Growth and growth trajectory among infants in early life: Contributions of food insecurity and water insecurity in rural Zimbabwe

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Study Justification:
This study aimed to investigate the contributions of household-level food insecurity (FI) and water insecurity (WI) to the growth and growth trajectory of infants in rural Zimbabwe. Stunting or linear growth faltering is a significant public health challenge in low-income and middle-income countries, and understanding the role of resource insecurities in infant growth is important for addressing this issue.
Highlights:
– The study used data from the Sanitation Hygiene and Infant Nutrition Efficacy trial, which randomly assigned clusters in rural Zimbabwe to receive different interventions.
– The analysis focused on the standard of care (SOC) arm, which received only education modules on optimal breastfeeding practices.
– The study found that low food availability and quality, as well as poor food access, were associated with lower length-for-age Z-scores (LAZ) among infants.
– Water insecurity dimensions were not associated with LAZ or LAZ trajectory over time.
– The findings suggest that food insecurity, but not water insecurity, is a significant factor in poor linear growth among rural Zimbabwean infants.
Recommendations:
Based on the study findings, the following recommendations can be made:
1. Addressing household-level food insecurity should be a priority in interventions aimed at improving infant growth and reducing stunting.
2. Strategies to improve food availability and quality, as well as access to nutritious food, should be implemented in rural areas.
3. Further research is needed to understand the specific factors contributing to food insecurity and to develop targeted interventions.
Key Role Players:
To address the recommendations, the following key role players are needed:
1. Government agencies responsible for public health and nutrition policies.
2. Non-governmental organizations (NGOs) working on food security and nutrition.
3. Healthcare providers and community health workers involved in infant care.
4. Local community leaders and organizations.
Cost Items for Planning Recommendations:
While the actual cost will depend on the specific interventions implemented, the following cost items should be considered in planning:
1. Development and implementation of food security programs, including provision of nutritious food and support for agricultural initiatives.
2. Training and capacity building for healthcare providers and community health workers on infant nutrition and growth monitoring.
3. Awareness campaigns and educational materials on the importance of food security for infant growth.
4. Monitoring and evaluation of interventions to assess their effectiveness and make necessary adjustments.
Please note that the provided cost items are general suggestions and a detailed budget would require a more comprehensive analysis of the specific context and interventions planned.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides detailed information about the study design, methods, and results. However, it does not mention the statistical significance of the findings or provide any specific effect sizes. To improve the evidence, the abstract could include p-values or confidence intervals for the associations between food insecurity (FI) and water insecurity (WI) with linear growth (LAZ). Additionally, it could provide effect sizes or odds ratios to quantify the magnitude of the associations. This would make the evidence more robust and easier to interpret.

Introduction Stunting or linear growth faltering, measured by length-for-age Z-score (LAZ), remains a significant public health challenge, particularly in rural low-income and middle-income countries. It is a marker of inadequate environments in which infants are born and raised. However, the contributions of household resource insecurities, such as food and water, to growth and growth trajectory are understudied. Methods We used the cluster-randomised Sanitation Hygiene and Infant Nutrition Efficacy trial to determine the association of household-level food insecurity (FI) and water insecurity (WI) on LAZ and LAZ trajectory among infants during early life. Dimensions of FI (poor access, household shocks, low availability and quality) and WI (poor access, poor quality, low reliability) were assessed with the multidimensional household food insecurity and the multidimensional household water insecurity measures. Infant length was converted to LAZ based on the 2006 WHO Child Growth Standards. We report the FI and WI fixed effects from multivariable growth curve models with repeated measures of LAZ at 1, 3, 6, 12 and 18 months (M1-M18). Results A total of 714 and 710 infants were included in our analyses of LAZ from M1 to M18 and M6 to M18, respectively. Mean LAZ values at each time indicated worsening linear growth. From M1 to M18, low food availability and quality was associated with lower LAZ (β=-0.09; 95% -0.19 to -0.13). From M6 to M18, poor food access was associated with lower LAZ (β=-0.11; 95% -0.20 to -0.03). None of the WI dimensions were associated with LAZ, nor with LAZ trajectory over time. Conclusion FI, but not WI, was associated with poor linear growth among rural Zimbabwean infants. Specifically, low food availability and quality and poor food access was associated with lower LAZ. There is no evidence of an effect of FI or WI on LAZ trajectory.

