Factors consistently associated with utilisation of essential maternal and child health services in Nigeria: analysis of the five Nigerian national household surveys (2003-2018)

BMJ Open, Volume 12, No. 9, Year 2022

Interpretation

Objective This study aims to identify the individual and contextual factors consistently associated with utilisation of essential maternal and child health services in Nigeria across time and household geolocation. Design, setting and participants Secondary data from five nationally representative household surveys conducted in Nigeria from 2003 to 2018 were used in this study. The study participants are women and children depending on essential maternal and child health (MCH) services. Outcome measures The outcome measures were indicators of whether participants used each of the following essential MCH services: antenatal care, facility-based delivery, modern contraceptive use, childhood immunisations (BCG, diphtheria, tetanus, pertussis/Pentavalent and measles) and treatments of childhood illnesses (fever, cough and diarrhoea). Methods We estimated generalised additive models with logit links and smoothing terms for households’ geolocation and survey years. Results Higher maternal education and households’ wealth were significantly associated with utilisation of all types of essential MCH services (p<0.05). On the other hand, households with more children under 5 years of age and in poor communities were significantly less likely to use essential MCH services (p<0.05). Except for childhood immunisations, greater access to transport was positively associated with utilisation (p<0.05). Households with longer travel times to the most accessible health facility were less likely to use all types of essential MCH services (p<0.05), except modern contraceptive use and treatment of childhood fever and/or cough. Conclusion This study adds to the evidence that maternal education and household wealth status are consistently associated with utilisation of essential MCH services across time and space. To increase utilisation of essential MCH services across different geolocations, interventions targeting poor communities and households with more children under 5 years of age should be appropriately designed. Moreover, additional interventions should prioritise to reduce inequities of essential MCH service utilisation between the wealth quantiles and between education status. This study uses secondary data from national representative cross-sectional surveys of geolocated households conducted in Nigeria from 2003 to 2018. We combined publicly available data from four Nigeria Demographic Health Surveys (DHS) in 2003, 2008, 2013 and 2018 and Multiple Indicator Cluster Surveys (MICS) in 2016–2017. DHS data were extracted from IPUMS DHS.16 Detailed methodologies of the four DHS surveys are published elsewhere.17–21 All surveys employed stratified two-stage or three-stage cluster sampling techniques. The primary sampling unit (PSU) for DHS 2003 was defined as one or more enumeration areas (EAs) used for Population and Housing Census 1991, while the PSU for DHS 2008, DHS 2013, DHS 2018 and MICS 2016/17 was defined as one or more EAs used for the Population and Housing Census 2006. The counts of households interviewed in DHS 2003, DHS 2008, DHS 2013, DHS 2018 and MICS 2016/17 are shown in table 1. In DHS 2018, 11 of 27 local government areas in Borno State were excluded from the sampling frame due to insecurity in those districts. Likewise, in MICS 2016/17, a total of 101 EAs in Borno, Yobe and Adamawa states were not surveyed due to insecurity. Total number of households interviewed in DHS 2003, 2008, 2013 and 2018 and MICS 2016/17 DHS, Demographic Health Survey; MICS, Multiple Indicator Cluster Survey. To protect the confidentiality of PSU geolocations, the Global Positioning System coordinates of urban PSU locations were randomly displaced within a 2 km buffer, and rural PSUs were displaced within a 5 km buffer (and in 1% of cases, a 10 km buffer). The direction and distance of the displacement for each PSU was randomly selected using a uniform distribution.22 23 Prior research found that the effect of random displacement across a 10 km2 grid to be negligible for estimating measles vaccination coverage.24 Geolocation data for 16 of 3533 PSUs (0.5%) were missing across the four DHS. Similarly, geolocation data for 1 of 2239 PSUs (0.0004%) was missing in MICS 2016/17. After initial random displacement, 14 PSUs (1 in DHS 2008 and 13 in MICS 2016/17) were ‘located’ either in the sea or out of country’s boundaries. We resampled the random displacement of those PSUs until their displaced positions lay inside the relevant boundaries (using a 5 km buffer if possible, and a 10 km buffer if necessary). Of these 14 PSUs, 8 were successfully resampled and 6 cases that could not be appropriately displaced across 10 000 attempts were discarded. The essential maternal and child health services considered in this study consist of ANC, facility-based delivery, modern contraceptive use, childhood immunisations (BCG, first and third diphtheria, tetanus, pertussis/pentavalent, measles) and treatments for childhood illnesses. The target group for ANC and facility-based delivery was women aged 15–49 years having given a live birth in the last 23 months, while that for modern contraceptive use was women aged 15–49 years not having wanted to have more children. Children aged 12–23 months and aged 0–59 months were the target groups for immunisation and treatments of childhood illnesses, respectively. Table 2 provides further details on the definitions of and study populations for these essential services. Definitions and target populations of essential health services DPT, diphtheria, tetanus, pertussis. Independent variables across essential maternal and child health services were selected