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Influenza-like illness by National Health Insurance databases

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

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Influenza-like illness by National Health Insurance databases

  1. 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. 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. 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. 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. 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
  6. 6. 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
  7. 7. 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”
  8. 8. 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
  9. 9. 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%
  10. 10. 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
  11. 11. Basic Statistics
  12. 12. 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)
  13. 13. 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
  14. 14. 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))
  15. 15. 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))
  16. 16. Results: Formula
  17. 17. 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).
  18. 18. Internal Validation
  19. 19. Future Directions • Covariates weights in formulae • Combined effect • External validation: 2011~ data? • Advanced models: agent-base models (Dr. Nathaniel Osgood)
  20. 20. Thank you!
  21. 21. Results: Cost
  22. 22. Results: LOS
  23. 23. Como-wise Statistics
  24. 24. Como-wise Statistics
  25. 25. Age-wise Statistics
  26. 26. 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
  27. 27. Selected Como 2007-2008
  28. 28. Selected Como 2007-2008
  29. 29. Selected Como 2009-2010
  30. 30. Selected Como 2009-2010

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