69. Epidemiology of Nontuberculous Mycobacterial Infections in the U.S. Veterans Health Administration
Session: Oral Abstract Session: Advances in Epidemiology and Diagnosis of Mycobacterial Diseases
Thursday, October 27, 2016: 9:15 AM
Room: 275-277

Background: We identified patients with non-tuberculous mycobacterial (NTM) disease in the US Veterans Health Administration (VHA), examined the distribution of diseases by NTM species, and explored the association between NTM disease and the frequency of clinic visits and mortality.

Methods: We used natural language processing to identify mycobacterial isolates from VHA data between 2008 and 2012 and used algorithms to apply modified ATS/IDSA guidelines for NTM diagnosis. We performed validation against a reference standard of chart review. Incidence rates were calculated. Two nested case-control studies (matched by age and location) were used to measure the association between NTM disease and each of 1) the frequency of outpatient clinic visits and 2) mortality, adjusted by chronic obstructive pulmonary disease (COPD), other structural lung diseases, and immunomodulatory factors.

Results: NTM cases were identified with a sensitivity of 94%, a specificity of >99%. The incidence of NTM was 12.6/100k patient-years (Figure 1 breaks incidence down by region and group; Figure 3 by first 3 digits of zipcode and also shows spatial autocorrelation). COPD was present in 68% of pulmonary NTM. NTM incidence was highest in the southeastern US. Extra-pulmonary NTM rates increased during the study period (Figure 2 shows incidence over time). The incidence rate ratio of clinic visits in the first year after diagnosis was 1.3 [95%CI 1.34-1.35]. NTM patients had a hazard ratio of mortality of 1.4 [95%CI 1.1-1.9] in the 6 months after NTM identification compared to controls and 1.99 [95%CI 1.8-2.3] thereafter.

Conclusion:  In VHA, pulmonary NTM disease is commonly associated with COPD, with the highest rates in the southeastern US. After adjustment, NTM patients had more clinic visits and greater mortality compared to matched patients.

Figure 1.

Figure 2.

Figure 3.

Makoto Jones, MD, MS1, Kevin L. Winthrop, MD, MPH2, Scott D. Nelson, PharmD, MS3,4, Scott Duvall, PhD5,6, Olga Patterson, PhD5,6, Kevin Nechodom, BS7, Kimberly Findley, RN8, Lewis Radonovich, MD9, Matthew Samore, MD, FSHEA10 and Kevin Fennelly, MD, MPH11,12, (1)Internal Medicine, VA Salt Lake City Health Care System, Salt Lake City, UT, (2)Oregon Health & Science University, Portland, OR, (3)Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, (4)College of Pharmacy, University of Utah, Salt Lake City, UT, (5)VA Salt Lake City Health Care System, Salt Lake City, UT, (6)University of Utah, Salt Lake City, UT, (7)Ideas Center, VA Salt Lake City Health Care System, Salt Lake City, UT, (8)National Center for Occupational Health and Infection Control, Patient Care Services (Public Health), Veterans Health Administration, Gainesville, FL, (9)Department of Veterans Affairs Veterans Health Administration Office of Public Health, Gainesville, FL, (10)University of Utah School of Medicine, Division of Epidemiology, Salt Lake City, UT, (11)National Center for Occupational Health and Infection Control, Formerly with Veterans Health Administration, Gainesville, FL, (12)National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD


M. Jones, None

K. L. Winthrop, Pfizer: Grant Investigator , Research grant
Bristol Myers Squibb: Grant Investigator , Research grant
Pfizer: Consultant , Consulting fee
Bristol Myers Squibb: Consultant , Consulting fee
Lilly: Consultant , Consulting fee
Amgen: Consultant , Consulting fee
Abbvie: Consultant , Consulting fee
UCB: Consultant , Consulting fee

S. D. Nelson, None

S. Duvall, None

O. Patterson, None

K. Nechodom, None

K. Findley, None

L. Radonovich, None

M. Samore, None

K. Fennelly, None

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