Socioeconomic status and response to antiretroviral therapy in high-income countries
Bibliographic record
Abstract
It has been shown that socioeconomic factors are associated with the prognosis of several chronic diseases; however, there is no recent systematic review of their effect on HIV treatment outcomes. We aimed to review the evidence regarding the existence of an association of socioeconomic status with virological and immunological response to antiretroviral therapy (ART). We systematically searched the current literature using the database PubMed. We identified and summarized original research studies in high-income countries that assessed the association between socioeconomic factors (education, employment, income/financial status, housing, health insurance, and neighbourhood-level socioeconomic factors) and virological response, immunological response, and ART nonadherence among people with HIV-prescribed ART. A total of 48 studies met the inclusion criteria (26 from the United States, six Canadian, 13 European, and one Australian), of which 14, six, and 35 analysed virological, immunological, and ART nonadherence outcomes, respectively. Ten (71%), four (67%), and 23 (66%) of these studies found a significant association between lower socioeconomic status and poorer response, and none found a significant association with improved response. Several studies showed that adjustment for nonadherence attenuated the association between socioeconomic status and ART response. Our review provides strong support that socioeconomic disadvantage is associated with poorer response to ART. However, most studies have been conducted in settings such as the United States without universal free healthcare access. Further study in settings with free access to ART could help assess the impact of socioeconomic status on ART outcomes and the mechanisms by which it operates.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".