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Am J Prev Med. 2008 Apr;34(4 Suppl):S134-9. doi: 10.1016/j.amepre.2007.11.004.

Passive surveillance of shaken baby syndrome using hospital inpatient data.

Author information

1
Epidemiology and Prevention for Injury Control Branch, California Department of Public Health, Sacramento, California 95899-7377, USA. steve.wirtz@cdph.ca.gov

Abstract

BACKGROUND:

The conference from which these articles came addressed the question of public health surveillance for shaken baby syndrome (SBS) and explores one component of a comprehensive SBS surveillance system that would be relatively easy to implement and maintain: passive surveillance based on hospital inpatient data. Provisional exclusion and inclusion criteria are proposed for a two-level case definition of diagnosed SBS (strict definition) and cases presumed to be SBS (broad definition). The strict SBS definition is based on the single SBS code in the ICD-9-CM (995.55). The broader presumptive SBS definition is based on research studies that have identified a pattern of diagnostic codes often considered part of the clinical diagnosis of SBS.

RESULTS:

Based on 2006 analyses, California inpatient data are presented for 1998-2004. The strict SBS definition identified 366 cases over the 7 years, whereas the broader definition captured nearly 1000 cases. Annual rates show little fluctuation from the overall rate of 5.1 for strict SBS and 14.0 for broad SBS (per 100,000 children aged <2 years). Selected demographic and outcome characteristics are presented for each definition. The broad definition produces rates that are roughly comparable to those produced in careful clinical and population-based studies that also included children who died without being hospitalized.

CONCLUSIONS:

Despite the limitations of inpatient data, a passive surveillance system like the one proposed here can provide a critical component for a comprehensive SBS surveillance system and may be adequate for some purposes, including identifying high-risk areas or groups for intervention and monitoring trends over time.

PMID:
18374263
DOI:
10.1016/j.amepre.2007.11.004
[Indexed for MEDLINE]

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