Household Microenvironment and Under-Fives Health Outcomes in Uganda: Focusing on Multidimensional Energy Poverty and Women Empowerment Indices

International Journal of Environmental Research and Public Health, Volume 19, No. 11, Year 2022

Interpretation

Young children in low-and middle-income countries (LMICs) are vulnerable to adverse effects of household microenvironments. The UN Sustainable Development Goals (SDGs)—specifically SDG 3 through 7—urge for a comprehensive multi-sector approach to achieve the 2030 goals. This study addresses gaps in understanding the health effects of household microenvironments in resourcepoor settings. It studies associations of household microenvironment variables with episodes of acute respiratory infection (ARI) and diarrhoea as well as with stunting among under-fives using logistic regression. Comprehensive data from a nationally representative, cross-sectional demographic and health survey (DHS) in Uganda were analysed. We constructed and applied the multidimensional energy poverty index (MEPI) and the three-dimensional women empowerment index in multivariate regressions. The multidimensional energy poverty was associated with higher risk of ARI (OR = 1.32, 95% CI 1.10 to 1.58). Social independence of women was associated with lower risk of ARI (OR= 0.91, 95% CI 0.84 to 0.98), diarrhoea (OR = 0.93, 95% CI 0.88 to 0.99), and stunting (OR = 0.83, 95% CI 0.75 to 0.92). Women’s attitude against domestic violence was also significantly associated with episodes of ARI (OR = 0.88, 95% CI 0.82 to 0.93) and diarrhoea (OR = 0.89, 95% CI 0.84 to 0.93) in children. Access to sanitation facilities was associated with lower risk of ARI (OR = 0.55, 95% CI 0.45 to 0.68), diarrhoea (OR = 0.83, 95% CI 0.71 to 0.96), and stunting (OR = 0.64, 95% CI 0.49 to 0.86). Investments targeting synergies in integrated energy and water, sanitation and hygiene, and women empowerment programmes are likely to contribute to the reduction of the burden from early childhood illnesses. Research and development actions in LMICs should address and include multi-sector synergies. In Figure 1, we present a simplified conceptual framework to show possible links between energy poverty, access to clean water, sanitation, hygiene, women empowerment, and health outcomes of children. Our conceptual approach incorporated long-standing accepted linkages between household microenvironments, women empowerment, and health outcomes of children. We also incorporated recently developed indices for environmental exposure [39] and women empowerment [41] to have a better understanding of the context-specific relationship between the exposures and health outcomes. The women empowerment index is useful to identify what types of empowerment are linked with health outcomes of children. Conceptual framework: household environment (including social dimension) and health outcomes of children below 5 years of age. The bolder lines show key associations that we aim to test while the lighter lines show control variables. The data analysed in this study are from the 2016 Uganda Demographic and Health Surveys (UDHS), collected by the Uganda Bureau of Statistics with technical support from ICF international [42]. The UDHS is a nationally representative survey that provides comprehensive data about households, health outcomes of children, and maternal characteristics. The survey was carried out from 20 June to 16 December 2016 on key demographic and health indicators, including nutritional status of children and women and gender-related variables. A stratified and multistage sampling method was used in the 2016 UDHS to collect key information on child and maternal health indicators, which is nationally representative. A detailed description of methods, design, collected data, study participants and other important information is documented in the 2016 Demographic and Health Survey of Uganda [42]. The survey has rich data including information about children’s morbidity, though most of them are symptomatic data. It also has rich data on household and parents’ socioeconomic and demographic characteristics. Therefore, the UDHS survey data are attractive for rigorous quantitative analysis to establish an association between morbidity among children under 5 years of age and household microenvironments and women empowerment. The key health outcome variables in this study came from the mother’s responses to questions on episodes of various child morbidity within two weeks before the survey date. As indicated in Figure 1, the child health outcomes in this study are ARI, diarrhoea, and nutritional status of children using stunting as a key indicator. In the 2016 DHS surveys, symptoms of ARI are defined as short, rapid breathing which was chest-related and/or difficult breathing which was chest-related [43]. Following this definition, the children were categorised into two groups: those who experienced ARI symptoms and those who did not within 2 weeks before the survey. A limitation of this indicator is that it is based on the mothers’ perception of the morbidity, not a definitive diagnosis. The DHS data also contains diarrhoea prevalence by asking mothers whether the child had diarrhoea in the two weeks preceding the survey. This health outcome is dichotomous, identifying children who suffered diarrhoea and those who did not. Stunting in children below 5 years of age is the other health outcome examined in this study. Following the WHO Multicentre Growth Reference guideline [44,45], a child is stunted if the height-for-age z-score (HAZ) is below -2SD of the median for their age, including both mildly and severely stunted children. Key predictors of interest in this study are a comprehensive set of variables related to household microenvironments and women empowerment, as presented in Figure 1. Most household-environment-related variables in the DHS are standardized in the recode files and often used as they are with moderate modifications; for example, see [46]. In this study, however, indices were constructed and globally set standards were used to categorize key variables of interest. The multidimensional energy poverty index [39] was constructed and used as an indicator for household air pollution. We also estimated an women empowerment index, relevant in African settings. To include water quality and sanitation and hygiene facilities, we used the revised standard ladder by WHO/UNICEF Joint Monitoring Program [8]. We discuss these measurements and standards in the following sections. An indicator for household air pollution is a key predictor in this study. Previous studies often considered households’ consumption of solid fuel as an indicator of household air pollution to explain childhood morbidity [18,21,47,48,49,50]. In this study, we constructed a multidimensional energy poverty index (MEPI) at the household level to explain childhood morbidity. The MEPI captures a set of energy deprivations that affect a person or household [39,51]. The MEPI provides a framework to identify the categories of households