Health Economics Review最新文献

Background: COVID - 19 has had a profound impact on the economy, health systems within countries, and individuals around the world. To provide insight that may enhance the preparedness for future pandemics, a comprehensive cost assessment is vital. This study aims to estimate the direct cost of illness (CoI), as well as the national burden of treating hospitalised COVID-19 patients.

Methods: This study is prevalence-based retrospective study containing all patients admitted to a single designated hospital in Kuwait for the treatment of COVID-19. Micro (bottom-up) and macro (top-down) costing methods were used to evaluate direct medical CoI from a hospital perspective. Cost components were grouped as consumables, equipment, and human resources, and sensitivity analysis was used to account for uncertainty of inputs. The cost per admission was reported in local currency and international dollars (PPP$).

Results: Data on 7569 patients was analysed, 52.8% of whom were male, 69.2% were above 41 years, 22% had previously vaccinated for COVID-19, 22% were admitted to the ICU, and 18% had ≥ 3 pre-existing comorbidities. The mean CoI per admission was 12,063 PPP$, with overheads accounting for 45% of this figure, while consumables, human resources, and equipment accounted for 30%, 19%, and 7%, respectively. The sensitivity analysis demonstrated that overall cost uncertainty was primarily driven by variations in human resource costs rather than by uncertainties related to personal protective equipment (PPE) or ventilator use.

Conclusion: The substantial economic impact of COVID-19 on Kuwait's healthcare system has emphasised the significant role human resource costs has on overall expenditure. These findings provide valuable insights for future pandemic preparedness.

Background: While out-of-pocket health expenses continue to rise, households' coping strategies remain largely unexplored. When individuals and families rely on unsustainable mechanisms such as borrowing, they may face heightened financial vulnerability, which can be particularly severe among certain socio-economic groups. This study examined the determinants of medical borrowing and the associated inequalities in Saudi Arabia.

Methods: Nationally representative data from the 2021 World Bank Global Financial Inclusion (Global Findex) database were analysed. Descriptive statistics summarized respondents' characteristics, and Chi-squared tests assessed bivariate associations between socio-economic factors and medical borrowing. Multivariate logistic regression models were then estimated to identify independent determinants of medical borrowing. Socioeconomic inequalities were further evaluated using concentration curves and concentration indices.

Results: Approximately 16.3% of the 1019 respondents from the KSA reported borrowing money for medical purposes within the preceding 12 months. Medical borrowing was less common among higher-income and more educated individuals [Model 3 odds ratio = 0.561; 95% confidence interval: 0.391-0.807; p < 0.01). Borrowing incidence was slightly lower for males than for females. Across all models, government employees showed consistently higher odds of borrowing for medical expenses. Inequality analysis showed a negative education-based concentration index (-0.117, p ˂ 0.01), indicating that medical borrowing was disproportionately concentrated among individuals with lower educational attainment.

Conclusion: Socio-economic inequalities in borrowing for medical purposes exist in Saudi Arabia, highlighting the need to curb distress financing, particularly among lower-income groups, less-educated individuals, and public sector employees. These findings underscore the importance of expanding equitable insurance coverage and reducing reliance on out-of-pocket spending. Strengthening public healthcare quality and aligning reforms with Vision 2030 goals will be critical to curbing medical indebtedness and enhancing financial protection for all in Saudi Arabia.

Background: Relatively little is known about subnational life expectancy disparities in low and middle-income countries (LMICs). We construct indicators of subnational disparities in life expectancy, offering critical insights into health inequalities within countries. Moreover, we investigate the factors that account for cross-country and over-time variations in subnational life expectancy in less developed countries.

Methods: We measure subnational disparities in life expectancy within a country by way of a population-weighted coefficient of variation indicator. Our sample covers 101 LMICs during the period 2000-2021 and we consider overall, male and female health disparities. We employ regression analysis to identify important drivers of subnational disparities in life expectancy and focus on the role of subnational disparities in income and education.

Results: The findings reveal that subnational disparities in life expectancy are markedly higher in Sub-Saharan Africa compared to other LMIC world regions. Moreover, while subnational disparities in life expectancy have decreased in most regions, Sub-Saharan Africa stands out as an exception, with persistently high disparities alongside rising average life expectancy. A gender-specific analysis highlights that, while women generally live longer than men, subnational disparities are greater for women. Regression analysis identifies a range of factors influencing life expectancy disparities. Variables such as good governance and public health spending help reduce subnational disparities, while decentralization, country size, geographic diversity and ethnic fractionalization tend to increase them. Subnational disparities in income and education emerge as the most significant drivers, with disparities in female education playing a particularly critical role.