based on three earlier studies.25–27 We considered five types of explanatory variables that might influence health seeking behaviours: (i) individual characteristics; (ii) the built environment; (iii) neighbourhood demographics; (iv) the social environment and (v) the healthcare environment. Maternal and households’ characteristics include the explanatory variables of maternal age, household head, education level, marital status, possession of television and radio, possession of means of transport and household’s wealth index. Possession of television and radio was categorised into three groups: (i) households possessing both a television and a radio; (ii) households possessing either of them and (iii) households possessing neither of them. Possession of means of transport means was generated using possession of car, motorcycle and bike and categorised into three groups: (i) no means of transport; (ii) one means of transport and (iii) two to three means of transport. The household wealth index was the first principal component estimated by a principal component analysis on the household assets, sources of drinking water, sanitation facilities, type of fuel for cooking and materials of floors for housing units. Gridded estimates of population density provided by WorldPop were used as a proxy for the built environment.28 The proportion of households in a PSU living under the poverty line was used as a proxy for neighbourhood demographics and the social environment. As proxy for the healthcare environment, we measured each PSU’s travel time to the most accessible health facility using the friction surface developed by the Malaria Atlas Project.29 Geolocations of health facilities managed by government, community-based organisations and faith-based organisations in Nigeria were provided by the Nature Scientific database, which records locations of health facilities as of 2018.30 We assume these facilities were present in all years surveyed by DHS; however, because Nature Scientific does not provide the date on which each facility was established, some health facilities may not have existed at the time of some of the five surveys, a possible limitation of our analysis. Finally, the number of health facilities within a 20 km buffer around each PSU was employed as the proxy for the healthcare environment, indicating the availability of accessible health facility options. In analyses of the utilisation of childhood immunisations and treatments of childhood illnesses, we added additional explanatory variables related to child characteristics (ie, age, sex and birth month). Children’s ages were rounded to whole months. In addition to descriptive analyses, we estimated generalised additive models (GAMs) with logit links to identify factors associated with the utilisation of essential service v by the ith individual in the dth PSU in the jth state at the t year. The systematic component of the model of vidjt is: s(longidj , latidj ): smooth function of longitude and latitude using isotropic smooths on the sphere to account for spatial autocorrelation. s(montht ): smooth function of time trends. DHSdjt : binary indicator recording 1 if the data source for year t is DHS and 0 if not. Headidjt : binary indicator recording 1 if the household head in year t was a mother and 0 if not. Ageidjt : maternal age of the mother in a household in year t. U5 idjt : the number of children under 5 years of age in a household in year t. Educationidjt : the education level of the mother in year t. Wealthidjt : the wealth quantile of the household in year t. Maritalidjt : the marital status of the mother in year t. Mediaidjt : possession of TV and/or radio by the household in year t. Transportidjt : possession of means of transport by the household in year t. Povertydjt : proportion of the households living below the poverty line in the dth PSU of the jth state in year t. Accessdj : travel time in minutes from the household’s PSU to the most accessible health facility. Choicedj : the number of health facilities within 20 km from the household’s PSU. PopDensitydjt : population density in the dth PSU of the jth state in year t. We included childhood covariates ChildAgeijt and ChildSexijt (child’s age and sex, respectively) in the models of childhood immunisation and care seeking for common childhood illnesses. We log-transformed population density, the number of health facilities and the number of children under 5 to improve model fit and to account for diminishing marginal effects of these variables. Two of these variables—the count of health facilities within 20 km and the count of children under 5 years of age—could in some cases have a value of precisely zero, posing a problem for taking logs. Rather than adding an arbitrary positive quantity to these count variables, we directly estimate the effect of having zero children (or zero health facilities) by including dummy variables in the model to indicate cases where each is precisely zero. In turn, and without loss of generality, before logging the count of health facilities (or children), we replaced zeros with ones, so that cases in which there are zero health facilities within 20 km (or no children under 5 years of age) affect the outcome only through the dummy variable for that case.31 32 We listwise deleted missing data, which accounted for <2% of total cases. Because the guidelines for DHS 2018 recommend against using weights for estimating relationships, we do not use sampling weights in estimating the GAMs.33 However, sampling weights were used for estimating health service coverage reported in table 3. Essential health service coverage from 2003 to 2018 in Nigeria Finally, we estimated an additional eight models for each outcome as sensitivity analyses to check the robustness of our findings: Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research.

