Barriers for health care access affects maternal continuum of care utilization in Ethiopia; spatial analysis and generalized estimating equation

PLoS ONE, Volume 17, No. 4 April, Year 2022

Interpretation

Background Although Ethiopia had made a significant change in maternal morbidity and mortality over the past decades, it remains a major public health concern. World Health Organization designed maternal continuum of care to reduce maternal morbidity and mortality. However, majority of the mothers didn’t utilize the maternal continuum of care. Therefore, this study aimed to assess the spatial distribution of incomplete utilization of maternal continuum of care and its associated factors in Ethiopia. Methods This study was based on 2016 Demographic and Health Survey data of Ethiopia. A total weighted sample of 4,772 reproductive aged women were included. The study used ArcGIS and SaTScan software to explore the spatial distribution of incomplete utilization of maternal continuum of care. Besides, multivariable Generalized Estimating Equation was fitted to identify the associated factors of incomplete utilization of maternal continuum of care using STATA software. Model comparison was made based on Quasi Information Criteria. An adjusted odds ratio with 95% confidence interval of the selected model was reported to identify significantly associated factors of incomplete utilization of maternal continuum of care. Results The spatial analysis revealed that incomplete utilization of maternal continuum of care had significant spatial variation across the country. Primary clusters were detected at Somali, North-Eastern part of Oromia, and East part of Southern Nation Nationalities while secondary clusters were detected in the Central Amhara region. In multivariate GEE, rural residency, secondary education, higher education, Protestant religious follower’s, Muslim religious follower’s, poorer wealth index, richer wealth index, richest wealth index, currently working, having barriers for accessing health care, and exposure for mass media were significantly associated with incomplete utilization maternal continuum of care. Conclusion Incomplete utilization of maternal continuum of care had significant spatial variations in Ethiopia. Residence, wealth index, education, religion, and barriers for health care access, mass media exposure, and currently working were significantly associated with incomplete utilization of maternal continuum of care. Therefore, public health interventions targeted to enhance maternal service utilization and women empowerment in hotspot areas of incomplete utilization of maternal continuum of care are crucial for reducing maternal morbidity and mortality. The present study used 2016 EDHS data. The survey was collected every 5 years to assess population and health indicators at the national and regional levels using a structured, validated, and standardized questionnaire. It was also conducted for four times in Ethiopia. Hence, the 2016 EDHS is the latest and the fourth survey conducted in the country. Ethiopia is an East African country with an estimated population of 109.2 million that makes second most populous country in Africa [18]. Ethiopia is federally decentralized in to nine regions and two city administrations and the regions are further divided into zones, and zones into administrative units called districts [19]. The district again subdivided into kebele which is the lowest administrative unit. Regarding to the health care system in Ethiopia, the fourth health sector development plan introduced a three-tier health-service delivery system. This system were arranged by including Primary health care unities (i.e. health posts and health centers) and primary hospitals at primary level, general hospitals at secondary level, and specialized hospitals at tertiary level [20]. All reproductive aged women who were booked for ANC service and giving birth within 5 years preceding the 2016 survey in Ethiopia were the source population, while all reproductive aged women who were booked for ANC service and giving birth in the selected Enumeration Areas (EAs) within 5 years before the 2016 survey were the study population. The most recent birth characteristics was used for those who give more than one birth within five years preceding the survey. A two stage stratified cluster sampling technique were employed to select study participants. Stratification of regions into urban and rural areas were considered. In the first stage, 645 EAs (202 from urban area) were selected using probability sampling proportional to the EAs size and with independent selection in each sampling stratum. In the second stage, 28 households from each cluster were selected with an equal probability of selection from the household listing [21]. A total of 4,772 weighted reproductive aged women were included in the study. The response variable for this study was maternal continuum of care. Maternal continuum of care is a series of cares provided for mothers during the three periods of maternity [11, 17]. It is a composite variable obtained from ANC, institutional delivery, and Post Natal care (PNC) services. The response variable for the ith mother from jth cluster (EAs) was represented by a random variable Yij, with two possible values coded as 1 and 0. The outcome variable of the ith mother in the jth cluster (Yij) = 1 if ith mother had incomplete maternal continuum of care or if the women had not utilize one of the three maternity services (i.e. 4 and above ANC visits, institutional delivery or postnatal checkup) and Yij = 0 if the mother had complete continuum of maternal care (if the women’s had utilize all the