Childhood undernutrition in three disadvantaged East African Districts: A multinomial analysis

Background: Undernutrition is an important public health indicator for monitoring nutritional status and survival. In spite of its importance, undernutrition is a significant problem health problem in many East African communities. The aim of this study was to identify factors associated with childhood undernutrition in three disadvantaged East African Districts. Methods: We examined data for 9270 children aged 0-59 months using cross-sectional survey from Gicumbi District in Rwanda, Kitgum District in Uganda and Kilindi District in Tanzania. We considered the level of undernutrition (stunting, wasting and underweight) as the outcome variables with four ordinal categories (severely undernourished, moderately undernourished, mildly undernourished, and nourished). Generalized linear latent and mixed models (GLLAMM) with the mlogit link and binomial family that adjusted for clustering and sampling weights were used to identify factors associated with undernutrition among children aged 0-59 months in three disadvantaged East African Districts. Results: After adjusting for potential confounding factors, the odds of a child being stunted were higher in Gicumbi District in Rwanda while the odds of a child being wasted and underweight were higher in Kitgum District in Uganda. Having diarrhoea two weeks prior to the survey was significantly associated with severe undernutrition. Wealth index (least poor household), increasing child’s age, sex of the child (male) and unavailability of water all year were reported to be associated with moderate or severe stunting/wasting. Children of women who did not attend monthly child growth monitoring sessions and children who had Acute Respiratory Infection (ARI) symptoms were significantly associated with moderate or severe underweight. Conclusions: Findings from our study indicated that having diarrhoea, having ARI, not having water availability all year and not attending monthly child growth monitoring sessions were associated with undernutrition among children aged 0-59 months. Interventions aimed at improving undernutrition in these disadvantaged communities should target all children especially those children from households with poor sanitation practices. Gicumbi district is situated in the Northern Province of Rwanda and has a population of 395,606 residents. Gicumbi District covers 21 sectors, 109 cells, 630 villages (Imidugudu). It has 23 health centres, 1 district hospital and 1 prison clinic. Rwanda has a health development strategy based on decentralized management and district-level care [10]. Kilindi district is located in the northern zone of Tanzania with a population of 236,833 residents. Kilindi District comprises of 16 rural wards and 102 villages. It has 30 dispensaries, 3 health centres and one hospital [11]. Tanzania has a hierarchical health system made up of the dispensaries found in every village, health centres at the ward level, district hospital at the district level, the regional referral hospital at the regional level, the zone hospitals at the tertiary level and the national hospital at the national level. There are also some specialized hospitals which do not fit directly into this hierarchy and therefore are directly linked to the ministry of health [12]. Kitgum district is located in the northern region of Uganda and has a population of 247,800 residents. It comprises 51 parishes and 437 village councils [13]. Kitgum district has 2 hospitals and 23 health centres; 21 are government owned while 4 are owned by non -government Organizations. Health services delivery in Uganda is decentralized within national, districts and health sub-districts levels with referral hospitals at the national level and health centres at the district and sub-district levels [14]. The sample was in two stages. In the first stage, a total of 20 villages (clusters) were selected from cells for Gicumbi, wards for Kilindi and Parishes for Kitgum. In the second stage, 32 households were randomly selected in each selected villages (clusters). The detailed sampling procedure for Gicumbi in Rwanda has been reported elsewhere [15]. For 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 for Gicumbi, wards for Kilindi and Parishes for Kitgum). For the combined analysis of the three datasets, we re-normalised our sampling weights by computing the total sum of weights for each district and divide each district survey sampling weights with the total sum of weights. Our dataset was obtained from a survey conducted during the harvest period, from 21st– 31st of January, 2016 in Gicumbi district in Rwanda, Kitgum district in Uganda and Kilindi district in Tanzania. The survey was commissioned as part of World Vision Rwanda, Uganda and Tanzania funding service agreement to generate evidence to influence maternal and child health programmes which aimed to reach 36,250 disadvantaged beneficiaries in these East African districts. The Maternal Newborn Child Health (MNCH) Project aimed to collect health and related indicators to identify the health needs of women and children and to establish priorities for evidence-based planning, decision-making in these regions. The program was an opportunity for