Real-World Pitfalls of Analyzing Real-World Data: A Cautionary Note and Path Forward.

PURPOSE: Real-world data (RWD) are pervasive in oncology research and offer insights into clinical trends and patient outcomes. However, RWD have shortcomings, making them prone to pitfalls during survival analyses. The American Society of Clinical Oncology (ASCO) CancerLinQ Discovery (CLQD) multiple myeloma (MM) data set was used to demonstrate some common pitfalls when analyzing survival from RWD: using incorrect surrogate markers for missing data and/or classification errors, ignoring deaths at time zero, and failing to account for guarantee-time bias. METHODS: The ASCO CLQD MM data set (July 19, 2021, release) was used to compare overall survival (OS) in patients with a known versus presumed date of MM diagnosis, in patients with secondary AML (sAML) with early deaths (ie, 0 months) included versus dropped, and in patients with second primary malignancies (SPMs) matched versus unmatched to control for time-related confounding factors (ie, guarantee-time bias). Analyses were conducted using STATA Version 17.0 (College Station, TX). RESULTS: In the CLQD MM data set, 28% of patients were missing a diagnosis date. Attempts to use the presumed diagnosis date (ie, first bortezomib or lenalidomide administration) as a surrogate marker for missing diagnosis dates were not successful as median OS was significantly different in patients with a recorded versus presumed diagnosis date (107 v 40 months, hazard ratio [HR], 2.5; 95% CI, 2.39 to 2.64; P < .001). Dropping deaths within 1 month of sAML diagnosis resulted in an exaggerated median OS (46 v 39 months). OS in patients with MM with SPMs differed substantially before and after incorporation of matching methods to account for guarantee-time bias (HR, 0.73; 95% CI, 0.67 to 0.78; P < .001 before matching, HR, 1.30; 95% CI, 1.18 to 1.43; P < .001 after matching). CONCLUSION: To fully maximize the benefits of RWD in oncology research, clinicians must be aware of analytic methods that can overcome pitfalls in survival analyses.

Short-term association of CO and NO2 with hospital visits for glomerulonephritis in Hefei, China: a time series study.

Objective: Recent studies suggest air pollution as an underlying factor to kidney disease. However, there is still limited knowledge about the short-term correlation between glomerulonephritis (GN) and air pollution. Thus, we aim to fill this research gap by investigating the short-term correlation between GN clinical visits and air pollution exposure. Methods: Between 2015 and 2019, daily GN visit data from two grade A tertiary hospitals in Hefei City were collected, along with corresponding air pollution and meteorological data. A generalized linear model integrated with a distributed lag nonlinear model was employed to analyze the relationship between GN visits and air pollutants. Moreover, we incorporated a dual pollutant model to account for the combined effects of multiple pollutants. Furthermore, subgroup analyses were performed to identify vulnerable populations based on gender, age, and season. Results: The association between 23,475 GN visits and air pollutants was assessed, and significant positive associations were found between CO and NO2 exposure and GN visit risk. The single-day lagged effect model for CO showed increased risks for GN visits from lag0 (RR: 1.129, 95% CI: 1.031-1.236) to lag2 (RR: 1.034, 95% CI: 1.011-1.022), with the highest risk at lag0. In contrast, NO2 displayed a more persistent impact (lag1-lag4) on GN visit risk, peaking at lag2 (RR: 1.017, 95% CI: 1.011-1.022). Within the dual-pollutant model, the significance persisted for both CO and NO2 after adjusting for each other. Subgroup analyses showed that the cumulative harm of CO was greater in the cold-season and older adult groups. Meanwhile, the female group was more vulnerable to the harmful effects of cumulative exposure to NO2. Conclusion: Our study indicated that CO and NO2 exposure can raise the risk of GN visits, and female and older adult populations exhibited greater susceptibility.

The role of migration networks in the development of Botswana's generalized HIV epidemic.

