EFFECT OF UPADACITINIB ON REDUCING PAIN IN PATIENTS WITH ACTIVE ANKYLOSING SPONDYLITIS AND INADEQUATE RESPONSE TO BIOLOGIC THERAPY

BackgroundPain is a debilitating symptom of ankylosing spondylitis (AS) that negatively affects patients' lives. Upadacitinib (UPA), a Janus kinase inhibitor approved for the treatment of AS and other inflammatory diseases, showed significant efficacy vs placebo (PBO) in the phase 2/3 SELECT-AXIS 1 study in patients with AS who were biologic-naive and in the phase 3 SELECT-AXIS 2 study in patients with active AS who had an inadequate response (IR) to biological therapy [1,2]. Improvement in pain outcomes with UPA was also previously demonstrated in the SELECT-AXIS 1 study [3].ObjectivesThe objective of this post-hoc analysis of SELECT-AXIS 2 was to evaluate the efficacy of UPA vs PBO on multiple pain assessments through 14 weeks in patients with IR to a biologic disease-modifying antirheumatic drug (bDMARD-IR).MethodsSELECT-AXIS 2 (NCT04169373) enrolled adults with active AS with IR to biological therapy, including patients who discontinued biologics due to lack of efficacy or intolerance [1]. Patients were randomized 1:1 to UPA 15 mg once daily (QD) or PBO for 14 weeks. Pain endpoints evaluated here included the proportion of patients achieving ≥30%, ≥50%, and ≥70% reduction from baseline, minimal clinically important difference (MCID, defined as ≥1 point reduction or ≥15% reduction from baseline), and much better improvement (MBI, defined as ≥2 point reduction and ≥33% reduction from baseline) in Patient's Global Assessment (PGA) of pain, total back pain, and nocturnal back pain on a 0–10 numeric rating scale [3,4]. Non-responder imputation incorporating multiple imputation to handle missing data due to COVID-19 was used.ResultsA total of 211 patients received UPA 15 mg QD and 209 patients received PBO. Higher proportions of patients receiving UPA vs PBO achieved ≥30% and ≥50% reductions in PGA of pain, total back pain, and nocturnal back pain as early as week 2 that were sustained at all time points through 14 weeks (nominal P<0.05;Figure 1a-c). Achievement of ≥70% reductions in PGA of pain and nocturnal back pain were higher at week 4 and sustained thereafter (Figures 1a and 1c), and achievement of ≥70% reduction in total back pain was higher at week 2 and week 8, but not week 4, and sustained thereafter (Figure 1b). Results were similar for the proportion of patients achieving MCID and MBI, with improvements in PGA of pain, total back pain, and nocturnal back pain for UPA vs PBO as early as week 1 (MCID) or week 2 (MBI) that were sustained through week 14 (all nominal P<0.001;Table 1).Table 1.Achievement of MCID and MBI in Pain Outcomes at Week 14 (NRI-MI)Responder Rate (95% CI), %Pain OutcomesUPA 15 mgPBONominal P ValuePGA of painMCID81.0 (75.8–86.3)62.7 (56.1–69.2)<0.0001MBI60.7 (54.1–67.3)24.9 (19.0–30.7)<0.0001Total back painMCID80.1 (74.7–85.5)65.1 (58.6–71.5)0.0005MBI58.3 (51.6–64.9)25.4 (19.5–31.3)<0.0001Nocturnal back painMCID82.9 (77.9–88.0)61.3 (54.7–67.9)<0.0001MBI61.6 (55.0–68.2)32.1 (25.7–38.4)<0.0001MBI, much better improvement;MCID, minimal clinically important difference;NRI-MI, non-responder imputation incorporating multiple imputation to handle missing data due to COVID-19;PBO, placebo;PGA, Patient's Global Assessment;UPA, upadacitinib.ConclusionIn patients with active AS who were bDMARD-IR, greater proportions of patients treated with UPA achieved rapid and clinically meaningful reductions in pain vs PBO as early as week 2 that were sustained through 14 weeks across multiple pain assessments.References[1]van der Heijde D, et al. Ann Rheum Dis. 2022;81(11):1515-1523.[2]van der Heijde D, et al. Lancet. 2019;394(10214):2108-2117.[3]McInnes IB, et al. RMD Open. 2022;8(1):doi:10.1136/rmdopen-2021-002049.[4]Salaffi F, et al. Eur J Pain. 2004;8(4):283-291.AcknowledgementsAbbVie funded this study and participated in the study design, research, analysis, data collection, interpretation of data, reviewing, and approval of the publication. All authors had access to relevant data and participated in the drafting, review, and approval of this p blication. No honoraria or payments were made for authorship. Medical writing support was provided by M. Hovenden and J. Matsuura of ICON plc (Blue Bell, PA, USA) and was funded by AbbVie.Disclosure of InterestsXenofon Baraliakos Consultant of: Novartis, Pfizer, AbbVie, Eli Lilly, UCB Pharma, Galapagos, Janssen, Celgene, and Amgen, Grant/research support from: Novartis, Pfizer, AbbVie, Eli Lilly, UCB Pharma, Galapagos, Janssen, Celgene, and Amgen, Marina Magrey Consultant of: UCB, Novartis, Eli Lilly, Pfizer, and Janssen, Grant/research support from: Amgen, AbbVie, BMS, and UCB Pharma, Louis Bessette Speakers bureau: Amgen, BMS, Janssen, UCB, AbbVie, Pfizer, Merck, Celgene, Lilly, Novartis, Organon, and Sanofi, Grant/research support from: Amgen, BMS, Janssen, UCB, AbbVie, Pfizer, Merck, Celgene, Lilly, Novartis, Sanofi, and Gilead, Kurt de Vlam Speakers bureau: Amgen, Celgene, Eli Lilly, Galapagos, Novartis, and UCB, Consultant of: Amgen, AbbVie, Celgene, Eli Lilly, Galapagos, Novartis, and UCB, Grant/research support from: Amgen, UCB, and MSD, Tianming Gao Shareholder of: AbbVie, Employee of: AbbVie, Anna Shmagel Shareholder of: AbbVie, Employee of: AbbVie, Ralph Lippe Shareholder of: AbbVie, Employee of: AbbVie, Ana Biljan Shareholder of: AbbVie, Employee of: AbbVie, Victoria Jasion Shareholder of: AbbVie, Employee of: AbbVie, Peter C. Taylor Speakers bureau: AbbVie, Consultant of: Lilly, AbbVie, Pfizer, Galapagos, Gilead, Janssen, GlaxoSmithKline, Sanofi, Fresenius, Nordic Pharma, UCB, and Biogen, Grant/research support from: Galapagos.

