Assessment of Inequalities in Coverage of Essential Reproductive, Maternal, Newborn, Child, and Adolescent Health Interventions in Kenya

JAMA network open, Volume 1, No. 8, Year 2018

Interpretação

Importance: Previous work has underscored subnational inequalities that could impede additional health gains in Kenya. Objective: To provide a comprehensive assessment of the burden, distribution, and change in inequalities in reproductive, maternal, newborn, child, and adolescent health (RMNCAH) interventions in Kenya from 2003 to 2014. Design, Setting, and Participants: This population-based cross-sectional study used data from the 2003, 2008, and 2014 Kenya Demographic and Health Surveys. The study included women of reproductive age (ages 15-49 years) and children younger than years, with national, regional, county, and subcounty level representation. Data analysis was conducted from April 2018 to November 2018. Exposures: Socioeconomic position that was derived from asset indices and presented as wealth quintiles. Urban and rural residence and regions of Kenya were also considered. Main Outcomes and Measures: Absolute and relative measures of inequality in coverage of RMNCAH interventions. Results: For this analysis, representative samples of 31 380 women of reproductive age and 29 743 children younger than 5 years from across Kenya were included. The RMNCAH interventions examined demonstrated pro-rich and bottom inequality patterns. The most inequitable interventions were skilled birth attendance, family planning needs satisfied, and 4 or more antenatal care visits, whereby the absolute difference in coverage between the wealthiest (quintile 5) and poorest quintiles (quintile 1) was 61.6% (95% CI, 60.1%-63.1%), 33.4% (95% CI, 31.9%-34.9%), and 31.0% (95% CI, 30.5%-31.6%), respectively. The most equitable intervention was early initiation of breastfeeding, with an absolute difference (quintile 5 minus quintile 1) of -7.9% (95% CI, -11.1% to -4.8%), although antenatal care (1 visit) and diphtheria-tetanus-pertussis immunization (3 doses) demonstrated the best combination of high coverage and low inequalities. Our geospatial analysis revealed significant socioeconomic disparities in the northern and eastern regions of Kenya that have translated to suboptimal intervention coverage. A significant gap remains for rural, disadvantaged populations. Conclusions and Relevance: Coverage of RMNCAH interventions has improved over time, but wealth and geospatial inequalities in Kenya are persistent. Policy and programming efforts should place more emphasis on improving the accessibility of health facility-based interventions, which generally demonstrate poor coverage and high inequalities, and focus on integrated approaches to maternal health service delivery at the community level when access is poor. Scaling up of health services for the urban and, in particular, rural poor areas and those residing in Kenya’s former north eastern province will contribute toward achievement of universal health coverage. We used data from the 2014 Kenya Demographic and Health Survey (K-DHS), the most recent, large-scale national survey conducted in Kenya that sampled 14 741 women of reproductive age (ages 15-49 years) and 18 702 children younger than 5 years.16 This survey is powered at the subnational (county) level and provides estimates for maternal and child health and nutrition indicators across the continuum of care. The K-DHS also contains comprehensive information on household assets that were used to compute wealth indices. For trend analyses, we included data from the 2003 and 2008 K-DHS surveys, which each sampled 5560 and 5481 children under the age of 5 years and 8195 and 8444 women of reproductive age, respectively. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. As this study consisted of secondary data analysis only, ethical review was waived. All ethics procedures were the responsibility of the institutions that commissioned, funded, or carried out the DHS surveys. We examined a diverse set of essential preventative and curative coverage indicators including the following: family planning needs satisfied (FPS), antenatal care with a skilled provider (ANCS), 4 or more antenatal care visits (ANC4), skilled attendant at birth (SBA), early initiation of breastfeeding (within 1 hour) (EIBF), 3 doses of diphtheria-tetanus-pertussis vaccine (DPT3), measles vaccination (MSL), full immunization of children (FULL), vitamin A supplementation (within 6 months) (VITA), oral rehydration therapy (ORT) and continued feeding for children with diarrhea, and care seeking for children with pneumonia (CPNM). Indicators selected for detailed subanalysis were those that represented opposite ends of the continuum of care and had diverse delivery strategies (ie, health systems based, outreach focused, or community led). All indicators were defined as per the Countdown to 2015 guidelines1 and have been detailed in the eTable in the Supplement. We analyzed 2 summary measures of coverage, the composite coverage index (CCI)17 and the co-coverage indicator.18 These widely used complementary indices are useful for within- and between-country comparisons, and for measuring change over time.6 An aggregated index, the CCI is an equally weighted average of 4 stages of interventions across the continuum of care (eTable in the Supplement): family planning, maternal and newborn care, immunization, and case management of sick children. The co-coverage index is measured at the individual or family level and includes the following: ANCS, 2 doses of tetanus toxoid during pregnancy, SBA, VITA, BCG vaccine (vaccine for tuberculosis), DPT3, MSL, access to improved drinking water, and use of an insecticide-treated bed net for children. Co-coverage is calculated as the proportion of essential interventions received by a mother and child pair, ranging from 0 (being no interventions received) to 9 (being 100% of interventions received). We also reported co-coverage with 6 or more preventive interventions (CC6+) by mother and child pair. We estimated socioeconomic position using the wealth score derived from Principals Components Analysis applied to household asset data.19 The creation of asset indices is considered to be more reliable than using a single-proxy measure for socioeconomic position, such as maternal education or place of residence, and is a method that has been widely adopted for use in low- and middle-income countries.20 Where sample size permitted, coverage indicators were single- and double-disaggregated by wealth quintiles (quintiles 1-5 of the asset score) and urban and rural residence. Equiplots that show the distance in coverage between various population strata (eg, wealth quintiles) are useful to determine patterns of inequality, including linear, top, and bottom inequality, which can then be used for appropriate targeting of interventions.6 Linear inequality exists when the distance between each estimate is equal, whereas top inequality represents a situation where the widest gap exists for the highest quintile and the opposite is true of bottom inequality.6 Equity literature stresses the importance of examining both absolute and relative inequalities which are complementary and together reveal the full picture of disparities.6,7 Absolute inequality highlights the actual coverage gap that exists between extreme wealth groups and the corresponding efforts that are required to close it. Relative inequality shows the degree of unfairness between the richest and the poorest. We calculated both simple and sophisticated measures for both absolute and relative inequality. Simple measures are useful for conveying messages to the lay-audience (eg, policymakers in Kenya), although they incorporate only the top (quintile 5) and bottom (quintile 1) quintiles of the population. Sophisticated measures use the full data distribution (quintiles 1-5) and thus more accurately show the magnitude of metrics. Absolute inequalities were evaluated using the basic gap between extreme quintiles (quintile 5 minus quintile 1) and the slope index of inequality (SII). Relative inequalities were estimated using the relative ratio (quintile 5 to quintile 1) and the concentration index (CIX). The SII was interpreted as the percentage point difference between the rich and poor, where greater values correspond to the intervention having higher coverage in the wealthier subgroup and 0 implying absence of inequality. The CIX is related to the Gini coefficient, which is a widely used summary measure to judge income inequality in a given country.6 The Gini index will equal 0 in a society that is perfectly equal in terms of income.6 Similarly, CIX values fall between −1 and 1, where negative values imply higher intervention coverage among the poor, positive values imply higher coverage among the rich, and 0, again, indicates the absence of inequality. For easier interpretation, the CIX values were multiplied by 100. The CIX (values) and SII (%) were also grouped into low (<15), moderate (15-40), high (40-60), and very high (>60) categories of socioeconomic inequality.7 The SII and CIX were calculated with standard errors and 95% CIs, using standardized methods.6,21 Where sample size permitted, analyses of intervention coverage and inequalities from Kenya’s 2014 DHS were disaggregated into 8 regions, 47 counties, and 290 subcounties (constituencies). Socioeconomic inequality patterns were examined across Kenya’s 8 regions—central, coast, eastern, Nairobi, north eastern, Nyanza, Rift Valley, and western—where adequate sample size ensured statistical power of derived estimates. Prior to introduction of a devolved government under the constitution change in 2010, these regions constituted administrative provinces. The SII and CIX were examined for SBA, MSL, and CC6+ indicators. To examine geospatial patterns in RMNCAH intervention coverage across the nation, county and constituency level estimates were calculated. The CCI was estimated for Kenya’s 47 counties. Given that the K-DHS 2014 was not powered for subcounty estimates, Bayesian small area estimation spatial models22,23,24 were used to generate constituency level coverage for key RMNCAH indicators. Constituency level estimates were generated for 2 key socioeconomic indicators given their importance in health care service use and care-seeking behavior of the family; household poverty (% households in the 2 poorest wealth quintiles) and maternal illiteracy. Data analysis was conducted from April 2018 to November 2018. All analyses were carried out in Stata, version 12.0 (Stata Corp) and Arc Map 9.3 was used to create high-resolution country maps for CCI.

