Spatial and multilevel analysis of unskilled birth attendance in Chad

BMC Public Health, Volume 22, No. 1, Year 2022

Interpretação

Background: Unskilled birth attendance is a major public health concern in Sub-Saharan Africa (SSA). Existing studies are hardly focused on the socio-demographic correlates and geospatial distribution of unskilled birth attendance in Chad (a country in SSA), although the country has consistently been identified as having one of the highest prevalence of maternal and neonatal deaths in the world. This study aimed to analyse the socio-demographic correlates and geospatial distribution of unskilled birth attendance in Chad. Methods: The study is based on the latest Demographic and Health Survey (DHS) data for Chad. A total of 10,745 women aged between 15 and 49 years were included in this study. A multilevel analysis based on logistic regression was conducted to estimate associations of respondents’ socio-demographic characteristics with unskilled birth attendance. Geographic Information System (GIS) mapping tools, including Getis-Ord Gi hotspot analysis tool and geographically weighted regression (GWR) tool, were used to explore areas in Chad with a high prevalence of unskilled birth attendance. Results: The findings show that unskilled birth attendance was spatially clustered in four Chad departments: Mourtcha, Dar-Tama, Assoungha, and Kimiti, with educational level, occupation, birth desire, birth order, antenatal care, and community literacy identified as the spatial predictors of unskilled birth attendance. Higher educational attainment, higher wealth status, cohabitation, lowest birth order, access to media, not desiring more births, and higher antenatal care visits were associated with lower odds of unskilled birth attendance at the individual level. On the other hand, low community literacy level was associated with higher odds of unskilled birth attendance in Chad whereas the opposite was true for urban residency. Conclusions: Unskilled birth attendance is spatially clustered in some parts of Chad, and it is associated with various disadvantaged individual and community level factors. When developing interventions for unskilled birth attendance in Chad, concerned international bodies, the Chad government, maternal health advocates, and private stakeholders should consider targeting the high-risk local areas identified in this study. The study was based on secondary data obtained from the Chad 2014–2015 demographic and health survey (DHS) conducted from October 2014 to April 2015. The survey included a nationally representative sample of 17,719 women aged 15–49 years selected from 17, 233 households. Respondent selection was based on a two-stage stratified cluster sampling procedure. For the first stage, 626 enumeration areas were selected from a list of clusters nationwide [11]. Households were then selected from the complete list of households in each selected cluster during the second stage. For this study, the analysis excluded women who had never given birth within the five years preceding the survey in 2014–2015. The analytic sample, thus, was made up of 10,745 women of reproductive age whose last birth occurred during the five years preceding the survey. The outcome variable – unskilled birth attendance – was constructed from the categories used in the DHS concerning the person who assisted with the delivery of the respondent’s last child. In the DHS, the respondents were asked to identify the personnel that assisted them during delivery such as a doctor, nurse or midwife, community health officer or nurse, traditional birth attendant, traditional health volunteer, community or village health volunteer, relative, other, or no one. A binary outcome was constructed by categorising baby deliveries performed by traditional birth attendants, traditional health volunteers, community/village health volunteers, relatives, and others as unskilled birth attendance and the remaining health personnel as skilled birth attendance. The independent variables of the current study were mainly informed by findings of existing studies on unskilled birth attendance [12–14]. The independent variables considered in the current study are both individual and community level variables measured at the cluster level. The individual-level variables comprised socio-demographic characteristics such as age (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49), educational level (No formal education, Primary, Secondary, Higher), wealth index (Poorest, Poorer, Middle, Richer, Richest), marital status (Never in a marital union, Married, Cohabitation, Widowed, Divorced, Separated), occupation (Not working, Working), birth order (1, 2–3, 4 +), media exposure (No, Yes), desire for birth (Then, later, no more), and antenatal care visits (Less than 4, 4 or more). Community variables were computed at the primary sampling unit level. Community socioeconomic status was computed from occupation, wealth, and education of study participants who resided in each sampled cluster as all community variables are for only women. Principal component analyses were applied based on women who were unemployed, uneducated, or from poor households. A standardised rating was derived with an average rating (zero) and standard deviation [15]. The rankings were then segregated into tertiles where the lower scores (tertile 1) denote high socioeconomic status, and average scores (tertile 2) as moderate socioeconomic status while the higher scores (tertile 3) denote lower socioeconomic status. Respondents who attended higher than secondary school were considered literate while all other respondents were given a sentence to read and were considered literate if they could read all or part of the sentence. Therefore, if respondents had higher than secondary education or had no school/primary/secondary education but could read a whole sentence, it was considered as high literacy. Medium