Geographical Inequalities and Social and Environmental Risk Factors for Under-Five Mortality in Ghana in 2000 and 2010: Bayesian Spatial Analysis of Census Data

PLoS Medicine, Volume 13, No. 6, Year 2016

Interprétation

Background: Under-five mortality is declining in Ghana and many other countries. Very few studies have measured under-five mortality—and its social and environmental risk factors—at fine spatial resolutions, which is relevant for policy purposes. Our aim was to estimate under-five mortality and its social and environmental risk factors at the district level in Ghana. Methods and Findings: We used 10% random samples of Ghana’s 2000 and 2010 National Population and Housing Censuses. We applied indirect demographic methods and a Bayesian spatial model to the information on total number of children ever born and children surviving to estimate under-five mortality (probability of dying by 5 y of age, 5q0) for each of Ghana’s 110 districts. We also used the census data to estimate the distributions of households or persons in each district in terms of fuel used for cooking, sanitation facility, drinking water source, and parental education. Median district 5q0 declined from 99 deaths per 1,000 live births in 2000 to 70 in 2010. The decline ranged from <5% in some northern districts, where 5q0 had been higher in 2000, to >40% in southern districts, where it had been lower in 2000, exacerbating existing inequalities. Primary education increased in men and women, and more households had access to improved water and sanitation and cleaner cooking fuels. Higher use of liquefied petroleum gas for cooking was associated with lower 5q0 in multivariate analysis. Conclusions: Under-five mortality has declined in all of Ghana’s districts, but the cross-district inequality in mortality has increased. There is a need for additional data, including on healthcare, and additional environmental and socioeconomic measurements, to understand the reasons for the variations in mortality levels and trends. We used 10% random samples of Ghana’s 2000 and 2010 National Population and Housing Censuses. Using a set of predefined questions, both censuses gathered information on a number of individual- and household-level variables related to socioeconomic factors (e.g., literacy and educational attainment for persons 11 y or older), living environment (e.g., household’s main water supply source, sanitation facility type, and cooking fuel type, as a commonly used metric for household air pollution [4,22]), and children’s births and deaths for females 12 y or older. Each record in the census data had information on the census enumeration area (EA), the smallest geographical unit, with an average population of 750. The 2000 and 2010 censuses had nearly 26,000 and 38,000 EAs, respectively. Our analysis was conducted at the district level, the country’s second smallest level of subnational administrative divisions, with EAs mapped to the district of residence. We used districts as units of analysis because they are administrative units for resource allocation and for program implementation. Further, EAs were defined separately in each census and could not be mapped from one census to the other. There were 110 and 170 districts in the 2000 and 2010 censuses, respectively. We merged the 2010 districts, linking them to their original districts that had split since 2000, to create 110 common districts for our analyses. The 110 districts are administratively assembled into ten regions: Ashanti, Brong-Ahafo, Greater Accra, Central, Eastern, Northern, Western, Upper East, Upper West, and Volta. We used the data to calculate the distribution of households or persons in each district for the following variables that are associated with child survival: household’s main source of cooking fuel (wood, charcoal, other biomass, kerosene, liquefied petroleum gas [LPG], electricity), type of sanitation (toilet) facility usually used by households (improved, unimproved), household’s main source of drinking water (improved, unimproved), maternal education (highest educational grade completed: none, primary, secondary or higher), paternal education (highest educational grade completed: none, primary, secondary or higher), and urban versus rural place of residence. We classified census responses on drinking water source and sanitation facility as improved versus unimproved based on WHO/UNICEF joint monitoring program categories for water supply and sanitation (http://www.wssinfo.org). Our measure of under-five mortality was the probability of dying by 5 y of age (5q0). The censuses asked all women of childbearing age to report on the total number of children ever born and children surviving. This information was the basis for the application of indirect demographic models to estimate under-five mortality, an approach commonly used by researchers and by national and international statistical and health agencies. The method converts the proportion of deaths among children ever born to women in each 5-y age group of the reproductive period into estimates of the probability of dying by exact ages of childhood, and calculates the number of years before the survey date to which the estimates refer [23,24]. Estimates derived from the two youngest age groups of women (15–19 and 20–24 y) tend to be overestimated compared to the population average because children of women in these age groups tend to have a higher risk of dying than children of older women [23]. Therefore, and following other analyses (including those by the UN Inter-agency Group for Child Mortality Estimation [IGME]) [23], we excluded 5q0 estimates based on reports of women aged 15–24 y. The remaining five estimates (one for each 5-y age group between 25 and 49 y), each with a reference date in years prior to the survey date, were used in our analysis. The reference dates for 5q0 estimates for the 2000 census covered the period 1987–1996; for the 2010 census, the period was 1997–2007. To obtain a single 5q0 estimate for each district for the index years 2000 and 2010, we fitted a Bayesian space-time model to the five estimates per district from each census. The model included a linear time trend for estimates from each census in each district. The district intercepts and slopes were modeled using the Besag, York, and Mollié (BYM) model [25]. In the BYM model, information is shared both locally (amongst neighboring districts), through spatially structured random effects with a conditional autoregressive (CAR) prior, and globally, through spatially unstructured Gaussian random effects. District-specific intercept and slope values are estimated by the sum of their respective spatially structured and spatially unstructured random effects. The prior distributions in the Bayesian framework allow district-specific parameters to be estimated on the basis of a district’s own data and those of its neighbors. This approach balances between overly unstable within-district estimates and overly simplified aggregate national estimates. Samples from the posterior distributions of the intercepts and slopes were used to estimate 5q0 for the years 2000 and 2010. The national 5q0 estimates based on the Ghana censuses alone were different from the UN IGME estimates, which use a larger number of data sources [1], especially for 2000 (the additional sources used in the UN IGME estimates are not available at the district level). We adjusted our estimates to match the UN IGME estimates in 2000 and 2010, because the additional data sources likely help provide more reliable estimates. As detailed above, the linear trends fitted to the census-based national estimates were used to obtain 5q0 in 2000 and 2010. We then applied a correction factor, calculated as the ratio of the national estimate of 5q0 from the UN IGME to that from the census alone, to the 5q0 estimates for each district. The means of the corrected district 5q0 estimates for the 2000 and 2010 censuses were 104.7 and 77.5, respectively, compared to 102.1 and 75.1 deaths per 1,000 live births as estimated by the UN IGME. We estimated the associations of under-five mortality with its social and environmental determinants at the district level. We analyzed the associations in 2000 and 2010, as well as the associations of the change in under-five mortality between 2000 and 2010 with changes in these factors. The model for associations in each census year was where 5q0 is the district-level under-five mortality (per 1,000 live births); X is a vector of district-level risk factors (each as percent of households or persons), including cooking fuel type (wood, charcoal, other biomass, kerosene, LPG, electricity), sanitation facility (improved, unimproved), drinking water source (improved, unimproved), maternal education (none, primary, secondary or higher), paternal education (none, primary, secondary or higher), and place of residence (urban, rural); U is a district-specific spatially structured random effect; V is a district-specific unstructured random effect; and α and β are regression coefficients. When analyzing change in under-five mortality, Ln(5q0) and X were replaced with their 2000–2010 differences. Noninformative normal priors with mean 0 and variance 10,000 were placed on all fixed effects parameters; gamma priors, Gamma(0.5,0.0005), were specified for the precision parameters of all random effects. For each analysis, two chains were run, and convergence was monitored using Brooks-Gelman-Rubin diagnostics [26] and visual inspection of the chains. Following convergence, which was before 10,000 iterations in all analyses, a further 200,000 iterations were run, with thinning to every tenth iteration, yielding final samples of 20,000 iterations for inference. All analyses were implemented using R2WinBUGS and sqldf libraries in the open-source statistical package R version 3.1.0 (R Project for Statistical Computing) and WinBUGS version 1.4 [27].

