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Background: Under-five children born in a fragile and war-affected setting of South Sudan are faced with a high risk of death as reflecting in high under-five mortality. In South Sudan health inequities and inequitable condition of daily living play a significant role in childhood mortality. This study examines factors associated with under-five mortality in South Sudan. Methods: The study population includes 8125 singleton, live birth, under-five children born in South Sudan within 5 years prior to the 2010 South Sudan Household Survey. Factors associated with neonatal, infant and under-five deaths were examined using generalised linear latent and mixed models with the logit link and binomial family that adjusted for cluster and survey weights. Results: The multivariate analysis showed that mothers who reported a previous death of a child reported significantly higher risk of neonatal (adjusted OR (AOR)=3.74, 95% confidence interval (CI 2.88 to 4.87), P<0.001), infant (AOR=3.19, 95% CI (2.62 to 3.88), P<0.001) and under-five deaths (AOR=3.07, 95% CI (2.58 to 3.64), P<0.001). Other associated factors included urban dwellers (AOR=1.37, 95% CI (1.01 to 1.87), P=0.045) for neonatal, (AOR=1.35, 95% CI (1.08 to 1.69), P=0.009) for infants and (AOR=1.39, 95% CI (1.13 to 1.71), P=0.002) for under-five death. Unimproved sources of drinking water were significantly associated with neonatal mortality (AOR=1.91, 95% CI (1.11 to 3.31), P=0.02). Conclusions: This study suggested that the condition and circumstances in which the child is born into, and lives with, play a role in under-five mortality, such as higher mortality among children born to teenage mothers. Ensuring equitable healthcare service delivery to all disadvantaged populations of children in both urban and rural areas is essential but remains a challenge, while violence continues in South Sudan.
We used a data set collected during the 2010 South Sudan Household Health Survey second round (SSHHSII), which is a nationally representative, stratified, cluster sample survey, covering the 10 states of South Sudan. The survey was largely based on the Unicef’s Multiple Indicator Cluster Survey (MICS) methodology.21 It aimed to collect health and related indicators essential for identifying the health needs of women and children, and for establishing priorities for evidence-based planning, decision-making and reporting. The SSHHSII comprised a general questionnaire to collect basic demographic information on all household members, with three individual questionnaires addressed to specific target groups: women and men aged 15–49 years and under-five children. The individual questionnaire was used to collect information on reproductive history, use of family planning, information about child health indicators and other health-related issues. The questionnaire for under-five children was administered to mothers or caretakers of children under 5 years of age.21 A two-stage cluster sampling design was employed for the selection of the sample in each of the 10 states of South Sudan. The first stage consisted of the selection of the required number of enumeration areas separately by urban and rural strata. Systematic probability proportional to size sampling procedure was used for the selection of 40 enumeration sites in each of the 10 states of South Sudan. The second stage was the selection of the total number of households in each cluster using random systematic selection procedures to select on average 25 households in each enumeration area. From the selected households, a total sample of 9369 households were interviewed with information from 9069 ever-married women, and 4344 men aged 15–49 years, and information from 8338 under-five children collected from their mother/caretaker yielding a response rate of 83%. The details of the SSHHSII sampling method have been reported elsewhere.21 Our study population consisted of 9125 (8125 weighted) singleton live-born children under the age of 5 years, who were born within 5 years prior to the survey. We excluded multiple pregnancies (n=303) in this analysis because of higher risk of newborn death, as the result of preterm birth and pregnancy complications among this group compared with singleton pregnancies.22 We modified and used the conceptual framework developed by WHO14 as a guide in identifying the key social determinants of health inequalities and their impact on the well-being of under-five children in this study. According to this framework, a set of the social economic positions, such as education, income, occupation, gender and social class, is shaped by the structural social, economic and political context.14 Furthermore, these socioeconomic positions influence an individual’s health and well-being through more specific factors called intermediate factors such as material circumstances, behaviours, biological factors and health services. According to the framework, we identified 26 possible determinants and predictors of under-five mortality in South Sudan based on the available information from the 2010 SSHHSII data sets. Figure 1 presents the modified conceptual framework used in this analysis. Conceptual framework for factors associated with under-five mortality, adapted from the WHO social determinants of health inequalities. The outcome variable for this analysis was neonatal, infant and under-five mortality expressed in a binary form (0 for living child and 1 for a child death). Neonatal mortality is defined as the probability of dying in the first month of life (0 to 28 days), infant mortality is the probability of dying between birth and first birthday (0 to <12 months) and under-five mortality is the death of a child under the age of 5 years (0 to <60 months). We obtained information on under-five deaths collected from the birth history section of the questionnaire administered to individual female respondents aged 15–49 years, who had ever given birth during the 5-year