Coverage and equity of maternal and newborn health care in rural Nigeria, Ethiopia and India

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Study Justification:
– The study aimed to assess the coverage, coverage change, and inequity of maternal and newborn health care in rural areas of Nigeria, Ethiopia, and India.
– Despite progress towards meeting the Sustainable Development Goals, there is still a significant burden of maternal and neonatal mortality in rural areas, particularly among vulnerable populations.
– The study focused on these three countries because they represent rural, poor populations with high rates of maternal and neonatal deaths.
– The study was conducted to inform policymakers and stakeholders about the current state of maternal and newborn health care in these areas and to identify areas for improvement.
Study Highlights:
– The study conducted household surveys in 2012 and 2015 in Gombe State, Nigeria; four regions of Ethiopia; and six districts of Uttar Pradesh, India.
– The surveys collected data on eight priority indicators related to maternal and newborn health care, disaggregated by household socioeconomic status.
– Positive coverage changes were observed in Gombe, Nigeria; Uttar Pradesh, India; and Ethiopia for various indicators.
– Coverage improvements occurred equally for all socioeconomic groups, indicating equitable progress.
– However, underlying inequities persisted, with the poorest women still experiencing lower coverage compared to the least poor women.
Study Recommendations:
– Action is needed to address the needs of the most vulnerable women, particularly those living in the most rural areas.
– Interventions should focus on improving access to essential care and promoting recommended interventions and behaviors for all families.
– Community-based programming should be tailored to each setting and designed to improve survival, reach the poor, be feasible for large-scale implementation, and support government priorities.
Key Role Players:
– Government agencies responsible for health and social services
– Community leaders and organizations
– Non-governmental organizations (NGOs) working in maternal and newborn health
– Health care providers and professionals
– Researchers and academics specializing in maternal and newborn health
Cost Items for Planning Recommendations:
– Training and capacity building for health care providers and community workers
– Infrastructure development, including the establishment of health facilities and transportation systems
– Community outreach and awareness campaigns
– Data collection and monitoring systems
– Supply chain management for essential health care commodities
– Research and evaluation activities to assess the impact of interventions
– Coordination and collaboration between different stakeholders and organizations

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 conducted cluster-based household surveys in three different countries, collecting data on 8 maternal and newborn health care indicators. The coverage changes and inequities were assessed between 2012 and 2015. The study provides specific data on coverage improvements and inequities in each country. However, the abstract could be improved by providing more details on the methodology used, such as the sampling technique and sample size calculation. Additionally, it would be helpful to include information on the limitations of the study and any potential biases that may have influenced the results.

BACKGROUND: Despite progress toward meeting the Sustainable Development Goals, a large burden of maternal and neonatal mortality persists for the most vulnerable people in rural areas. We assessed coverage, coverage change and inequity for 8 maternal and newborn health care indicators in parts of rural Nigeria, Ethiopia and India. METHODS: We examined coverage changes and inequity in 2012 and 2015 in 3 high-burden populations where multiple actors were attempting to improve outcomes. We conducted cluster-based household surveys using a structured questionnaire to collect 8 priority indicators, disaggregated by relative household socioeconomic status. Where there was evidence of a change in coverage between 2012 and 2015, we used binomial regression models to assess whether the change reduced inequity. RESULTS: In 2015, we interviewed women with a birth in the previous 12 months in Gombe, Nigeria (n = 1100 women), Ethiopia (n = 404) and Uttar Pradesh, India (n = 584). Among the 8 indicators, 2 positive coverage changes were observed in each of Gombe and Uttar Pradesh, and 5 in Ethiopia. Coverage improvements occurred equally for all socioeconomic groups, with little improvement in inequity. For example, in Ethiopia, coverage of facility delivery almost tripled, increasing from 15% (95% confidence interval [CI] 9%-25%) to 43% (95% CI 33%-54%). This change was similar across socioeconomic groups (p = 0.2). By 2015, the poorest women had about the same facility delivery coverage as the least poor women had had in 2012 (32% and 36%, respectively), but coverage for the least poor had increased to 60%. INTERPRETATION: Although coverage increased equitably because of various community-based interventions, underlying inequities persisted. Action is needed to address the needs of the most vulnerable women, particularly those living in the most rural areas.

