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      A Multilevel Analysis of Factors Associated with Childhood Diarrhea in Ethiopia

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          Abstract

          Background:

          Childhood diarrhea is the major contributor to the deaths of children under the age of 5 years in Ethiopia, but evidence at the national level to identify the contributing factors associated with diarrhea by considering the clustering effects is limited. Hence, this study aimed to identify factors associated with childhood diarrhea at the individual and community levels.

          Methods:

          A secondary data analysis was conducted using the 2011 and 2016 Ethiopian Demographic and Health Survey (EDHS) data. A total of 23 321 children with their mothers were included in this study, and multilevel logistic regression models were applied for the data analysis.

          Results:

          The odds of diarrhea among female children were 13% lower (AOR = 0.87; 95% CI: 0.79-0.94) compared with male children. The odds of diarrhea among children aged between 13 and 24 months were 31% higher than (AOR = 1.31; 95% CI: 1.17-1.47) their younger counter parts. Children aged ⩾25 months (AOR = 0.50; 95% CI: 0.45-0.56), those whose mothers were unemployed (AOR = 0.79; 95% CI: 0.73-0.87), and children live in households between 2 and 3 under-5 children (AOR = 0.87; 95% CI: 0.79-0.96) were associated with lower odds of experiencing diarrhea. The odds of diarrhea among children whose mother had no formal education were 49% higher than (AOR = 1.49; 95% CI: 1.08-2.07) their counterparts. Besides, children residing in city administrations (AOR = 0.69; 95% CI: 0.58-0.82) had lower odds of experiencing diarrhea than children living in agrarian regions.

          Conclusions:

          At the individual level (sex and age of the child, mother’s employment status, and educational level, and the number of under-5 children) and the community-level (contextual region) were found to be significant factors associated with childhood diarrhea in Ethiopia.

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          Most cited references43

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          Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016

