Our investigation aimed to describe the all-cause mortality rates by COVID-19 vaccination groups in Hungary for an epidemic period (1 April 2021–20 June 2021) and a nonepidemic period (21 June 2021–15 August 2021), and to determine the vaccines’ effectiveness in preventing all-cause mortality utilizing nonepidemic effectiveness measures to adjust for the healthy vaccinee effect (HVE). Sociodemographic status, comorbidity, primary care structural characteristics, and HVE-adjusted survival difference between fully vaccinated and unvaccinated cohorts in the epidemic period had been computed by Cox regression models, separately for each vaccine (six vaccines were available in Hungary). Hazard ratio (HR) reduction in epidemic period corrected with nonepidemic period’s HR with 95% confidence interval for each vaccine was used to describe the vaccine effectiveness (VE). The whole adult population (N = 6,404,702) of the country was followed in this study (4,026,849 fully vaccinated). Each vaccine could reduce the HVE-corrected all-cause mortality in the epidemic period (VE_{Oxford/AstraZeneca} = 0.592 [0.518–0.655], VE_Janssen = 0.754 [0.628–0.838], VE_Moderna = 0.573 [0.526–0.615], VE_{Pfizer-BioNTech} = 0.487 [0.461–0.513], VE_Sinopharm = 0.530 [0.496–0.561], and VE_{Sputnik V} = 0.557 [0.493–0.614]). The HVE-corrected general mortality for COVID-19 vaccine cohorts demonstrated the real-life effectiveness of vaccines applied in Hungary, and the usefulness of this indicator to convince vaccine hesitants.

Introduction: Before the mass vaccination, epidemiological control measures were the only means of containing the COVID-19 epidemic. Their effectiveness determined the consequences of the COVID-19 epidemic. Our study evaluated the impact of sociodemographic, lifestyle, and clinical factors on patient-reported epidemiological control measures. Methods: A nationwide representative sample of 1008 randomly selected adults were interviewed in person between 15 March and 30 May 2021. The prevalence of test-confirmed SARS-CoV-2 infection was 12.1%, of testing was 33.7%, and of contact tracing among test-confirmed infected subjects was 67.9%. The vaccination coverage was 52.4%. Results: According to the multivariable logistic regression models, the occurrence of infection was not influenced by sociodemographic and lifestyle factors or by the presence of chronic disease. Testing was more frequent among middle-aged adults (aOR = 1.53, 95% CI 1.10–2.13) and employed adults (aOR = 2.06, 95% CI 1.42–3.00), and was more frequent among adults with a higher education (aOR_secondary = 1.93, 95% CI 1.20–3.13; aOR_tertiary = 3.19, 95% CI 1.81–5.63). Contact tracing was more frequently implemented among middle-aged (aOR41-7y = 3.33, 95% CI 1.17–9.45) and employed (aOR = 4.58, 95% CI 1.38–15.22), and those with chronic diseases (aOR = 5.92, 95% CI 1.56–22.47). Positive correlation was observed between age groups and vaccination frequency (aOR41-70y = 2.94, 95% CI 2.09–4.15; aOR71+y = 14.52, 95% CI 7.33–28.77). Higher than primary education (aOR_secondary = 1.69, 95% CI 1.08–2.63; aOR_tertiary = 4.36, 95% CI 2.46–7.73) and the presence of a chronic disease (aOR = 2.58, 95% CI 1.75–3.80) positively impacted vaccination. Regular smoking was inversely correlated with vaccination (aOR = 0.60; 95% CI 0.44–0.83). Conclusions: The survey indicated that testing, contact tracing, and vaccination were seriously influenced by socioeconomic position; less so by chronic disease prevalence and very minimally by lifestyle. The etiological role of socioeconomic inequalities in epidemic measure implementation likely generated socioeconomic inequality in COVID-19-related complication and death rates.

Screening for visual acuity loss (VAL) is not applied systematically because of uncertain recommendations based on observations from affordable countries. Our study aimed to evaluate the effectiveness of primary health care-based screening. A cross-sectional investigation was carried out among adults who did not wear glasses and did not visit an ophthalmologist in a year (N = 2070). The risk factor role of sociodemographic factors and the cardiometabolic status for hidden VAL was determined by multivariable linear regression models. The prevalence of unknown VAL of at least 0.5 was 3.7% and 9.1% in adults and in the above-65 population. Female sex (b = 1.27, 95% CI: 0.35; 2.18), age (b = 0.15, 0.12; 0.19), and Roma ethnicity (b = 2.60, 95% CI: 1.22; 3.97) were significant risk factors. Higher than primary school (b_{secondaryschoolwithoutgraduation} = −2.06, 95% CI: −3.64; −0.47; and b_{secondaryschoolwithgraduation} = −2.08, 95% CI: −3.65; −0.51), employment (b = −1.33, 95% CI: −2.25; 0.40), and properly treated diabetes mellitus (b = −2.84, 95% CI: −5.08; −0.60) were protective factors. Above 65 years, female sex (b = 3.85, 95% CI: 0.50; 7.20), age (b = 0.39, 95% CI: 0.10; 0.67), Roma ethnicity (b = 24.79, 95% CI: 13.83; 35.76), and untreated diabetes (b = 7.30, 95% CI: 1.29; 13.31) were associated with VAL. Considering the huge differences between the health care and the population’s social status of the recommendation-establishing countries and Hungary which represent non-high-income countries, the uncertain recommendation of VAL screening should not discourage general practitioners from organizing population-based screening for VAL in non-affordable populations.

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.

Background/Objectives: Primary healthcare in Hungary faces acute physician shortages; in the Heves district, eight general practices are permanently vacant, leaving 11,191 residents without continuous care. In September 2025, the Hungarian Charity Service of the Order of Malta launched the Heves Haziorvosi Pilot (HHP), combining on-site nurse triage with remote telemedicine consultations. This study qualitatively evaluated its implementation and early operation. Methods: Three focus group discussions were held in two rounds, before and after implementation, with all 19 professionals delivering the programme (10 practice nurses, 4 general practitioners, 5 degreed professionals). Verbatim Hungarian transcripts were analysed in NVivo 15 (Lumivero, Denver, CO, USA) using the thematic analysis framework of Braun and Clarke with hybrid deductive-inductive coding by three independent researchers until full consensus. Results: Daily physician availability rose from approximately 2 to 8 h, and one remote physician concurrently supported four practices. Participants reported reduced professional isolation among nurses, longer and more focused consultations, and perceived, though unquantified, decreases in non-urgent ambulance use. Implementation was constrained by duplicate paper and digital documentation, patients’ limited familiarity with triage, and unresolved software interoperability and IT ergonomic problems. Conclusions: The hybrid model was perceived as operationally feasible; national scaling would require nursing curriculum reform, administrative simplification, interoperable medical software, and community health literacy programmes.