Effect of the COVID-19 Pandemic on the Well-Being of Middle-Aged and Older Adults from 27 European Countries: Evidence from a Longitudinal Analysis of Population-Based Surveys
Bibliographic record
Abstract
Abstract Background Containment and restriction measures introduced during the coronavirus (COVID-19) pandemic have had a negative effect on the health and well-being of individuals, regardless of whether or not they were infected by the SARS-CoV-2 virus. We assessed how the pandemic impacted the overall well-being of middle-aged and older adults residing in 27 European countries. In particular, we assessed how having COVID-19 illness or experiencing symptoms associated with Long COVID impacted change in the level of well-being. Methods We used data from the Survey of Health, Ageing and Retirement in Europe's Corona Surveys collected in 2020 and 2021. The sample consisted of 47,964 respondents aged 50 years and older. The outcome variable was the across-time change in the repeated measures of well-being which were operationalized as two latent variables. We used structural equation modeling techniques (i.e., latent change score models) to assess the effects of the relevant risk factors (demographics, socio-economic, chronic conditions, social support) on change in the level of well-being. Results Overall, 8% or respondents were infected by the SARS-CoV-2 virus and 73% of those who were infected reported at least one persistent symptom associated with Long COVID. The results from the analyses indicate that individuals who were affected by COVID-19 illness had a significantly larger decline in the level of well-being than those not affected by this illness and that this decline was more pronounced among those who had Long COVID. We also observed that the degree of change in the level of well-being was associated with the risk factors. Conclusions The COVID-19 pandemic impacted the well-being of Europeans and its effects were more pronounced among those directly affected by COVID-19 illness. However, the magnitude of this effect differed across sub-groups of individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".