left behind on access to clean, safe, and sustainable household energy [52]. Methodologically, the MEPI is derived from the multidimensional poverty measures developed by Alkire and Foster [53]. Literature on methodological developments of multidimensional poverty have their root in Amartya Sen’s discussion of deprivations and capabilities [54] which argues for the need to focus on the absence of opportunities and choices for living a basic human life. The MEPI is composed of five dimensions representing basic energy services with six indicators. More specifically, it is composed of indicators of a household using modern cooking fuel and cooking places, having access to electricity for lighting, having a refrigerator, having a TV or radio for entertainment and education, and having a phone or mobile phone for communication (see Table 1). Multidimensional energy poverty dimensions and respective variables with cut-offs, including relative weights (in parenthesis). Source: taken from Nussbaumer, Bazilian, and Modi [39]. Following previous studies [39,51,52] and the relative importance of indicators to human health, we unequally assigned weights to the various dimensions and indicators. This reflects the relative importance of the various energy poverty variables considered in household pollution and human health. We refer readers to Nussbaumer, Bazilian, and Modi [39] for further understanding of dimensions, indicators, and weights used in MEPI construction. A household is identified as energy poor if the respective set of deprivation scores (Ci) exceeds a predefined threshold, k. Previous studies in LMICs used a multidimensional energy poverty cut-off score at k=0.3 [39,52]. Nussbaumer, Nerini, Onyeji, and Howells [51] further categorized a household multidimensional energy poor level as acute when the MEPI exceed 0.7, moderate between 0.3 and 0.7, and low below 0.3. We also followed these cut-off points. However, in our analysis, very few (0.16%) of the observations fell in the low multidimensional energy poor category. Consequently, we combined the ‘low’ and ‘moderate’ energy poor category and coded as ‘moderate’ multidimensional energy poor. Therefore, households were categorised into ‘moderate’ and ‘acute’ multidimensional energy poor. The MEPI captures information on both the incidence and the intensity or severity of energy poverty. We computed the poverty headcount as H=qn, where q is the number of energy poor households (where ci>k) and n the total number of households. The severity of poverty indicates the average proportion of indicators in which multidimensional energy poor households is obtained as A=∑i=1nci (k)/q. Finally, the MEPI is obtained as the product of the multidimensional energy poverty headcount ratio (H) and multidimensional energy poverty intensity (A): MEPI=H×A. In coding quality and source of household water and sanitation, previous studies used the dichotomous improved vs. unimproved or safe vs. unsafe definitions [17,48,55]. As our focus in this study is household microenvironments, we opted for definitions and categorization that are clearer and more distinct. We followed the revised water and sanitation ladder by WHO/UNICEF joint monitoring programme (JMP) [8] to define quality of water and sanitation facilities but with some modifications. We defined household access to a hygiene facility following the WHO/UNICEF joint monitoring programme ladder for hygiene: basic, limited, and no facility. The WHO/UNICEF defines and categorizes quality of water and sanitation facilities into five levels. This was not possible in the Uganda DHS dataset due to less clarity in wording used in the questionnaire to match with WHO/UNICEF JMP ladders for water and sanitation. We categorised sanitation facilities, slightly deviating from the WHO/UNICEF JMP ladder for sanitation, into three: no facility, unimproved, and improved. We categorized the quality of the source of drinking water into two: improved and unimproved. We adopted the women empowerment index construction method developed by [41] using DHS data from Africa. This composite index consists of three domains of empowerment: attitude to violence, social independence, and decision making. These empowerment dimensions overlaps with most of the dimensions considered by [56] focusing on sub-Saharan Africa, particularly East African countries. Similar empowerment dimensions were considered in other studies in LMICs [29]. The three empowerment dimensions comprise various information. ‘Attitude to violence’ is composed of information related to the respondent’s opinion about whether wife-beating is justified or not in various scenarios. ‘Social independence’ includes items related to education, frequency of information consumption (reading), age at first cohabitation and first childbirth, and differences between age and their years of schooling of the woman and her husband. The ‘decision making’ domain is comprised of information related to a woman’s involvement in household decisions and labour force participation. In this study, the three dimensions were weighed following [41]. In addition to the household microenvironments and women empowerment-related predictors, other relevant predictors such as individual and parental variables are included in the analysis. Potential predictors associated with the health of children below 5 years of age were included, considering their relevance in previous studies [16,21,23,25,29,30,46,47,57,58] as control variables. A summary of definitions of key predictors used in the analysis is presented in (Table 2). Description of key predictors used in the analysis. The statistical analyses used in this study include descriptive statistics, bivariate analyses, and logistic regression models. The DHS sampling weights are applied in all analyses to account for the complex survey design. We used descriptive analyses, percentages, and numbers to show the distribution of childhood morbidity and nutritional status by predictor variables. Associations between childhood health outcomes and predictors were first analysed using bivariate analyses, the χ2 tests, before fitting the regression models. These analyses were carried out to compare the prevalence of childhood morbidities and stunting among the levels of the selected predictors and to inform further analyses using regression models. The dependent variables considered in this study are dichotomous variables. Therefore, binary response econometric models are the natural choice. Logistic regression models were estimated to evaluate the association between key predictors and health outcomes of children considered for this study. The logistic regression model we estimate as: where pij is a dichotomous health outcome for child i in household j, β denotes vector of coefficients estimated, Χ denotes a set of values of predictors: household energy poverty, water, sanitation and hygiene, women empowerment, and control variables. We used the Stata 15 software package for all data analyses and logistic regressions, and we reported odds ratios.

