Search

Presents abstracts of papers and posters for presentation at the 2005 American Society for Clinical Laboratory Science Annual Meeting and Clinical Laboratory Exposition, July 26-30, 2005 in Orlando, Florida. "In Vitro Effects of Chloramine-T on Select Wound Pathogens," by Linda J. Laatsch; "Prospective Students' Preparation for a CLS Program," by Leonce H. Thierry Jr.; "Utility of Lecithin Cholesterol Acyl Transferase Mass as a Diagnostic Marker for Liver Disease and Liver Transplant," by Karen R. Murray.

Objective: To investigate the potential spillover effects of the Hospital Readmissions Reduction Program (HRRP) on readmissions for nontargeted conditions and patient populations. We examine HRRP effects on nontargeted conditions separately and on non-Medicare populations in Florida and California.Data Sources: From 2007-2013, 100 percent Medicare inpatient claims data, 2007-2013 State Inpatient Database (SID) for Florida, and 2007-2011 SID for California.Study Design: We conducted an interrupted time series analysis to estimate the change in 30-day all-cause unplanned readmission trends after the start of HRRP using logistic regression.Principal Findings: Hospitals with the largest reductions in targeted Medicare readmissions experienced higher reductions in nontargeted Medicare readmissions. Among nontargeted conditions, reductions were higher for neurology and surgery conditions than for the cardiovascular and cardiorespiratory conditions, which are clinically similar to the targeted conditions. For non-Medicare patients, readmission trends for targeted conditions in Florida and California did not change after HRRP.Conclusions: Our findings are consistent with positive spillover benefits associated with HRRP. The extent of these benefits, however, varies across condition and patient groups. The observed patterns suggest a complex response, including a role of nonfinancial factors, in driving lower readmissions. [ABSTRACT FROM AUTHOR]

Objective: To analyze health care disparities in pediatric quality of care measures and determine the impact of data imputation.Data Sources: Five HEDIS measures are calculated based on 2012 administrative data for 145,652 children in two public insurance programs in Florida.Methods: The Bayesian Improved Surname and Geocoding (BISG) imputation method is used to impute missing race and ethnicity data for 42 percent of the sample (61,954 children). Models are estimated with and without the imputed race and ethnicity data.Principal Findings: Dropping individuals with missing race and ethnicity data biases quality of care measures for minorities downward relative to nonminority children for several measures.Conclusions: These results provide further support for the importance of appropriately accounting for missing race and ethnicity data through imputation methods. [ABSTRACT FROM AUTHOR]