418 Matching Annotations
  1. Dec 2020
    1. 2020-12-01

    2. Sisson, N. M., Willroth, E. C., Le, B. M., & Ford, B. Q. (2020, December 1). The Mental Health Effects of Living with Close Others Before, During, and After the Onset of COVID-19. https://doi.org/10.31234/osf.io/v9mc4

    3. 10.31234/osf.io/v9mc4
    4. The COVID-19 pandemic not only threatens physical health, but is also a multi-faceted stressor that threatens mental health. Given the public health focus on staying home to stem the tide of COVID-19, it is crucial to determine how the close others we live with (i.e., romantic partners or children) affect our mental health, for better or worse. We examined the month-to-month mental health (i.e., well-being and ill-being) of parents living with child(ren) and people living with romantic partners (versus people not living with these close others) from February through September 2020 in two diverse samples of U.S. adults (N=656; N=544). This longitudinal approach distinguishes three unique effects: differences existing before COVID-19 was declared a pandemic, differences due to the onset of the pandemic, and differences that persisted across the first six months of the pandemic. In both samples, living with child(ren) or living with a romantic partner were both protective for mental health, before and during the first six months of the pandemic. Some evidence suggests these groups experienced unique increases in ill-being during the onset of the pandemic, but their ill-being also recovered more quickly. These findings highlight the crucial protective function of close relationships for mental health both in general and amidst a pandemic, suggesting that people living without these close others may need additional support.
    5. The Mental Health Effects of Living with Close Others Before, During, and After the Onset of COVID-19
    1. 2020-11-30

    2. Hu, C., Zhu, K., Huang, K., Yu, B., Jiang, W., Peng, K., & Wang, F. (2020, November 30). Using Natural Intervention to Promote Subjective Well-being of COVID-19 Essential Workers. https://doi.org/10.31234/osf.io/mc57s

    3. 10.31234/osf.io/mc57s
    4. Essential workers such as medical workers and police officers have been playing crucial roles in the fight against the COVID-19 pandemic, and are under heavy stress both physically and mentally. The goal of the present study was to develop a novel nature-based intervention to promote their well-being. A representative sample of essential workers in China was recruited for a five-day intervention program, and were randomly assigned to two groups. The experimental group watched two-minute video clips of natural scenes every day, while the control group watched urban scenes. Results indicated that after five days, the natural stimuli intervention yielded overall improvements in various indices of subjective well-being. Furthermore, analyses of nested longitudinal data confirmed that everyday nature stimuli exposure provided both immediate and cumulative restorative benefits. The proposed natural-based intervention is brief and easy-to-use, offering a cost-efficient psychological booster to promote subjective well-being of essential workers during this crisis time.
    5. Using Natural Intervention to Promote Subjective Well-being of COVID-19 Essential Workers
    1. 2020-12-01

    2. Graupmann, V., & Pfundmair, M. (2020, December 1). When social exclusion is mandated: COVID-19, social distancing, gender and psychological needs. https://doi.org/10.31234/osf.io/u362n

    3. 10.31234/osf.io/u362n
    4. In light of evidence from ostracism research, social distancing to limit the spread of COVID-19 poses a unique psychological challenge. In a German (N=546) and a US (N=199) sample, we examined how different degrees of social distancing impact outcomes related to social exclusion, measuring self-related needs: self-esteem, belonging, control, and meaning. Across both samples social distancing was associated with decreased need fulfillment. German participants reported higher need fulfillment compared to American participants. In comparison to previous studies, self-related needs associated with social distancing were less impacted than under experimental manipulations of social exclusion, however more so than under the baseline condition of inclusion. Working while social distancing was associated with greater need fulfillment, as was identifying as male. Women reported lower need fulfillment in both samples and this difference was mediated by need to belong. Results are discussed in terms of understanding self-related needs in different contexts of exclusion.
    5. When social exclusion is mandated: COVID-19, social distancing, gender and psychological needs
    1. 2020-12-03

    2. Julienne, H., Lavin, C., Belton, C., Barjakova, M., Timmons, S., & Lunn, P. D. (2020, December 3). Behavioural pre-testing of COVID Tracker, Ireland’s contact-tracing app. https://doi.org/10.31234/osf.io/9cguq

    3. 10.31234/osf.io/9cguq
    4. Contact-tracing mobile phone apps have the potential to play a role in controlling the spread of COVID-19, but their success hinges on widespread uptake by the public. We report a study that behaviourally pre-tested COVID Tracker, Ireland's contact-tracing app, prior to its launch with a large sample of smartphone users. The study was funded by the Department of Health and run in cooperation with the app's developers, NearForm. Participants were randomised to receive different versions of a trial app. They responded to an online survey while downloading and using the app on their phones in real time. The experimental manipulations focused on three broad areas: (i) the level of privacy assurance provided in the app, (ii) the goal-framing of the purpose of the app and (iii) the structuring of the exposure notification received by users if they are recorded as a close contact. Almost one in five participants mentioned privacy concerns in relation to their likelihood of downloading the app. Including additional assurances regarding the privacy of users' data in the app successfully lowered participants' privacy concerns and boosted engagement. This finding fed into the final version of the app released in July 2020. We also found minor beneficial effects of restructuring the exposure notification, but did not find any significant differences between two different types of goal-framing, other than a subtle effect on how the exposure notification is interpreted. Overall, our results demonstrate the value of pre-testing contact-tracing apps from a behavioural perspective to boost uptake, trust and participation.
    5. Behavioural pre-testing of COVID Tracker, Ireland’s contact-tracing app
    1. 2020-11-29