AI Digest

Study Justification:

– The study aims to assess the burden, distribution, and change in inequalities in reproductive, maternal, newborn, child, and adolescent health (RMNCAH) interventions in Kenya from 2003 to 2014.
– Previous work has highlighted subnational inequalities that could hinder further health improvements in Kenya.
– The study provides a comprehensive assessment of the coverage of essential RMNCAH interventions in Kenya.

Study Highlights:

– The study analyzed data from the 2003, 2008, and 2014 Kenya Demographic and Health Surveys, including women of reproductive age and children under 5 years old.
– The analysis revealed pro-rich and bottom inequality patterns in the RMNCAH interventions examined.
– Skilled birth attendance, family planning needs satisfied, and 4 or more antenatal care visits showed the greatest inequalities in coverage.
– Early initiation of breastfeeding demonstrated the most equitable coverage.
– Geospatial analysis revealed significant socioeconomic disparities in the northern and eastern regions of Kenya, leading to suboptimal intervention coverage.
– Rural and disadvantaged populations still face significant gaps in coverage.

Recommendations for Lay Reader and Policy Maker:

– Policy and programming efforts should focus on improving the accessibility of health facility-based interventions, which have poor coverage and high inequalities.
– Integrated approaches to maternal health service delivery at the community level should be prioritized in areas with poor access.
– Scaling up of health services is needed for urban and rural poor areas, as well as Kenya’s former north eastern province, to achieve universal health coverage.

Key Role Players:

– Ministry of Health: Responsible for implementing and coordinating interventions to address inequalities in RMNCAH coverage.
– County Health Departments: Play a crucial role in delivering health services and addressing disparities at the subnational level.
– Non-Governmental Organizations (NGOs): Provide support and resources for implementing interventions and addressing inequalities.
– Community Health Workers: Act as a bridge between communities and health facilities, ensuring access to RMNCAH interventions.
– Donors and Funding Agencies: Provide financial resources to support the implementation of interventions and address disparities.

Cost Items for Planning Recommendations:

– Health Facility Infrastructure: Budget for improving and expanding health facilities to ensure accessibility for all populations.
– Training and Capacity Building: Allocate funds for training healthcare providers and community health workers to deliver quality RMNCAH interventions.
– Outreach Programs: Budget for community-based outreach programs to reach rural and disadvantaged populations.
– Health Information Systems: Invest in robust data collection and analysis systems to monitor intervention coverage and inequalities.
– Monitoring and Evaluation: Allocate resources for regular monitoring and evaluation of interventions to ensure effectiveness and equity.

Força da prova

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study used data from a large-scale national survey conducted in Kenya, which provides a representative sample of women of reproductive age and children under 5 years. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline and used established indicators to assess coverage of reproductive, maternal, newborn, child, and adolescent health interventions. The study also analyzed socioeconomic inequalities using both absolute and relative measures. However, to improve the evidence, the abstract could provide more information on the methodology used for data analysis, such as the statistical methods employed. Additionally, it would be helpful to include the main findings and implications of the study.

