Determinants of neonatal, infant and under-five mortality in a war-affected country: Analysis of the 2010 Household Health Survey in South Sudan

BMJ Global Health, Volume 3, No. 1, Year 2018

Interprétation

Background: Under-five children born in a fragile and war-affected setting of South Sudan are faced with a high risk of death as reflecting in high under-five mortality. In South Sudan health inequities and inequitable condition of daily living play a significant role in childhood mortality. This study examines factors associated with under-five mortality in South Sudan. Methods: The study population includes 8125 singleton, live birth, under-five children born in South Sudan within 5 years prior to the 2010 South Sudan Household Survey. Factors associated with neonatal, infant and under-five deaths were examined using generalised linear latent and mixed models with the logit link and binomial family that adjusted for cluster and survey weights. Results: The multivariate analysis showed that mothers who reported a previous death of a child reported significantly higher risk of neonatal (adjusted OR (AOR)=3.74, 95% confidence interval (CI 2.88 to 4.87), P<0.001), infant (AOR=3.19, 95% CI (2.62 to 3.88), P<0.001) and under-five deaths (AOR=3.07, 95% CI (2.58 to 3.64), P<0.001). Other associated factors included urban dwellers (AOR=1.37, 95% CI (1.01 to 1.87), P=0.045) for neonatal, (AOR=1.35, 95% CI (1.08 to 1.69), P=0.009) for infants and (AOR=1.39, 95% CI (1.13 to 1.71), P=0.002) for under-five death. Unimproved sources of drinking water were significantly associated with neonatal mortality (AOR=1.91, 95% CI (1.11 to 3.31), P=0.02). Conclusions: This study suggested that the condition and circumstances in which the child is born into, and lives with, play a role in under-five mortality, such as higher mortality among children born to teenage mothers. Ensuring equitable healthcare service delivery to all disadvantaged populations of children in both urban and rural areas is essential but remains a challenge, while violence continues in South Sudan. We used a data set collected during the 2010 South Sudan Household Health Survey second round (SSHHSII), which is a nationally representative, stratified, cluster sample survey, covering the 10 states of South Sudan. The survey was largely based on the Unicef’s Multiple Indicator Cluster Survey (MICS) methodology.21 It aimed to collect health and related indicators essential for identifying the health needs of women and children, and for establishing priorities for evidence-based planning, decision-making and reporting. The SSHHSII comprised a general questionnaire to collect basic demographic information on all household members, with three individual questionnaires addressed to specific target groups: women and men aged 15–49 years and under-five children. The individual questionnaire was used to collect information on reproductive history, use of family planning, information about child health indicators and other health-related issues. The questionnaire for under-five children was administered to mothers or caretakers of children under 5 years of age.21 A two-stage cluster sampling design was employed for the selection of the sample in each of the 10 states of South Sudan. The first stage consisted of the selection of the required number of enumeration areas separately by urban and rural strata. Systematic probability proportional to size sampling procedure was used for the selection of 40 enumeration sites in each of the 10 states of South Sudan. The second stage was the selection of the total number of households in each cluster using random systematic selection procedures to select on average 25 households in each enumeration area. From the selected households, a total sample of 9369 households were interviewed with information from 9069 ever-married women, and 4344 men aged 15–49 years, and information from 8338 under-five children collected from their mother/caretaker yielding a response rate of 83%. The details of the SSHHSII sampling method have been reported elsewhere.21 Our study population consisted of 9125 (8125 weighted) singleton live-born children under the age of 5 years, who were born within 5 years prior to the survey. We excluded multiple pregnancies (n=303) in this analysis because of higher risk of newborn death, as the result of preterm birth and pregnancy complications among this group compared with singleton pregnancies.22 We modified and used the conceptual framework developed by WHO14 as a guide in identifying the key social determinants of health inequalities and their impact on the well-being of under-five children in this study. According to this framework, a set of the social economic positions, such as education, income, occupation, gender and social class, is shaped by the structural social, economic and political context.14 Furthermore, these socioeconomic positions influence an individual’s health and well-being through more specific factors called intermediate factors such as material circumstances, behaviours, biological factors and health services. According to the framework, we identified 26 possible determinants and predictors of under-five mortality in South Sudan based on the available information from the 2010 SSHHSII data sets. Figure 1 presents the modified conceptual framework used in this analysis. Conceptual framework for factors associated with under-five mortality, adapted from the WHO social determinants of health inequalities. The outcome variable for this analysis was neonatal, infant and under-five mortality expressed in a binary form (0 for living child and 1 for a child death). Neonatal mortality is defined as the probability of dying in the first month of life (0 to 28 days), infant mortality is the probability of dying between birth and first birthday (0 to <12 months) and under-five mortality is the death of a child under the age of 5 years (0 to <60 months). We obtained information on under-five deaths collected from the birth history section of the questionnaire