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Mam Ibraheem, MD, MPH

Using Hospitalization Data To Evaluate and Improve Invasive Pneumococcal Disease Surveillance — New Mexico, 2007–2009. Mam Ibraheem, MD, MPH. New Mexico Department of Health EIS Field Assignments Branch, DAS, SEPDPO, OSELS 2011 CSTE Annual Conference June 15, 2011.

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Mam Ibraheem, MD, MPH

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  1. Using Hospitalization Data To Evaluate and Improve Invasive Pneumococcal Disease Surveillance — New Mexico, 2007–2009 Mam Ibraheem, MD, MPH New Mexico Department of Health EIS Field Assignments Branch, DAS, SEPDPO, OSELS 2011 CSTE Annual Conference June 15, 2011 Office of Surveillance, Epidemiology, and Laboratory Services Scientific Education and Professional Development Program Office

  2. Invasive Pneumococcal Disease (IPD) • Isolation of Streptococcus pneumoniae from normallysterile sites • Serious and vaccine-preventable • Typically manifests as pneumonia, septicemia, or meningitis • Leading cause of bacterial meningitis in young children in the United States

  3. Importance of IPD Surveillance Systems • Monitor pneumococcal vaccination programs • Monitor changes in IPD epidemiology • Inconsistent reporting adversely impacts policy decisions

  4. IPD Surveillance in New Mexico • IPD reportable since 2000 • Passive surveillance • Statewide • Healthcare providers/Laboratories • Active Bacterial Core Surveillance (ABCs) • Population-based: cases among non-residents of NM excluded • Audits of clinical laboratory records used to identify cases not reported passively • In 2009, access to hospitalization data

  5. Questions • How complete is the combined (passive and active) IPD surveillance in New Mexico? • Can hospitalization data identify additional IPD cases?

  6. Capture-Recapture Method • Degree of undercount for a surveillance • Compares results of 2 ‘independent’ reporting systems • Calculates number of cases missed by both systems • Estimated total number of cases derived • Determines reporting completeness for a surveillance system • Assumptions: • Closed population • No loss of tags • Simple randomness • Independency

  7. Methods • Linked IPD surveillance data with Hospital Inpatient Discharge Data (HIDD) by deterministic data linkage • Identified potential IPD cases in HIDD by ICD-9 codes Specific Nonspecific

  8. Data Linkage Results HIDD n=1,287 Surveillance n=1,191 (~67% initially passive)

  9. Data Linkage Results HIDD n=1,287 Surveillance n=1,191 (~67% initially passive) Linked n=523

  10. Data Linkage Results HIDD n=1,287 Surveillance n=1,191 (~67% initially passive) HIDD only n=764 Linked n=523 Surveillance only n=668 (~79% hospitalized)

  11. Data Linkage Results HIDD n=1,287 Surveillance n=1,191 (~67% initially passive) HIDD only n=764 Linked n=523 Surveillance only n=668 (79% hospitalized)

  12. Data Linkage Results HIDD n=1,287 Surveillance n=1,191 (~67% initially passive) HIDD only n=764 Linked n=523 Surveillance only n=668 (79% hospitalized) IPD-specific codes n=62 Nonspecific codes n=702

  13. Data Linkage Results HIDD n=1,287 Surveillance n=1,191 (~67% initially passive) HIDD only n=764 Linked n=523 Surveillance only n=668 (79% hospitalized) IPD-specific codes n=62 Nonspecific codes n=702 Census approach 62 (100%) Reviewed

  14. Data Linkage Results HIDD n=1,287 Surveillance n=1,191 (~67% initially passive) HIDD only n=764 Linked n=523 Surveillance only n=668 (79% hospitalized) Census approach 62 (100%) Reviewed 4 confirmed cases IPD-specific codes n=62 Nonspecific codes n=702 IPD cases n=4 Not cases N=58

  15. Data Linkage Results HIDD n=1,287 Surveillance n=1,191 (~67% initially passive) HIDD only n=764 Linked n=523 Surveillance only n=668 (79% hospitalized) IPD-specific codes n=62 Nonspecific codes n=702 102 (15%) Systematic sampling 102 (100%) Reviewed 4 were confirmed IPD cases Sample requestedn=102

  16. Data Linkage Results HIDD n=1,287 Surveillance n=1,191 (~67% initially passive) HIDD only n=764 Linked n=523 Surveillance only n=668 (79% hospitalized) IPD-specific codes n=62 Nonspecific codes n=702 Systematic sampling 102 (100%) Reviewed 4 were confirmed IPD cases Sample requestedn=102 Estimated IPD cases n=28 95%CI (8-68) Estimated Not cases N=674

  17. Data Linkage Results HIDD n=1,287 Surveillance n=1,191 (~67% initially passive) HIDD only n=764 Linked n=523 Surveillance only n=668 (79% hospitalized) Census approach 62 (100%) Reviewed 4 confirmed cases IPD-specific codes n=62 Nonspecific codes n=702 Systematic sampling 102 (100%) Reviewed 4 confirmed IPD cases Sample requestedn=102 IPD cases n=4 Not cases N=58 Estimated IPD cases n=28 95%CI (8-68) Estimated Not cases N=674

