Tuberculosis Genotyping Project, United States
Molecular Epidemiology
of Tuberculosis in a Sentinel Surveillance Population
Barbara A. Ellis,* Jack T. Crawford,* Christopher R. Braden,*
Scott J. N. McNabb,* Marisa Moore,* Steve Kammerer,* and the National
Tuberculosis Genotyping and Surveillance Network Work Group1
*Centers for Disease Control and Prevention, Atlanta, Georgia,
USA
Suggested citation for this article: Ellis
BA, Crawford JT, Braden CR, McNabb SJN, Moore M, Kammerer S, et
al. Molecular epidemiology of tuberculosis in a sentinel surveillance
population. Emerg Infect Dis [serial online] 2002 Nov [date
cited];8. Available from: URL: http://www.cdc.gov/ncidod/EID/vol8no11/02-0403.htm
We conducted
a population-based study to assess demographic and risk-factor
correlates for the most frequently occurring Mycobacterium
tuberculosis genotypes from tuberculosis (TB) patients. The
study included all incident, culture-positive TB patients from
seven sentinel surveillance sites in the United States from 1996
to 2000. M. tuberculosis isolates were genotyped by IS6110-based
restriction fragment length polymorphism and spoligotyping. Genotyping
was available for 90% of 11,923 TB patients. Overall, 48% of cases
had isolates that matched those from another patient, including
64% of U.S.-born and 35% of foreign-born patients. By logistic
regression analysis, risk factors for clustering of genotypes
were being male, U.S.-born, black, homeless, and infected with
HIV; having pulmonary disease with cavitations on chest radiograph
and a sputum smear with acid-fast bacilli; and excessive drug
or alcohol use. Molecular characterization of TB isolates permitted
risk correlates for clusters and specific genotypes to be described
and provided information regarding cluster dynamics over time.
Since 1990, characterization of Mycobacterium tuberculosis
isolates by molecular methods has been useful in confirming suspected
laboratory contamination and as an adjunct to epidemiology-based
contact investigation (1-3). Most studies used
the restriction fragment length polymorphism (RFLP) technique, based
on IS6110 and specific to the M. tuberculosis complex.
This genetic element may be present in different positions on the
chromosome, resulting in a unique genotype useful for characterizing
the strain of M. tuberculosis infecting a patient. Although
RFLP has disadvantages (e.g., cost, time required to culture the
organism, and specialized training and laboratory equipment), IS6110-based
RFLP is the established method considered most discriminatory for
genetic characterization of M. tuberculosis strains worldwide
(4).
In 1996, the Centers for Disease Control and Prevention (CDC)
established seven sentinel surveillance sites in the United States
(National Tuberculosis Genotyping and Surveillance Network) to assess
the utility of molecular genotyping for improving tuberculosis (TB)
prevention and control. The TB genotyping network used standardized
protocols for molecular characterization of M. tuberculosis
isolates from patients in all sentinel sites. The network was designed
to address specific epidemiologic questions regarding the natural
history, transmission, and potential applicability of molecular
genotyping of M. tuberculosis strains to augment TB control
activities (5). Two objectives were to identify
and determine the prevalence of specific M. tuberculosis
genotype clustering in populations of sentinel surveillance TB patients
and to describe the demographic characteristics of these populations
and the genotypic characteristics of M. tuberculosis strains
in clustered and nonclustered TB cases. We describe demographic
and risk factor correlates for the most frequently occurring M.
tuberculosis genotypes in isolates collected from sentinel TB
patients.
Methods
This population-based sentinel study included all incident culture-positive
TB patients from sentinel sites from January 1996 to December 2000.
In brief, the seven sentinel surveillance sites included the states
of Arkansas, Maryland, Massachusetts, Michigan, and New Jersey;
Dallas, Tarrant, Cameron, and Hidalgo Counties in Texas; and Alameda,
Contra Costa, Marin, San Mateo, Santa Clara, and Solano Counties
in California. A detailed description of the study's design, participants,
population, and laboratory and epidemiologic methods is provided
elsewhere (6).
All patients included in the study were reported to the CDC national
TB case registry on the form Report of a Verified Case of Tuberculosis,
a standardized electronic form submitted for TB surveillance to
CDC by all state public health reporting areas. Data reported include
patient demographics, laboratory test results, drug susceptibilities,
information on chest radiographs, and treatment outcomes (7).
Investigators from the sentinel surveillance sites submitted patient
isolates to the corresponding regional laboratory for genotyping
and conducted routine contact investigations. In addition, participants
from the surveillance sites performed detailed epidemiologic investigations
on groups of persons with M. tuberculosis isolates that had
matching genetic patterns or clusters (see below). The regional
genotyping laboratories conducted IS6110 RFLP on isolates
from sentinel patients. Since low-copy numbers of IS6110
(i.e., six or fewer copies) reduce test specificity, spacer oligonucleotide
typing (spoligotyping) was conducted on such isolates. A cluster,
which was identified by analysis of the entire TB genotyping network
database, was defined as two or more isolates with either identical
RFLP patterns (at least seven copies of IS6110) or identical
RFLP and spoligotype patterns for isolates with RFLP patterns that
had six or fewer copies of IS6110.
Differences in the proportion of TB patients from the TB genotyping
network population living in cities with populations of <100,000,
100,001 to 250,000, 250,001 to 500,000, and >500,000 were compared
with those of the national TB patients for the year 2000 only. Statistics
were obtained from the U.S. Census Bureau (available at: URL: http://www.census.gov/population/cen2000/phc-t6/tab04.pdf
).
Correlation of average TB incidence among cases at the seven sentinel
sites and percentage of cases with isolates that clustered genetically
were examined by year by using the Spearman rank correlation statistic.
Clustering was determined by examining each year's cases independently.
A Mantel-Haenszel chi-square or Fisher exact test was used, as appropriate,
to ascertain whether the sentinel population was representative
of TB patients in the United States in terms of demographic, clinical,
behavioral, or outcome characteristics.
We used multiple logistic regression to assess the importance of
demographic, clinical, behavioral, or outcome variables in predicting
the occurrence of a given genotype for those genetic clusters that
occurred most frequently (>20 isolates). The dependent
variable was the presence or absence of a given genotype. The best-fit
logistic regression model was determined by the strategy of Hosmer
and Lemeshow (8). In brief, a univariate analysis
of the categorical independent variables was done by using the Mantel-Haenszel
chi-square or Fisher exact test, as appropriate; any variable with
a significance value of <0.20 was included in a best subset,
multivariate logistic regression model. Collinearity of independent
variables was assessed by using the variance/covariance matrix from
PROC LOGISTIC (SAS Institute, Inc., Cary, NC) to generate condition
indices and a matrix of variance decomposition proportions to detect
dependencies among the variables (9). Backward
elimination of independent variables was performed if the probability
of the independent variable was >0.20. Both the Wald statistic
and 95% confidence interval were used on each coefficient to assess
the significance of variables in each model; the log-likelihood
ratio was used to assess the overall significance of the final models,
and the Hosmer-Lemeshow statistic was used to evaluate the fit of
each of the final models. Data were analyzed by SAS version 8.0
software (SAS Institute, Inc.) (10).
