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BACKGROUND: The Agency for Toxic Substances and Disease Registry (ATSDR) established Amyotrophic Lateral Sclerosis (ALS) surveillance projects in three states and eight metropolitan areas to evaluate the completeness of its National ALS Registry, and to better describe local incidence, prevalence, and demographic characteristics of ALS. The purpose of this abstract is to describe geographical variation in incidence of the disease in New Jersey (NJ), based on population-based surveillance data collected in the state over a three-year period.
METHODS: Neurologists were asked to submit case reports for NJ residents with ALS under their care from January 1, 2009, through December 31, 2011. Death and hospital data were reviewed to identify and collect additional case reports. Case addresses were provided by the reporting neurologists. Addresses were standardized, geocoded, and assigned to county and 2010 U.S. census tracts. Analyses were restricted to incident cases during the three year period. Average annual age-adjusted incidence rates are calculated by county using 2010 U.S. Census population data, and standardized to the year 2000 U.S. Standard Population. Smoothed rate maps based on the census tract or aggregated tracts as the unit of analysis are generated using a spatially adaptive filter method. Finally, geographic clustering is assessed using a spatial scan statistic at the census tract level. Since ALS incidence varies by sex, race and ethnicity, analyses are adjusted for or stratified by these factors.
RESULTS: A total of 494 cases were diagnosed in 2009-2011 and 99% (491/494) of them had geocodeable street addresses (three were P.O. boxes). County-level average annual age-adjusted ALS incidence rates will be presented. Smoothed rate maps and the presence of any geographic clusters, will also be shown.
CONCLUSIONS: Neurologists reported usable addresses of their ALS patients. The presentation will discuss whether there is any evidence that ALS varies geographically across New Jersey, using traditional county-level analyses as well as smoothed maps and cluster identification methods. The influence of race and ethnicity composition on geographic rates will also be explored and discussed. An understanding of geographic variation may be useful to suggest potential risk factors for ALS for further investigation.