slower rate than the Q5 group, the absolute difference may still shrink.
Concentration index Concentration indexes were generated from the concentration curves. The detailed calculation method could refer to the World Bank instruction a Concentration index quantified the degree of socioeconomic-related inequality in a health variable, which incorporates information from all income groups instead of simply the poorest and the richest It has higher data requirements than the other equality measurements. Table A5. Table A7. Figure A1. Figure A2.
Descripción de editorial
Table A8. Table A9. Table A Appendix Method. Disclosure statement No potential conflict of interest was reported by the authors. Paper context While previous studies on child health inequalities exclusively focused on interventions in the health sector, we yielded a comprehensive evaluation of child health, tracking inequalities in three outcome indicators mortality and stunting and 17 interventions including access to improved water and sanitation.
Supplemental Material Supplemental files for this article can be accessed here. Reducing inequality - the missing MDG: a content review of prsps and bilateral donor policy statements. IDS Bull. Mind the Gap! Widening child mortality disparities. J Hum Dev. On decomposing the causes of health sector inequalities with an application to malnutrition inequalities in Vietnam.
ZW conceived of the study, and participated in its design and coordination and helped to draft the manuscript. YY participated in the design of the study. ZZ is the guarantor. All authors read and approved the final manuscript. Skip to main content Skip to sections. Advertisement Hide. Download PDF. Open Access. First Online: 17 November Background China is now under a period of social transition, and inequity is evident in the field of health care. Methods In our study, monitoring data of Zhejiang Province from to was collected.
Results From to , overall mortality rate in children under five decreased, and regional disparity reduced markedly, and with a reduced disparity of mortality rate among children from urban and rural areas. Conclusions The survival status was near to equity. The trend of mortality rate reduction in children under five From to , overall mortality rate in children under five decreased, and regional disparity reduced remarkably.
The trend of reduction was significant in infants and children under five Fig.
Mortality rate in the newborns was floating, but the overall decreasing trend can be observed obviously Fig. Per capita GDP in the studied regions increased by years, and with increased regional disparity Fig. Open image in new window. The children were separated into different groups based on different household registry: urban area and rural area; native residency and migrant population.
The disparity of mortality rate between urban and rural areas decreased most remarkably Fig. In , the mortality rate between in children from urban and rural areas was similar. In contrast, a significant difference was found between the children with native residency and migrant population Fig. Mortality rates of newborns, infants and children under five reduced in all the 30 regions.
The difference of mortality rate among the three tiers was still very significant; overall mortality rate of children under five in the first tier was significantly lower than those in the other two tiers Fig. Mortality rate of all children under five including neonates and infants in the second and third tier was overlapping which indicating other factors may affect the mortality rate of children in these two tiers.
The concentration curve and concentration index CI were two commonly used indicators for indicating health equity, which can reveal the relation of regional economical level and health indexes accurately. CI was more sensitive to indicate the social-economical effect and most used in health economics.
Our results revealed that the mortality rate of children was negatively related with economical level Fig. Acknowledgements We thank Mrs. Availability of data and materials All the data in this study can not be shared now. Competing interests The authors declare that they have no competing interests. Income inequality and health: a critical review of the literature.
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Regional health-care inequity in children’s survival in Zhejiang Province, China
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