Association of insurance status among cancer patients and survival outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
Health insurance coverage is a critical determinant of cancer care access. However, the association of different insurance statuses affecting survival outcomes remains understudied worldwide. This meta-analysis provides global evidence on the association between insurance status and survival and highlights structural health inequities across national health insurance systems. We searched five databases for cohort studies published between 1 January 2000 and 15 July 2025. Random-effect multilevel and traditional meta-analyses were employed to address heterogeneity. The Newcastle-Ottawa Scale (NOS) and the ROBINS-I method assessed all studies for quality. We included 37 studies between 2000 and 2025, contributing 219 effect sizes. In the United States (US), patients insured in Medicare (HR: 1.29, 95% CI : 1.17–1.42, P < 0.001; τ 2 (2) = 0.046, I 2 (2) = 67.28%; τ 2 (3) = 0.022, I 2 (3) = 31.89%), Medicaid (HR: 1.39; 95% CI : 1.28–1.51, P < 0.001; τ 2 (2) = 0.049, I 2 (2) = 74.07%; τ 2 (3) = 0.016, I 2 (3) = 24.60%), or without insurance (HR: 1.42, 95% CI : 1.31–1.53, P = 0.001; τ 2 (2) = 0.032, I 2 (2) = 65.99%; τ 2 (3) = 0.015, I 2 (3) = 30.77%) had worse overall survival (OS) than private insurers. Cancer stage, cancer type, and the adjustment variables are moderators of effect size heterogeneity in the US. The association between insurance status and survival was stronger in early-stage (I-II) cancers and among patients with breast and prostate cancer, whereas survival disparity across insurance statuses was smaller or not statistically significant for advanced (III-IV) stages and patients diagnosed with lung, liver, and colorectal cancer. In China, patients without Urban Employee Basic Medical Insurance (non-UEBMI) showed worse OS (HR: 1.39; 95% CI : 1.22–1.59; I 2 = 60.0%; τ 2 = 0.012) than UEBMI patients. Qualitative evidence from Germany, South Korea, Thailand, and Brazil did not identify statistically significant associations between insurance status and cancer survival outcomes. Uninsured individuals were experiencing poorer OS than those with any other form of insurance status globally. The association between insurance status and cancer survival differs across national health insurance systems. Insurance policies should prioritize early-stage cancer care, cancer types with a favorable prognosis, and uninsured groups. Future research should use prospective international cohorts to explore how insurance structures and covariate interactions affect survival and to achieve equity in global cancer care.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.023 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".