Cancer screening uptake among migrant background populations: an umbrella review
Bibliographic record
Abstract
Abstract Background Cancer is one of the leading causes of death worldwide, with colorectal, breast, cervical, and prostate cancers being among the most common types. Uptake of prevention and early detection strategies varies between native and migrant background populations. While research on disparities in cancer screening uptake among migrant groups exist, many studies do not account for the heterogeneity of these populations, including differences between first- and second-generation migrants. This umbrella review evaluates evidence on cancer screening uptake across these four cancers and identifies barriers and facilitators influencing participation. Methods Reviews on cancer screening uptake and related factors in migrant populations were searched in PubMed and Google Scholar. All types of reviews were included. Evidence was synthesised by distinguishing two groups: first-generation migrants (those who experienced migration) and second-generation descendants (those born in the host country to migrant parents). Results Twenty reviews were included, representing 439 primary studies. Most primary studies lacked clear definitions of migrant populations and relied on non-random sampling methods. When migration background was considered, first-generation migrants had lower cancer screening uptake (e.g., 0-50% for colorectal cancer) than second-generation migrants (23-50%). Screening rates were generally higher in native populations (e.g., 38-86% for cervical cancer) compared to migrant populations (11-69%). Barriers included lower socioeconomic conditions, language, cultural beliefs, limited cancer knowledge and the complexity of the healthcare system. Conclusions Cancer screening rates are lower among migrant populations compared to natives, but second-generation descendants have higher uptake compared to their migrant parents. However, most reviews were based on studies with non-representative samples and limited distinction between migrant subgroups. Key messages • Second-generation descendant of migrants have higher cancer screening uptake than first-generation migrants. • To capture meaningful disparities, research should reflect the diversity of migrant background population - by origin, generation and ethnicity.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".