A survey of quality of life indicators in the Romanian Roma population following the ‘Decade of Roma Inclusion’ [version 3; referees: 2 approved, 1 approved with reservations]

Background: This study explores how the Roma in Romania, the EU’s most concentrated population, are faring in terms of a number of quality of life indicators, including poverty levels, healthcare, education, water, sanitation, and hygiene. It further explores the role of synthetic populations and modelling in identifying at-risk populations and delivering targeted aid. Methods: 135 surveys were conducted across five geographically diverse Romanian communities. Household participants were selected through a comprehensive random walk method. Analyses were conducted on all data using Pandas for Python. Combining land scan data, time-use survey analyses, interview data, and ArcGIS, the resulting synthetic population was analysed via classification and regression tree (CART) analysis to identify hot-spots of need, both ethnically and geographically. Results: These data indicate that the Roma in Romania face significant disparities in education, with Roma students less likely to progress beyond 8 th grade. In addition, the Roma population remains significantly disadvantaged with regard to safe and secure housing, poverty, and healthcare status, particularly in connection to diarrheal disease. In contrast, however, both Roma and non-Roma in rural areas face difficulties regarding full-time employment, sanitation, and water, sanitation, and hygiene infrastructure. In addition, the use of a synthetic population can generate information about ‘hot spots’ of need, based on geography, ethnicity, and type of aid required. Conclusions: These data demonstrate the challenges that remain to the Roma population in Romania, and also point to the myriad of ways in which all rural Romanians, regardless of ethnicity, are encountering hardship. This study highlights an approach that combines traditional survey data with more wide-reaching geographically based data and CART analysis to determine ‘hot spot’ areas of need in a given population. With the appropriate inputs, this tool can be extrapolated to any population in any country

Spatial Epidemiology in an Appalachian Context

Once only spoken of by physicians and epidemiologists, exotic tropical diseases have recently become household names. Chikungunya, Ebola, SARS, Zika, even the local evening news talks about them. But for all the attention we give to each tropical menace, we seem willfully ignorant of home-grown epidemics. This is especially true of diseases which prey upon residents of disadvantaged rural areas. Appalachia is no exception; in fact it is burdened by several such diseases. With almost no news coverage, it may surprise some to learn that Hepatitis C kills more Americans than any other infectious disease including HIV. The Hepatitis C outbreak is inexorably intertwined with the ongoing heroin and opioid-abuse epidemic, and sadly, Appalachia serves as hot spots for both. For all the talk of Zika-carrying mosquitoes, it is easy to lose track of the fact that tick-borne Lyme disease is the most significant vector-borne agent in the country. Lyme continues its slow southerly advance, and has found Appalachian farming communities to be particularly hospitable. Through the use of spatiotemporal analysis, we investigate the impact of Hepatitis C and Lyme disease on rural Appalachia. In addition to describing the where and the when of each epidemic, we also investigate why Appalachia, in particular, is so vulnerable to these intractable threats