scholarly journals Mapping Geographical Differences and Examining the Determinants of Childhood Stunting in Ethiopia: A Bayesian Geostatistical Analysis

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 2104
Author(s):  
Kedir Ahmed ◽  
Kingsley Agho ◽  
Andrew Page ◽  
Amit Arora ◽  
Felix Ogbo ◽  
...  
Keyword(s):  
Employment Status ◽  
Regression Models ◽  
Credible Interval ◽  
Household Wealth ◽  
Geographical Differences ◽  
Tigray Region ◽  
Geographical Variations ◽  
Special Zone ◽  
Childhood Stunting ◽  
Children Under 5

Understanding the specific geographical distribution of stunting is essential for planning and implementing targeted public health interventions in high-burdened countries. This study investigated geographical variations in the prevalence of stunting sub-nationally, and the determinants of stunting among children under 5 years of age in Ethiopia. We used the 2016 Ethiopia Demographic and Health Survey (EDHS) dataset for children aged 0–59 months with valid anthropometric measurements and geographic coordinates (n = 9089). We modelled the prevalence of stunting and its determinants using Bayesian geospatially explicit regression models. The prevalence of stunting among children under five years was 36.3% (95% credible interval (CrI); 22.6%, 51.4%) in Ethiopia, with wide variations sub-nationally and by age group. The prevalence of childhood stunting ranged from 56.6% (37.4–74.6%) in the Mekelle Special zone of the Tigray region to 25.5% (10.5–48.9%) in the Sheka zone of the Southern Nations, Nationalities and Peoples region. Factors associated with a reduced likelihood of stunting in Ethiopia included non-receipt of breastmilk, mother’s BMI (overweight/obese), employment status (employed), and higher household wealth, while the enablers were residence in the “arid” geographic areas, small birth size of the child, and mother’s BMI (underweight). The prevalence and determinants of stunting varied across Ethiopia. Efforts to reduce the burden of childhood stunting should consider geographical heterogeneity and modifiable risk factors.

scholarly journals Household structure, maternal characteristics and children’s stunting in sub-Saharan Africa: evidence from 35 countries

2020 ◽  
Author(s):  
Sanni Yaya ◽  
Olanrewaju Oladimeji ◽  
Emmanuel Kolawole Odusina ◽  
Ghose Bishwajit
Keyword(s):  
Birth Interval ◽  
Household Wealth ◽  
Household Structure ◽  
Sub Saharan Africa ◽  
Physical Growth ◽  
Multiple Birth ◽  
Rural Children ◽  
Sub Saharan ◽  
Ssa Countries ◽  
Childhood Stunting

Abstract Background Adequate nutrition in early childhood is a necessity to achieve healthy growth and development, as well as a strong immune system and good cognitive development. The period from conception to infancy is especially vital for optimal physical growth, health and development. In this study we examined the influence of household structure on stunting in children <5 yrs of age in sub-Saharan Africa (SSA) countries. Methods Demographic and Health Survey data from birth histories in 35 SSA countries were used in this study. The total sample of children born within the 5 yrs before the surveys (2008 and 2018) was 384 928. Children whose height-for-age z-score throughout was <−2 SDs from the median of the WHO reference population were considered stunted. Percentages and χ2 tests were used to explore prevalence and bivariate associations of stunting. In addition, a multivariable logistic regression model was fitted to stunted children. All statistical tests were conducted at a p<0.05 level of significance. Results More than one-third of children in SSA countries were reportedly stunted. The leading countries include Burundi (55.9%), Madagascar (50.1%), Niger (43.9%) and the Democratic Republic of the Congo (42.7%). The percentage of stunted children was higher among males than females and among rural children than their urban counterparts in SSA countries. Children from polygamous families and from mothers who had been in multiple unions had a 5% increase in stunting compared with children from monogamous families and mothers who had only one union (AOR 1.05 [95% CI 1.02 to 1.09]). Furthermore, rural children were 1.23 times as likely to be stunted compared with urban children (AOR 1.23 [95% CI 1.16 to 1.29]). Children having a <24-mo preceding birth interval were 1.32 times as likely to be stunted compared with first births (AOR 1.32 [95% CI 1.26 to 1.38]). In addition, there was a 2% increase in stunted children for every unit increase in the age (mo) of children (AOR 1.02 [95% CI 1.01 to 1.02]). Multiple-birth children were 2.09 times as likely to be stunted compared with a singleton (AOR 2.09 [95% CI 1.91 to 2.28]). Conclusions The study revealed that more than one-third of children were stunted in SSA countries. Risk factors for childhood stunting were also identified. Effective interventions targeting factors associated with childhood stunting, such as maternal education, advanced maternal age, male sex, child’s age, longer birth interval, multiple-birth polygamy, improved household wealth and history of mothers’ involvement in multiple unions, are required to reduce childhood stunting in the region.

