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

Linking (matching) Mortality Records and Inpatient Discharge Records 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? Lessons learned

Why Combine Mortality Records and Inpatient Discharge Records? Improve surveillance of CVD and other chronic diseases Enhanced surveillance analysis opportunities –Mortality records capture CVD only if an underlying or contributing cause –Inpatient records capture CVD treated in that setting, but the case history ends at discharge Capture hospital record information on demographics, co-morbidities, complications, and surgical procedures. Measure treatment outcomes on a population basis

The Time Frame for Linked Records 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: 2 million inpatient discharge records Smaller number of individual patients 140,000 mortality records How to find matching records? How to define links between records?

False Positives and Negatives Matching records involves finding a balance between false positive and false negative matches.  False positive matches combine records for different people.  False negatives fail to include all persons in the dataset of matched records – possibly introducing bias.

Method 1. Deterministic Record Linkage 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: –uniquely identifying elements often not available; –recorded data have small differences between records –some records have some fields with missing values.

Method 2. Probabilistic Record Linkage 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”  A common criterion is 0.9 probability of being a true match  This defines the risk of accepting false positives Some methods impute missing matches to pairs that look unlikely due to possible reporting and recording errors.

Part I. Deterministic Linkage among Inpatient Records Identifying Patients = de-duplicating inpatient records Method: Iterative application of combinations of elements with person-matching face validity. Available fields: Initials 3-digit encryption of last name (Miller = M460) Date of birth Gender ZIP code of residence Insurance ID >> “SSN-like string” Hospital and medical record number

Part I: Deterministic Linkage among Inpatient Records Uniqueness of Patient Identifiers Wisconsin Inpatients Discharged , N=2,017,339 “Patient” Identifier % Records with identifier % with unique values Initials + DOB + sex100%56.2% Initials + DOB + sex + ZIP99.9%63.4 Policy number + DOB + sex92.1%64.7 SSN-like string + DOB + sex78.2%61.2 Hospital + medical record number 99.7%70.9

Part I: Deterministic Linkage among Inpatient Records Record links were evaluated by looking for three indicators of false positive matches: 1.Any later admission date preceding the earliest admission’s discharge date. 2.Any admission date preceding the previous admission’s discharge date. 3.Records indicating the patient died but patient has later hospitalizations.

Part II: Deterministic Matching of Patients to Mortality Records 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). Construct a linking identifier Select records with unique values of the “linker” Sort each set by that linking identifier Matching and merge those records with identical linker values Collect the remaining records Construct an alternative linking combination Repeat until plausible linking combinations have been exhausted.

Part II: Deterministic Matching of Patients to Mortality Records Iterative matching in two phases: I. Match the records for in- hospital deaths  Less time between events and more data elements in common  Date of death = discharge date  Hospital is match element  25% of deaths; 2% of inpatients. II. Examine the remaining records for matches

Part II: Deterministic Matching of Inpatient Records to Mortality Records Phase I. Linked In-Hospital Deaths Linker# Pairs Matched Matched RecordsRemaining Unmatched Records % of inpatient records % of mortality records # of inpatient records # of mortality records All In-Hospital Deaths32,81635,745 Initials + DOB + Sex + ZIP26,02279%73%6,7949,723 Initials + DOB + Sex + SSN2,666874,1287,057 Initials + Sex + ZIP3 + DOD1,496442,6325,561 Hospital + DOD + DOB833221,7994,728 Initials + Sex + DOB ,7624,691 All Linked Pairs31, %86.9%

Part II: Deterministic Matching of Inpatient Records to Mortality Records Phase 2: Linked Residual Deaths and Patients Linker# Pairs Matched Matched RecordsRemaining Unmatched Records % of inpatient records % of mortality records # of inpatient records # of mortality records Residual Deaths1,195,638104,023 Initials + DOB + Sex + ZIP53,0594%51%1,142,57950,964 Initials + DOB + Sex + SSN 5,514<1111,137,06545,450 All Residual Linked Pairs58,5734.9%56.3%

Part II: Deterministic Matching of Inpatient Records to Mortality Records Combined results: Linked 66% of the mortality records to a hospital patient 89,627 of the 135,077 total resident and occurrence deaths Evaluated results with logic tests Admission date after previous discharge date Not hospitalized again after discharged ‘expired’ Agreement rates among other data elements

Part III: Probabilistic Matching of Inpatient Records to Mortality Records 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 – our choice LinkPlus LinkPro LinkSolv is based on Bayesian statistics as applied by Fellegi and Sunter and considerably developed by Dr. Michael McGlincy, the software developer.

