Effects of short birth interval on neonatal, infant and under-five child mortality in Ethiopia: A nationally representative observational study using inverse probability of treatment weighting

BMJ Open, Volume 11, No. 8, Year 2021

Interpretação

Objective To assess the effect of short birth interval (SBI) on neonatal, infant, and under-five mortality in Ethiopia. Design A nationally representative cross-sectional survey. Setting This study used data from the Ethiopia Demographic and Health Survey 2016. Participants A total of 8448 women who had at least two live births during the 5 years preceding the survey were included in the analysis. Outcome measures Neonatal mortality (death of the child within 28 days of birth), infant mortality (death between birth and 11 months) and under-five mortality (death between birth and 59 months) were the outcome variables. Methods Weighted logistic regression analysis based on inverse probability of treatment weights was used to estimate exposure effects adjusted for potential confounders. Results The adjusted ORs (AORs) of neonatal mortality were about 85% higher among women with SBI (AOR=1.85, 95% CI=1.19 to 2.89) than those without. The odds of infant mortality were twofold higher (AOR=2.16, 95% CI=1.49 to 3.11) among women with SBI. The odds of under-five child mortality were also about two times (AOR=2.26, 95% CI=1.60 to 3.17) higher among women with SBI. Conclusion SBI has a significant effect on neonatal, infant and under-five mortality in Ethiopia. Interventions targeting SBI are warranted to reduce neonatal, infant and under-five mortality. This analysis used data from the Ethiopia Demographic and Health Survey (EDHS) 2016. The EDHS is a nationally representative cross-sectional study conducted in nine geographical regions (Tigray, Afar, Amhara, Oromia, Somali, Benishangul-Gumuz, Southern Nations Nationalities and Peoples’ region, Gambela and Harari) and two administrative cities (Addis Ababa and Dire Dawa). A two-stage, stratified, clustered random sampling design was employed to collect data from women who gave birth within the 5 years preceding the survey. Further descriptions of the sampling procedure for the EDHS are presented elsewhere.5 A total of 8448 women who had at least two live births during the 5 years preceding the 2016 survey were included in the analysis. When women had more than two births in the 5 years preceding the survey, the birth interval between the most recent index child and the immediately preceding child was considered for all the study participants. The outcome variables in the current study were neonatal mortality (death of the child within 28 days of birth), infant mortality (death between birth and 11 months) and under-five mortality (death between birth and 59 months).5 43 These outcomes were coded as binary variables (1/0). SBI was the treatment variable and was defined as a birth-to-birth interval of less than 33 months as per the WHO definition.1 A preceding birth interval, the amount of time between the birth of the child under study (index child) and the immediately preceding birth, was considered in this study. Women’s birth interval data were collected by extracting the date of birth of their biological children data from the children’s birth/immunisation certificate, and/or asking for information regarding their children’s date of birth from the women. Mothers were asked to confirm the accuracy of the information before documenting children’s date of birth from children’s birth/immunisation certificates. This crosschecking was performed to avoid errors, since in some cases the documented birth date may represent the date when the birth was recorded, rather than the actual birth date. In the absence of children’s birth certificates, information regarding children’s date of birth was obtained from their mothers. Further information regarding birth interval data collection is provided elsewhere.2 3 44 After reviewing relevant literature,2 18–21 23–25 28 29 39 45 46 direct acyclic graphs (DAGs) were constructed using DAGitty V.3.047 to identify confounders for the association between SBI and neonatal, infant and under-five child mortality. Adjustment for such confounders is necessary to estimate the unbiased effect of SBI on neonatal, infant and under-five mortality (figure 1). DAG is a formal system of mapping variables and the direction of causal relationships among them.48 49 This graphical representation of causal effects among variables helps understand whether bias is potentially reduced or increased when conditioning on covariates. Moreover, it illustrates covariates that lie in the causal pathway between the treatment and outcomes, which should not be included in the analysis as a confounder. These variables are indicated by green lines in figure 1. This is because a propensity score (PS) that includes covariates affected by the treatment (ie, variables on the causal pathway between treatment and outcome) obscures part of the treatment effect that one is trying to estimate.50 Identified confounders were maternal age at the birth of the index child, maternal education, maternal occupation, husband’s education, husband’s occupation, household wealth status, survival status of the preceding child, the total number of the preceding child, place of residence (urban/rural), regions, access to media and decision-making autonomy. A list of all variables considered in the DAG is provided in online supplemental material I. Direct acyclic graph used to select controlling variables. ANC, antenatal care; Birth_ord, birth order; Birth_wt, birth weight; H_Educ, husband education; H_Occup, husband occupation; IM, infant mortality; M_age_atBirth_chil, maternal age at birth of the index child; M_Edu, maternal education; M_Occu, maternal occupation; Multiple_preg, multiple pregnancy; NM, neonatal