Socio-demographic and environmental determinants of under-5 stunting in Rwanda: Evidence from a multisectoral study

Child stunting is an important household, socio-economic, environmental and nutritional stress indicator. Nationally, 33% of children under 5 in Rwanda are stunted necessitating the need to identify factors perpetuating stunting for targeted interventions. Our study assessed the individual and community-level determinants of under-5 stunting essential for designing appropriate policy and program responses for addressing stunting in Rwanda. A cross-sectional study was conducted between September 6 and October 9, 2022, in five districts of Rwanda including, Kicukiro, Ngoma, Burera, Nyabihu and Nyanza. 2788 children and their caregivers were enrolled in the study and data on the individual level (child, caregiver/household characteristics), and community-level variables were collected. A multilevel logistic regression model was used to determine the influence of individual and community-level factors on stunting. The prevalence of stunting was 31.4% (95% CI: 29.5–33.1). Of this, 12.2% were severely stunted while 19.2% were moderately stunted. In addition, male gender, age above 11 months, child disability, more than six people in the household, having two children below the age of five, a child having diarrhea 1–2 weeks before the study, eating from own plate when feeding, toilet sharing, and open defecation increased the odds of childhood stunting. The full model accounted for 20% of the total variation in the odds of stunting. Socio-demographic and environmental factors are significant determinants of childhood stunting in Rwanda. Interventions to address under-five stunting should be tailored toward addressing individual factors at household levels to improve the nutritional status and early development of children.