administered to individual female respondents aged 15–49 years, who had ever given birth during the 5-year period prior to the survey. The under-five mortality rate was estimated directly from the information on the birth history using the child’s date of birth, date of interview and age at death. We calculated the mortality rate for this analysis as the number of children dying during each age period (neonatal, infant and under-five) per 1000 live births in a given year. The independent variables for this analysis were categorised based on the WHO conceptual framework. At the socioeconomic position, 14 distal factors were identified and classified as follows: (1) community factors consisting of cluster type and region (representing the characteristics of a cluster); the mean household wealth index (representing economic status); the proportion of mothers with at least intermediate education (representing maternal factors); and the mean number of antenatal care visits, percentage of mothers receiving postnatal care and the percentage of deliveries assisted by skilled birth attendants in the cluster (representing community access to maternal health services); and (2) household factors including household wealth, the gender and education of the household head, maternal literacy and education, maternal marital status and polygamy status. The entire list of the independent variables with their definitions and the categories can be found in the online supplementary material. bmjgh-2017-000510supp001.pdf In this analysis, we constructed the household wealth index variable from an inventory of 24 household facilities and assets (such as the material of the dwelling floor, roof and walls; the number of persons per sleeping room; the fuel used for cooking; main source of drinking water; availability of electricity; ownership of radio, television, mobile phone, telephone, refrigerator and watch; ownership of transport devices, such as bicycles, motorcycles/scooters, animal-drawn carts, cars/trucks, and boats; the source of drinking water and type of sanitation facility; ownership land) using principal components analysis to weight the contribution of the items to the index.23 This index was divided into three categories: the bottom one-third of households that were referred to as poor households, the next one-third as the middle-level households and the top one-third as the wealthier households. At the proximal individual’s circumstances/conditions, nine factors were identified and categorised according to: (1) maternal conditions/behaviours including maternal age at childbirth, ever had a child who later died, cooking location, garbage disposal, ever heard of family planning and mother’s experience of domestic violence; and (2) under-five conditions including the child’s gender, access to improved sanitation facilities and access to improved source of drinking water. Unimproved source of water consisted of unprotected wells and springs; unfiltered water from rivers, streams, dams and hafirs; water transported by tankers/carts; and bottled water from unimproved source. Improved source of drinking water consisted of piped water (into dwelling, compound, yard or plot, to neighbour, public tap/standpipe), tube wells/boreholes, protected wells, protected springs, bottled water and water transported by tankers/carts from improved source. All respondents to the survey provided verbal informed consent; consent for children was obtained through parents, caregivers or guardians when data were originally collected. In 2013, the first author requested for data access from the director of Health Social and Demographic Statistics and from the Ministry of Health of South Sudan, and access was granted to use the data for research. Currently, the data are available from MICS website (http://mics.unicef.org/surveys). Preliminary analyses were conducted by producing frequency tabulations of all the selected characteristics examined in this study. The preliminary analyses were carried out using STATA/MP V.12 (StataCorp, College Station, TX, USA).24 The ‘Svy’ survey commands were used to allow for adjustments for the cluster sampling design and sampling weights. This was followed by calculating neonatal, infant and under-five mortality rate using a method similar to that described by Rutstein and Rojas.25 Univariable and multivariable logistic regression generalised linear latent and mixed models with the logit link and binomial family25 that adjusted for cluster and survey weights were used to identify those factors associated with neonatal, infant and under-five mortality. Univariable logistic regression was conducted to determine the unadjusted ORs of the study factors for neonatal, infant and under-five mortality. In the multivariable logistic regression analysis, a three-stage hierarchical model based on a conceptual framework described by Victora et al26 was performed in this analysis. According to this approach, the effect of distal variables could be assessed without inappropriate adjustment by proximate or intermediate variables that could be mediators of the effects of more distal variables.26 In the first-stage model (model 1), all the distal socioeconomic community factors were entered into the model and this was followed by manually executed backward elimination process. Only variables associated with the outcome were retained (model 1). In the second-stage model (model 2), the significant factors (P<0.05) in model 1 were added to socioeconomic (household) level factors and this was followed by a backward elimination procedure but retaining all the significant factors from model 1. In the third and final-stage model (model 3), the individual (maternal and child condition and circumstance) factors were added into model 2 and those variables with P<0.05 in model 3 were retained in the final model including all factors from model 2. The ORs and their 95% CIs obtained from the adjusted multiple logistics model were used to measure the factors associated with neonatal, infant and under-five mortality.