mortality; PNC, postnatal care; Prev_Chi_Survival, previous child survival; Respiratory_infn, respiratory infection; SBI, short birth interval; Total_Prec_child, total number of preceding child; TT_vaccin, tetanus toxoid vaccination status; U5M, under-five mortal. bmjopen-2020-047892supp001.pdf A yellowish-green circle with a triangle at its centre indicates the main treatment/exposure variable, a blue circle with a vertical bar at its centre indicates the outcome variable, light red circles indicate ancestors of exposure and outcome (ie, confounders). Blue circles indicate the ancestors of the outcome variable. Green lines indicate a causal pathway. Red lines indicate open paths by which confounding may occur; this confounding can be removed by adjusting for one or several variables on the pathway. Participants’ characteristics were described using frequency with per cent. P values were calculated using Pearson’s χ2 test. Given that the outcomes (ie, neonatal, infant and under-five mortality) were relatively infrequent, the unbiased effect of SBI on each outcome was estimated using PSs with a stabilised method of inverse probability of treatment weighting (IPTW). A previous study51 has shown that IPTW with stabilised weights preserves the sample size of the original data, provides an appropriate estimation of the variance of the main effect and maintains an appropriate type I error rate. The other methods, such as IPTW with normalised weight and greedy algorithm with 1:1 matching methods, are discussed elsewhere.52–54 A PS is defined as the probability of treatment assignment given observed baseline covariates (described in online supplemental material II).54 PSs are used to estimate treatment effects on outcomes using observational data when confounding bias due to non-random treatment assignment is likely.50 IPTW weights the entire study sample by the inverse of the PS55; a differential amount of information is used from each participant, depending on their conditional probability of receiving treatment. This means observations are less likely to be lost than when using matching for confounder adjustment.56 57 PSs are a robust alternative to covariate adjustment when the outcome variable is rare, resulting in data sparsity and estimation issues in multivariable models.57 In this study, the weighted prevalence of the outcome variables of neonatal, infant and under-five mortality were 2.9% (95% CI=2.39% to 3.61%), 4.8% (95% CI=4.11% to 5.58%) and 5.5% (95% CI=4.73% to 6.44%), respectively. bmjopen-2020-047892supp002.pdf The analysis procedure was as follows. First, the PS was estimated using a logistic regression model in which treatment assignment (SBI vs non-SBI) was regressed on the 11 covariates identified using the DAG. The balance of measured covariates/confounders was then assessed across treatment groups (ie, women with SBI) and comparison groups (ie, women with non-SBI) before and after weighting, by computing standardised differences (online supplemental material II).57 58 For a continuous covariate, the standardised difference58 59 is defined as: where x¯treatment and x¯control denote the sample mean of the covariate in treated and untreated subjects, respectively and streatment2 and streatment2 denote the corresponding sample variances of the covariate. The standardised difference58 59 for a dichotomous variable is given as: where p^treatment and p^control denote the prevalence of the dichotomous variable in treated and untreated subjects, respectively. A standard difference <0.1 has been suggested as indicating a negligible difference in the mean or prevalence of a covariate between treatment and control groups and was used here.58 In addition, kernel densities were plotted to graphically demonstrate the PS balance in the treatment group (ie, women with SBI) and control groups (women with non-SBI). Balance in PSs was considered to be achieved when the kernel density line for the treatment group and control group lay closer together.60 The IPTWs was then calculated as 1/PS for those exposed to SBI and 1/(1−PS) for those who were not. The sample was then reweighted by the IPTW and the balance of the covariates checked in the reweighted sample.50 61 Stabilisation of weights was made to preserve the sample size of the original data, reduce the effect of weights of either treated subjects with low PSs or untreated subjects with high PSs, and improve the estimation of variance estimates and CIs for the treatment effect.51 Since the EDHS employed a two-stage, stratified, clustered random sampling, which is a complex sampling procedure, sampling weights were also used to adjust for the non-proportional allocation of sample participants to different regions, including urban and rural areas, and consider the possible differences in response rates.5 Finally, a weighted logistic regression was fit to estimate the effect of the treatment (SBI) on each outcome variable (neonatal, infant and under-five mortality). Estimation of the treatment effect on outcome variables in the final model used the grand weight, which was formed as the product of the survey weight and the stabilised weight. Literature has shown that combining a PS method and survey weighting is necessary to estimate unbiased treatment effects which are generalisable to the original survey target population.62 The treatment effect on the outcome variables was expressed as adjusted ORs (AORs) with a 95% CI. Statistical analysis was performed using Stata V.14 statistical software (StataCorp Stata Statistical Software: Release V.14. College Station, Texas: StataCorp LP 2015). Figure 2 presents a schematic summary of the overall analysis procedure. Schematic presentation of the overall steps followed in the analysis. Patients and/or the general public were not involved in the design, or conduct or drafting of this secondary analysis.

