Health equity
Individual SES and cancer survival
Open access · cc by · source: Europe PMC
UK linked-data analysis shows both area deprivation and individual SES shape cancer survival, with wider absolute gaps in the most deprived areas.
Study at a glance
- Design
- Cohort — ONS Longitudinal Study; excess mortality models by SES markers
- N
- N=9276 · 1522 men + 1237 women colorectal; 3044 prostate; 3473 breast (ONS Longitudinal Study analysis cohort)
- Population
- People with colorectal, prostate, or breast cancer in the ONS Longitudinal Study
- Outcome
- Excess mortality / net-survival gaps by area deprivation and individual SES
Structured fields used in claim comparison tables when every cited study has a complete layer.
Key findings
Less deprived areas had lower excess hazard (e.g., prostate EHR 0.80); absolute net-survival gaps between best/worst individual SES profiles were 29.6% in most deprived vs 9.1% in least deprived areas.
Methodology
Using ONS Longitudinal Study cancer patients, authors modelled excess mortality for colorectal, prostate, and breast cancers by area deprivation and individual education/occupation/income markers.
Limitations
Observational inequalities do not identify a single causal mechanism or evaluate a specific intervention.
How this study connects
Role on claims
Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.
In observational cancer and sleep cohorts, Cox models turn deprivation, education and disease severity into excess-hazard or event-rate contrasts. ONS Longitudinal Study analyses found less-deprived areas had lower excess hazard for prostate cancer (EHR 0.80) and that absolute net-survival gaps between extreme individual SES profiles were 29.6% in the most deprived areas versus 9.1% in the least; an OSA sleep-lab cohort of 10,149 patients had 11.5% composite events over a median 68 months, with higher AHI linked to higher hazard (e.g. univariate HR 1.49 for high versus low AHI).
Evidence for the claim as stated.
The same modelling family is asked to do two different jobs: describe inequalities and exposures in cohorts, versus test a randomised intervention. The SES-cancer and OSA papers cannot name a single causal mechanism or a treatment that would close the HR gap, whereas the propofol–sevoflurane trial can say the assigned anaesthetic did not shift 5-year survival under its protocol. Reading every HR as if it were that trial overclaims the observational papers and underclaims the trial's design.
Evidence for the claim as stated.
Open questions
Tensions this paper is part of
From concept pages' “where studies disagree.” Disagreement means the same question; scope means different assays, populations, or outcomes.
The same modelling family is asked to do two different jobs: describe inequalities and exposures in cohorts, versus test a randomised intervention. The SES-cancer and OSA papers cannot name a single causal mechanism or a treatment that would close the HR gap, whereas the propofol–sevoflurane trial can say the assigned anaesthetic did not shift 5-year survival under its protocol. Reading every HR as if it were that trial overclaims the observational papers and underclaims the trial's design.
- Supports · OSA severity and cardiovascular risk
- Supports · Propofol vs sevoflurane and breast cancer survival
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Same topic cluster — not a recommendation engine.