Internet searches for "insomnia" and "suicide" mediated by stay-at-home behaviors in 45 countries during the first 12 months of the COVID-19 pandemic.

BACKGROUND: This study aimed to investigate (1) the mental health impacts (i.e., insomnia and suicide ideas) of the COVID-19 pandemic and (2) the mediation effects of stay-at-home levels on those impacts. METHODS: This study investigated monthly national COVID-19 deaths, stay-at-home levels, and internet searches for words for "insomnia" and "suicide" across 45 countries during the first year of the COVID-19 pandemic (March 1, 2020, to February 28, 2021). We used the changes of internet search volumes for "insomnia" and "suicide" (from the Google Trends database) to represent the mental health impacts, and the time of cell phone activity at the residence (from Google Location History) to estimate the stay-at-home effects. We computed the proportion mediated (PM) caused by stay-at-home levels in the COVID-19 impacts on insomnia and suicide ideas, respectively. RESULTS: Throughout the first year of the COVID-19 pandemic, national COVID-19 deaths significantly correlated to increased internet searches for "insomnia" but decreased searches for "suicide". In addition, the mediation effect was significant in the first six-month of COVID-19-related increases in insomnia (PM = 42.6 %, p = 0.016), but this effect was not significant (PM = 13.1 %, p = 0.270) in the second six-month. By contrast, the mediation effect was not significant in the first six-month of COVID-19-related decrease in suicide ideation (PM = 8.1 %, p = 0.180), but this effect was significant (PM = 39.6 %, p = 0.014) in the second six-month. CONCLUSIONS: Stay-at-home levels significantly mediated both increased insomnia and decreased suicide ideas, but within different time frames.

Using the Novel Mortality-Prevalence Ratio to Evaluate Potentially Undocumented SARS-CoV-2 Infection: Correlational Study.

BACKGROUND: The high prevalence of COVID-19 has resulted in 200,000 deaths as of early 2020. The corresponding mortality rate among different countries and times varies. OBJECTIVE: This study aims to investigate the relationship between the mortality rate and prevalence of COVID-19 within a country. METHODS: We collected data from the Johns Hopkins Coronavirus Resource Center. These data included the daily cumulative death count, recovered count, and confirmed count for each country. This study focused on a total of 36 countries with over 10,000 confirmed COVID-19 cases. Mortality was the main outcome and dependent variable, and it was computed by dividing the number of COVID-19 deaths by the number of confirmed cases. RESULTS: The results of our global panel regression analysis showed that there was a highly significant correlation between prevalence and mortality (ρ=0.8304; P<.001). We found that every increment of 1 confirmed COVID-19 case per 1000 individuals led to a 1.29268% increase in mortality, after controlling for country-specific baseline mortality and time-fixed effects. Over 70% of excess mortality could be attributed to prevalence, and the heterogeneity among countries' mortality-prevalence ratio was significant (P<.001). Further, our results showed that China had an abnormally high and significant mortality-prevalence ratio compared to other countries (P<.001). This unusual deviation in the mortality-prevalence ratio disappeared with the removal of the data that was collected from China after February 17, 2020. It is worth noting that the prevalence of a disease relies on accurate diagnoses and comprehensive surveillance, which can be difficult to achieve due to practical or political concerns. CONCLUSIONS: The association between COVID-19 mortality and prevalence was observed and quantified as the mortality-prevalence ratio. Our results highlight the importance of constraining disease transmission to decrease mortality rates. The comparison of mortality-prevalence ratios between countries can be a powerful method for detecting, or even quantifying, the proportion of individuals with undocumented SARS-CoV-2 infection.

Stepwise Signal Extraction via Marginal Likelihood.

This paper studies the estimation of stepwise signal. To determine the number and locations of change-points of the stepwise signal, we formulate a maximum marginal likelihood estimator, which can be computed with a quadratic cost using dynamic programming. We carry out extensive investigation on the choice of the prior distribution and study the asymptotic properties of the maximum marginal likelihood estimator. We propose to treat each possible set of change-points equally and adopt an empirical Bayes approach to specify the prior distribution of segment parameters. Detailed simulation study is performed to compare the effectiveness of this method with other existing methods. We demonstrate our method on single-molecule enzyme reaction data and on DNA array CGH data. Our study shows that this method is applicable to a wide range of models and offers appealing results in practice.