# Presentation: 475938

## Probabilistic Linkage and the CODES Data Network

- Larry Cook, MStat
- University of Utah

## CODES

- Crash Outcome Data Evaluation System
- Study medical outcomes of motor vehicle crashes
- Seatbelts and car seats
- Motorcycle helmets
- Teens & Older drivers
- Drivers with medical conditions

## Crash Outcomes

- Crash databases do not typically contain medical outcomes
- Hospital data do not contain event information about the crash
- In order to describe the entire crash event, one must combine databases

## EMS

- ED

- Inpatient

- Analysis Database

- Crash

## Purpose of Record Linkage

- Linkage allows the combination of
- different data sets into one extensive
- data set for analysis

## Types of Record Linkage

- Deterministic
- Exact matching on a key
- Linkage scores derived by some predetermined scoring system
- No consideration on how likely values are to agree by chance
- Probabilistic
- Linkage scores based on properties of fields being matched
- Linkage score depends on individual values
- Probability that two records match

## Deterministic Example

- 8 points for agreement on first name
- 6 points for agreement on last name
- 5 points for agreement on date of birth
- 5 points for agreement on event date
- 1 point for agreement on gender
- All records with a score of 16 or higher are matches

## Probabilistic Linkage

- Probabilistic record linkage is a method that uses variables common to databases to determine the probability that two records refer to the same person and/or event and should be linked

## Probabilistic Linkage Theory

- Probability that a common variable agrees on a **matched** pair.  Approximately 1 - error rate.

- Probability that a common variable agrees on an **unmatched** pair.
- # of times a value occurs/ # of records.

**Reliability (m)**

**Discriminating Power (u)**

## Record Linkage with Imperfect Data

- Let us choose a pair of imperfect records and try to decide if they are a match.  That is, do they refer to the same individual and event?

**Crash**
**Records**

**Health**
**Records**

## Record Linkage with Imperfect Data

**Mary Smith                  F  05/05/45  07/15/96 11:40  Weber  US5  Seat=1  Belt=N**

**Mary Smith Sanchez   F  05/05/44  07/15/96 11:51  Weber  Fracture  Mem Hosp**

**Crash Record**

**Ambulance Record**

## Probabilistic Record Linkage

- Let’s revisit our pair of imperfect records and try to decide if they are a true match by applying the theory of probabilistic linkage.

**Crash**
**Records**

**Ambulance**
**Records**

## Probabilistic Record Linkage

- If each ambulance record matches to one crash record in a file of 100,000 crashes then the odds for a match at random are
**1:99,999  **

**Crash**
**Records**

**Ambulance**
**Records**

## Probabilistic Linkage Theory

- Probability that a common variable agrees on a **matched** pair.  Approximately 1 - error rate.

- Probability that a common variable agrees on an **unmatched** pair.
- # of times a value occurs/ # of records

**Reliability (m)**

**Discriminating Power (u)**

## Probabilistic Record Linkage

**Mary Smith                  F  05/05/45  07/15/96 11:47  Weber  US5  Seat=1  Belt=N**

**Mary Smith Sanchez   F  05/05/44  07/15/96 11:55  Weber  Fracture  Mem Hosp**

**Crash Record**

**Ambulance Record**

- First name agrees...
- m = 0.90  u = 0.01  ratio = 90**:**1

- Agreement on first name improves the odds for a match:
- 1**:**99,999 x 90**:**1 = **1:1,111**

## Probabilistic Record Linkage

**Mary Smith                  F  05/05/45  07/15/96 11:47  Weber  US5  Seat=1  Belt=N**

**Mary Smith Sanchez   F  05/05/44  07/15/96 11:55  Weber  Fracture  Mem Hosp**

**Crash Record**

**Ambulance Record**

- Last or middle name agrees with last or middle...
- m = 0.90  u = 0.04  ratio = 22**:**1

- Agreement on last name improves the odds for a match:
- 1**:**1,111 x 22**:**1 = **1:51**

