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

* Tanya Marchant
* Emma Beaumont
* Krystyna Makowiecka
* Della Berhanu
* Tsegahun Tessema
* Meenakshi Gautham
* Kultar Singh
* Nasir Umar
* Adamu Umar Usman
* Keith Tomlin
* Simon Cousens
* Elizabeth Allen
* Joanna Armstrong Schellenberg

## Abstract

**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.

In September 2015, the United Nations Member States adopted the Sustainable Development Goals, a set of 17 goals for which the target date for achievement is 2030 ([https://sustainabledevelopment.un.org/](https://sustainabledevelopment.un.org/)). The health agenda has a central place in these goals, which aim to be relevant to all countries and to meet the needs of women, children and the poorest, most disadvantaged people of the world. Despite important gains in maternal and newborn health, faster progress is needed in many countries to meet the Sustainable Development Goals for improved survival.1,2 More families need access to better-quality care across the continuum from pregnancy to newborn periods, so that no opportunity is lost to improve outcomes, including for those who are most vulnerable.3,4 Addressing inequities in access to and quality of health care is a priority now more than ever, as the poorest members of communities continue to bear the largest burden of ill health and consistently receive lower-quality care.5,6

Recognizing this situation, health programmers in low- and middle-income countries have, over the past decade, aimed to implement strategies that favour the poorest members of communities and promote community linkages within primary health care.7–10 Certain interventions, such as training community members to make health promotion visits to families, have improved the practice of healthy behaviours in communities and increased care-seeking in health facilities.11,12

In 2012, in regions across Nigeria, Ethiopia and India, the implementation of community-based programming was prioritized. The aim of these programs was to improve maternal and newborn health outcomes through multiple approaches applied simultaneously by government, working alongside nongovernment organizations.

Here, we quantify the coverage and coverage change of 8 predefined essential maternal and newborn health indicators in these 3 regions. We assessed whether the community-based programming was associated with changes in outcomes, whether the changes observed were equitable and whether health care improved for the most vulnerable members of the population.

## Methods

### Study setting

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).

View this table:
[Table 1:](http://www.cmaj.ca/content/191/43/E1179/T1)

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](http://www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.190219/-/DC1)).

Box 1: #### **Illustrative components of community-based programming**

**Gombe State, Nigeria***

*   Train and deploy female community volunteers to make home visits to families to promote healthy maternal and newborn care practices; equip volunteers with clean delivery kits

*   Initiate a call centre, with toll-free numbers, to offer free health advice to families

*   Initiate an emergency transport scheme whereby community members with vehicles are trained in a transportation protocol for women who need urgent care in a health facility

*   Conduct limited upgrading of primary health facilities

**Ethiopia**†

*   Facilitate community-based data for decision-making, helping community health workers and volunteers to work together to identify families in need of care; this activity was fully integrated within the government’s health strategy of deploying health extension workers and a Health Development Army

*   Provide nonfinancial incentives for the Health Development Army

*   Provide supplemental training to the Health Development Army

*   Strengthen partnerships between community actors and primary health facility structures

**Uttar Pradesh, India**‡

*   Expand community-based women’s self-help groups to include maternal and newborn care topics

*   Strengthen linkages between self-help groups and health facility structures

*   Develop and deploy a mobile phone app as a job aid to support the work of community health workers

*   Initiate a mother and child tracking system to facilitate record-keeping and tracking of care provision for individual families

*   * For further detail, see the Maternal and Neonatal Health Project — Gombe ([http://www.sfhnigeria.org/the-maternal-and-neonatal-health-project-2012-2016/](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/](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/](http://www.rgmvp.org/)).

### Survey overview

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).

View this table:
[Table 2:](http://www.cmaj.ca/content/191/43/E1179/T2)

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](http://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.

### Statistical analysis

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](http://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.

### Ethics approval

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).

## Results

### Gombe State, Nigeria

In Gombe State, 74% (2021/2718) of all women residing in sampled households were interviewed about their recent fertility history in 2012, and 91% (7419/8125) in 2015. Of these, 17% (349/2021) in 2012 and 15% (1100/7419) in 2015 reported a recent live birth (Table 2). There was no evidence of a difference in demographic characteristics between survey years. In 2012, the women interviewed had a mean age of 26 years, 93% were currently married, 66% had no formal education, 83% were Muslim, and 17% were Christian (Table 3).

View this table:
[Table 3:](http://www.cmaj.ca/content/191/43/E1179/T3)

Table 3: 
Characteristics of household survey respondents (resident women with a live birth in 12 mo preceding survey date)*

All access and care indicators had coverage below 50% in both years (Table 4). In 2012, 40% (139/348; 95% CI 30%–51%) of women had at least 4 antenatal care visits, and 30% (104/348; 95% CI 21%–41%) had a facility delivery, but very few women or newborns had timely postnatal care. Large-scale inequities were present for all indicators of access to care.

