What Is the Outcome of Location-based Targeted Tuberculosis Screening Based on Universal Genotyping?

Running head: Tuberculosis and GIS Patrick K. Moonan1,2,4, Joseph Oppong3, Behzad Sahbazian1,2,6, Karan P. Singh2, Raghbir Sandhu 2, Gerry Drewyer1,4, Terry LaFon4, Marco Marruffo1,2, Teresa N. Quitugua5, Charles Wallace6, and Stephen E. Weis1,2,4,6 From the 1School of Medicine, 2School of Public Health, University of North Texas Health Science Center at Fort Worth, Fort Worth, Texas; 3Department of Geography, University of North Texas, Denton, Texas; 4Tarrant County Public Health Department, Fort Worth, Texas; 5Department of Microbiology, University of Texas Health Science Center at San Antonio, San Antonio, Texas; and the 6Bureau of Tuberculosis Elimination, Texas Department of Health and Human Services, Austin, Texas. Corresponding author: Stephen E. Weis, DO, University of North Texas Health Science Center at Fort Worth, Patient Care Center, 855 Montgomery, Fort Worth, TX, 76107, Telephone: (817) 321-4948, Fax: (817) 321-4920 e-mail: sweis@hsc.unt.edu Abstract Word Count: 179 Word count: 3,670 Tables: 5 Figures: 1

AJRCCM Articles in Press. Published on May 25, 2006 as doi:10.1164/rccm.200512-1977OC

Copyright (C) 2006 by the American Thoracic Society.

Abstract

Rationale and Objectives: Identifying and treating persons with latent tuberculosis

infection (LTBI) at high risk for developing tuberculosis is part of current tuberculosis

elimination strategy. There are no specific criteria, other than medical risks, to designate

groups as high-risk for developing tuberculosis. We hypothesized that if location-based

screenings were done in communities where persons with genotypically clustered M.

tuberculosis resided, then persons with LTBI from recent transmission and with

undiagnosed tuberculosis could be identified.

Methods: Location-based tuberculosis screenings were done in partnership with multiple

community-based organizations using resources previously used for other types of

screening.

Main Results: Location-based screenings identified one person with tuberculosis for

every 83 screened and one person with latent tuberculosis infection for every 5 screened.

The yield of this targeted screening program for discovering persons with tuberculosis

and LTBI exceeded what would be expected from non-targeted screening in a county

with a tuberculosis incidence of 5.7 per 100,000 population year.

Conclusions: Genotyping combined with Geographic Information Systems (GIS)

analysis can potentially be used to define high-risk status and to define areas for location-

based tuberculosis-screenings.

Keywords: genotyping, M. tuberculosis, location-based screening

Introduction

For over 40 years screening and treating persons with latent tuberculosis infection

(LTBI) have been important parts of the tuberculosis control strategy in the United States

[1]. Reduction of reported cases of tuberculosis has resulted in the Centers for Disease

Control and Prevention (CDC) altering its national tuberculosis elimination strategy from

universal screening to targeted screening of specific high-risk populations [2]. Consistent

with this change in strategy, the Institute of Medicine recommended developing more

effective methodologies for targeting populations with recently acquired infection for

treatment to limit the spread of tuberculosis [3]. Targeted tuberculosis screening allows

for optimum performance of the tuberculin skin test (TST) used to diagnosis LTBI and is

economically more efficient [3, 4]. Developing targeted testing strategies to identify the

estimated 15 million individuals with LTBI in the United States is one of the current

challenges of the tuberculosis elimination policy [5].

Assessing M. tuberculosis strains within a community by molecular genotyping

may be useful to design intervention strategies [6]. Studies from Europe and the United

States combining molecular surveillance with epidemiologic investigations have shown

that unrecognized transmission of tuberculosis can result in additional tuberculosis cases

[7 - 14]. Some of these studies have suggested that greater than 40% of persons with

tuberculosis were a result of recently acquired infection [8 - 10]. Population-based

molecular epidemiological surveillance combined with Geographic Information Systems

analysis (GIS) can identify discrete geographical areas where on-going tuberculosis

transmission is likely occurring [15]. This suggests genotyping-GIS based analyses can

determine locations where traditional person-orientated contact investigations are not

successfully controlling transmission.