World Vision to embed knowledge and action of the organisation’s ‘7–11’ interventions for maternal and child survival in the Region [16]. World Vision uses the 7–11 approach to prevent maternal and child mortality and morbidity through 7 key interventions for a mother and 11 interventions for the child. The intervention for the mother are: diet, deworming and iron supplements, prevention of infectious diseases, malaria prevention and treatment, appropriate pregnancy spacing, birth preparedness, and access to antenatal and postnatal maternity services. The 11 interventions for the child are appropriate breastfeeding, newborn care, timely complementary feeding, age-appropriate immunisation, sufficient iron intake, consistent hand washing prevention and treatment for acute malnutrition, prevention and treatment of malaria, and acute respiratory infection. Others are timely administration of oral rehydration therapy to treat diarrhoea, prevention and care for pediatric Human Immunodeficiency Virus (HIV), and timely deworming [17]. The nutritional status of children under five years of age was measured anthropometrically. We considered height-for-age (stunting), weight-for-height (wasting) and weight-for-age (underweight). The height-for-age index is an indicator of linear growth retardation and cumulative growth deficits in children, Weight-for-height index measures body mass in relation to height and reflects the current nutritional status of the child. Weight-for-age takes into account both acute malnutrition (wasting) and chronic malnutrition (stunting), but it does not distinguish between stunting and wasting. The index is calculated using growth standards published by WHO in 2006. These growth standards were generated through data collected in the WHO Multicentre Growth Reference Study and expressed in standard deviation units from the Multicentre Growth Reference Study median [18]. Child undernutrition status was categorized into four categories – severe undernutrition (< − 3.0 Z-score), moderate undernutrition (− 3.0 to − 2.0 Z-score) and mild undernutrition (− 2.0 to − 1.0 Z-score) and proper nutrition (≥ − 1.0 Z-score). The level of childhood undernutrition (stunting, wasting and underweight) was considered as the outcome variables with four ordinal categories (severely undernourished, moderately undernourished, mildly undernourished, and nourished). The potential confounding factors were organised into four distinct groups: Socio-economic and demographic (Districts, primary caregiver, education level, marital status, household wealth index and food security); child (fever in two weeks preceding each survey, acute respiratory infection (ARI) in two weeks prior to each survey, diarrhoea in the two weeks preceding each survey, sex of baby and child’s age in months); maternal and child health (antenatal care and attended child monthly growth monitoring sessions); 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 principal components analysis. The household wealth index was divided into three categories: poorest, middle and least poor [19]. Improved and unimproved sources of drinking water and type of toilet facility were categorised based on the WHO and UNICEF Joint Monitoring Programme guidelines [20]. Birth order and number of children per household were not collected. Higher birth order and number of children per household may result in higher susceptibility to undernutrition due to impact on income. Re-normalised weight was used for Survey (SVY) tabulation that adjusts for clustering, and sampling weights were used to determine the percentage and frequency count of all selected characteristics and, district-specific weights were used for the Taylor series linearization method in the surveys when estimating 95% confidence intervals around prevalence estimates of undernutrition by severity in each district. Multivariate multinomial logistic regression model guided by the conceptual framework was used to determine factors associated with childhood undernutrition (stunting, wasting and underweight) with nourished children used as reference category. In the multivariate analyses, a four-stage model was carried out, in the first stage model, socio-economic and demographic factors were entered into the model, and a stepwise backward elimination method was used to remove the non-significant factors (p > 0.05). In the second stage model, the significant factors in the first stage model were added to the child level factors, and this was followed by another stepwise backward elimination procedure which retained all the significant factors. A similar procedure was employed for the third stage model which included the individual (maternal and child) level factors as well as health services factors and the final stage model which introduced environmental factors. After completion of all four modelling stages, the factors that were significantly associated with the outcomes were retained. All statistical analyses were conducted using STATA/MP Version.14.1 (StataCorp, College Station, Texas, USA) and adjusted odds ratios (AORs) and their 95% confidence intervals (CIs) obtained from the adjusted multivariate multinomial logistic regression model were used to measure the factors associated with childhood undernutrition.