Dataset Information

Determinants of nutritional status among primary school students in Dilla Town; Application of an ordinal logistic regression model.

ABSTRACT:

Background

One of the most frequent reasons for children's poor physical and mental development is malnutrition, becoming a more significant issue in most developing nations, including Ethiopia. Prior research used multiple anthropometric measurements separately to pinpoint undernutrition concerns in children. However, the impact of each explanatory variable on a single response category was not considered in these investigations. This study used a single composite index of anthropometric parameters to identify the factors affecting elementary school students' nutritional condition.

Methods

In Dilla, Ethiopia, 494 primary school students took part in a cross-sectional institutional survey during the 2021 academic year. Principal component analysis was used to create a single composite measure of nutritional status using z-scores for the anthropometric indices of height-for-age and body mass index-for-age. The relative effectiveness of a partial proportional odds model was compared with several other ordinal regression models to identify the important variables for children's nutritional status.

Results

27.94% of primary school students were undernourished (7.29% severely and 20.65% moderately). According to the fitted partial proportional odds model, the mother's education level (secondary or higher) was positively correlated with the nutritional status of primary school students, given that in this case the students ate three or more times per day and had a high dietary diversity score (OR = 5.94; CI: 2.2-16.0). Nevertheless, there was a negative correlation between larger family size (OR = 0.56; CI: 0.32-0.97), unprotected groundwater (OR = 0.76; CI: 0.6-0.96), and severely food insecure households (OR = 0.3; CI: 0.14-0.68).

Conclusion

In Dilla, Ethiopia, undernutrition among primary school students is a serious issue. It is essential to implement nutrition education and school feeding programs, improve drinking water sources, and boost the community's economy to alleviate the problems.

SUBMITTER: Berhanu G

PROVIDER: S-EPMC9988510 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Determinants of nutritional status among primary school students in Dilla Town; Application of an ordinal logistic regression model.

Berhanu Getasew G Dessalegn Behailu B Ali Helen H Animut Kassahun K

Heliyon 20230221 3

<h4>Background</h4>One of the most frequent reasons for children's poor physical and mental development is malnutrition, becoming a more significant issue in most developing nations, including Ethiopia. Prior research used multiple anthropometric measurements separately to pinpoint undernutrition concerns in children. However, the impact of each explanatory variable on a single response category was not considered in these investigations. This study used a single composite index of anthropometri ...[more]

PMID: 36895335

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Project description:Background:Every student has the potential to do well in school. Failing to provide good nutrition puts them at risk for missing out on meeting that potential and leads to long-term irreversible damage to cognitive development. However, taking action today to provide healthier choices in schools can help set students up for a successful future full of possibilities. So, this study is aimed at assessing the effect of nutritional status on academic performance of school-aged children. Methods:A school-based cross-sectional study was conducted from Feb 05- March 30, 2017 in Lalibela town. A total of 505 primary school students were included in the study. The child anthropometric measures were calculated using AnthroPlus software version 1.0.4 WHO 2007 standards. Bivariable and multivariable ordinal logistic regression were fitted. The proportional odds ratio (POR) with a 95% CI and P value < 0.05 were considered statistically significant. Results:The overall prevalence of stunting, underweight, and thinness was 29.5%, 35.7%, and 29.5%, respectively. In this study, 14.7% of the study participants had low academic achievement in the semester. The multivariable analysis showed that rural residence (POR = 0.39; 95% CI: (0.21, 0.75)), not studying regularly (POR = 0.49; 95% CI: (0.29, 0.82)), severe underweight (POR = 0.25; 95% CI: (0.09, 0.71)), and severe stunting (POR = 0.03; 95% CI: (0.01, 0.37)) were associated with decreased odds of high average semester score achievement of school-aged children. Additionally, higher maternal educational status (POR = 2.12; 95% CI: (1.10, 4.07)) and family income from 550 to 2999 ETB (POR = 1.71; 95% CI: (1.04, 2.81)) were associated with increased odds of high semester average score achievement. Conclusion:Nearly one-third of school-aged children in Lalibela town were stunted, thin, and underweight. Rural place of residence, not studying regularly study, underweight, and stunting were associated with decreased academic achievement. While, family monthly income from 550 to 2999 ETB and higher maternal education status were associated with increased academic achievement.

Project description:BackgroundLogistic random effects models are a popular tool to analyze multilevel also called hierarchical data with a binary or ordinal outcome. Here, we aim to compare different statistical software implementations of these models.MethodsWe used individual patient data from 8509 patients in 231 centers with moderate and severe Traumatic Brain Injury (TBI) enrolled in eight Randomized Controlled Trials (RCTs) and three observational studies. We fitted logistic random effects regression models with the 5-point Glasgow Outcome Scale (GOS) as outcome, both dichotomized as well as ordinal, with center and/or trial as random effects, and as covariates age, motor score, pupil reactivity or trial. We then compared the implementations of frequentist and Bayesian methods to estimate the fixed and random effects. Frequentist approaches included R (lme4), Stata (GLLAMM), SAS (GLIMMIX and NLMIXED), MLwiN ([R]IGLS) and MIXOR, Bayesian approaches included WinBUGS, MLwiN (MCMC), R package MCMCglmm and SAS experimental procedure MCMC.Three data sets (the full data set and two sub-datasets) were analysed using basically two logistic random effects models with either one random effect for the center or two random effects for center and trial. For the ordinal outcome in the full data set also a proportional odds model with a random center effect was fitted.ResultsThe packages gave similar parameter estimates for both the fixed and random effects and for the binary (and ordinal) models for the main study and when based on a relatively large number of level-1 (patient level) data compared to the number of level-2 (hospital level) data. However, when based on relatively sparse data set, i.e. when the numbers of level-1 and level-2 data units were about the same, the frequentist and Bayesian approaches showed somewhat different results. The software implementations differ considerably in flexibility, computation time, and usability. There are also differences in the availability of additional tools for model evaluation, such as diagnostic plots. The experimental SAS (version 9.2) procedure MCMC appeared to be inefficient.ConclusionsOn relatively large data sets, the different software implementations of logistic random effects regression models produced similar results. Thus, for a large data set there seems to be no explicit preference (of course if there is no preference from a philosophical point of view) for either a frequentist or Bayesian approach (if based on vague priors). The choice for a particular implementation may largely depend on the desired flexibility, and the usability of the package. For small data sets the random effects variances are difficult to estimate. In the frequentist approaches the MLE of this variance was often estimated zero with a standard error that is either zero or could not be determined, while for Bayesian methods the estimates could depend on the chosen "non-informative" prior of the variance parameter. The starting value for the variance parameter may be also critical for the convergence of the Markov chain.

