The Causes of Racial and Ethnic Differences in Influenza Vaccination Rates among Elderly Medicare Beneficiaries
Bibliographic record
Abstract
Objective. To explore three potential causes of racial/ethnic differences in influenza vaccination rates in the elderly: (1) resistant attitudes and beliefs regarding vaccination by African‐American and Hispanic Medicare beneficiaries, (2) poor access to care during influenza vaccination weeks, and (3) discriminatory behavior by providers. Data Sources. Medicare beneficiaries who responded to both the 1995 and 1996 Medicare Current Beneficiary Survey (MCBS) (n=6,746). Study Design. We combined survey information from the MCBS with Medicare claims. We measured resistance to vaccination by self‐reported reasons for not receiving vaccination, access to care by claims submitted during vaccination weeks, and discrimination by racial differences in vaccinations among beneficiaries who visited the same providers during vaccination weeks. Principal Findings. White beneficiaries (66.6 percent) were more likely to self‐report having received vaccination than were African Americans (43.3 percent) or Hispanics (52.5 percent). Resistance to vaccination plays a role in low vaccination rates of African‐American (−11.8 percentage points), but not Hispanic beneficiaries. Unequal access accounts for <2 percent of the disparity. Minority beneficiaries remained unvaccinated despite having medical encounters with their usual providers on days when those same providers were administering vaccinations to white beneficiaries. This disparity is attributable not to provider discrimination but to a 1.6−5 × higher likelihood of white beneficiaries initiating encounters for the purpose of receiving vaccination. Conclusion. Disparities in access to care and provider discrimination play little role in explaining racial/ethnic disparities in influenza vaccination. Eliminating missed opportunities for vaccination in 1995 would have raised vaccination rates in three racial/ethnic groups to the Healthy People 2000 goal of 60 percent vaccination.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".