Impact of Neighborhood-Level Socioeconomic Status on HIV Disease Progression in a Universal Health Care Setting
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to examine neighborhood measures of socioeconomic status and their effect on the risk of mortality among HIV-positive persons accessing and not accessing treatment, the effects of late access to treatment by CD4 cell count, and survival among those who accessed treatment. METHODS: We limited our analysis to the era of highly active antiretroviral therapy (HAART). We used individual-level patient and clinical characteristics and neighborhood-level socioeconomic data to address our objectives. The Pearson chi2 and Wilcoxon sign rank tests were used to compare mortality among HIV-positive persons accessing and not accessing treatment, logistic regression models were used to compare persons who accessed treatment with low CD4 cell counts (<50 cells/mm(3)) with those who accessed treatment earlier (CD4 count > or =50 cells/mm(3)), and Weibull survival models were used to compare mortality among those who accessed treatment. RESULTS: Forty percent of people who died from HIV/AIDS-related causes never accessed treatment. Among those who accessed treatment, 16% did so when their CD4 counts were <50 cells/mm(3). Unemployment was associated with delayed access to treatment (odds ratio = 1.41, 95% confidence interval [CI]: 1.14 to 1.74). Postsecondary education (hazard ratio [HR] = 0.80, 95% CI: 0.71 to 0.91) and percent of residents below the poverty line (HR = 1.07, 95% CI: 1.01 to 1.13) were associated with mortality. CONCLUSIONS: In a setting where treatment for HIV is free of charge, a significant number of HIV-positive persons did not access HAART. Low socioeconomic status was associated with this delay and with increased mortality among persons receiving HAART. Social and health policy initiatives, beyond free and universal health care, are required to optimize access to HAART.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".