scholarly journals Spatial heterogeneity and determinants of childhood anaemia in Nigeria

2020 ◽  
Author(s):  
Chigozie Louisa Jane Ugwu ◽  
Temesgen Zewotir
Keyword(s):  
Spatial Heterogeneity ◽  
National Level ◽  
Credible Interval ◽  
Household Wealth ◽  
Influential Factors ◽  
World Health ◽  
Targeted Interventions ◽  
Malaria Indicator Survey ◽  
Additive Regression ◽  
Structured Additive Regression

Abstract Background Childhood anaemia is highly prevalent in Nigeria. According to the 2015 Nigeria Malaria Indicator Survey (NMIS) report, more than 68% of children aged 6-59 months were found to be anaemic. This estimate is far above the World Health Organization’s 40% cut off point which classifies anaemia a severe public health challenge in the country. Identifying environmental, health, socioeconomic and demographic influential factors and mapping the prevalence of anaemia can help guide geographically targeted intervention programmes to reduce the risk of anaemia associated morbidity among vulnerable children in Nigeria. Methods Geographically linked national level datasets obtained from the 2015 Nigeria Demographic and Health Survey (NDHS) and Malaria Indicator Survey (NMIS) programmes were used for this study. For the analysis, a binary structured additive regression (BSAR) model was explored. This model incorporated a Markov Random Field (MRF) prior, with posterior parameters estimated via Bayesian framework. Results After accounting for the spatial heterogeneity, we found a strong negative association between the odds of anaemia and the child’s demographic variables in terms of increasing age and being female. Increased odds of anaemia were also associated with child’s malaria and fever status, living in a rural environment, lower household wealth quintile, having younger and illiterate mothers. We also found that a decreased distance to vegetated areas was associated with increased childhood anaemia risk, while the odds of anaemia decreased as cluster altitude increased at 95% credible interval. The maps of the posterior means of the spatial random effects revealed evidence of spatial variation in the odds of childhood anaemia, while accounting for the model covariates (Fig. 4). Greater risk of anaemia was observed for children who resided in Adamawa, Ebonyi, Edo, Cross river, FCT, Jigawa, Kaduna, Kano and Kebbi states in Nigeria. Conclusions In this study, a binary structured additive regression model was utilized, allowing for a flexible semiparametric predictor that accounted for the effects of different types of covariates, while simultaneously incorporating spatial variables directly. Our results revealed significant spatial variability of childhood anaemia, suggesting that spatially targeted interventions could result in efficiency gains for anaemia control in Nigeria.

Association between Household Livestock Ownership and Childhood Stunting in Bangladesh – A Spatial Analysis

2019 ◽  
Vol 66 (3) ◽  
pp. 248-256 ◽  
Author(s):  
Md Belal Hossain ◽  
Jahidur Rahman Khan
Keyword(s):  
Child Nutrition ◽  
Dietary Diversity ◽  
Credible Interval ◽  
Nutrition Status ◽  
Spatial Effects ◽  
Posterior Odds ◽  
Small Effect Size ◽  
Livestock Ownership ◽  
The Relationship ◽  
Childhood Stunting

Abstract Livestock is an integrated part of agriculture, yet the relationship between household livestock ownership and child nutrition is a significant knowledge gap. The present study aimed to assess the association between household livestock ownership and childhood stunting and to explore the geospatial variations at district level in Bangladesh. A complete data of 19 295 children aged below 5 years were extracted from the latest Bangladesh Multiple Indicator Cluster Survey 2012–13. The tropical livestock unit (TLU) score calculated as a weighted value for each livestock and categorized as low, medium, and high using tertile. A hierarchical Bayesian spatial logistic model was used to assess the association between TLU and childhood stunting. Children from the household with high TLU were 10% less likely to be stunted (adjusted posterior odds ratio: 0.90, 95% credible interval: 0.84–0.97) after controlling for demographic, socioeconomic, morbidity, place of residence and spatial effects. There was also a substantial spatial variation in childhood stunting across districts in Bangladesh with the highest burden in the Northern and North-Eastern regions. The positive effect of household livestock ownership on reducing child stunting suggests that, in addition to nutritional intervention in Bangladesh, efforts to strengthen livestock production would be beneficial for improving child nutrition status. However, a small effect size may be owing to the lack of dietary diversity, livestock health and productivity data as well as the complexity of the relationship, requiring further study. Furthermore, a significant regional disparity in stunting highlighted the importance of spatial targeting during the design of interventions and implementation.

