Relationships between the Structural Characteristics of General Medical Practices and the Socioeconomic Status of Patients with Diabetes-Related Performance Indicators in Primary Care.
Undraa JargalsaikhanFeras KasabjiFerenc VinczeAnita PálinkásLászló KőrösiJános SándorPublished in: Healthcare (Basel, Switzerland) (2024)
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.
Keyphrases
- healthcare
- quality improvement
- primary care
- end stage renal disease
- palliative care
- biofilm formation
- ejection fraction
- case report
- newly diagnosed
- chronic kidney disease
- physical activity
- fatty acid
- type diabetes
- uric acid
- computed tomography
- adipose tissue
- pain management
- glycemic control
- magnetic resonance
- patient reported outcomes
- staphylococcus aureus
- cystic fibrosis
- red blood cell
- pseudomonas aeruginosa
- big data
- deep learning
- diffusion weighted imaging