Estimation of an individual-level deprivation index in a cohort of HIV-HCV co-infected Canadians and its relationship with health outcomes
Bibliographic record
Abstract
Abstract:Background: HIV-HCV co-infected individuals are often more deprived than the general population. These factors may lead to disengagement from care. However, deprivation is difficult to measure, and often relies on aggregate data which don’t capture individual heterogeneity. We developed an individual-level deprivation index for HIV-HCV co-infected persons that encapsulated social, material, and lifestyle factors. Methods: We estimated an individual-level deprivation index with data from the Canadian Co-infection Cohort, a national prospective cohort study. We used multiple correspondence analyses and exploratory data analyses to select 9 dichotomous variables at baseline visit: income >$1500/month; education >high school; employment; identifying as gay or bisexual; Indigenous status; injection drug use in last 6 months; injection drug use ever; past incarceration, and past psychiatric hospitalization. We fitted an item response theory model with: severity parameters (how likely an item was reported), discriminatory parameters, (how well a variable distinguished index levels), and an individual parameter (the index). We considered two models: a simple one with no provincial variation and a hierarchical model by province. The Widely Applicable Information Criterion was used to compare the models. Finally, we evaluated the association of the index with non-attendance to a second clinic visit (as a measure of disengagement) using logistic regression. Results: We analyzed 1547 complete cases of 1842 enrolled participants; 457 (30%) failed to attend a second visit. The hierarchical model was found to have the best fit. Values of the index were similarly distributed across the provinces. Overall, past incarceration, education, and unemployment had the greatest discriminatory parameters. However, in each province different components of the index were associated with being deprived reflecting local epidemiology. For example, Saskatchewan had the highest severity parameter for Indigenous status while Quebec the lowest. A one-unit increase in the index was associated with 17% odds (95% credible interval, 2% to 34%) of not attending a second visit.Conclusion: We estimated an individual-level deprivation index with weights of variables varying by province. The index was associated with disengagement from care. This index and methodology may be useful in studying health and treatment outcomes that are influenced by social disparities in co-infected Canadians
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".