Tuberculosis 

Laboratory 

Aggregate Report

Fifth Edition

2019

CS309239-A
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

ii

Suggested Citation: Centers for Disease Control and Prevention. 2019. Tuberculosis Laboratory Aggregate 

Report: Fifth Edition. U.S. Department of Health and Human Services, CDC, Atlanta, Georgia.

Use of trade names and commercial sources is for identification only and does not imply endorsement by 

Centers for Disease Control and Prevention or U.S. Department of Health and Human Services.

For accessibility, a full explanation of figures can be found in Appendix A: Explanation of Figures for 

Accessibility on page 16. 
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

1

Inside This Report

LCT Contact Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Acronyms and Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Technical Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Laboratory Workload . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Trends in Nucleic Acid Amplification Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Turnaround Times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Methods in Public Health Laboratories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Journal Articles of Interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Updates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Appendix A: Explanation of Figures for Accessibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

LCT Contact Details

Stephanie Johnston, MS

sjohnston@cdc.gov

(404) 639-5019

Robert Domaoal, PhD

rdomaoal@cdc.gov

(404) 639-0868

Cortney Stafford, MPH

cstafford@cdc.gov

(404) 639-3420

Mitchell Yakrus, MS, MPH

myakrus@cdc.gov

(404) 639-1288

Monica Youngblood, MPH

myoungblood@cdc.gov

(404) 718-2079

Other Sites:

Stephanie Johnston—Houston (HOU), Los Angeles (LAX), 

San Diego (SAN), San Francisco (SFO), New York City (NYC)

Robert Domaoal—Philadelphia (PHI), District of Columbia (DC)

Monica Youngblood—Puerto Rico (PR)
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

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Introduction

The Laboratory Capacity Team (LCT) in the Laboratory Branch (LB) of the Division of Tuberculosis Elimination 

(DTBE) at the U.S. Centers for Disease Control and Prevention (CDC) provides oversight for the laboratory 

component of the CDC Tuberculosis (TB) Elimination and Laboratory Strengthening Cooperative Agreement 

(CoAg). Each year, 58 state and local public health laboratories (PHL) submit an Annual Performance Report 

(APR) with self-reported laboratory workload and turnaround time (TAT) data, testing algorithms, an organizational chart, and responses to three required TB laboratory elements of the CoAg. The data are compiled 

into a practical report for PHL to use as a tool for benchmark assessments and peer comparisons. 

As part of a robust quality assurance program, laboratories should monitor workload volume and TAT 

indicators. By assessing these indicators internally over time, laboratories can track performance progress 

and set realistic laboratory-specific goals. This report provides a resource for external comparison against 

peer data. Use of national averages and trends data from this report should be used to document your 

laboratory’s accomplishments or to provide evidence to substantiate change (e.g., methodologies, protocols, 

or need for additional resources) to improve TAT indicators. 

Please contact your LCT consultant with any questions regarding data requirements for the CDC TB CoAg or 

your laboratory’s specific data. In addition, any recommendations concerning the report and its content are 

always welcomed and appreciated.

Executive Summary

Data in this TB Laboratory Aggregate Report—Fifth Edition include a comparison of aggregate workload data 

and nucleic acid amplification test (NAAT) trends for calendar years 2015, 2016, and 2017. TAT data for 2017 

and current public health laboratory TB testing methods are also included in this report. 

CoAg PHL self-reported workload and TAT benchmark data suggest: 

• Decrease in the number of clinical specimens received and number of patients for whom a specimen 

was submitted from 2015 to 2017, but an increase of 3.9% for the number of patients positive for 

Mycobacterium tuberculosis complex (MTBC) by culture. 

• At the PHL, only 44% of patients with MTBC positive cultures had a positive NAAT performed within 

48 hours. 

• CoAg PHL utilize a variety of NAAT algorithms with the most common described as testing of new acid 

fast bacilli (AFB) smear positive specimens and when requested, those that are AFB smear negative. 

• 2017 national averages for each of the four TAT indicators remain below national targets. However, more 

laboratories met or exceeded the national target compared to 2015 for AFB smear, identification (ID), 

and drug susceptibility testing (DST), indicating improved TAT performance.

• Since 2016, 4 additional laboratories implemented IGRA testing and 9 laboratories changed the NAAT 

method performed.

LCT encourages laboratorians to review the details of each section for more in-depth assessments and 

comparisons.
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

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Acronyms and Abbreviations

AFB: Acid-fast bacilli

AP: Agar proportion

APR: Annual Performance Report for the CDC TB 

Cooperative Agreement

BACTEC MGITTM: Mycobacterium Growth Indicator 

Tube. A commercial non-radiometric broth-based 

mycobacterial culture system by Becton Dickinson 

and Co.

CoAg: CDC TB Elimination Cooperative Agreement

CDC: U.S. Centers for Disease Control and Prevention

DST: Drug susceptibility testing. Inoculation of 

bacteria in/on media containing a particular drug 

for determination of susceptibility or resistance 

based on growth.

HHS: U.S. Department of Health and Human Services 

HP 2020: Healthy People 2020 

HPLC: High Performance Liquid Chromatography. 

