Introduction

This concept provides a working definition of tuberculosis (TB) and describes the methods used at MCHP to investigate TB in the administrative data held in the Manitoba Population Research Data Repository. The concept lists the ICD-9-CM and ICD-10-CA diagnostic codes to identify TB in the hospital abstracts data and the medical services data, and lists the codes that are used in laboratory tests to investigate its presence in the population. SAS code (internal access only) is also available for working with the laboratory data.

An April 2023 update to this concept documents a 2018 project that used the Manitoba Tuberculosis (TB) Registry to investigate the identification of tuberculosis and latent tuberculosis infection (LTBI), as well the validation of TB diagnoses with the TB Registry and other administrative data sources (e.g. hospital abstracts data). Please note, the TB Registry was acquired for this project only and is not part of the Manitoba Population Research Data Repository.

Definition of Tuberculosis

Tuberculosis (TB) is a disease that is acquired through an infection from a Mycobacterium, specifically Mycobacterium tuberculosis, Mycobacterium africanum, Mycobacterium canetti, and Mycobacterium bovis (Lix et al., 2012).

TB is highly contagious: it is spread through the air by individuals with infected lungs or throats when they cough, sneeze, or talk. An individual with a spreading TB disease will become sick; and if left untreated, the individual may die (Martens et al. 2010). Because of its significant public health implications, TB is a notifiable disease in Canada since 1924. All clinical and laboratory-confirmed cases are reportable to the Canadian Tuberculosis Reporting System (CTBRS). For more information on TB, see the Public Health Agency of Canada Website - Tuberculosis.

MCHP Tuberculosis Algorithms

The following methods have been used at MCHP to identify testing for tuberculosis and individuals diagnosed with tuberculosis.

1. Martens et al. (2010) and Smith et al. (2013)

In Martens et al. (2010) and Smith et al. (2013), the average annual hospital episode rates for TB per 100,000 residents for all ages were calculated. ICD-9-CM codes 011-018 and ICD-10-CA codes A15-A19 were used to identify TB cases recorded in the Hospital Abstracts data. All diagnosis fields were included. Only those who had a diagnosis of TB were counted for this indicator. Individuals with a code of "primary tuberculosis infection" (ICD-9-CM code 010.xx - skin test for TB) were excluded.

In Martens et al., (2010) , hospital abstracts were reviewed from 1984 to 2008 and divided in five time periods. From the first time period (1984/85 - 1988/89) to the last time period (2004/05 - 2007/08), the average annual hospitalization for TB decreased from 16.67 per 100,000 residents to 12.81 per 100,000 residents.

The rates of hospitalization due to TB by income quintiles were calculated for rural and urban residents. The disparity rate ratios (DRRs) were similar across time for the urban neighborhood income quintiles, but increased significantly over time in the rural neighborhood income quintiles. The socioeconomic gap in rates of hospitalization due to TB was very high in both rural and urban neighborhood income quintiles across time, however, the gap appeared to be widening over time for rural Manitoba. For more information, read the section Hospitalization due to Tuberculosis in Martens et al., (2010) and the section Hospitalizations due to Tuberculosis in Smith et al. (2013) .

2. Lix et al. (2012)

In Lix et al. (2012), the Cadham Provincial Laboratory (CPL) data from 1992/93 to 2000/01 was used to identify the total number of TB tests, the number of individuals with at least one TB test, and the frequency of positive TB tests (laboratory-confirmed cases) by fiscal year in Manitoba. Data from the CPL Clinical Microbiology Section - Results file was used to identify the number of TB tests using the acid-fast bacillus (AFB) smear. For more information on AFB, see Lab Tests Online Website - AFB Smear and Culture.

Table 1 identifies the codes used in the Clinical Microbiology Section - Results data to report the results of the AFB smear. NOTE: Results from the smear do not indicate a positive test for TB. Only the presence of Mycobacterium tuberculosis and Mycobacterium bovis in the CPL Clinical Microbiology Section - Organism data indicate a positive test for TB. Table 2 identifies the codes for these two Mycobacterium organisms. For more information on CPL, see the CADHAM Provincial Laboratory (CPL) - Overview of Services and Data concept.

