Multilevel geospatial analysis of factors associated with unskilled birth attendance in Ghana

PLoS ONE, Volume 16, No. 6 June, Year 2021

Ukuhunyushwa

Background Globally, about 810 women die every day due to pregnancy and its related complications. Although the death of women during pregnancy or childbirth has declined from 342 deaths to 211 deaths per 100, 000 live births between 2000 and 2017, maternal mortality is still higher, particularly in sub-Saharan Africa and South Asia, where 86% of all deaths occur. Methods A secondary analysis was carried out using the 2014 Ghana Demographic and Health Survey. A sample total of 4, 290 women who had a live birth in the 5 years preceding the survey was included in the analysis. GIS software was used to explore the spatial distribution of unskilled birth attendance in Ghana. The Geographic Weighted Regression (GWR) was employed to model the spatial relationship of some predictor of unskilled birth attendance. Moreover, a multilevel binary logistic regression model was fitted to identify factors associated with unskilled birth attendance. Results In this study, unskilled birth attendance had spatial variations across the country. The hotspot, cluster and outlier analysis identified the concerned districts in the north-eastern part of Ghana. The GWR analysis identified different predictors of unskilled birth attendance across districts of Ghana. In the multilevel analysis, mothers with no education, no health insurance coverage, and mothers from households with lower wealth status had higher odds of unskilled birth attendance. Being multi and grand multiparous, perception of distance from the health facility as not a big problem, urban residence, women residing in communities with medium and higher poverty level had lower odds of unskilled birth attendance. Conclusion Unskilled birth attendance had spatial variations across the country. Areas with high levels of unskilled birth attendance had mothers who had no formal education, not health insured, mothers from poor households and communities, primiparous women, mothers from remote and border districts could get special attention in terms of allocation of resources including skilled human power, and improved access to health facilities. The Ghana Demographic and Health Survey (GDHS) used a standard Demographic and Health Survey (DHS) model questionnaire developed by the Measure DHS programme. This study used the most recent DHS data and a cross-sectional study design. The DHS are national surveys carried out every five years in low-and middle-income countries globally. The surveys concentrate on maternal and child health, physical activity, sexually transmitted infections, fertility, health insurance, tobacco use, and alcohol consumption. They provide data to monitor the demographic and health profiles of the respective countries. For the study, women with birth history who had given birth up to five years before the survey were included. Only the last birth of the women aged 15–49 years preceding the survey was included in the study. A sample of 4,290 women with complete data required for our analysis participated in this study. Permission to use the data set was given by the MEASURE DHS following the assessment of our concept note. This study is a secondary analysis of the de-identified 2014 Ghana Demographic and Health Survey (GDHS), a publicly available dataset. Therefore, ethics approval or consent to participate is not applicable. The datasets are freely available to the public at www.measuredhs.com. Outcome variable. The primary outcome variable was unskilled birth attendance. The outcome variable was derived from the response to the question “who assisted with the delivery?” Responses were categorized under Health Personnel and Other Person. Health personnel included doctor, nurse, nurse/midwife, auxiliary midwife, and other people consisting of a traditional birth attendant, traditional health volunteer, community/village health volunteer, neighbours/ friends/relatives, etc. For this study, unskilled birth attendance referred to births assisted by a traditional birth attendant, traditional health volunteer, community/village health volunteer, neighbours/ friends/relatives, other [11]. Explanatory variables. Eleven explanatory variables were used. These were grouped into individual and community level variables. The individual characteristics consist of level of education, age, parity, health insurance coverage, wealth status, distance to a health facility, and media exposure. The community-level characteristics comprised the type of residence, community socioeconomic status, community literacy, and region of residence. We employed both descriptive and inferential analytical approaches. First, we computed the proportion of women who utilized the service of unskilled birth attendants during delivery. This ensued with bivariate analysis between individual characteristics (level of education, age, parity, health insurance coverage, wealth status, distance to a health facility, and media exposure), community characteristics (Type of residence, Community socioeconomic status, Community