Introduction: Social factors might bring about health inequities. Vulnerable population groups, i... more Introduction: Social factors might bring about health inequities. Vulnerable population groups, including those suffering from noncommunicable diseases such as type 2 diabetes and depression, might be more prone to suffering the effects of such inequities. This study aimed to identify patients with type 2 diabetes with depression in a primary care setting, with the objective of describing health inequities among urban and suburban dwellers. Methods: A quantitative, retrospective and descriptive study was carried out among patients with diabetes attending public primary healthcare centres in different regions of Malta. Participants completed a self-administered questionnaire to identify patient and disease characteristics. Convenience sampling was used. Results: The logistic regression model predicting the likelihood of different factors occurring with suburban patients with diabetes as opposed to those residing in urban areas contained five independent variables (severity of depression, monthly income, blood capillary glucose readings, weight and nationality). The full model containing all predictors was statistically significant, c (5, n=400), p<0.001, indicating that the model was able to distinguish between urban and suburban areas. The model as a whole explained between 10% (Cox and Snell R) and 20% (Nagelkerke R) of the variance in urban and suburban areas, and correctly classified 73.8% of cases.
Introduction: Social factors might bring about health inequities. Vulnerable population groups, i... more Introduction: Social factors might bring about health inequities. Vulnerable population groups, including those suffering from noncommunicable diseases such as type 2 diabetes and depression, might be more prone to suffering the effects of such inequities. This study aimed to identify patients with type 2 diabetes with depression in a primary care setting, with the objective of describing health inequities among urban and suburban dwellers. Methods: A quantitative, retrospective and descriptive study was carried out among patients with diabetes attending public primary healthcare centres in different regions of Malta. Participants completed a self-administered questionnaire to identify patient and disease characteristics. Convenience sampling was used. Results: The logistic regression model predicting the likelihood of different factors occurring with suburban patients with diabetes as opposed to those residing in urban areas contained five independent variables (severity of depression, monthly income, blood capillary glucose readings, weight and nationality). The full model containing all predictors was statistically significant, c (5, n=400), p<0.001, indicating that the model was able to distinguish between urban and suburban areas. The model as a whole explained between 10% (Cox and Snell R) and 20% (Nagelkerke R) of the variance in urban and suburban areas, and correctly classified 73.8% of cases.
Uploads
Papers by tania cardona