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Improving the linkage of deliveries over time using vital records and hospital discharge data

Explore how linking successive deliveries can provide insights into maternal and infant health risks, birth outcomes, and healthcare practices. Learn about the PELL Data System and its role in improving maternal linkage data using advanced methodologies.

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Improving the linkage of deliveries over time using vital records and hospital discharge data

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  1. Improving the linkage of deliveries over time using vital records and hospital discharge data Mark McLaughlin, Judy Weiss, ScD, Milton Kotelchuck, MPH, PhD, MA, Stephen Evans, MPH Boston University School of Public Health Dept. of Maternal and Child Health Boston, MA, November 6, 2006 Contact info: Mark.E.Mclaughlin@state.ma.us APHA, Nov. 2006

  2. Why link successive deliveries? • Create a reproductive history of individual women to study, for example: • Risk of maternal and infant morbidity • Risk & consequences of premature birth • Impact of OB practices (e.g., method of delivery) on subsequent birth outcomes • Past linkage efforts in the US have been limited and/or have ended • Birth certificates +/- fetal death certificates • Need for improved ‘case-finding’ methods to capture most complete maternal linkage possible APHA, Nov. 2006

  3. Pregnancy to Early Life Longitudinal (PELL) Data System • CDC-funded collaborative project • MCH Dept. Boston Univ. School of Public Health • MA Department of Public Health (MDPH) • A population-based relational data system connected by unique identifiers for infants, deliveries and mothers • Consists of a Core plus additional program and other surveillance data • Is longitudinal and expandable APHA, Nov. 2006

  4. PELL Data System Vital and Health Status Data Program Participation Data Newborn Hearing Screening (2006) CORE WIC (2006) Birth Defects Registry Child and Mother deaths Hospital Discharge (HD) Mother’s delivery Early Intervention Birth Certificate Pregnancy-associated deaths Linked birth-infant deaths Other DPH programs (future) Fetal Death Hospital Discharge (HD) Child’s birth Health Services Utilization Data Non-birth hospital discharge Contextual Data Geocoded birth data (2006) Emergency Department 2000 Census (2006) Observational Stays Other contextual data (e.g., environmental data) Other Future Data Sets (e.g., School, ‘ART’, NICU, Medicaid) APHA, Nov. 2006

  5. PELL Core Data Sets Birth and Fetal Death Certificate with Hospital Discharge Maternal Delivery Record and Child Birth Record CORE Hospital Discharge (HD) Mother’s delivery Birth Certificate Fetal Death Hospital Discharge (HD) Child’s birth APHA, Nov. 2006

  6. Creation of PELL Core LinkagesBirth/fetal death to Hospital records • Deterministic and probabilistic linkage methodology using LinkPro 2.0 (SAS-based) software • Core Linkage Variables: • Facility code • Maternal Date of Birth • Date of Delivery • Zipcode No Unique Identifier APHA, Nov. 2006

  7. PELL Core Data Linkage Rates1998-2004 • 574,442 occurrence live births and fetal death records • Overall, 98.6% linked to maternal hospital delivery record • 77.8% of fetal deaths linked to maternal hospital delivery record Now that the data are linked… APHA, Nov. 2006

  8. …can we create a better maternal linkage? Is there a way to combine Vitals (both births and fetal deaths) and Hospital Discharge data to create a more robust maternal linkage? APHA, Nov. 2006

  9. Creation of longitudinally-linked delivery data • 3-step process using vital records and HD data individually and jointly • Step 1: identify unique women within each data source and count the number of deliveries per woman • Vitals linkage based on names, DOB • HD linkage based on encrypted SSN and/or facility code plus medical record number APHA, Nov. 2006

  10. Example 2 3 * Encrypted SSN ** Medical record number APHA, Nov. 2006

  11. Creation of longitudinally-linked delivery data • Step 2: Merge vital records and HD data by delivery and compare the number of deliveries • Three scenarios • Both classify equal number of deliveries (92.8%) • Vitals classifies more deliveries than HD (5.5%) • HD classifies more deliveries than Vitals (1.7%) • Accept whichever data (Vitals or HD) classified more deliveries APHA, Nov. 2006

  12. Example APHA, Nov. 2006

  13. Creation of longitudinally-linked delivery data • Step 3: Assess validity of linkages • Validate names • Compare names from first delivery to names from all subsequent deliveries using SPEDIS function in SAS • Women with at least one name that appears significantly different (score>30) are flagged for manual review • When reviewing names, judgment of validity is made based on names, dates, parity, and other demographic variables • Review performed by two people and compared APHA, Nov. 2006

  14. Examples APHA, Nov. 2006

  15. Creation of longitudinally-linked delivery data • Step 3, cont’d: Assess validity of links • Validate short inter-pregnancy intervals • Calculate the time between deliveries • Women with short delivery intervals (<250 days) are output for review • Review performed by two people and compared • After review, maternal ID is assigned APHA, Nov. 2006

  16. Creation of longitudinally-linked delivery data RESULTS (1998-2004): • 427,817 unique women identified • 122,094 women found with >1 delivery • 118,860 women found with >1 delivery in Vitals alone • 114,014 women found with >1 delivery in HD alone • 2.7% more women with >1 delivery than Vitals alone • ~500 women with >1 delivery saw additional increase in deliveries APHA, Nov. 2006

  17. Practical Challenges • Incomplete information • Early fetal losses (prior to 20 weeks) • Non-occurrence fetal deaths • Births to women who move out of state • Initial linkage can be time consuming (if spanning several years) • Subsequent years require less time • 350-400 records reviewed each year APHA, Nov. 2006

  18. Conclusions • Using 3 data sources (births, fetal deaths, hospital discharge data) provides a more complete reproductive history than births and fetals alone • Validation of linkages makes final data linkage more robust • Longitudinally-linked delivery data provide a rich source for exploration of MCH issues APHA, Nov. 2006

  19. Caveats • Vitals data • Name changes (marriage, divorce, etc.) • Spelling errors, typographical errors • Hospital Discharge data • Only includes births in MA hospitals (no home births, out of state births, most births at birthing centers • Encrypted SSN (UHIN) sometimes missing (n=32,963 [5.6%]) so reliance on medical record number and assumption that woman goes to the same hospital • No Hospital Discharge data on unlinked deliveries (n=14,021 [2.5%]) APHA, Nov. 2006

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