          Summary Background Monitoring levels and trends in premature mortality is crucial to understanding how societies can address prominent sources of early death. The Global Burden of Disease 2016 Study (GBD 2016) provides a comprehensive assessment of cause-specific mortality for 264 causes in 195 locations from 1980 to 2016. This assessment includes evaluation of the expected epidemiological transition with changes in development and where local patterns deviate from these trends. Methods We estimated cause-specific deaths and years of life lost (YLLs) by age, sex, geography, and year. YLLs were calculated from the sum of each death multiplied by the standard life expectancy at each age. We used the GBD cause of death database composed of: vital registration (VR) data corrected for under-registration and garbage coding; national and subnational verbal autopsy (VA) studies corrected for garbage coding; and other sources including surveys and surveillance systems for specific causes such as maternal mortality. To facilitate assessment of quality, we reported on the fraction of deaths assigned to GBD Level 1 or Level 2 causes that cannot be underlying causes of death (major garbage codes) by location and year. Based on completeness, garbage coding, cause list detail, and time periods covered, we provided an overall data quality rating for each location with scores ranging from 0 stars (worst) to 5 stars (best). We used robust statistical methods including the Cause of Death Ensemble model (CODEm) to generate estimates for each location, year, age, and sex. We assessed observed and expected levels and trends of cause-specific deaths in relation to the Socio-demographic Index (SDI), a summary indicator derived from measures of average income per capita, educational attainment, and total fertility, with locations grouped into quintiles by SDI. Relative to GBD 2015, we expanded the GBD cause hierarchy by 18 causes of death for GBD 2016. Findings The quality of available data varied by location. Data quality in 25 countries rated in the highest category (5 stars), while 48, 30, 21, and 44 countries were rated at each of the succeeding data quality levels. Vital registration or verbal autopsy data were not available in 27 countries, resulting in the assignment of a zero value for data quality. Deaths from non-communicable diseases (NCDs) represented 72·3% (95% uncertainty interval [UI] 71·2–73·2) of deaths in 2016 with 19·3% (18·5–20·4) of deaths in that year occurring from communicable, maternal, neonatal, and nutritional (CMNN) diseases and a further 8·43% (8·00–8·67) from injuries. Although age-standardised rates of death from NCDs decreased globally between 2006 and 2016, total numbers of these deaths increased; both numbers and age-standardised rates of death from CMNN causes decreased in the decade 2006–16—age-standardised rates of deaths from injuries decreased but total numbers varied little. In 2016, the three leading global causes of death in children under-5 were lower respiratory infections, neonatal preterm birth complications, and neonatal encephalopathy due to birth asphyxia and trauma, combined resulting in 1·80 million deaths (95% UI 1·59 million to 1·89 million). Between 1990 and 2016, a profound shift toward deaths at older ages occurred with a 178% (95% UI 176–181) increase in deaths in ages 90–94 years and a 210% (208–212) increase in deaths older than age 95 years. The ten leading causes by rates of age-standardised YLL significantly decreased from 2006 to 2016 (median annualised rate of change was a decrease of 2·89%); the median annualised rate of change for all other causes was lower (a decrease of 1·59%) during the same interval. Globally, the five leading causes of total YLLs in 2016 were cardiovascular diseases; diarrhoea, lower respiratory infections, and other common infectious diseases; neoplasms; neonatal disorders; and HIV/AIDS and tuberculosis. At a finer level of disaggregation within cause groupings, the ten leading causes of total YLLs in 2016 were ischaemic heart disease, cerebrovascular disease, lower respiratory infections, diarrhoeal diseases, road injuries, malaria, neonatal preterm birth complications, HIV/AIDS, chronic obstructive pulmonary disease, and neonatal encephalopathy due to birth asphyxia and trauma. Ischaemic heart disease was the leading cause of total YLLs in 113 countries for men and 97 countries for women. Comparisons of observed levels of YLLs by countries, relative to the level of YLLs expected on the basis of SDI alone, highlighted distinct regional patterns including the greater than expected level of YLLs from malaria and from HIV/AIDS across sub-Saharan Africa; diabetes mellitus, especially in Oceania; interpersonal violence, notably within Latin America and the Caribbean; and cardiomyopathy and myocarditis, particularly in eastern and central Europe. The level of YLLs from ischaemic heart disease was less than expected in 117 of 195 locations. Other leading causes of YLLs for which YLLs were notably lower than expected included neonatal preterm birth complications in many locations in both south Asia and southeast Asia, and cerebrovascular disease in western Europe. Interpretation The past 37 years have featured declining rates of communicable, maternal, neonatal, and nutritional diseases across all quintiles of SDI, with faster than expected gains for many locations relative to their SDI. A global shift towards deaths at older ages suggests success in reducing many causes of early death. YLLs have increased globally for causes such as diabetes mellitus or some neoplasms, and in some locations for causes such as drug use disorders, and conflict and terrorism. Increasing levels of YLLs might reflect outcomes from conditions that required high levels of care but for which effective treatments remain elusive, potentially increasing costs to health systems. Funding Bill & Melinda Gates Foundation.
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            Estimates of the global, regional, and national morbidity, mortality, and aetiologies of diarrhoea in 195 countries: a systematic analysis for the Global Burden of Disease Study 2016

