Improving the power of chronic disease surveillance by incorporating residential history

Abstract

We present a global test for disease clustering with power to identify disturbances from the null population distribution which accounts for the lag time between the date of exposure and the date of diagnosis. Location at diagnosis is often used as a surrogate for the location of exposure, however, the causative exposure could have occurred at a previous address in a case’s residential history. We incorporate models for the incubation distribution of a disease to weight each address in the residential history by the corresponding probability of the exposure occurring at that address. We then introduce a test statistic which uses these incubation-weighted addresses to test for a difference between the spatial distribution of the cases and the spatial distribution of the controls, or the background population. We follow the construction of the M statistic to evaluate the significance of these new distance distributions. Our results show that gains in detection power when residential history is accounted for are of such a degree that it might make the qualitative difference between the presence of spatial clustering being detected or not, thus making a strong argument for the inclusion of residential history in the analysis of such data.

Bayesian importance parameter modeling of misaligned predictors: soil metal measures related to residential history and intellectual disability in children

“…Various approaches have been used in the literature to model residential history (Cook et al 2009; Hoffman et al 2010; Kim et al 2009, 2010; Manjourides and Pagano 2011; Van den Hooven et al 2012; Vieira et al 2002, 2005; Wheeler et al 2012). Recently, with the advancement of geographical information systems, it has become easier to obtain point location data, resulting in an increased need for the development of statistical methods appropriate for this type of data.…”

The utility of EMR address histories for assessing neighborhood exposures

“…Residential mobility data offer spatiotemporal records patient movement through space over time and are frequently studied in sociology [5,17–19], demography [20–24], economics [25–28], and public health [8,29,30]. A number of recent epidemiologic studies include information gleaned via address histories [31–34]. Despite current U.S. law mandating adoption of electronic medical records (EMRs), many of which contain address histories [35,36], no epidemiologic studies to date utilize this increasingly available resource.…”

Creating Residential and Tenure Histories from Multi‐Year White Pages

“…2004) and as necessary for improving the power of disease surveillance and statistical analysis (Jacquez et al. 2011, Manjourides and Pagano 2011). Many studies, for lack of better information, have made the assumption that location at diagnosis was location of exposure and that latency was negligible.…”

“…An example might be: Residence A: {hadTenant B, 1970-1982, hadTenant C, 1982-1987, hadTenant D, 1987-1998, hadTenant E, 1998. Residential histories have recently been documented as crucial information for the analysis of cancer registry data (Pickle et al 2005, Boscoe et al 2004) and as necessary for improving the power of disease surveillance and statistical analysis (Jacquez et al 2011, Manjourides andPagano 2011). Many studies, for lack of better information, have made the assumption that location at diagnosis was location of exposure and that latency was negligible.…”

“…Given residential mobility, which suggests that persons in the U.S. move on average every 5-7 years, such assumptions have lead to inaccuracies in disease cluster detection (Jacquez et al 2007). Several recent articles have made strong cases for more informative and accurate residential histories that can enhance the power to detect possible disease clusters (Jacquez et al 2011b) and to refine exposure models (Manjourides and Pagano 2011).…”