three maternity services). Age of the women, residence, marital status, religion, maternal education, wealth index, currently working, mass media exposure, number of births/parity, contraceptive use, barriers for accessing health care (women reported at least one challenge of health care access (money, distance, companionship, and permission) considered as having barriers of for accessing health care while if a woman didn’t report none of the above challenges were considered as no barriers for accessing health care) [22], wanted pregnancy were included as independent variables. After accessing the data, the variables of the study were extracted from birth recorded data set of EDHS data, data cleaning, and recoding were conducted in STATA version 14.1. The data were weighted using sampling weight and complex survey design was used to adjust for unequal probability of selection due to the sampling design employed in EDHS data. The spatial analysis was done using ArcGIS V.10.7 and SaTScan V.9.6 software. These study conducts the spatial autocorrelation, hot spot analysis, spatial interpolation, and SaTScan analysis of incomplete utilization of maternal continuum of care. Spatial autocorrelation was conducted to test whether the spatial distribution of incomplete utilization of maternal continuum of care was randomly distributed or not. The Global Moran’s I statistics which ranges from −1 to +1 was used to measure whether the distribution of incomplete utilization of maternal continuum of care was dispersed, random, or clustered in the study area [23]. The statistic values close to −1 indicate spatial distribution of incomplete utilization of maternal continuum of care is dispersed, a statistic close to value 0 indicates incomplete utilization of maternal continuum of care is randomly distributed, and a statistic close to +1 means the spatial distribution of incomplete utilization of maternal continuum of care was clustered [24]. Getis-Ord Gi* statistics was used to identify areas with higher rates of incomplete utilization of maternal continuum of care (significant hot spots areas), and areas with lower rates of incomplete utilization of maternal continuum of care (cold spot areas) [25]. To assess the presence of statistically significant spatial clusters of incomplete utilization of maternal continuum of care, Bernoulli based spatial scan statistical analysis with circular window was done. Women with incomplete utilization of maternal continuum of care were taken as cases and women with complete utilization of maternal continuum of care was taken as controls to fit the Bernoulli based model. The default maximum spatial cluster size of less than 50% of the population was used as an upper limit for the identification of both small and large clusters. Log Likelihood Ratio (LLR) test was used to the significance of the clusters and the 999 Monte Carlo replications were used to calculate p values and to rank using their LLR test. Finally, the primary cluster was chosen as the spatial window when it has greatest LLR test [26]. To predict incomplete utilization maternal continuum of care in unsampled areas in the country based on the data in sampled clusters /EA, spatial interpolation technique was employed. Although various spatial interpolation techniques are available, this study used an Empirical Bayesian Kriging (EBK) technique which are considered the best methods since it incorporates spatial autocorrelation and statistically optimize the weight [27]. In the EDHS data, women are nested within a cluster/EAs and those who reside within the same clusters have similar characteristics compared to those from another clusters. This violates the independence and equal variance assumptions of the ordinary logistic regression model. Thus, Intra-class Correlation Coefficient (ICC) was computed to measure the variability between clusters after fitting a model without any covariate. It quantifies the degree of heterogeneity of incomplete utilization of maternal continuum care between clusters (the proportion of variance explained by the between cluster variability). It also computed as; ICC=σμ2σμ2+π23; Where: the ordinary logit distribution has variance of π23, σμ2 indicates the cluster variance [28]. The calculated ICC was 36.37%, showed that about 36.37% of the variation in incomplete continuum of care was explained by the between cluster variation. This implies the need to take into account between-cluster variability by using advanced modelling techniques. Therefore, Generalized Estimating Equation (GEE) model was fitted to identify the associated factors of incomplete utilization of maternal continuum of care among reproductive aged women [29]. It is a marginal model that considers working correlation structure among clusters that estimates a robust standard error and also controlled for within-cluster correlations. Generalized Estimating Equation (GEE) model was fitted with a logit link function and binomial family with independent and exchangeable working correlation structures. Quasi Information Criteria (QIC) was used to select the best-fitted model. The model with exchangeable correlation structure was selected as the best fitted model since it had smaller QIC value. Variables with p-value <0.2 in the bi-variable GEE were considered for the multivariable GEE model. To assess the strength of association between outcome variable and independent factors both crude and adjusted odds ratio with a 95% Confidence Interval (CI) were computed. Variables having less than 5% p-value in the multivariable GEE model were considered as the associated factors with the incomplete utilization of maternal continuum care.

