This study reports perceived stress and associated sociodemographic factors from an international sample of adults, during the COVID-19 pandemic. The Perceived Stress Scale (PSS-10) along with socio-demographic questions were conducted between 8 April 2020 and 11 May 2020. The survey was translated from English into five languages. Recruitment was conducted worldwide using social media. A total of 1685 survey responses were collected across 57 countries with eleven countries (≥30 responses/country) included in the sub-analyses. Overall, the mean PSS-10 score was 19.08 (SD = 7.17), reflecting moderate stress compared to previously reported norms. Female gender was associated with a higher PSS score (3.03, p < 0.05) as well as four-year degree holders (3.29, p < 0.05), while adults over 75 years (−7.46, p < 0.05) had lower PSS scores. Personal care composite score (including hours of sleep, exercise, and meditation) was associated with lower PSS scores (−0.39, p < 0.01). Increases in personal care and changes in work expectations were associated with lower PSS scores (−1.30 (p < 0.05) and −0.38 (p < 0.01), respectively). Lower total PSS scores were reported in Germany (−4.82, p < 0.01) compared to the global response sample mean. This information, collected during the initial period of global mitigation orders, provides insight into potential mental health risks and protective factors during crises.

Inmate welfare, staff security, public health concerns, and the need for recovery-friendly prison environments have been cited as supporting efforts to control in prison substance use. This article reports on the California Department of Corrections (CDC) Drug Reduction Strategy Project, which involved systematic random urine testing and drug interdiction measures. The two-phase evaluation took place at four CDC institutions, with three serving as test sites and one serving as a standard-procedures comparison site. The results of Phase I, random urine testing of 150 inmates per week from the eligible inmate general population, supported the effectiveness of systematic random urine testing in reducing in-prison substance use, as measured by the number of inmates refusing to test or testing positive for illicit substances. The results of Phase II, which involved continued random urine testing at the three test sites in addition to K-9 drug detection teams at one institution and drug detection equipment at another institution, led to further reductions in substance use, but few drug finds resulted from the additional interdiction measures.

Massive molecular testing for COVID-19 has been pointed out as fundamental to moderate the spread of the pandemic. Pooling methods can enhance testing efficiency, but they are viable only at low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of informed Dorfman testing for COVID-19 by arranging samples into all-negative pools. To do this, we ran an automated method to train numerous machine learning models on a retrospective dataset from more than 8000 patients tested for SARS-CoV-2 from April to July 2020 in Bogotá, Colombia. We estimated the efficiency gains of using the predictor to support Dorfman testing by simulating the outcome of tests. We also computed the attainable efficiency gains of non-adaptive pooling schemes mathematically. Moreover, we measured the false-negative error rates in detecting the ORF1ab and N genes of the virus in RT-qPCR dilutions. Finally, we presented the efficiency gains of using our proposed pooling scheme on proof-of-concept pooled tests. We believe Smart Pooling will be efficient for optimizing massive testing of SARS-CoV-2.

Massive molecular testing for COVID-19 has been pointed as fundamental to moderate the spread of the disease. Pooling methods can enhance testing efficiency, but they are viable only at very low prevalences of the disease. We propose Smart Pooling, a machine learning method that uses sociodemographic data from patients to increase the efficiency of pooled molecular testing for COVID-19 by arranging samples into all-negative pools. We show efficiency gains of 42% with respect to individual testing at disease prevalence of up to 25%, a regime in which two-step pooling offers marginal efficiency gains. Additionally, we calculate the possible efficiency gains of one- and two-dimensional two-step pooling strategies and present the optimal strategies for disease prevalences up to 25%. We discuss practical limitations to conduct pooling in the laboratory.

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