The majority of people with HIV live in sub-Saharan Africa, where epidemics are generalized. For these epidemics to develop, populations need to be mobile. However, the role of population-level mobility in the development of generalized HIV epidemics has not been studied. Here we do so by studying historical migration data from Botswana, which has one of the most severe generalized HIV epidemics worldwide; HIV prevalence was 21% in 2021. The country reported its first AIDS case in 1985 when it began to rapidly urbanize. We hypothesize that, during the development of Botswana's epidemic, the population was extremely mobile and the country was highly connected by substantial migratory flows. We test this mobility hypothesis by conducting a network analysis using a historical time series (1981-2011) of micro-census data from Botswana. Our results support our hypothesis. We found complex migration networks with very high rates of rural-to-urban, and urban-to-rural, migration: 10% of the population moved annually. Mining towns (where AIDS cases were first reported, and risk behavior was high) were important in-flow and out-flow migration hubs, suggesting that they functioned as 'core groups' for HIV transmission and dissemination. Migration networks could have dispersed HIV throughout Botswana and generated the current hyperendemic epidemic.

Over 25 million people in sub-Saharan Africa live with HIV. After reporting its first AIDS case in 1985, Botswana is one of the most severely affected countries in the region, with one in five adults now living with HIV. Movement of the population is likely to have contributed to a geographically dispersed, and high-prevalence, HIV epidemic in Botswana. Since 1985, urbanization, rapid economic and population growth, and migration have transformed Botswana. Yet, few studies have analyzed the role of population-level movement patterns in the spread of HIV during this time. By studying micro-census data from Botswana between 1981 and 2011, Song et al. found that the country's population was highly mobile during this period. Reconstructions of internal migration patterns show very high rates of rural-to-urban and urban-to-rural migration, with 10% of Botswana's population moving each year. The first reported AIDS cases in Botswana occurred in mining towns and cities where high-risk behavior was prevalent. These areas were also migration hubs during this period and could have contributed to the rapid spread of HIV throughout the country as infected individuals moved back to rural districts. Understanding human migration patterns and how they affect the spread of infectious diseases using current data could help public health authorities in Botswana and additional sub-Saharan African countries design control strategies for HIV and other important infections that occur in the region.

Heat exposure and hospitalizations for chronic kidney disease in China: a nationwide time series study in 261 major Chinese cities.

BACKGROUND: Climate change profoundly shapes the population health at the global scale. However, there was still insufficient and inconsistent evidence for the association between heat exposure and chronic kidney disease (CKD). METHODS: In the present study, we studied the association of heat exposure with hospitalizations for cause-specific CKD using a national inpatient database in China during the study period of hot season from 2015 to 2018. Standard time-series regression models and random-effects meta-analysis were developed to estimate the city-specific and national averaged associations at a 7 lag-day span, respectively. RESULTS: A total of 768,129 hospitalizations for CKD was recorded during the study period. The results showed that higher temperature was associated with elevated risk of hospitalizations for CKD, especially in sub-tropical cities. With a 1 °C increase in daily mean temperature, the cumulative relative risks (RR) over lag 0-7 d were 1.008 [95% confidence interval (CI) 1.003-1.012] for nationwide. The attributable fraction of CKD hospitalizations due to high temperatures was 5.50%. Stronger associations were observed among younger patients and those with obstructive nephropathy. Our study also found that exposure to heatwaves was associated with added risk of hospitalizations for CKD compared to non-heatwave days (RR = 1.116, 95% CI 1.069-1.166) above the effect of daily mean temperature. CONCLUSIONS: Short-term heat exposure may increase the risk of hospitalization for CKD. Our findings provide insights into the health effects of climate change and suggest the necessity of guided protection strategies against the adverse effects of high temperatures.

Representations of older people in Turkish prime-time TV series and Netflix original Turkish series: A comparative content analysis.