The SHINE trial design and primary outcomes have been published previously.59 62 Additional information on the protocol and statistical analysis plan are available elsewhere (https://osf.io/w93hy). In summary, SHINE randomly assigned clusters, in two rural Zimbabwean districts (Shurugwi and Chirumanzu), to receive one of four interventions: (1) standard of care (SOC), (2) infant and young child feeding (IYCF), (3) water, sanitation, hygiene (WASH) and (4) IYCF+WASH. The clusters were defined as the catchment area of 1–4 village health workers employed by the Ministry of Health and Child Care. Between 22 November 2012 and 27 March 2015, pregnant women aged 15–49 years old who were permanent residents of those rural areas were enrolled. The infants born to the pregnant women were followed over time to ascertain stunting and anaemia at M18. The analyses presented in this paper focus on the SOC arm (n=1166 live born infants), which received only the WHO recommended education modules on optimal breastfeeding practices for all infants from birth to M6. Thus, the SOC arm was considered appropriate for investigating the effects of FI and WI on infant growth and growth trajectory, independent of the SHINE interventions. Research nurses made home visits at multiple times to collect relevant information from households, mothers and infants: at baseline (during pregnancy) and at infant ages 1, 3, 6, 12 and 18 months (M1–M18). Growth: We used LAZ as the indicator for growth. Recumbent length was measured to the nearest 0.1 cm using a Seca 417 infantometer by trained nurses. The length measurements at each time were converted to LAZ based on the 2006 WHO Child Growth Standards.63 FI and WI: The multidimensional household food insecurity (MHFI) and the multidimensional household waterinsecurity (MHWI) measures, developed specifically for the rural Zimbabwean households, were used.64 These measures were created from separate factor analyses using groups of food-related and water-related variables collected at baseline (during pregnancy) from the SHINE trial. From these analyses, FI and WI were characterised by three dimensions each. MHFI includes (1) poor food access, (2) household shocks and (3) low food availability and quality; whereas MHWI includes (1) poor water access, (2) poor water quality and (3) low water reliability. A description of the variables making up each dimension is provided in table 1. Each MHFI and MHWI dimension was scored in postestimation commands in the ‘PCAmix’ package from the R software (R Foundation for Statistical Computing, Vienna, Austria) V.4.0.2. We used each of these three dimensions of FI and WI as the main continuous exposure variables in this study. These variables were included simultaneously in the statistical models. An important note is that higher scores on the dimensions of FI and WI as described in table 1 represent higher levels of insecurity. Description of MHFI and water insecurity MHFI, multidimensional household food insecurity; MHWI, multidimensional household water insecurity. Covariates: At baseline (during pregnancy), a structured questionnaire was used to collect information on maternal and household characteristics such as maternal age (years), maternal height (cm), maternal education (some primary, some secondary, completed secondary), formal employment outside the home (yes/ no), marital status (married vs other), religion (apostolic, other Christian, other), parity (parous, nulliparous, missing), household size (number of household members), presence of improved latrine (yes/no), household location (Shurugwi/Chirumanzu) and season at baseline (during pregnancy) interview (calendar quarter). The HIV status of women was determined using a rapid test algorithm; those who tested positive were directed to local clinics for follow-up and treatment. Socioeconomic status (SES) was based on a household wealth index created specifically for this population.65 Maternal depression, based on Edinburgh’s Postnatal Depression Scale,66 and mothering self-efficacy67 were collected using validated scales for the Zimbabwean population as described previously. Pregnant women’s diet adequacy was assessed based on food group consumption, as described in the FANTA project Minimum Dietary Diversity for Women (yes/ no).68 Infant characteristics such as date of birth, sex, birth weight and prematurity (born at<37 weeks of gestation) were abstracted from health facility records. Infant breast feeding in the 24 hours prior to interviews at M6, M12 and M18 was self-reported by the mother. Since SHINE was household based, the intermediate visits were conducted only when mother–infant dyads still lived at the address where they consented. If after two contact attempts the participants remained inaccessible, they were considered missing at those time points. At M18, participants were visited anywhere in Zimbabwe even if they had moved on from their initial residence. In addition to our sample being restricted to infants from the SOC arm, analyses were further limited to infants who had complete information on FI, WI, at least one LAZ measure out of five and the above prespecified covariates. Infants, who had died prior to the end of the trial (n=67), whose mothers signed voluntary consent to exit the study (n=5) and who had implausible LAZ patterns over time, were also excluded (n=3). Descriptive statistics were used to summarise the characteristics of the infants included in the analysis. Frequencies and percentages were used for categorical variables. Medians (p50) and IQRs were used for the distributions of the FI and WI dimensions. After graphically confirming normal LAZ distribution of our sample, LAZ values were summarised using means and SDs. The associations of FI and WI with LAZ and LAZ trajectory were investigated through multivariable growth curve modelling of their fixed effects. We used unstructured covariance structure to account for multiple measurements of length on the same infant over time. Time interactions with FI and WI represented growth trajectory associated with these exposures in our models. Two groups of variables were defined a priori. Group 1 included only variables that were considered theoretically critical given the main predictors and population: season at baseline (during pregnancy) interview, household SES, infant sex, residence location, improved latrine and maternal HIV status. Group 2 additionally included risk factors for poor growth: maternal age, height, education, religion, parity, maternal depression, mothering self-efficacy, infant birth weight, prematurity, breast feeding and household size. Group 2 variables and time-covariate interactions were selected using backward stepwise regressions with retention at p<0.2 at each modelling stage. Multicollinearity was tested with variance inflation factors (VIF <5). The best subset of covariates for the growth models was identified by comparing AIC and BIC between models. Two models are presented in the results section. Model 1 consists of group 1 variables and time interaction with infant sex (minimally adjusted model (Min-AM)). Model 2 includes Min-AM, plus maternal age, height, education, religion, infant birth weight, preterm birth, household size, continued breast feeding until M18 and time interactions with maternal height, infant birth weight and continued breast feeding until M18 (fully AM (Full-AM)). All analyses were performed in Stata/MP V.17 (StataCorp). Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of the research presented in this paper.

