Socioeconomic inequalities in child growth failure in Ethiopia: Findings from the 2000 and 2016 Demographic and Health Surveys

BMJ Open, Volume 11, No. 12, Year 2021

Interpretation

Objective Socioeconomic inequalities in child growth failure (CGF) remain one of the main challenges in Ethiopia. This study examined socioeconomic inequalities in CGF and determinants that contributed to these inequalities in Ethiopia. Methods The Ethiopia Demographic and Health Surveys 2000 and 2016 data were used in this study. A pooled unweighted sample of the two surveys yielded 21514 mother-child pairs (10873 in 2000 and 10641 in 2016). We assessed socioeconomic inequalities in CGF indicators using the concentration curve and concentration index (CI). We then decomposed the CI to identify percentage contribution of each determinant to inequalities. Results Socioeconomic inequalities in CGF have increased in Ethiopia between 2000 and 2016. The CI increased from -0.072 and -0.139 for stunting, -0.088 and -0.131 for underweight and -0.015 and -0.050 for wasting between 2000 and 2016, respectively. Factors that mainly contributed to inequalities in stunting included geographical region (49.43%), number of antenatal care visits (31.40%) and child age in months (22.20%) in 2000. While in 2016, inequality in stunting was contributed mainly by wealth quintile (46.16%) and geographical region (-13.70%). The main contributors to inequality in underweight were geographical regions (82.21%) and wealth quintile (27.21%) in 2000, while in 2016, wealth quintile (29.18%), handwashing (18.59%) and access to improved water facilities (-17.55%) were the main contributors. Inequality in wasting was mainly contributed to by maternal body mass index (-66.07%), wealth quintile (-45.68%), geographical region (36.88%) and paternal education (33.55%) in 2000, while in 2016, wealth quintile (52.87%) and urban areas of residence (-17.81%) were the main driving factors. Conclusions This study identified substantial socioeconomic inequalities in CGF, and factors that relatively contributed to the disparities. A plausible approach to tackling rising disparities may involve developing interventions on the identified predictors and prioritising actions for the most socioeconomically disadvantaged groups. Data for this study were taken from two rounds of EDHS: 2000 and 2016. The EDHS was financially sponsored by the US Agency for International Development. The survey was implemented in collaboration with the Ethiopian Ministry of Health, Central Statistical Agency and ICF International (previous Macro International). The population and health indicators were collected from nine regional states and two city administrative jurisdictions of the country. The sampling frame used for the 2000 EDHS was taken from the Population and Housing Census (PHC) in 1994, while the 2016 EDHS was taken from PHC 2007. The source population were all children aged 0–59 months as well as their mothers or caregivers in the enumeration areas (EA) of EDHS who slept in the selected households the night before the survey. We included all children under-5 years of age and women aged 15–49 years with valid anthropometric measurements in the selected households. The datasets were downloaded with permission from the Demographic and Health Survey (DHS) Programme website.27 A pooled unweighted sample of the two surveys yielded 21 514 children, of which 17 880 had valid information for HAZ, 17 947 for WAZ and 18 148 for WHZ. Of 10 873 children 0 to 59 months of age in 2000, we found 8903 children had valid information on HAZ, 8903 on WAZ and 9085 for WHZ data. A total of 10 641 children had valid anthropometric indices in 2016, including 8855 for HAZ, 9033 for WAZ and 8919 for WHZ. Detailed sample size calculations are provided in the main publications of each survey.19 28 Two-stage stratified and cluster random sampling techniques were used in both EDHS. Each region was stratified into urban and rural areas. Stratification and proportional allocation to size were achieved at each of the lower administrative levels. In the first stage, EA or clusters were randomly selected with probability proportional to EA size from the list of EAs or clusters created during the 1994 and 2007 PHC. The selected EAs or clusters included 540 (139 from urban and 401 from rural) in 2000 and 645 (202 from urban and 443 from rural area) in the 2016 EDHS. Selected EAs were with a fixed number of households in each survey. A household listing operation (served as the sampling frame for household selection) was carried out in all selected EAs. In the second stage, 27 households per EA in the 2000 EDHS and 28 households per EA in the 2016 EDHS were selected. Detailed survey methods and sampling procedures are found in the respective EDHS reports.19 28 CGF was assessed according to WHO 2006 Child Growth standards.4 29 Children’s height/length, weight and age data were used to calculate the three CGF indicators (ie, stunting, underweight or wasting). Height was measured with a measuring board (Shorr Board) by lying down on the board (recumbent length) for children younger than age 24 months, while standing height was measured for older children. Each measure (index) provides different information about child growth and body composition. For example, stunting (low HAZ) is an indicator of chronic undernutrition which reflects inadequate nutrition over a long period and the effects of recurrent and chronic illness. Wasting (low WHZ) is a measure of acute undernutrition that shows inadequate food intake or recent episodes of illness which caused weight loss. Underweight is a composite index of WHZ and HAZ which includes both acute (wasting) and/or chronic (stunting) undernutrition.19 CGF is referred to as a specific subset of undernutrition characterised by inadequate height or weight for the specific age of a child on growth reference standards.3 30 Previous studies11 21 22 31 32 have identified factors associated with CGF. After reviewing these factors, the following variables were included in the analyses: sociodemographic characteristics of children, households and parents’ characteristics such as place