Résumé de l’IA

Study Justification:

This study examines the factors associated with neonatal, infant, and under-five mortality in South Sudan, a war-affected country. The high under-five mortality rate in South Sudan is a significant concern, and understanding the determinants of child mortality is crucial for developing effective interventions and policies to reduce child deaths. This study aims to provide evidence-based information to guide decision-making and planning for improving child health outcomes in South Sudan.

Highlights:

– The study population includes 8,125 singleton, live birth, under-five children born in South Sudan within 5 years prior to the 2010 South Sudan Household Survey.
– The multivariate analysis shows that mothers who reported a previous death of a child had a significantly higher risk of neonatal, infant, and under-five deaths.
– Other factors associated with child mortality include urban dwellers, unimproved sources of drinking water, and higher mortality among children born to teenage mothers.
– The study suggests that the conditions and circumstances in which a child is born and lives play a role in under-five mortality.
– Ensuring equitable healthcare service delivery to all disadvantaged populations of children in both urban and rural areas is essential but remains a challenge, especially in the context of ongoing violence in South Sudan.

Recommendations:

– Improve access to healthcare services for disadvantaged populations of children in both urban and rural areas.
– Address the issue of unimproved sources of drinking water to reduce neonatal mortality.
– Implement interventions to reduce teenage pregnancies and improve maternal and child health outcomes.
– Strengthen efforts to address the underlying causes of the war and violence in South Sudan to create a more stable and peaceful environment for children.

Key Role Players:

– Ministry of Health of South Sudan
– Health professionals and healthcare providers
– Community leaders and organizations
– Non-governmental organizations (NGOs) working in the field of child health
– International organizations and donors supporting child health initiatives in South Sudan

Cost Items for Planning Recommendations:

– Healthcare infrastructure development and improvement
– Training and capacity building for healthcare providers
– Outreach programs and community health initiatives
– Provision of clean and safe drinking water sources
– Education and awareness campaigns on reproductive health and family planning
– Peacebuilding and conflict resolution efforts to address the underlying causes of violence in South Sudan

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 nationally representative survey with a large sample size. The study used multivariate analysis to examine factors associated with under-five mortality in South Sudan. The results showed significant associations between various factors and neonatal, infant, and under-five deaths. The study also provided a detailed description of the survey methodology and data analysis. To improve the evidence, the abstract could include information on the statistical significance of the associations and the effect sizes of the risk factors.

