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An R-Based Landscape Validation of a Competing Risk Model
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Testing for changes in spatial relative risk.

Martin L Hazelton1

  • 1Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand.

Statistics in Medicine
|May 9, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to detect changes in disease spatial patterns over time. Researchers found significant shifts in campylobacteriosis risk in New Zealand, linked to public health initiative responses.

Keywords:
bandwidthcampylobacteriosiscase-control datadensity estimationkernel smoothingrandomization test

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Area of Science:

  • Epidemiology
  • Spatial Analysis
  • Biostatistics

Background:

  • Geographical variations in disease incidence are often described using spatial relative risk functions.
  • Detecting changes in these spatial patterns is crucial for public health, independent of overall incidence rate shifts.

Purpose of the Study:

  • To develop and validate a statistical method for comparing spatial relative risk functions between two distinct time periods.
  • To identify alterations in the geographical distribution of disease risk.

Main Methods:

  • Utilized case-control datasets from two time periods.
  • Employed kernel smoothing methods to estimate the difference between log-relative risk functions, termed the log-relative risk ratio.
  • Computed p-values for hypothesis testing using randomization and asymptotic normal approximation.

Main Results:

  • Applied the methodology to campylobacteriosis data in New Zealand (2006-2013).
  • Found statistically significant evidence of a change in the spatial pattern of disease risk.
  • Observed differences in risk patterns between urban and rural communities, potentially related to public health interventions.

Conclusions:

  • The developed log-relative risk ratio method effectively detects changes in spatial disease risk patterns.
  • The study highlights a dynamic spatial risk profile for campylobacteriosis in New Zealand.
  • Results suggest differential community responses to public health initiatives influenced spatial risk distribution.