Mapping Geographical Differences and Examining the Determinants of Childhood Stunting in Ethiopia: A Bayesian Geostatistical Analysis.
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SUBMITTER: Ahmed KY
PROVIDER: S-EPMC8234472 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The prevalence of stunting in Ethiopia declined from 57% in 2000 to 44% in 2011, yet the factors producing this change are not fully understood. Data on 23,999 children 0-59 months of age from three Demographic and Health Surveys (DHS) from 2000, 2005, and 2011 were analyzed to assess the trends in stunting prevalence, mean height-for-age z-scores (HAZ) and the associations between potential factors and HAZ. Associations were determined separately using three separate generalized linear models for children age less than 6?months, 6-23 months, and 24-59 months of age. Significant variables were then analyzed to determine if they showed an overall trend between the 2000 and 2011 surveys. In children?<?6?months of age, only mother's height was both a significant predictor of HAZ and showed a progressive, albeit non-significant, increase from 2000 to 2011. In children 6-23 months of age, only mother's use of modern contraception showed substantial changes in a direction consistent with improving HAZ, but improvements in maternal nutrition status were observed from 2000 to 2005. For children 24-59 months of age a consistent and progressive change is seen in child's diarrhea, fever, mother's education, and the occurrence of open defecation. Our analysis demonstrated that factors associated with HAZ vary by child's age and the dominant livelihood practice in the community. Variables that could have contributed to the decline of stunting in Ethiopia in children less than 5?years of age include markers of child health, mother's nutritional status, mother's educational level, and environmental hygiene.
| S-EPMC6866086 | biostudies-literature
Project description:BackgroundStunting is still a major public health problem in low- and middle-income countries, including Indonesia. Previous studies have reported the complexities associated with understanding the determinants of stunting. This study aimed to examine the household-, subdistrict- and province-level determinants of stunting in Indonesia using a multilevel hierarchical mixed effects model.MethodsWe analyzed data for 8045 children taken from the 2007 and 2014 waves of the Indonesian Family and Life Surveys (IFLS). We included individual-, family-/household- and community-level variables in the analyses. A multilevel mixed effects model was employed to take into account the hierarchical structure of the data. Moreover, the model captured the effect of unobserved household-, subdistrict- and province-level characteristics on the probability of children being stunted.ResultsOur findings showed that the odds of childhood stunting vary significantly not only by individual child- and household-level characteristics but also by province- and subdistrict-level characteristics. Among the child-level covariates included in our model, dietary habits, neonatal weight, a history of infection, and sex significantly affected the risk of stunting. Household wealth status and parental education are significant household-level covariates associated with a higher risk of stunting. Finally, the risk of stunting is higher for children living in communities without access to water, sanitation and hygiene.ConclusionsStunting is associated with not only child-level characteristics but also family- and community-level characteristics. Hence, interventions to reduce stunting should also take into account family and community characteristics to achieve effective outcomes.
| S-EPMC8604318 | biostudies-literature
Project description:There is increasing interest to control or eradicate the major neglected tropical diseases. Accurate modelling of the geographic distributions of parasitic infections will be crucial to this endeavour. We used 664 community level infection prevalence data collated from the published literature in conjunction with eight environmental variables, altitude and population density, and a multivariate Bayesian generalized linear spatial model that allows explicit accounting for spatial autocorrelation and incorporation of uncertainty in input data and model parameters, to construct the first spatially-explicit map describing LF prevalence distribution in Africa. We also ran the best-fit model against predictions made by the HADCM3 and CCCMA climate models for 2050 to predict the likely distributions of LF under future climate and population changes. We show that LF prevalence is strongly influenced by spatial autocorrelation between locations but is only weakly associated with environmental covariates. Infection prevalence, however, is found to be related to variations in population density. All associations with key environmental/demographic variables appear to be complex and non-linear. LF prevalence is predicted to be highly heterogenous across Africa, with high prevalences (>20%) estimated to occur primarily along coastal West and East Africa, and lowest prevalences predicted for the central part of the continent. Error maps, however, indicate a need for further surveys to overcome problems with data scarcity in the latter and other regions. Analysis of future changes in prevalence indicates that population growth rather than climate change per se will represent the dominant factor in the predicted increase/decrease and spread of LF on the continent. We indicate that these results could play an important role in aiding the development of strategies that are best able to achieve the goals of parasite elimination locally and globally in a manner that may also account for the effects of future climate change on parasitic infection.