AI Digest

Study Justification:

– The study aims to identify the factors consistently associated with the utilization of essential maternal and child health services in Nigeria over a period of time and across different geographical locations.
– Understanding these factors is crucial for improving the utilization of these services and addressing health disparities in Nigeria.
– The study adds to the existing evidence on the importance of maternal education and household wealth in determining the utilization of essential maternal and child health services.

Highlights:

– Higher maternal education and household wealth were consistently associated with the utilization of all types of essential maternal and child health services.
– Households with more children under 5 years of age and in poor communities were less likely to use essential maternal and child health services.
– Greater access to transport was positively associated with utilization, except for childhood immunizations.
– Longer travel times to the most accessible health facility were associated with lower utilization of essential maternal and child health services.

Recommendations for Lay Reader and Policy Maker:

– Interventions should be designed to target poor communities and households with more children under 5 years of age in order to increase the utilization of essential maternal and child health services.
– Efforts should be made to reduce inequities in service utilization between different wealth quantiles and education levels.
– Improving access to transport can positively impact the utilization of these services.
– Attention should be given to reducing travel times to the most accessible health facility to encourage utilization.

Key Role Players:

– Government agencies responsible for healthcare policy and implementation
– Non-governmental organizations (NGOs) working in the field of maternal and child health
– Community leaders and organizations
– Healthcare providers and professionals
– Researchers and academics

Cost Items for Planning Recommendations:

– Development and implementation of targeted interventions for poor communities and households with more children under 5 years of age
– Improving access to transport, which may involve infrastructure development or transportation subsidies
– Enhancing healthcare facilities and services in underserved areas
– Training and capacity building for healthcare providers
– Research and monitoring to evaluate the impact of interventions and track progress towards reducing inequities in service utilization

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong as it is based on secondary data from five nationally representative household surveys conducted in Nigeria from 2003 to 2018. The study used generalised additive models with logit links and smoothing terms for households’ geolocation and survey years to analyze the data. The findings consistently show that higher maternal education and households’ wealth are significantly associated with the utilization of essential maternal and child health services. However, there are actionable steps to improve the evidence. First, the abstract could provide more specific information about the sample size and representativeness of the surveys. Second, it would be helpful to include the statistical significance levels of the associations mentioned. Finally, the abstract could mention any limitations of the study, such as potential biases or confounding factors, to provide a more comprehensive assessment of the evidence.