Results
Sentinel Population
Characteristics
The incidence of TB cases in the sentinel surveillance sites varied
within and among sites over time (Table 1).
From 1996 to 2000, the overall incidence of TB in the United States
declined from 8.0 to 5.8 per 100,000 inhabitants, and similar downward
trends were observed in each of the TB genotyping network sites.
The California, New Jersey, Arkansas, and Texas sites had a higher
incidence of TB than the overall national rates. The incidence rates
in California and Texas (sites that included only six and four counties
from each state) were similar to the overall incidence rates for
each state (data not shown).
In the surveillance area, 15,035 patients with verified TB represented
16% of the TB patients in the United States during the 5-year study
period (Table 2). Overall, 11,923 TB patients
were culture-positive (721 from Arkansas, 2,842 from California,
1,192 from Maryland, 1,022 from Massachusetts, 1,481 from Michigan,
2,599 from New Jersey, and 2,066 from Texas). Of TB patients in
the surveillance areas, 79.3% (11,923) were culture positive, and
RFLP results were available for 91.2% (10,883). However, spoligotyping
results were not available for 131 of the isolates that had six
or fewer copies of IS6110 (5%; n=2,638); thus, these patients
were excluded from our analysis. Of 1,171 isolates not genotyped
by RFLP or spoligotyping, 12 (1%) were from Michigan, 35 (3%) from
Maryland, 40 (3%) from Massachusetts, 110 (9%) from Arkansas, 156
(13%) from Texas, 327 (28%) from California, and 491 (42%) from
New Jersey. Primary reasons for lack of genotyping results included
inability to obtain cultures from private health-care providers,
contamination of cultures, or poorly growing or nonviable cultures.
Characteristics of the TB patient population from the genotyping
network sentinel sites were comparable with those from the entire
United States, with some exceptions (Table 2).
Sentinel surveillance populations had higher proportions of women
(42% for the genotyping network vs. 37% for the United States overall)
and patients in the 15- to 44-year age category, and were more often
homeless or lived in correctional or long-term care facilities.
Higher proportions of genotyping network patients used intravenous
drugs, but fewer patients used noninjecting drugs or alcohol excessively.
Of the study population, about 4% reported previous episodes of
TB (652 of 15,035; Table 2). Of persons with
a previous recent history of TB, 28 had TB after completing >1
year of therapy within the study period; genotyping data on isolates
from both episodes were available for 22 of these persons. A higher
number of persons from the TB genotyping network study population
lived within city limits (97% vs. 87%). However, when compared with
national averages, genotyping network populations were generally
from smaller towns and cities: 1,446 (69%) of 2,099 genotyping network
patients were from cities and towns with <250,000 inhabitants,
compared with 10,093 (62%) of 16,377 TB patients nationwide (Mantel-Haenszel
chi square= 41.8; p<0.0001).
The proportion of foreign-born patients was higher in genotyping
network populations compared with the overall national average (50%
for genotyping network vs. 41% for the United States). Numbers of
foreign-born TB patients increased over time at about the same rate
for both genotyping network populations and national TB patients.
From 1996 to 2000, national proportions of foreign-born TB patients
increased from 37% (7,725/21,045) to 47% (7,593/16,281); in the
genotyping network populations, the proportions of foreign-born
TB patients increased from 44% (1,153/2,642) to 58% (1,222/2,092).
Characteristics of the genotyping network population between sites
were similar, as were culture-positive genotyping network populations
compared with the overall genotyping network case population.
Analysis of Genotyping
Data
The distribution and diversity of RFLP and spoligotyping pattern
results from the genotyping network have been discussed in detail
(11). In contrast to that analysis, we used both
RFLP and spoligotyping results to define genetic clusters. Overall,
6,609 distinct patterns were identified, including 1,029 that contained
>2 isolates per cluster. When analyzed by site, 1,018
clusters were identified: 71 clusters were from Arkansas (611 cases
genotyped, 2-16 cases per cluster), 233 from California (2,511 cases,
2-128 cases per cluster), 104 from Maryland (1,157 cases, 2-36 cases
per cluster), 85 from Massachusetts (982 cases, 2-16 cases per cluster),
125 from Michigan (1,469 cases, 2-102 cases per cluster), 196 from
New Jersey (2,112 cases, 2-40 cases per cluster), and 204 from Texas
(1,910 cases, 2-96 cases per cluster). Overall, 970 distinct genotypes,
including 235 representing clusters, had <6 copies (2,507
cases, 24% clustered, 2-93 cases per cluster). In contrast, 794
clusters from 5,639 distinct genotypes had >7 IS6110
copies (8,245 cases, 14% clustered, 2-105 cases per cluster). Most
clusters included seven or fewer persons (85%; 900/1,029).
Longitudinal Analysis
Most clusters occurred in only a single site (66%; 680/1,029).
However, 260 (25%) were found in two sites, 55 (5%) in three sites,
19 (2%) in four, 8 (1%) in five, and 7 (1%) in six sites. As expected,
clusters that spanned multiple sites were larger. Clusters found
at a single site averaged four persons per cluster (mean=3.65; standard
error [SE] ± 0.22; n=680), in contrast to 61 persons per
cluster for the genotypes found at six sites (mean=61.14; SE ±
23.6; n=7; Kruskal-Wallis test, p<0.0001). Most (62%) of the
34 clusters that occurred in at least four sites occurred in all
5 years of the study; 26% in 4 years; and 6% each in 3 and 2 years
of the study.
Changes in proportions of patients with isolates that clustered
were observed over time. In the first 2 years of the study, the
percentage of the cumulative total number of cases that clustered
increased from 28% to 45%; smaller increases occurred thereafter
(Figure 1). Overall, the proportion of clustered
cases was 48% (5,171/10,752). The percentages of clustered cases
by sites were 28% (276/982) for Massachusetts; 34% (393/1,157) for
Maryland; 41% (873/2,112) for New Jersey; 42% (1,046/2,511) for
California; 44% (266/611) for Arkansas; 49% (720/1,469) for Michigan;
and 57% (1,093/1,910) for Texas. Maximum cluster size and absolute
numbers of cases with isolates that clustered continued to increase
through the end of the study.
Overall, cases with isolates that clustered showed a concomitant
decline with average incidence of TB over the 5-year period (Figure
2). A significant positive association was observed between
the percentage of cases with clustered genotypes and TB incidence
over time (Spearman rho=0.90; p=0.037).
