OBJECTIVES: Estimating the prevalence of undiagnosed HIV in emergency departments (EDs) is not straightforward. Regional epidemiologic data are unlikely to translate directly to a single ED setting, and the prevalence of undiagnosed HIV likely differs between EDs within a region. We propose a simple method for estimating the prevalence of undiagnosed HIV in individual EDs. METHODS: First, incident cases are grouped by zip codes and combined with census data to calculate zip code-specific case rates. Second, the proportion of ED patients living in each zip code is determined. Third, the prevalence of undiagnosed disease is estimated as the mean zip code case rate, weighted by the proportion of ED patients living in each zip code, multiplied by the estimated time from infection to diagnosis. We applied this method to 3 EDs in a metropolitan region with an annual HIV/AIDS case rate of 6.2 per 100,000. RESULTS: From 1999 through 2003, the annual HIV case rate was estimated to range from 6.4 to 12.7 at an urban academic ED, 5.9 to 10.2 at an urban community ED, and 2.1 to 4.9 at a suburban community ED. The estimated prevalence of undiagnosed disease was 0.05% (urban academic), 0.04% (urban community), and 0.02% (suburban community). CONCLUSION: Publicly reported regional AIDS or HIV statistics do not reflect ED-specific HIV epidemiology, but ED-specific case rates can be crudely estimated from readily available data. This method promises to be a valuable aid for translating HIV screening to ED settings.
OBJECTIVES: Estimating the prevalence of undiagnosed HIV in emergency departments (EDs) is not straightforward. Regional epidemiologic data are unlikely to translate directly to a single ED setting, and the prevalence of undiagnosed HIV likely differs between EDs within a region. We propose a simple method for estimating the prevalence of undiagnosed HIV in individual EDs. METHODS: First, incident cases are grouped by zip codes and combined with census data to calculate zip code-specific case rates. Second, the proportion of ED patients living in each zip code is determined. Third, the prevalence of undiagnosed disease is estimated as the mean zip code case rate, weighted by the proportion of ED patients living in each zip code, multiplied by the estimated time from infection to diagnosis. We applied this method to 3 EDs in a metropolitan region with an annual HIV/AIDS case rate of 6.2 per 100,000. RESULTS: From 1999 through 2003, the annual HIV case rate was estimated to range from 6.4 to 12.7 at an urban academic ED, 5.9 to 10.2 at an urban community ED, and 2.1 to 4.9 at a suburban community ED. The estimated prevalence of undiagnosed disease was 0.05% (urban academic), 0.04% (urban community), and 0.02% (suburban community). CONCLUSION: Publicly reported regional AIDS or HIV statistics do not reflect ED-specific HIV epidemiology, but ED-specific case rates can be crudely estimated from readily available data. This method promises to be a valuable aid for translating HIV screening to ED settings.
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