Résumé de l’IA

Study Justification:

– The study aimed to estimate under-five mortality and its social and environmental risk factors at the district level in Ghana.
– This fine spatial resolution analysis is relevant for policy purposes and can help identify areas with high mortality rates and specific risk factors.

Highlights:

– Under-five mortality in Ghana declined from 99 deaths per 1,000 live births in 2000 to 70 in 2010.
– The decline varied across districts, with some experiencing less than 5% decline and others more than 40%.
– Inequalities in mortality between districts increased over time.
– Primary education increased for both men and women, and more households had access to improved water and sanitation facilities and cleaner cooking fuels.
– The use of liquefied petroleum gas for cooking was associated with lower under-five mortality.

Recommendations:

– Additional data, including healthcare information, environmental factors, and socioeconomic measurements, are needed to understand the reasons for variations in mortality levels and trends.
– Policies should focus on reducing geographical inequalities in under-five mortality and addressing the specific risk factors identified in the study.
– Investments should be made in improving access to primary education, clean water, sanitation facilities, and cleaner cooking fuels.

Key Role Players:

– Researchers and statisticians to analyze and interpret the data.
– Policy makers and government officials to implement policies based on the study findings.
– Healthcare professionals to provide insights on healthcare-related factors.
– Education officials to address issues related to primary education.
– Environmental experts to provide guidance on improving access to clean water, sanitation, and cleaner cooking fuels.

Cost Items for Planning Recommendations:

– Data collection and analysis costs.
– Investments in primary education infrastructure and resources.
– Improvements in water and sanitation facilities.
– Promotion and distribution of cleaner cooking fuels.
– Training and capacity building for healthcare professionals.
– Monitoring and evaluation costs to track the progress of implemented policies.

Force de la preuve

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a Bayesian spatial analysis of census data, which provides a robust methodology for estimating under-five mortality and its social and environmental risk factors at the district level in Ghana. The study used a large sample size from Ghana’s 2000 and 2010 National Population and Housing Censuses, which enhances the generalizability of the findings. The study also adjusted its estimates to match the UN IGME estimates, which helps ensure the reliability of the results. However, to further improve the evidence, the study could consider including additional data sources, such as healthcare data, to better understand the reasons for the variations in mortality levels and trends.

Abstrait

We used 10% random samples of Ghana’s 2000 and 2010 National Population and Housing Censuses. Using a set of predefined questions, both censuses gathered information on a number of individual- and household-level variables related to socioeconomic factors (e.g., literacy and educational attainment for persons 11 y or older), living environment (e.g., household’s main water supply source, sanitation facility type, and cooking fuel type, as a commonly used metric for household air pollution [4,22]), and children’s births and deaths for females 12 y or older. Each record in the census data had information on the census enumeration area (EA), the smallest geographical unit, with an average population of 750. The 2000 and 2010 censuses had nearly 26,000 and 38,000 EAs, respectively. Our analysis was conducted at the district level, the country’s second smallest level of subnational administrative divisions, with EAs mapped to the district of residence. We used districts as units of analysis because they are administrative units for resource allocation and for program implementation. Further, EAs were defined separately in each census and could not be mapped from one census to the other. There were 110 and 170 districts in the 2000 and 2010 censuses, respectively. We merged the 2010 districts, linking them to their original districts that had split since 2000, to create 110 common districts for our analyses. The 110 districts are administratively assembled into ten regions: Ashanti, Brong-Ahafo, Greater Accra, Central, Eastern, Northern, Western, Upper East, Upper West, and Volta. We used the data to calculate the distribution of households or persons in each district for the following variables that are associated with child survival: household’s main source of cooking fuel (wood, charcoal, other biomass, kerosene, liquefied petroleum gas [LPG], electricity), type of sanitation (toilet) facility usually used by households (improved, unimproved), household’s main source of drinking water (improved, unimproved), maternal education (highest educational grade completed: none, primary, secondary or higher), paternal education (highest educational grade completed: none, primary, secondary or higher), and urban versus rural place of residence. We classified census responses on drinking water source and sanitation facility as improved versus unimproved based on WHO/UNICEF joint monitoring program categories for water supply and sanitation (http://www.wssinfo.org). Our