AI Digest

Study Justification:

– Maternal morbidity and mortality remain a major public health concern in Ethiopia.
– The World Health Organization has designed the maternal continuum of care to reduce maternal morbidity and mortality.
– However, the majority of mothers in Ethiopia do not utilize the maternal continuum of care.
– This study aimed to assess the spatial distribution of incomplete utilization of maternal continuum of care and its associated factors in Ethiopia.

Highlights:

– The study used 2016 Demographic and Health Survey data of Ethiopia.
– Spatial analysis revealed significant spatial variation in incomplete utilization of maternal continuum of care across the country.
– Primary clusters were detected in Somali, North-Eastern part of Oromia, and East part of Southern Nation Nationalities, while secondary clusters were detected in the Central Amhara region.
– Factors significantly associated with incomplete utilization of maternal continuum of care included rural residency, education level, religion, wealth index, employment status, barriers for accessing healthcare, and mass media exposure.
– Public health interventions targeted at hotspot areas of incomplete utilization of maternal continuum of care are crucial for reducing maternal morbidity and mortality.

Recommendations:

– Enhance maternal service utilization and women empowerment in hotspot areas of incomplete utilization of maternal continuum of care.
– Implement targeted public health interventions to address the identified factors associated with incomplete utilization of maternal continuum of care.

Key Role Players:

– Ministry of Health, Ethiopia
– Regional Health Bureaus
– District Health Offices
– Non-governmental organizations (NGOs) working in maternal health
– Community health workers
– Health facility staff

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers
– Development and implementation of targeted public health interventions
– Awareness campaigns and community mobilization activities
– Monitoring and evaluation of interventions
– Data collection and analysis
– Infrastructure improvement in healthcare facilities
– Procurement of essential medical supplies and equipment

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study utilized a large sample size and employed spatial analysis and generalized estimating equation to identify factors associated with incomplete utilization of maternal continuum of care in Ethiopia. The study also used the latest and nationally representative data from the 2016 Demographic and Health Survey. However, the abstract does not provide information on the specific methodology used for data collection and analysis, and it does not mention any limitations of the study. To improve the strength of the evidence, the abstract should include a clear description of the data collection process, the statistical methods used, and any potential limitations of the study, such as sampling bias or measurement error.