If genotyping-GIS analysis can identify locations where traditional methods of

surveillance are unsuccessful at identifying transmitted tuberculosis then geographically

based screening in those areas could identify persons with previously undiagnosed

tuberculosis and latent tuberculosis infection (LTBI). This paper presents the results of a

targeted geographical tuberculosis screening program with genotyping-GIS analysis

defined boundaries for identifying tuberculosis and LTBI.

Methods

The Tarrant County Health Department (TCHD) coordinated and implemented

the tuberculosis screening program. The TCHD serves the western portion of the Fort

Worth-Dallas metropolitan area in north central Texas and includes a population of

approximately 1.5 million [16]. The TCHD conducts screening of high-risk populations

as part of the required tuberculosis control activities specified by its contract with the

Bureau of Tuberculosis Elimination of the Texas Department of Health and Human

Services. Previously, the screening efforts were based on epidemiological risk factors

and not on geographical risk. Between January 1, 1993 and December 31, 2000 these

screening efforts identified two persons with tuberculosis out of 2,911 persons screened

[17]. The epidemiologic risks of the screened population were unstable housing which

included living in congregate living centers, substance abuse, and low socioeconomic

status [17]. In the current study, tuberculosis screening resources were focused on

location-based targeted screening program implemented in collaboration with local

community-based organizations. These screenings were conducted within Zip codes

previously demonstrated by GIS to have genotypically clustered tuberculosis isolates

consistent with uninterrupted transmission [15]. The methods used to define geographic

and genotypic clustering of M. tuberculosis have been previously described [15, 18, 19].

Briefly, prospectively collected isolates had IS6110-based RFLP and spoligotyping

genotyping performed to identify patients infected with the same strain [18, 19]. Isolates

were defined as genotypically clustered if they had seven or more identical IS6110 band

pattern or if they had identical IS6110 patterns containing six or fewer bands and

identical spoligotypes [15]. GIS analysis demonstrated that spatial distribution of

tuberculosis and of molecular clustering within the county was not homogenous and

displayed a distinct geographical distribution [15]. The average tuberculosis annual

incidence for the county from 1993 to 2000 was 5.9 cases per 100,000. When the

incidence was divided using zip-code boundaries, the average annual incidence in the 3

Zip codes screened was 94, 55, and 32 cases per 100,000 population. Countywide 55%

of the isolates collected during the previous study period were molecularly clustered. In

the 3 screened Zip codes 95 out of 117 (81%) isolates collected were molecularly

clustered [15]. In the current study, we conducted screening in the 3 Zip codes that had

both the highest incidence and molecular clustering of tuberculosis and were contiguous.

The area of these zip codes was 4.62, 5.74, and 5.88 square miles. The analysis was

performed on data collected on persons screened at multiple locations between

September 1, 2002 and December 31, 2004. Screening and treatment were done at the

community-based organizations for patient's convenience, to take advantage of their

client volume and because they were considered to be locations where transmission was

occurring.

The community-based organizations participating in implementation of this

program included: mental health facilities; temporary labor services; job training and

living facilities sponsored by the Veterans Administration; dialysis centers, churches,

community services center, HIV congregate living facility, and several congregate living

facilities sponsored by different faith-based organizations. Persons screened included

those using the services and living within the community. Screened individuals were

identifiable by a county-issued screening identification card. To receive service at these

community-based organizations the clients needed their county-issued screening

identification card. Those without a card were allowed to use services for up to 2 weeks

and were encouraged to attend the next scheduled screening or to go to health

department. Each community-based organization had a screening schedule. The same

locations were used for screening throughout the study and were accessible for persons

with limited transportation and 4 of the locations were less than 1.5 miles apart. After 2

weeks without presenting a screening card clients were no longer allowed access to

services. Most, but not all, of the faith-based organizations participating in the screening

required identification card for entry to their services. The wide overlap of the need for

the community-based organizations services were strong incentive to be screened. Many

of the persons screened used multiple services e.g. employed through the temporary labor

center, received mental health care from mental health facility, washed clothing and used

telephone at community center, ate meals at church and stayed in community-based

organization supported housing.