AI Digest

Study Justification:

This study aims to address gaps in understanding the health effects of household microenvironments in resource-poor settings, specifically focusing on the impact on under-five children. By analyzing comprehensive data from a nationally representative survey in Uganda, the study investigates the associations between household microenvironment variables and health outcomes such as acute respiratory infection (ARI), diarrhea, and stunting. The study also examines the multidimensional energy poverty index (MEPI) and women empowerment index to better understand the relationship between these factors and health outcomes. The findings of this study can contribute to the development of integrated energy, water, sanitation, and hygiene (WASH), and women empowerment programs to reduce the burden of childhood illnesses.

Highlights:

– The study found that multidimensional energy poverty was associated with a higher risk of ARI in under-five children.
– Women’s social independence and attitude against domestic violence were associated with lower risks of ARI and diarrhea in children.
– Access to sanitation facilities was associated with lower risks of ARI, diarrhea, and stunting in children.
– Investments targeting synergies in integrated energy, WASH, and women empowerment programs are likely to contribute to reducing the burden of childhood illnesses.

Recommendations:

– Policy makers should prioritize investments in integrated energy, WASH, and women empowerment programs to improve the health outcomes of under-five children.
– Efforts should be made to reduce multidimensional energy poverty by providing access to clean, safe, and sustainable household energy.
– Women’s social independence and empowerment should be promoted through education, information dissemination, and involvement in decision-making processes.
– Access to improved sanitation facilities should be increased to reduce the risk of ARI, diarrhea, and stunting in children.

Key Role Players:

– Government agencies responsible for health, energy, water, and women empowerment programs.
– Non-governmental organizations (NGOs) working in the fields of health, energy, water, and women empowerment.
– Community leaders and local authorities.
– Researchers and academics specializing in public health, energy, water, and women empowerment.

Cost Items for Planning Recommendations:

– Investments in energy infrastructure to provide clean, safe, and sustainable household energy.
– Implementation of WASH programs to improve access to sanitation facilities and clean water sources.
– Educational and awareness campaigns to promote women empowerment and social independence.
– Research and development activities to identify effective strategies and interventions.
– Monitoring and evaluation systems to assess the impact of the implemented programs.
Please note that the cost items provided are general categories and do not represent actual cost estimates. The specific costs will depend on the context, scale, and implementation strategies of the recommended interventions.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it is based on a nationally representative, cross-sectional demographic and health survey in Uganda. The study uses logistic regression to analyze the associations between household microenvironment variables and health outcomes in under-fives. The study also constructs and applies multidimensional energy poverty and women empowerment indices. The findings show significant associations between household microenvironments and episodes of acute respiratory infection, diarrhea, and stunting. The study suggests that investments targeting synergies in integrated energy and water, sanitation and hygiene, and women empowerment programs can reduce the burden of childhood illnesses. To improve the evidence, future research could consider longitudinal studies to establish causality and explore the effectiveness of specific interventions in reducing childhood morbidity.