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Figure 1. Numbers of tuberculosis
cases, cumulative proportion of cases with isolates in genetic
clusters, and maximum genetic cluster size from seven sentinel
surveillance sites by quarter that verified case was counted,
1996-2000...
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Figure
2
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Figure 2. Average annual
incidence of tuberculosis for seven sentinel surveillance sites
and percentage of cases with isolates in genetic clusters, 1996
to 2000... |
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Figure
3
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Figure 3. Number of cases
with isolates that had unique genotypes ("not clustered")
and those in genetic clusters... |
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Risk Factor Analyses
of Genetic Clusters
Compared with persons whose isolates had unique genotypes, persons
with isolates that clustered were more likely to be non-Hispanic,
black men born in the United States. They were more likely to have
pulmonary disease and abnormal chest radiographs with cavities;
in addition, they more often had positive sputum smears; were HIV-positive,
homeless, or residents of a correctional facility; and used drugs
or alcohol excessively (Table 3). Patients
with unclustered isolates were 5 years older on the average than
those with isolates that clustered (44.8 years vs. 49.4 years, respectively;
Table 3). Multiple logistic regression efforts
resulted in models that were not robust (data not shown).
Except for 4 genotypes, all 34 clusters with ³ 20 isolates
per cluster had significant demographic, clinical, and behavioral
risk factors (Table 4). Race, ethnicity, and
place of birth were frequently significant predictors for a given
genotype. Other predictors included gender, age, site of disease,
resistance to first-line drugs, and alcohol or drug abuse (Table
4). Twelve (40%) of 30 of these larger clusters were observed
in four or more sites over a 5-year period. Lower percentages of
foreign-born patients than U.S.-born patients clustered, regardless
of the number of IS6110 copies (Figure
3). More than 50% (1,025/1,825) of the foreign-born patients
whose isolates clustered had been in the United States for ³5
years. Clustering of isolates from foreign-born patients ranged
from 15% (49/316) in Michigan to 38% (309/816) in Texas.
Discussion
This population-based study is the largest that has been conducted
in the United States to assess risk factors related to specific
M. tuberculosis genotypes. Generally, clustered isolates
have been considered recently acquired infections (12).
However, this assumption may not always be correct. Clustering does
not prove that transmission occurred, and its demonstration depends
on adequate sampling of the population, incidence of TB, and characteristics
of the study population (e.g., age structure, population mobility,
duration of residence, and immune status) (1,13)
. Only 25%-42% of patients in genetic clusters were shown to have
epidemiologic connections with another member of the cluster (14-16).
Conventional epidemiologic investigation of these TB patients (including
interviews) was conducted, but inclusion in this analysis was outside
the scope of this article. Thus, results that indicate clustered
genotypes are representative of recent transmission should be interpreted
with caution.
Given this caveat, our results nevertheless demonstrate several
consistent patterns. Differences in demographic and other risk factors
for persons with isolates that clustered corroborated those from
smaller studies conducted in the United States and larger surveys
in Europe. Extensive surveys from the Netherlands (17)
also demonstrated that persons with isolates that clustered genetically
were younger than those with unique genotypes. Other risk factors
for clustering included being male, born in the United States, non-Hispanic
black, or homeless; using drugs and alcohol excessively; and having
pulmonary disease and cavitations on chest radiograph, a sputum
smear with acid-fast bacilli, and HIV infection. These risk factors
have been observed for TB patients in different communities (12,18,19).
The heterogeneity and diversity of the study population may account
for our failure to produce a multivariate logistic model to predict
clustering.
A third of the foreign-born cases were recent immigrants to the
United States, and overall, the percentage of clustered isolates
from foreign-born persons was lower than the percentage from nonimmigrants
(Figure 3), indicating that at least a portion
of these cases resulted from reactivation of latent disease or recent
infection in the country of origin. In addition, for foreign-born
persons, clustering of M. tuberculosis increased with the
duration of residence in the United States. These results suggest
that recently imported strains of M. tuberculosis from foreign-born
persons may not commonly spread to U.S. residents or that transmission
may be occurring after a lag time before the imported strains manifest
as disease in contacts. Similar observations have been published
in studies from San Francisco, New York, Switzerland, and Norway
(20-24). These data may also
reflect gaps in our knowledge of M. tuberculosis genotypes
in circulation; a comparison of the U.S. TB genotyping network results
with other databases worldwide may be warranted.
Logistic regression analysis of the most commonly occurring strains
demonstrated that different risk factors were associated with specific
genotypes. Several genotypes were associated with ethnic origin
(e.g., Asian or Pacific Islander and Hispanic patients with six
and three genotypes, respectively; Table 4).
A recent study in Norway showed that several clusters consisted
of patients of the same ethnic origin (23). An
association has also been observed between the patient's ethnic
origin and IS6110 copy number (25). These
results, in conjunction with additional epidemiologic data, may
be useful in tracking the geographic origin and spread of M.
tuberculosis strains of public health importance (26).
A small proportion of clustered isolates were from persons from
more than four sites spanning 5 years of study (Table
4). Although an in-depth analysis of epidemiologic links was
not possible in this study, we found no evidence of recent transmission
between patients with identical genotypes from the different states
(data not shown); this lack of transmission was also noted in a
smaller study in the United States (27). Since
TB transmission is generally considered a local event, these ubiquitous
genotypes may be widespread because of social factors (e.g., homelessness
or alcohol or drug abuse; Table 4). In addition,
these genotypes may represent older, endemic domestic strains that
have been in the United States for centuries and have dispersed
more widely throughout the United States than the more recently
imported strains. Further molecular characterization of these genotypes
may show additional differences not detected by RFLP. Nonetheless,
the effect of M. tuberculosis virulence or host factors on
the distribution of these genotypes cannot be ascertained.
The proportion of strains that were classified into clusters of
identical genotypes (48%) was comparable with proportions in the
Netherlands and Denmark (50%) (2,28),
but the proportion was considerably higher than in two other countries
(17% in Switzerland [29]; 20% in Norway [23]).
The cumulative percentage of clustered strains reached a plateau
by the end of the study's second year (Figure
1), a finding consistent with other molecular epidemiologic
TB studies (2). Increases in maximum cluster size
were anticipated because, as sample sizes increase with time, the
number of isolates in each cluster would be expected to increase.
In addition, higher proportions of clustered cases were observed
for low-band number patterns (Figure 3),
which had the maximum cluster size and may indicate that the low-copy
IS6110 patterns are not specific, even with the addition
of spoligotyping. The sensitivity and specificity of IS6110
RFLP in molecular epidemiologic studies have not been quantified
and represent a potential limitation of this study. Although the
stability of IS6110 is relatively high, the half-life of
IS6110 RFLP is estimated to be 3-10 years (29-31)
based on typing of serial isolates from individual patients. A study
of isolates from patients in confirmed chains of transmission showed
little change in IS6110 patterns (32).