Analytical technique for the identification of 

mycobacteria species based on differences in cell 

wall mycolic acids.

ID: Identification

IGRA: Interferon-Gamma Release Assay. 

Whole-blood test used to measure a person’s 

immune reactivity to MTBC.

INNO-LiPA®: Commercial line probe assays by 

Fujirebio that identifies MTBC and can detect 

mutations associated with rifampin resistance.

LCT: Laboratory Capacity Team

LIMS: Laboratory Information Management System

Maldi-TOF: Matrix-assisted Laser Desorption 

Ionization Time of Flight. A mass-spectrometry 

based assay for bacterial identification based on 

time of flight of proteins and peptides. 

MTBC: Mycobacterium tuberculosis complex

MTD®: Amplified Mycobacterium tuberculosis 

Direct Test. A commercial molecular assay by 

Hologic® for direct detection of MTBC in clinical 

specimens.

MTBDRplus: Commercial line probe assay by 

Bruker (previously by HAIN LifeScience) that detects 

mutations associated with both rifampin and 

isoniazid resistance.

NAAT: Nucleic acid amplification test. In this report, 

generic terminology for molecular methods used 

for direct detection of MTBC in clinical specimens.

PHL: Public health laboratories

PRA: PCR restriction analysis. Analysis of amplified 

DNA fragments produced by the cleaving of DNA 

by restriction enzymes. 

Quantiferon®: A commercial IGRA blood test 

by QIAGEN that is used to aid in diagnosis of TB 

infection. 

TAT: Turnaround time

TB: Tuberculosis

Trek Sensititre® MYCOTB: A commercial microtiter 

plate by ThermoScientific for minimum inhibitory 

concentration (MIC) testing of 12 antituberculosis 

drugs, simultaneously.

T-SPOT®.TB: A commercial IGRA blood test by 

Oxford ImmunoTec used to aid in diagnosis of TB 

infection.

XpertTM MTB/RIF: A commercial molecular assay 

by Cepheid, Inc. for direct detection of MTBC and 

mutations associated with rifampin resistance.

Technical Notes

1. Unless otherwise specified, the source of all 

data and information for the tables and figures 

in this report originates from APR submitted 

to CDC by U.S. PHL that receive TB Elimination 

and Laboratory Strengthening CoAg funding. 

2. For Figure 3, PHL were asked to describe their 

NAAT algorithm for inclusion in the analysis.

3. For Figure 5, data for regions were based on 

defined HHS Regions with the addition of a 

City/County group. 

4. For Figures 6–10, data for test methods were 

interpreted as accurately as possible from 

project narratives of CoAg APR. 
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

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Laboratory Workload

Table 1. National Workload Data Among 58 PHL, 2015–201

Workload Variable Total Number 

2015

Total Number 

2016

Total Number 

2017

Three Year 

Change Number 

(% change)

Clinical specimensa received 207,018

(251–21,071)

210,508

(157–21,018)

201,374

(124–18,357) -5,644 (-2.7)

Patients for whom a specimen 

was submitted

92,169

(130–10,017)

93,383

(100–9,407)

86,700

(79–9,939) -5,469 (-5.9)

Patients culture positive for MTBC 3,868

(2–663)

3,826

(0–706)

4,017

(0–741) 149 (3.9)

Patients culture positive for MTBC 

that were NAAT positive

2125

(0–301)

1981

(0–295)

1957

(0–261) -168 (-7.9)

Patients tested by NAAT 

or other rapid testb

20,053

(0–5,101)

20,027

(0–5,256)

20,203

(0–5,134) 150 (0.7)

Patients NAAT positive for MTBCb 3,244

(1–485)

2,760

(0–384)

2,540

(0–307) -704 (-21.7)

Patients for whom a reference 

isolate was submittedc

15,766

(0–2,313)

15,757

(0–2,547)

16,105

(0–2,480) 339 (2.2)

Patients with a reference 

isolate identified as MTBC

3,307

(0–592)

3,263

(0–740)

3,378

(0–723) 71 (2.1)

Patients for whom DST was performed 6,006

(2–834)

5,877

(1–863)

5,672

(1–849) -334 (-5.6)

IGRA performed 91,519

(0–23,709)

107,065

(0–28,803)

108,829

(0–29,743) 17,310 (18.9)

a

 Processed and cultured, not including isolates referred from other laboratories,

b Includes sediments received only for NAAT

c

 Received to rule out or confirm the ID of MTBC

Summary of workload variable changes for 2015–2017: 

• Number of patients culture positive for MTBC increased by 3.9% over the three-year period, however, 

patients culture positive for MTBC that were NAAT positive decreased by 7.9%. 

• Patients tested by NAAT or another rapid test remained relatively unchanged from 2015 through 2017, 

increasing only 0.7%. 

• Patients for whom a DST was performed decreased each year for a total decrease of 5.6%.

