{"paper_id":"2211dc6c-96c4-4ba7-94c6-e9804481d456","body_text":"Climate variability, crop production, and child undernutrition: A mediation analysis from a drought-prone area in the rural Sidama Region, Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Climate variability, crop production, and child undernutrition: A mediation analysis from a drought-prone area in the rural Sidama Region, Ethiopia Eyob Fitalo1*, Dawit Jember Tesfaye2, Taye Gari2*, Bernt Lindtjørn2 This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7558344/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Ethiopia is a predominantly agricultural community that relies on farming and animal husbandry for subsistence. It is vulnerable to the adverse effects of climate variability. However, the association between weather conditions and health has not been well studied. Therefore, the primary objective of this study was to assess the impact of climate variability on child acute malnutrition, mediated by crop production. Methods This study was part of a large, open, dynamic cohort study designed to examine the relationship between weather conditions, nutrition, and health. For this study, a cohort of 395 children was monitored quarterly from June 2023 to March 2024. Using a cohort study design, we established the temporal sequence in which the exposures occurred before the mediators and the outcome. A two-stage sampling method was employed to select households, and data were collected using a pre-tested, structured questionnaire. Structural equation modelling was employed to assess the direct, indirect, and total effects of a proxy for crop production on acute malnutrition. Results An increase in Normalised Difference Vegetation Index (NDVI) had a positive direct effect and an indirect impact on wasting via household food. Similarly, rainfall had a positive effect on the NDVI. Furthermore, children who initiated complementary feeding late and large families were at increased risk of wasting. However, children from wealthier families had less wasting. Conclusion Climate variability and crop production were risk factors for wasting, and this relationship is primarily mediated by household food insecurity. This finding could imply that rural communities depending on rain-fed subsistence agriculture exhibit vulnerability to the impacts of climate variability. climate variability mediation analysis acute malnutrition Sidama Region Ethiopia Figures Figure 1 Figure 2 Figure 3 Introduction Child undernutrition is characterised by insufficient nutrient intake or utilisation and can manifest as wasting (low weight for height) or stunting (low height for age) ( 1 ). It is a major public health concern in developing countries, and the leading causes are a lack of food and high prevalence rates of infectious diseases ( 2 ). In Ethiopia, the prevalence of child stunting declined from 58% in 2000 to 37% by 2019, and the prevalence of wasting fell from 12–7% over the same time frame ( 3 , 4 ). However, Ethiopia is frequently affected by drought, which can lead to decreased food production ( 5 ). Consequently, climate variability often exacerbates undernutrition by disrupting agricultural production, intensifying food insecurity, and contributing to the spread of infectious diseases ( 6 ). Weather refers to atmospheric conditions, such as temperature, precipitation, and wind speed, at a specific location and time; these conditions are measured over short periods ( 7 ). Climate variability “includes all the variations in the climate that last longer than individual weather events.” In contrast, climate change refers to variations that persist for a more extended period, typically decades or more ( 8 ). Climate variability is a pressing global challenge for vulnerable populations in developing countries, such as Ethiopia. It can have implications for food security, livelihoods, and child health. Undernutrition has become one of the top five adverse impacts of climate change ( 9 ). Ethiopia, particularly our study area in the Ethiopian Rift Valley, is an agricultural community that relies on farming and raising animals for subsistence. It is vulnerable to the adverse effects of climate variability, including erratic rainfall patterns and temperature fluctuations ( 10 ). Previous cohort studies in the study area have also demonstrated seasonal variations in the prevalence rates of child malnutrition and household food security ( 11 , 12 ). However, weather condition-related variability has not been well studied, and previous studies on climate variability and nutritional status were mainly cross-sectional ( 13 , 14 ). A cohort design may help establish potential causal pathways between climate variability, crop production, and child malnutrition. Therefore, the primary objective of this study was to assess the impact of climate variability on child acute malnutrition, mediated by crop production. Method and materials Study setting, design and duration This study was part of a dynamic cohort study aimed at measuring the association between climate variability, nutrition, and health. By using a cohort study design, we established the temporality of the association in which the exposures occurred before the mediator and the outcome. Children were followed quarterly from June 2023 to March. It was conducted in the two woredas Boricha and Bilate Zuria. The region covers approximately 600 square kilometres and is relatively flat, with a gradual decrease in altitude from east to west. The altitude ranges from 1320 meters in the west to 2080 meters in the east. Approximately 14% of the total population of 315,000 consists of children aged 6 to 59 months. Most of the population (95%) lives in rural areas and relies on subsistence farming. Around 90% of the woreda receives yearly rainfall ranging from 850 to 1000 mm. The districts experience bimodal rainfall, with most of the rain falling during the Belg season (March-May) and the Kiremt season (June-September). This pattern yields two harvests: short-cycle crops (e.g., potatoes, sweet potatoes, peppers, and beans) in August, and long-cycle crops (e.g., maize) in October ( 12 ). Study Population The study includes all children under five who were living in the selected kebeles of Boricha and Bilate districts. Eligible respondents were those found within the household during the study period, while children absent from the cluster during the period of climate exposure, children with severe illnesses, and those whose mothers or caregivers could not communicate were excluded. Data collection procedure A multistage sampling method was used to select the study participants. Initially, nine rural kebeles out of 30 in the districts were chosen randomly. Subsequently, the cluster sampling method was employed to select households within these kebeles. The final sample consisted of 395 households with children aged 6 to 59 months. An interview was conducted with biological mothers unless they were deceased or divorced. To determine the appropriate sample size for the study, we used G*Power software ( 15 ). The assumptions included were a two-tailed test, an effect size of |ρ| = 0.18 (based on previous study findings) ( 11 ). We used a significance level (α) of 0.05 and a statistical power of 80%. Based on these parameters, the minimum required sample size was calculated to be 237 participants. To account for a potential non-response rate of 20%, the sample size was adjusted to 297. However, to further enhance the power and robustness of the study, a total of 395 children on follow-up were included in the analysis. We had also evaluated the power of negative results in the study (post hoc test). This was done for non-significant variables (mother’s occupation and child vaccination status. This was done to avoid a Type II error. Post hoc power analysis of vaccination status had 43% power to detect a difference in WHZ between vaccinated and non-vaccinated children, which is lower than the accepted standard threshold of 80%. This low power primarily derives from the fact that only a small effect difference (Cohen’s d = 0.10) emerges between groups due to small sample size, or other confounding variables may dilute the effect of vaccination on WHZ. However, for the mother occupation, the groups showed a large effect difference (Cohen’s d = 0.67), and with an adequate sample distribution, the study had over 80% power to detect the mean WHZ difference observed in each group. Study variables The outcome variable was the weight-for-height Z-score (WHZ). The child’s age was determined using the mother’s memory and memorable experiences to aid in recall. Height was measured in the recumbent position for children under 24 months using a Seca stadiometer, and the measurement was taken to the nearest 0.1cm. Vertical height was taken for children greater than 24 months after removing shoes. The procedure was repeated for the same child, and the average value was used. The child's weight was measured by asking the mother/caretaker to remove the child’s heavy clothing and was recorded to the nearest 0.1 kg using a Seca weight scale. The scale calibration was performed daily, and during weighing, the scale was checked for data accuracy. The data related to socio-demographic factors (family size, educational status of the mother and father, occupational status of the mother and father, religion, source of water) and child characteristics (age, sex, initiation of complementary feeding, initiation of breastfeeding, vaccination status) were collected by interviewing the household head and reviewing the vaccination card for child-related factors. Weather data Rainfall information was downloaded from the Climate Hazards Infra-Red Precipitation with Stations (CHIRPS) dataset, which provides daily precipitation at a 0.05 ° by 0.05 ° spatial resolution (5 km ²) ( 16 ). We used monthly lag rainfall, prepared from the sum of daily precipitation data (for each round of data collection). In this method, rainfall measurements were taken simultaneously with the NDVI data. Normalised Difference Vegetation Index (NDVI) For this study, historical Normalised Difference Vegetation Index (NDVI) data were downloaded from the United States Geological Survey (USGS) server, incorporating monthly-resolution datasets. NDVI quantifies vegetation greenness and density in satellite imagery, serving as a tool for evaluating crop health, agricultural productivity, and the ecological effects of rainfall fluctuations. The NDVI data were sourced from Sentinel-2 MSI (Multispectral Instrument, Level-2A) imagery, offering a 10-meter spatial resolution. Custom JavaScript algorithms were employed to compute monthly NDVI averages across the nine kebeles (administrative units) under investigation. The index is derived by analysing the contrast between visible red light (absorbed by vegetation) and near-infrared light (reflected by healthy plant cells), yielding values between − 1 and + 1. Values near + 1 denote thriving, dense vegetation, while those approaching − 1 indicate sparse vegetation or non-vegetated surfaces ( 17 ). Household food insecurity was evaluated based on a recall period of four weeks preceding the survey, using 9 occurrence questions on the household experience of food insecurity. If a respondent responds “yes” to any of the questions, a follow-up question about the frequency of occurrence was asked to determine the frequency of the experience. The frequency of occurrence was classified as rarely “if occurring once or twice”, sometimes “3–10 times, or often “more than 10 times”. A food-secure household is the one that experienced none of these nine questions (conditions) or just experienced the first condition, “worrying about food”, though with a frequency of once or twice over the past four weeks “rarely”. Then by summing up the total score (HHFIS), it is categorized into “secured” for score 0–1, “mildly insecure” for score 2–7, “moderately insecure” for score 8–14 and “severely insecure” for score 15–27 ( 18 ). Wealth index : For wealth index construction, which is an indicator of the socio economic status of the community, data were collected on the availability of different assets in the household, like radio, TV, mobile phone, fixed phone, electricity, and housing condition, like type of floor, type of roof, number of windows, separate house for livestock, separate sleeping room, and the number of livestock in the household. A relative household wealth index was created using Principal Component Analysis (PCA) by utilising household assets ( 19 ). Variables exhibiting similar responses of 95% higher and or 5% lower were removed from factor analysis. Finally, by using the first factor, it was categorised into 3 quintiles: poor “1”, middle “2”, and rich “3”. Data collectors were chosen based on their familiarity with the local area and their ability to use the questionnaire in the local language. Data collectors also received orientation on the purpose of the