or residence (urban/rural), geographical region (nine regional states and two city administrative), maternal and paternal educational level, maternal age, child age, childbirth order, access to water, sanitation and handwashing (WASH) facilities. We used the household wealth index as a main study variable because measuring socioeconomic-related inequalities in health outcomes requires information with which to rank households from the poorest to the richest. The DHS wealth index enables identification of disparities in health outcomes. The index also allows governments to evaluate whether public health services, vaccination campaigns, education and other crucial interventions are reaching the poorest households. The wealth index is particularly valuable in countries that lack reliable data on income and expenditure, which are proxy indicators of household economic status.33 This index was calculated based on EDHS data on household ownership of selected assets such as major source of drinking water, type of toilet, sharing of toilet facilities, major type of cooking fuel, principal material of floors, walls and rooves, number of members per sleeping room, household services and possessions, such as electricity, television, radio, watch, telephone, computer, refrigerator, table, chair, bed with cotton/spring mattress, electric mitad, kerosene lamp/pressure lamp, mobile phone, bicycle, motorcycle or scooter, animal-drawn cart, car or truck, boat with a motor, bagag, domestic staff, a house and land. Using these assets, a wealth index was initially devised through the use of principal component analysis (PCA).34 In conducting PCA, each of the above asset categorical items (such as type of water facility) were first categorised into binary indicator variables (ie, has/does not have), and together with continuous variables (such as number of members per sleeping room) were included in the PCA. In this case, the first principal component (ie, having the asset) was considered as the underlying index of wealth and each household’s position on it was calculated using the PCA weights. The PCA approach produced an index that is ‘normalised’ which has a mean value of zero and SD of one. Dividing the position of households into five equal parts in the normal curve produced household wealth quintiles. Details of the DHS wealth index construction, including steps, is given elsewhere.35 We estimated socioeconomic-related inequalities in CGF using the concentration index (CI) following an approach described by Wagstaff et al36 and O’Donnell et al.37 The CI is a measure of relative inequality, capturing the extent to which CGF differs across households ranked by some indicator of living standards, and is directly related to the concentration curve.38 The concentration curve plots the cumulative percentage of a health outcome (y-axis) vs the cumulative percentage of the population ranked by an indicator of socioeconomic status in ascending order (x-axis).37 The CI is defined as twice the area between the concentration curve and the line of equality (45° line) and can be calculated as shown in equation one.39 The Erreygers CI for stunting, underweight and wasting during 2000 and 2016 was calculated using the ‘conindex’ user written Stata command.40 O’Donnell et al37 has summarised CI formula as follows: CI=2μcov(h,r)equation (1) Where, µ is the mean of the outcome variable (stunting, underweight and wasting), h is the value of the outcome variable for each observation and r is the rank of individual households in the wealth distribution. The values of CI range between −1 and +1. A negative CI value indicates that CGF is concentrated among lower ranked households and the concentration curve lies above the line of equality and vice versa. A CI with the value of 0, shows the absence of socioeconomic-related inequalities in CGF and the concentration curve coincides with the line of equality. When inequality is skewed towards the worse-off, for instance with the higher CGF level in the lower group, it can be referred to as a pro-poor inequality and vice versa. We performed analyses to decompose CI so as to estimate the contribution of determinant variables to socioeconomic inequalities in the outcome variable.36 37 For any continuous outcome variable, a linear regression model linking the outcome variable (y) to a set of k determinants (xk), Wagstaff et al36 suggested the following formula: y=α+∑kβkxk+Ɛequation (2) where α is an intercept, βk is a regression coefficient and ε is an error term. Equation 2 can be transformed to the CI of y, and it can also be rearranged as follows: CI=∑k(βkx-K/µ)CK+GCϵ/μ equation (3) where μ is the mean of y (outcome variable), x−K is the mean of xk (for the kth determinant), CK is the CI of global xk and GCϵ is the generalised CI for error term (ε). The term (βkx-K/µ)CK represents an explained component of determinants to inequality, while the term GCϵ/μ or residual shows the part of inequality in outcome variables that cannot be explained by systematic variation in the determinants. The term βkx-K/µ shows the sensitivity (elasticity) of each CK on the sum of CI of outcome variable. Each absolute contribution is estimated by multiplying the sensitivity of the outcome variable with respect to the determinant and the degree of socioeconomic-related inequality in that determinant. Finally, the percentage contribution is obtained by dividing the absolute contribution by the total CI and multiplying by 100%. We considered the complex sampling design in the DHS with utilising an appropriate sample weighting so that statistics in the current study could be generalised to the population. The survey-specific Stata command ‘svy’ was used to adjust for the sampling design. A p<0.05 was considered to indicate statistically significant estimates. All analyses were performed using Stata V.15.0. This paper followed the standard for reporting observational studies outlined in Strengthening the Reporting of Observational Studies in Epidemiology statement (online supplemental file 1). bmjopen-2021-051304supp001.pdf Patients and the public were not involved in the design and conduct of this research.

