Epidemiological Studies: Using Iot Data To Conduct Epidemiological Research On The Prevalence And Risk Factors Of Ent Diseases In Different Populations

Abstract

Background:ENT diseases are common the world over and their occurrence often as a result of environmental factors which include air pollution and other particles causing allergens. Since the usage of IoT devices continues to rise, the current environmental details may be employed to diagnose and estimate the ENT disease's causative elements. For this reason, the role of IoT-derived environmental data and its association with the occurrence and manifestation of ENT conditions within different groups of individuals will be investigated in this research study.

Objective:To explore the use of IoT data in defining the occurrence and distribution of ENT diseases and for determining the effects of environmental drives including pollution on ENT health.

Methods:A cross-sectional descriptive survey research design was adopted for this study using data collected through smart devices and structured questionnaires. The participants included 250 people with differences in geographical location and age. Score of ENT symptoms, days lost due to ENT diseases, and various environmental factors like air pollution, etc, were examined. The techniques used for data analysis were the Shapiro-Wilk test to determine the level of normality and Cronbach’s Alpha regarding internal reliability or consistency of the items in the set questionnaires besides logistic regression. The multicollinearity of the independent variable was tested using the Variance Inflation Factor (VIF).

Results:Descriptive analysis also showed that ENT symptoms severity, sick day, and other likes were not normally distributed ( = < 0.001). There was a poor internal consistency of all the measurement items in terms of reliability and Cronbach’s Alpha was low (-0.168). The statistical analysis said that by using logistic regression, both pollution exposure and IoT device use, it was only possible to predict sick days with an accuracy of 10.67%. The VIF analysis suggested that there was no problem with multicollinearity amongst the control variables. ENT symptoms severity distribution and RATM Sick Days were positively skewed.

Conclusion:Though IoT devices are beneficial for obtaining real-time environmental data, the findings suggest that the environmental variables may not independently act as effective predictor variables for the severity of ENT disease or sick days. Because the reliability of the measurement tools used was low, and due to the poor performance of the predictive model, we recommend that future studies use more variables and better methods of assessment. More elaborate frameworks are required to capture the link between IoT data, environmental effects, and ENT health-related outcomes.