[Purpose]
Many studies have observed a high prevalence of erectile dysfunction among individuals performing physical activity in less leisure-time. However, this relationship in patients with type 2 diabetic patients is not well studied. In exposure outcome studies with ordinal outcome variables, investigators often try to make the outcome variable dichotomous and lose information by collapsing categories. Several statistical models have been developed to make full use of all information in ordinal response data, but they have not been widely used in public health research. In this paper, we discuss the application of two statistical models to determine the association of physical inactivity with erectile dysfunction among patients with type 2 diabetes.
[Methods]
A total of 204 married men aged 20-60 years with a diagnosis of type 2 diabetes at the outpatient unit of the Department of Endocrinology at PSG hospitals during the months of May and June 2019 were studied. We examined the association between physical inactivity and erectile dysfunction using proportional odds ordinal logistic regression models and continuation ratio models.
[Results]
The proportional odds model revealed that patients with diabetes who perform leisure time physical activity for over 40 minutes per day have reduced odds of erectile dysfunction (odds ratio=0.38) across the severity categories of erectile dysfunction after adjusting for age and duration of diabetes.
[Conclusion]
The present study suggests that physical inactivity has a negative impact on erectile function. We observed that the simple logistic regression model had only 75% efficiency compared to the proportional odds model used here; hence, more valid estimates were obtained here.
Background Corona virus disease (COVID-2019) is a severe ongoing novel pandemic that is spreading rapidly across the world. Analyzing and predicting COVID-19 prevalence will help governments to take necessary actions in controlling the spread. Time series analysis has proved to be efficient for estimating future impact in such circumstances. Methods Auto Regressive Integrated Moving Average (ARIMA) models were developed to predict the epidemiological trend of pandemic in India, The USA and Italy up-to 20 July 2020. A 15 day forecast were made to study the future scenario. Results We could identify that all the countries had a linear trend in the increase of daily confirmed COVID-19 cases. Our studies produced a 15 day forecast results for India, US and Italy. the 95% PI for 20 July 2020 are, (25821.72 , 32991.38), (44278.89 , 87095.68) and (-2935.20, 2685.14) respectively for the three countries. Conclusion The US and India will have a rise in number of daily counts of COVID-19. Italy will gradually slow the pace. It is supposed that the present prediction models will assist the government and medical personnel to be prepared for the upcoming conditions and have more readiness in healthcare systems.
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