    2. Cameron, E. E., Joyce, K. M., Rollins, K., & Roos, L. E. (2020, November 29). Paternal Depression & Anxiety During the COVID-19 Pandemic. https://doi.org/10.31234/osf.io/drs9u

    3. 10.31234/osf.io/drs9u
    4. Background: The COVID-19 pandemic has changed family functioning and increased parenting demands, leading to increased risk for poor psychosocial outcomes. Emerging evidence underscores the significant impact the pandemic has had on maternal mental health concerns. In contrast, paternal mental health has yet to be described. The current study describes the prevalence of depression and anxiety in fathers of young children as well as associated risk and protective factors. Methods: As part of the Parenting during the Pandemic study, fathers (N = 70) of children age 0-8 years old self-reported on mental health symptoms and additional concerns, while mothers (N = 236) provided a partner-report of father perinatal depression. Results:. Clinically significant depression (37.1%) and anxiety (22.9%) were prevalent in fathers. Partner reported perinatal depression was prevalent in 61.9% of fathers. Higher financial strain and previous mental health history were associated with increased risk of both depression and anxiety. Maternal report of paternal depression was associated with higher financial strain, greater number of children in the home, and lower maternal-reported marital quality. Limitations: The current study used cross-sectional data from an online cohort. The sample size limits the generalizability of the findings; future research should continue evaluating this important topic with larger samples. Conclusions: Compared to pre-pandemic population comparisons, paternal depression and anxiety are elevated in the context of the COVID-19 pandemic. Intervention recommendations and implications are discussed.
    5. Paternal Depression & Anxiety During the COVID-19 Pandemic
    1. 2020-11-29

    2. Bialobrzeska, O., Baba, J., Bedyńska, S., Cichocka, A., Cislak, A., Formanowicz, M., … Kozakiewicz, K. (2020, November 29). Keep kind and carry on. Everyday kindness enhances well-being and prosocial behavior in the time of COVID-19. https://doi.org/10.31234/osf.io/n2g3m

    3. 10.31234/osf.io/n2g3m
    4. Acts of everyday kindness are voluntary, low-cost actions intended to express a friendly attitude toward a specific person or persons. In two pre-registered studies we examined whether practicing everyday kindness can help people maintain well-being and prosocial orientation in times of pandemic. In correlational Study 1 (N = 497), performing everyday kindness was positively linked to well-being, social connectedness, and a willingness to engage in more costly prosocial behavior. In an experimental Study 2 (N = 482), practicing acts of everyday kindness increased well-being and actual prosocial behavior, although it did not affect feelings of social connectedness. The results point to the role of everyday kindness in counteracting the negative psychological and social consequences of COVID-19 pandemic. Furthermore, even simple online interventions can be used to elicit everyday kindness.
    5. Keep kind and carry on. Everyday kindness enhances well-being and prosocial behavior in the time of COVID-19.
    1. 2020-12-01

    2. Albrecht, R., Jarecki, J. B., Meier, D., & Rieskamp, J. (2020, December 1). Risk Preferences and Risk Perception Affect the Acceptance of Digital Contact Tracing. https://doi.org/10.31234/osf.io/4x5f3

    3. 10.31234/osf.io/4x5f3
    4. Digital contact-tracing applications (DCTAs) can control the spread of epidemics, like the COVID-19 pandemic. But people in Western societies fail to accept DCTAs. Understanding the low acceptance is key to policymakers who support DCTAs to avoid harsh nationwide lock-downs. In a preregistered study in a representative Swiss sample (N=757), we compare the role of individual risk perception, risk preferences, social preferences, and social values in the acceptance of and compliance with DCTA. The results show a low acceptance of DCTAs but high compliance with the measures recommended by DCTAs. Risk preferences and perceptions, but not social preferences, influenced accepting DCTAs; a high health risk perception and a low data-security risk perception increased acceptance. Additionally, supporting political measures, technical abilities, and understanding the DCTA functionality had large effects on accepting DCTAs. Therefore, we recommend highlighting personal health risks and clearly explaining DCTAs, focusing on data security, to enhance DCTA acceptance.
    5. Risk Preferences and Risk Perception Affect the Acceptance of Digital Contact Tracing
  2. Oct 2020
    1. 2020-10-13

    2. Robert, Alexis. “Lessons from New Zealand’s COVID-19 Outbreak Response.” The Lancet Public Health 0, no. 0 (October 13, 2020). https://doi.org/10.1016/S2468-2667(20)30237-1.

