Visualizing the diffusion of digital mammography in New York State

Digital mammography saw rapid adoption during the first decade of the 2000s. According to data maintained by the Food and Drug Administration, which regulates mammography machines, fewer than 1 percent of the machines in 2001 were digital. By 2014, this figure had risen to 94%. We were interested in identifying the times and locations where the technology was introduced within the state of New York as a way of illustrating the uneven introduction of this technology. While the diffusion of medical innovation has been well-studied, there have not been many instances where maps or geographic information science methods have been used to inform the interpretation. Here we illustrate the adoption of digital mammography in New York as of 2005, 2008, and 2011

Current practices in spatial analysis of cancer data: data characteristics and data sources for geographic studies of cancer

The use of spatially referenced data in cancer studies is gaining in prominence, fueled by the development and availability of spatial analytic tools and the broadening recognition of the linkages between geography and health. We provide an overview of some of the unique characteristics of spatial data, followed by an account of the major types and sources of data used in the spatial analysis of cancer, including data from cancer registries, population data, health surveys, environmental data, and remote sensing data. We cite numerous examples of recent studies that have used these data, with a focus on etiological research

Geographic disparities in colorectal cancer survival

© 2009 Henry et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Public domain small-area cancer incidence data for New York State, 2005-2009

There has long been a demand for cancer incidence data at a fine geographic resolution for use in etiologic hypothesis generation and testing, methodological evaluation, and teaching. In this paper we describe a public domain data set containing data for 23 anatomic sites of cancer diagnosed in New York State between 2005 and 2009 at the level of the census block group. The data set includes 524,503 tumors distributed across 13,823 block groups with an average population of about 1,400. In addition, the data have been linked with race and ethnicity and with socioeconomic indicators such as income, educational attainment, and language proficiency. We demonstrate the application of the data set by confirming two well-established relationships: that between breast cancer and median household income, and that between stomach cancer and Asian race. We foresee that this data set will serve as the basis for a wide range of spatial analyses and serve as a benchmark data set for evaluating spatial methods in the future

Estimating the accuracy of geographical imputation

<p>Abstract</p> <p>Background</p> <p>To reduce the number of non-geocoded cases researchers and organizations sometimes include cases geocoded to postal code centroids along with cases geocoded with the greater precision of a full street address. Some analysts then use the postal code to assign information to the cases from finer-level geographies such as a census tract. Assignment is commonly completed using either a postal centroid or by a geographical imputation method which assigns a location by using both the demographic characteristics of the case and the population characteristics of the postal delivery area. To date no systematic evaluation of geographical imputation methods ("geo-imputation") has been completed. The objective of this study was to determine the accuracy of census tract assignment using geo-imputation.</p> <p>Methods</p> <p>Using a large dataset of breast, prostate and colorectal cancer cases reported to the New Jersey Cancer Registry, we determined how often cases were assigned to the correct census tract using alternate strategies of demographic based geo-imputation, and using assignments obtained from postal code centroids. Assignment accuracy was measured by comparing the tract assigned with the tract originally identified from the full street address.</p> <p>Results</p> <p>Assigning cases to census tracts using the race/ethnicity population distribution within a postal code resulted in more correctly assigned cases than when using postal code centroids. The addition of age characteristics increased the match rates even further. Match rates were highly dependent on both the geographic distribution of race/ethnicity groups and population density.</p> <p>Conclusion</p> <p>Geo-imputation appears to offer some advantages and no serious drawbacks as compared with the alternative of assigning cases to census tracts based on postal code centroids. For a specific analysis, researchers will still need to consider the potential impact of geocoding quality on their results and evaluate the possibility that it might introduce geographical bias.</p

Using Imputation to Provide Location Information for Nongeocoded Addresses

Background: The importance of geography as a source of variation in health research continues to receive sustained attention in the literature. The inclusion of geographic information in such research often begins by adding data to a map which is predicated by some knowledge of location. A precise level of spatial information is conventionally achieved through geocoding, the geographic information system (GIS) process of translating mailing address information to coordinates on a map. The geocoding process is not without its limitations, though, since there is always a percentage of addresses which cannot be converted successfully (nongeocodable). This raises concerns regarding bias since traditionally the practice has been to exclude nongeocoded data records from analysis. Methodology/Principal: Findings In this manuscript we develop and evaluate a set of imputation strategies for dealing with missing spatial information from nongeocoded addresses. The strategies are developed assuming a known zip code with increasing use of collateral information, namely the spatial distribution of the population at risk. Strategies are evaluated using prostate cancer data obtained from the Maryland Cancer Registry. We consider total case enumerations at the Census county, tract, and block group level as the outcome of interest when applying and evaluating the methods. Multiple imputation is used to provide estimated total case counts based on complete data (geocodes plus imputed nongeocodes) with a measure of uncertainty. Results indicate that the imputation strategy based on using available population-based age, gender, and race information performed the best overall at the county, tract, and block group levels. Conclusions/Significance: The procedure allows for the potentially biased and likely under reported outcome, case enumerations based on only the geocoded records, to be presented with a statistically adjusted count (imputed count) with a measure of uncertainty that are based on all the case data, the geocodes and imputed nongeocodes. Similar strategies can be applied in other analysis settings

Persistent and extreme outliers in causes of death by state, 1999–2013

In the United States, state-specific mortality rates that are high relative to national rates can result from legitimate reasons or from variability in coding practices. This paper identifies instances of state-specific mortality rates that were at least twice the national rate in each of three consecutive five-year periods (termed persistent outliers), along with rates that were at least five times the national rate in at least one five-year period (termed extreme outliers). The resulting set of 71 outliers, 12 of which appear on both lists, illuminates mortality variations within the country, including some that are amenable to improvement either because they represent preventable causes of death or highlight weaknesses in coding techniques. Because the approach used here is based on relative rather than absolute mortality, it is not dominated by the most common causes of death such as heart disease and cancer

Subdividing the Age Group of 85 Years and Older to Improve US Disease Reporting

The standard terminal age category in disease reporting in the United States has been 85 years and older since the 1940s, but the dramatically increasing share of the US population reaching this age has rendered the single category inadequate for surveillance, research, and analysis. Important age-specific variations in mortality among the oldest old are masked by the continued use of this category