Use of medical care biases associations between Parkinson disease and other medical conditions.

Neurology

From the Department of Neurology (A.G., B.A.R., A.C.-S., U.D., S.S.N.), Washington University School of Medicine, St. Louis, MO; and School of Public Health (B.A.R.), Faculty of Health Sciences, University of the Witwatersrand, Parktown, South Africa.

Published: June 2018

Objective: To examine how use of medical care biases the well-established associations between Parkinson disease (PD) and smoking, smoking-related cancers, and selected positively associated comorbidities.

Methods: We conducted a population-based, case-control study of 89,790 incident PD cases and 118,095 randomly selected controls, all Medicare beneficiaries aged 66 to 90 years. We ascertained PD and other medical conditions using ICD-9-CM codes from comprehensive claims data for the 5 years before PD diagnosis/reference. We used logistic regression to estimate age-, sex-, and race-adjusted odds ratios (ORs) between PD and each other medical condition of interest. We then examined the effect of also adjusting for selected geographic- or individual-level indicators of use of care.

Results: Models without adjustment for use of care and those that adjusted for geographic-level indicators produced similar ORs. However, adjustment for individual-level indicators consistently decreased ORs: Relative to ORs without adjustment for use of care, all ORs were between 8% and 58% lower, depending on the medical condition and the individual-level indicator of use of care added to the model. ORs decreased regardless of whether the established association is known to be positive or inverse. Most notably, smoking and smoking-related cancers were positively associated with PD without adjustment for use of care, but appropriately became inversely associated with PD with adjustment for use of care.

Conclusion: Use of care should be considered when evaluating associations between PD and other medical conditions to ensure that positive associations are not attributable to bias and that inverse associations are not masked.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5996836PMC
http://dx.doi.org/10.1212/WNL.0000000000005678DOI Listing

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