• Number of IGRA performed to detect TB infection increased 18.9% from 2015 to 2017. During this time 

period, four additional laboratories began performing IGRA. Of note was the large range in volumes 

among PHL performing IGRA. 
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

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Figure 1. Distribution of Public Health Laboratory Testing Volumes Measured by Total Number of

Clinical Specimens Received, 2015 and 2017

0 

2 

4 

6 

8 

10 

12 

14 

16 

1–500 501–1,000 1,001–2,000 2,001–3,000 3,001–5,000 5,001–8,000 8,001–15,000 >15,000 

Number of PHL 

Distribution of Clinical Specimens Received 

2015 2017 

CO 

DC* 

DE 

VT 

WY 

DC* 

WY 

VT 

NH

NM

NY

OH

OR

PHI

WV

CA 

CO 

DE 

NH 

NM 

NY 

OH 

OR 

PHI 

UT 

WV 

CA

HI*

ID

IN

MO

NV

PA

RI

SAN

UT

WA

CT 

HI* 

ID 

LA 

MN 

MO 

MT 

NV 

PA 

SD 

WA 

AR 

AZ 

CT 

IA 

IL 

KY 

LA* 

ME 

MN 

MT 

ND 

NE 

NJ 

SFO 

IL 

IN 

ME 

ND 

NE 

NJ 

PR 

RI 

SFO 

SAN

AK 

HOU 

KS 

NC 

NYC 

OK 

SD 

TN 

VA 

WI

AK 

AR 

AZ 

IA 

KS 

KY 

NC 

SC 

TN 

VA 

WI 

GA 

MD 

MI 

MS 

PR 

SC 

GA 

HOU 

MD 

MI 

MS 

NYC 

OK 

AL 

LAX 

AL 

LAX 

FL 

MA 

TX 

FL 

MA 

TX 

*These jurisdictions referred all TB testing to another laboratory. 

Note: DC=Washington, D.C., PHI=Philadelphia, SAN=San Diego, SFO=San Francisco, HOU=Houston, NYC=New York City, 

LAX=Los Angeles. All others are U.S. Postal Service abbreviations.

The distribution of TB testing volumes, as measured by clinical specimens received, is variable and has 

changed from 20151 to 2017. Twenty-one laboratories changed volume ranges in the three-year time frame; 

these transitions are observed among PHL receiving less than 8,000 clinical specimens.

Public health mycobacteriology laboratories across the United States may function differently (e.g., 

diagnostic versus primarily reference) based on their populations served and the availability of mycobacteriology testing at other laboratories in their jurisdiction. Although clinical specimens typically account for 

the majority of the workload, some PHL may serve primarily as reference laboratories in which isolates and 

inoculated media are received for identification and subsequent testing2. A full picture of workload data 

requires an understanding of the nature of the work for both diagnostic and reference testing by a public 

health laboratory.
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

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Trends in Nucleic Acid Amplification Testing

Figure 2. Trends in NAAT, 2015–201

3293

1884

1476

(45%)

3297

1778 1498

(45%)

3578

1762 1558

(44%)

0

500

1000

1500

2000

2500

3000

3500

4000

N

u

m

b

er of P

atie

nts

2015 (n=52 PHL) 2016 (n=48 PHL) 2017 (n=50 PHL)

Number of patients with

MTBC positive culture 

(Workload Indicator 2a)

Number of patients with MTBC 

positive culture with a positive

NAAT (Workload Indicator 2b)

Number of patients with MTBC 

positive culture with a postive

NAAT reported within 48 hours 

(Healthy People 2020)

Note: Laboratories were excluded from the analysis if data were missing for any of the three indicators or if the number of patients 

with MTBC positive culture with a positive NAAT reported within 48 hours (Healthy People 2020) was greater than either of the other 

two workload indicators.

Summary of Trends in NAAT from 2015–2017:

• The Healthy People 2020 goal is to detect MTBC by NAAT for 77% of patients with culture-confirmed 

TB within 48 hours. In CoAg funded PHL, only 44% (2017) of patients with MTBC positive cultures had a 

positive NAAT within 48 hours. 

• The number of patients with MTBC positive culture increased, however, the number of patients with MTBC 

positive culture initially detected by NAAT decreased from 2015 to 2017.
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

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Figure 3. NAAT Algorithm, 201

1

1

4

4

2

33

13

0 5 10 15 20 25 30 35

Number of PHL

Testing per request only

New AFB smear positivepatients (routinely

only first smear positive specimen); AFB

smear negative patients on request only

New AFB smear positivepatients (automatically

testing >1 specimen per patient); AFB smear

negative patients on request only

Combination of AFB smear status & non-clinical

indicators printed on submission form

One specimen from all new patients

regardless of AFB smear status

All specimens received regardless of AFB smear status

except patientspreviously positive for MTBC

All specimens received regardless of AFB smear

status and previous positivity

PHL clarified their current NAAT algorithms based on information provided in the APR. In 2018, seven NAAT 

algorithms were used for rapid detection of MTBC among the 58 PHL funded through the TB CoAg. 

• Testing of specimens from new AFB smear-positive patients and when requested, those that are AFB 

smear-negative, was the most frequent NAAT algorithm in place; however, this algorithm was subdivided 

by the number of specimens per patient that are routinely tested. Thirty-three (57%) PHL perform NAAT 

routinely on the first smear-positive specimen per patient only, while two (3%) laboratories automatically 

perform NAAT on >1 smear-positive specimen per patient.

