Recent case reports in medical literatures suggest that more and more second-generation atypical antipsychotics (AAs) have been prescribed for off-label use; quetiapine (Brand name: Seroquel^®) showed increase in its trend for off-label use. Little is known about the reasons behind this trend, although historical sedative and hypnotic prescription patterns suggest that despite relatively superior safety profiles of quetiapine (especially for movement disorders), it may be used for treating substance abuse disorder. In addition, recent studies have shown a strong potential for misuse and abuse (MUA) of quetiapine beyond Food and Drug Administration-approved indications. This includes drug-seeking behaviors, such as feigning symptoms, motivated by quetiapine and use of quetiapine in conjunction with alcohol. Quetiapine appears to be the most documented AA with street values bartered illicitly on the street. A recent report from the Drug Abuse Warning Network has shown a high prevalence of quetiapine-related emergency department visits involving MUA. Several other case studies have found that quetiapine causes seeking behaviors observed in substance use disorder. In fact, the majority of quetiapine MUA involved patients diagnosed with substance use disorder. In the absence of a definitive mechanism of action of quetiapine's reinforcing properties, it is imperative to gather robust evidence to support or refute increasing off-label use of AAs.

Cancer screening tests are important tools to combat cancer-related morbidity and mortality. There is limited up-to-date research on spatial and temporal variations of colorectal and breast cancer screening in the United States. County-level data of cancer screening adherence rates were generated from 2008 to 2012 Behavioral Risk Factor Surveillance System. We performed the univariate local indicators for spatial analyses (LISA) for the geographic differences of screening adherence rate and the differential LISA for the change of screening adherence rate from 2008 to 2012. In the univariate LISA, low-to-low clusters were consistently identified in counties of New Mexico, Wyoming, and Mississippi (P < 0.05) for both screenings. In the differential LISA, we found low-to-low clusters in Indiana counties (P < 0.05) for mammography screening, which implied that counties with a below-average difference in mammography adherence were surrounded by counties of below-average difference in adherence rates. A high-to-high cluster was also identified in the southern Appalachian counties for mammography screening (P < 0.05). No obvious spatial pattern was found for the colorectal cancer screening adherence rate across the United States. We found low-to-low clusters over time in adherence to screening guidelines for both cancer types in New Mexico, Wyoming, and Mississippi, and clusters of potential decrease in adherence to mammography screening guideline in counties of Indiana. The study also showed improvement on mammography screening clustered in southern Appalachia. The methodology adopted in this study identified areas with clusters of consistent low adherence to screening and a decrease in adherence, which implies that further research and intervention is warranted.

Purpose: Due to a limited number of studies with generalizable findings on the relationships between market conditions and RN staffing levels in hospitals, this study examined such relationships employing a longitudinal design with a representative national sample. Materials and Methods: We used longitudinal panel datasets from 2006 to 2010, drawn from various datasets including the American Hospital Association Annual Survey Database and the Area Health Resource File. A random-effects linear regression model was used to measure the influence of market conditions on RN staffing levels. Results: The results of this study showed that market conditions were significantly associated with RN staffing levels in hospitals. First, an increase in per capita income and being located in urban rather than rural areas were associated with a greater number of RNs per 1,000 inpatient days and a higher ratio of RNs to LPNs and nursing aides. In addition, an increase in the number of physician specialists was associated with an increase in the number of RNs per 1,000 inpatient days. Second, an increase in Medicare HMO penetration in the environment was related to an increase in the RNs to LPNs and nursing aides ratio. Lastly, an increase in market competition was associated with an increase in the number RNs per 1,000 inpatient days and the ratio of RNs to LPNs and nursing aides. Conclusion: The findings of this study suggest that staffing decision makers in hospitals should consider how to best align their RN staffing levels with their operating environment. In addition, health policy makers may improve the levels the RN supply in communities that needs more RNs by modulating external environmental forces (eg, specialist resources) that influence RN staffing levels in hospitals.