The influence of health insurance on coverage of a country’s population with medical services

  • 150 Views
  • 34 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

One of the effective ways to increase the level of population coverage with medical services is health insurance. The paper aims to determine what type of health insurance (compulsory, social, or voluntary) has the greatest impact on a country’s ability to provide large-scale and timely medical services to citizens, as measured by the number of unmet needs for medical examination, treatable and preventable mortality. The control variables included a country’s population size, the level of economic well-being, and the scale of the public health system (number of doctors and hospital beds) based on EUROSTAT data for all 27 EU countries in 2012–2021. Modelling (regression models of panel data with fixed and random effects in STATA 18, Wald test, Hausman test, Breusch and Pagan test) proved that only one of three researched types of insurance – voluntary health insurance – positively influences a country’s ability to provide large-scale and timely medical services to citizens: an increase in its volume by 1% leads to a decrease in unmet needs in medical examination on average across all EU countries by 0.26%, treatable mortality rate by 0.08%, preventive mortality rate by 0.27%. The influence of the other two types – compulsory and social – was not confirmed (received regression coefficients for these variables are not statistically significant). This emphasizes the importance of citizens’ conscious attitude to their health (due to the increase in voluntary health insurance) both in strengthening public health and in ensuring faster and better access to medical services.

view full abstract hide full abstract
    • Figure 1. Geographical map of treatable and preventable mortality of EU countries residents in 2021
    • Figure 2. Comparative analysis of the rate of treatable and preventable mortality of EU countries residents in 2021
    • Figure 3. Cross-country analysis of the share of key health insurance types as a source of financing health care in 2021
    • Figure 4. Results of the Breusch and Pagan Lagrangian multiplier test for random effects (STATA 18 screen).
    • Figure 5. Hausman test results (STATA 18 screen) (1st stage).
    • Figure 6. Results of the Breusch and Pagan test (STATA 18 screen).
    • Figure 7. Hausman test results (STATA 18 screen) (2nd stage).
    • Figure 8. Breusch and Pagan test results (STATA 18 screen)
    • Figure 9. Hausman test results (STATA 18 screen) (3rd stage)
    • Figure A1. Cross-country analysis of the share dynamics of unmet needs for medical examination in the EU for 2012–2021
    • Table 1. Descriptive statistics results
    • Table 2. Fixed-effect regression results to formalize the impact of different types of health insurance on the level of unmet needs for medical examination
    • Table 3. Random-effect GLS regression results to formalize the impact of different types of health insurance on the level of unmet needs for medical examination
    • Table 4. Results of fixed-effects regression to formalize the impact of health insurance on the treatable mortality rate
    • Table 5. Results of random-effects regression to formalize the impact of health insurance on the treatable mortality rate
    • Table 6. Results of fixed-effects regression to formalize the impact of health insurance on the preventable mortality rate
    • Table 7. Results of random-effects regression to formalize the impact of health insurance on the preventable mortality rate
    • Conceptualization
      Olena Dobrovolska, Wolfgang Ortmanns, Svitlana Kachula, Ralph Sonntag
    • Data curation
      Olena Dobrovolska, Svitlana Kachula
    • Formal Analysis
      Olena Dobrovolska, Oksana Pavlenko
    • Funding acquisition
      Olena Dobrovolska, Wolfgang Ortmanns, Ralph Sonntag
    • Investigation
      Olena Dobrovolska, Svitlana Kachula, Oksana Pavlenko, Ralph Sonntag
    • Methodology
      Olena Dobrovolska, Svitlana Kachula
    • Project administration
      Olena Dobrovolska, Oksana Pavlenko
    • Resources
      Olena Dobrovolska, Svitlana Kachula
    • Software
      Olena Dobrovolska, Svitlana Kachula, Ralph Sonntag
    • Supervision
      Olena Dobrovolska, Wolfgang Ortmanns, Ralph Sonntag
    • Validation
      Olena Dobrovolska, Svitlana Kachula
    • Visualization
      Olena Dobrovolska, Oksana Pavlenko
    • Writing – original draft
      Olena Dobrovolska, Wolfgang Ortmanns, Svitlana Kachula, Oksana Pavlenko, Ralph Sonntag
    • Writing – review & editing
      Olena Dobrovolska, Wolfgang Ortmanns, Svitlana Kachula, Oksana Pavlenko, Ralph Sonntag