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-  2019 

Regional Risk: Mapping Single and Multiple Chronic Conditions in the United States

DOI: 10.1177/2158244018822385

Keywords: adult,Behavioral Risk Factor Surveillance System,multiple chronic conditions,hierarchical logistic modeling,population surveillance

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Abstract:

Prevalence of single and multiple chronic conditions continues to increase in the United States. Chronic conditions predict significant morbidity and health care costs, especially when complicated by additional conditions. Likewise, many conditions are linked to health risk behaviors, and thus amenable to prevention. We examine regional differences in prevalence of single and multiple chronic conditions. In the process, we examine the ability of health risk behaviors to predict condition prevalence in each region. We recommend national prevention strategies with targeted content for specific geographic regions. We used 2009 Behavioral Risk Factor Surveillance System (BRFSS) data (N = 432,607) for all analyses. After grouping states into nine U.S. Census divisions, we fitted generalized linear mixed regression models and compared regional odds ratios with national averages. Analyses controlled for helpful and harmful behaviors, health insurance coverage, and demographic characteristics. Odds ratios for single and multiple chronic conditions deviated significantly from national averages in all nine regions. Health behaviors significantly predicted prevalence for both single and multiple conditions within regions, but differences in behaviors between regions did not fully account for observed disparities in prevalence. Significant regional differences in disease prevalence suggest priority areas for prevention efforts. Promoting healthy behaviors and mitigating harmful behaviors in high-risk regions may help to reduce overall chronic condition prevalence, but is unlikely to obviate disparities between regions. Targeted needs assessment should be conducted within each region with higher-than-average risk to determine intervention strategies with the greatest likelihood of near-term impact

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