Association between non-adequate health literacy and cancer screening adherence: a meta-analysis
Bibliographic record
Abstract
Abstract Objective Individuals with non-adequate health literacy (NAHL) are more likely to have poor health outcomes and behaviors, including a limited use of preventive services. This study aimed at summarizing the evidence on the association between NAHL and adherence to cancer screening programs. Methods PubMed, Scopus, and Web of Science were searched. Cross-sectional studies conducted in any country, published through January 2020 and quantifying the association between NAHL and cancer screening adherence, were included. An adapted Newcastle-Ottawa Scale was used to assess quality. Inverse-variance random-effects methods were used to produce pooled estimates: overall, by cancer and by HL tool. Results Seventeen articles of heterogeneous quality were included in the systematic review and 45 analyses were combined. NAHL was found to negatively influence screening adherence in both the crude (n = 26) and adjusted (n = 19) pooled analyses, and the association was slightly stronger in the latter (OR = 0.70, 95%CI: 0.62-0.79 and aOR=0.66, 95%CI: 0.57-0.76, respectively). Moreover, NAHL was significantly associated with lower adherence in all the types of cancer screening investigated: breast cancer (n = 7), aOR=0.55, 95%CI: 0.38-0.78; cervical cancer (n = 4), aOR=0.63, 95%CI: 0.53-0.75; prostate cancer (n = 1), aOR=0.60, 95%CI: 0.36-0.99, and colorectal cancer (n = 7), aOR=0.85, 95%CI: 0.74-0.97. Lastly, tools measuring NAHL with reading comprehension/numeracy items yielded the strongest association with the outcome (n = 5, aOR= 0.53, 95%CI: 0.35-0.80), that was attenuated in tools with either self-reported comprehension items (n = 7, aOR=0.72, 95%CI: 0.65-0.80) or medical term recognition items (n = 7, aOR=0.74, 95%CI: 0.57-0.95). Conclusions This study shows that NAHL may have a significant impact on adherence in all types of screening programs analysed, independently of the tools applied to measure it. Hence, it should be a useful focus for interventions to improve screening participation rates. Key messages Non-adequate health literacy negatively influences cancer screening adherence. Efforts to promote the uptake of cancer screening programs should consider and address NAHL.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.053 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".