literacy, Region of residence) and utilization of unskilled birth attendants (see Table 1). Following the hierarchical nature of the data set, the Multilevel Logistic Regression Model (MLRM) was employed. This comprises fixed effects and random effects [23]. The fixed effects of the model were gauged with binary logistic regression, which resulted in odds ratios (ORs) and adjusted odds ratios (aORs) (see Table 2). Model 1 was an empty table, where model 2 looked at the relationship between the individual variable and the outcome variable. Model 3 looked at the relationship between community variables and the outcome variable. Model 4 was the complete model that looked at the relationship with both the individual and community variables and the outcome variable. The random effects, on the other hand, were assessed with Intra-Cluster Correlation (ICC) [23] (Table 3). The sample weight (v005/1,000,000) was applied in all the analyses to control for over and under-sampling. All the analyses were carried out using STATA version 14. Source: GDHS, 2014 Source: GDHS, 2014 *p<0.05 **p<0.01 *** p<0.001 Source: GDHS, 2014 We assessed the fitness of all the models with the Likelihood Ratio (LR) test. The presence of multicollinearity between the independent variables was checked before fitting the models. The variance inflation factor (VIF) test revealed the absence of high multicollinearity between the variables (Mean VIF = 2.28). In the conduct of the survey, instead of mapping outhouses in which the data were collected, clusters were mapped to protect the actual identity and location of respondents [24]. These clusters are developed to suit the district-level data, making it easy to merge the household records with spatial data. During the data collection period, there were 216 administrative districts in Ghana; however, not all districts had respondents drawn from for the survey. This aided in the merger of the data gathered with the district shapefiles obtained from the Department of Geography and Regional Planning, University of Cape Coast, Ghana. This was done to permit the analysis to be made at a district level. The data is best analysed at the district level since the information is more representative at the cluster level [24]. This study extracted the required variables from the 2014 GDHS. The extracted data maintained the mapped clusters information. This mapped cluster information was used to help join the extracted non-spatial data to the coordinates gathered for the clusters. All the data required (GDHS data and 216 district boundary) were projected into the projected coordinate system of Ghana Meter Grid to aid in the spatial analysis. The extracted GDHS data were merged with coordinate, and a spatial join was undertaken to transfer the cluster point to the 216-district boundary (polygon) layer using ArcMap version 10.5. This activity enabled us to easily identify and trace where each case is located within a district. It was identified that some of the district boundaries had more than one cluster. In such cases, the data from the clusters were aggregated, and their means were computed to represent the respective district they fell within [24]. With regards to the geospatial analyses, four spatial statistical tools were applied to analyse the data. These tools were spatial autocorrelation (Global Moran’s I), hot spot analysis (Getis-Ord G), outlier and cluster analysis, and Geographically Weighted Regression. The spatial autocorrelation was used to assess whether unskilled birth attendance in Ghana had a clustering or dispersion pattern at the district level. This study hypothesized that unskilled birth attendance is randomly distributed across various districts in the country. The null hypothesis is rejected if a calculated p-value is small (95% confidence interval), which implies an unlikely situation that the observed spatial pattern results from random processes [24]. Further, hot spot analysis (Getis-Ord G) was used to ascertain statistically significant spatial variations in unskilled birth attendance [24, 25]. This analysis was conducted to determine districts with high prevalence against areas of the low prevalence of unskilled birth attendance. In addition, an outlier and cluster analysis was conducted to identify districts that appeared as outliers. Outlier districts could either be a hot spot district that is surrounded by cold spot districts and vice-versa. The geographically weighted regression (GWR) modelling was conducted after ascertaining the hot spot and cluster and outlier analysis of unskilled birth attendance, the geographically weighted regression (GWR) modelling was conducted. This spatial regression modelling was performed to identify which explanatory variables best account for the observed spatial patterns of unskilled birth attendance [25]. To be specific, the GWR uses the OLS coefficient from the clusters concerning its nearest neighbours in modelling the predictability of the explanatory variable. The output shows how the strength of each explanatory variable changed across space. Therefore, maps of the statistically significant coefficients were generated.