The role of heritage sites and other influential factors in domestic tourism inflow to Australian states: a gravity model approach

Domestic tourism plays a crucial role in the Australian economy, generating revenue, creating employment opportunities, fostering cultural identity, and facilitating tourism growth and development. The remote regions of Australia are particularly reliant on domestic inbound tourism to stimulate their local economies. This study investigates the influence of heritage sites and various factors on domestic tourism inflows to eight states in the Australia between 1998-2021. The gravity method and random effect model are employed for the empirical analysis. The results indicate that the macro determinants, including population of origin state, gross state product per capita, infrastructural development, shared border between states, and the number of heritage sites, have significant and positive impact on domestic tourism inflow. Conversely, the consumer price index, distance, and pandemic outbreak have a negative influence on domestic tourism inflow. These findings hold important practical implications. Given Australia's geographical remoteness, promoting domestic tourism becomes imperative to boost the tourism industry and local economies. Therefore, it is recommended that authorities prioritize domestic tourism flows and invest in infrastructure, preserve heritage sites, stabilize prices, implement effective marketing strategies, and respond swiftly to public emergencies such as the Covid-19 pandemic.

Application of Artificial Intelligence to Predict CO2 Emissions: Critical Step towards Sustainable Environment

Prediction of carbon dioxide (CO2) emissions is a critical step towards a sustainable environment. In any country, increasing the amount of CO2 emissions is an indicator of the increase in environmental pollution. In this regard, the current study applied three powerful and effective artificial intelligence tools, namely, a feed-forward neural network (FFNN), an adaptive network-based fuzzy inference system (ANFIS) and long short-term memory (LSTM), to forecast the yearly amount of CO2 emissions in Saudi Arabia up to the year 2030. The data were collected from the "Our World in Data” website, which offers the measurements of the CO2 emissions from the years 1936 to 2020 for every country on the globe. However, this study is only concerned with the data related to Saudi Arabia. Due to some missing data, this study considered only the measurements in the years from 1954 to 2020. The 67 data samples were divided into 2 subsets for training and testing with the optimal ratio of 70:30, respectively. The effect of different input combinations on prediction accuracy was also studied. The inputs were combined to form six different groups to predict the next value of the CO2 emissions from the past values. The group of inputs that contained the past value in addition to the year as a temporal index was found to be the best one. For all the models, the performance accuracies were assessed using the root mean squared errors (RMSEs) and the coefficient of determination (R2). Every model was trained until the smallest RMSE of the testing data was reached throughout the entire training run. For the FFNN, ANFIS and LSTM, the averages of the RMSEs were 19.78, 20.89505 and 15.42295, respectively, while the averages of the R2 were found to be 0.990985, 0.98875 and 0.9945, respectively. Every model was applied individually to forecast the next value of the CO2 emission. To benefit from the powers of the three artificial intelligence (AI) tools, the final forecasted value was considered the average (ensemble) value of the three models' outputs. To assess the forecasting accuracy, the ensemble was validated with a new measurement for the year 2021, and the calculated percentage error was found to be 6.8675% with an accuracy of 93.1325%, which implies that the model is highly accurate. Moreover, the resulting forecasting curve of the ensembled models showed that the rate of CO2 emissions in Saudi Arabia is expected to decrease from 9.4976 million tonnes per year based on the period 1954–2020 to 6.1707 million tonnes per year in the period 2020–2030. Therefore, the finding of this work could possibly help the policymakers in Saudi Arabia to take the correct and wise decisions regarding this issue not only for the near future but also for the far future.

DeCoP: Deep Learning for COVID-19 Prediction of Survival

The current ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus, has severely affected our daily life routines and behavior patterns. According to the World Health Organization, there have been 93 million confirmed cases with more than 1.99 million confirmed death around 235 Countries, areas or territories until 15 January 2021, 11:00 GMT+11. People who are affected with COVID-19 have different symptoms from people to people. When large amounts of patients are affected with COVID-19, it is important to quickly identify the health conditions of patients based on the basic information and symptoms of patients. Then the hospital can arrange reasonable medical resources for different patients. However, existing work has a low recall of 15.7% for survival predictions based on the basic information of patients (i.e., false positive rate (FPR) with 84.3%, FPR: actually survival but predicted as died). There is much room for improvement when using machine learning-based techniques for COVID-19 prediction. In this paper, we propose DeCoP to train a classifier to predict the survival of COVID-19 patients with high recall and F1 score. DeCoP is a deep learning (DL)-based scheme of Bidirectional Long Short-Term Memory (BiLSTM) along with Fuzzy-based Information Decomposition (FID) to predict the survival of patients. First of all, we apply FID oversampling to redistribute the training data of the Open COVID-19 Data Working Group. Then, we employ BiLSTM to learn the high-level feature representations from the redistributed dataset. After that, the high-level feature vector will be used to train the prediction model. Experimental results show that our proposed scheme achieves outstanding performances. Precisely, the improvement achieves about 19% and 18% in terms of recall and F1-measure.

Sleep Deficit as a Risk Factor for Hypertension in Korean Adults

This study aims to evaluate the association between sleep duration and hypertension in Korean adults aged 30 and older. This is a population-based cross-sectional study using the 2020 Korean National Health and Nutritional Examination Survey data. Study subjects numbered 3984 after excluding people with missing data for key exposures and outcome variables. Of the study subjects, 18.8% (n = 748) sleep for less than 6 h a day. Increased risk for hypertension was associated with being male, of old age, unemployed, of low educational achievement, and overweight, as well as drinking, smoking, stress, and short sleep duration. The prevalence of sleep deficit was associated with sex, age, education level, income, and health insurance type. Logistic regression analyses were performed to identify whether sleep duration affects the risk of hypertension. In the unadjusted model, the odds ratio (OR) of having hypertension was lower among people sleeping for 7.0–7.9 h (OR = 0.52, 95% confidence interval (95% CI) = 0.42–0.64) than those sleeping for fewer than 6 h per day. After adjusting for sociodemographic factors (sex, age, education level, occupation, and health insurance), the OR for 7.0–7.9 h remained significant (OR = 0.74, 95% CI = 0.59–0.92). This association was not significant when the model was further adjusted for health-related factors (smoking, drinking, physical activity, BMI level, and stress). Measures to promote adequate sleep duration should be included in programs to prevent and manage hypertension.