Part III: Probabilistic Matching of Inpatient Records to Mortality Records LinkSolv compares pairs of fields, incorporating a number of adjustments to account for real-world violations of statistical assumptions: The probability that apparently different values may both be correct; Rates of missing data; Estimated rates of reporting errors; and Discounting some weights for matching/mismatching values if agreements/disagreements on one field are related to agreements/disagreements on another. Comparisons may be for exact matches or acceptable differences

Part III: Probabilistic Matching of Inpatient Records to Mortality Records Some simplifying decisions:  Use the most recent inpatient discharge identified by the deterministic linkage process  Drop the 30% of patients who are mothers and their newborns  Work only with the patients whose last hospitalization was in 2006

Part III: Probabilistic Matching of Inpatient Records to Mortality Records Experimented with comparison fields: Disaggregate birth date or not? Break up ZIP in ZIP-3 and ZIP-2 components or not? Break up name into separate initials and encrypted field? Use full SSN or just last 4 digits (SSN-4)? Use elements only available for the in-hospital deaths?

Part III: Probabilistic Matching of Inpatient Records to Mortality Records Final model was relatively simple: Last initial + encryption (Miller = M460) First initial SSN-4 Date of birth as one field Gender M/F ZIP-3

Part III: Probabilistic Matching of Inpatient Records to Mortality Records 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. –Identifies additional record pairs that have a low - but real - probability of being true matches, due to possible measurement errors. For evaluation purposes, we de-duplicated these 5 sets to identify a final set of 36,562 inpatient-mortality records linked with probabilistic methods.

Comparison of Results Combined Linked Pairs  93% of deterministic matches were confirmed by the probabilistic matches  14% of probabilistic matches were not captured by deterministic linking.

Comparison of Results Evaluating the discrepant results: High-probability matches not found in the deterministic matches. The most common issue was discrepancies in the last two ZIP digits. Low-probability matches 2% of the record pairs identified by both methods were evaluated by LinkSolv as having a low probability of being a true match. This suggests that some deterministic criteria are weaker than would be desirable, notably last name encryption and SSN. Deterministic matches not confirmed by probabilistic matching. Should we be wary of this 5% of matches? Disproportionately are in-hospital deaths

Conclusions De-duplicating patients The strongest linking combination was patient’s initials + date of birth + sex + ZIP. Yielded reasonable and apparently robust results. Given the observed instability of ZIP code in the population of deceased recent patients, we should experiment with substituting ZIP-3. This will result in fewer ‘patients’ being identified. The trade-off is the creation of more false-positive matches.

Conclusions Linking patients to mortality records The probabilistic process yields more matched pairs than the deterministic process, but not dramatically so. Overall, the more rigorous probabilistic method validated the results of the deterministic linkage. Initials, date of birth, and sex Patient and mortality records generally reliable and consistent. ZIP Less reliable - small moves often result in different ZIPs. Older patients particularly likely to make such moves. Probabilistic models only used ZIP-3 SSN Using full SSN limited the success of exact matching. SSNs were teased out of policy numbers but are often missing or are a spouse’s SSN. Probabilistic models used only SSN-4.

Conclusions Both methods created reasonable sets of matched pairs of records Those sets had a high degree of common pairs. The deterministic process is probably more accessible and efficient for the general user. However, the quality is heavily dependent on the completeness and accuracy of the recorded data.

Conclusions The probabilistic process, particularly as developed in LinkSolv, is more statistically rigorous and will more thoroughly identify matched pairs. Using multiply-imputed output datasets requires sophisticated statistical treatment by well-trained researchers.  Useful lessons can be learned from the application of both methods to the same datasets. The probabilistic process provides a rigorous evaluation and, perhaps, validation of the results of deterministic exact-matching.  The probabilistic process provides insights into the utility of particular data elements; this may be used to refine and improve a deterministic matching process.

Acknowledgments 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.

Linking Mortality and Inpatient Records: Comparing Deterministic and Probabilistic Methods Richard Miller HerngLeh (Mike) Yuan Wisconsin Division of Public Health