Abstrait

We used a data set collected during the 2010 South Sudan Household Health Survey second round (SSHHSII), which is a nationally representative, stratified, cluster sample survey, covering the 10 states of South Sudan. The survey was largely based on the Unicef’s Multiple Indicator Cluster Survey (MICS) methodology.21 It aimed to collect health and related indicators essential for identifying the health needs of women and children, and for establishing priorities for evidence-based planning, decision-making and reporting. The SSHHSII comprised a general questionnaire to collect basic demographic information on all household members, with three individual questionnaires addressed to specific target groups: women and men aged 15–49 years and under-five children. The individual questionnaire was used to collect information on reproductive history, use of family planning, information about child health indicators and other health-related issues. The questionnaire for under-five children was administered to mothers or caretakers of children under 5 years of age.21 A two-stage cluster sampling design was employed for the selection of the sample in each of the 10 states of South Sudan. The first stage consisted of the selection of the required number of enumeration areas separately by urban and rural strata. Systematic probability proportional to size sampling procedure was used for the selection of 40 enumeration sites in each of the 10 states of South Sudan. The second stage was the selection of the total number of households in each cluster using random systematic selection procedures to select on average 25 households in each enumeration area. From the selected households, a total sample of 9369 households were interviewed with information from 9069 ever-married women, and 4344 men aged 15–49 years, and information from 8338 under-five children collected from their mother/caretaker yielding a response rate of 83%. The details of the SSHHSII sampling method have been reported elsewhere.21 Our study population consisted of 9125 (8125 weighted) singleton live-born children under the age of 5 years, who were born within 5 years prior to the survey. We excluded multiple pregnancies (n=303) in this analysis because of higher risk of newborn death, as the result of preterm birth and pregnancy complications among this group compared with singleton pregnancies.22 We modified and used the conceptual framework developed by WHO14 as a guide in identifying the key social determinants of health inequalities and their impact on the well-being of under-five children in this study. According to this framework, a set of the social economic positions, such as education, income, occupation, gender and social class, is shaped by the structural social, economic and political context.14 Furthermore, these socioeconomic positions influence an individual’s health and well-being through more specific factors called intermediate factors such as material circumstances, behaviours, biological factors and health services. According to the framework, we identified 26 possible determinants and predictors of under-five mortality in South Sudan based on the available information from the 2010 SSHHSII data sets. Figure 1 presents the modified conceptual framework used in this analysis. Conceptual framework for factors associated with under-five mortality, adapted from the WHO social determinants of health inequalities. The outcome variable for this analysis was neonatal, infant and under-five mortality expressed in a binary form (0 for living child and 1 for a child death). Neonatal mortality is defined as the probability of dying in the first month of life (0 to 28 days), infant mortality is the probability of dying between birth and first birthday (0 to <12 months) and under-five mortality is the death of a child under the age of 5 years (0 to <60 months). We obtained information on under-five deaths collected from the birth history section of the questionnaire administered to individual female respondents aged 15–49 years, who had ever given birth during the 5-year period prior to the survey. The under-five mortality rate was estimated directly from the information on the birth history using the child’s date of birth, date of interview and age at death. We calculated the mortality rate for this analysis as the number of children dying during each age period (neonatal, infant and under-five) per 1000 live births in a given year. The independent variables for this analysis were categorised based on the WHO conceptual framework. At the socioeconomic position, 14 distal factors were identified and classified as follows: (1) community factors consisting of cluster type and region (representing the characteristics of a cluster); the mean household wealth index (representing economic status); the proportion of mothers with at least intermediate education (representing maternal factors); and the mean number of antenatal care visits, percentage of mothers receiving postnatal care and the percentage of deliveries assisted by skilled birth attendants in the cluster (representing community access to maternal health services); and (2) household factors including household wealth, the gender and education of the household head, maternal literacy and education, maternal marital status and polygamy status. The entire list of the independent variables with their definitions and the categories can be found in the online supplementary material. bmjgh-2017-000510supp001.pdf In this analysis, we constructed the household wealth index variable from an inventory of 24 household facilities and assets (such as the material of the dwelling floor, roof and walls; the number of persons per sleeping room; the fuel used for cooking; main source of drinking water; availability of electricity; ownership of radio, television, mobile phone, telephone, refrigerator and watch; ownership of transport devices, such as bicycles, motorcycles/scooters, animal-drawn carts, cars/trucks, and boats; the source of drinking water and type of sanitation facility; ownership land) using principal components analysis to weight the contribution of the items to the index.23 This index was divided into three categories: the bottom one-third of households that were referred to as poor households, the next one-third as the middle-level households and the top one-third as the wealthier households. At the proximal individual’s circumstances/conditions, nine factors were identified and categorised according to: (1) maternal conditions/behaviours including maternal age at childbirth, ever had a child who later died, cooking location, garbage disposal, ever heard of family planning and mother’s experience of domestic violence; and (2) under-five conditions including the child’s gender, access to improved sanitation facilities and access to improved source of drinking water. Unimproved source of water consisted of unprotected wells and springs; unfiltered water from rivers, streams, dams and hafirs; water transported by tankers/carts; and bottled water from unimproved source. Improved source of drinking water consisted of piped water (into dwelling, compound, yard or plot, to neighbour, public tap/standpipe), tube wells/boreholes, protected wells, protected springs, bottled water and water transported by tankers/carts from improved source. All respondents to the survey provided verbal informed consent; consent for children was obtained through parents, caregivers or guardians when data were originally collected. In 2013, the first author requested for data access from the director of Health Social and Demographic Statistics and from the Ministry of Health of South Sudan, and access was granted to use the data for research. Currently, the data are available from MICS website (http://mics.unicef.org/surveys). Preliminary analyses were conducted by producing frequency tabulations of all the selected characteristics examined in this study. The preliminary analyses were carried out using STATA/MP V.12 (StataCorp, College Station, TX, USA).24 The ‘Svy’ survey commands were used to allow for adjustments for the cluster sampling design and sampling weights. This was followed by calculating neonatal, infant and under-five mortality rate using a method similar to that described by Rutstein and Rojas.25 Univariable and multivariable logistic regression generalised linear latent and mixed models with the logit link and binomial family25 that adjusted for cluster and survey weights were used to identify those factors associated with neonatal, infant and under-five mortality. Univariable logistic regression was conducted to determine the unadjusted ORs of the study factors for neonatal, infant and under-five mortality. In the multivariable logistic regression analysis, a three-stage hierarchical model based on a conceptual framework described by Victora et al26 was performed in this analysis. According to this approach, the effect of distal variables could be assessed without inappropriate adjustment by proximate or intermediate variables that could be mediators of the effects of more distal variables.26 In the first-stage model (model 1), all the distal socioeconomic community factors were entered into the model and this was followed by manually executed backward elimination process. Only variables associated with the outcome were retained (model 1). In the second-stage model (model 2), the significant factors (P<0.05) in model 1 were added to socioeconomic (household) level factors and this was followed by a backward elimination procedure but retaining all the significant factors from model 1. In the third and final-stage model (model 3), the individual (maternal and child condition and circumstance) factors were added into model 2 and those variables with P<0.05 in model 3 were retained in the final model including all factors from model 2. The ORs and their 95% CIs obtained from the adjusted multiple logistics model were used to measure the factors associated with neonatal, infant and under-five mortality.

