Association between maternal age at childbirth and child and adult outcomes in the offspring: A prospective study in five low-income and middle-income countries (COHORTS collaboration)

The Lancet Global Health, Volume 3, No. 7, Year 2015

Interprétation

Background: Both young and advanced maternal age is associated with adverse birth and child outcomes. Few studies have examined these associations in low-income and middle-income countries (LMICs) and none have studied adult outcomes in the offspring. We aimed to examine both child and adult outcomes in five LMICs. Methods: In this prospective study, we pooled data from COHORTS (Consortium for Health Orientated Research in Transitioning Societies)-a collaboration of five birth cohorts from LMICs (Brazil, Guatemala, India, the Philippines, and South Africa), in which mothers were recruited before or during pregnancy, and the children followed up to adulthood. We examined associations between maternal age and offspring birthweight, gestational age at birth, height-for-age and weight-for-height Z scores in childhood, attained schooling, and adult height, body composition (body-mass index, waist circumference, fat, and lean mass), and cardiometabolic risk factors (blood pressure and fasting plasma glucose concentration), along with binary variables derived from these. Analyses were unadjusted and adjusted for maternal socioeconomic status, height and parity, and breastfeeding duration. Findings: We obtained data for 22 188 mothers from the five cohorts, enrolment into which took place at various times between 1969 and 1989. Data for maternal age and at least one outcome were available for 19 403 offspring (87%). In unadjusted analyses, younger (≤19 years) and older (≥35 years) maternal age were associated with lower birthweight, gestational age, child nutritional status, and schooling. After adjustment, associations with younger maternal age remained for low birthweight (odds ratio [OR] 1·18 (95% CI 1·02-1·36)], preterm birth (1·26 [1·03-1·53]), 2-year stunting (1·46 [1·25-1·70]), and failure to complete secondary schooling (1·38 [1·18-1·62]) compared with mothers aged 20-24 years. After adjustment, older maternal age remained associated with increased risk of preterm birth (OR 1·33 [95% CI 1·05-1·67]), but children of older mothers had less 2-year stunting (0·64 [0·54-0·77]) and failure to complete secondary schooling (0·59 [0·48-0·71]) than did those with mothers aged 20-24 years. Offspring of both younger and older mothers had higher adult fasting glucose concentrations (roughly 0·05 mmol/L). Interpretation: Children of young mothers in LMICs are disadvantaged at birth and in childhood nutrition and schooling. Efforts to prevent early childbearing should be strengthened. After adjustment for confounders, children of older mothers have advantages in nutritional status and schooling. Extremes of maternal age could be associated with disturbed offspring glucose metabolism. Funding: Wellcome Trust and the Bill & Melinda Gates Foundation. COHORTS (Consortium for Health Orientated Research in Transitioning Societies) is a collaboration of five birth cohorts from LMICs, in which mothers were recruited before or during pregnancy, and the children followed up to adulthood.26 In this prospective study, the cohorts include the 1982 Pelotas Cohort (Brazil); the Institute of Nutrition of Central America and Panama Nutrition Trial Cohort (Guatemala); the New Delhi Cohort (India); the Cebu Longitudinal Health and Nutrition Survey (Philippines); and the Birth to Twenty Cohort (South Africa; appendix p 1).26 All studies were approved by appropriate institutional ethics committees. Informed verbal or written consent was obtained at recruitment from the mothers in the original birth cohort studies. Informed verbal or written consent was obtained at each round of follow-up, from a parent for childhood follow-ups and from the cohort member themselves for the adult follow-up. The mother’s age at the birth of the index child was calculated from data obtained at interview before or during pregnancy, or, for South African data, from birth notification forms. Birthweight was measured by researchers (Brazil, India, and Guatemala) or obtained from hospital records (the Philippines and South Africa). Gestational age was calculated from the last menstrual period date obtained by prospective surveillance (Guatemala and India), from the mother at recruitment (the Philippines), or from medical records (Brazil and South Africa). In the Philippines, gestational age was obtained for low birthweight babies by newborn clinical assessment.27 Low birthweight was defined as <2500 g, preterm birth as gestational age <37 weeks, and smallness-for-gestational-age as birthweight below the age-specific and sex-specific 10th percentile of a US reference population.28 In all sites, post-natal weight and height were measured longitudinally with standardised methods. Measurements at 2 years were available in all sites, and 4-year measurements in all except the Philippines, where the next available age (8·5 years) was used to define so-called mid-childhood size. Height-for-age and weight-for-height were converted into Z-scores (HAZ and WHZ, respectively) with the WHO growth reference. Stunting and wasting were defined as HAZ and WHZ below −2 SDs, respectively. Breastfeeding data were recorded prospectively with different methods in each cohort; for this analysis we used duration in months of any breastfeeding (as opposed to exclusive breastfeeding), which was available for all cohorts except India. Height, weight, and waist circumference were measured with standardised techniques. Fat and fat-free