ArticleSpatial and spatio-temporal epidemiology2012
The effect of administrative boundaries and geocoding error on cancer rates in California.
Article in Spatial and spatio-temporal epidemiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Application of Spatial Methods in Rural Cancer Control Research in the United States.Current epidemiology reports · 2026Review
- Area-Based Geocoding: An Approach to Exposure Assessment Incorporating Positional Uncertainty.GeoHealth · 2021Article
- GIScience and cancer: State of the art and trends for cancer surveillance and epidemiology.Cancer · 2019Review
- Article
- Precision of provider licensure data for mapping member accessibility to Medicaid managed care provider networks.BMC health services research · 2018Observational
- Article
- An analysis of the process and results of manual geocode correction.Geospatial health · 2017Article
- Smartphone-assisted spatial data collection improves geographic information quality: pilot study using a birth records dataset.Geospatial health · 2016Article
- Air pollution affects lung cancer survival.Thorax · 2016Article
- Use of attribute association error probability estimates to evaluate quality of medical record geocodes.International journal of health geographics · 2015Article
- Geocoding large population-level administrative datasets at highly resolved spatial scales.Transactions in GIS : TG · 2014Article
- An evaluation framework for comparing geocoding systems.International journal of health geographics · 2013Article
- Examining the spatial congruence between data obtained with a novel activity location questionnaire, continuous GPS tracking, and prompted recall surveys.International journal of health geographics · 2013Article
- A research agenda: does geocoding positional error matter in health GIS studies?Spatial and spatio-temporal epidemiology · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Geocoding is often used to produce maps of disease rates from the diagnosis addresses of incident cases to assist with disease surveillance, prevention, and control. In this process, diagnosis addresses are converted into latitude/longitude pairs which are then aggregated to produce rates at varying geographic scales such as Census tracts, neighborhoods, cities, counties, and states. The specific techniques used within geocoding systems have an impact on where the output geocode is located and can therefore have an effect on the derivation of disease rates at different geographic aggregations. This paper investigates how county-level cancer rates are affected by the choice of interpolation method when case data are geocoded to the ZIP code level. Four commonly used areal unit interpolation techniques are applied and the output of each is used to compute crude county-level five-year incidence rates of all cancers in California. We found that the rates observed for 44 out of the 58 counties in California vary based on which interpolation method is used, with rates in some counties increasing by nearly 400% between interpolation methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.