BACKGROUND: National point prevalence surveys (PPS) of healthcare-associated infection (HAI) and antimicrobial prescribing in hospitals were conducted in 2011 and 2016 in Scotland. When comparing results of PPS, it is important to adjust for any differences in patient case-mix that may confound the comparison. AIM: To describe the methodology used to compare prevalence for the two surveys and illustrate the importance of taking case-mix (patient and hospital stay characteristics) into account. METHODS: Multivariate models (clustered logistic regression) that adjusted for differences in patient case-mix were used to describe the difference in prevalence of six outcomes (HAI, antimicrobial prescribing and four devices: central vascular catheter, peripheral vascular catheter, urinary catheterisation and intubation) between the 2011 and 2016 PPS. Univariate models that did not adjust for these differences were also developed for comparison to show the importance of adjusting for confounders. RESULTS: Without adjustment for case-mix, HAI and intubation prevalence estimates were not significantly different in 2016 compared with 2011 although with adjustment, the prevalence of both was significantly lower (P=0.03 and P=0.02, respectively). These associations were only identified after adjustment for confounding by case-mix. CONCLUSIONS: While prevalence surveys do not provide intelligence on temporal trends as an incidence-based surveillance system would, if limitations and caveats are acknowledged, it is possible to compare two prevalence surveys to describe changing epidemiology. Adjusting for differences in case-mix is essential for robust comparisons. This methodology may be useful for other countries that are conducting large, repeated prevalence surveys.
BACKGROUND: National point prevalence surveys (PPS) of healthcare-associated infection (HAI) and antimicrobial prescribing in hospitals were conducted in 2011 and 2016 in Scotland. When comparing results of PPS, it is important to adjust for any differences in patient case-mix that may confound the comparison. AIM: To describe the methodology used to compare prevalence for the two surveys and illustrate the importance of taking case-mix (patient and hospital stay characteristics) into account. METHODS: Multivariate models (clustered logistic regression) that adjusted for differences in patient case-mix were used to describe the difference in prevalence of six outcomes (HAI, antimicrobial prescribing and four devices: central vascular catheter, peripheral vascular catheter, urinary catheterisation and intubation) between the 2011 and 2016 PPS. Univariate models that did not adjust for these differences were also developed for comparison to show the importance of adjusting for confounders. RESULTS: Without adjustment for case-mix, HAI and intubation prevalence estimates were not significantly different in 2016 compared with 2011 although with adjustment, the prevalence of both was significantly lower (P=0.03 and P=0.02, respectively). These associations were only identified after adjustment for confounding by case-mix. CONCLUSIONS: While prevalence surveys do not provide intelligence on temporal trends as an incidence-based surveillance system would, if limitations and caveats are acknowledged, it is possible to compare two prevalence surveys to describe changing epidemiology. Adjusting for differences in case-mix is essential for robust comparisons. This methodology may be useful for other countries that are conducting large, repeated prevalence surveys.
Authors: Evangelos I Kritsotakis; Ioannis Dimitriadis; Maria Roumbelaki; Emelia Vounou; Maria Kontou; Panikos Papakyriakou; Maria Koliou-Mazeri; Ioannis Varthalitis; George Vrouchos; George Troulakis; Achilleas Gikas Journal: Infect Control Hosp Epidemiol Date: 2008-08 Impact factor: 3.254