BACKGROUND: Comparing and ranking hospitals based on health outcomes is becoming increasingly popular, although case-mix differences between hospitals and random variation are known to distort interpretation. The aim of this study was to explore whether surgical-site infection (SSI) rates are suitable for comparing hospitals, taking into account case-mix differences and random variation. METHODS: Data from the national surveillance network in the Netherlands, on the eight most frequently registered types of surgery for the year 2009, were used to calculate SSI rates. The variation in SSI rate between hospitals was estimated with multivariable fixed- and random-effects logistic regression models to account for random variation and case mix. 'Rankability' (as the reliability of ranking) of the SSI rates was calculated by relating within-hospital variation to between-hospital variation. RESULTS: Thirty-four hospitals reported on 13 629 patients, with overall SSI rates per surgical procedure varying between 0 and 15·1 per cent. Statistically significant differences in SSI rate between hospitals were found for colonic resection, caesarean section and for all operations combined. Rankability was 80 per cent for colonic resection but 0 per cent for caesarean section. Rankability was 8 per cent in all operations combined, as the differences in SSI rates were explained mainly by case mix. CONCLUSION: When comparing SSI rates in all operations, differences between hospitals were explained by case mix. For individual types of surgery, case mix varied less between hospitals, and differences were explained largely by random variation. Although SSI rates may be used for monitoring quality improvement within hospitals, they should not be used for ranking hospitals.
BACKGROUND: Comparing and ranking hospitals based on health outcomes is becoming increasingly popular, although case-mix differences between hospitals and random variation are known to distort interpretation. The aim of this study was to explore whether surgical-site infection (SSI) rates are suitable for comparing hospitals, taking into account case-mix differences and random variation. METHODS: Data from the national surveillance network in the Netherlands, on the eight most frequently registered types of surgery for the year 2009, were used to calculate SSI rates. The variation in SSI rate between hospitals was estimated with multivariable fixed- and random-effects logistic regression models to account for random variation and case mix. 'Rankability' (as the reliability of ranking) of the SSI rates was calculated by relating within-hospital variation to between-hospital variation. RESULTS: Thirty-four hospitals reported on 13 629 patients, with overall SSI rates per surgical procedure varying between 0 and 15·1 per cent. Statistically significant differences in SSI rate between hospitals were found for colonic resection, caesarean section and for all operations combined. Rankability was 80 per cent for colonic resection but 0 per cent for caesarean section. Rankability was 8 per cent in all operations combined, as the differences in SSI rates were explained mainly by case mix. CONCLUSION: When comparing SSI rates in all operations, differences between hospitals were explained by case mix. For individual types of surgery, case mix varied less between hospitals, and differences were explained largely by random variation. Although SSI rates may be used for monitoring quality improvement within hospitals, they should not be used for ranking hospitals.
Authors: Hester F Lingsma; Alex Bottle; Steve Middleton; Job Kievit; Ewout W Steyerberg; Perla J Marang-van de Mheen Journal: BMC Health Serv Res Date: 2018-02-14 Impact factor: 2.655
Authors: Peter C Austin; Iris E Ceyisakar; Ewout W Steyerberg; Hester F Lingsma; Perla J Marang-van de Mheen Journal: BMC Med Res Methodol Date: 2019-06-26 Impact factor: 4.615
Authors: I E Ceyisakar; N van Leeuwen; Diederik W J Dippel; Ewout W Steyerberg; H F Lingsma Journal: BMC Med Res Methodol Date: 2021-01-06 Impact factor: 4.615
Authors: Carl T Gustafson; Felix Boakye-Agyeman; Cassandra L Brinkman; Joel M Reid; Robin Patel; Zeljko Bajzer; Mahrokh Dadsetan; Michael J Yaszemski Journal: PLoS One Date: 2016-01-13 Impact factor: 3.240
Authors: Michael S Calderwood; Ken Kleinman; Susan S Huang; Michael V Murphy; Deborah S Yokoe; Richard Platt Journal: Med Care Date: 2017-01 Impact factor: 2.983
Authors: Veronique M A Voorn; Perla J Marang-van de Mheen; Anja van der Hout; Cynthia So-Osman; M Elske van den Akker-van Marle; Ankie W M M Koopman-van Gemert; Albert Dahan; Thea P M Vliet Vlieland; Rob G H H Nelissen; Leti van Bodegom-Vos Journal: BMJ Open Date: 2017-07-20 Impact factor: 2.692