measure of under-five mortality was the probability of dying by 5 y of age (5q0). The censuses asked all women of childbearing age to report on the total number of children ever born and children surviving. This information was the basis for the application of indirect demographic models to estimate under-five mortality, an approach commonly used by researchers and by national and international statistical and health agencies. The method converts the proportion of deaths among children ever born to women in each 5-y age group of the reproductive period into estimates of the probability of dying by exact ages of childhood, and calculates the number of years before the survey date to which the estimates refer [23,24]. Estimates derived from the two youngest age groups of women (15–19 and 20–24 y) tend to be overestimated compared to the population average because children of women in these age groups tend to have a higher risk of dying than children of older women [23]. Therefore, and following other analyses (including those by the UN Inter-agency Group for Child Mortality Estimation [IGME]) [23], we excluded 5q0 estimates based on reports of women aged 15–24 y. The remaining five estimates (one for each 5-y age group between 25 and 49 y), each with a reference date in years prior to the survey date, were used in our analysis. The reference dates for 5q0 estimates for the 2000 census covered the period 1987–1996; for the 2010 census, the period was 1997–2007. To obtain a single 5q0 estimate for each district for the index years 2000 and 2010, we fitted a Bayesian space-time model to the five estimates per district from each census. The model included a linear time trend for estimates from each census in each district. The district intercepts and slopes were modeled using the Besag, York, and Mollié (BYM) model [25]. In the BYM model, information is shared both locally (amongst neighboring districts), through spatially structured random effects with a conditional autoregressive (CAR) prior, and globally, through spatially unstructured Gaussian random effects. District-specific intercept and slope values are estimated by the sum of their respective spatially structured and spatially unstructured random effects. The prior distributions in the Bayesian framework allow district-specific parameters to be estimated on the basis of a district’s own data and those of its neighbors. This approach balances between overly unstable within-district estimates and overly simplified aggregate national estimates. Samples from the posterior distributions of the intercepts and slopes were used to estimate 5q0 for the years 2000 and 2010. The national 5q0 estimates based on the Ghana censuses alone were different from the UN IGME estimates, which use a larger number of data sources [1], especially for 2000 (the additional sources used in the UN IGME estimates are not available at the district level). We adjusted our estimates to match the UN IGME estimates in 2000 and 2010, because the additional data sources likely help provide more reliable estimates. As detailed above, the linear trends fitted to the census-based national estimates were used to obtain 5q0 in 2000 and 2010. We then applied a correction factor, calculated as the ratio of the national estimate of 5q0 from the UN IGME to that from the census alone, to the 5q0 estimates for each district. The means of the corrected district 5q0 estimates for the 2000 and 2010 censuses were 104.7 and 77.5, respectively, compared to 102.1 and 75.1 deaths per 1,000 live births as estimated by the UN IGME. We estimated the associations of under-five mortality with its social and environmental determinants at the district level. We analyzed the associations in 2000 and 2010, as well as the associations of the change in under-five mortality between 2000 and 2010 with changes in these factors. The model for associations in each census year was where 5q0 is the district-level under-five mortality (per 1,000 live births); X is a vector of district-level risk factors (each as percent of households or persons), including cooking fuel type (wood, charcoal, other biomass, kerosene, LPG, electricity), sanitation facility (improved, unimproved), drinking water source (improved, unimproved), maternal education (none, primary, secondary or higher), paternal education (none, primary, secondary or higher), and place of residence (urban, rural); U is a district-specific spatially structured random effect; V is a district-specific unstructured random effect; and α and β are regression coefficients. When analyzing change in under-five mortality, Ln(5q0) and X were replaced with their 2000–2010 differences. Noninformative normal priors with mean 0 and variance 10,000 were placed on all fixed effects parameters; gamma priors, Gamma(0.5,0.0005), were specified for the precision parameters of all random effects. For each analysis, two chains were run, and convergence was monitored using Brooks-Gelman-Rubin diagnostics [26] and visual inspection of the chains. Following convergence, which was before 10,000 iterations in all analyses, a further 200,000 iterations were run, with thinning to every tenth iteration, yielding final samples of 20,000 iterations for inference. All analyses were implemented using R2WinBUGS and sqldf libraries in the open-source statistical package R version 3.1.0 (R Project for Statistical Computing) and WinBUGS version 1.4 [27].