Project description:BackgroundAnemia is a major public health problem affecting more than half of children under the age of five globally. It has serious short- and long-term consequences including growth retardation, impaired motor and cognitive development, and increased morbidity and mortality. Despite anemia is the leading cause of child mortality in sub-Saharan Africa, there is limited evidence on the prevalence and determinants of anemia among under-five children in sub-Saharan Africa. Therefore, this study aimed to investigate the prevalence and determinants of severity levels of anemia among children aged 6-59 months in sub-Saharan Africa.MethodsThis study was based on the most recent Demographic and Health Survey (DHS) data of 32 sub-Saharan African countries. A total weighted sample of 135,619 children aged 6-59 months was included in the study. Considering the hierarchical nature of DHS data and the ordinal nature of anemia, a multilevel ordinal logistic regression model was applied. Proportional odds assumption was tested by Brant test and it was satisfied (p-value = 0.091). Besides, deviance was used for model comparison. Variables with a p-value ≤0.2 in the bivariable analysis were considered for the multivariable analysis. In the multivariable multilevel proportional odds model, the Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) were reported for potential determinant factors of severity levels of anemia.ResultsThe overall prevalence of anemia among children aged 6-59 months in sub-Saharan Africa was 64.1% [95% CI: 63.9%, 64.4%]. Of which, 26.2% were mildly anemic, 34.9% moderately anemic and 3% severely anemic. Poor maternal education, lower household wealth status, large family size, being male child, multiple births, having fever in the last two weeks, having diarrhea in the last two weeks, higher-order birth, maternal anemia, underweight, wasted, and stunted were significantly associated with increased odds of higher levels of anemia. Whereas, being 24-59 months age, taking drugs for an intestinal parasite, and born from mothers aged ≥ 20 years were significantly associated with lower odds of higher levels of anemia.ConclusionSeverity levels of anemia among children aged 6-59 months in sub-Saharan Africa was a major public health problem. Enhancing maternal education, providing drugs for an intestinal parasite, designing interventions that address maternal anemia, febrile illness, and diarrheal disease, and strengthening the economic status of the family are recommended to reduce childhood anemia. Furthermore, it is better to strengthen the strategies of early detection and management of stunted, wasted, and underweight children to decrease childhood anemia.

Project description:PurposeResearch on the novel coronavirus diseases 2019 (COVID-19) mainly relies on cross-sectional data, but this approach fails to consider the temporal dimension of the pandemic. This study assesses three temporal dimensions of the COVID-19 infection risk in US counties, namely probability of occurrence, duration of the pandemic, and intensity of transmission, and investigate local patterns of the factors associated with these risks.MethodsAnalyzing daily data between January 22 and September 11, 2020, we categorize the contiguous US counties into four risk groups-High-Risk, Moderate-Risk, Mild-Risk, and Low-Risk-and then apply both conventional (i.e., non-spatial) and geographically weighted (i.e., spatial) ordinal logistic regression model to understand the county-level factors raising the COVID-19 infection risk. The comparisons of various model fit diagnostics indicate that the spatial models better capture the associations between COVID-19 risk and other factors.ResultsThe key findings include (1) High- and Moderate-Risk counties are clustered in the Black Belt, the coastal areas, and Great Lakes regions. (2) Fragile labor markets (e.g., high percentages of unemployed and essential workers) and high housing inequality are associated with higher risks. (3) The Monte Carlo tests suggest that the associations between covariates and COVID-19 risk are spatially non-stationary. For example, counties in the northeastern region and Mississippi Valley experience a stronger impact of essential workers on COVID-19 risk than those in other regions, whereas the association between income ratio and COVID-19 risk is stronger in Texas and Louisiana.ConclusionsThe COVID-19 infection risk levels differ greatly across the US and their associations with structural inequality and sociodemographic composition are spatially non-stationary, suggesting that the same stimulus may not lead to the same change in COVID-19 risk. Potential interventions to lower COVID-19 risk should adopt a place-based perspective.

