In North America, the last two decades saw continued increases in population multimorbidity across all age groups. This trend, which is expected to endure in the coming years, has been attributed in large part to population aging and unhealthy lifestyle choices. While the societal consequences of multimorbidity have focused primarily on the burden it imposes on the sustainability of health systems and the need to implement innovative ways to deliver care, latent costs, such as possible increases in motor-vehicle accidents (MVAs) have received relatively little attention. The principal objective of this thesis was to investigate the relationship between multimorbidity and MVAs. To complement current knowledge on the topic, we conducted observational studies based on information recorded in electronic health records (EHR). The hypothesis that increasing levels of multimorbidity would translate into increasing risk of MVA was tested in both a general population of health care recipients and in persons with epilepsy, a subgroup of individuals predisposed to comorbidities and MVAs. To gain a better understanding of morbidity ascertainment in EHR data, preliminary validation studies were performed to evaluate the performance of Elixhauser comorbidity measures for predicting hospital mortality in our data source. A systematic review of risk factors contributing to the onset and progression of epilepsy was also performed in hopes of identifying elements that would help improve the methodological design of the principal thesis study limited to persons with epilepsy. Study results confirmed the excellent performance of the Elixhauser comorbidity measures for predicting hospital mortality in the Cerner Health Facts data repository. In the general health care recipient population, a positive exposure-outcome relationship was observed between multimorbidity and MVA risk. This relationship was consistent in adults across the lifespan and more pronounced in women than in men. In persons with epilepsy, the observed exposure-outcome relationship between multimorbidity and MVAs did not reach statistical significance. However, comorbid depression was identified as a risk factor for MVAs. Given increasing rates of multimorbidity in the general population, the findings of this thesis strongly support the need for replication and better characterization of the disease combinations that drive increases in MVA risk. Future work on this topic should also include estimates of MVA risk attributable to multimorbidity; this would inform and gauge the relevance of novel driving policies targeting individuals diagnosed with specific health conditions.

Prognostic scores, and more specifically comorbidity scores, are important and widely used measures in the health care field and in health services research. A comorbidity is an existing disease an individual has in addition to a primary condition of interest, such as cancer. A comorbidity score is a summary score that can be created from these individual comorbidities for prognostic purposes, as well as for confounding adjustment. Despite their widespread use, the properties of and conditions under which comorbidity scores are valid dimension reduction tools in statistical models is largely unknown. This dissertation explores the use of summary comorbidity measures in statistical models. Three particular aspects are examined. First, it is shown that, under standard conditions, the predictive ability of these summary comorbidity measures remains as accurate as the individual comorbidities in regression models, which can include factors such as treatment variables and additional covariates. However, these results are only true when no interaction exists between the individual comorbidities and any additional covariate. The use of summary comorbidity measures in the presence of such interactions leads to biased results. Second, it is shown that these measures are also valid in the causal inference framework through confounding adjustment in estimating treatment effects. Lastly, we introduce a time dependent extension of summary comorbidity scores. This time dependent score can account for changes in patients' health over time and is shown to be a more accurate predictor of patient outcomes. A data example using breast cancer data from the SEER Medicare Database is used throughout this dissertation to illustrate the application of these results to the health care field. / Statistics

Objective: The objective of this study was to compare the use of different comorbidity measures to predict future healthcare utilization and expenditures for diabetic patients. Methods: This was a retrospective study that included 8,704 diabetic patients enrolled continuously for three years in the Department of Defense TRICARE program. Administrative claims data were used to calculate six comorbidity measures: number of distinct medications, index-year healthcare expenditures, two versions of the Charlson Comorbidity Index (CCI), and two versions of the Chronic Disease Score (CDS). Linear regression models were used to estimate three health outcomes for one- and two-year post-index periods: healthcare expenditures (COST), number of hospitalizations (HOS), and number of emergency department visits (ED). Logistic regression models were used to estimate binary outcomes (above or below the 90th percentile of COST; [greater than or equal to] 1 HOS or none; [greater than or equal to] 1 ED or none). Comparisons were based on adjusted R², areas under the receiver-operator-curve (c statistics), and the Hosmer-Lemeshow goodness-of-fit tests. Results: The study population had a mean age of 51.0 years (SD = 10.5), and 46.3 percent were male. After adjusting for age and sex, the updated CCI was the best predictor of one-year and two-year HOS (adjusted R² = 8.1%, 9.3%), the number of distinct medications was superior in predicting one-year and two-year ED (adjusted R² = 9.9%, 12.4%), and the index-year healthcare expenditures explained the most variance in one-year and two-year COST (adjusted R² = 35.6%, 31.6%). In logistic regressions, the number of distinct medications was the best predictor of one-year and two-year risks of emergency department use (c = 0.653, 0.654), but the index-year healthcare expenditures performed the best in predicting one-year and two-year risks of hospitalizations (c = 0.684, 0.676) and high-expenditure cases (c = 0.810, 0.823). The updated CCI consistently outperformed the original CCI in predicting the outcomes of interest. Conclusions: In a diabetic population under age 65, the number of distinct medications and baseline healthcare expenditures appeared to have superior or similar powers compared to the CCI or CDS for the prediction of future healthcare utilization and expenditures. The updated CCI was a better predictor than the original CCI in this population. / text