Multilevel Analysis Of Risk Factors Research Articles

Multilevel analysis of risk factors associated with severe underweight among children under 5 years: evidence from the 2014 Ghana Demographic and Health Survey

Background: Undernutrition among children <5yr. is a global public health problem, especially in developing countries like Ghana. Undernutrition increases the risk of child morbidity and mortality. There is paucity of data on household-level effects on severe underweight and associated factors. Objective: The study investigated the risk factors associated with severe underweight among children < 5yr.in Ghana and examined unobserved differences across households based on the 2014 Ghana Demographic and Health surveys (GDHS) data. Methods: Data from a population-based cross-sectional study was obtained from the 2014 Ghana Demographic and Health Survey. Data on 2720 children nested within 1972 households was extracted for analysis. Our regression analysis used data on 2716 children with complete data on the outcome and risk factors. Children with weight-for-age Z-score below -3 standard deviations were classified as severely underweight based on the 2006 WHO child growth standards. We applied random intercept multilevel logistic regression to examine whether severe underweight status in children differ across households while simultaneously identifying potential risk factors. Results: A total of 2720 children had valid weight-for-age z-score and 53 (1.95%) of them were identified as severely underweight. In the univariate model, child level variables such as multiple birth [odds ratio (OR), 4.03;95%confidence interval (CI): 1.85-8.76]and child born average (OR, 2.17;95%CI: 1.09-4.32) or small (OR, 4.08; 95%CI: 2.01-8.28) in size at birth are associated with increased odds of severe underweight. Maternal/household level variables such as increase in number of children below 5 yr. (OR, 1.61; 95%CI: 1.28-2.04), poorest households (OR, 4.85; 95%CI: 1.14-20.59) and increase in number of births in last 5 yr. (OR, 1.80, 95%CI: 1.24-2.60) were associated with increased odds of severe underweight. Increase in maternal years of education (OR, 0.89; 95%CI: 0.83-0.95) was associated with reduced odds of severe underweight. In the multilevel logistic model, only type of birth (OR, 1.61;95%CI: 1.28-2.04), size of child (average: OR, 2.12;95%CI: 1.04-4.33; small: OR, 3.87;95%CI: 1.80-8.33) at birth, and maternal education (OR, 0.92;95%CI: 0.84-1.00) were independently associated with severe underweight. There were no significant residual household-level variations in severe underweight status. Conclusion: Our findings suggest that improving maternal education, socioeconomic conditions of families, and family planning are critical in addressing severe underweight

School staff perpetration of physical violence against students in Uganda: a multilevel analysis of risk factors

ObjectiveTo conduct a multilevel analysis of risk factors for physical violence perpetration by school staff against Ugandan students.DesignMultilevel logistic regression analysis of cross-sectional survey data from 499 staff and 828...

Mapping an agenda for the study of youth sexual aggression in Europe: assessment, principles of good practice, and the multilevel analysis of risk factors

ABSTRACTSexual aggression is a serious threat to young people's sexual health in Europe, but establishing the exact scale of the problem has been hampered by a variety of conceptual and methodological problems. This article presents a framework for studying youth sexual aggression that addresses both prevalence and risk factors of victimisation and perpetration. It proposes a research tool to comprehensively assess the perpetration of, and victimisation by, sexual aggression that captures different coercive strategies, sexual acts, victim–perpetrator relations, and gender constellations. The instrument is rooted in a clear conceptual definition of sexual aggression and was pilot-tested in 10 countries of the European Union (EU). Furthermore, a list of good practice criteria is proposed to promote the quality and comparability of research on youth sexual aggression in Europe. A multilevel approach combining individual-level and country-level predictors of sexual aggression is outlined and illustrated with data from the pilot study in 10 countries.

A Multi-level Analysis of Risk Factors for Depression Among Elderly Women: Estimating the Effects of Individual Traits and Government Intervention

A Multi-level Analysis of Risk Factors for Depression Among Elderly Women: Estimating the Effects of Individual Traits and Government Intervention

A multi-level analysis of risk factors for Schistosoma japonicum infection in China

The aim of this study was to explore the risk factors of schistosomiasis japonica in China, using a hierarchical multi-level model with individuals nested within villages. A cross-sectional survey of schistosomiasis japonica was conducted in 16 villages in the Chinese province of Hunan. A multi-level modeling technique (HLM version 6.04) was used to assess risk factors of schistosomiasis. The results from this multi-level model were compared with those from a conventional single-level logistic regression model. A total of 10,245 individuals were enrolled in this study, of whom about 4.1% were infected with Schistosoma japonicum. In the multi-level model analysis, individual level variables such as gender, age, and occupation, and village level variables such as type of S. japonicum endemic area, drinking water source, sewage treatment, June temperature, and April rainfall were associated with schistosomiasis japonica infection. Conventional single-level logistic regression analysis selected more independent variables, and had narrower confidence intervals around the corresponding regression coefficients. In particular, per capita income, precipitation in October, and density of infected snails were statistically significant in the conventional single-level logistic regression analysis but not in the multi-level model. Multi-level modeling is a useful tool in the analysis of risk factors of schistosomiasis japonica. Because the multi-level model captures the hierarchical structure of the data, it may be considered a more appropriate analytical tool for data of this type. This technique may also be useful in the analysis of other infectious diseases with a similar hierarchical structure.

Multilevel analysis of risk factors for Salmonella shedding in Ontario finishing pigs

The objectives of this study were to identify factors associated with Salmonella status at the farm, pen, and pig level; explore the nature of variation in the association between the pen-level Salmonella status and pen-level covariates, and pig-level Salmonella status and pig- and pen-level covariates; and to identify the relative importance of factors operating at geographical, farm, and pen level for Salmonella shedding of pigs. For these purposes, samples from 799 pigs and 374 pens on 80 farms in Ontario in 2004 were collected and bacteriologically tested in a cross-sectional study. Census division was the least variable level, and farm the most variable level for shedding. Increased frequency of disinfection and washing with cold water were positively associated with Salmonella positivity, whereas liquid and mash feed and completely closed barns were sparing factors. After farm, pen was the second most variable level for shedding. However, no measured pen-level variables were associated with Salmonella status of pigs or pens. The shedding of Salmonella at the pig level tended to be associated with pig weight, and there was no random variation around this association. Results of this study suggest that a herd test based on bacteriological culture would probably have higher sensitivity if growing animals of lower weight were sampled instead of market weight animals, and this might be beneficial for Salmonella monitoring.

Multilevel Analysis of Risk Factors Related to Musculoskeletal Symptoms among Caterers for Elementary School Lunch Services

Dept. of Preventive Medicine, Ewha Womans University, Medical College, Armed Forces Medical Command , Institute for Occupational and Environmental Health, Korea University , Federation of Korean Trade Unions Industrial Safety Bureau , Dept. of Occupational Medicine. Hallym University Sacred Heart Hospital , Dept. of Occupational and Environmental Medicine, Dankook University , Dept. of Preventive Medicine. Eulji University School of Medicine , School of Public Health and Institute of Health and Environment, Seoul National University )