• Thirteen (22%) laboratories specified that NAAT was only performed when testing was requested by the 

TB program or healthcare provider. 

PHL are encouraged to assess their NAAT algorithm in conjunction with laboratory-specific data from 

workload indicators presented in Figure 2. Laboratories could examine results for patients with MTBC positive 

cultures that did not receive a NAAT or those that were not tested within 48 hours as a means of evaluating 

their algorithm. PHL should collaborate with their TB program to adjust NAAT algorithms, if necessary, to 

capture additional MTBC positive culture cases within 48 hours of specimen receipt. It should be noted that 

NAAT is not solely performed in PHL; clinical laboratories may be performing NAAT initially thereby capturing 

a portion of patients with MTBC positive cultures. In these cases, PHL might not repeat NAAT. Analysis of 

laboratory-specific data and discussions with the TB program will help to discern the number of patients this 

applies to and whether an adjustment to the NAAT algorithm would capture missed TB patients. 
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

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Turnaround Times

Table 2. TAT Indicators, 201

TAT Measurement

Specimen receipt 

within 1 day of 

collection

AFB smear 

result within 

1 day of receipt

ID of MTBC within 

21 days of receipt*

DST within 17 days 

of ID of MTBC

National Target: 

(% of specimens that should 

meet the benchmark)

67% 92% 74% 69%

Number of laboratories 

meeting or exceeding 

national target

12 33 30 26

National Average 

(reported % of specimens 

meeting the benchmark)

49% 89% 72% 57%

Number of laboratories at 

or above national average 28 35 33 32 

*Number of laboratories = 56 (2 laboratories had no MTBC identified)

Figure 4. TAT, 201

57%

81%

95%

48%

77%

77%

93%

48%

52%

72%

91%

53%

57%

72%

85%

46%

0% 20% 40% 60% 80% 100%

Average percent meeting benchmark

Specimen Receipt: % received within 1 day

Smear: % reported within 1 day

ID: % reportedwithin 21days

DST: % rifampin reportedwithin 17 days of ID
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

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In 2017, the national average for the four TAT indicators remained below the national targets. PHL improved 

TAT performance for AFB smear, ID, and DST when measured by the number of PHL that met or exceeded the 

national targets (Table 2) compared to 2015. The number of PHL in 2015 that met or exceeded the national 

targets were n=31 for AFB smear, n=25 for ID, and n=20 for DST.1 Specimen receipt remained the same for 

both years with 12 PHL who met or exceeded the national TAT target within 1 day and continues to be a 

challenge for state PHL.

Regarding the 2017 TAT data, there does not appear to be a consistent trend for PHL among any of the four 

volume ranges of clinical specimens received and the four TAT indicators.

• All PHL had similar metrics for specimen receipt within 1 day, except PHL with specimen volume of 

2,001–5,000, which received more specimens within 1 day at 53%. 

• Larger volume laboratories achieved higher average percentages meeting TAT benchmarks for AFB smear 

within 1 day and ID within 21 days.

• Laboratories receiving 5,001–8,000 clinical specimens per year had a greater percentage of DST reported 

within 17 days of ID than all other PHL. 

Testing Algorithms and Improvement of TAT

Each year in CoAg APR, recipients describe how their laboratory has improved efficiency and quality 

assurance through use of laboratory-specific data. Changes in the laboratory to improve TAT have included:

• Assessing specimen receipt TAT data by submitter for focused education/intervention 

• Improving specimen processing efficiency by reviewing staffing, laboratory hours, arrival times of 

specimens, equipment availability, use of commercial kits, contamination rates, and policies

• Reviewing specimen submission guidelines and providing annual refreshers for TB Control Program and 

submitters

• Evaluating alternative AFB smear fixation procedures

• Increasing frequency of specific tests performed each week

• Implementing new testing methods or algorithms (e.g., Maldi-TOF, XpertTM MTB/RIF, real-time PCR, 

sequencing)

• Cross-training of staff

• Implementing new LIMS and report queries

• Establishing isolate shipment policies to the National DST Reference Center3

Public Health Laboratory Barriers to Meeting TAT

PHL across the U.S. experience challenges when striving to meet recommended TAT. These include:

• Resources (staffing and funding)

• Hours of operation (closed on weekends and holidays)

• Lack of courier service

• Specimen batching from patients and submitters 

• Delayed specimen shipments 

• LIMS limitations
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

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Figure 5. TAT for Specimen Receipt by Health and Human Services (HHS) Region*, 201

