Adjusting for Underrepresentation Reveals Widespread Underestimation of Parkinson's Disease Symptom Burden

Background: Clinical research is limited by underrepresentation, but the impact of underrepresentation on patient-reported outcomes in Parkinson's disease (PD) is unknown. Objectives: To produce nationwide estimates of non-motor symptom (NMS) prevalence and PD-related quality of life (QOL) limitations while accounting for underrepresentation. Methods: We performed a cross-sectional analysis of data from the Fox Insight (FI) study, an ongoing prospective longitudinal study of persons with self-reported PD. Using epidemiologic literature and United States (US) Census Bureau, Medicare, and National Health and Aging Trends Study data, we simulated a “virtual census” of the PD population. To compare the PD census to the FI cohort, we used logistic regression to model the odds of study participation and calculate predicted probabilities of participation for inverse probability weighting. Results: There are an estimated 849,488 persons living with PD in the US. Compared to 22,465 eligible FI participants, non-participants are more likely to be older, female, and non-White; live in rural regions; have more severe PD; and have lower levels of education. When these predictors were incorporated into a multivariable regression model, predicted probability of participation was much higher for FI participants than non-participants, indicating a significant difference in the underlying populations (propensity score distance 2.62). Estimates of NMS prevalence and QOL limitation were greater when analyzed using inverse probability of participation weighting compared to unweighted means and frequencies. Conclusions: PD-related morbidity may be underestimated because of underrepresentation, and inverse probability of participation weighting can be used to give greater weight to underrepresented groups and produce more generalizable estimates.