Institute of Medical Biometry and Statistics, Division Methods in Clinical Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
Freiburg Center for Data Analysis and Modeling, Freiburg, Germany.
Infect Control Hosp Epidemiol. 2022 Aug;43(8):1022-1031. doi: 10.1017/ice.2021.295. Epub 2021 Aug 5.
In 2017, a point-prevalence survey was conducted with 12,931 patients in 96 hospitals across Switzerland as part of the national strategy to prevent healthcare-associated infections (HAIs). We present novel statistical methods to assess incidence proportions of HAI and attributable length-of-stay (LOS) in point-prevalence surveys.
Follow-up data were collected for a subsample of patients and were used to impute follow-up data for all remaining patients. We used weights to correct length bias in logistic regression and multistate analyses. Methods were also tested in simulation studies.
The estimated incidence proportion of HAIs during hospital stay and not present at admission was 2.3% (95% confidence intervals [CI], 2.1-2.6), the most common type being lower respiratory tract infections (0.8%; 95% CI, 0.6-1.0). Incidence proportion was highest in patients with a rapidly fatal McCabe score (7.8%; 95% CI, 5.7-10.4). The attributable LOS for all HAI was 6.4 days (95% CI, 5.6-7.3) and highest for surgical site infections (7.1 days, 95% CI, 5.2-9.0). It was longest in the age group of 18-44 years (9.0 days; 95% CI, 5.4-12.6). Risk-factor analysis revealed that McCabe score had no effect on the discharge hazard after infection (hazard ratio [HR], 1.21; 95% CI, 0.89-1.63). Instead, it only influenced the infection hazard (HR, 1.84; 95% CI, 1.39-2.43) and the discharge hazard prior to infection (HR, 0.73; 95% CI, 0.66-0.82).
In point-prevalence surveys with limited follow-up data, imputation and weighting can be used to estimate incidence proportions and attributable LOS that would otherwise require complete follow-up data.
2017 年，瑞士全国预防医源性感染（HAI）战略的一部分，对 96 家医院的 12931 名患者进行了一项时点患病率调查。我们提出了新的统计方法来评估时点患病率调查中 HAI 的发病率比例和归因住院时间（LOS）。
对患者的亚样本进行了随访数据收集，并将其用于推断所有其他患者的随访数据。我们使用权重来纠正逻辑回归和多状态分析中的长度偏倚。还在模拟研究中测试了方法。
住院期间和入院时不存在的 HAI 的估计发病率比例为 2.3%（95%置信区间[CI]，2.1-2.6），最常见的类型是下呼吸道感染（0.8%；95%CI，0.6-1.0）。McCabe 评分快速致命的患者发病率比例最高（7.8%；95%CI，5.7-10.4）。所有 HAI 的归因 LOS 为 6.4 天（95%CI，5.6-7.3），手术部位感染最高（7.1 天，95%CI，5.2-9.0）。在 18-44 岁年龄组最长（9.0 天；95%CI，5.4-12.6）。风险因素分析表明，McCabe 评分对感染后出院的危险没有影响（危险比[HR]，1.21；95%CI，0.89-1.63）。相反，它只影响感染的危险（HR，1.84；95%CI，1.39-2.43）和感染前的出院危险（HR，0.73；95%CI，0.66-0.82）。
在随访数据有限的时点患病率调查中，可以使用推断和加权来估计发病率比例和归因 LOS，否则需要完整的随访数据。
