Seasonality and predictors of childhood stunting and wasting in drought-prone areas in Ethiopia: a cohort study

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Study Justification:
– Ethiopia has a history of severe famines and droughts, and malnutrition is a significant public health issue.
– The study aims to understand the seasonal variations in child stunting and wasting in drought-prone areas.
– By identifying the factors associated with child malnutrition, the study can inform interventions and policies to address the problem.
Highlights:
– The study followed a cohort of 909 children in rural southern Ethiopia for one year.
– Measurements of height-for-age and weight-for-height indices were taken every three months.
– The study found that child wasting rates varied with seasonal household food insecurity, while stunting rates did not.
– Household participation in a social safety net program was associated with decreased stunting and wasting.
– Access to protected drinking water enhanced the association between social safety net program participation and decreased stunting.
– Factors such as absence of a household latrine, lower maternal education level, larger family size, and lack of non-farming income were associated with increased child stunting.
Recommendations:
– Strengthen community-based food security programs, such as the Ethiopian social safety net program, to reduce child undernutrition in drought-prone areas.
– Improve clean water access and sanitation to decrease child undernutrition.
Key Role Players:
– Government agencies responsible for implementing and monitoring social safety net programs.
– Non-governmental organizations working on food security and nutrition programs.
– Health and education departments to promote maternal education and provide support for families.
– Water and sanitation departments to improve access to clean water and promote hygiene practices.
Cost Items for Planning Recommendations:
– Budget for expanding and strengthening the social safety net program.
– Investment in infrastructure for clean water access and sanitation facilities.
– Funding for education programs targeting maternal education and awareness.
– Resources for monitoring and evaluation of interventions and programs.
Please note that the cost items provided are general suggestions and may vary depending on the specific context and requirements of the interventions.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a detailed description of the study design, methods, and findings. However, it lacks information on the statistical significance of the associations and the magnitude of the effect sizes. To improve the evidence, the abstract could include p-values and effect size estimates for the associations between the exposure variables and child malnutrition outcomes. Additionally, it would be helpful to provide a summary of the main findings and their implications for future research or interventions.

Background and objectives Over centuries, Ethiopia has experienced severe famines and periods of serious drought, and malnutrition remains a major public health problem. The aims of this study were to estimate seasonal variations in child stunting and wasting, and identify factors associated with both forms of child malnutrition in drought-prone areas. Methods This cohort study was conducted among a random sample of 909 children in rural southern Ethiopia. The same children were followed for 1 year (2017-2018) with quarterly repeated measurements of their outcomes: height-for-age and weight-for-height indices (Z-scores). Linear regression models were used to analyse the association between both outcomes and baseline factors (eg, household participation in a social safety net programme and water access) and some time-varying factors (eg, household food insecurity). Results Child wasting rates varied with seasonal household food insecurity (ᵪ 2 trend = 15.9, p=0.001), but stunting rates did not. Household participation in a social safety net programme was associated with decreased stunting (p=0.001) and wasting (p=0.002). In addition to its association with decreased wasting (p=0.001), protected drinking water access enhanced the association between household participation in a social safety net programme and decreased stunting (p=0.009). Absence of a household latrine (p=0.011), lower maternal education level (p=0.001), larger family size (p=0.004) and lack of non-farming income (p=0.002) were associated with increased child stunting. Conclusions Seasonal household food insecurity was associated with child undernutrition in rural Ethiopia. Strengthening community-based food security programmes, such as the Ethiopian social safety net programme, could help to reduce child undernutrition in drought-prone areas. Improving clean water access and sanitation could also decrease child undernutrition. Key terms: Z-scores; Social safety net program; Water access

