BACKGROUND: Hospitals will increasingly bear the costs for healthcare-acquired conditions such as infection. Our goals were to estimate the costs attributable to healthcare-acquired infection (HAI) and conduct a sensitivity analysis comparing analytic methods. METHODS: A random sample of high-risk adults hospitalized in the year 2000 was selected. Measurements included total and variable medical costs, length of stay (LOS), HAI site, APACHE III score, antimicrobial resistance, and mortality. Medical costs were measured from the hospital perspective. Analytic methods included ordinary least squares linear regression and median quantile regression, Winsorizing, propensity score case matching, attributable LOS multiplied by mean daily cost, semi-log transformation, and generalized linear modeling. Three-state proportional hazards modeling was also used for LOS estimation. Attributable mortality was estimated using logistic regression. RESULTS: Among 1253 patients, 159 (12.7%) developed HAI. Using different methods, attributable total costs ranged between $9310 to $21,013, variable costs were $1581 to $6824, LOS was 5.9 to 9.6 days, and attributable mortality was 6.1%. The semi-log transformation regression indicated that HAI doubles hospital cost. The totals for 159 patients were $1.48 to $3.34 million in medical cost and $5.27 million for premature death. Excess LOS totaled 844 to 1373 hospital days. CONCLUSIONS: Costs for HAI were considerable from hospital and societal perspectives. This suggests that HAI prevention expenditures would be balanced by savings in medical costs, lives saved and available hospital days that could be used by overcrowded hospitals to enhance available services. Our results obtained by applying different economic methods to a single detailed dataset may inform future cost analyses.
BACKGROUND: Hospitals will increasingly bear the costs for healthcare-acquired conditions such as infection. Our goals were to estimate the costs attributable to healthcare-acquired infection (HAI) and conduct a sensitivity analysis comparing analytic methods. METHODS: A random sample of high-risk adults hospitalized in the year 2000 was selected. Measurements included total and variable medical costs, length of stay (LOS), HAI site, APACHE III score, antimicrobial resistance, and mortality. Medical costs were measured from the hospital perspective. Analytic methods included ordinary least squares linear regression and median quantile regression, Winsorizing, propensity score case matching, attributable LOS multiplied by mean daily cost, semi-log transformation, and generalized linear modeling. Three-state proportional hazards modeling was also used for LOS estimation. Attributable mortality was estimated using logistic regression. RESULTS: Among 1253 patients, 159 (12.7%) developed HAI. Using different methods, attributable total costs ranged between $9310 to $21,013, variable costs were $1581 to $6824, LOS was 5.9 to 9.6 days, and attributable mortality was 6.1%. The semi-log transformation regression indicated that HAI doubles hospital cost. The totals for 159 patients were $1.48 to $3.34 million in medical cost and $5.27 million for premature death. Excess LOS totaled 844 to 1373 hospital days. CONCLUSIONS: Costs for HAI were considerable from hospital and societal perspectives. This suggests that HAI prevention expenditures would be balanced by savings in medical costs, lives saved and available hospital days that could be used by overcrowded hospitals to enhance available services. Our results obtained by applying different economic methods to a single detailed dataset may inform future cost analyses.
Authors: Xiaoxi Zeng; Gearoid M McMahon; Steven M Brunelli; David W Bates; Sushrut S Waikar Journal: Clin J Am Soc Nephrol Date: 2013-10-31 Impact factor: 8.237
Authors: Michael W Climo; Deborah S Yokoe; David K Warren; Trish M Perl; Maureen Bolon; Loreen A Herwaldt; Robert A Weinstein; Kent A Sepkowitz; John A Jernigan; Kakotan Sanogo; Edward S Wong Journal: N Engl J Med Date: 2013-02-07 Impact factor: 91.245
Authors: Bryce Haac; Clare Rock; Anthony D Harris; Lisa Pineles; Deborah Stein; Thomas Scalea; Peter Hu; George Hagegeorge; Stephen Y Liang; Kerri A Thom Journal: Injury Date: 2016-08-17 Impact factor: 2.586