In this study, older characters that appeared in all the episodes of the first seasons of eight most popular Turkish TV series on prime-time televisions in Turkey and those appeared in all the episodes of the first seasons of eight "original" Turkish series on Netflix were submitted to a comparative quantitative and qualitative content analysis. In this sense, the aim of this study was to reveal what kind of old age is promised to viewers by such media environments as TV and Netflix. Findings revealed that, when compared to the Turkish population, older people were significantly underrepresented in prime-time series and that, in other words, they were symbolically eliminated and exposed to age discrimination. Another finding is that older people were portrayed more fairly on Netflix than TV. Although older individuals were inadequately represented on Netflix as well, the difference between the two platforms was not statistically significant. On the other hand, while older women are significantly less represented than older men on both platforms, the study found no significant difference in gender representation between Netflix and prime-time TV. The findings also indicated that no older character, when evaluated qualitatively, was represented as the major character on either platforms and that, especially when it comes to having a profession, older people, specifically older women, were portrayed more negatively on both platforms, which means that older women faced a double jeopardy.

Provision of evaluation and management visits by nurse practitioners and physician assistants in the USA from 2013 to 2019: cross-sectional time series study.

OBJECTIVE: To examine the proportion of healthcare visits are delivered by nurse practitioners and physician assistants versus physicians and how this has changed over time and by clinical setting, diagnosis, and patient demographics. DESIGN: Cross-sectional time series study. SETTING: National data from the traditional Medicare insurance program in the USA. PARTICIPANTS: Of people using Medicare (ie, those older than 65 years, permanently disabled, and people with end stage renal disease), a 20% random sample was taken. MAIN OUTCOME MEASURES: The proportion of physician, nurse practitioner, and physician assistant visits in the outpatient and skilled nursing facility settings delivered by physicians, nurse practitioners, and physician assistants, and how this proportion varies by type of visit and diagnosis. RESULTS: From 1 January 2013 to 31 December 2019, 276 million visits were included in the sample. The proportion of all visits delivered by nurse practitioners and physician assistants in a year increased from 14.0% (95% confidence interval 14.0% to 14.0%) to 25.6% (25.6% to 25.6%). In 2019, the proportion of visits delivered by a nurse practitioner or physician assistant varied across conditions, ranging from 13.2% for eye disorders and 20.4% for hypertension to 36.7% for anxiety disorders and 41.5% for respiratory infections. Among all patients with at least one visit in 2019, 41.9% had one or more nurse practitioner or physician assistant visits. Compared with patients who had no visits from a nurse practitioner or physician assistant, the likelihood of receiving any care was greatest among patients who were lower income (2.9% greater), rural residents (19.7%), and disabled (5.6%). CONCLUSION: The proportion of visits delivered by nurse practitioners and physician assistants in the USA is increasing rapidly and now accounts for a quarter of all healthcare visits.

Time series analysis of daily reported number of new positive cases of COVID-19 in Japan from January 2020 to February 2023.

This study investigated temporal variations of the COVID-19 pandemic in Japan using a time series analysis incorporating maximum entropy method (MEM) spectral analysis, which produces power spectral densities (PSDs). This method was applied to daily data of COVID-19 cases in Japan from January 2020 to February 2023. The analyses confirmed that the PSDs for data in both the pre- and post-Tokyo Olympics periods show exponential characteristics, which are universally observed in PSDs for time series generated from nonlinear dynamical systems, including the so-called susceptible/exposed/infectious/recovered (SEIR) model, well-established as a mathematical model of temporal variations of infectious disease outbreaks. The magnitude of the gradient of exponential PSD for the pre-Olympics period was smaller than that of the post-Olympics period, because of the relatively high complex variations of the data in the pre-Olympics period caused by a deterministic, nonlinear dynamical system and/or undeterministic noise. A 3-dimensional spectral array obtained by segment time series analysis indicates that temporal changes in the periodic structures of the COVID-19 data are already observable before the commencement of the Tokyo Olympics and immediately after the introduction of mass and workplace vaccination programs. Additionally, the possibility of applying theoretical studies for measles control programs to COVID-19 is discussed.

