Changes in Preventable Hospitalization Patterns Among the Adults

Abstract

The study examines the variation and changes in preventable hospitalization (PH) rates across small areas over 1995-2005 in 5 US states for adults (aged 18-64 years). Using hospital discharge data from the Agency for Healthcare Research and Quality and contextual data from Health Resources and Services Administration, the study examines the role of managed care, primary care physician supply, and sociodemographic factors on adult PH rates. A stronger influence of minority and uninsured status, weaker contributions of managed care enrollment in the commercial as well as in the Medicaid markets, and weaker contributions of primary care density may have caused slower than expected reduction in adult PH rates.

Disparities in Potentially Preventable Hospitalizations: Near‐National Estimates for Hispanics

“…To estimate adjusted differences by race-ethnicity in PHH rates, we used area-level covariates covering five domains: clinical risk, SES, health care access, acculturation, and provider availability (DeLia 2003;Hadley and Cunningham 2004;Vargas et al 2004;Basu, Thumula, and Mobley 2012). To adjust for variations in prevalence of clinical risk factors, we used individuallevel BRFSS 2011 data from the 15 study states on prevalence of five risk indicators (hypertension, high cholesterol, diabetes, coronary heart disease, and COPD/asthma) and produced aggregate prevalence rate of each indicator for the aforementioned age-sex-race-ethnicity demographic cohorts (N = 24) by state; the top half of the cohorts by prevalence in each state were categorized as having "high" prevalence and the remaining cohorts as having "low" prevalence.…”

“…However, these studies were limited to local or regional analyses for Hispanics (Djojonegoro et al. ; Cable ; DeLia ; Basu, Thumula, and Mobley ); obtaining the national PPH rate has been challenging due to high rates of misclassification and incompleteness of Hispanic ethnicity data. Medicare utilization data, the principal source for a wide range of national estimates of black–white disparities, are “completely unreliable for the Hispanic population” (Arias ) as a large proportion—differently estimated at 8 percent (Arias ) and 33 percent (McBean )—are misclassified, primarily as whites.…”

“…Our second objective was to examine the role of potential mediators of race-ethnic disparities in PPH rates, focusing on factors identified in previous studies (DeLia 2003;Hadley and Cunningham 2004;Vargas et al 2004;Basu, Thumula, and Mobley 2012), including prevalence of chronic conditions, SES, lack of insurance, cost barriers, acculturation, provider availability, and rural/urban location. An advantage of our database, over that developed by AHRQ, is that it includes all nonfederal short-term acute hospitals and discharges, and thus permits analysis of small area variations in the aforementioned factors; the AHRQ disparities analysis database is based on 40 percent sample of the nonfederal short-term acute hospitals.…”

Understanding Hierarchical Structure Construction Strategies and Biomimetic Design Principles: A Review

“…[269] For example, unique optical properties can provide functions for organisms, such as protection, mating, and information transfer. [270,271] Figure 12a-d shows the appearance and skeletal structure of two species of Ophiocomid brittle stars, with different features of photosensitivity. [32] Ophiocoma pumila exhibits no color change and little reaction, whereas Ophiocoma wendtii is an extremely photosensitive species.…”