This was a cross-sectional study conducted between September 6 and October 9 2022, in five districts of Rwanda including, Kicukiro, Ngoma, Burera, Nyabihu, and Nyanza. Gikuriro Kuri Bose is a multisectoral and transdisciplinary project being implemented in five districts of Rwanda, each being drawn from one province. Rwanda has four geopolitical provinces and the City of Kigali. The provinces and the City of Kigali are further subdivided into 30 districts and districts subdivided into sectors (416 sectors in total) and sectors subdivided into cells (2,148 cells) and cells subdivided into villages (14,837 villages). Villages comprise about 100 households while cells constitute between five-seven villages. The study districts included Nyabihu from the western province, Burera from the northern province, Kicukiro from the city of Kigali, Nyanza in the south and Ngoma in the eastern province (Figure 1). Map of Rwanda showing the administrative districts and project implementation areas. This study was based on 2,788 children and their mothers/legal guardians. To determine the sample size, the current prevalence of stunting (33%) (7) was considered as an indicator of the nutritional status. Using a 95% confidence interval and the equation proposed by Lwanga et al. (11) as n = Z1-a22 (1-p)/ ε2p, where p = prevalence, ε = relative precision, and n = sample size with a relative precision for the study to be between 5 and 10% of the true prevalence (0.05 < ε < 0.10), a sample size of 713–2,854 pairs of mothers/guardians and children as adequate. From each household, children under five and their legal guardians were selected for inclusion in the study. In this study, the sampling unit was a cell. To obtain a representative sample, the study used a two-stage probabilistic sampling method. The first stage involved the random selection of cells from the sector and the second stage involved a systematic sampling of households from the selected cells. Approval to conduct the study was granted by the University of Global Health Equity Institutional Review Board (UGHE-IRB: Ref: UGHE-IRB/2022/034). Furthermore, legal guardians of children were asked for consent, and this was provided in writing. The dependent variable in this study was stunting, and this was a categorical binary variable (yes = 1 or no = 0). Stunting was defined as height for age z-score <-2 standard deviations using the WHO growth standards (12). Furthermore, using WHO classifications, children with height for age z-score of ≤-2 standard deviations and ≥-3 standard deviation were classified as moderately stunted while those with height for age z-score <-3 standard deviations were classified as severely stunted (13). There were three levels of the independent variables. These were categorized as individual (child and maternal/household) characteristics, community and environmental factors which included topography of the area, water, hygiene, and sanitation variables. To collect this information, a structured pre-tested questionnaire was administered to mothers/legal guardians of the children who had been included in the study. The questionnaire collected information on the child's age, sex, maternal/guardian's age, level of education, socio-economic class also called Ubudehe, breastfeeding and complementary feeding practices, hygiene and handwashing practices, household water availability and access, availability, and types of sanitary facilities, and socio-economic characteristics of the household. Additionally, information about the guardian and child's illnesses and disabilities (yes = 1 or no = 0) was collected. The classification of Ubudehe in Rwanda has been explained further in Supplementary material 1. The weight of the children was measured using the SECA electronic scales to a precision of 0.1 kg while the height was taken to the nearest 0.1 cm using a UNICEF height/length board. To measure the height, children between the age of 24–59 months were made stand-upright without shoes and their height was taken using a stadiometer in a Frankfurt vertical position and to the nearest 0.1 cm. For children aged 0–23 months, their height/length was taken using a vertical measuring board while in a horizontal position. Before the measurements, it was ensured that the head, shoulders, and buttocks touched the board. To ascertain the validity of the anthropometry measurements, duplicate measurements were done for 10–15% of the sample and the variations for the duplicate measurements were below 5%. The age of the children was obtained from the Ifishi Y'Ubuzima Bw'umwana (vaccination card). The study included children aged between 0 and 59 months who were attending routine hospital outpatient visitations. Furthermore, the study included those without medical complications and those whose legal guardians consented to participate and signed the consent forms. All children in this age category but not fulfilling the inclusion criteria were excluded from the study. To enhance the precision of the measurements, the SECA weighing scales were calibrated daily before the commencement of data collection. All data collectors were trained in the taking of child anthropometric measurements and administration of the face-to-face questionnaire interviews before data collection. Community health workers who were part of the data collection teams assisted with the taking of anthropometric measurements on all children. For children who could not be weighed on the SECA scale, the weight of the mother/legal guardian was initially taken. Thereafter, the weight of the mother/legal guardian while holding the child was taken. The difference between the two weights was taken as the weight of the child. Descriptive analysis was used to summarize continuous and categorical variables, showing their distribution with the outcome variable. The Z-score value for height-for-age was calculated using the ANTHRO PLUS software (14). In the bivariate and multivariate analysis, the response variable, stunting, was turned into a binary variable thus allowing us to logistic models. To determine the relationship between the various individual, community and environmental factors, a bivariate analysis was used. A multivariate multilevel logistic regression was used to examine the individual, community and environmental factors associated with under-five stunting. The multilevel models were deemed suitable for the analysis because of the hierarchical structure of the data and its ability to allow for the determination of the residual components associated with each level of the hierarchy. Furthermore, the multilevel models also allow for the estimation of group-level variables while estimating the group effects. Three models were fit in the overall analysis. The first model was a null model, and this included the response variable only without any predictor variable and this was done to estimate its variance. In the second model which was a fixed effects model, we controlled for individual-level variables, and this included the children's demographic characteristics, history of diarrhea, breastfeeding and complementary feeding practices and child morbidity. In this model, district and sector were added as random intercept terms. Maternal (legal guardian) variables included education level and feeding structure, age and morbidity and water, hygiene, and environmental variables such as sanitation practices were also included. District and place of residence were added as random effects. The final model included both individual and contextual level factors which were the place of residence and district. The results demonstrating measures of association have been presented as adjusted odds ratios (aOR) together with their corresponding 95% confidence intervals (CIs) and p-values. The intraclass correlation coefficient (ICC), median odds ratio (MOR) and proportional change in variance (PCV) were used as a measure of the random effect. The ICC, which shows the proportion of total variance in the outcome attributable to districts, sectors and cells was calculated as shown by Merlo et al. (15). MOR is the measure of heterogeneity, and the PVC is the measure of the total variation of stunting in the final model (models with individual and environmental variables) comparative to the null model and was determined as described elsewhere (16, 17). Data analysis was carried out using StataSE STATA version 17 (StataCorp, College Station, TX, USA).

• kalinda, chester
• phri, million
• qambayot, maria albin
• ishimwe, marie consolatrice sage
• gebremariam, alemayehu digssie
• bekele, abebe
• wong, rex