Abstract

In Figure 1, we present a simplified conceptual framework to show possible links between energy poverty, access to clean water, sanitation, hygiene, women empowerment, and health outcomes of children. Our conceptual approach incorporated long-standing accepted linkages between household microenvironments, women empowerment, and health outcomes of children. We also incorporated recently developed indices for environmental exposure [39] and women empowerment [41] to have a better understanding of the context-specific relationship between the exposures and health outcomes. The women empowerment index is useful to identify what types of empowerment are linked with health outcomes of children. Conceptual framework: household environment (including social dimension) and health outcomes of children below 5 years of age. The bolder lines show key associations that we aim to test while the lighter lines show control variables. The data analysed in this study are from the 2016 Uganda Demographic and Health Surveys (UDHS), collected by the Uganda Bureau of Statistics with technical support from ICF international [42]. The UDHS is a nationally representative survey that provides comprehensive data about households, health outcomes of children, and maternal characteristics. The survey was carried out from 20 June to 16 December 2016 on key demographic and health indicators, including nutritional status of children and women and gender-related variables. A stratified and multistage sampling method was used in the 2016 UDHS to collect key information on child and maternal health indicators, which is nationally representative. A detailed description of methods, design, collected data, study participants and other important information is documented in the 2016 Demographic and Health Survey of Uganda [42]. The survey has rich data including information about children’s morbidity, though most of them are symptomatic data. It also has rich data on household and parents’ socioeconomic and demographic characteristics. Therefore, the UDHS survey data are attractive for rigorous quantitative analysis to establish an association between morbidity among children under 5 years of age and household microenvironments and women empowerment. The key health outcome variables in this study came from the mother’s responses to questions on episodes of various child morbidity within two weeks before the survey date. As indicated in Figure 1, the child health outcomes in this study are ARI, diarrhoea, and nutritional status of children using stunting as a key indicator. In the 2016 DHS surveys, symptoms of ARI are defined as short, rapid breathing which was chest-related and/or difficult breathing which was chest-related [43]. Following this definition, the children were categorised into two groups: those who experienced ARI symptoms and those who did not within 2 weeks before the survey. A limitation of this indicator is that it is based on the mothers’ perception of the morbidity, not a definitive diagnosis. The DHS data also contains diarrhoea prevalence by asking mothers whether the child had diarrhoea in the two weeks preceding the survey. This health outcome is dichotomous, identifying children who suffered diarrhoea and those who did not. Stunting in children below 5 years of age is the other health outcome examined in this study. Following the WHO Multicentre Growth Reference guideline [44,45], a child is stunted if the height-for-age z-score (HAZ) is below -2SD of the median for their age, including both mildly and severely stunted children. Key predictors of interest in this study are a comprehensive set of variables related to household microenvironments and women empowerment, as presented in Figure 1. Most household-environment-related variables in the DHS are standardized in the recode files and often used as they are with moderate modifications; for example, see [46]. In this study, however, indices were constructed and globally set standards were used to categorize key variables of interest. The multidimensional energy poverty index [39] was constructed and used as an indicator for household air pollution. We also estimated an women empowerment index, relevant in African settings. To include water quality and sanitation and hygiene facilities, we used the revised standard ladder by WHO/UNICEF Joint Monitoring Program [8]. We discuss these measurements and standards in the following sections. An indicator for household air pollution is a key predictor in this study. Previous studies often considered households’ consumption of solid fuel as an indicator of household air pollution to explain childhood morbidity [18,21,47,48,49,50]. In this study, we constructed a multidimensional energy poverty index (MEPI) at the household level to explain childhood morbidity. The MEPI captures a set of energy deprivations that affect a person or household [39,51]. The MEPI provides a framework to identify the categories of households left behind on access to clean, safe, and sustainable household energy [52]. Methodologically, the MEPI is derived from the multidimensional poverty measures developed by Alkire and Foster [53]. Literature on methodological developments of multidimensional poverty have their root in Amartya Sen’s discussion of deprivations and capabilities [54] which argues for the need to focus on the absence of opportunities and choices for living a basic human life. The MEPI is composed of five dimensions representing basic energy services with six indicators. More specifically, it is composed of indicators of a household using modern cooking fuel and cooking places, having access to electricity for lighting, having a refrigerator, having a TV or radio for entertainment and education, and having a phone or mobile phone for communication (see Table 1). Multidimensional energy poverty dimensions and respective variables with cut-offs, including relative weights (in parenthesis). Source: taken from Nussbaumer, Bazilian, and Modi [39]. Following previous