🧵: #COVID19 will only exacerbate the health disparities between the richest and poorest in the US. A new study demonstrates that, while we tend to ignore the ongoing COVID risks, those risks are not gone nor evenly distributed.
The study found that “Public facing industries, including teaching and education, social care, healthcare, civil service, retail and transport industries and occupations, had the highest likelihood of long-COVID.” (1/9)
People working in caring, leisure and other services had a 44% higher risk of experiencing Long COVID than average.
We could do much to mitigate the risks to people in less safe jobs as COVID continues to evolve and our immunity declines. (And it is declining: The CDC Director, Mandy Cohen, just noted, “What I want folks to understand is that protection is decreasing over time — and this virus is changing.”) Protecting those who don't have the privilege to work from home would mean: (2/9)
▪️ All of us getting the latest vaccine to decrease infections and transmission.
▪️ Employers investing in filtration and ventilation systems to help create safer public spaces.
▪️ Governments setting new rules for safe air, including CO2 monitoring, in public spaces.
▪️ And as a last line of defense, continued masking, particularly when COVID signs (such as wastewater surveillance or the positive rate of testing) is higher.
(3/9)
We used to say this back in 2020, but it's no less true today: We're all in this together. Even if you are convinced COVID is no big deal or are willing to accept the risk of Long COVID or other chronic health problems, our behaviors still can increase or decrease the risk of those working in public-facing jobs.
Getting the new vaccine jab or taking a little caution not only decreases your risk, but also the risks faced by others.
(8/9)
And here is the study referenced in this the beginning of this thread:
Objectives To establish whether prevalence and severity of long-COVID symptoms vary by industry and occupation. Methods We used Office for National Statistics COVID-19 Infection Survey (CIS) data (February 2021–April 2022) of working-age participants (16–65 years). Exposures were industry, occupation and major Standard Occupational Classification (SOC) group. Outcomes were self-reported: (1) long-COVID symptoms and (2) reduced function due to long-COVID. Binary (outcome 1) and ordered (outcome 2) logistic regression were used to estimate odds ratios (OR)and prevalence (marginal means). Results Public facing industries, including teaching and education, social care, healthcare, civil service, retail and transport industries and occupations, had the highest likelihood of long-COVID. By major SOC group, those in caring, leisure and other services (OR 1.44, 95% CIs 1.38 to 1.52) had substantially elevated odds than average. For almost all exposures, the pattern of ORs for long-COVID symptoms followed SARS-CoV-2 infections, except for professional occupations (eg, some healthcare, education, scientific occupations) (infection: OR<1 ; long-COVID: OR>1). The probability of reporting long-COVID for industry ranged from 7.7% (financial services) to 11.6% (teaching and education); whereas the prevalence of reduced function by ‘a lot’ ranged from 17.1% (arts, entertainment and recreation) to 22%–23% (teaching and education and armed forces) and to 27% (not working). Conclusions The risk and prevalence of long-COVID differs across industries and occupations. Generally, it appears that likelihood of developing long-COVID symptoms follows likelihood of SARS-CoV-2 infection, except for professional occupations. These findings highlight sectors and occupations where further research is needed to understand the occupational factors resulting in long-COVID. Data may be obtained from a third party and are not publicly available. ONS CIS data can be accessed only by researchers who are Office of National Statistics (ONS) accredited researchers. Researchers can apply for accreditation through the Research Accreditation Service. Access is through the Secure Research Service (SRS) and approved on a project basis. For further details see: <https://www.ons.gov.uk/aboutus/whatwedo/statistics/requestingstatistics/approvedresearcherscheme>.
Augie Ray
MARGARET D
Dana das Grau 🧙🏻🧝♂️
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