The study took place in 2012 and 2015 in Gombe State, Nigeria; in the 4 most populous regions of Ethiopia; and in the state of Uttar Pradesh, India. We focused on these 3 diverse settings because they reflect areas of interest of the Bill & Melinda Gates Foundation, a global health funder, and because they represent rural, poor populations experiencing a high number of maternal and neonatal deaths.13 Gombe State in northeast Nigeria has a population of 3.1 million. In 2015, the state had an estimated maternal mortality ratio of 1549 per 100 000 population and a neonatal mortality rate of 35 per 1000 population.14 In Ethiopia, the implementation area covered a population of about 6 million people living in 59 districts in the 4 regions of Oromia, Tigray, Amhara and Southern Nations Nationalities and Peoples. In 2015, the country had an estimated maternal mortality ratio of 353 per 100 000 population and a neonatal mortality rate of 28 per 1000 population.15 In Uttar Pradesh, implementation took place within a population of about 13 million people living in 6 districts. In 2016, the maternal mortality ratio in this state was estimated at 201 per 100 000 population and the neonatal mortality rate at 35 per 1000 population.16 The prioritization process for what was to be implemented in each study setting involved consultation with government and community leaders and was guided by a global recommendation concerning the basic package of care for all women and newborns, emphasizing a core set of interventions that could be delivered at the community or primary health level.17 Following detailed characterization of the individual implementation approaches,18 8 indicators for improvement were identified across the study areas: 4 for access to essential care and 4 for interventions or behaviours recommended for all families (Table 1). Indicators of maternal and newborn health targeted for change in all 3 jurisdictions* Community-based programming differed according to setting; however, all interventions were designed to improve survival, to reach the poor, to be feasible for large-scale implementation and to support government priorities (Box 1). For example, the interventions included an emergency transport scheme to remove access barriers in Gombe, improved use of local data to identify families in need of care in Ethiopia and the establishment of women’s groups in Uttar Pradesh. For further detail, see Appendix 1 (available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.190219/-/DC1). Gombe State, Nigeria* Ethiopia† Uttar Pradesh, India‡ *For further detail, see the Maternal and Neonatal Health Project — Gombe (http://www.sfhnigeria.org/the-maternal-and-neonatal-health-project-2012-2016/).†For further detail, see the Last Ten Kilometers Project (http://l10k.jsi.com/).‡For further detail, see the RGMVP (Rajiv Gandhi Mahila Vikas Pariyojana), a poverty reduction program of the Rajiv Gandhi Charitable Trust (http://www.rgmvp.org/). *For further detail, see the Maternal and Neonatal Health Project — Gombe (http://www.sfhnigeria.org/the-maternal-and-neonatal-health-project-2012-2016/). †For further detail, see the Last Ten Kilometers Project (http://l10k.jsi.com/). ‡For further detail, see the RGMVP (Rajiv Gandhi Mahila Vikas Pariyojana), a poverty reduction program of the Rajiv Gandhi Charitable Trust (http://www.rgmvp.org/). We performed cluster-based household surveys in 2012 and 2015, which involved interviewing women who reported a live birth in the preceding 12 months.19,20 The same methods and sampling frames were applied in both years and covered the entire area of implementation (Appendix 1). We applied multistage random sampling to generate a representative sample of women living in the implementation areas. In Gombe State, clusters were defined as enumeration areas. The enumeration areas were listed alphabetically, and their population size cumulated; areas were then systematically selected with probability proportional to population size. Households in the selected enumeration areas were listed and enumeration areas segmented into groups of about 75 households, with 1 segment in each enumeration area randomly selected for the survey. In Ethiopia, clusters were defined as villages. The 59 implementation districts (woreda) and their subdistricts (kebele) were listed geographically from north to south, and their population size cumulated; subdistricts were then systematically selected with probability proportional to population size. One village was randomly sampled for each selected subdistrict. Within each village, households were listed and villages segmented into groups of about 75 households, with 1 segment in each village randomly selected for the survey. In Uttar Pradesh, clusters were defined as villages. All villages from the 6 implementation districts were listed alphabetically, and their population size cumulated; villages were then systematically selected with probability