Disease mapping method comparing the spatial distribution of a disease with a control disease

Area of Science:

• Spatial epidemiology
• Biostatistics
• Public health

Background:

• Small-area methods are crucial for spatial epidemiology, aiding in health analysis and identifying elevated disease risk areas.
• Traditional disease mapping relies on internal standardization for expected case calculations, which necessitates accurate population data and disease rates by demographic groups.
• Data limitations, such as confidentiality concerns or lack of detailed population information, often hinder accurate expected value calculations.

Purpose of the Study:

• To propose a novel case-control approach for disease mapping in lattice data settings when accurate population data is unavailable.
• To address the uncertainty in estimating expected values by utilizing a control disease to represent a fraction of the population.
• To validate the proposed method's efficacy using a real-world study and simulation analyses.

Main Methods:

• Developed a case-control methodology adapted for lattice data, employing a spatially unstructured control disease.
• Used the observed cases of a control disease (pancreatic cancer) to estimate population fractions, correcting for estimation uncertainty.
• Applied the method to a Belgian mesothelioma risk study and conducted simulation studies with varying spatial structures.

Main Results:

• The proposed method yielded analysis results highly consistent with traditional disease mapping models that use internally standardized expected counts.
• Simulation studies confirmed the method's robustness across different spatial data structures.
• The approach effectively handles inaccuracies or complete absence of population data in disease mapping analyses.

Conclusions:

• The novel case-control method provides a viable alternative for disease mapping when population data is limited or unavailable.
• This approach enhances the applicability of spatial epidemiology in data-scarce environments.
• The study demonstrates the method's reliability and accuracy in identifying disease risk hotspots.