By: Elizabeth Lesher
This project was an epidemiological study that seeks to identify both risk and protective factors for the common cold (i.e., characteristics that make a person more or less susceptible to contracting the common cold). To carry out this project, I collected demographic, lifestyle, and genetic information from over 200 participants and used a range of statistical tools to analyze my data. From this analysis, I was able to determine many factors that increase or decrease a person’s chance of contracting the cold. The information discovered in this study contributes to scientific knowledge on the common cold, and because many lifestyle factors are alterable, this study contributes to a better informed population on how to maintain health. Although its impact may seem insignificant, common cold has no cure or effective treatments and is a major economic, social, and scientific burden. It can also cause serious and potentially fatal health problems in at-risk populations and is known to mutate and have reservoirs in various animal species (as in the case of COVID-19). Identifying risk factors to the cold can help create effective methods of diagnosis and prevention, as well as lead scientists closer to finding a cure or vaccination. This year I was an ISEF finalist for this work.
Abstract: In this study, the question, “What lifestyle, demographic, and genetic factors increase the risk of contracting the common cold?” was researched and tested. Background research suggests that susceptibility to the cold is a good proxy of overall immune health; the higher one’s immune system function, the fewer colds they get. Thus, it was hypothesized that there are certain lifestyle, demographic, and genetic factors that increase susceptibility to the common cold, and quantitative standards were created to determine if the data met the criteria for each considered risk factor. Two hundred forty-one human participants completed a questionnaire which asked about lifestyle, demographic, and genetic information and served as a means of data collection for the experiment. Four statistical tests were performed on the data: a chi-square test for independence, Cramer’s V strength test, a two-sample z-test for the difference between two proportions, and a t-test for the slope in least-squares regression. For the regression, each variable was transformed using exponential, power, or linear models achieved through logistic regression. Based on the results of this experiment—that age, hours of exercise per day, and servings of fermented foods have the strongest associations with the common cold, followed by minutes to fall asleep each night, sleep efficiency, servings of refined carbohydrates, family history, and exposure to e-cigarettes or smoke at work or school, and lastly, servings of processed meats, history of e-cigarette usage, asthmatic status, and medication usage—the hypothesis was partially supported. To allow for further research on this topic or similar topics, a longitudinal study using a tracking app to collect data could be implemented or the effect of risk factors on the severity of colds rather than frequency could be examined. The findings of this study contribute to scientific knowledge about the common cold, and identifying risk factors to the common cold can help create effective methods of prevention.