Peter Oh, CDPH TB Control Branch
April 28th 2011
CTCA breakout session on the use of data to
inform TB control practices and priorities

Describe an “in house” methodology to assess
annual increases (or decreases) in TB cases at
the local health jurisdiction level
◦ Present a case study
◦ Highlight some reasons why increases may occur
◦ Summarize statistical testing approaches

Provoke thought and discussion about how
this type of data analysis approach can inform
TB control actions and priorities
2

Is the increase (or decrease) greater than
‘expected’?
◦ If so, what explains it?
◦ If there are plausible explanations, what can be
done to slow the increase/accelerate the decrease?
3
Categories:
1. Reporting artifacts
2. Detection and diagnosis of TB disease
3. Population changes
4. Importation of TB disease versus reactivation
of remote infection
5. Recent transmission and outbreaks
Data Sources:
 Surveillance (RVCT)
 Genotyping, B-notification
4
Non-statistical
 N (%) in a year, compared to average of prior
years (e.g., 3-year period)
Statistical
 Chi-square test for trend in proportions (e.g.,
EpiInfo Statcalc, Cochran-Armitage test in SAS)
 Other possibilities
5
New cases per 100,000 pop’n
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
Example: TB case increase in County A
County A
California
6
TB case trend in County A, 2007-’10
Number of cases
Percent change
2010
2007-2009
(average)
2009
2008 2007
177
146.3
156
134
+20.9%
+13.5%
149
Percent change in County A case count:
2007-09 (average) to 2010: +20.9%
Chi-square test for trend: there was a significant
increasing trend in the TB case rate in County A from
2008-2010 (p=0.02)
7


Case report date vs. Case count date
Cases reported near the end of one calendar
year may be counted at the beginning of the
next
8
Count Year
Report
Year
2007
2008
2009
2010
2007
145 (97%)
8 (6%)
-
-
2008
-
126 (94%)
3 (2%)
-
2009
-
-
153 (98%)
11 (6%)
2010
-
-
-
166 (94%)
9
~5%
Provider
diagnoses
~15% Clinical
cases
~80% Laboratory confirmed cases
10

Laboratory-confirmed case proportion slightly
decreased from 75% (2007-09) to 72% (2010)

Provider diagnosed cases increased from 7% (200709) to 12% (2010)

Recent changes in case confirmation practices may
have contributed to the overall case increase in 2010
in County A
11





Number
Age structure
Nativity
Race / ethnicity
New immigrants’ countries of origin
12
Age:
• Median age decreased from 48 y (2007-09) to 40 y
(2010)
Race:
• U.S.-born Asian cases increased from
3 (2007-09) to 10 (2010)
• African-American cases decreased from 19 (2007-09)
to 14 (2010)
13
Example: Foreign-born TB cases in County A
Increase in the proportion of cases reported in
2010 compared to 2007-09 (average) by world
region:
World region
Percent change
Africa
+3.3
Latin America
+5.9
Asia
+2.6
Europe
+2.2
Pacific
+0.2
Southeast Asia
+1.8
14

Foreign born TB case patients
◦ New arrivers (<= 3 months)
◦ Recent arrivers (<= 1 year)
◦ Arriving with B-notification
15
2010
No. (%)
2007-’09 (average)
No. (%)
141 (79.7)
112.3 (76.8)
FB cases with B-notification
3 (2.1)
4.7 (4.2)
FB cases in U.S. 1-90 days
2 (1.4)
11.0 (9.8)
13 (9.2)
18.7 (16.6)
2 (1.4)
4.3 (3.9)
Group
Foreign-born (FB) cases
FB cases in U.S. ≤1 year
FB cases in U.S. with B note ≤1 year
16
12
11
Number of cases
10
1
2
3
9
8
7
6
5
4
3
2
1
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51
Time in U.S. (years)
(1) 18% of cases reported <2 years after U.S. arrival
(2) 50% <14 years
(3) Average=16 years
17


Pediatric (< 5y) cases
Diagnoses in congregate settings
◦ Corrections (jail, prison)
 Inmate
 Corrections employee
◦ Long term care facility
◦ Homeless patient
◦ Health care worker (occupational risk)
18
Group
TB cases
2010
TB cases
2007-’09
(average)
Change from
2007-’09 (average) to
2010
Pediatric (<5 y)
3*
2.7
+12.5%
Diagnosed in a
correctional facility
0
1.3
-100%
Diagnosed in a long
term care facility
4
4.3
-7.7%
Homeless
2
3.0
-33.3%
Health care worker
8
5.7
+41.2%
Corrections worker
0
0
no change
*U.S.-born pediatric TB cases (n=3)
19
2010
No. (%)
2009
No. (%)
2008
No. (%)
2007
No. (%)
177
156
134
149
Total cases
Genotyped
93
(73)
92
(82)
79
(80)
98
(82)
Clustered cases
58
(62)
65
(71)
49
(62)
63
(64)
2
(3)
4
(6)
4
(6)
2
(4)
5
(8)
4
(8)
9
(14)
2
(4)
East Asian (“Beijing”)*
Indo-Oceanic (“Manila”)*
Euro-American Haarlem
4
(7)
East-African-Indian
M. bovis type
2
Euro-American LAM
(3)
2
(3)
* Only sub-clusters determined by the MIRU2 method are shown.
This lineage is one of the most commonly found in CA.
20
The +32% increase (p=0.02) in TB cases in County A
2008-2010 is attributable to a combination of the
following factors:
Reporting artifacts
•
A higher proportion of cases in the year of analysis (2010)
were actually identified at the end of the previous year
Detection & Diagnosis of TB Disease
•
Provider diagnoses increased in 2010
Population changes
•
Small, concurrent case increases in several population
groups (e.g., U.S.-born Asians; immigrants from North
Africa, Mexico, Eastern Europe)
21
Recent Transmission & Outbreaks


Possible recent transmission among foreign born persons
A slight increase in health care worker TB cases in 2010
Importation of TB Disease

Decreases in measures of importation of disease suggest that
this did not contribute to the 2010 case increase
Reactivation of Remote Infection

Half of foreign-born cases in 2010 arrived in the U.S. >14 years
ago, underscoring the importance of reactivation of remote
infection
22
County A example



The increase in the TB case count from 2008-2010 in County A is
significant and warrants further investigation
The increase was due to several factors instead of a single
overriding factor
Interventions designed to address the increase in reported cases
in 2010 will need to be tailored to address conditions specific to
County A
The case increase analysis approach in general


This methodology has shown promise, utility in recent
collaborations with local health jurisdictions
Can inform TB control practices and priorities (e.g., case
detection and confirmation-related provider, over diagnosis
issues)
23




Other (unmeasured) contributing factors may not be
captured in this analysis
Accuracy of denominator data
Small numbers in lower-morbidity LHJs
LHJ may not always be the ideal analysis level (e.g.,
regional could be preferable)
24



What are the strengths and weaknesses of this case
increase analysis approach?
What questions does this type of analysis raise?
How can data analysts use these kinds of results to
inform TB control approaches and priorities?
◦ Existing situations or forums?
◦ Opportunities, barriers?
25




Phil Lowenthal
Lisa Pascopella
TB program colleagues in County A
Kathy DeRiemer
26
Peter Oh
Peter.Oh@cdph.ca.gov
(510) 620-3018
27