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Accommodating the ecological fallacy in disease mapping in the absence of individual exposures.

Publication ,  Journal Article
Wang, F; Wang, J; Gelfand, A; Li, F
Published in: Statistics in medicine
December 2017

In health exposure modeling, in particular, disease mapping, the ecological fallacy arises because the relationship between aggregated disease incidence on areal units and average exposure on those units differs from the relationship between the event of individual incidence and the associated individual exposure. This article presents a novel modeling approach to address the ecological fallacy in the least informative data setting. We assume the known population at risk with an observed incidence for a collection of areal units and, separately, environmental exposure recorded during the period of incidence at a collection of monitoring stations. We do not assume any partial individual level information or random allocation of individuals to observed exposures. We specify a conceptual incidence surface over the study region as a function of an exposure surface resulting in a stochastic integral of the block average disease incidence. The true block level incidence is an unavailable Monte Carlo integration for this stochastic integral. We propose an alternative manageable Monte Carlo integration for the integral. Modeling in this setting is immediately hierarchical, and we fit our model within a Bayesian framework. To alleviate the resulting computational burden, we offer 2 strategies for efficient model fitting: one is through modularization, the other is through sparse or dimension-reduced Gaussian processes. We illustrate the performance of our model with simulations based on a heat-related mortality dataset in Ohio and then analyze associated real data.

Duke Scholars

Published In

Statistics in medicine

DOI

EISSN

1097-0258

ISSN

0277-6715

Publication Date

December 2017

Volume

36

Issue

30

Start / End Page

4930 / 4942

Related Subject Headings

  • Statistics & Probability
  • Ohio
  • Normal Distribution
  • Monte Carlo Method
  • Models, Statistical
  • Incidence
  • Humans
  • Heat Stress Disorders
  • Environmental Exposure
  • Computer Simulation
 

Citation

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ICMJE
MLA
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Wang, F., Wang, J., Gelfand, A., & Li, F. (2017). Accommodating the ecological fallacy in disease mapping in the absence of individual exposures. Statistics in Medicine, 36(30), 4930–4942. https://doi.org/10.1002/sim.7494
Wang, Feifei, Jian Wang, Alan Gelfand, and Fan Li. “Accommodating the ecological fallacy in disease mapping in the absence of individual exposures.Statistics in Medicine 36, no. 30 (December 2017): 4930–42. https://doi.org/10.1002/sim.7494.
Wang F, Wang J, Gelfand A, Li F. Accommodating the ecological fallacy in disease mapping in the absence of individual exposures. Statistics in medicine. 2017 Dec;36(30):4930–42.
Wang, Feifei, et al. “Accommodating the ecological fallacy in disease mapping in the absence of individual exposures.Statistics in Medicine, vol. 36, no. 30, Dec. 2017, pp. 4930–42. Epmc, doi:10.1002/sim.7494.
Wang F, Wang J, Gelfand A, Li F. Accommodating the ecological fallacy in disease mapping in the absence of individual exposures. Statistics in medicine. 2017 Dec;36(30):4930–4942.
Journal cover image

Published In

Statistics in medicine

DOI

EISSN

1097-0258

ISSN

0277-6715

Publication Date

December 2017

Volume

36

Issue

30

Start / End Page

4930 / 4942

Related Subject Headings

  • Statistics & Probability
  • Ohio
  • Normal Distribution
  • Monte Carlo Method
  • Models, Statistical
  • Incidence
  • Humans
  • Heat Stress Disorders
  • Environmental Exposure
  • Computer Simulation