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Objectives: Some of the most pressing health problems are found in rural America. However, the surveillance needed to track and prevent disease in these regions is lacking. Our objective was to perform a comprehensive health survey of a single rural county to assess the validity of using emergency claims data to estimate rural disease prevalence at a sub-county level., Design: We performed a cross-sectional study of chronic disease prevalence estimates using emergency department (ED) claims data versus mailed health surveys designed to capture a substantial proportion of residents in New York's rural Sullivan County., Setting: Sullivan County, a rural county ranked second-to-last for health outcomes in New York State., Participants: Adult residents of Sullivan County aged 25 years and older who responded to the health survey in 2017-2018 or had at least one ED visit in 2011-2015., Outcome Measures: We compared age and gender-adjusted prevalence of hypertension, hyperlipidaemia, diabetes, cancer, asthma and chronic obstructive pulmonary disease/emphysema among nine sub-county areas., Results: Our county-wide mailed survey obtained 6675 completed responses for a response rate of 30.4%. This sample represented more than 12% of the estimated 53 020 adults in Sullivan County. Using emergency claims data, we identified 34 576 adults from Sullivan County who visited an ED at least once during 2011-2015. At a sub-county level, prevalence estimates from mailed surveys and emergency claims data correlated especially well for diabetes (r=0.90) and asthma (r=0.85). Other conditions were not well correlated (range: 0.23-0.46). Using emergency claims data, we created more geographically detailed maps of disease prevalence using geocoded addresses., Conclusions: For select conditions, emergency claims data may be useful for tracking disease prevalence in rural areas and providing more geographically detailed estimates. For rural regions lacking robust health surveillance, emergency claims data can inform how to geographically target efforts to prevent chronic disease., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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.