129 Matching Annotations
  1. Jun 2020
    1. Dr Rageshri Dhairyawan on Twitter. " The PHE COVID-19 report today shows significant racial disparities https://tinyurl.com/y9x99yem consistent with ONS data https://tinyurl.com/y8utg3td & ICNARC data https://tinyurl.com/ybpk9ur3.

      But racial disparities in health in the UK are not new. Thread /1" /Twitter (n.d.). Twitter Retrieved June 15 2020, from https://twitter.com/crageshri/status/1267821605847044110

    2. 2020-06-02

    3. I wrote about COVID-19 & racial disparities in April with @rapclassroom https://discoversociety.org/2020/04/15/covid-19-racism-and-health-outcomes/… /17
    4. Here’s Macpherson’s definition of institutional racism in the Stephen Lawrence Inquiry, 1999 (first described by Carmichael & Hamilton in 1967) ./16
    5. Finally I think it’s vital that we empower the affected communities to lead on research & interventions for their own communities. They are best placed to know what may work. Funding should reflect this. /15
    6. Reasons for racial disparities health are complex. Here is Angela Saini on the topic in @theLancet https://tinyurl.com/y8bn5we3. I hope COVID-19 has highlighted the need to address racial inequalities in health, & will lead to long lasting changes in many areas. /14
    7. UK born people from Black and Asian communities are more likely to be diagnosed with asthma. /13
    8. The highest rates of hypertension (high blood pressure) are in Black groups. This is a risk factor for many health issues including stroke, chronic kidney disease, cardiovascular disease, retinopathy. /12
    9. Rates of Type 2 diabetes are approximately three to five times higher than in BAME groups than the white British population. Diagnosis is more likely to occur at a younger age. /11
    10. People from South Asian and Black backgrounds are three to five times more likely to start kidney dialysis than people from white backgrounds. /10 https://tinyurl.com/y6u8ec8z
    11. Black people were more likely to have severe mental health symptoms, but were the least likely to receive treatment for mental illness https://tinyurl.com/ybxxxd9v. They are more likely to be detained in hospital https://tinyurl.com/y9dx2w8x /9
    12. Black men are 2x as likely to be diagnosed with prostate cancer in the UK than white men and proportionately more Black men die of prostate cancer than other groups. /8
    13. Jo’s Trust @JoTrust conducted a survey showing that women from BAME backgrounds are more likely to have never attended cervical screening. /7
    14. The British National Survey of Sexual Attitudes and Lifestyles showed emergency contraception use was most commonly reported by Black Caribbean (30%) and mixed ethnicity women (28%) than White British women (23%) /6
    15. 74% heterosexual people receiving HIV care in the UK in 2018 were BAME of which 57% were of Black African ethnicity. The highest rate of late diagnosis (the most important predictor of HIV-related illness and death) was in heterosexual Black men (65%) /5 https://tinyurl.com/ybqmffsb
    16. Rates of sexually transmitted infections are highest in Black communities especially Black Caribbean /4 https://tinyurl.com/y25z4v9w
    17. Here we go. It's not a comfortable read (and neither should it be). Black women are 5x more likely to die in pregnancy than white women. /3 https://npeu.ox.ac.uk/downloads/file
    18. This thread attempts to show racial health disparities in the UK are common & existed long before COVID-19. I have compiled a list of racial disparities in different areas of health in the UK, but this is not exhaustive and there are many that I haven't been able to include /2
    1. 2020-06-17

    2. BehSciMeta repost. (2020, June 17). "Reproducibility scores for behavioural science: what are the merits and drawbacks?" Reddit. https://www.reddit.com/r/BehSciMeta/comments/har2np/reproducibility_scores_for_behavioural_science/

    3. I have been wondering about this tool (that seems to be targeted at biological sciences): https://twitter.com/SciscoreReportsIt makes me wonder, what would be an ideal 'reproducibility score' for work in the behavioural science?Certainly there are now badges for reproducibility (e.g., preregistration, open materials etc.)—a step in the right direction, but we should always be trying to improve.So what elements best define scientific quality in our research, and what is the best way to put this into practice?And maybe a controversial question: should it be up to the journals to mete it out?
    4. Reproducibility scores for behavioural science: what are the merits and drawbacks?
    1. David Fisman on Twitter. "I'm not sure if anyone's in the mood for this, given the state of N America right now, but I keep getting asked whether another wave of COVID-19 is "possible".