Each member of the screening team had experience in meeting the needs of

vulnerable populations, and had successfully completed the CDC core TB training

modules [20]. This training includes standardized placement and interpretation of

Mantoux testing (TST), identifying common tuberculosis symptoms, principles of direct

observation of therapy, recognition of medication side effects, and patient follow-up.

The team included public health nurses and physicians; however, the majority of the staff

involved did not have medical licenses. Licensed and bi-lingual staff members were

onsite during each screening.

The screening program included active tuberculosis case finding and testing for

LTBI. Screenings were conducted on-site and included a brief history with

demographics, local employment and residential history, length of time utilizing services

in the target area, the number of facilities utilized in the target area, number of days per

week utilizing services in the targeted area, symptom assessment, TST, chest radiograph,

issuing proof of screening, and creation of a photo-identification card. TST screening was

done with 5-tuberculin unit–strength purified protein derivative. Based on results of the

prior GIS analysis, all area residents were presumed to have TB exposure and TST

reaction greater than or equal to 5 mm induration was defined as positive [21]. Persons

reporting a history of a prior positive TST were not re-tested but underwent all other

aspects of the evaluation. TCHD personnel were available for 5 evening hours Monday

through Friday to evaluate TST results and conduct directly observed therapy (DOT) and

directly observed preventive therapy (DOPT). Persons with symptoms, positive TST or

abnormal radiograph had an additional medical evaluation. The photo-identification card

expired after 12 months. Renewal of the card required another complete evaluation. At

retest an increase in TST induration of 5 mm or greater was defined as positive. Any

person who developed symptoms consistent with tuberculosis prior to the card expiration

date had another complete evaluation. All data were stored in a single database.

Statistical analysis was performed utilizing SPSS 13.0 (SPSS Inc, Chicago, IL).

Logistic regression analyses were performed and the method of maximum likelihood was

used to estimate odds ratios and 95% confidence intervals for all variables. Age was the

only continuous variable. The age variable statistics were analyzed by comparing the

means between groups. A 95% confidence interval for the mean age difference was

calculated by using the normal approximation, and an independent sample two-tailed

student's t-test was used to assess the statistical significance of the mean age difference.

Each eligible tuberculosis patient prospectively enrolled and participated in a structured

interview by S. Weis as part of their routine initial medical evaluation using an interview

tool approved by University of North Texas Health Science Center Institutional Review

Board.

Results

Between September 1, 2002 and December 31, 2004, we conducted 5,672

complete evaluations for 3,645 individuals evaluated at 48 screenings [2,770 (76%)

evaluated at least once; 1,987 (55%) evaluated at least twice; 756 (21%) evaluated at

least three times; 159 (4%), evaluated at least 4 times]. Eight hundred and seventy-five

(24%) had a partial evaluation; consisting of epidemiologic and symptom assessment,

chest radiograph and placement of TST, but did not return to have the skin test read.

Those with partial evaluation did not differ statistically from those with a completed

evaluation based on race (p = 0.61), age (p = 0.55), or gender (p = 0.23).

During the study period, 44 persons (1.2%) were diagnosed with pulmonary

tuberculosis and 681 individuals (18.6%) were diagnosed with LTBI. 1,987 individuals

(54.5%) had repeat evaluation, 104 of them (5.2%) demonstrated tuberculin skin test

conversion (range of increase 13 – 37 mm). Over this period the total number of persons

identified active tuberculosis, LTBI, and TST conversions decreased significantly. The

number of persons identified with tuberculosis decreased from 28.5 cases per 1,000 to 2.4

cases per 1,000 during the 28 months of screening. The number of persons identified

with LTBI decreased during the same period from 300.6 per 1,000 to 18.9 per 1,000

[Table 1].

