As hypotheses are tested and rejected, the remaining hypotheses are those that plausibly might explain the observed pattern. But how often do we include medications contaminated with foreign agents — fungus, bacteria, or otherwise — in our set of explanatory hypotheses? Until now, rarely, if ever. What we are learning from the New England Compounding Center is that contaminated medications largely explain the observed outbreak of fungal meningitis.
Genetic GIS: A call and a research agenda.
Genetic GIS provides a comprehensive model of human health and its determinants including genetic, environmental and behavioral dimensions.
Part 3: Spatial Autocorrelation and Clusters of Health Events
Part 3 Neutral models This is the third in a series on spatial autocorrelation and clusters of health events. The first part presented a framework for analyzing disease clusters that builds on the principles of strong inference. Strong inference involves enumeration...
Part 2: Spatial Autocorrelation and Clusters of Health Events
Part 2 Sources of Spatial Autocorrelation Summary: This blog presents several of the sources of spatial autocorrelation in health event data. Many of these could plausibly lead to clusters of health events, others (such as interpolation autocorrelation) may act...
Part 1: Spatial Autocorrelation and Clusters of Health Events
Part 1 Strong Inference The Centers for Disease Control as well as state and local health agencies use information on clusters of health events to respond to cluster allegations brought forward by a concerned public; identify impacted local populations (where are...
The small numbers problem Part 3: Diagnostics for the small numbers problem
To follow along with the analyses in this blog, download and install a trial version of SpaceStat here. An earlier blog defined the small numbers problem and illustrated that rates calculated with small denominators (e.g. small at-risk populations) have high variance...
The small numbers problem–Part 2
Using persistence in spatial time series as a diagnostic for extreme rates in small areas. In my last blog on the small numbers problem, we found that rates calculated with small denominators (e.g. small at-risk populations) have high variance and we thus have little...
The small numbers problem Part 1: What you see is not necessarily what you get
The ability to quickly create maps of health outcomes such as cancer incidence and mortality in counties, census areas and even Zip codes is now available through websites and data portals. (See for example Atlasplus, State Cancer Profiles, and Cardiovascular Disease,...