Evaluation of Acute Myocardial Infarction In-hospital Mortality Using a Risk-adjustment Model Based on Japanese Administrative Data

The relationship between hospital ownership, in-hospital mortality, and medical expenses: an analysis of three common conditions in China

BACKGROUND

Private hospitals expanded rapidly in China since 2009 following its national health reform encouraging private investment in the hospital sector. Despite long-standing debates over the performance of different types of hospitals, empirical evidence under the context of developing countries remains scant. We investigated the disparities in health care quality and medical expenses among public, private not-for-profit, and private for-profit hospitals.

METHODS

A total of 64,171 inpatients (51,933 for pneumonia (PNA), 9,022 for heart failure (HF) and 3,216 for acute myocardial infarction (AMI)) who were admitted to 528 secondary hospitals in Sichuan province, China, during the fourth quarters of 2016, 2017, and 2018 were selected for this study. Multilevel logistic regressions and multilevel linear regressions were utilized to assess the relationship between hospital ownership types and in-hospital mortality, as well as medical expenses for PNA, HF, and AMI, after adjusting for relevant hospital and patient characteristics, respectively.

RESULTS

The private not-for-profit (adjusted OR, 1.69; 95% CI, 1.08, 2.64) and for-profit (adjusted OR, 1.67; 95% CI, 1.06, 2.62) hospitals showed higher in-hospital mortality than the public ones for PNA, but not for AMI and HF. No significant differences were found in medical expenses across hospital ownership types for AMI, but the private not-for-profit was associated with 9% higher medical expenses for treating HF, while private not-for-profit and for-profit hospitals were associated with 10% and 11% higher medical expenses for treating PNA than the public hospitals. No differences were found between the private not-for-profit and private for-profit hospitals both in in-hospital mortality and medical expenses across the three conditions.

CONCLUSION

The public hospitals had at least equal or even higher healthcare quality and lower medical expenses than the private ones in China, while private not-for-profit and for-profit hospitals had similar performances in these aspects. Our results added evidences on hospitals' performances among different ownership types under China's context, which has great potential to inform the optimization of healthcare systems implemented among developing countries confronted with similar challenges.

Development and validation of an acute heart failure-specific mortality predictive model based on administrative data

BACKGROUND

Acute heart failure (AHF) with its high in-hospital mortality is an increasing burden on healthcare systems worldwide, and comparing hospital performance is required for improving hospital management efficiency. However, it is difficult to distinguish patient severity from individual hospital care effects. The aim of this study was to develop a risk adjustment model to predict in-hospital mortality for AHF using routinely available administrative data.

METHODS

Administrative data were extracted from 86 acute care hospitals in Japan. We identified 8620 hospitalized patients with AHF from April 2010 to March 2011. Multivariable logistic regression analyses were conducted to analyze various patient factors that might affect mortality. Two predictive models (models 1 and 2; without and with New York Heart Association functional class, respectively) were developed and bootstrapping was used for internal validation. Expected mortality rates were then calculated for each hospital by applying model 2.

RESULTS

The overall in-hospital mortality rate was 7.1%. Factors independently associated with higher in-hospital mortality included advanced age, New York Heart Association class, and severe respiratory failure. In contrast, comorbid hypertension, ischemic heart disease, and atrial fibrillation/flutter were found to be associated with lower in-hospital mortality. Both model 1 and model 2 demonstrated good discrimination with c-statistics of 0.76 (95% confidence interval, 0.74-0.78) and 0.80 (95% confidence interval, 0.78-0.82), respectively, and good calibration after bootstrap correction, with better results in model 2.

CONCLUSIONS

Factors identifiable from administrative data were able to accurately predict in-hospital mortality. Application of our model might facilitate risk adjustment for AHF and can contribute to hospital evaluations.