LB9. Rising High Rate of Invasive Group A Streptococcus Infections Among Persons Experiencing Homelessness in San Francisco, 2010–2017
Bibliographic record
Abstract
Abstract Background Rates of invasive group A Streptococcus (iGAS) disease in the United States have risen since 2014; reasons remain unclear. Outbreaks of iGAS infection among persons experiencing homelessness (PEH) and persons who inject drugs in Europe, Canada, and the United States have been described. Using active, population-based surveillance data from California’s Emerging Infections Program, we describe incidence trends and characteristics of iGAS infection among PEH and persons not experiencing homelessness (PNEH) in San Francisco (SF) County during 2010–2017. Methods We defined an iGAS case as infection with GAS isolated from a normally sterile site (e.g., blood) in an SF resident. We calculated annual iGAS disease incidence rates (cases per 100,000 population) for PEH and PNEH using denominators from SF’s Department of Homelessness and Supportive Housing and the State of California Department of Finance. Demographic, clinical, and exposure characteristics of PEH and PNEH were compared by chi-square or t-test. Results We identified 673 iGAS cases in SF during 2010–2017. Among these, 34% (229/673) were among PEH. Annual iGAS incidence among PEH rose from ~300 (2010–2014) to 547 (95% CI: 379–714) per 100,000 in 2017 (P < 0.001, Cochran-Armitage trend test); rates peaked at 758 (95% CI: 561–955) in 2016. Annual iGAS incidence in PNEH rose from a mean of 5 in 2010–2013 to 9.3 (95% CI: 7.3–11.4) per 100,000 in 2017 (P < 0.001). Annual iGAS incidence in PEH was 42–72 times that in PNEH. PEH with iGAS infections were significantly younger and more likely to be male, white, and uninsured or enrolled in Medicaid (P < 0.05 for each) compared with PNEH with iGAS disease. Case fatality ratios, ICU admission, infection type, and length of hospital stay did not differ significantly. Smoking, current injection drug use, current alcohol abuse, and AIDS diagnosis were significantly more common among PEH with iGAS. Obesity, diabetes, and cancer were significantly more common among PNEH with iGAS. Conclusion In San Francisco, iGAS rates among both PEH and PNEH have risen significantly. Incidence of iGAS is strikingly higher in PEH than in PNEH and exposures differed between PEH and PNEH with iGAS. This information could inform development of disease control and prevention strategies. Disclosures All authors: No reported disclosures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".