## 4 Discussion  
### Strengths and Limitations  
Our study has several strengths. First, we studied the entire population of Navarra, an area in the north of Spain with over 600,000 inhabitants. This minimized the risk of selection bias. We used cluster analysis to classify GPPs' compliance with the indicators. Cluster analysis creates groups to minimize intragroup variability and maximize extra group variability. This analysis allowed us to correct the drawbacks of more straightforward methods, such as deciles or quartiles and others that include individuals with outliers that may produce bias. Our population was so large that slight differences, without clinical relevance, could be significant. Therefore, using the hazard ratio or odds ratio to measure the association's strength allowed us to visualize better the most clinically relevant differences between the different GPPs in terms of compliance with the quality indicators. We did not know how many years general practitioners were in a GPP. In the future, it would be interesting to analyze in other studies the continuity of the same physician in each GPP. Another limitation was that the database did not include deaths by CV diseases. Our study was carried out based on the electronic records of health professionals during patient care, being a faithful reflection of clinical practice. This is one strength of this type of study. However, it could have biases because of the limitations in the quality of the records. Studies with large administrative databases often show high accuracy in the diagnostic and treatment aspects. Still, they have underreported or lack information on crucial aspects of the diet or physical activity. This lack of information on lifestyle produces misclassification problems that may affect the outcome of interest. In contrast, in many traditional \"ad hoc\" epidemiological studies, there is a great deal of information on risk factors. However, the clinical data are incomplete or of lower quality. Administrative databases may not obtain reliable estimates of the effects of preventive actions because of unmeasured confounding factors [37]. This can occur in both longitudinal and cross-sectional studies [38]. Our study's lifestyle or clinical examination data may have underreported smoking, BP, and weight measurements. However, our results were similar to those of previous studies in Spain [18; 39; 40; 41; 42; 43; 44; 45; 46; 47]. Laboratory data were collected automatically, accurately reflecting what had been requested for each patient. The registry of Navarra was rated as the highest quality among the 17 regional health services in Spain in patients with T2D [42]. In Navarra, studies have shown the usefulness of electronic records for assessing the quality of care of patients with T2D [7; 27; 29]. This study could only access data on hospital admissions for CVEs, but not mortality data. Future studies would be of great interest in analyzing the risk of CV mortality in patients with T2D [39]. It would have been interesting to include the duration of diabetes in the analysis [47]. However, this was not possible. In the year 2012, primary care medical records were \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **HRa * & **95\% CI** & \(p\) \\ \hline HbA1c \(\geq\) 9\% ** & 1.339 & 1.070–1.676 & 0.010 \\ \hline HbA1c \(<\) 7\% ** & 0.736 & 0.632–0.856 & \textless{}0.001 \\ \hline BP \textless{} 140/90 mmHg ** & 0.732 & 0.628–0.853 & \textless{}0.001 \\ \hline LDL-C \textless{} 100 mg/dL \(\ddagger\) or \textless{}70 mg/dL \(\ddagger\) & 0.240 & 0.196–0.294 & \textless{}0.001 \\ \hline Cluster inadequate control HbA1c \(\geq\) 9\% ** & 1.119 & 1.003–1.250 & 0.045 \\ \hline \hline \end{tabular}  
* Adjusted hazard ratio (full model in Table S4). ** Average five-year compliance. \(\ddagger\) Primary prevention. \(\ddagger\) Secondary prevention.  
\end{table}