Project description:IntroductionSoil-transmitted helminth infections and malnutrition are major health problems of school-age children in developing countries. Malnutrition and soil-transmitted helminth infections often co-exist with synergetic consequences.ObjectiveThe aim of this study was to determine the prevalence and intensity of soil-transmitted helminths and its association with nutritional status of school-age children.MethodsSchool-based cross-sectional study was carried out from April to May 2014 among 404 elementary school-age children in Jimma Town, Southwest Ethiopia. Data on background characteristics were collected using structured interviewer administered questionnaire. Anthropometric measurements were taken according to World Health Organization standard. Fresh single stool sample was collected from each study participant and examined using direct wet mount and McMaster techniques. Anthropometric indices were generated using WHO AnthroPlus software. Multivariable logistic regression models were fitted to isolate independent predictors of intestinal parasitic infection and nutritional status using STATA-MP software. All tests were two-sided and P < 0.05 was used to declare statistical significance.ResultsThe overall prevalence of intestinal parasites and soil-transmitted helminths were 68.6% (n = 277) and 55.0% (n = 222), respectively. A total of eight species of intestinal parasites were identified in this study, Trichuris trichiura being the most common parasite identified followed by Ascaris lumbricoides. Study participants who had a habit of open defecation were two times more likely to be infected with soil-transmitted helminths (adjusted odds ratio = 1.9, 95% confidence interval: 1.0-3.4). The overall prevalence of stunting and thinness were 21.0% (n = 85) and 6.9% (n = 28), respectively. The odds of stunting was significantly high (adjusted odds ratio = 4.0, 95% confidence interval: 1.7-9.7) among children who had fathers working as daily labourers and children with personal dietary diversity score of ⩽3 (adjusted odds ratio = 3.5, 95% confidence interval: 1.5-8.0). T. trichiura infection (adjusted odds ratio = 9.4, 95% confidence interval: 2.0-44.8) was identified as an independent predictor of stunting among school-age children.ConclusionBoth the prevalence of soil-transmitted helminths and stunting are high among school-age children in Jimma Town. Although there was no statistically significant association between the STHs, in general, T. trichiura was reported as predictor of stunting. The results imply the need for strengthening strategies for reduction of parasitic infection to curb the pervasively high prevalence of stunting.

Project description:BackgroundGeneralized anxiety disorder is characterized by excessive and uncontrollable worry about a variety of events. It is critical to ensure a pregnant mother's mental health in order to reduce pregnancy and birth-related problems. The major goal of current study was to identify the factors associated with generalized anxiety disorder among mothers attending perinatal services in the study area during COVID-19 using ordinal logistic regression.MethodsThe institution-based cross-sectional study was conducted from July 10th, 2020 to August 10th, 2020 at Kembata Tembaro zone, Southern Ethiopia. The current study included 423 mothers. The GAD-7 scale was used to assess the anxiety level among mothers. An Ordered logit model was used to identify the determinants of GAD. Brant test of the parallel line was utilized to check proportionality assumption. The statistical significance was determined using an adjusted proportional odd ratio with a 95%CI, and a p-value <5%. STATA software version 14 was used to analyze statistical data.ResultsOf all 423 mothers attending perinatal service during COVID-19; 134(31.7%), 171(40.4%), 85(20.1%), and 33 (7.8%) had non/minimal to severe generalized anxiety disorder respectively. The results of multivariable proportional odds model (POM) showed that the variables town residents [aPOR = 1.827; 95% CI:1.233-2.708], having alcohol habit [aPOR = 3.437, 95% CI = 1.397-8.454], having occupation [aPOR = 0.509, 95% CI: 0.303-0.857], being health care worker [aPOR = 0.117, 95% CI = 0.044-0.311], having chronic illness [aPOR = 7.685, 95% CI = 3.045-19.39], having family history of anxiety/mood disorder [aPOR = 7.839, 95% CI = 2.656-23.12], fear of contracting COVID-19 [aPOR = 1.704, 95% CI = 1.152-2.521], having moderate social support [aPOR = 0.648, 95% CI = 0.425-0.989], having strong social support [aPOR = 0.495, 95% CI = 0.272-0.901] were significantly associated with generalized anxiety disorder at 5% level of significance.ConclusionCurrent findings concluded that the prevalence of GAD among mothers attending perinatal service during COVID-19 was high. The covariates like being town resident, lower-income status, occupation status, having a chronic illness, having a positive family history of anxiety or mood disorder, perceived social support, and fear of the COVID-19 were significantly associated with generalized anxiety disorder among mothers. Mothers who visit perinatal services should be given special consideration to improve health care services and ensure their mental health.

Dataset Information

Determinants of nutritional status among primary school students in Dilla Town; Application of an ordinal logistic regression model.

Background

Methods

Results

Conclusion

Publications

Determinants of nutritional status among primary school students in Dilla Town; Application of an ordinal logistic regression model.

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