Forecasting ICU Census by Combining Time Series and Survival Models.

Capacity planning of ICUs is essential for effective management of health safety, quality of patient care, and the allocation of ICU resources. Whereas ICU length of stay (LOS) may be estimated using patient information such as severity of illness scoring systems, ICU census is impacted by both patient LOS and arrival patterns. We set out to develop and evaluate an ICU census forecasting algorithm using the Multiple Organ Dysfunction Score (MODS) and the Nine Equivalents of Nursing Manpower Use Score (NEMS) for capacity planning purposes. DESIGN: Retrospective observational study. SETTING: We developed the algorithm using data from the Medical-Surgical ICU (MSICU) at University Hospital, London, Canada and validated using data from the Critical Care Trauma Centre (CCTC) at Victoria Hospital, London, Canada. PATIENTS: Adult patient admissions (7,434) to the MSICU and (9,075) to the CCTC from 2015 to 2021. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We developed an Autoregressive integrated moving average time series model that forecasts patients arriving in the ICU and a survival model using MODS, NEMS, and other factors to estimate patient LOS. The models were combined to create an algorithm that forecasts ICU census for planning horizons ranging from 1 to 7 days. We evaluated the algorithm quality using several fit metrics. The root mean squared error ranged from 2.055 to 2.890 beds/d and the mean absolute percentage error from 9.4% to 13.2%. We show that this forecasting algorithm provides a better fit when compared with a moving average or a time series model that directly forecasts ICU census. Additionally, we evaluated the performance of the algorithm using data during the global COVID-19 pandemic and found that the error of the forecasts increased proportionally with the number of COVID-19 patients in the ICU. CONCLUSIONS: It is possible to develop accurate tools to forecast ICU census. This type of algorithm may be important to clinicians and managers when planning ICU capacity as well as staffing and surgical demand planning over a short time horizon.

Food Insecurity Is Under-reported in Surveys That Ask About the Past Year.

INTRODUCTION: Food insecurity affects 1 in 10 Americans in a typical year; recent U.S. Department of Agriculture data show that this food insecurity rate was stable from 2019 to 2021. However, data from Los Angeles County and other U.S. regions show that food insecurity spiked during the early months of the COVID-19 pandemic. One reason for this discrepancy may be that food insecurity measures assess experiences over different time frames. This study investigated the discrepancies in food insecurity rates by comparing past-week and past-year food insecurity measures and explored the role of recall bias. METHODS: Data were obtained from a representative survey panel of Los Angeles adults (N=1,135). Participants were surveyed about past-week food insecurity 11 times throughout 2021 and once about past-year food insecurity in December 2021. Data were analyzed in 2022. RESULTS: Of the participants who reported past-week food insecurity at any time in 2021, only two thirds also reported past-year food insecurity in December 2021, suggesting that one third of participants under-reported past-year food insecurity. Logistic regression models indicated that 3 characteristics were significantly associated with under-reporting of past-year food insecurity: having reported past-week food insecurity at fewer survey waves, not reporting recent past-week food insecurity, and having a relatively high household income. CONCLUSIONS: These results suggest substantial under-reporting of past-year food insecurity, related to recall bias and social factors. Measuring food insecurity at multiple points throughout the year may help to improve the accuracy of reporting and public health surveillance of this issue.

Geographic Variations in Human Mobility Patterns during the First Six Months of the COVID-19 Pandemic in California

Human mobility influenced the spread of the COVID-19 virus, as revealed by the high spatiotemporal granularity location service data gathered from smart devices. We conducted time series clustering analysis to delineate the relationships between human mobility patterns (HMPs) and their social determinants in California (CA) using aggregated smart device tracking data from SafeGraph. We first identified four types of temporal patterns for five human mobility indicator changes by applying dynamic-time-warping self-organizing map clustering methods. We then performed an analysis of variance and linear discriminant analysis on the HMPs with 17 social, economic, and demographic variables. Asians, children under five, adults over 65, and individuals living below the poverty line were found to be among the top contributors to the HMPs, including the HMP with a significant increase in the median home dwelling time and the HMP with emerging weekly patterns in full-time and part-time work devices. Our findings show that the CA shelter-in-place policy had varying impacts on HMPs, with socially disadvantaged places showing less compliance. The HMPs may help practitioners to anticipate the efficacy of non-pharmaceutical interventions on cases and deaths in pandemics.

Regular Cannabis Use During the First Year of the Pandemic: Studying Trajectories Rather Than Prevalence.