AI Digest

Study Justification:

This study aimed to assess the effects of short birth intervals (SBI) on neonatal, infant, and under-five mortality in Ethiopia. The justification for this study is to provide evidence on the impact of SBI on child mortality in order to inform interventions and policies aimed at reducing neonatal, infant, and under-five mortality rates.

Highlights:

– The study used data from the Ethiopia Demographic and Health Survey 2016, which is a nationally representative cross-sectional survey.
– A total of 8,448 women who had at least two live births in the 5 years preceding the survey were included in the analysis.
– The study found that women with SBI had significantly higher odds of neonatal mortality (85% higher), infant mortality (twofold higher), and under-five child mortality (twofold higher) compared to women without SBI.
– These findings highlight the importance of addressing SBI as a risk factor for child mortality in Ethiopia.

Recommendations:

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

1. Interventions targeting SBI should be implemented to reduce neonatal, infant, and under-five mortality rates.
2. Family planning programs should prioritize education and access to contraceptives to help women and families plan and space their pregnancies appropriately.
3. Health education campaigns should raise awareness about the risks of SBI and promote the benefits of longer birth intervals for maternal and child health.
4. Healthcare providers should provide counseling and support to women and families regarding birth spacing and family planning options.

Key Role Players:

1. Ministry of Health: Responsible for developing and implementing policies and programs related to maternal and child health.
2. Non-governmental organizations (NGOs): Involved in implementing interventions and providing support for family planning and maternal and child health services.
3. Healthcare providers: Play a crucial role in counseling and educating women and families about birth spacing and family planning options.
4. Community health workers: Engage with communities to raise awareness about the importance of birth spacing and provide information on available services.
5. Education sector: Can contribute to health education campaigns by incorporating information on birth spacing and family planning into school curricula.

Cost Items for Planning Recommendations:

1. Training and capacity building for healthcare providers and community health workers on birth spacing counseling and family planning services.
2. Development and dissemination of educational materials and resources for health education campaigns.
3. Provision of contraceptives and family planning services, including the cost of supplies and equipment.
4. Monitoring and evaluation of interventions to assess their impact on reducing SBI and child mortality rates.
5. Research and data collection to monitor progress and inform evidence-based interventions.
Please note that the cost items provided are general categories and the actual costs will depend on the specific context and implementation strategies.

Força da prova

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is rated 8 because it is based on a nationally representative cross-sectional survey and uses weighted logistic regression analysis. The study includes a large sample size of 8448 women and adjusts for potential confounders. However, it is important to note that this is an observational study, which limits the ability to establish causation. To improve the evidence, future research could consider conducting a longitudinal study or a randomized controlled trial to provide stronger evidence of the causal relationship between short birth interval and neonatal, infant, and under-five mortality.