AI Digest

Study Justification:

– The study aimed to examine socioeconomic inequalities in child growth failure (CGF) in Ethiopia.
– CGF is a significant challenge in Ethiopia, and understanding the factors contributing to these inequalities is crucial for addressing the issue.
– By analyzing data from the Ethiopia Demographic and Health Surveys in 2000 and 2016, the study provides valuable insights into the changes in socioeconomic inequalities in CGF over time.

Study Highlights:

– Socioeconomic inequalities in CGF have increased in Ethiopia between 2000 and 2016.
– The concentration index (CI) for stunting, underweight, and wasting all showed an increase in inequalities over time.
– Factors contributing to inequalities varied between the two time periods, with geographical region, number of antenatal care visits, and child age being significant factors in 2000, while wealth quintile and geographical region were major contributors in 2016.
– Maternal body mass index, wealth quintile, geographical region, and paternal education were identified as key drivers of inequality in wasting in 2000, while wealth quintile and urban areas of residence were the main factors in 2016.

Recommendations for Lay Readers and Policy Makers:

– The study highlights the need for interventions to address the rising socioeconomic inequalities in CGF in Ethiopia.
– Priority should be given to the most socioeconomically disadvantaged groups in implementing interventions.
– Actions should focus on the identified predictors of inequalities, such as geographical region, wealth quintile, and access to healthcare and sanitation facilities.
– Policy makers should consider targeted interventions to reduce disparities in child growth and nutrition outcomes.