scholarly journals Risk factors and regional variations of malnutrition among children under 5 in Myanmar: cross-sectional analyses at national and subnational levels

BMJ Open ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. e030894 ◽  
Author(s):  
Hnin Thiri Khaing ◽  
Shuhei Nomura ◽  
Daisuke Yoneoka ◽  
Peter Ueda ◽  
Kenji Shibuya
Keyword(s):  
Risk Factors ◽  
Nutritional Status ◽  
Potential Risk ◽  
Regression Models ◽  
Cross Sectional ◽  
Regional Variations ◽  
Targeted Interventions ◽  
Maternal Nutritional Status ◽  
Potential Risk Factors ◽  
Children Under 5

ObjectiveThe levels, distributions of child malnutrition and its potential risk factors are not very well known in Myanmar. The objectives included in this study were: to estimate the current national and subnational prevalence of four types of malnutrition (stunting, wasting, underweight and overweight) among children under 5 in Myanmar; to identify potential risk factors associated with each type of malnutrition and to investigate how the identified risk factors’ distributions explained the regional disparities in malnutrition prevalence.Design/methodsData from the Myanmar Demographic and Health Survey 2015–2016 were used to estimate the prevalence of four types of malnutrition at both national and subnational levels (15 regions). Logistic regression models were applied to examine the association between each type of malnutrition and its risk factors, including child’s factors, parental social status and household conditions. The risk factor-adjusted prevalence of the malnutrition was estimated at the subnational level based on the estimated parameters from the regression models.ResultsThe national prevalence of stunting, wasting, underweight and overweight in children under 5 was estimated to be 29.1% (95% CI 27.7% to 30.6%), 6.8% (6.0% to 7.6%), 18.3% (17.0% to 19.5%) and 1.5% (1.1% to 1.9%), respectively. Substantial regional variations in the prevalence of each type of malnutrition were observed. Several risk factors of each type of malnutrition were identified, including low birth weight (LBW) and inadequate maternal nutritional status. Except for overweight, regional variations largely persisted even after adjustment for the risk factors investigated.ConclusionThe prevalence of malnutrition among children under 5 is still high in Myanmar, most commonly stunting. Targeted interventions aimed at prevention of LBW, improving the maternal nutritional status, in addition to other sociodemographic conditions should be encouraged urgently. Further research is necessary to investigate the potential sources of regional variation in prevalence of malnutrition among children under 5 in the country.

Geographical Distribution and Risk Factors of Low Birth Weight in Nigeria

2020 ◽  
Vol 10 (4) ◽  
pp. 234-248
Author(s):  
Richard Adeleke ◽  
Tolulope Osayomi ◽  
Ayodeji E. Iyanda
Keyword(s):  
Risk Factors ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Prevalence Rate ◽  
Spatial Epidemiology ◽  
Ordinary Least Squares ◽  
Health Concern ◽  
Geographical Differences ◽  
Spatial Error ◽  
Geographical Variations

BACKGROUNDLow birth weight (LBW) directly or indirectly accounts for 60% to 80% of all neonatal deaths globally, and it has become an issue of serious health concern with Nigeria with one of the highest infant mortality rates (74/1,000) in the world. Despite the severe health impact, little is understood on the geographical differences in maternal socioeconomic and environmental factors that affect LBW across the states in Nigeria.METHODUsing the spatial epidemiological approach, this study examined the geographical variations in LBW and associated risk factors in Nigeria with the aid of spatial statistics.RESULTSThere was a regional LBW corridor in the extreme north with Yobe state with the highest prevalence rate. Maternal educational attainment and acute maternal malnutrition explained 65.4% (ordinary least squares model) and 70.5% (spatial error model, SEM) of the variation in the geographical pattern of LBW.CONCLUSIONLBW remains an issue of serious health concern in Nigeria. The finding of this study would shed more light on the spatial epidemiology of LBW in Nigeria and also guide public health programs in curtailing the high prevalence rate of LBW.RECOMMENDATIONSThe study recommends health education on nutrition in pregnancy and the need to improve health literacy among women so as to check the high LBW prevalence.

scholarly journals Bayesian Modeling of Spatiotemporal patterns of TB-HIV co-infection risk in Kenya

2019 ◽  
Author(s):  
Verrah A Otiende ◽  
Thomas N O Achia ◽  
Henry G Mwambi
Keyword(s):  
Relative Risk ◽  
Temporal Trends ◽  
Control Program ◽  
Credible Interval ◽  
Infection Risk ◽  
Geographical Variations ◽  
Hiv Coinfection ◽  
Notification Data ◽  
Best Fit ◽  
Case Notification

