Analyzing Bladder Cancer Risk in Michigan
Author Information
Author(s): Geoffrey M. Jacquez, Jaymie R. Meliker, Gillian A. AvRuskin, Pierre Goovaerts, Andy Kaufmann, Mark L. Wilson, Jerome Nriagu
Primary Institution: BioMedware and The University of Michigan, School of Public Health
Hypothesis
Can a new method for analyzing case-control data account for residential mobility and known risk factors in bladder cancer cases?
Conclusion
The study found significant geographic clustering of bladder cancer cases that cannot be fully explained by known risk factors.
Supporting Evidence
- The study analyzed residential histories of 219 bladder cancer cases and 437 controls.
- Statistically significant global clustering was found at 12 of the 14 levels of k considered.
- Focused clusters were identified around 22 industries known to release bladder cancer carcinogens.
Takeaway
The researchers looked at where people lived to see if it affected their chances of getting bladder cancer, and they found some areas had more cases than expected.
Methodology
The study used case-control data from 219 bladder cancer cases and 437 controls, analyzing residential histories and applying logistic regression to account for risk factors.
Potential Biases
Potential geographic bias in case and control selection.
Limitations
Data collection is incomplete, and the study is only halfway through the data collection phase.
Participant Demographics
{"age_distribution":{"30-39":"1%","40-49":"6%","50-59":"20%","60-69":"33%","70+":"40%"},"gender_distribution":{"male":"77%","female":"23%"},"race_distribution":{"Caucasian/White":"95%","African American/Black":"1%","Asian/Asian American":"1%","American Indian or Alaskan Native":"3%"},"education_distribution":{"≤ High School":"39%","Some Post-High School":"30%","College Graduate":"19%","Post-Graduate Education":"12%"}}
Statistical Information
P-Value
p<0.001
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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