Household cooking fuel type and childhood anaemia in sub-Saharan Africa: Analysis of cross-sectional surveys of 123, 186 children from 29 countries

BMJ Open, Volume 11, No. 7, Year 2021

Interpretation

Objective This study sought to investigate the joint effect of household cooking fuel type and urbanicity (rural-urban residency) on anaemia among children under the age of five in sub-Saharan Africa. Design We analysed cross-sectional data of 123, 186 children under the age of five from 29 sub-Saharan African countries gathered between 2010 and 2019 by the Demographic and Health Survey programme. Bivariate (χ 2 test of independence) and multilevel logistic regression were used to examine the effect of urbanicity-household cooking fuel type on childhood anaemia. Results were reported as adjusted odds ratios (aORs) with 95% CIs at p<0.05. Outcome measures Anaemia status of children. Results More than half (64%) of children had anaemia. The percentage of children who suffered from anaemia was high in those born to mothers in Western Africa (75%) and low among those born in Southern Africa (54%). Children from rural households that depend on unclean cooking fuels (aOR=1.120; 95% CI 1.033 to 1.214) and rural households that depend on clean cooking fuels (aOR=1.256; 95% CI 1.080 to 1.460) were more likely to be anaemic as compared with children from urban households using clean cooking fuel. Child's age, sex of child, birth order, perceived birth size, age of mother, body mass index of mother, education, marital status, employment status, antenatal care, wealth quintile, household size, access to electricity, type of toilet facility, source of drinking water and geographic region had significant associations with childhood anaemia status. Conclusions Our study has established a joint effect of type of household cooking fuel and urbanicity on anaemia among children under the age of five in sub-Saharan Africa. It is therefore critical to promote the usage of clean cooking fuels among households and women in rural areas. These should be done taking into consideration the significant child, maternal, household, and contextual factors identified in this study. Nationally representative data from the Demographic and Health Surveys (DHS) Program for 29 countries in SSA from 2010 to 2019 were acquired for analysis in this study. The DHS Program provides large secondary data gathered from surveys using probability sampling methods, following standard protocols that are internationally accepted. Different sets of questionnaires designed and pretested to ensure reliability and amenability for comparison of data gathered on various spatial and temporal scales are used in the survey. Some questionnaires the Program uses include the ‘children’s questionnaire’ ‘mother’s questionnaire’, ‘men’s questionnaire’ and ‘household questionnaire’. These questionnaires cover a broad range of variables cutting across demographics and anthropometrics, water and sanitation, health, wealth, nutrition among others. The Program recruits and trains field officers to collect accurate data and measurement of weight, height, anaemia using recommended guidelines and instruments. Data on other important variables such as household cooking fuel, urbanicity, wealth, water and sanitation are taken at the household level. The dataset used in this study are: Children’s Data—Children’s Recode, and Household Data—Household Recode (variable names: caseid; v000; v001 v007; v013; v102; v113; v116; v136; v149; v151; v152; v190; bord; b4; b8; m19; hw2; v457; v005; hhid; hv001, hv000; hv226). A sample of 123, 182 was drawn from 29 countries (figure 1) accros the five geographic regions—Western, Eastern, Central and Southern Africa in SSA. For a country to be selected, it must meet the following criteria: should be found in SSA based on the United Nations regional groupings; it must have a DHS dataset with standardised questions and observations on anaemia level of children under 5 years as well as household cooking fuel type, urbanicity, source of drinking water and type of toilet facility. Where multiple datasets exist for a country, the most recent dataset is used. Map of study countries. The dataset provided information on household cooking fuel type, source of drinking water and toilet facility type at the household level. The observations for household cooking fuel were classified into ‘clean’ and ‘unclean’ (polluting fuels) following the criteria used in previous studies25 26 (see table 1). The weight of the child at birth named as ‘birth weight’ was categorised as ‘underweight’ (<2.5 kg) and ‘normal’ (≥2.5 kg) (see Yaya et al27). Also, the observations for the household source of drinking water and type of toilet facility were classified into ‘improved’ and ‘unimproved’ using the revised definitions by the WHO/UNICEF Joint Monitoring Programme report.28 The improved and unimproved drinking water sources and toilet facilities categorisation as described by Armah et al29 are summarised in Table 2. Classification of the source of drinking water and toilet facilities under the WHO/UNICEF Joint Monitoring Programme and cooking fuel Distribution of child anaemia status by predictor variables Anaemia status of children is the outcome variable considered in this study. According to DHS, the anaemia status of living children within the age bracket 0–4 years before the survey night was taken. It has its responses classified into four categories according to the WHO recommendation as (i) ‘not anaemic’ for children with Hb count (g/L) measuring above 110 g/L; (ii) ‘mild anaemia’ for Hb count of 100–109 g/L; (iii) ‘moderate anaemia’ for Hb count between 70 and 99 g/L and (iv) ‘severe anaemia’ for Hb count less than 70 g/L. Children with no observations for anaemia count (not tested) and those whose mothers were not listed in the household questionnaire were excluded. Observations under mild, moderate and severe were combined and recoded as ‘1’ for ‘anaemic’ and observations under not anaemic was recoded as ‘0’ ‘normal’ (see Chandran and Kirby30). This produced the dichotomous outcome variable ‘anaemia status’. The predictor chosen for this study is a composite variable derived from household cooking fuel type and urbanicity. The selection of the predictor variable was based on parsimony, literature review, theoretical relevance as well as practical significance. Household cooking fuel type and urbanicity both had two categories since the former was classified as ‘clean’ and ‘unclean’ and the latter measured as ‘rural’ and ‘urban’ per the DHS. This, therefore, gave four mutually exclusive groups: unclean-urban (households relying on ‘unclean’ cooking fuel and found in urban areas); unclean-rural (households relying on ‘unclean’ cooking fuel and found in rural areas); clean-urban (households using ‘clean’ cooking fuels and found in urban areas) and clean-rural (households using ‘clean’ cooking fuels and found in rural areas). This variable combination technique has been widely applied in previous studies.29 31 Out of the four responses, ‘clean-urban’ was chosen as the reference category in the models. Even though urban residents are commonly exposed to outdoor pollution, emissions from household cooking fuels (indoor pollution) is a major problem to rural residents owing to their relatively high dependence on unclean cooking fuels32 33 This formed the basis for the choice of reference category. There is a plethora of evidence on the independent associations between the type of household cooking fuel and urbanicity with childhood anaemia.14 22 33–35 Even though the UNICEF categorisation of the factors that influence the association between household cooking fuel and child anaemia—proximal, immediate and distal factors serves as a useful framework, for parsimony, practical and theoretical considerations, we categorise independent variables drawn from literature under ‘individual-level characteristics’, ‘household characteristics’ and ‘contextual factors’ (see Nambiema et al4 and Amadu et al36). The individual-level characteristics considered in this study are child age in years (0, 1, 2, 3 and 4); sex of child (male, female); birth order (1, 2, 3 and above); perceived birth size (larger than average, average, smaller than average, don’t know); birth weight (underweight, normal); age of the mother (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49); body mass index (BMI) of the mother (thin, normal and obese); education (no formal, primary, secondary and tertiary); marital status (never married, married, living with a partner, widowed/divorced/separated); employment status (no, yes); number of antenatal care (ANC) by mother (no, yes); and number of postnatal care (PNC) by mother (no, yes). The relevant household-level factors included in this study are wealth status (poor, middle, rich); household size (‘small’ for those with 1–5 members; ‘medium’ 5–10 members and ‘large’ for more than 10 members); age of household head (‘young-aged adults’ for those age below 35; ‘middle-aged adults’ for those between 35 and 64 years and ‘old-aged adults’ for those 65 years and above); sex of household head (male, female); access to electricity (no, yes); type of toilet facility (improved, unimproved); and source of drinking water (improved, unimproved). Finally, we adjusted for the effect of the contextual factor ‘geographic region’ (Western Africa, Eastern Africa, Central Africa, Southern Africa). Variables in this category of factors relate to the attributes of respondent’s neighbourhood, and opportunities and services that are space-bound.37 38 The Stata V.14.0 MP (Stata Corporation) software was used for the analysis of data. The data were first declared as a survey dataset to prevent potential errors that could arise from the complex survey design using the ‘svyset’ command with the cluster, weighting and strata variables. To understand the distribution of childhood anaemia and the influence of predictive factors on anaemia, descriptive analysis was performed. Using ArcGIS V.10.6, the data were integrated with ESRI Shapefiles to construct a map of the study countries showing the distribution of childhood anaemia. We then determined the associations between the anaemia status of children under five and the relevant predictors using the χ2 test of independence. These relationships were further examined by implementing five multilevel logistic regression models. The first model (model 0) with no independent variable indicated the variance in child anaemia as a result of clustering of the primary sampling units. In model I, only the main predictor variable (urbanicity-type of cooking fuel) was included. Model II adjusted for the individual-level characteristics. The effects of both individual-level and the relevant household-level factors were adjusted in model III. Model IV controlled the individual-level and household-level characteristics, and the contextual-level factors. The Akaike’s Information Criterion was the model comparison metric estimated. Results were presented using adjusted ORs (aORs) at p<0.05 and 95% CIs. Patients and the public were not involved in the design and conduct of this research.

