The role of race and place in drug use and mortality in the United States
Bibliographic record
Abstract
Background: Much of the research on the emergence and recent trends in the opioid epidemic in the United States examines differences by either race or place.Yet, the few studies which assess the intersection of these dimensions reveal unexpected findings, which challenge initial assumptions about the uniformity of the epidemic's impact.As such, this thesis aims to better describe differences in trends of drug overdose mortality and drug-use outcomes for non-Hispanic Blacks and Whites across three metropolitan categories: large metro, small metro, and nonmetro.Objectives: This thesis aims to 1) present trends in all-drug and opioid-related mortality rates between 2003-2018; 2) highlight differences in changes in drug-related mortality before and after the peak of the epidemic in 2015; 3) delineate time trends in illicit drug use and prescription pain reliever misuse between 2003-2018; and 4) describe recent patterns in access to and source of drugs, for Blacks and Whites based on their metropolitan status.Methods: Drug mortality data was obtained from the Centers for Disease Control and Prevention (CDC) WONDER database for national and population level data.Drug use data was obtained from the National Survey for Drug Use and Health (NSDUH), a nationally representative annual survey.Results: For Blacks, drug-related mortality rates between 2003 to 2018 were consistently higher in large and small metro areas than in non-metro areas; such disparities by metro status did not emerge among Whites until 2011.In 2018, opioid-related deaths continued to rise for Blacks in large and small metro areas, but declined for Blacks in nonmetro areas, and for Whites in all metro categories.In contrast to drug-related mortality trends, self-reported drug use trends did not vary greatly between 2003-2018.Despite similar mortality rates prior to 2011, metro-based differences in illicit drug use and prescription pain reliever misuse among Whites were apparent over the entire time period.Among Blacks, illicit drug use was always higher in large and small metro areas; however, there were no metro-based differences in trends in prescription pain reliever misuse.Although reported drug use rates are generally lower, Blacks in all metro categories were more likely than Whites to report being approached by someone selling drugs, and this likelihood was highest among Blacks in large and small metro areas. Conclusions:The findings demonstrate that many of the common narratives in drug use and mortality trends cannot be applied across racial and metropolitan groups.In contrast to common narratives, in recent years the opioid drug overdose epidemic is worsening for Blacks in both large and small metro areas, while it is declining for Whites.Thus, recent intervention efforts may be overlooking a particularly vulnerable subpopulation.Moreover, efforts to address drug use and its outcomes among Blacks should not be limited to large-urban areas, as patterns of drug use and mortality between small and large urban areas are consistently similar.Among rural Blacks, drug-related mortality and illicit drug use were consistently lowest, in spite of heightened risks for drug use for this population.The persistence of this trend is significant and not explained by just barriers in access to prescription opioids.Empirical research is needed to better understand why rates are escalating among more urban Blacks, while remaining low for rural Blacks and declining for Whites in all metro categories.Overall, studying drug-related mortality
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".