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The misuse of count data aggregated over time for disease mapping
1Escuela Andaluza de Salud Pública, Campus Universitario de Cartuja, Cuesta del Observatorio, 4, Apdo de Correos 2070, 18080 Granada, Spain. ricardo.ocana.easp@juntadeandalucia.es
Statistics in Medicine
|March 6, 2007
Summary
Aggregating disease data over several years can distort area-specific risk estimates, impacting healthcare policies. A Bayesian model offers a more accurate approach for disease mapping with temporal data.
Area of Science:
- Spatial analysis
- Biostatistics
- Epidemiology
- Public Health
Background:
- Spatial analysis techniques are increasingly used for small-area disease mapping, often aggregating data over multiple years.
- This practice, while common for creating mortality and morbidity atlases, may lead to inaccurate relative risk estimations.
- Inappropriate model specifications using aggregated temporal data can negatively influence healthcare policies and decision-making.
Purpose of the Study:
- To demonstrate how aggregating count data over time can introduce bias in area-specific relative risk estimation for disease mapping.
- To quantify the bias resulting from using time-aggregated data in spatial analysis.
- To propose an alternative statistical model for more accurate disease mapping when data spans several years.
Main Methods:
- Utilized properties of the Poisson distribution to quantify bias in relative risk estimation from aggregated count data.
- Developed and proposed a hierarchical Bayesian model incorporating a spatio-temporal random structure.
- Applied the discussed methods to a real-world dataset: a small-area survey of male mortality in Southern Spain (1985-1999).
Main Results:
- Confirmed that aggregating count data over extended periods can lead to inappropriate area-specific relative risk estimates.
- Quantified the bias introduced by temporal aggregation in disease mapping.
- The proposed hierarchical Bayesian model demonstrated potential for more accurate risk smoothing compared to models lacking temporal structure.
Conclusions:
- Caution is advised when interpreting disease risk maps derived from data aggregated over multiple years without accounting for temporal dynamics.
- Models used for disease mapping should incorporate temporal structures to accurately represent area-specific risks.
- The proposed spatio-temporal Bayesian model provides a more reliable alternative for analyzing small-area health data spanning several years.
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