Risk Prediction of high blood glucose among women (15–49 years) and men (15–54 years) in India: An analysis from National Family Health Survey-5 (2019–21) - Anjan Kumar Karri; Visweswara Rao Guthi; P Sri Sai Githa (Journal of Family Medicine and Primary Care)
Context: Approximately 500 million individuals worldwide are known to have diabetes, representing roughly 1 out of every 11 adults in the world. Approximately 45.8% of adult diabetes cases are believed to be undiagnosed. Aim: This study aimed to identify the predictors for high blood glucose and to develop a risk score which helps in early detection of high blood glucose among Indian men (15–54 years) and women (15–49 years). Methods and Material: This study utilised data from the National Family Health Survey-5, which were gathered between 2019 and 2021. The study population comprises women aged 15–49 years and men aged 15–54 years in India. Statistical Analysis Used: A logistic regression analysis was conducted to determine the predictors of high blood glucose. The results were expressed as odds ratios with 95% confidence intervals. The risk score for high blood glucose was derived through variable shrinking and by employing regression coefficients obtained from the standard logistic regression model. Data were analysed using IBM SPSS version 26. Results: The prevalence of high blood glucose in India was 9.3%. The study findings indicated an association between age and the occurrence of high blood glucose levels. The prevalence of high blood glucose was higher among males (11.1% vs 7.5%), individuals living in urban areas (10.7% vs 8.9%), those with a waist circumference exceeding the specified limit (11.7% vs 5.9%), and individuals who were overweight or obese (11.3%). The prevalence of high blood glucose was higher among alcoholics (13.2% vs 8.8%) and various forms of tobacco users (12.1% vs 8.4%). Conclusions: Age, sex, place of residence (urban), consumption of alcohol, hypertension, and waist circumference were found to be the significant predictor variables and were used to develop the risk prediction score using the logistic regression model.