Higher continuity of care with usual providers for diabetic care is significantly associated with lower risk of future hospitalization for long-term diabetic complication admissions. To avoid future hospitalization, health policy stakeholders are encouraged to improve the continuity of care through strengthening the provider-patient relationships.

Examining whether the causal relationships among the performance indicators of the balanced scorecard (BSC) framework exist in hospitals is the aim of this article. Data were collected from all twenty-one general hospitals in a public hospital system and their supervising agency for the 3-year period, 2000-2002. The results of the path analyses identified significant causal relationships among four perspectives in the BSC model. We also verified the relationships among indicators within each perspective, some of which varied as time changed. We conclude that hospital administrators can use path analysis to help them identify and manage leading indicators when adopting the BSC model. However, they should also validate causal relationships between leading and lagging indicators periodically because the management environment changes constantly.

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Generic grouping, reduction of the flat payment rate and delegation of financial responsibility were effective in controlling PE. A global budget alone would be unable to control PE without other direct financial incentives. Neither drug co-payment nor brand-specific price adjustment based on prices of international/existing products had a significant impact on PE.

Background Global burden of COVID-19 has not been well studied, disability-adjusted life years(DALYs) and value of statistical life(VSL) metrics were therefore proposed to quantify its impacts on health and economic loss globally. Methods The life expectancy, cases, and death numbers of COVID-19 until 30 th April 2021 were retrieved from open data to derive the epidemiological profiles and DALYs (including years of life lost (YLL) and years loss due to disability (YLD)) by four periods. The VSL estimates were estimated by using hedonic wage method(HWM) and contingent valuation method(CVM). The estimate of willingness to pay using CVM was based on the meta-regression mixed model. Machine learning method was used for classification. Results Globally, DALYs (in thousands) due to COVID-19 was tallied as 31,930 from Period I to IV. YLL dominated over YLD. The estimates of VSL were US$591 billion and US$5,135 billion based on HWM and CVM, respectively. The estimate of VSL increased from US$579 billion in Period I to US$2160 billion in Period IV using CVM. The higher the human development index(HDI), the higher the value of DALYs and VSL. However, there exits the disparity even at the same level of HDI. Machine learning analysis categorized eight patterns of global burden of COVID-19 with a large variation from US$0.001 billion to US$691.4 billion. Conclusions Global burden of COVID-19 pandemic resulted in substantial health and value of life loss particularly in developed economies. Classifications of such health and economic loss is informative to early preparation of adequate resource to reduce impacts.