Multi-state Models for Natural History of Disease

Longitudinal studies are a useful tool for investigating the course of chronic diseases. Many chronic diseases can be characterized by a set of health states. We can improve our understanding of the natural history of the disease by modeling the sequence of visited health states and the duration in each state. However, in most applications, subjects are observed only intermittently. This observation scheme creates a major modeling challenge: the transition times are not known exactly, and in some cases the path through the health states is not known. In this manuscript we review existing approaches for modeling multi-state longitudinal data. We introduce Bayesian estimation methods and illustrate their use in two longitudinal studies of chronic disease

A Joint Model for Multistate Disease Processes and Random Informative Observation Times, with Applications to Electronic Medical Records Data

Multistate models are used to characterize individuals\u27 natural histories through diseases with discrete states. Observational data resources based on electronic medical records pose new opportunities for studying such diseases. However, these data consist of observations of the process at discrete sampling times, which may either be pre-scheduled and non-informative, or symptom-driven and informative about an individual\u27s underlying disease status. We have developed a novel joint observation and disease transition model for this setting. The disease process is modeled according to a latent continuous time Markov chain; and the observation process, according to a Markov-modulated Poisson process with observation rates that depend on the individual\u27s underlying disease status. The disease process is observed at informative or non-informative sampling times, with possible misclassification error. We demonstrate that the model is computationally tractable and devise an expectation-maximization algorithm for parameter estimation. Using simulated data, we show how estimates from our joint observation and disease transition model lead to less biased and more precise estimates of the disease rate parameters. We apply the model to a study of secondary breast cancer events, utilizing mammography and biopsy records from a sample of women with a history of primary breast cancer

Exploring Student Persistence in STEM Programs: A Motivational Model

To address continually decreasing enrollment and rising attrition in post-secondary STEM degree (science, technology, engineering, and mathematics) programs, particularly for women, the present study examines the utility of motivation and emotion variables to account for persistence and achievement in science in male and female students transitioning from high school to junior college. Consistent with self-determination theory (Deci & Ryan, 2012) and achievement-goal theory (Senko, Hulleman, & Harackiewicz, 2011),structural equation modelling based on data from 1,309 students from four English-language CEGEPs showed students’ achievement goals, self-efficacy, and perceived autonomy support to impact intrinsic motivation, emotions, and achievement that, in turn, predicted persistence in the science domain

Validation of a Medicare Claims-based Algorithm for Identifying Breast Cancers Detected at Screening Mammography

The breast cancer detection rate is a benchmark measure of screening mammography quality, but its computation requires linkage of mammography interpretive performance information with cancer incidence data. A Medicare claims-based measure of detected breast cancers could simplify measurement of this benchmark and facilitate mammography quality assessment and research

Association of Rideshare-Based Transportation Services and Missed Primary Care Appointments: A Clinical Trial

In a pragmatic trial, offering complimentary ridesharing services broadly to Medicaid patients did not reduce rates of missed primary care appointments. The uptake of free rides was low, and rates of missed appointments remained unchanged at 36%. Efforts to reduce missed appointments due to transportation barriers may require more targeted approaches

Mobility Disability in Older Adults: At the Intersection of People and Places

Mobility disability is associated with poor lower body function among older adults. This study examines whether specific types of neighborhood characteristics moderate that association

A community study of the effect of particulate matter on blood measures of inflammation and thrombosis in an elderly population

BACKGROUND: The mechanism behind the triggering effect of fine particulate matter (PM) air pollution on cardiovascular events remains elusive. We postulated that elevated levels of PM would be associated with increased blood levels of inflammatory and thrombotic markers in elderly individuals. We also hypothesized that elevated PM would increase levels of cytokines in individuals with heart disease. METHODS: We measured these blood markers in 47 elderly individuals with (23) and without (16 COPD and 8 healthy) cardiovascular disease (CVD) on 2 or 3 mornings over a 5 or 10-day period between February 2000 and March 2002. Blood measures were paired with residence level outdoor PM measured by nephelometry. Analyses determined the within-individual effect of 24-hour averaged outdoor PM on blood measures. RESULTS: Analyses found no statistically significant effect of a same day 10 ug/m(3 )increase in fine PM on log transformed levels of CRP 1.21 fold-rise [95% CI: 0.86, 1.70], fibrinogen 1.02 fold-rise [95% CI: 0.98, 1.06], or D-dimer 1.02 fold-rise [95% CI: 0.88, 1.17] in individuals with CVD. One-day lagged analyses in the CVD subgroup found similar null results. These same models found no change in these blood markers at the same-day or 1-day lag in the group without CVD. In 21 individuals with CVD, a 10 μg/m(3 )increase in same-day PM was associated with a 1.3 fold-rise [95% CI: 1.1, 1.7] in the level of monocyte chemoattractant protein-1. CONCLUSION: We did not find consistent effects of low ambient levels of PM on blood measures of inflammation or thrombosis in elderly individuals

Increased Risk of Developing Breast Cancer after a False-Positive Screening Mammogram

Women with a history of false-positive mammogram result may be at increased risk of developing subsequent breast cancer

Is the Closest Facility the One Actually Used? An Assessment of Travel Time Estimation Based on Mammography Facilities

Characterizing geographic access depends on a broad range of methods available to researchers and the healthcare context to which the method is applied. Globally, travel time is one frequently used measure of geographic access with known limitations associated with data availability. Specifically, due to lack of available utilization data, many travel time studies assume that patients use the closest facility. To examine this assumption, an example using mammography screening data, which is considered a geographically abundant health care service in the United States, is explored. This work makes an important methodological contribution to measuring access--which is a critical component of health care planning and equity almost everywhere. We analyzed one mammogram from each of 646,553 women participating in the US based Breast Cancer Surveillance Consortium for years 2005-2012. We geocoded each record to street level address data in order to calculate travel time to the closest and to the actually used mammography facility. Travel time between the closest and the actual facility used was explored by woman-level and facility characteristics