Matériaux

N/A

Innovations

Based on the information provided, here are some potential innovations that could improve access to maternal health in South Sudan:

1. Mobile Health (mHealth) Solutions: Implementing mobile health technologies, such as text messaging and mobile apps, to provide pregnant women and new mothers with important health information, reminders for prenatal and postnatal care appointments, and access to teleconsultations with healthcare providers.

2. Community Health Workers: Training and deploying community health workers to provide maternal health education, support, and basic healthcare services to pregnant women and new mothers in remote and underserved areas.

3. Telemedicine: Establishing telemedicine networks to connect healthcare providers in urban areas with pregnant women and new mothers in rural and remote areas, allowing for remote consultations, diagnosis, and treatment.

4. Maternal Health Vouchers: Introducing voucher programs that provide pregnant women with financial assistance to access essential maternal health services, including prenatal care, skilled birth attendance, and postnatal care.

5. Transportation Solutions: Improving transportation infrastructure and implementing innovative transportation solutions, such as ambulances or mobile clinics, to ensure that pregnant women can safely and easily access healthcare facilities for prenatal care, delivery, and postnatal care.

6. Maternal Waiting Homes: Establishing maternal waiting homes near healthcare facilities to provide temporary accommodation for pregnant women who live far away, ensuring they have a safe place to stay before and after giving birth.

7. Task-Shifting: Training and empowering non-physician healthcare providers, such as nurses and midwives, to perform certain tasks traditionally done by doctors, thereby increasing the availability of skilled healthcare providers in underserved areas.

8. Public-Private Partnerships: Collaborating with private sector organizations, such as pharmaceutical companies or technology companies, to leverage their resources, expertise, and networks to improve access to maternal health services.

9. Health Financing Innovations: Exploring innovative financing mechanisms, such as microinsurance or community-based health financing schemes, to make maternal health services more affordable and accessible to low-income populations.

10. Quality Improvement Initiatives: Implementing quality improvement programs in healthcare facilities to ensure that maternal health services are delivered in a safe, effective, and patient-centered manner, thereby improving outcomes and patient satisfaction.

These are just a few potential innovations that could be considered to improve access to maternal health in South Sudan. It is important to assess the feasibility, acceptability, and effectiveness of these innovations in the local context before implementing them on a larger scale.

