The impact of health insurance models on reducing DALYS from cardiovascular diseases and neoplasms: A panel study across 51 OECD member and candidate countries

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As health systems worldwide increasingly focus on mitigating the burden of non-communicable diseases, the strategic role of insurance schemes in facilitating early detection and preventive care, thereby reducing the substantial costs associated with advanced-stage treatment, has become a critical area of policy and research attention. This study aims to evaluate the impact of various health financing models, specifically voluntary, compulsory, and social insurance, on the burden of cardiovascular diseases and neoplasms, measured by Disability-Adjusted Life Years (DALYs), across working-age and older populations. The analysis is based on unbalanced panel data from 51 countries covering the period 2000–2021, drawing from the Global Burden of Disease database for DALY rates and the OECD and WHO Global Health Expenditure Database for health financing indicators. Fixed and random effects panel regression models with clustered robust standard errors were employed to estimate the associations. Results show that voluntary private insurance significantly reduces DALY rates from cardiovascular diseases, by approximately 19-28%, among working-age (15-49) and older adults (50-69). Compulsory and social insurance models also exhibit protective effects, but of smaller magnitude. Government health financing schemes similarly correlate with improved outcomes. In contrast, enterprise-based financing is positively associated with higher DALY rates, especially in older age groups. Insurance schemes demonstrate weaker and more inconsistent associations for neoplasms, with compulsory insurance and government schemes showing the most stable links to reduced burden among older adults.

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    • Table 1. Variables and sources
    • Table 2. Outputs of the FE and RE models of the influence of healthcare financing schemes on the DALY rate for cardiovascular diseases in the 15-49 age group
    • Table 3. Outputs of the robust standard errors calculations of the fixed effects model of the healthcare financing schemes’ influence on the DALY rate for cardiovascular diseases in the 15-49 age group
    • Table 4. Outputs of the RE and RE models of the healthcare financing schemes’ influence on the DALY rate for cardiovascular diseases in the 50-69 age group
    • Table 5. Output of the robust inference results from the RE model of the healthcare financing schemes’ influence on the DALY rate for cardiovascular diseases in the 50-69 age group
    • Table 6. Outputs of the robust standard errors calculations of the fixed effects model of the healthcare financing schemes’ influence on the DALY rate for neoplasms in the 15–49 age group
    • Table 7. Outputs of the robust standard errors calculations of the fixed effects model showing the healthcare financing schemes’ influence on the DALY rate for neoplasms in the 50-69 age group
    • Table B1. Descriptive statistics output
    • Table B2. Outputs of the temporal t-coefficient analysis using year dummies of the influence of healthcare financing schemes on the DALY rate for cardiovascular diseases in the 15-49 age group
    • Table B3. Outputs of the temporal t-coefficient analysis using year dummies, the influence of healthcare financing schemes on the DALY rate for neoplasms in the 15-49 age group
    • Table B4. Outputs of the temporal t-coefficient analysis using year dummies, the influence of healthcare financing schemes on the DALY rate for neoplasms in the 50-69 age group
    • Conceptualization
      Aleksandra Kuzior, Zhanat Khishauyeva, Narek M. Kesoyan, Dmytro Sukov, Natalia Sidelnyk
    • Funding acquisition
      Aleksandra Kuzior
    • Project administration
      Aleksandra Kuzior
    • Resources
      Aleksandra Kuzior
    • Writing – original draft
      Aleksandra Kuzior, Zhanat Khishauyeva, Narek M. Kesoyan, Dmytro Sukov, Natalia Sidelnyk, Nataliia Sheliemina, Tetiana Vasylieva
    • Writing – review & editing
      Aleksandra Kuzior, Zhanat Khishauyeva, Narek M. Kesoyan, Dmytro Sukov, Natalia Sidelnyk, Nataliia Sheliemina, Tetiana Vasylieva
    • Data curation
      Zhanat Khishauyeva, Nataliia Sheliemina
    • Software
      Narek M. Kesoyan, Nataliia Sheliemina
    • Validation
      Dmytro Sukov, Nataliia Sheliemina
    • Visualization
      Natalia Sidelnyk, Nataliia Sheliemina
    • Formal Analysis
      Nataliia Sheliemina
    • Investigation
      Nataliia Sheliemina
    • Methodology
      Nataliia Sheliemina
    • Supervision
      Nataliia Sheliemina, Tetiana Vasylieva