Abstract

The present study used 2016 EDHS data. The survey was collected every 5 years to assess population and health indicators at the national and regional levels using a structured, validated, and standardized questionnaire. It was also conducted for four times in Ethiopia. Hence, the 2016 EDHS is the latest and the fourth survey conducted in the country. Ethiopia is an East African country with an estimated population of 109.2 million that makes second most populous country in Africa [18]. Ethiopia is federally decentralized in to nine regions and two city administrations and the regions are further divided into zones, and zones into administrative units called districts [19]. The district again subdivided into kebele which is the lowest administrative unit. Regarding to the health care system in Ethiopia, the fourth health sector development plan introduced a three-tier health-service delivery system. This system were arranged by including Primary health care unities (i.e. health posts and health centers) and primary hospitals at primary level, general hospitals at secondary level, and specialized hospitals at tertiary level [20]. All reproductive aged women who were booked for ANC service and giving birth within 5 years preceding the 2016 survey in Ethiopia were the source population, while all reproductive aged women who were booked for ANC service and giving birth in the selected Enumeration Areas (EAs) within 5 years before the 2016 survey were the study population. The most recent birth characteristics was used for those who give more than one birth within five years preceding the survey. A two stage stratified cluster sampling technique were employed to select study participants. Stratification of regions into urban and rural areas were considered. In the first stage, 645 EAs (202 from urban area) were selected using probability sampling proportional to the EAs size and with independent selection in each sampling stratum. In the second stage, 28 households from each cluster were selected with an equal probability of selection from the household listing [21]. A total of 4,772 weighted reproductive aged women were included in the study. The response variable for this study was maternal continuum of care. Maternal continuum of care is a series of cares provided for mothers during the three periods of maternity [11, 17]. It is a composite variable obtained from ANC, institutional delivery, and Post Natal care (PNC) services. The response variable for the ith mother from jth cluster (EAs) was represented by a random variable Yij, with two possible values coded as 1 and 0. The outcome variable of the ith mother in the jth cluster (Yij) = 1 if ith mother had incomplete maternal continuum of care or if the women had not utilize one of the three maternity services (i.e. 4 and above ANC visits, institutional delivery or postnatal checkup) and Yij = 0 if the mother had complete continuum of maternal care (if the women’s had utilize all the three maternity services). Age of the women, residence, marital status, religion, maternal education, wealth index, currently working, mass media exposure, number of births/parity, contraceptive use, barriers for accessing health care (women reported at least one challenge of health care access (money, distance, companionship, and permission) considered as having barriers of for accessing health care while if a woman didn’t report none of the above challenges were considered as no barriers for accessing health care) [22], wanted pregnancy were included as independent variables. After accessing the data, the variables of the study were extracted from birth recorded data set of EDHS data, data cleaning, and recoding were conducted in STATA version 14.1. The data were weighted using sampling weight and complex survey design was used to adjust for unequal probability of selection due to the sampling design employed in EDHS data. The spatial analysis was done using ArcGIS V.10.7 and SaTScan V.9.6 software. These study conducts the spatial autocorrelation, hot spot analysis, spatial interpolation, and SaTScan analysis of incomplete utilization of maternal continuum of care. Spatial autocorrelation was conducted to test whether the spatial distribution of incomplete utilization of maternal continuum of care was randomly distributed or not. The Global Moran’s I statistics which ranges from −1 to +1 was used to measure whether the distribution of incomplete utilization of maternal continuum of care was dispersed, random, or clustered in the study area [23]. The statistic values close to −1 indicate spatial distribution of incomplete utilization of maternal continuum of care is dispersed, a statistic close to value 0 indicates incomplete utilization of maternal continuum of care is randomly distributed, and a statistic close to +1 means the spatial distribution of incomplete utilization of maternal continuum of care was clustered [24]. Getis-Ord Gi* statistics was used to identify areas with higher rates of incomplete utilization of maternal continuum of care (significant hot spots areas), and areas with lower rates of incomplete utilization of maternal continuum of care (cold spot areas) [25]. To assess the presence of statistically significant spatial clusters of incomplete utilization of maternal continuum of care, Bernoulli based spatial scan statistical analysis with circular window was done. Women with incomplete utilization of maternal continuum of care were taken as cases and women with complete utilization of maternal continuum of care was taken as controls to fit the Bernoulli based model. The default maximum spatial cluster size of less than 50% of the population was used as an upper limit for the identification of both small and large clusters. Log Likelihood Ratio (LLR) test was used to the significance of the clusters and the 999 Monte Carlo replications were used to calculate p values and to rank using their LLR test. Finally, the primary cluster was chosen as the spatial window when it has greatest LLR test [26]. To predict incomplete utilization maternal continuum of care in unsampled areas in the country based on the data in sampled clusters /EA, spatial interpolation technique was employed. Although various spatial interpolation techniques are available, this study used an Empirical Bayesian Kriging (EBK) technique which are considered the best methods since it incorporates spatial autocorrelation and statistically optimize the weight [27]. In the EDHS data, women are nested within a cluster/EAs and those who reside within the same clusters have similar characteristics compared to those from another clusters. This violates the independence and equal variance assumptions of the ordinary logistic regression model. Thus, Intra-class Correlation Coefficient (ICC) was computed to measure the variability between clusters after fitting a model without any covariate. It quantifies the degree of heterogeneity of incomplete utilization of maternal continuum care between clusters (the proportion of variance explained by the between cluster variability). It also computed as; ICC=σμ2σμ2+π23; Where: the ordinary logit distribution has variance of π23, σμ2 indicates the cluster variance [28]. The calculated ICC was 36.37%, showed that about 36.37% of the variation in incomplete continuum of care was explained by the between cluster variation. This implies the need to take into account between-cluster variability by using advanced modelling techniques. Therefore, Generalized Estimating Equation (GEE) model was fitted to identify the associated factors of incomplete utilization of maternal continuum of care among reproductive aged women [29]. It is a marginal model that considers working correlation structure among clusters that estimates a robust standard error and also controlled for within-cluster correlations. Generalized Estimating Equation (GEE) model was fitted with a logit link function and binomial family with independent and exchangeable working correlation structures. Quasi Information Criteria (QIC) was used to select the best-fitted model. The model with exchangeable correlation structure was selected as the best fitted model since it had smaller QIC value. Variables with p-value <0.2 in the bi-variable GEE were considered for the multivariable GEE model. To assess the strength of association between outcome variable and independent factors both crude and adjusted odds ratio with a 95% Confidence Interval (CI) were computed. Variables having less than 5% p-value in the multivariable GEE model were considered as the associated factors with the incomplete utilization of maternal continuum care.