Estimands and their Estimators for Clinical Trials Impacted by the COVID-19 Pandemic: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

The COVID-19 pandemic continues to affect the conduct of clinical trials globally. Complications may arise from pandemic-related operational challenges such as site closures, travel limitations and interruptions to the supply chain for the investigational product, or from health-related challenges such as COVID-19 infections. Some of these complications lead to unforeseen intercurrent events in the sense that they affect either the interpretation or the existence of the measurements associated with the clinical question of interest. In this article, we demonstrate how the ICH E9(R1) Addendum on estimands and sensitivity analyses provides a rigorous basis to discuss potential pandemic-related trial disruptions and to embed these disruptions in the context of study objectives and design elements. We introduce several hypothetical estimand strategies and review various causal inference and missing data methods, as well as a statistical method that combines unbiased and possibly biased estimators for estimation. To illustrate, we describe the features of a stylized trial, and how it may have been impacted by the pandemic. This stylized trial will then be revisited by discussing the changes to the estimand and the estimator to account for pandemic disruptions. Finally, we outline considerations for designing future trials in the context of unforeseen disruptions.Copyright © 2022 American Statistical Association.

Supply Chain Resilience of Mineral Resources Industry in China

Improving the supply chain resilience of the mineral resources industry is crucial for ensuring national economic security in China. Based on the supply and demand data of China's mineral resources industry from 2002 to 2018, this study adopts system dynamics model to simulate the supply chain resilience of the mineral resources industry, the mining industry, and the smelting and processing industry under the scenario of steady economic development and the scenario of supply chain crisis. From the simulation results, the reserves of the mineral resources industry and the smelting and processing industry under the two scenarios are nearly the same, indicating that they are weakly affected by the foreign market, and both have strong resilience. The mining industry has a high dependence on imports and a lack of supply chain resilience. Under the condition of steady economic development, the output of the mining industry needs to develop at a low speed to reduce production capacity. More attention should be paid to the high level of import dependence and insufficient supply chain resilience of the mining industry. In the stable international trade situation, reserves of important minerals should be increased to alleviate the resource shortage during the supply chain crisis.

A mixed effects changepoint quantile regression model for longitudinal data with application on COVID-19 data

Introduction: Longitudinal individual response profiles could exhibit a mixture of two or more phases of increase or decrease in trend throughout the follow-up period, with one or more unknown transition points (changepoints). The detection and estimation of these changepoints is crucial. Most of the proposed statistical methods for detecting and estimating changepoints in literature rely on distributional assumptions that may not hold. In this case, a good alternative is to use a robust approach;the quantile regression model. There are methods in the literature to deal with quantile regression models with a changepoint. These methods ignore the within-subject dependence of longitudinal data. Methods: We propose a mixed effects quantile regression model with changepoints to account for dependence structure in the longitudinal data. Fixed effects parameters, in addition to the location of the changepoint, are estimated using the profile estimation method. The stochastic approximation EM algorithm is proposed to estimate the fixed effects parameters exploiting the link between an asymmetric Laplace distribution and the quantile regression. In addition, the location of the changepoint is estimated using the usual optimization methods. Results and discussion: A simulation study shows that the proposed estimation and inferential procedures perform reasonably well in finite samples. The practical use of the proposed model is illustrated using COVID-19 data. The data focus on the effect of global economic and health factors on the monthly death rate due to COVID-19 from 1 April 2020 to 30th April 2021. the results show a positive effect on the monthly number of patients with COVID-19 in intensive care units (ICUs) for both 0.5th and 0.8th quantiles of new monthly deaths per million. The stringency index, hospital beds, and diabetes prevalence have no significant effect on both 0.5th and 0.8th quantiles of new monthly deaths per million. Copyright © 2023 Ibrahim, Gad and Abd-Rabou.

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

BACKGROUND: In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as ​​reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients. METHODS: We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern. RESULTS: With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors. CONCLUSION: In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.

Ekonomik Faaliyet Kollarında COVID-19 Pandemi Etkisinin Çok Boyutlu Ölçekleme ve K-Ortalamalar Kümeleme Analiziyle Íncelenmesi

