Determining Chronic Disease Prevalence in Local Populations Using Emergency Department Surveillance

• Published in: American journal of public health (1971) 2015-09, Vol.105 (9), p.e67-e74
• Main Authors: Lee, David C, Long, Judith A, Wall, Stephen P, Carr, Brendan G, Satchell, Samantha N, Braithwaite, R Scott, Elbel, Brian
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Age; Aged; Asthma; Asthma - epidemiology; Census; Censuses; Chronic Disease - epidemiology; Chronic illnesses; Community; Correlation coefficient; Diabetes; Diabetes Mellitus - epidemiology; Disease; Emergency medical care; Emergency medical services; Emergency Service, Hospital - statistics & numerical data; Estimates; Female; Geography; Health surveillance; Health Surveys; Hospitals; Humans; Hypertension; Hypertension - epidemiology; Insurance Claim Review; Local population; Male; Methods; Middle Aged; Neighborhoods; New York City - epidemiology; Online Research and Practice; Patients; Population; Population Surveillance - methods; Postal codes; Prevalence; Public health; Regression analysis; Reporting requirements; Reproducibility of Results; Residence Characteristics - statistics & numerical data; Small-Area Analysis; Socioeconomic Factors; Studies; Surveillance; Young Adult
• ISSN: 0090-0036; 1541-0048
• DOI: 10.2105/AJPH.2015.302679

We sought to improve public health surveillance by using a geographic analysis of emergency department (ED) visits to determine local chronic disease prevalence. Using an all-payer administrative database, we determined the proportion of unique ED patients with diabetes, hypertension, or asthma. We compared these rates to those determined by the New York City Community Health Survey. For diabetes prevalence, we also analyzed the fidelity of longitudinal estimates using logistic regression and determined disease burden within census tracts using geocoded addresses. We identified 4.4 million unique New York City adults visiting an ED between 2009 and 2012. When we compared our emergency sample to survey data, rates of neighborhood diabetes, hypertension, and asthma prevalence were similar (correlation coefficient = 0.86, 0.88, and 0.77, respectively). In addition, our method demonstrated less year-to-year scatter and identified significant variation of disease burden within neighborhoods among census tracts. Our method for determining chronic disease prevalence correlates with a validated health survey and may have higher reliability over time and greater granularity at a local level. Our findings can improve public health surveillance by identifying local variation of disease prevalence.