Health Economics Review最新文献

Background: Antibiotic resistance poses a significant global health threat, exacerbated by over-prescription of antibiotics, which often happens in primary care for respiratory tract infections (RTIs). Studies indicate that up to half of these prescriptions may be unnecessary. Little is known about how general practitioners' (GPs) remuneration schemes influence prescribing. GPs compensated via fee-for-service (FFS) and capitation (CAP) may face stronger incentives to prescribe antibiotics compared to salaried GPs, as prescriptions can signal quality, reduce consultation time, and aid patient retention - critical where reimbursements depend on consultations and list size. This study examines how GP remuneration influences antibiotic prescribing for RTIs using Norwegian register data.

Methods: We utilized linked registry data (2015-2019) from the Control and Payment of Health Reimbursements Database (KUHR), National GP Registry, Norwegian Prescribed Drug Registry (NorPD), and Statistics Norway. We matched antibiotic prescriptions to patient-GP contacts for RTIs. The dataset covers regular GPs (mixed FFS/CAP or fixed salary) and locum GPs (FFS or salary).

Outcomes: (1) probability of antibiotic prescription during RTI contacts; (2) probability of selecting non-phenoxymethylpenicillin (non-PcV). We used linear probability, logit, and probit models, controlling for GP, patient, contact, and practice attributes. To mitigate selection bias, we exploited within-GP variation among those who switched remuneration type during the study period.

Results: Regular FFS/CAP GPs had a 12-15% higher relative probability of prescribing antibiotics for RTIs than salaried GPs, especially at initial contacts. When prescribing, they were 9-11% more likely to choose non-PcV. Switchers analyses showed that FFS/CAP increased prescription rates by 14% and non-PcV choice by 8%. Among locums, we did not find any significant difference in overall prescription rates between FFS and salary, but FFS locums favoured non-PcV by 7%.

Conclusions: Remuneration schemes may influence antibiotic prescribing behaviour. FFS/CAP is linked to higher prescription rates and broader-spectrum antibiotic use among regular GPs, likely due to patient retention and time-efficiency incentives. Policy interventions, such as monitoring of prescriptions depending on the remuneration type or adjustments to the remuneration scheme (e.g., antibiotic-related pay-for-performance), could promote prudent prescribing. Further research is needed on prescription appropriateness and quality impacts.

Background: Cardiometabolic risk factors and conditions are the leading contributors to morbidity and mortality, yet quantifying their causal effects on socioeconomic outcomes using observational data is challenging due to endogeneity. Using genetic variants as instrumental variables, Mendelian randomization (MR) offers a unique approach to strengthen causal inference in this context and has also gained popularity in health economic literature.

Aims: This study aimed to: i) map the current landscape of MR studies evaluating the impact of cardiometabolic exposures on healthcare and socioeconomic outcomes; ii) describe how core MR assumptions were tested and reported; iii) summarize how additional assumptions underlying causal interpretation were discussed.

Methods: We searched MEDLINE and EMBASE for studies applying MR to examine the impact of cardiometabolic risk factors or conditions (e.g., obesity, blood pressure, cholesterol, coronary artery disease, type 2 diabetes) on socioeconomic and healthcare outcomes (e.g., education, income, occupational status, social deprivation, healthcare use and costs, health-related quality of life). Study characteristics, MR design choices, and reporting of assumption testing and causal interpretation were extracted and narratively summarized.

Results: Sixteen studies were included, covering 79 exposure-outcome pairs. Most studies examined the effects of body mass index on employment or healthcare costs. Only one study assessed home ownership, social income transfers, resource utilization, and quality-adjusted life years as outcomes, respectively. Effects of childhood cardiometabolic exposures were rarely examined beyond educational outcomes. UK Biobank was the predominant data source. None of the core MR assumptions were mentioned across all studies. While weak instrument bias was frequently tested, less than 40% of studies assessed associations between instruments and observable confounders as falsification tests. Only few studies discussed monotonicity or homogeneity assumptions.

Conclusions: Although MR is a promising identification strategy for assessing causal effects of cardiometabolic risk on healthcare and socioeconomic outcomes, reporting practices for assumption testing and causal interpretation vary widely. This review highlights opportunities to strengthen transparency and coherence in future MR applications. With increasing data availability and clearer methodological guidance, MR could complement conventional observational approaches in supporting policy decisions.

Objectives: Our main objective is to measure the economic impact of managing diabetes-related visual impairment and blindness in the Kingdom of Saudi Arabia (KSA) to inform policy and resource allocation for effective disease management and prevention.

