ArticleInternational journal of environmental research and public health2013
Mapping disease at an approximated individual level using aggregate data: a case study of mapping New Hampshire birth defects.
Article in International journal of environmental research and public health, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed, 20 citations in OpenAlex.
- Geospatial heterogeneity of hotspots for incidence and late-stage diagnosis of breast, colorectal, and lung cancer.Cancer causes & control : CCC · 2026Article
- Cancer incidence data at the ZIP Code Tabulation Area level in the United States interpolated by Monte Carlo simulation with multiple constraints.Scientific data · 2025Article
- A multi-constraint Monte Carlo Simulation approach to downscaling cancer data.Health & place · 2025Article
- Assessing the influence of the modifiable areal unit problem on Bayesian disease mapping in Queensland, Australia.PloS one · 2025Article
- Evaluating Geographic Health Disparities in Cancer Care: Example of the Modifiable Areal Unit Problem.Annals of surgical oncology · 2023Article
- Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease.Journal of geographical systems · 2022Article
- Spatial interaction between breast cancer and environmental pollution in the Monterrey Metropolitan Area.Heliyon · 2021Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundLimited by data availability, most disease maps in the literature are for relatively large and subjectively-defined areal units, which are subject to problems associated with polygon maps. High resolution maps based on objective spatial units are needed to more precisely detect associations between disease and environmental factors.
methodWe propose to use a Restricted and Controlled Monte Carlo (RCMC) process to disaggregate polygon-level location data to achieve mapping aggregate data at an approximated individual level. RCMC assigns a random point location to a polygon-level location, in which the randomization is restricted by the polygon and controlled by the background (e.g., population at risk). RCMC allows analytical processes designed for individual data to be applied, and generates high-resolution raster maps.
resultsWe applied RCMC to the town-level birth defect data for New Hampshire and generated raster maps at the resolution of 100 m. Besides the map of significance of birth defect risk represented by p-value, the output also includes a map of spatial uncertainty and a map of hot spots.
conclusionsRCMC is an effective method to disaggregate aggregate data. An RCMC-based disease mapping maximizes the use of available spatial information, and explicitly estimates the spatial uncertainty resulting from aggregation.
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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.