Conclusions: Subnational life expectancy disparities in some LMICs, especially in Sub-Saharan Africa, are high and persistent. Reducing subnational disparities in female education emerges as a key strategy.

Background: Health expenditure is influenced by complex interactions between economic, demographic, social factors, with significant variations across countries. This study aims to investigate the determinants of health expenditures employing robust regression methods offering a more flexible and reliable approach to dealing with outliers and high data variation.

Methods: This study employs robust regression methods, Weighted Least Squares (WLS) and MM-estimator regression, to examine the determinants of health expenditures. The analyses were conducted using data from 179 countries for the year 2021 with the R Studio.

Results: The findings indicate that income and ageing are significant determinants of health expenditures, and sixteen outliers were identified. In contrast, education level, public health expenditure, disease patterns showed no significant effect.

Conclusion: This study fills gap in the literature by using robust regression methods to account for outliers and provides new insights into the role of economic and demographic factors in health expenditures.

Background: Diagnosis-related group (DRG) payment methods are increasingly being used to decrease the costs of healthcare worldwide. However, the effectiveness of cost controls varies from region to region. This study aimed to analyze the impacts of DRG payments on medical costs in China and provide theoretical support for the promotion of DRG payments in other countries.

Methods: Patients from City Wuxi in China was selected, which underwent a reform from fee-for-service (FFS) payment to DRG payment during the study period. Ordinary least regression analysis (OLS) and propensity-score-matching (PSM) were used to analyze the effects of DRG, Causal Forest (CF) of machine learning algorithm was used to analyze the underlying reasons for the results.

Results: The OLS model revealed that personal total medical costs decreased by 28.3% after the DRG reform and the total personal out-of-pocket payment (OPP) decreased by 21.3% after the DRG reform, but the personal out-of-pocket ratio increased by 15% after the DRG reform. The PSM-OLS model regression and the DRG reform results indicated decreases of 29.4% and 24.2% in personal total cost and OPP costs, respectively. The proportion of OPP costs increased by 9%. The causal forest model suggested that age and the number of surgeries played a significant role in the impact of DRG reform on patients' medical burden (total medical expenses, OPP costs, and OPP Ratio). Results indicate that the impacts of the DRG reform was associated with a 27% reduction in patients' medical burden (SE = 0.007), a 19.4% reduction in out-of-pocket expenses (SE = 0.012), and a 1.4% increase in utilization costs (SE = 0.002).

Conclusions: DRG payment can control the growth of medical expenses and ease the burden on the medical insurance fund. However, the current rules may increase the OPP ratio and the economic burden on patients. A regulatory model in line with China's national conditions still must be explored.

Economic Evaluation (EE) is increasingly used to inform the decision-making of various health care systems about which health care interventions to fund with the available resources. Until now, majority of cost-effectiveness analyses have been performed with Microsoft Excel (ME). Today, the trend is to use software that can improve the decision-making model and that can resolve complex problems, as well as ensure reproducibility and transparency. The intention of this tutorial paper is not to show the "best" way of developing decision models in R, but to provide two different codes described in a step-by-step guide on how to implement a Markov model, with an explanation to help beginners in modeling (e.g., health economists new to R) and MS Excel users and to switch to R without having any great knowledge of programming with R. This paper is offered to facilitate the wider use of R to implement decision-making models.

Background: Sugar-sweetened beverage (SSB) taxes have been implemented in various regions as a public health strategy to reduce obesity and associated chronic diseases. While previous research has examined the effectiveness of these taxes, findings remain mixed regarding their impact on long-term health outcomes. This study assesses the effect of Washington State's 2009 soft drink syrup tax on obesity prevalence and related health conditions. This study mitigates selection bias and cross-border purchasing effects, providing a clear picture of the policy's effectiveness.

Methods: We employ the Synthetic Control Method (SCM) to estimate the causal impact of the syrup tax on obesity rates and obesity-related diseases using 1996-2021 data from the Behavioral Risk Factor Surveillance System (BRFSS) and the American Community Survey (ACS). The SCM constructs a counterfactual state to compare against Washington's post-tax obesity trends. Key outcome variables include obesity rates (Body Mass Index [BMI] $\ge$ 30 and BMI $\ge$ 35), prevalence of diabetes, hypertension, and high cholesterol. Statistical significance is determined using Monte Carlo simulations ( $n=999$ ) with false discovery rate corrections ( $p<0.05$ ).