99%

84%

86%

84%

86%

68%

79%

70%

82%

74%

87%

92%

71%

79%

72%

73%

54%

66%

56%

64%

54%

74%

87%

53%

57%

49%

46%

36%

44%

32%

45%

32%

56%

0% 20% 40% 60% 80% 100%

City/County

HOU, LAX, NYC, PHI, SAN, SFO

Region 10

AK, ID, OR, WA

Region 9

AZ, CA, HI, NV

Region 8

CO, MT, ND, SD, UT,WY

Region 7

IA, KS, MO, NE

Region 6

AR, LA, NM, OK, TX

Region 5

IL, IN, MI, MN, OH, WI

Region 4

AL, FL, GA, KY, MS, NC, SC, TN

Region 3

DC, DE, MD, PA, VA,WV

Region 2

NJ, NY, PR

Region 1

CT, MA, ME, NH, RI, VT

Percent of specimens receivedwithinthe time frame

Region*

1 day 2 days 3 days Range

*States organized by HHS Region with the addition of a City/County group

Rapid specimen delivery continues to be a challenge for PHL. Specimen receipt TAT, presented by HHS region 

in Figure 5, indicates there are differences based on geographical regions, however, many HHS regions have 

wide ranges in TAT suggesting variability between sites within each region. The smaller geographical size of 

cities and thus prompt courier services likely contribute to City/County PHL receiving more specimens within 

1 day. Laboratories can use Figure 5 to compare their specimen receipt TAT performance to others within 

their HHS region and to other regions that may have similar size states, populations, or geography. 
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

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Methods in Public Health Laboratories

Methods performed by PHL supported, in part, by the TB CoAg for NAAT, ID, DST, molecular testing for 

detection of drug resistance, and IGRA, are displayed in Figures 6–10. For each figure, the total number of 

PHL equals 58, with the exception of molecular testing for detection of drug resistance which has a total of 7 

PHL and IGRA with 30 PHL. As new technology emerges and laboratories adjust testing algorithms, methods 

performed will continue to evolve. 

Figure 6. NAAT Methods, 201

Cepheid Xpert

MTB/RIF, 36

H Pyrosequencing, 1 ologic MTD, 2

Referred, 4

Real-timePCR, 15

Direct detection of MTBC in clinical specimens by NAAT provides the earliest opportunity for treatment and 

intervention. In 2018, Cepheid XpertTM MTB/RIF and laboratory-developed real-time PCR assays accounted for 

88% of NAAT methods performed.

Figure 7. Primary ID Methods, 201

Sequencing, 2 PRA, 1 PCR Melting

Curve Analysis, 1

Hologic Accuprobe, 30

Cepheid Xpert

MTB/RIF, 2

Maldi-TOF, 5

Real-timePCR, 6

HPLC, 7

Fujirebio INNO-LiPA, 2

Referred, 2

Although many PHL utilize more than one ID method within their testing algorithm, the primary ID method 

described from APR is included for all 58 laboratories. 
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

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Figure 8. First-line Growth-based DST Methods, 201

BACTEC

MGIT, 38

Thermoscientific

Sensititre, 1

Indirect AP, 1

Referred—

Other Laboratory, 4

Referred—

DST Reference Center, 14

The majority of PHL continue to perform first-line DST via the BACTEC MGITTM system. Fourteen laboratories, 

performing less than 50 DST per year, have utilized the APHL/CDC National TB DST Reference Center3 for 

susceptibility testing; additionally, 4 laboratories have established individual DST referral practices with 

another laboratory.

Figure 9. Molecular Testing for Detection of Drug Resistance, 201

Bruker MTBDRplus

Line Probe Assay, 1

Whole Genome

Sequencing*, 1

Cepheid Xpert

MTB/RIF*, 2

Targeted

Sequencing, 5

*Performed on culture growth

Table 3. Sites Performing Molecular Testing for Detection of Drug Resistance by Method, 201

Method Public Health Laboratory

Targeted Sequencing CA, FL, IN, MO, NY

Cepheid XpertTM MTB/RIF IL, SAN

Whole Genome Sequencing NY

Bruker MTBDRplus Line Probe Assay FL
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

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Figure 10. IGRA Methods, 201

Qiagen QuantiFERON in

other section of PHL, 20

Qiagen QuantiFERON

in Mycobacteriology

Laboratory, 8

Oxford Immunotec

T-Spot.TB, 2

Table 4. Sites Performing IGRA by Method, 201

Method Public Health Laboratory

Oxford Immunotec T-SPOT®.TB AR, LA

Qiagen QuantiFERON® in Mycobacteriology Laboratory DE, HOU, KS, ME, MD, MT, NV, TN

Qiagen QuantiFERON® in other section of PHL CT, FL, GA, IA, LAX, MS, NE, NJ, NM, ND, OR, PA, SAN, SFO, 

SC, SD, TX, UT, VT, WY

Thirty of the 58 funded PHL reported use of IGRA testing; 4 laboratories implemented IGRA testing since 2016.

References

1. CDC. 2017. Tuberculosis Laboratory Aggregate Report: Fourth Edition. U.S. Department of Health 

and Human Services, CDC, Atlanta, Georgia.

2. Livingston KA, Lobato MN, Sosa LE, Budnick GE, Bernardo J. 2011. Mycobacterium tuberculosis 

testing practices in hospital, commercial and state laboratories in the New England states. 

Int J Tuberc Lung Dis 15:1218–22, i.