study and the method of data collection. The entire data collection process was under the supervision of the primary investigator and project manager, and a 5% pretest was performed in nearby kebeles outside of the selected kebeles prior to data collection. At the end of each day, the submitted data was randomly chosen from the submitted data and checked for completeness and consistency by field supervision. We enrolled 395 children in our study, following them up every three months for a year. The number of children with complete outcome measurement was 382 in June 2023, 369 in September 2023, 382 in December 2023, and 376 in March 2024. We excluded the observations with incomplete data or extreme outliers observed in each measurement, and a new observation was added as we followed an open dynamic cohort (Fig. 1 ). Data processing and analysis After completion of household and individual data collection, the data were checked for completeness and consistency. Descriptive statistics, including frequency and summary measures such as mean, median, and interquartile range, were used to present the data. STATA Version 16.1 (Stata Corp, Texas 77845 USA) statistical software was used for analysis. The Weight-for-Height Z-score (WHZ) was calculated using the WHO Anthro Software, Version 3.2.2 (WHO, Geneva, Switzerland). A total of 27 children (13 in March, 8 in September, 4 in December, and 4 in March 2024) with doubtful values of WHZ (below − 6 or above + 6) and missing values in two or more rounds of the survey were excluded from the analysis. To compare mean values of the outcome variable (WHZ), a t-test was used. Pearson's correlation coefficient was used to measure the strength and direction of the association between continuous variables, and multiple linear regression analysis was employed to establish the existing association. Normality assumption tests were performed for continuous outcome variables WHZ using graphical methods such as histograms, the quantile function for the normal distribution (qnorm), and kurtosis statistical tests; kurtosis was between − 1 and 1. Linearity between WHZ and NDVI was also examined using a scatter plot, a linear fit line, and residual plots. In the final multivariable analysis, the coefficient with a P value less than 0.05 and a 95% CI was used to declare a statistically significant association. Since the study involved hierarchical data, it included kebeles and repeated measurements (four rounds). Measurements (level 1) were nested within child (level 2) and children nested within kebele (level 3). Due to this, the probability of developing undernutrition among children under five years old may be similar within the kebeles but differ among them. There could also be dependence between measurements. This leads to incorrect parameter estimation by violating the assumption of the standard regression model (independent observations). Therefore, to account for the effect of dependency observed in clustering and repeated measurements, mediation analysis using generalised structural equation models (GSEM) was employed, based on the hypothesis that a decrease in rainfall and NDVI would result in a reduction of WHZ, which would be mediated by household food insecurity. The final model was chosen through a series of steps. Initially, an unadjusted mediation model was created with NDVI (independent variable), household food security score (as the mediator), and WHZ (as the outcome variable). The AIC was 29796 and the BIC was 29877 for the unadjusted model. Subsequently, the complete mediation analysis model was constructed after accounting for potential confounding variables. Finally, a model with the lowest AIC (29445) and BIC (29579), which was improved compared to the unadjusted model, was considered for identifying direct and indirect factors associated with the outcome variable (WHZ). Results Socio-demographic characteristics of the study participants The mean (SD) age of the mothers was 31.6 ( 6 ) years, and households with an average (SD) family size had 5 (1.5) members. The majority of the mothers, 98% (387 of 395), were married, and 65% (258 of 395) of the mothers were illiterate, while 56% (221 of 395) of the fathers were illiterate. Around 32% of households had a poor wealth status, and 79% of households were using a protected source of drinking water (Table 1 ). Table 1 Household-related Baseline characteristics of the study participants (n = 395) Socio-demographic variables Frequency $ Percentage Mother’s age in years, mean (SD) = 31.6 ( 6 ) Family size, mean (SD) = 5(1.5) Father education Illiterate 221 56.0 Read and write without formal education 55 13.9 Attended formal education 119 30.1 Mother education Illiterate 258 65.3 Read and write without formal education 43 10.9 Attended formal education 94 23.8 Marital status Married 387 98.0 Other # 8 2.0 Mother occupation House wife and farmer 381 96.4 Trader/ other 14 3.6 Wealth status Poor 127 32.2 Medium 139 35.2 Rich 129 32.6 Source of drinking water Protected source 313 79.2 Not protected source 82 20.8 #=Widowed/divorced/separated, $ =unless it is indicated in the row Child-related baseline characteristics The study involved 395 children, with a nearly equal split by sex: 51.4% (203 of 395) were female and 48.6% (192 of 395) male. Over two-thirds (64.8%) of the children were aged 6–24 months, with 34.4% in the 6–11 months group and 31.4% in the 12–23 months group. 92.9% (387 of 395) of mothers-initiated breastfeeding within one hour of birth, and 71.9% introduced complementary feeding at the recommended time. The majority, 71.4% (282 of 395), received age-appropriate vaccinations, although a notable proportion, 28.6% (113 of 395), remained unvaccinated (Table 2) .Table 2: Child-related baseline characteristics (n = 395) Child characteristics Frequency $ Percentage Sex of child Female 203 51.4 Male 192 48.6 Child age 6–11 month 136 34.4 12–23 month 124 31.4 24–35 month 73 18.5 36–59 month 62 15.6 Breast feeding initiation Within one hours 387 92.9 Not started Within a day 28 7.1 Vaccination from card Vaccinated for age 282 71.4 Not vaccinated for age 113 28.6 Complementary feeding initiation At recommended time 284 71.9 Early introduction of CF 24 6.1 Late introduction of CF 87 22.0 $ =unless it is indicated in the row. CF = Complementary feeding Rainfall and Normalised Difference Vegetation Index (NDVI) Figure 2 shows the variation of rainfall and NDVI observed over different months. The highest rainfall was from February to May 2023 and from mid-August to October, and the lowest was observed from June to August 2023. There was also a statistically significant positive correlation between NDVI and a month after rainfall (3-month lag rainfall and 2-month lag NDVI) (correlation coefficient: r = 0.17, p < 0.001). Weight-for-Height Z-scores (WHZ) of the children As shown in Table 3 , the WHZ scores were similar for girls (0.51, 95% CI: 0.48–0.53) and boys (0.48, 95% CI: 0.46–0.52). But children aged 12–23 months had the highest mean WHZ (0.45, 95% CI: 0.4–0.47), while those aged 24–35 months exhibited the lowest (0.18, 95% CI: 0.16–0.19). Access to protected water sources was associated with a higher mean WHZ (0.79, 95% CI: 0.77–0.81) compared to unprotected sources (0.21, 95% CI: 0.19–0.23). Similarly, children from households of middle-level wealth status had the highest mean WHZ (0.35, 95% CI: 0.33–0.38) compared to those from poor households (0.33, 95% CI: 0.30–0.35). Table 3 Mean WHZ of the children by socio-demographic variables, June 2023 to March 2024 Variable Mean WHZ (95% CI) Standard Deviation (SD) Child sex Female 0.51 (0.48, 0.53) 1.36 Male 0.48 (0.46, 0.52) 1.47 Child age 6–11 month 0.12 (0.13, 0.15) 1.32 12–23 month 0.45 (0.42, 0.47) 1.34 24–35 month 0.18 (0.16, 0.19) 1.64 36–59 month 0.23 (0.21, 0.26) 1.37 Mother education Illiterate 0.66 (0.63–0.68) 1.40 Read/write 0.11 (0.09–0.12) 1.62 Attended 0.24 (0.21–0.26) 1.29 Father education Illiterate 0.56 (0.54–0.59) 1.35 Read/write 0.14 (0.12–0.15) 1.58 Attended 0.30 (0.28–0.33) 1.40 Wealth status Poor 0.32 (0.30–0.35) 1.37 Middle 0.35 (0.33–0.38) 1.37 Rich 0.32 (0.30–0.35) 1.46 Mother occupation Housewife/farmer 0.97 (0.96–0.98) 1.40 Trader/other 0.03 (0.03–0.04) 1.44 Marital status Married 0.98 (0.97–0.99) 1.41 Other 0.02 (0.01–0.03) 1.08 Source of water Protected source 0.79 (0.77–0.81) 1.34 Not protected source 0.21 (0.19–0.23) 1.57 Rainfall, crop production proxy (NDVI) and WHZ score Table 4 shows the overall correlation between Rainfall, NDVI and WHZ. There were significant positive correlations between 2-month lag NDVI and child nutritional status (WHZ). Likewise, 1-month lag rainfall also showed a positive correlation with NDVI and WHZ. Both NDVI and WHZ showed significant negative correlations with HHFIAS. Table 4 Correlation matrix between NDVI, HHFIAS and WHZ Correlations NDVI 2 WHZ Rainfall NDVI 1.00 0.08 ** 0.16** WHZ 0.08 ** 1.00 0.01** HHFIAS 0.13 ** -0.12 ** -0.02** 2 = 2month lag Normalized Difference Vegetation Index ** Correlation is significant at the 0.01 level Predictors of WHZ score. In multiple linear regression analysis (Table 5 ), a higher NDVI value was associated with increased WHZ (β = 0.69, 95% CI (0.06, 1.32)), while a higher household food insecurity score (HHFIAS) was associated with lower WHZ (β = -0.02, 95% CI (-0.03, -0.004)). Larger family size (β = -0.10, 95% CI (-0.15, -0.04)) and late initiation of complementary feeding (β = -0.59, 95% CI (-0.77, -0.40)) significantly reduced WHZ. Wealth status was associated with higher WHZ compared to poorer households (β = 0.18, 95% CI (0.01, 0.36)). Table 5 Multiple linear regression of factors Associated with under-five children Weight for height Z score in the study area Boricha and Bilate zuria District, 2023/24 Variable β Coefficient (95% CI) p-value NDVI 0.69 (0.06, 1.32) 0.031* HHFIAS -0.02 (-0.03, -0.01) 0.008* Child age -0.001 (-0.01, 0.01) 0.832 Family size -0.10 (-0.15, -0.04) < 0.001*** Mother’s occupation Housewife/farmer 1.00 Trader/other -0.18 (-0.56, 0.21) 0.354 Vaccination status Vaccinated 1.00 Not vaccinated -0.07 (-0.23, 0.09) 0.368 Complementary feeding On recommended time 1.00 Early initiation 0.25 (-0.05, 0.55) 0.102 Late initiation -0.58 (-0.76, -0.40) < 0.001*** Wealth status Poor 1.00 Middle 0.18 (0.002, 0.36) 0.047* Rich 0.28 (0.10, 0.47) 0.003** Mother’s education Illiterate 1.00 Read/write without formal education 0.18 (-0.05, 0.41) 0.119 Attended formal education -0.02 (-0.16, 0.20) 0.820 Mediation analysis of factors associated with under-five children’s Weight for Height Z score In the adjusted mediation model presented below (Table 6 or Fig. 4 ), a significant positive relationship between rainfall and NDVI was observed. The total effect of NDVI on WHZ was 0.97 (95% CI: 0.37, 1.59), of which 15% was mediated by Household Food Insecurity (HHFIS). Furthermore, NDVI showed a negative effect on Household Food Insecurity (HHFIS) of -8.53 (95% CI: -10.91, -6.15). Finally, children from higher wealth households had higher WHZ scores of 0.16 (95% CI: 0.07, 0.25), with nearly one quarter (27%) of this effect mediated by Household Food Insecurity (HHFIS). Table 6 Mediation analysis of the direct, indirect and total effect estimates of under the age five years children weight for height Z score in the study area Boricha and Bilate zuria District, 2023/24 Causal effect pathway Direct effect (95% CI) Indirect effect (95%) Total effect (95% CI) %(Mediation analysis ) @ RF 1 → NDVI 2 0.0003 (0.0002, 0.0003)*** 0.0003 (0.0002, 0.0003)*** RF 1 → WHZ Via NDVI 2 0.0003 (0.0001, 0.0005)** 0.0003 (0.0002, 0.0003)** NDVI→ HHFIS 3 -8.53 (-10.91, -6.15)*** -8.53 (-10.91, -6.15)*** HHFIS →WHZ -0.02 (-0.03, -0.01)*** -0.02 (-0.03, -0.01)*** NDVI →WHZ via HHFIS 0.91 (0.24, 1.58)** 0.15 (0.03, 0.26)** 0.97 (0.36, 1.59)** 15% (Partial mediation) Mother occupation → WHZ -0.17 (-0.56, 0.22) -0.17 (-0.56, 0.22) Wealth $ → WHZ via HHFIS 0.12 (0.03, 0.21)* 0.04 (0.01, 0.07)** 0.16 (0.07, 0.25)*** 27% (Partial mediation) Family size $ →WHZ -0.24 (-0.39, -0.08)** -0.24 (-0.39, -0.08)** Vaccination $ →WHZ -0.11 (-0. 27, 0.05) -0.11 (-0. 