Key Role Players:

– Ethiopian Ministry of Health: Responsible for implementing interventions and policies related to child health and nutrition.
– Central Statistical Agency: Provides data and statistical support for monitoring and evaluating the impact of interventions.
– ICF International (previous Macro International): Collaborates in conducting the Demographic and Health Surveys and provides technical expertise in data collection and analysis.

Cost Items for Planning Recommendations:

– Development and implementation of interventions targeting the most socioeconomically disadvantaged groups.
– Improving access to healthcare services, including antenatal care visits and nutrition programs.
– Enhancing access to improved water facilities and sanitation infrastructure.
– Strengthening educational programs and awareness campaigns on child nutrition and growth.
– Monitoring and evaluation of interventions to assess their effectiveness and make necessary adjustments.
Note: The actual cost of implementing these recommendations is not provided in the study. The cost items listed here are general budget items that may be considered in planning interventions.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on data from two rounds of the Ethiopia Demographic and Health Surveys, which included a large sample size of 21,514 mother-child pairs. The study used the concentration index to assess socioeconomic inequalities in child growth failure (CGF) indicators and decomposed the index to identify the contribution of different determinants to the inequalities. The findings show that socioeconomic inequalities in CGF have increased in Ethiopia between 2000 and 2016, and the study identifies specific factors that contribute to these disparities. To improve the evidence, the abstract could provide more details on the methodology used, such as the specific statistical tests employed and any limitations of the study. Additionally, including information on the representativeness of the sample and the generalizability of the findings would further strengthen the evidence.

