The economic impact of cancer diagnosis to individuals and their families: a systematic review
Bibliographic record
Abstract
BACKGROUND: The effect of a cancer diagnosis is wide-ranging with the potential to affect income, employment and risk of poverty. The aim of this systematic review is to identify the economic impact of a cancer diagnosis for patients and their families/caregivers. METHODS: The search covered peer-reviewed journals using MEDLINE, EMBASE, CINAHL, Cochrane Library, Epistemonikos and PsycINFO databases. Quality appraisal was undertaken using CASP tools. Monetary values were converted to US Dollars/2019 using a purchasing power parities (PPP) conversion factor. The review included articles up to and including January 2020, written in English language, for patients with cancer aged ≥ 18 years and focused on the costs up to 5 years following a cancer diagnosis. RESULTS: The search was run in January 2020 and updated in November 2021. Of the 7973 articles identified, 18 met the inclusion criteria. Studies were undertaken in the USA, Ireland, Canada, Australia, France, UK, Malaysia, Pakistan, China and Sri Lanka. The majority were cohort studies. Twelve reported out-of-pocket costs (range US$16-US$2523/month per patient/caregiver) consisting of medical expenses (e.g. surgery, radiotherapy and chemotherapy) and non-medical expenses (e.g. travel, food and childcare). Fourteen studies reported patient/caregiver loss of income and lost productivity (range 14-57.8%). CONCLUSIONS: A high percentage of cancer patients and their families/caregivers experience out-of-pocket expenditure, loss of income and lost productivity. Future research is needed to observe the effects of continuing changes to healthcare policies and social protections on the economic burden among cancer patients and their families/caregivers.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".