Examining the impact of sex differences and the COVID-19 pandemic on health and health care: findings from a national cross-sectional study

Abstract

Lay Summary The COVID-19 pandemic has had unprecedented and potentially irreversible impacts on health and health care globally. Many complex issues and factors therefore need to be accounted for as we look at the long-term impact of COVID-19 on humanity and society and how this ongoing crisis continues to affect health and health care outcomes for different populations. There are significant health disparities and sex inequalities associated with the COVID-19 pandemic. Sex-responsive interventions are imperative to address widening sex gaps resulting from the pandemic. Although many researchers have studied the impact of the COVID-19 pandemic on health disparities, there is a lack of research on the sole and joint association of the pandemic or sex differences and their specific impact on health and health care. The study examined the association of the COVID-19 pandemic, the association of sex, and the joint association of sex and the COVID-19 pandemic with health communication, physical activity, mental health, and behavioral health. We also highlighted the application of Bonferroni corrections when conducting multiple hypotheses testing simultaneously. Objective To examine the association of the COVID-19 pandemic, the association of sex, and the joint association of sex and the COVID-19 pandemic with health communication, physical activity, mental health, and behavioral health. Methods We drew data from the National Cancer Institute's 2020 Health Information National Trends Survey (HINTS). We described and compared the characteristics of social determinants of health, physical activity, mental health, alcohol use, patterns of social networking service use, and health information data sharing. Analyses were weighted to provide nationally representative estimates. Multivariate models (multiple linear regression, multiple logistic regression, and multinomial logistic model) were used to assess the sole and joint association with sex and pandemic. In addition, we applied the Bonferroni correction to adjust p-values to decrease the risks of type I errors when making multiple statistical tests. Results Females were more likely to use mobile health and health communication technologies than males, and the difference increased after the pandemic. The association between sex and mental health was significant after the COVID-19 pandemic. Females were more likely to experience depression or anxiety disorders. Both males and females had a slight decrease in terms of the quantity and intensity of physical activity and females were less likely to perform moderate exercise and strength training regularly. Males were likely to drink more alcohol than females. Conclusion The COVID-19 pandemic amplifies the differences between males and females in health communication, physical activity, mental health, and behavioral health. Intersectional analyses of sex are integral to addressing issues that arise and mitigating the exacerbation of inequities. Responses to the pandemic should consider diverse perspectives, including sex.

Leveraging natural language processing and geospatial time series model to analyze COVID-19 vaccination sentiment dynamics on Tweets

“…In addition, an iterative learning cycle based on the analytical process can help identify unforeseen areas of public concerns as well as potential barriers for required interventions, thus maximizing vaccine uptake and minimizing health care disparities across demographic communities. 6 During the pandemic, NLP has been applied to address many clinical and health care problems. NLP was shown to be useful for extracting signs and symptoms of COVID-19 from clinical notes, 7 and extracting risk factors related to severe or nonsevere COVID-19 cases from unstructured free text.…”

The role of artificial intelligence in the application of the integrated electronic health records and patient-generated health data

“…In addition, the use of PGHD must be implemented in a way that prevents the exacerbation of health disparities. [85] Pernicious disparities, such as gender or race differences in health conditions related to social inequality within health data, have negative impacts on AI model performance and results. [86] Developers of such software should draw heavily from research in the fields of user-focused design and human-computer interaction to create intuitive data visualizations that focus on important clinical information and allow health care providers to identify patterns that provide clinically meaningful insights.…”

Patterns of Dietary Quality, Physical Activity, and Sleep Duration among Cancer Survivors and Caregivers

“…Weighted ranges in our sample demonstrated that most long-sleepers (i.e., ≥9 hours per night) were in the Least Engaged-Sedentary pro le, further supporting the potential utility of health behavior intervention for this group. Data included in this analysis were collected prior to the COVID-19 pandemic, which may have changed population engagement in health behaviors, although this remains unclear (Cole et al, 2023;Donzella et al, 2021;Ye & Ren, 2022). Finally, the temporality of the relationships examined and changes over time cannot be determined in this cross-sectional study.…”