27, 0.05) Child age →WHZ -0.06 (-0.21, -0.09)* -0.06 (-0.21, -0.09)* CF $ →WHZ -0.27 (-0.36, -0.18)*** -0.27 (-0.36, -0.18)*** *P < 0.05, ** p < 0.01, *** p < 0.001, @= Proportion of mediated, $ =Reference category is first, CF = Complementary feeding, 1 = 1 month lag of Rainfall from NDVI, 2 = 2 month lag of average Normalized Deference Vegetation Index from survey time, 3 = Household food insecurity scale, Categorical variables coding: Family size (1 for < = 5, 2 for > 5), Vaccination (1 for vaccinated and 2 for not vaccinated), Mother occupation (1 for housewife/farmer and 2 trader/other), Wealth (1 = Poor,2 = Middle, 3 = Rich), Complementary feeding (1 = on recommended time, 2 = early initiation, 3 = late initiation) , Path diagram Below, Fig. 4 shows an alternative presentation of the mediation analysis path model showing the direction of association between exogenous and endogenous variables along with the regression coefficient. Discussion As hypothesised, we observed a direct impact of rainfall on agricultural crop production (measured using NDVI) and an indirect influence of NDVI on WHZ through household food insecurity (HHFIS). After controlling for household and individual factors, NDVI showed a significant positive effect on WHZ. Additionally, late initiation of complementary feeding, larger family size, and higher family wealth status had notable direct effects on WHZ. The study has shown that rainfall had a positive direct effect on NDVI. In line with the study conducted in China ( 20 ). Furthermore, this study showed that NDVI has a significant direct and indirect effect on weight-for-height Z score. This finding is consistent with a study conducted in Kenya that showed a positive association between NDVI and child nutritional status ( 21 ). A community-based study conducted in Ethiopia reported that the prevalence of undernutrition was high among children engaging in inappropriate complementary feeding practices ( 22 ). Our study also showed that children who recently started complementary feeding had lower WHZ scores. Conversely, children living in larger family sizes also had lower WHZ compared to those in smaller families, as documented in earlier studies from Ethiopia ( 23 ). This may suggest that larger family sizes increase the risk of acute malnutrition in children, potentially due to an imbalance between the number of family members and available resources. On the other hand, being part of the highest wealth category family improves a child's WHZ, in agreement with earlier studies from Ethiopia ( 24 ). The possible reason may be that family wealth status significantly impacts the quality and quantity of food consumption, often leading to inadequate nutrition among children. Families with limited resources may rely on less nutritious and cheaper food options, which can contribute to poorer health outcomes. One of the strengths of our study is that we used a prospective cohort design to measure the temporal relationship between NDVI and child nutrition status. Additionally, we adjusted for clustering effects at the kebele level and accounted for time variations due to repeated measurements of some variables. The limited statistical power of this study may explain the borderline significance observed in the multiple linear regression analysis. Therefore, the results should be interpreted with caution and verified through larger studies. In this study, NDVI obtained from satellite data was used as a proxy for crop production. Previous studies have indicated that NDVI is a reliable measure for crop monitoring, showing strong correlations with yields of crops ( 25 ). In addition, a study from the same area as the current study has also shown an association between NDVI, household food insecurity, and women’s nutrition status ( 26 , 27 ). However, NDVI may not provide the actual crop produced at the household level, and therefore, care should be taken while interpreting this study. While NDVI strongly correlates with agricultural output, the reliance on remote sensing data (NDVI) rather than direct assessments of household food production raises questions. Future investigations should integrate satellite-derived data with on-ground evaluations of crop diversity to better capture local food production. Additionally, our measurement of household food security relied on self-reported data, which risks information bias, as participants’ awareness of potential food aid benefits may have influenced their responses, potentially skewing reported levels of food insecurity. In conclusion, Climate variability and crop production were risk factors for wasting, and this relationship is primarily mediated by household food insecurity. This finding could imply that rural communities depending on rain-fed subsistence agriculture exhibit vulnerability to the impacts of climate variability. There may be a need to use local specific climate service by the community to prevent acute malnutrition in children. Declarations Ethics approval and consent to participate The study fulfilled with the declaration of Helsinki and Ethical approval was obtained from Hawassa University College of Medicine and Health Sciences Institutional Review Board (Ref. No: IRB/141/16). Informed written consent was obtained from the mothers or caregivers before the interview began. They were told to withdraw at any time and/or to abstain from responding to questions they were not interested or not willing to respond. Child parents were also informed that all the data obtained would be kept confidential and if there is a problem, it was link nearby health facility for malnutrition management and advice was given to their parents. We express our gratitude to the SENUPH II projects for funding this study. Consent for publication Not applicable Availability of data and materials The dataset used during this study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing Interests Funding This study was funded by the Southern Ethiopia Network of Universities in Public Health (SENUPH II) project conducted under Hawassa University. The funders were not involved in the study design, data collection and analysis, publication decision, or manuscript preparation. Authors' contributions Conceptualization: Taye Gari, Eyob Fitalo, Bernt Lindtjørn. Data curation: Eyob Fitalo, Taye Gari, Bernt Lindtjørn, Dawit Jember Formal analysis: Eyob Fitalo, Taye Gari, Dawit Jember, Bernt Lindtjørn. Funding acquisition: Taye Gari, Bernt Lindtjørn. Investigation: Eyob Fitalo, Taye Gari, Dawit Jember , Bernt Lindtjørn. Methodology: Eyob Fitalo ,Taye Gari, Dawit Jember, Bernt Lindtjørn. Project administration: Eyob Fitalo, Taye Gari, Dawit Jember , Bernt Lindtjørn. Software: Eyob Fitalo, Taye Gari, Dawit Jember, Bernt Lindtjørn. Supervision: Eyob Fitalo, Taye Gari, Dawit Jember , Bernt Lindtjørn. Validation: Eyob Fitalo, Taye Gari, Dawit Jember, Bernt Lindtjørn. Visualization: Eyob Fitalo, Taye Gari, Dawit Jember, Bernt Lindtjørn. Writing – original draft: Eyob Fitalo, Taye Gari, Dawit Jember, Bernt Lindtjørn. Acknowledgments We extend our sincere thanks to the SENUPH project for funding this study and Hawassa University for providing the necessary infrastructure and resources to support this research. We further extend heartfelt appreciation to our field supervisors and data collection teams for their dedication and diligence. Finally, we extend our deepest gratitude to all participants of the study for their patience and willingness to engage in multiple interview phases. References Mank I, Belesova K, Bliefernicht J, Traoré I, Wilkinson P, Danquah I et al (2021) The Impact of Rainfall Variability on Diets and Undernutrition of Young Children in Rural Burkina Faso. Front Public Health. ;9 Ghebrezgabher MG, Yang T, Yang X, Eyassu Sereke T (2020) Assessment of NDVI variations in responses to climate change in the Horn of Africa. Egypt J Remote Sens Space Sci 23(3):249–261 Central Statistical Authority Addis Ababa E (2000) Ethiopia Demographic and Health Survey Central Statistical Authority Addis Ababa ER (2019) Ethiopia Mini Demographic and Health Survey Bekana T (2025) Drought Risk Management in Ethiopia: A Systematic Review. J Energy Environ Chem Eng 10(1):1–11 Dimitrova A (2020) ‘No rain, no harvest, no food': Impacts of droughts on undernutrition among children aged under five in Ethiopia. ISEE Conference Abstracts. ;2020(1):1–34 Thanvisitthpon N, Kallawicha K, Chao HJ (2024) Chapter 12 - Introduction to meteorology, weather, and climate. In: Dehghani MH, Karri RR, Vera T, Hassan SKM (eds) Health and Environmental Effects of Ambient. Academic, Air Pollution, pp 303–329 Field CB, Barros V, Stocker T, Dahe Q, Dokken DJ, Ebi K et al (2012) IPCC, : Summary for policymakers: Managing the risks of extreme events and disasters to advance climate change adaptation. 2018. pp. 111 – 28 Phalkey RK, Aranda-Jan C, Marx S, Höfle B, Sauerborn R (2015) Systematic review of current efforts to quantify the impacts of climate change on undernutrition. Proc Natl Acad Sci USA 112(33):E4522–E9 Brown ME, Grace K, Shively G, Johnson KB, Carroll M (2014) Using satellite remote sensing and household survey data to assess human health and nutrition response to environmental change. Popul Environ 36(1):48–72 Belayneh M, Loha E, Lindtjørn B (2020) Seasonal Variation of Household Food Insecurity and Household Dietary Diversity on Wasting and Stunting among Young Children in A Drought Prone Area in South Ethiopia: A Cohort Study. Ecol Food Nutr 60(1):1–26 Mezgebe B, Gari T, Belayneh M, Lindtjørn B (2024) Seasonal variations in household food security and consumption affect women’s nutritional status in rural South Ethiopia. PLOS Global Public Health 4(8):1–18 Bloom N, Reenen JV (2014) Climate Change and Multidimensional Vulnerability to Child Undernutrition: Evidence from Ethiopia Heather. NBER Working Papers. :89- Dimitrova A (2020) ‘No rain, no harvest, no food’: Impacts of droughts on undernutrition among children aged under five in Ethiopia. ISEE Conference Abstracts. ;2020(1):1–34 Faul F, Erdfelder E, Buchner A, Lang A-G (2009) Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behav Res Methods 41(4):1149–1160 Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S et al (2015) The climate hazards infrared precipitation with stations - A new environmental record for monitoring extremes. Sci Data 2:1–21 al MGGe (2017) Analyzing drought conditions, interventions and mapping of vulnerable areas using ndvi and spi indices in eastern ethiopia, somali region. Ethiop J Environ Stud Managemen 4(9):9–15 Coates J, Swindale a, Bilinsky P. Household Food Insecurity Access Scale (HFIAS)for measurement of food access: indicator guide. Washington, DC: Food and Nutrition Technical … August):Version 3-Version Hjelm L, Mathiassen A, Wadhwa A (2016) Measuring Poverty for Food Security Analysis: Consumption- Versus Asset-Based Approaches. Food Nutr Bull 37(3):275–289 Chen Z, Wang W, Fu J (2020) Vegetation response to precipitation anomalies under different climatic and biogeographical conditions in China. Sci Rep 10(1):1–16 Bauer JM, Mburu S (2017) Effects of drought on child health in Marsabit District, Northern Kenya. Econ Hum Biol 24:74–79 Derseh NM, Shewaye DA, Agimas MC, Alemayehu MA, Aragaw FM (2023) Spatial variation and determinants of inappropriate complementary feeding practice and its effect on the undernutrition of infants and young children aged 6 to 23 months in Ethiopia by using the Ethiopian Mini-demographic and health survey, 2019: spatial. Front Public Health. ;11(October) Abate KH, Belachew T (2017) Women ’ s autonomy and men ’ s involvement in child care and feeding as predictors of infant and young child anthropometric indices in coffee farming households of Jimma Zone. South West Ethiopia. :1–16 Girma S, Alenko A (2020) Women’s Involvement in Household Decision-Making and Nutrition Related-Knowledge as Predictors of Child Global Acute Malnutrition in Southwest Ethiopia: A Case–Control Study. Nutr Diet Supplements 12(June):87–95 Rodimtsev SA, Pavlovskaya NE, Vershinin SV, Gorkova IV, Gagarina IN (2023) The use of the vegetative index NDVI to predict grain crop yields. Bull NSAU (Novosibirsk State Agrarian University). (4):56–67 Moussa Kourouma J, Eze E, Negash E, Phiri D, Vinya R, Girma A et al (2021) Assessing the spatio-temporal variability of NDVI and VCI as indices of crops productivity in Ethiopia: a remote sensing approach. Geomatics Nat Hazards Risk 12(1):2880–2903 Taye Gari BM, Mehretu Belayneh (2025) Bernt Lindtjørn Effect of climate variability, crop production, and household food insecurity on malnutrition among women: A mediation analysis from a droughtprone area in Southern Ethiopia. PLOS Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7558344\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":511495808,\"identity\":\"4b703b9b-4d3d-4b9f-a3ab-d69bae20d7c9\",\"order_by\":0,\"name\":\"Eyob Fitalo1*\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYHACNoYKAwYGPgb2gw8SKoB8ZuYGwlrOGBgASZ5kgwdnQFoYidHCANLCYCb5sA0kQEALv0TysQcHCv7Is7E3JEgkzquN5m8HavlRsQ2nFskZaekGBwwMDNt4Dh4wSNx2PHfGYcYGxp4zt3FqMbiRYyb9wcCAsU0iISEhcdux3AagFmbGNtxa7IFaJIC22AO1GBxInHMsdz4hLQYSEC2JQC2GDYkNNbkbCGmROPMM5Bfj5DaeM8kMCccO5G4EajmIzy/87aAQ+yNn28/efvznj5q63HnnDx988KMCtxYGgQQU7mEweQC3epA1qNJ1eBWPglEwCkbByAQAnUdfnqeE2+IAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Hawassa College of Health Sciences, Sidama, Ethiopia\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Eyob\",\"middleName\":\"\",\"lastName\":\"Fitalo1*\",\"suffix\":\"\"},{\"id\":511496436,\"identity\":\"097ac6de-a998-4380-bb5e-aa514348ed28\",\"order_by\":1,\"name\":\"Dawit Jember Tesfaye2\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0003-3831-5465\",\"institution\":\"School of Public Health, Hawassa