AI Digest

Study Justification:

This study aimed to investigate the joint effect of household cooking fuel type and urbanicity on childhood anaemia in sub-Saharan Africa. Anaemia is a significant public health issue in the region, particularly among children under the age of five. Understanding the factors contributing to anaemia can help inform interventions and policies to reduce its prevalence. The study utilized nationally representative data from the Demographic and Health Surveys (DHS) Program, which provides reliable and comprehensive information on various health and socio-demographic indicators.

Highlights:

– More than half (64%) of children in sub-Saharan Africa were found to have anaemia.
– Children from rural households that depend on unclean cooking fuels and rural households that depend on clean cooking fuels were more likely to be anaemic compared to children from urban households using clean cooking fuel.
– Several individual-level, household-level, and contextual factors were found to be significantly associated with childhood anaemia, including child’s age, sex, birth order, maternal age and education, household wealth, access to electricity, type of toilet facility, and geographic region.

Recommendations:

Based on the study findings, the following recommendations can be made:

1. Promote the usage of clean cooking fuels among households and women in rural areas to reduce the risk of childhood anaemia.
2. Improve access to clean cooking fuels in rural areas through targeted interventions and policies.
3. Enhance maternal and child health services, including antenatal and postnatal care, to address the risk factors associated with childhood anaemia.
4. Implement comprehensive interventions that address the identified individual-level, household-level, and contextual factors to reduce the prevalence of childhood anaemia in sub-Saharan Africa.

Key Role Players:

To address the recommendations, the involvement of the following key role players is crucial:

1. Government health departments and ministries responsible for public health and nutrition policies.
2. Non-governmental organizations (NGOs) working in the field of child health and nutrition.
3. Community health workers and volunteers who can provide education and support to households.
4. Health professionals, including doctors, nurses, and nutritionists, who can provide guidance and treatment for anaemia.
5. Researchers and academics who can contribute to the evidence base and evaluation of interventions.

Cost Items:

While the actual cost of implementing the recommendations will vary depending on the specific context and interventions chosen, some key cost items to consider in planning include:

1. Awareness and education campaigns to promote the usage of clean cooking fuels: costs for materials, media campaigns, and community outreach.
2. Infrastructure development to improve access to clean cooking fuels in rural areas: costs for fuel distribution networks, storage facilities, and equipment.
3. Training and capacity building for health professionals and community health workers: costs for workshops, materials, and ongoing support.
4. Monitoring and evaluation of interventions: costs for data collection, analysis, and reporting to assess the impact and effectiveness of interventions.
5. Research and data collection: costs for conducting further studies and surveys to gather more evidence and inform future interventions.
It is important to note that the provided cost items are general considerations and a more detailed budget would need to be developed based on the specific context and resources available.

Strength of Evidence

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 large sample size (123,186 children from 29 countries) and uses nationally representative data from the Demographic and Health Surveys (DHS) Program. The study design includes bivariate and multilevel logistic regression analyses to examine the effect of household cooking fuel type and urbanicity on childhood anaemia. The results are reported as adjusted odds ratios (aORs) with 95% confidence intervals (CIs) at a significance level of p<0.05. The study also considers a wide range of potential confounding factors. To improve the evidence, the abstract could provide more details on the specific methods used for data collection, sampling, and statistical analysis.