As screening was mandatory to use the services available at the community-based

organizations we believe the screened population reflects the demographics of the target

population. The screened population was significantly different by age (p = 0.001) [Data

not shown], gender (p < 0.001), and race (p < 0.001) than the overall Tarrant County

population [Table 2]. When compared with the general Tarrant County population, the

screened population was 2.9 times more likely to be male [OR 2.9; 95% CI: 2.7, 3.1] and

5.9 times more likely to be African-American [OR 5.9; 95% CI: 5.6, 6.3]. The majority

of the population screened was between the ages of 35 to 54 years (64.9%) [Figure 1].

The mean age was 43.7 ± 12.9 (SD) for the overall screened population; 46.2 ± 10.3 (SD)

for persons with LTBI; 46.9 ± 10.3 (SD) for persons with TST converting to positive on

subsequent TST; and 51.2 ± 10.5 (SD) for persons with tuberculosis. Individuals with

LTBI differed significantly by age (p < 0.001) from those without LTBI. On average,

those with LTBI were 5.9 years older than those without identified infection (mean

difference 5.9; 95% CI: 4.8, 7.1). Individuals with TST conversion also differed

significantly by age from those who did not convert (p < 0.001). On average, those with

TST converted to positive were 5.4 years older than those who did not (mean difference

5.4; 95% CI: 2.6, 7.9). Finally, tuberculosis cases differed significantly by age from non-

cases (p < 0.001). On average identified cases of TB were 9.4 years older than the study

population (mean difference = 9.4 95% CI = 6.1, 12.3) [Data not shown].

Of the 44 individuals identified as having tuberculosis, 29 (65.9%) had culture

confirmation, 15 (51.7%) of these were smear-positive, and in 15 (34.1%) the diagnosis

was established on clinical characteristics. During the 28 months following the initiation

of the screening program 9 (20%) developed tuberculosis following a normal chest

radiograph. HIV status was available in 43 (97.7%) of the cases; 3 (7.0%) were HIV

seropositive. The majority of the cases were White (56.8%), followed by Black (34.1%).

Male gender (OR: 4.8; 95% CI: 1.4, 24.9); White race (OR: 1.9; 95% CI: 1.0, 3.6) were

the only demographic variables significantly associated with tuberculosis. Self-

identifying as being American Indian appeared to be an important factor for tuberculosis,

however, the association was not statistically significant (OR: 13.9.; 95% CI: 0.3, 118.3)

[Table 3]. Three (7%) of the diagnosed cases self identified as born outside the United

States.

The screening identified 681 (18.6%) individuals with LTBI. Male gender (OR:

2.8; 95% CI: 2.2, 3.6), Black race (OR: 1.7; 95% CI: 1.4, 2.0), and Hispanic (OR: 1.8;

95% CI: 1.4, 2.3) were significantly associated with LTBI. Self-identifying as White race

(OR: 0.4; 95% CI: 0.3, 0.5), was associated with reduced risk [Table 4].

1,987 (54.5%) individuals received at least one follow-up evaluation. Of these

104 (5.2%) developed a positive TST on retest. There were 1,326 person-years of

observations. The overall risk of TST conversion in this targeted community was 8.9 per

100 person-years of exposure. TST conversion rates decreased after the first 14 months

from 14.3 to 2.2 per 100 person-years of exposure over the last 14 months. Being male

(OR: 2.5; 95% CI: 1.4, 4.7) was the only demographic variable significantly associated

with developing a positive TST on retest. Of those with positive TST, Blacks (48.1%)

and Whites (39.4%) had the highest incidence of conversion, however stratified risk

estimates remained non-significant (Blacks: p = 0.724, Whites: p = 0.488) [Table 5].