    3. 10.1016/S2468-2667(20)30237-1
    4. In the absence of a vaccine for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or of highly effective pharmaceutical treatments for COVID-19, countries have implemented a large range of non-pharmaceutical interventions to control the spread of the virus.1Petherick A Kira B Hale T et al.Variation in government responses to COVID-19.https://www.bsg.ox.ac.uk/research/publications/variation-government-responses-covid-19Date: Sept 1, 2020Date accessed: October 9, 2020Google Scholar These interventions differ in their level of stringency (ie, the severity of the measures) and their ultimate objective (eg, prevent health systems being overwhelmed, suppress incidence to low levels, or reduce incidence to zero and keep it there). With many countries facing epidemic resurgence, evaluating the impact of different strategies implemented in the early phases of the pandemic is crucial for developing an effective long-term response.
    5. Lessons from New Zealand's COVID-19 outbreak response
    1. 2020-10-20

    2. Rubin, M. (2020, October 20). Does preregistration improve the credibility of research findings?. https://doi.org/10.20982/tqmp.16.4.p376

    3. 10.31222/osf.io/vgr89
    4. Preregistration entails researchers registering their planned research hypotheses, methods, and analyses in a time-stamped document before they undertake their data collection and analyses. This document is then made available with the published research report to allow readers to identify discrepancies between what the researchers originally planned to do and what they actually ended up doing. This historical transparency is supposed to facilitate judgments about the credibility of the research findings. The present article provides a critical review of 17 of the reasons behind this argument. The article covers issues such as HARKing, multiple testing, p-hacking, forking paths, optional stopping, researchers’ biases, selective reporting, test severity, publication bias, and replication rates. It is concluded that preregistration’s historical transparency does not facilitate judgments about the credibility of research findings when researchers provide contemporary transparency in the form of (a) clear rationales for current hypotheses and analytical approaches, (b) public access to research data, materials, and code, and (c) demonstrations of the robustness of research conclusions to alternative interpretations and analytical approaches.
    5. Does preregistration improve the credibility of research findings?
    1. 2020-10-22

    2. Jaeger, B., Oud, B., Williams, T., Krumhuber, E., Fehr, E., & Engelmann, J. B. (2020, October 20). Trustworthiness detection from faces: Does reliance on facial impressions pay off?. https://doi.org/10.31234/osf.io/ayqeh

    3. 10.31234/osf.io/ayqeh
    4. While people readily form and rely on trustworthiness impressions from faces, the question of whether these impressions are accurate remains debated. The present research examines whether having access to the facial appearance of counterparts provides a strategic advantage to participants when making trust decisions. Furthermore, we investigated whether people show above-chance accuracy in trustworthiness detection (a) when they make trust decisions vs. provide explicit trustworthiness ratings, (b) when judging male vs. female counterparts, and (c) when rating cropped images (with non-facial features removed) vs. uncropped images. Results showed that incentivized trust decisions (Study 1, n = 131) and predictions of counterparts’ trustworthiness (Study 2, n = 266) were unrelated to actual trustworthiness. Moreover, accuracy was not moderated by stimulus type (cropped vs. uncropped faces) or counterparts’ gender. Overall, these findings suggest that people are unable to detect the trustworthiness of strangers based on their facial appearance.
    5. Trustworthiness detection from faces: Does reliance on facial impressions pay off?
    1. 2020-10-22

    2. Bauch, Chris T. “Estimating the COVID-19 R Number: A Bargain with the Devil?” The Lancet Infectious Diseases 0, no. 0 (October 22, 2020). https://doi.org/10.1016/S1473-3099(20)30840-9.

    3. 10.1016/S1473-3099(20)30840-9
    4. Bob May's limerick alludes to both the promises and dangers of characterising epidemic control by a single number. The basic reproduction number (R0) is the average number of infections produced by a single infectious person in a population with no immunity. R0 has a close relative named the effective reproduction number (R), which is the average number of infections produced by a single infected person in a population with partial immunity. In The Lancet Infectious Diseases, You Li and colleagues2Li Y Campbell H Kulkarni D et al.The temporal association of introducing and lifting non-pharmaceutical interventions with the time-varying reproduction number (R) of SARS-CoV-2: a modelling study across 131 countries.Lancet Infect Dis. 2020; (published online Oct 22.)https://doi.org/10.1016/S1473-3099(20)30785-4Google Scholar estimate how the imposition and lifting of non-pharmaceutical interventions (NPIs) changed the R number for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 131 countries in the first half of 2020.
    5. Estimating the COVID-19 R number: a bargain with the devil?
    1. 2020-10-09