Abstract

Nationally representative data from the Demographic and Health Surveys (DHS) Program for 29 countries in SSA from 2010 to 2019 were acquired for analysis in this study. The DHS Program provides large secondary data gathered from surveys using probability sampling methods, following standard protocols that are internationally accepted. Different sets of questionnaires designed and pretested to ensure reliability and amenability for comparison of data gathered on various spatial and temporal scales are used in the survey. Some questionnaires the Program uses include the ‘children’s questionnaire’ ‘mother’s questionnaire’, ‘men’s questionnaire’ and ‘household questionnaire’. These questionnaires cover a broad range of variables cutting across demographics and anthropometrics, water and sanitation, health, wealth, nutrition among others. The Program recruits and trains field officers to collect accurate data and measurement of weight, height, anaemia using recommended guidelines and instruments. Data on other important variables such as household cooking fuel, urbanicity, wealth, water and sanitation are taken at the household level. The dataset used in this study are: Children’s Data—Children’s Recode, and Household Data—Household Recode (variable names: caseid; v000; v001 v007; v013; v102; v113; v116; v136; v149; v151; v152; v190; bord; b4; b8; m19; hw2; v457; v005; hhid; hv001, hv000; hv226). A sample of 123, 182 was drawn from 29 countries (figure 1) accros the five geographic regions—Western, Eastern, Central and Southern Africa in SSA. For a country to be selected, it must meet the following criteria: should be found in SSA based on the United Nations regional groupings; it must have a DHS dataset with standardised questions and observations on anaemia level of children under 5 years as well as household cooking fuel type, urbanicity, source of drinking water and type of toilet facility. Where multiple datasets exist for a country, the most recent dataset is used. Map of study countries. The dataset provided information on household cooking fuel type, source of drinking water and toilet facility type at the household level. The observations for household cooking fuel were classified into ‘clean’ and ‘unclean’ (polluting fuels) following the criteria used in previous studies25 26 (see table 1). The weight of the child at birth named as ‘birth weight’ was categorised as ‘underweight’ (<2.5 kg) and ‘normal’ (≥2.5 kg) (see Yaya et al27). Also, the observations for the household source of drinking water and type of toilet facility were classified into ‘improved’ and ‘unimproved’ using the revised definitions by the WHO/UNICEF Joint Monitoring Programme report.28 The improved and unimproved drinking water sources and toilet facilities categorisation as described by Armah et al29 are summarised in Table 2. Classification of the source of drinking water and toilet facilities under the WHO/UNICEF Joint Monitoring Programme and cooking fuel Distribution of child anaemia status by predictor variables Anaemia status of children is the outcome variable considered in this study. According to DHS, the anaemia status of living children within the age bracket 0–4 years before the survey night was taken. It has its responses classified into four categories according to the WHO recommendation as (i) ‘not anaemic’ for children with Hb count (g/L) measuring above 110 g/L; (ii) ‘mild anaemia’ for Hb count of 100–109 g/L; (iii) ‘moderate anaemia’ for Hb count between 70 and 99 g/L and (iv) ‘severe anaemia’ for Hb count less than 70 g/L. Children with no observations for anaemia count (not tested) and those whose mothers were not listed in the household questionnaire were excluded. Observations under mild, moderate and severe were combined and recoded as ‘1’ for ‘anaemic’ and observations under not anaemic was recoded as ‘0’ ‘normal’ (see Chandran and Kirby30). This produced the dichotomous outcome variable ‘anaemia status’. The predictor chosen for this study is a composite variable derived from household cooking fuel type and urbanicity. The selection of the predictor variable was based on parsimony, literature review, theoretical relevance as well as practical significance. Household cooking fuel type and urbanicity both had two categories since the former was classified as ‘clean’ and ‘unclean’ and the latter measured as ‘rural’ and ‘urban’ per the DHS. This, therefore, gave four mutually exclusive groups: unclean-urban (households relying on ‘unclean’ cooking