Exploration of Older Adults’ Travel Behavior and Their Transportation Barriers

Both the number of older adults and their proportion of the population are increasing rapidly in the United States. By 2040, about 20.7% of the U.S. population will be 65 and older (Harrison & Ragland, 2003a). These dramatic changes in the composition of the population will bring new challenges to the provision of transportation services. This is because the travel patterns and needs of older adults are likely to become more complicated. A growing number of people will find it increasingly difficult to meet their transportation needs. As the life expectancy of older adults is likely to continue to increase, a greater number of older people will face mobility issues alone (Alsnih & Hensher, 2003). Researchers widely agree that the aging population in the U.S. relies heavily on cars (as drivers or passengers) because they are convenient, flexible, and allow them to live independently and participate in normal daily activities (Haustein, 2012; Rosenbloom, 2005). However, dispersed land use patterns in the United States, the growing number of older adults living in suburban areas, and the current transportation infrastructure in the country make the use of a car a necessity rather than an option for a large proportion of older adults. However, as they age, their physical and mental health deteriorates, making driving dangerous for them. Therefore, it is of great importance to understand the transportation problems of older adults and provide them with reliable and acceptable alternative modes of transportation to help them meet their transportation needs. The study presented here aims to examine the transportation problems of older adults living in urban and suburban areas, make policy recommendations, and identify effective strategies to help them meet their mobility needs. To this end, the study used a mixed-method approach to identify the factors that influence older adults\u27 travel behavior and the issues they face when walking, biking, and using transit. In-depth, one-on-one surveys were conducted in three counties in southeastern Wisconsin with 178 English-speaking older adults aged 65 and older living independently in institutionalized senior housing (i.e., subsidized housing and retirement communities) and in noninstitutionalized buildings. The first main chapter of the thesis (Chapter 4) examines the factors that influence older adults\u27 mode choice for grocery shopping and aims to predict older adults\u27 travel behavior for going to the grocery store. A quantitative analysis involving statistical and machine learning techniques was conducted with older adults who traveled to the grocery store by car, carpool, walking, or public transit (N=153). The results of the study show that household car ownership and having a valid driver\u27s license are the most important factors influencing travel mode choice by older adults. However, age group (65-74 or 75+) and physical disability were not significant factors influencing older adults\u27 choice of transportation mode for grocery shopping. The second main chapter of this study (Chapter 5) examines the reasons why older adults who hold a valid driver\u27s license intend to renew their license when it expires (yes), or whether they do not intend to do so or are hesitant (no/not sure). Using a mixed-method approach including binomial logit regression and qualitative analysis, 116 older adults were surveyed. Results suggest that being 75 years of age and older, having a physical disability, and having a lower level of education (high school and below) negatively influence older adults\u27 decision to renew their driver\u27s license. Older adults who drive frequently and indicate that they would like to be able to drive to destinations easily are more likely to renew their driver\u27s license after it expires. The third main chapter of this thesis (Chapter 6) aims to examine the barriers and challenges older adults face when using modes of transportation other than the personal automobile, such as walking, bicycling, public transit, and ride-hailing. A qualitative content analysis of the 103 open-ended responses was used to fit the results into an ecological model. The study recommends four main actions to help policymakers and city governments overcome these barriers: (1) implement transportation education and outreach programs, (2) improve accessibility to services and facilities through land use policies, (3) improve transportation infrastructure and services, and (4) help for-profit and nonprofit organizations organize informal groups to walk, bike, or carpool together. This thesis has important implications for policy makers and urban practitioners to meet the transportation needs of older adults. Improving transportation infrastructure and providing older adults with reliable and high-standard non-automobile transportation alternatives, managing future land use dynamics and investing in sustainable land use patterns, and coordinating with organizations to support social networks (such as informal clubs and local groups) that help older adults meet their travel needs are among some of these important implications

A Systematic Approach for Developing a Corpus of Patient Reported Adverse Drug Events: A Case Study for SSRI and SNRI Medications

Psychiatric Treatment Adverse Reactions (PsyTAR) corpus is an annotated corpus that has been developed using patients narrative data for psychiatric medications, particularly SSRIs (Selective Serotonin Reuptake Inhibitor) and SNRIs (Serotonin Norepinephrine Reuptake Inhibitor) medications. This corpus consists of three main components: sentence classification, entity identification, and entity normalization. We split the review posts into sentences and labeled them for presence of adverse drug reactions (ADRs) (2168 sentences), withdrawal symptoms (WDs) (438 sentences), sign/symptoms/illness (SSIs) (789 sentences), drug indications (517), drug effectiveness (EF) (1087 sentences), and drug infectiveness (INF) (337 sentences). In the entity identification phase, we identified and extracted ADRs (4813 mentions), WDs (590 mentions), SSIs (1219 mentions), and DIs (792). In the entity normalization phase, we mapped the identified entities to the corresponding concepts in both UMLS (918 unique concepts) and SNOMED CT (755 unique concepts). Four annotators double coded the sentences and the span of identified entities by strictly following guidelines rules developed for this study. We used the PsyTAR sentence classification component to automatically train a range of supervised machine learning classifiers to identifying text segments with the mentions of ADRs, WDs, DIs, SSIs, EF, and INF. SVMs classifiers had the highest performance with F-Score 0.90. We also measured performance of the cTAKES (clinical Text Analysis and Knowledge Extraction System) in identifying patients\u27 expressions of ADRs and WDs with and without adding PsyTAR dictionary to the core dictionary of cTAKES. Augmenting cTAKES dictionary with PsyTAR improved the F-score cTAKES by 25%. The findings imply that PsyTAR has significant implications for text mining algorithms aimed to identify information about adverse drug events and drug effectiveness from patients\u27 narratives data, by linking the patients\u27 expressions of adverse drug events to medical standard vocabularies. The corpus is publicly available at Zolnoori et al. [30]