Geographic Distribution of Parkinsonʹs Disease and Stroke in South Carolina

Purpose In South Carolina (SC) stroke remains a serious public health problem, and rates for both incidence and mortality continue to be among the nationʹs highest. Descriptive epidemiology of Parkinsonʹs Disease (PD) is difficult to estimate with accuracy due to difficulty in diagnosis, and, more, lack of dedicated surveillance programs. Since stroke sequelae can mimic symptoms of Parkinsonism, we set out to better understand the relationship of these diseases by first describing their distributions together. Methods We queried a population-based, statewide, integrated administrative database operated by the SC Office of Research and Statistics to determine the distribution of both stroke and PD in SC. The research dataset consists of all emergency department (ED) visits as well as hospitalizations resulting from admissions through EDs with diagnosis of cerebrovascular disease for 2002–2005, and all hospitalizations and ED encounters with diagnosis of PD for 1996–2004. For PD, all patients with conditions that confound the diagnosis were restricted from analyses. Next, average rates for each condition by county were calculated using U.S. Census standard population data. These data were then analyzed using GIS for spatial patterns and STATA software for statistical associations. Results Spatial analyses revealed significant variation in rates for both stroke and PD. Incidence of stroke and prevalence of PD were found to be positively correlated in SC for the total population (Spearman rho =0.57, p< 0.001) and for whites (Spearman rho =0.72, p<0.001) but not for non-whites (Spearman rho = 0.095, p= 0.53). Rates of PD and stroke tended to be different with respect to urban/rural location for the total population and for whites, but, again, not in non-whites. Conclusion We found that race and urban/rural status were important factors in the distribution of these diseases and how they correlated with each other. Further research is needed to investigate the relationship of stroke and PD, and to understand how racial diversity, population density, and other socio-economic factors influence the epidemiology of PD.