proportional to population size. All households within selected villages were listed and villages segmented into groups of about 75 households, with 1 segment in each village randomly selected for the survey. The final sample size was sufficient to measure, with 90% power and a 5% level of significance, changes of a minimum of 20 percentage points across the range of indicators, representing the magnitude of change that was anticipated by project partners. In 2015, partly because of declining trends in fertility, 2 changes were made to increase the sample size. In Gombe, the number of clusters was doubled, and in all 3 jurisdictions, cluster size was increased from 50 to 75 households (Table 2). Household survey samples in 2012 and 2015 A modular household questionnaire was applied by trained interviewers. In 2012, there was relatively little guidance available on best practice for measurement of maternal and newborn health.21 We conducted extensive pretesting of questions and pilot testing of survey protocols, and we reviewed existing surveys, including the Demographic and Health Survey.22 In brief, household heads were asked about socioeconomic characteristics, and resident women aged 13–49 years were asked about their access to health care in the past year. Further questions were asked of women who reported a recent birth. As an example, the questionnaire implemented in Ethiopia in 2015 is provided in English in Appendix 2 (available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.190219/-/DC1). After translation and back-translation, surveys were implemented in Hausa in Gombe; in Amharic, Oromifa and Tigrinya in Ethiopia; and in Hindi in Uttar Pradesh. In Gombe and Uttar Pradesh, the data were collected using hand-held digital devices. In Ethiopia, the data were collected in 2012 using paper questionnaires, which were double-entered and reconciled; digital data collection was introduced in 2015. To enhance response rates, survey teams revisited each household up to 3 times for call-backs. Informed, written consent was obtained from all participants. We performed the analyses separately for each study setting. We adjusted coverage indicators for clustering of the segmented villages using the svy command in Stata 14 (StataCorp). We calculated odds ratios (ORs) for the difference in coverage over time using individual-level binomial regression models. For each survey, we used principal components analysis to construct an indicator of relative household socioeconomic status. We divided the resulting continuous index variable into quintiles of households from quintile 1 (poorest) to quintile 5 (least poor). The characteristics of poorest and least poor families in the 3 jurisdictions are illustrated in Appendix 3 (available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.190219/-/DC1). We examined the association between household socioeconomic status quintile and indicator coverage at each time point using binomial regression. We tested linearity of the association between socioeconomic status and indicators using a likelihood ratio test, comparing a model with socioeconomic status quintiles treated as a continuous variable with a model in which quintiles were treated as categories. Where there was no evidence of nonlinearity, we calculated the ORs and 95% confidence intervals (CIs) for a 1-unit change in socioeconomic status. Where there was evidence of nonlinearity, we calculated separate ORs for each socioeconomic status quintile. Where there was evidence of coverage change between survey years, to determine whether the association between household socioeconomic status quintile and indicator coverage changed over time, we included data from both time points in a binomial regression model and tested for an interaction between time point and socioeconomic status quintile. All regression models were at the individual-woman level and included robust standard errors to account for clustering of the data. We used interaction tests to examine whether change was inequitable for multiple indicators. A p value of less than 0.05 was used to indicate statistical significance. In Nigeria, national-level approval was obtained from the National Health Research Ethics Committee, Federal Ministry of Health, Abuja, and in Gombe State from the State Ministry of Health in both Gombe and Abuja. In Ethiopia, national-level support was obtained from the Ethiopian Ministry of Health and ethics approval from the Ministry of Science and Technology; at the regional level, approval was granted by the Regional Institutional Review Boards in Oromia, Tigray, Amhara, and Southern Nations Nationalities and Peoples. In Uttar Pradesh, India, approval was obtained from SPECT-ERB, an independent ethics review board, and written permission was obtained from the National Rural Health Mission of Uttar Pradesh. Ethics approval was also obtained from the London School of Hygiene & Tropical Medicine (reference 6088).

