Impacts on labour force and healthcare services related to mental-health issues following an acute SARS-CoV-2 infection: rapid review
Bibliographic record
Abstract
ABSTRACT Purpose The impact on the labour force, including healthcare services, from the emergence of mental health symptoms after COVID-19 is uncertain. This rapid review examined the impacts on the labour force and healthcare services and costs related to mental health issues following an acute SARS-CoV-2 infection. Methods We searched Medline, Embase, and PsycInfo in January 2024, conducted forward citation searches in Scopus, and searched reference lists for studies reporting labour force outcomes (among those with mental health symptoms after COVID-19) and mental health services use among people of any age at least 4 weeks after confirmed/suspected SARS-CoV-2 infection. Titles/abstracts required one reviewer to include but two to exclude; we switched to single reviewer screening after 50% of citations were screened. Selection of full texts used two independent reviewers. Data extraction and risk of bias assessments by one reviewer were verified. Studies were sorted into categories based on the population and outcomes, including timing of outcome assessment, and, if suitable, study proportions were pooled using Freeman-Tukey transformation with assessment of heterogeneity using predetermined subgroups. Results 45 studies were included with 20 reporting labour force and 28 mental healthcare services use outcomes. 60% were rated as high risk of bias, mainly due to difficulty attributing the outcomes to COVID-19 from potential confounding from employment status or mental healthcare services use prior to infection. Studies on labour force outcomes mostly (85%) reported on populations with symptoms after acute infection that was cared for in outpatient/mixed care settings. Among studies reporting mental healthcare use, 50% were among those hospitalized for acute care and 43% assessed outcomes among populations with post-acute or prolonged symptoms. Across 13 studies (N=3,106), on average 25% (95% CI 14%, 38%) of participants with symptoms after COVID-19 had mental health symptoms and were unable to work for some duration of time. It was difficult to associate inability to work with having any mental health symptom, because studies often focused on one or a couple of symptoms. The proportion of participants unable to work ranged from 4% to 71%, with heterogeneity being very high across studies (I 2 >98%) and not explained by subgroup analyses. Most of these studies focused on people infected with pre-Omicron strains. There was scarce data to inform duration of inability to work. For outcomes related to work capacity and productivity, there was conceptual variability between studies and often only single studies reporting on an outcome among a narrowly focused mental health symptom. On average across 21 studies (N=445,994), 10% (95% CI 6%, 14%) of participants reported seeing a mental healthcare professional of any type (psychiatrist, psychologist, or unspecified). Heterogeneity was very high and not explained after investigation. There was very limited information on the number of sessions attended. Among seven studies, mainly reporting on populations with post-COVID-19 symptoms, participant referrals to mental health services ranged from 4.2% to 45.3% for a variety of types of mental health symptoms including neuropsychology, psychiatric, and psychological. Though at high risk of bias, findings from one large study suggested 1-2% of those hospitalized during their acute infection may be re-hospitalized due to mental health symptoms attributed to COVID-19. Conclusions A large minority of people (possibly 25%) who experience persisting symptoms after COVID-19 may not be able to work for some period of time because of mental health symptoms. About 10% of people experiencing COVID-19 may have use for mental health care services after the acute phase, though this rate may be most applicable for those hospitalized for COVID-19. A small minority (possibly 1-2%) may require re-hospitalization for mental health issues. There is limited applicability of the results in most cases to populations with post-COVID-19 symptoms rather than more broadly post-COVID-19 or general populations. Overall, this rapid review highlights the variability of measurement, definition of outcomes and difficulty attributing the outcomes to mental health symptoms after COVID-19 infection. PROSPERO CRD42024504369
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".