Discussion

While the currently recommended strategy for tuberculosis elimination based on

the concept of targeted testing and treatment of persons most likely to develop

tuberculosis is ideal, how best to expand this screening beyond groups traditionally

considered at high risk is uncertain. We have used, for the first time, genotyping-GIS

analysis to design and implement a geographical screening program. This geographically

targeted screening identified one person with tuberculosis for every 83 screened and one

person with latent tuberculosis infection for every 5 screened. The yield of this targeted

screening program for discovering persons with tuberculosis, LTBI, and TST converters

far exceeded what would be expected from non-targeted screening in a county with a

tuberculosis incidence of 5.7 per 100,000 population year. These data suggest that

genotyping-GIS analysis can identify high-risk groups for targeted tuberculosis testing

programs.

There is the potential to identify tuberculosis during the evaluation of each TST-

positive individual [2, 21]. Persons with tuberculosis identified through active case

finding are more likely to be asymptomatic than persons passively identified [22]. There

is then, while not the primary purpose, a component of active case finding in every

targeted testing program. Our data on screening based on genotyping-GIS analysis

found a greater yield than many other targeted tuberculosis-testing programs conducted

by other methods and circumstances. A study of the most common form of targeted

testing, contact investigation, conducted in 5 geographically diverse TB programs across

the U.S. found one person with tuberculosis for every 319 screened and one person with

LTBI for every 8 screened [23]. A previous study of tuberculosis contacts from our

community identified one person with tuberculosis for every 222 persons screened and

one person with LTBI per every 5 screened [24]. A screening program of persons

incarcerated in Tarrant County Jail found that screening 3,274 persons was needed to

identify a person with tuberculosis and 108 persons to identify one individual with LTBI

[25]. Other novel efforts targeting screening persons at increased risk who would not

normally be evaluated for tuberculosis were all less successful at identifying tuberculosis

[26-28]. An advantage of screening based on genotyping-GIS analysis is that the entire

community can be surveyed for tuberculosis risk and then screening targeted to the

highest risk locations.

TST conversion is an important indicator for detecting recent transmission. We

found a significant reduction in TST conversion from 14.3 per 100 person-years of

exposure in the first 14 months to 2.2 per 100 person-years of exposure in the last 14

months. This magnitude of reduction is similar to the risk reduction that occurred in a

high-incidence inner city medical centers after full implementation of expanded infection

control measures. TST conversion rates decreased from 5.9 to 1.1 per 100 person-years

worked over 5-year period [29]. It is likely that decreases in transmission are a result of

reduced transmission from active case finding as well as the treatment of tuberculosis and

LTBI. These data demonstrate that screening and treatment of tuberculosis based on

genotyping-GIS analysis can reduce tuberculosis transmission in a community.

Political and managerial challenges are as important impediments to tuberculosis

control as medical challenges [30]. Public concern and political will are critical to

overcoming these barriers [3, 30]. The genotyping-GIS analysis proved to be important in

mobilizing community support for this screening. Several community-based

organizations that previously would not fully cooperate with tuberculosis screening

agreed to make it mandatory for individuals who wished to use their services after

reviewing the molecular surveillance-GIS analysis indicating community transmission.

The community support was crucial to the success of the screening and subsequently to

the treatment program. Universal genotyping-GIS analysis has the potential to be a useful

tool to increase community support and political involvement for tuberculosis control in

other communities.

Persons recently infected with tuberculosis are one of the groups at the highest

risk for developing tuberculosis [31]. In most of the developed world, where relatively

few persons in the community at any one time have tuberculosis, the strategy for

identifying persons most likely to have been recently infected is through investigation of

the contacts of persons with tuberculosis. Currently contact evaluation uses a concentric

circle model that was developed at a time when most contacts occurred within the home

and family [32 – 34]. Molecular cluster analysis of M. tuberculosis isolates conducted in

developed countries have demonstrated transmission links at times and in places not

identified through conventional concentric circle person-based contact investigation [8, 9,

11–14, 35 – 38] . These data have led to the suggestion that contact investigations as

currently designed are insufficient for identifying transmission pathways in multicultural

urban areas of the U.S. and that they need to be updated to be not only person-orientated

but also place-orientated in design [34]. In view of the large number of persons identified

through this targeted screening with LTBI and active tuberculosis, this targeted screening

approach demonstrates that genotyping-GIS analysis can help to delineate transmission

pathways.