AI Innovations Description

Based on the information provided, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Strengthen healthcare service delivery: Ensure equitable access to healthcare services for all disadvantaged populations, both in urban and rural areas. This can be achieved by improving the availability and quality of maternal health services, such as antenatal care, postnatal care, and skilled birth attendance.

2. Address social determinants of health: Recognize and address the social and economic factors that contribute to maternal and child mortality. This includes improving education and income opportunities for women, reducing gender inequalities, and addressing the impact of war and violence on maternal and child health.

3. Improve access to clean drinking water: Unimproved sources of drinking water were found to be significantly associated with neonatal mortality. Therefore, efforts should be made to improve access to clean drinking water, especially in areas with high neonatal mortality rates.

4. Enhance community-based interventions: Implement community-based interventions that focus on raising awareness about maternal and child health, promoting healthy behaviors, and providing support to mothers and families. This can be done through community health workers, outreach programs, and community engagement initiatives.

5. Utilize data for evidence-based decision-making: Continue to collect and analyze data on maternal and child health to identify areas of improvement and track progress. This will help inform evidence-based decision-making and ensure that interventions are targeted and effective.

By implementing these recommendations, it is possible to develop innovative solutions that can improve access to maternal health and reduce maternal and child mortality rates in war-affected countries like South Sudan.

AI Innovations Methodology

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

1. Strengthen healthcare infrastructure: Invest in improving healthcare facilities, including hospitals, clinics, and maternity centers, particularly in rural and underserved areas. This can help ensure that pregnant women have access to quality maternal healthcare services.

2. Increase availability of skilled birth attendants: Train and deploy more skilled birth attendants, such as midwives and obstetricians, especially in areas with high maternal mortality rates. Skilled birth attendants can provide essential care during childbirth and reduce the risk of complications.

3. Enhance community-based healthcare services: Implement community-based programs that focus on educating and empowering women and families about maternal health. These programs can provide prenatal and postnatal care, promote healthy behaviors, and raise awareness about the importance of seeking timely medical assistance during pregnancy and childbirth.

4. Improve transportation and infrastructure: Enhance transportation systems, including roads and ambulances, to ensure that pregnant women can access healthcare facilities in a timely manner. This is particularly crucial in remote and conflict-affected areas where transportation infrastructure may be limited.

5. Increase availability of essential medicines and supplies: Ensure that healthcare facilities have an adequate supply of essential medicines, equipment, and supplies needed for safe childbirth and emergency obstetric care. This includes medications for preventing and managing complications during pregnancy and childbirth.

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

1. Collect baseline data: Gather data on the current state of maternal health access, including indicators such as maternal mortality rates, availability of healthcare facilities, skilled birth attendants, and transportation infrastructure.

2. Define simulation parameters: Determine the specific variables and parameters that will be used to simulate the impact of the recommendations. This could include factors such as the number of healthcare facilities to be built or upgraded, the number of skilled birth attendants to be trained and deployed, and the improvements in transportation infrastructure.

3. Develop a simulation model: Create a mathematical or computational model that incorporates the baseline data and simulation parameters. This model should simulate the impact of the recommendations on maternal health access, taking into account factors such as population size, geographical distribution, and healthcare utilization patterns.

4. Run the simulation: Use the simulation model to project the potential impact of the recommendations over a specified time period. This could involve running multiple scenarios to assess the effects of different combinations of recommendations.

5. Analyze the results: Evaluate the simulation results to determine the projected improvements in access to maternal health. This could include assessing changes in maternal mortality rates, the number of women receiving prenatal and postnatal care, and the reduction in travel time to healthcare facilities.

6. Validate the simulation: Compare the simulation results with real-world data, if available, to validate the accuracy and reliability of the simulation model.

7. Refine and iterate: Based on the simulation results and validation, refine the recommendations and simulation model as needed. Iterate the simulation process to assess the impact of different scenarios and refine the recommendations further.

By following this methodology, policymakers and healthcare stakeholders can gain insights into the potential impact of different innovations and recommendations on improving access to maternal health. This can inform decision-making and resource allocation to prioritize interventions that are most likely to have a positive impact.

Auteur

Dima Health

Statistics:

Citations: 19

Identifiers:

DOI: 10.1136/bmjgh-2017-000510

Research Areas:

Community Interventions, Disparities, Environmental, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Violence and Injury, Workforce

Study Countries:

Multi-Countries, South Sudan, Sudan

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female

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