3. APHL. National Public Health Laboratory Drug Susceptibility Testing Reference Center. 

Silver Springs, MD. 2015. 
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

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Resources

APHL TB Web page https://www.aphl.org/programs/infectious_disease/tuberculosis/Pages/default.aspx

CDC TB Website http://www.cdc.gov/tb/

CDC Molecular Detection of Drug Resistance (MDDR) Service http://www.cdc.gov/tb/topic/laboratory/

default.htm

CDC Model Performance Evaluation Program http://www.cdc.gov/tb/topic/laboratory/mpep/

Guide to the Application of Genotyping to Tuberculosis Prevention and Control http://www.cdc.gov/tb/

programs/genotyping/manual.htm

TB Notes Newsletter http://www.cdc.gov/tb/publications/newsletters/default.htm

FIND TB Education and Training Resources http://www.findtbresources.org/

Journal Articles of Interest

Laboratory Practices

Theron G, Venter R, Smith L, Esmail A, Randall P, Sood V, Oelfese S, Calligaro G, Warren R, Dheda K. 2018. 

False-Positive Xpert MTB/RIF Results in Retested Patients with Previous Tuberculosis: Frequency, Profile, and 

Prospective Clinical Outcomes. J Clin Microbiol 56. 

Han LL, Elvin P, Bernardo J. 2012. Nonclinical selection criteria for maximizing yield of nucleic acid amplification tests in tuberculosis diagnosis. J Clin Microbiol 50:2592–5.

Morlock GP, Tyrrell FC, Baynham D, Escuyer VE, Green N, Kim Y, Longley-Olson PA, Parrish N, Pennington C, 

Tan D, Austin B, Posey JE. 2017. Using Reduced Inoculum Densities of Mycobacterium tuberculosis in MGIT 

Pyrazinamide Susceptibility Testing to Prevent False-Resistant Results and Improve Accuracy: A Multicenter 

Evaluation. Tuberc Res Treat 2017:3748163. 

Houben R, Lalli M, Kranzer K, Menzies NA, Schumacher SG, Dowdy DW. 2019. What if They Don’t Have Tuberculosis? The Consequences and Trade-offs Involved in False-positive Diagnoses of Tuberculosis. Clin Infect Dis

68:150–156.

Forbes BA, Hall GS, Miller MB, Novak SM, Rowlinson MC, Salfinger M, Somoskovi A, Warshauer DM, Wilson ML. 

2018. Practice Guidelines for Clinical Microbiology Laboratories: Mycobacteria. Clin Microbiol Rev 31.

Drug Resistance

Technical Report on critical concentrations for drug susceptibility testing of medicines used in the treatment 

of drug-resistant tuberculosis. Geneva: World Health Organization; 2018 (WHO/CDS/TB/2018.5). License: CC 

BY-NC-SA 3.0 IGO. 

Georghiou SB, Seifert M, Catanzaro DG, Garfein RS, Rodwell TC. 2017. Increased Tuberculosis Patient Mortality 

Associated with Mycobacterium tuberculosis Mutations Conferring Resistance to Second-Line Antituberculous Drugs. J Clin Microbiol 55:1928–1937. 

Miotto P, Cabibbe AM, Borroni E, Degano M, Cirillo DM. 2018. Role of Disputed Mutations in the rpoB Gene 

in Interpretation of Automated Liquid MGIT Culture Results for Rifampin Susceptibility Testing of Mycobacterium tuberculosis. J Clin Microbiol 56. 

Shah NS, Grace Lin SY, Barry PM, Cheng YN, Schecter G, Desmond E. 2016. Clinical Impact on Tuberculosis 

Treatment Outcomes of Discordance Between Molecular and Growth-Based Assays for Rifampin Resistance, 

California 2003–2013. Open Forum Infect Dis 3:ofw150. 
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

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Next Generation Sequencing

Shea J, Halse TA, Lapierre P, Shudt M, Kohlerschmidt D, Van Roey P, Limberger R, Taylor J, Escuyer V, Musser 

KA. 2017. Comprehensive Whole-Genome Sequencing and Reporting of Drug Resistance Profiles on Clinical 

Cases of Mycobacterium tuberculosis in New York State. J Clin Microbiol 55:1871–1882. 

Doyle RM, Burgess C, Williams R, Gorton R, Booth H, Brown J, Bryant JM, Chan J, Creer D, Holdstock J, Kunst H, 

Lozewicz S, Platt G, Romero EY, Speight G, Tiberi S, Abubakar I, Lipman M, McHugh TD, Breuer J. 2018. Direct 

Whole-Genome Sequencing of Sputum Accurately Identifies Drug-Resistant Mycobacterium tuberculosis 

Faster than MGIT Culture Sequencing. J Clin Microbiol 56. 

Quan TP, Bawa Z, Foster D, Walker T, Del Ojo Elias C, Rathod P, Iqbal Z, Bradley P, Mowbray J, Walker AS, Crook 

DW, Wyllie DH, Peto TEA, Smith EG. 2018. Evaluation of Whole-Genome Sequencing for Mycobacterial Species 

Identification and Drug Susceptibility Testing in a Clinical Setting: a Large-Scale Prospective Assessment of 

Performance against Line Probe Assays and Phenotyping. J Clin Microbiol 56. 