Results: Our findings indicate that Washington's syrup tax led to a significant reduction in obesity rates, decreasing by 2.2 to 4.0 percentage points relative to the synthetic control. The tax's effect was most pronounced among college graduates, males, individuals under 65, and White/Asian populations. However, the tax did not yield significant changes in diabetes, hypertension, or high cholesterol prevalence, suggesting a more limited impact on broader obesity-related health conditions.

Conclusions: The study provides evidence that an SSB tax can effectively reduce obesity rates, particularly among certain demographic groups. However, the lack of significant effects on other obesity-related diseases suggests that additional policy measures may be necessary to achieve broader public health improvements. These findings contribute to ongoing policy discussions on the role of taxation in the fight against obesity and highlight the need for targeted interventions to improve the health benefits of such policies.

This paper evaluates the effectiveness of the 2009 French Diagnosis-Related Group (DRG) classification reform, which introduced four severity levels within each DRG, ranging from low to very high, with corresponding increases in fixed-price reimbursements. Notably, the reform incorporates the Medicare Severity Diagnosis-Related Group (MS-DRG) system, first implemented in the United States in 2007, giving the French system international relevance. The French Public Health Insurance system (NHI) reimburses both public and private healthcare establishments through a DRG-based payment system. This study focuses on variations in hospital resource costs for four different health conditions. The paper begins by discussing the theoretical challenges of constructing DRG categories, particularly the trade-off between greater clinical detail (granularity) and the risk of distorting incentives for hospital efficiency. It then presents an empirical analysis of hospital resource cost variations both within and between DRGs for the same pathology or clinically meaningful group (DRG-root), using data from 2012 to 2019. Our findings suggest that a one-size-fits-all approach to severity classification is inadequate. In some cases, broader categories improve statistical validity, while in others, more granular distinctions are necessary. We conclude that a tailored, case-by-case approach is the most effective solution. Specifically, the analysis reveals significant overlap in confidence intervals for hospital resource costs across DRG severity levels, suggesting that the current classification system fails to effectively capture cost differences related to severity. Additionally, a large portion of cost variation within DRGs is driven by factors unrelated to severity, such as hospital-specific characteristics. Overall, the results underscore the need to revise the current DRG system in France in order to reduce financial discrepancies and to prevent incentives for patient selection, especially before implementing bundled payment models that include both inpatient and outpatient care.

Objectives: To estimate the financial burden and the determinants of catastrophic healthcare expenditure(CHE) in patients with rheumatoid arthritis(RA) residing in the state of Karnataka, India.

Methods: This was a cross-sectional, questionnaire-based study carried out by the practicing rheumatologists across 17 centers in Karnataka, India. Patients with RA diagnosed as per ACR classification criteria and on follow-up for at least 1-year were interviewed regarding disease-related expenditures which included direct medical and non-medical costs. CHE defined as > 20% of annual family income was analysed for various sociodemographic and clinical variables. Results are presented in Indian currency (INR), wherein 100 INR = 1.17 USD = 1.06 EURO.

Results: We included 2141 patients with RA (M: F::11:89), mean age 50.9 ± 12 years. The median annual expenditure towards treatment of RA including all direct medical and non-medical costs was ₹32200(IQR 21600,45200), the largest proportion (41.0%) being for RA medications. More than 10% annual income was being spent for treatment of RA by 48.1%(n = 1029)] of patients while CHE (> 20%) was noted in 582(27.1%) patients. Longer time taken for referral to rheumatologist [β = 1.28 (1.15,1.43)], longer duration of illness [β = 1.002(1.001,1.003)], presence of comorbidity [β = 1.12(1.04,1.22)] and disability HAQ-DI > 2 [β = 1.37(1.20,1.56)] had significant association with higher direct expenditure. Patients belonging to lower SES [AOR 2.66(1.99,3.56)], primary and middle level of patient education [AOR 1.57(1.05,2.36) & 2.01(1.32,3.07)] and hospitalisation [β = 9.20(6.25,13.6)] incurred CHE.

Conclusion: The primary drivers of high direct expenditure in patients with RA in Karnataka, India are cost of medications, delayed specialist referral, high disease activity, disability and comorbidities. Additionally, hospitalization significantly contributes to CHE.