INTRODUCTION: Cannabis use in the U.S. rose early in the COVID-19 pandemic, but it is unclear whether that rise was temporary or permanent. This study estimated the nature and sociodemographic correlates of U.S. adult subpopulations regularly using cannabis by examining weekly trajectories of use during the first year of the pandemic. METHODS: Data came from the Understanding America Study, a nationally representative panel of U.S. adults (N=8,397; March 10, 2020-March 29, 2021). A growth mixture model was deployed to identify subgroups with similar regular cannabis use. Sociodemographic correlates of subgroups were examined using multinomial logistic regression. RESULTS: Four cannabis-use groups were identified. Most participants did not regularly use cannabis (no regular use; 81.7%). The other groups increased regular use until April 2020 but then diverged. Some (7.1%) decreased thereafter, whereas others (3.4%) maintained their elevated use until October 26, 2020 before decreasing. The last group (7.7%) sustained their elevated use throughout. Individuals aged between 18 and 39 years, unmarried, living in poverty, without a college degree, and with longer unemployment or underemployment spells had higher odds of being in the other groups with more weekly use than in the no-regular-use group. CONCLUSIONS: The analyses revealed population subgroups with prolonged regular cannabis use and a disproportionate concentration of socioeconomically vulnerable members of society in these subgroups. These findings elucidate important heterogeneity in the subpopulations using cannabis, highlighting the urgent need to tailor public health programs for subgroups that may have unique service needs.

MBA Futures: Images from Principles for Responsible Management Education (PRME) Signatories during the COVID-19 Pandemic

This paper presents key ideas from a Futures study relating to part-time (PT) Master of Business Administration (MBA) degree programmes. The objective of the study was to determine the likely nature of PT MBA programmes approximately 30 years in the future, i.e., 2050, and to do so in the context of an assessment of possible long-term impact of disruptions caused or accelerated by the COVID-19 pandemic. The research involved strategic conversations with ten PT MBA Programme Directors or equivalent across seven countries and each of whom was based in Principles for Responsible Management Education (PRME)-signatory institutions. The findings suggest that MBA programmes of the future are likely to be significantly different to current PT MBAs. To close the learning loop associated with the original term of strategic conversations, higher education institutions (HEIs) are invited to consider the findings to inform strategic conversations within their own institutions in respect of future PT MBA design and provision.

In COVID-19 Health Messaging, Loss Framing Increases Anxiety with Little-to-No Concomitant Benefits: Experimental Evidence from 84 Countries.

The COVID-19 pandemic (and its aftermath) highlights a critical need to communicate health information effectively to the global public. Given that subtle differences in information framing can have meaningful effects on behavior, behavioral science research highlights a pressing question: Is it more effective to frame COVID-19 health messages in terms of potential losses (e.g., "If you do not practice these steps, you can endanger yourself and others") or potential gains (e.g., "If you practice these steps, you can protect yourself and others")? Collecting data in 48 languages from 15,929 participants in 84 countries, we experimentally tested the effects of message framing on COVID-19-related judgments, intentions, and feelings. Loss- (vs. gain-) framed messages increased self-reported anxiety among participants cross-nationally with little-to-no impact on policy attitudes, behavioral intentions, or information seeking relevant to pandemic risks. These results were consistent across 84 countries, three variations of the message framing wording, and 560 data processing and analytic choices. Thus, results provide an empirical answer to a global communication question and highlight the emotional toll of loss-framed messages. Critically, this work demonstrates the importance of considering unintended affective consequences when evaluating nudge-style interventions.

Do They Really Work? Quantifying Fabric Mask Effectiveness to Improve Public Health Messaging.

The purpose of this study is to compare masks (non-medical/fabric, surgical, and N95 respirators) on filtration efficiency, differential pressure, and leakage with the goal of providing evidence to improve public health messaging. Masks were tested on an anthropometric face filtration mount, comparing both sealed and unsealed. Overall, surgical and N95 respirators provided significantly higher filtration efficiency (FE) and differential pressure (dP). Leakage comparisons are one of the most significant factors in mask efficiency. Higher weight and thicker fabric masks had significantly higher filtration efficiency. The findings of this study have important implications for communication and education regarding the use of masks to prevent the spread of COVID-19 and other respiratory illnesses, specifically the differences between sealed and unsealed masks. The type and fabric of facial masks and whether a mask is sealed or unsealed has a significant impact on the effectiveness of a mask. Findings related to differences between sealed and unsealed masks are of critical importance for health care workers. If a mask is not completely sealed around the edges of the wearer, FE for this personal protective equipment is misrepresented and may create a false sense of security. These results can inform efforts to educate health care workers and the public on the importance of proper mask fit.

Alcohol use trajectories among U.S. adults during the first 42 weeks of the COVID-19 pandemic.

