Preventing Chronic Disease | Administrative Data Linkage to Evaluate a Quality Improvement Program in Acute Stroke Care, Georgia, 2006–2009 - CDC
Administrative Data Linkage to Evaluate a Quality Improvement Program in Acute Stroke Care, Georgia, 2006–2009
Moges Seyoum Ido, MD, MS, MPH; Rana Bayakly, MPH; Michael Frankel, MD; Rodney Lyn, PhD; Ike S. Okosun, MPH, PhD
Suggested citation for this article: Ido MS, Bayakly R, Frankel M, Lyn R, Okosun IS. Administrative Data Linkage to Evaluate a Quality Improvement Program in Acute Stroke Care, Georgia, 2006–2009. Prev Chronic Dis 2015;12:140238. DOI:http://dx.doi.org/10.5888/pcd12.140238
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.PEER REVIEWED
Abstract
Introduction
Tracking the vital status of stroke patients through death data is one approach to assessing the impact of quality improvement in stroke care. We assessed the feasibility of linking Georgia hospital discharge data with mortality data to evaluate the effect of participation in the Georgia Coverdell Acute Stroke Registry on survival rates among acute ischemic stroke patients.
Tracking the vital status of stroke patients through death data is one approach to assessing the impact of quality improvement in stroke care. We assessed the feasibility of linking Georgia hospital discharge data with mortality data to evaluate the effect of participation in the Georgia Coverdell Acute Stroke Registry on survival rates among acute ischemic stroke patients.
Methods
Multistage probabilistic matching, using a fine-grained record integration and linkage software program and combinations of key variables, was used to link Georgia hospital discharge data for 2005 through 2009 with mortality data for 2006 through 2010. Data from patients admitted with principal diagnoses of acute ischemic stroke were analyzed by using the extended Cox proportional hazard model. The survival times of patients cared for by hospitals participating in the stroke registry and of those treated at nonparticipating hospitals were compared.
Multistage probabilistic matching, using a fine-grained record integration and linkage software program and combinations of key variables, was used to link Georgia hospital discharge data for 2005 through 2009 with mortality data for 2006 through 2010. Data from patients admitted with principal diagnoses of acute ischemic stroke were analyzed by using the extended Cox proportional hazard model. The survival times of patients cared for by hospitals participating in the stroke registry and of those treated at nonparticipating hospitals were compared.
Results
Average age of the 50,579 patients analyzed was 69 years, and 56% of patients were treated in Georgia Coverdell Acute Stroke Registry hospitals. Thirty-day and 365-day mortality after first admission for stroke were 8.1% and 18.5%, respectively. Patients treated at nonparticipating facilities had a hazard ratio for death of 1.14 (95% confidence interval, 1.03–1.26; P = .01) after the first week of admission compared with patients cared for by hospitals participating in the registry.
Average age of the 50,579 patients analyzed was 69 years, and 56% of patients were treated in Georgia Coverdell Acute Stroke Registry hospitals. Thirty-day and 365-day mortality after first admission for stroke were 8.1% and 18.5%, respectively. Patients treated at nonparticipating facilities had a hazard ratio for death of 1.14 (95% confidence interval, 1.03–1.26; P = .01) after the first week of admission compared with patients cared for by hospitals participating in the registry.
Conclusion
Hospital discharge data can be linked with death data to assess the impact of clinical-level or community-level chronic disease control initiatives. Hospitals need to undertake quality improvement activities for a better patient outcome.
Hospital discharge data can be linked with death data to assess the impact of clinical-level or community-level chronic disease control initiatives. Hospitals need to undertake quality improvement activities for a better patient outcome.
Acknowledgments
The GCASR was funded by the Centers for Disease Control and Prevention under agreement no. U50/CCU 420275-01. We acknowledge the help of Cherie L. Drenzek, DVM, MS, Georgia state epidemiologist, in preparing this manuscript.
Author Information
Corresponding Author: Moges Seyoum Ido, MD, MS, MPH, Epidemiologist, Georgia Department of Public Health, 2 Peachtree St, NW, Suite 14-277, Atlanta, GA 30303-3142. Telephone: 404-463-8918. E-mail: Moges.Ido@dph.ga.gov.
Author Affiliations: Rana Bayakly, Georgia Department of Public Health, Atlanta, Georgia; Michael Frankel, Emory University, Atlanta, Georgia; Rodney Lyn, Ike S. Okosun, Georgia State University, Atlanta, Georgia.
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