Co-morbidity of malnutrition with falciparum malaria parasitaemia among children under the aged 6-59 months in Somalia: a geostatistical analysis.

BACKGROUND: Malnutrition and malaria are both significant causes of morbidity and mortality in African children. However, the extent of their spatial comorbidity remains unexplored and an understanding of their spatial correlation structure would inform improvement of integrated interventions. We aimed to determine the spatial correlation between both wasting and low mid upper arm circumference (MUAC) and falciparum malaria among Somalian children aged 6-59 months. METHODS: Data were from 49 227 children living in 888 villages between 2007 to 2010. We developed a Bayesian geostatistical shared component model in order to determine the common spatial distributions of wasting and falciparum malaria; and low-MUAC and falciparum malaria at 1 × 1 km spatial resolution. RESULTS: The empirical correlations with malaria were 0.16 and 0.23 for wasting and low-MUAC respectively. Shared spatial residual effects were statistically significant for both wasting and low-MUAC. The posterior spatial relative risk was highest for low-MUAC and malaria (range: 0.19 to 5.40) and relatively lower between wasting and malaria (range: 0.11 to 3.55). Hotspots for both wasting and low-MUAC with malaria occurred in the South Central region in Somalia. CONCLUSIONS: The findings demonstrate a relationship between nutritional status and falciparum malaria parasitaemia, and support the use of the relatively simpler MUAC measurement in surveys. Shared spatial distribution and distinct hotspots present opportunities for targeted seasonal chemoprophylaxis and other forms of malaria prevention integrated within nutrition programmes.

Conflict in Somalia: impact on child undernutrition.

INTRODUCTION: In Somalia, protracted conflict and drought have caused population displacement and livelihood destruction. There is also widespread childhood undernutrition. We aimed to determine the independent effects of conflict on wasting and stunting among children aged 6-59 months nationwide in Somalia. METHODS: Data were from household surveys during 2007-2010, including 73 778 children in 1066 clusters, the Armed Conflict Location and Event Data project database and remote sensing. We used Bayesian hierarchical spatial-temporal regression to examine the effects of conflict on wasting and stunting. Models included individual, household and environmental covariates and recent (<3 months) or longer term (3-12 months) conflict events. RESULTS: 15 355 (21%) and 22 739 (31%) observations were from wasted and stunted children, respectively. The conflict was associated with undernutrition independently of the individual, household and environmental factors, and its inclusion improved model performance. Recent conflict was associated with wasting (OR 1.37, 95% credible interval (CrI): (1.33, 1.42) and attributable fraction (AF) 7.6%)) and stunting (OR 1.21, 95% CrI (1.15, 1.28), AF 6.9%). Longer term conflict had greater effects on wasting (OR 1.76, 95% CrI (1.71, 1.81), AF 6.0%) and stunting (OR 1.88, 95% CrI = (1.83, 1.94), AF 7.4%). After controlling for conflict, the harmful effect of internal displacement and protective effects of rainfall and vegetation cover on undernutrition were enhanced. CONCLUSION: Conflict and internal displacement have large effects on undernutrition in ways not fully captured by simply measuring individual, household and environmental factors or drought.

Modelling the Ecological Comorbidity of Acute Respiratory Infection, Diarrhoea and Stunting among Children Under the Age of 5 Years in Somalia.

The aim of this study was to assess spatial co-occurrence of acute respiratory infections (ARI), diarrhoea and stunting among children of the age between 6 and 59 months in Somalia. Data were obtained from routine biannual nutrition surveys conducted by the Food and Agriculture Organization 2007-2010. A Bayesian hierarchical geostatistical shared component model was fitted to the residual spatial components of the three health conditions. Risk maps of the common spatial effects at 1×1 km resolution were derived. The empirical correlations of the enumeration area proportion were 0.37, 0.63 and 0.66 for ARI and stunting, diarrhoea and stunting and ARI and diarrhoea, respectively. Spatially, the posterior residual effects ranged 0.03-20.98, 0.16-6.37 and 0.08-9.66 for shared component between ARI and stunting, diarrhoea and stunting and ARI and diarrhoea, respectively. The analysis showed clearly that the spatial shared component between ARI, diarrhoea and stunting was higher in the southern part of the country. Interventions aimed at controlling and mitigating the adverse effects of these three childhood health conditions should focus on their common putative risk factors, particularly in the South in Somalia.

Environmental predictors of stunting among children under-five in Somalia: cross-sectional studies from 2007 to 2010.

BACKGROUND: Stunting among children under five years old is associated with long-term effects on cognitive development, school achievement, economic productivity in adulthood and maternal reproductive outcomes. Accurate estimation of stunting and tools to forecast risk are key to planning interventions. We estimated the prevalence and distribution of stunting among children under five years in Somalia from 2007 to 2010 and explored the role of environmental covariates in its forecasting. METHODS: Data from household nutritional surveys in Somalia from 2007 to 2010 with a total of 1,066 clusters covering 73,778 children were included. We developed a Bayesian hierarchical space-time model to forecast stunting by using the relationship between observed stunting and environmental covariates in the preceding years. We then applied the model coefficients to environmental covariates in subsequent years. To determine the accuracy of the forecasting, we compared this model with a model that used data from all the years with the corresponding environmental covariates. RESULTS: Rainfall (OR = 0.994, 95 % Credible interval (CrI): 0.993, 0.995) and vegetation cover (OR = 0.719, 95 % CrI: 0.603, 0.858) were significant in forecasting stunting. The difference in estimates of stunting using the two approaches was less than 3 % in all the regions for all forecast years. CONCLUSION: Stunting in Somalia is spatially and temporally heterogeneous. Rainfall and vegetation are major drivers of these variations. The use of environmental covariates for forecasting of stunting is a potentially useful and affordable tool for planning interventions to reduce the high burden of malnutrition in Somalia.

Space-time mapping of wasting among children under the age of five years in Somalia from 2007 to 2010.

OBJECTIVE: To determine the sub-national seasonal prevalence and trends in wasting from 2007 to 2010 among children aged 6-59 months in Somalia using remote sensing and household survey data from nutritional surveys. METHODS: Bayesian hierarchical space-time model was implemented using a stochastic partial differential equation (SPDE) approach in integrated nested Laplace approximations (INLA) to produce risk maps of wasting at 1 × 1 km(2) spatial resolution and predict to seasons in each year of study from 2007 to 2010. RESULTS: The prevalence of wasting was generally at critical levels throughout the country, with most of the areas remaining in the upper classes of critical and very critical levels. There was minimal variation in wasting from year-to-year, but a well-defined seasonal variation was observed. The mean difference of the prevalence of wasting between the dry and wet season ranges from 0% to 5%. The risks of wasting in the South Central zone were highest in the Gedo (37%) and Bay (32%) regions. In North East zone the risk was highest in Nugaal (25%) and in the North West zone the risk was high in Awdal and Woqooyi Galbeed regions with 23%. CONCLUSION: There was a clear seasonal variation in wasting with minimal year-to-year variability from 2007 to 2010 in Somalia. The prevalence was high during the long dry season, which affects the prevalence in the preceding long rainy season. Understanding the seasonal fluctuations of wasting in different locations and at different times is important to inform timely interventions.