mass were measured with site-specific methods, as previously described.29 In Brazil, bioelectrical impedance was measured and results corrected based on a validation study that used isotopic methods; data are only available for men. In Guatemala, weight, height, and abdominal circumference were measured and entered into a hydrostatic-weighing validated equation. The Indian and Filipino cohorts used published equations that have been validated for use in Asian populations for estimation of body fat from skinfold measures. South Africa used dual X-ray absorptiometry (Hologic Delphi, Bedford, MA, USA). Fat mass was calculated as: % body fat × body weight, and fat-free mass as weight–fat mass. Overweight was defined as a body-mass index (BMI) of 25 kg/m2 or more and obesity as BMI 30 kg/m2 or more. Blood pressure was measured seated, after a 5–10 min rest, with appropriate cuff sizes and a variety of devices, as previously described.30 High blood pressure was defined as systolic blood pressure 130 mm Hg or greater or diastolic blood pressure 85 mm Hg or greater.31 Fasting glucose was measured in all sites except Brazil, where a random sample was collected and glucose values adjusted for time since the last meal.32 Impaired fasting glucose was defined as a fasting glucose concentration of 6·1 mmol/L or greater but less than 7·0 mmol/L and diabetes as a concentration of 7·0 mmol/L or greater.33 Pregnant women were excluded from all these analyses. Socioeconomic status was considered a potential confounding factor and was assessed with five variables: maternal schooling, marital status, wealth index, urban or rural residence, and ethnic origin. Wealth index was a score derived in each cohort based on type of housing and ownership of household assets (appendix p 2). The Brazilian, Indian, and South African cohorts are urban, and the Guatemalan cohort is rural; in the mixed Filipino cohort, a so-called urbanicity index was used.34 Brazil and South Africa had white, black, Asian, and other ethnic subgroups. Maternal height was potentially both a confounder of maternal age effects (due to secular trends in height) and a mediator (due to younger mothers not having attained final height). Maternal parity and breastfeeding duration were potential mediators (older mothers tend to have higher parity and younger mothers might breastfeed for a shorter time). Parity was coded as 1, 2, 3, or 4 or more. The number of offspring for whom outcomes were available diminished with age at follow-up; for example, n=17 903 (81%) for birthweight and n=10 376 (47%) for adult blood pressure. The maternal age distribution was similar in successive waves of follow-up (appendix p 3). For each outcome, we used the maximum sample with available data. To test the representativeness of our analysis sample, we compared maternal age in those included in the analysis (with data for any childhood or adult outcome of interest) and those not included, using t tests (appendix p 4). We used maternal age as a continuous variable where possible, but used categories (≤19 years, ≥35 years, and 5-year intervening bands) to check for linearity and for tables, figures, and odds ratio calculations. Percentage of body fat and wealth index were non-normally distributed and were Fisher-Yates transformed.35 We first analysed associations between maternal age and outcomes in each cohort with multiple linear regression for continuous outcomes and multiple logistic regression for dichotomous outcomes. We assessed non-linear associations with quadratic terms. We then produced pooled analyses, including main effects for each cohort and interaction terms for cohort and control variables. We tested for heterogeneity among cohorts with F tests, comparing sums of squares explained when effects were or were not allowed to vary across sites.35 We used a sequence of regression models: (1) adjusted for sex, and adult age (adult outcomes only); (2) further adjusted for socioeconomic variables; (3) further adjusted for maternal height; (4) further adjusted for breastfeeding duration; and (5) further adjusted for parity. Missing maternal wealth and schooling values were imputed with regression analysis of known values on other socioeconomic variables. Missing maternal height values were not imputed, and a dummy variable (0=not missing; 1=missing) to represent missing value was included in regression models. The Guatemala cohort is based on a randomised controlled trial of a protein and energy supplement for pregnant women and children;26 it comprises children living in the trial villages who were born or were younger than 7 years of age at any time between 1969 and 1977. We tested for interactions between maternal age and intervention group in this cohort, but noted no consistent evidence of interactions. In Guatemala, the 2344 participants came from 768 families; in India, the 5395 came from 5313 families; there were no siblings in the other cohorts. We used linear mixed modelling to assess whether siblings affected the associations of outcomes with maternal age, and found that they made little difference (appendix p 9). We therefore present our findings without adjustment for sibships. All analyses were done with SPSS version 21 and Stata version 12. The funders had no role in the study design or conduct; the management, analysis, or interpretation of the data; the preparation, review, or approval of the report; or the decision to submit the manuscript for publication. CO and CHDF had full access to all the data and take responsibility for the integrity of the data and the accuracy of the data analysis. CHDF had the responsibility to submit the report for publication.