    2. Weisz, E., & Cikara, M. (2020, October 9). Strategic Regulation of Empathy. https://doi.org/10.31234/osf.io/2kr46

    3. 10.31234/osf.io/2kr46
    4. Empathy is an integral part of socio-emotional well-being, yet recent research has highlighted some of its downsides. Here we examine literature that establishes when, how much, and what aspects of empathy promote specific outcomes. After reviewing a theoretical framework which characterizes empathy as a suite of separable components, we examine evidence showing how dissociations of these components affect important socio-emotional outcomes and describe emerging evidence suggesting that these components can be independently and deliberately modulated. Finally, we advocate for a new approach to a multi-component view of empathy which accounts for the interrelations among components. This perspective advances scientific conceptualization of empathy and offers suggestions for tailoring empathy to help people realize their social, emotional, and occupational goals.
    5. Strategic Regulation of Empathy
    1. 2020-10-12

    2. Meeter, M., Bele, T., Hartogh, C. d., Bakker, T., de Vries, R. E., & Plak, S. (2020, October 11). College students’ motivation and study results after COVID-19 stay-at-home orders. https://doi.org/10.31234/osf.io/kn6v9

    3. 10.31234/osf.io/kn6v9
    4. Due to the COVID-19 pandemic, many institutions of higher education had to close their campuses and shift to online education. Here, we investigate how stay-at-home orders impacted students. We investigated results obtained by 15,125 bachelor students at a large Dutch research university during a semester in which the campus was closed and all education had shifted online. Moreover, we surveyed 166 students of the bachelor of psychology program of the same university. Results showed that students rated online education as less satisfactory than campus-based education, and rated their own motivation as having gone down. This was reflected in a lower time investment: lectures and small-group meetings were attended less frequently, and student estimates of hours studied went down. Lower motivation predicted this drop in effort. Moreover, a drop in motivation was related to fewer credits being obtained during stay-at-home orders. However, on average students reported obtaining slightly more credits than before, which was indeed found in an analysis of administered credits. In a qualitative analysis of student comments, it was found that students missed social interactions, but reported being much more efficient during online education. It is concluded that whereas student satisfaction and motivation dropped during the shift to online education, increased efficiency meant results were not lower than they would normally have been.
    5. College students’ motivation and study results after COVID-19 stay-at-home orders
    1. 2020-10-13

    2. Florida, R & Mellander, C. (2020) The Geography of COVID-19 in Sweden. Working Paper Series in Economics and Institutions of Innovation, Royal Institute of Technology.

    3. The Geography of COVID-19 in Sweden
    4. This paper examines the geographic factors that are associated with the spread of COVID-19 in Sweden. The country is a useful case study to examine because it did not impose mandatory lockdowns, and thus we would expect the virus to spread in a more unimpeded way across communities. A growing body of research has examined the role of factors like density, household size, air connectivity, income, race and ethnicity, age, political affiliation, temperature and climate, and policy measure like lockdowns and physical distancing among others. The research examines the effects of some of these factors on the geographic variation of COVID-19 cases and on deaths, across both municipalities and neighborhoods. Our findings show that the geographic variation in COVID-19 is significantly but modestly associated with variables like density, population size, and the socio-economic characteristics of places, and somewhat more associated with variables for household size. What matters more is the presence of high-risk nursing homes and the onset of infections with places that were hit earlier by COVID-19 cases experiencing more severe outbreaks. Still, all these variables explain little of the geographic variation in COVID-19 across Sweden. There appears to be a high degree of randomness in the geographic variation of COVID-19 across Sweden and the degree to which some places were hit harder than others.
    1. von Gaudecker, H., Holler, R., Janys, L., Siflinger, B. M., & Zimpelmann, C. (2020). Labour Supply during Lockdown and a “New Normal”: The Case of the Netherlands. IZA Discussion Paper, 13623.

    2. 2020-08

    3. We document the evolution of hours of work using monthly data from February to June 2020. During this period, the Nether­lands ex­pe­ri­enced a quick spread of the SARS-CoV-2 virus, enacted a lockdown for a period of six weeks and gradually opened there­after. We show that during lock-down, sub­sti­tutabil­ity between work from home and at the workplace or essential worker status are key to maintain a large fraction of pre-​crisis hours of work. These pandemic-​specific mech­a­nisms become much less important as social dis­tanc­ing re­stric­tions are eased in May and June. Labor supply recovers quickly in sectors affected heavily during lockdown, but goes down in other areas of the economy. The latter is unlikely caused by pandemic-​induced supply changes; di­min­ished demand is a more plausible ex­pla­na­tion. Analyzing take-up of economic support programs, we find sug­ges­tive evidence that wage subsidies and other programs helped limit the early-​stage impact of the crisis along the extensive margin.
    4. Labour Supply during Lockdown and a “New Normal”: The Case of the Nether­lands
    1. Tani, M., Cheng, Z., Mendolia, S., Paloyo, A. R., & Savage, D. (2020). Working Parents, Financial Insecurity, and Child-​Care: Mental Health in the Time of COVID-19. IZA Discussion Paper, 13588.