fuel and found in urban areas); unclean-rural (households relying on ‘unclean’ cooking fuel and found in rural areas); clean-urban (households using ‘clean’ cooking fuels and found in urban areas) and clean-rural (households using ‘clean’ cooking fuels and found in rural areas). This variable combination technique has been widely applied in previous studies.29 31 Out of the four responses, ‘clean-urban’ was chosen as the reference category in the models. Even though urban residents are commonly exposed to outdoor pollution, emissions from household cooking fuels (indoor pollution) is a major problem to rural residents owing to their relatively high dependence on unclean cooking fuels32 33 This formed the basis for the choice of reference category. There is a plethora of evidence on the independent associations between the type of household cooking fuel and urbanicity with childhood anaemia.14 22 33–35 Even though the UNICEF categorisation of the factors that influence the association between household cooking fuel and child anaemia—proximal, immediate and distal factors serves as a useful framework, for parsimony, practical and theoretical considerations, we categorise independent variables drawn from literature under ‘individual-level characteristics’, ‘household characteristics’ and ‘contextual factors’ (see Nambiema et al4 and Amadu et al36). The individual-level characteristics considered in this study are child age in years (0, 1, 2, 3 and 4); sex of child (male, female); birth order (1, 2, 3 and above); perceived birth size (larger than average, average, smaller than average, don’t know); birth weight (underweight, normal); age of the mother (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49); body mass index (BMI) of the mother (thin, normal and obese); education (no formal, primary, secondary and tertiary); marital status (never married, married, living with a partner, widowed/divorced/separated); employment status (no, yes); number of antenatal care (ANC) by mother (no, yes); and number of postnatal care (PNC) by mother (no, yes). The relevant household-level factors included in this study are wealth status (poor, middle, rich); household size (‘small’ for those with 1–5 members; ‘medium’ 5–10 members and ‘large’ for more than 10 members); age of household head (‘young-aged adults’ for those age below 35; ‘middle-aged adults’ for those between 35 and 64 years and ‘old-aged adults’ for those 65 years and above); sex of household head (male, female); access to electricity (no, yes); type of toilet facility (improved, unimproved); and source of drinking water (improved, unimproved). Finally, we adjusted for the effect of the contextual factor ‘geographic region’ (Western Africa, Eastern Africa, Central Africa, Southern Africa). Variables in this category of factors relate to the attributes of respondent’s neighbourhood, and opportunities and services that are space-bound.37 38 The Stata V.14.0 MP (Stata Corporation) software was used for the analysis of data. The data were first declared as a survey dataset to prevent potential errors that could arise from the complex survey design using the ‘svyset’ command with the cluster, weighting and strata variables. To understand the distribution of childhood anaemia and the influence of predictive factors on anaemia, descriptive analysis was performed. Using ArcGIS V.10.6, the data were integrated with ESRI Shapefiles to construct a map of the study countries showing the distribution of childhood anaemia. We then determined the associations between the anaemia status of children under five and the relevant predictors using the χ2 test of independence. These relationships were further examined by implementing five multilevel logistic regression models. The first model (model 0) with no independent variable indicated the variance in child anaemia as a result of clustering of the primary sampling units. In model I, only the main predictor variable (urbanicity-type of cooking fuel) was included. Model II adjusted for the individual-level characteristics. The effects of both individual-level and the relevant household-level factors were adjusted in model III. Model IV controlled the individual-level and household-level characteristics, and the contextual-level factors. The Akaike’s Information Criterion was the model comparison metric estimated. Results were presented using adjusted ORs (aORs) at p<0.05 and 95% CIs. Patients and the public were not involved in the design and conduct of this research.

