Understanding the Burden of Tuberculosis among American Indians/Alaska Natives in the U.S.: A Validation Study

Abstract

Objective.

We validated cases of active tuberculosis (TB) recorded in the Indian Health Service (IHS) National Patient Information Reporting System (NPIRS) and evaluated the completeness of TB case reporting from IHS facilities to state health departments.

Methods.

We reviewed the medical records of American Indian/Alaska Native (AI/AN) patients at IHS health facilities who were classified as having active TB using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnostic codes from 2006 to 2009 for clinical and laboratory evidence of TB disease. Individuals were reclassified as having active TB disease; recent latent TB infection (LTBI); past positive tuberculin skin test (TST) only; or as having no evidence of TB, LTBI, or a past positive TST. We compared validated active TB cases with corresponding state records to determine if they were reported.

Results.

The study included 596 patients with active TB as per ICD-9-CM codes. Based on chart review, 111 (18.6%) had active TB; 156 (26.2%) had LTBI; 104 (17.4%) had a past positive TST; and 221 (37.1%) had no evidence of TB disease, LTBI, or a past positive TST. Of the 111 confirmed cases of active TB, 89 (80.2%) resided in participating states; 81 of 89 (91.2%) were verified as reported TB cases.

Conclusions.

ICD-9-CM codes for active TB disease in the IHS NPIRS do not accurately reflect the burden of TB among AI/ANs. Most confirmed active TB cases in the IHS health system were reported to the state; the national TB surveillance system may accurately represent the burden of TB in the AI/AN population.

References

Centers for Disease Control and Prevention (US). Reported tuberculosis in the United States, 2011 Atlanta: CDC; 2012 Also available from: URL: http://www.cdc.gov/tb/statistics/reports/2011/pdf/report2011.pdf [cited 2013 Mar 5].

Centers for Disease Control and Prevention (US). National Notifiable Diseases Surveillance System (NNDSS): case definitions [cited 2013 Mar 5]. Available from: URL: http://wwwn.cdc.gov/nndss/script/casedefDefault.aspx

Indian Health Service (IHS). Indian Health Service: quick look [cited 2014 Jan 31]. Available from: URL: http://www.ihs.gov/newsroom/factsheets/quicklook

Indian Health Service (US). Direct/CHS inpatient data: all diseases, fiscal years 1998–2006, National Patient Information Reporting System. Albuquerque (NM): IHS; 2009.

National Center for Health Statistics, Centers for Medicare & Medicaid Services (US). International classification of diseases, ninth revision, clinical modification (ICD-9-CM) [cited 2013 Sep 14]. Available from: URL: http://www.cdc/gov/nchs/icd/icd9cm.htm

Indian Health Service (US). IHS EHR GUI deployment status [cited 2013 Apr 26]. Available from: URL: http://www.ihs.gov/ehr/index.cfm?module=gui_facilities

Trepka

Beyer

Proctor

Davis

. An evaluation of the completeness of tuberculosis case reporting using hospital billing and laboratory data: Wisconsin, 1995. Ann Epidemiol 1999; 9:419–23.

Curtis

Martin

Saag

Patkar

Kramer

Shatin

Confirmation of administrative claims-identified opportunistic infections and other serious potential adverse events associated with tumor necrosis factor α antagonists and disease-modifying antirheumatic drugs. Arthritis Rheum 2007; 57:343–6.

Schneeweiss

Robicsek

Scranton

Zuckerman

Solomon

. Veteran's Affairs hospital discharge databases coded serious bacterial infections accurately. J Clin Epidemiol 2007; 60:397–409.

10.

Calderwood

Platt

Hou

Malenfant

Haney

Kruskal

Real-time surveillance for tuberculosis using electronic health record data from an ambulatory practice in eastern Massachusetts. Public Health Rep 2010; 125:843–50.

11.

Winthrop

Baxter

Liu

McFarland

Austin

Varley

The reliability of diagnostic coding and laboratory data to identify tuberculosis and nontuberculous mycobacterial disease among rheumatoid arthritis patients using anti-tumor necrosis factor therapy. Pharmacoepidemiol Drug Saf 2011; 20:229–35.

12.

Fiske

Griffin

Mitchel

Sterling

Grijalva

. Accuracy of pharmacy and coded-diagnosis information in identifying tuberculosis in patients with rheumatoid arthritis. Pharmacoepidemiol Drug Saf 2012; 21:666–9.

13.

Barber

Lacaille

Fortin

. A systematic review of validation studies of the use of administrative data to identify serious infections. Arthritis Care Res (Hoboken) 2013; 65:1343–57.

14.

Indian Health Service (IHS). Indian Health Service glossary [cited 2013 Mar 5]. Available from: URL: http://www.ihs.gov/index_old.cfm?module=ihsGlossary

15.

SAS Institute, Inc. SAS®: Version 9.3 for Windows. Cary (NC): SAS Institute, Inc.; 2012.

16.

Redd

Susser

. Controlling tuberculosis in an urban emergency department: a rapid decision instrument for patient isolation. Am J Public Health 1997; 87:1543–7.

17.

Keck

Redd

Cheek

Layne

Groom

Kitka

Influenza surveillance using electronic health records in the American Indian and Alaska Native population. J Am Med Inform Assoc 2014; 21:132–8.

18.

Driver

Braden

Nieves

Navarro

Rullan

Valwy

Completeness of tuberculosis case reporting, San Juan and Caguas regions, Puerto Rico, 1992. Public Health Rep 1996; 111:157–61.

19.

Curtis

McCray

McKenna

Onorato

. Completeness and timeliness of tuberculosis case reporting: a multistate study. Am J Prev Med 2001; 20:108–12.

20.

Language is key in clinical documentation. Hosp Case Manag 2011; 19:84–6.

21.

Batres

. EHRs offer coders opportunities, challenges. J AHIMA 2012; 83:76–7.

22.

Taylor

. Coding in an EMR: impact on clinicians and coders. Revenue-cycle Strateg 2010; 7:1–3.

23.

Featheringham

. Lowering the language barrier: data quality manager helps coders and physicians understand each other. J AHIMA 2009; 80:86.