Understanding how social determinants of health shape Long COVID outcomes: a rapid review of evidence
Bibliographic record
Abstract
BACKGROUND: Long COVID affects over 65 million people worldwide, yet the impact of social determinants of health (SDoH), such as socioeconomic status, race/ethnicity, education, occupation, and geography, remains poorly understood. To evaluate the association between SDoH and the risk and severity of Long COVID. METHODS: A rapid review of observational studies was conducted using MEDLINE, Embase, and Web of Science (up to September 29, 2024). Studies reporting original data on SDoH and Long COVID outcomes were included. Data were extracted on study characteristics, population demographics, Long COVID definitions, and SDoH-related findings. Study quality was assessed using the Newcastle-Ottawa Scale. RESULTS: Seventy-one studies (43 cohort, 28 cross-sectional) were included. Definitions of Long COVID varied. Commonly studied SDoH included age, sex, race/ethnicity, education, financial security, employment, and geography. Female sex and older age were consistently associated with increased risk and severity of Long COVID. Black and Hispanic individuals were more likely to experience Long COVID. Lower education and financial insecurity were also linked to greater prevalence and symptom burden. Frontline and essential workers were found to be at increased risk. Geographic disparities were evident but varied across rural and urban residence. CONCLUSIONS: SDoH play a key role in shaping Long COVID outcomes. Addressing these disparities requires targeted public health efforts and standardized case definitions.
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".