We conducted a prospective cohort study using a random sample of 909 households in the rural Wolaita area in southern Ethiopia. We recruited one child per household at the start of the study (in June 2017), and followed the same children by measuring their outcomes, that is, height-for-age and weight-for-height indices, every 3 months for 1 year (June 2017 to June 2018). Our exposure variables included background factors (measured at baseline) and some time-varying factors (measured each season). Quarterly repeated measurements were performed in the first month of each season (ie, June, September, December and March).10 Wolaita is located between the Great Rift Valley and the Omo Valley in southern Ethiopia. Rural villages in this area mainly represent two agro-ecological areas: the hot and semi-dry ‘lowlands’ and the relatively cooler and subhumid ‘midlands’.3 22 Mean annual rainfall ranges from 800 mm in the lowlands to 1200 mm in the midlands, with a bimodal distribution.10 Farming of staple crops, such as maize, occurs during the Belg rains from approximately March to early May.10 23 Root crops, such as taro and sweet potato, are farmed in both seasons and help to bridge seasonal gaps in food security.3 10 The main outcome measures were height-for-age and weight-for-height indices (Z-scores), measured each season for 1 year and defined based on the WHO 2006 child growth standards.24 Stunting and wasting were defined as HAZ (height-for-age Z-scores) and WHZ (weight-for-height Z-scores) of −2 SD below the respective WHO standard median. Our exposure variables included background factors (baseline data) and some time-varying factors (measured each season). Baseline factors comprised the following: (1) child age and sex; (2) parent age and education; (3) household socioeconomic conditions, such as family size, source of income, wealth index and participation in the food security programme (PSNP); (4) household latrine ownership and (5) drinking water access. We considered HFI, dietary diversity and child diarrhoeal illness as time-varying exposures, for which we carried out repeated measurements. Our repeated measurements were performed during the four seasons based on agricultural cycles: Kiremt is the sowing season in June, July and August; Belg is the main harvest season in September, October and November; Bega is the postharvest season in December, January and February and Tsedey is the dry preharvest season in March, April and May.10 These quarterly repeated measurements were carried out at the same time for outcomes and time-varying exposures. A multistage random selection of households was conducted. First, we selected two rural districts, or Woredas, representing the two agroecological strata in Wolaita: the Humbo district in the lowlands and Soddo Zuria district in the midland area, with the assumption that HFI would be more prevalent in the lowland areas.25 26 Population density was higher in the midland villages than in the lowland villages in the study area. As such, we selected three kebeles (the smallest administrative unit) from the lowland district and two kebeles from the midland district using the complex samples selection feature in SPSS V.25.0 (IBM). Finally, we selected households with children under 5 years-old and enrolled one child aged 6–59 months per household. To estimate the sample size, we followed an earlier cohort study assessing seasonal variations in wasting prevalence.27 The estimated sample size to estimate differences in prevalence rates of wasting 6.6% and 13%, with a 95% level of confidence and 80% power, was 820 children (OpenEpi software). Our study included 909 children. No subject involvement. Height and weight measurements were performed each season. We trained four data collectors on standard techniques for height and weight measurements. After the training, we validated the consistency of their measurements by recruiting 10 children aged below 5 years from another rural village and having all four data collectors (observers) measure each child’s height twice. The overall measurements showed approximately 92% average internal consistency. These four observers recorded height and weight measurements for the actual study. Height (or recumbent length for children younger than 24 months) was measured to the nearest 0.1 cm using a local wooden length board. Weight was measured to the nearest 0.1 kg using a Seca weight scale (Seca GmbH & Co. Kg, Hamburg, Germany). Children’s age in months, mothers’ age in years and highest grade of school completed by both parents were recorded. We also recorded family size (number of household members), source of income (exclusively farming vs generates other additional income), possession of common household assets and participation in the food security programme (data collectors observed PSNP beneficiary cards during household visits). In addition, we recorded household latrine ownership (yes vs no) and drinking water access (protected vs unprotected), and only water piped via public tap was as a protected source.28 We used principal component analysis to construct a wealth index based on common household assets: (1) housing material of the roof, interior ceilings, floors and walls; (2) number of livestock owned by the household; (3) land size in hectares and (4) possession of common assets, such as a radio, mobile telephone, bed and mattress, kerosene lamp, watch, electric or solar panels, chairs and tables, wooden boxes, and donkey carts.10 29 The time-varying variable, that is, HFI, was measured using nine questions in the Household Food Insecurity Access Scale, which has been validated in the study area.10 Household dietary diversity was scored using 24-hour recall measurements. Household members were asked about the 12 common food groups in Ethiopia: (1) cereals and breads; (2) potatoes and other roots or tubers; (3) vegetables; (4) fruits; (5) eggs; (6) dairy products; (7) pulses; (8) fish; (9) meat; (10) oil, fat or butter; (11) sugar or honey and (12) other foods or condiments (eg, coffee, tea, other spices, etc.). The responses for the 12 food-groups were used to generate a scale of food intake diversity, that is, the household dietary diversity score (HDDS).30 The occurrence of