Based on the provided information, it appears that the study focuses on the association between household-level food insecurity (FI) and water insecurity (WI) with linear growth among infants in rural Zimbabwe. The study utilizes the Sanitation Hygiene and Infant Nutrition Efficacy (SHINE) trial, which includes interventions related to infant and young child feeding (IYCF), water, sanitation, and hygiene (WASH), and a combination of both.

In terms of potential innovations to improve access to maternal health, based on the information provided, the following recommendations can be considered:

1. Integrated interventions: Implement integrated interventions that address both food insecurity and water insecurity simultaneously. This could involve combining efforts to improve access to nutritious food and clean water sources, as well as promoting hygiene practices to ensure safe food preparation and consumption.

2. Community-based approaches: Utilize community-based approaches to identify and address the specific needs and challenges related to maternal health in rural areas. This could involve engaging local community members, including village health workers, to provide education, support, and resources to pregnant women and new mothers.

3. Mobile health (mHealth) solutions: Explore the use of mobile health technologies to improve access to maternal health information and services. This could include mobile apps or text messaging platforms that provide educational resources, reminders for prenatal and postnatal care appointments, and access to telehealth consultations.

4. Sustainable agriculture and water management: Promote sustainable agriculture practices and water management strategies to enhance food production and ensure reliable access to clean water sources. This could involve initiatives such as community gardens, rainwater harvesting systems, and water purification technologies.

5. Policy and advocacy: Advocate for policies and programs that prioritize maternal health and address the underlying factors contributing to food and water insecurity. This could involve working with local and national governments to develop and implement strategies that improve access to nutritious food, clean water, and healthcare services for pregnant women and new mothers.

It’s important to note that these recommendations are based on the provided information and may need to be further tailored and contextualized to the specific needs and resources of the rural Zimbabwean population.
AI Innovations Description
Based on the provided description, the recommendation to improve access to maternal health is to address household-level food insecurity (FI) and water insecurity (WI). The study found that low food availability and quality, as well as poor food access, were associated with lower linear growth among rural Zimbabwean infants. However, none of the WI dimensions were associated with linear growth. Therefore, interventions should focus on improving food security by ensuring adequate food availability, quality, and access for pregnant women and new mothers in rural areas. This can be achieved through various strategies such as promoting sustainable agriculture, providing nutritional education and support, improving transportation infrastructure for food distribution, and implementing social safety nets to alleviate poverty and food insecurity. Additionally, efforts should be made to address water insecurity by improving access to clean and reliable water sources, especially in rural communities. This can involve implementing water infrastructure projects, promoting water conservation and management practices, and ensuring proper sanitation and hygiene practices. By addressing these underlying factors, access to maternal health can be improved, leading to better health outcomes for both mothers and infants.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Enhance household food availability and quality: Implement interventions that focus on improving access to nutritious and diverse food options for pregnant women and new mothers. This can include initiatives such as community gardens, agricultural training programs, and food subsidies.

2. Improve food access: Address barriers that prevent pregnant women and new mothers from accessing nutritious food, such as financial constraints, transportation issues, and lack of nearby food markets. This can be achieved through strategies like mobile food markets, food vouchers, and transportation assistance.

3. Enhance water access and quality: Implement interventions to ensure reliable access to clean and safe drinking water for pregnant women and new mothers. This can involve infrastructure improvements, water purification systems, and community education on water hygiene practices.

4. Strengthen health education: Provide comprehensive health education to pregnant women and new mothers, focusing on topics such as proper nutrition during pregnancy, breastfeeding techniques, and hygiene practices. This can be done through community health workers, mobile clinics, and educational campaigns.

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

1. Data collection: Gather baseline data on maternal health indicators, such as maternal mortality rates, infant mortality rates, and rates of stunting or growth faltering. Collect information on the current status of food and water insecurity, as well as existing interventions and resources available.

2. Define simulation parameters: Determine the specific variables and factors that will be simulated, such as the implementation of the recommended interventions, changes in access to food and water, and improvements in health education. Set realistic targets and timelines for the simulation.

3. Model development: Develop a simulation model that incorporates the collected data and parameters. This can be done using statistical software or simulation tools. The model should consider the interplay between different variables and their potential impact on maternal health outcomes.

4. Simulation runs: Run the simulation multiple times, adjusting the parameters and variables to explore different scenarios and potential outcomes. This can help identify the most effective interventions and strategies for improving access to maternal health.

5. Analysis and interpretation: Analyze the simulation results to assess the impact of the recommended interventions on maternal health outcomes. Compare the simulated outcomes with the baseline data to determine the potential improvements in access to maternal health.

6. Recommendations and implementation: Based on the simulation findings, make recommendations for implementing the most effective interventions identified. Consider factors such as feasibility, cost-effectiveness, and sustainability. Develop an implementation plan that includes monitoring and evaluation strategies to assess the real-world impact of the recommended interventions.

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 above steps provide a general framework for conducting such simulations.

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