Abstract

Data for this study were taken from two rounds of EDHS: 2000 and 2016. The EDHS was financially sponsored by the US Agency for International Development. The survey was implemented in collaboration with the Ethiopian Ministry of Health, Central Statistical Agency and ICF International (previous Macro International). The population and health indicators were collected from nine regional states and two city administrative jurisdictions of the country. The sampling frame used for the 2000 EDHS was taken from the Population and Housing Census (PHC) in 1994, while the 2016 EDHS was taken from PHC 2007. The source population were all children aged 0–59 months as well as their mothers or caregivers in the enumeration areas (EA) of EDHS who slept in the selected households the night before the survey. We included all children under-5 years of age and women aged 15–49 years with valid anthropometric measurements in the selected households. The datasets were downloaded with permission from the Demographic and Health Survey (DHS) Programme website.27 A pooled unweighted sample of the two surveys yielded 21 514 children, of which 17 880 had valid information for HAZ, 17 947 for WAZ and 18 148 for WHZ. Of 10 873 children 0 to 59 months of age in 2000, we found 8903 children had valid information on HAZ, 8903 on WAZ and 9085 for WHZ data. A total of 10 641 children had valid anthropometric indices in 2016, including 8855 for HAZ, 9033 for WAZ and 8919 for WHZ. Detailed sample size calculations are provided in the main publications of each survey.19 28 Two-stage stratified and cluster random sampling techniques were used in both EDHS. Each region was stratified into urban and rural areas. Stratification and proportional allocation to size were achieved at each of the lower administrative levels. In the first stage, EA or clusters were randomly selected with probability proportional to EA size from the list of EAs or clusters created during the 1994 and 2007 PHC. The selected EAs or clusters included 540 (139 from urban and 401 from rural) in 2000 and 645 (202 from urban and 443 from rural area) in the 2016 EDHS. Selected EAs were with a fixed number of households in each survey. A household listing operation (served as the sampling frame for household selection) was carried out in all selected EAs. In the second stage, 27 households per EA in the 2000 EDHS and 28 households per EA in the 2016 EDHS were selected. Detailed survey methods and sampling procedures are found in the respective EDHS reports.19 28 CGF was assessed according to WHO 2006 Child Growth standards.4 29 Children’s height/length, weight and age data were used to calculate the three CGF indicators (ie, stunting, underweight or wasting). Height was measured with a measuring board (Shorr Board) by lying down on the board (recumbent length) for children younger than age 24 months, while standing height was measured for older children. Each measure (index) provides different information about child growth and body composition. For example, stunting (low HAZ) is an indicator of chronic undernutrition which reflects inadequate nutrition over a long period and the effects of recurrent and chronic illness. Wasting (low WHZ) is a measure of acute undernutrition that shows inadequate food intake or recent episodes of illness which caused weight loss. Underweight is a composite index of WHZ and HAZ which includes both acute (wasting) and/or chronic (stunting) undernutrition.19 CGF is referred to as a specific subset of undernutrition characterised by inadequate height or weight for the specific age of a child on growth reference standards.3 30 Previous studies11 21 22 31 32 have identified factors associated with CGF. After reviewing these factors, the following variables were included in the analyses: sociodemographic characteristics of children, households and parents’ characteristics such as place or residence (urban/rural), geographical region (nine regional states and two city administrative), maternal and paternal educational level, maternal age, child age, childbirth order, access to water, sanitation and handwashing (WASH) facilities. We used the household wealth index as a main study variable because measuring socioeconomic-related inequalities in health outcomes requires information with which to rank households from the poorest to the richest. The DHS wealth index enables identification of disparities in health outcomes. The index also allows governments to evaluate whether public health services, vaccination campaigns, education and other crucial interventions are reaching the poorest households. The wealth index is particularly valuable in countries that lack reliable data on income and expenditure, which are proxy indicators of household economic status.33 This index was calculated based on EDHS data on household ownership of selected assets such as major source of drinking water, type of toilet, sharing of toilet facilities, major type of cooking fuel, principal material of floors, walls and rooves, number of members per sleeping room, household services and possessions, such as electricity, television, radio, watch, telephone, computer, refrigerator, table, chair, bed with cotton/spring mattress, electric mitad, kerosene lamp/pressure lamp, mobile phone, bicycle, motorcycle or scooter, animal-drawn cart, car or truck, boat with a motor, bagag, domestic staff, a house and land. Using these assets, a wealth index was initially devised through the use of principal component analysis (PCA).34 In conducting PCA, each of the above asset categorical items (such as type of water facility) were first categorised into binary indicator variables (ie, has/does not have), and together with continuous variables (such as number of members per sleeping room) were included in the PCA. In this case, the first principal component (ie, having the asset) was considered as the underlying index of wealth and each household’s position on it was calculated using the PCA weights. The PCA approach produced an index that is ‘normalised’ which has a mean value of zero and SD of one. Dividing the position of households into five equal parts in the normal curve produced household wealth quintiles. Details of the DHS wealth index construction, including steps, is given elsewhere.35 We estimated socioeconomic-related inequalities in CGF using the concentration index (CI) following an approach described by Wagstaff et al36 and O’Donnell et al.37 The CI is a measure of relative inequality, capturing the extent to which CGF differs across households ranked by some indicator of living standards, and is directly related to the concentration curve.38 The concentration curve plots the cumulative percentage of a health outcome (y-axis) vs the cumulative percentage of the population ranked by an indicator of socioeconomic status in ascending order (x-axis).37 The CI is defined as twice the area between the concentration curve and the line of equality (45° line) and can be calculated as shown in equation one.39 The Erreygers CI for stunting, underweight and wasting during 2000 and 2016 was calculated using the ‘conindex’ user written Stata command.40 O’Donnell et al37 has summarised CI formula as follows: CI=2μcov(h,r)equation (1) Where, µ is the mean of the outcome variable (stunting, underweight and wasting), h is the value of the outcome variable for each observation and r is the rank of individual households in the wealth distribution. The values of CI range between −1 and +1. A negative CI value indicates that CGF is concentrated among lower ranked households and the concentration curve lies above the line of equality and vice versa. A CI with the value of 0, shows the absence of socioeconomic-related inequalities in CGF and the concentration curve coincides with the line of equality. When inequality is skewed towards the worse-off, for instance with the higher CGF level in the lower group, it can be referred to as a pro-poor inequality and vice versa. We performed analyses to decompose CI so as to estimate the contribution of determinant variables to socioeconomic inequalities in the outcome variable.36 37 For any continuous outcome variable, a linear regression model linking the outcome variable (y) to a set of k determinants (xk), Wagstaff et al36 suggested the following formula: y=α+∑kβkxk+Ɛequation (2) where α is an intercept, βk is a regression coefficient and ε is an error term. Equation 2 can be transformed to the CI of y, and it can also be rearranged as follows: CI=∑k(βkx-K/µ)CK+GCϵ/μ equation (3) where μ is the mean of y (outcome variable), x−K is the mean of xk (for the kth determinant), CK is the CI of global xk and GCϵ is the generalised CI for error term (ε). The term (βkx-K/µ)CK represents an explained component of determinants to inequality, while the term GCϵ/μ or residual shows the part of inequality in outcome variables that cannot be explained by systematic variation in the determinants. The term βkx-K/µ shows the sensitivity (elasticity) of each CK on the sum of CI of outcome variable. Each absolute contribution is estimated by multiplying the sensitivity of the outcome variable with respect to the determinant and the degree of socioeconomic-related inequality in that determinant. Finally, the percentage contribution is obtained by dividing the absolute contribution by the total CI and multiplying by 100%. We considered the complex sampling design in the DHS with utilising an appropriate sample weighting so that statistics in the current study could be generalised to the population. The survey-specific Stata command ‘svy’ was used to adjust for the sampling design. A p<0.05 was considered to indicate statistically significant estimates. All analyses were performed using Stata V.15.0. This paper followed the standard for reporting observational studies outlined in Strengthening the Reporting of Observational Studies in Epidemiology statement (online supplemental file 1). bmjopen-2021-051304supp001.pdf Patients and the public were not involved in the design and conduct of this research.

Materials

Innovations

The study titled “Socioeconomic inequalities in child growth failure in Ethiopia: Findings from the 2000 and 2016 Demographic and Health Surveys” examines the socioeconomic disparities in child growth failure (CGF) in Ethiopia and identifies the factors contributing to these inequalities. The study suggests several recommendations for improving access to maternal health based on its findings:

1. Geographically targeted interventions: Implementing targeted interventions in regions with higher disparities in stunting and underweight can help improve access to maternal health. This can involve increasing the availability and accessibility of healthcare facilities, antenatal care services, and nutrition programs in these regions.

2. Increasing antenatal care visits: Promoting and incentivizing regular antenatal care visits can help improve maternal health outcomes and reduce disparities in child growth failure, particularly in terms of stunting.

3. Improving access to improved water facilities and handwashing: Innovations that focus on improving access to clean water sources and promoting proper handwashing practices can have a positive impact on maternal and child health, specifically in reducing underweight.

4. Addressing maternal nutrition: Developing interventions that focus on improving maternal nutrition, including access to nutritious food and education on healthy eating habits, can help reduce disparities in child growth failure, particularly in terms of wasting.

5. Targeting socioeconomically disadvantaged groups: Prioritizing actions and interventions for the most socioeconomically disadvantaged groups can help improve access to maternal health and reduce disparities in child growth failure across all indicators (stunting, underweight, and wasting).

By implementing these recommendations as innovative interventions, it is possible to improve access to maternal health and reduce socioeconomic inequalities in child growth failure in Ethiopia.

AI Innovations Description

The study titled “Socioeconomic inequalities in child growth failure in Ethiopia: Findings from the 2000 and 2016 Demographic and Health Surveys” highlights the socioeconomic inequalities in child growth failure (CGF) in Ethiopia and identifies the determinants contributing to these inequalities. The study suggests a plausible approach to addressing these disparities by developing interventions based on the identified predictors and prioritizing actions for the most socioeconomically disadvantaged groups.