Resumo

This analysis used data from the Ethiopia Demographic and Health Survey (EDHS) 2016. The EDHS is a nationally representative cross-sectional study conducted in nine geographical regions (Tigray, Afar, Amhara, Oromia, Somali, Benishangul-Gumuz, Southern Nations Nationalities and Peoples’ region, Gambela and Harari) and two administrative cities (Addis Ababa and Dire Dawa). A two-stage, stratified, clustered random sampling design was employed to collect data from women who gave birth within the 5 years preceding the survey. Further descriptions of the sampling procedure for the EDHS are presented elsewhere.5 A total of 8448 women who had at least two live births during the 5 years preceding the 2016 survey were included in the analysis. When women had more than two births in the 5 years preceding the survey, the birth interval between the most recent index child and the immediately preceding child was considered for all the study participants. The outcome variables in the current study were neonatal mortality (death of the child within 28 days of birth), infant mortality (death between birth and 11 months) and under-five mortality (death between birth and 59 months).5 43 These outcomes were coded as binary variables (1/0). SBI was the treatment variable and was defined as a birth-to-birth interval of less than 33 months as per the WHO definition.1 A preceding birth interval, the amount of time between the birth of the child under study (index child) and the immediately preceding birth, was considered in this study. Women’s birth interval data were collected by extracting the date of birth of their biological children data from the children’s birth/immunisation certificate, and/or asking for information regarding their children’s date of birth from the women. Mothers were asked to confirm the accuracy of the information before documenting children’s date of birth from children’s birth/immunisation certificates. This crosschecking was performed to avoid errors, since in some cases the documented birth date may represent the date when the birth was recorded, rather than the actual birth date. In the absence of children’s birth certificates, information regarding children’s date of birth was obtained from their mothers. Further information regarding birth interval data collection is provided elsewhere.2 3 44 After reviewing relevant literature,2 18–21 23–25 28 29 39 45 46 direct acyclic graphs (DAGs) were constructed using DAGitty V.3.047 to identify confounders for the association between SBI and neonatal, infant and under-five child mortality. Adjustment for such confounders is necessary to estimate the unbiased effect of SBI on neonatal, infant and under-five mortality (figure 1). DAG is a formal system of mapping variables and the direction of causal relationships among them.48 49 This graphical representation of causal effects among variables helps understand whether bias is potentially reduced or increased when conditioning on covariates. Moreover, it illustrates covariates that lie in the causal pathway between the treatment and outcomes, which should not be included in the analysis as a confounder. These variables are indicated by green lines in figure 1. This is because a propensity score (PS) that includes covariates affected by the treatment (ie, variables on the causal pathway between treatment and outcome) obscures part of the treatment effect that one is trying to estimate.50 Identified confounders were maternal age at the birth of the index child, maternal education, maternal occupation, husband’s education, husband’s occupation, household wealth status, survival status of the preceding child, the total number of the preceding child, place of residence (urban/rural), regions, access to media and decision-making autonomy. A list of all variables considered in the DAG is provided in online supplemental material I. Direct acyclic graph used to select controlling variables. ANC, antenatal care; Birth_ord, birth order; Birth_wt, birth weight; H_Educ, husband education; H_Occup, husband occupation; IM, infant mortality; M_age_atBirth_chil, maternal age at birth of the index child; M_Edu, maternal education; M_Occu, maternal occupation; Multiple_preg, multiple pregnancy; NM, neonatal mortality; PNC, postnatal care; Prev_Chi_Survival, previous child survival; Respiratory_infn, respiratory infection; SBI, short birth interval; Total_Prec_child, total number of preceding child; TT_vaccin, tetanus toxoid vaccination status; U5M, under-five mortal. bmjopen-2020-047892supp001.pdf A yellowish-green circle with a triangle at its centre indicates the main treatment/exposure variable, a blue circle with a vertical bar at its centre indicates the outcome variable, light red circles indicate ancestors of exposure and outcome (ie, confounders). Blue circles indicate the ancestors of the outcome variable. Green lines indicate a causal pathway. Red lines indicate open paths by which confounding may occur; this confounding can be removed by adjusting for one or several variables on the pathway. Participants’ characteristics were described using frequency with per cent. P values were calculated using Pearson’s χ2 test. Given that the outcomes (ie, neonatal, infant and under-five mortality) were relatively infrequent, the unbiased effect of SBI on each outcome was estimated using PSs with a stabilised method of inverse probability of treatment weighting (IPTW). A previous study51 has shown that IPTW with stabilised weights preserves the sample size of the original data, provides an appropriate estimation of the variance of the main effect and maintains an appropriate type I error rate. The other methods, such as IPTW with normalised weight and greedy algorithm with 1:1 matching methods, are discussed elsewhere.52–54 A PS is defined as the probability of treatment assignment given observed baseline covariates (described in online supplemental material II).54 PSs are used to estimate treatment effects on outcomes using observational data when confounding bias due to non-random treatment assignment is likely.50 IPTW weights the entire study sample by the inverse of the PS55; a differential amount of information is used from each participant, depending on their conditional probability of receiving treatment. This means observations are less likely to be lost than when using matching for confounder adjustment.56 57 PSs are a robust alternative to covariate adjustment when the outcome variable is rare, resulting in data sparsity and estimation issues in multivariable models.57 In this study, the weighted prevalence of the outcome variables of neonatal, infant and under-five mortality were 2.9% (95% CI=2.39% to 3.61%), 4.8% (95% CI=4.11% to 5.58%) and 5.5% (95% CI=4.73% to 6.44%), respectively. bmjopen-2020-047892supp002.pdf The analysis procedure was as follows. First, the PS was estimated using a logistic regression model in which treatment assignment (SBI vs non-SBI) was regressed on the 11 covariates identified using the DAG. The balance of measured covariates/confounders was then assessed across treatment groups (ie, women with SBI) and comparison groups (ie, women with non-SBI) before and after weighting, by computing standardised differences (online supplemental material II).57 58 For a continuous covariate, the standardised difference58 59 is defined as: where x¯treatment and x¯control denote the sample mean of the covariate in treated and untreated subjects, respectively and streatment2 and streatment2 denote the corresponding sample variances of the covariate. The standardised difference58 59 for a dichotomous variable is given as: where p^treatment and p^control denote the prevalence of the dichotomous variable in treated and untreated subjects, respectively. A standard difference <0.1 has been suggested as indicating a negligible difference in the mean or prevalence of a covariate between treatment and control groups and was used here.58 In addition, kernel densities were plotted to graphically demonstrate the PS balance in the treatment group (ie, women with SBI) and control groups (women with non-SBI). Balance in PSs was considered to be achieved when the kernel density line for the treatment group and control group lay closer together.60 The IPTWs was then calculated as 1/PS for those exposed to SBI and 1/(1−PS) for those who were not. The sample was then reweighted by the IPTW and the balance of the covariates checked in the reweighted sample.50 61 Stabilisation of weights was made to preserve the sample size of the original data, reduce the effect of weights of either treated subjects with low PSs or untreated subjects with high PSs, and improve the estimation of variance estimates and CIs for the treatment effect.51 Since the EDHS employed a two-stage, stratified, clustered random sampling, which is a complex sampling procedure, sampling weights were also used to adjust for the non-proportional allocation of sample participants to different regions, including urban and rural areas, and consider the possible differences in response rates.5 Finally, a weighted logistic regression was fit to estimate the effect of the treatment (SBI) on each outcome variable (neonatal, infant and under-five mortality). Estimation of the treatment effect on outcome variables in the final model used the grand weight, which was formed as the product of the survey weight and the stabilised weight. Literature has shown that combining a PS method and survey weighting is necessary to estimate unbiased treatment effects which are generalisable to the original survey target population.62 The treatment effect on the outcome variables was expressed as adjusted ORs (AORs) with a 95% CI. Statistical analysis was performed using Stata V.14 statistical software (StataCorp Stata Statistical Software: Release V.14. College Station, Texas: StataCorp LP 2015). Figure 2 presents a schematic summary of the overall analysis procedure. Schematic presentation of the overall steps followed in the analysis. Patients and/or the general public were not involved in the design, or conduct or drafting of this secondary analysis.