studies [39,51,52] and the relative importance of indicators to human health, we unequally assigned weights to the various dimensions and indicators. This reflects the relative importance of the various energy poverty variables considered in household pollution and human health. We refer readers to Nussbaumer, Bazilian, and Modi [39] for further understanding of dimensions, indicators, and weights used in MEPI construction. A household is identified as energy poor if the respective set of deprivation scores (Ci) exceeds a predefined threshold, k. Previous studies in LMICs used a multidimensional energy poverty cut-off score at k=0.3 [39,52]. Nussbaumer, Nerini, Onyeji, and Howells [51] further categorized a household multidimensional energy poor level as acute when the MEPI exceed 0.7, moderate between 0.3 and 0.7, and low below 0.3. We also followed these cut-off points. However, in our analysis, very few (0.16%) of the observations fell in the low multidimensional energy poor category. Consequently, we combined the ‘low’ and ‘moderate’ energy poor category and coded as ‘moderate’ multidimensional energy poor. Therefore, households were categorised into ‘moderate’ and ‘acute’ multidimensional energy poor. The MEPI captures information on both the incidence and the intensity or severity of energy poverty. We computed the poverty headcount as H=qn, where q is the number of energy poor households (where ci>k) and n the total number of households. The severity of poverty indicates the average proportion of indicators in which multidimensional energy poor households is obtained as A=∑i=1nci (k)/q. Finally, the MEPI is obtained as the product of the multidimensional energy poverty headcount ratio (H) and multidimensional energy poverty intensity (A): MEPI=H×A. In coding quality and source of household water and sanitation, previous studies used the dichotomous improved vs. unimproved or safe vs. unsafe definitions [17,48,55]. As our focus in this study is household microenvironments, we opted for definitions and categorization that are clearer and more distinct. We followed the revised water and sanitation ladder by WHO/UNICEF joint monitoring programme (JMP) [8] to define quality of water and sanitation facilities but with some modifications. We defined household access to a hygiene facility following the WHO/UNICEF joint monitoring programme ladder for hygiene: basic, limited, and no facility. The WHO/UNICEF defines and categorizes quality of water and sanitation facilities into five levels. This was not possible in the Uganda DHS dataset due to less clarity in wording used in the questionnaire to match with WHO/UNICEF JMP ladders for water and sanitation. We categorised sanitation facilities, slightly deviating from the WHO/UNICEF JMP ladder for sanitation, into three: no facility, unimproved, and improved. We categorized the quality of the source of drinking water into two: improved and unimproved. We adopted the women empowerment index construction method developed by [41] using DHS data from Africa. This composite index consists of three domains of empowerment: attitude to violence, social independence, and decision making. These empowerment dimensions overlaps with most of the dimensions considered by [56] focusing on sub-Saharan Africa, particularly East African countries. Similar empowerment dimensions were considered in other studies in LMICs [29]. The three empowerment dimensions comprise various information. ‘Attitude to violence’ is composed of information related to the respondent’s opinion about whether wife-beating is justified or not in various scenarios. ‘Social independence’ includes items related to education, frequency of information consumption (reading), age at first cohabitation and first childbirth, and differences between age and their years of schooling of the woman and her husband. The ‘decision making’ domain is comprised of information related to a woman’s involvement in household decisions and labour force participation. In this study, the three dimensions were weighed following [41]. In addition to the household microenvironments and women empowerment-related predictors, other relevant predictors such as individual and parental variables are included in the analysis. Potential predictors associated with the health of children below 5 years of age were included, considering their relevance in previous studies [16,21,23,25,29,30,46,47,57,58] as control variables. A summary of definitions of key predictors used in the analysis is presented in (Table 2). Description of key predictors used in the analysis. The statistical analyses used in this study include descriptive statistics, bivariate analyses, and logistic regression models. The DHS sampling weights are applied in all analyses to account for the complex survey design. We used descriptive analyses, percentages, and numbers to show the distribution of childhood morbidity and nutritional status by predictor variables. Associations between childhood health outcomes and predictors were first analysed using bivariate analyses, the χ2 tests, before fitting the regression models. These analyses were carried out to compare the prevalence of childhood morbidities and stunting among the levels of the selected predictors and to inform further analyses using regression models. The dependent variables considered in this study are dichotomous variables. Therefore, binary response econometric models are the natural choice. Logistic regression models were estimated to evaluate the association between key predictors and health outcomes of children considered for this study. The logistic regression model we estimate as: where pij is a dichotomous health outcome for child i in household j, β denotes vector of coefficients estimated, Χ denotes a set of values of predictors: household energy poverty, water, sanitation and hygiene, women empowerment, and control variables. We used the Stata 15 software package for all data analyses and logistic regressions, and we reported odds ratios.