childhood diarrhoeal illness was also assessed, which was defined as the passage of three or more loose or watery stools in the preceding 24-hours,31 32 and was assessed during the 2 weeks prior to the survey dates.33 We generated two categorical variables from the actual HFI observations in our data set and the time series of our repeated measurements. Quantified as person-time observations, an ordinal HFI measure (ie, number of seasons with HFI) was generated as an exposure variable to explore dose–response relationships (eg, between child wasting and HFI). Quantified also as person-time observations, we generated a multinomial HFI measure summarising incidence rates of HFI by the four seasons (0=food-secure; 1=HFI in the sowing season; 2=HFI in the main harvest season; 3=HFI in the postharvest season and 4=HFI in the dry preharvest season) as an exposure variable in our main analysis (ie, multivariable models). As household food security and dietary diversity are highly correlated entities, we accounted for HDDS as the null category for HFI multinomial measure (ie, 0=food-secure) as an exposure variable in our main analysis. Moreover, child diarrhoeal illness was considered as a covariate for the effect of the other time-varying exposures (eg, seasonal HFI). Based on a systematic review paper, Phalkey and colleagues suggested complex pathways from climate variability to undernutrition in subsistence communities.34 Our current work used their work, but we adapted it to the scope of our study, and we focused on human nutrition (figure 1). Conceptual framework for a possible chain of relationships between seasonal food insecurity and child undernutrition, Wolaita, rural Ethiopia, 2017–2018. Data were double-entered in EpiData software V.3.1 (EpiData Association 2000–2021, Aarhus, Denmark) and corrected for entry errors. First, we entered our baseline data by unique identification numbers for the subjects (ID). We then entered repeated measurements by the subject ID and recorded each round of measurement with different variable names for each variable. After cleaning our data in the short format (by ID), we reshaped the data set into long format for statistical analysis, with which a new variable (season) was generated to specify the discrete time series of our repeated measurements. We generated nutritional indices (HAZ and WHZ) from anthropometric data using ENA and WHO Anthro software 3.2.2 (WHO, Geneva, Switzerland). As we measured each child in each of the four seasons, we compiled counts of observations totalling 3636 HAZ and WHZ measurements at the end of the study period. However, we excluded 46 HAZ and 126 WHZ observations that had incomplete data or that were severe outliers.35 Accordingly, our units of analysis were counts of measurements totalling 3571 HAZ estimates and 3510 WHZ estimates (figure 2). We analysed complete WHZ data (n=3510) of 897 children and complete HAZ data (n=3571) of 907 children. Flow chart of child anthropometric measurements considered for this cohort study, Wolaita, rural Ethiopia, 2017–2018. As we measured the same children in each season, age changes during the study period could lead to certain deviations in outcome estimates (ie, cohort effects). As such, we generated a separate variable (age in months divided by age in the logarithmic scale) to account for cohort effects. Time-varying effects could also be due to external factors (eg, seasonality). Accordingly, we considered HFI as a multinomial variable to estimate the seasonally variable effect of HFI on child undernutrition. Furthermore, we accounted for the time series of our repeated measurements using some dummy variables as measurement components of time-varying exposures. We used Stata V.15 (Stata Corp LLC, College Station, TX, USA) for our statistical works. To explore our data distributions (bivariate analysis), we used parametric tests, such as t-tests to compare two means, analysis of variance tests to compare more than two means, and correlation tests to assess the associations between two continuous variables. We analysed our normally distributed data for both outcomes with background factors (baseline data) and some time-varying factors (repeated measurements) using hierarchical linear regression models. Our data comprised two categories: (1) clustering effects at the primary sampling stage (at the kebele level) or (2) time-varying effects within our repeated measurements. We first estimated between-variations as main effects of baseline factors on our outcome measures, and then analysed within-variations as main effects to explore time-varying exposure effects on outcome estimates (additional details are provided under separate subheadings hereafter). HAZ data were analysed using a multivariable linear regression model with adjustment for the clustering effect of stunting at the primary sampling stage, but we ignored observed insignificant clustering when analysing the WHZ data.36 37 HAZ and WHZ estimates in the preceding season were considered to control for cohort effects when analysing baseline factors associated with stunting and wasting. At this stage, we aimed to estimate the time-varying exposure effects on outcome estimates, and further analysed the fit between-variation models to account for an exposure-season interaction effect (eg, HFI by the four seasons as a multinomial exposure variable) to estimate seasonally variable effects of relevant exposure variables on outcome estimates. Time-varying exposure effects were estimated as main effects with adjustment for other time-varying effects (ie, cohort and time series effects) and main effects of all baseline factors included in the fitted models for between-variations. We further analysed the fitted models for both outcomes to explore interactions, for example, additive, or multiplicative effects (eg, PSNP participation and protected drinking water access on our outcome estimates) or effect modification (eg, variations in the effect of PSNP participation on child wasting across HFI levels). We reported main effects for between-variations and within-variations using standardised model coefficients (β) with 95% CIs. Decreased model coefficients refer to increased stunting (HAZ) and wasting (WHZ).