literacy represented respondents who had no school/primary/secondary education and could read part of the sentence. Low literacy comprised respondents who had no school/primary/secondary education and could not read at all. Thus, socioeconomic status and literacy level were measured as low, moderate, and high at the primary sampling unit level (cluster) while place of residence was measured as urban or rural. The explanatory variables of the current study were mainly informed by findings of existing studies on unskilled birth attendance [12–14]. Two levels of analysis were conducted. A descriptive analysis was performed by calculating the proportion of respondents’ socio-demographic characteristics. Multilevel logistic regression analysis was conducted to estimate associations of respondents’ socio-demographic characteristics with unskilled birth attendance. The Model 1 (null model) was estimated to establish whether there is a significant variance in unskilled birth attendance at the cluster level to justify the multilevel analysis, while Model 2 comprises the fixed effects analysis of individual-level socio-demographic factors and assumes that these socio-demographic factors have fixed or constant association with unskilled birth attendance across all the primary sampling units. Model 3 estimated the effects of the community-level variables and assumed that the effects may vary across the primary sampling units. Model 4 contains the full model (Models 1, 2, and 3) and the mainstay of the analysis. Adjusted odds ratios and 95% confidence intervals were calculated for the variables. All analyses were conducted with Stata software (Version 14) and the results were weighted to cater for potential over-sampling and under-sampling. Regarding the spatial analysis, the coordinates of the surveyed respondents were obtained from the DHS website. These data were projected to UTM Zone 33 N to aid the spatial analysis. Relying on just the surveyed point was insufficient, so Chad’s departments’ shapefile was obtained from the Humanitarian Data Exchange for the spatial analysis [16]. The departments’ shapefile was also projected to UTM Zone 33 N. This dataset had 70 departments, but 55 departments were found to have had respondents sampled for the survey. Therefore, the 15 additional departments were excluded from the analysis. As part of the dependent variable’s data processing, birth attendance was coded 0 for skilled birth attendance and 1 for unskilled birth attendance. The independent variables were in their original categories, but proportions were generated from the lower categories at each sub-regional level. Therefore, the extracted surveyed data was linked with its corresponding coordinates. These data were then merged with the sub-regional shapefile using the join tool. In joining, the average computation system was adopted to estimate averages of unskilled birth attendance at the sub-regional level (departments). After obtaining the sub-regional level estimate of unskilled birth attendance, the independent variables’ proportions were added to asssist with the computation of the geographically weighted regression (GWR). The actual data analysis began with using the spatial autocorrelation tool (Moran’s I) to assess the distribution of unskilled birth attendance in Chad. The spatial autocorrelation assesses the distribution of a phenomenon being studied; thus, either random, dispersed, or clustered. The output does not show the exact sub-regional distribution of unskilled birth attendance. Therefore, the Getis-Ord Gi hotspot analysis tool was used to examine areas that had a higher tendency of experiencing unskilled birth attendance. A limitation of the hotspot tool is that it considers areas with high values of the dependent variables to create hot and cold spots. However, it is advisable to use the Anselin Local Moran’s I cluster and outlier analysis tool for policy implications to explore other areas that may not be identified as a hotspot but have a high incidence of the phenomenon being studied. The final analysis was to determine the spatial explanation of the independent variables by running the GWR. GWR is a spatial regression technique that focuses on the spatial differentiation in the explanatory power of independent variables. This is done by estimating separate regression equations between the dependent and independent variables to every feature in the dataset. Before running the GWR, significant independent variables were identified using the exploratory regression analysis. This regression method was adopted because it assesses all possible combinations of the independent variables that best explain the dependent variable. Thus, it looks for models that meet all of the requirements and assumptions of the OLS method. The first model of the fifth category was accepted since it had the criteria [AICc = -52.19, JB = 0.08, K(BP)0.02, VIF = 1.71, SA = 0.46] that contributed to the highest adjusted R2 (0.32). This revealed the best combination of independent variables that are significant predictors of unskilled birth attendance in Chad. This allowed the use of the identified independent variables that were used for the GWR. In conducting the GWR, the identified independent variables found to be significant predictors of unskilled birth attendance in Chad were used as the explanatory variables with kernel type being fixed as well as bandwidth method using the AICc. The obtained adjusted R2 was about 0.309 percent which implies that the results explain about 31% of the entire data. All the data processing and analyses were conducted in ArcGIS version 10.7. The Likelihood Ratio (LR) test was used to evaluate the fitness of all the models. Before fitting the models, the presence of multicollinearity between the independent variables was evaluated. The variance inflation factor (VIF) test found the absence of high multicollinearity between the variables (Mean VIF = 1.94).