Table 1: Tuberculosis: Clinical Microbiology Section - Results

NOTE: All records are RECTYPE=8



AUXCD5	DESCR70
AFB	ACID FAST BACILLI DETECTED ON CULTURE
AFBC	ACID FAST BACILLI ON CULTURE. SEE PREVIOUS REPORT
AFBCF	A.F.B. NOT SEEN ON SPECIMEN(S) RECEIVED. CULTURE REPORT TO FOLLOW
AFBNS	ACID FAST BACILLI NOT SEEN
AFG	ACID FAST GROWTH DETECTED RADIOMETRICALLY
AFIF	ACID FAST BACILLI ISOLATED
AFL	ACID FAST-LIKE ORGANISM ISOLATED
AFNI	ACID FAST BACILLI NOT ISOLATED
AFS	ACID FAST-LIKE ORGANISMS SEEN
FAFB	FEW ACID FAST BACILLI SEEN
FCFB	FEW ACID FAST BACILLI SEEN
MAFB	MANY ACID FAST BACILLI SEEN
MCC	MYCOBACTERIAL CULTURE IS CONTAMINATED, PLEASE REPEAT
NTMI	NON TUBERCULOUS MYCOBACTERIA
ODR	ORGANISMS DETECTED RADIOMETRICALLY ARE NOT ACID FAST BACILLI
OGNT	ORANGE GROWTH. NOT LIKELY M. TUBERCULOSIS
ONT	ORANGE GROWTH. NOT LIKELY M. TUBERCULOSIS
SAFB	SCREEN FOR AFB NEGATIVE, CULTURE REPORT TO FOLLOW
SCOMB	10 ML. REQ. FOR TB INVEST. SPECIMENS FROM THIS PATIENT WERE COMBINED

Table 2: Tuberculosis: Clinical Microbiology Section - Organisms

NOTE: All records are RECTYPE=9



AUXCD5	DESCR40
MYC4	MYCOBACTERIUM BOVIS
MYC20	MYCOBACTERIUM TUBERCULOSIS

Comparison of Lab Data to Hospital Discharge Abstracts and Medical Services Data

TB diagnoses from the Hospital Abstracts data and the Medical Services data were compared to the CPL data. ICD-9-CM codes 010-018 and ICD-10-CA codes A15-A19 were used to identify the diagnoses of TB. The frequency of individuals with a TB diagnosis in hospital abstracts and medical services data was substantially higher than in the CPL data. The agreement between the two data sources was examined and indicated a moderate concordance. For more information on how TB was investigated in the CPL data, read the section titled Case Study #2 : Tuberculosis Tests in CPL Data and Tuberculosis Diagnoses in Hospital and Physician Billing Records in Lix et al., (2012) .

3. Lix et al. (2018)

In Lix et al. (2018), they investigated the identification of Tuberculosis (TB) and Latent Tuberculosis Infection (LTBI) in Manitoba using administrative data. They also investigated the validation of diagnoses for active TB using administrative data, comparing data in the Manitoba Tuberculosis (TB) Registry with hospital discharge abstract data and physician billing claims (Medical Services) data.

3.1. Identifying Tuberculosis (TB) in Manitoba

The following sections describe how the active TB cohort was developed, and provides links to discussion and research findings from the Deliverable.

Developing the Active TB Cohort

To create an active TB cohort, information from the Manitoba Tuberculosis (TB) Registry was used to define the case criteria for TB diagnosis. NOTE: the Manitoba Tuberculosis (TB) Registry data was acquired for specific use in this project and is not currently available for use as part of the Manitoba Population Research Data Repository.

The Manitoba TB Registry, managed and maintained by Manitoba Health (MH), contains information about all cases of active TB in the province. The collection of information is required under legislation because TB is a notifiable communicable disease under Schedule B of the Public Health Act. The Manitoba TB Registry captures detailed information about active TB cases, including demographic and geographic characteristics, contact assessments, bacteriology and x-ray results, course and outcome of treatment, selected measures of healthcare use, and identified drug sensitivities. The population of origin: First Nations on-reserve, First Nations off-reserve, Canadian-born non-First Nations, and foreign-born (i.e., individuals not born in Canada) is also collected. The years of data available in the Manitoba TB Registry provided by MH to MCHP extend from 1993 to 2014. Appendix 1: Contents of the Manitoba Health (MH) Tuberculosis Registry provides an overview of the information captured in the Manitoba TB Registry.

The TB Registry was used to determine new TB diagnoses or subsequent (i.e., recurrent) diagnoses, and to classify individuals as pulmonary (i.e., infectious) or non-pulmonary TB cases (note: there were no individuals classified as both pulmonary and non-pulmonary; a few cases did, however, have missing information as to whether they were pulmonary or non-pulmonary cases). Non-pulmonary TB involves organs other than the lungs, such as the lymph nodes, abdomen, skin, joints, or bones.