Calculation of these rates may be influenced by the duration between
time of disease onset and time of sampling and may be proportional
to the effectiveness of the TB control program (30).
Because genotyping results were not available for 10% of TB cases
in this study, estimates of the degree of clustering and the size
of clusters are conservative. Some unique isolates might have clustered
if some of the missing isolates had been aaavailable or if other
cases with the same strain were present outside the study area (33).
Sentinel surveillance sites defined by artificial boundaries (i.e.,
state lines) not entirely representative of TB patients from the
United States were included in this study. More than 90% of the
isolates from patients from the surveillance areas were genotyped,
and these isolates were representative of those culture-positive
patients from the sentinel surveillance areas. However, 16% of all
TB case-patients reported in the United States were included in
these sentinel surveillance sites during the 5-year study period.
In addition, the sentinel surveillance population had higher proportions
of foreign-born persons than the national average. Because of the
propensity of foreign-born persons to have isolates with unique
genotypes, the actual rate of clustering may have been underestimated.
Nonetheless, sentinel surveillance of TB cases has provided a useful
method for documenting genotypes in circulation in the United States
and for identifying risk factor correlates of common genotypes.
Annual declines in TB incidence were paralleled by similar declines
in the proportion of cases with genotypes in clusters (Figure
2), a finding consistent with the hypothesis that decreased
clustering is expected with declining incidence (20).
Since effort was similar each year, this association is not likely
to be an artifact related to sample size (i.e., as sample size or
number of cases becomes smaller, the probability of detecting clusters
decreases). These findings underscore the importance of long-term
longitudinal molecular studies and the potential usefulness of these
methods in evaluating program effectiveness and improving program
management.
Acknowledgments
We thank Ida Onorato, Ken Castro, Tom Shinnick, and Thomas Navin
for their scientific guidance and logistic support; Elsa Villarino
and James Mills for valuable comments on an earlier version of the
manuscript; Annie Faye Prescott for excellent editorial assistance;
and the health officials at local and state TB control offices that
supported the activities of the National Tuberculosis Genotyping
and Surveillance Network.
Dr. Ellis is an epidemiologist with the National Center for Infectious
Diseases, Centers for Disease Control and Prevention. Her research
interests focus on the molecular epidemiology of infectious diseases.
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- Niemann S, Rusch-Gerdes S, Richter E, Thielen H, Heykes-Uden
H, Diel R. Stability
of IS6110 restriction fragment length polymorphism patterns
of Mycobacterium tuberculosis strains in actual chains
of transmission. J Clin Microbiol 2000;38:2563-7.
- Murray M, Alland D. Methodological
problems in the molecular epidemiology of tuberculosis. Am
J Epidemiol 2002;155:565-71.
Table 1. Incidence
of tuberculosis cases in the United States and in the sentinel
surveillance areas of the National Tuberculosis Genotyping
Surveillance Network, 1996–2000a
|
|
Sentinel surveillance site
|
1996
|
1997
|
1998
|
1999
|
2000
|
Mean
|
|
Arkansas
|
9.0
|
7.9
|
6.7
|
7.1
|
7.4
|
7.6
|
Californiab
|
16.3
|
13.9
|
13.9
|
12.9
|
11.6
|
13.7
|
Maryland
|
6.3
|
6.7
|
6.3
|
5.7
|
5.3
|
6.1
|
Massachusetts
|
4.3
|
4.4
|
4.6
|
4.4
|
4.5
|
4.4
|
Michigan
|
4.6
|
3.8
|
3.9
|
3.6
|
2.9
|
3.8
|
New Jersey
|
10.3
|
8.9
|
7.9
|
7.0
|
6.7
|
8.2
|
Texasb
|
12.7
|
12.8
|
12.5
|
10.9
|
9.6
|
11.7
|
United States
|
8.0
|
7.4
|
6.8
|
6.4
|
5.8
|
6.9
|
|
aNumber
per 100,000 inhabitants.
bSentinel surveillance areas
for California and Texas did not include the entire states. |
Table
2. Demographic and risk behavior factors and clinical, laboratory,
and treatment outcomes for the sentinel surveillance patients
(National Tuberculosis and Genotyping Surveillance Network),
compared with factors and outcomes of all tuberculosis patients,
United States, 1996–2000a,b |
|
Variable
|
Category
|
All U.S. TB cases
(n=93,097) (%)
|
All NTGSN cases
(n=15,035) (%)
|
Probabilityc
|
|
Gender
|
Male
|
58,356 (62.7)
|
8,767 (58.3)
|
<0.001
|
Female
|
