Project description:Travel time to hospital is a key measure of health service accessibility, and impacts patients' experiences of care and health outcomes. Methods used to estimate travel time vary across studies. In Australia the smallest geographical areas defined by the Australian Bureau of Statistics for the release of population counts are mesh blocks (MBs) and the smallest geographical areas for the release of health-related statistics are statistical areas level 2 (SA2). SA2s are built up from whole MBs. This project used the Open Source Routing Machine (OSRM) HTTP server to compute estimated travel times between the centroid of each inhabited MB and each hospital in Australia, as well as the shortest travel times between MBs and any hospital. By computing population-weighted averages across MBs, the average travel times to hospitals and the shortest travel time to any hospital were estimated for each SA2. This dataset will promote consistency across studies investigating geographic influences on health care in Australia, and the methods are applicable to generating similar datasets for other countries.
Project description:BackgroundA positive delirium screen at skilled-nursing facility (SNF) admission can trigger a simultaneous diagnosis of Alzheimer's Disease or related dementia (AD/ADRD) and lead to psychoactive medication treatment despite a lack of evidence supporting use.MethodsThis was a nationwide historical cohort study of 849,086 Medicare enrollees from 2011-2013 who were admitted to the SNF from a hospital without a history of dementia. Delirium was determined through positive Confusion Assessment Method screen and incident AD/ADRD through active diagnosis or claims. Cox proportional hazard models predicted the risk of receiving one of three psychoactive medications (i.e., antipsychotics, benzodiazepines, antiepileptics) within 7 days of SNF admission and within the entire SNF stay.ResultsOf 849,086 newly-admitted SNF patients (62.6% female, mean age 78), 6.1% had delirium (of which 35.4% received an incident diagnosis of AD/ADRD); 12.6% received antipsychotics, 30.4% benzodiazepines, and 5.8% antiepileptics. Within 7 days of admission, patients with delirium and incident dementia were more likely to receive an antipsychotic (relative risk [RR] 3.09; 95% confidence interval [CI] 2.99 to 3.20), or a benzodiazepine (RR 1.23; 95% CI 1.19 to 1.27) than patients without either condition. By the end of the SNF stay, patients with both delirium and incident dementia were more likely to receive an antipsychotic (RR 3.04; 95% CI 2.95 to 3.14) and benzodiazepine (RR 1.32; 95% CI 1.29 to 1.36) than patients without either condition.ConclusionIn this historical cohort, a positive delirium screen was associated with a higher risk of receiving psychoactive medication within 7 days of SNF admission, particularly in patients with an incident AD/ADRD diagnosis. Future research should examine strategies to reduce inappropriate psychoactive medication prescribing in older adults admitted with delirium to SNFs.
Project description:During the COVID-19 pandemic, many countries implemented international travel restrictions that aimed to contain viral spread while still allowing necessary cross-border travel for social and economic reasons. The relative effectiveness of these approaches for controlling the pandemic has gone largely unstudied. Here we developed a flexible network meta-population model to compare the effectiveness of international travel policies, with a focus on evaluating the benefit of policy coordination. Because country-level epidemiological parameters are unknown, they need to be estimated from data; we accomplished this using approximate Bayesian computation, given the nature of our complex stochastic disease transmission model. Based on simulation and theoretical insights we find that, under our proposed policy, international airline travel may resume up to 58% of the pre-pandemic level with pandemic control comparable to that of a complete shutdown of all airline travel. Our results demonstrate that global coordination is necessary to allow for maximum travel with minimum effect on viral spread.
Project description:Comparable data on spatial accessibility by different travel modes are frequently needed to understand how city regions function. Here, we present a spatial dataset called the Helsinki Region Travel Time Matrix that has been calculated for 2013, 2015 and 2018. This longitudinal dataset contains travel time and distance information between all 250 metres statistical grid cell centroids in the Capital Region of Helsinki, Finland. The dataset is multimodal and multitemporal by nature: all typical transport modes (walking, cycling, public transport, and private car) are included and calculated separately for the morning rush hour and midday for an average working day. We followed a so-called door-to-door principle, making the information between travel modes comparable. The analyses were based primarily on open data sources, and all the tools that were used to produce the data are openly available. The matrices form a time-series that can reveal the accessibility conditions within the city and allow comparisons of the changes in accessibility in the region, which support spatial planning and decision-making.
Project description:The COVID-19 pandemic has prompted the re-emergence of staycations to the fore, as many people were forced to spend their vacations at or close to home due to travel restrictions. This phenomenon first went mainstream during the 2008 financial crisis, and has now been further accelerated by the COVID-19 pandemic. This study investigated the growth and practice of staycations during the first two years of the pandemic by analyzing social media and internet search data using Latent Dirichlet Allocation (LDA) topic modeling and Google Trends analytics. Key findings suggest that, while spatially close to home, people tried to achieve a psychological distance away from home. This was demonstrated by a strong global search interest in spending staycations at hotels close to home. The optimal LDA topic model produced 38 topics which were classified under four aggregate dimensions of antecedents, attributes, activities, and consequences of staycations. The findings provide useful insights to managers and policymakers on boosting revenue through this practice, and the role of staycations in promoting leisure activities close to home and sustainable tourism.