    2. 2020-08

    3. The COVID-19 pandemic and the policy measures to control its spread – lockdowns, physical dis­tanc­ing, and social isolation – has coincided with the de­te­ri­o­ra­tion of people’s mental well-​being. We use data from the UK Household Lon­gi­tu­di­nal Study (UKHLS) to document how this phe­nom­e­non is related to the situation of working parents who now have to manage competing time demands across the two life domains of work and home. We show that the worsening of mental health in the United Kingdom is worse for working parents, and that it is es­pe­cially related to the increased financial in­se­cu­rity and the time spent on childcare and home schooling. We find that this burden is not shared equally between men and women, and between richer and poorer house­holds. In crafting public policy responses to the pandemic, better outcomes can be achieved if pol­i­cy­mak­ers are cognizant of these in­equal­i­ties. Keywords
    4. Working Parents, Financial In­se­cu­rity, and Child-​Care: Mental Health in the Time of COVID-19
    1. 2020-10-07

    2. Shauna Brail on Twitter. (n.d.). Twitter. Retrieved October 9, 2020, from https://twitter.com/shaunabrail/status/1313818873163067392

    3. Next week we'll launch the mobility dashboard. In the meantime, our intro video tells the story of the full initiative, supported by a Mitacs Research Trainee Award and produced by research assistant extraordinaire, Katherine Hovdestad
    4. We had hoped to access publicly-collected mental health data-however, it's not available yet though researchers are working on it. The pandemic has revealed the importance of data, and yet despite quantity of data collected, making that data useable & accessible is a challenge.
    5. The dashboard highlights the current status of increasing COVID-19 cases/hospitalizations amidst the context of other public health measures: few school outbreaks, surgical backlogs, shortened ER waits, fewer visits to supervised injection sites + more overdose-related deaths
    6. Toronto After the First Wave: Tracking Toronto's experience and response to COVID-19 in Fall 2020, our first dashboard focuses on measures of public health https://torontoafterthefirstwave.com/dashboards/health/
    1. 2020-10

    2. Burel, Gregoire; Farrell, Tracie; Mensio, Martino; Khare, Prashant and Alani, Harith (2020). Co-Spread of Misinformation and Fact-Checking Content during the Covid-19 Pandemic. In: Proceedings of the 12th International Social Informatics Conference (SocInfo), LNCS.

    3. Co-Spread of Misinformation and Fact-Checking Content during the Covid-19 Pandemic
    4. In the context of the Covid-19 pandemic, the consequences of misinformation are a matter of life and death. Correcting misconceptions and false beliefs are important for injecting reliable information about the outbreak. Fact-checking organisations produce content with the aim of reducing misinformation spread, but our knowledge of its impact on misinformation is limited. In this paper, we explore the relation between misinformation and fact-checking spread during the Covid-19 pandemic. We specifically follow misinformation and fact-checks emerging from December 2019 to early May 2020. Through a combination of spread variance analysis, impulse response modelling and causal analysis, we show similarities in how misinformation and fact-checking information spread and that fact-checking information has a positive impact in reducing misinformation. However, we observe that its efficacy can be reduced, due to the general amount of online misinformation and the short-term spread of fact-checking information compared to misinformation
    1. 2020-10-06

    2. Alexandra Freeman on Twitter. (n.d.). Twitter. Retrieved October 8, 2020, from https://twitter.com/alex_freeman/status/1313533304696639489

    3. Numbers need context. People don’t normally think in numbers. When forced to put a number on risks of people dying from COVID-19 if they caught it, they over-estimate (and the effect of the number format they are asked to use is evident). But they do know the major risk factors.
    4. People liked ‘positive framing’ (how many likely survive), but the numbers were rated a bit harder to understand. Probably safest to stick with familiar ‘likelihood of dying’. A visual scale helps: despite the difficulties of a linear scale, it was more trusted over log scale.
    5. hen our advice on how to communicate the numbers: Percentages seem clearest format, but also convey the smallest feeling of risk. For balance maybe include an ‘x out of 1000’ translation, which gives a larger feeling of risk. Do not describe as ‘your risk’ – maybe ‘risk result’
    6. 2) What are you trying to communicate? The risk of catching it and dying from it, or the risk of dying from it if you catch it? Whichever it is, be very clear 3) Who are your audience? UK public keen for this information in our surveys, and GPs don’t want to be ‘gatekeepers’
    7. Preprint out – our work on communicating personal risks from COVID-19: https://doi.org/10.1101/2020.10.05.20206961… In summary, for those developing personal risk calculators: 1) Are you trying to change behaviour or simply inform people? It changes your communication approach. UK public opinion
    1. 2020-10-07

    2. Travelling Tabby on Twitter. (n.d.). Twitter. Retrieved October 8, 2020, from https://twitter.com/travellingtabby/status/1313882542425112579

