Implementation and maintenance of infant dietary diversity in Zimbabwe: contribution of food and water insecurity

BMC Nutrition, Volume 8, No. 1, Year 2022

Interpretation

Background: Inadequate food and water resources negatively affect child health and the efficiency of nutrition interventions. Methods: We used data from the SHINE trial to investigate the associations of food insecurity (FI) and water insecurity (WI) on mothers’ implementation and maintenance of minimum infant dietary diversity (MIDD). We conducted factor analysis to identify and score dimensions of FI (poor access, household shocks, low availability & quality), and WI (poor access, poor quality and low reliability). MIDD implementation (n = 636) was adequate if infants aged 12 months (M12) ate ≥ four food groups. MIDD maintenance (n = 624) was categorized into four mutually exclusive groups: A (unmet MIDD at both M12 and M18), B (unmet MIDD at M12 only), C (unmet MIDD at M18 only), and D (met MIDD at both M12 and M18). We used multivariable-adjusted binary logistic and multinomial regressions to determine likelihood of MIDD implementation, and of belonging to MIDD maintenance groups A-C (poor maintenance groups), compared to group D, respectively. Results: Low food availability & quality were negatively associated with implementation (OR = 0.81; 0.69, 0.97), and maintenance (ORB = 1.29; 1.07, 1.56). Poor water quality was positively associated with implementation (OR = 1.25; 1.08, 1.44), but inconsistently associated with maintenance, with higher odds of infants being in group C (OR = 1.39; 1.08, 1.79), and lower odds of being in group B (OR = 0.80; 0.66, 0.96). Conclusion: Food security should be prioritized for adequate implementation and maintenance of infant diets during complementary feeding. The inconsistent findings with water quality indicate the need for further research on WI and infant feeding. The SHINE trial design and outcomes have been published previously [45]. Briefly, SHINE was a 4-arm cluster-randomized trial testing the independent and combined effects of infant and young child feeding (IYCF) and household water, sanitation, and hygiene (WASH) on child stunting and anemia. It was a community-based intervention implemented in rural areas of Shurugwi and Chirumanzu districts in Zimbabwe. The districts were divided into clusters, each defined as a catchment area serviced by 1–4 village health workers (VHW) of the Ministry of Health and Child Care. Between 22 November 2012 and 27 March 2015, pregnant women, 15–49 years old, who were permanent residents of those rural areas were enrolled. The infants born to these mothers were then followed over time to ascertain stunting prevalence. The analyses presented in this paper focus on the IYCF arm (n = 1148 live born infants). The IYCF intervention included six nutrition education modules delivered by VHW during 15 home visits to participating women. The intervention which promoted WHO recommended feeding practices, adapted to the local context, were delivered at monthly intervals starting at infant age 5 months (Table (Table1).1). From the 6-month home visit until the 18-month visit, a daily supply of 20 g of Nutributter® was provided to the caregiver to supplement the diet of the index infant. Additional detail on the IYCF protocol is available online. SHINE’s IYCF arm was chosen for our analyses for two reasons: 1) the children showed improvements in growth compared to the other SHINE arms [45], and 2) it allows the exploration of the effects of FI and WI on recommended feeding practices because nutrition education was targeted without direct intervention on food or water. Education modules in the Infant and Young Child Feeding (IYCF) Nutrition Intervention arm of the Sanitation Hygiene and Infant Nutrition Efficacy (SHINE) Trial Research nurses made home visits at multiple times to collect relevant information from mothers and infants: at baseline (during the pregnancy period) and at infant ages 1, 3, 6, 12 and 18 months. Since SHINE was household-based, the intermediate visits were conducted only when participants were available at the address where they consented. If the participants remained inaccessible after two attempts to reach out during the intermediate visits, the mother-infant dyad data were considered missing. At M18, participants were visited anywhere in Zimbabwe, even if they had moved from their initial residence. Our sample excluded infants who had died (n = 50), whose mothers rescinded consent (n = 2), and who were lost to follow-up (n = 33 at M12; and an additional n = 4 at M18). Twins were also excluded (n = 21 pairs) because diet was reported for only one of the infants, and it was not possible to distinguish which infant the data belonged to. Figure 1 illustrates the sample of included and excluded participants. Flowchart of eligible and included mother-infant dyads. aTwins were excluded since only one diet questionnaire was filled and it was not possible to determine which infant the information belonged to. bMissing covariates: unknown HIV−status (n=1), maternal age (n=20), maternal education (n=3), all other covariates (n=0). MHFI= Multidimensional Household Food Insecurity. MHWI= Multidimensional Household Water Insecurity Infant diet was assessed using the WHO infant diet assessment questionnaire, and the MIDD indicator was defined as infants who were fed at least four out of the following seven food groups: 1) grains, roots and tubers, 2) legumes and nuts, 3) dairy products, 4) flesh foods, 5) eggs, 6) vitamin-A rich fruits and vegetables, and 7) other fruits and vegetables [8]. During the post-partum period, when infants were aged M12 and M18, mothers were asked to recall what they fed their infants in the 24 h prior to the home visit interviews. The first main outcome, MIDD