| Title |
Environmental predictors of stunting among children under-five in Somalia: cross-sectional studies from 2007 to 2010
|
|---|---|
| Published in |
BMC Public Health, July 2016
|
| DOI | 10.1186/s12889-016-3320-6 |
| Pubmed ID | |
| Authors |
Damaris K. Kinyoki, James A. Berkley, Grainne M. Moloney, Elijah O. Odundo, Ngianga-Bakwin Kandala, Abdisalan M. Noor |
| Abstract |
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. 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. 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. 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. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 195 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sudan | 1 | <1% |
| Unknown | 194 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 35 | 18% |
| Lecturer | 21 | 11% |
| Student > Bachelor | 19 | 10% |
| Researcher | 17 | 9% |
| Student > Ph. D. Student | 12 | 6% |
| Other | 25 | 13% |
| Unknown | 66 | 34% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 38 | 19% |
| Social Sciences | 22 | 11% |
| Medicine and Dentistry | 17 | 9% |
| Agricultural and Biological Sciences | 8 | 4% |
| Environmental Science | 8 | 4% |
| Other | 27 | 14% |
| Unknown | 75 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 August 2017.
All research outputs
#14,857,703
of 22,881,964 outputs
Outputs from BMC Public Health
#10,943
of 14,922 outputs
Outputs of similar age
#226,171
of 365,664 outputs
Outputs of similar age from BMC Public Health
#264
of 349 outputs
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