Time series, seasonality and trend evaluation of 7 years (2015-2021) of OSHA severe injury data.

PROBLEM: Employers are required to report severe work-related injuries (e.g., amputation, inpatient hospitalization, or loss of an eye), to the Occupational Safety and Health Administration (OSHA). This study examined the OSHA severe injury reports (SIRs) public microdata to understand time-related trends and patterns. METHODS: This study included all SIRs from January 2015 to December 2021 (84 months). We employed time series decomposition models (classical additive and multiplicative, X-11, and X-13ARIMA-SEATS) to evaluate monthly seasonal effect and seasonally adjusted trend of SIRs. We developed data visuals to display trends from different models with the original data series. We compared number of daily SIRs by day of the week, and yearly trends by 2-digit NAICS and separately by 1-digit OIICS injury event. RESULTS: There were a total of 70,241 SIRs in this 7 year period; ranging from 8,704 to 11,156 per year, and 600 to 1,100 per month. Seasonally adjusted trend indicated a gradual increase of SIRs over time until October 2018, then a steeper decrease until August 2020, and staying somewhat flat for the rest of the months. Seasonality indicated more SIRs were reported in the summer months (June, July, August). Daily SIRs indicated a weekday average of 34 (SD = 9) and weekend average of 11 (SD = 5). The Manufacturing and Construction industries reported the highest yearly SIRs. Contact with objects and equipment, and falls, slips, trips were the most numerous injury events associated with SIRs. DISCUSSION: Although Federal OSHA SIR data do not include SIRs from state-plan jurisdictions, the data provide a timely national trend of SIR. This is the first known time series analysis of SIRs. PRACTICAL APPLICATIONS: The findings of this study highlight the ability of researchers to use the SIRs as a timely indicator to understand occupational injury trends by specific industries and injury events.

A long short-temory relation network for real-time prediction of patient-specific ventilator parameters.

Accurate prediction of patient-specific ventilator parameters is crucial for optimizing patient-ventilator interaction. Current approaches encounter difficulties in concurrently observing long-term, time-series dependencies and capturing complex, significant features that influence the ventilator treatment process, thereby hindering the achievement of accurate prediction of ventilator parameters. To address these challenges, we propose a novel approach called the long short-term memory relation network (LSTMRnet). Our approach uses a long, short-term memory bank to store rich information and an important feature selection step to extract relevant features related to respiratory parameters. This information is obtained from the prior knowledge of the follow up model. We also concatenate the embeddings of both information types to maintain the joint learning of spatio-temporal features. Our LSTMRnet effectively preserves both time-series and complex spatial-critical feature information, enabling an accurate prediction of ventilator parameters. We extensively validate our approach using the publicly available medical information mart for intensive care (MIMIC-III) dataset and achieve superior results, which can be potentially utilized for ventilator treatment (i.e., sleep apnea-hypopnea syndrome ventilator treatment and intensive care units ventilator treatment.

Simple Bayesian reconstruction and forecasting of stream water temperature for ecologists-A tool using air temperature, optionally flow, in a time series decomposition approach.

Mitigating the impacts of global warming on wildlife entails four practical steps. First, we need to study how processes of interest vary with temperature. Second, we need to build good temperature scenarios. Third, processes can be forecast accordingly. Only then can we perform the fourth step, testing mitigating measures. While having good temperature data is essential, this is not straightforward for stream ecologists and managers. Water temperature (WT) data are often short and incomplete and future projections are currently not routinely available. There is a need for generic models which address this data gap with good resolution and current models are partly lacking. Here, we expand a previously published hierarchical Bayesian model that was driven by air temperature (AT) and flow (Q) as a second covariate. The new model can hindcast and forecast WT time series at a daily time step. It also allows a better appraisal of real uncertainties in the warming of water temperatures in rivers compared to the previous version, stemming from its hybrid structure between time series decomposition and regression. This model decomposes all-time series using seasonal sinusoidal periodic signals and time varying means and amplitudes. It then links the contrasted frequency signals of WT (daily and six month) through regressions to that of AT and optionally Q for better resolution. We apply this model to two contrasting case study rivers. For one case study, AT only is available as a covariate. This expanded model further improves the already good fitting and predictive capabilities of its earlier version while additionally highlighting warming uncertainties. The code is available online and can easily be run for other temperate rivers.

