Patients’ no-shows, scheduled but unattended medical appointments, have a direct negative impact on patients’ health, due to discontinuity of treatment and late presentation to care. They also lead to inefficient use of medical resources in hospitals and clinics. The ability to predict a likely no-show in advance could enable the design and implementation of interventions to reduce the risk of it happening, thus improving patients’ care and clinical resource allocation. In this study, we develop a new interpretable deep learning-based approach for predicting the risk of no-shows at the time when a medical appointment is first scheduled. The retrospective study was conducted in an academic pediatric teaching hospital with a 20% no-show rate. Our approach tackles several challenges in the design of a predictive model by (1) adopting a data imputation method for patients with missing information in their records (77% of the population), (2) exploiting local weather information to improve predictive accuracy, and (3) developing an interpretable approach that explains how a prediction is made for each individual patient. Our proposed neural network-based and logistic regression-based methods outperformed persistence baselines. In an unobserved set of patients, our method correctly identified 83% of no-shows at the time of scheduling and led to a false alert rate less than 17%. Our method is capable of producing meaningful predictions even when some information in a patient’s records is missing. We find that patients’ past no-show record is the strongest predictor. Finally, we discuss several potential interventions to reduce no-shows, such as scheduling appointments of high-risk patients at off-peak times, which can serve as starting point for further studies on no-show interventions.
Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data Pablo M De Salazar, Fred Lu, James A Hay, Diana Gómez-Barroso, Pablo Fernández-Navarro, Elena V Martínez, Jenaro Astray-Mochales, Rocío Amillategui, Ana García-Fulgueiras, Maria D Chirlaque, Alonso Sánchez-Migallón, Amparo Larrauri, María J Sierra, Marc Lipsitch, Fernando Simón, Mauricio Santillana, Miguel A Hernán PLoS computational biology 18 (3), e100996442022
Abstract
When responding to infectious disease outbreaks, rapid and accurate estimation of the epidemic trajectory is critical. However, two common data collection problems affect the reliability of the epidemiological data in real time: missing information on the time of first symptoms, and retrospective revision of historical information, including right censoring. Here, we propose an approach to construct epidemic curves in near real time that addresses these two challenges by 1) imputation of dates of symptom onset for reported cases using a dynamically-estimated “backward” reporting delay conditional distribution, and 2) adjustment for right censoring using the NobBS software package to nowcast cases by date of symptom onset. This process allows us to obtain an approximation of the time-varying reproduction number (Rt) in real time. We apply this approach to characterize the early SARS-CoV-2 outbreak in two Spanish regions between March and April 2020. We evaluate how these real-time estimates compare with more complete epidemiological data that became available later. We explore the impact of the different assumptions on the estimates, and compare our estimates with those obtained from commonly used surveillance approaches. Our framework can help improve accuracy, quantify uncertainty, and evaluate frequently unstated assumptions when recovering the epidemic curves from limited data obtained from public health systems in other locations.
The ongoing COVID-19 pandemic continues to affect communities around the world. To date, almost 6 million people have died as a consequence of COVID-19, and more than one-quarter of a billion people are estimated to have been infected worldwide. The design of appropriate and timely mitigation strategies to curb the effects of this and future disease outbreaks requires close monitoring of their spatio-temporal trajectories. We present machine learning methods to anticipate sharp increases in COVID-19 activity in US counties in real-time. Our methods leverage Internet-based digital traces -- e.g., disease-related Internet search activity from the general population and clinicians, disease-relevant Twitter micro-blogs, and outbreak trajectories from neighboring locations -- to monitor potential changes in population-level health trends. Motivated by the need for finer spatial-resolution epidemiological insights to improve local decision-making, we build upon previous retrospective research efforts originally conceived at the state level and in the early months of the pandemic. Our methods -- tested in real-time and in an out-of-sample manner on a subset of 97 counties distributed across the US -- frequently anticipated sharp increases in COVID-19 activity 1-6 weeks before the onset of local outbreaks (defined as the time when the effective reproduction number becomes larger than 1 consistently). Given the continued emergence of COVID-19 variants of concern -- such as the most recent one, Omicron -- and the fact that multiple countries have not had full access to vaccines, the framework we present, while conceived for the county-level
Prevalence of firearm ownership among individuals with major depressive symptoms Roy H Perlis, Matthew D Simonson, Jon Green, Jennifer Lin, Alauna Safarpour, Kristin Lunz Trujillo, Alexi Quintana, Hanyu Chwe, John Della Volpe, Katherine Ognyanova, Mauricio Santillana, James Druckman, David Lazer, Matthew A Baum JAMA network open 5 (3), e223245-e22324512022
Abstract