View this table:
[Table 4:](http://www.cmaj.ca/content/191/43/E1179/T4)

Table 4: 
Evidence of coverage change between 2012 and 2015, overall and by socioeconomic status, for targeted maternal and newborn health indicators in Gombe State, Nigeria

By 2015, coverage had increased for 2 intervention indicators: hygienic cord care from 26% (92/348; 95% CI 19%–36%) in 2012 to 45% (490/1100; 95% CI 41%–49%) in 2015, and breastfeeding initiation within 1 hour after birth increased from 40% (135/340; 95% CI 33%–47%) in 2012 to 49% (533/1081; 95% CI 46%–53%) in 2015. All socioeconomic status quintiles benefited equally from this positive coverage change (*p* values for interaction between time and socioeconomic status 0.1 and 0.3, respectively).

### Four regions of Ethiopia

In Ethiopia, in both 2012 and 2015, about 90% of resident women were interviewed. Of these, 14% (277/1934) in 2012 and 13% (404/3170) in 2015 reported a recent live birth (Table 2). There was no evidence of a difference in demographic characteristics between survey years. In 2012, the women interviewed had a mean age of 26 years, 96% were currently married, 61% had no formal education, 67% were Christian, and 33% were Muslim (Table 3).

In 2012, no intervention indicator had coverage higher than 50%, and no indicator of access to care had coverage higher than 25%, being lowest for newborn postnatal care (4% [10/277], 95% CI 2%–7%) (Table 5). Coverage inequity in relation to socioeconomic status was present in 2012 for facility delivery (*p* = 0.001) and birth attendant’s use of gloves (*p* < 0.001).

View this table:
[Table 5:](http://www.cmaj.ca/content/191/43/E1179/T5)

Table 5: 
Evidence of coverage change between 2012 and 2015, overall and by socioeconomic status, for targeted maternal and newborn health indicators in Ethiopia

By 2015, some large-scale changes had occurred for both access and intervention indicators. Coverage of at least 4 antenatal visits almost doubled, from 22% (62/277; 95% CI 14%–34%) to 39% (156/404; 95% CI 30%–48%), although coverage in 2015 for the poorest women was equivalent to coverage in 2012 for the least poor women (35% and 31%, respectively). Facility delivery almost tripled from 15% (41/277; 95% CI 9%–25%) in 2012 to 43% (172/404; 95% CI 33%–54%) in 2015, although 2015 coverage for the poorest women was equivalent to 2012 coverage for the least poor women (32% and 36%, respectively). Delayed bathing increased from 39% (107/274; 95% CI 30%–49%) to 50% (204/404; 95% CI 41%–60%), with 2015 coverage for the poorest women approaching 2012 coverage for the least poor women (42% and 48%, respectively). Breastfeeding initiation within 1 hour increased from 50% (136/274; 95% CI 42%–58%) to 66% (264/400; 95% CI 59%–72%) with no evidence of inequity in either year. Birth attendant’s use of gloves doubled in coverage, from 26% (61/239; 95% CI 17%–37%) in 2012 to 54% (192/357; 95% CI 43%–64%) in 2015, with evidence of the greatest increase for the poorest women (*p* value for interaction 0.03; Table 5).

### Six districts of Uttar Pradesh, India

In Uttar Pradesh, 94% (8120/8641) of all resident women were interviewed in 2012, and 90% (8522/9446) in 2015. Of these, 7% (604/8120) in 2012 and 7% (584/8522) in 2015 reported a recent live birth (Table 2). There was no evidence of a difference in demographic characteristics between survey years. In 2012, the women had a mean age of 26 years, 100% were currently married, 40% had no formal education, 93% were Hindu, and 7% were Muslim (Table 3).

In 2012, coverage levels were all about 50% or above, with the exception of at least 4 antenatal visits (28% [170/604]; 95% CI 24%–33%) and postnatal care for the newborn (19% [114/604]; 95% CI 15%–23%); these 2 indicators still had the lowest coverage in 2015 (Table 6). Evidence of inequity in relation to socioeconomic status was present in 2012 for antenatal care (*p* < 0.001), facility delivery (*p* = 0.007), postnatal care for newborn (*p* = 0.01) and birth attendant’s use of gloves (*p* = 0.04). By 2015, coverage for 2 of the access-to-care indicators had improved. Facility delivery increased from 76% (459/604; 95% CI 71%–80%) in 2012 to 81% (475/584; 95% CI 77%–85%) in 2015, reaching 80% even among the poorest group of women. In addition, postnatal care for the mother increased from 54% (324/604; 95% CI 48%–59%) to 63% (365/584; 95% CI 58%–67%). Testing for an interaction between time and socioeconomic status showed no evidence that any socioeconomic status group benefited more than another for either of these indicators.

