Methods: We applied a retrospective approach using a top-down costing method to estimate the cost of health care services. Clinical and Administrative departments divided into cost centres, and the unit cost was calculated by dividing the total cost of final care cost centres into the total number of patients discharged in one year. The average cost of inpatient services was calculated based on the average cost of each ward and the number of patients treated.
Results: The average cost per patient stayed in KFCH was SAR 19,034, with the highest cost of SAR 108,561 for patients in the Orthopedic ward. The average cost of the patient in the Surgery ward, Plastic surgery, Neurosurgery, Medical ward, Pediatric ward and Gynecology ward was SAR 33,033, SAR 29,425, SAR 23,444, SAR 20,450, SAR 9579 and SAR 8636 respectively.
Conclusion: This study provides necessary information about the cost of health care services in a tertiary care setting. This information can be used as a primary tool and reference for further studies in other regions of the country. Hence, this data can help to provide a better understanding of tertiary hospital costing in the region to achieve the privatization objective.
Materials and Methods: A retrospective cohort study was conducted in the ED of the largest tertiary medical center in Taiwan. Trends of adult, non-trauma patients who visited the ED during February-April 2019 were compared with those during February-April 2020. The number of visits, their dispositions, crowding parameters, and turnover rates were analyzed. The primary outcome was the change in ED attendance between the two periods. The secondary outcomes were changes in hospital admission rates, crowding parameters, and turnover rates.
Results: During the outbreak, there were decreased non-trauma ED visits by 33.45% (p < 0.001) and proportion of Taiwan Triage and Acuity Scale (TTAS) 3 patients (p=0.02), with increased admission rates by 4.7% (p < 0.001). Crowding parameters and turnover rate showed significant improvements.
Conclusion: Comparison of periods before and during the COVID-19 outbreak showed an obvious decline in adult, non-trauma ED visits. The reduction in TTAS 3 patient visits and the increased hospital admission rates provide references for future public-health policy-making to optimise emergency medical resource allocations globally.
METHODS: A decision-analytic Markov model was developed to simulate the impact of S. suis infection and its major complications: death, meningitis and infective endocarditis among Thai people in 2019 with starting age of 51 years. Transition probabilities, and inputs pertaining to costs, utilities and productivity impairment associated with long-term complications were derived from published sources. A lifetime time horizon with follow-up until death or age 100 years was adopted. The simulation was repeated assuming that the cohort had not been infected with S.suis. The differences between the two set of model outputs in years of life, QALYs, and PALYs lived reflected the impact of S.suis infection. An annual discount rate of 3% was applied to both costs and outcomes. One-way sensitivity analyses and Monte Carlo simulation modeling technique using 10,000 iterations were performed to assess the impact of uncertainty in the model.
KEY RESULTS: This cohort incurred 769 (95% uncertainty interval [UI]: 695 to 841) years of life lost (14% of predicted years of life lived if infection had not occurred), 826 (95% UI: 588 to 1,098) QALYs lost (21%) and 793 (95%UI: 717 to 867) PALYs (15%) lost. These equated to an average of 2.46 years of life, 2.64 QALYs and 2.54 PALYs lost per person. The loss in PALYs was associated with a loss of 346 (95% UI: 240 to 461) million Thai baht (US$11.3 million) in GDP, which equated to 1.1 million Thai baht (US$ 36,033) lost per person.
CONCLUSIONS: S.suis infection imposes a significant economic burden both in terms of health and productivity. Further research to investigate the effectiveness of public health awareness programs and disease control interventions should be mandated to provide a clearer picture for decision making in public health strategies and resource allocations.
METHODS: A systematic review was performed for economic burden studies in schizophrenia using four electronic databases (Medline, EMBASE, PsycINFO, and EconLit) from inception to August 31, 2014.
RESULTS: A total of 56 articles were included in this review. More than 80% of the studies were conducted in high-income countries. Most studies had undertaken a retrospective- and prevalence-based study design. The bottom-up approach was commonly employed to determine cost, while human capital method was used for indirect cost estimation. Database and literature were the most commonly used data sources in cost estimation in high-income countries, while chart review and interview were the main data sources in low and middle-income countries. Annual costs for the schizophrenia population in the country ranged from US$94 million to US$102 billion. Indirect costs contributed to 50%-85% of the total costs associated with schizophrenia. The economic burden of schizophrenia was estimated to range from 0.02% to 1.65% of the gross domestic product.
CONCLUSION: The enormous economic burden in schizophrenia is suggestive of the inadequate provision of health care services to these patients. An informed decision is achievable with the increasing recognition among public and policymakers that schizophrenia is burdensome. This results in better resource allocation and the development of policy-oriented research for this highly disabling yet under-recognized mental health disease.