Economic impact on direct healthcare costs of missing opportunities for diagnosing HIV within healthcare settings
Bibliographic record
Abstract
BACKGROUND: The economic consequences of a missed opportunity for HIV testing at an earlier stage of infection within a healthcare setting are poorly described. METHODS: For all newly diagnosed HIV patients followed at the Southern Alberta HIV/AIDS Clinic (SAC), Calgary, Canada, between 1 April 2011 and 1 April 2016, all clinical encounters occurring < 3 years prior to diagnosis within the region were obtained. The direct costs of HIV care after diagnosis to 31 March 2019 were determined from a payers' perspective and reported as mean cost per patient per month (PPPM) in 2019 Canadian dollars (CDN$). Patients with no encounters for 3 years prior to diagnosis were compared with patients with encounters, with special attention to patients with HIV clinical indicator conditions (HCICs). RESULTS: Of 388 patients, 60% had one or more prior encounter without HIV testing; 14% had been treated for an HCIC. Females, older patients and heterosexuals were more likely to have prior encounters. At diagnosis, patients with previous encounters presented with lower CD4 counts and higher rates of AIDS. The mean PPPM costs for patients with any prior encounter or for an HCIC-based encounter were 16% and 33% higher, respectively, than for patients with no prior encounters. While mean PPPM costs for antiretroviral drugs and outpatient visits were slightly higher, in-patient costs were 10 times higher for people with HIV who had a previous HCIC encounter vs. those with no encounters (CDN$316 vs. $31, respectively). CONCLUSIONS: Any healthcare visit, especially for an HCIC, represents relatively easy opportunities for HIV testing. Not testing can result in poorer health and higher costs. Targeted clinical testing and novel interventions to correct overlooked testing opportunities within healthcare settings may be an easy way to implement cost savings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".