34,734 (37.3)
|
6,266 (41.7)
|
|
Unknown
|
7 (0.0)
|
2 (0.0)
|
|
Age (yrs)
|
<4
|
3,289 (3.5)
|
518 (3.4)
|
NS
|
5–14
|
2,397 (2.6)
|
393 (2.6)
|
NS
|
15–24
|
7,988 (8.6)
|
1,462 (9.7)
|
<0.001
|
25–44
|
32,433 (34.8)
|
5,413 (36.0)
|
0.005
|
45–64
|
25,319 (27.2)
|
3,850 (25.6)
|
<0.001
|
>64
|
21,662 (23.3)
|
3,397 (22.6)
|
NS
|
Unknown
|
9 (0.0)
|
2 (0.0)
|
|
Race/ethnicity
|
White, non-Hispanic
|
22,655 (24.3)
|
3,087 (20.5)
|
<0.001
|
Black, non-Hispanic
|
30,201 (32.4)
|
4,775 (31.8)
|
NS
|
Hispanic
|
20,475 (22.0)
|
2,923 (19.4)
|
<0.001
|
American Indian/Native
|
1,280 (1.4)
|
38 (0.3)
|
<0.001
|
Asian/Pacific Islander
|
18,346 (19.7)
|
4,195 (27.9)
|
<0.001
|
Unknown
|
140 (0.2)
|
17 (0.1)
|
|
Place of birth
|
U.S.-born
|
54,341 (58.4)
|
7,530 (50.1)
|
<0.001
|
Foreign-born
|
38,252 (41.1)
|
7,468 (49.7)
|
|
Unknown
|
504 (0.5)
|
37 (0.2)
|
|
Years in United States (foreign-born only)
|
<1
|
7,425 (19.4)
|
1,494 (20.0)
|
NS
|
1
|
2,612 (6.8)
|
567 (7.6)
|
NS
|
2
|
2,073 (5.4)
|
477 (6.4)
|
<0.005
|
3
|
1,827 (4.8)
|
406 (5.4)
|
<0.05
|
4
|
1,676 (4.4)
|
361 (4.8)
|
NS
|
>5
|
19,396 (50.7)
|
3,688 (49.4)
|
<0.001
|
Unknown
|
3,243 (8.5)
|
475 (6.4)
|
|
Country of origind
|
Philippines
|
4,862 (12.7)
|
1,113 (14.9)
|
<0.0001
|
Mexico
|
8,795 (23.0)
|
1,100 (14.7)
|
<0.0001
|
Vietnam
|
3,824 (10.0)
|
968 (13.0)
|
<0.0001
|
India
|
2,527 (6.6)
|
883 (11.8)
|
<0.0001
|
China
|
1,930 (5.0)
|
370 (5.0)
|
NS
|
Haiti
|
1,470 (3.8)
|
225 (3.0)
|
<0.0005
|
Peru
|
636 (1.7)
|
207 (2.8)
|
<0.0001
|
Republic of Korea
|
1,176 (3.1)
|
202 (2.7)
|
NS
|
Ethiopia
|
578 (1.5)
|
153 (2.0)
|
<0.001
|
Ecuador
|
627 (1.6)
|
115 (1.5)
|
NS
|
Other
|
11,827 (30.9)
|
2,132 (28.5)
|
<0.0001
|
Status at diagnosis
|
Alive
|
90,141 (96.8)
|
14,611 (97.2)
|
0.02
|
Dead
|
2,925 (3.1)
|
422 (2.8)
|
|
Unknown
|
31 (0.0)
|
2 (0.0)
|
|
Site of disease
|
Pulmonary
|
68,611 (73.7)
|
10,576 (70.3)
|
<0.001
|
Extrapulmonary
|
17,406 (18.7)
|
3,210 (21.4)
|
<0.001
|
Pulmonary and Extrapulmonary
|
7,046 (7.6)
|
1,241 (8.3)
|
0.003
|
Unknown
|
34 (0.0)
|
8 (0.1)
|
|
Primary disease site
|
Pulmonary
|
73,157
(78.6)
|
11,365
(75.6)
|
<0.0001
|
Lymph:
cervical
|
4,312
(4.6)
|
1,020
(6.8)
|
<0.0001
|
Pleural
|
3,842
(4.1)
|
674
(4.5)
|
<0.05
|
Miliary
|
1,407
(1.5)
|
241
(1.6)
|
NS
|
All
other
|
10,345
(11.1)
|
1,727
(11.5)
|
NS
|
Unknown
|
34
(0.0)
|
8
(0.0)
|
|
Sputum smear for acid-fast organisms
|
Negative
|
36,912 (39.6)
|
5,995 (39.9)
|
<0.0001
|
Positive
|
33,235 (35.7)
|
4,735 (31.5)
|
|
Not done/unknown
|
22,950 (24.6)
|
4,305 (28.7)
|
|
TST at diagnosis
|
Negative
|
13,215 (14.2)
|
1,947 (12.9)
|
<0.001
|
Positive
|
54,113 (58.1)
|
8,799 (58.5)
|
|
Not done/unknown
|
25,769 (27.6)
|
4,289 (28.6)
|
|
Case verification criteria
|
Positive culture
|
74,940 (80.5)
|
11,967 (79.6)
|
<0.01
|
Positive smear
|
765 (0.8)
|
136 (0.9)
|
NS
|
Clinical case
|
11,286 (12.1)
|
1,858 (12.4)
|
NS
|
Provider diagnosis
|
6,106 (6.6)
|
1,074 (7.1)
|
<0.01
|
Chest radiographe
|
Cavitary
|
18,742 (24.8)
|
2,990 (25.3)
|
NS
|
Noncavitary
|
50,652 (66.9)
|
7,897 (66.8)
|
NS
|
Normal
|
2,495 (3.3)
|
360 (3.0)
|
NS
|
Not done/unknown
|
3,802 (5.0)
|
578 (4.9)
|
|
Total
|
75,691
|
11,825
|
|
HIV statusf
|
Positive
|
6,062 (18.8)
|
884 (16.7)
|
NS
|
Negative
|
16,525 (51.2)
|
2,406 (45.5)
|
|
Indeterminate
|
47 (0.1)
|
6 (0.1)
|
|
Refused
|
1,959 (6.1)
|
325 (6.1)
|
|
Not offered
|
4,130 (12.8)
|
899 (17.0)
|
|
Test done, unknown
|
714 (2.2)
|
115 (2.2)
|
|
Unknown
|
2,812 (8.7)
|
658 (12.4)
|
|
Total
|
32,249
|
5,293
|
|
Homeless within past year
|
Yes
|
5,789 (6.2)
|
646 (4.3)
|
<0.001
|
No
|
84,873 (91.2)
|
14,185 (94.3)
|
|
Unknown
|
2,435 (2.6)
|
204 (1.4)
|
|
Resident of correctional facility at diagnosis
|
Yes
|
3,352 (3.6)
|
377 (2.5)
|
<0.001
|
No
|
89,479 (96.1)
|
14,617 (97.2)
|
|
Unknown
|
266 (0.3)
|
41 (0.3)
|
|
Correctional facility type
|
Federal prison
|
164 (4.9)
|
6 (1.6)
|
<0.005
|
State prison
|
1,036 (30.9)
|
97 (25.7)
|
<0.05
|
Local jail
|
1,905 (56.8)
|
231 (61.3)
|
NS
|
Juvenile facility
|
33 (1.0)
|
8 (2.1)
|
NS
|
Other
|
161 (4.8)
|
34 (9.0)
|
<0.001
|
Unknown
|
53 (1.6)
|
1 (0.3)
|
|
Total
|
3,352
|
377
|
|
Resident, long-term care facility at diagnosis
|
Yes
|
3,157 (3.4)
|
441 (2.9)
|
0.004
|
No
|
89,656 (96.3)
|
14,552 (96.8)
|
|
Unknown
|
284 (0.3)
|
42 (0.3)
|
|
Long-term care facility type
|
Nursing home
|
1,794 (56.8)
|
279 (63.3)
|
<0.01
|
Hospital-based
|
441 (14.0)
|
66 (15.0)
|
NS
|
Residential
|
356 (11.3)
|
34 (7.7)
|
<0.05
|
All other
|