BACKGROUND: This study characterized the prevalence, drinking patterns, and sociodemographic characteristics of U.S. adult subpopulations with distinct drinking trajectories during the COVID-19 pandemic's first 42 weeks. METHODS: Adult respondents (n = 8130) in a nationally representative prospective longitudinal study completed 21 biweekly web surveys (March 2020 to January 2021). Past-week alcohol drinking frequency (drinking days [range: 0 to 7]) and intensity (binge drinking on usual past-week drinking day [yes/no]) were assessed at each timepoint. Growth mixture models identified multiple subpopulations with homogenous drinking trajectories based on mean drinking days or binge drinking proportional probabilities across time. RESULTS: Four drinking frequency trajectories were identified: Minimal/stable (72.8% [95% CI = 71.8 to 73.8]) with <1 mean past-week drinking days throughout; Moderate/late decreasing (6.7% [95% CI = 6.2 to 7.3) with 3.13 mean March drinking days and reductions during summer, reaching 2.12 days by January 2021; Moderate/early increasing (12.9% [95% CI = 12.2 to 13.6) with 2.13 mean March drinking days that increased in April and then plateaued, ending with 3.20 mean days in January 2021; and Near daily/early increasing (7.6% [95% CI = 7.0 to 8.2]) with 5.58 mean March drinking days that continued increasing without returning to baseline. Four drinking intensity trajectories were identified: Minimal/stable (85.8% [95% CI = 85.0% to 86.5%]) with <0.01 binge drinking probabilities throughout; Low-to-moderate/fluctuating (7.4% [95% CI = 6.8% to 8%]) with varying binge probabilities across timepoints (range:0.12 to 0.26); Moderate/mid increasing (4.2% [95% CI = 3.7% to 4.6%]) with 0.39 April binge drinking probability rising to 0.65 during August-September without returning to baseline; High/early increasing trajectory (2.7% [95% CI = 2.3% to 3%]) with 0.84 binge drinking probability rising to 0.96 by June without returning to baseline. Males, Whites, middle-aged/older adults, college degree recipients, those consistently working, and those above the poverty limit were overrepresented in various increasing (vs. minimal/stable) frequency trajectories. Males, Whites, nonmarried, those without college degree, 18 to 39-year-olds, and middle aged were overrepresented in increasing (vs. minimal/stable) intensity trajectories. CONCLUSIONS: Several distinct U.S. adult sociodemographic subpopulations appear to have acquired new drinking patterns during the pandemic's first 42 weeks. Frequent alcohol use assessment in the COVID-19 era could improve personalized medicine and population health efforts to reduce drinking.

Timely goals of care documentation in patients with frailty in the COVID-19 era: a retrospective multi-site study.

BACKGROUND: Older frail patients are more likely to have timely goals of care (GOC) documentation than non-frail patients. AIMS: To investigate whether timely documentation of GOC within 72 h differed in the context of the COVID-19 pandemic (2020), compared with the pre-COVID-19 era (2019) for older frail patients. METHODS: Multi-site retrospective cohort study was conducted in two public hospitals where all consecutive frail adult patients aged ≥65 years were admitted under medical units for at least 24 h between 1 March 31 and October in 2019 and between 1 March and 31 October 2020 were included. The GOC was derived from electronic records. Frailty status was derived from hospital coding data using hospital frailty risk score (frail ≥5). The primary outcome was the documentation of GOC within 72 h of hospital admission. Secondary outcomes included hospital mortality, rapid response call, intensive care unit admission, prolonged hospital length of stay (≥10 days) and time to the documentation of GOC. RESULTS: The study population comprised 2021 frail patients admitted in 2019 and 1849 admitted in 2020, aged 81.2 and 90.9 years respectively. The proportion of patients with timely GOC was lower in 2020, than 2019 (48.3% (893/1849) vs 54.9% (1109/2021); P = 0.021). After adjusting for confounding factors, patients in 2020 were less likely to receive timely GOC (odds ratio = 0.77; 95% confidence interval (CI) 0.68-0.88). Overall time to GOC documentation was longer in 2020 (hazard ratio = 0.86; 95% CI 0.80-0.93). CONCLUSION: Timely GOC documentation occurred less frequently in frail patients during the COVID-19 pandemic than in the pre-COVID-19 era.

Do They Really Work? Quantifying Fabric Mask Effectiveness to Improve Public Health Messaging

The purpose of this study is to compare masks (non-medical/fabric, surgical, and N95 respirators) on filtration efficiency, differential pressure, and leakage with the goal of providing evidence to improve public health messaging. Masks were tested on an anthropometric face filtration mount, comparing both sealed and unsealed. Overall, surgical and N95 respirators provided significantly higher filtration efficiency (FE) and differential pressure (dP). Leakage comparisons are one of the most significant factors in mask efficiency. Higher weight and thicker fabric masks had significantly higher filtration efficiency. The findings of this study have important implications for communication and education regarding the use of masks to prevent the spread of COVID-19 and other respiratory illnesses, specifically the differences between sealed and unsealed masks. The type and fabric of facial masks and whether a mask is sealed or unsealed has a significant impact on the effectiveness of a mask. Findings related to differences between sealed and unsealed masks are of critical importance for health care workers. If a mask is not completely sealed around the edges of the wearer, FE for this personal protective equipment is misrepresented and may create a false sense of security. These results can inform efforts to educate health care workers and the public on the importance of proper mask fit.