Résumé de l’IA

Study Justification:

This study aimed to examine the association between maternal age at childbirth and child and adult outcomes in low-income and middle-income countries (LMICs). Previous research has shown that both young and advanced maternal age can have adverse effects on birth and child outcomes, but few studies have focused on LMICs and none have explored adult outcomes in the offspring. Understanding these associations is important for informing policies and interventions to improve maternal and child health in LMICs.

Highlights:

– The study included data from five birth cohorts in LMICs (Brazil, Guatemala, India, the Philippines, and South Africa) and followed the children into adulthood.
– Younger (≤19 years) and older (≥35 years) maternal age were associated with lower birthweight, gestational age, child nutritional status, and schooling.
– After adjusting for confounding factors, younger maternal age remained associated with increased risk of low birthweight, preterm birth, 2-year stunting, and failure to complete secondary schooling.
– Older maternal age remained associated with increased risk of preterm birth, but children of older mothers had lower rates of 2-year stunting and failure to complete secondary schooling.
– Offspring of both younger and older mothers had higher adult fasting glucose concentrations.

Recommendations for Lay Reader:

– Efforts to prevent early childbearing should be strengthened to improve birth and child outcomes in LMICs.
– Policies and interventions should focus on improving nutrition and schooling for children of young mothers.
– Further research is needed to understand the long-term effects of extreme maternal age on offspring glucose metabolism.

Recommendations for Policy Maker:

– Strengthen efforts to prevent early childbearing through comprehensive sex education, access to contraception, and support for young mothers.
– Implement interventions to improve nutrition and schooling for children of young mothers, including targeted programs and support services.
– Consider the potential long-term effects of extreme maternal age on offspring glucose metabolism when developing policies and programs for maternal and child health.

Key Role Players:

– Researchers and scientists involved in the COHORTS collaboration
– Representatives from the Wellcome Trust and the Bill & Melinda Gates Foundation (funders of the study)
– Policy makers and government officials responsible for maternal and child health in LMICs
– Healthcare providers and organizations working in LMICs
– Non-governmental organizations (NGOs) and community-based organizations focused on maternal and child health

Cost Items for Planning Recommendations:

– Comprehensive sex education programs
– Access to contraception and family planning services
– Nutritional support programs for pregnant women and young mothers
– School-based interventions to improve education outcomes for children of young mothers
– Healthcare services for maternal and child health, including prenatal care and postnatal support
– Training and capacity building for healthcare providers and community workers
– Monitoring and evaluation systems to assess the impact of interventions
– Research and data collection to inform evidence-based policies and interventions

Force de la preuve

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it is based on a prospective study that pooled data from five birth cohorts in low-income and middle-income countries. The study examined associations between maternal age and various child and adult outcomes, and the findings were adjusted for confounding factors. However, to improve the evidence, it would be beneficial to provide more details on the methodology, such as the specific data collection methods and statistical analyses used. Additionally, including information on the sample size and characteristics of the study population would further strengthen the evidence.