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women with access to important maternal health information, such as prenatal care guidelines, nutrition advice, and reminders for appointments and medication.

2. Telemedicine Services: Implement telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare providers through video calls or phone calls. This can help overcome geographical barriers and provide timely medical advice and support.

3. Community Health Workers: Train and deploy community health workers who can provide maternal health education, conduct regular check-ups, and refer women to healthcare facilities when necessary. These workers can also help address cultural and language barriers that may prevent women from seeking care.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to access maternal health services, including prenatal care, delivery, and postnatal care. This can help reduce financial barriers and increase utilization of healthcare services.

5. Mobile Clinics: Set up mobile clinics that travel to remote or underserved areas to provide maternal health services, including prenatal check-ups, vaccinations, and health education. This can improve access for women who have limited transportation options.

6. Health Information Systems: Implement electronic health information systems that enable healthcare providers to easily access and share maternal health records. This can improve coordination of care and ensure that women receive appropriate and timely interventions.

7. Maternal Health Education Programs: Develop and implement comprehensive maternal health education programs that target women, families, and communities. These programs can increase awareness about the importance of prenatal care, nutrition, and safe delivery practices.

8. Maternal Health Hotlines: Establish toll-free hotlines staffed by trained healthcare professionals who can provide information, support, and guidance to pregnant women. This can be particularly useful for women who have questions or concerns but may not have immediate access to healthcare providers.

9. Public-Private Partnerships: Foster collaborations between government agencies, non-profit organizations, and private sector companies to improve access to maternal health services. This can involve leveraging private sector resources and expertise to expand healthcare infrastructure and service delivery.

10. Maternal Health Monitoring Systems: Develop systems for monitoring and tracking maternal health indicators, such as maternal mortality rates, prenatal care coverage, and birth outcomes. This can help identify areas of improvement and guide resource allocation for targeted interventions.
AI Innovations Description
The study conducted in rural southern Ethiopia aimed to estimate seasonal variations in child stunting and wasting and identify factors associated with both forms of child malnutrition in drought-prone areas. The study found that child wasting rates varied with seasonal household food insecurity, while stunting rates did not. Household participation in a social safety net program was associated with decreased stunting and wasting. Additionally, protected drinking water access enhanced the association between household participation in a social safety net program and decreased stunting. Factors such as absence of a household latrine, lower maternal education level, larger family size, and lack of non-farming income were associated with increased child stunting.

Based on the findings, the study recommends strengthening community-based food security programs, such as the Ethiopian social safety net program, to reduce child undernutrition in drought-prone areas. Improving clean water access and sanitation can also help decrease child undernutrition. These recommendations can be developed into innovations by implementing targeted interventions that focus on improving access to nutritious food, providing support to vulnerable households through social safety net programs, and implementing water and sanitation initiatives in drought-prone areas. These innovations can help improve access to maternal health by addressing the underlying factors that contribute to child malnutrition.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Strengthen community-based food security programs: Building on the findings that seasonal household food insecurity is associated with child undernutrition in rural Ethiopia, it is important to enhance and expand community-based food security programs. These programs can provide support to vulnerable households, ensuring access to nutritious food throughout the year.

2. Improve clean water access and sanitation: The study found that protected drinking water access enhanced the association between household participation in a social safety net program and decreased stunting. Therefore, efforts should be made to improve clean water access and sanitation facilities in drought-prone areas. This can help reduce the risk of waterborne diseases and improve overall maternal and child health.

3. Enhance maternal education and non-farming income: The study identified lower maternal education level and lack of non-farming income as factors associated with increased child stunting. Therefore, interventions should focus on improving maternal education and creating opportunities for income diversification beyond farming. This can empower women and families to better meet their nutritional needs and improve maternal health outcomes.

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

1. Baseline data collection: Collect data on the current status of maternal health and access to healthcare services in the target area. This can include information on maternal mortality rates, availability of healthcare facilities, and utilization of maternal health services.

2. Define indicators: Identify specific indicators that reflect the desired outcomes of the recommendations, such as the percentage of households with access to clean water, the percentage of women with increased educational attainment, or the reduction in child stunting rates.

3. Develop a simulation model: Use statistical modeling techniques to simulate the potential impact of the recommendations on the defined indicators. This can involve creating a mathematical model that incorporates various factors, such as population demographics, socio-economic conditions, and healthcare infrastructure.

4. Data analysis and interpretation: Analyze the simulated data to assess the potential impact of the recommendations on improving access to maternal health. This can involve comparing the simulated outcomes with the baseline data to determine the magnitude of change that can be expected.

5. Sensitivity analysis: Conduct sensitivity analysis to assess the robustness of the simulation results. This can involve varying the input parameters within a certain range to evaluate the potential range of outcomes.

6. Communication and decision-making: Present the simulation results to relevant stakeholders, such as policymakers, healthcare providers, and community leaders. Use the findings to inform decision-making and prioritize interventions that are likely to have the greatest impact on improving access to maternal health.

It is important to note that the methodology described above is a general framework and can be tailored to the specific context and data availability of the target area.

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