| S-EPMC3741112 | biostudies-literature
Project description:BackgroundStunting is a worldwide public health problem caused by factors that vary across regions, including in Ethiopia. Limited evidence to prevent stunting makes it difficult to design and prioritize appropriate interventions. Therefore, this study investigated the intervention priorities for the prevention of stunting among children 6-59 months old in Kemissie City Administration, northeastern Ethiopia.MethodsA community-based individual matched case-control study was conducted from January to April 2017 including 107 cases and 214 controls. Controls were selected and matched with cases using the matching variable of child's age. Data were collected by open data kit (ODK) software using a structured questionnaire. Data were analyzed using STATA version 13.0 and WHO (World Health Organization) Anthro 2005. A conditional logistic regression model was used for data analysis. From multivariable conditional logistic regression analysis, determinants of stunting were identified. A statistically significant level was declared by a conditional adjusted odds ratio (cAOR) with 95% confidence interval (CI) and p-value of less than 0.05.Main findingsThe wealth index 52 (48.6%) of the cases and 108 (50.5%) controls were categorized as poor. The mean height-for-age z-score (HAZ) for the cases and controls was -2.79±.67 and -0.55±.92, respectively. One-sixth (16.8%) of the cases and 29 (13.6%) of the controls were given prelacteal feeding. A majority 82 (86.9%) of the cases and 137 (69.1%) of the controls had undernourished mothers/care-givers. Slightly less than one-third 35 (32.7%) of cases and one-fourth 53 (24.8%) of controls were affected by repeated episodes of diarrhea. Mother's body mass index (BMI) (conditional adjusted odds ratio [cAOR]) = 2.64; 95% CI: 1.28, 5.43), giving food priority to father (cAOR = 2.42; 95% CI: 1.23, 4.75), lack of exclusive breastfeeding for at least 6 months (cAOR = 2.44; 95% CI: 1.15, 5.17), no intake of meat by child (cAOR = 2.35; 95% CI: 1.21, 4.58) and child having repeated diarrheal episodes (cAOR = 2.0: 95% CI: 1.07, 3.86) were factors associated with childhood stunting.ConclusionMaternal nutritional status, food priority, duration of exclusive breastfeeding, no intake of meat and repeated episodes of diarrhea were the main determinants of stunting among children aged 6-59 months. Therefore, intervention measures to avert childhood stunting should include strengthening action on provision of essential nutrition, providing counseling to parents on giving food priority to children, promotion of optimal duration of breastfeeding and preventing diarrheal disease among children 6-59 months old.
| S-EPMC7514084 | biostudies-literature
Project description:BackgroundIn Uganda, malaria and lymphatic filariasis (causative agent Wuchereria bancrofti) are transmitted by the same vector species of Anopheles mosquitoes, and thus are likely to share common environmental risk factors and overlap in geographical space. In a comprehensive nationwide survey in 2000-2003 the geographical distribution of W. bancrofti was assessed by screening school-aged children for circulating filarial antigens (CFA). Concurrently, blood smears were examined for malaria parasites. In this study, the resultant malariological data are analysed for the first time and the CFA data re-analysed in order to identify risk factors, produce age-stratified prevalence maps for each infection, and to define the geographical patterns of Plasmodium sp. and W. bancrofti co-endemicity.MethodsLogistic regression models were fitted separately for Plasmodium sp. and W. bancrofti within a Bayesian framework. Models contained covariates representing individual-level demographic effects, school-level environmental effects and location-based random effects. Several models were fitted assuming different random effects to allow for spatial structuring and to capture potential non-linearity in the malaria- and filariasis-environment relation. Model-based risk predictions at unobserved locations were obtained via Bayesian predictive distributions for the best fitting models. Maps of predicted hyper-endemic malaria and filariasis were furthermore overlaid in order to define areas of co-endemicity.ResultsPlasmodium sp. parasitaemia was found to be highly endemic in most of Uganda, with an overall population adjusted parasitaemia risk of 47.2% in the highest risk age-sex group (boys 5-9 years). High W. bancrofti prevalence was predicted for a much more confined area in northern Uganda, with an overall population adjusted infection risk of 7.2% in the highest risk age-group (14-19 year olds). Observed overall prevalence of individual co-infection was 1.1%, and the two infections overlap geographically with an estimated number of 212,975 children aged 5 - 9 years living in hyper-co-endemic transmission areas.ConclusionsThe empirical map of malaria parasitaemia risk for Uganda presented in this paper is the first based on coherent, national survey data, and can serve as a baseline to guide and evaluate the continuous implementation of control activities. Furthermore, geographical areas of overlap with hyper-endemic W. bancrofti transmission have been identified to help provide a better informed platform for integrated control.
