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Record W2316883951 · doi:10.1093/occmed/kqt105

Occupational screening of health care workers for tuberculosis infection: tuberculin skin testing or interferon-  release assays?

2013· editorial· en· W2316883951 on OpenAlexaff
Madhukar Pai, Niaz Banaei

Bibliographic record

VenueOccupational Medicine · 2013
Typeeditorial
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsTuberculinMedicineTuberculosisOccupational exposureImmunologyInterferon γOccupational medicineInterferon gammaEnvironmental healthPathology

Abstract

fetched live from OpenAlex

While incidence rates of tuberculosis (TB) have steadily declined in high-income countries, TB incidence continues to be high in many low-income countries. In general, TB disease and infection rates among health care workers (HCWs) are higher than that in the general population, although this differential is likely to be small in countries where TB incidence has now dropped to very low levels (e.g. North America) [1]. Despite the current low rates of TB in countries such as the USA and Canada, occupational health programmes continue to screen large numbers of HCWs, at recruitment and annually. In contrast, most high-TB-burden countries do not routinely screen HCWs, and TB rates in HCWs remain exceptionally high because of inadequate TB infection control [1]. Until recently, occupational health programmes had no choice but to rely on the conventional tuberculin skin test (TST) for HCW screening. The TST has variable specificity and can be affected by Bacillus Calmette–Guérin (BCG) vaccination, especially when the vaccine is given after infancy or given multiple times. It is subjective and needs to be read carefully and interpreted with risk factor data. When repeated, interpretation of boosting, conversions and reversions can pose challenges [2]. To overcome the problem of boosting, a baseline two-step TST is helpful [2]. To avoid confusing nonspecific changes with real conversions, TST conversions are defined as a change from negative to positive and an increase of 10mm induration above the baseline level (established after the two-step testing). Using these approaches, occupational programmes can maximize the specificity of TST conversions, and HCWs with conversions can be targeted for isoniazid preventive therapy, since conversions (i.e. new TB infection) increase the risk of progression to TB disease, and preventive therapy is efficacious. Occupational health programmes now have a choice of test—newer interferon-γ release assays (IGRAs) are increasingly available and gaining popularity in high-income countries. Based on a large body of evidence, summarized in several systematic reviews, it is clear that both TST and IGRA are acceptable but are imperfect tests for latent TB infection (LTBI). They represent surrogate markers of Mycobacterium tuberculosis (MTB) exposure and indicate a cellular immune response to MTB. Neither test can accurately differentiate between LTBI and active TB [3,4], but IGRAs are unaffected by BCG vaccination. In addition, both have suboptimal sensitivity in immunocompromised populations and children [5,6]. Both tests have low predictive value for progression to active TB—a vast majority of individuals with positive test results do not progress to develop active TB disease [7]. Available evidence on TST versus IGRAs among HCWs has been systematically reviewed [8,9]. Zwerling and colleagues found that in low and moderate TB incidence settings, the prevalence of a positive IGRA in HCWs was significantly lower than the prevalence of a positive TST [8]. However, in high-incidence settings, there were no consistent differences in the prevalence of positive tests. Thus, the use of IGRAs instead of TST for one-time screening (e.g. pre-employment screening) may result in a lower prevalence of positive tests and fewer HCWs who require LTBI treatment, particularly in low TB incidence settings. The challenge, however, is in understanding what happens when IGRA tests are repeated annually, to identify conversions for targeted isoniazid preventive therapy [10]. Several studies have reported very high rates of IGRA conversions and reversions [8,9,11], and early adopters of IGRAs for HCW screening in North America are now reporting major challenges in the implementation of IGRAs for serial testing [12–16]. For example, IGRA conversion rates of 5–8% have been reported in these studies, and such high conversion rates are not compatible with the current low rates of TB incidence in the USA and Canada as indicated by TST conversion rates <1% in many hospitals. These studies have also reported high rates of IGRA reversions (exceeding 50%) when HCWs with positive IGRA results are re-tested. Thus, it appears that IGRAs are inherently dynamic in a serial testing context, and when manufacturer’s dichotomous cut-offs (e.g. change from negative to positive) are used for conversions, it will result in conversion rates that are much higher than what is epidemiologically expected for a given setting. While the interpretation of IGRA results is not prone to the subjectivity that adversely affects the reading of TST induration, there is growing concern about reproducibility of IGRAs in settings where repeat testing is necessary. Studies show that IGRAs are susceptible to variability by numerous factors including assay manufacturing [17], pre-analytical processing [18], analytical testing [19] and immunomodulation [20,21]. All of the pre-analytical sources of variability are within the ranges recommended by the manufacturers. The total variability of IGRAs is the net sum of individual sources of variability, not all of which are understood completely. While systematic (predictable) sources of variability can be eliminated or minimized through standardization by the assay manufacturers and users of the test, random sources of variability are unavoidable and must be accounted for in interpretation. Therefore, further studies are needed to better define the range of total assay variability attributed to each source of variability. If the total variability is determined, appropriate borderline zones and conversion cut-offs can be derived for interpreting serial testing results, just like the cut-off for TST conversions. Until future iterations of guidelines address these issues, occupational health programmes must be cautious in interpreting serial IGRA results, and consider the magnitude of change in quantitative interferon-γ responses, not rely on simplistic cut-offs for conversions, and repeat testing when positive results do not fit the clinical presentation. It is particularly import ant to consider clinical context, risk profile of the HCW and history of exposure in making decisions about preventive therapy. Occupational health programmes must also work harder to standardize IGRA testing protocols to minimize variations in test results (Table 1). Recommendations to occupational health programmes to further standardize the QFT assay in order to minimize sources of variabilitya IGRA, interferon-γ release assay; PPD, purified protein derivative; QFT, QuantiFERON-TB Gold In Tube, Qiagen Inc., USA; TST, tuberculin skin test. aSome of the sources of variability also apply to the T-SPOT.TB assay, Oxford Immunotec, UK (phlebotomy, delay in incubation and boosting by PPD). The T-SPOT.TB assay has unique sources of variability such as counting of spots, and this will also require standardization. Recommendations to occupational health programmes to further standardize the QFT assay in order to minimize sources of variabilitya IGRA, interferon-γ release assay; PPD, purified protein derivative; QFT, QuantiFERON-TB Gold In Tube, Qiagen Inc., USA; TST, tuberculin skin test. aSome of the sources of variability also apply to the T-SPOT.TB assay, Oxford Immunotec, UK (phlebotomy, delay in incubation and boosting by PPD). The T-SPOT.TB assay has unique sources of variability such as counting of spots, and this will also require standardization. From a policy perspective, LTBI screening should be reserved only for those who are at sufficiently high risk of TB exposure, which depends on a number of factors, including TB incidence in the region, number of TB cases managed by a health care facility, adherence to TB infection control policies, risk profile of individual HCWs and the activities they perform. If low-risk HCWs in low incidence settings are routinely screened, poor predictive value of LTBI tests can pose major challenges for occupational health programmes, especially if IGRAs are used with simplistic cut-offs. Yet, millions of HCWs in high-income countries get screened for LTBI every year with huge resource implications, and a majority of HCWs tested are low-risk employees who probably should not be screened at all. Thus, regardless of whether TST or IGRAs are used for serial testing, we need to reconsider the current strategy of annual testing of HCWs in low TB incidence countries.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.033
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.057
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0050.002
Science and technology studies0.0030.005
Scholarly communication0.0080.006
Open science0.0060.002
Research integrity0.0330.031
Insufficient payload (model declined to judge)0.0050.006

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.

Opus teacher head0.079
GPT teacher head0.413
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations17
Published2013
Admission routes1
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