Interventions to increase follow-up of abnormal cervical cancer screening results: A systematic literature review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Ensuring timely follow-up of abnormal screening results is essential for eliminating cervical cancer. OBJECTIVE: The purpose of the study was to review single and multicomponent interventions designed to improve follow-up of women with abnormal cervical cancer screening results. We report on effectiveness across studies, and describe what aspects of these interventions might be more impactful. METHODS: Publications were searched between January 2000 and December 2022. The search included observational, quasi-experimental (pre-post studies) and randomized controlled studies describing at least one intervention to increase follow-up of women with abnormal cervical cancer screening results. Outcomes of studies included completion of any follow-up (i.e., attending a follow-up appointment), timely diagnosis (i.e., colposcopy results within 90 days of screening) and time to diagnostic resolution (i.e., days between screening and final diagnosis). We assessed risk of bias for observational and quasi-experimental studies using the Newcastle-Ottawa Scale (NOS) tool and the Cochrane collaboration tool for randomized studies. We conducted a meta-analysis using studies where data were provided to estimate a summary average effect of the interventions on follow-up of patients and to identify characteristics of studies associated with an increased effectiveness of interventions. We extracted the comparison and intervention proportions of women with follow-up before and after the intervention (control and intervention) and plotted the odds ratios (ORs) of completing follow-up along with the 95% confidence intervals (CIs) using forest plots for the interventions vs. controls when data were available. FINDINGS: From 7,457 identified studies, 28 met the inclusion criteria. Eleven (39%) of the included studies had used a randomized design. Most studies (63%) assessed completion of any follow-up visit as the primary outcome, whereas others measured time to definite diagnosis (15%) or diagnostic resolution (22%). Navigation was used as a type of intervention in 63% of the included studies. Most interventions utilized behavioral approaches to improve outcomes. The overall estimate of the OR for completion of follow-up for all interventions was 1.81 (1.36-2.42). The highest impact was for programs using more than one approach (multicomponent interventions) to improve outcomes with OR = 3.01 (2.03-4.46), compared with studies with single intervention approaches with OR = 1.56 (1.14-2.14). No statistical risks were noted from publication bias or small-study effects in the studies reviewed. CONCLUSION: Our findings revealed large heterogeneity in how follow-up of abnormal cervical cancer screening results was defined. Our results suggest that multicomponent interventions were more effective than single component interventions and should be used to improve follow-up after abnormal cervical cancer screening results. Navigation appears to be an important tool for improving follow-up. We also provide recommendations for future studies and implications for policy in terms of better defining outcomes for these interventions.
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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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.043 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".