University, Hawassa, Ethiopia\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Dawit\",\"middleName\":\"Jember\",\"lastName\":\"Tesfaye2\",\"suffix\":\"\"},{\"id\":511496437,\"identity\":\"6f971f39-d357-4004-87c8-dc43f22e1fa1\",\"order_by\":2,\"name\":\"Taye Gari2*\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBADHn4JEMVGjFIYLTkDpoWQNpgWBoMbxGqxZz9j+PBLzT0Z49s9Bgwfyg4z8Ms3ELCFJ8fYWOZYMY/ZnTMGjDPOHWaQbCPosBwzaQm2BB6zGzkGzLxthxkMjhHSwv/G/LfEvwQe4xlALX+BWuwJapHIMWP82JbAYyAB1MIIsoVgiN14VizN2JfAI3EjreBgz7l0HoljCfi1sPcnb/z441uCPf+M5I0PfpRZy/E3HyBgDQOHATMsckBqefAohdvzgPEHEcpGwSgYBaNgBAMAg7Q8AWhZMTQAAAAASUVORK5CYII=\",\"orcid\":\"https://orcid.org/0000-0003-4091-4491\",\"institution\":\"School of Public Health, Hawassa University, Hawassa, Ethiopia\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Taye\",\"middleName\":\"\",\"lastName\":\"Gari2*\",\"suffix\":\"\"},{\"id\":511496438,\"identity\":\"cfd0249d-1eb7-4143-8f5e-69f0ec63f63c\",\"order_by\":3,\"name\":\"Bernt Lindtjørn2\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-6267-6984\",\"institution\":\"School of Public Health, Hawassa University, Hawassa, Ethiopia\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Bernt\",\"middleName\":\"\",\"lastName\":\"Lindtjørn2\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-09-07 19:56:00\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":true,\"vertebrateSubjects\":false,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":true,\"humanSubjectConsent\":true,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":false},\"doi\":\"10.21203/rs.3.rs-7558344/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7558344/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":90911984,\"identity\":\"c03f33c6-60e3-42eb-ae77-15fe9673e24d\",\"added_by\":\"auto\",\"created_at\":\"2025-09-09 13:44:51\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":47814,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlow chart of child outcome measurements in the study area Boricha and Bilate zuria District, 2023/24\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7558344/v1/a4c01828219a2fca42c1d81d.png\"},{\"id\":90911992,\"identity\":\"97a235d5-d855-459e-a0dd-4e825d361139\",\"added_by\":\"auto\",\"created_at\":\"2025-09-09 13:44:51\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":39744,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMonthly Normalised difference vegetation index distribution and Rainfall in Boricha and Bilate Zuria 2023/2024.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7558344/v1/b3c778bab1588e9ef89d730c.png\"},{\"id\":90912751,\"identity\":\"19f53c8b-6905-4a58-97ae-c8867c434f01\",\"added_by\":\"auto\",\"created_at\":\"2025-09-09 13:52:51\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":59973,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFigure 4 Path diagram shows the link between Rainfall, NDVI and WHZ\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7558344/v1/3e4a0ad0406112b54a2824b2.png\"},{\"id\":90914232,\"identity\":\"a43fe9c9-da45-4ed9-b873-588c4cbb5044\",\"added_by\":\"auto\",\"created_at\":\"2025-09-09 14:00:52\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1003212,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7558344/v1/35150ec9-6f0f-499d-ae82-5acd2c4257e0.pdf\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003eClimate variability, crop production, and child undernutrition: A mediation analysis from a drought-prone area in the rural Sidama Region, Ethiopia\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eChild undernutrition is characterised by insufficient nutrient intake or utilisation and can manifest as wasting (low weight for height) or stunting (low height for age) (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). It is a major public health concern in developing countries, and the leading causes are a lack of food and high prevalence rates of infectious diseases (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). In Ethiopia, the prevalence of child stunting declined from 58% in 2000 to 37% by 2019, and the prevalence of wasting fell from 12–7% over the same time frame (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eHowever, Ethiopia is frequently affected by drought, which can lead to decreased food production (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e). Consequently, climate variability often exacerbates undernutrition by disrupting agricultural production, intensifying food insecurity, and contributing to the spread of infectious diseases (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eWeather refers to atmospheric conditions, such as temperature, precipitation, and wind speed, at a specific location and time; these conditions are measured over short periods (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e). Climate variability “includes all the variations in the climate that last longer than individual weather events.” In contrast, climate change refers to variations that persist for a more extended period, typically decades or more (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). Climate variability is a pressing global challenge for vulnerable populations in developing countries, such as Ethiopia. It can have implications for food security, livelihoods, and child health. Undernutrition has become one of the top five adverse impacts of climate change (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eEthiopia, particularly our study area in the Ethiopian Rift Valley, is an agricultural community that relies on farming and raising animals for subsistence. It is vulnerable to the adverse effects of climate variability, including erratic rainfall patterns and temperature fluctuations (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e). Previous cohort studies in the study area have also demonstrated seasonal variations in the prevalence rates of child malnutrition and household food security (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). However, weather condition-related variability has not been well studied, and previous studies on climate variability and nutritional status were mainly cross-sectional (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). A cohort design may help establish potential causal pathways between climate variability, crop production, and child malnutrition. Therefore, the primary objective of this study was to assess the impact of climate variability on child acute malnutrition, mediated by crop production.\\u003c/p\\u003e\"},{\"header\":\"Method and materials\",\"content\":\"\\u003cp\\u003eStudy setting, design and duration\\u003c/p\\u003e\\u003cp\\u003eThis study was part of a dynamic cohort study aimed at measuring the association between climate variability, nutrition, and health. By using a cohort study design, we established the temporality of the association in which the exposures occurred before the mediator and the outcome. Children were followed quarterly from June 2023 to March.\\u003c/p\\u003e\\u003cp\\u003eIt was conducted in the two woredas Boricha and Bilate Zuria. The region covers approximately 600 square kilometres and is relatively flat, with a gradual decrease in altitude from east to west. The altitude ranges from 1320 meters in the west to 2080 meters in the east. Approximately 14% of the total population of 315,000 consists of children aged 6 to 59 months. Most of the population (95%) lives in rural areas and relies on subsistence farming. Around 90% of the woreda receives yearly rainfall ranging from 850 to 1000 mm. The districts experience bimodal rainfall, with most of the rain falling during the \\u003cem\\u003eBelg\\u003c/em\\u003e season (March-May) and the \\u003cem\\u003eKiremt\\u003c/em\\u003e season (June-September). This pattern yields two harvests: short-cycle crops (e.g., potatoes, sweet potatoes, peppers, and beans) in August, and long-cycle crops (e.g., maize) in October (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eStudy Population\\u003c/p\\u003e\\u003cp\\u003eThe study includes all children under five who were living in the selected kebeles of Boricha and Bilate districts. Eligible respondents were those found within the household during the study period, while children absent from the cluster during the period of climate exposure, children with severe illnesses, and those whose mothers or caregivers could not communicate were excluded.\\u003c/p\\u003e\\u003cp\\u003eData collection procedure\\u003c/p\\u003e\\u003cp\\u003eA multistage sampling method was used to select the study participants. Initially, nine rural kebeles out of 30 in the districts were chosen randomly. Subsequently, the cluster sampling method was employed to select households within these kebeles. The final sample consisted of 395 households with children aged 6 to 59 months. An interview was conducted with biological mothers unless they were deceased or divorced.\\u003c/p\\u003e\\u003cp\\u003eTo determine the appropriate sample size for the study, we used G*Power software (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). The assumptions included were a two-tailed test, an effect size of |ρ| = 0.18 (based on previous study findings) (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e). We used a significance level (α) of 0.05 and a statistical power of 80%. Based on these parameters, the minimum required sample size was calculated to be 237 participants. To account for a potential non-response rate of 20%, the sample size was adjusted to 297. However, to further enhance the power and robustness of the study, a total of 395 children on follow-up were included in the analysis.\\u003c/p\\u003e\\u003cp\\u003eWe had also evaluated the power of negative results in the study (post hoc test). This was done for non-significant variables (mother’s occupation and child vaccination status. This was done to avoid a Type II error. Post hoc power analysis of vaccination status had 43% power to detect a difference in WHZ between vaccinated and non-vaccinated children, which is lower than the accepted standard threshold of 80%. This low power primarily derives from the fact that only a small effect difference (Cohen’s d = 0.10) emerges between groups due to small sample size, or other confounding variables may dilute the effect of vaccination on WHZ. However, for the mother occupation, the groups showed a large effect difference (Cohen’s d = 0.67), and with an adequate sample distribution, the study had over 80% power to detect the mean WHZ difference observed in each group.\\u003c/p\\u003e\\u003cp\\u003eStudy variables\\u003c/p\\u003e\\u003cp\\u003eThe outcome variable was the weight-for-height Z-score (WHZ). The child’s age was determined using the mother’s memory and memorable experiences to aid in recall. Height was measured in the recumbent position for children under 24 months using a Seca stadiometer, and the measurement was taken to the nearest 0.1cm. Vertical height was taken for children greater than 24 months after removing shoes. The procedure was repeated for the same child, and the average value was used. The child's weight was measured by asking the mother/caretaker to remove the child’s heavy clothing and was recorded to the nearest 0.1 kg using a Seca weight scale. The scale calibration was performed daily, and during weighing, the scale was checked for data accuracy.\\u003c/p\\u003e\\u003cp\\u003eThe data related to socio-demographic factors (family size, educational status of the mother and father, occupational status of the mother and father, religion, source of water) and child characteristics (age, sex, initiation of complementary feeding, initiation of breastfeeding, vaccination status) were collected by interviewing the household head and reviewing the vaccination card for child-related factors.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eWeather data\\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003eRainfall information was downloaded from the Climate Hazards Infra-Red Precipitation with Stations (CHIRPS) dataset, which provides daily precipitation at a 0.05 ° by 0.05 ° spatial resolution (5 km\\u003csup\\u003e²)\\u003c/sup\\u003e (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e). We used monthly lag rainfall, prepared from the sum of daily precipitation data (for each round of data collection). In this method, rainfall measurements were taken simultaneously with the NDVI data.