Impact of cancer on income, wealth and economic outcomes of adult cancer survivors: a scoping review
Bibliographic record
Abstract
OBJECTIVE: To summarise peer-reviewed evidence on the effect of a cancer diagnosis on the different sources of income of individuals diagnosed with cancer during adulthood (age ≥18 years). DESIGN: A scoping review following the Joanna Briggs Institute's methodological framework for conducting scoping reviews and reporting results following the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews checklist. DATA SOURCES: Ovid MEDLINE, PsycINFO, CINAHL, EMBASE, Econ-Lit and Evidence-based Medicine Reviews, and reference lists of evidence syntheses. Published literature of any study type in English was searched from January 2000 to December 2020. ELIGIBILITY AND CRITERIA: Study participants were individuals diagnosed with cancer during adulthood (age ≥18 years). Studies from any country and/or healthcare system were included. Primary outcomes were employment income (eg, individual or household); investment income (eg, stocks/bonds, properties, savings); government transfer payments (eg, disability income/pension); debt and bankruptcy. DATA EXTRACTION AND SYNTHESIS: Findings are summarised descriptively and in tabular form. RESULTS: From 6297 citations retrieved, 63 studies (67 articles) met our inclusion criteria. Most (51%) were published in 2016-2020; 65% were published in the USA or Scandinavia. Survivors incurred debt (24 studies), depleted savings (13 studies) and liquidated stocks/bonds (7 studies) in response to a cancer diagnosis. 41 studies reported changes to employment income; of these, 12 case-control studies reported varying results: 5 reported survivors earned less than controls, 4 reported no significant differences, 2 reported mixed results and 1 reported income increased. Initial declines in income tended to lessen over time. CONCLUSIONS: Cancer's impact on survivors' income is complex and time-varying. Longitudinal studies are needed to document the trend of initial declines in income, with declines lessening over time, and its variations. Study designs using standardised income measures and capturing treatment type and follow-up time will improve our understanding of cancer's impact on survivors' income.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".