The mitigating role of climate smart villages to the impacts of COVID-19 pandemic in the Myanmar rural communities.

Climate smart village approach is identified as an important strategy laid out in the Myanmar Climate Smart Agriculture Strategy (MCSAS, 2016) Four climate smart villages were established in 2017 to facilitate participatory action research to develop the CSV approach as well as to generate evidence of outcomes. The CSV approach is based on the principle of community-directed research process where community-members collaborate with an external researcher to investigate community challenges and their solutions. Like other countries in 2020, the height of the COVID-19 pandemic, Myanmar implemented wide-scale national and local restrictions on mobility that impacted trade and business resulting to an economic slowdown. Rural communities dominated by smallholder agriculture in Myanmar are not spared from the negative impacts of these restrictions. This paper seeks to assess the impacts of the COVID-19 pandemic to the 4 climate smart villages in Myanmar by analyzing household survey data (N = 527) collected in 2020 during the height of economic disruptions and comparing these data to the household survey conducted during the pre-pandemic period of 2018. Our analysis indicated that overall, the effect of the pandemic to agriculture production in 2020 production season in the 4 CSVs has been minimal as evidenced by the continued agriculture production at the same levels as the pre-pandemic conditions in 2018. The effects to household food security and diet diversity has been varied. Sakta village in Chin state in the highlands have demonstrated that diversified production systems enable them to achieve food security in the pandemic year of 2020.

Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact;while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.

Filtration efficiency and differential pressure of fabrics used in non-medical masks based on SARS COVID-19 particle size

Non-medical fabric masks, recommended by the Centers for Disease Control and Prevention and the World Health Organization, are available in various fabrics. There is limited research on the overall effectiveness of fabrics used to make masks. The purpose of this study was to assess fabrics commonly used in non-medical masks against their ability to mitigate the spread of COVID-19 based on the size and throughput of aerosols and particles (<1??m). Seven different fabrics were evaluated on filtration efficiency (FE), differential pressure (dP), and filtration quality (Q factor). Results indicate <16% FE against particles the size of COVID-19, dP <0.51 in w.c., and Q factor <0.004?Pa?1. FE results are lower than previously reported research with dP and Q factors within international guidelines. Using non-medical fabric masks as the sole mitigation strategy is not effective. It is critical to combine non-medical fabric masks with physical distancing to slow the spread of COVID-19 further.

Making Information Measurement Meaningful: The United Nations’ Sustainable Development Goals and the Social and Human Capital Protocol

Drucker’s saying that “What gets measured gets managed” is examined in the context of corporate social responsibility. The United Nations’ Sustainable Development Goals have encouraged sustainability reporting, and a reporting tool, the Social and Human Capital Protocol, has been developed to assist measurement and provide information to support the achievement of sustainability. This information should be valid and reliable;however, it is not easy to measure social and human capital factors. Additionally, companies use a large number of methodologies and indicators that are difficult to compare, and they may sometimes only present positive outcomes as a form of greenwashing. This lack of full transparency and comparability with other companies has the potential to discredit their reports, thereby supporting the claims of climate change deniers, free-market idealogues and conspiracy theorists who often use social media to spread their perspectives. This paper will describe the development of environmental reporting and CSR, discuss the natural capital protocol, and assess the extent to which the Social and Human Capital Protocol is able to fulfil its purpose of providing SMART objective measurements. It is the first academic article to provide a detailed examination of the Social and Human Capital Protocol.

Rapid greening response of China's 2020 spring vegetation to COVID-19 restrictions: Implications for climate change.

The 2019 novel coronavirus pandemic (COVID-19) negatively affected global public health and socioeconomic development. Lockdowns and travel restrictions to contain COVID-19 resulted in reduced human activity and decreased anthropogenic emissions. However, the secondary effects of these restrictions on the biophysical environment are uncertain. Using remotely sensed big data, we investigated how lockdowns and traffic restrictions affected China's spring vegetation in 2020. Our analyses show that travel decreased by 58% in the first 18 days following implementation of the restrictions across China. Subsequently, atmospheric optical clarity increased and radiation levels on the vegetation canopy were augmented. Furthermore, the spring of 2020 arrived 8.4 days earlier and vegetation 17.45% greener compared to 2015-2019. Reduced human activity resulting from COVID-19 restrictions contributed to a brighter, earlier, and greener 2020 spring season in China. This study shows that short-term changes in human activity can have a relatively rapid ecological impact at the regional scale.

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 12 May 2020. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.c.4878591.v1.