1. Peter Oh, CDPH TB Control Branch April 28 th 2011 CTCA breakout session on the use of data to inform TB control practices and priorities

2.  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

3.  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

4. 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

5. 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

6. Example: TB case increase in County A 6

7. 7 20102007-2009 (average) 200920082007 Number of cases177146.3156134149 Percent change+20.9%+13.5% TB case trend in County A, 2007-’10 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)

8.  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

9. Count Year 2007200820092010 Report Year 2007145 (97%)8 (6%)-- 2008-126 (94%)3 (2%)- 2009--153 (98%)11 (6%) 2010---166 (94%) 9

10. 10

11.  Laboratory-confirmed case proportion slightly decreased from 75% (2007-09) to 72% (2010)  Provider diagnosed cases increased from 7% (2007- 09) to 12% (2010)  Recent changes in case confirmation practices may have contributed to the overall case increase in 2010 in County A 11

12.  Number  Age structure  Nativity  Race / ethnicity  New immigrants’ countries of origin 12

13. 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

14. 14 Increase in the proportion of cases reported in 2010 compared to 2007-09 (average) by world region: World regionPercent change Africa +3.3 Latin America +5.9 Asia +2.6 Europe +2.2 Pacific +0.2 Southeast Asia +1.8 Example: Foreign-born TB cases in County A

15.  Foreign born TB case patients ◦ New arrivers (<= 3 months) ◦ Recent arrivers (<= 1 year) ◦ Arriving with B-notification 15

16. 16 Group 2010 No. (%) 2007-’09 (average) No. (%) Foreign-born (FB) cases141(79.7)112.3(76.8) FB cases with B-notification3(2.1)4.7(4.2) FB cases in U.S. 1-90 days2(1.4)11.0(9.8) FB cases in U.S. ≤1 year13(9.2)18.7(16.6) FB cases in U.S. with B note ≤1 year2(1.4)4.3(3.9)

17. 17 Time in U.S. (years) (1) 18% of cases reported <2 years after U.S. arrival (2) 50% <14 years (3) Average=16 years 1 23 Number of cases

18.  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

19. GroupTB 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 01.3-100% Diagnosed in a long term care facility 44.3-7.7% Homeless 23.0-33.3% Health care worker 85.7+41.2% Corrections worker 00no change *U.S.-born pediatric TB cases (n=3) 19

20. 20 2010 No. (%) 2009 No. (%) 2008 No. (%) 2007 No. (%) Total cases177156134149 Genotyped93(73)92(82)79(80)98(82) Clustered cases58(62)65(71)49(62)63(64) East Asian (“Beijing”)*2(3)4(6) Indo-Oceanic (“Manila”)*4(6)2(4) Euro-American Haarlem4(7)5(8)4 9(14) East-African-Indian2(4) M. bovis type2(3) Euro-American LAM2(3) * Only sub-clusters determined by the MIRU2 method are shown. This lineage is one of the most commonly found in CA.

21. 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

22. 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

23. 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

24.  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

25.  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

26.  Phil Lowenthal  Lisa Pascopella  TB program colleagues in County A  Kathy DeRiemer 26

27. Peter Oh Peter.Oh@cdph.ca.gov (510) 620-3018 27