Abstrait

COHORTS (Consortium for Health Orientated Research in Transitioning Societies) is a collaboration of five birth cohorts from LMICs, in which mothers were recruited before or during pregnancy, and the children followed up to adulthood.26 In this prospective study, the cohorts include the 1982 Pelotas Cohort (Brazil); the Institute of Nutrition of Central America and Panama Nutrition Trial Cohort (Guatemala); the New Delhi Cohort (India); the Cebu Longitudinal Health and Nutrition Survey (Philippines); and the Birth to Twenty Cohort (South Africa; appendix p 1).26 All studies were approved by appropriate institutional ethics committees. Informed verbal or written consent was obtained at recruitment from the mothers in the original birth cohort studies. Informed verbal or written consent was obtained at each round of follow-up, from a parent for childhood follow-ups and from the cohort member themselves for the adult follow-up. The mother’s age at the birth of the index child was calculated from data obtained at interview before or during pregnancy, or, for South African data, from birth notification forms. Birthweight was measured by researchers (Brazil, India, and Guatemala) or obtained from hospital records (the Philippines and South Africa). Gestational age was calculated from the last menstrual period date obtained by prospective surveillance (Guatemala and India), from the mother at recruitment (the Philippines), or from medical records (Brazil and South Africa). In the Philippines, gestational age was obtained for low birthweight babies by newborn clinical assessment.27 Low birthweight was defined as <2500 g, preterm birth as gestational age <37 weeks, and smallness-for-gestational-age as birthweight below the age-specific and sex-specific 10th percentile of a US reference population.28 In all sites, post-natal weight and height were measured longitudinally with standardised methods. Measurements at 2 years were available in all sites, and 4-year measurements in all except the Philippines, where the next available age (8·5 years) was used to define so-called mid-childhood size. Height-for-age and weight-for-height were converted into Z-scores (HAZ and WHZ, respectively) with the WHO growth reference. Stunting and wasting were defined as HAZ and WHZ below −2 SDs, respectively. Breastfeeding data were recorded prospectively with different methods in each cohort; for this analysis we used duration in months of any breastfeeding (as opposed to exclusive breastfeeding), which was available for all cohorts except India. Height, weight, and waist circumference were measured with standardised techniques. Fat and fat-free mass were measured with site-specific methods, as previously described.29 In Brazil, bioelectrical impedance was measured and results corrected based on a validation study that used isotopic methods; data are only available for men. In Guatemala, weight, height, and abdominal circumference were measured and entered into a hydrostatic-weighing validated equation. The Indian and Filipino cohorts used published equations that have been validated for use in Asian populations for estimation of body fat from skinfold measures. South Africa used dual X-ray absorptiometry (Hologic Delphi, Bedford, MA, USA). Fat mass was calculated as: % body fat × body weight, and fat-free mass as weight–fat mass. Overweight was defined as a body-mass index (BMI) of 25 kg/m2 or more and obesity as BMI 30 kg/m2 or more. Blood pressure was measured seated, after a 5–10 min rest, with appropriate cuff sizes and a variety of devices, as previously described.30 High blood pressure was defined as systolic blood pressure 130 mm Hg or greater or diastolic blood pressure 85 mm Hg or greater.31 Fasting glucose was measured in all sites except Brazil, where a random sample was collected and glucose values adjusted for time since the last meal.32 Impaired fasting glucose was defined as a fasting glucose concentration of 6·1 mmol/L or greater but less than 7·0 mmol/L and diabetes as a concentration of 7·0 mmol/L or greater.33 Pregnant women were excluded from all these analyses. Socioeconomic status was considered a potential confounding factor and was assessed with five variables: maternal schooling, marital status, wealth index, urban or rural residence, and ethnic origin. Wealth index was a score derived in each cohort based on type of housing and ownership of household assets (appendix p 2). The Brazilian, Indian, and South African cohorts are urban, and the Guatemalan cohort is rural; in the mixed Filipino cohort, a so-called urbanicity index was used.34 Brazil and South Africa had white, black, Asian, and other ethnic subgroups. Maternal height was potentially both a confounder of maternal age effects (due to secular trends in height) and a mediator (due to younger mothers not having attained final height). Maternal parity and breastfeeding duration were potential mediators (older mothers tend to have higher parity and younger mothers might breastfeed for a shorter time). Parity was coded as 1, 2, 3, or 4 or more. The number of offspring for whom outcomes were available diminished with age at follow-up; for example, n=17 903 (81%) for birthweight and n=10 376 (47%) for adult blood pressure. The maternal age distribution was similar in successive waves of follow-up (appendix p 3). For each outcome, we used the maximum sample with available data. To test the representativeness of our analysis sample, we compared maternal age in those included in the analysis (with data for any childhood or adult outcome of interest) and those not included, using t tests (appendix p 4). We used maternal age as a continuous variable where possible, but used categories (≤19 years, ≥35 years, and 5-year intervening bands) to check for linearity and for tables, figures, and odds ratio calculations. Percentage of body fat and wealth index were non-normally distributed and were Fisher-Yates transformed.35 We first analysed associations between maternal age and outcomes in each cohort with multiple linear regression for continuous outcomes and multiple logistic regression for dichotomous outcomes. We assessed non-linear associations with quadratic terms. We then produced pooled analyses, including main effects for each cohort and interaction terms for cohort and control variables. We tested for heterogeneity among cohorts with F tests, comparing sums of squares explained when effects were or were not allowed to vary across sites.35 We used a sequence of regression models: (1) adjusted for sex, and adult age (adult outcomes only); (2) further adjusted for socioeconomic variables; (3) further adjusted for maternal height; (4) further adjusted for breastfeeding duration; and (5) further adjusted for parity. Missing maternal wealth and schooling values were imputed with regression analysis of known values on other socioeconomic variables. Missing maternal height values were not imputed, and a dummy variable (0=not missing; 1=missing) to represent missing value was included in regression models. The Guatemala cohort is based on a randomised controlled trial of a protein and energy supplement for pregnant women and children;26 it comprises children living in the trial villages who were born or were younger than 7 years of age at any time between 1969 and 1977. We tested for interactions between maternal age and intervention group in this cohort, but noted no consistent evidence of interactions. In Guatemala, the 2344 participants came from 768 families; in India, the 5395 came from 5313 families; there were no siblings in the other cohorts. We used linear mixed modelling to assess whether siblings affected the associations of outcomes with maternal age, and found that they made little difference (appendix p 9). We therefore present our findings without adjustment for sibships. All analyses were done with SPSS version 21 and Stata version 12. The funders had no role in the study design or conduct; the management, analysis, or interpretation of the data; the preparation, review, or approval of the report; or the decision to submit the manuscript for publication. CO and CHDF had full access to all the data and take responsibility for the integrity of the data and the accuracy of the data analysis. CHDF had the responsibility to submit the report for publication.