implementation, occurred if mothers reported feeding their infants a minimum of four food groups at M12 as described above. MIDD implementation was determined at M12 because the last nutrition education module was delivered at 9 months, with a reminder of all previous modules provided at M12 (Table (Table2).2). The second main outcome, MIDD maintenance, was categorized into four mutually exclusive groups based on the combined MIDD practices at M12 and M18 as follows: Group A = unmet MIDD at both M12 and M18, Group B = unmet MIDD at M12 only, Group C = unmet MIDD at M18 only, and Group D = met MIDD at both M12 and M18. Description of multidimensional household food insecurity and water insecurity variables Type of drinking source, non-drinking source (improved (piped, protected ground), unimproved ground (unprotected boreholes/ wells), surface) Drinking water satisfaction (3-level) MHFI Multidimensional Household Food Insecurity MHWI Multidimensional Household Water Insecurity The Multidimensional Household Food Insecurity (MHFI) and the Multidimensional Household Water Insecurity (MHWI) measures were used as primary FI and WI exposures, respectively [46]. These measures were developed specifically for the rural Zimbabwean households and their validities were tested to ensure robustness and usefulness. In brief, factor analyses were run on groups of food- and water-related variables. The process identified multiple dimensions representing the different aspects of each FI and WI. The resulting MHFI measure was characterized by 1) poor food access, 2) household shocks, and 3) low food availability & quality, whereas MHWI was characterized by 1) poor water access, 2) poor water quality, and 3) low water reliability. Each dimension of the MHFI and MHWI was made up of aggregated groups of variables as summarized in Table Table2.2. Standardized scores were obtained from post-estimation commands using the PCAmix package in R4.0.2 (R Foundation for Computational Statistics, Austria, Vienna). At baseline, a structured questionnaire was used to collect information on socio-demographic characteristics such as maternal age, maternal education, marital status, parity, religion, maternal employment outside the home and household size. Maternal depression, based on Edinburgh’s Postnatal Depression Scale and Mothering Self-Efficacy were collected using validated scales as described previously [47, 48]. The HIV status of women was determined using the rapid test algorithm; those who tested positive were directed to local clinics for follow-up and treatment. Socio-economic status (SES) was based on a wealth index created specifically for this population and reported in a prior publication [49]. Season at enrolment was characterized as calendar quarter during the baseline interview. Infant characteristics such as date of birth, sex, birthweight, and premature birth (gestational age < 37 weeks) were abstracted from health facility records by trained nurses. Our analyses excluded the mother-infant dyads who had incomplete information on MHFI (n = 95), MHWI (n = 148), MIDD implementation at M12 (n = 17), MIDD maintenance from M12 to M18 (n = 8) and the above-mentioned covariates (n = 24). We used descriptive statistics to summarize the characteristics of participants included in the analysis. We report medians and interquartile ranges (IQR) for the distributions of FI and WI dimensions; means and standard deviations (SD) for normally distributed variables; and frequencies and percentages for categorical variables. We fit binary logistic regression models to investigate the association of MIDD implementation at M12 (yes vs. no) with FI and WI. Since MIDD maintenance from M12 to M18 was a non-ordered categorical outcome (groups A-D), we used multinomial logistic regressions for the assessment of the relationship between MIDD maintenance and household-level FI and WI. All regression models utilized cluster-robust estimations to account for clustering of participants within study districts. All analyses were performed with all FI, and WI dimensions included simultaneously. To identify relevant covariates for inclusion in the models, three groups of variables were defined. Group 1 included only variables that were considered theoretically critical given the exposures and population: season at baseline interview (calendar quarter), wealth index (tercile), and household location (Chirumanzu vs. Shurugwi). Group 2 included variables that are commonly controlled for in this population and in nutrition behavior change interventions: maternal HIV status (positive vs. negative), maternal age (years), maternal education (some primary, some secondary, completed secondary), maternal religion (Apostolic, other Christian, other religion), and infant sex (male vs. female). Group 3 included mothering self-efficacy (scores: 1–5), maternal depression (scores: 0–30), household size, and parity (parous, nulliparous, missing). To select model covariates, we implemented backward elimination logistic regression by forcing retention of all Group 1 and Group 2 variables and setting retention of Group 3 variables at p < 0.2. MIDD outcomes were then modelled using all variables retained. However, since none of the identified Group 3 variables changed the measures of associations by ± 10% or more, the final models were the most parsimonious models with Group 1 and Group 2 variables only, determined by AIC and BIC. The main analyses described in this section were conducted in Stata IC v.16 (StataCorp LP, College Station, TX). The Medical Research Council of Zimbabwe (MRCZ) and the Institutional Review Board (IRB) of the Johns Hopkins Bloomberg School of Public Health reviewed and approved the SHINE trial protocol. Written informed consent was obtained from women in the local languages (Ndebele, Shona and English).