Abstract

This study uses secondary data from national representative cross-sectional surveys of geolocated households conducted in Nigeria from 2003 to 2018. We combined publicly available data from four Nigeria Demographic Health Surveys (DHS) in 2003, 2008, 2013 and 2018 and Multiple Indicator Cluster Surveys (MICS) in 2016–2017. DHS data were extracted from IPUMS DHS.16 Detailed methodologies of the four DHS surveys are published elsewhere.17–21 All surveys employed stratified two-stage or three-stage cluster sampling techniques. The primary sampling unit (PSU) for DHS 2003 was defined as one or more enumeration areas (EAs) used for Population and Housing Census 1991, while the PSU for DHS 2008, DHS 2013, DHS 2018 and MICS 2016/17 was defined as one or more EAs used for the Population and Housing Census 2006. The counts of households interviewed in DHS 2003, DHS 2008, DHS 2013, DHS 2018 and MICS 2016/17 are shown in table 1. In DHS 2018, 11 of 27 local government areas in Borno State were excluded from the sampling frame due to insecurity in those districts. Likewise, in MICS 2016/17, a total of 101 EAs in Borno, Yobe and Adamawa states were not surveyed due to insecurity. Total number of households interviewed in DHS 2003, 2008, 2013 and 2018 and MICS 2016/17 DHS, Demographic Health Survey; MICS, Multiple Indicator Cluster Survey. To protect the confidentiality of PSU geolocations, the Global Positioning System coordinates of urban PSU locations were randomly displaced within a 2 km buffer, and rural PSUs were displaced within a 5 km buffer (and in 1% of cases, a 10 km buffer). The direction and distance of the displacement for each PSU was randomly selected using a uniform distribution.22 23 Prior research found that the effect of random displacement across a 10 km2 grid to be negligible for estimating measles vaccination coverage.24 Geolocation data for 16 of 3533 PSUs (0.5%) were missing across the four DHS. Similarly, geolocation data for 1 of 2239 PSUs (0.0004%) was missing in MICS 2016/17. After initial random displacement, 14 PSUs (1 in DHS 2008 and 13 in MICS 2016/17) were ‘located’ either in the sea or out of country’s boundaries. We resampled the random displacement of those PSUs until their displaced positions lay inside the relevant boundaries (using a 5 km buffer if possible, and a 10 km buffer if necessary). Of these 14 PSUs, 8 were successfully resampled and 6 cases that could not be appropriately displaced across 10 000 attempts were discarded. The essential maternal and child health services considered in this study consist of ANC, facility-based delivery, modern contraceptive use, childhood immunisations (BCG, first and third diphtheria, tetanus, pertussis/pentavalent, measles) and treatments for childhood illnesses. The target group for ANC and facility-based delivery was women aged 15–49 years having given a live birth in the last 23 months, while that for modern contraceptive use was women aged 15–49 years not having wanted to have more children. Children aged 12–23 months and aged 0–59 months were the target groups for immunisation and treatments of childhood illnesses, respectively. Table 2 provides further details on the definitions of and study populations for these essential services. Definitions and target populations of essential health services DPT, diphtheria, tetanus, pertussis. Independent variables across essential maternal and child health services were selected based on three earlier studies.25–27 We considered five types of explanatory variables that might influence health seeking behaviours: (i) individual characteristics; (ii) the built environment; (iii) neighbourhood demographics; (iv) the social environment and (v) the healthcare environment. Maternal and households’ characteristics include the explanatory variables of maternal age, household head, education level, marital status, possession of television and radio, possession of means of transport and household’s wealth index. Possession of television and radio was categorised into three groups: (i) households possessing both a television and a radio; (ii) households possessing either of them and (iii) households possessing neither of them. Possession of means of transport means was generated using possession of car, motorcycle and bike and categorised into three groups: (i) no means of transport; (ii) one means of transport and (iii) two to three means of transport. The household wealth index was the first principal component estimated by a principal component analysis on the household assets, sources of drinking water, sanitation facilities, type of fuel for cooking and materials of floors for housing units. Gridded estimates of population density provided by WorldPop were used as a proxy for the built environment.28 The proportion of households in a PSU living under the poverty line was used as a proxy for neighbourhood demographics and the social environment. As proxy for the healthcare environment, we measured each PSU’s travel time to the most accessible health facility using the friction surface developed by the Malaria Atlas Project.29 Geolocations of health facilities managed by government, community-based organisations and faith-based organisations in Nigeria were provided by the Nature Scientific database, which records locations of health facilities as of 2018.30 We assume these facilities were present in all years surveyed by DHS; however, because Nature Scientific does not provide the date on which each facility was established, some health facilities may not have existed at the time of some of the five surveys, a possible limitation of our analysis. Finally, the number of health facilities within a 20 km buffer around each PSU was employed as the proxy for the healthcare environment, indicating the availability of accessible health facility options. In analyses of the utilisation of childhood immunisations and treatments of childhood illnesses, we added additional explanatory variables related to child characteristics (ie, age, sex and birth month). Children’s ages were rounded to whole months. In addition to descriptive analyses, we estimated generalised additive models (GAMs) with logit links to identify factors associated with the utilisation of essential service v by the ith individual in the dth PSU in the jth state at the t year. The systematic component of the model of vidjt is: s(longidj , latidj ): smooth function of longitude and latitude using isotropic smooths on the sphere to account for spatial autocorrelation. s(montht ): smooth function of time trends. DHSdjt : binary indicator recording 1 if the data source for year t is DHS and 0 if not. Headidjt : binary indicator recording 1 if the household head in year t was a mother and 0 if not. Ageidjt : maternal age of the mother in a household in year t. U5 idjt : the number of children under 5 years of age in a household in year t. Educationidjt : the education level of the mother in year t. Wealthidjt : the wealth quantile of the household in year t. Maritalidjt : the marital status of the mother in year t. Mediaidjt : possession of TV and/or radio by the household in year t. Transportidjt : possession of means of transport by the household in year t. Povertydjt : proportion of the households living below the poverty line in the dth PSU of the jth state in year t. Accessdj : travel time in minutes from the household’s PSU to the most accessible health facility. Choicedj : the number of health facilities within 20 km from the household’s PSU. PopDensitydjt : population density in the dth PSU of the jth state in year t. We included childhood covariates ChildAgeijt and ChildSexijt (child’s age and sex, respectively) in the models of childhood immunisation and care seeking for common childhood illnesses. We log-transformed population density, the number of health facilities and the number of children under 5 to improve model fit and to account for diminishing marginal effects of these variables. Two of these variables—the count of health facilities within 20 km and the count of children under 5 years of age—could in some cases have a value of precisely zero, posing a problem for taking logs. Rather than adding an arbitrary positive quantity to these count variables, we directly estimate the effect of having zero children (or zero health facilities) by including dummy variables in the model to indicate cases where each is precisely zero. In turn, and without loss of generality, before logging the count of health facilities (or children), we replaced zeros with ones, so that cases in which there are zero health facilities within 20 km (or no children under 5 years of age) affect the outcome only through the dummy variable for that case.31 32 We listwise deleted missing data, which accounted for <2% of total cases. Because the guidelines for DHS 2018 recommend against using weights for estimating relationships, we do not use sampling weights in estimating the GAMs.33 However, sampling weights were used for estimating health service coverage reported in table 3. Essential health service coverage from 2003 to 2018 in Nigeria Finally, we estimated an additional eight models for each outcome as sensitivity analyses to check the robustness of our findings: Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research.

