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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".