No abstract
Sensor-based remote monitoring could help better track Parkinson’s disease (PD) progression, and measure patients’ response to putative disease-modifying therapeutic interventions. To be useful, the remotely-collected measurements should be valid, reliable, and sensitive to change, and people with PD must engage with the technology. We developed a smartwatch-based active assessment that enables unsupervised measurement of motor signs of PD. Participants with early-stage PD (N = 388, 64% men, average age 63) wore a smartwatch for a median of 390 days. Participants performed unsupervised motor tasks both in-clinic (once) and remotely (twice weekly for one year). Dropout rate was 5.4%. Median wear-time was 21.1 h/day, and 59% of per-protocol remote assessments were completed. Analytical validation was established for in-clinic measurements, which showed moderate-to-strong correlations with consensus MDS-UPDRS Part III ratings for rest tremor (⍴ = 0.70), bradykinesia (⍴ = −0.62), and gait (⍴ = −0.46). Test-retest reliability of remote measurements, aggregated monthly, was good-to-excellent (ICC = 0.75–0.96). Remote measurements were sensitive to the known effects of dopaminergic medication (on vs off Cohen’s d = 0.19–0.54). Of note, in-clinic assessments often did not reflect the patients’ typical status at home. This demonstrates the feasibility of smartwatch-based unsupervised active tests, and establishes the analytical validity of associated digital measurements. Weekly measurements provide a real-life distribution of disease severity, as it fluctuates longitudinally. Sensitivity to medication-induced change and improved reliability imply that these methods could help reduce sample sizes needed to demonstrate a response to therapeutic interventions or disease progression.
Many matching markets are naturally dynamic. Every year thousands of incompatible patient-donor pairs register to kidney exchange clearinghouses that search periodically to match these pairs. Online platforms (dating, online workplace, etc.), labor markets, and even housing markets can be viewed as dynamic matching markets. The matching policy, which determines when and who to match, plays an important role in the efficiency of the marketplace. A myopic policy, which attempts to match agents upon arrival, may have short run benefits but could harm future arriving agents. So a centralized clearinghouse may wait to thicken the market before identifying matches. In various marketplaces, the matching technology is also instrumental for efficiency. While kidney exchanges were first conducted in 2-way cycles (bilateral matches), most transplants are now conducted through chains initiated by an altruistic donor. This paper is concerned with the effects matching technologies and matching policies have on the efficiency of markets with different thickness levels. We study a stylized infinite horizon model with two types of agents distinguished by their difficulty to match. Every period a single agent arrives to the market whose type is either hard-to-match (H), or easy-to-match (E). So the fraction of H agents joining the market, can be viewed as a measure for the market thickness, with a higher fraction interpreted as a thinner market. Agents in our model prefer to match as early as possible and are indifferent between acceptable matches. We focus on two types of matchings; bilateral and chains, and we assume that only one of these take place. We are interested in the behavior of the average waiting time for small values of p H. Our main contribution is identifying a tight connection between the fraction of H agents joining the market and the effect the matching technology has on efficiency. In particular, we find that when E agents join the market more frequently than H ones, myopic matching policies that use a chain, or just bilateral matches, are both approximately optimal. Otherwise, any bilateral matching policy is highly sub-optimal. Further, we show that no matching policy can reduce the average waiting time of H agents without significantly harming E agents, and thus any attempt to thicken the pool is artificial. Finally, we discuss extensions of our model to include departures, independent arrival rates, two-sided markets, and decentralized markets.
Numerous kidney exchange (kidney paired donation [KPD]) registries in the United States have gradually shifted to high-frequency match-runs, raising the question of whether this harms the number of transplants. We conducted simulations using clinical data from 2 KPD registries-the Alliance for Paired Donation, which runs multihospital exchanges, and Methodist San Antonio, which runs single-center exchanges-to study how the frequency of match-runs impacts the number of transplants and the average waiting times. We simulate the options facing each of the 2 registries by repeated resampling from their historical pools of patient-donor pairs and nondirected donors, with arrival and departure rates corresponding to the historical data. We find that longer intervals between match-runs do not increase the total number of transplants, and that prioritizing highly sensitized patients is more effective than waiting longer between match-runs for transplanting highly sensitized patients. While we do not find that frequent match-runs result in fewer transplanted pairs, we do find that increasing arrival rates of new pairs improves both the fraction of transplanted pairs and waiting times.
As society has moved past the initial phase of the COVID-19 crisis that relied on broad-spectrum shutdowns as a stopgap method, industries and institutions have faced the daunting question of how to return to a stabilized state of activities and more fully reopen the economy. A core problem is how to return people to their workplaces and educational institutions in a manner that is safe, ethical, grounded in science, and takes into account the unique factors and needs of each organization and community. In this paper, we introduce an epidemiological model (the “Community-Workplace” model) that accounts for SARS-CoV-2 transmission within the workplace, within the surrounding community, and between them. We use this multi-group deterministic compartmental model to consider various testing strategies that, together with symptom screening, exposure tracking, and nonpharmaceutical interventions (NPI) such as mask wearing and physical distancing, aim to reduce disease spread in the workplace. Our framework is designed to be adaptable to a variety of specific workplace environments to support planning efforts as reopenings continue. Using this model, we consider a number of case studies, including an office workplace, a factory floor, and a university campus. Analysis of these cases illustrates that continuous testing can help a workplace avoid an outbreak by reducing undetected infectiousness even in high-contact environments. We find that a university setting, where individuals spend more time on campus and have a higher contact load, requires more testing to remain safe, compared to a factory or office setting. Under the modeling assumptions, we find that maintaining a prevalence below 3% can be achieved in an office setting by testing its workforce every two weeks, whereas achieving this same goal for a university could require as much as fourfold more testing (i.e., testing the entire campus population twice a week). Our model also simulates the dynamics of reduced spread that result from the introduction of mitigation measures when test results reveal the early stages of a workplace outbreak. We use this to show that a vigilant university that has the ability to quickly react to outbreaks can be justified in implementing testing at the same rate as a lower-risk office workplace. Finally, we quantify the devastating impact that an outbreak in a small-town college could have on the surrounding community, which supports the notion that communities can be better protected by supporting their local places of business in preventing onsite spread of disease.
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