| S-EPMC3216645 | biostudies-literature
Project description:The problem of simultaneous covariate selection and parameter inference for spatial regression models is considered. Previous research has shown that failure to take spatial correlation into account can influence the outcome of standard model selection methods. A Markov chain Monte Carlo (MCMC) method is investigated for the calculation of parameter estimates and posterior model probabilities for spatial regression models. The method can accommodate normal and non-normal response data and a large number of covariates. Thus the method is very flexible and can be used to fit spatial linear models, spatial linear mixed models, and spatial generalized linear mixed models (GLMMs). The Bayesian MCMC method also allows a priori unequal weighting of covariates, which is not possible with many model selection methods such as Akaike's information criterion (AIC). The proposed method is demonstrated on two data sets. The first is the whiptail lizard data set which has been previously analyzed by other researchers investigating model selection methods. Our results confirmed the previous analysis suggesting that sandy soil and ant abundance were strongly associated with lizard abundance. The second data set concerned pollution tolerant fish abundance in relation to several environmental factors. Results indicate that abundance is positively related to Strahler stream order and a habitat quality index. Abundance is negatively related to percent watershed disturbance.
| S-EPMC3213087 | biostudies-literature
Project description:BackgroundSuicide death rates in Australia are higher in rural than urban communities however the contributors to this difference remain unclear. Geographical differences in suicidal ideation and attempts were explored using two datasets encompassing urban and rural community residents to examine associations between socioeconomic, demographic and mental health factors. Differing patterns of association between psychiatric disorder and suicidal ideation and attempts as geographical remoteness increased were investigated.MethodsParallel cross-sectional analyses were undertaken using data from the 2007 National Survey of Mental Health and Wellbeing (2007-NSMHWB, n = 8,463), under-representative of remote and very remote residents, and selected participants from the Australian Rural Mental Health Study (ARMHS, n = 634), over-representative of remote and very remote residents. Uniform measures of suicidal ideation and attempts and mental disorder using the World Mental Health Composite International Diagnostic Interview (WMH-CIDI-3.0) were used in both datasets. Geographic region was classified into major cities, inner regional and other. A series of logistic regressions were undertaken for the outcomes of 12-month and lifetime suicidal ideation and lifetime suicide attempts, adjusting for age, gender and psychological distress. A sub-analysis of the ARMHS sample was undertaken with additional variables not available in the 2007-NSMHWB dataset.ResultsRates and determinants of suicidal ideation and suicide attempts across geographical region were similar. Psychiatric disorder was the main determinant of 12-month and lifetime suicidal ideation and lifetime suicide attempts across all geographical regions. For lifetime suicidal ideation and attempts, marital status, employment status, perceived financial adversity and mental health service use were also important determinants. In the ARMHS sub-analysis, higher optimism and better perceived infrastructure and service accessibility tended to be associated with a lower likelihood of lifetime suicidal ideation, when age, gender, psychological distress, marital status and mental health service use were taken into account.ConclusionsRates and determinants of suicidal ideation and attempts did not differ according to geographical location. Psychiatric disorder, current distress, employment and financial adversity remain important factors associated with suicidal ideation and attempts across all regions in Australia. Regional characteristics that influence availability of services and lower personal optimism may also be associated with suicidal ideation in rural communities.
| S-EPMC4227072 | biostudies-literature
Project description:BACKGROUND:Progress has been made worldwide in reducing chronic undernutrition and rates of linear growth stunting in children under 5 y of age, although rates still remain high in many regions. Policies, programs, and interventions supporting maternal and child health and nutrition have the potential to improve child growth and development. OBJECTIVE:This article synthesizes the available global evidence on the drivers of national declines in stunting prevalence and compares the relative effect of major drivers of stunting decline between countries. METHODS:We conducted a systematic review of published peer-reviewed and gray literature analyzing the relation between changes in key determinants of child linear growth and contemporaneous changes in linear growth outcomes over time. RESULTS:Among the basic determinants of stunting assessed within regression-decomposition analyses, improvement in asset index score was a consistent and strong driver of improved linear growth outcomes. Increased parental education was also a strong predictor of improved child growth. Of the underlying determinants of stunting, reduced rates of open defecation, improved sanitation infrastructure, and improved access to key maternal health services, including optimal antenatal care and delivery in a health facility or with a skilled birth attendant, all accounted for substantially improved child growth, although the magnitude of variation explained by each differed substantially between countries. At the immediate level, changes in several maternal characteristics predicted modest stunting reductions, including parity, interpregnancy interval, and maternal height. CONCLUSIONS:Unique sets of stunting determinants predicted stunting reduction within countries that have reduced stunting. Several common drivers emerge at the basic, underlying, and immediate levels, including improvements in maternal and paternal education, household socioeconomic status, sanitation conditions, maternal health services access, and family planning. Further data collection and in-depth mixed-methods research are required to strengthen recommendations for those countries where the stunting burden remains unacceptably high.