ECG Matching: An Approach to Synchronize ECG Datasets for Data Quality Comparisons.

Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data.

A novel hybrid framework based on temporal convolution network and transformer for network traffic prediction.

BACKGROUND: Accurately predicting mobile network traffic can help mobile network operators allocate resources more rationally and can facilitate stable and fast network services to users. However, due to burstiness and uncertainty, it is difficult to accurately predict network traffic. METHODOLOGY: Considering the spatio-temporal correlation of network traffic, we proposed a deep-learning model, Convolutional Block Attention Module (CBAM) Spatio-Temporal Convolution Network-Transformer, for time-series prediction based on a CBAM attention mechanism, a Temporal Convolutional Network (TCN), and Transformer with a sparse self-attention mechanism. The model can be used to extract the spatio-temporal features of network traffic for prediction. First, we used the improved TCN for spatial information and added the CBAM attention mechanism, which we named CSTCN. This model dealt with important temporal and spatial features in network traffic. Second, Transformer was used to extract spatio-temporal features based on the sparse self-attention mechanism. The experiments in comparison with the baseline showed that the above work helped significantly to improve the prediction accuracy. We conducted experiments on a real network traffic dataset in the city of Milan. RESULTS: The results showed that CSTCN-Transformer reduced the mean square error and the mean average error of prediction results by 65.16%, 64.97%, and 60.26%, and by 51.36%, 53.10%, and 38.24%, respectively, compared to CSTCN, a Long Short-Term Memory network, and Transformer on test sets, which justified the model design in this paper.

Effect Modifications of Overhead-View and Eye-Level Urban Greenery on Heat-Mortality Associations: Small-Area Analyses Using Case Time Series Design and Different Greenery Measurements.

BACKGROUND: The protective effect of urban greenery from adverse heat impacts remains inconclusive. Existing inconsistent findings could be attributed to the different estimation techniques used. OBJECTIVES: We investigated how effect modifications of urban greenery on heat-mortality associations vary when using different greenery measurements reflecting overhead-view and eye-level urban greenery. METHODS: We collected meteorological and daily mortality data for 286 territory planning units between 2005 and 2018 in Hong Kong. Three greenery measurements were extracted for each unit: a) the normalized difference vegetation index (NDVI) from Landsat remote sensing images, b) the percentage of greenspace based on land use data, and c) eye-level street greenery from street view images via a deep learning technique. Time-series analyses were performed using the case time series design with a linear interaction between the temperature term and each of the three greenery measurements. Effect modifications were also estimated for different age groups, sex categories, and cause-specific diseases. RESULTS: Higher mortality risks were associated with both moderate and extreme heat, with relative risks (RRs) of 1.022 (95% CI: 1.000, 1.044) and 1.045 (95% CI: 1.013, 1.079) at the 90th and 99th percentiles of temperatures relative to the minimum mortality temperature (MMT). Lower RRs were observed in greener areas whichever of the three greenery measurements was used, but the disparity of RRs between areas with low and high levels of urban greenery was more apparent when using eye-level street greenery as the index at high temperatures (99th percentile relative to MMT), with RRs for low and high levels of greenery, respectively, of 1.096 (95% CI: 1.035, 1.161) and 0.985 (95% CI: 0.920, 1.055) for NDVI (p=0.0193), 1.068 (95% CI: 1.021, 1.117) and 0.990 (95% CI: 0.906, 1.081) for the percentage of greenspace (p=0.1338), and 1.103 (95% CI: 1.034, 1.177) and 0.943 (95% CI: 0.841, 1.057) for eye-level street greenery (p=0.0186). Health discrepancies remained for nonaccidental mortality and cardiorespiratory diseases and were more apparent for older adults (≥65 years of age) and females. DISCUSSION: This study provides new evidence that eye-level street greenery shows stronger associations with reduced heat-mortality risks compared with overhead-view greenery based on NDVI and percentage of greenspace. The effect modification of urban greenery tends to be amplified as temperatures rise and are more apparent in older adults and females. Heat mitigation strategies and health interventions, in particular with regard to accessible and visible greenery, are needed for helping heat-sensitive subpopulation groups in coping with extreme heat. https://doi.org/10.1289/EHP12589.