Abstract Background Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) diseases are globally acknowledged as a public health challenge that exhibits adverse bidirectional relations due to the co-epidemic overlap. To understand the co-infection burden we used the case notification data to generate spatiotemporal maps that described the distribution and exposure hypotheses for further epidemiologic investigations in areas with unusual case notification levels. Methods We analyzed the TB and TB-HIV case notification data from the Kenya national TB control program aggregated for forty-seven counties over a seven-year period (2012 – 2018). Using the Integrated Nested Laplace Approach (INLA), we modeled the risk of TB-HIV co-infection. Six competing models with varying space-time formulations were compared to determine the best fit model. We then assessed the geographic patterns and temporal trends of coinfection risk by mapping the posterior marginal from the best fit model. Results Of the total 608312 TB case notifications, 194129 were HIV co-infected. The proportion of TB-HIV co-infection was higher in females (39.7%) than in males (27.0%). A significant share of the co-infection was among adults aged 35 to 44 years (46.7%) and 45 to 54 years (42.1%). Based on the Bayesian Defiance Information (DIC) and the effective number of parameters comparisons, the spatiotemporal model 3b was the best in explaining the geographical variations in TB-HIV coinfection. The model results suggested that the risk of TB-HIV coinfection was influenced by infrastructure index (Relative risk (RR)=5.75, Credible Interval (Cr.I) = (1.65, 19.89)) and gender ratio . The lowest and highest temporal relative risks were in the years 2016 at 0.9 and 2012 at 1.07 respectively. The spatial pattern presented an increased co-infection risk in a number of counties. For the spatiotemporal interaction, only a few counties had a relative risk greater than 1 that varied in different years. Conclusions We identified elevated risk areas for TB/HIV co-infection and fluctuating temporal trends which could be because of improved TB case detection or surveillance bias caused by spatial heterogeneity in the co-infection dynamics. Focused interventions and continuous TB-HIV surveillance will ensure adequate resource allocation and significant reduction of HIV burden amongst TB patients

scholarly journals Multiple Binomial Regression Models of Learning Style Preferences of Students of Sidhu School, Wilkes University

2018 ◽  
Vol 7 (3) ◽  
pp. 9
Author(s):  
Adekanmbi, D. B
Keyword(s):  
Learning Style ◽  
Data Model ◽  
Employment Status ◽  
Regression Models ◽  
Probit Model ◽  
Effect Modifier ◽  
Learning Style Preferences ◽  
Binomial Regression ◽  
Style Preferences ◽  
Model C

The interest of this study is to explore the relationship between a dichotomous response, learning style preferences by university students of Sidhu School, Wilkes University, as a function of the following predictors: Gender, Age, employment status, cumulative grade point assessment (GPA) and level of study, as in usual generalized linear model. The response variable is the students’ preference for either Behaviorist or Humanist learning style. Four different binomial regression models were fitted to the data. Model A is a logit regression model that fits all the predictors, Model B is a probit model that fits all the predictors, Model C is a logit model with an effect modifier, while Model D is a probit model also with an effect modifier. Models A and B appeared to have performed poorly in fitting the data. Models C and D fit the data well as confirmed by the non-significant chi-square lack of fit with p-values 0.1409 and 0.1408 respectively. Among the four models considered for fitting the data, Model D, the probit model with effect modifier fit best. There was a marginal difference in the measure of goodness-of-fit for models C and D. Since probit model usually do not lend itself to ease of interpretation, model C was focused on for interpretation of results. The four variables that made significant contributions to model C were gender, age, employment status and the interaction variable. Academic performance of the students measured by their GPA and the level of study of the students were not significant predictors of the learning style preference by the students. The results of Model C revealed that the likelihood that a student prefers Behaviorist learning style is negatively related to his or her gender, age, employment status, GPA and level of study. However, the likelihood is positively related to the interaction term: Gender* Age. The result also showed that every one year increase in age of the students leads to decrease in the log-odds of preference for Behaviorist learning style. Also the odds of an MBA student preference for Behaviorist learning style are 1.1925 times greater than the odds of an undergraduate student. The association between gender and age was significant, so that gender modifies the association between age and preference. The interaction term showed that both the male and female odds ratio indicate an increase of odds of Behaviorist learning style, with increasing age of students, but the rate of increase is greater for male students. Plots of residuals and other diagnostic procedures conducted further confirmed that models A and B did not yield good fit, while both models C and D though identified an outlier which was not influential, but the functional forms of the models appeared suitable and seemed to fit the data well, and were therefore considered adequate. The residual mean deviance of model C was slightly above 1 which an indication of a slight overdispersion problem in the model. Important issues arising from the study were also discussed.