AI Digest

Study Justification:

– Unskilled birth attendance is a major public health concern in Sub-Saharan Africa.
– Chad has one of the highest prevalence of maternal and neonatal deaths in the world.
– Existing studies on unskilled birth attendance in Chad are limited.
– This study aims to analyze the socio-demographic correlates and geospatial distribution of unskilled birth attendance in Chad.

Study Highlights:

– Unskilled birth attendance is spatially clustered in four Chad departments: Mourtcha, Dar-Tama, Assoungha, and Kimiti.
– Educational level, occupation, birth desire, birth order, antenatal care, and community literacy are identified as predictors of unskilled birth attendance.
– Higher educational attainment, higher wealth status, cohabitation, lowest birth order, access to media, not desiring more births, and higher antenatal care visits are associated with lower odds of unskilled birth attendance at the individual level.
– Low community literacy level is associated with higher odds of unskilled birth attendance in Chad, while urban residency is associated with lower odds.

Recommendations:

– Develop interventions targeting the high-risk areas identified in the study.
– Involve concerned international bodies, the Chad government, maternal health advocates, and private stakeholders in the development and implementation of interventions.
– Focus on improving educational attainment, wealth status, access to media, and antenatal care utilization to reduce unskilled birth attendance.
– Promote community literacy and urban residency to decrease the odds of unskilled birth attendance.

Key Role Players:

– Concerned international bodies
– Chad government
– Maternal health advocates
– Private stakeholders

Cost Items for Planning Recommendations:

– Educational programs and resources
– Media campaigns and outreach
– Antenatal care services and facilities
– Community literacy initiatives
– Infrastructure development in high-risk areas
– Training and capacity building for healthcare providers
– Monitoring and evaluation systems

Força da prova

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study is based on a nationally representative sample and uses multilevel analysis and spatial analysis techniques. The abstract clearly presents the objectives, methods, and findings of the study. However, it would be helpful to include information on the statistical significance of the associations found and the limitations of the study. Additionally, providing more details on the specific actionable steps to improve interventions for unskilled birth attendance in Chad would enhance the practicality of the study.