ImportanceBoth major depression and firearm ownership are associated with an increased risk for death by suicide in the United States, but the extent of overlap among these major risk factors is not well characterized. ObjectiveTo assess the prevalence of current and planned firearm ownership among individuals with depression. Design, Setting, and ParticipantsCross-sectional survey study using data pooled from 2 waves of a 50-state nonprobability internet survey conducted between May and July 7, 2021. Internet survey respondents were 18 years of age or older and were sampled from all 50 US states and the District of Columbia. Main Outcomes and MeasuresSelf-reported firearm ownership; depressive symptoms as measured by the 9-item Patient Health Questionnaire. ResultsOf 24 770 survey respondents (64.6% women and 35.4% men; 5.0% Asian, 10.8% Black, 7.5% Hispanic, and 74.0% White; mean [SD] age 45.8 [17.5]), 6929 (28.0%) reported moderate or greater depressive symptoms; this group had mean (SD) age of 38.18 (15.19) years, 4587 were female (66.2%), and 406 were Asian (5.9%), 725 were Black (10.5%), 652 were Hispanic (6.8%), and 4902 were White (70.7%). Of those with depression, 31.3% reported firearm ownership (n = 2167), of whom 35.9% (n = 777) reported purchasing a firearm within the past year. In regression models, the presence of moderate or greater depressive symptoms was not significantly associated with firearm ownership (adjusted odds ratio [OR], 1.07; 95% CI, 0.98-1.17) but was associated with greater likelihood of a first-time firearm purchase during the COVID-19 pandemic (adjusted OR, 1.77; 95% CI, 1.56-2.02) and greater likelihood of considering a future firearm purchase (adjusted OR, 1.53; 95% CI, 1.23-1.90). Conclusions and RelevanceIn this study, current and planned firearm ownership was common among individuals with major depressive symptoms, suggesting a public health opportunity to address this conjunction of suicide risk factors.
Global modeling of atmospheric chemistry is a great computational challenge because of the cost of integrating the kinetic equations for chemical mechanisms with typically over 100 coupled species. Here we present an adaptive algorithm to ease this computational bottleneck with no significant loss in accuracy and apply it to the GEOS-Chem global 3-D model for tropospheric and stratospheric chemistry (228 species, 724 reactions). Our approach is inspired by unsupervised machine learning clustering techniques and traditional asymptotic analysis ideas. We locally define species in the mechanism as fast or slow on the basis of their total production and loss rates, and we solve the coupled kinetic system only for the fast species assembled in a submechanism of the full mechanism. To avoid computational overhead, we first partition the species from the full mechanism into 13 blocks, using a machine learning approach that analyzes the chemical linkages between species and their correlated presence as fast or slow in the global model domain. Building on these blocks, we then preselect 20 submechanisms, as defined by unique assemblages of the species blocks, and then pick locally and on the fly which submechanism to use in the model based on local chemical conditions. In each submechanism, we isolate slow species and slow reactions from the coupled system of fast species to be solved. Because many species in the full mechanism are important only in source regions, we find that we can reduce the effective size of the mechanism by 70 % globally without sacrificing complexity where/when it is needed. The computational cost of the chemical integration decreases by 50 % with relative biases smaller than 2 % for important species over 8-year simulations. Changes to the full mechanism including the addition of new species can be accommodated by adding these species to the relevant blocks without having to reconstruct the suite of submechanisms.
The dengue virus affects millions of people every year worldwide, causing large epidemic outbreaks that disrupt people’s lives and severely strain healthcare systems. In the absence of a reliable vaccine against dengue or an effective treatment to manage the illness in humans, most efforts to combat dengue infections have focused on preventing its vectors, mainly the Aedes aegypti mosquito, from flourishing across the world. These mosquito-control strategies need reliable disease activity surveillance systems to be deployed. Despite significant efforts to estimate dengue incidence using a variety of data sources and methods, little work has been done to understand the relative contribution of the different data sources to improved prediction. Additionally, scholarship on the topic had initially focused on prediction systems at the national- and state-levels, and much remains to be done at the finer spatial resolutions at which health policy interventions often occur. We develop a methodological framework to assess and compare dengue incidence estimates at the city level, and evaluate the performance of a collection of models on 20 different cities in Brazil. The data sources we use towards this end are weekly incidence counts from prior years (seasonal autoregressive terms), weekly-aggregated weather variables, and real-time internet search data. We find that both random forest-based models and LASSO regression-based models effectively leverage these multiple data sources to produce accurate predictions, and that while the performance between them is comparable on average, the former method produces fewer extreme outliers, and can thus be considered more robust. For real-time predictions that assume long delays (6–8 weeks) in the availability of epidemiological data, we find that real-time internet search data are the strongest predictors of dengue incidence, whereas for predictions that assume short delays (1–3 weeks), in which the error rate is halved (as measured by relative RMSE), short-term and seasonal autocorrelation are the dominant predictors. Despite the difficulties inherent to city-level prediction, our framework achieves meaningful and actionable estimates across cities with different demographic, geographic and epidemic characteristics.