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eNormalised Difference Vegetation Index (NDVI)\\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003eFor this study, historical Normalised Difference Vegetation Index (NDVI) data were downloaded from the United States Geological Survey (USGS) server, incorporating monthly-resolution datasets. NDVI quantifies vegetation greenness and density in satellite imagery, serving as a tool for evaluating crop health, agricultural productivity, and the ecological effects of rainfall fluctuations. The NDVI data were sourced from Sentinel-2 MSI (Multispectral Instrument, Level-2A) imagery, offering a 10-meter spatial resolution. Custom JavaScript algorithms were employed to compute monthly NDVI averages across the nine kebeles (administrative units) under investigation. The index is derived by analysing the contrast between visible red light (absorbed by vegetation) and near-infrared light (reflected by healthy plant cells), yielding values between − 1 and + 1. Values near + 1 denote thriving, dense vegetation, while those approaching − 1 indicate sparse vegetation or non-vegetated surfaces (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eHousehold food insecurity\\u003c/b\\u003e was evaluated based on a recall period of four weeks preceding the survey, using 9 occurrence questions on the household experience of food insecurity. If a respondent responds “yes” to any of the questions, a follow-up question about the frequency of occurrence was asked to determine the frequency of the experience. The frequency of occurrence was classified as rarely “if occurring once or twice”, sometimes “3–10 times, or often “more than 10 times”. A food-secure household is the one that experienced none of these nine questions (conditions) or just experienced the first condition, “worrying about food”, though with a frequency of once or twice over the past four weeks “rarely”. Then by summing up the total score (HHFIS), it is categorized into “secured” for score 0–1, “mildly insecure” for score 2–7, “moderately insecure” for score 8–14 and “severely insecure” for score 15–27 (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eWealth index\\u003c/b\\u003e: For wealth index construction, which is an indicator of the socio economic status of the community, data were collected on the availability of different assets in the household, like radio, TV, mobile phone, fixed phone, electricity, and housing condition, like type of floor, type of roof, number of windows, separate house for livestock, separate sleeping room, and the number of livestock in the household. A relative household wealth index was created using Principal Component Analysis (PCA) by utilising household assets (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e). Variables exhibiting similar responses of 95% higher and or 5% lower were removed from factor analysis. Finally, by using the first factor, it was categorised into 3 quintiles: poor “1”, middle “2”, and rich “3”.\\u003c/p\\u003e\\u003cp\\u003eData collectors were chosen based on their familiarity with the local area and their ability to use the questionnaire in the local language. Data collectors also received orientation on the purpose of the study and the method of data collection. The entire data collection process was under the supervision of the primary investigator and project manager, and a 5% pretest was performed in nearby \\u003cem\\u003ekebeles\\u003c/em\\u003e outside of the selected kebeles prior to data collection. At the end of each day, the submitted data was randomly chosen from the submitted data and checked for completeness and consistency by field supervision.\\u003c/p\\u003e\\u003cp\\u003eWe enrolled 395 children in our study, following them up every three months for a year. The number of children with complete outcome measurement was 382 in June 2023, 369 in September 2023, 382 in December 2023, and 376 in March 2024. We excluded the observations with incomplete data or extreme outliers observed in each measurement, and a new observation was added as we followed an open dynamic cohort (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eData processing and analysis\\u003c/p\\u003e\\u003cp\\u003eAfter completion of household and individual data collection, the data were checked for completeness and consistency. Descriptive statistics, including frequency and summary measures such as mean, median, and interquartile range, were used to present the data. STATA Version 16.1 (Stata Corp, Texas 77845 USA) statistical software was used for analysis. The Weight-for-Height Z-score (WHZ) was calculated using the WHO Anthro Software, Version 3.2.2 (WHO, Geneva, Switzerland). A total of 27 children (13 in March, 8 in September, 4 in December, and 4 in March 2024) with doubtful values of WHZ (below − 6 or above + 6) and missing values in two or more rounds of the survey were excluded from the analysis.\\u003c/p\\u003e\\u003cp\\u003eTo compare mean values of the outcome variable (WHZ), a t-test was used. Pearson's correlation coefficient was used to measure the strength and direction of the association between continuous variables, and multiple linear regression analysis was employed to establish the existing association. Normality assumption tests were performed for continuous outcome variables WHZ using graphical methods such as histograms, the quantile function for the normal distribution (qnorm), and kurtosis statistical tests; kurtosis was between − 1 and 1. Linearity between WHZ and NDVI was also examined using a scatter plot, a linear fit line, and residual plots. In the final multivariable analysis, the coefficient with a P value less than 0.05 and a 95% CI was used to declare a statistically significant association.\\u003c/p\\u003e\\u003cp\\u003eSince the study involved hierarchical data, it included kebeles and repeated measurements (four rounds). Measurements (level 1) were nested within child (level 2) and children nested within \\u003cem\\u003ekebele\\u003c/em\\u003e (level 3). Due to this, the probability of developing undernutrition among children under five years old may be similar within the \\u003cem\\u003ekebeles\\u003c/em\\u003e but differ among them. There could also be dependence between measurements. This leads to incorrect parameter estimation by violating the assumption of the standard regression model (independent observations). Therefore, to account for the effect of dependency observed in clustering and repeated measurements, mediation analysis using generalised structural equation models (GSEM) was employed, based on the hypothesis that a decrease in rainfall and NDVI would result in a reduction of WHZ, which would be mediated by household food insecurity. The final model was chosen through a series of steps. Initially, an unadjusted mediation model was created with NDVI (independent variable), household food security score (as the mediator), and WHZ (as the outcome variable). The AIC was 29796 and the BIC was 29877 for the unadjusted model. Subsequently, the complete mediation analysis model was constructed after accounting for potential confounding variables. Finally, a model with the lowest AIC (29445) and BIC (29579), which was improved compared to the unadjusted model, was considered for identifying direct and indirect factors associated with the outcome variable (WHZ).\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eSocio-demographic characteristics of the study participants\\u003c/p\\u003e\\u003cp\\u003eThe mean (SD) age of the mothers was 31.6 (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e) years, and households with an average (SD) family size had 5 (1.5) members. The majority of the mothers, 98% (387 of 395), were married, and 65% (258 of 395) of the mothers were illiterate, while 56% (221 of 395) of the fathers were illiterate. Around 32% of households had a poor wealth status, and 79% of households were using a protected source of drinking water (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eHousehold-related Baseline characteristics of the study participants (n\\u0026thinsp;=\\u0026thinsp;395)\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSocio-demographic variables\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eFrequency\\u003csup\\u003e$\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePercentage\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eMother\\u0026rsquo;s age in years, mean (SD)\\u0026thinsp;=\\u0026thinsp;31.6 (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e)\\u003c/p\\u003e\\u003cp\\u003eFamily size, mean (SD)\\u0026thinsp;=\\u0026thinsp;5(1.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFather education\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIlliterate\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e221\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e56.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRead and write without formal education\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e55\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e13.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAttended formal education\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e119\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e30.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMother education\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIlliterate\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e258\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e65.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRead and write without formal education\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e43\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e10.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAttended formal education\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e94\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e23.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMarital status\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMarried\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e387\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e98.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOther\\u003csup\\u003e#\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMother occupation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHouse wife and farmer\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e381\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e96.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTrader/ other\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWealth status\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePoor\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e127\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e32.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMedium\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e139\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e35.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRich\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e129\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e32.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSource of drinking water\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eProtected source\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e313\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e79.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNot protected source\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e82\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e20.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e#=Widowed/divorced/separated, \\u003cspan\\u003e$\\u003c/span\\u003e=unless it is indicated in the row\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eChild-related baseline characteristics\\u003c/p\\u003e\\u003cp\\u003eThe study involved 395 children, with a nearly equal split by sex: 51.4% (203 of 395) were female and 48.6% (192 of 395) male. Over two-thirds (64.8%) of the children were aged 6\\u0026ndash;24 months, with 34.4% in the 6\\u0026ndash;11 months group and 31.4% in the 12\\u0026ndash;23 months group. 92.9% (387 of 395) of mothers-initiated breastfeeding within one hour of birth, and 71.9% introduced complementary feeding at the recommended time. The majority, 71.4% (282 of 395), received age-appropriate vaccinations, although a notable proportion, 28.6% (113 of 395), remained unvaccinated (Table\\u0026nbsp;2)\\u003c/p\\u003e\\u003cp\\u003e.Table\\u0026nbsp;2: Child-related baseline characteristics (n\\u0026thinsp;=\\u0026thinsp;395)\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Taba\\\" border=\\\"1\\\"\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eChild characteristics\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eFrequency\\u003csup\\u003e$\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePercentage\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSex of child\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFemale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e203\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e51.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e192\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e48.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eChild age\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e6\\u0026ndash;11 month\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e136\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e34.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e12\\u0026ndash;23 month\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e124\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e31.