In response to the outbreak of the COVID-19 pandemic, many governments instituted “stay-at-home” orders to prevent the spread of the coronavirus. The resulting changes in work and life routines had the potential to substantially perturb typical patterns of urban water use. We present here an analysis of how these pandemic responses affected California’s urban water consumption. Using water demand modeling that fuses an integrated water use database, we first simulated the water use in a business-as-usual (non-pandemic) scenario for essentially all urban areas in California. We then subtracted the business-as-usual water use from the actual use to isolate the changes caused solely by the pandemic response. We found that the pandemic response decreased California’s urban water use by 7.9%, which can be largely attributed to an 11.2% decrease in the commercial, industrial, and institutional sector that more than offset a 1.4% increase in the residential sector. The influence of the stay-at-home practices on urban water use is slightly stronger than the combined influences of all non-pandemic factors. This study covers both metropolitans and suburbs; therefore, the results could also be useful for analysis of the impacts of COVID-19 on water use in other urban areas.
Objective: The associations between maternal use of antidepressant during pregnancy and preterm birth (PTB) has been the subject of much discussion and controversy. The aim of the present study was to systematically review the association between antidepressant use during pregnancy and the risk of PTB, especially in depressed women.Methods: A computerized search was conducted in PubMed, PsycINFO, and Embase before June 30, 2019, supplemented with a manual search of the reference lists, to identify original research regarding PTB rates in women taking antidepressants during pregnancy. A random-effects model was used to calculate the summarized relative risks (RRs) and 95% confidence intervals (CIs). The potential for publication bias was examined through Begg' s and Egger' s tests.Results: A total of 2,279 articles were reviewed, 23 of which were selected. The risk of PTB was increased in women with depression [1.58 (1.23−2.04)] and in the general pregnant female population [1.35 (1.11−1.63)] who used antidepressants during pregnancy. Similar results were observed in depressed women treated with selective serotonin reuptake inhibitors (SSRIs) during pregnancy [1.46 (1.32−1.61)]. There was no significantly increased risk of PTB observed with SSRI use in the general pregnant female population [1.25 (1.00−1.57)], and the heterogeneity of these studies was high. Conclusions:The results of this meta-analysis indicate maternal antidepressant use is associated with a significantly increased risk of PTB in infants. Health care providers and pregnant women must weigh the risk-benefit potential of these drugs when making decisions about whether to treat with antidepressant during pregnancy.
ObjectiveTo investigate the prevalence of depression or anxiety in patient with multiple myeloma (MM) in China during maintenance treatment and its associated influencing factors.MethodsPatients with MM (n = 160) received maintenance therapy, and control subjects (without MM, n = 160) matched on age, sex, and BMI were recruited. Patients completed questionnaires, including the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder 7-item Scale (GAD-7), and the Verbal Pain Rating Scale (VPRS). Data on the Clinical characteristics, biochemical indicators of de novo MM were from the database of the Hematology Department of Beijing Chao-yang Hospital. Multiple linear regression model analysis was used to compare the differences in PHQ-9 and GAD-7 scale scores between the control group and the case group after correction for relevant variables. Multiple logistic regression models were subsequently used to analyze the correlation between the presence or absence of anxiety and depression and clinical indicators in the MM groups.ResultsDepression symptoms was present in 33.33% and anxiety symptoms in 24.68% of first-episode MM in the maintenance phase of treatment, and depression symptoms in the index-corrected MM group was significantly different from that in the control group (t = 2.54, P < 0.05). Analyses of multiple logistic regressions: biochemical indicators and clinical typing were not significantly associated with anxiety and depression. Compared to the pain rating 1, the risk of depressive mood was greater in the case group with the pain rating 2 (OR = 2.38) and the pain rating ≥ 3 (OR = 4.32). The risk of anxiety was greater in the case group with the pain rating ≥ 3 than the pain rating 1 (OR = 2.89).ConclusionDespite being in clinical remission, depressive mood problems in patients with MM remain prominent. Clinicians should enhance mood assessment and management in patients with concomitant pain.
Abstract. Accurate remote sensing-based snow water equivalent (SWE) estimates have been elusive, particularly in mountain areas, however, there now appears to be some potential for direct satellite-based SWE observations along ground tracks that only cover a portion of a spatial domain (e.g., watershed). Fortunately, spatiotemporally continuous meteorological and surface variables could be leveraged to infer SWE in the gaps between satellite ground tracks. Here, we evaluate statistical and machine learning (ML) approaches to perform a track-to-area (TTA) transformation of synthetic SWE observations in California’s Upper Tuolumne River Watershed. We construct relationships between multiple meteorological and surface variables and synthetic SWE observations along observation tracks, and we then extend this relationship to unobserved areas between ground tracks to estimate SWE over the entire watershed. Domain-wide April 1st SWE inferred using two satellite tracks (~4.5 % basin coverage) resulted in percent error of basin-averaged SWE of 24.5 %, 4.5 %, and 6.3 % in an extreme dry year (WY2015), a normal year (WY2008) and an extraordinarily wet year (WY2017), respectively. Assuming a 10-day overpass interval, percent errors in basin-averaged SWE in both snow accumulation and snowmelt seasons were mostly less than 10 %. We employ feature sensitivity analysis to overcome the black-box nature of ML methods and increase the explainability of the ML results. Our feature sensitivity analysis shows that precipitation is the dominant variable controlling the TTA SWE estimation, followed by net longwave radiation. We find a modest increase in SWE estimation accuracy when more than two ground tracks are leveraged. Accuracy of Apr 1st SWE estimation is only modestly improved for track repeats more often than about 15 days.
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