Associations between work schedule characteristics and occupational injury and illness.

BACKGROUND: Nurses often endure working irregular day, night and evening shifts as well as mandatory overtime (i.e. employer-imposed work time in excess of one's assigned schedule). While these work characteristics are examined as potential risks for nurses' safety and health, it is not clear whether negative health impacts occur simply because of working long hours or in combination with other mechanisms. AIM: This study investigates how these work characteristics are associated with nurses' work-related injury and illness over and above long work hours. METHODS: In this cross-sectional study, questionnaire data were collected from a sample of 655 registered nurses in the Philippines. Multiple logistic regression was used to assess associations of shift work and mandatory overtime with four work-related health outcomes. RESULTS: After weekly work hours, shift length and demographic variables were accounted for, non-day shifts were associated with work-related injury [odds ratio (OR) = 1.54; 95% confidence interval (CI): 1.07, 2.24] and work-related illness (OR = 1.48; 95% CI: 1.02, 2.16). Also, frequency of working mandatory overtime was associated with work-related injury (OR = 1.22; 95% CI: 1.06, 1.41), work-related illness (OR = 1.19; 95% CI: 1.04, 1.37) and missing more than 2 days of work because of a work-related injury or illness (OR = 1.25; 95% CI: 1.08, 1.44). CONCLUSIONS: These findings suggest that non-day shifts and mandatory overtime may negatively impact nurses' health independent of working long hours. Mechanisms through which these work characteristics affect health, such as circadian rhythm disturbance, nurse-to-patient ratios and work-family conflict, should be examined in future studies.

Neighbourhood environment as a predictor of television watching among girls.

BACKGROUND: Television watching, a sedentary activity, has been associated with overweight in children. While the family environment is known to influence television watching, little is known about the influence of the neighbourhood environment. This study is an exploratory examination of the association of socioeconomic characteristics of the neighbourhood environment with television watching among 9-10 year old girls. METHODS: Data collected by the Berkeley site of the National Heart, Lung and Blood Institute Growth and Health Study (NGHS) in 1987-8 from 787 girls who had a complete set of measurements relevant to the analysis were used. These measures included parental education, household income, race and weekly hours spent watching television. Addresses of the girls were geocoded and the median household income for the census tracts in which they lived was used to indicate neighbourhood socioeconomic characteristics. Multilevel modelling procedures were used to estimate fixed effect coefficients for individual and neighbourhood level variables. RESULTS: Living in high income areas was associated with less television watching, a finding that held even when controlling for parental education, household income and race. Race and parental education were also associated with television watching. CONCLUSION: Television watching among girls was associated not only with the socioeconomic characteristics of their households, but also of their neighbourhoods. Future studies should explore the mechanisms that mediate this relation and determine if these results are generalisable to other populations.

Levels of analysis for the study of environmental health disparities.

Reducing racial/ethnic and socioeconomic environmental health disparities requires a comprehensive multilevel conceptual and quantitative approach that recognizes the various levels through which environmental health disparities are produced and perpetuated. We propose a conceptual framework that incorporates the micro level, contained within the local level, which in turn is contained within the macro level. We discuss the utility of multilevel techniques to examine environmental level (both physical and social) and individual-level factors to appropriately quantify and improve our understanding of environmental health disparities. We discuss the reasoning and the methodological approach behind multilevel modeling, including differentiating between individual and contextual influences on individual outcomes. Next we address the questions and principles that guide the choice of levels or geographic units in multilevel studies. Finally, we address the ways in which different data sources can be combined to produce suitable data for multilevel analyses. We provide some examples of how such data sources can be linked to create multilevel data structures, and offer suggestions to facilitate the integration of multilevel techniques in environmental health disparities research and monitoring.