Individuals were excluded from the active TB cohort if they:

• had an invalid or missing personal health identification number (PHIN) in the Manitoba TB Registry; and
  
• did not have a minimum of 365 days of healthcare coverage before the index date and 30 days of coverage after the index date. Individuals could only enter the cohort once, based on their first study index date in the Manitoba TB Registry. The diagnosis date in the Manitoba TB Registry was the study index date. If an individual did not have a diagnosis date in the Manitoba TB Registry, then the TB Registry entry date was used as a proxy for the diagnosis date.

For more detailed information on how the active TB cohort was developed, and the total number of individuals in the cohort, see Figure 2.3: Study Flow Chart for the Active TB Cohort in the Deliverable.

Discussion and Research Findings

This section provides links to the related discussion and selected tables within the Deliverable. For more information, see:


• Developing the Study Cohorts
  
• Table 2.1: Sociodemographic Characteristics of Active TB Cohort
  
• Table 2.2: Comorbidity Characteristics of Active TB Cohort

Additional discussion and results available from the Deliverable focus on the use of healthcare services for active TB cases. For more information, see Chapter 4: Healthcare Use for Active TB Cases.

3.2. Identifying Latent Tuberculosis Infection (LTBI) in Manitoba

The following sections describe how the Treated Latent TB Infection (LTBI) cohort was developed, and provides links to discussion and research findings from the Deliverable.

Developing the Treated Latent Tuberculosis Infection (LTBI) Cohort

Two tests are commonly used to confirm a diagnosis of LTBI: the tuberculin skin test, and the measurement of interferon-γ in whole blood. However, neither is the “gold standard” test for diagnosing LTBI. Individuals with LTBI have an elevated lifelong risk of developing active TB. Individuals at greatest risk of progressing from LTBI to active TB include those who have recently been infected, have HIV, or have chronic conditions that compromise their immune systems. LTBI is not reportable by law and there is no single database that accurately identifies individuals with LTBI in Manitoba. In Lix et al. (2018) they used specific prescription drug administrative data found in Manitoba’s Drug Program Information Network (DPIN) data to identify individuals being treated for LTBI.

The methods to define the Treated LTBI cohort were based on the work of Rivest et al. (2013), who examined medication completion rates for a cohort of individuals receiving treatment for LTBI using the Régie de l’assurance maladie du Québec database. This database contains information about all prescription dispensations for Québec residents. In addition, input from Manitoba’s clinical and public health experts (e.g., co-authors Plourde and Larcombe; epidemiologists from MHSAL) guided the development of the inclusion and exclusion criteria for this cohort.

The Treated LTBI Cohort comprised individuals who had received at least one prescription dispensation for rifampin (RIF) or isoniazid (INH) between April 1, 1999 and March 31, 2014, common antibiotics used to treat LTBI. Individuals could only enter the Treated LTBI Cohort once, based on the first study index date in the prescription drug data. The study index date was the date of the first medication dispensation on or after April 1, 1999.

Individuals were excluded from the Treated LTBI Cohort if they:

• had a diagnosis for leprosy within 30 days prior to the study index date;
  
• had a prescription for a medication used to treat chronic bacterial infections within 14 days of their index RIF or INH prescription;
  
• received a 2-day or 4-day course of RIF;
  
• did not have at least 365 days of healthcare coverage before the index date and 30 days of coverage after the index date; and/or
  
• had a prescription for RIF or INH in the 180-day period prior to the study index date.
  
• As well, individuals who initiated LTBI treatment with INH and continued treatment with RIF were not included in the Treated LTBI Cohort.
  
• For individuals with an index prescription for INH, prescriptions used to treat active TB or chronic non-TB mycobacterial infections resulted in exclusion from the cohort. These prescriptions included RIF, rifabutin, ethambutol, pyrazinamide, amikacin, capreomycin, cycloserine, linezolid, moxifloxacin, para-aminosalicylic acid, or streptomycin.
  
• For individuals with an index prescription for RIF, prescriptions used to treat active TB or chronic non-TB mycobacterial infections resulted in exclusion from the cohort. These prescriptions included INH, clofazimime, ethambutol, pyrazinamide, amikacin, capreomycin, cycloserine, linezolid, moxifloxacin, para-aminosalicylic acid, or streptomycin, and prescriptions used to treat other chronic bacterial infections (such as methicillin-resistant Staphylococcus aureus) including azithromycin, cefazolin, cefotaxime, cefoxitin, ceftriaxone, cefuroxime, ciprofloxacin, clarithromycin, clindamycin, cloxacillin, cycloserine, dapsone, daptomycin, doxycycline, erythromycin, flucloxacillin, fusidic acid, gentamicin, imipenem, levofloxacin, meropenem, minocycline, mupirocin, sulfamethoxazole/trimethoprim, or vancomycin. These medications were identified in DPIN using ATC codes.