Resumo

The study was based on secondary data obtained from the Chad 2014–2015 demographic and health survey (DHS) conducted from October 2014 to April 2015. The survey included a nationally representative sample of 17,719 women aged 15–49 years selected from 17, 233 households. Respondent selection was based on a two-stage stratified cluster sampling procedure. For the first stage, 626 enumeration areas were selected from a list of clusters nationwide [11]. Households were then selected from the complete list of households in each selected cluster during the second stage. For this study, the analysis excluded women who had never given birth within the five years preceding the survey in 2014–2015. The analytic sample, thus, was made up of 10,745 women of reproductive age whose last birth occurred during the five years preceding the survey. The outcome variable – unskilled birth attendance – was constructed from the categories used in the DHS concerning the person who assisted with the delivery of the respondent’s last child. In the DHS, the respondents were asked to identify the personnel that assisted them during delivery such as a doctor, nurse or midwife, community health officer or nurse, traditional birth attendant, traditional health volunteer, community or village health volunteer, relative, other, or no one. A binary outcome was constructed by categorising baby deliveries performed by traditional birth attendants, traditional health volunteers, community/village health volunteers, relatives, and others as unskilled birth attendance and the remaining health personnel as skilled birth attendance. The independent variables of the current study were mainly informed by findings of existing studies on unskilled birth attendance [12–14]. The independent variables considered in the current study are both individual and community level variables measured at the cluster level. The individual-level variables comprised socio-demographic characteristics such as age (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49), educational level (No formal education, Primary, Secondary, Higher), wealth index (Poorest, Poorer, Middle, Richer, Richest), marital status (Never in a marital union, Married, Cohabitation, Widowed, Divorced, Separated), occupation (Not working, Working), birth order (1, 2–3, 4 +), media exposure (No, Yes), desire for birth (Then, later, no more), and antenatal care visits (Less than 4, 4 or more). Community variables were computed at the primary sampling unit level. Community socioeconomic status was computed from occupation, wealth, and education of study participants who resided in each sampled cluster as all community variables are for only women. Principal component analyses were applied based on women who were unemployed, uneducated, or from poor households. A standardised rating was derived with an average rating (zero) and standard deviation [15]. The rankings were then segregated into tertiles where the lower scores (tertile 1) denote high socioeconomic status, and average scores (tertile 2) as moderate socioeconomic status while the higher scores (tertile 3) denote lower socioeconomic status. Respondents who attended higher than secondary school were considered literate while all other respondents were given a sentence to read and were considered literate if they could read all or part of the sentence. Therefore, if respondents had higher than secondary education or had no school/primary/secondary education but could read a whole sentence, it was considered as high literacy. Medium literacy represented respondents who had no school/primary/secondary education and could read part of the sentence. Low literacy comprised respondents who had no school/primary/secondary education and could not read at all. Thus, socioeconomic status and literacy level were measured as low, moderate, and high at the primary sampling unit level (cluster) while place of residence was measured as urban or rural. The explanatory variables of the current study were mainly informed by findings of existing studies on unskilled birth attendance [12–14]. Two levels of analysis were conducted. A descriptive analysis was performed by calculating the proportion of respondents’ socio-demographic characteristics. Multilevel logistic regression analysis was conducted to estimate associations of respondents’ socio-demographic characteristics with unskilled birth attendance. The Model 1 (null model) was estimated to establish whether there is a significant variance in unskilled birth attendance at the cluster level to justify the multilevel analysis, while Model 2 comprises the fixed effects analysis of individual-level socio-demographic factors and assumes that these socio-demographic factors have fixed or constant association with unskilled birth attendance across all the primary sampling units. Model 3 estimated the effects of the community-level variables and assumed that the effects may vary across the primary sampling units. Model 4 contains the full model (Models 1, 2, and 3) and the mainstay of the analysis. Adjusted odds ratios and 95% confidence intervals were calculated for the variables. All analyses were conducted with Stata software (Version 14) and the results were weighted to cater for potential over-sampling and under-sampling. Regarding the spatial analysis, the coordinates of the surveyed respondents were obtained from the DHS website. These data were projected to UTM Zone 33 N to aid the spatial analysis. Relying on just the surveyed point was insufficient, so Chad’s departments’ shapefile was obtained from the Humanitarian Data Exchange for the spatial analysis [16]. The departments’ shapefile was also projected to UTM Zone 33 N. This dataset had 70 departments, but 55 departments were found to have had respondents sampled for the survey. Therefore, the 15 additional departments were excluded from the analysis. As part of the dependent variable’s data processing, birth attendance was coded 0 for skilled birth attendance and 1 for unskilled birth attendance. The independent variables were in their original categories, but proportions were generated from the lower categories at each sub-regional level. Therefore, the extracted surveyed data was linked with its corresponding coordinates. These data were then merged with the sub-regional shapefile using the join tool. In joining, the average computation system was adopted to estimate averages of unskilled birth attendance at the sub-regional level (departments). After obtaining the sub-regional level estimate of unskilled birth attendance, the independent variables’ proportions were added to asssist with the computation of the geographically weighted regression (GWR). The actual data analysis began with using the spatial autocorrelation tool (Moran’s I) to assess the distribution of unskilled birth attendance in Chad. The spatial autocorrelation assesses the distribution of a phenomenon being studied; thus, either random, dispersed, or clustered. The output does not show the exact sub-regional distribution of unskilled birth attendance. Therefore, the Getis-Ord Gi hotspot analysis tool was used to examine areas that had a higher tendency of experiencing unskilled birth attendance. A limitation of the hotspot tool is that it considers areas with high values of the dependent variables to create hot and cold spots. However, it is advisable to use the Anselin Local Moran’s I cluster and outlier analysis tool for policy implications to explore other areas that may not be identified as a hotspot but have a high incidence of the phenomenon being studied. The final analysis was to determine the spatial explanation of the independent variables by running the GWR. GWR is a spatial regression technique that focuses on the spatial differentiation in the explanatory power of independent variables. This is done by estimating separate regression equations between the dependent and independent variables to every feature in the dataset. Before running the GWR, significant independent variables were identified using the exploratory regression analysis. This regression method was adopted because it assesses all possible combinations of the independent variables that best explain the dependent variable. Thus, it looks for models that meet all of the requirements and assumptions of the OLS method. The first model of the fifth category was accepted since it had the criteria [AICc = -52.19, JB = 0.08, K(BP)0.02, VIF = 1.71, SA = 0.46] that contributed to the highest adjusted R2 (0.32). This revealed the best combination of independent variables that are significant predictors of unskilled birth attendance in Chad. This allowed the use of the identified independent variables that were used for the GWR. In conducting the GWR, the identified independent variables found to be significant predictors of unskilled birth attendance in Chad were used as the explanatory variables with kernel type being fixed as well as bandwidth method using the AICc. The obtained adjusted R2 was about 0.309 percent which implies that the results explain about 31% of the entire data. All the data processing and analyses were conducted in ArcGIS version 10.7. The Likelihood Ratio (LR) test was used to evaluate the fitness of all the models. Before fitting the models, the presence of multicollinearity between the independent variables was evaluated. The variance inflation factor (VIF) test found the absence of high multicollinearity between the variables (Mean VIF = 1.94).