Materiais

Inovações

Based on the information provided, the study conducted a nationally representative observational study in Ethiopia to assess the effect of short birth interval (SBI) on neonatal, infant, and under-five mortality. The study found that SBI has a significant effect on neonatal, infant, and under-five mortality in Ethiopia.

To improve access to maternal health and address the issue of SBI, here are some potential recommendations for innovation:

1. Education and awareness programs: Implement educational campaigns to raise awareness about the importance of birth spacing and the risks associated with short birth intervals. This can be done through community outreach programs, media campaigns, and targeted messaging to reach women and their families.

2. Family planning services: Strengthen and expand access to family planning services, including counseling, contraceptives, and reproductive health education. This can help women and couples make informed decisions about birth spacing and prevent unintended pregnancies.

3. Antenatal care (ANC) services: Enhance ANC services to include counseling on birth spacing and the importance of healthy timing and spacing of pregnancies. ANC visits provide an opportunity to educate women about the risks of short birth intervals and promote healthy behaviors.

4. Postpartum care: Improve postpartum care services to include counseling on birth spacing and family planning options. This can help women make informed decisions about their reproductive health after childbirth and prevent unintended pregnancies.

5. Integration of maternal and child health services: Integrate maternal and child health services to provide comprehensive care for women and their children. This can ensure that women receive appropriate care during pregnancy, childbirth, and the postpartum period, including counseling on birth spacing.

6. Community-based interventions: Engage community health workers and volunteers to promote birth spacing and provide information and support to women and families. Community-based interventions can help reach women in remote areas and increase awareness about the importance of birth spacing.

7. Mobile health (mHealth) solutions: Utilize mobile health technologies to deliver information and reminders about birth spacing, family planning, and maternal health. Mobile apps, SMS messaging, and interactive voice response systems can be used to provide personalized support and education to women and their families.

8. Strengthening health systems: Invest in strengthening health systems to ensure access to quality maternal and reproductive health services. This includes training healthcare providers, improving infrastructure and equipment, and ensuring the availability of essential medicines and supplies.

These recommendations aim to improve access to maternal health and address the issue of short birth intervals, ultimately reducing neonatal, infant, and under-five mortality rates in Ethiopia.

AI Innovations Description

The recommendation based on the study is to implement interventions targeting short birth intervals (SBI) in order to reduce neonatal, infant, and under-five mortality in Ethiopia. The study found that women with SBI had significantly higher odds of neonatal mortality, infant mortality, and under-five child mortality compared to those without SBI. The analysis used data from the Ethiopia Demographic and Health Survey 2016 and employed weighted logistic regression analysis based on inverse probability of treatment weights to estimate the effects of SBI on mortality outcomes. The study identified several confounders that should be considered when implementing interventions, including maternal age, education, occupation, husband’s education and occupation, household wealth status, survival status of the preceding child, total number of preceding children, place of residence, regions, access to media, and decision-making autonomy. By addressing these factors and implementing interventions to promote longer birth intervals, access to maternal health can be improved and maternal and child mortality rates can be reduced.

AI Innovations Methodology

The study you provided is focused on assessing the effect of short birth intervals (SBI) on neonatal, infant, and under-five mortality in Ethiopia. The methodology used in the study involves analyzing data from the Ethiopia Demographic and Health Survey (EDHS) 2016, which is a nationally representative cross-sectional survey. Here is a brief description of the methodology used:

1. Study Design: The study used a cross-sectional design, utilizing data from the EDHS 2016.

2. Participants: The analysis included 8,448 women who had at least two live births during the 5 years preceding the survey.

3. Outcome Measures: The outcome variables were neonatal mortality (death within 28 days of birth), infant mortality (death between birth and 11 months), and under-five mortality (death between birth and 59 months).

4. Data Collection: Birth interval data were collected by extracting the date of birth of the biological children from birth/immunization certificates or by asking the mothers for information regarding their children’s date of birth.

5. Confounders: Confounders were identified using direct acyclic graphs (DAGs) to understand the causal relationships between variables. Confounders included maternal age, education, occupation, husband’s education and occupation, household wealth status, survival status of the preceding child, total number of preceding children, place of residence, regions, access to media, and decision-making autonomy.

6. Propensity Score (PS): A PS was estimated using logistic regression, regressing treatment assignment (SBI vs. non-SBI) on the identified confounders.

7. Inverse Probability of Treatment Weighting (IPTW): IPTW was used to estimate the treatment effects on outcomes. Stabilized weights were calculated using the PS, and the entire study sample was weighted accordingly.

8. Statistical Analysis: Weighted logistic regression analysis was performed to estimate the effect of SBI on each outcome variable. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were calculated.

9. Survey Weights: Sampling weights were used to adjust for the complex sampling design of the EDHS, ensuring the results are representative of the target population.

10. Statistical Software: Stata V.14 was used for data analysis.

The study found that SBI had a significant effect on neonatal, infant, and under-five mortality in Ethiopia, suggesting the need for interventions targeting SBI to reduce mortality rates.

It’s important to note that this methodology specifically addresses the research question of the study you provided. If you are looking for a methodology to simulate the impact of recommendations on improving access to maternal health, a different approach would be needed.

Autores & Coautores

Statistics:

Citations: 1

Authors: 4

Identifiers:

DOI: 10.1136/bmjopen-2020-047892

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory, Quasi Experimental Study, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Female

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