Comorbidity effect on influenza-like illness (ILI) inpatients outcomes; method by National Health Insurance databases of Taiwan; SAS, Stata, SQL

1. Comorbidity Attributes that Link to
Unfavorable Outcomes of Influenza-
like Illness-related Inpatients -- A
Nationwide Cohort Analysis
以台灣全民健保資料庫分析探索類
流感關聯病人不良預後之共病特質
及預測模式
陳勁辰 金傳春 …

2. Introduction
• Susceptible –(1)-> Influenza –(2)-> Outcome
• Comorbidity (Como) affects (2)
• Real world: symptoms/syndrome groups
• Influenza-like illness (ILI)
• Aim: Como effect of ILI on outcomes

3. Methods
• National Health Insurance Database (NHID)
• Materials: One-million samples of NHID of
2007, 2008, 2009, 2010
• Roughly: Seasonal in 2007+2008; Pandemic in
2009+2010
• ILI defined by EID
• ILI-related inpatients: hospitalized with ILI or
ambulatory visits for ILI =< 1 day

4. Marsden-Haug N, Foster VB, Gould PL, Elbert E, Wang H, Pavlin JA. Code-based syndromic
surveillance for influenzalike illness by International Classification of Diseases, Ninth
Revision. Emerging Infect. Dis. 2007;13(2):207–216.

5. Methods
• ILIR cohort: EID definition
• Como: Selected by relevance and advisors
• Outcomes:
– Cost: from NHID
– Length of stay (LOS): from NHID
– Daily cost: Cost/LOS
– Death: by endpoint code
– ICU: by treatment code
– Adverse event (AE): Death or ICU

7. Strata by Age
• By social-economic status
• [0, 6): pre-school
• [6, 15): school children
• [15, 25): the youth
• [25, 45): young adults
• [45, 65): middle-aged
• [65, oo): the elderly

8. Methods
• Data management by SAS 9.3, SAS-SQL
programs
• “Big Data” computation on NTU virtual
machine remotely
• Statistics by StataMP 13.1 Mac
• Programs and files shared in “Clouds”

9. Models
• Outcome Y
• Como X
• Adjusted by Sex
• Y = B0 + B1 X + B2 Sex (Each Age stratum)
• Cost, LOS, Dcost -> log-transformed -> Linear
regression
• Death, ICU, AE -> Logistic regression

10. Algorithms
• ILI hetero each year? Checked by sex, age,
como’s, outcomes, ILI top-ten codes;
• Combine homo years, then proceed;
• In each age stratum, como is selected if:
– Sig in all regression models (all endpts)
– Age-specific prevalence>5%

11. Algorithm
• ILI Score = Sum of <Como> * <In Age>
• Internal validation by modeling ILI score on
outcomes
– Cost/LOS/Dcost by Spearman correlation
– Death/ICU/AE by ROC

12. Basic Statistics

13. Results: Yearly Comparison
• ILI ICD9: freq rank; fisher's exact test p=0.9090
• Sex: fisher's exact test p=0.2380
• Age: Mann-Whitney U test as scale; Fisher's exact
test as strata nominal; p<0.001 (age up with year)
• Como: each p<0.001 except preg, cong, imdef,
autoimm
• Endpt: each p<0.001 (cost, los, daycost, die, icu,
ae)

14. Results: Data Merge by Pandemic State
• Yearly data cannot be merged into one;
• Yearly formulae are not practical;
• Formulae by pandemic attribute (pan=0 or 1)
• Seasonal (pan=0): 2007+2008
• Pandemic (pan=1): 2009+2010

15. Results: Seasonal (pan=0)
• Como: cancer, cong, cv, cva, htn, esrd, imdef, dm;
• 0-6: cancer, cong;
6-15: cv, cva;
15-25: cv, cva;
25-45: cv, cva, htn, esrd, imdef;
45-65: dm, esrd;
65+: esrd.
• => (cancer*[0-6), cong*[0-6), cv*[6-45), cva*[6-
45), htn*[25-45), esrd*[25+), dm*[45-65),
imdef*[25-45))

16. Results: Pandemic (pan=1)
• Como: cv, cancer, cong, cva, esrd, imdef;
• 0-6: cv, cong;
6-15: cva, cancer;
15-25: cv, cva, cancer;
25-45: cv, cva, cancer, esrd, imdef;
45-65: esrd;
65+: esrd.
• => (cancer*[6-45), cong*[0-6), cv*[0-6, 15-45),
cva*[6-45), esrd*[25+), imdef*[25-45))

17. Results: Formula

18. Results: Formula
• 1. Cong*[0-6)+cva*[6-
45)+esrd*[25+)+imdef*[25-45);
• +2. Seasonal: dm*[45-65) +htn*[25-45)
+cancer*[0-6) +cv*[6-45).
• +2'. Pandemic: cancer*[6-45) +cv*[0-6, 15-45).

19. Internal Validation

20. Future Directions
• Covariates weights in formulae
• Combined effect
• External validation: 2011~ data?
• Advanced models: agent-base models (Dr.
Nathaniel Osgood)

21. Thank you!

22. Results: Cost

23. Results: LOS

24. Como-wise Statistics

25. Como-wise Statistics

26. Age-wise Statistics

27. Age-wise Como Prevalence
Comorbidity prevalence in each age stratum
2007+2008 0-6 6-15 15-25 25-45 45-65 65+ 2009+2010 0-6 6-15 15-25 25-45 45-65 65+
Allergy 27.64 24.25 9.17 7.54 6.80 7.74 Allergy 28.38 25.30 10.37 7.68 6.72 7.36
DM 0.56 1.67 2.93 7.17 29.10 37.20 DM 0.34 1.69 2.74 8.26 29.28 37.89
Hlipid 0.14 0.45 1.63 6.26 21.17 18.25 Hlipid 0.10 0.67 1.26 6.92 22.85 20.70
CV 0.67 2.03 3.61 7.03 26.64 50.39 CV 0.68 1.24 3.27 7.17 25.86 49.70
HTN 0.05 0.36 2.04 9.09 41.66 65.47 HTN 0.07 0.60 1.83 10.25 42.61 67.52
CVA 0.13 1.28 2.70 3.42 13.76 31.68 CVA 0.95 0.73 2.29 3.44 13.07 30.99
Dementia 0.06 0.24 1.45 3.89 3.86 16.98 Dementia 0.17 0.34 1.65 3.40 3.90 18.11
Cancer 1.56 7.30 3.18 10.06 27.28 22.90 Cancer 3.61 4.29 4.83 10.05 28.54 24.09
Pregnancy 0.00 0.03 18.95 24.70 0.17 0.00 Pregnancy 0.00 0.10 14.10 24.21 0.14 0.00
congenital 4.85 7.90 3.06 1.91 2.13 2.11 congenital 4.75 6.85 3.24 2.16 2.06 2.26
Uremia NA 0.09 0.78 1.52 6.58 10.74 Uremia NA 0.10 0.90 1.68 6.93 11.73
Lungs 23.40 14.75 4.55 5.07 13.14 34.35 Lungs 26.66 15.23 3.88 5.17 11.69 32.08
Imdef 0.02 0.32 0.21 0.62 1.09 0.99 Imdef 0.02 0.33 0.67 0.81 1.25 0.80
Autoimm 0.03 0.86 2.06 2.25 3.01 2.96 Autoimm 0.07 1.13 1.80 2.32 2.90 3.20

28. Selected Como 2007-2008

29. Selected Como 2007-2008

30. Selected Como 2009-2010

31. Selected Como 2009-2010