## Probabilistic Record Linkage

**Mary Smith                  F  05/05/45  07/15/96 11:47  Weber  US5  Seat=1  Belt=N**

**Mary Smith Sanchez   F  05/05/44  07/15/96 11:55  Weber  Fracture  Mem Hosp**

- Sex agrees...
- m = 0.99  u = 0.50  ratio = 2**:**1

**Crash Record**

**Ambulance Record**

- Agreement on sex improves the odds for a match:
- 1**:**51 x 2**:**1 = **1:25**

## Probabilistic Record Linkage

**Mary Smith                  F  05/05/45  07/15/96 11:47  Weber  US5  Seat=1  Belt=N**

**Crash Record**

**Ambulance Record**

**Mary Smith Sanchez   F  05/05/44  07/15/96 11:55  Weber  Fracture  Mem Hosp**

- Date of Birth
- Month agrees...
- m = 0.99  u = 0.08  ratio = 12**:**1
- Day agrees...
- m = 0.99  u = 0.03  ratio = 30**:**1
- Year disagrees
- m = 0.99  u = 0.01  ratio = 1**:**99

- Agreement on birth date improves the odds for a match:
- 1**:**25 x 4**:**1 = **1:6**

## Probabilistic Record Linkage

**Mary Smith                  F  05/05/45  07/15/96 11:47  Weber  US5  Seat=1  Belt=N**

**Crash Record**

**Ambulance Record**

**Mary Smith Sanchez   F  05/05/44  07/15/96 11:55  Weber  Fracture  Mem Hosp**

- Date of Crash
- Month agrees...
- m = 0.99  u = 0.08  ratio = 12**:**1
- Day agrees...
- m = 0.99  u = 0.03  ratio = 30**:**1
- Year agrees
- m = 1.00  u = 1.00  ratio = 1**:**1

- Agreement on crash date improves the odds for a match:
- 1**:**6 x 360**:**1 = **60:1**

## Probabilistic Record Linkage

**Mary Smith                  F  05/05/45  07/15/96 11:47  Weber  US5  Seat=1  Belt=N**

**Crash Record**

**Ambulance Record**

- Agreement on crash time improves the odds for a match:
- 60**:**1 x 12**:**1 = **1,699:1**

**Mary Smith Sanchez   F  05/05/44  07/15/96 11:55  Weber  Fracture  Mem Hosp**

- Time of Crash
- Hour agrees...
- m = 0.90  u = 0.04  ratio = 23**:**1
- Minute disagrees...
- m = 0.50  u = 0.02  ratio = 1**:**2

## Probabilistic Record Linkage

**Mary Smith                  F  05/05/45  07/15/96 11:47  Weber  US5  Seat=1  Belt=N**

**Crash Record**

**Ambulance Record**

- Agreement on crash location improves the odds for a match:
- 1,699**:**1 x 10**:**1 = **16,990:1**

**Mary Smith Sanchez   F  05/05/44  07/15/96 11:55  Weber  Fracture  Mem Hosp**

- Place of crash agrees...
- m = 0.99  u = 0.10  ratio = 10**:**1

## Probabilistic Record Linkage

**Mary Smith                  F  05/05/45  07/15/96 11:47  Weber  US5  Seat=1  Belt=N**

**Mary Smith Sanchez   F  05/05/44  07/15/96 11:55  Weber  Fracture  Mem Hosp**

**Crash Record**

**Ambulance Record**

- This pair of records has both agreements and disagreements.  Our calculations say that the odds are **16,990:1 **that the records refer to the same individual and crash event (p = 0.9999).