            Summary Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 provides an up-to-date analysis of the burden of diarrhoea in 195 countries. This study assesses cases, deaths, and aetiologies in 1990–2016 and assesses how the burden of diarrhoea has changed in people of all ages. Methods We modelled diarrhoea mortality with a Bayesian hierarchical modelling platform that evaluates a wide range of covariates and model types on the basis of vital registration and verbal autopsy data. We modelled diarrhoea incidence with a compartmental meta-regression tool that enforces an association between incidence and prevalence, and relies on scientific literature, population representative surveys, and health-care data. Diarrhoea deaths and episodes were attributed to 13 pathogens by use of a counterfactual population attributable fraction approach. Diarrhoea risk factors are also based on counterfactual estimates of risk exposure and the association between the risk and diarrhoea. Each modelled estimate accounted for uncertainty. Findings In 2016, diarrhoea was the eighth leading cause of death among all ages (1 655 944 deaths, 95% uncertainty interval [UI] 1 244 073–2 366 552) and the fifth leading cause of death among children younger than 5 years (446 000 deaths, 390 894–504 613). Rotavirus was the leading aetiology for diarrhoea mortality among children younger than 5 years (128 515 deaths, 105 138–155 133) and among all ages (228 047 deaths, 183 526–292 737). Childhood wasting (low weight-for-height score), unsafe water, and unsafe sanitation were the leading risk factors for diarrhoea, responsible for 80·4% (95% UI 68·2–85·0), 72·1% (34·0–91·4), and 56·4% (49·3–62·7) of diarrhoea deaths in children younger than 5 years, respectively. Prevention of wasting in 1762 children (95% UI 1521–2170) could avert one death from diarrhoea. Interpretation Substantial progress has been made globally in reducing the burden of diarrhoeal diseases, driven by decreases in several primary risk factors. However, this reduction has not been equal across locations, and burden among adults older than 70 years requires attention. Funding Bill & Melinda Gates Foundation.
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              Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC).

              This article reviews the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) in model selection and the appraisal of psychological theory. The focus is on latent variable models, given their growing use in theory testing and construction. Theoretical statistical results in regression are discussed, and more important issues are illustrated with novel simulations involving latent variable models including factor analysis, latent profile analysis, and factor mixture models. Asymptotically, the BIC is consistent, in that it will select the true model if, among other assumptions, the true model is among the candidate models considered. The AIC is not consistent under these circumstances. When the true model is not in the candidate model set the AIC is efficient, in that it will asymptotically choose whichever model minimizes the mean squared error of prediction/estimation. The BIC is not efficient under these circumstances. Unlike the BIC, the AIC also has a minimax property, in that it can minimize the maximum possible risk in finite sample sizes. In sum, the AIC and BIC have quite different properties that require different assumptions, and applied researchers and methodologists alike will benefit from improved understanding of the asymptotic and finite-sample behavior of these criteria. The ultimate decision to use the AIC or BIC depends on many factors, including the loss function employed, the study's methodological design, the substantive research question, and the notion of a true model and its applicability to the study at hand. (c) 2012 APA, all rights reserved
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                Author and article information

                Journal
                Environ Health Insights
                Environ Health Insights
                EHI
                spehi
                Environmental Health Insights
                SAGE Publications (Sage UK: London, England )
                1178-6302
                15 April 2021
                2021
                : 15
                : 11786302211009894
                Affiliations
                [1 ]Department of Public Health, Madda Walabu University Goba Referral Hospital, Bale-Goba, Ethiopia
                [2 ]Department of Midwifery, Goba Referral Hospital, School of Health Science, Madda Walabu University, Bale-Goba, Ethiopia
                [3 ]Department of Human Anatomy, Goba Referral Hospital, School of Health Science, Madda Walabu University, Bale-Goba, Ethiopia
                [4 ]Department of Medicine, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
                Author notes
                [*]Biniyam Sahiledengle, Department of Public Health, Madda Walabu University Goba Referral Hospital, P.O. Box 302, Bale-Goba, Ethiopia. Email: biniyam.sahiledengle@ 123456gmail.com
                Author information
                https://orcid.org/0000-0002-1114-4849
                https://orcid.org/0000-0001-6628-8180
                https://orcid.org/0000-0003-4112-7764
                https://orcid.org/0000-0002-2968-4958
                Article
                10.1177_11786302211009894
                10.1177/11786302211009894
                8056729
                33953568
                66f199ed-a67a-4d89-a8b2-ed2e57af2a8d
                © The Author(s) 2021

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 11 December 2020
                : 18 March 2021
                Categories
                Original Research
                Custom metadata
                January-December 2021
                ts1

                Public health
                diarrhea,ethiopia,edhs,multilevel,pooled data,under-5 children
                Public health
                diarrhea, ethiopia, edhs, multilevel, pooled data, under-5 children

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