Time series of cases and treatment outcomes from tuberculosis in Sergipe, 2012-2021.

OBJECTIVE: The objectives of this study were: (1) to analyze the temporal trend of tuberculosis treatment outcomes in the state of Sergipe; (2) to identify the existence of seasonality of tuberculosis; (3) to verify the influence of the rapid molecular test (MTB-RIF) in the time series of tuberculosis and its treatment outcomes in the state of Sergipe; and (4) to verify treatment outcomes. METHODS: Ecological study on tuberculosis and three treatment outcomes (cure, interruption of treatment, and death) extracted from Datasus. Incidence and mortality rates were calculated for the crude occurrences of cases and deaths and the proportions of cure and interruption of treatment (%). The time series was analyzed using Prais-Winsten regression from Jan to Dec/2021. RESULTS: The total incidence rate was 36.35 cases per 100,000 inhabitants, with an increase of 0.44% per month (95%CI 0.35; 0.54). The cure rate was 64.0% with a steady trend (p>0.05). The percentage of treatment interruption was 13.3%, with a reduction of -0.73%/month (95%CI -1.11; -0.34). The total mortality rate was 1.92 deaths/100,000 inhabitants with a stationary trend. After the implementation of the MTB-RIF, there was an increase in the incidence rate of 0.65% per month. Seasonality was not identified in any of the analyses performed (p>0.05). CONCLUSION: There was an increase in incidence rates, reduction in treatment interruption and mortality in the state of Sergipe. Seasonality was not identified. The rapid molecular test showed a growth effect on the incidence rate.

Comparison of the Speed of Enrollment for Clinical Trials Conducted by Japanese and Global Pharmaceutical Companies.

Since the establishment of the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), many countries in the world have rapidly improved their clinical trial performance, and the era has come to compare the clinical trial performance of each country. Japan's clinical trials are considered excellent quality, but costly and slow. In this study, we examined the speed of enrollment period in clinical trials. We surveyed clinical trials from January 1, 2010, to December 31, 2019, covering the top 10 pharmaceutical companies in each global sales ranking (Global 10) and the Japanese sales ranking (Japan 10). Clinical trial data were obtained from ClinicalTrials.gov, a clinical trial registration information database, and the speed of participant enrollment (cases/month) was compared for each phase of the trials. The number of clinical trials conducted during the 10 years was 8938 trials for Global 10 and 1439 trials for Japan 10. Comparing the speed of participant enrollment by phase, Japan 10 was significantly faster in phase 1 for both healthy subjects and oncology patients. [Japan 10: Global 10; 15.1 : 12.0 cases/month (healthy subjects) and 5.5 : 1.8 cases/month (oncology), respectively. p < 0.001]. Global 10 was also significantly faster in phase 3. [Japan 10: Global 10; 12.4: 36.9 cases/month, p < 0.001). No significant difference was observed in phase 2 and phase 4. There was a possibility that the speed of enrollment differed by phase between global companies and Japanese domestic companies.

Inhaled Fluticasone Furoate for Outpatient Treatment of Covid-19.