| S-EPMC7487433 | biostudies-literature
Project description:BackgroundClonorchiasis, one of the most important food-borne trematodiases, affects more than 12 million people in the People's Republic of China (P.R. China). Spatially explicit risk estimates of Clonorchis sinensis infection are needed in order to target control interventions.MethodologyGeoreferenced survey data pertaining to infection prevalence of C. sinensis in P.R. China from 2000 onwards were obtained via a systematic review in PubMed, ISI Web of Science, Chinese National Knowledge Internet, and Wanfang Data from January 1, 2000 until January 10, 2016, with no restriction of language or study design. Additional disease data were provided by the National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention in Shanghai. Environmental and socioeconomic proxies were extracted from remote-sensing and other data sources. Bayesian variable selection was carried out to identify the most important predictors of C. sinensis risk. Geostatistical models were applied to quantify the association between infection risk and the predictors of the disease, and to predict the risk of infection across P.R. China at high spatial resolution (over a grid with grid cell size of 5×5 km).Principal findingsWe obtained clonorchiasis survey data at 633 unique locations in P.R. China. We observed that the risk of C. sinensis infection increased over time, particularly from 2005 onwards. We estimate that around 14.8 million (95% Bayesian credible interval 13.8-15.8 million) people in P.R. China were infected with C. sinensis in 2010. Highly endemic areas (? 20%) were concentrated in southern and northeastern parts of the country. The provinces with the highest risk of infection and the largest number of infected people were Guangdong, Guangxi, and Heilongjiang.Conclusions/significanceOur results provide spatially relevant information for guiding clonorchiasis control interventions in P.R. China. The trend toward higher risk of C. sinensis infection in the recent past urges the Chinese government to pay more attention to the public health importance of clonorchiasis and to target interventions to high-risk areas.
| S-EPMC5416880 | biostudies-literature
Project description:BackgroundThe Zambia Malaria Indicator Survey (ZMIS) of 2006 was the first nation-wide malaria survey, which combined parasitological data with other malaria indicators such as net use, indoor residual spraying and household related aspects. The survey was carried out by the Zambian Ministry of Health and partners with the objective of estimating the coverage of interventions and malaria related burden in children less than five years. In this study, the ZMIS data were analysed in order (i) to estimate an empirical high-resolution parasitological risk map in the country and (ii) to assess the relation between malaria interventions and parasitaemia risk after adjusting for environmental and socio-economic confounders.MethodsThe parasitological risk was predicted from Bayesian geostatistical and spatially independent models relating parasitaemia risk and environmental/climatic predictors of malaria. A number of models were fitted to capture the (potential) non-linearity in the malaria-environment relation and to identify the elapsing time between environmental effects and parasitaemia risk. These models included covariates (a) in categorical scales and (b) in penalized and basis splines terms. Different model validation methods were used to identify the best fitting model. Model-based risk predictions at unobserved locations were obtained via Bayesian predictive distributions for the best fitting model.ResultsModel validation indicated that linear environmental predictors were able to fit the data as well as or even better than more complex non-linear terms and that the data do not support spatial dependence. Overall the averaged population-adjusted parasitaemia risk was 20.0% in children less than five years with the highest risk predicted in the northern (38.3%) province. The odds of parasitaemia in children living in a household with at least one bed net decreases by 40% (CI: 12%, 61%) compared to those without bed nets.ConclusionsThe map of parasitaemia risk together with the prediction error and the population at risk give an important overview of the malaria situation in Zambia. These maps can assist to achieve better resource allocation, health management and to target additional interventions to reduce the burden of malaria in Zambia significantly. Repeated surveys will enable the evaluation of the effectiveness of on-going interventions.
| S-EPMC2845589 | biostudies-literature