Materiais

https://efashare.b-cdn.net/materials/14df9e16a8aea232801fb5ff280ab38a55ef3dc476c74da359cf8ae7c8184366/12889_2022_13972_MOESM1_ESM.docx

Inovações

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women and new mothers with information and resources related to maternal health. These apps could include features such as appointment reminders, educational materials, and access to telemedicine consultations.

2. Community Health Workers: Train and deploy community health workers to provide maternal health services and education in remote or underserved areas. These workers could conduct antenatal visits, assist with deliveries, and provide postnatal care, helping to bridge the gap in skilled birth attendance.

3. Telemedicine: Establish telemedicine networks to connect healthcare providers in urban areas with pregnant women in rural or hard-to-reach locations. This would allow for remote consultations, monitoring, and guidance during pregnancy and childbirth.

4. Maternal Waiting Homes: Set up maternal waiting homes near healthcare facilities to accommodate pregnant women who live far away and need to travel for delivery. These homes would provide a safe and comfortable place for women to stay during the final weeks of pregnancy, ensuring they have timely access to skilled birth attendance.

5. Transportation Support: Improve transportation infrastructure and provide transportation subsidies or vouchers for pregnant women to facilitate their access to healthcare facilities for antenatal care, delivery, and postnatal care.

6. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of skilled birth attendance and the availability of maternal health services. These campaigns could use various mediums, such as radio, television, and community outreach programs.

7. Strengthening Health Systems: Invest in strengthening the overall healthcare system in Chad, including training and retaining skilled healthcare professionals, improving infrastructure and equipment, and ensuring the availability of essential medicines and supplies for maternal health services.

It is important to note that the specific implementation and effectiveness of these innovations would require further research, planning, and collaboration with relevant stakeholders in Chad’s healthcare system.

AI Innovations Description

Based on the study titled “Spatial and multilevel analysis of unskilled birth attendance in Chad,” the following recommendation can be developed into an innovation to improve access to maternal health:

1. Target high-risk local areas: The study identified four departments in Chad (Mourtcha, Dar-Tama, Assoungha, and Kimiti) with a high prevalence of unskilled birth attendance. To improve access to maternal health, interventions should be developed specifically for these high-risk areas. This could involve setting up maternal health clinics or mobile health units in these departments to provide skilled birth attendance and other essential maternal health services.

2. Improve educational attainment: The study found that higher educational attainment was associated with lower odds of unskilled birth attendance. Therefore, efforts should be made to improve access to education, especially for women and girls in Chad. This can be achieved through initiatives such as scholarships, awareness campaigns, and community-based education programs.

3. Enhance antenatal care services: The study also identified antenatal care visits as a predictor of skilled birth attendance. To improve access to maternal health, it is crucial to strengthen antenatal care services in Chad. This can be done by ensuring that pregnant women have access to regular check-ups, screenings, and counseling during pregnancy. Additionally, efforts should be made to educate women about the importance of antenatal care and encourage them to seek these services.

