Variability of the QuantiFERON®-TB gold in-tube test using automated and manual methods.

BACKGROUND: The QuantiFERON®-TB Gold In-Tube test (QFT-GIT) detects Mycobacterium tuberculosis (Mtb) infection by measuring release of interferon gamma (IFN-γ) when T-cells (in heparinized whole blood) are stimulated with specific Mtb antigens. The amount of IFN-γ is determined by enzyme-linked immunosorbent assay (ELISA). Automation of the ELISA method may reduce variability. To assess the impact of ELISA automation, we compared QFT-GIT results and variability when ELISAs were performed manually and with automation. METHODS: Blood was collected into two sets of QFT-GIT tubes and processed at the same time. For each set, IFN-γ was measured in automated and manual ELISAs. Variability in interpretations and IFN-γ measurements was assessed between automated (A1 vs. A2) and manual (M1 vs. M2) ELISAs. Variability in IFN-γ measurements was also assessed on separate groups stratified by the mean of the four ELISAs. RESULTS: Subjects (N = 146) had two automated and two manual ELISAs completed. Overall, interpretations were discordant for 16 (11%) subjects. Excluding one subject with indeterminate results, 7 (4.8%) subjects had discordant automated interpretations and 10 (6.9%) subjects had discordant manual interpretations (p = 0.17). Quantitative variability was not uniform; within-subject variability was greater with higher IFN-γ measurements and with manual ELISAs. For subjects with mean TB Responses ±0.25 IU/mL of the 0.35 IU/mL cutoff, the within-subject standard deviation for two manual tests was 0.27 (CI95 = 0.22-0.37) IU/mL vs. 0.09 (CI95 = 0.07-0.12) IU/mL for two automated tests. CONCLUSION: QFT-GIT ELISA automation may reduce variability near the test cutoff. Methodological differences should be considered when interpreting and using IFN-γ release assays (IGRAs).

Within-subject interlaboratory variability of QuantiFERON-TB gold in-tube tests.

BACKGROUND: The QuantiFERON®-TB Gold In-Tube test (QFT-GIT) is a viable alternative to the tuberculin skin test (TST) for detecting Mycobacterium tuberculosis infection. However, within-subject variability may limit test utility. To assess variability, we compared results from the same subjects when QFT-GIT enzyme-linked immunosorbent assays (ELISAs) were performed in different laboratories. METHODS: Subjects were recruited at two sites and blood was tested in three labs. Two labs used the same type of automated ELISA workstation, 8-point calibration curves, and electronic data transfer. The third lab used a different automated ELISA workstation, 4-point calibration curves, and manual data entry. Variability was assessed by interpretation agreement and comparison of interferon-γ (IFN-γ) measurements. Data for subjects with discordant interpretations or discrepancies in TB Response >0.05 IU/mL were verified or corrected, and variability was reassessed using a reconciled dataset. RESULTS: Ninety-seven subjects had results from three labs. Eleven (11.3%) had discordant interpretations and 72 (74.2%) had discrepancies >0.05 IU/mL using unreconciled results. After correction of manual data entry errors for 9 subjects, and exclusion of 6 subjects due to methodological errors, 7 (7.7%) subjects were discordant. Of these, 6 (85.7%) had all TB Responses within 0.25 IU/mL of the manufacturer's recommended cutoff. Non-uniform error of measurement was observed, with greater variation in higher IFN-γ measurements. Within-subject standard deviation for TB Response was as high as 0.16 IU/mL, and limits of agreement ranged from -0.46 to 0.43 IU/mL for subjects with mean TB Response within 0.25 IU/mL of the cutoff. CONCLUSION: Greater interlaboratory variability was associated with manual data entry and higher IFN-γ measurements. Manual data entry should be avoided. Because variability in measuring TB Response may affect interpretation, especially near the cutoff, consideration should be given to developing a range of values near the cutoff to be interpreted as "borderline," rather than negative or positive.

Public health delivery systems: evidence, uncertainty, and emerging research needs.

The authors review empirical studies published between 1990 and 2007 on the topics of public health organization, financing, staffing, and service delivery. A summary is provided of what is currently known about the attributes of public health delivery systems that influence their performance and outcomes. This review also identifies unanswered questions, highlighting areas where new research is needed. Existing studies suggest that economies of scale and scope exist in the delivery of public health services, and that key organizational and governance characteristics of public health agencies may explain differences in service delivery across communities. Financial resources and staffing characteristics vary widely across public health systems and have expected associations with service delivery and outcomes. Numerous gaps and uncertainties are identified regarding the mechanisms through which organizational, financial, and workforce characteristics influence the effectiveness and efficiency of public health service delivery. This review suggests that new research is needed to evaluate the effects of ongoing changes in delivery system structure, financing, and staffing.