Assessing the Reliability of Performing Citywide Chronic Disease Surveillance Using Emergency Department Data from Sentinel Hospitals.
Given the inequalities in the distribution of disease burden, geographically detailed methods of disease surveillance are needed to identify local hot spots of chronic disease. However, few data sources include the patient-level addresses needed to perform these studies. Given that individual hospitals would have access to this geographically granular data, this study assessed the reliability of estimating chronic disease prevalence using emergency department surveillance at specific hospitals. Neighborhood-level diabetes, hypertension, and asthma prevalence were estimated using emergency claims data from each individual hospital in New York City from 2009-2012. Estimates were compared to prevalence obtained from a traditional health survey. A multivariable analysis also was performed to identify which individual hospitals were more accurate at estimating citywide disease prevalence. Among 52 hospitals, variation was found in the accuracy of disease prevalence estimates using emergency department surveillance. Estimates at some hospitals, such as NYU Langone Medical Center, had strong correlations for all diseases studied (diabetes: 0.81, hypertension: 0.84, and asthma: 0.84). Hospitals with patient populations geographically distributed throughout New York City had better accuracy in estimating citywide disease prevalence. For diabetes and hypertension, hospitals with racial/ethnic patient distributions similar to Census estimates and higher fidelity of diagnosis coding also had more accurate prevalence estimates. This study demonstrated how citywide chronic disease surveillance can be performed using emergency data from specific sentinel hospitals. The findings may provide an alternative means of mapping chronic disease burden by using existing data, which may be critical in regions without resources for geographically detailed health surveillance.
1 Ronald O. Perelman Department of Emergency Medicine, New York University School of Medicine , New York, New York.|2 Department of Population Health, New York University School of Medicine , New York, New York.
David C
DC
Lee
1 Ronald O. Perelman Department of Emergency Medicine, New York University School of Medicine , New York, New York.
Jordan L
JL
Swartz
1 Ronald O. Perelman Department of Emergency Medicine, New York University School of Medicine , New York, New York.
Christian A
CA
Koziatek
1 Ronald O. Perelman Department of Emergency Medicine, New York University School of Medicine , New York, New York.
Andrew J
AJ
Vinson
2 Department of Population Health, New York University School of Medicine , New York, New York.
Jessica K
JK
Athens
2 Department of Population Health, New York University School of Medicine , New York, New York.
Stella S
SS
Yi
D000293:Adolescent; D000328:Adult; D000368:Aged; D002908:Chronic Disease / Q000453:epidemiology*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D040362:Geographic Information Systems; D006761:Hospitals; D006801:Humans; D008297:Male; D008875:Middle Aged; D009519:New York City / Q000453:epidemiology; D015995:Prevalence; D018571:Sentinel Surveillance; D016896:Treatment Outcome; D055815:Young Adult
administrative claims data; chronic disease prevalence; emergency department surveillance; geographic information systems