AI Digest

Study Justification:

This study aimed to investigate the impact of food and water insecurity on the implementation and maintenance of minimum infant dietary diversity (MIDD) in Zimbabwe. The study is important because inadequate food and water resources can negatively affect child health and the effectiveness of nutrition interventions. By understanding the associations between food and water insecurity and infant dietary diversity, policymakers and stakeholders can develop targeted interventions to improve infant nutrition.

Study Highlights:

1. The study used data from the SHINE trial, a cluster-randomized trial conducted in rural areas of Zimbabwe, to examine the effects of food and water insecurity on infant dietary diversity.
2. The study found that low food availability and quality were negatively associated with the implementation and maintenance of MIDD.
3. Poor water quality was positively associated with MIDD implementation but inconsistently associated with maintenance.
4. The findings suggest that food security should be prioritized to ensure adequate implementation and maintenance of infant diets during complementary feeding.
5. The study highlights the need for further research on water quality and its impact on infant feeding practices.

Recommendations for Lay Readers and Policy Makers:

1. Prioritize food security: Policies and interventions should focus on improving food availability and quality to ensure infants have access to a diverse and nutritious diet.
2. Address water quality issues: Further research is needed to better understand the relationship between water quality and infant feeding practices. Policies and interventions should aim to improve water quality to support infant nutrition.
3. Strengthen nutrition education: Nutrition education programs should be implemented to educate mothers and caregivers on the importance of diverse and nutritious diets for infants. These programs should be tailored to the local context and delivered by trained health workers.
4. Enhance household-level support: Interventions should target households experiencing food and water insecurity, providing support and resources to improve access to nutritious foods and clean water.
5. Foster collaboration: Stakeholders, including government agencies, NGOs, and community organizations, should collaborate to develop and implement comprehensive strategies that address food and water insecurity and promote infant dietary diversity.

Key Role Players:

1. Ministry of Health and Child Care: Responsible for implementing and coordinating nutrition interventions and programs.
2. Non-governmental organizations (NGOs): Organizations specializing in nutrition and food security can provide technical expertise and support in developing and implementing interventions.
3. Community health workers: Trained health workers who can deliver nutrition education and provide support to mothers and caregivers at the community level.
4. Research institutions: Conduct further research to better understand the relationship between food and water insecurity and infant feeding practices, and evaluate the effectiveness of interventions.

Cost Items for Planning Recommendations:

1. Nutrition education materials: Development and production of educational materials for nutrition interventions.
2. Training and capacity building: Training programs for community health workers and other stakeholders involved in implementing interventions.
3. Food supplementation: Provision of supplementary food, such as Nutributter®, to caregivers to supplement infant diets.
4. Water quality improvement: Investments in water treatment infrastructure and technologies to improve water quality.
5. Monitoring and evaluation: Resources for monitoring and evaluating the effectiveness of interventions and making necessary adjustments.
Note: The cost items provided are general categories and do not represent actual costs. The specific budget items would depend on the context and scale of the interventions.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study design is a cluster-randomized trial, which is a robust method. The sample size is adequate, with 636 participants for MIDD implementation and 624 participants for MIDD maintenance. The study uses multivariable-adjusted binary logistic and multinomial regressions to analyze the data. However, the abstract does not provide information on the statistical significance of the associations found. To improve the evidence, the abstract should include p-values or confidence intervals for the reported odds ratios. Additionally, the abstract mentions the SHINE trial design and outcomes being published previously, but it does not provide a reference to the publication. Including the reference would allow readers to access more detailed information about the trial.

