Modelling the prevalence of diabetes mellitus risk factors based on artificial neural network and multiple regression

Background: Type 2 diabetes mellitus [T2DM] is a metabolic disease with complex causes, manifestations, complications and management. Understanding the wide range of risk factors for T2DM can facilitate diagnosis, proper classification and cost-effective management of the disease.

Aims: To compare the power of an artificial neural network [ANN] and logistic regression in identifying T2DM risk factors.

Methods: This descriptive and analytical study was conducted in 2013. The study samples were all residents aged 15–64 years of rural and urban areas in East Azerbaijan, Islamic Republic of Iran, who consented to participate [n = 990]. The latest data available were collected from the Noncommunicable Disease Surveillance System of East Azerbaijan Province [2007]. Data were analysed using SPSS version 19.

Results: Based on multiple logistic regression, age, family history of T2DM and residence were the most important risk factors for T2DM. Based on ANN, age, body mass index and current smoking were most important. To test for generalization, ANN and logistic regression were evaluated using the area under the receiver operating characteristic curve [AUC]. The AUC was 0.726 [SE = 0.025] and 0.717 [SE = 0.026] for logistic regression and ANN, respectively [P < 0.001].

Conclusions: The logistic regression model is better than ANN and it is clinically more comprehensible.

Households encountering with catastrophic health expenditures in Ferdows, Iran

Out-of-pocket payments are the main sources of healthcare financing in most developing countries. Healthcare services can impose a massive cost burden on households, especially in developing countries. The purpose of this study was to calculate households encountered with catastrophic healthcare expenditures in Ferdows, Iran. The sample included 100 households representing 20% of all households in Ferdows, Iran. The data were collected using self-administered questionnaire. The ability to pay of households was calculated, and then if costs of household health were at least 40% of their ability to pay, it was considered as catastrophic expenditures. Rate of households encountered to catastrophic health expenditures was estimated to be 24%, of which dentistry services had the highest part in catastrophic health expenditures. Low ability to pay of households should be supported against these expenditures. More equitable health system would solve the problem, although more financial aid should be provided for households encountered to catastrophic costs