      I think that it is all but a certainty. Why are multiple waves a signature feature of pandemics?" / Twitter. (n.d.). Twitter. Retrieved June 15, 2020, from https://twitter.com/DFisman/status/1267964828691431424

    2. 2020-06-03

    3. behaves as one might expect, it is better to identify these glitches before a big winter wave hits. Here endeth ye tweetorial.
    4. It's why preparation now is of the essence. In Ontario we have identified a lot of bugs in our public health and healthcare systems, particularly related to lab capacity, information systems, and communication. In a sense that's great, because if this disease...
    5. which jibes with that idea. If that's true, that means we are likely to have a very challenging winter ahead of us: lots of susceptibility, weariness of distancing, and a seasonally juiced virus with lots of susceptible folks to infect.
    6. Why is this important? Because the very waning of COVID-19 in the northern hemisphere right now, despite pretty crappy disease control efforts in many places, suggests it is indeed a very seasonal pattern. I've also noted the concave up patterns in S America right now
    7. As in this excellent figure showing us the very irregular patterns of seasonal waves in influenza pandemics:
    8. And ultimately will turn into predictable seasonal disease. But that initial crazy trajectory of a pandemic depends in part on the random element of when the disease emerges. Early waves can die but then come back with a vengeance due to seasonal boosting.
    9. Here are the average trajectories across 20 batched runs. Starting to look a bit regular.
    10. We can batch a few runs and see very different patterns. These are 5 runs and each color represents a different run. The patterns look very different, but it's the same (exactly the same) disease.
    11. They look pretty different. It's the same model, same parameters. Just seeding it at different times means that the epidemics get boosted or suppressed by seasonal changes in R.
    12. Because the fraction susceptible is around 1, we can get out of season epidemics with pandemic pathogens. These are called "herald waves". Let's add a single stochastic element to our model...I'll seed the model with a single case at a random time of year.
    13. Here's another.
    14. Here's a run of the same model:
    15. We don't know when a novel pathogen is going to emerge. Perhaps it'll be at "peak season" (with respect to its R0) when it emerges, perhaps it'll be "off season". E.g., summertime emergence for a flu virus, wintertime emergence for (summertime seasonal) cholera.
    16. Now let's throw in some randomness ("stochasticity")...because again, although these waves look irregular, this model produces exactly the same outputs every time I run it.
    17. Here we go...I messed about by shortening duration of immunity and we now have a disease that explodes onto the scene as a pandemic but then becomes "seasonal flu" once there's some immunity in the population.
    18. See here for genius work on this by @jd_mathbio
    19. This isn't annual periodicity. We could muck about with the numbers and get this to have an "intrinsic" oscillatory frequency that's the same as the oscillatory frequency of R0, and then we could have seasonal epidemics as with flu.
    20. So we've got some cool waves. That looks like what happens with pandemics. If I run this out over a long time (200 years, here) u can see that the combination of replenishment of susceptibility (births, deaths, viral drift) with some seasonal forcing gives us periodic epidemics
    21. Epidemic waves in my deterministic model look like this, and are a function of the interplay between seasonality and replenishment of susceptibles over time.
    22. I'm going to make an SEIRS model (susceptible-latent-infectious-removed-susceptible) model such as we might use for flu. People lose immunity over time...perhaps as a result of viral drift. This model is initially deterministic (get the same result every time).
    23. Let's make a simple SIR model with a seasonally oscillatory R. My R looks, arbitrarily, like this... In winter it's COVID-y...up in the low 2's. In summer it drops to 1-point-something.
    24. One reason may be a seasonally oscillatory R0, which we might expect to see with a coronavirus and which has been anticipated by investigators like @mlipsitch
    25. That both reduced the R of H1N1, and also attenuated mortality, because those at greatest risk of death, conditional on infection, didn't get infected.
    26. SARS-CoV-2 is different, because nobody, in any age group, has pre-existing immunity. Those who are predisposed to death, conditional on infection, are not protected against infection, as they were in 2009. Hence mortality patterns that look like this in Ontario (X-axis = age)
    27. Also, because nobody has baseline immunity R ~ R0 so attack rates are predictably high. But wait: why doesn't this just rip through this susceptible population in a single wave? Why did we have an R ~ 3 in Ontario in March and now (despite weak distancing) do we have an R ~ 1?
    28. Pandemics have initial R ~ R0. That's why the epidemics are so large. In the 2009 influenza pandemic, this wasn't true. Those born prior to 1957 had early life experience with a related H1N1 influenza A virus, and were protected against infection.
    29. I think I've used this analogy before, but epidemics are like gardens: you need the seed (pathogen) and the soil (susceptible population and conditions that permit R0 > 1). As R ~ R0 x S (proportion of the population that's susceptible), and S ~ 1 at the beginning of a pandemic
    30. I'm not sure if anyone's in the mood for this, given the state of N America right now, but I keep getting asked whether another wave of COVID-19 is "possible". I think that it is all but a certainty. Why are multiple waves a signature feature of pandemics?
    1. Nivi Mani on Twitter. "I cannot stop smiling! Here is a first peek at the data from our online browser-based intermodal preferential looking set-up! We replicate the prediction effect (boy eats big cake, Mani & Huettig, 2012) using our online webcam testing software @julien__mayor @Kindskoepfe_Lab" / Twitter. (n.d.). Twitter. Retrieved June 15, 2020, from https://twitter.com/nivedita_mani/status/1265556217486815232