For more detailed information on how the treated LTBI cohort was developed, and the total number of individuals in the cohort, see Figure 2.4: Study Flow Chart for Treated LTBI Cohort in the Deliverable.

Discussion and Research Findings

This section provides links to the related discussion and selected tables within the Deliverable. For more information, see:


• Developing the Study Cohorts
  
• Table 2.3: Sociodemographic Characteristics of Treated LTBI Cohort (n = 6,217)
  
• Table 2.4: Comorbidity Characteristics of Treated LTBI Cohort (n= 6,217)

Additional discussion and results available from the Deliverable focus on the treatment completion for Latent TB Infections (LTBI) cases. For more information, see Chapter 6: Treatment Completion for Latent TB Infections.

3.3. Validity of TB Diagnoses for Active TB in Administrative Data

One of the objectives of this deliverable was to validate diagnoses for active TB in administrative health data. The following sections describe the methods used to validate TB diagnoses from the TB Registry with two types of administrative data: hospital abstracts and physician billing claims, and provides a link to the validity estimates for the entire cohort, along with a summary of the findings.

Methods for Validating TB Diagnoses

The methods/approach used to validate TB diagnoses in this research are summarized below:


• individuals in the Manitoba population who met specific inclusion criteria (e.g., new cases), during a specific time period, were classified into four mutually exclusive groups: 1). Active TB Cases, 2). Treated LTBI Cases, 3). TB Contacts, and 4). Disease, Treatment and Contact-Free Individuals. Subsequently, all individuals in Group 1 were classified as TB cases, and all others were classified as non-cases.
  
• TB diagnoses were identified from hospital discharge abstracts and physician billing claims (Medical Services) data using the following ICD diagnosis codes: ICD-9-CM: 010-018 and ICD-10-CA: A15-A19.
  
• the following accuracy estimates were calculated: sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), Youden's index and Cohen's kappa. In addition, 95% confidence intervals were computed for each of these measures, except Youden's index.
  
• nine different ascertainment algorithms were applied to the administrative data, based on different periods of time and different data sources.

For more detailed information on these methods, see Chapter 3: Validating Manitoba TB Registry Data and MCHP’s Administrative Health Data

Discussion and Research Findings

Discussions of the validity of TB Diagnoses in administrative health data begin in the deliverable with the reporting of validity estimates for the entire cohort - see Table 3.3: Validity Estimates for TB Diagnoses in Hospital Records and Physician Billing Claims in the deliverable. Additional tables provide estimates of validity by age groups and health region of residence.

A summary of the research findings indicate:


• recommending a single algorithm may be difficult, and
  
• overall, TB diagnosis codes in hospital abstract records and physician billing claims had excellent specificity for identifying active TB cases. However, sensitivity and positive predictive value were frequently low, indicating that TB diagnoses in administrative data are not a valid way to identify active TB cases. In jurisdictions where TB Registry data cannot be linked to administrative data, researchers will have difficulty conducting accurate studies about TB outcomes and healthcare use.

SAS Code

• SAS code for identifying TB tests in the Cadham Provincial Laboratory is available in the SAS code and formats section below (internal access only).

Limitations / Cautions

• The CPL data files do not contain all laboratory tests for TB. Prior to 2001/02 approximately half of the TB tests were conducted by CPL. In subsequent years, all TB testing was conducted by Diagnostic Services of Manitoba (DSM).
  
  
• A value of P [Positive] to identify a positive test result in the POSNEG field [Positive-Negative] for specific codes in the CPL data was used to determine a positive test result.
  
  
• In the Medical Services / Physician Claims data, the diagnosis recorded is limited to one, 3-digit ICD-9-CM code. In some cases, the 3-digit code is not specific enough to uniquely identify conditions and if used, may over report certain conditions. NOTE: In December 2018, a 5-digit diagnosis code variable was added to the Medical Services data. If recorded, the first 3-digits of both the 3-digit and 5-digit diagnosis code variables will match exactly. The 5-digit diagnosis code variable applies to records from 2015/16 forward.
  
  
• The codes used to identify Tuberculosis (TB) in the Clinical Microbiology Section should be verified at the beginning of each new study to ensure they are up-to-date.