Matériaux

https://efashare.b-cdn.net/materials/56da5d405a3175bfd5bbd30033a7991469cf6e040934134d321b06490f1fcb73/mmc1.pdf

Innovations

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

1. Mobile health (mHealth) applications: Develop mobile apps that provide pregnant women with access to information, resources, and support for maternal health. These apps can provide guidance on prenatal care, nutrition, and exercise, as well as reminders for appointments and medication.

2. Telemedicine services: Implement telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare professionals remotely. This can help overcome geographical barriers and provide access to prenatal care and consultations.

3. Community health workers: Train and deploy community health workers to provide education, support, and basic healthcare services to pregnant women in their communities. These workers can help identify high-risk pregnancies, provide prenatal care, and refer women to appropriate healthcare facilities.

4. Maternal health clinics: Establish dedicated maternal health clinics in underserved areas to provide comprehensive prenatal care, including regular check-ups, screenings, and vaccinations. These clinics can also offer counseling and support services for pregnant women.

5. Financial incentives: Implement financial incentives, such as cash transfers or vouchers, to encourage pregnant women to seek and receive prenatal care. This can help overcome financial barriers and improve access to essential maternal health services.

6. Public-private partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private sector resources and expertise to expand healthcare infrastructure, improve service delivery, and increase availability of maternal health services.

7. Health education programs: Develop and implement health education programs that focus on maternal health and target women, families, and communities. These programs can raise awareness about the importance of prenatal care, nutrition, and healthy behaviors during pregnancy.

8. Maternal health hotlines: Establish toll-free hotlines staffed by trained healthcare professionals who can provide information, support, and guidance to pregnant women. These hotlines can be accessible 24/7 and offer assistance in multiple languages.

9. Transportation support: Provide transportation support for pregnant women in remote or underserved areas to ensure they can access healthcare facilities for prenatal care, delivery, and postnatal care. This can involve arranging transportation services or subsidizing transportation costs.

10. Maternal health monitoring systems: Develop and implement systems for monitoring maternal health indicators and outcomes in real-time. This can help identify areas with low access to maternal health services and enable targeted interventions to improve access and outcomes.

It is important to note that the implementation of these innovations should be context-specific and tailored to the needs and resources of each community or country.