There are no specific criteria to designate a group as high-risk for developing

tuberculosis [38]. Determining groups that are at increased risk at a local level requires

public health authorities to develop an epidemiologic profile of persons through

systematic screening for LTBI in their community. With currently available methods for

diagnosing LTBI this would be difficult and expensive to do in each community.

Beginning in 2004, the CDC has sponsored a universal genotyping program for

tuberculosis. This program expands the availability of genotypes for tuberculosis

diagnosed in United States. Isolates are characterized by PCR-based molecular

techniques, using spoligotyping, mycobacterial interspersed repetitive unit (MIRU)

typing [39, 40]. These typing methods can now provide local programs with access to

genotypes of isolates that are relatively easy to interpret and rapid enough that the

information can be used for epidemiological and clinical decision making. The

widespread availability of genotyping could allow for the identification of high-risk

tuberculosis status defined by geography. The genotyping-GIS analysis could be

performed at a local, state or national level. If our results are confirmed then genotyping-

GIS analysis could become a method to rapidly develop and adjust community-screening

programs based on location risk.

An important limitation to potential widespread implementation of these methods

is the need for community-based organizations willing to partner with health authorities

to perform screening. This program was successful because it met the needs of persons

being screened to obtain services and of the community-based organizations that did not

want their members exposed to tuberculosis. Additionally, tuberculosis programs in

communities with smaller populations or lower TB morbidity may not have patient

volume or expertise to do this level of analysis locally and therefore this analysis may

need to be conducted at a state or national level. Universal submission of at least one

isolate per culture-positive case is also needed as selective submission may affect cluster

size and the determination of location-based risk. Additionally, clustering can represent

prevalent community strains and not recently transmitted tuberculosis. It is also possible

that circumstances may exist where geographic clustering could represent recently

transmitted strains that were acquired from a different location. Treatment of LTBI, not

screening, prevents tuberculosis, and for this method to be useful it must be combined

with successful treatment for LTBI. It will be important for these findings to be

duplicated in other communities to ensure that they are generalizable.

Dating back to the pioneering work of John Snow on cholera, disease mapping

has provided important insights into geographic patterns of disease, risk, and mode of

spread [41]. These insights subsequently were used to provide the first practical epidemic

control. We have similarly, through a targeted screening program based on evaluating the

geographic pattern of disease, demonstrated that genotyping-GIS analysis can identify

areas with prevalent undiagnosed tuberculosis and LTBI. While there are many other

associations with the risk of developing tuberculosis, including: socioeconomic status,

country of birth, immunological status, and exposure; genotyping-GIS analysis is an

integrated view of the expression of these risks from the perspective of location and is a

novel approach for defining high-risk status.

Acknowledgements

The team of public health professionals at the Tarrant County Public Health Department

that did the screening: Lisa Adame, John Biboa, Guadalupe Munguia-Bayona, Catalina

Blanco, Stephen Fulmer, Debbie Greever, Nicole Hines, Karen Kardaras, Barbara King,

Douglas Melendy, Coy Mickens, Mack Neal, Nho Nguyen, Thieu Nguyen, Debbi

Roulston, Lee Sewell, Norma Shafer, Louise Slade, Gloria Stevenson, Kelly Taylor, Le

Turk, Rose Young, and Catherine Zimmerman. Also the leadership that made the

screening possible: Elvin Adams, Lou Brewer, Alex Hathaway, and John Suggs. Manuel

Bayona provided direction. Francesca Sanchez provided several critical reviews of the

manuscript.

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Figure Legend

Figure 1. Age distribution of target population and Tarrant County.

