Theses (Master In Health Informatics & Analytics)

Although men are diagnosed with depression half as frequently as women and are unlikely to seek suicide, men are in fact more prone to death by suicide up to three to four times as often. Ultimately, it is highlighted that rarely do men pursue help yet are engaging in detrimental behaviours all the same at a greater prevalence than that of women. To improve mental health among men, a crucial measure to take is to raise their inclination in seeking aid for depression and associated functional impairment. Consequently, two research objectives are determined for the present study. Firstly, to ascertain the direction and strength of association between sociodemographic characteristics and Major Depressive Episode with Severe Impairment (MDESI) in men by utilising a nomogram. Secondly, to develop a logistic regression predictive model to classify men diagnosed with MDESI into categories with and without severe functional impairment. Data on adult men aged 18 years and above who have participated in the National Survey on Drug Use and Health (NSDUH), 2020 to 2021, are pooled and analysed. The nomogram has revealed that Native American men are at highest risk of experiencing MDESI compared to men of other ethnicities. Additionally, for men, being at an age between 50 to 64 years, having a family income of less than 20,000$ (US), being gay, strongly disagreeing with the importance of friends sharing religious beliefs, strongly agreeing with the importance of personal religious beliefs, agreeing with religious beliefs influencing personal decisions, and living at a non-metro area further increase the risk of experiencing MDESI. Using the training data set, the logistic regression predictive model has produced AUC = 0.733, accuracy = 0.638, recall = 0.638, and precision = 0.697 . Using the test data set, the scores have slightly increased for all measures (AUC = 0.746, accuracy = 0.678, recall = 0.678, precision = 0.729). Study results have, however, indicated that the current logistic model, when utilised as a classifier, is presently performing inadequately. Further work is required in order to enhance the overall model to be at a more adequate state. Keywords men’s mental health, major depression, severe impairment, National Survey on Drug Use and Health, machine learning, logistic regression classifier