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.