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Household food insecurity (HFI) plays an important role in child malnutrition in many low-income countries. We determined the association between HFI and stunting and severe stunting among Rwandan children from the Gicumbi district, aged 6–59 months using a cross-sectional study of 2,222 children. HFI factor was calculated by summing all seven HFI (access) frequency questions and was categorised into food security, mildly food insecurity, moderately food insecurity, and severe food insecurity. The association between stunting, severe stunting, and HFI was determined using the multiple logistic regression analyses that adjust for clustering and sampling weights. The odds of moderate and severe HFI were significantly higher among stunted children aged 6–59 months than those who were not stunted (adjusted odds ratio [AOR] = 1.43; 95% confidence interval [CI] [1.11, 1.84] and AOR = 1.35; 95% CI [1.08, 1.69], respectively). Children from households with moderate food insecurity were 2.47 times more likely to be severely stunted (AOR = 2.47; 95% CI [1.77, 3.46]), and those from households with severe food insecurity were more likely to be severely stunted (AOR = 1.82; 95% CI [1.34, 2.48]), compared with children aged 6–59 months from households with food security. Other factors included male children and children who did not attend monthly growth monitoring sessions. This study showed that moderate and severe HFI correlated with stunting and severe stunting. Interventions to improve stunting in Gicumbi children should also focus on male children, children who did not attend monthly growth monitoring sessions, and households with moderate and severe food insecurity.
Gicumbi district is located in the northern province of Rwanda closer to the border with Uganda. Gicumbi district comprises 21 sectors, 109 cells, and 630 villages (Imidugudu). The population is more of rural than urban. The topography of Gicumbi is more of steep slopes and mountainous but surrounded by steep ravines with small valleys segmented by multiple swamps. A cross‐sectional study was conducted during harvest period, from January 21 to 31, 2016, in Gicumbi district covering 32 villages as part of World Vision Rwanda’s funding service agreement to generate evidence to influence maternal and child health programmes. The study population shared similar characteristics (homogeneous, i.e., all household from a low socio‐economic group). The respondents were enrolled in a Maternal Newborn Child Health intervention at the household level with the specific criteria for household inclusion being a presence of a pregnant woman or breastfeeding mother. The sampling frame produced by the 2010 Rwanda Population and Housing Census projection was used in the sampling process of the survey (RDHS, 2010; Rurangirwa, Mogren, Nyirazinyoye, Ntaganira, & Krantz, 2017). The survey sample was selected in two stages. In the first stage, a total of 20 villages (clusters) were selected from the cells. In the second stage, 32 households were randomly selected in each selected villages (clusters). All selected villages were visited, and none was replaced, regardless of reason(s) encountered or given. The total sample of the survey consists of 20 clusters. All 660 (including nonresponse rate) households completed the mother’s/caregiver interviews, yielding a response rate of 100%. The high response rate for this survey was because before conducting the interview, World Vision Rwanda mobilised the local leaders, community health workers, and team leaders of community health workers for the survey. For reporting district‐level results, sample weights will be used, and sampling weight was calculated by the product of the reciprocal of the sampling fractions employed in the selection of cells and villages. For the analysis to be achieved, it is important to calculate the required sample size that will be enough to detect any statistical difference. We estimate that this sample has 90% power and alpha level of 5%, to detect an odds ratio (OR) of at least 1.6, assuming an alpha level of 5%, prevalence of <4 times antenatal care (ANC) of 55% (Rurangirwa et al., 2017), a design effect of 3.2 (based on the average of 32 children per cluster and expected relative difference of about 10%) and a total sample of about 664 households is required for the study, and we consider this sufficient statistical power to examine differences in <4 times ANC that would be of public health significance. The questionnaires that were used in the survey included household information, which was used to collect information on household members (usual residents), and women's questionnaire administered to mothers or caretakers for all children under 5 years. The women or caretaker's questionnaire included the women or caretaker's demographic characteristics: antenatal, delivery, and post‐natal care, breastfeeding, and child nutrition. The questionnaires were installed on tablets using the Open Data Kit. Open Data Kit is a suite tool that allows data collection using mobile devices and data submission to an online server. World Vision office in Kigali provided the tablets that were used in the data collection exercise. Data were posted daily after fieldwork, and this enabled