The urban safety net: can it keep people healthy and out of the hospital?
There is much discussion and debate over the relative vulnerability and capacity of the health care safety net to care for the growing numbers of uninsured and disenfranchised persons in urban poor communities. In this study, we present findings from a community-based survey of 248 adults identified at eight safety net provider sites in Baltimore, Maryland, to contextualize recent findings that described Baltimore's safety net capacity as having more hospital- and intensive service-based interventions, with higher proportions of the population reportedly unable to get care when needed compared with other cities. The average age of respondents was 41.2 years, most (87.3%) were African American, unemployed (75.8%), homeless (57.0%), and with at least one chronic medical problem (77.8%). Almost one half (47.6%) also reported a chronic mental health condition, and 51.2% reported having difficulty accessing health care services in the past. Overall, 76.9% reported accessing additional community sites for daily sustenance needs, with most of these sites community non-profit or faith-based organizations. In the multiple logistic regression model, only individuals with chronic mental health conditions were significantly more likely to report difficulties accessing health care. The lack of a Community Access Program or other structured efforts to facilitate integration of services among providers in Baltimore and an "all-payer" system that reimburses uncompensated care only for hospital admissions are postulated as two structural elements that may contribute to these findings.
O'Toole
Thomas P
TP
Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA. totoole@jhmi.edu
Arbelaez
Jose
J
Haggerty
Christine
C
D000328:Adult; D015142:Baltimore; D003153:Community Health Services / Q000706:statistics & numerical data*; D019538:Health Care Surveys; D006297:Health Services Accessibility; D006703:Ill-Housed Persons / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D017057:Medically Uninsured / Q000706:statistics & numerical data; D017664:Uncompensated Care; D014481:United States; D019037:Urban Health Services / Q000706:statistics & numerical data*