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Using Hospitalization Data To Evaluate and Improve Invasive Pneumococcal Disease Surveillance New Mexico, 20072009 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 Invasive Pneumococcal Disease (IPD) Isolation of Streptococcus pneumoniae from normally sterile sites Serious and vaccine-preventable
Typically manifests as pneumonia, septicemia, or meningitis Leading cause of bacterial meningitis in young children in the United States Importance of IPD Surveillance Systems Monitor pneumococcal vaccination programs Monitor changes in IPD epidemiology Inconsistent reporting adversely impacts policy decisions IPD Surveillance in New Mexico IPD reportable since 2000 Passive surveillance
Active Bacterial Core Surveillance (ABCs) Statewide Healthcare providers/Laboratories 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 Questions How complete is the combined (passive and active) IPD
surveillance in New Mexico? Can hospitalization data identify additional IPD cases? 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 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 ICD-9 Codes Definitions 320.1
Meningitis due to S.pneumoniae 038.2 Septicemia due to S. pneumoniae 481 Pneumonia due to S. pneumoniae 320.2 Streptococcal meningitis 041.2 S. pneumoniae as the cause of bacterial infection classified elsewhere and of unspecified site Specific Nonspecific
Data Linkage Results HIDD n=1,287 Surveillance n=1,191 (~67% initially passive) Data Linkage Results HIDD n=1,287 Surveillance n=1,191 (~67% initially passive) Linked n=523 Data Linkage Results HIDD n=1,287
HIDD only n=764 Surveillance n=1,191 (~67% initially passive) Linked n=523 Surveillance only n=668 (~79% hospitalized) Data Linkage Results HIDD n=1,287 HIDD only n=764 Surveillance n=1,191 (~67% initially passive)
Linked n=523 Surveillance only n=668 (79% hospitalized) Data Linkage Results HIDD n=1,287 HIDD only n=764 IPD-specific codes n=62 Surveillance n=1,191 (~67% initially passive) Linked n=523
Nonspecific codes n=702 Surveillance only n=668 (79% hospitalized) Data Linkage Results HIDD n=1,287 Surveillance n=1,191 (~67% initially passive) HIDD only n=764 Census approach 62 (100%) Reviewed Linked n=523
IPD-specific codes n=62 Surveillance only n=668 (79% hospitalized) Nonspecific codes n=702 Data Linkage Results HIDD n=1,287 Surveillance n=1,191 (~67% initially passive) HIDD only n=764 Census approach 62 (100%) Reviewed 4 confirmed cases
IPD cases n=4 Linked n=523 IPD-specific codes n=62 Not cases N=58 Surveillance only n=668 (79% hospitalized) Nonspecific codes n=702 Data Linkage Results HIDD n=1,287
Not cases N=58 Estimated IPD cases n=28 95%CI (8-68) Systematic sampling 102 (100%) Reviewed 4 confirmed IPD cases Estimated Not cases N=674 Final Capture-Recapture Results Identified by HIDD Yes No Detected by Yes 523 Surveillance No 32 (12-72) * System
Total 555 668 41 Total 1,191 1,264* IPD Surveillance System Sensitivity : 1,191/1,264 = 94%* HIDD IPD Sensitivity : 555/1264 : 44%* * 95% confidence interval estimate pending further review/validation ICD-9 Code Distribution by IPD Case Status within HIDD post Laboratory Reports Review IPD-specific codes IPD 272 Not IPD
58 Total 330 Nonspecific codes 283 674 957 Total 555 732 1,287 SEN = 49%
PVP = 82% SEN = 51% PVP = 30% Missed Cases by Culture Site Case 1 2 3 4 5 6 7 8 Culture Site Blood Blood Blood Blood Blood Body fluid
Body fluid Reason Missed Failure of lab to code all sterile site isolates as invasive Infection control practitioner generated list of cases Unknown Unknown Unknown Lab misnomer of body fluid; actually synovial fluid Initially considered not a case by NMDOH Reclassified: body fluid was actually pleural fluid/empyema Abscess aspirate Initially considered not a case by NMDOH Reclassified: aspirate was from sternoclavicular joint abscess HIDD identified Cases 1-6, not previously identified by IPD surveillance Excluded Hospital Admissions by Final Status Combined Specific and Nonspecific Sample n=4 (2.564%) n=18 (11.54%)
n=63 (40.38%) n=28 (17.95%) n=43 (27.56%) No Micro Non S. Pneum. Isolated S. Pneum./non Sterile Site Non IPD(Others/Unclassified) Negative Micro 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 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 Conclusions High NM IPD surveillance sensitivity ABCs
A sample of hospitalization data yielded eight additional IPD cases HIDD IPD sensitivity? ICD-code dependent 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 Acknowledgments 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 For more information please contact Centers for Disease Control and Prevention 1600 Clifton Road NE, Atlanta, GA 30333 Telephone, 1-800-CDC-INFO (232-4636)/TTY: 1-888-232-6348
E-mail: [email protected] Web: www.cdc.gov The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Office of Surveillance, Epidemiology, and Laboratory Services Scientific Education and Professional Development Program Office Excluded Hospital Admissions by Final Status Specific Nonspecific n=3 (5.172%) n=15 (15.31%) n=11 (18.97%) n=22 (37.93%) n=41 (41.84%) n=2 (3.448%)
n=32 (32.65%) n=20 (34.48%) n=8 (8.163%) n=2 (2.041%) S. Pneum./non Sterile Site No Micro Non IPD(Others/Unclassified) Graphs by IPD ICD-9 Codes Non S. Pneum. Isolated Negative Micro Calculation of Completeness of Reporting by the Two-Source Capture-Recapture Method Source 2 cases Source 1 cases Reported Not reported
Reported Not reported Total C N1 R N2 X Total S N 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 ICD-9 Distribution within HIDD Preliminary Analysis ICD-9 codes Nonlinked (Unreported) 104 Total IPD-Specific Linked
(Reported) 317 Nonspecific 241 625 866 Total 558 729 1287 Prevalence Rate Ratio ~ 2.6 421
ICD-9 Distribution within HIDD IPD-specific Nonspecific 24.70% 27.83% Linked(Reported) Linked(Reported) NonLinked(Unreported) NonLinked(Unreported) 72.17% 75.30% NonLinked(Unreported)
Linked(Reported) 14.27% IPD-specific Nonspecific IPD-specific Nonspecific 43.19% 56.81% 85.73% 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 Linkage Lessons Sequential deterministic linkage Overall rate of false +ve matches: 5.97% Overall rate of false -ve matches: 0.41% 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 Demo Scenario 1: Source 2 Source 1 yes no
yes 6 1 7 no 9 2 15 18 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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