Abstract

The SHINE trial design and outcomes have been published previously [45]. Briefly, SHINE was a 4-arm cluster-randomized trial testing the independent and combined effects of infant and young child feeding (IYCF) and household water, sanitation, and hygiene (WASH) on child stunting and anemia. It was a community-based intervention implemented in rural areas of Shurugwi and Chirumanzu districts in Zimbabwe. The districts were divided into clusters, each defined as a catchment area serviced by 1–4 village health workers (VHW) of the Ministry of Health and Child Care. Between 22 November 2012 and 27 March 2015, pregnant women, 15–49 years old, who were permanent residents of those rural areas were enrolled. The infants born to these mothers were then followed over time to ascertain stunting prevalence. The analyses presented in this paper focus on the IYCF arm (n = 1148 live born infants). The IYCF intervention included six nutrition education modules delivered by VHW during 15 home visits to participating women. The intervention which promoted WHO recommended feeding practices, adapted to the local context, were delivered at monthly intervals starting at infant age 5 months (Table (Table1).1). From the 6-month home visit until the 18-month visit, a daily supply of 20 g of Nutributter® was provided to the caregiver to supplement the diet of the index infant. Additional detail on the IYCF protocol is available online. SHINE’s IYCF arm was chosen for our analyses for two reasons: 1) the children showed improvements in growth compared to the other SHINE arms [45], and 2) it allows the exploration of the effects of FI and WI on recommended feeding practices because nutrition education was targeted without direct intervention on food or water. Education modules in the Infant and Young Child Feeding (IYCF) Nutrition Intervention arm of the Sanitation Hygiene and Infant Nutrition Efficacy (SHINE) Trial Research nurses made home visits at multiple times to collect relevant information from mothers and infants: at baseline (during the pregnancy period) and at infant ages 1, 3, 6, 12 and 18 months. Since SHINE was household-based, the intermediate visits were conducted only when participants were available at the address where they consented. If the participants remained inaccessible after two attempts to reach out during the intermediate visits, the mother-infant dyad data were considered missing. At M18, participants were visited anywhere in Zimbabwe, even if they had moved from their initial residence. Our sample excluded infants who had died (n = 50), whose mothers rescinded consent (n = 2), and who were lost to follow-up (n = 33 at M12; and an additional n = 4 at M18). Twins were also excluded (n = 21 pairs) because diet was reported for only one of the infants, and it was not possible to distinguish which infant the data belonged to. Figure 1 illustrates the sample of included and excluded participants. Flowchart of eligible and included mother-infant dyads. aTwins were excluded since only one diet questionnaire was filled and it was not possible to determine which infant the information belonged to. bMissing covariates: unknown HIV−status (n=1), maternal age (n=20), maternal education (n=3), all other covariates (n=0). MHFI= Multidimensional Household Food Insecurity. MHWI= Multidimensional Household Water Insecurity Infant diet was assessed using the WHO infant diet assessment questionnaire, and the MIDD indicator was defined as infants who were fed at least four out of the following seven food groups: 1) grains, roots and tubers, 2) legumes and nuts, 3) dairy products, 4) flesh foods, 5) eggs, 6) vitamin-A rich fruits and vegetables, and 7) other fruits and vegetables [8]. During the post-partum period, when infants were aged M12 and M18, mothers were asked to recall what they fed their infants in the 24 h prior to the home visit interviews. The first main outcome, MIDD implementation, occurred if mothers reported feeding their infants a minimum of four food groups at M12 as described above. MIDD implementation was determined at M12 because the last nutrition education module was delivered at 9 months, with a reminder of all previous modules provided at M12 (Table (Table2).2). The second main outcome, MIDD maintenance, was categorized into four mutually exclusive groups based on the combined MIDD practices at M12 and M18 as follows: Group A = unmet MIDD at both M12 and M18, Group B = unmet MIDD at M12 only, Group C = unmet MIDD at M18 only, and Group D = met MIDD at both M12 and M18. Description of multidimensional household food insecurity and water insecurity variables Type of drinking source, non-drinking source (improved (piped, protected ground), unimproved ground (unprotected boreholes/ wells), surface) Drinking water satisfaction (3-level) MHFI Multidimensional Household Food Insecurity MHWI Multidimensional