AI Innovations Description

The recommendation to improve access to maternal health based on the study is to strengthen efforts to prevent early childbearing. The study found that children of young mothers in low-income and middle-income countries (LMICs) are disadvantaged at birth and in childhood nutrition and schooling. Therefore, it is important to provide comprehensive reproductive health education and services to young women to delay childbearing and ensure they are physically and emotionally prepared for pregnancy and childbirth.

Additionally, the study also found that children of older mothers had advantages in nutritional status and schooling. This suggests that efforts should also be made to support older mothers in accessing adequate healthcare and resources during pregnancy and childbirth. This may include providing targeted support and interventions to address the specific needs and challenges faced by older mothers.

Overall, the recommendation is to implement comprehensive maternal health programs that address the unique needs of both young and older mothers in LMICs. This can include improving access to reproductive health education, family planning services, antenatal care, skilled birth attendance, and postnatal care. By addressing these factors, it is possible to improve maternal and child health outcomes and ensure better access to quality healthcare for all women.

AI Innovations Methodology

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

1. Strengthen efforts to prevent early childbearing: Young mothers in low-income and middle-income countries (LMICs) are at a disadvantage when it comes to birth outcomes, childhood nutrition, and schooling. Implementing comprehensive sex education programs, increasing access to contraception, and providing support for young mothers can help prevent early childbearing and improve maternal and child health outcomes.

2. Improve access to prenatal care: Prenatal care plays a crucial role in ensuring a healthy pregnancy and reducing the risk of complications. Efforts should be made to improve access to prenatal care services, especially in rural and underserved areas. This can be done by increasing the number of healthcare facilities, training more healthcare providers, and implementing mobile health initiatives to reach remote communities.

3. Enhance maternal nutrition programs: Adequate nutrition during pregnancy is essential for the health and development of both the mother and the baby. Implementing maternal nutrition programs that provide education, counseling, and access to nutritious food can help improve maternal and child health outcomes. These programs can also address common nutritional deficiencies, such as iron and folic acid deficiencies, which are prevalent in LMICs.

4. Promote breastfeeding support: Breastfeeding provides numerous health benefits for both the mother and the baby. Efforts should be made to promote and support breastfeeding, including providing education and counseling to mothers, establishing breastfeeding-friendly environments in healthcare facilities and workplaces, and implementing policies that protect and support breastfeeding mothers.

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

1. Define the target population: Identify the specific population group or region that will be the focus of the simulation. This could be based on factors such as geographical location, socioeconomic status, or specific health indicators.

2. Collect baseline data: Gather relevant data on the current state of maternal health in the target population. This could include information on maternal mortality rates, access to prenatal care, breastfeeding rates, and other relevant indicators.

3. Develop a simulation model: Create a mathematical or statistical model that represents the target population and incorporates the recommended interventions. The model should take into account factors such as population size, demographic characteristics, healthcare infrastructure, and the potential impact of the interventions.

4. Input intervention parameters: Specify the parameters of the recommended interventions in the simulation model. This could include factors such as the coverage and effectiveness of prenatal care services, the reach and impact of nutrition programs, and the level of support provided for breastfeeding.

5. Run the simulation: Use the model to simulate the impact of the interventions over a specified time period. This could involve running multiple iterations of the simulation to account for uncertainty and variability in the data.

6. Analyze the results: Evaluate the outcomes of the simulation to assess the potential impact of the recommended interventions on improving access to maternal health. This could include analyzing changes in maternal mortality rates, improvements in prenatal care utilization, increases in breastfeeding rates, and other relevant indicators.

7. Refine and validate the model: Review the simulation results and refine the model as needed. Validate the model by comparing the simulated outcomes with real-world data, if available.

8. Communicate the findings: Present the findings of the simulation in a clear and concise manner, highlighting the potential benefits of the recommended interventions in improving access to maternal health. This information can be used to inform policy decisions, resource allocation, and program planning.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific context and available data. The steps outlined above provide a general framework for conducting such simulations.

Statistics:

Citations: 244

Authors: 31

Identifiers:

DOI: 10.1016/S2214-109X(15)00038-8

Research Areas:

Community Interventions, Food Security, Genetics and Genomics, Health System and Policy, Maternal and Child Health, Noncommunicable Diseases, Sexual and Reproductive Health, Social Determinants

Study Countries:

South Africa

Study Design:

Case-Control Study, Cohort Study, Cross Sectional Study, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Male & Female

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