daily review of work done to check for inconsistencies and errors. In the child nutrition questionnaire, measurements of height were obtained for children under the age of five in all of the selected households. Each enumerator carried a scale and measuring board. Measurements were made using lightweight SECA scales (with digital screens) designed and manufactured under the authority of the UNICEF. The measuring boards employed were specially made by Shorr Productions for use in survey settings. Children under the age of 2 were measured lying down on the board (recumbent length), and standing height was measured for all other children. The primary outcome variables were stunting and severe stunting. The outcome variables were expressed as a dichotomous variable, that is, Category 0 (not stunted [greater than −2 standard deviations {SDs} of the WHO Child Growth Standards median] or not severely stunted [greater than −3 SD]) and Category 1 (stunted [less than −2 SD] or severely stunted [less than −3 SD]). The household food security tool consists of seven questions, which are aimed at extracting information required for defining the household's food security status. The responses are “rarely,” “something,” or “often” or “rarely” in the past 12 months. The HFI factor was calculated by summing all the seven HFI (access) frequency questions with scores ranging from 0 to 21. The households were also categorised into four groups, such as food secure (0), mildly food insecure (1–2), moderately food insecure (3–10), and severely food insecure (more than 10; Swindale & Bilinsky, 2007). Our choice of potential confounding factors was based on similar studies that examined the relationship between stunting and severe stunting by food security status in developing countries (Ali et al., 2013; Ali Naser et al., 2014; Singh, Singh, & Ram, 2014). These potential confounders were classified into four distinct groups: socio‐economic and demographic (sectors, primary caregiver, education level, marital status, and household wealth index); child (sex of baby and child's age in months); maternal and child health (ANC, duration of breastfeeding, and attended child monthly growth monitoring sessions); and health services and environmental factors (quality of care from health services, place of delivery, water available all year, sources of drinking water, and type of toilet facility). The household wealth index variable measures basic household needs for all children 5–18 years. The household wealth index was constructed by assigning weights to three basic household needs for children 5–18 years (i.e., difficulty providing at least two sets of clothes for all children aged 5–18 years living in the household, difficulty providing a pair of shoes for all children aged 5–18 years living in the household, and difficulty paying school fees or school contribution for all children aged 5–18 years living in the household) using the principle components analysis. The household wealth index was divided into three categories (poorest, middle and least poor; Filmer & Pritchett, 2001), and improved and unimproved sources of drinking water and type of toilet facility were categorised based on the WHO and UNICEF Joint Monitoring Programme guidelines (WHO/UNICEF, 2014). Data analysis was performed using the survey (SVY) commands of Stata version 14.1 (Stata Corp, College Station, TX, USA), which adjust for sampling weights and cluster sampling design and the calculation of standard errors. Preliminary analyses involved percentage and frequency count of all selected characteristics; this was followed by estimation of prevalence of stunting and severe stunting by HFI among children aged 6–59 months. The Taylor series linearization method was used in the surveys when estimating 95% confidence intervals (CIs) around prevalence estimates. Survey logistic regression that adjusted for cluster and survey weights was used to determine the association between HFI and stunting and severe stunting among Rwandan children aged 6–59 months. First, univariate binary logistic regression analysis was performed to examine the unadjusted OR. A staged modelling technique was employed for the multiple logistic regression analyses. In the first stage, the socio‐economic and demographic factors were entered into the baseline multiple logistic regression model to examine their association with the study outcome. After that, a manual elimination process was performed, and variables that were associated with the study outcomes were retained in the model. Second, child factors were added into significant model retained in the first stage. In the third and fourth stages, maternal and child's health factors and health services and environmental factors were added to the significant variable retained in the second stage. As before, those factors with p values <0.05 were retained. In the final stage of the analysis, the main study factor (HFI) was added to the significant variables obtained from the third and fourth stages, and variables with a p value <0.05 were retained in the final. The ORs and their 95% CIs obtained from the adjusted multiple logistics model were used to determine the association of HFI fuels on stunting and severe stunting.
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