Materials

https://efashare.b-cdn.net/materials/363926ee253519d528b7a56821e876e88a4741e2f268f1e59aa86c948410aec6/pone.0266490.s001.docx

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 with information about prenatal care, reminders for appointments, and educational resources.

2. Telemedicine: Implement telemedicine programs that allow pregnant women in remote or underserved areas to consult with healthcare providers through video calls or phone consultations, reducing the need for travel.

3. Community Health Workers: Train and deploy community health workers who can provide basic prenatal care, education, and support to pregnant women in their communities, particularly in areas with limited access to healthcare facilities.

4. Transportation Solutions: Establish transportation services or partnerships to ensure that pregnant women can easily access healthcare facilities for prenatal visits, delivery, and postnatal care.

5. Financial Incentives: Introduce financial incentives, such as cash transfers or vouchers, to encourage pregnant women to seek and utilize maternal health services.

6. Public Awareness Campaigns: Launch targeted public awareness campaigns to educate communities about the importance of maternal health and the available services, addressing cultural beliefs and misconceptions.

7. Infrastructure Development: Invest in the construction and improvement of healthcare facilities, particularly in rural and underserved areas, to ensure that pregnant women have access to quality maternal health services.

8. Task Shifting: Train and empower non-physician healthcare providers, such as nurses and midwives, to provide comprehensive prenatal and postnatal care, reducing the burden on doctors and increasing access to care.

9. Quality Improvement Initiatives: Implement quality improvement programs in healthcare facilities to ensure that maternal health services are delivered in a safe and effective manner, improving the overall experience for pregnant women.

10. Health Information Systems: Establish or enhance health information systems to track and monitor maternal health indicators, enabling policymakers to make informed decisions and allocate resources effectively.

These innovations can help address the barriers to accessing maternal health services and contribute to reducing maternal morbidity and mortality in Ethiopia.

