An algorithm for matching anonymous hospital discharge records used in occupational disease surveillance: Anonymous record matching algorithm

Abstract

The expense of collecting primary data, coupled with limited authority to mandate reporting, requires alternative methods of implementing an occupational disease registry in Illinois. One alternative data source for surveillance of some occupational diseases is hospital discharge records. Because these records lack personal identifiers, it has been impossible historically to match records belonging to the same individual and obtain reliable case estimates. To circumvent this difficulty, an algorithm has been developed to match anonymous hospital discharge records collected from all Illinois hospitals. The algorithm was based on the assumption that specific combinations of occupational disease code, sex, zip code, and date of birth would identify an individual to whom multiple hospitalizations belong. Matching with the algorithm reduced the 1986 case estimates from 597 to 499 for all cases of coal workers' pneumoconiosis, asbestosis, and silicosis.

Accuracy and validity of using medical claims data to identify episodes of hospitalizations in patients with COPD

“…Linkage procedure A deterministic linkage method [31][32][33][34][35] (i.e., an exact agreement on one or more matching variables) was used to link estimated episodes of hospitalizations from the RAMQ and MED-ECHO databases. For any given patient (i.e., same patient identifier), an estimated hospitalization episode from the RAMQ database was considered to have matched one from MED-ECHO if the admission dates of both estimates were the same or within 1 day of one another (admission date AE 1 day).…”

Characteristics of occupational burns in Oregon, 2001–2006

“…Given that the RBC records are a subset of HDI records, as are some of the hospitalized WC claims, it does not appear that using HDI data in its current form augments the burn surveillance system. Other authors have worked on algorithms for matching claims data to anonymous hospital discharge data [Keller et al, 1991]; future projects could attempt this or other methodologies for matching to improve the usefulness of this data source.…”