# Detecting Spatial Patterns of Disease in Large Collections of Electronic Medical Records Using Neighbor-Based Bootstrapping (NB2)

## TL;DR

This paper presents NB2, a neighbor-based bootstrapping method for quantifying and analyzing geospatial disease variation using large-scale electronic medical records, aligning well with existing spatial autocorrelation techniques.

## Contribution

The paper introduces NB2, a novel, flexible method for detecting spatial patterns in disease incidence from EMR data, capable of identifying variations at multiple scales.

## Key findings

- NB2 agrees with Moran's I and kriging in detecting spatial autocorrelation.
- NB2 can identify both large and small area geospatial variations.
- Applied to 100 million records, it effectively ranks diseases by spatial variation.

## Abstract

We introduce a method called neighbor-based bootstrapping (NB2) that can be used to quantify the geospatial variation of a variable. We applied this method to an analysis of the incidence rates of disease from electronic medical record data (ICD-9 codes) for approximately 100 million individuals in the US over a period of 8 years. We considered the incidence rate of disease in each county and its geospatially contiguous neighbors and rank ordered diseases in terms of their degree of geospatial variation as quantified by the NB2 method. We show that this method yields results in good agreement with established methods for detecting spatial autocorrelation (Moran's I method and kriging). Moreover, the NB2 method can be tuned to identify both large area and small area geospatial variations. This method also applies more generally in any parameter space that can be partitioned to consist of regions and their neighbors.