Abstract 4365359: Ethnic disparities in the effectiveness of electronically delivered nudges to increase influenza vaccination uptake: A secondary analysis of the NUDGE-FLU-CHRONIC trial
Bibliographic record
Abstract
Background: Persistent racial and ethnic disparities exist in influenza vaccination coverage. Increasing uptake in this population could potentially reduce influenza infection and associated complications in a at-risk population. Purpose: To explore differences in the effectiveness of behavioral nudges on influenza vaccination rates according to ethnicity. Methods: We conducted a secondary analysis of the nationwide, pragmatic, registry-based, randomized, implementation trial NUDGE-FLU-CHRONIC, which evaluated the effect of 6 different electronic nudging letter strategies on influenza uptake compared with usual care among persons aged 18-64 years, with a chronic disease associated with an increased risk of adverse influenza-related outcomes. Ethnicity was categorized into two groups, white or non-white. Effect modification by ethnicity was tested on the absolute scale. Absolute differences and relative risks (RR) for each comparison were calculated at a significance level of 0.0071. Results: Of 299,881 participants (53.2% female, median age 52.0 years [IQR, 39.8-59.0]) randomized, 90.4% were white. Vaccination uptake was significantly lower compared with non-whites (22.0% vs. 37.7%, p<0.001). White residents were older (median [IQR] age: 52.5 [40.5, 59.2] vs. 46.6 [36.0, 55.8]), and had a higher prevalence of heart failure (4.0% vs 2.7%, p<0.001), atrial fibrillation (6.9% vs 3.2%, p<0.001, and hypertension (36.2% vs 28.9, p<0.001), whereas non-whites had a higher prevalence of ischemic heart disease (10.5% vs. 8.9%, p<0.001), and diabetes (24.6% vs. 18.7%, p<0.001). White residents were more likely to be educated, be employed, and had higher income (all p<0.001). Each letter successfully increased influenza vaccination irrespective of ethnicity; however, the magnitude of effectiveness of each letter was significantly modified by ethnicity (Figure 1; all p<0.001), such that effectiveness was less pronounced among non-whites with the largest difference in effect size observed with the repeated letter and the smallest difference in effect size observed with the cardiovascular gain letter. Conclusion: Electronic nudging letters consistently led to clinically relevant relative gains in vaccine uptake, irrespective of ethnicity. However, absolute gains from these nudging strategies were somewhat attenuated in non-white residents. These results highlight the need for greater emphasis on vaccine implementation strategies tailored according to 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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".