Health Economics Review最新文献

Contraception is crucial for women's well-being and national development, which is particularly salient in Thailand's rapidly aging society where maximizing the health of the working-age population is essential. While health insurance is pivotal, this research moves beyond analyzing financial barriers to examine the distinct structural influence of Thailand's three major health insurance systems-the Universal Coverage Scheme (UCS), Social Security Scheme (SSS), and Civil Servant Medical Benefit Scheme (CSMBS)-on modern contraceptive method choices among reproductive-age women. Using data from the 2022 Multiple Indicator Cluster Survey (MICS) (n = 10,922 women currently using contraception), a Multinomial Logistic Regression model yielded significant findings. The results demonstrate that the specific scheme structure, rather than just the presence of coverage, significantly predicts method selection. Women under the Social Security Scheme (SSS) showed a statistically significant higher propensity for hormonal methods (pills, injections, implants). In contrast, members of the Civil Servant Medical Benefit Scheme (CSMBS) showed a significant preference for device-based methods (condoms, IUDs, diaphragms). These distinct preferences align with the structural incentives of the schemes: the SSS's capitation payment model may favor time-efficient hormonal dispensing, while the CSMBS's fee-for-service model and provider flexibility facilitate access to medical devices and non-hormonal options. Additionally, religious beliefs and concerns about hormonal side effects (observed among highly educated, affluent women) played a substantial role in the adoption of natural methods. This study underscores the need for public health policies that move beyond guaranteeing financial access and consider the structural diversity of health insurance systems to promote appropriate and sustainable contraceptive access and informed choice.

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.