Materials

Innovations

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

1. Mobile Health (mHealth) Solutions: Develop mobile applications or text messaging services that provide pregnant women and new mothers with important information about prenatal care, postnatal care, and child health. These platforms can also be used to send reminders for appointments and medication schedules.

2. Telemedicine Services: Implement telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare professionals through video calls or phone consultations. This can help address the issue of long travel times to the nearest health facility.

3. Community Health Workers: Train and deploy community health workers to provide maternal health education, counseling, and basic healthcare services in rural and marginalized communities. These workers can also help identify and refer high-risk pregnancies to appropriate healthcare facilities.

4. Financial Incentives: Introduce financial incentives, such as conditional cash transfers or vouchers, to encourage pregnant women to seek antenatal care, facility-based delivery, and postnatal care. This can help address the issue of households’ wealth being a barrier to accessing essential maternal and child health services.

5. Infrastructure Development: Improve the physical infrastructure of health facilities, particularly in poor communities, to ensure they are well-equipped and accessible. This can include constructing new facilities, upgrading existing ones, and providing necessary medical equipment and supplies.

6. Health Education Programs: Implement comprehensive health education programs that target women and their families, focusing on the importance of maternal health, family planning, and child immunizations. These programs can be conducted through community workshops, radio broadcasts, and educational materials.

7. Public-Private Partnerships: Foster collaborations between the government, private sector, and non-profit organizations to improve access to maternal health services. This can involve leveraging private sector resources and expertise to support the development and implementation of innovative solutions.

8. Quality Improvement Initiatives: Implement quality improvement initiatives in healthcare facilities to ensure that essential maternal and child health services are provided in a timely and effective manner. This can involve training healthcare providers, improving service delivery processes, and monitoring and evaluating the quality of care.

These are just a few potential innovations that could be considered to improve access to maternal health based on the information provided. It is important to carefully assess the local context and engage relevant stakeholders to determine the most appropriate and effective strategies for each specific setting.