Materials

Innovations

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

1. Clean Cooking Fuel Promotion: Promote the usage of clean cooking fuels among households and women in rural areas. This can help reduce indoor air pollution, which has been linked to childhood anaemia.

2. Maternal Education and Awareness Programs: Implement education and awareness programs targeting mothers and caregivers to increase their knowledge about the importance of maternal health and anaemia prevention. This can include information on nutrition, antenatal care, and the use of clean cooking fuels.

3. Improved Access to Antenatal Care: Enhance access to antenatal care services, particularly in rural areas, to ensure early detection and management of anaemia during pregnancy. This can involve mobile clinics, community health workers, and transportation support.

4. Nutritional Support: Provide nutritional support to pregnant women and young children, focusing on iron-rich foods and supplements. This can help prevent and treat anaemia in both mothers and children.

5. Infrastructure Development: Improve infrastructure in rural areas, such as access to clean water and sanitation facilities. This can contribute to better overall health and reduce the risk of anaemia.

6. Community Engagement: Engage local communities and traditional leaders in promoting maternal health and anaemia prevention. This can help overcome cultural barriers and increase acceptance of interventions.

7. Data Collection and Monitoring: Strengthen data collection systems to monitor the prevalence of anaemia and track progress in maternal health interventions. This can inform evidence-based decision-making and resource allocation.

It is important to note that these recommendations are based on the specific context and findings of the study mentioned. Further research and contextual adaptation may be necessary for effective implementation.

AI Innovations Description

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

Promote the usage of clean cooking fuels among households and women in rural areas in sub-Saharan Africa.

This recommendation is based on the joint effect of household cooking fuel type and urbanicity on childhood anaemia in sub-Saharan Africa. The study found that children from rural households that depend on unclean cooking fuels and rural households that depend on clean cooking fuels were more likely to be anaemic compared to children from urban households using clean cooking fuel.

By promoting the usage of clean cooking fuels, such as liquefied petroleum gas (LPG) or improved cookstoves, in rural areas, the indoor air pollution caused by unclean cooking fuels can be reduced. This can have a positive impact on maternal health by reducing the risk of anaemia among children under the age of five.

To implement this recommendation, it is important to consider the significant child, maternal, household, and contextual factors identified in the study. These factors include child’s age, sex of child, birth order, perceived birth size, age of mother, body mass index of mother, education, marital status, employment status, antenatal care, wealth quintile, household size, access to electricity, type of toilet facility, source of drinking water, and geographic region.

Innovations to promote the usage of clean cooking fuels in rural areas could include awareness campaigns, subsidies or financial incentives for clean cooking fuel adoption, distribution of improved cookstoves, and partnerships with local communities and organizations. It is important to tailor these interventions to the specific needs and contexts of each rural area in sub-Saharan Africa.

By improving access to clean cooking fuels in rural areas, the overall health and well-being of mothers and children can be enhanced, contributing to better maternal health outcomes.

AI Innovations Methodology

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

1. Promote the usage of clean cooking fuels: Encourage households, especially those in rural areas, to switch from unclean cooking fuels to clean cooking fuels. This can be done through awareness campaigns, subsidies, and incentives.

2. Improve access to clean cooking fuels: Ensure that clean cooking fuels are readily available and affordable for households. This may involve establishing distribution networks, improving supply chains, and reducing the cost of clean cooking fuels.

3. Enhance maternal education and awareness: Provide education and information to mothers about the importance of clean cooking fuels for maternal and child health. This can be done through antenatal care visits, community health programs, and educational materials.

4. Strengthen antenatal and postnatal care services: Ensure that antenatal and postnatal care services include information and support related to clean cooking fuels. This can be integrated into existing maternal health programs and services.

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

1. Define the indicators: Identify key indicators that reflect access to maternal health, such as maternal mortality rates, antenatal care coverage, and postnatal care utilization.

2. Collect baseline data: Gather data on the current status of these indicators in the target population. This can be done through surveys, health facility records, and existing data sources.

3. Develop a simulation model: Create a mathematical or statistical model that simulates the impact of the recommendations on the selected indicators. This model should take into account factors such as population size, geographical distribution, and existing healthcare infrastructure.

4. Input data and parameters: Input the baseline data and parameters related to the recommendations into the simulation model. This may include data on the prevalence of clean cooking fuel use, access to maternal health services, and demographic information.

5. Run simulations: Run the simulation model multiple times, varying the parameters related to the recommendations. This will allow for the exploration of different scenarios and the estimation of the potential impact on access to maternal health.

6. Analyze results: Analyze the results of the simulations to determine the potential impact of the recommendations on the selected indicators. This may involve comparing different scenarios, identifying trends, and assessing the magnitude of the impact.

7. Validate and refine the model: Validate the simulation model by comparing the simulated results with real-world data, if available. Refine the model based on feedback and further analysis.

8. Communicate findings: Present the findings of the simulation study to relevant stakeholders, such as policymakers, healthcare providers, and community members. Use the results to advocate for the implementation of the recommendations and inform decision-making processes.

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: 5

Identifiers:

DOI: 10.1136/bmjopen-2021-048724

Research Areas:

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

Study Design:

Cross Sectional Study

Study Approach:

Mixed-methods, Quantitative, Systematic Review

Participants Gender:

Male & Female