BACKGROUND: The effectiveness of inhaled glucocorticoids in shortening the time to symptom resolution or preventing hospitalization or death among outpatients with mild-to-moderate coronavirus disease 2019 (Covid-19) is unclear. METHODS: We conducted a decentralized, double-blind, randomized, placebo-controlled platform trial in the United States to assess the use of repurposed medications in outpatients with confirmed coronavirus disease 2019 (Covid-19). Nonhospitalized adults 30 years of age or older who had at least two symptoms of acute infection that had been present for no more than 7 days before enrollment were randomly assigned to receive inhaled fluticasone furoate at a dose of 200 µg once daily for 14 days or placebo. The primary outcome was the time to sustained recovery, defined as the third of 3 consecutive days without symptoms. Key secondary outcomes included hospitalization or death by day 28 and a composite outcome of the need for an urgent-care or emergency department visit or hospitalization or death through day 28. RESULTS: Of the 1407 enrolled participants who underwent randomization, 715 were assigned to receive inhaled fluticasone furoate and 692 to receive placebo, and 656 and 621, respectively, were included in the analysis. There was no evidence that the use of fluticasone furoate resulted in a shorter time to recovery than placebo (hazard ratio, 1.01; 95% credible interval, 0.91 to 1.12; posterior probability of benefit [defined as a hazard ratio >1], 0.56). A total of 24 participants (3.7%) in the fluticasone furoate group had urgent-care or emergency department visits or were hospitalized, as compared with 13 participants (2.1%) in the placebo group (hazard ratio, 1.9; 95% credible interval, 0.8 to 3.5). Three participants in each group were hospitalized, and no deaths occurred. Adverse events were uncommon in both groups. CONCLUSIONS: Treatment with inhaled fluticasone furoate for 14 days did not result in a shorter time to recovery than placebo among outpatients with Covid-19 in the United States. (Funded by the National Center for Advancing Translational Sciences and others; ACTIV-6 ClinicalTrials.gov number, NCT04885530.).

A large-scale observational study of the causal effects of a behavioral health nudge.

Nudges are interventions promoting healthy behavior without forbidding options or substantial incentives; the Apple Watch, for example, encourages users to stand by delivering a notification if they have been sitting for the first 50 minutes of an hour. On the basis of 76 billion minutes of observational standing data from 160,000 subjects in the public Apple Heart and Movement Study, we estimate the causal effect of this notification using a regression discontinuity design for time series data with time-varying treatment. We show that the nudge increases the probability of standing by up to 43.9% and remains effective with time. The nudge's effectiveness increases with age and is independent of gender. Closing Apple Watch Activity Rings, a visualization of participants' daily progress in Move, Exercise, and Stand, further increases the nudge's impact. This work demonstrates the effectiveness of behavioral health interventions and introduces tools for investigating their causal effect from large-scale observations.

Effects of scaling direction on adults' spatial scaling in different perceptual domains.

The current study investigated adults' strategies of spatial scaling from memory in three perceptual conditions (visual, haptic, and visuo-haptic) when scaling up and down. Following previous research, we predicted the usage of mental transformation strategies. In all conditions, participants (N = 90, aged 19-28 years) were presented with tactile, colored graphics which allowed to visually and haptically explore spatial information. Participants were first asked to encode a map including a target. Then, they were instructed to place a response object at the same place on an empty, constant-sized referent space. Maps had five different sizes resulting in five scaling factors (3:1, 2:1, 1:1, 1:2, 1:3). This manipulation also allowed assessing potentially symmetric effects of scaling direction on adults' responses. Response times and absolute errors served as dependent variables. In line with our hypotheses, the changes in these dependent variables were best explained by a quadratic function which suggests the usage of mental transformation strategies for spatial scaling. There were no differences between perceptual conditions concerning the influence of scaling factor on dependent variables. Results revealed symmetric effects of scaling direction on participants' accuracy whereas there were small differences for response times. Our findings highlight the usage of mental transformation strategies in adults' spatial scaling, irrespective of perceptual modality and scaling direction.