    3. Can you look at the age groups Scotland publishes to avoid the 15-19 hiding the school kids. They had data up to the 27th Sept 0-4, 5-11, 12-17 and the whole group 2-17 but it’s hard to find.
    4. A couple of charts showing the age of the new positive cases in England/Scotland over the past week, per 1m pop Englands figures are quite high in comparison because of the backlog issue, but it looks like 15-24 year olds are behind the bulk of the new cases (uni outbreaks)
    1. Tufekci, Z. (2020, September 30). UThis Overlooked Variable Is the Key to the Pandemic. The Atlantic. https://www.theatlantic.com/health/archive/2020/09/k-overlooked-variable-driving-pandemic/616548/

    2. 2020-09-30

    3. There’s something strange about this coronavirus pandemic. Even after months of extensive research by the global scientific community, many questions remain open.Why, for instance, was there such an enormous death toll in northern Italy, but not the rest of the country? Just three contiguous regions in northern Italy have 25,000 of the country’s nearly 36,000 total deaths; just one region, Lombardy, has about 17,000 deaths. Almost all of these were concentrated in the first few months of the outbreak. What happened in Guayaquil, Ecuador, in April, when so many died so quickly that bodies were abandoned in the sidewalks and streets?* Why, in the spring of 2020, did so few cities account for a substantial portion of global deaths, while many others with similar density, weather, age distribution, and travel patterns were spared? What can we really learn from Sweden, hailed as a great success by some because of its low case counts and deaths as the rest of Europe experiences a second wave, and as a big failure by others because it did not lock down and suffered excessive death rates earlier in the pandemic? Why did widespread predictions of catastrophe in Japan not bear out? The baffling examples go on
    4. This Overlooked Variable Is the Key to the Pandemic
    1. 2020-09-29

    2. Carter, J. (2020, September 29). The American Public Still Trusts Scientists, Says a New Pew Survey. Scientific American. https://www.scientificamerican.com/article/the-american-public-still-trusts-scientists-says-a-new-pew-survey/

    3. Public trust of the scientific community in the United States is as strong as ever, according to a new poll just released today by the Pew Research Center, confirming polling results dating back to the 1970s. Thirty-eight percent of those polled in Pew’s survey in the U.S. say that they have a lot of trust in scientists to do what is right for the public. Those polled also place a lot of trust in scientific institutions as compared to others in the U.S. Pew’s data show respondents only ranked the military as more trustworthy than scientific institutions, while ranking lower trust in others like the national government, news media and business leaders.
    4. The American Public Still Trusts Scientists, Says a New Pew Survey
    1. 2020-09-29

    2. Surkova, E., Nikolayevsskyy, V., Drobniewski, F. (2020). False-positive COVID-19 results: hidden problems and costs. The Lancet Respiratory Medicine. https://doi.org/10.1016/S2213-2600(20)30453-7

    3. 10.1016/S2213-2600(20)30453-7
    4. RT-PCR tests to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA are the operational gold standard for detecting COVID-19 disease in clinical practice. RT-PCR assays in the UK have analytical sensitivity and specificity of greater than 95%, but no single gold standard assay exists.1Watson J Whiting PF Brush JE Interpreting a covid-19 test result.BMJ. 2020; 369m1808PubMed Google Scholar,  2Mayers C Baker K Impact of false-positives and false-negatives in the UK's COVID-19 RT-PCR testing programme.https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/895843/S0519_Impact_of_false_positives_and_negatives.pdfDate: June 3, 2020Date accessed: August 8, 2020Google Scholar New assays are verified across panels of material, confirmed as COVID-19 by multiple testing with other assays, together with a consistent clinical and radiological picture. These new assays are often tested under idealised conditions with hospital samples containing higher viral loads than those from asymptomatic individuals living in the community. As such, diagnostic or operational performance of swab tests in the real world might differ substantially from the analytical sensitivity and specificity.2
    5. False-positive COVID-19 results: hidden problems and costs
  3. Sep 2020
    1. 2020-09-28

    2. Natalie E. Dean en Twitter: “VACCINE EFFICACY 101: A biostatistician's primer. Ten tweets to cover:- How is vaccine efficacy calculated?- Distinguishing between infection, disease, & severe disease.- Measuring reduced infectiousness.- Vaccine efficacy vs. effectiveness!n” / Twitter. (n.d.). Twitter. Retrieved September 30, 2020, from https://twitter.com/nataliexdean/status/1310613702476017666