I-AI Digest

Study Justification:

– Maternal mortality rates in Ghana and other low- and middle-income countries are still high, particularly in sub-Saharan Africa and South Asia.
– Understanding the factors associated with unskilled birth attendance can help inform interventions and policies to improve maternal health outcomes.
– Geospatial analysis can provide valuable insights into the spatial distribution of unskilled birth attendance and identify areas that require targeted interventions.

Study Highlights:

– The study used data from the 2014 Ghana Demographic and Health Survey to analyze the spatial distribution of unskilled birth attendance in Ghana.
– The analysis identified spatial variations in unskilled birth attendance across the country, with hotspot districts in the north-eastern part of Ghana.
– Different predictors of unskilled birth attendance were identified across districts using Geographic Weighted Regression (GWR) analysis.
– Factors associated with higher odds of unskilled birth attendance included mothers with no education, no health insurance coverage, and mothers from households with lower wealth status.
– Factors associated with lower odds of unskilled birth attendance included being multi and grand multiparous, perceiving distance from health facilities as not a big problem, urban residence, and residing in communities with medium and higher poverty levels.

Recommendations:

– Areas with high levels of unskilled birth attendance, particularly in the hotspot districts, should receive special attention in terms of resource allocation and improved access to health facilities.
– Targeted interventions should focus on mothers with no formal education, those without health insurance coverage, and those from poor households and communities.
– Skilled human power should be allocated to these areas to ensure safe and skilled birth attendance.

Key Role Players:

– Ministry of Health: Responsible for implementing policies and allocating resources to address unskilled birth attendance.
– District Health Authorities: Responsible for coordinating and implementing interventions at the district level.
– Health Facilities: Provide skilled birth attendance services and ensure access to quality maternal healthcare.
– Community Health Workers: Play a crucial role in educating and supporting pregnant women in accessing skilled birth attendance.

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers to ensure skilled birth attendance.
– Infrastructure development and improvement of health facilities in areas with high unskilled birth attendance.
– Outreach programs and community engagement to raise awareness and promote the importance of skilled birth attendance.
– Health insurance coverage for vulnerable populations to ensure access to maternal healthcare services.
– Monitoring and evaluation systems to assess the impact of interventions and make necessary adjustments.
Please note that the above information is a summary of the study and may not include all details. For a comprehensive understanding, it is recommended to refer to the original publication in PLoS ONE, Volume 16, No. 6, June, Year 2021.

Amandla Obufakazi

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it is based on a secondary analysis of a nationally representative survey and utilizes geospatial analysis techniques. However, to improve the evidence, the abstract could provide more details on the methodology, such as the specific statistical tests used and the significance levels. Additionally, it would be helpful to include information on the sample size and any limitations of the study.

