Geospatial analysis and determinant factors of comorbidity presence in patients with diabetes in Peru

Given the significant increase in diabetes mellitus (DM) prevalence and its associated complications in recent decades, this study aimed to explore the determinant factors and geographical distribution of comorbidities and their number in patients with diabetes in Peru. Cross-sectional study based on a database providing detailed demographic and clinical information on DM patients affiliated with the Seguro Integral de Salud (SIS) in Peru. The dependent variables in this study are twofold: the type of comorbidities present in DM patients and the number of comorbidities they have. Comorbidities were categorized into three groups: DM with obesity/dyslipidemia, DM with hypertension, and DM with mental health disorders. The number of comorbidities was classified as none, one, two, or three comorbidities. A total of 1,355,354 patients were included. Male patients, older individuals, and those with a longer time since diagnosis have different probabilities of presenting the comorbidities and a higher number of them. Additionally, the geospatial analysis showed apparent regional variations in the prevalence and number of comorbidities, highlighting the influence of environmental and socioeconomic factors and access to healthcare services. This study identified significant demographic and clinical factors associated with comorbidities in patients with DM in Peru. These findings showed the need for personalized, region-specific diabetes management. Therefore, public health policies should adapt to meet the needs of different regions and groups. Improving healthcare access is crucial, especially where comorbidity prevalence is high. Further education programs must address diet and exercise comorbidities, focusing on vulnerable people. • Analysis of 1,355,354 diabetic patients reveals patterns of comorbidities in Peru. • Male patients show lower probability of presenting comorbidities than females. • Probability of comorbidities increases with age and time since diabetes diagnosis. • Regional variations in comorbidity prevalence suggest influence of environmental factors. • Geospatial analysis identifies clusters of high and low comorbidity prevalence across Peru.