Enduring Challenge of Latent Tuberculosis in Older Nursing Home Residents: A Brief Review
Bibliographic record
Abstract
Tuberculosis (TB) kills almost 4,000 people a day and is competing with human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS) as the most deadly infectious disease in the world. The gold standards of detection and management of latent tuberculosis infection (LTBI) have not been successful in complete eradication of the disease. Current screening modalities of TB include tuberculin skin testing (TST) and/or interferon-γ release assay (IGRA). However, these screening tests have been heavily studied in healthy populations but not in the elderly who are more likely to have multiple risk factors for progression to active TB from LTBI. The largest population that is harboring LTBI is the elderly, specifically those residing in nursing homes. Yet, unfortunately, guidelines for standards of detection and treatment for this specific group are lacking. In this review, we look at TST versus IGRA screening for LTBI in the elderly living in nursing homes. We review a cross-sectional study done at Staten Island University Hospital, and several other assessments of the sensitivity and accuracy of both screening tools. Furthermore, this review looks at the appropriateness of current LTBI treatment and prophylaxis in elderly patients residing in close quarters. The reviews point to the superiority of IGRA testing in the elderly for screening LTBI. The IGRA has been shown to be more sensitive to the detection of LTBI than TST. Additionally, medical complexities that the elderly population possesses may present challenges and resistance to standard treatments of LTBI. It is recommended via the literature that the addition of vitamin D, or alternative therapies (e.g. rifampin) could produce better outcomes for elderly patients with LTBI than the current 9 months of isoniazid (INH). As the older adults represent the fastest growing segment of our population and the largest LTBI reservoir in the USA, revisiting screening and treatment of LTBI in the elderly living in nursing homes may prove to lead to a path of TB eradication once and for all.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".