Household Water Insecurity The Multidimensional Household Food Insecurity (MHFI) and the Multidimensional Household Water Insecurity (MHWI) measures were used as primary FI and WI exposures, respectively [46]. These measures were developed specifically for the rural Zimbabwean households and their validities were tested to ensure robustness and usefulness. In brief, factor analyses were run on groups of food- and water-related variables. The process identified multiple dimensions representing the different aspects of each FI and WI. The resulting MHFI measure was characterized by 1) poor food access, 2) household shocks, and 3) low food availability & quality, whereas MHWI was characterized by 1) poor water access, 2) poor water quality, and 3) low water reliability. Each dimension of the MHFI and MHWI was made up of aggregated groups of variables as summarized in Table Table2.2. Standardized scores were obtained from post-estimation commands using the PCAmix package in R4.0.2 (R Foundation for Computational Statistics, Austria, Vienna). At baseline, a structured questionnaire was used to collect information on socio-demographic characteristics such as maternal age, maternal education, marital status, parity, religion, maternal employment outside the home and household size. Maternal depression, based on Edinburgh’s Postnatal Depression Scale and Mothering Self-Efficacy were collected using validated scales as described previously [47, 48]. The HIV status of women was determined using the rapid test algorithm; those who tested positive were directed to local clinics for follow-up and treatment. Socio-economic status (SES) was based on a wealth index created specifically for this population and reported in a prior publication [49]. Season at enrolment was characterized as calendar quarter during the baseline interview. Infant characteristics such as date of birth, sex, birthweight, and premature birth (gestational age < 37 weeks) were abstracted from health facility records by trained nurses. Our analyses excluded the mother-infant dyads who had incomplete information on MHFI (n = 95), MHWI (n = 148), MIDD implementation at M12 (n = 17), MIDD maintenance from M12 to M18 (n = 8) and the above-mentioned covariates (n = 24). We used descriptive statistics to summarize the characteristics of participants included in the analysis. We report medians and interquartile ranges (IQR) for the distributions of FI and WI dimensions; means and standard deviations (SD) for normally distributed variables; and frequencies and percentages for categorical variables. We fit binary logistic regression models to investigate the association of MIDD implementation at M12 (yes vs. no) with FI and WI. Since MIDD maintenance from M12 to M18 was a non-ordered categorical outcome (groups A-D), we used multinomial logistic regressions for the assessment of the relationship between MIDD maintenance and household-level FI and WI. All regression models utilized cluster-robust estimations to account for clustering of participants within study districts. All analyses were performed with all FI, and WI dimensions included simultaneously. To identify relevant covariates for inclusion in the models, three groups of variables were defined. Group 1 included only variables that were considered theoretically critical given the exposures and population: season at baseline interview (calendar quarter), wealth index (tercile), and household location (Chirumanzu vs. Shurugwi). Group 2 included variables that are commonly controlled for in this population and in nutrition behavior change interventions: maternal HIV status (positive vs. negative), maternal age (years), maternal education (some primary, some secondary, completed secondary), maternal religion (Apostolic, other Christian, other religion), and infant sex (male vs. female). Group 3 included mothering self-efficacy (scores: 1–5), maternal depression (scores: 0–30), household size, and parity (parous, nulliparous, missing). To select model covariates, we implemented backward elimination logistic regression by forcing retention of all Group 1 and Group 2 variables and setting retention of Group 3 variables at p < 0.2. MIDD outcomes were then modelled using all variables retained. However, since none of the identified Group 3 variables changed the measures of associations by ± 10% or more, the final models were the most parsimonious models with Group 1 and Group 2 variables only, determined by AIC and BIC. The main analyses described in this section were conducted in Stata IC v.16 (StataCorp LP, College Station, TX). The Medical Research Council of Zimbabwe (MRCZ) and the Institutional Review Board (IRB) of the Johns Hopkins Bloomberg School of Public Health reviewed and approved the SHINE trial protocol. Written informed consent was obtained from women in the local languages (Ndebele, Shona and English).

