Variation of Daily Care Demand in Swiss General Hospitals: Longitudinal Study on Capacity Utilization, Patient Turnover and Clinical Complexity Levels

Potentially Avoidable Hospitalizations by Asthma and COPD in Switzerland from 1998 to 2018: A Cross-Sectional Study

Potentially avoidable hospitalizations (PAH) are commonly used as an indicator for healthcare quality and primary care performance. However, data are usually presented in a restricted timeframe and for a specific region, limiting the identification of trends and national patterns. We aimed in this study to calculate rates, identify clinical determinants, and estimate costs of PAH for two frequent lung diseases (asthma and COPD) in Switzerland between 1998 and 2018 using hospital discharge data available for patients aged ≥20 years. PAH were defined according to the Health Care Quality Indicators Project (HCQIP) from the Organisation for Economic Co-operation and Development (OECD). The distribution of PAH in seven administrative regions (Leman, Mittelland, Northwest, Zurich, Eastern, Central, and Ticino) was calculated, along with PAH-associated total hospital days and Diagnosis-Related Group (DRG) estimated costs. Totals of 25,260 PAH for asthma and 135,069 PAH for COPD were identified in the 20-year period. The standardized rates of PAH per 100,000 people for asthma fluctuated from 18.7 in 1998 to 22.5 on 2018. The standardized rates of PAH per 100,000 people from COPD almost doubled from 77.4 in 1998 to 142.7 in 2018. In 2018, the estimated total costs of PAH amounted to 7.7 million CHF for asthma and 91.2 million CHF for COPD. We conclude that PAH for asthma and COPD represent a significant and unnecessary burden and costs of hospitalizations in Switzerland.

The effect of time-varying capacity utilization on 14-day in-hospital mortality: a retrospective longitudinal study in Swiss general hospitals

BACKGROUND

High bed-occupancy (capacity utilization) rates are commonly thought to increase in-hospital mortality; however, little evidence supports a causal relationship between the two. This observational study aimed to assess three time-varying covariates-capacity utilization, patient turnover and clinical complexity level- and to estimate causal effect of time-varying high capacity utilization on 14 day in-hospital mortality.

METHODS

This retrospective population-based analysis was based on routine administrative data (n = 1,152,506 inpatient cases) of 102 Swiss general hospitals. Considering the longitudinal nature of the problem from available literature and expert knowledge, we represented the underlying data generating mechanism as a directed acyclic graph. To adjust for patient turnover and patient clinical complexity levels as time-varying confounders, we fitted a marginal structure model (MSM) that used inverse probability of treatment weights (IPTWs) for high and low capacity utilization. We also adjusted for patient age and sex, weekdays-vs-weekend, comorbidity weight, and hospital type.

RESULTS

For each participating hospital, our analyses evaluated the ≥85th percentile as a threshold for high capacity utilization for the higher risk of mortality. The mean bed-occupancy threshold was 83.1% (SD 8.6) across hospitals and ranged from 42.1 to 95.9% between hospitals. For each additional day of exposure to high capacity utilization, our MSM incorporating IPTWs showed a 2% increase in the odds of 14-day in-hospital mortality (OR 1.02, 95% CI: 1.01 to 1.03).

CONCLUSIONS

Exposure to high capacity utilization increases the mortality risk of inpatients. Accurate monitoring of capacity utilization and flexible human resource planning are key strategies for hospitals to lower the exposure to high capacity utilization.

Evaluating the Hospital Standardized Home-Transition Ratios for Cerebral Infarction in Japan: A Retrospective Observational Study from 2016 through 2020

Discharge to home is considered appropriate as a treatment goal for diseases that often leave disabilities such as cerebral infarction. Previous studies showed differences in risk-adjusted in-hospital mortality and readmission rates; however, studies assessing the rate of hospital-to-home transition are limited. We developed and calculated the hospital standardized home-transition ratio (HSHR) using Japanese administrative claims data from 2016–2020 to measure the quality of in-hospital care for cerebral infarction. Overall, 24,529 inpatients at 35 hospitals were included. All variables used in the analyses were associated with transition to another hospital or facility for inpatients, and evaluation of the HSHR model showed good predictive ability with c-statistics (area under curve, 0.73 standard deviation; 95% confidence interval, 0.72–0.73). All HSHRs of each consecutive year were significantly correlated. HSHRs for cerebral infarction can be calculated using Japanese administrative claims data. It was found that there is a need for support for low HSHR hospitals because hospitals with high/low HSHR were likely to produce the same results in the following year. HSHRs can be used as a new quality indicator of in-hospital care and may contribute to assessing and improving the quality of care.

Hospital-level characteristics of the standardised mortality ratio for ischemic heart disease: a retrospective observational study using Japanese administrative claim data from 2012 to 2019

Background

Ischemic heart disease (IHD) is one of the leading causes of mortality worldwide and imposes a heavy burden on patients. Previous studies have indicated that the optimal care for IHD during hospitalisation may reduce the risk of in-hospital mortality. The standardised mortality ratio (SMR) is an indicator for assessing the risk-adjusted in-hospital mortality ratio based on case-mix. This indicator can crucially identify hospitals that can be changed to improve patient safety and the quality of care. This study aimed to determine the hospital-level characteristics of the SMR for IHD in Japan.

Methods

This study was designed as a retrospective observational study using the Japanese administrative claim data from 2012 to 2019. The data of all hospital admissions with a primary diagnosis of IHD (ICD-10, I20-I25) were used. Patients with complete variables data were included in this study. Hospitals with less than 200 IHD inpatients in each 2-year period were excluded. The SMR was defined as the ratio of the observed number of in-hospital deaths to the expected number of in-hospital deaths multiplied by 100.The observed number of in-hospital deaths was the sum of the actual number of in-hospital deaths at that hospital, and the expected number of in-hospital deaths was the sum of the probabilities of in-hospital deaths. Ratios of in-hospital mortality was risk-adjusted using multivariable logistic regression analyses. The c-statistic and Hosmer-Lemeshow test were used to evaluate the predictive accuracy of the logistic models. Changes in SMRs in each consecutive period were assessed using Spearman's correlation coefficient.

Results

A total of 64,831 were admitted patients with IHD in 27 hospitals as complete submission data. The SMRs showed wide variation among hospitals, ranging from 35.4 to 197.6, and analysis models indicated good predictive ability with a c-statistic of 0.93 (95% CI [0.92-0.94]) and Hosmer-Lemeshow test of 0.30. The results of chi-square tests and t-tests for all variables to assess the association with in-hospital mortality were P < 0.001. In the analysis of trends in each consecutive period, the SMRs showed positive correlations.

Conclusions

This study denoted that the SMRs for IHD could be calculated using Japanese administrative claim data. The SMR for IHD might contribute to the development of more appropriate benchmarking systems for hospitals to improve quality of care.