The inevitable rising costs of health care and the accompanying risk of increasing inequalities raise concerns. In order to make tailored policies and interventions that can reduce this risk, it is necessary to investigate whether vulnerable groups (such as Roma, the largest ethnic minority in Europe) are being left out of access to medical advances. Objectives: The study aimed to describe the association between general medical practice (GMP) level of average per capita expenditure of the National Health Insurance Fund (NHIF), and the proportion of Roma people receiving GMP in Hungary, controlled for other socioeconomic and structural factors. Methods: A cross-sectional study that included all GMPs providing care for adults in Hungary (N = 4818) was conducted for the period 2012–2016. GMP specific data on health expenditures and structural indicators (GMP list size, providing care for adults only or children also, type and geographical location of settlement, age of GP, vacancy) for secondary analysis were obtained from the NHIF. Data for the socioeconomic variables were from the last census. Age and sex standardized specific socioeconomic status indicators (standardized relative education, srEDU; standardized relative employment, srEMP; relative housing density, rHD; relative Roma proportion based on self-reported data, rRP) and average per capita health expenditure (standardized relative health expenditure, srEXP) were computed. Multivariate linear regression model was applied to evaluate the relationship of socioeconomic and structural indicators with srEXP. Results: The srEDU had significant positive (b = 0.199, 95% CI: 0.128; 0.271) and the srEMP had significant negative (b = −0.282, 95% CI: −0.359; −0.204) effect on srEXP. GP age > 65 (b = −0.026, 95% CI: −0.036; −0.016), list size <800 (b = −0.043, 95% CI: −0.066; −0.020) and 800–1200 (b = −0.018, 95% CI: −0.031; −0.004]), had significant negative association with srEXP, and GMP providing adults only (b = 0.016, 95% CI: 0.001;0.032) had a positive effect. There was also significant expenditure variability across counties. However, rRP proved not to be a significant influencing factor (b = 0.002, 95% CI: −0.001; 0.005). Conclusion: As was expected, lower education, employment, and small practice size were associated with lower NHIF expenditures in Hungary, while the share of self-reported Roma did not significantly affect health expenditures according to our GMP level study. These findings do not suggest the necessity for Roma specific indicators elaborating health policy to control for the risk of widening inequalities imposed by rising health expenses.

Background: Since the effectiveness of primary DM care (PDMC) is influenced by both health-care-related and external factors, its indicator set must include indicators that are easy-to-understand for all stakeholders, such as composite indicator-based ranking. Objectives: Our study aimed to prepare a composite PDMC indicator, which is adjusted with the GP-independent characteristics of a GMP, in order to evaluate the usefulness of composite indicators in performance-improving programs. Methods: Using indicators standardized by age, sex, and eligibility for exemption certificates (on hemoglobin A1C, lipid status, serum creatinine, and ophthalmological examination, and on influenza vaccination) for Hungarian adult DM care, factor analysis was applied to create a composite DM care quality indicator (CDMI). It was adjusted (ACDMI) by a multivariable linear regression model of the association between structural characteristics of GMPs and the CDMI. Results: There were 516,052 DM patients provided for by 4784 GMPs. The CDMI exhibited significant associations with patients’ lower education (β = −0.139, 95%CI: −0.182; −0.095), GPs’ age over 65 (β = −0.083, 95%CI: −0.109; −0.056), GMPs with more than 2000 adult patients (β = −0.059, 95%CI: −0.090; −0.027), and urban location (β = 0.096, 95%CI: 0.058; 0.134). The average difference in GMPs’ ranks by the CDMI and ACDMI was 583. Extreme poor (N = 147) and extreme good (N = 176) performances of GMPs were identified, and those were categorized further by the role of GP-independent factors in causing the extreme performances (N_{healthcareunrelated} = 84; N_{healthcarerelated} = 239). Conclusions: Our findings suggest a stepwise and widely communicable process for PDMC monitoring, which starts with the evaluation of the CDMI and ACDMI to identify the GMPs requiring interventions, making a distinction between extreme GMPs requiring health-care-related interventions and those requiring non-health-care-related interventions.

The implementation of monitoring for general medical practice (GMP) can contribute to improving the quality of diabetes mellitus (DM) care. Our study aimed to describe the associations of DM care performance indicators with the structural characteristics of GMPs and the socioeconomic status (SES) of patients. Using data from 2018 covering the whole country, GMP-specific indicators standardized by patient age, sex, and eligibility for exemption certificates were computed for adults. Linear regression models were applied to evaluate the relationships between GMP-specific parameters (list size, residence type, geographical location, general practitioner (GP) vacancy and their age) and patient SES (education, employment, proportion of Roma adults, housing density) and DM care indicators. Patients received 58.64% of the required medical interventions. A lower level of education (hemoglobin A1c test: β = −0.108; ophthalmic examination: β = −0.100; serum creatinine test: β = −0.103; and serum lipid status test: β = −0.108) and large GMP size (hemoglobin A1c test: β = −0.068; ophthalmological examination β = −0.031; serum creatinine measurement β = −0.053; influenza immunization β = −0.040; and serum lipid status test β = −0.068) were associated with poor indicators. A GP age older than 65 years was associated with lower indicators (hemoglobin A1c test: β = −0.082; serum creatinine measurement: β = −0.086; serum lipid status test: β = −0.082; and influenza immunization: β = −0.032). Overall, the GMP-level DM care indicators were significantly influenced by GMP characteristics and patient SES. Therefore, proper diabetes care monitoring for the personal achievements of GPs should involve the application of adjusted performance indicators.