SM2-PD-02 Introduction: The susceptibility of people to develop ill health can be influenced by characteristics of their neighborhood. Moreover, populations in socioeconomic deprived areas are considered more susceptible to poor health compared with those in affluent areas. Here, we aimed to use methods of spatial statistics and apply them in the context of environmental epidemiology. Specifically, we aimed to test for spatial autocorrelation to investigate clustering of indicators of health, in the Northeast of England. Methods: We abstracted data on the English Health Deprivation and Disability Index (HDDI), which comprises of 4 measures of ill health. These were downloaded for the smallest possible spatial unit comprising of a mean population of 1500, called Lower Super Output Areas (LSOA). We used Geographical Information Systems to map HDDI for the Northeast of England, as well as for each county and unitary authority (UA) in the region. We used the global Moran's I statistic to investigate spatial autocorrelation. Global autocorrelation statistics provide a single measure for clustering (positive spatial autocorrelation) or dispersion (negative spatial autocorrelation) of an attribute, in a spatial unit. A Moran's Index value near +1.0 indicates clustering, whereas an index value near −1.0 indicates dispersion. Results: We detected positive spatial autocorrelation in all spatial units assessed. A high positive z-score for all tests indicated that the observed clustering was statistically significant (P < 0.01). The strongest positive spatial autocorrelation was detected in counties Durham (Moran's I = 0.68, z = 10.4) and Northumberland (Moran's I = 0.68, z = 8.5). Clustering of health deprived areas was apparent in the eastern part of both counties. Weak positive spatial autocorrelation was found in Darlington UA (Moran's I = 0.18, z = 3.8) and Hartlepool UA (Moran's I = 0.24, z = 3.2). Moderate positive spatial autocorrelation was detected for the Northeast of England (Moran's I = 0.34, z = 22.4) and the other spatial units. Discussion and Conclusions: The global Moran's test was successful in identifying spatial autocorrelation of health indicators, separating spatial units with high, moderate, and weak clustering. In those areas where county and UA level clustering was moderate or weak, the local Moran's I test may be able to detect more localized clustering in smaller spatial units. We conclude that susceptibility to develop ill health is strongly clustered at county level in some counties but not in others.