504 (16.0)
|
55 (12.5)
|
NS
|
Unknown
|
62 (2.0)
|
7 (1.6)
|
|
Total
|
3,157
|
441
|
|
Injecting drug useg
|
Yes
|
2,569 (2.8)
|
515 (3.4)
|
<0.001
|
No
|
83,141 (89.3)
|
13,771 (91.6)
|
|
Unknown
|
7,387 (7.9)
|
749 (5.0)
|
|
Noninjecting drug useg
|
Yes
|
6,557 (7.0)
|
811 (5.4)
|
<0.001
|
No
|
78,622 (84.5)
|
13,367 (88.9)
|
|
Unknown
|
7,918 (8.5)
|
857 (5.7)
|
|
Excessive alcohol useh
|
Yes
|
13,646 (14.7)
|
1,661 (11.0)
|
<0.001
|
No
|
71,924 (77.3)
|
12,552 (83.5)
|
|
Unknown
|
7,527 (8.1)
|
822 (5.5)
|
|
Drug resistancei
|
First-line drugs
|
Yes
|
8,456 (11.7)
|
1,482 (12.6)
|
<0.001
|
No
|
57,029 (79.0)
|
8,886 (75.5)
|
|
Not tested/unknown
|
6,703 (9.3)
|
1,399 (11.9)
|
|
Total
|
72,188
|
11,767
|
|
Second-line drugs
|
Yes
|
1,341 (1.9)
|
208 (1.8)
|
<0.001
|
No
|
175 (0.2)
|
78 (0.7)
|
|
Not tested/unknown
|
70,672 (97.9)
|
11,481 (97.6)
|
|
Total
|
72,188
|
11,767
|
|
DOT
|
Yes—total DOT
|
40,511 (43.5)
|
4,936 (32.8)
|
<0.001
|
Yes—both DOT and self-administered
|
20,555 (22.1)
|
3,648 (24.3)
|
<0.001
|
No
|
23,337 (25.1)
|
5,326 (35.4)
|
<0.001
|
Unknown
|
8,694 (9.3)
|
1,125 (7.5)
|
|
Within city limits
|
Yes
|
80,775 (86.8)
|
14,603 (97.1)
|
<0.001
|
No
|
10,916 (11.7)
|
374 (2.5)
|
|
Unknown
|
1,406 (1.5)
|
58 (0.4)
|
|
Previous diagnosis of TB
|
Yes
|
4,794 (5.1)
|
652 (4.3)
|
<0.001
|
No
|
87,567 (94.1)
|
14,336 (95.4)
|
|
Unknown
|
736 (0.8)
|
47 (0.3
|
|
Duration of therapy (days)
|
Mean
|
246
|
245
|
NS
|
Median
|
217
|
214
|
|
Std. dev.
|
135
|
130
|
|
No.
|
65,344
|
10,822
|
|
|
aNTGSN,
National Tuberculosis Genotyping Surveillance Network;TB,
tuberculosis; DOT, directly observed therapy; TST, tuberculin
skin test; Std. dev., standard deviation; NS, not significant
(p>0.05).
bSubtotals for each category are listed if different
from the total case numbers.
cProbability of significant differences between
U.S. TB patients and all NTGSN surveillance patients (chi-square
test; t-test for duration of therapy); referent group is all
other groups combined, excluding not done or unknown categories,
unless otherwise noted.
dTop 10 countries for foreign-born patients only.
eExcludes cases with extrapulmonary TB only.
fHIV cases from California are excluded because
this site does not report HIV results on Report of a Verified
Case of Tuberculosis forms; ages 15–44 years only.
gInjecting or noninjecting drug use within last
year; includes use of licensed, prescription, or illegal drugs
(not prescribed by a physician).
hExcessive use of alcohol
within the past year as indicated by participation in alcohol
treatment programs, diagnosis of alcoholism, or observation
of intoxication during visits to health-care facilities.
iDrug resistance on initial
testing of isolate. First-line drug resistance is resistance
to at least one of the following: isoniazid, rifampin, ethambutol,
or streptomycin. Second-line drug resistance is resistance
to one or more of the following: ethionamide, kanamycin, cycloserine,
capreomycin, para-amino salicylic acid, amikacin, rifabutin,
ciprofloxacin, ofloxacin, or other drugs. Testing results
for one or more of the drugs could have been missing.
|
Table
3. Comparison of demographic and behavioral risk factors
and clinical and treatment outcomes of tuberculosis (TB) case-patients
who have genetically clustered genotypes with factors and outcomes
of patients who had unique genotype patternsa |
|
Variableb
|
Clustered
(%)
|
Unclustered
(%)
|
Relative risk
(95% CI)
|
Probabilityc
|
|
Total cases (n=10,752)
|
5.171(48.1)
|
|
5,581 (51.9)
|
|
|
Gender
|
Male
|
3,289 (63.6)
|
3,107 (55.7)
|
1.19 (1.14 to 1.24)
|
<0.001
|
Female
|
1,881 (36.4)
|
2,473 (44.3)
|
|
|
Unknown
|
1 (0.0)
|
1 (0.0)
|
|
|
Mean age (yrs; ±S.E.)
|
|
44.8 (±0.26)
|
49.4 (±0.28)
|
|
<0.0001
|
Race/ethnicity
|
White, non-Hispanic
|
1,018 (19.7)
|
1,201 (21.5)
|
0.94 (0.90 to 0.99)
|
0.02
|
Black, non-Hispanic
|
2,254 (43.6)
|
1,237 (22.2)
|
1.61 (1.55 to 1.67)
|
<0.001
|
Hispanic
|
914 (17.7)
|
1,112 (19.9)
|
0.92 (0.88 to 0.97)
|
0.003
|
American Indian/Native
|
17 (0.3)
|
10 (0.2)
|
|
|
Asian/Pacific Islander
|
961 (18.6)
|
2,014 (36.1)
|
0.60 (0.56 to 0.63)
|
<0.001
|
Unknown
|
7 (0.1)
|
7 (0.1)
|
)
|
|
Place of birth
|
U.S.-born
|
3,331 (64.4)
|
2,023 (36.2)
|
1.83 (1.75 to 1.90)
|
<0.001
|
Foreign-born
|
1,825 (35.3)
|
3,552 (63.6)
|
|
|
Unknown
|
15 (0.3)
|
6 (0.1)
|
|
|
Recent arrival in United Statesd
|
Yes
|
535 (29.3)
|
1,225 (34.5)
|
0.59 (0.55 to 0.63
|
<0.001
|
No
|
1,181 (64.7)
|
2,111 (59.4)
|
|
|
Unknown
|
109 (6.0)
|
