Sex-differences in COVID-19 diagnosis, risk factors and disease comorbidities: A large US-based cohort study.

Introduction: Morbidity and mortality from COVID-19 are higher among men, however, underlying pathways remain controversial. We aim to investigate sex-gender differences in COVID-19 in a large US-based cohort, namely COVID-19 Research Database. More specifically, the objectives are to explore the socio-economic characteristics of COVID-19 male and female patients and to examine potential sex differences in lifestyle factors and disease comorbidities among diagnosed patients. Methods: This is a retrospective cohort study contrasting male vs. female patients with test-confirmed COVID-19. The study used Healthjump electronic medical records (e.g., demographics, encounters, medical history, and vitals) extracted from January 2020 to December 2021 (N = 62,310). Results: Significant sociodemographic and comorbidity differences were observed between males and females (p < 0.05). For example, a significantly higher proportion of males (vs. females) were aged ≥70-year-old (17.04 vs. 15.01%) and smokers (11.04 vs. 9.24%, p < 0.0001). In addition, multiple logistic regression showed that hypertension and diabetes were significantly more frequent in males [adjusted odds ratio (ORa) = 66.19 and ORa = 22.90]. Conclusions: Understanding the differences in outcomes between male and female patients will inform gender equity responsive approach to COVID-19 and enhance the effectiveness of clinical practice, health policy and interventions.

Modeling SF-6D Health Utilities: Is Bayesian Approach Appropriate?

BACKGROUND: Valuation studies of preference-based health measures like SF6D have been conducted in many countries. However, the cost of conducting such studies in countries with small populations or low- and middle-income countries (LMICs) can be prohibitive. There is potential to use results from readily available countries' valuations to produce better valuation estimates. METHODS: Data from Lebanon and UK SF-6D value sets were analyzed, where values for 49 and 249 health states were extracted from samples of Lebanon and UK populations, respectively, using standard gamble techniques. A nonparametric Bayesian model was used to estimate a Lebanon value set using the UK data as informative priors. The resulting estimates were then compared to a Lebanon value set obtained using Lebanon data by itself via various prediction criterions. RESULTS: The findings permit the UK evidence to contribute potential prior information to the Lebanon analysis by producing more precise valuation estimates than analyzing Lebanon data only under all criterions used. CONCLUSIONS: The positive findings suggest that existing valuation studies can be merged with a small valuation set in another country to produce value sets, thereby making own country value sets more attainable for LMICs.