Cost Effectiveness and Resource Allocation最新文献

Background: Early cancer diagnosis is crucial in improving the survival. The main goal in NHS in cancer diagnosis is detection rate of 75% by 2028. Our study presents the economic analysis of impact of early versus late-cancer diagnosis on healthcare resources use and costs within our Trust also exploring the influence of deprivation index.

Methods: We retrospectively analyse the cost-of-care and patient-level data for 4596 patients across nine cancer groups who fully completed their cancer pathway between April 2020 and September 2024. Costs were compared between early (stage 1 and 2) versus late (stage 3 and 4) diagnosis.

Results: Significant variations in costs were determine across cancer types, with colorectal and haematological malignancies being most costly. Early-stage diagnosis averaged £11,2K, significantly lower than late- stage £23,8K with largest differences seen in haematological, colorectal and breast cancers. A hypothetical 75% early detection rate could save the trust £14.7 million over four years. Successful treatment yielded an average 10.74 years of healthy life expectancy, further increased by early detection.

Conclusions: Late cancer diagnosis dramatically increases healthcare costs underscoring the importance of early detection and advanced screening methods. Extrapolating a 75% early detection rate across the NHS could yield substantial financial savings, highlighting its impact on healthcare efficiency.

Introduction: Breast cancer is an evolving non-communicable disease and a global disease burden, which accounts for a momentous portion of worldwide mortality and morbidity with significant economic impacts. In low- and middle-income countries like Ethiopia, the most common problem with cancer treatment is scarcity of medical resources, and financial supply, for providing care, which includes expensive medical costs, cost of service provision, and medical equipment. In Ethiopia, the cost of breast cancer care increased and imposed an enormous financial burden on healthcare providers and the healthcare system. The study aimed to estimate the annual total direct medical cost of breast cancer and identify the cost drivers of breast cancer treatment at Jimma University Medical Center (JUMC).

Methods: A facility-based retrospective medical record review was employed using the provider's perspective. Both mixed bottoms-up and top-down costing approaches were used to estimate the cost. Medical records of 130 breast cancer patients who were diagnosed between September 2022 and August 2023 were reviewed retrospectively.

Results: The mean age of patients was 41.9 (SD + 10.4) years. The total direct medical cost incurred at the hospital per patient to treat breast cancer across all stages was US$24,727 at JUMC. The total median cost per patient at the hospital to treat breast cancer at stages I, II, III, and IV could have been US$218.6, US$638.7, US$846.2, and US$753.4 respectively. At JUMC, the major cost drivers were the cost of drugs for chemotherapy accounts for 47.2%, followed by laboratory tests at 14.8%, and radiotherapy at 14.2%.

Conclusion: The hospital's direct medical costs associated with breast cancer disease were extensive. The highest medical cost was incurred in stage III of the disease. The main cost drivers at JUMC were drugs for chemotherapy, laboratory tests, and radiotherapy (radiologic imaging) added higher direct medical costs followed by surgery and pathology. The stage-specific cost analysis study focused on cost identification and valuation that provides profound information related to the estimation of direct medical cost, and the main cost drivers of the disease, and helps to encourage the treatment and diagnosis of breast cancer at an early stage of the disease.

Background: Increased appropriate use of self-care for minor conditions can reduce the number of healthcare professional appointments and, hence, provide opportunity cost savings to the National Health Service (NHS). The receipt of over-the-counter medications can lead to faster access to treatment, improved health-related quality of life, and fewer education and work days lost.

Methods: A model was developed to evaluate the economic impact of a change in the proportion of people using self-care with ibuprofen to manage three conditions (dysmenorrhoea, migraine and acute rhinosinusitis) on preventable healthcare resource use from the perspective of the NHS and Personal Social Services (PSS). The total number of appointments for each condition was estimated from NHS Digital and was based on the number of primary (n = 230,298,091) and secondary (n = 22,839,832) care visits, and the proportion of visits due to each condition (informed by clinical opinion). Work and school days lost were also modelled to estimate the wider indirect costs associated with preventable absences due to delays in receiving treatment. Deterministic sensitivity and scenario analyses were also conducted to estimate the uncertainty associated with the analysis.

Results: The use of self-care with ibuprofen was increased by 5% in the base case analysis. The results indicate that this increase could prevent 409,243 appointments in the United Kingdom over a one-year time horizon. 882,875 and 117,114 work and school hours lost could also be prevented, respectively. Sensitivity analysis suggests the magnitude of change in self-care, average working hours/pay and appointment waiting times are the main drivers of the model results.

Conclusion: Self-care with ibuprofen provides opportunity cost-savings to the NHS and frees up the capacity of healthcare professionals so that they can focus on more severe conditions.