    3. 9) Household studies can be very useful here, where we follow the household contacts of infected vaccinated individuals and infected unvaccinated individuals, and compare how frequently they are infected. Pioneering work from my mentor @betzhallo. http://courses.washington.edu/b578a/readings/PreziosiHalloranVaccine2003.pdf…
    4. 10) Finally, vaccine efficacy vs. effectiveness? We like to reserve "efficacy" for estimates from randomized trials, where everyone receives the vaccine as intended (proper cold chain, no missed doses). We distinguish this idealized measure from real-world "effectiveness." END
    5. 8) A vaccine that prevents infection entirely provides indirect protection to others. If I can't get infected, I can't infect you. But it is possible to have a vaccine that prevents disease but individuals can still be infectious.
    6. 7) So far I have talked about how well a vaccine directly protects the vaccinated individual. Another important type of vaccine effect is the ability of a vaccine to reduce infectiousness to others. This is known as indirect protection, and is related to herd immunity.
    7. 6) Preventing infection entirely is the hardest to achieve. And of course a vaccine that prevents infection will also prevent disease and severe disease. But we can have vaccines where people are still infected but their disease severity is lessened.Quote Tweet
    8. 5) Most Phase 3 trials are measuring efficacy to prevent disease as the primary analysis, with efficacy against infection and against severe disease as secondary analyses.Quote Tweet
    9. 4) Though we talk about vaccine efficacy as a single number, there are actually several different types of vaccine efficacy, such as: - Efficacy to prevent infection (sterilizing immunity) - Efficacy to prevent disease - Efficacy to prevent severe disease
    10. 3) Vaccine efficacy of 50% roughly means you have a 50% reduced risk of becoming sick compared to an otherwise similar unvaccinated person. Or you have a 50% chance of becoming sick given that you were exposed to enough infectious virus to make an unvaccinated person sick.
    11. 2) Vaccine efficacy (VE) measures the relative reduction in infection/disease for the vaccinated arm versus the unvaccinated arm. A perfect vaccine would eliminate risk entirely, so VE = 1 or 100%. This can be calculated from the risk ratio, incidence rate ratio, or hazard ratio.
    12. VACCINE EFFICACY 101: A biostatistician's primer Ten tweets to cover: - How is vaccine efficacy calculated? - Distinguishing between infection, disease, & severe disease. - Measuring reduced infectiousness. - Vaccine efficacy vs. effectiveness!
    1. 2020-04-28

    2. Digital transformation is a buzzword that has been around for some time. For many, digital transformation started to kick into full gear in the 1990s as personal computers started to become more prevalent. This is when businesses looked at providing more digital experiences for customers and end users and also began transforming the way they worked internally to keep up with the pace of technology. While nearly every industry is undergoing or at least considering digital transformation, according to McKinsey, “the average digital transformation ... stands a 45 percent chance of delivering less profit than expected.” Nonetheless, businesses are still investing in these initiatives; IDC estimates that worldwide spending on the technologies and services that enable digital transformation will reach $2.3 trillion in 2023. So, why should you embark or continue on a digital transformation journey? And what is digital transformation, exactly? As the president of a company that offers software development and digital transformation services, I'll attempt to outline what success looks like and how to get there.
    3. What's Powering Digital Transformation?
    1. 10.1007/s40471-020-00245-2
    2. 2020-09-23

    3. Caniglia, E.C., Murray, E.J. (2020) Difference-in-Difference in the Time of Cholera: a Gentle Introduction for Epidemiologists. Current Epidemiology Reports. https://doi.org/10.1007/s40471-020-00245-2

    4. Purpose of ReviewThe goal of this article is to provide an introduction to the intuition behind the difference-in-difference method for epidemiologists. We focus on the theoretical aspects of this tool, including the types of questions for which difference-in-difference is appropriate, and what assumptions must hold for the results to be causally interpretable.Recent FindingsWhile currently under-utilized in epidemiologic research, the difference-in-difference method is a useful tool to examine effects of population level exposures, but relies on strong assumptions.SummaryWe use the famous example of John Snow’s investigation of the cause of cholera mortality in London to illustrate the difference-in-difference approach and corresponding assumptions. We conclude by arguing that this method deserves a second look from epidemiologists interested in asking causal questions about the impact of a population level exposure change on a population level outcome for the group that experienced the change.
    1. 10.1101/2020.09.15.20191957
    2. 2020-09-18

    3. Smith, E. L., Potts, W.W. H., Amlot R., Fear, T. N., Michie, S., Rubin, J. (2020).Adherence to the test, trace and isolate system: results from a time series of 21 nationally representative surveys in the UK (the COVID-19 Rapid Survey of Adherence to Interventions and Responses [CORSAIR] study).medRxiv. https://doi.org/10.1101/2020.09.15.20191957