216 (6.1)
|
|
|
Site of disease
|
Pulmonary
|
3,902 (75.5)
|
3,835 (68.7)
|
1.20 (1.14 to1.26)
|
<0.001
|
Extrapulmonary
|
788 (15.2)
|
1,254 (22.5)
|
0.77 (0.72 to 0.81)
|
<0.001
|
Pulmonary and extrapulmonary
|
476 (9.2)
|
492 (8.8)
|
|
NS
|
Unknown
|
5 (0.1)
|
0
|
|
|
Sputum smear
|
Positive
|
2,270 (43.9)
|
2,011 (36.0)
|
1.22 (1.11 to 1.33)
|
<0.001
|
Negative
|
1,802 (34.8)
|
1,943 (34.8)
|
|
|
|
Not done/unknown
|
1,099 (21.3)
|
1,627 (29.1)
|
|
|
Chest radiographe
|
Cavitary
|
1,345 (30.7)
|
1,172 (27.1)
|
1.09 (1.04 to 1.14)
|
<0.001
|
Noncavitary
|
2,639 (60.2)
|
2,826 (65.3)
|
|
|
Normal
|
146 (3.3)
|
118 (2.73)
|
|
|
Not done/unknown
|
253 (5.8)
|
211 (4.9)
|
|
|
Total
|
4,383
|
4,327
|
|
|
HIV statusf
|
Positive
|
458 (22.2)
|
223 (11.8)
|
1.37 (1.29 to 1.46)
|
<0.001
|
Negative
|
978 (47.4)
|
847 (44.8)
|
|
NS
|
Indeterminate
|
0
|
4 (0.2)
|
|
|
Refused
|
106 (5.1)
|
138 (7.3)
|
|
|
Not offered
|
252 (12.2)
|
354 (18.7)
|
|
|
Unknown
|
270 (13.0)
|
323 (17.1)
|
|
|
Total
|
2,064
|
1,889
|
|
|
Homeless within past year
|
Yes
|
370 (7.2)
|
139 (2.5)
|
1.55 (1.46 to 1.64)
|
<0.001
|
No
|
4,724 (91.4)
|
5,370 (96.2)
|
|
|
Unknown
|
77 (1.5)
|
72 (1.3)
|
|
|
Resident of correctional facility at diagnosis
|
Yes
|
190 (3.7)
|
69 (1.2)
|
1.55 (1.43 to 1.67)
|
<0.001
|
No
|
4,966 (96.0)
|
5,503 (98.6)
|
|
|
Unknown
|
15 (0.3)
|
9 (0.2)
|
|
|
Injecting drug useg
|
Yes
|
312 (6.0)
|
72 (1.3)
|
1.73 (1.65 to 1.83)
|
<0.001
|
No
|
4,540 (87.8)
|
5,231 (93.7)
|
|
|
Unknown
|
319 (6.2)
|
278 (5.0)
|
|
|
Noninjecting drug useg
|
Yes
|
460 (8.9)
|
140 (2.5)
|
1.65 (1.57 to 1.73)
|
<0.001
|
No
|
4,335 (83.8)
|
5,140 (92.1)
|
|
|
Unknown
|
376 (7.3)
|
301 (5.4)
|
|
|
Excessive alcohol useg
|
Yes
|
948 (18.3)
|
371 (6.6)
|
1.61 (1.54 to 1.67)
|
<0.001
|
No
|
3,897 (75.4)
|
4,893 (87.7)
|
|
|
Unknown
|
326 (6.3)
|
317 (5.7)
|
|
|
First-line drugsh
|
Yes
|
622 (12.1)
|
755 (13.7)
|
0.93 (0.87 to 0.99)
|
0.016
|
No
|
2,718 (53.0)
|
3,337 (60.5)
|
|
|
Not done
|
1,748 (34.1)
|
1,356 (24.6)
|
|
|
|
Unknown
|
45 (0.9
|
66 (1.2)
|
|
|
|
Total
|
5,133
|
5,514
|
|
|
|
aCI, confidence
interval; S.E., standard error.
bOnly factors that had significant
differences are shown.
cProbability of chi-square
statistic is shown, except for t-test results from analysis
of age from each group.
dForeign-born only; arrived
in the United States within 2 years.
eExcludes cases with extrapulmonary
TB only.
fCalifornia TB cases not included;
ages 15–44 years only.
gExcessive drug or alcohol
use within last year.
hFirst-line drug resistance
is resistance to at least one of the following: isoniazid, rifampin,
ethambutol, or streptomycin. Second-line drug resistance is
resistance to one or more of the following: ethionamide, kanamycin,
cycloserine, capreomycin, para-amino salicylic acid, amikacin,
rifabutin, ciprofloxacin, ofloxacin, or other drugs. Testing
results for one or more of the drugs could have been missing. |
Table 4.
Odds ratios from best-fit logistic regression analyses of the
presence or absence of a specific genetic cluster of Mycobacterium
tuberculosis on demographic, clinical, behavioral, or treatment
outcome variablesa |
|
Designationc
|
IS6110 copies
|
Spoligotypec
|
N
|
Main effect
|
Odds ratio estimates (95 CI)b
|
Wald pb
|
|
00003c
|
1
|
777777777760771
|
40
|
Asian/Pacific Islander
|
3.70 (1.51 to 9.02)
|
0.004
|
|
|
|
|
Age
|
0.98 (0.96 to 0.99)
|
0.017
|
|
|
|
|
Foreign-born
|
12.4 (3.83 to 39.9)
|
<0.0001
|
00129d
|
1
|
777777777413771
|
25
|
Asian/Pacific Islander
|
73.3 (17.0 to 315.6)
|
<0.0001
|
|
|
|
|
Extrapulmonary infection
|
2.57 (1.10 to 6.03)
|
0.03
|
00129d
|
1
|
777777774413771
|
83
|
Asian/Pacific Islander
|
282.8 (88.06 to 908.11)
|
<0.0001
|
00129d
|
1
|
477777777413071
|
23
|
Asian/Pacific Islander
|
6.34 (1.52 to 26.44)
|
0.01
|
|
|
|
|
Foreign-born
|
10.4 (1.55 to 70.12)
|
0.02
|
00129d
|
1
|
777777777413731
|
13
|
Asian/Pacific Islander
|
13.88 (3.71 to 51.92)
|
<0.0001
|
|
|
|
|
Resistance to first-line drugsd
|
3.80 (1.22 to 11.86)
|
0.02
|
00129
|
1
|
777776407760601
|
40
|
Female
|
2.73 (1.43 to 5.23)
|
0.0025
|
|
|
|
|
Black, non-Hispanic
|
3.57 (1.47 to 8.68)
|
0.005
|
|
|
|
|
Injecting drug use
|
3.81 (1.81 to 8.03)
|
0.0004
|
00016
|
2
|
701776777760601
|
129
|
Male
|
0.58 (0.40 to 0.84)
|
0.004
|
|
|
|
|
Black, non-Hispanic
|
10.88 (5.48 to 21.6)
|
0.006
|
00016c
|
2
|
777776777760771
|
82
|
Hispanic
|
16.36 (10.15 to 26.37)
|
<0.0001
|
00016
|
2
|
037776777760601
|
30
|
Age
|
1.03 (1.01 to 1.05)
|
0.006
|
|
|
