Associations between social determinants of health and comorbidity and multimorbidity in people of black ethnicities with HIV
Bibliographic record
Abstract
OBJECTIVE: Social determinants of health (SDH) are important determinants of long-term conditions and multimorbidity in the general population. The intersecting relationship between SDH and multimorbidity in people with HIV remains poorly studied. DESIGN: A cross-sectional study investigating the relationships between eight socio-economic parameters and prevalent comorbidities of clinical significance and multimorbidity in adults of African ancestry with HIV aged 18-65 years in South London, UK. METHODS: Multivariable logistic regression analysis was used to evaluate associations between SDH and comorbidities and multimorbidity. RESULTS: Between September 2020 and January 2022, 398 participants (median age 52 years, 55% women) were enrolled; 85% reported at least one SDH and 72% had at least one comorbidity. There were no associations between SDH and diabetes mellitus or kidney disease, few associations between SDH (job and food insecurity) and cardiovascular or lung disease, and multiple associations between SDH (financial, food, housing and job insecurity, low educational level, social isolation, and discrimination) and poor mental health or chronic pain. Associations between SDH and multimorbidity mirrored those for constituent comorbidities. CONCLUSION: We demonstrate strong associations between SDH and poor mental health, chronic pain and multimorbidity in people of black ethnicities living with HIV in the UK. These findings highlight the likely impact of enduring socioeconomic hardship in these communities and underlines the importance of holistic health and social care for people with HIV to address these adverse psychosocial conditions.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".