Materials

https://efashare.b-cdn.net/materials/b47ca945617637ff13ba1b900cc1651350ec2511e06132c1c42c9cfd0063c238/ijerph-19-06684-s001.zip

Innovations

Based on the provided information, 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 and new mothers with access to important health information, reminders for prenatal and postnatal care appointments, and educational resources.

2. Telemedicine: Implement telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare professionals through video calls, reducing the need for travel and improving access to medical advice.

3. Community Health Workers: Train and deploy community health workers who can provide basic prenatal and postnatal care, conduct health education sessions, and refer women to appropriate healthcare facilities when necessary.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to access essential maternal health services, such as antenatal care visits, delivery services, and postnatal care.

5. Maternal Health Clinics: Establish dedicated maternal health clinics that offer comprehensive services, including prenatal care, skilled birth attendance, emergency obstetric care, and postnatal care, to ensure that women receive the necessary care throughout their pregnancy and after childbirth.

6. Transportation Support: Develop transportation initiatives that provide pregnant women with affordable and reliable transportation options to reach healthcare facilities for prenatal and postnatal care visits, as well as for emergency obstetric care.

7. Maternal Health Education Campaigns: Launch targeted education campaigns to raise awareness about the importance of maternal health, promote healthy behaviors during pregnancy, and encourage women to seek timely and appropriate care.

8. Maternity Waiting Homes: Set up maternity waiting homes near healthcare facilities, where pregnant women from remote areas can stay during the final weeks of pregnancy to ensure timely access to skilled birth attendance and emergency obstetric care.