Matériaux

Innovations

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

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

2. Telemedicine: Establish telemedicine programs that allow pregnant women in remote or underserved areas to consult with healthcare professionals through video conferencing or phone calls. This can help overcome geographical barriers and provide access to specialized care.

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

4. Maternal Health Vouchers: Implement voucher programs that provide pregnant women with subsidized or free access to essential maternal health services, including antenatal care, skilled birth attendance, and emergency obstetric care. This can help reduce financial barriers to accessing quality care.

5. Public-Private Partnerships: Foster collaborations between public and private healthcare providers to improve access to maternal health services. This can involve leveraging private sector resources and expertise to expand service delivery, improve infrastructure, and enhance the quality of care.

6. Data-driven Decision Making: Use data from population censuses, surveys, and health information systems to identify areas with high maternal mortality rates and disparities in access to care. This information can guide targeted interventions and resource allocation to areas with the greatest need.

7. Maternal Health Financing: Explore innovative financing mechanisms, such as social health insurance or community-based health financing, to ensure sustainable funding for maternal health services. This can help reduce out-of-pocket expenses for pregnant women and increase financial protection.

8. Maternal Health Quality Improvement Initiatives: Implement quality improvement programs that focus on enhancing the availability, accessibility, acceptability, and quality of maternal health services. This can involve training healthcare providers, improving infrastructure, and strengthening referral systems.

9. Maternal Health Task Forces: Establish multi-sectoral task forces or committees at the national and local levels to coordinate efforts and monitor progress in improving maternal health. These task forces can bring together stakeholders from the health sector, government agencies, civil society organizations, and communities to drive change and accountability.