    2. I cannot stop smiling! Here is a first peek at the data from our online browser-based intermodal preferential looking set-up! We replicate the prediction effect (boy eats big cake, Mani & Huettig, 2012) using our online webcam testing software @julien__mayor
    3. 2020-05-27

    4. I cannot stop smiling! Here is a first peek at the data from our online browser-based intermodal preferential looking set-up! We replicate the prediction effect (boy eats big cake, Mani & Huettig, 2012) using our online webcam testing software @julien__mayor @Kindskoepfe_Lab
    1. 2020-06-14

    2. Countries where citizens report higher openness to diversity are more likely to become democratic. The authors say that this trait is important because 'it predicts peaceful coexistence of competing viewpoints'.
    3. In the wake of recent events, I keep thinking about this paper, published by @damianjruck et al. earlier this year. https://nature.com/articles/s41562-019-0769-1… short thread:
    4. Raihani, Nichola. (2020, June 14) "In the wake of recent events, I keep thinking about this paper, published by @damianjruck et al. earlier this year. https://nature.com/articles/s41562-019-0769-1 short thread:" Twitter. https://twitter.com/nicholaraihani/status/1272150848467087360

    5. This paper challenges the widespread assumption that we can create democracies by introducing democratic institutions, and that we can inculcate support for democracy with the right set of societal rules. Spoiler: we can't.
    6. There is no enshrined rule or law that democratic nations must remain democratic. Democracy is a political choice - it can be swept away with the tide of public opinion. These findings make me worry about the fate of some countries - including my own - over the coming years.
    7. Ends.
    8. Rather, the strongest predictor of whether a nation becomes democratic or not hinges on the values of its citizens in the preceding years. One cultural value is especially important in this transition: openness to diversity.
    9. The paper explores how nations become democratic as opposed to, say, autocratic. Democracy is not an inevitable or per-ordained state of affairs. As recently as the 50s, just 20 countries were considered democratic.
    10. In the wake of recent events, I keep thinking about this paper, published by @damianjruck et al. earlier this year. https://nature.com/articles/s41562-019-0769-1… short thread:
    1. 2020-05-22

    2. Bell, Kirsten, and Judith Green. “Premature Evaluation? Some Cautionary Thoughts on Global Pandemics and Scholarly Publishing.” Critical Public Health 0, no. 0 (May 22, 2020): 1–5. https://doi.org/10.1080/09581596.2020.1769406.

    3. 10.1080/09581596.2020.1769406
    4. In the space of two short months, the coronavirus pandemic has transformed the social, economic, and political landscape across the globe. For many, our research plans and projects have been one of the casualties of the virus, but we are also increasingly being assured that the virus is not just an impediment but an opportunity. Inboxes are daily flooded with requests to contribute to special issues or blogs on the coronavirus, and research funders have been fast to develop funding calls for research on the pandemic. Thus, among the many uncertainties of the COVID-19 pandemic, one clear outcome has been an incitement to publish.
    5. Premature evaluation? Some cautionary thoughts on global pandemics and scholarly publishing
    1. 2020-05-28

    2. Popovich, Nadja, and Margot Sanger-Katz. “The World Is Still Far From Herd Immunity for Coronavirus.” The New York Times, May 28, 2020, sec. The Upshot. Retrieved June 1, 2020, from https://www.nytimes.com/interactive/2020/05/28/upshot/coronavirus-herd-immunity.html.

