Sociodemographic variation in experiences with medication shortages among US adults
Bibliographic record
Abstract
Objective: To investigate sociodemographic factors associated with prescribed and over-the-counter medication shortage experiences. Methods: We analyzed repeated cross-sectional data from the 2023 US Census Household Pulse Survey, a nationwide survey of US adults. Outcomes were based on the following question: "In the past month, have you or a member of your household been directly affected by the following?" We created binary indicators based on the following response options: (1) "Shortage of prescription medications, which includes any medicine required or provided by a healthcare provider, pharmacist, or hospital" and (2) "Shortage of over-the-counter medications, encompassing any medication available without a prescription." Sociodemographic factors included age, gender identity, race/ethnicity, marital status, educational attainment, household income, number of children, employment status, health insurance coverage, at risk for depression/anxiety, disability status, and region. Weighted multivariable models accounted for the complex survey design and estimated adjusted odds ratios with 95 % confidence intervals. Results: We found that more experiences with prescribed and over-the-counter medication shortages were associated with middle age, transgender/other gender identity, non-Hispanic Other race/ethnicity, higher educational attainment, having at least one child, at risk for depression or anxiety, and being disabled. In contrast, fewer experiences with prescribed and over-the-counter medication shortages were associated with higher household income. Conclusions: Sociodemographic variation exist in experiences with medication shortages among US adults. These findings underscore the need to bolster the pharmaceutical supply chain to mitigate inequities in medication access.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".