Is Discrimination Related to Markers of Systemic Inflammation? A Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Discrimination, or unfair treatment of individuals based on social group membership, is a major public health concern. To understand health inequities, it is critical to examine the physiological mechanisms-such as systemic inflammation-through which discrimination impacts health. However, estimations of the discrimination-inflammation association vary widely across studies, and it is unclear if the magnitude of the association varies as a function of methodological and sample characteristics. METHODS: We conducted a systematic review and meta-analysis of the association between discrimination and inflammation in 47 articles that yielded 161 effects. A series of meta-regressions were conducted using random effects models to estimate the overall effect size and effect sizes among subgroups of different combinations of discrimination measures and inflammatory markers. RESULTS: Results revealed a significant, positive overall association, such that greater discrimination was associated with higher levels of systemic inflammation among ∼74,763 participants ( r = 0.087, p < .001). Subgroup analyses showed that the magnitude of the association varied by the type of discrimination measured, the specific inflammatory marker, and methodological features. Discrimination was significantly associated with CRP and IL-6. There was a significant, positive association between discrimination and inflammation in studies that measured racial/ethnic discrimination specifically. Statistical power is also a significant contributor to our ability to estimate effects between discrimination and inflammation. CONCLUSIONS: Overall, the current literature provides evidence that greater discrimination is associated with higher levels of inflammation. We need greater theoretical and methodological precision to advance our understanding of the mechanistic pathways by which discrimination gets under the skin.
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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 | 0.031 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".