10. Maternal Health Education and Awareness Campaigns: Conduct targeted education and awareness campaigns to empower women and their families with knowledge about maternal health, including the importance of antenatal care, skilled birth attendance, and postnatal care. These campaigns can also address cultural and social barriers that prevent women from seeking care.

It is important to note that these recommendations are general and may need to be adapted to the specific context and needs of Ghana.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health in Ghana would be to focus on addressing the geographical inequalities and social and environmental risk factors identified in the study. Here are some potential innovations that could be developed based on this recommendation:

1. Targeted healthcare interventions: Develop targeted healthcare interventions that specifically address the social and environmental risk factors identified in the study. For example, implement programs that provide education and resources to improve cooking fuel options, sanitation facilities, and access to clean drinking water in districts with higher under-five mortality rates.

2. Mobile health (mHealth) solutions: Utilize mobile technology to improve access to maternal health services in remote or underserved areas. Develop mobile applications or SMS-based platforms that provide information, reminders, and support for pregnant women and new mothers. This could include prenatal care reminders, nutrition advice, and access to teleconsultations with healthcare providers.

3. Community health worker programs: Expand and strengthen community health worker programs to reach vulnerable populations in districts with higher under-five mortality rates. Train and equip community health workers to provide essential maternal health services, including prenatal care, postnatal care, and family planning education. This would help bridge the gap between healthcare facilities and remote communities.

4. Infrastructure development: Invest in improving healthcare infrastructure in districts with higher under-five mortality rates. This could involve building or upgrading healthcare facilities, ensuring the availability of essential medical equipment and supplies, and improving transportation networks to facilitate access to healthcare services.

5. Data-driven decision making: Enhance data collection and analysis systems to better understand the reasons for variations in mortality levels and trends. This could involve integrating health information systems, conducting regular surveys, and using advanced analytics to identify areas of need and monitor the impact of interventions.

It is important to note that these recommendations are based on the specific findings of the study mentioned and may need to be tailored to the local context and resources available in Ghana.

AI Innovations Methodology

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

1. Strengthen healthcare infrastructure: Invest in building and upgrading healthcare facilities, particularly in rural areas where access to maternal health services is limited. This includes ensuring the availability of skilled healthcare professionals, essential medical equipment, and necessary medications.

2. Increase awareness and education: Implement comprehensive maternal health education programs to raise awareness about the importance of prenatal care, safe delivery practices, and postnatal care. This can be done through community outreach programs, workshops, and the use of multimedia platforms.

3. Improve transportation systems: Enhance transportation networks to facilitate the timely and safe transportation of pregnant women to healthcare facilities. This can involve providing ambulances or other means of transportation in remote areas, as well as improving road infrastructure.

4. Strengthen referral systems: Establish effective referral systems between primary healthcare centers and higher-level facilities to ensure that pregnant women with complications can access specialized care when needed. This includes training healthcare providers on recognizing and managing obstetric emergencies.

5. Enhance maternal health financing: Develop and implement policies to improve financial access to maternal health services, such as providing subsidies or health insurance coverage for pregnant women. This can help reduce the financial barriers that prevent women from seeking appropriate care.

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

1. Collect baseline data: Gather data on the current state of maternal health access, including indicators such as the number of healthcare facilities, healthcare provider-to-patient ratios, transportation availability, and maternal health outcomes.

2. Define simulation parameters: Determine the specific variables and parameters that will be used to simulate the impact of the recommendations. This may include factors such as the number of new healthcare facilities to be built, the percentage increase in healthcare providers, the improvement in transportation infrastructure, and the expected increase in awareness and education.

3. Develop a simulation model: Create a mathematical or computational model that incorporates the collected data and simulation parameters. This model should simulate the potential changes in access to maternal health services based on the recommended interventions.

4. Run the simulation: Input the baseline data and simulation parameters into the model and run the simulation to generate projected outcomes. This may involve running multiple iterations of the simulation to account for uncertainties and variations in the input parameters.

5. Analyze the results: Analyze the simulated outcomes to assess the potential impact of the recommendations on improving access to maternal health. This may include evaluating changes in indicators such as the number of women accessing prenatal care, the percentage of deliveries attended by skilled birth attendants, and maternal mortality rates.

6. Refine and validate the model: Continuously refine and validate the simulation model based on feedback and additional data. This may involve incorporating new data sources, adjusting simulation parameters, and comparing simulated outcomes with real-world data.

By following this methodology, policymakers and stakeholders can gain insights into the potential impact of different interventions on improving access to maternal health and make informed decisions on resource allocation and program implementation.

Auteur