AI Innovations Description

Based on the study titled “Factors consistently associated with utilization of essential maternal and child health services in Nigeria: analysis of the five Nigerian national household surveys (2003-2018)”, the following recommendations can be developed into an innovation to improve access to maternal health:

1. Target interventions towards poor communities and households with more children under 5 years of age: The study found that households in poor communities and with more children under 5 years of age were significantly less likely to use essential maternal and child health services. To address this, innovative interventions should be designed specifically for these communities and households, taking into account their unique challenges and needs.

2. Increase maternal education and household wealth: The study also found that higher maternal education and households’ wealth were significantly associated with the utilization of essential maternal and child health services. Therefore, innovative programs should focus on improving access to education for women and increasing household income to enhance the utilization of these services.

3. Improve transportation infrastructure: The study found that greater access to transport was positively associated with the utilization of essential maternal and child health services. To improve access, innovative solutions could include the development of transportation networks, such as improved road infrastructure, public transportation systems, or mobile health clinics that can reach remote areas.

4. Reduce travel time to health facilities: The study found that households with longer travel times to the most accessible health facility were less likely to use essential maternal and child health services. Innovative approaches could include the establishment of more health facilities in underserved areas or the use of telemedicine and mobile health technologies to provide remote consultations and services.

5. Address inequities in service utilization: The study highlighted the need to reduce inequities in the utilization of essential maternal and child health services between different wealth quantiles and education statuses. Innovative interventions should prioritize reducing these disparities and ensuring that all women and children have equal access to quality healthcare services.

Overall, these recommendations can guide the development of innovative solutions to improve access to maternal health services in Nigeria. By addressing the identified factors and implementing targeted interventions, it is possible to enhance utilization and ultimately improve maternal and child health outcomes.

AI Innovations Methodology

Based on the description provided, here are some potential recommendations for improving access to maternal health:

1. Strengthen maternal education programs: Given that higher maternal education is consistently associated with increased utilization of essential maternal and child health services, implementing programs that focus on improving maternal education can help improve access to maternal health. These programs can include providing education on pregnancy, childbirth, and postnatal care, as well as promoting awareness of the importance of seeking timely and appropriate healthcare services.

2. Target interventions for poor communities: The study found that households in poor communities were significantly less likely to use essential maternal and child health services. To address this disparity, interventions should be designed specifically for poor communities, taking into account their unique challenges and barriers to accessing healthcare. This can include providing financial assistance, improving infrastructure and transportation options, and increasing the availability of healthcare facilities in these communities.

3. Focus on households with more children under 5 years of age: The study also identified households with more children under 5 years of age as being less likely to use essential maternal and child health services. Interventions should be targeted towards these households, with a focus on providing comprehensive healthcare services for both mothers and children. This can include promoting family planning methods, ensuring access to immunizations and treatments for childhood illnesses, and providing support for childcare and parenting.

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, such as the percentage of women receiving antenatal care, facility-based delivery, and modern contraceptive use.

2. Collect baseline data: Gather data on the current utilization of maternal health services in the target population. This can be done through surveys, interviews, or analysis of existing data sources.

3. Develop a simulation model: Create a simulation model that incorporates the identified factors associated with utilization of maternal health services. This model should take into account the individual and contextual factors identified in the study, such as maternal education, household wealth, number of children under 5 years of age, and access to transportation and healthcare facilities.

4. Define intervention scenarios: Define different scenarios that represent the potential impact of the recommended interventions. For example, one scenario could simulate the impact of increasing maternal education levels, while another scenario could simulate the impact of targeting interventions for poor communities.

5. Run the simulation: Use the simulation model to run the defined intervention scenarios and assess their impact on access to maternal health services. This can be done by comparing the indicators of access under different scenarios to the baseline data.

6. Analyze the results: Analyze the results of the simulation to determine the potential impact of the recommended interventions on improving access to maternal health. This can include quantifying the changes in utilization rates and identifying any disparities or variations across different population groups.

7. Refine and iterate: Based on the analysis of the simulation results, refine the interventions and the simulation model as needed. Iterate the simulation process to further explore different scenarios and assess their potential impact on improving access to maternal health.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different interventions and make informed decisions on how to improve access to maternal health services.

Author

Dima Health

Identifiers:

DOI: 10.1136/bmjopen-2022-061747

Research Areas:

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

Study Countries:

Nigeria

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female