Mam Ibraheem, MD, MPH

Mam Ibraheem, MD, MPHNew Mexico Department of Health

EIS Field Assignments Branch, DAS, SEPDPO, OSELS2011 CSTE Annual Conference

June 15, 2011

Using Hospitalization Data To Evaluate and Improve Invasive Pneumococcal Disease Surveillance —

New Mexico, 2007–2009

Office of Surveillance, Epidemiology, and Laboratory ServicesScientific 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

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

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 infectionclassified elsewhere and of unspecified site

Specific

Nonspecific

Data Linkage Results

HIDD n=1,287

Surveillance n=1,191 (~67% initially passive)


HIDD n=1,287


Linked n=523


HIDD n=1,287


Surveillance only n=668 (~79% hospitalized)

HIDD only n=764

Linked n=523


HIDD n=1,287


Surveillance only n=668 (79% hospitalized)

HIDD only n=764

Linked n=523


IPD-specific codes n=62

Nonspecific codes n=702

HIDD n=1,287



HIDD only n=764

Linked n=523


Census approach 62 (100%) Reviewed



HIDD n=1,287



HIDD only n=764

Linked n=523


Census approach 62 (100%) Reviewed 4 confirmed cases



HIDD n=1,287



HIDD only n=764

Linked n=523

IPD casesn=4

Not casesN=58




102 (15%) Systematic sampling102 (100%) Reviewed4 were confirmed IPD cases

HIDD n=1,287



HIDD only n=764

Linked n=523

Sample requestedn=102




Systematic sampling102 (100%) Reviewed4 were confirmed IPD cases

HIDD n=1,287



HIDD only n=764

Linked n=523

Estimated IPD casesn=28 95%CI (8-68)

Estimated Not casesN=674



Census approach 62 (100%) Reviewed 4 confirmed cases



HIDD n=1,287



HIDD only n=764

Linked n=523

IPD casesn=4

Not casesN=58

Systematic sampling102 (100%) Reviewed4 confirmed IPD cases

Estimated IPD casesn=28 95%CI (8-68)


Estimated Not casesN=674

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

Identified by HIDDYes No Total

Detected by Surveillance System

Yes 523 668 1,191No 32 (12-72) * 41Total 555 1,264*

ICD-9 Code Distribution by IPD Case Status within HIDD post Laboratory Reports Review

IPD Not IPD TotalIPD-specific codes 272 58 330

Nonspecific codes 283 674 957

Total 555 732 1,287

SEN = 49%PVP = 82%

SEN = 51%PVP = 30%

Missed Cases by Culture Site

HIDD identified Cases 1-6, not previously identified by IPD surveillance

Case Culture Site Reason Missed

1 Blood Failure of lab to code all sterile site isolates as invasive

2 Blood Infection control practitioner generated list of cases

3 Blood Unknown

4 Blood Unknown

5 Blood Unknown

6 Body fluid Lab misnomer of body fluid; actually synovial fluid

7 Body fluid Initially considered not a case by NMDOHReclassified: body fluid was actually pleural fluid/empyema

8 Abscess aspirate Initially considered not a case by NMDOHReclassified: aspirate was from sternoclavicular joint abscess

n=4 (2.564%)n=18 (11.54%)

n=28 (17.95%)

n=43 (27.56%)

n=63 (40.38%)

No Micro Non IPD(Others/Unclassified)Non S. Pneum. Isolated Negative MicroS. Pneum./non Sterile Site

Combined Specific and Nonspecific SampleExcluded Hospital Admissions by Final Status

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

For more information please contact Centers for Disease Control and Prevention

1600 Clifton Road NE, Atlanta, GA 30333Telephone, 1-800-CDC-INFO (232-4636)/TTY: 1-888-232-6348E-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.

Acknowledgments

Office of Surveillance, Epidemiology, and Laboratory ServicesScientific Education and Professional Development Program Office

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 MagriDiana Bensyl, Betsy Gunnels, Sheryl Lyss

n=22 (37.93%)

n=20 (34.48%)

n=2 (3.448%)

n=11 (18.97%)

n=3 (5.172%)

n=41 (41.84%)

n=8 (8.163%)n=2 (2.041%)

n=32 (32.65%)

n=15 (15.31%)

Specific Nonspecific

S. Pneum./non Sterile Site Non S. Pneum. IsolatedNo Micro Negative MicroNon IPD(Others/Unclassified)

Graphs by IPD ICD-9 Codes

Excluded Hospital Admissions by Final Status

Calculation of Completeness of Reporting by the Two-Source Capture-Recapture Method

Source 2 casesSource 1 cases

TotalReported Not reported

Reported C N2 SNot reported N1 XTotal R N

C = number of people identified by both sourcesN2 = number of people identified only in data source 2S = number of people identified in data source 2N1 = number of people identified only in data source 1R = number of people identified in data source 1X = number of cases not reported to either system(estimated)N = estimate of total number of cases

......................................................N = RS/CCompleteness of source 1 = R/NCompleteness 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 HIDDPreliminary Analysis

ICD-9 codes Linked (Reported)

Nonlinked (Unreported)

Total

IPD-Specific 317 104 421

Nonspecific 241 625 866

Total 558 729 1287

Prevalence Rate Ratio ~ 2.6

ICD-9 Distribution within HIDD

75%

25%

IPD-specific

Linked(Reported)

NonLinked(Unreported)

28%

72%

Nonspecific

Linked(Reported)


57%

43%

Linked(Reported)

IPD-specificNonspecific

14%

86%


IPD-specificNonspecific

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

Source 1 yes noyes 6 9 15no 1 2

7 18

Scenario 1:Source 2

Slides Master1-Title2-Invasive Pneumococcal Disease (IPD)3-Importance of IPD Surveillance Systems4-IPD Surveillance in New Mexico5-Questions/Objectives6-Capture-Recapture Method7-Methods/ ICD codes(8-16) Data Linkage Results17-Full flow diagram18-Final Cap-Recap Results19-ICD-9 Codes by IPD Case Status20-Missed Cases by Culture Site21-Excluded Hospital Admissions by Final Status22-Limitations23-Strengths24-Conclusions25-Recommendations26-Acknowledgments27-Empty28-Excluded Hospital Admissions by Final Status by ICD-9 codes29-Cap-Recap Calculus30-ICD-9 Distribution: Preliminary analysis31-ICD-9 Distribution: Pie Charts 32-Some Reasons for HIDD IPD Cases Misclassifications33-Linkage Lessons34-Recommendations to Improve IPD Surveillance35-Cap-Recap Sampling Demo36-Cap-Recap IPD Assumptions37-Scenarios38-Slide Master

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Mam Ibraheem, MD, MPH

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