Lee RS, Pai M. 2017. Real-Time Sequencing of Mycobacterium tuberculosis: Are We There Yet? J Clin Microbiol 

55:1249–1254. 

Votintseva AA, Bradley P, Pankhurst L, Del Ojo Elias C, Loose M, Nilgiriwala K, Chatterjee A, Smith EG, 

Sanderson N, Walker TM, Morgan MR, Wyllie DH, Walker AS, Peto TEA, Crook DW, Iqbal Z. 2017. Same-Day 

Diagnostic and Surveillance Data for Tuberculosis via Whole-Genome Sequencing of Direct Respiratory 

Samples. J Clin Microbiol 55:1285–1298. 

Interferon Gamma Release Assay

Moon HW, Gaur RL, Tien SS, Spangler M, Pai M, Banaei N. 2017. Evaluation of QuantiFERON-TB Gold-Plus in 

Health Care Workers in a Low-Incidence Setting. J Clin Microbiol 55:1650–1657.

Theel ES, Hilgart H, Breen-Lyles M, McCoy K, Flury R, Breeher LE, Wilson J, Sia IG, Whitaker JA, Clain J, Aksamit 

TR, Escalante P. 2018. Comparison of the QuantiFERON-TB Gold Plus and QuantiFERON-TB Gold In-Tube 

Interferon Gamma Release Assays in Patients at Risk for Tuberculosis and in Health Care Workers. J Clin 

Microbiol 56. 

Updates

Clinical and Laboratory Standards Institute (CLSI). 2018. Laboratory Detection and Identification of Mycobacteria, 2nd ed. CLSI guideline M48. Wayne, PA.

Clinical and Laboratory Standards Institute (CLSI). 2018. Susceptibility Testing of Mycobacteria, Nocardia spp., 

and Other Aerobic Actinomycetes; 3rd ed. CLSI standard M24. Wayne, PA. 

Clinical and Laboratory Standards Institute (CLSI). 2018. Performance Standards for Susceptibility Testing of 

Mycobacteria, Nocardia spp., and Other Aerobic Actinomycetes. CLSI supplement M62. Wayne, PA.

Sosa LE, Njie GJ, Lobato MN, et al. Tuberculosis Screening, Testing, and Treatment of U.S. Health Care 

Personnel: Recommendations from the National Tuberculosis Controllers Association and CDC, 2019. MMWR 

Morb Mortal Wkly Rep 2019;68:439–443. 
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Appendix A: Explanation of Figures for Accessibility

LCT Contact Details Map of the U.S. divided by LCT consultant. Each consultant is assigned a color:

• Stephanie Johnston, MS (Yellow)—Alaska, Arizona, California, Hawaii, Nevada, New Mexico, Oregon, 

Texas, Washington

• Robert Domaoal, PhD (Orange)—Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, 

New York, North Carolina, Pennsylvania, Rhode Island, Vermont, Virginia

• Cortney Stafford, MPH (Green)—Alabama, Arkansas, Florida, Georgia, Illinois, Indiana, Kentucky, Louisiana, 

Michigan, Mississippi, Ohio, South Carolina, Tennessee, West Virginia, Wisconsin

• Monica Youngblood, MPH (Blue)—Colorado, Idaho, Iowa, Kansas, Minnesota, Missouri, Montana, Nebraska, 

New Jersey, North Dakota, Oklahoma, Puerto Rico, South Dakota, Utah, Wyoming ◄ back

Figure 1. The distribution of public health laboratory testing volumes measured by total number of clinical 

specimens received in 2015 and 2017 is displayed in a vertical bar graph. The vertical y-axis is the number of 

PHL ranging from 0 to 16, by increments of 2. Along the horizontal x-axis are sixteen vertical bars (eight for 

2015 and eight for 2017) representing the number of clinical specimens received. From left to right: 

5 laboratories in 2015 and 3 laboratories in 2017 received between 1 and 500 clinical specimens; 

7 laboratories in 2015 and 11 laboratories in 2017 received between 501 and 1,000 clinical specimens; 

11 laboratories in 2015 and 11 laboratories in 2017 received between 1,001 and 2,000 clinical specimens; 

14 laboratories in 2015 and 10 laboratories in 2017 received between 2,001 and 3,000 clinical specimens; 

10 laboratories in 2015 and 11 laboratories in 2017 received between 3,001 and 5,000 clinical specimens; 

6 laboratories in 2015 and 7 laboratories in 2017 received between 5,001 and 8,000 clinical specimens; 

2 laboratories in 2015 and 2 laboratories in 2017 received between 8,001 and 15,000 clinical specimens; 

3 laboratories in 2015 and 3 laboratories in 2017 received greater than 15,000 clinical specimens. ◄back

Figure 2. Trends in NAAT for 2015–2017 are presented in a vertical bar graph. The vertical y-axis is the 

number of patients ranging from 0 to 4,000, by increments of 500. Along the horizontal x-axis are nine 

vertical bars (three each for 2015, 2016, and 2017), clustered for the following workload indicators: 

