Estimating the impact of healthcare-associated infections on length of stay and costs

Abstract

Healthcare-associated infections (HAIs) unquestionably have substantial effects on morbidity and mortality. However, quantifying the exact economic burden attributable to HAIs still remains a challenging issue. Inaccurate estimations may arise from two major sources of bias. First, factors other than infection may affect patients' length of stay (LOS) and healthcare utilization. Second, HAI is a time-varying exposure, as the infection can impact on LOS and costs only after the infection has started. The most frequent mistake in previously published evidence is the introduction of time-dependent information as time-fixed, on the assumption that the impact of such exposure on the outcome was already present on admission. Longitudinal and multistate models avoid time-dependent bias and address the time-dependent complexity of the data. Appropriate statistical methods are important in analysis of excess costs and LOS associated with HAI, because informed decisions and policy developments may depend on them.

The Importance of Good Data, Analysis, and Interpretation for Showing the Economics of Reducing Healthcare-Associated Infection

“…4 Although some believed this was an effective strategy to reduce HAIs, others were concerned that not all HAIs were preventable and that the incentive under consideration would present challenges to hospitals caring for patients at high risk for HAIs. 5 To estimate the proportion of preventable HAIs in the most efficient and accurate manner, we used an up-to-date federally funded systematic review that examined the effectiveness of single and multimodal interventions on HAI prevention 6 as well as the most recent and valid estimates of HAI incidence. 7 We also conducted our own systematic review of studies examining the incremental costs of individual HAIs.…”

“…6 The consequence is that results are biased upward. 7 Barnett et al 8 showed that 11.23 extra days in the hospital were attributable to a case of healthcareassociated infection when the timing of infection was ignored and that, when the timing of infection was appropriately included, the additional duration of hospital stay was reduced to 1.35 days.…”

Improved Treatment of Multidrug-Resistant Bacterial Infections: Utility of Clinical Studies

“…Identifying a real, well-justified instrumental variable may be very difficult [72]. These last methods have scarcely been used in the study of antimicrobial therapy of MDROs, but exploring their applicability in this field could be interesting.…”

Analysis of osteoporosis treatment patterns with bisphosphonates and outcomes among postmenopausal veterans

“…For these GEE models, we assumed a gamma distributed dependent variable with a log link. Similar to the fracture analyses, the dependent variables in the cost regressions were lagged forward one quarter to allow us to estimate the impact of a change behavior on expenditures in the subsequent quarter, all of which will have occurred after the change behavior occurred [53,54]. We ran separate models for each outcome: total costs, osteoporosisrelated costs, and osteoporosis-related pharmacy costs (see Cost outcomes, above) for overall patients, high-risk patients, and not highrisk patients.…”