SARS-CoV-2 RNA concentrations in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases Fuqing Wu, Amy Xiao, Jianbo Zhang, Katya Moniz, Noriko Endo, Federica Armas, Richard Bonneau, Megan A Brown, Mary Bushman, Peter R Chai, Claire Duvallet, Timothy B Erickson, Katelyn Foppe, Newsha Ghaeli, Xiaoqiong Gu, William P Hanage, Katherine H Huang, Wei Lin Lee, Mariana Matus, Kyle A McElroy, Jonathan Nagler, Steven F Rhode, Mauricio Santillana, Joshua A Tucker, Stefan Wuertz, Shijie Zhao, Janelle Thompson, Eric J Alm Science of The Total Environment 805, 1501211452022
Abstract
Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we quantify the SARS-CoV-2 concentration and track its dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. SARS-CoV-2 RNA concentrations in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4–10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral load as a convolution of back-dated new clinical cases with the average population-level viral shedding function. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. This finding suggests that SARS-CoV-2 concentrations in wastewater may be primarily driven by viral shedding early in infection. This work shows that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and infer early viral shedding dynamics for newly infected individuals, which are difficult to capture in clinical investigations.
ImportanceMisinformation about COVID-19 vaccination may contribute substantially to vaccine hesitancy and resistance. ObjectiveTo determine if depressive symptoms are associated with greater likelihood of believing vaccine-related misinformation. Design, Setting, and ParticipantsThis survey study analyzed responses from 2 waves of a 50-state nonprobability internet survey conducted between May and July 2021, in which depressive symptoms were measured using the Patient Health Questionnaire 9-item (PHQ-9). Survey respondents were aged 18 and older. Population-reweighted multiple logistic regression was used to examine the association between moderate or greater depressive symptoms and endorsement of at least 1 item of vaccine misinformation, adjusted for sociodemographic features. The association between depressive symptoms in May and June, and new support for misinformation in the following wave was also examined. ExposuresDepressive symptoms. Main Outcomes and MeasuresThe main outcome was endorsing any of 4 common vaccine-related statements of misinformation. ResultsAmong 15 464 survey respondents (9834 [63.6%] women and 5630 [36.4%] men; 722 Asian respondents [4.7%], 1494 Black respondents [9.7%], 1015 Hispanic respondents [6.6%], and 11 863 White respondents [76.7%]; mean [SD] age, 47.9 [17.5] years), 4164 respondents (26.9%) identified moderate or greater depressive symptoms on the PHQ-9, and 2964 respondents (19.2%) endorsed at least 1 vaccine-related statement of misinformation. Presence of depression was associated with increased likelihood of endorsing misinformation (crude odds ratio [OR], 2.33; 95% CI, 2.09-2.61; adjusted OR, 2.15; 95% CI, 1.91-2.43). Respondents endorsing at least 1 misinformation item were significantly less likely to be vaccinated (crude OR, 0.40; 95% CI, 0.36-0.45; adjusted OR, 0.45; 95% CI, 0.40-0.51) and more likely to report vaccine resistance (crude OR, 2.54; 95% CI, 2.21-2.91; adjusted OR, 2.68; 95% CI, 2.89-3.13). Among 2809 respondents who answered a subsequent survey in July, presence of depression in the first survey was associated with greater likelihood of endorsing more misinformation compared with the prior survey (crude OR, 1.98; 95% CI, 1.42-2.75; adjusted OR, 1.63; 95% CI, 1.14-2.33). Conclusions and RelevanceThis survey study found that individuals with moderate or greater depressive symptoms were more likely to endorse vaccine-related misinformation, cross-sectionally and at a subsequent survey wave. While this study design cannot address causation, the association between depression and spread and impact of misinformation merits further investigation.
The COVID-19 pandemic has had a devastating impact on global health, the magnitude of which appears to differ intercontinentally: for example, reports suggest 271,900 per million people have been infected in Europe versus 8,800 per million people in Africa. While Africa is the second largest continent by population, its reported COVID-19 cases comprise <3% of global cases. Although social, environmental, and environmental explanations have been proposed to clarify this discrepancy, systematic infection under ascertainment may be equally responsible.
Characterizing the dynamics of epidemic trajectories is critical to understanding the potential impacts of emerging outbreaks and to designing appropriate mitigation strategies. As the COVID-19 pandemic evolves, however, the emergence of SARS-CoV-2 variants of concern has complicated our ability to assess in real-time the potential effects of imminent outbreaks, such as those presently caused by the Omicron variant. Here, we report that SARS-CoV-2 outbreaks across regions exhibit strain-specific times from onset to peak, specifically for Delta and Omicron variants. Our findings may facilitate real-time identification of peak medical demand and may help fine-tune ongoing and future outbreak mitigation deployment efforts.
Politics and science have become increasingly intertwined. Salient scientific issues such as climate change, evolution, and stem cell research become politicized, pitting partisans against one another. This creates a challenge of how to effectively communicate on such issues. Recent work emphasizes the need for tailored messages to specific groups. Here, we focus on whether generalized messages also can matter. We do so in the context of a highly polarized issue – extreme COVID-19 vaccine resistance. The results show that science-based, moral frame, and social norm messages move behavioral intentions, and do so by the same amount across the population (i.e., homogenous effects). Counter to common portrayals, the politicization of science does not preclude using broad messages that resonate with the entire population.