Introduction: The rapid evolution of technology has reshaped the insurance industry, with artificial intelligence (AI) taking center stage as a key driver of innovation. This paper examines the transformative impact of AI in health insurance, focusing on its applications and potential to revolutionize the sector.

Method: This scoping review examines literature published between 2000 and 2024, focusing on the application of AI in health insurance. We used relevant keywords related to artificial intelligence and health insurance to search the PubMed, Scopus, and Web of Science databases.

Findings: AI presents numerous opportunities in health insurance, including contributions to shaping international and national agendas, such as aligning goals, establishing indicators, and achieving objectives, financial management, fraud detection, monitoring capabilities, diagnostics and medical innovations, private insurance applications, risk management, technical analysis, and value creation. However, there are ethical challenges that must be addressed if AI is to be effectively implemented.

Conclusion: Policies for AI applications in health insurance should prioritize the protection of personal health and medical data, address ethical concerns, and ensure robust data privacy and security. Additionally, these policies should promote the use of AI to enhance customer experiences, optimize risk selection, and generate revenue for both insurers and policyholders.

Objectives: Endometriosis-related dysmenorrhea and pelvic pain impose significant economic and quality-of-life burdens. This study evaluated the cost-effectiveness of dienogest compared to gonadotropin-releasing hormone agonists (GnRH-a) for managing dysmenorrhea and pelvic pain in Vietnam.

Design: The cost-effectiveness analysis using a Markov model was conducted from a healthcare payer perspective. Model input parameters were obtained from meta-analyses, published literature, and local data sources. One-way sensitivity, and probabilistic sensitivity analyses (PSA) were performed to assess the robustness of the findings.

Setting: Vietnamese healthcare system context.

Participants: Hypothetical cohort of women with endometriosis experiencing dysmenorrhea or pelvic pain.

Interventions: Dienogest compared with GnRH-a therapies (triptorelin, leuprorelin, goserelin).

Main outcome measures: Costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs) were calculated over two years.

Results: Dienogest was the dominant treatment for dysmenorrhea, with the lowest cost ($363.3) and highest QALYs (1.74) compared to triptorelin ($739.3; 1.62 QALYs; ICER -$3,292/QALY) and leuprorelin ($744.0; 1.70 QALYs; ICER -$11,454/QALY). For pelvic pain, dienogest ($381.5, 1.64 QALYs) also dominated triptorelin ($720.4; 1.60 QALYs; ICER -$10,919/QALY), leuprorelin ($773.4; 1.54 QALYs; ICER -$4,300/QALY), and goserelin ($753.1; 1.49 QALYs; ICER -$2,609/QALY).One-way sensitivity analysis identified the probability of symptom resolution and utility values as key drivers of cost-effectiveness. PSA confirmed dienogest's high probability (≥ 99%) of being cost-effective at a willingness-to-pay threshold of one GDP per capita.

Conclusion: Dienogest is a cost-effective alternative to GnRH-a drugs for treating dysmenorrhea and pelvic pain in Vietnam, offering improved health outcomes at a lower cost. These findings support its broader adoption in clinical practice and healthcare policy.

Background: Out-of-pocket (OOP) expenditure is one of the most common payment strategies for hospitalization care in Bangladesh, and the share of OOP expenditure has been increasing at an alarming rate. This study aimed to investigate the OOP costs of hospitalization care, the impact of OOP on catastrophic healthcare expenditure (CHE) and financial distress, and the associated factors.

Methods: We used data from the most recent nationally representative dataset, the Bangladesh Household Income and Expenditure Survey 2022. A total of 14,395 households were surveyed, with 1973 household members hospitalized due to various illnesses. Respondents were asked to provide information regarding hospitalization care for the year preceding the survey. Households were considered to have CHE if they spent at least 25% of their total consumption expenditure or 40% of their non-food consumption expenditure on healthcare. Distress financing was defined as covering OOP healthcare costs by selling assets, borrowing money, or receiving financial assistance from friends or relatives. Multivariate logistic regression models were used to identify the determinants of CHE and distress financing.

Results: The annual average OOP cost of hospitalization was USD 418, with the OOP cost nearly twice as high in private facilities compared to public ones (USD 538 vs. USD 283). The highest OOP costs were observed for cancer treatment (USD 2365), followed by COVID-19 (USD 1391). Overall, 6.72% and 9.03% of hospitalized patients experienced CHE at 25% of total expenditure and 40% of non-food expenditure, respectively, while about 61% of patients faced distress financing due to hospitalization.

Conclusion: Financial hardship due to hospitalization remains high in Bangladesh. These findings will help policymakers adopt more effective healthcare financing strategies and improve the efficiency of public health investments.