    3. Official case counts often substantially underestimate the number of coronavirus infections. But in new studies that test the population more broadly, the percentage of people who have been infected so far is still in the single digits. The numbers are a fraction of the threshold known as herd immunity, at which the virus can no longer spread widely. The precise herd immunity threshold for the novel coronavirus is not yet clear; but several experts said they believed it would be higher than 60 percent.
    4. The World Is Still Far From Herd Immunity for Coronavirus
    1. 2020-04-10

    2. Smith-Keiling, Beverly L., Archana Sharma, Sheritta M. Fagbodun, Harsimranjit K. Chahal, Keyaira Singleton, Hari Gopalakrishnan, Katrina E. Paleologos, et al. “Starting the Conversation: Initial Listening and Identity Approaches to Community Cultural Wellness,.” Journal of Microbiology & Biology Education 21, no. 1 (April 10, 2020). https://doi.org/10.1128/jmbe.v21i1.2073.

    3. Inclusion of multiple viewpoints increases when teams are diverse and provides value in scientific communication and discovery. To promote retention and raise the critical mass of underrepresented persons in science, all voices must be heard “at the table” to include “ways of knowing” outside the dominant institutional culture. These community-based inclusive concepts promote hearing all diverse perspectives for inclusive recognition of deeper socio-historical cultural wealth—collectively termed cultural wellness. When undergraduates and graduates in active-learning groups in class, or faculty collaborative teams on campus, start a project too quickly on task, opportunities are missed to be inclusive. While beginning a larger science project, we, student and faculty co-authors, first addressed this challenge —the need for greater inclusion of diverse perspectives—by starting a conversation. Here, we share ideas from our inclusive process. Based on social constructivist theories of co-constructing learning interpersonally, we co-mentored each other, learning from one another in community. We experientially considered how to inclusively collaborate across a demographically, geographically, and structurally heterogeneous group including multiple academic tiers from multiple ethnic backgrounds, cultural experiences, and institutions. Through an asset-based process grounded in several frameworks, we documented our introduction process of listening deeply, being mindful of identities including invisible cultural identities, recognizing each other with mutual respect, applying inclusive practices, and developing mutual trust and understanding. Building community takes time. Initial conversations can, and should, go deeper than mere introductions to build trust beyond social norms for relationships promoting cultural wellness.
    4. 10.1128/jmbe.v21i1.2073
    5. Starting the Conversation: Initial Listening and Identity Approaches to Community Cultural Wellness
  2. May 2020
    1. 2020-05-19

    2. Jørgensen, F. J., Bor, A., & Petersen, M. (2020, May 19). Compliance Without Fear: Predictors of Protective Behavior During the First Wave of the COVID-19 Pandemic. https://doi.org/10.31234/osf.io/uzwgf

    3. 10.31234/osf.io/uzwgf
    4. The COVID-19 pandemic requires rapid public compliance with advice from health authorities. Here, we ask who was most likely to do so during the first wave of the pandemic. We conducted surveys asking 26,508 citizens of eight Western democracies in the period between March 19 and April 3 about their protective behavior relating to COVID-19. Consistent with prior research on epidemics, we find that perceptions of threat and risk factors are crucial and culturally uniform determinants of protective behavior. On this basis, authorities could potentially foster further compliance by appealing to fear of COVID-19, but there may be normative and practical limits to such a strategy. Instead, we find that another major source of compliance are feelings of efficacy. Importantly, the effects of such feelings are especially strong among those who do not feel threatened, creating a path to compliance without fear. In contrast, two other major candidates for facilitating compliance from the social sciences, interpersonal trust and institutional evaluations, have surprisingly little motivational power. To combat future waves of the pandemic, health authorities should thus focus on facilitating efficacy in the public.
    5. Behavior During the First Wave of the COVID-19 Pandemic
    1. 2020-05-17

