Identifying the predictors of severe psychological distress by auto-machine learning methods.

Social stress in daily life and the COVID-19 pandemic have greatly impacted the mental health of the population. Early detection of a predisposition to severe psychological distress is essential for timely interventions. This paper analyzed 4036 samples participating in the 2019-2020 National Health Information Trends Survey (HINTS) and identified 57 candidate predictors of severe psychological distress based on univariate chi-square and t-test analyses. Five machine learning methods, namely logistic regression (LR), automatic generalized linear models (Auto-GLM), automatic random forests (Auto-Random Forests), automatic deep neural networks (Auto-Deep learning) and automatic gradient boosting machines (Auto-GBM), were employed to model synthetic minority oversampling technique-based (SMOTE) resampled data and identify predictors of severe psychological distress. Predictors were evaluated by odds ratios in logistic models and variable importance in the other models. Forty-seven variables were identified as significant predictors of severe psychological distress, including 13 sociodemographic variables and 34 variables related to individual lifestyle and behavioral habits. Among them, new potentially relevant variables related to an individual's level of concern and trust in cancer information, exposure to health care providers, and cancer screening and awareness are included. The performance of each model was evaluated using five-fold cross-validation. The optimal model performance-wise was Auto-GBM with an accuracy of 89.75%, a precision of 89.68%, a recall of 89.31%, an F1-score of 89.48% and an AUC of 95.57%. Significant predictors of severe psychological distress were identified in this study and the value of machine learning methods in predicting severe psychological distress is demonstrated, thereby enhancing pre-prediction and clinical decision-making of severe psychological distress problems.

Gender Differences in the Nonspecific and Health-Specific Use of Social Media Before and During the COVID-19 Pandemic: Trend Analysis Using HINTS 2017-2020 Data.

The use of social media has changed since the outbreak of coronavirus disease 2019 (COVID-19). However, little is known about the gender disparity in social media use for nonspecific and health-specific issues before and during the COVID-19 pandemic. Based on a gender difference perspective, this study aimed to examine how the nonspecific and health-specific uses of social media changed in 2017-2020. The data came from the Health Information National Trends Survey Wave 5 Cycle 1-4. This study included 10,426 participants with complete data. Compared to 2017, there were higher levels of general use in 2019 and 2020, and an increased likelihood of health-related use in 2020 was reported among the general population. Female participants were more likely to be nonspecific and health-specific users than males. Moreover, the relationship of gender with general use increased in 2019 and 2020; however, concerning health-related use, it expanded in 2019 but narrowed in 2020. The COVID-19 global pandemic led to increased use of social media, especially for health-related issues among males. These findings further our understanding of the gender gap in health communication through social media, and contribute to targeted messaging to promote health and reduce disparities between different groups during the pandemic.

Impact of COVID-19 Preventative Measures on Otolaryngology in Taiwan: A Nationwide Study.

BACKGROUND: Taiwan always had low case rates of COVID-19 compared with other countries due to its immediate control and preventive measures. However, the effects of its policies that started on 2020 for otolaryngology patients were unknown; therefore, the aim of this study was to analyze the nationwide database to know the impact of COVID-19 preventative measures on the diseases and cases of otolaryngology in 2020. METHOD: A case-compared, retrospective, cohort database study using the nationwide database was collected from 2018 to 2020. All of the information from outpatients and unexpected inpatients with diagnoses, odds ratios, and correlation matrix was analyzed. RESULTS: The number of outpatients decreased in 2020 compared to in 2018 and 2019. Thyroid disease and lacrimal system disorder increased in 2020 compared to 2019. There was no difference in carcinoma in situ, malignant neoplasm, cranial nerve disease, trauma, fracture, and burn/corrosion/frostbite within three years. There was a highly positive correlation between upper and lower airway infections. CONCLUSIONS: COVID-19 preventative measures can change the numbers of otolaryngology cases and the distributions of the disease. Efficient redistribution of medical resources should be developed to ensure a more equitable response for the future.

Reducing Medication Problems among Minority Individuals with Low Socioeconomic Status through Pharmacist Home Visits.

INTRODUCTION: In this study, pharmacists conducted home visits for individuals of medically underserved populations in Taiwan (i.e., socioeconomically disadvantaged individuals, middle-aged or older adults, and individuals living alone, with dementia, or with disabilities) to understand their medication habits. We quantified medication problems among various groups and investigated whether the pharmacist home visits helped to reduce the medication problems. MATERIALS AND METHODS: From April 2016 to March 2019, pharmacists visited the homes of the aforementioned medically underserved individuals in Taipei to evaluate their drug-related problems and medication problems. Age, living alone, diagnoses of dementia or disabilities, and socioeconomic disadvantages contributed significantly to inadequate disease and medical treatment knowledge and self-care skills as well as lifestyle inappropriateness among patients. The patients who were living alone and socioeconomically disadvantaged stored their drugs in inappropriate environments. RESULTS: After the pharmacists visited the patients' homes twice, the patients improved considerably in their disease and medical treatment knowledge, self-care skills, and lifestyles (p < 0.001). Problems related to the uninstructed reduction or discontinuation of drug use (p < 0.05) and use of expired drugs (p < 0.001) were also mitigated substantially. DISCUSSION AND CONCLUSION: Through the home visits, the pharmacists came to fully understand the medicine (including Chinese medicine) and health food usage behaviors of the patients and their lifestyles, enabling them to provide thorough health education. After the pharmacists' home visits, the patients' drug-related problems were mitigated, and their knowledge of diseases, drug compliance, and drug storage methods and environments improved, reducing drug waste. Our findings can help policymakers address the medication problems of various medically underserved groups, thereby improving the utilization of limited medical resources.

Analysis of the COVID-19 Epidemic Transmission Network in Mainland China: K-Core Decomposition Study.

BACKGROUND: Since the outbreak of COVID-19 in December 2019 in Wuhan, Hubei Province, China, frequent interregional contacts and the high rate of infection spread have catalyzed the formation of an epidemic network. OBJECTIVE: The aim of this study was to identify influential nodes and highlight the hidden structural properties of the COVID-19 epidemic network, which we believe is central to prevention and control of the epidemic. METHODS: We first constructed a network of the COVID-19 epidemic among 31 provinces in mainland China; after some basic characteristics were revealed by the degree distribution, the k-core decomposition method was employed to provide static and dynamic evidence to determine the influential nodes and hierarchical structure. We then exhibited the influence power of the above nodes and the evolution of this power. RESULTS: Only a small fraction of the provinces studied showed relatively strong outward or inward epidemic transmission effects. The three provinces of Hubei, Beijing, and Guangzhou showed the highest out-degrees, and the three highest in-degrees were observed for the provinces of Beijing, Henan, and Liaoning. In terms of the hierarchical structure of the COVID-19 epidemic network over the whole period, more than half of the 31 provinces were located in the innermost core. Considering the correlation of the characteristics and coreness of each province, we identified some significant negative and positive factors. Specific to the dynamic transmission process of the COVID-19 epidemic, three provinces of Anhui, Beijing, and Guangdong always showed the highest coreness from the third to the sixth week; meanwhile, Hubei Province maintained the highest coreness until the fifth week and then suddenly dropped to the lowest in the sixth week. We also found that the out-strengths of the innermost nodes were greater than their in-strengths before January 27, 2020, at which point a reversal occurred. CONCLUSIONS: Increasing our understanding of how epidemic networks form and function may help reduce the damaging effects of COVID-19 in China as well as in other countries and territories worldwide.

Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index.

Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series of SMSI to predict new suspected COVID-19 case numbers during this period. To avoid overfitting, five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new suspected COVID-19 case numbers from 20 January 2020 to 9 February 2020. We further validated the optimal method for new confirmed cases of COVID-19 from 31 December 2019 to 17 February 2020. The new suspected COVID-19 case numbers correlated significantly with the lagged series of SMSI. SMSI could be detected 6-9 days earlier than new suspected cases of COVID-19. The optimal method was the subset selection method, which had the lowest estimation error and a moderate number of predictors. The subset selection method also significantly correlated with the new confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new confirmed COVID-19 cases. SMSI could be a significant predictor of the number of COVID-19 infections. SMSI could be an effective early predictor, which would enable governments' health departments to locate potential and high-risk outbreak areas.