Flexible design and analysis methods in diverse data sources for the evaluation of progressive neurological diseases
Keywords: Epidemiology.

Neurological disorders represent a large and growing burden as populations age, both in the United States and globally. The prevalence of noncommunicable neurological conditions, in particular, increases with age and entails a significant societal cost in terms of disability and mortality as well as care and support. This reality is complicated by the high degree of uncertainty around the causes, modifiable risk factors, and natural history of many of these disorders, including Alzheimer's and other dementias, Parkinson's, multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS). Rigorous, classical epidemiologic investigations into these conditions are needed, but must be designed with sufficient flexibility and creativity to succeed despite a dearth of gold standard measures or firm mechanistic knowledge of disease processes. To that end, in this dissertation we employed a combination of flexible design and analytic approaches with traditional epidemiologic principles to achieve two primary objectives: (1) assess the prevalence of antecedent autoimmune conditions in newly diagnosed ALS patients within the US Medicare population, and (2) characterize the longitudinal trajectories of MS disease progression in the absence of disease modifying therapies (DMTs). In the largest and most comprehensive US-based study to date, we found little evidence of autoimmune conditions and ALS co-occurring at a higher rate than expected when requiring a rigorous, 24-month exclusion period prior to ALS diagnosis in which no new autoimmune conditions could accrue. Sensitivity analyses evaluating shortened exclusion period demonstrated increasing numbers of autoimmune diagnoses in the periods directly preceding ALS diagnosis, suggesting uncertainty in the differential diagnosis of ALS during the early period of non-specific symptoms. Additionally, we found that MS progression trajectories among placebo-treated patients are highly heterogeneous and that trajectories described by various functional measures do not correspond to the commonly understood disease course phenotypes. Moreover, despite frequent usage in clinical trials and cohort studies to evaluate progression, many of the functional measures we assessed only identified longitudinal changes in a small subset of MS patients, indicating that they may have more utility as measures of inter-individual heterogeneity than intra-individual progression. Lastly, we have included a methodological study introducing a novel sampling strategy that utilizes unsupervised machine learning to cluster geographic regions on the basis of disease risk factors. When employed to recruit a representative study population for estimating the prevalence of COVID-19 in the San Francisco Bay Area, the approach proved effective for rapid and flexible identification of high-risk communities. While this study was conducted in the context of infectious disease surveillance, the methodology has far-ranging applications in clinical and epidemiologic research for targeted geographical recruitment of study participants. These findings provide insight into the particular challenges of population-level neuroepidemiology and highlight clear opportunities for future research in the field