|
|
Black, non-Hispanic
|
7.13 (2.36 to 21.53)
|
0.0005
|
|
|
|
|
Resident, long-term care facility
|
3.67 (1.17 to 11.70)
|
0.026
|
00016d
|
2
|
777776777760601
|
175
|
U.S.-born
|
3.12 (1.85 to 5.26)
|
<0.0001
|
|
|
|
|
Excessive alcohol use
|
0.55 (0.37 to 0.83)
|
0.0048
|
00370
|
3
|
700036777760731
|
13
|
White, non-Hispanic
|
5.20 (1.52 to 17.79)
|
0.0087
|
|
|
|
|
HIV positive
|
5.87 (1.69 to 20.41)
|
0.005
|
|
|
|
|
Noninjecting drug use
|
3.74 (1.17 to 12.01)
|
0.03
|
00017d
|
4
|
700076777760771
|
25
|
Hispanic
|
4.97 (2.16 to 11.44)
|
0.0002
|
00017d
|
4
|
777776777760771
|
64
|
Hispanic
|
15.7 (9.24 to 26.71)
|
<0.0001
|
01285
|
4
|
777776777760771
|
20
|
Resident, correctional facility
|
8.23 (3.08 to 22.01)
|
<0.0001
|
00015
|
7
|
|
28
|
Black, non-Hispanic
|
7.04 (1.64 to 30.3)
|
0.0087
|
|
|
|
|
Injecting drug use
|
4.84 (2.11 to 11.09)
|
0.0002
|
|
|
|
|
Excessive alcohol use
|
2.28 (1.02 to 5.13)
|
0.05
|
00768
|
9
|
|
19
|
Black, non-Hispanic
|
11.68 (1.54 to 88.87)
|
0.02
|
|
|
|
|
Noninjecting drug use
|
2.77 (1.11 to 6.92)
|
0.03
|
00242d
|
10
|
|
95
|
Male
|
2.12 (1.27 to 3.56)
|
0.004
|
|
|
|
|
Age
|
0.97 (0.96 to 0.98)
|
<0.0001
|
|
|
|
|
U.S.-born
|
8.44 (2.63 to 27.09)
|
0.0003
|
|
|
|
|
Homeless
|
3.60 (2.16 to 5.98)
|
<0.0001
|
|
|
|
|
Noninjecting drug use
|
0.46 (0.24 to 0.90)
|
0.02
|
00028
|
11
|
|
70
|
Black, non-Hispanic
|
17.57 (5.50 to 56.12)
|
<0.0001
|
00159
|
11
|
|
24
|
Excessive alcohol use
|
2.76 (1.23 to 6.22)
|
0.01
|
00325
|
11
|
|
20
|
Age
|
1.03 (1.01 to 1.06)
|
0.01
|
|
|
|
|
Excessive alcohol use
|
3.08 (1.22 to 7.70)
|
0.02
|
00673
|
11
|
|
25
|
Asian/Pacific Islander
|
84.6 (19.85 to 361.9)
|
<0.0001
|
00757
|
11
|
|
16
|
Age
|
0.90 (0.85 to 0.94)
|
<0.0001
|
|
|
|
|
HIV positive
|
4.86 (1.60 to 14.79)
|
0.005
|
00019c
|
12
|
|
27
|
Male
|
3.68 (1.10 to 12.39)
|
0.03
|
|
|
|
|
White, non-Hispanic
|
5.4 (2.35 to 11.08)
|
<0.0001
|
00372
|
12
|
|
20
|
Homeless
|
6.09 (2.43 to 15.20)
|
0.0001
|
|
|
|
|
Resident, long-term care facility
|
5.52 (1.535 to 20.0)
|
0.009
|
00035
|
13
|
|
33
|
Black, non-Hispanic
|
6.96 (2.3 to 21.0)
|
0.0006
|
|
|
|
|
Resistance to second-line drugse
|
40.59 (16.5 to 99.85)
|
<0.0001
|
00867
|
14
|
|
20
|
Black, non-Hispanic
|
11.68 (1.54 to 88.87)
|
0.02
|
|
|
|
|
Noninjecting drug use
|
2.77 (1.11 to 6.92)
|
0.03
|
01284
|
17
|
|
46
|
Black, non-Hispanic
|
2.40 (1.22 to 3.57)
|
<0.0001
|
|
|
|
|
Pulmonary disease
|
0.92 (-0.01 to 1.86)
|
0.054
|
00237c
|
21
|
|
98
|
White, non-Hispanic
|
2.80 (1.81 to 4.33)
|
<0.0001
|
|
|
|
|
Excessive alcohol use
|
2.09 (1.36 to 3.22)
|
0.0007
|
01693
|
21
|
|
29
|
HIV positive
|
3.16 (1.39 to 7.18)
|
0.006
|
|
|
|
|
Injecting drug use
|
3.08 (1.26 to 7.56)
|
0.014
|
|
|
|
|
Extrapulmonary disease
|
3.99 (1.69, 9.42)
|
0.002
|
00027
|
22
|
|
78
|
Black, non-Hispanic
|
1.74 (1.05 to 2.90)
|
0.03
|
|
|
|
|
Sputum-smear positive
|
3.07 (1.75 to 5.39)
|
<0.0001
|
|
aCI, confidence interval.
bOnly genetic clusters that had >20 isolates
were included in the analysis; some samples sizes are <20
because of missing data among independent variables (Wald 95%
confidence intervals given in parentheses). Only genetic clusters
with significant predictors are listed. Age was modeled as a
continuous variable.
cThe National Tuberculosis Genotyping and Surveillance
Network (NTGSN) designation for the IS6110 RFLP pattern
is represented; spoligotype octal code designations are presented
only for those genetic clusters from isolates that had <6
copies of IS6110. RFLP patterns and spoligotypes are
detailed elsewhere (11) .
dsolates observed in >4
sites over 5 years.
eFirst-line drug resistance
is resistance to at least one of the following: isoniazid, rifampin,
ethambutol, or streptomycin. Second-line drug resistance is
resistance to one or more of the following: ethionamide, kanamycin,
cycloserine, capreomycin, para-amino salicylic acid, amikacin,
rifabutin, ciprofloxacin, ofloxacin, or other drugs. |
1Members of the
National Tuberculosis Genotyping and surveillance Network Work Group,
in addition to the listed authors, included Joseph Bates, William
Benjamin, Pablo Bifani, M. Donald Cave, Rebecca Cox, Wendy Cronin,
Ed Desmond, Jeffrey Driscoll, Nancy Dunlap, Jennifer Flood, Kashef
Ijaz,, Michael Kucab, Barry Kreiswirth, Zary Liu, D. Mitchell Magee,
Jeffrey Massey, Ann Miller, Donna Mulcahy, Robert Pratt, Teresa
Quitugua, Barbara Schable, Kenneth Shilkret, Harry Taber, Jeffrey
Taylor, Sharon Sharnprapai, Sumi Sun, and Zhenhua Yang.
|