Resumo

We used data from the 2014 Kenya Demographic and Health Survey (K-DHS), the most recent, large-scale national survey conducted in Kenya that sampled 14 741 women of reproductive age (ages 15-49 years) and 18 702 children younger than 5 years.16 This survey is powered at the subnational (county) level and provides estimates for maternal and child health and nutrition indicators across the continuum of care. The K-DHS also contains comprehensive information on household assets that were used to compute wealth indices. For trend analyses, we included data from the 2003 and 2008 K-DHS surveys, which each sampled 5560 and 5481 children under the age of 5 years and 8195 and 8444 women of reproductive age, respectively. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. As this study consisted of secondary data analysis only, ethical review was waived. All ethics procedures were the responsibility of the institutions that commissioned, funded, or carried out the DHS surveys. We examined a diverse set of essential preventative and curative coverage indicators including the following: family planning needs satisfied (FPS), antenatal care with a skilled provider (ANCS), 4 or more antenatal care visits (ANC4), skilled attendant at birth (SBA), early initiation of breastfeeding (within 1 hour) (EIBF), 3 doses of diphtheria-tetanus-pertussis vaccine (DPT3), measles vaccination (MSL), full immunization of children (FULL), vitamin A supplementation (within 6 months) (VITA), oral rehydration therapy (ORT) and continued feeding for children with diarrhea, and care seeking for children with pneumonia (CPNM). Indicators selected for detailed subanalysis were those that represented opposite ends of the continuum of care and had diverse delivery strategies (ie, health systems based, outreach focused, or community led). All indicators were defined as per the Countdown to 2015 guidelines1 and have been detailed in the eTable in the Supplement. We analyzed 2 summary measures of coverage, the composite coverage index (CCI)17 and the co-coverage indicator.18 These widely used complementary indices are useful for within- and between-country comparisons, and for measuring change over time.6 An aggregated index, the CCI is an equally weighted average of 4 stages of interventions across the continuum of care (eTable in the Supplement): family planning, maternal and newborn care, immunization, and case management of sick children. The co-coverage index is measured at the individual or family level and includes the following: ANCS, 2 doses of tetanus toxoid during pregnancy, SBA, VITA, BCG vaccine (vaccine for tuberculosis), DPT3, MSL, access to improved drinking water, and use of an insecticide-treated bed net for children. Co-coverage is calculated as the proportion of essential interventions received by a mother and child pair, ranging from 0 (being no interventions received) to 9 (being 100% of interventions received). We also reported co-coverage with 6 or more preventive interventions (CC6+) by mother and child pair. We estimated socioeconomic position using the wealth score derived from Principals Components Analysis applied to household asset data.19 The creation of asset indices is considered to be more reliable than using a single-proxy measure for socioeconomic position, such as maternal education or place of residence, and is a method that has been widely adopted for use in low- and middle-income countries.20 Where sample size permitted, coverage indicators were single- and double-disaggregated by wealth quintiles (quintiles 1-5 of the asset score) and urban and rural residence. Equiplots that show the distance in coverage between various population strata (eg, wealth quintiles) are useful to determine patterns of inequality, including linear, top, and bottom inequality, which can then be used for appropriate targeting of interventions.6 Linear inequality exists when the distance between each estimate is equal, whereas top inequality represents a situation where the widest gap exists for the highest quintile and the opposite is true of bottom inequality.6 Equity literature stresses the importance of examining both absolute and relative inequalities which are complementary and together reveal the full picture of disparities.6,7 Absolute inequality highlights the actual coverage gap that exists between extreme wealth groups and the corresponding efforts that are required to close it. Relative inequality shows the degree of unfairness between the richest and the poorest. We calculated both simple and sophisticated measures for both absolute and relative inequality. Simple measures are useful for conveying messages to the lay-audience (eg, policymakers in Kenya), although they incorporate only the top (quintile 5) and bottom (quintile 1) quintiles of the population. Sophisticated measures use the full data distribution (quintiles 1-5) and thus more accurately show the magnitude of metrics. Absolute inequalities were evaluated using the basic gap between extreme quintiles (quintile 5 minus quintile 1) and the slope index of inequality (SII). Relative inequalities were estimated using the relative ratio (quintile 5 to quintile 1) and the concentration index (CIX). The SII was interpreted as the percentage point difference between the rich and poor, where greater values correspond to the intervention having higher coverage in the wealthier subgroup and 0 implying absence of inequality. The CIX is related to the Gini coefficient, which is a widely used summary measure to judge income inequality in a given country.6 The Gini index will equal 0 in a society that is perfectly equal in terms of income.6 Similarly, CIX values fall between −1 and 1, where negative values imply higher intervention coverage among the poor, positive values imply higher coverage among the rich, and 0, again, indicates the absence of inequality. For easier interpretation, the CIX values were multiplied by 100. The CIX (values) and SII (%) were also grouped into low (60) categories of socioeconomic inequality.7 The SII and CIX were calculated with standard errors and 95% CIs, using standardized methods.6,21 Where sample size permitted, analyses of intervention coverage and inequalities from Kenya’s 2014 DHS were disaggregated into 8 regions, 47 counties, and 290 subcounties (constituencies). Socioeconomic inequality patterns were examined across Kenya’s 8 regions—central, coast, eastern, Nairobi, north eastern, Nyanza, Rift Valley, and western—where adequate sample size ensured statistical power of derived estimates. Prior to introduction of a devolved government under the constitution change in 2010, these regions constituted administrative provinces. The SII and CIX were examined for SBA, MSL, and CC6+ indicators. To examine geospatial patterns in RMNCAH intervention coverage across the nation, county and constituency level estimates were calculated. The CCI was estimated for Kenya’s 47 counties. Given that the K-DHS 2014 was not powered for subcounty estimates, Bayesian small area estimation spatial models22,23,24 were used to generate constituency level coverage for key RMNCAH indicators. Constituency level estimates were generated for 2 key socioeconomic indicators given their importance in health care service use and care-seeking behavior of the family; household poverty (% households in the 2 poorest wealth quintiles) and maternal illiteracy. Data analysis was conducted from April 2018 to November 2018. All analyses were carried out in Stata, version 12.0 (Stata Corp) and Arc Map 9.3 was used to create high-resolution country maps for CCI.