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

1. Community-based interventions: Implementing community-based interventions, such as women’s groups or local data systems, can help identify and reach vulnerable women in rural areas who are in need of maternal health care. These interventions can improve access to essential care and ensure that services are delivered at the community or primary health level.

2. Emergency transport schemes: Establishing emergency transport schemes can help remove access barriers for pregnant women in rural areas. By providing transportation services, pregnant women can easily reach healthcare facilities for delivery or in case of emergencies, reducing delays and improving access to timely care.

3. Digital data collection: Utilizing hand-held digital devices for data collection can streamline the process and improve the accuracy and efficiency of data collection in rural areas. This can help monitor and evaluate the impact of interventions, identify gaps in coverage, and inform decision-making for targeted improvements.

4. Strengthening healthcare infrastructure: Investing in the improvement and expansion of healthcare infrastructure in rural areas can enhance access to maternal health services. This includes building and equipping healthcare facilities, ensuring the availability of skilled healthcare providers, and improving the quality of care provided.

5. Public-private partnerships: Collaborating with private sector organizations and leveraging their resources and expertise can help improve access to maternal health services in rural areas. Public-private partnerships can support the implementation of innovative solutions, increase the availability of healthcare facilities and services, and address the needs of the most vulnerable women.

6. Mobile health (mHealth) interventions: Utilizing mobile technology, such as mobile phones or applications, can facilitate access to maternal health information, appointment reminders, and teleconsultations. mHealth interventions can bridge the gap between healthcare providers and pregnant women in remote areas, providing them with timely and accurate information and support.

7. Capacity building and training: Investing in the training and capacity building of healthcare providers in rural areas can improve the quality of maternal health services. This includes training healthcare workers on evidence-based practices, emergency obstetric care, and respectful maternity care, ensuring that they have the necessary skills and knowledge to provide comprehensive and safe care.

It is important to note that the specific recommendations for improving access to maternal health should be tailored to the context and needs of each setting, taking into consideration the local healthcare system, cultural factors, and available resources.
AI Innovations Description
Based on the information provided, the study conducted in rural Nigeria, Ethiopia, and India identified several indicators for improving access to maternal and newborn health care. The study found that coverage of certain indicators improved between 2012 and 2015, but inequities in access to care persisted. Here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Strengthen Community-Based Interventions: The study showed that community-based interventions were effective in improving coverage of maternal and newborn health care. Building on this success, it is recommended to further strengthen and expand community-based interventions. This can be done by training and empowering community health workers to provide essential maternal health services, such as antenatal care, postnatal care, and skilled birth attendance, in rural areas where access to health facilities is limited.

Innovation: Develop a mobile health (mHealth) application that provides training and support to community health workers. The application can include educational materials, guidelines, and tools for conducting antenatal and postnatal visits, as well as facilitating communication and coordination with health facilities. This innovation will enhance the capacity of community health workers to deliver quality maternal health services in remote areas, improving access to care for vulnerable women.

By implementing this recommendation and innovation, it is expected that access to maternal health care will be improved, particularly for women living in rural areas. This will contribute to reducing maternal and neonatal mortality rates and achieving the Sustainable Development Goals related to maternal and child health.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Strengthen community-based interventions: Community-based interventions have shown promise in improving coverage and access to maternal health care. These interventions can include training and empowering community health workers, establishing women’s groups, and implementing emergency transport schemes to remove access barriers.

2. Improve use of local data: Enhancing the use of local data can help identify families in need of care and target interventions more effectively. This can involve implementing systems for data collection, analysis, and dissemination at the community level.

3. Enhance collaboration between multiple actors: To address the complex challenges of improving maternal health access, collaboration between various stakeholders is crucial. This can include partnerships between governments, non-governmental organizations, community leaders, and international donors to coordinate efforts and share resources.

4. Focus on the needs of the most vulnerable women: Efforts should be directed towards addressing the needs of the most vulnerable women, particularly those living in rural areas. This can involve targeted interventions, such as providing financial support for transportation or establishing maternal health facilities in underserved areas.

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

1. Define the indicators: Identify specific indicators that measure access to maternal health care, such as facility delivery rates, antenatal care coverage, or postnatal care utilization.

2. Collect baseline data: Conduct a baseline survey to collect data on the selected indicators in the target population. This can involve household surveys, interviews with women who have recently given birth, and collection of socioeconomic data.

3. Implement interventions: Implement the recommended interventions in the target population. This can be done through partnerships with local health authorities, community organizations, and other relevant stakeholders.

4. Monitor and evaluate: Continuously monitor the implementation of interventions and collect data on the selected indicators. This can involve regular surveys, data collection from health facilities, and monitoring of program activities.

5. Analyze the data: Use statistical analysis techniques to assess the impact of the interventions on the selected indicators. This can include comparing pre- and post-intervention data, conducting regression analysis, and assessing changes in equity across socioeconomic groups.

6. Interpret the results: Interpret the findings to understand the effectiveness of the interventions in improving access to maternal health care. Identify any gaps or areas for improvement and make recommendations for future interventions.

7. Iterate and refine: Based on the results and lessons learned, refine the interventions and methodology as needed. Continuously iterate and improve the approach to maximize the impact on improving access to maternal health care.

By following this methodology, it is possible to simulate the impact of the recommended interventions on improving access to maternal health and make evidence-based decisions for future interventions.

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