AI Innovations Description

Based on the study’s findings, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Targeted interventions in hotspot areas: Develop and implement targeted public health interventions in areas with significant spatial variations in incomplete utilization of maternal continuum of care. These interventions should focus on enhancing maternal service utilization and empowering women in these areas.

2. Women empowerment programs: Implement programs that aim to empower women in terms of education, employment, and decision-making power. This can be done through providing educational opportunities, vocational training, and promoting gender equality. Empowered women are more likely to seek and utilize maternal health services.

3. Addressing barriers to healthcare access: Identify and address the barriers that prevent women from accessing maternal health services. These barriers may include financial constraints, distance to healthcare facilities, lack of transportation, and cultural or social norms. Implement strategies to overcome these barriers, such as providing financial support, improving transportation infrastructure, and raising awareness about the importance of maternal health.

4. Strengthening healthcare infrastructure: Invest in improving the healthcare infrastructure, particularly in rural areas where access to maternal health services is limited. This can involve building and equipping health centers and hospitals, training healthcare providers, and ensuring the availability of essential medical supplies and equipment.

5. Enhancing health education and awareness: Implement comprehensive health education programs that target both women and their communities. These programs should focus on raising awareness about the importance of maternal health, promoting healthy behaviors during pregnancy, childbirth, and postpartum, and dispelling myths and misconceptions related to maternal health.

6. Collaboration and coordination: Foster collaboration and coordination among various stakeholders, including government agencies, non-governmental organizations, healthcare providers, and community leaders. This can help ensure a comprehensive and integrated approach to improving access to maternal health services.

By implementing these recommendations, it is possible to develop innovative solutions that can effectively improve access to maternal health and reduce maternal morbidity and mortality in Ethiopia.

AI Innovations Methodology

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

1. Strengthening Primary Health Care: Enhance the capacity and resources of primary health care facilities, such as health posts and health centers, to provide comprehensive maternal health services. This includes ensuring availability of skilled health workers, essential medicines, and equipment.

2. Community Engagement and Education: Implement community-based interventions to raise awareness about the importance of maternal health care and promote utilization of services. This can involve community health workers conducting outreach activities, organizing health education sessions, and addressing cultural and social barriers to accessing care.

3. Addressing Barriers to Access: Identify and address specific barriers that prevent women from accessing maternal health services, such as financial constraints, distance to health facilities, lack of transportation, and cultural beliefs. This can be done through targeted interventions, such as providing financial support for transportation or establishing mobile health clinics in remote areas.

4. Improving Health Information Systems: Enhance the collection, analysis, and utilization of data related to maternal health. This includes strengthening health information systems to track maternal health indicators, identify gaps in service utilization, and monitor the impact of interventions.

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

1. Define Outcome Measures: Identify specific indicators that reflect improved access to maternal health, such as increased utilization of antenatal care, institutional delivery, and postnatal care services.

2. Baseline Data Collection: Collect baseline data on the selected outcome measures, using methods such as surveys or health facility records. This will provide a starting point for comparison.

3. Intervention Implementation: Implement the recommended interventions in selected areas or communities. Ensure that the interventions are implemented consistently and with fidelity.

4. Data Collection Post-Intervention: Collect data on the outcome measures after the intervention has been implemented. This can be done using the same methods as the baseline data collection.

5. Data Analysis: Analyze the collected data to assess the impact of the interventions on the selected outcome measures. This can involve comparing the pre- and post-intervention data, as well as conducting statistical tests to determine the significance of any observed changes.

6. Interpretation and Reporting: Interpret the findings of the data analysis and report on the impact of the interventions on improving access to maternal health. This can include presenting the results in a clear and concise manner, highlighting any significant changes or trends.

By following this methodology, it will be possible to simulate the impact of the recommended interventions on improving access to maternal health and provide evidence for decision-making and further interventions.

Authors & Co-Authors

Statistics:

Citations: 1

Authors: 9

Identifiers:

DOI: 10.1371/journal.pone.0266490

Research Areas:

Community Interventions, Genetics and Genomics, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female