Materials

https://efashare.b-cdn.net/materials/1ca15a52ea3d3b6509dc5966b6f148fe11feab72d40c896f65bac6dc983d8362/40795_2022_622_MOESM1_ESM.docx

Innovations

Based on the provided information, it appears that the study focuses on the associations between food and water insecurity and the implementation and maintenance of minimum infant dietary diversity (MIDD) in Zimbabwe. The study uses data from the SHINE trial, which tested the effects of infant and young child feeding (IYCF) and household water, sanitation, and hygiene (WASH) on child stunting and anemia.

To improve access to maternal health in this context, some potential innovations and recommendations could include:

1. Strengthening food security programs: Given the negative associations between food insecurity and MIDD implementation and maintenance, it is important to prioritize food security interventions. This could involve implementing programs that improve access to nutritious and diverse food options for pregnant women and new mothers.

2. Enhancing water quality and reliability: While the findings on water quality were inconsistent, it is still important to address water insecurity to ensure safe and reliable access to clean water for mothers and infants. This could involve implementing water treatment and purification systems, improving water infrastructure, and promoting hygiene practices to reduce waterborne diseases.

3. Integrating nutrition education and counseling: The SHINE trial included nutrition education modules delivered by village health workers. To further improve access to maternal health, it could be beneficial to integrate nutrition education and counseling into routine antenatal and postnatal care services. This would ensure that all pregnant women and new mothers receive information and support regarding optimal infant feeding practices.

4. Strengthening social support networks: Social support plays a crucial role in maternal health. Implementing programs that promote community engagement, peer support, and knowledge sharing among mothers could help improve access to maternal health services and encourage the adoption of recommended feeding practices.

5. Addressing socio-economic factors: Socio-economic factors, such as maternal education and household wealth, can influence access to maternal health services. It is important to address these factors through targeted interventions, such as providing financial support for vulnerable mothers and improving access to education and employment opportunities.

These are just a few potential innovations and recommendations that could be considered to improve access to maternal health in the context of the study. It is important to note that further research and contextual analysis would be needed to determine the most effective and feasible strategies for implementation.

AI Innovations Description

Based on the provided description, the recommendation to improve access to maternal health is to prioritize food security for adequate implementation and maintenance of infant diets during complementary feeding. This recommendation is based on the findings that low food availability and quality were negatively associated with the implementation and maintenance of minimum infant dietary diversity (MIDD).

Additionally, further research is needed to explore the effects of water quality on MIDD implementation and maintenance, as the findings were inconsistent in this regard.

It is important to note that the recommendation is based on data from the SHINE trial, which was a community-based intervention implemented in rural areas of Zimbabwe. The trial focused on infant and young child feeding (IYCF) and household water, sanitation, and hygiene (WASH) interventions to improve child stunting and anemia. The IYCF intervention included nutrition education modules delivered by village health workers, and a daily supply of Nutributter® to supplement the diet of infants.

AI Innovations Methodology

Based on the provided information, the study focuses on the implementation and maintenance of infant dietary diversity in Zimbabwe, specifically looking at the impact of food and water insecurity on maternal practices. The methodology used in the study involves data collection from the SHINE trial, a 4-arm cluster-randomized trial conducted in rural areas of Shurugwi and Chirumanzu districts in Zimbabwe. Pregnant women aged 15-49 years old were enrolled, and their infants were followed over time to assess stunting prevalence and feeding practices.

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

1. Identify potential recommendations: Review the findings of the study and identify key areas where improvements can be made to enhance access to maternal health. This could include interventions related to food security, water quality, nutrition education, and healthcare services.

2. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on improving access to maternal health. The model should consider various factors such as the target population, geographical location, existing infrastructure, and available resources.

3. Define outcome measures: Determine the outcome measures that will be used to evaluate the impact of the recommendations. This could include indicators such as the percentage of pregnant women receiving adequate prenatal care, the reduction in maternal mortality rates, or the improvement in infant feeding practices.

4. Collect baseline data: Gather relevant data on the current state of maternal health in the target population. This could include information on healthcare utilization, maternal and infant health outcomes, and socio-economic factors.

5. Implement the simulation: Input the baseline data into the simulation model and apply the recommended interventions. The model should simulate the impact of these interventions over a specified time period, taking into account factors such as population growth, resource availability, and potential barriers to implementation.

6. Analyze the results: Evaluate the outcomes generated by the simulation model and assess the impact of the recommended interventions on improving access to maternal health. This could involve comparing the simulated outcomes with the baseline data to determine the effectiveness of the interventions.

7. Refine and iterate: Based on the results of the simulation, refine the recommendations and the simulation model as needed. Iterate the process to further optimize the interventions and improve access to maternal health.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of specific recommendations on improving access to maternal health. This can inform decision-making and resource allocation to prioritize interventions that have the greatest potential for positive outcomes.

Author

Dima Health

Identifiers:

DOI: 10.1186/s40795-022-00622-8

Research Areas:

Community Interventions, Disparities, Environmental, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Mental Health, Sexual and Reproductive Health, Social Determinants

Study Countries:

Zimbabwe

Study Design:

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

Study Approach:

Quantitative

Participants Gender:

Male & Female