Additional file 1 of The effect of time-varying capacity utilization on 14-day in-hospital mortality: a retrospective longitudinal study in Swiss general hospitals
Creators: name: Sharma, Narayan
name: Moffa, Giusi
name: Schwendimann, René
name: Endrich, Olga
name: Ausserhofer, Dietmar
name: Simon, Michael
Publisher: name: figshare
Year: 2023
Subjects: subject: Medicine
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: Sociology
subject: FOS: Sociology
subject: 69999 Biological Sciences not elsewhere classified
subject: FOS: Biological sciences
subject: Computational Biology

description: Additional file 1: Figure F1. Flow diagram of the study participants from the study year for the analysis. Table S1. Description of the study variables. Table S2. Capacity utilization (%) at different percentile of annual distribution for each of the hospital. Table S3. Sensitivity analysis to evaluate robustness to different specifications of the capacity utilization tipping points in Swiss hospitals, splitting capacity utilization distribution at 75th, 80th, 85th, 90th, and 95th percentile. The table displays total/short-term effect of daily exposure on 14-day in-hospital mortality as estimated with generalized estimating equations (GEEs), for different values of the thresholds defining potential tipping points. Figure F2. DAGs A-D: Causal directed acyclic graphs (DAGs) showing causal effects of capacity utilization (CU) on in-hospital mortality (M). Note: the arrows are limited to prevent overcrowding. Green represents causal paths; black represents adjusted (blocked) paths; and pink represents biasing paths. A: Causal DAG for time-fixed confounders, exposure and outcome (PT1, PCCL1, CU1, M1: respectively, patient turnover, patient clinical complexity level, capacity utilization, and mortality at fixed-day/baseline). B: Causal DAG for time-varying confounders, exposure and outcome at day one (PT1, PCCL1, CU1, M1) and day two (PT2, PCC2, CU2, M2). C: Causal DAG for time-varying exposure, confounders and outcome at day one and day two with other fixed covariates (C) (age, sex, comorbidity weights, weeks). D: Causal DAG for time-varying exposure, confounders and outcome at day one and day two with other fixed covariates (C) and unmeasured variables (U) (e.g., leadership, staffing). Note: the arrows are limited to prevent overcrowding and main issue remained same if we add more arrows and to avoid complexity in DAG representation. Table S4. Daily distribution of time-varying variables capacity utilization, patient turnover and patient clinical complexity level per hospital by hospital type. Table S5. Patients’ treatment-exposure day distribution by hospital type. Figure F3. Density plot for inverse probability of treatment weight (IPTW) of capacity utilization with two time-varying confounders: patient turnover and patient clinical complexity level (PCCL). Inverse probability of treatment weight (IPTW) of capacity utilization with two time-varying confounders: patient turnover and patient clinical complexity level (PCCL) adjusting other fixed covariates. Table S6. Total/short-term effect of time-varying exposure (capacity utilization) on 14-day in-hospital mortality without and with IPTW. Table S7. Adjusted causal effect (total/short term and cumulative daily) of time-varying exposure (capacity utilization) on 14-day in-hospital mortality with truncation of the top and bottom 1% of IPTW.