Online Program

Creation and evaluation of a multi-layered maternal and child health database for comparative effectiveness research

Tuesday, November 5, 2013

Jason L. Salemi, MPH, Department of Epidemiology and Biostatistics, University of South Florida, College of Public Health, Tampa, FL
Jean Paul Tanner, MPH, Birth Defects Surveillance Program, Department of Community and Family Health, College of Public Health, University of South Florida, Tampa, FL
Marie Bailey, MA, MSW, Division of Public Health Statistics and Performance Management, Florida Department of Health, Tallahassee, FL
Alfred Mbah, PhD, Department of Epidemiology and Biostatistics, University of South Florida, College of Public Health, Tampa, FL
Hamisu Salihu, MD, PhD, Department of Epidemiology and Biostatistics, University of South Florida, College of Public Health, Tampa, FL
Background: Comparative effectiveness research (CER) is a rigorous scientific approach designed to generate valid scientific evidence to make informed decisions that will improve health care quality and patient outcomes. As high-speed computers and sophisticated software packages for data linkage become increasingly available, investigators are creating massive databases for CER. Considering their potential use in informing health care decisions, we must increase transparency of these data so that potential biases can be addressed. Methods: We used an innovative stepwise deterministic record linkage strategy to construct a clinically-enhanced maternal and child health (MCH) database for CER. Mothers and infants that were part of a 12-year cohort were followed up for at least one year (up to 12) after the child's birth or until death, investigating inpatient, outpatient, and emergency department visits. We used multivariable modeling to investigate linkage disparities across a host of maternal and infant demographic, reproductive, and geospatial characteristics. Results: We were able to link 92.1% of the 2,549,738 birth certificate records to an infant birth hospitalization record. The highest crude unlinked rates were seen among infants who died during their first year of life (35.9%), and infants who born to mothers without health insurance (28.1%), with less than a 9th grade education (26.0%), who were foreign-born (12.9%), and who self-identified as Hispanic (12.8%). We discuss potential biases in using these data, particularly for MCH disparities research. Conclusions: Our findings describe a unique algorithm for linking MCH data and demonstrate the importance of evaluating routinely collected and linked health data.

Learning Areas:

Conduct evaluation related to programs, research, and other areas of practice
Public health or related public policy
Public health or related research

Learning Objectives:
Describe the advantages and disadvantages of observational data in comparative effectiveness research. Evaluate and compare strategies for linking administrative and clinical maternal and child health data. Discuss the biases associated with linked databases that disproportionately exclude records with selected socio-demographic and perinatal characteristics.

Keyword(s): MCH Epidemiology, Quality Assurance

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: As an Epidemiology and Data Analysis Manager on multiple grants (federal and state-funded), I have amassed a substantial and versatile proficiency in database data linkage, management, and analysis. I have served as an expert birth defects surveillance and data linkage consultant for the Florida Department of Health and the CDC’s National Center on Birth Defects and Developmental Disabilities.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.