Preventive medicine
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Preventive medicine · Mar 2020
'Home is where the health is': Housing quality and adult health outcomes in the Survey of Income and Program Participation.
Nearly a quarter of the homes in the United States were considered unhealthy or inadequate, but whether these housing characteristics have direct effects on health or whether they are driven by other contextual housing and neighborhood characteristics remains unclear. The purpose of this study was to quantify the independent associations between poor housing quality and adult health outcomes, adjusting for socioeconomic factors (e.g. income to poverty ratio, food insecurity) and other contextual housing characteristics (e.g. rental status, number of people per household, unsafe neighborhood). Using in-person household interview data from wave 1 of the 2014 Survey of Income and Program Participation (SIPP), a secondary analysis was performed using a series of logistic regression models. ⋯ Non-housing-related government assistance, food security, and safe neighborhoods only partially explained associations between housing quality and health outcomes. Evaluating current local, state, and federal policy on housing quality standards may help determine if these standards decrease the number of Americans residing in inadequate homes or result in improvements in health and reductions in healthcare costs. Simply put, the home is where [we suggest] the health is.
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Preventive medicine · Mar 2020
Development of a prediction model to target screening for high blood pressure in children.
Targeted screening for childhood high blood pressure may be more feasible than routine blood pressure measurement in all children to avoid unnecessary harms, overdiagnosis or costs. Targeting maybe based e.g. on being overweight, but information on other predictors may also be useful. Therefore, we aimed to develop a multivariable diagnostic prediction model to select children aged 9-10 years for blood pressure measurement. ⋯ Using the model and a cut-off of 5% for predicted risk, sensitivity and specificity were 59% and 76%; using child overweight only, sensitivity and specificity were 47% and 84%. In conclusion, our diagnostic prediction model uses easily obtainable information to identify children at increased risk of high blood pressure, offering an opportunity for targeted screening. This model enables to detect a higher proportion of children with high blood pressure than a strategy based on child overweight only.
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Preventive medicine · Mar 2020
Rural-urban differences in cannabis detected in fatally injured drivers in the United States.
While there is a vast literature on rural and urban differences in substance use, little is known in terms of cannabis positive drug tests among fatally injured drivers. In the present study, we examined rural-urban differences in cannabis detected in fatally-injured drivers. Data were drawn from the 2015-2017 Fatality Analysis Reporting System. ⋯ Those aged at least 25 years had lower odds of a positive test for cannabinoids. Drivers involved in a weekend nighttime crash (aOR: 1.14, 95% CI 1.03-1.26) and weekday nighttime (aOR: 1.15, 95% CI 1.05-1.26) had higher odds of testing positive for cannabinoids compared to drivers involved in a weekend daytime crash. Results showed significant rural-urban differences in the prevalence of cannabis detected in fatally-injured drivers.
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Preventive medicine · Mar 2020
The association between health care coverage and prevalence of cardiovascular diseases and diabetes over a 10-year period.
Persons without health care coverage have poorer health outcomes. We investigated the association between health care coverage and trends in the prevalence of cardiovascular disease (CVD) and diabetes pre- and post-Affordable Care Act (ACA) periods. Using data from 3,824,678 surveyed adults in the Behavioral Risk Factor Surveillance System survey from 2007 - 2016, we calculated the yearly prevalence of CVD and diabetes. ⋯ The prevalence of CVD and diabetes increased from pre- to post-ACA periods. After adjustment, in pre-ACA period, the odds ratio (OR) for the association between health care coverage and CVD and diabetes was 1.32 (95% CI:1.30-1.34) and 1.44 (95% CI:1.41-1.46), respectively; in the post-ACA period, the OR was 1.26 (95% CI:1.22-1.30) and 1.48 (95% CI:1.44-1.52), respectively. We found a significant association between health care coverage and trends in the prevalence of CVD and diabetes in the pre- and post-ACA periods.