Abstract

The Ghana Demographic and Health Survey (GDHS) used a standard Demographic and Health Survey (DHS) model questionnaire developed by the Measure DHS programme. This study used the most recent DHS data and a cross-sectional study design. The DHS are national surveys carried out every five years in low-and middle-income countries globally. The surveys concentrate on maternal and child health, physical activity, sexually transmitted infections, fertility, health insurance, tobacco use, and alcohol consumption. They provide data to monitor the demographic and health profiles of the respective countries. For the study, women with birth history who had given birth up to five years before the survey were included. Only the last birth of the women aged 15–49 years preceding the survey was included in the study. A sample of 4,290 women with complete data required for our analysis participated in this study. Permission to use the data set was given by the MEASURE DHS following the assessment of our concept note. This study is a secondary analysis of the de-identified 2014 Ghana Demographic and Health Survey (GDHS), a publicly available dataset. Therefore, ethics approval or consent to participate is not applicable. The datasets are freely available to the public at www.measuredhs.com. Outcome variable. The primary outcome variable was unskilled birth attendance. The outcome variable was derived from the response to the question “who assisted with the delivery?” Responses were categorized under Health Personnel and Other Person. Health personnel included doctor, nurse, nurse/midwife, auxiliary midwife, and other people consisting of a traditional birth attendant, traditional health volunteer, community/village health volunteer, neighbours/ friends/relatives, etc. For this study, unskilled birth attendance referred to births assisted by a traditional birth attendant, traditional health volunteer, community/village health volunteer, neighbours/ friends/relatives, other [11]. Explanatory variables. Eleven explanatory variables were used. These were grouped into individual and community level variables. The individual characteristics consist of level of education, age, parity, health insurance coverage, wealth status, distance to a health facility, and media exposure. The community-level characteristics comprised the type of residence, community socioeconomic status, community literacy, and region of residence. We employed both descriptive and inferential analytical approaches. First, we computed the proportion of women who utilized the service of unskilled birth attendants during delivery. This ensued with bivariate analysis between individual characteristics (level of education, age, parity, health insurance coverage, wealth status, distance to a health facility, and media exposure), community characteristics (Type of residence, Community socioeconomic status, Community literacy, Region of residence) and utilization of unskilled birth attendants (see Table 1). Following the hierarchical nature of the data set, the Multilevel Logistic Regression Model (MLRM) was employed. This comprises fixed effects and random effects [23]. The fixed effects of the model were gauged with binary logistic regression, which resulted in odds ratios (ORs) and adjusted odds ratios (aORs) (see Table 2). Model 1 was an empty table, where model 2 looked at the relationship between the individual variable and the outcome variable. Model 3 looked at the relationship between community variables and the outcome variable. Model 4 was the complete model that looked at the relationship with both the individual and community variables and the outcome variable. The random effects, on the other hand, were assessed with Intra-Cluster Correlation (ICC) [23] (Table 3). The sample weight (v005/1,000,000) was applied in all the analyses to control for over and under-sampling. All the analyses were carried out using STATA version 14. Source: GDHS, 2014 Source: GDHS, 2014 *p<0.05 **p<0.01 *** p<0.001 Source: GDHS, 2014 We assessed the fitness of all the models with the Likelihood Ratio (LR) test. The presence of multicollinearity between the independent variables was checked before fitting the models. The variance inflation factor (VIF) test revealed the absence of high multicollinearity between the variables (Mean VIF = 2.28). In the conduct of the survey, instead of mapping outhouses in which the data were collected, clusters were mapped to protect the actual identity and location of respondents [24]. These clusters are developed to suit the district-level data, making it easy to merge the household records with spatial data. During the data collection period, there were 216 administrative districts in Ghana; however, not all districts had respondents drawn from for the survey. This aided in the merger of the data gathered with the district shapefiles obtained from the Department of Geography and Regional Planning, University of Cape Coast, Ghana. This was done to permit the analysis to be made at a district level. The data is best analysed at the district level since the information is more representative at the cluster level [24]. This study extracted the required variables from the 2014 GDHS. The extracted data maintained the mapped clusters information. This mapped cluster information was used to help join the extracted non-spatial data to the coordinates gathered for the clusters. All the data required (GDHS data and 216 district boundary) were projected into the projected coordinate system of Ghana Meter Grid to aid in the spatial analysis. The extracted GDHS data were merged with coordinate, and a spatial join was undertaken to transfer the cluster point to the 216-district boundary (polygon) layer using ArcMap version 10.5. This activity enabled us to easily identify and trace where each case is located within a district. It was identified that some of the district boundaries had more than one cluster. In such cases, the data from the clusters were aggregated, and their means were computed to represent the respective district they fell within [24]. With regards to the geospatial analyses, four spatial statistical tools were applied to analyse the data. These tools were spatial autocorrelation (Global Moran’s I), hot spot analysis (Getis-Ord G), outlier and cluster analysis, and Geographically Weighted Regression. The spatial autocorrelation was used to assess whether unskilled birth attendance in Ghana had a clustering or dispersion pattern at the district level. This study hypothesized that unskilled birth attendance is randomly distributed across various districts in the country. The null hypothesis is rejected if a calculated p-value is small (95% confidence interval), which implies an unlikely situation that the observed spatial pattern results from random processes [24]. Further, hot spot analysis (Getis-Ord G) was used to ascertain statistically significant spatial variations in unskilled birth attendance [24, 25]. This analysis was conducted to determine districts with high prevalence against areas of the low prevalence of unskilled birth attendance. In addition, an outlier and cluster analysis was conducted to identify districts that appeared as outliers. Outlier districts could either be a hot spot district that is surrounded by cold spot districts and vice-versa. The geographically weighted regression (GWR) modelling was conducted after ascertaining the hot spot and cluster and outlier analysis of unskilled birth attendance, the geographically weighted regression (GWR) modelling was conducted. This spatial regression modelling was performed to identify which explanatory variables best account for the observed spatial patterns of unskilled birth attendance [25]. To be specific, the GWR uses the OLS coefficient from the clusters concerning its nearest neighbours in modelling the predictability of the explanatory variable. The output shows how the strength of each explanatory variable changed across space. Therefore, maps of the statistically significant coefficients were generated.

Izinto zokwakha

Emisha

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women with information about prenatal care, nutrition, and safe delivery practices. These apps can also include features for tracking appointments, receiving reminders, and connecting with healthcare providers.

2. Telemedicine: Implement telemedicine services to enable pregnant women in remote areas to consult with healthcare professionals through video calls. This can help address the issue of distance to health facilities and provide timely advice and support.

3. Community Health Workers: Train and deploy community health workers who can provide basic prenatal care, education, and support to pregnant women in underserved areas. These workers can also help identify high-risk pregnancies and refer women to appropriate healthcare facilities.

4. Transportation Solutions: Develop innovative transportation solutions, such as mobile clinics or ambulances, to ensure that pregnant women can access healthcare facilities in a timely manner, especially in rural or hard-to-reach areas.

5. Financial Incentives: Introduce financial incentives, such as cash transfers or subsidies, to encourage pregnant women to seek skilled birth attendance and access antenatal and postnatal care. This can help address financial barriers that prevent women from accessing maternal health services.

