American journal of epidemiology
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A recent trial showed that universal decolonization in adult intensive care units (ICUs) resulted in greater reductions in all bloodstream infections and clinical isolates of methicillin-resistant Staphylococcus aureus (MRSA) than either targeted decolonization or screening and isolation. Since regional health-care facilities are highly interconnected through patient-sharing, focusing on individual ICUs may miss the broader impact of decolonization. Using our Regional Healthcare Ecosystem Analyst simulation model of all health-care facilities in Orange County, California, we evaluated the impact of chlorhexidine baths and mupirocin on all ICU admissions when universal decolonization was implemented for 25%, 50%, 75%, and 100% of ICU beds countywide (compared with screening and contact precautions). ⋯ MRSA prevalence decreased by a relative 3.2% countywide, with similar effects for methicillin-susceptible S. aureus. We showed that a large proportion of decolonization's benefits are missed when accounting only for ICU impact. Approximately 70% of the countywide cases of MRSA carriage averted after 1 year of universal ICU decolonization were outside the ICU.
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We live in the era of genomics and big data. Evaluating the impact on health of large-scale biological, social, and environmental data is an emerging challenge in the field of epidemiology. In the past 3 years, major discussions and plans for the future of epidemiology, including with several recommendations for actions to transform the field, have been launched by 2 institutes within the National Institutes of Health. ⋯ Ongoing engagement within the epidemiology community is needed to determine how to shape the evolution of the field and what truly matters for changing population health. We also need to assess how to leverage existing epidemiology resources and develop new studies to improve human health. Readers are invited to examine these recommendations, consider others that might be important, and join in the conversation about the future of epidemiology.
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Thiazolidinediones, a class of medications indicated for the treatment of type 2 diabetes mellitus, reduce inflammation and have been shown to provide a therapeutic benefit in animal models of Parkinson disease. We examined the association between treatment with thiazolidinediones and the onset of Parkinson disease in older individuals. We performed a cohort study of 29,397 Medicare patients enrolled in state pharmaceutical benefits programs who initiated treatment with thiazolidinediones or sulfonylureas during the years 1997 through 2005 and had no prior diagnosis of Parkinson disease. ⋯ In the primary analysis, thiazolidinedione users had a hazard ratio for a diagnosis of Parkinson disease of 1.09 (95% confidence interval: 0.71, 1.66) when compared with sulfonylurea users. Increasing the duration-of-use requirements to 10 months did not substantially change the association; the hazard ratios ranged from 1.00 (95% confidence interval: 0.49, 2.05) to 1.17 (95% confidence interval: 0.60, 2.25). Thiazolidinedione use was not associated with a longer time to diagnosis of Parkinson disease than was sulfonylurea use, regardless of duration of exposure.
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Researchers conducting observational studies need to consider 3 types of biases: selection bias, information bias, and confounding bias. A whole arsenal of statistical tools can be used to deal with information and confounding biases. ⋯ In this paper, we propose general bounding formulas for bias, including selection bias and unmeasured confounding. This should help researchers make more prudent interpretations of their (potentially biased) results.