Background Online surveys have triggered a heated debate regarding their scientific validity. Many authors have adopted weighting methods to enhance the quality of online survey findings, while others did not find an advantage for this method. This work aims to compare weighted and unweighted association measures after adjustment over potential confounding, taking into account dataset properties such as the initial gap between the population and the selected sample, the sample size, and the variable types. Methods This study assessed seven datasets collected between 2019 and 2021 during the COVID-19 pandemic through online cross-sectional surveys using the snowball sampling technique. Weighting methods were applied to adjust the online sample over sociodemographic features of the target population. Results Despite varying age and gender gaps between weighted and unweighted samples, strong similarities were found for dependent and independent variables. When applied on the same datasets, the regression analysis results showed a high relative difference between methods for some variables, while a low difference was found for others. In terms of absolute impact, the highest impact on the association measure was related to the sample size, followed by the age gap, the gender gap, and finally, the significance of the association between weighted age and the dependent variable. Conclusion The results of this analysis of online surveys indicate that weighting methods should be used cautiously, as weighting did not affect the results in some databases, while it did in others. Further research is necessary to define situations in which weighting would be beneficial.
Background Our aim was to examine whether the length of stay, hospital charges and in-hospital mortality attributable to healthcare- and community-associated infections due to antimicrobial-resistant bacteria were higher compared with those due to susceptible bacteria in the Lebanese healthcare settings using different methodology of analysis from the payer perspective . Methods We performed a multi-centre prospective cohort study in ten hospitals across Lebanon. The sample size consisted of 1289 patients with documented healthcare-associated infection (HAI) or community-associated infection (CAI). We conducted three separate analysis to adjust for confounders and time-dependent bias: (1) Post-HAIs in which we included the excess LOS and hospital charges incurred after infection and (2) Matched cohort, in which we matched the patients based on propensity score estimates (3) The conventional method, in which we considered the entire hospital stay and allocated charges attributable to CAI. The linear regression models accounted for multiple confounders. Results HAIs and CAIs with resistant versus susceptible bacteria were associated with a significant excess length of hospital stay (2.69 days [95% CI,1.5–3.9]; p < 0.001) and (2.2 days [95% CI,1.2–3.3]; p < 0.001) and resulted in additional hospital charges ($1807 [95% CI, 1046–2569]; p < 0.001) and ($889 [95% CI, 378–1400]; p = 0.001) respectively. Compared with the post-HAIs analysis, the matched cohort method showed a reduction by 26 and 13% in hospital charges and LOS estimates respectively. Infections with resistant bacteria did not decrease the time to in-hospital mortality, for both healthcare- or community-associated infections. Resistant cases in the post-HAIs analysis showed a significantly higher risk of in-hospital mortality (odds ratio, 0.517 [95% CI, 0.327–0.820]; p = 0.05). Conclusion This is the first nationwide study that quantifies the healthcare costs of antimicrobial resistance in Lebanon. For cases with HAIs, matched cohort analysis showed more conservative estimates compared with post-HAIs method. The differences in estimates highlight the need for a unified methodology to estimate the burden of antimicrobial resistance in order to accurately advise health policy makers and prioritize resources expenditure.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.