5 research outputs found
Flexible Basis Representations for Modeling High-Dimensional Hierarchical Spatial Data
Nonstationary and non-Gaussian spatial data are prevalent across many fields
(e.g., counts of animal species, disease incidences in susceptible regions, and
remotely-sensed satellite imagery). Due to modern data collection methods, the
size of these datasets have grown considerably. Spatial generalized linear
mixed models (SGLMMs) are a flexible class of models used to model
nonstationary and non-Gaussian datasets. Despite their utility, SGLMMs can be
computationally prohibitive for even moderately large datasets. To circumvent
this issue, past studies have embedded nested radial basis functions into the
SGLMM. However, two crucial specifications (knot placement and bandwidth
parameters), which directly affect model performance, are typically fixed prior
to model-fitting. We propose a novel approach to model large nonstationary and
non-Gaussian spatial datasets using adaptive radial basis functions. Our
approach: (1) partitions the spatial domain into subregions; (2) employs
reversible-jump Markov chain Monte Carlo (RJMCMC) to infer the number and
location of the knots within each partition; and (3) models the latent spatial
surface using partition-varying and adaptive basis functions. Through an
extensive simulation study, we show that our approach provides more accurate
predictions than competing methods while preserving computational efficiency.
We demonstrate our approach on two environmental datasets - incidences of plant
species and counts of bird species in the United States
Quantifying polarization across political groups on key policy issues using sentiment analysis
There is growing concern that over the past decade, industrialized democratic
nations are becoming increasingly politically polarized. Indeed, elections in
the US, UK, France, and Germany have all seen tightly won races, with notable
examples including the 2016 Trump vs. Clinton presidential election and the
UK's Brexit referendum. However, while there has been much qualitative
discussion of polarization on key issues, there are few examples of formal
quantitative assessments examining this topic. Therefore, in this paper, we
undertake a statistical evaluation of political polarization for
representatives elected to the US congress on key policy issues between
2021-2022. The method is based on applying sentiment analysis to Twitter data
and developing quantitative analysis for six political groupings defined based
on voting records. Two sets of policy groups are explored, including
geopolitical policies (e.g., Ukraine-Russia, China, Taiwan, etc.) and domestic
policies (e.g., abortion, climate change, LGBTQ, immigration, etc.). We find
that out of the twelve policies explored here, gun control was the most
politically polarizing, with significant polarization results found for all
groups (four of which were P < 0.001). The next most polarizing issues include
immigration and border control, fossil fuels, and Ukraine-Russia.
Interestingly, the least polarized policy topics were Taiwan, LGBTQ, and the
Chinese Communist Party, potentially demonstrating the highest degree of
bipartisanship on these issues. The results can be used to guide future policy
making, by helping to identify areas of common ground across political groups.Comment: 31 pages, 7 figure
Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries
Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely
Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks
Abstract Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi‐sector dynamics and the design of risk‐management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example, the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (a) stepwise line search, (b) precalibration or screening, and (c) the Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle‐based approach that takes advantage of the massive parallelization afforded by modern high‐performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections