Dynamics of stunting from childhood to youthhood in Ethiopia: Evidence from the Young Lives panel data

PLoS ONE, Volume 15, No. 2, Year 2020

Interpretation

Introduction Stunting continues to be a public health challenge with grave health, cognitive and economic consequences. Yet, its dynamics along the life course remain not well investigated in Ethiopia and beyond. Methods Longitudinal data generated by following two (younger and older) cohorts of about 3000 children for nearly 15 years were analyzed to investigate the longitudinal dynamics of stunting in Ethiopia. The cross-sectional prevalence of stunting in each round, longitudinal prevalence, and transition probabilities were determined. Multilevel mixed effects ordinal regression was applied to identify the determinants of stunting accounting for child-level and cluster-level variations. Results The cross-sectional prevalence of severe stunting for the younger cohort fluctuated between 21% and 6%, while for the older cohort it fluctuated between 12% and 3%. Moderate stunting fluctuated between 23% and 16% for the younger cohort and between 22% and 8% for the older cohort. The longitudinal prevalence of severe stunting was 10% in both the younger and older cohorts, whereas that of moderate stunting was 20% for the younger cohort and 18% for the older cohort. Children not stunted at baseline had very high probabilities of remaining not stunted through youthhood (87% for the younger and 90% for the older cohorts). Conversely, children with moderate stunting at baseline had high probabilities either remaining moderately stunted or progressing to severe stunting. Furthermore, children who had severe stunting at baseline had high probabilities of either remaining severely stunted or transitioning to moderate stunting. In both cohorts, older age of the child, female sex, having an educated mother, and being from a household with educated head significantly reduced the risk of stunting. Children from households in the top wealth tertile had a significantly lower risk of stunting in the younger cohort, but not in the older cohort. Similarly, Productive Safety Net Programme reduced the risk of stunting in the younger cohort, but not in the older cohort. Conclusion Children not stunted early in life are highly likely to grow into non-stunted adults while children stunted early in life are highly likely to grow into stunted adults. Several child-level, maternal, household and programmatic factors affect the risk of stunting. Efforts to prevent stunting shall commence early in life. This study is based on the Young Lives study, which is a longitudinal panel study of approximately 12,000 children carried out over 15 years (in five rounds) in four low- and middle-income countries namely Ethiopia, Peru, Vietnam and India [25, 26]. The present study utilizes the constructed Young Lives dataset for Ethiopia which covers Rounds 1 to 5 [27]. The longitudinal study comprised of two cohorts of children–a younger cohort of 1999 children who were about 1 year old and an older cohort of 1000 children who were about eight years old at the start of the study in 2002 (Round 1). Both cohorts were followed for about 15 years in five rounds–Round 1 (2002), Round 2 (2006), Round 3 (2009), Round 4 (2013) and Round 5 (2016). The younger cohort was followed from 1 year to 15 years of age, while the older cohort was followed from 8 years to 22 years of age. In each round, the study participants in the younger cohort were 1, 5, 8, 12, and 15 years old, respectively while those in the older cohort were 8, 12, 15, 19 and 22 years old, respectively [25, 26]. The Young Lives sample for Ethiopia, as well as for the other countries, was not intended to be nationally representative, but to be a sample suitable to investigate the longitudinal dynamics of child-related variables and the impact of children’s early-life circumstances on children’s later outcomes. The sample size was, therefore, decided to be large enough for general statistical analyses such as modeling child welfare and its dynamics overtime. Accordingly, the younger cohort comprised of 1999 study participants and the older cohort comprised of 1000 study participants. Though not incepted to be nationally representative, the sample has been shown to cover children characteristically as diverse as those involved in nationally representative samples such as the Demographic and Health Survey (DHS) and the Welfare Monitoring Survey (WMS) [25, 26, 28]. The number of children who actually participated in each round and the size of the longitudinal attrition are given in Fig 1. Detailed descriptions of the sampling procedure are given elsewhere[25, 26]. Briefly, the sampling was accomplished using a multistage sampling technique at the start of the study in 2002. In the first stage, out of the nine administrative regions and two city administrations in Ethiopia, four regions–namely Amhara, Oromia, Southern Nations, Nationalities and Peoples (SNNP), and Tigray–and one city administration–namely Addis Ababa–were selected purposefully to ensure national coverage. These five administrative areas account for about 96% of the national population. In the second stage, three to five woredas (districts) were selected per region ensuring representation of different poverty levels, urban and rural areas and food deficit status. Totally 20 woredas were selected. In the third stage of selection, kebeles (lowest administrative units) were selected. At least one kebele was selected from each woreda. A kebele was considered a sentinel site for the panel data collection or was merged with adjacent kebeles to form a sentinel site depending on the number of eligible households in each kebele. Finally, 100 households with a 1-year old child and 50 households with an 8-year old child were selected randomly from each sentinel site. If a selected household had both a 1-year old child and an 8-year old child, the 1-year old child was selected as the study required larger number of younger children. Poor children were purposively over-sampled. In the present analysis, the dependent (outcome) variable is stunting. It was measured as an ordinal categorical variable with three mutually exclusive categories, namely not stunted (a height-for-age [HFA] z-score of greater than or equal to -2), moderately stunted (a HFA of between -3 and -2), and severely stunted (a HFA of less than -3) [29]. Three measures of outcome are used in this article–viz., cross-sectional prevalence, longitudinal prevalence and transition probabilities. The cross-sectional prevalence measured the point prevalence of stunting in each round. It is used to show fluctuations in the prevalence of stunting across rounds. The longitudinal prevalence measures the proportion of times a person has the disease (in this case, stunting) in longitudinal studies[30, 31]. It is a useful measure of disease occurrence in longitudinal studies as it avoids problem of defining an outcome in the presence of repeated episodes[30]. Transition probabilities refer to the probability of transitioning (change over time) of categorical variable (in this case, stunting) from one category (level) to another[32]. The Independent variables investigated as possible determinants of stunting included child’s age, mother’s age, and age of the household head (all in years); child’s sex; area of residence (rural vs. urban); levels of education of the mother and of the household head (illiterate, grade 1–4, grade 5–8, above grade 8, other [adult literacy, religious or other]); household size (5 or less vs. greater than 5); household wealth tertile (bottom, middle, top); and participation of at least one household member in PSNP–public works or direct support programme (no vs. yes). The Young Lives wealth index is used as a measure of the socioeconomic status of households[29] and is computed based on three sub-indices, namely housing quality, access to services, and ownership of consumer durables[33]. The PSNP was introduced in Ethiopia to support food insecure households in 2005 (after the Young Lives panel study was launched)[34]. Hence, data on participation in PSNP was collected as of Round 3. For rounds 1 and 2, all households were considered as having not participated in PSNP. A detailed description of the data collection methodology of the Young Lives study is provided elsewhere [25]. Briefly, the data on which this article is based were collected in each round using interviewer-administered questionnaires from the children (8 years and older) and their primary caregivers. While the core content of the questionnaire remains unchanged across rounds, modifications have been done to take account of life course and contextual changes and based on lessons learned from preceding rounds. Anthropometric measurements such as height have also been taken based on which z-scores were computed to define children’s malnutrition status[29]. Data were collected using paper-based questionnaires in the first three rounds. Computer-assisted personal interviewing (CAPI) was implemented in Rounds 4 and 5. Data collectors and supervisors comprised of men and women recruited based on minimum educational requirements and prior experience in data collection. They were all fluent in speaking and writing the languages of the localities in which they were assigned for field work[25]. In all rounds, data collection took place between October and December[28]. The data were analyzed using Stata/IC 15.1 [StataCorp LLC, College Station, Texas, USA]. The data for the younger and older cohort were analyzed separately. Descriptive analyses were performed to obtain summary measures for the basic background characteristics of the study participants and the prevalence and transition probabilities of stunting. The Stata command xttrans was used to estimate the transition probabilities of stunting status across rounds. A multilevel mixed-effects ordered logistic regression with three levels–in which observations in each round were the first-level units, children were the second-level units and clusters (sentinel sites) were the third-level units–was conducted to identify the determinants of stunting accounting for child-level and cluster-level variations. The mixed-effects ordered logistic regression commenced with a crude analysis, in which each potential determinant was examined separately for its possible effect on stunting. Consequently, potential determinants with p-values less than 0.25 on crude analysis were included in the adjusted (multivariable) model. Variables with a large number of missing observations (father’s age and father’s level of education) were excluded from the adjusted analysis. In addition to missing observations, father’s level of education was also found to be redundant with the education level of the household head as evidenced by a high correlation coefficient. Furthermore, caregiver’s level of education was excluded from the adjusted model because it correlated highly with mother’s level of education. The initial (full) model was successively refined and re-fit by iteratively excluding variables the exclusion of which does not significantly affect the model as a whole (based on likelihood ratio test) and the variables remaining in the model (based on changes in the odds ratios of individual variables). The importance of the multilevel model over the standard ordinal regression model was tested using likelihood ratio test. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were used to determine the presence and strength of association between stunting and potential determinants. AORs with 95% CIs that do not embrace unity (1) were considered statistically significant. Details of the ethical considerations of the Young Lives study have been described elsewhere[25]. Briefly, the study was conducted in compliance with the ethical standards of the countries in which the study was conducted. The study proposal was reviewed by the London School of Hygiene and Tropical Medicine and the pilot phase of the study was approved by the Rand Afrikaans University in South Africa. Subsequent approvals of the study have been obtained from ethics committees in each of the countries where the Young Lives study was conducted. In Ethiopia, the study was approved by the Institutional Review Board at the College of Health Sciences of Addis Ababa University[28]. Informed consent was obtained in each round from the parents or caregivers and from the children themselves from as early age as possible. The confidentiality and identities of the study participants were protected by excluding names of persons and places from the datasets. Anonymized dataset for this work was obtained from the UK Data Service after submitting the intent of the work (Project id: 118166).

AI Digest

Study Justification:

– Stunting is a significant public health challenge with serious health, cognitive, and economic consequences.
– The dynamics of stunting throughout the life course have not been well investigated in Ethiopia and beyond.
– Understanding the longitudinal dynamics of stunting is crucial for developing effective interventions and policies.

Study Highlights:

– The study analyzed longitudinal data from the Young Lives panel study in Ethiopia, which followed two cohorts of children for nearly 15 years.
– The cross-sectional prevalence of severe stunting fluctuated between 21% and 6% for the younger cohort and between 12% and 3% for the older cohort.
– Moderate stunting fluctuated between 23% and 16% for the younger cohort and between 22% and 8% for the older cohort.
– Children not stunted at baseline had high probabilities of remaining not stunted through youthhood, while children with moderate or severe stunting at baseline had high probabilities of remaining stunted or transitioning to a higher level of stunting.
– Factors such as older age of the child, female sex, having an educated mother, and being from a household with an educated head significantly reduced the risk of stunting.
– Household wealth and participation in the Productive Safety Net Programme also influenced the risk of stunting.

Study Recommendations:

– Efforts to prevent stunting should start early in life.
– Interventions should focus on improving maternal education, household wealth, and access to programs like the Productive Safety Net Programme.
– Policies should prioritize addressing the determinants of stunting, including child-level, maternal, household, and programmatic factors.

Key Role Players:

– Ministry of Health: Responsible for implementing and coordinating interventions to prevent stunting.
– Ministry of Education: Involved in promoting maternal education and ensuring access to quality education for children.
– Ministry of Finance: Responsible for allocating funds for stunting prevention programs.
– Non-governmental organizations (NGOs): Play a crucial role in implementing community-based interventions and providing support to vulnerable households.
– Community health workers: Involved in delivering health education and interventions at the community level.
– Researchers and academics: Conduct further studies to deepen the understanding of stunting dynamics and evaluate the effectiveness of interventions.

Cost Items for Planning Recommendations:

– Maternal education programs: Budget for training and capacity building of educators, development of educational materials, and monitoring and evaluation.
– Household wealth improvement programs: Budget for income generation activities, vocational training, and access to credit and financial services.
– Productive Safety Net Programme: Budget for program implementation, including cash transfers, public works projects, and monitoring and evaluation.
– Community-based interventions: Budget for training and support for community health workers, awareness campaigns, and provision of nutrition supplements.
– Research and evaluation: Budget for data collection, analysis, and dissemination of findings to inform policy and programmatic decisions.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a longitudinal panel study with a large sample size. The study analyzes data from two cohorts of children over a 15-year period, providing valuable insights into the dynamics of stunting in Ethiopia. The study also uses multilevel mixed effects regression to identify determinants of stunting, accounting for child-level and cluster-level variations. To improve the evidence, the abstract could provide more information on the representativeness of the sample and the generalizability of the findings to the broader population in Ethiopia.

Abstract

This study is based on the Young Lives study, which is a longitudinal panel study of approximately 12,000 children carried out over 15 years (in five rounds) in four low- and middle-income countries namely Ethiopia, Peru, Vietnam and India [25, 26]. The present study utilizes the constructed Young Lives dataset for Ethiopia which covers Rounds 1 to 5 [27]. The longitudinal study comprised of two cohorts of children–a younger cohort of 1999 children who were about 1 year old and an older cohort of 1000 children who were about eight years old at the start of the study in 2002 (Round 1). Both cohorts were followed for about 15 years in five rounds–Round 1 (2002), Round 2 (2006), Round 3 (2009), Round 4 (2013) and Round 5 (2016). The younger cohort was followed from 1 year to 15 years of age, while the older cohort was followed from 8 years to 22 years of age. In each round, the study participants in the younger cohort were 1, 5, 8, 12, and 15 years old, respectively while those in the older cohort were 8, 12, 15, 19 and 22 years old, respectively [25, 26]. The Young Lives sample for Ethiopia, as well as for the other countries, was not intended to be nationally representative, but to be a sample suitable to investigate the longitudinal dynamics of child-related variables and the impact of children’s early-life circumstances on children’s later outcomes. The sample size was, therefore, decided to be large enough for general statistical analyses such as modeling child welfare and its dynamics overtime. Accordingly, the younger cohort comprised of 1999 study participants and the older cohort comprised of 1000 study participants. Though not incepted to be nationally representative, the sample has been shown to cover children characteristically as diverse as those involved in nationally representative samples such as the Demographic and Health Survey (DHS) and the Welfare Monitoring Survey (WMS) [25, 26, 28]. The number of children who actually participated in each round and the size of the longitudinal attrition are given in Fig 1. Detailed descriptions of the sampling procedure are given elsewhere[25, 26]. Briefly, the sampling was accomplished using a multistage sampling technique at the start of the study in 2002. In the first stage, out of the nine administrative regions and two city administrations in Ethiopia, four regions–namely Amhara, Oromia, Southern Nations, Nationalities and Peoples (SNNP), and Tigray–and one city administration–namely Addis Ababa–were selected purposefully to ensure national coverage. These five administrative areas account for about 96% of the national population. In the second stage, three to five woredas (districts) were selected per region ensuring representation of different poverty levels, urban and rural areas and food deficit status. Totally 20 woredas were selected. In the third stage of selection, kebeles (lowest administrative units) were selected. At least one kebele was selected from each woreda. A kebele was considered a sentinel site for the panel data collection or was merged with adjacent kebeles to form a sentinel site depending on the number of eligible households in each kebele. Finally, 100 households with a 1-year old child and 50 households with an 8-year old child were selected randomly from each sentinel site. If a selected household had both a 1-year old child and an 8-year old child, the 1-year old child was selected as the study required larger number of younger children. Poor children were purposively over-sampled. In the present analysis, the dependent (outcome) variable is stunting. It was measured as an ordinal categorical variable with three mutually exclusive categories, namely not stunted (a height-for-age [HFA] z-score of greater than or equal to -2), moderately stunted (a HFA of between -3 and -2), and severely stunted (a HFA of less than -3) [29]. Three measures of outcome are used in this article–viz., cross-sectional prevalence, longitudinal prevalence and transition probabilities. The cross-sectional prevalence measured the point prevalence of stunting in each round. It is used to show fluctuations in the prevalence of stunting across rounds. The longitudinal prevalence measures the proportion of times a person has the disease (in this case, stunting) in longitudinal studies[30, 31]. It is a useful measure of disease occurrence in longitudinal studies as it avoids problem of defining an outcome in the presence of repeated episodes[30]. Transition probabilities refer to the probability of transitioning (change over time) of categorical variable (in this case, stunting) from one category (level) to another[32]. The Independent variables investigated as possible determinants of stunting included child’s age, mother’s age, and age of the household head (all in years); child’s sex; area of residence (rural vs. urban); levels of education of the mother and of the household head (illiterate, grade 1–4, grade 5–8, above grade 8, other [adult literacy, religious or other]); household size (5 or less vs. greater than 5); household wealth tertile (bottom, middle, top); and participation of at least one household member in PSNP–public works or direct support programme (no vs. yes). The Young Lives wealth index is used as a measure of the socioeconomic status of households[29] and is computed based on three sub-indices, namely housing quality, access to services, and ownership of consumer durables[33]. The PSNP was introduced in Ethiopia to support food insecure households in 2005 (after the Young Lives panel study was launched)[34]. Hence, data on participation in PSNP was collected as of Round 3. For rounds 1 and 2, all households were considered as having not participated in PSNP. A detailed description of the data collection methodology of the Young Lives study is provided elsewhere [25]. Briefly, the data on which this article is based were collected in each round using interviewer-administered questionnaires from the children (8 years and older) and their primary caregivers. While the core content of the questionnaire remains unchanged across rounds, modifications have been done to take account of life course and contextual changes and based on lessons learned from preceding rounds. Anthropometric measurements such as height have also been taken based on which z-scores were computed to define children’s malnutrition status[29]. Data were collected using paper-based questionnaires in the first three rounds. Computer-assisted personal interviewing (CAPI) was implemented in Rounds 4 and 5. Data collectors and supervisors comprised of men and women recruited based on minimum educational requirements and prior experience in data collection. They were all fluent in speaking and writing the languages of the localities in which they were assigned for field work[25]. In all rounds, data collection took place between October and December[28]. The data were analyzed using Stata/IC 15.1 [StataCorp LLC, College Station, Texas, USA]. The data for the younger and older cohort were analyzed separately. Descriptive analyses were performed to obtain summary measures for the basic background characteristics of the study participants and the prevalence and transition probabilities of stunting. The Stata command xttrans was used to estimate the transition probabilities of stunting status across rounds. A multilevel mixed-effects ordered logistic regression with three levels–in which observations in each round were the first-level units, children were the second-level units and clusters (sentinel sites) were the third-level units–was conducted to identify the determinants of stunting accounting for child-level and cluster-level variations. The mixed-effects ordered logistic regression commenced with a crude analysis, in which each potential determinant was examined separately for its possible effect on stunting. Consequently, potential determinants with p-values less than 0.25 on crude analysis were included in the adjusted (multivariable) model. Variables with a large number of missing observations (father’s age and father’s level of education) were excluded from the adjusted analysis. In addition to missing observations, father’s level of education was also found to be redundant with the education level of the household head as evidenced by a high correlation coefficient. Furthermore, caregiver’s level of education was excluded from the adjusted model because it correlated highly with mother’s level of education. The initial (full) model was successively refined and re-fit by iteratively excluding variables the exclusion of which does not significantly affect the model as a whole (based on likelihood ratio test) and the variables remaining in the model (based on changes in the odds ratios of individual variables). The importance of the multilevel model over the standard ordinal regression model was tested using likelihood ratio test. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were used to determine the presence and strength of association between stunting and potential determinants. AORs with 95% CIs that do not embrace unity (1) were considered statistically significant. Details of the ethical considerations of the Young Lives study have been described elsewhere[25]. Briefly, the study was conducted in compliance with the ethical standards of the countries in which the study was conducted. The study proposal was reviewed by the London School of Hygiene and Tropical Medicine and the pilot phase of the study was approved by the Rand Afrikaans University in South Africa. Subsequent approvals of the study have been obtained from ethics committees in each of the countries where the Young Lives study was conducted. In Ethiopia, the study was approved by the Institutional Review Board at the College of Health Sciences of Addis Ababa University[28]. Informed consent was obtained in each round from the parents or caregivers and from the children themselves from as early age as possible. The confidentiality and identities of the study participants were protected by excluding names of persons and places from the datasets. Anonymized dataset for this work was obtained from the UK Data Service after submitting the intent of the work (Project id: 118166).

Materials

Innovations

Based on the provided information, it seems that the study titled “Dynamics of stunting from childhood to youthhood in Ethiopia: Evidence from the Young Lives panel data” focuses on investigating the longitudinal dynamics of stunting in Ethiopia and identifying the determinants of stunting. The study utilizes data from the Young Lives study, a longitudinal panel study conducted over 15 years in Ethiopia and other low- and middle-income countries.

In terms of innovations to improve access to maternal health, it is important to note that the study does not directly address this topic. However, based on the broader context of maternal health and the challenges associated with stunting, here are some potential recommendations for innovations that could be used to improve access to maternal health:

1. Telemedicine: Implementing telemedicine programs that allow pregnant women in remote or underserved areas to access prenatal care and consultations with healthcare providers through video conferencing or mobile applications.

2. Mobile health (mHealth) interventions: Developing mobile applications or SMS-based platforms that provide pregnant women with information, reminders, and guidance on prenatal care, nutrition, and healthy practices during pregnancy.

3. Community health workers: Expanding the role of community health workers to provide maternal health services, including prenatal care, education, and support, especially in rural or marginalized communities where access to healthcare facilities is limited.

4. Maternal health clinics: Establishing dedicated maternal health clinics or centers that provide comprehensive prenatal care, including regular check-ups, screenings, and counseling services, to ensure early detection and management of potential health issues.

5. Transportation solutions: Implementing innovative transportation solutions, such as mobile clinics or ambulances, to improve access to healthcare facilities for pregnant women living in remote or hard-to-reach areas.

6. Maternal health financing models: Developing innovative financing models, such as community-based health insurance or microfinance programs, to improve affordability and access to maternal health services for low-income women.

7. Health information systems: Strengthening health information systems to collect, analyze, and utilize data on maternal health outcomes and service utilization, enabling evidence-based decision-making and targeted interventions.

It is important to note that these recommendations are based on general knowledge and may not be directly related to the specific findings of the mentioned study. Further research and analysis would be needed to determine the most effective and context-specific innovations for improving access to maternal health in Ethiopia.

AI Innovations Description

The study titled “Dynamics of stunting from childhood to youthhood in Ethiopia: Evidence from the Young Lives panel data” provides valuable insights into the prevalence and determinants of stunting in Ethiopia. Stunting, which refers to impaired growth and development in children due to chronic malnutrition, has significant health, cognitive, and economic consequences. The study highlights the need for early interventions to prevent stunting and improve maternal and child health outcomes.

Based on the findings of the study, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Early Childhood Nutrition and Health Program: Develop and implement a comprehensive program that focuses on improving nutrition and health outcomes for children from birth to five years of age. This program should include interventions such as promoting exclusive breastfeeding, providing nutritious complementary foods, ensuring access to clean water and sanitation facilities, and delivering essential healthcare services to prevent and treat common childhood illnesses.

This program should also prioritize maternal health by providing prenatal and postnatal care, promoting healthy pregnancies, and educating mothers on proper nutrition and care practices for themselves and their children. Additionally, the program should involve community engagement and awareness campaigns to promote behavior change and create a supportive environment for optimal maternal and child health.

By implementing this early childhood nutrition and health program, it is expected to reduce the prevalence of stunting and improve overall maternal and child health outcomes in Ethiopia. This innovation can be scaled up and replicated in other low- and middle-income countries facing similar challenges in maternal and child health.

AI Innovations Methodology

The study you provided focuses on the dynamics of stunting in Ethiopia using data from the Young Lives panel study. While the study does not directly address innovations to improve access to maternal health, I can provide some general recommendations and a methodology to simulate the impact of these recommendations on improving access to maternal health.

Recommendations for improving access to maternal health:

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, equipment, and trained healthcare professionals can improve access to maternal health services.
2. Mobile health (mHealth) interventions: Utilizing mobile technology to provide information, reminders, and access to healthcare services can help overcome barriers to accessing maternal health services, especially in remote areas.
3. Community-based interventions: Implementing community health worker programs and community outreach initiatives can improve access to maternal health services by bringing care closer to women in underserved areas.
4. Financial incentives: Providing financial incentives, such as conditional cash transfers or vouchers, can encourage pregnant women to seek and utilize maternal health services.
5. Transportation support: Addressing transportation barriers by providing affordable or free transportation options for pregnant women can improve access to maternal health facilities.

Methodology to simulate the impact of recommendations:

1. Define the target population: Determine the specific population group for which you want to simulate the impact of the recommendations (e.g., pregnant women in a specific region).
2. Collect baseline data: Gather data on the current access to maternal health services, including the number of facilities, healthcare providers, and utilization rates.
3. Identify key indicators: Select indicators that reflect access to maternal health services, such as the number of antenatal care visits, facility-based deliveries, or maternal mortality rates.
4. Develop a simulation model: Create a mathematical or statistical model that incorporates the recommendations and their potential impact on the selected indicators. This model should consider factors such as population size, geographical distribution, and existing healthcare infrastructure.
5. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to estimate the potential impact of each recommendation on the selected indicators. Adjust the parameters of the recommendations (e.g., coverage, effectiveness) to explore different scenarios.
6. Analyze results: Analyze the simulation results to determine the potential impact of each recommendation on improving access to maternal health services. Compare the outcomes of different scenarios to identify the most effective interventions.
7. Validate and refine the model: Validate the simulation model by comparing the simulated results with real-world data, if available. Refine the model based on feedback and additional data to improve its accuracy and reliability.
8. Communicate findings: Present the findings of the simulation study to relevant stakeholders, policymakers, and healthcare providers to inform decision-making and prioritize interventions for improving access to maternal health services.

Please note that the methodology provided is a general framework, and the specific details and data requirements may vary depending on the context and scope of the study.

Author


Dima Health

Statistics:

Citations: 10

Identifiers:

DOI: 10.1371/journal.pone.0229011

Research Areas:

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

Study Countries:

Ethiopia, Multi-Countries, South Africa

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Male & Female