    4. Objectives: To investigate rates of adherence to the UKs test, trace and isolate system over time. Design: Time series of cross-sectional online surveys. Setting: Data were collected between 2 March and 5 August 2020. Participants: 42,127 responses from 31,787 people living in the UK, aged 16 years or over, are presented (21 survey waves, n≈2,000 per wave). Main outcome measures: Identification of the key symptoms of COVID-19 (cough, high temperature / fever, and loss of sense of smell or taste), self-reported adherence to self-isolation if symptomatic, requesting an antigen test if symptomatic, intention to share details of close contacts, self-reported adherence to quarantine if alerted that you had been in contact with a confirmed COVID-19 case. Results: Only 48.9% of participants (95% CI 48.2% to 49.7%) identified key symptoms of COVID-19. Self-reported adherence to test, trace and isolate behaviours was low (self-isolation 18.2%, 95% CI 16.4% to 19.9%; requesting an antigen test 11.9%, 95% CI 10.1% to 13.8%; intention to share details of close contacts 76.1%, 95% CI 75.4% to 76.8%; quarantining 10.9%, 95% CI 7.8% to 13.9%) and largely stable over time. By contrast, intention to adhere to protective measures was much higher. Non-adherence was associated with: men, younger age groups, having a dependent child in the household, lower socio-economic grade, greater hardship during the pandemic, and working in a key sector. Conclusions: Practical support and financial reimbursement is likely to improve adherence. Targeting messaging and policies to men, younger age groups, and key workers may also be necessary.
    5. Adherence to the test, trace and isolate system: results from a time series of 21 nationally representative surveys in the UK (the COVID-19 Rapid Survey of Adherence to Interventions and Responses [CORSAIR] study)
    1. 10.31234/osf.io/wqdx8
    2. Gurung, R. A. R., & Stone, A. (2020, September 14). You Can’t Always Get What You Want and It Hurts: Learning During the Pandemic. https://doi.org/10.31234/osf.io/wqdx8

    3. 2020-09-14

    4. Colleges and universities around the world switched to remote teaching in early 2020. In this study we assessed the experiences of students who experienced different operationalizations of remote teaching during the first full term of instruction during the COVID pandemic. Students (N = 649) in 11 sections of Introductory Psychology participated in an online assessment of their learning after completing their final exam. We examined the level of alignment between student preferences (e.g., for synchronous lectures) with the format of the classes they were in (e.g., featuring synchronous lectures) and used this measure of fit (aligned, misaligned, no preference) and students’ modality based self-efficacy as predictors of learning. Self-efficacy predicted final exam scores and students’ ratings of the skills learned, value of science, student learning outcomes, class behaviors, and attitudes toward their class. Fit predicted differences in attitude and class related behaviors (e.g., studying). Self-efficacy also predicted the extent to which students changed their learning behaviors during the pandemic. Our results provide educators with key ways to prepare for additional remote teaching.
    5. You Can’t Always Get What You Want and It Hurts: Learning During the Pandemic
    1. Leicester UCU en Twitter: “Universities are conducting an experiment, an experiment that involves human beings (university staff and students) and a life-threatening virus. But experimental subjects must give informed consent. (That's basic research ethics.)” / Twitter. (n.d.). Twitter. Retrieved September 26, 2020, from https://twitter.com/leicesterucu/status/1309107917879156737

    2. 2020-09-24

    3. Members of host communities are also part of this experiment, of course. (We should have mentioned this above.) University leaders haven't sought their consent either.
    4. But university leaders, including at @uniofleicester, are neither informing us of the risks, nor seeking our consent to participate in this experiment. (This important point was just made by a member in our emergency general meeting.)
    5. Universities are conducting an experiment, an experiment that involves human beings (university staff and students) and a life-threatening virus. But experimental subjects must give informed consent. (That's basic research ethics.)
    1. 2020-09-14

    2. Racine, S. E., Miller, A. E., Mehak, A., & Trolio, V. (2020, September 14). Examining Risk and Protective Factors for Psychological Health during the COVID-19 Pandemic. https://doi.org/10.31234/osf.io/ys8fn

    3. 10.31234/osf.io/ys8fn
    4. Examining Risk and Protective Factors for Psychological Health during the COVID-19 Pandemic
    5. Background: The coronavirus disease 2019 (COVID-19) pandemic has impacted the lives of people globally, and the significant mental health consequences of this pandemic are beginning to be documented. In addition to sociodemographic and COVID-19 specific factors, psychological risk and protective mechanisms likely influence individual differences in mental health symptoms in the context of the COVID-19 pandemic. We examined associations between a broad set of risk and protective factors with symptoms of depression, anxiety, alcohol problems, and eating pathology, and investigated interactions between objective stress due to COVID-19 and risk/protective variables in predicting psychopathology. Methods: Participants were 877 adults (73.7% female) recruited via internet sources from around the globe, but primarily residing in North America (87.4%). Results: Structural equation modelling revealed that certain risk and protective factors (e.g., loneliness, latent protective factor, mindfulness) were broadly related to psychopathology, whereas others showed unique relations with specific forms of psychopathology (e.g., greater repetitive thinking and anxiety; low meaning and purpose and depression). COVID-19 objective stress interacted with risk factors, but not protective factors, to predict greater anxiety symptoms, but not other forms of psychopathology. Limitations: This is a cross-sectional study of non-randomly recruited participants who reported high levels of income and education. Rates of problematic alcohol use were low. Conclusions: Findings contribute to our understanding of psychological mechanisms underlying individual differences in psychopathology in the context of a global stressor. Strategies that reduce loneliness and increase mindfulness will likely impact the greatest number of mental health symptoms.