## What Do You Need For Probabilistic Linkage 

## Data Files

- Variables common to both files
- Variable definitions same on each file
- Missing values represented by NULL

## Common Linkage Variables

- First and Last Names
- Soundex of Names (Sounds like)
- Lawrence Cook	= L652 C200
- Laurence Cooke 	= L652 C200
- Date of Birth and Age
- Incident Date
- Time of Incident
- Location:  state, county, and city

## Are Names Necessary for Probabilistic Linkage?

## Usefulness of Names

- Names are powerful identifiers
- Confidentiality concerns
- Names may be impossible to obtain ins some case

## Objective

- The objective of our study was to determine the impact of name information on  probabilistic record linkage

## Crash Data

- 110,000 records

- 110,000 records

- 10,000 records

- 100,000 records

- 100,000 records

- 92 - 93

- 94 - 95

- 1996

## Simulated Databases

- 2 databases 110,000 records each
- 10,000 records known to be exact matches
- 100,000 records known not to match to any record in the other file

## Linking Information

- First and Last Name
- Date of Birth
- Gender

- County of Crash
- Crash Time
- Crash Date

## Levels of Name Information

- Full first and last names
- SOUNDEX of first and last names
- First and last initials
- No name information

## Levels of Non-Name Information

- All available non-name information
- Remove date of birth	(personal)
- Also remove county	(location)
- Also remove time		(time)

## Simulated Key Punch Error Rates

- No errors
- 1%
- 5%
- 10%
- 25%
- Errors could only be valid entries

## Linkage Performance Measures

- Sensitivity - Ability to recognize true matches
- # correct matches identified / 10,000
- Specificity - Ability to recognize incorrect matches
- 1 - # incorrect matches / total matches

## DOB, Gender, County, Time, Incident Date

## DOB, Age, Gender, County, Time, Incident Date

## DOB, Age, Gender, County, Time, Incident Date

## DOB, Age, Gender, County, Time, Incident Date

## Summary

- Lack of name information had no effect on linkages when all available non-name information was present.
- Performance declines when non-name information is removed.
- Linkages using names and SOUNDEX were less affected
- Linkages using initials and no name information most affected
- Performance declines with increasing error rates.

## Conclusions

- Identifiers are needed for probabilistic linkage
- Is name information necessary?
- If many non-name identifiers are available then name information may not be needed
- If few non-name identifiers are available then name information becomes crucial

## CODES Data Network

## CODES Data Network

- National Highway Traffic Safety Administration (NHTSA) has funded CODES grants in several states
- Grantees link state data and share results with NHTSA
- Counts or totals
- Predefined tables or analyses
- Record level data

## CODES States – FY 2007  

- Arizona
- Connecticut
- Delaware
- Georgia
- Iowa
- Illinois
- Indiana
- Kentucky
- Massachusetts

- Maryland
- Maine
- Minnesota
- Missouri
- Nebraska
- New Hampshire
- New York
- Ohio
- Oklahoma

- Pennsylvania
- Rhode Island
- S. Carolina
- S. Dakota
- Tennessee
- Texas
- Utah
- Virginia
- Wisconsin

- White = CODES Data Network

## Current Events in CODES

- CODES Working Group
-  NHTSA and selected CODES states
- Areas of focus
- Motorcycles
- Older adults
- Analysis techniques
- Using costs as outcome measure

## Older Adults – Preliminary Results

- Compare mean hospital charges by age group
- Restraint use
- Primary Payer
- Age groups of interest
- 30-50 (comparison)
- 65-74
- 75-84
- 85+
- Included drivers and occupants of passenger vehicles

## CODES States and Data Years

- Contributed to Older Adult Analysis

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**Older Adult Model -**

## Older Adult Model- Pilot Test Results

| 17 out of 20 states have higher mean charges for age group 65-74 compared with control group when restraint used
17 out of 20 states have higher mean charges for age group 75-84 compared with control group when restraint used
14 out of 20 states have higher mean charges for age group 85+ compared with control group when restraint used |
| --- |

**Older Adult Model **

## Older Adult Model- Pilot Test Results

**Older Adult Model **

## Questions?

- Larry Cook
- 295 Chipeta Way
- P.O. Box 581289
- Salt Lake City, UT 84158-1289
- 801.585.9760
- larry.cook@hsc.utah.edu