Materiais

https://efashare.b-cdn.net/materials/10ad40ba128d9fdab0913d1ac251c8cb7872c97098edbd3b737b93089686e4aa/jamanetwopen-1-e185152-s001.pdf

Inovações

Based on the provided information, here are some potential innovations that could be used to improve access to maternal health:

1. Mobile Health (mHealth) Solutions: Develop mobile applications or SMS-based platforms to provide pregnant women and new mothers with information, reminders, and support for antenatal care visits, skilled birth attendance, and postnatal care.

2. Community Health Workers (CHWs): Train and deploy CHWs to provide maternal health services and education in rural and disadvantaged areas. CHWs can conduct home visits, provide basic antenatal and postnatal care, and refer women to health facilities when necessary.

3. Telemedicine: Implement telemedicine programs to connect pregnant women in remote areas with healthcare providers. This allows for virtual consultations, remote monitoring of pregnancies, and timely access to medical advice.

4. Maternal Health Vouchers: Introduce voucher programs that provide financial assistance to pregnant women, particularly those from low-income backgrounds, to cover the costs of maternal health services. This can help reduce financial barriers to accessing care.

5. Transport Solutions: Develop transportation initiatives, such as ambulance services or community-based transportation networks, to ensure that pregnant women can easily reach health facilities for antenatal care, delivery, and emergency obstetric care.

6. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare providers and facilities to expand service coverage and reduce geographical disparities.