period prior to the survey. The under-five mortality rate was estimated directly from the information on the birth history using the child’s date of birth, date of interview and age at death. We calculated the mortality rate for this analysis as the number of children dying during each age period (neonatal, infant and under-five) per 1000 live births in a given year. The independent variables for this analysis were categorised based on the WHO conceptual framework. At the socioeconomic position, 14 distal factors were identified and classified as follows: (1) community factors consisting of cluster type and region (representing the characteristics of a cluster); the mean household wealth index (representing economic status); the proportion of mothers with at least intermediate education (representing maternal factors); and the mean number of antenatal care visits, percentage of mothers receiving postnatal care and the percentage of deliveries assisted by skilled birth attendants in the cluster (representing community access to maternal health services); and (2) household factors including household wealth, the gender and education of the household head, maternal literacy and education, maternal marital status and polygamy status. The entire list of the independent variables with their definitions and the categories can be found in the online supplementary material. bmjgh-2017-000510supp001.pdf In this analysis, we constructed the household wealth index variable from an inventory of 24 household facilities and assets (such as the material of the dwelling floor, roof and walls; the number of persons per sleeping room; the fuel used for cooking; main source of drinking water; availability of electricity; ownership of radio, television, mobile phone, telephone, refrigerator and watch; ownership of transport devices, such as bicycles, motorcycles/scooters, animal-drawn carts, cars/trucks, and boats; the source of drinking water and type of sanitation facility; ownership land) using principal components analysis to weight the contribution of the items to the index.23 This index was divided into three categories: the bottom one-third of households that were referred to as poor households, the next one-third as the middle-level households and the top one-third as the wealthier households. At the proximal individual’s circumstances/conditions, nine factors were identified and categorised according to: (1) maternal conditions/behaviours including maternal age at childbirth, ever had a child who later died, cooking location, garbage disposal, ever heard of family planning and mother’s experience of domestic violence; and (2) under-five conditions including the child’s gender, access to improved sanitation facilities and access to improved source of drinking water. Unimproved source of water consisted of unprotected wells and springs; unfiltered water from rivers, streams, dams and hafirs; water transported by tankers/carts; and bottled water from unimproved source. Improved source of drinking water consisted of piped water (into dwelling, compound, yard or plot, to neighbour, public tap/standpipe), tube wells/boreholes, protected wells, protected springs, bottled water and water transported by tankers/carts from improved source. All respondents to the survey provided verbal informed consent; consent for children was obtained through parents, caregivers or guardians when data were originally collected. In 2013, the first author requested for data access from the director of Health Social and Demographic Statistics and from the Ministry of Health of South Sudan, and access was granted to use the data for research. Currently, the data are available from MICS website (http://mics.unicef.org/surveys). Preliminary analyses were conducted by producing frequency tabulations of all the selected characteristics examined in this study. The preliminary analyses were carried out using STATA/MP V.12 (StataCorp, College Station, TX, USA).24 The ‘Svy’ survey commands were used to allow for adjustments for the cluster sampling design and sampling weights. This was followed by calculating neonatal, infant and under-five mortality rate using a method similar to that described by Rutstein and Rojas.25 Univariable and multivariable logistic regression generalised linear latent and mixed models with the logit link and binomial family25 that adjusted for cluster and survey weights were used to identify those factors associated with neonatal, infant and under-five mortality. Univariable logistic regression was conducted to determine the unadjusted ORs of the study factors for neonatal, infant and under-five mortality. In the multivariable logistic regression analysis, a three-stage hierarchical model based on a conceptual framework described by Victora et al26 was performed in this analysis. According to this approach, the effect of distal variables could be assessed without inappropriate adjustment by proximate or intermediate variables that could be mediators of the effects of more distal variables.26 In the first-stage model (model 1), all the distal socioeconomic community factors were entered into the model and this was followed by manually executed backward elimination process. Only variables associated with the outcome were retained (model 1). In the second-stage model (model 2), the significant factors (P<0.05) in model 1 were added to socioeconomic (household) level factors and this was followed by a backward elimination procedure but retaining all the significant factors from model 1. In the third and final-stage model (model 3), the individual (maternal and child condition and circumstance) factors were added into model 2 and those variables with P<0.05 in model 3 were retained in the final model including all factors from model 2. The ORs and their 95% CIs obtained from the adjusted multiple logistics model were used to measure the factors associated with neonatal, infant and under-five mortality.
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