4. Address community-level factors: The study highlighted community literacy level as a predictor of unskilled birth attendance. To address this, interventions should focus on improving community literacy rates and promoting health literacy specifically related to maternal health. This can be achieved through community-based education programs, partnerships with local organizations, and the use of culturally appropriate communication materials.

5. Collaborate with stakeholders: To effectively implement these recommendations, collaboration is essential. Concerned international bodies, the Chad government, maternal health advocates, and private stakeholders should work together to develop and implement interventions. This can involve sharing resources, expertise, and best practices to ensure a comprehensive and sustainable approach to improving access to maternal health in Chad.

By implementing these recommendations, it is possible to develop innovative solutions that address the specific challenges faced in Chad and improve access to maternal health services, ultimately reducing the prevalence of unskilled birth attendance and maternal and neonatal deaths.

AI Innovations Methodology

Based on the information provided, here are some potential recommendations for improving access to maternal health in Chad:

1. Strengthening education and awareness programs: Implement initiatives to increase education and awareness about the importance of skilled birth attendance and the risks associated with unskilled birth attendance. This can be done through community-based education programs, media campaigns, and targeted messaging.

2. Enhancing antenatal care services: Improve access to and quality of antenatal care services, including regular check-ups, screenings, and counseling. This can help identify and address potential complications during pregnancy and promote the importance of skilled birth attendance.

3. Training and capacity building: Invest in training and capacity building programs for healthcare providers, particularly in rural and underserved areas. This can help increase the number of skilled birth attendants and improve the quality of care provided during childbirth.

4. Strengthening healthcare infrastructure: Improve the availability and accessibility of healthcare facilities, particularly in remote and rural areas. This includes ensuring the presence of skilled birth attendants, necessary medical equipment, and emergency obstetric care services.

5. Addressing socio-economic factors: Implement interventions to address socio-economic factors that contribute to unskilled birth attendance, such as poverty, low literacy rates, and limited access to transportation. This can involve providing financial support for maternal healthcare, promoting income-generating activities, and improving transportation infrastructure.

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

1. Data collection: Gather data on key indicators related to maternal health, such as the prevalence of unskilled birth attendance, educational levels, antenatal care utilization, healthcare infrastructure, and socio-economic factors. This can be done through surveys, existing databases, and collaboration with relevant stakeholders.

2. Baseline assessment: Analyze the current situation and identify the gaps and challenges in accessing maternal health services. This can involve conducting a spatial and multilevel analysis, similar to the methodology described in the provided study, to understand the socio-demographic correlates and geospatial distribution of unskilled birth attendance.

3. Scenario development: Develop different scenarios based on the recommended interventions. This can involve estimating the potential impact of each intervention on key indicators, such as the increase in skilled birth attendance rates, reduction in unskilled birth attendance rates, and improvement in access to antenatal care.

4. Modeling and simulation: Use modeling and simulation techniques, such as statistical models or agent-based models, to simulate the impact of the different scenarios on improving access to maternal health. This can involve running simulations based on the available data and assumptions about the effectiveness and coverage of the interventions.

5. Evaluation and analysis: Evaluate the results of the simulations and analyze the potential impact of the recommended interventions. This can include comparing the outcomes of different scenarios, identifying the most effective interventions, and assessing the cost-effectiveness of the interventions.

6. Policy and decision-making: Use the findings from the simulation to inform policy and decision-making processes. This can involve presenting the results to relevant stakeholders, policymakers, and healthcare providers to guide the implementation of interventions and allocate resources effectively.

It’s important to note that the specific methodology for simulating the impact of recommendations may vary depending on the available data, resources, and expertise. It’s recommended to consult with experts in the field of maternal health and data analysis to develop a robust methodology tailored to the specific context of improving access to maternal health in Chad.

Autores & Coautores

Statistics:

Authors: 6

Identifiers:

DOI: 10.1186/s12889-022-13972-6

Research Areas:

Community Interventions, Health System and Policy, Maternal Access, Maternal and Child Health, Sexual and Reproductive Health, Social Determinants, Workforce

Study Countries:

Chad

Study Design:

Cohort Study, Cross Sectional Study, Exploratory Study

Study Approach:

Quantitative

Participants Gender:

Female

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