7. Maternal Waiting Homes: Establish maternal waiting homes near health facilities in remote areas. These homes provide accommodation for pregnant women in the weeks leading up to their expected delivery date, ensuring they are closer to healthcare facilities and can receive timely care.

8. Task-Shifting and Task-Sharing: Train and empower non-physician healthcare providers, such as nurses and midwives, to perform certain tasks traditionally carried out by doctors. This can help alleviate the shortage of skilled birth attendants and increase access to maternal health services.

9. Quality Improvement Initiatives: Implement programs to improve the quality of maternal health services, including training healthcare providers, strengthening health systems, and ensuring the availability of essential equipment and supplies.

10. Health Education and Awareness Campaigns: Conduct targeted health education campaigns to raise awareness about the importance of antenatal care, skilled birth attendance, and postnatal care. This can help address cultural and social barriers that prevent women from seeking timely and appropriate care.

It is important to note that the specific context and needs of the target population should be considered when implementing these innovations.

AI Innovations Description

The recommendation to improve access to maternal health based on the provided information is to focus on improving the accessibility of health facility-based interventions and implementing integrated approaches to maternal health service delivery at the community level in areas where access is poor. This includes scaling up health services for rural and disadvantaged populations, particularly in Kenya’s former north eastern province. Additionally, efforts should be made to address the persistent wealth and geospatial inequalities in coverage of reproductive, maternal, newborn, child, and adolescent health (RMNCAH) interventions in Kenya. By targeting these areas and populations with tailored interventions and resources, progress can be made towards achieving universal health coverage and reducing disparities in maternal health outcomes.

AI Innovations Methodology

Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Strengthen health facility-based interventions: Focus on improving the accessibility and quality of health facility-based interventions such as skilled birth attendance, family planning services, and antenatal care visits. This can be achieved by increasing the number of skilled health workers, improving infrastructure and equipment, and ensuring availability of essential supplies and medications.

2. Integrated approaches to maternal health service delivery: Implement integrated approaches that combine multiple maternal health interventions and deliver them at the community level. This can include community-based antenatal care, home visits by trained health workers, and mobile clinics to reach remote areas. By bringing services closer to the community, access to maternal health care can be improved, especially for rural and disadvantaged populations.

3. Address socioeconomic disparities: Target interventions towards addressing socioeconomic disparities in access to maternal health care. This can involve providing financial support or subsidies for maternal health services to low-income individuals and communities. Additionally, efforts should be made to improve education and awareness about maternal health among disadvantaged populations.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Data collection: Collect data on key indicators related to maternal health, such as coverage of skilled birth attendance, antenatal care visits, and family planning services. This data can be obtained from national surveys, health facility records, and community-based assessments.

2. Baseline assessment: Analyze the current status of access to maternal health care and identify existing gaps and disparities. This can be done by disaggregating the data by socioeconomic factors (e.g., wealth quintiles, urban/rural residence) and geographic regions.

3. Modeling and simulation: Use statistical modeling techniques to simulate the impact of the recommended interventions on improving access to maternal health care. This can involve creating scenarios where the interventions are implemented and estimating the potential changes in coverage and reduction in inequalities.

4. Sensitivity analysis: Conduct sensitivity analysis to assess the robustness of the simulation results. This can involve varying key parameters and assumptions to understand the potential range of outcomes.

5. Policy recommendations: Based on the simulation results, provide policy recommendations on the most effective interventions and strategies to improve access to maternal health care. Consider the feasibility, cost-effectiveness, and sustainability of the recommended interventions.

6. Monitoring and evaluation: Implement the recommended interventions and establish a monitoring and evaluation system to track progress and assess the actual impact on access to maternal health care. This can involve regular data collection, analysis, and reporting to inform ongoing improvements and adjustments to the interventions.

Autor

Dima Health

Statistics:

Citations: 14

Identifiers:

DOI: 10.1001/jamanetworkopen.2018.5152

Research Areas:

Community Interventions, Disparities, Environmental, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Kenya

Study Design:

Cross Sectional Study, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Female

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