Linking Mortality and Inpatient Discharge Records: Comparing Deterministic and Probabilistic Methodologies. Richard Miller Office of Health Informatics. Mike Yuan Bureau of Community Health Promotion. Wisconsin Division of Public Health June 2011.
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Linking Mortality and Inpatient Discharge Records: Comparing Deterministic and Probabilistic Methodologies
Office of Health Informatics
Bureau of Community Health Promotion
Wisconsin Division of Public Health
Why Combine Mortality Records and Inpatient Discharge Records?
How to link or match records
Method 1: Deterministic record linkage
Method 2: Probabilistic record linkage
How do the results compare?
Improve surveillance of CVD and other chronic diseases
Enhanced surveillance analysis opportunities
Capture hospital record information on demographics, co-morbidities, complications, and surgical procedures.
Measure treatment outcomes on a population basis
Analyses are more complete the more time there is to find a death record following a hospitalization
The scale of mortality and inpatient records in Wisconsin:
Smaller number of individual patients
How to find matching records? How to define links between records?
Pairs of records are compared for exactly matching indentifying information. Exact matches determine true record matches.
Works perfectly only if information that uniquely identifies the same individual in two datasets is available, is captured perfectly, and is recorded perfectly
In real world data systems:
Every pair of records has some probability of being a “true match.”
Specialized software estimates that probability by applying statistical principles and tools.
Set some threshold for “high probability matches”
Some methods impute missing matches to pairs that look unlikely due to possible reporting and recording errors.
Identifying Patients = de-duplicating inpatient records
Method: Iterative application of combinations of elements with person-matching face validity.
Uniqueness of Patient Identifiers
Wisconsin Inpatients Discharged 2006-08, N=2,017,339
Record links were evaluated by looking for three indicators of false positive matches:
Matches between the 1,280,000 resident patients and the 135,000 Wisconsin occurrence deaths to residents.
Which inpatient record? The most recent one…
Iterative procedures use a succession of identifiers (combinations of the available data elements).
Iterative matching in two phases:
I. Match the records for in- hospital deaths
II. Examine the remaining records for matches
Linked 66% of the mortality records to a hospital patient
Evaluated results with logic tests
A “probabilistic record linkage methodology” recognizes that a pair of records has some probability of being a “true match.”
Specialized software products estimate that probability:
LinkSolv is based on Bayesian statistics as applied by Fellegi and Sunter and considerably developed by Dr. Michael McGlincy, the software developer.
LinkSolv compares pairs of fields, incorporating a number of adjustments to account for real-world violations of statistical assumptions:
Comparisons may be for exact matches or acceptable differences
Some simplifying decisions:
Experimented with comparison fields:
Final model was relatively simple:
This model was applied to three over-lapping subsets of records, along with estimated corrections to statistical assumptions.
We merged the three linkage passes in a multiple imputation process that applies Markov Chain-Monte Carlo techniques to create five alternative sets of paired records.
For evaluation purposes, we de-duplicated these 5 sets to identify a final set of 36,562 inpatient-mortality records linked with probabilistic methods.
Combined Linked Pairs
We gratefully acknowledge the support of CSTE’s Cardiovascular Disease Surveillance Data Pilot Project
We are indebted to Dr. Michael McGlincy, Strategic Matching Inc., for his thoughtful advice.