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

7. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of skilled birth attendance and the availability of maternal health services. These campaigns can be conducted through various channels, including mass media, community outreach programs, and social media.

8. Strengthening Health Systems: Invest in strengthening healthcare infrastructure, training healthcare professionals, and ensuring the availability of essential medical supplies and equipment. This can help improve the quality and accessibility of maternal health services.

It is important to note that these recommendations are based on the information provided and may need to be tailored to the specific context and needs of Ghana’s healthcare system.

AI Innovations Description

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

Develop a targeted intervention program that focuses on improving access to skilled birth attendance in districts with high levels of unskilled birth attendance in Ghana. This program should prioritize the following groups:

1. Mothers with no formal education: Implement initiatives to increase awareness and education about the importance of skilled birth attendance among women with no formal education. This can include community-based education programs, outreach activities, and partnerships with local organizations.

2. Mothers without health insurance coverage: Work towards increasing health insurance coverage among pregnant women to ensure that they have access to affordable and quality maternal healthcare services. This can involve advocacy efforts, policy changes, and collaborations with health insurance providers.

3. Mothers from poor households and communities: Implement strategies to address the socio-economic barriers that prevent women from accessing skilled birth attendance. This can include providing financial support for transportation to health facilities, improving infrastructure in underserved areas, and offering incentives for healthcare providers to work in remote areas.

4. Primiparous women: Develop targeted interventions to support first-time mothers in accessing skilled birth attendance. This can involve providing antenatal care services, childbirth education, and emotional support to help alleviate fears and concerns.

5. Mothers from remote and border districts: Improve access to healthcare facilities in remote and border districts by establishing mobile clinics, expanding telemedicine services, and providing transportation options for pregnant women to reach healthcare facilities.

By implementing these targeted interventions, it is expected that access to skilled birth attendance will improve in areas with high levels of unskilled birth attendance, ultimately reducing maternal mortality rates in Ghana.

AI Innovations Methodology

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

1. Strengthening education and awareness programs: Implementing comprehensive education and awareness programs that target women and communities can help increase knowledge about the importance of skilled birth attendance and the risks associated with unskilled birth attendance.

2. Enhancing health insurance coverage: Expanding health insurance coverage to include maternal health services can reduce financial barriers and improve access to skilled birth attendance.

3. Improving infrastructure and transportation: Investing in the development of healthcare facilities, especially in remote and border districts, and improving transportation networks can help overcome geographical barriers and ensure that pregnant women have timely access to skilled birth attendance.

4. Training and deploying skilled healthcare professionals: Increasing the number of skilled healthcare professionals, such as doctors, nurses, and midwives, and ensuring their equitable distribution across districts can improve access to skilled birth attendance.

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

1. Data collection: Gather data on the current state of maternal health access, including information on unskilled birth attendance rates, education levels, health insurance coverage, infrastructure, transportation, and healthcare workforce distribution.

2. Spatial analysis: Use geographic information system (GIS) software to analyze the spatial distribution of unskilled birth attendance and identify hotspots and areas with low access to skilled birth attendance.

3. Statistical modeling: Utilize multilevel geospatial analysis techniques, such as geographic weighted regression (GWR), to model the spatial relationship between various factors (e.g., education, health insurance coverage, infrastructure) and unskilled birth attendance. This can help identify the predictors that have the most significant impact on access to skilled birth attendance.

4. Scenario development: Based on the identified predictors, develop different scenarios that represent the potential impact of the recommendations. For example, simulate the effect of increasing education levels or improving transportation infrastructure on reducing unskilled birth attendance rates.

5. Impact assessment: Evaluate the simulated scenarios to assess the potential impact on improving access to maternal health. This can be done by comparing the predicted outcomes (e.g., reduction in unskilled birth attendance rates) between the different scenarios.

6. Policy recommendations: Based on the results of the impact assessment, provide evidence-based policy recommendations to stakeholders, policymakers, and healthcare providers. These recommendations should prioritize the most effective interventions that can be implemented to improve access to skilled birth attendance.

It is important to note that this methodology is a general framework and may require customization based on the specific context and available data. Additionally, ongoing monitoring and evaluation should be conducted to assess the actual impact of implemented interventions and make necessary adjustments to further improve access to maternal health.

Ababhali & Co-Ababhali

Statistics:

Citations: 3

Authors: 4

Identifiers:

DOI: 10.1371/journal.pone.0253603

Research Areas:

Community Interventions, Disparities, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Substance Abuse, Workforce

Study Countries:

Ghana

Study Design:

Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female

Yabelana ngalokhu: