Summary Evidence for the use of automated or partly automated contact-tracing tools to contain severe acute respiratory syndrome coronavirus 2 is scarce. We did a systematic review of automated or partly automated contact tracing. We searched PubMed, EMBASE, OVID Global Health, EBSCO Medical COVID Information Portal, Cochrane Library, medRxiv, bioRxiv, arXiv, and Google Advanced for articles relevant to COVID-19, severe acute respiratory syndrome, Middle East respiratory syndrome, influenza, or Ebola virus, published from Jan 1, 2000, to April 14, 2020. We also included studies identified through professional networks up to April 30, 2020. We reviewed all full-text manuscripts. Primary outcomes were the number or proportion of contacts (or subsequent cases) identified. Secondary outcomes were indicators of outbreak control, uptake, resource use, cost-effectiveness, and lessons learnt. This study is registered with PROSPERO (CRD42020179822). Of the 4036 studies identified, 110 full-text studies were reviewed and 15 studies were included in the final analysis and quality assessment. No empirical evidence of the effectiveness of automated contact tracing (regarding contacts identified or transmission reduction) was identified. Four of seven included modelling studies that suggested that controlling COVID-19 requires a high population uptake of automated contact-tracing apps (estimates from 56% to 95%), typically alongside other control measures. Studies of partly automated contact tracing generally reported more complete contact identification and follow-up compared with manual systems. Automated contact tracing could potentially reduce transmission with sufficient population uptake. However, concerns regarding privacy and equity should be considered. Well designed prospective studies are needed given gaps in evidence of effectiveness, and to investigate the integration and relative effects of manual and automated systems. Large-scale manual contact tracing is therefore still key in most contexts.

BACKGROUND: Particulate air pollution's physical health effects are well known, but associations between particulate matter (PM) exposure and mental illness have not yet been established. However, there is increasing interest in emerging evidence supporting a possible etiological link. OBJECTIVES: This systematic review aims to provide a comprehensive overview and synthesis of the epidemiological literature to date by investigating quantitative associations between PM and multiple adverse mental health outcomes (depression, anxiety, bipolar disorder, psychosis, or suicide). METHODS: We undertook a systematic review and meta-analysis. We searched Medline, PsycINFO, and EMBASE from January 1974 to September 2017 for English-language human observational studies reporting quantitative associations between exposure to PM <1:0 lm in aerodynamic diameter (ultrafine particles) and PM <2:5 and <10 lm in aerodynamic diameter (PM 2:5 and PM 10 , respectively) and the above psychiatric outcomes. We extracted data, appraised study quality using a published quality assessment tool, summarized methodological approaches, and conducted metaanalyses where appropriate. RESULTS: Of 1,826 citations identified, 22 met our overall inclusion criteria, and we included 9 in our primary meta-analyses. In our meta-analysis of associations between long-term (>6 months) PM 2:5 exposure and depression (n = 5 studies), the pooled odds ratio was 1.102 per 10-lg=m 3 PM 2:5 increase (95% CI: 1.023, 1.189; I 2 = 0:00%). Two of the included studies investigating associations between long-term PM 2:5 exposure and anxiety also reported statistically significant positive associations, and we found a statistically significant association between short-term PM 10 exposure and suicide in meta-analysis at a 0-2 d cumulative exposure lag. DISCUSSION: Our findings support the hypothesis of an association between long-term PM 2:5 exposure and depression, as well as supporting hypotheses of possible associations between long-term PM 2:5 exposure and anxiety and between short-term PM 10 exposure and suicide. The limited literature and methodological challenges in this field, including heterogeneous outcome definitions, exposure assessment, and residual confounding, suggest further high-quality studies are warranted to investigate potentially causal associations between air pollution and poor mental health.

Background People experiencing homelessness are vulnerable to COVID-19 due to the risk of transmission in shared accommodation and the high prevalence of comorbidities. In England, as in some other countries, preventive policies have been implemented to protect this population. We aimed to estimate the avoided deaths and health-care use among people experiencing homelessness during the so-called first wave of COVID-19 in England—ie, the peak of infections occurring between February and May, 2020—and the potential impact of COVID-19 on this population in the future. Methods We used a discrete-time Markov chain model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection that included compartments for susceptible, exposed, infectious, and removed individuals, to explore the impact of the pandemic on 46 565 individuals experiencing homelessness: 35 817 living in 1065 hostels for homeless people, 3616 sleeping in 143 night shelters, and 7132 sleeping outside. We ran the model under scenarios varying the incidence of infection in the general population and the availability of prevention measures: specialist hotel accommodation, infection control in homeless settings, and mixing with the general population. We divided our scenarios into first wave scenarios (covering Feb 1–May 31, 2020) and future scenarios (covering June 1, 2020–Jan 31, 2021). For each scenario, we ran the model 200 times and reported the median and 95% prediction interval (2·5% and 97·5% quantiles) of the total number of cases, the number of deaths, the number hospital admissions, and the number of intensive care unit (ICU) admissions. Findings Up to May 31, 2020, we calibrated the model to 4% of the homeless population acquiring SARS-CoV-2, and estimated that 24 deaths (95% prediction interval 16–34) occurred. In this first wave of SARS-CoV-2 infections in England, we estimated that the preventive measures imposed might have avoided 21 092 infections (19 777–22 147), 266 deaths (226–301), 1164 hospital admissions (1079–1254), and 338 ICU admissions (305–374) among the homeless population. If preventive measures are continued, we projected a small number of additional cases between June 1, 2020, and Jan 31, 2021, with 1754 infections (1543–1960), 31 deaths (21–45), 122 hospital admissions (100–148), and 35 ICU admissions (23–47) with a second wave in the general population. However, if preventive measures are lifted, outbreaks in homeless settings might lead to larger numbers of infections and deaths, even with low incidence in the general population. In a scenario with no second wave and relaxed measures in homeless settings in England, we projected 12 151 infections (10 718–13 349), 184 deaths (151–217), 733 hospital admissions (635–822), and 213 ICU admissions (178–251) between June 1, 2020, and Jan 31, 2021. Interpretation Outbreaks of SARS-CoV-2 in homeless settings can lead to a high attack rate among people experiencing h...

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An observational study was carried out to assess the impact of the service on the financial and environmental impacts of healthcare use. GP appointments, psychotropic medications and secondary-care referrals were measured. Findings Results demonstrate no statistical difference in the financial and carbon costs of healthcare use between groups. Social prescribing showed a trend towards reduced healthcare use, mainly due to a reduction in secondary-care referrals compared with controls. The associations found did not achieve significance due to the small sample size leading to a large degree of uncertainty regarding differences. This study demonstrates that these services are potentially able to pay for themselves through reducing future healthcare costs and are effective, low-carbon interventions, when compared with cognitive behavioral therapy or antidepressants. This is an important finding in light of Government targets for the NHS to reduce its carbon footprint by 80% by 2050. Larger studies are required to investigate the potentials of social prescribing services further.