    2. Socially responsible behavior is crucial for slowing the spread of infectious diseases. However, economic and epidemiological models of disease transmission abstract from prosocial motivations as a driver of behaviors that impact the health of others. In an incentivized study, we show that a large majority of people are very reluctant to put others at risk for their personal benefit. Moreover, this experimental measure of prosociality predicts health behaviors during the COVID-19 pandemic, measured in a separate and ostensibly unrelated study with the same people. Prosocial individuals are more likely to follow physical distancing guidelines, stay home when sick, and buy face masks. We also find that prosociality measured two years before the pandemic predicts health behaviors during the pandemic. Our findings indicate that prosociality is a stable, long-term predictor of policy-relevant behaviors, suggesting that the impact of policies on a population may depend on the degree of prosociality.
    3. Prosociality predicts health behaviors during the COVID-19 pandemic
    1. 2020-05-19

    2. 10.1111/bjhp.12428
    3. Purpose To describe and discuss a systematic method for producing a very rapid response (3 days) to a UK government policy question in the context of reducing SARS‐CoV‐2 transmission. Methods A group of behavioural and social scientists advising the UK government on COVID‐19 contributed to the analysis and writing of advice through the Government Office for Science. The question was as follows: What are the options for increasing adherence to social distancing (staying at home except for essential journeys and work) and shielding vulnerable people (keeping them at home and away from others)? This was prior to social distancing legislation being implemented. The first two authors produced a draft, based on analysis of the current government guidance and the application of the Behaviour Change Wheel (BCW) framework to identify and evaluate the options. Results For promoting social distancing, 10 options were identified for improving adherence. They covered improvements in ways of achieving the BCW intervention types of education, persuasion, incentivization, and coercion. For promoting shielding of vulnerable people, four options were identified covering the BCW intervention types of incentivization, coercion, and enablement. Conclusions Responding to policymakers very rapidly as has been necessary during the COVID‐19 pandemic can be facilitated by using a framework to structure the thinking and reporting of multidisciplinary academics and policymakers.
    4. Reducing SARS‐CoV‐2 transmission in the UK: A behavioural science approach to identifying options for increasing adherence to social distancing and shielding vulnerable people
    1. 2020-05-04

    2. Correia, Rion Brattig, Ian B. Wood, Johan Bollen, and Luis M. Rocha. “Mining Social Media Data for Biomedical Signals and Health-Related Behavior.” Annual Review of Biomedical Data Science, May 4, 2020. https://doi.org/10.1146/annurev-biodatasci-030320-040844.

    3. /10.1146/annurev-biodatasci-030320-040844
    4. Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.
    5. Mining Social Media Data for Biomedical Signals and Health-Related Behavior
    1. 2020-05-21

    2. Pichler, Anton, Marco Pangallo, R. Maria del Rio-Chanona, François Lafond, and J. Doyne Farmer. “Production Networks and Epidemic Spreading: How to Restart the UK Economy?” ArXiv:2005.10585 [Physics, q-Fin], May 21, 2020. http://arxiv.org/abs/2005.10585.

    3. 2005.10585v1
    4. We analyse the economics and epidemiology of different scenarios for a phased restart of the UK economy. Our economic model is designed to address the unique features of the COVID-19 pandemic. Social distancing measures affect both supply and demand, and input-output constraints play a key role in restricting economic output. Standard models for production functions are not adequate to model the short-term effects of lockdown. A survey of industry analysts conducted by IHS Markit allows us to evaluate which inputs for each industry are absolutely necessary for production over a two month period. Our model also includes inventory dynamics and feedback between unemployment and consumption. We demonstrate that economic outcomes are very sensitive to the choice of production function, show how supply constraints cause strong network effects, and find some counter-intuitive effects, such as that reopening only a few industries can actually lower aggregate output. Occupation-specific data and contact surveys allow us to estimate how different industries affect the transmission rate of the disease. We investigate six different re-opening scenarios, presenting our best estimates for the increase in R0 and the increase in GDP. Our results suggest that there is a reasonable compromise that yields a relatively small increase in R0 and delivers a substantial boost in economic output. This corresponds to a situation in which all non-consumer facing industries reopen, schools are open only for workers who need childcare, and everyone who can work from home continues to work from home.
    5. Production networks and epidemic spreading: How to restart the UK economy?