Background: Many studies have reported significant associations between exposure to PM2.5 and hospital admissions, but all have focused on the effects of short-term exposure. In addition all these studies have relied on a limited number of PM2.5 monitors in their study regions, which introduces exposure error, and excludes rural and suburban populations from locations in which monitors are not available, reducing generalizability and potentially creating selection bias. Methods: Using our novel prediction models for exposure combining land use regression with physical measurements (satellite aerosol optical depth) we investigated both the long and short term effects of PM2.5 exposures on hospital admissions across New-England for all residents aged 65 and older. We performed separate Poisson regression analysis for each admission type: all respiratory, cardiovascular disease (CVD), stroke and diabetes. Daily admission counts in each zip code were regressed against long and short-term PM2.5 exposure, temperature, socio-economic data and a spline of time to control for seasonal trends in baseline risk. Results: We observed associations between both short-term and long-term exposure to PM2.5 and hospitalization for all of the outcomes examined. In example, for respiratory diseases, for every10-μg/m3 increase in short-term PM2.5 exposure there is a 0.70 percent increase in admissions (CI = 0.35 to 0.52) while concurrently for every10-μg/m3 increase in long-term PM2.5 exposure there is a 4.22 percent increase in admissions (CI = 1.06 to 4.75). Conclusions: As with mortality studies, chronic exposure to particles is associated with substantially larger increases in hospital admissions than acute exposure and both can be detected simultaneously using our exposure models.