Number of patients with MTBC positive culture (workload indicator 2a), Number of patients with MTBC 

positive culture with a positive NAAT (workload indicator 2b), and Number of patients with MTBC positive 

culture with a positive NAAT reported within 48 hours (Healthy People 2020). ◄ back

Figure 3. The NAAT algorithms used by PHL in 2018 are displayed in a horizontal bar graph. The vertical 

y-axis contains a list of NAAT algorithms and the horizontal x-axis contains the number of PHL ranging 

from 0 to 35, by increments of 5. There are 7 horizontal bars with each bar representing the number of 

laboratories using a particular NAAT algorithm. ‘New smear positive patients (routinely only first smear 

positive specimen); smear negative patients on request only’ is the most commonly used algorithm with 

33 PHL. ‘Testing by request only’ is the second most commonly used algorithm with 13 PHL. ◄ back

Figure 4. The 2017 turnaround time data stratified by four ranges of clinical specimens received is 

displayed in a horizontal bar graph. The vertical y-axis contains a list of the four turnaround time indicators 

and the horizontal x-axis contains the percent of specimens meeting the benchmark with ranges from 

0% to 100%, by increments of 20%. Along the vertical y-axis are sixteen horizontal bars (four for specimen 

receipt, four for smear, four for ID, and four for DST). For specimen receipt within 1 day: PHL receiving ≤2,000 

clinical specimen achieved 46%, PHL receiving between 2,001 and 5,000 achieved 53%, PHL receiving 

between 5,001 and 8,000 achieved 48%, and PHL receiving ≥8,001 achieved 48%. For smear within 1 day: 

PHL receiving ≤2,000 clinical specimen achieved 85%, PHL receiving between 2,001 and 5,000 achieved 91%, 

PHL receiving between 5,001 and 8,000 achieved 93%, and PHL receiving ≥8,001 achieved 95%. For ID within 

21 days: PHL receiving ≤2,000 clinical specimen achieved 72%, PHL receiving between 2,001 and 5,000 
2019 Tuberculosis Laboratory Aggregate Report—Fifth Edition

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achieved 72%, PHL receiving between 5,001 and 8,000 achieved 77%, and PHL receiving ≥8,001 achieved 

81%. For DST within 17 days of ID: PHL receiving ≤2,000 clinical specimen achieved 57%, PHL receiving 

between 2,001 and 5,000 achieved 52%, PHL receiving between 5,001 and 8,000 achieved 77%, and PHL 

receiving ≥8,001 achieved 57%. ◄ back

Figure 5. The 2017 specimen receipt turnaround time comparisons by HHS Region are displayed in a 

horizontal bar graph. The vertical y-axis contains a list of each region and the horizontal x-axis contains the 

percent of specimens received ranging from 0% to 100%, by increments of 20%. Each of the eleven regions 

have three horizontal bars representing the average percent of specimens received within 1 day, 2 days, and 

3 days and each bar includes a small thin line representing the range. ◄ back

Figure 6. The NAAT methods used by PHL in 2018 are presented in a pie chart. The largest slice represents 

the 36 PHL that perform Cepheid Xpert MTB/RIF. The second largest slice represents the 15 PHL that perform 

real-time PCR testing. The next three slices represent 4 PHL that refer testing, 2 PHL that perform Hologic 

MTD, and 1 public health laboratory that performs pyrosequencing. ◄ back

Figure 7. The primary ID methods used by PHL in 2018 are presented in a pie chart. The largest slice 

represents the 30 PHL that perform Hologic Accuprobe. The following nine slices represent: 7 PHL that 

perform HPLC, 6 PHL that perform real-time PCR, 5 PHL that perform Maldi-TOF, 2 PHL that perform Cepheid 

Xpert MTB/RIF assay, 2 PHL that perform Fujirebio INNP-LiPA, 2 PHL that refer testing, 2 PHL that perform 

sequencing, 1 public health laboratory that performs PRA, and 1 public health laboratory that performs PCR 

melting curve analysis. ◄ back

Figure 8. The first-line growth-based DST methods used by PHL in 2018 are presented in a pie chart. The 

largest slice represents the 38 PHL that perform DST using BACTEC MGIT. The next four slices represent 

14 PHL that refer testing to the DST Reference Center, 4 PHL that refer testing to another PHL, 1 public 

health laboratory that performs indirect agar proportion, and 1 public health laboratory that performs 

Thermoscientific Sensititre. ◄ back

Figure 9. The molecular testing for detection of drug resistance used by PHL in 2018 are presented in a pie 

chart. The largest slice represents the 5 PHL that perform targeted DNA sequencing. The next three slices 

represent 2 PHL that use the Cepheid Xpert MTB/RIF assay, 1 public health laboratory that performs whole 

genome sequencing, and 1 public health laboratory that performs Bruker MTBDRplus line probe assay. ◄ back

Figure 10. The IGRA methods used by PHL in 2018 are presented in a pie chart. The largest slice represents 

the 20 PHL that perform Qiagen QuantiFERON in another section of the PHL. The other two slices represent 

8 PHL that perform Qiagen QuantiFERON in the mycobacteriology section of the PHL and 2 PHL that use 

Oxford Immunotec T-SPOT.TB. ◄ back