Table 1. Detection Yield by Period

Period* M. tb Culture

Positive Clinical TB LTBI Negative

TST Incomplete Evaluation Total

Case Rate/1000

LTBI Rate/1000

1 14 6 211 291 180 702 28.5 300.6 2 5 2 143 304 139 593 11.8 241.1 3 4 3 113 242 126 488 14.4 231.6 4 3 0 103 383 153 642 4.7 160.4 5 1 2 77 249 121 450 6.7 171.1 6 1 2 26 248 70 347 8.5 74.9 7 1 0 8 328 86 423 2.4 18.9 Total 29 15 681† 2,045 875 3,645 12.1 186.9 * Each period duration is 4 months; beginning September, 2002 and ending December, 2004 † Includes persons identified at retest

Table 2. Demographic Characteristics Tested Population Tarrant County* 3,645 1,446,219

Demographic No. (%) No. (%) Odds Ratio 95% CI P value

Sex Male 2,700 74.1 715,878 49.5 2.9 2.7, 3.1 < 0.0001 Female 945 25.9 730,341 50.5 Race/ethnicity Black 1,698 46.6 185,695 12.8 5.9† 5.6, 6.3 < 0.0001 Hispanic 399 10.9 285,484 19.7 White 1,509 41.4 895,788 61.9 Asian 24 0.7 52,642 3.6 American Indian 7 0.2 9,256 0.6 Other Race 8 0.2 17,355 1.2 * U.S. Census, 2002 † When compared to all other races

Table 3. Risk Factors Associated with Tuberculosis Tuberculosis Case

Yes No Odds Ratio

95% Confidence Interval p value

N (%) N (%) Gender Male 41 1.5 2,659 98.5 4.8 1.4 24.9 <0.006* Female 3 0.3 942 99.7 Race Black 15 0.9 1,683 99.1 0.6 0.3 1.1 <0.129 White 25 1.7 1,484 92.3 1.9 1.0 3.6 <0.053 Hispanic 3 0.8 396 99.2 0.6 0.1 1.9 <0.274 Asian 0 - - 24 100 - -† - - - - - - American Indian 1 14.3 6 85.7 13.9 0.3 118.3 <0.082* Other 0 - - 8 100 - -† - - - - - - * Fisher's exact test used with individual cell counts below 5 † Zero cell count

Table 4. Risk Factors Associated with Latent Tuberculosis Infection (LTBI)* † LTBI Yes No

Odds Ratio

95% Confidence Interval p value

N (%) N (%) Gender Male 580 30.0 1,375 70.0 2.8 2.2 3.6 <0.001 Female 101 13.0 670 87.0 Race Black 373 30.3 57 69.7 1.7 1.4 2.0 <0.001

White 194 16.6 973

83.4 0.4 0.3 0.5 <0.001 Hispanic 105 35.5 191 64.5 1.8 1.4 2.3 <0.001 Asian 8 36.4 14 63.6 1.8 0.7 4.5 0.299 American Indian 1 20.0 4 80.0 0.8 0.02 7.6 0.633‡ Other 0 - - 6 100 - -§ - - - - - - * For those with TST read within 72 hours and not identified as tuberculosis cases [n = 2,726] † Includes persons identified at retest ‡ Fisher's exact test used with individual cell counts below 5 § Zero cell count

Table 5. Risk Factors Associated with Tuberculin Skin Test Conversion* TST Conversion Yes No

Odds Ratio

95% Confidence Interval p value

N (%) N (%) Gender Male 90 6.3 1,349 93.7 2.5 1.4 4.7 0.001 Female 14 2.6 534 97.4 Race Black 50 5.3 896 94.7 0.9 0.6 1.4 0.724 White 41 5.0 772 95.0 0.9 0.6 1.3 0.488 Hispanic 12 5.9 193 94.1 1.1 0.5 2.1 0.954 Asian 0 - - 18 100 - -‡ - - - - - - American Indian 1 20.0 4 80.0 4.6 0.6 56.7 0.236† Other 0 - - 0 - - - -‡ - - - - - - * For those with one or more subsequent TST [n = 1,987] † Fisher's exact test used with individual cell counts below 5 ‡ Zero cell count

Figure 1

> 7565 to 7460 to 6455 to 5945 to 5435 to 4425 to 3420 to 2415 to 1910 to 145 to 9< 5

Age Group (in years)

50.00

40.00

30.00

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Perc

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TST ConvertorsLTBITB CasesTarrant CountyTarget Population