scholarly journals Quantifying the impact of mass vaccination programmes on notified cases in the Netherlands

2018 ◽  
Vol 146 (6) ◽  
pp. 716-722 ◽  
Author(s):  
M. van Wijhe ◽  
A. D. Tulen ◽  
H. Korthals Altes ◽  
S. A. McDonald ◽  
H. E. de Melker ◽  
...  
Keyword(s):  
The Netherlands ◽  
Infectious Diseases ◽  
Regression Models ◽  
Continuous Monitoring ◽  
Monitoring And Evaluation ◽  
Credible Interval ◽  
Secular Trends ◽  
Auto Correlation ◽  
The Difference ◽  
The Impact

AbstractVaccination programmes are considered a main contributor to the decline of infectious diseases over the 20th century. In recent years, the national vaccination coverage in the Netherlands has been declining, highlighting the need for continuous monitoring and evaluation of vaccination programmes. Our aim was to quantify the impact of long-standing vaccination programmes on notified cases in the Netherlands. We collected and digitised previously unavailable monthly case notifications of diphtheria, poliomyelitis, mumps and rubella in the Netherlands over the period 1919–2015. Poisson regression models accounting for seasonality, multi-year cycles, secular trends and auto-correlation were fit to pre-vaccination periods. Cases averted were calculated as the difference between observed and expected cases based on model projections. In the first 13 years of mass vaccinations, case notifications declined rapidly with 82.4% (95% credible interval (CI): 74.9–87.6) of notified cases of diphtheria averted, 92.9% (95% CI 85.0–97.2) cases of poliomyelitis, and 79.1% (95% CI 67.1–87.4) cases of mumps. Vaccination of 11-year-old girls against rubella averted 49.9% (95% CI 9.3–73.5) of cases, while universal vaccination averted 68.1% (95% CI 19.4–87.3) of cases. These findings show that vaccination programmes have contributed substantially to the reduction of infectious diseases in the Netherlands.

Is overweight a risk factor for wheezing in pre-school children? A study in 14 Brazilian communities

2007 ◽  
Vol 10 (9) ◽  
pp. 878-882 ◽  
Author(s):  
S R D M Saldiva ◽  
M M Escuder ◽  
S I Venâncio ◽  
M H A Benicio ◽  
A M O Assis ◽  
...  
Keyword(s):  
School Children ◽  
Odds Ratio ◽  
Multivariate Regression ◽  
Regression Models ◽  
The State ◽  
Overweight Children ◽  
Cross Sectional ◽  
Increased Risk ◽  
Confounding Variables ◽  
Children Under 5

AbstractObjectiveTo evaluate the association between overweight and wheezing in pre-school children in 14 small Brazilian communities.MethodsCross-sectional epidemiological study, conducted between 2001 and 2002. A sample of 3453 children under 5 years of age was taken from nine communities in the state of Bahia and five in the state of São Paulo. Data on housing, family and children were obtained by applying structured questionnaires in loco. Weight and height for each child were also measured. The association between wheezing and overweight was assessed by unconditional logistic multivariate regression models.ResultsOverweight children had a greater frequency of wheezing and an odds ratio of 2.57 (95% confidence interval 1.51–4.37) was estimated after controlling for several potential confounding variables. The magnitude of the risk was not affected by several different model specifications.ConclusionExcess weight is associated with increased risk for wheezing in this population of children below 5 years of age.

scholarly journals How Financial Literacy Affects Household Wealth Accumulation

2012 ◽  
Vol 102 (3) ◽  
pp. 300-304 ◽  
Author(s):  
Jere R Behrman ◽  
Olivia S Mitchell ◽  
Cindy K Soo ◽  
David Bravo
Keyword(s):  
Linear Regression ◽  
Instrumental Variables ◽  
Financial Literacy ◽  
Regression Models ◽  
Household Wealth ◽  
Causal Effects ◽  
Wealth Accumulation ◽  
Linear Regression Models

This study isolates the causal effects of financial literacy and schooling on wealth accumulation using a new household dataset and an instrumental variables (IV) approach. Financial literacy and schooling attainment are both strongly positively associated with wealth outcomes in linear regression models, whereas the IV estimates reveal even more potent effects of financial literacy. They also indicate that the schooling effect only becomes positive when interacted with financial literacy. Estimated impacts are substantial enough to imply that investments in financial literacy could have large wealth payoffs.

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