  18. Final Capture-Recapture Results IPD Surveillance System Sensitivity : 1,191/1,264 = 94%* HIDD IPD Sensitivity : 555/1264 : 44%* * 95% confidence interval estimate pending further review/validation

  19. ICD-9 Code Distribution by IPD Case Status within HIDD post Laboratory Reports Review SEN = 49% PVP = 82% SEN = 51% PVP = 30%

  20. Missed Cases by Culture Site HIDD identified Cases 1-6, not previously identified by IPD surveillance

  21. Limitations • Sampling instead of census approach • Sampling variability • Small sample size • Low precision • Incomplete sampling frame • Time period chosen • HIDD data only included hospital admissions • Systems were not entirely independent , potentials for: • Positive dependency phenomenon • Underestimation of total IPD cases • Overestimation of IPD surveillance system sensitivity

  22. Strengths • Accurate diagnosis of IPD • Correct identification of IPD cases • Closed population • Deterministic data linkage reduces false matches • Manually reviewing all the linked data and correcting for all the identified false matches • Systematic sampling

  23. Conclusions • High NM IPD surveillance sensitivity • ABCs • A sample of hospitalization data yielded eight additional IPD cases • HIDD IPD sensitivity? • ICD-code dependent

  24. Recommendations • In New Mexico, • Periodic review of HIDD data may be worthwhile. This identified additional IPD cases but required a lot of work • A study of the hospitalized IPD cases yet unidentified by HIDD is warranted • States relying on passive reporting without resources to do active surveillance might use IPD-specific ICD-9 codes to improve IPD surveillance • IPD case-ascertainment deficiencies, including hospitalization coding problems, should be addressed through coding study • Capture-Recapture methods may be used to improve surveillance case findings

  25. NM DOH: Michael Landen (co-author) Joseph Bareta, Joan Baumbach, Camille Clifford, Paul Ettestad, Jessica Jungk, Megin Nichols, Terry Reusser, Mack Sewell, Chad Smelser, Brian Woods CDC: Julie Magri Diana Bensyl, Betsy Gunnels, Sheryl Lyss Acknowledgments Office of Surveillance, Epidemiology, and Laboratory Services Scientific Education and Professional Development Program Office

  26. Calculation of Completeness of Reporting by the Two-Source Capture-Recapture Method C = number of people identified by both sources N2 = number of people identified only in data source 2 S = number of people identified in data source 2 N1 = number of people identified only in data source 1 R = number of people identified in data source 1 X = number of cases not reported to either system(estimated) N = estimate of total number of cases ...................................................... N = RS/C Completeness of source 1 = R/N Completeness of source 2 = S/N ...................................................... Var (N) = ( R * S * N1 * N2 ) / C3 95% CI = N ± 1.96 Var (N)1/2

  27. ICD-9 Distribution within HIDDPreliminary Analysis Prevalence Rate Ratio ~ 2.6

  28. ICD-9 Distribution within HIDD

  29. Some Reasons for Misclassification of HIDD IPD Cases • Keypunch • Coding error • Abstraction error • Physician error (Rule out IPD) • Physician error (other) • No error; clinically compatible

  30. Linkage Lessons • Sequential deterministic linkage • Overall rate of false +ve matches: 5.97% • Overall rate of false -ve matches: 0.41%

  31. Recommendations to Improve IPD Surveillance • Direct electronic reporting of laboratory data • Identification of missed opportunities for reporting • System to automatically remind treating doctors • Provision of updatable computer software • Hospital coders to seek evidence of documented reporting • Audit of selected laboratories • Studies to identify coding issues and reasons for under reporting

  32. Demo

  33. Scenario 1: Source 2

  34. Slides Master 1-Title 2-Invasive Pneumococcal Disease (IPD) 3-Importance of IPD Surveillance Systems 4-IPD Surveillance in New Mexico 5-Questions/Objectives 6-Capture-Recapture Method 7-Methods/ ICD codes (8-16) Data Linkage Results 17-Full flow diagram 18-Final Cap-Recap Results 19-ICD-9 Codes by IPD Case Status 20-Missed Cases by Culture Site 21-Excluded Hospital Admissions by Final Status 22-Limitations 23-Strengths 24-Conclusions 25-Recommendations 26-Acknowledgments 27-Empty 28-Excluded Hospital Admissions by Final Status by ICD-9 codes 29-Cap-Recap Calculus 30-ICD-9 Distribution: Preliminary analysis 31-ICD-9 Distribution: Pie Charts 32-Some Reasons for HIDD IPD Cases Misclassifications 33-Linkage Lessons 34-Recommendations to Improve IPD Surveillance 35-Cap-Recap Sampling Demo 36-Cap-Recap IPD Assumptions 37-Scenarios 38-Slide Master

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