Linking Hospitalizations and Death Certificates across Minnesota Hospitals

Transcription

1 Linking Hospitalizations and Death Certificates across Minnesota Hospitals AcademyHealth, Baltimore, June 2013 JMNaessens,ScD; SMPeterson; MBPine,MD; JSchindler; MSonneborn; JRoland; ASRahman; MGJohnson; DKOlson; SLVisscher,PhD; KRKoenig 2012 R slide-1

2 Disclosures Employed by health care provider, hospital association and consulting firm Nothing else of substance Effort funded under AHRQ grant 2012 R slide-2

3 Background Comparing hospital mortality across institutions has always been influenced by local variation on where deaths occur. Movement toward assessing longer perspective (30-day mortality) However, limited sources of data Medicare FFS (MN Medicare Advantage 47%) Selected Health Plans State of Minnesota has been sensitized to patient privacy issues 2012 R slide-3

4 Background Minnesota Hospital Association (MHA) started collecting hospital discharge abstracts since the 1980s Currently, 100% of community-based acute care hospitals contribute data, at least quarterly Data is used for hospital benchmarking, quality improvement and as a research base for hospital policy issues MHA has been actively involved with Michael Pine Associates in developing enhanced discharge abstracts under AHRQ funding 2012 R slide-4

5 Background In this project, we (MHA, MPA and Mayo Clinic HCPR) extend that work to link laboratory and medication orders with the hospital discharge information to explore the care of heart failure. However, MHA data has limited patient identifiers which hinder linking hospital stays between institutions and tracking patient survival after discharge. Our objective was to explore the possible identification of hospital readmissions and posthospital mortality within 30 days R slide-5

6 Methods Identify the best performing algorithm using available data to link hospital discharges with death certificates where no unique identifier exists Assessment based on 4.5 years of Mayo Minnesota hospitals with clinical data as a gold standard. Looked separately at matching hospital death and post-hospital deaths Algorithm performance based on sensitivity, as well as positive predictive value. Applied best algorithm to all MHA data from (1 st six months) 2012 R slide-6

7 Data Sources Three sources of data: MHA Hospital Discharge abstracts Mayo Clinic Registration (maintain a field for death date) State of Minnesota Death Certificates The gold standard was based Mayo Clinic registration field, enhanced with any death certificate matches (4.1%). For algorithm assessment, we matched on a variety of characteristics available to MHA 2012 R slide-7

8 Patients Assessment included use of discharges from 11 hospitals in Minnesota from Mayo Clinic Rochester and the Mayo Clinic Health System over 4.5 years 2012 R slide-8

9 Available Patient Characteristics Each of our datasets have the following demographic variables: Sex Date of birth Zip code of Residence [some report 5/some 9]. Death certificates only contain 5, but we used address to assign 9-digit zips on 88.9% of cases Social Security Number is not currently collected on MHA discharge abstracts, however it is generally available on death certificates Visit-level data: Admission Date Discharge Date Death Date 2012 R slide-9

10 Deterministic Algorithms Assessed matching on four algorithms: Algorithm 1: DOB, gender, 5-digit zip Algorithm 2: DOB, gender, 9-digit zip* Algorithm 3: DOB, gender, 4-digit SSN (not currently reported to MHA) Algorithm 4: DOB, gender, 4-digit SSN or 5- digit zip * 5 digit used where +4 not available 2012 R slide-10

11 Assessment Data 361,647 hospital discharges at the 11 hospitals from Jan 2008 June (1.7%) discharges were identified with death as a disposition code 8740 (2.4%) discharges known to have died within 30 days of discharge 184,604 death certificates from across Minnesota from 2008 through September R slide-11

12 In-hospital Deaths 54 (0.8%) of the 6009 discharges indicating hospital deaths were not confirmed in registration 46 occurred at one institution, while 38 occurred in had an indicator for death but no date 49 found in the certificates 5973 (96.4%) of the 6009 known hospital deaths were found in the death certificates R slide-12

13 4 years Minnesota Mayo Hospitals Deaths After Discharge 2012 R slide-13

14 Matching Post-hospital Deaths Death certificates on MN residents 98.8% Death certificates on non-mn residents 24.6% Therefore, assessment will be based on Minnesota residents only 2012 R slide-14

15 Algorithm Results: 30-day deaths, MN residents Sensitivity # deaths algorithm missed PPV # incorrectly identified deaths DOB, gender, 5- digit Algorithm 1zipcode 85.0% % 66 DOB, gender, 5 or 9-digit Algorithm 2zipcode 72.8% % 16 DOB, gender, last Algorithm 34 of SSN 96.1% % 2 DOB, gender, last 4 of SSN or 5-digit zip if Algorithm 4unavailable 97.2% % R slide-15

16 Mismatches Matching on last 4 digits of the SSN improved both the sensitivity and false positive rates of algorithms 9-digit zip codes not reliably supplied by all hospitals. Death certificate addresses included vague locations not compatible with Plus-4 algorithm R slide-16

17 Mismatches Algorithm 1 78 deaths (7.5% of false negatives) were not found in the death certificates with a 30 day match 960 deaths were in the death certificates: 32 had different dobs 923 had different zipcodes 5 had different sexes 2012 R slide-17

18 Applying Algorithm 1 to All Minnesota 2012 R slide-18

19 Observations about matching with death certificates Some patients have zip code changes after leaving the hospital (move to SNF, hospice or with relative) Twins are more likely to affect pediatric false positives based on zip codes Need to be aware of effect of out-ofhospital deaths and transfer/readmission patterns 2012 R slide-19

20 Next Steps and Policy Implications Currently exploring whether probabilistic methods would enhance matching Matching based on zip code, sex and date of birth is good Reporting of last 4 digits of SSN on discharge abstracts would greatly improve linkage for identifying posthospital mortality R slide-20

21 Questions and Discussion 2012 R slide-21