9. Task-Shifting and Training: Train and empower non-specialist healthcare providers, such as nurses and midwives, to deliver quality maternal health services, including antenatal care, safe delivery, and postnatal care.

10. Public-Private Partnerships: Foster collaborations between the public and private sectors to improve access to maternal health services, leveraging the resources and expertise of both sectors to expand service delivery and reach underserved populations.

These innovations aim to address various barriers to accessing maternal health services, such as geographical distance, lack of transportation, limited healthcare infrastructure, and inadequate knowledge about maternal health. By implementing these innovations, it is possible to improve access to essential maternal health services and ultimately reduce maternal and neonatal mortality rates.

AI Innovations Description

Based on the provided description, the recommendation to improve access to maternal health would be to invest in integrated energy and water, sanitation, and hygiene (WASH) programs, as well as women empowerment initiatives. This recommendation is based on the findings that multidimensional energy poverty, lack of access to sanitation facilities, and low women empowerment are associated with higher risks of acute respiratory infections (ARI), diarrhea, and stunting among children under five in Uganda.

By targeting synergies between energy and WASH programs, efforts can be made to provide clean and sustainable household energy sources, improve access to clean water, and enhance sanitation and hygiene facilities. This can help reduce the burden of early childhood illnesses and improve maternal health outcomes.

Additionally, investing in women empowerment programs can have a positive impact on maternal and child health. Women’s social independence, attitudes against domestic violence, and involvement in decision-making processes are associated with lower risks of ARI, diarrhea, and stunting in children. Therefore, empowering women through education, economic opportunities, and social support can contribute to better maternal health and improved access to healthcare services.

It is important for research and development actions in low- and middle-income countries to address and include these multi-sector synergies to effectively improve access to maternal health and reduce the burden of childhood illnesses.

AI Innovations Methodology

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

1. Improve access to clean water and sanitation facilities: Investments should be made to ensure that communities have access to clean water sources and proper sanitation facilities. This can help reduce the risk of waterborne diseases and improve overall maternal health.

2. Promote women empowerment: Programs and initiatives should be implemented to empower women, both socially and economically. This can include providing education and training opportunities, promoting gender equality, and supporting women’s decision-making power in matters related to their health and well-being.

3. Enhance energy access: Efforts should be made to improve access to clean and sustainable energy sources, particularly in resource-poor settings. This can help reduce household air pollution, which is associated with adverse health effects, including maternal health complications.

4. Strengthen integrated multi-sector approaches: Collaboration between different sectors, such as health, water and sanitation, energy, and women empowerment, is crucial to address the complex challenges related to maternal health. Integrated programs and policies should be developed and implemented to ensure a comprehensive approach.

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

1. Data collection: Collect relevant data on maternal health indicators, such as maternal mortality rates, access to prenatal care, and birth outcomes. Also, gather data on the proposed recommendations, such as access to clean water and sanitation, women empowerment indices, and energy access.

2. Data analysis: Analyze the collected data using appropriate statistical methods, such as logistic regression or other econometric models. This analysis will help identify the associations between the proposed recommendations and maternal health outcomes.

3. Simulation modeling: Develop a simulation model that incorporates the identified associations between the recommendations and maternal health outcomes. This model should consider the interdependencies and potential synergies between the different recommendations.

4. Scenario analysis: Use the simulation model to conduct scenario analysis. This involves simulating different scenarios by varying the levels of implementation and impact of the recommendations. For example, simulate the impact of different levels of access to clean water and sanitation facilities on maternal mortality rates.

5. Impact assessment: Assess the impact of the different scenarios on improving access to maternal health. This can be done by comparing the simulated outcomes, such as maternal mortality rates or access to prenatal care, across the different scenarios.

6. Policy recommendations: Based on the simulation results, provide policy recommendations on the most effective strategies to improve access to maternal health. These recommendations should consider the potential trade-offs and costs associated with implementing the different recommendations.

It is important to note that the methodology described above is a general framework and may need to be adapted based on the specific context and data availability.

Author

Dima Health

Statistics:

Citations: 1

Identifiers:

DOI: 10.3390/ijerph19116684

ISSN: 16617827

Research Areas:

Environmental, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants, Violence and Injury

Study Countries:

Uganda

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female