View this table:
[Table 6:](http://www.cmaj.ca/content/191/43/E1179/T6)

Table 6: 
Evidence of coverage change between 2012 and 2015, overall and by socioeconomic status, for targeted maternal and newborn health indicators in Uttar Pradesh, India

## Interpretation

In 2012, the coverage of maternal and newborn health care in 3 diverse settings was suboptimal, with the poorest families consistently having lowest access and intervention coverage. Following 3 years of large-scale, community-based intervention, some improvements were observed. Notably, more women in Ethiopia and Uttar Pradesh had access to maternity care in 2015 than in 2012; however, this was not the case in Gombe State, where sociocultural barriers to access persisted, and the Boko Haram insurgency presented a barrier to accessing care.23 In all 3 settings, coverage for early postnatal care remained low, despite strong government commitment to and intense nongovernment effort for community-based programming.

Improving outcomes for mothers and newborns requires not only structural changes in the provision of care, but also behaviour change by individuals, communities and health care providers. Such changes may take considerable time — longer than this study’s duration — to achieve.24 Where changes did occur, they were of a similar magnitude for all socioeconomic groups, with the exception only of birth attendant’s use of gloves in Ethiopia. However, because of the pre-existing inequities, this meant that the coverage of many indicators remained inequitable in 2015.

There is considerable evidence to support implementation of community-based interventions for maternal and newborn health.25–27 Our findings have both an optimistic and a pessimistic interpretation, in that families from all socioeconomic status groups benefited, but inequities have also persisted.28 In 2015, all 3 study settings were characterized as rural and poor with a high burden of mortality; the entire population was in need of better care. However, as population-level coverage increases, a more sophisticated examination of the patterns of inequity is required,29 along with greater agility in decision-making about how to target vulnerability.30,31 Meeting these requirements will necessitate more country-level coordination and leadership, with more investment in data that can be disaggregated for this purpose.32

It is also relevant to consider packages of care, rather than focusing on indicators independently. For example, if facility delivery increases, then so should indicators reflecting positive behaviours at birth, as observed in Ethiopia.33 Emphasis on packages of care demands a more quality-oriented, effective coverage approach to planning, implementation and measurement.34 Furthermore, it is crucial to understand why the poorest women receive the worst quality of care. One reason may be that they have the fewest choices in the type of provider they can access, because they lack the resources needed to bypass local facilities to seek better-quality care elsewhere.6,35,36

### Limitations

The strengths of this study included examination of progress on a large scale, at 2 points in time, using the same measurement approach, with a relatively short recall period. However, the study was limited by its observational design, the inability to assess the impact of individual interventions associated with change and consideration of the magnitude of change only in areas that were experiencing multiple improvement efforts concurrently. Other limitations included reliance on women’s self-reporting in response to interviewer-administered structured questionnaires, which may not always provide valid responses37,38 and a limited sample size that did not permit further subgroup analyses. Results from the multiple interaction tests examining whether change was inequitable should be interpreted with caution, because the study was not explicitly powered for these tests.

### Conclusion

Universal health coverage requires that countries strategize to reach the most vulnerable members of the population, who experience the worst health outcomes. In our study, representing a total population of more than 22 million people in Nigeria, Ethiopia and India, we found that the poorest families benefited in equal measure to the least poor families, for 8 maternal and newborn health care indicators, but without immediate redress of socioeconomic inequities in health care. To ensure that no one is left behind, specific efforts are needed to close these coverage gaps.

## Acknowledgements

The authors are grateful for support received by field teams and government officials in each geographic region during administration of the surveys. They also thank the many women who gave generously of their time during interviews.

## Footnotes

*   **Competing interests:** None declared.

*   This article has been peer reviewed.

*   **Contributors:** Tanya Marchant, Simon Cousens and Joanna Armstrong Schellenberg conceived the study. All of the other authors contributed to acquisition, analysis or interpretation of the data. All of the authors contributed to drafting the manuscript and revising it critically for important intellectual content, and to approval of the final version to be published. All of the authors agreed to be accountable for all aspects of the work.

*   **Funding:** This work was supported through a grant from the Bill & Melinda Gates Foundation to the IDEAS project at the London School of Hygiene & Tropical Medicine. The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

*   **Data sharing:** All data files are available from the COMPASS database of the London School of Hygiene & Tropical Medicine (doi: 10.17037/DATA.129).

*   Accepted October 15, 2019.

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