Comparing Disease Rate Detection Methods
Author Information
Author(s): Rhonda J Rosychuk
Primary Institution: University of Alberta
Hypothesis
When do confidence intervals for disease rates and a disease cluster detection method agree?
Conclusion
The cluster detection method is preferred when the cluster size exceeds the number of cases in an administrative area or when the expected number of cases exceeds a threshold.
Supporting Evidence
- Two areas were significant with both methods and one additional area was identified with the cluster detection method.
- The overall provincial rate was 105.3 cases per 100,000 population.
- Cells with confidence intervals above the overall rate were classified as areas of high rates.
Takeaway
Researchers looked at how to find areas with high disease rates and found that different methods can give similar results, but sometimes they don't agree.
Methodology
The study compared the Besag and Newell cluster detection method with confidence intervals for disease rates using a dataset on self-inflicted injuries.
Limitations
The methods may yield different results depending on the cluster size and the number of cases.
Participant Demographics
The analysis focused on the pediatric population under 18 years old.
Statistical Information
P-Value
0.026
Confidence Interval
(0.0009, 0.0016)
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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