To improve access to maternal health, based on the findings of the study, the following recommendations can be developed into innovations:

1. Geographically targeted interventions: Given that geographical region was a significant contributor to inequalities in stunting and underweight, implementing targeted interventions in regions with higher disparities can help improve access to maternal health. This can include increasing the availability and accessibility of healthcare facilities, antenatal care services, and nutrition programs in these regions.

2. Increasing antenatal care visits: The number of antenatal care visits was found to be a significant determinant of inequalities in stunting in 2000. Promoting and incentivizing regular antenatal care visits can help improve maternal health outcomes and reduce disparities in child growth failure.

3. Improving access to improved water facilities and handwashing: In 2016, access to improved water facilities and handwashing were identified as contributors to inequalities in underweight. Innovations that focus on improving access to clean water sources and promoting proper handwashing practices can have a positive impact on maternal and child health.

4. Addressing maternal nutrition: Maternal body mass index was a significant contributor to inequalities in wasting in 2000. Developing interventions that focus on improving maternal nutrition, including access to nutritious food and education on healthy eating habits, can help reduce disparities in child growth failure.

5. Targeting socioeconomically disadvantaged groups: Wealth quintile was identified as a significant contributor to inequalities in stunting, underweight, and wasting. Innovations that specifically target and prioritize actions for the most socioeconomically disadvantaged groups can help improve access to maternal health and reduce disparities in child growth failure.

By implementing these recommendations as innovative interventions, it is possible to improve access to maternal health and reduce socioeconomic inequalities in child growth failure in Ethiopia.

AI Innovations Methodology

To simulate the impact of the main recommendations on improving access to maternal health, the following methodology can be used:

1. Identify the target population: Determine the specific population group or region in Ethiopia that will be the focus of the simulation. This could be regions with higher disparities in child growth failure or socioeconomically disadvantaged groups.

2. Collect baseline data: Gather data on the current status of maternal health indicators, such as the number of antenatal care visits, access to healthcare facilities, availability of improved water facilities, handwashing practices, and maternal nutrition. This data will serve as the baseline for comparison.

3. Implement interventions: Based on the recommendations from the study, design and implement interventions that target the identified determinants of inequalities in child growth failure. For example, interventions could include increasing the availability and accessibility of healthcare facilities, promoting and incentivizing regular antenatal care visits, improving access to improved water facilities and promoting handwashing practices, and developing programs to address maternal nutrition.

4. Monitor and evaluate: Continuously monitor and evaluate the implementation of the interventions. Collect data on the impact of the interventions on maternal health indicators, such as the increase in antenatal care visits, improvement in access to healthcare facilities, changes in handwashing practices, and improvements in maternal nutrition.

5. Analyze the data: Analyze the data collected to assess the impact of the interventions on improving access to maternal health. Use statistical methods, such as regression analysis or descriptive statistics, to measure changes in maternal health indicators and determine the extent to which the interventions have reduced socioeconomic inequalities in child growth failure.

6. Compare results: Compare the results of the simulation with the baseline data to determine the effectiveness of the interventions in improving access to maternal health and reducing socioeconomic inequalities in child growth failure. Assess whether the interventions have achieved the desired outcomes and identify any areas for improvement.

7. Adjust and refine interventions: Based on the findings of the simulation, make any necessary adjustments or refinements to the interventions. This could involve scaling up successful interventions, modifying strategies to better target the most disadvantaged groups, or addressing any unforeseen challenges or barriers.

8. Repeat the simulation: Repeat the simulation periodically to track progress over time and assess the long-term impact of the interventions. This will help to ensure that efforts to improve access to maternal health are sustained and continue to address socioeconomic inequalities in child growth failure.

By following this methodology, policymakers and stakeholders can gain insights into the potential impact of implementing the recommendations from the study and make informed decisions on strategies to improve access to maternal health in Ethiopia.

Author

Dima Health

Identifiers:

DOI: 10.1136/bmjopen-2021-051304

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female