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e24\\u0026ndash;35 month\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e73\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e18.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e36\\u0026ndash;59 month\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e62\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e15.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eBreast feeding initiation\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWithin one hours\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e387\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e92.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNot started Within a day\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e28\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e7.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eVaccination from card\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVaccinated for age\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e282\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e71.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNot vaccinated for age\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e113\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e28.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eComplementary feeding initiation\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAt recommended time\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e284\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e71.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEarly introduction of CF\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e24\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLate introduction of CF\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e87\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e22.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cspan\\u003e$\\u003c/span\\u003e=unless it is indicated in the row. CF\\u0026thinsp;=\\u0026thinsp;Complementary feeding\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eRainfall and Normalised Difference Vegetation Index (NDVI)\\u003c/p\\u003e\\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e shows the variation of rainfall and NDVI observed over different months. The highest rainfall was from February to May 2023 and from mid-August to October, and the lowest was observed from June to August 2023. There was also a statistically significant positive correlation between NDVI and a month after rainfall (3-month lag rainfall and 2-month lag NDVI) (correlation coefficient: r\\u0026thinsp;=\\u0026thinsp;0.17, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eWeight-for-Height Z-scores (WHZ) of the children\\u003c/p\\u003e\\u003cp\\u003eAs shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, the WHZ scores were similar for girls (0.51, 95% CI: 0.48\\u0026ndash;0.53) and boys (0.48, 95% CI: 0.46\\u0026ndash;0.52). But children aged 12\\u0026ndash;23 months had the highest mean WHZ (0.45, 95% CI: 0.4\\u0026ndash;0.47), while those aged 24\\u0026ndash;35 months exhibited the lowest (0.18, 95% CI: 0.16\\u0026ndash;0.19). Access to protected water sources was associated with a higher mean WHZ (0.79, 95% CI: 0.77\\u0026ndash;0.81) compared to unprotected sources (0.21, 95% CI: 0.19\\u0026ndash;0.23). Similarly, children from households of middle-level wealth status had the highest mean WHZ (0.35, 95% CI: 0.33\\u0026ndash;0.38) compared to those from poor households (0.33, 95% CI: 0.30\\u0026ndash;0.35).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eMean WHZ of the children by socio-demographic variables, June 2023 to March 2024\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVariable\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMean WHZ (95% CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eStandard Deviation (SD)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eChild sex\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFemale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.51 (0.48, 0.53)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.36\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.48 (0.46, 0.52)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.47\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eChild age\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e6\\u0026ndash;11 month\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.12 (0.13, 0.15)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.32\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e12\\u0026ndash;23 month\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.45 (0.42, 0.47)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.34\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e24\\u0026ndash;35 month\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.18 (0.16, 0.19)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.64\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e36\\u0026ndash;59 month\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.23 (0.21, 0.26)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMother education\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIlliterate\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.66 (0.63\\u0026ndash;0.68)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.40\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRead/write\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.11 (0.09\\u0026ndash;0.12)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.62\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAttended\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.24 (0.21\\u0026ndash;0.26)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.29\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFather education\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIlliterate\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.56 (0.54\\u0026ndash;0.59)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.35\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRead/write\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.14 (0.12\\u0026ndash;0.15)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.58\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAttended\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.30 (0.28\\u0026ndash;0.33)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.40\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWealth status\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePoor\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.32 (0.30\\u0026ndash;0.35)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMiddle\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.35 (0.33\\u0026ndash;0.38)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRich\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.32 (0.30\\u0026ndash;0.35)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.46\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMother occupation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHousewife/farmer\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.97 (0.96\\u0026ndash;0.98)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.40\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTrader/other\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.03 (0.03\\u0026ndash;0.04)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.44\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMarital status\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMarried\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.98 (0.97\\u0026ndash;0.99)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.41\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOther\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.02 (0.01\\u0026ndash;0.03)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.08\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSource of water\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eProtected source\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.79 (0.77\\u0026ndash;0.81)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.34\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNot protected source\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.21 (0.19\\u0026ndash;0.23)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.57\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eRainfall, crop production proxy (NDVI) and WHZ score\\u003c/p\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e shows the overall correlation between Rainfall, NDVI and WHZ. There were significant positive correlations between 2-month lag NDVI and child nutritional status (WHZ). Likewise, 1-month lag rainfall also showed a positive correlation with NDVI and WHZ. Both NDVI and WHZ showed significant negative correlations with HHFIAS.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eCorrelation matrix between NDVI, HHFIAS and WHZ\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c4\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eCorrelations\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNDVI\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eWHZ\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eRainfall\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNDVI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.08 **\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.16**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWHZ\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.08 **\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.01**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHHFIAS\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.13 **\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-0.12 **\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.02**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c4\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e2\\u0026thinsp;=\\u0026thinsp;2month lag Normalized Difference Vegetation Index\\u003c/p\\u003e\\u003cp\\u003e** Correlation is significant at the 0.01 level\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003ePredictors of WHZ score.\\u003c/p\\u003e\\u003cp\\u003eIn multiple linear regression analysis (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e), a higher NDVI value was associated with increased WHZ (β\\u0026thinsp;=\\u0026thinsp;0.69, 95% CI (0.06, 1.32)), while a higher household food insecurity score (HHFIAS) was associated with lower WHZ (β = -0.02, 95% CI (-0.03, -0.004)). Larger family size (β = -0.10, 95% CI (-0.15, -0.04)) and late initiation of complementary feeding (β = -0.59, 95% CI (-0.77, -0.40)) significantly reduced WHZ. Wealth status was associated with higher WHZ compared to poorer households (β\\u0026thinsp;=\\u0026thinsp;0.18, 95% CI (0.01, 0.36)).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eMultiple linear regression of factors Associated with under-five children Weight for height Z score in the study area Boricha and Bilate zuria District, 2023/24\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVariable\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eβ Coefficient (95% CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ep-value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eNDVI\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.69 (0.06, 1.32)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.031*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eHHFIAS\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.02 (-0.03, -0.01)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.008*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eChild age\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.001 (-0.01, 0.01)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.832\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFamily size\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.10 (-0.15, -0.04)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001***\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eMother\\u0026rsquo;s occupation\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHousewife/farmer\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTrader/other\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.18 (-0.56, 0.21)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.354\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eVaccination status\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVaccinated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNot vaccinated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.07 (-0.23, 0.09)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.368\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eComplementary feeding\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOn recommended time\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEarly initiation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.25 (-0.05, 0.55)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.102\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLate initiation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.58 (-0.76, -0.40)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001***\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eWealth status\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePoor\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMiddle\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.18 (0.002, 0.36)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.047*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRich\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.28 (0.10, 0.47)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.003**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eMother\\u0026rsquo;s education\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIlliterate\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRead/write without formal education\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.18 (-0.05, 0.41)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.119\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAttended formal education\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.02 (-0.16, 0.20)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.820\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eMediation analysis of factors associated with under-five children\\u0026rsquo;s Weight for Height Z score\\u003c/p\\u003e\\u003cp\\u003eIn the adjusted mediation model presented below (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e or Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e), a significant positive relationship between rainfall and NDVI was observed. The total effect of NDVI on WHZ was 0.97 (95% CI: 0.37, 1.59), of which 15% was mediated by Household Food Insecurity (HHFIS). Furthermore, NDVI showed a negative effect on Household Food Insecurity (HHFIS) of -8.53 (95% CI: -10.91, -6.15). Finally, children from higher wealth households had higher WHZ scores of 0.16 (95% CI: 0.07, 0.25), with nearly one quarter (27%) of this effect mediated by Household Food Insecurity (HHFIS).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eMediation analysis of the direct, indirect and total effect estimates of under the age five years children weight for height Z score in the study area Boricha and Bilate zuria District, 2023/24\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCausal effect pathway\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eDirect effect (95% CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eIndirect effect (95%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eTotal effect (95% CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e%(Mediation analysis ) \\u003csup\\u003e@\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRF\\u003csup\\u003e1\\u003c/sup\\u003e\\u0026rarr; NDVI\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.0003 (0.0002, 0.0003)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.0003 (0.0002, 0.0003)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRF\\u003csup\\u003e1\\u003c/sup\\u003e\\u0026rarr; WHZ Via NDVI\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.0003 (0.0001, 0.0005)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.0003 (0.0002, 0.0003)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNDVI\\u0026rarr; HHFIS\\u003csup\\u003e3\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-8.53 (-10.91, -6.15)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-8.53 (-10.91, -6.15)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHHFIS \\u0026rarr;WHZ\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.02 (-0.03, -0.01)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.02 (-0.03, -0.01)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNDVI \\u0026rarr;WHZ via HHFIS\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.91 (0.24, 1.58)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.15 (0.03, 0.26)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.97 (0.36, 1.59)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e15% (Partial mediation)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMother occupation \\u0026rarr; WHZ\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.17 (-0.56, 0.22)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.17 (-0.56, 0.22)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWealth\\u003csup\\u003e$\\u003c/sup\\u003e \\u0026rarr; WHZ via HHFIS\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.12 (0.03, 0.21)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.04 (0.01, 0.07)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.16 (0.07, 0.25)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e27% (Partial mediation)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFamily size\\u003csup\\u003e$\\u003c/sup\\u003e \\u0026rarr;WHZ\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.24 (-0.39, -0.08)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.24 (-0.39, -0.08)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVaccination\\u003csup\\u003e$\\u003c/sup\\u003e \\u0026rarr;WHZ\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.11 (-0. 27, 0.05)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.11 (-0. 27, 0.05)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eChild age \\u0026rarr;WHZ\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.06 (-0.21, -0.09)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.06 (-0.21, -0.09)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCF\\u003csup\\u003e$\\u003c/sup\\u003e \\u0026rarr;WHZ\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.27 (-0.36, -0.18)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.27 (-0.36, -0.18)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e*P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, ** p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01, *** p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, @= Proportion of mediated, \\u003cspan\\u003e$\\u003c/span\\u003e=Reference category is first, CF\\u0026thinsp;=\\u0026thinsp;Complementary feeding,\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e1\\u0026thinsp;=\\u0026thinsp;1 month lag of Rainfall from NDVI, 2\\u0026thinsp;=\\u0026thinsp;2 month lag of average Normalized Deference Vegetation Index from survey time, 3\\u0026thinsp;=\\u0026thinsp;Household food insecurity scale,\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eCategorical variables coding: \\u003cem\\u003eFamily size (1 for \\u0026lt;\\u0026thinsp;=\\u0026thinsp;5, 2 for \\u0026gt;\\u0026thinsp;5), Vaccination (1 for vaccinated and 2 for not vaccinated), Mother occupation (1 for housewife/farmer and 2 trader/other), Wealth (1\\u0026thinsp;=\\u0026thinsp;Poor,2\\u0026thinsp;=\\u0026thinsp;Middle, 3\\u0026thinsp;=\\u0026thinsp;Rich), Complementary feeding (1\\u0026thinsp;=\\u0026thinsp;on recommended time, 2\\u0026thinsp;=\\u0026thinsp;early initiation, 3\\u0026thinsp;=\\u0026thinsp;late initiation)\\u003c/em\\u003e,\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003ePath diagram\\u003c/h2\\u003e\\u003cp\\u003eBelow, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e shows an alternative presentation of the mediation analysis path model showing the direction of association between exogenous and endogenous variables along with the regression coefficient.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eAs hypothesised, we observed a direct impact of rainfall on agricultural crop production (measured using NDVI) and an indirect influence of NDVI on WHZ through household food insecurity (HHFIS). After controlling for household and individual factors, NDVI showed a significant positive effect on WHZ. Additionally, late initiation of complementary feeding, larger family size, and higher family wealth status had notable direct effects on WHZ.\\u003c/p\\u003e\\u003cp\\u003eThe study has shown that rainfall had a positive direct effect on NDVI. In line with the study conducted in China (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e). Furthermore, this study showed that NDVI has a significant direct and indirect effect on weight-for-height Z score. This finding is consistent with a study conducted in Kenya that showed a positive association between NDVI and child nutritional status (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eA community-based study conducted in Ethiopia reported that the prevalence of undernutrition was high among children engaging in inappropriate complementary feeding practices (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e). Our study also showed that children who recently started complementary feeding had lower WHZ scores. Conversely, children living in larger family sizes also had lower WHZ compared to those in smaller families, as documented in earlier studies from Ethiopia (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e). This may suggest that larger family sizes increase the risk of acute malnutrition in children, potentially due to an imbalance between the number of family members and available resources. On the other hand, being part of the highest wealth category family improves a child's WHZ, in agreement with earlier studies from Ethiopia (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e). The possible reason may be that family wealth status significantly impacts the quality and quantity of food consumption, often leading to inadequate nutrition among children. Families with limited resources may rely on less nutritious and cheaper food options, which can contribute to poorer health outcomes.\\u003c/p\\u003e\\u003cp\\u003eOne of the strengths of our study is that we used a prospective cohort design to measure the temporal relationship between NDVI and child nutrition status. Additionally, we adjusted for clustering effects at the kebele level and accounted for time variations due to repeated measurements of some variables. The limited statistical power of this study may explain the borderline significance observed in the multiple linear regression analysis. Therefore, the results should be interpreted with caution and verified through larger studies.\\u003c/p\\u003e\\u003cp\\u003eIn this study, NDVI obtained from satellite data was used as a proxy for crop production. Previous studies have indicated that NDVI is a reliable measure for crop monitoring, showing strong correlations with yields of crops (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e). In addition, a study from the same area as the current study has also shown an association between NDVI, household food insecurity, and women\\u0026rsquo;s nutrition status (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e). However, NDVI may not provide the actual crop produced at the household level, and therefore, care should be taken while interpreting this study. While NDVI strongly correlates with agricultural output, the reliance on remote sensing data (NDVI) rather than direct assessments of household food production raises questions. Future investigations should integrate satellite-derived data with on-ground evaluations of crop diversity to better capture local food production. Additionally, our measurement of household food security relied on self-reported data, which risks information bias, as participants\\u0026rsquo; awareness of potential food aid benefits may have influenced their responses, potentially skewing reported levels of food insecurity.\\u003c/p\\u003e\\u003cp\\u003eIn conclusion, Climate variability and crop production were risk factors for wasting, and this relationship is primarily mediated by household food insecurity. This finding could imply that rural communities depending on rain-fed subsistence agriculture exhibit vulnerability to the impacts of climate variability. There may be a need to use local specific climate service by the community to prevent acute malnutrition in children.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eEthics approval and consent to participate\\u003c/p\\u003e\\n\\u003cp\\u003eThe study fulfilled with the declaration of Helsinki\\u0026nbsp;and Ethical approval was obtained from Hawassa University College of Medicine and Health Sciences Institutional Review Board (Ref. No: IRB/141/16). Informed written consent was obtained from the mothers or caregivers before the interview began. They were told to withdraw at any time and/or to abstain from responding to questions they were not interested or not willing to respond. Child parents were also informed that all the data obtained would be kept confidential and if there is a problem, it was link nearby health facility for malnutrition management and advice was given to their parents.\\u0026nbsp;We express our gratitude to the SENUPH II \\u0026nbsp;projects for funding this study.