ArticleStatistics in medicine2011
Improving the power of chronic disease surveillance by incorporating residential history.
Article in Statistics in medicine, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- Assessing residential neighborhood exposomes and their associations with cancer incidence: rationale, study design, and methods.American journal of epidemiology · 2025Article
- Estimating cumulative spatial risk over time with low-rank kriging multiple membership models.Statistics in medicine · 2022Article
- The Effect of Workforce Mobility on Intervention Effectiveness Estimates.Annals of work exposures and health · 2018Article
- The utility of EMR address histories for assessing neighborhood exposures.Annals of epidemiology · 2017Article
- Spatial measurement errors in the field of spatial epidemiology.International journal of health geographics · 2016Article
- Assessment of Residential History Generation Using a Public-Record Database.International journal of environmental research and public health · 2015Article
- Bayesian importance parameter modeling of misaligned predictors: soil metal measures related to residential history and intellectual disability in children.Environmental science and pollution research international · 2014Article
- Improving the power of chronic disease surveillance by incorporating residential history.Statistics in medicine · 2011Article
- Power to detect spatial disturbances under different levels of geographic aggregation.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
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Authors and funding
2 authors.
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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 into 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.
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