A new method of integrating epidemiological and health services modelling techniques for studying infectious diseases : an example using HIV/AIDS
Bibliographic record
Abstract
Since the introduction in 1996 of highly active antiretroviral therapy (HAART) for treating individuals infected with HIV, morbidity and mortality among individuals who access care in a timely fashion have dramatically decreased. As a result, patterns of health services utilization have shifted from hospitalizations and acute care services to outpatient services and medications. An additional implication of increased HAART coverage has been a reduction in HIV-1 viral load amongst treated individuals. Individuals with lower viral load are less likely to transmit HIV infection to uninfected individuals. A resulting hypothesis is that increasing treatment with HAART—either by treating individuals earlier in their infection or by expanding treatment coverage rate to a greater proportion of infected individuals—could potentially lead to reduced rates of HIV transmission. To date, the results of ecological studies and mathematical modelling studies have been consistent with this hypothesis. The objectives in this dissertation were: to build a computational tool that could be used to answer complex questions regarding the economics of HIV during the HAART era in British Columbia (BC), Canada; and to address specific economic questions relevant to the current treatment of HIV in BC. The particular questions of interest included: the monthly costs associated with various categories of health services utilization and the correlation between categories (Chapter 3); the expected long-term incidence and costs associated with hospitalizations after initiation of HAART (Chapter 4); the lifetime direct medical costs associated with an individual infected with HIV during the HAART era (Chapter 5); and the cost-effectiveness of a strategy to increase HAART coverage from 50% to 75% of clinically eligible individuals, accounting for individual-level and population-level effects of HAART (Chapter 6). These questions were addressed using a variety of statistical and mathematical techniques, based on a BC-specific population-based data source. A key finding with important public health relevance was that use of HAART was associated with decreases in other categories of direct medical costs. These cost offsets were due to both an improvement in morbidity resulting in a reduced need for acute health care services and, more importantly, a reduction in HIV incidence.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".