The Contribution of Socioeconomic Factors to HIV RNA Suppression in Persons With HIV Engaged in Care in the NA-ACCORD
Bibliographic record
Abstract
INTRODUCTION: Socioeconomic status (SES) influences well-being among people living with HIV (people with HIV [PWH]); when individual-level SES information is not available, area-level SES indicators may be a suitable alternative. We hypothesized that (1) select ZIP code-level SES indicators would be associated with viral suppression and (2) accounting for ZIP code-level SES would attenuate racial disparities in viral suppression among PWH. SETTING: The NA-ACCORD, a collaboration of clinical and interval cohorts of PWH, was used. METHODS: Participants with ≥1 viral load measurement and ≥1 US residential 5-digit ZIP code(s) between 2010 and 2018 were included. In this serial cross-sectional analysis, multivariable logistic regression models were used to quantify the annual association of race and ethnicity with viral suppression, in the presence of SES indicators and sex, hepatitis C status, and age. RESULTS: We observed a dose-response relationship between SES factors and viral suppression. Lower income and education were associated with 0.5-0.7-fold annual decreases in odds of viral suppression. We observed racial disparities of approximately 40% decreased odds of viral suppression among non-Hispanic Black compared with non-Hispanic White participants. The disparity persisted but narrowed by 3%-4% when including SES in the models. CONCLUSIONS: ZIP code-based SES was associated with viral suppression, and accounting for SES narrowed racial disparities in viral suppression among PWH in the NA-ACCORD. Inclusion of ZIP code-level indicators of SES as surrogates for individual-level SES should be considered to improve our understanding of the impact of social determinants of health and racial disparities on key outcomes among PWH in North America.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".