\\u003c/p\\u003e\\n\\u003cp\\u003eConsent for publication\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAvailability of data and materials\\u003c/p\\u003e\\n\\u003cp\\u003eThe dataset used during this study are available from the corresponding author on reasonable request.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eCompeting interests\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing Interests\\u003c/p\\u003e\\n\\u003cp\\u003eFunding\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was funded by the Southern Ethiopia Network of Universities in Public Health (SENUPH II) project conducted under Hawassa University. The funders were not involved in the study design, data collection and analysis, publication decision, or manuscript preparation.\\u003c/p\\u003e\\n\\u003cp\\u003eAuthors\\u0026apos; contributions\\u003c/p\\u003e\\n\\u003cp\\u003eConceptualization: Taye Gari, Eyob Fitalo, Bernt Lindtj\\u0026oslash;rn.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Data curation: Eyob Fitalo, Taye Gari, Bernt Lindtj\\u0026oslash;rn, Dawit Jember\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Formal analysis: Eyob Fitalo, Taye Gari, Dawit Jember, Bernt Lindtj\\u0026oslash;rn.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Funding acquisition: Taye Gari, Bernt Lindtj\\u0026oslash;rn.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Investigation: Eyob Fitalo, Taye Gari, Dawit Jember , Bernt Lindtj\\u0026oslash;rn.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Methodology: Eyob Fitalo ,Taye Gari, Dawit Jember, Bernt Lindtj\\u0026oslash;rn.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Project administration: Eyob Fitalo, Taye Gari, Dawit Jember , Bernt Lindtj\\u0026oslash;rn.\\u003c/p\\u003e\\n\\u003cp\\u003eSoftware: Eyob Fitalo, Taye Gari, Dawit Jember, Bernt Lindtj\\u0026oslash;rn.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Supervision: Eyob Fitalo, Taye Gari, Dawit Jember , Bernt Lindtj\\u0026oslash;rn.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Validation: Eyob Fitalo, Taye Gari, Dawit Jember, Bernt Lindtj\\u0026oslash;rn.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Visualization: Eyob Fitalo, Taye Gari, Dawit Jember, Bernt Lindtj\\u0026oslash;rn.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Writing \\u0026ndash; original draft: Eyob Fitalo, Taye Gari, Dawit Jember, Bernt Lindtj\\u0026oslash;rn.\\u003c/p\\u003e\\n\\u003cp\\u003eAcknowledgments\\u003c/p\\u003e\\n\\u003cp\\u003eWe extend our sincere thanks to the SENUPH project for funding this study and Hawassa University for providing the necessary infrastructure and resources to support this research. We further extend heartfelt appreciation to our field supervisors and data collection teams for their dedication and diligence. Finally, we extend our deepest gratitude to all participants of the study for their patience and willingness to engage in multiple interview phases.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eMank I, Belesova K, Bliefernicht J, Traor\\u0026eacute; I, Wilkinson P, Danquah I et al (2021) The Impact of Rainfall Variability on Diets and Undernutrition of Young Children in Rural Burkina Faso. Front Public Health. ;9\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGhebrezgabher MG, Yang T, Yang X, Eyassu Sereke T (2020) Assessment of NDVI variations in responses to climate change in the Horn of Africa. Egypt J Remote Sens Space Sci 23(3):249\\u0026ndash;261\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCentral Statistical Authority Addis Ababa E (2000) Ethiopia Demographic and Health Survey\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCentral Statistical Authority Addis Ababa ER (2019) Ethiopia Mini Demographic and Health Survey\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBekana T (2025) Drought Risk Management in Ethiopia: A Systematic Review. J Energy Environ Chem Eng 10(1):1\\u0026ndash;11\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eDimitrova A (2020) \\u0026lsquo;No rain, no harvest, no food': Impacts of droughts on undernutrition among children aged under five in Ethiopia. ISEE Conference Abstracts. ;2020(1):1\\u0026ndash;34\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eThanvisitthpon N, Kallawicha K, Chao HJ (2024) Chapter 12 - Introduction to meteorology, weather, and climate. In: Dehghani MH, Karri RR, Vera T, Hassan SKM (eds) Health and Environmental Effects of Ambient. Academic, Air Pollution, pp 303\\u0026ndash;329\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eField CB, Barros V, Stocker T, Dahe Q, Dokken DJ, Ebi K et al (2012) IPCC, : Summary for policymakers: Managing the risks of extreme events and disasters to advance climate change adaptation. 2018. pp. 111\\u0026thinsp;\\u0026ndash;\\u0026thinsp;28\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePhalkey RK, Aranda-Jan C, Marx S, H\\u0026ouml;fle B, Sauerborn R (2015) Systematic review of current efforts to quantify the impacts of climate change on undernutrition. Proc Natl Acad Sci USA 112(33):E4522\\u0026ndash;E9\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBrown ME, Grace K, Shively G, Johnson KB, Carroll M (2014) Using satellite remote sensing and household survey data to assess human health and nutrition response to environmental change. Popul Environ 36(1):48\\u0026ndash;72\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBelayneh M, Loha E, Lindtj\\u0026oslash;rn B (2020) Seasonal Variation of Household Food Insecurity and Household Dietary Diversity on Wasting and Stunting among Young Children in A Drought Prone Area in South Ethiopia: A Cohort Study. Ecol Food Nutr 60(1):1\\u0026ndash;26\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMezgebe B, Gari T, Belayneh M, Lindtj\\u0026oslash;rn B (2024) Seasonal variations in household food security and consumption affect women\\u0026rsquo;s nutritional status in rural South Ethiopia. PLOS Global Public Health 4(8):1\\u0026ndash;18\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBloom N, Reenen JV (2014) Climate Change and Multidimensional Vulnerability to Child Undernutrition: Evidence from Ethiopia Heather. NBER Working Papers. :89-\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eDimitrova A (2020) \\u0026lsquo;No rain, no harvest, no food\\u0026rsquo;: Impacts of droughts on undernutrition among children aged under five in Ethiopia. ISEE Conference Abstracts. ;2020(1):1\\u0026ndash;34\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFaul F, Erdfelder E, Buchner A, Lang A-G (2009) Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behav Res Methods 41(4):1149\\u0026ndash;1160\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFunk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S et al (2015) The climate hazards infrared precipitation with stations - A new environmental record for monitoring extremes. Sci Data 2:1\\u0026ndash;21\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eal MGGe (2017) Analyzing drought conditions, interventions and mapping of vulnerable areas using ndvi and spi indices in eastern ethiopia, somali region. Ethiop J Environ Stud Managemen 4(9):9\\u0026ndash;15\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCoates J, Swindale a, Bilinsky P. Household Food Insecurity Access Scale (HFIAS)for measurement of food access: indicator guide. Washington, DC: Food and Nutrition Technical \\u0026hellip; August):Version 3-Version\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHjelm L, Mathiassen A, Wadhwa A (2016) Measuring Poverty for Food Security Analysis: Consumption- Versus Asset-Based Approaches. Food Nutr Bull 37(3):275\\u0026ndash;289\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eChen Z, Wang W, Fu J (2020) Vegetation response to precipitation anomalies under different climatic and biogeographical conditions in China. Sci Rep 10(1):1\\u0026ndash;16\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBauer JM, Mburu S (2017) Effects of drought on child health in Marsabit District, Northern Kenya. Econ Hum Biol 24:74\\u0026ndash;79\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eDerseh NM, Shewaye DA, Agimas MC, Alemayehu MA, Aragaw FM (2023) Spatial variation and determinants of inappropriate complementary feeding practice and its effect on the undernutrition of infants and young children aged 6 to 23 months in Ethiopia by using the Ethiopian Mini-demographic and health survey, 2019: spatial. Front Public Health. ;11(October)\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAbate KH, Belachew T (2017) Women \\u0026rsquo; s autonomy and men \\u0026rsquo; s involvement in child care and feeding as predictors of infant and young child anthropometric indices in coffee farming households of Jimma Zone. South West Ethiopia. :1\\u0026ndash;16\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGirma S, Alenko A (2020) Women\\u0026rsquo;s Involvement in Household Decision-Making and Nutrition Related-Knowledge as Predictors of Child Global Acute Malnutrition in Southwest Ethiopia: A Case\\u0026ndash;Control Study. Nutr Diet Supplements 12(June):87\\u0026ndash;95\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRodimtsev SA, Pavlovskaya NE, Vershinin SV, Gorkova IV, Gagarina IN (2023) The use of the vegetative index NDVI to predict grain crop yields. Bull NSAU (Novosibirsk State Agrarian University). (4):56\\u0026ndash;67\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMoussa Kourouma J, Eze E, Negash E, Phiri D, Vinya R, Girma A et al (2021) Assessing the spatio-temporal variability of NDVI and VCI as indices of crops productivity in Ethiopia: a remote sensing approach. Geomatics Nat Hazards Risk 12(1):2880\\u0026ndash;2903\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eTaye Gari BM, Mehretu Belayneh (2025) Bernt Lindtj\\u0026oslash;rn Effect of climate variability, crop production, and household food insecurity on malnutrition among women: A mediation analysis from a droughtprone area in Southern Ethiopia. PLOS\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"climate variability, mediation analysis, acute malnutrition, Sidama Region, Ethiopia\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7558344/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7558344/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e\\u003cp\\u003eEthiopia is a predominantly agricultural community that relies on farming and animal husbandry for subsistence. It is vulnerable to the adverse effects of climate variability. However, the association between weather conditions and health has not been well studied. Therefore, the primary objective of this study was to assess the impact of climate variability on child acute malnutrition, mediated by crop production.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eThis study was part of a large, open, dynamic cohort study designed to examine the relationship between weather conditions, nutrition, and health. For this study, a cohort of 395 children was monitored quarterly from June 2023 to March 2024. Using a cohort study design, we established the temporal sequence in which the exposures occurred before the mediators and the outcome. A two-stage sampling method was employed to select households, and data were collected using a pre-tested, structured questionnaire. Structural equation modelling was employed to assess the direct, indirect, and total effects of a proxy for crop production on acute malnutrition.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eAn increase in Normalised Difference Vegetation Index (NDVI) had a positive direct effect and an indirect impact on wasting via household food. Similarly, rainfall had a positive effect on the NDVI. Furthermore, children who initiated complementary feeding late and large families were at increased risk of wasting. However, children from wealthier families had less wasting.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e\\u003cp\\u003eClimate variability and crop production were risk factors for wasting, and this relationship is primarily mediated by household food insecurity. This finding could imply that rural communities depending on rain-fed subsistence agriculture exhibit vulnerability to the impacts of climate variability.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Climate variability, crop production, and child undernutrition: A mediation analysis from a drought-prone area in the rural Sidama Region, Ethiopia\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-09-09 13:44:47\",\"doi\":\"10.21203/rs.3.rs-7558344/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"da673bdf-6443-4bac-ad30-fa35ea33969e\",\"owner\":[],\"postedDate\":\"September 9th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-09-09T13:44:47+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-09-09 13:44:47\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7558344\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7558344\",\"identity\":\"rs-7558344\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}