Has Long Covid contributed to labor market inactivity in the UK?

In a preprint recently uploaded to the medRxiv* server, researchers conducted an observational, longitudinal study to investigate the association between Long Covid and subsequent labor market outcomes in working-age adults from the United Kingdom.

*Important notice: medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Their pre-post study design findings suggest that Long Covid contributed to increased levels of labor market inactivity post-infection compared to pre-infection. These results were pronounced in individuals reporting Long Covid symptoms lasting more than 30 weeks post-infection.

What is Long Covid?

Long Covid is a condition some individuals have developed following contracting the coronavirus disease 2019 (COVID-19). Individuals experience symptoms persisting weeks, months, or even years after initial infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

It is characterized by symptoms including prolonged fatigue, breathlessness, joint and muscle aches, cognitive impairment, and sleep disorders.

The global prevalence of Long Covid is estimated to be between 7.5–89.0% of all COVID-19 patients. United Kingdom (UK) centric studies reported an estimated 3% of the country’s population, around 2,000,000 individuals, to be suffering from the condition as of January 2023. Of these, 77% of individuals, mainly working-age adults, reported symptoms disrupting normal daily activities.

There are no biological or clinical tests for Long Covid, making its diagnosis exclusion based. However, COVID-19 comorbidities may contribute to some of the depicted long-term symptoms, making studies investigating Long Covid difficult and rare.

On the global scale, the few cross-sectional studies that evaluated the impacts of Long Covid on employment reported significant reductions in working hours for individuals livings with the condition, with 19–22% of patients being unable to work entirely.

Furthermore, these studies included small sample populations with short follow-up periods, making discussions into the macroeconomic and livelihood impacts of Long Covid data deficient.

About the study

In the present study, researchers used large population longitudinal data within a pre-post study design to elucidate the associations between Long Covid prevalence and labor market inactivity. For the purposes of this study, labor market inactivity was defined as “being out of employment and not looking for work, or experiencing long-term absence while in employment.”

Data was gathered from the COVID-19 Infection Survey (CIS), a longitudinal dataset comprising individuals from randomly sampled households across the UK. Individuals of working age (16-64 years) who consented to monthly follow-ups between 3^rd February 2021 and 30^th September 2022 were included in the study dataset.

Individuals pursuing full-time education and those wherein the time of study enrolment was unlikely to correspond with the time of first SARS-CoV-2 infection were excluded.

Long Covid exposure was measured both clinically, via PCR test assessments taken during routine monthly follow-up, and via a self-reported questionnaire that asked the question, “Would you describe yourself as having ‘Long Covid,’ that is, you are still experiencing symptoms more than 4 weeks after you first had COVID-19 that are not explained by something else?”. The study defined Long Covid as persisting symptoms 12 or more weeks after initial infection. Downstream analyses stratified exposure duration into 12–17 weeks, 18–23 weeks, 24–29 weeks, 30–39 weeks, 40–51 weeks, and 52 weeks or more.

Sociodemographic variables related to Long Covid and labor market status were used as covariates. Long Covid variables included age, sex, ethnicity, country/region, area deprivation, and long-term disability status. Labor market variables included employment sector, status, Standard Occupational Classification (SOC) major group, and whether the individual was self-employed.

Statistical analyses used means and proportions to compute and remove confounders for continuous and categorical variables. For variables where measures varied with time, condition logit models were used. The effects of sociodemographic variables and SARS-CoV-2 reinfection (the latter of which has been reported to extend Long Covid duration) were treated as heterogeneous effects.

Finally, population attributable risk, the probability that an individual who was not working due to Long Covid would have been working had they not contracted the infection, was computed for July 2022 using adjusted Odds Ratios (aORs) combined with official inactivity statistics.

Study findings

The final dataset comprised 206,299 individuals between the ages of 16 and 64. An average of 12.3 monthly follow-ups per individual revealed 147,895 participants with long-term labor market inactivity, 97,751 SARS-CoV-2 reinfections, and 8,440 reported Long Covid patients.

Observational statistics revealed that Long Covid showed age-related signatures, with participants reporting the condition being, on average older (46.3 years) than those that did not (44.3 years) at CIS enrolment. Individuals working at CIS enrolment who reported Long Covid were more likely to be teachers, government employees, or service sector workers.

Analyses revealed that 31,248 study participants (15.1%) were ever inactive. Participants who reported Long Covid were more likely to be ever inactive than those who did not (17.7% versus 13.4%). Compared to pre-infection, analyses computed an estimated 27,000 inactive participants due to Long Covid, most of whom were ages 50–64 (16,000).

Long-term absence results revealed that 14,493 (9.8%) participants were on work leave for more than four weeks following infection. This proportion was higher for individuals reporting Long Covid than those not reporting the condition (13.1% versus 9.8%).

“Compared with the pre-infection period, SARS-CoV-2 infection <12 weeks previously was associated with an increased likelihood of long-term absence (aOR: 1.09); as too was reporting Long Covid 18 to <24 or 24 to <30 weeks post-infection, with aORs of 1.40 and 1.45, respectively.

Conversely, infection in the past 12 to <18 weeks without reporting Long Covid to date (0.84), or being 40 to <52 weeks (0.70) or ≥52 weeks (0.59) after infection having previously reported Long Covid, were both associated with reduced odds of long-term absence relative to pre-infection.”

Ayoubkhani et al. (2023)

Conclusions

In the present preprint, researchers conducted a longitudinal pre-post study-design analysis on UK-based, working-age adults (16–64 years) who were not pursuing full-time education between 3^rd February 2021 and 30^th September 2022.

An average of 12.3 monthly clinical and questionnaire-based follow-ups revealed that Long Covid was significantly associated with labor market inactivity. The strongest risk associations were revealed at 30 to <40 weeks following SARS-CoV-2 infection.

Long Covid was also found to be a significant contributor to long-term work leave between 18 to <30 weeks after infection. These results highlight that Long Covid has contributed to absence and overall reduced participation in the UK labor market.

At the same time, it is unlikely to have been the sole driver of inactivity, with fewer than 30,000 working-age adults estimated to be inactive because of their Long Covid symptoms in July 2022. The relative contribution of factors besides Long Covid to reduced levels of [labor] market participation (such as indirect health effects of the pandemic and extended healthcare waiting lists) remains unknown, and further research is required.

Future studies with longer follow-up could also provide insights on a broader range of socio-economic outcomes following SARS-CoV-2 infection, such as income and earnings, social security benefit claims, and socio-economic position.

Ayoubkhani et al. (2023)

*Important notice: medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Written by

Hugo Francisco de Souza

Hugo Francisco de Souza is a scientific writer based in Bangalore, Karnataka, India. His academic passions lie in biogeography, evolutionary biology, and herpetology. He is currently pursuing his Ph.D. from the Centre for Ecological Sciences, Indian Institute of Science, where he studies the origins, dispersal, and speciation of wetland-associated snakes. Hugo has received, amongst others, the DST-INSPIRE fellowship for his doctoral research and the Gold Medal from Pondicherry University for academic excellence during his Masters. His research has been published in high-impact peer-reviewed journals, including PLOS Neglected Tropical Diseases and Systematic Biology. When not working or writing, Hugo can be found consuming copious amounts of anime and manga, composing and making music with his bass guitar, shredding trails on his MTB, playing video games (he prefers the term ‘gaming’), or tinkering with all things tech.