Methods: We develop a prevalence-based cost of illness model using Microsoft Excel to assess the economic impact of treating diabetic-related visual impairment and blindness over a one-year period from societal perspective. The model categorizes costs according to the severity level of the condition: mild, moderate, and severe visual impairment and blindness. It encompasses direct medical expenses such as those for drug acquisitions, surgical procedures, healthcare resources, monitoring, visual aids, and psychological care, as well as indirect costs such as productivity losses in the financial year 2024. The model's outputs provide a cumulative and detailed breakdown of the costs associated with visual impairment and blindness in KSA. Sensitivity analysis was conducted.

Results: Our economic model shows that the total economic burden of diabetes-related visual impairment and blindness in KSA in one year is SAR 150,324,912,186 ($ 81.2 billion). This cost is divided into direct and indirect medical costs estimated at SAR 101 billion ($ 54.6 billion) and SAR 49 billion ($26.5 billion), respectively. The major component is medication costs, along with patients' productivity loss.

Conclusion: The significant economic repercussions of diabetes-related visual impairment and blindness across individuals, households, and the Saudi healthcare system impose a substantial financial burden. These findings highlight the urgent need for investment in innovative interventions and screening programs aimed at mitigating these costs, enhancing accessibility to essential treatments, improving the quality of life of affected individuals and families, and achieving sustainable healthcare and socioeconomic development goals in the Kingdom.

Background: Congenital anomalies represent a major cause of childhood morbidity and disability, imposing long-term health, social, and financial challenges. While medical expenditures are relatively well-documented, evidence on the broader financial and social impact on families-particularly in Central and Eastern Europe-remains limited. Accordingly, this study aimed to quantify the economic burden associated with congenital anomalies from a caregiver perspective.

Methods: A cross-sectional, questionnaire-based study was conducted in Lithuania in 2023 among 160 caregivers of children aged 5-18 years diagnosed with congenital anomalies. Participants were grouped by the number of affected organ systems to reflect the increasing disease complexity. Using standardized cost-of-illness methodology and both the human capital and opportunity cost approaches, we estimated annual direct medical and non-medical, indirect, and informal care costs.

Results: Families experienced a substantial and multidimensional financial burden that intensified with disease severity. The annual indirect cost, mainly reflecting productivity losses, represented the largest component of the total economic burden, with a median of €7,566 overall and €12,965 in the most severely affected families. Out-of-pocket payments were also considerable, with a median of €2,040 per year; in the most severe group they exceeded 20% of monthly household income for 42% of families. The economic value of informal care represented a smaller yet meaningful portion of total costs (median €1,967), increasing more than fivefold between the mildest and most severe groups.

Conclusions: Congenital anomalies place a substantial economic burden on families, extending far beyond healthcare expenses. These findings highlight the need for policies that improve financial protection, support caregivers' labor market participation, and acknowledge the economic value of informal care within health and welfare systems.

Background: Mental health conditions impose substantial economic burdens on healthcare systems globally, with growing evidence indicating disproportionate impacts on household-level out-of-pocket (OOP) expenditures. Despite Australia's universal healthcare system, the financial burden of mental health conditions on households remains underexplored.

Objective: To examine the longitudinal relationship between mental health status and OOP healthcare expenditures among Australian adults, and assess how education and income moderate this relationship.

Methods: We analyzed 17 waves (2006-2022) of the Household, Income and Labour Dynamics in Australia (HILDA) survey, encompassing 57,647 person-year observations from 3,391 unique individuals. Mental health was measured using the Mental Health Inventory-5 (MHI-5) scale and newly proposed expanded MHI-9 scales. We employed fixed-effects panel regression models and instrumental variable analysis to address unobserved heterogeneity.

Results: A one-unit decrease in MHI-5 score is associated with 0.18-0.25% increase in inflation-adjusted OOP healthcare expenditure, equivalent to AU$2.10-$3.00 per unit decline, with a 10-point decline in MHI-5 costing households an additional AU$21-$30. Instrumental variable estimates revealed larger causal effects of 0.80-1.00%. Individuals with good mental health and higher education demonstrated expenditure patterns consistent with Grossman's health capital theory, while those with poor mental health showed disrupted relationships between education and healthcare spending. Urban residents faced 11.00% higher inflation-adjusted OOP costs than the rural residents.

Conclusions: Mental health deterioration significantly increases household healthcare expenditure burdens in Australia. Traditional health economics theories apply primarily to individuals with good mental health, indicating the need for targeted rather than universal policy approaches.