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Emerging Infectious Diseases

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Article Contents

The Study
Conclusions
Acknowledgments
References
Figure
Table 1
Table 2
Suggested Citation

Volume 15, Number 5–May 2009

Dispatch

Evaluation of Surveillance Methods for Staphylococcal Toxic Shock Syndrome

Lindsey Lesher, Aaron DeVries, Richard Danila, and Ruth Lynfield
Author affiliations: Minnesota Department of Health, St. Paul, Minnesota, USA (L. Lesher, A. DeVries, R. Danila, R. Lynfield); and University of Minnesota, Minneapolis, Minnesota, USA (A. DeVries)

The Study

Figure

Figure. Flow diagram of toxic shock syndrome (TSS) case ascertainment. TSS cases were identified from International Classification of Diseases, 9th Revision (ICD-9), codes assigned at hospital discharge...

The MSP area is composed of 7 counties with a population of 2,642,056 (2000 US Census) and 24 acute-care hospitals. Requests were sent to medical record departments of these hospitals for data on inpatients discharged from hospitals from January 1, 2000, through December 31, 2003, whose medical records indicated >1 of the select ICD-9 study codes. Medical records from all hospitalizations receiving the TSS-specific code (040.82 or 040.89) were reviewed (Figure), and a 20% random sample of medical records from hospitalizations that received >1 nonspecific TSS study code (Table 2) from within each hospital was reviewed. Each medical record was reviewed for TSS case criteria and pertinent epidemiologic and clinical information. Additionally, death certificates assigned the ICD-10 code for TSS (A48.3) and cases from the Minnesota Unexplained Critical Illness and Death of Possible Infectious Etiology project (UNEX) (9) during 2000–2003 were reviewed. TSS cases identified through ICD-9 code searches were compared with cases reported to MDH during 2000–2003. Data were analyzed with Stata version 9 (StataCorp, College Station, TX, USA). Statistical analyses included Pearson and McNemar χ² tests.

Of 7,414 hospitalizations with >1 study code, 116 (1.6%) were assigned the TSS-specific code and were reviewed (Figure). Of the remaining 7,298 hospitalizations assigned >1 nonspecific TSS code, 1,575 (21.6%) randomly selected hospitalizations were reviewed. Of these 1,691 hospitalizations, 55 had 5 or 6 criteria for TSS, of which 12 (22%) met the CDC case definition for streptococcal TSS, and 7 were non-MSP residents. The remaining 36 cases were probable or confirmed TSS. No cases from UNEX or death certificate searches met the TSS case definition. Of the 36 TSS cases, 17 (47%) were reported to MDH by passive surveillance. Thirty-one (86%) cases were found by using TSS-specific ICD-9 codes. Five cases were found by using non-TSS–specific ICD-9 codes. After adjusting for 20% random sampling for cases identified by non-TSS–specific codes, we identified the estimated number of cases by using non-TSS–specific codes to be 25. This analysis resulted in 56 estimated TSS cases identified in the surveillance area during 2000–2003 by using ICD-9 codes.

The TSS-specific ICD-9 code search identified 31 of the 56 estimated TSS cases (sensitivity 55%, specificity 99%). Seventeen cases were reported to MDH by passive surveillance (sensitivity 30%, specificity 99.9%); all were coded with the TSS-specific code. The TSS-specific ICD-9 code search was more sensitive than passive surveillance (p = 0.0005, McNemar χ² 12.25). Of those cases reported to MDH, more were likely to be associated with menstruation (14/17 vs. 5/19; p<0.001) and to have had a positive test result for S. aureus (16/17 vs. 11/19; p = 0.01). Twenty-seven of 36 TSS cases detected had a bacterial culture positive for S. aureus. The 3 TSS cases with methicillin-resistant S. aureus isolates were not reported to MDH. The positive predictive value of being a case among those coded with the TSS-specific code was 27% (31/116). In 68 of 116 cases that received the TSS-specific code, there was clinical suspicion of TSS, but these cases did not meet the clinical case definition (<5 criteria): 10 were streptococcal TSS and 7 were in non-MSP residents. All 17 cases reported to MDH were detected through the ICD-9 code search.

Conclusions

Surveillance for TSS is challenging given the lack of a diagnostic test and a case definition with multiple components. Under the current passive surveillance system, between one third and half of potential TSS cases were identified. Discrepancies were found in reporting, with menstruation-associated cases more likely to be reported to MDH than nonmenstrual-associated cases. This discrepancy was observed with prior active surveillance efforts (10).

Using ICD-9 codes, we found 12 TSS cases that were of streptococcal etiology. Accuracy may be improved by developing separate ICD-9 codes specific for staphylococcal, streptococcal, or unidentified TSS. In addition to the TSS-specific ICD-9 code, we selected 5 other ICD-9 codes on the basis of previous studies to address the concern that TSS cases may be classified under a staphylococcal infection or sepsis code, but not the TSS-specific code (8–10). These 5 additional non-TSS–specific ICD-9 codes required reviewing 1,575 medical records; only 5 (0.3%) additional TSS cases were identified. The non-TSS–specific ICD-9 codes detected 25 estimated cases. However, this detection required 8 trained staff and substantial resources with ≈40 minutes required per medical record review.

Passive surveillance requires fewer public health resources because it relies on clinicians to report cases. Active surveillance involves public health resources in identifying cases. The disadvantage of passive surveillance is the potential for missed cases. Despite possible inaccuracies associated with the assignment of ICD-9 codes, these codes represent a standardized data source that may be readily available. In the absence of a specific diagnostic test, ICD-9 codes represent an efficient method for surveillance and following trends.

Medical record abstraction per hospitalization was labor- and resource-intensive and is not feasible for most health departments. With increasing use of automated electronic reporting for disease surveillance (11), querying hospital discharge data for the TSS-specific ICD-9 code is a feasible adjunct to passive surveillance to detect TSS trends over time. Consequently, it is imperative that clinicians and coders are thorough to ensure that ICD-9 codes are accurate.

We found it useful to add regular ICD-9 code searches for TSS-specific codes as an active surveillance adjunct to our passive surveillance system. This addition increases sensitivity of TSS surveillance with a minimal increase in resources. Use of this more sensitive system increases the ability to detect trends in TSS, which may develop because of changes in bacterial virulence characteristics, host characteristics such as the use of new devices or products, changes in human behavior, or changes in host susceptibility. Evaluation of this approach in other areas to assess sensitivity of TSS surveillance would be useful because coding practices may differ.

Acknowledgments

We thank Brenda Jewel, Craig Morin, Jean Rainbow, and Lori Triden for assistance with data collection; Elly Pretzel for graphics assistance; and Catherine Lexau and Tyson Rogers for statistical assistance.

This study was supported by grants from the National Institutes of Health (Ruth L. Kirschstein National Research Service Award T32 AI 055433-2), the Loan Repayment Program, the National Institute of Allergy and Infectious Disease (Extramural Clinical Research L30 AI 071582-01), and the Centers for Disease Control and Prevention (Emerging Infections Program CDC 5 R01 CI000209).

Ms Lesher is an epidemiologist in the Emerging Infections Program at the Minnesota Department of Health. Her research interests include epidemiologic methods and disease caused by S. aureus.

References

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Figure

Tables

Table 1. Staphylococcal toxic shock syndrome case definitions
Table 2. ICD-9 study codes used for staphylococcal toxic shock syndrome case ascertainment