COVID-19 pandemisinin tüm dünyaya yayılmasıyla ekonomik faaliyetlerde küresel bazda önemli deǧişiklikler meydana gelmiştir. Türkiye bu durumdan global ve lokal bazda önemli düzeyde etkilenen ülkelerdendir. Çeşitli ekonomik faaliyet kollarında iş yeri ve istihdam sayıları bu etkinin gözlenebildiǧi önemli göstergelerdendir. Bu çalışmada;pandeminin hemen öncesi (2019 yılı) ile hızlı ve yoǧun olarak görüldüǧü erken dönem (2020 yılı) iş yeri sayıları ve zorunlu sigortalı çalışan sayıları, iller bazında incelenerek faaliyet kollarına göre pandeminin etkisinin ortaya çıkarılması amaçlanmıştır. Yöntem olarak istatistiksel veri analizi ve veri madenciliǧi tekniklerinden çok boyutlu ölçekleme ve kümeleme analizleri kullanılmıştır. Bu yöntemler yardımıyla elde edilen bulgular görselleştirilmiş ve çalışmanın amacı doǧrultusunda yorumlanmıştır. Elde edilen sonuçlara göre, iki yılın verileri karşılaştırıldıǧında, toplamda iş yeri sayısı ve zorunlu sigortalı çalışan sayısının arttıǧı görülmüştür. Faaliyet kolları bazında sonuçlar incelendiǧinde deǧişimlerdeki pandemi etkisi göze çarpmaktadır. Mobiliteye dayalı ve pandemi tedbirlerinin engellediǧi faaliyet alanlarının iş yeri ve çalışan sayısı bakımından azalma yönünde etkilendiǧi görülmüştür. Öte yandan bu kısıtlamaların özellikle perakendecilik sektörlerini dijital ortamlara taşıyarak e-ticarette büyümeye sebep olması, posta ve kargo faaliyetlerinde yüksek oranlı artışa neden olmuştur. Bunun yanı sıra evde bakım faaliyetlerinin de pandemi etkisiyle en fazla artışın olduǧu kollardan olduǧu sonucuna ulaşılmıştır.Alternate :With the spread of the COVID-19 pandemic all over the world, significant changes have occurred globally with regard to economic activities. Turkey is one of the countries to be affected by this situation on a global and local basis. The number of workplaces and employment in various segments of economic activity are important indicators through which this impact can be observed. These changes have occurred locally in different regions and different lines of business. This study aims to reveal the pandemic's impact by examining by province the number of workplaces and number of employees with compulsory insurance just before the pandemic (2019) and in the pandemic's early period in 2020 when it was seen spread rapidly and intensely. The study uses multidimensional scaling and clustering analyses from the statistical data analysis and data mining techniques as the research methods. The findings obtained with these methods have been visualized and interpreted in line with the purpose of the study. When comparing the data of these two years in accordance with the obtained results, the number of workplaces and the number of employees with compulsory insurance were seen to have increased overall. When examining the results on the basis of operating segments, the pandemic is seen to have had a striking impact with regard to the changes, with the operation segments based on mobility and on those prohibited by the pandemic measures being observed to have been affected by a decrease in terms of the numbers of workplaces and employees. Meanwhile, these restrictions led to growth in e-commerce, particularly by moving retail sectors to digital environments, and this caused a high rate of increase in postal and cargo activities. Home care activities were additionally concluded to be among the segments with the highest increase due to the pandemic's effects.

Factors associated with adverse mental health outcomes during the COVID-19 pandemic.

Objectives: Even with significant advances to advance the health status of the general American population, the increased prevalence of mental health conditions and re-emergence of highly infectious diseases across all levels of society present a significant threat. This study aimed to quantify the effects of pandemic-, healthcare-related, and sociodemographic variables on adverse mental health outcomes, and determine their relative magnitudes. Study design: This study employed publicly available data from the Household Pulse Survey, conducted by the United States Census Bureau to examine the social and economic impacts of the COVID-19 pandemic on American households. Methods: A multiple regression model formed the basis of analysis, with adverse mental health as the outcome and various pandemic-, healthcare-related, and sociodemographic variables as predictors. Missing data was handled using multiple imputation. Results: The factors with significant contributions to adverse mental health outcomes were those associated with mental health services and prescriptions. General healthcare-related variables followed those specific to mental health, along with sociodemographic variables contributing smaller changes. There were differential outcomes in mental health that were in part attributable to sociodemographic factors, but also a lack of access to both mental and physical healthcare due to factors both related and unrelated to the ongoing pandemic. Conclusions: There is a need for policymakers and other stakeholders to work towards a mental health system that is more robust to restrictions brought on by events like the COVID-19 pandemic, and to address inequities in health care that have been exacerbated.

Mortality Prediction with Bidirectional Coupled and Gumbel Subset Network on Irregularly Multivariate Time Series

Mortality prediction is a crucial challenge because of multivariate time series (MTS) complexity, which are sparse, irregularly, asynchronous and hold missing values for various reasons in a single acquisition. Various methods are proposed to deal with missing values for the final mortality prediction. However, existing models only capture the temporal dependencies within a time series and are inefficient to capture the dependencies between time series to rebuild missing values for mortality prediction. To address these challenges, in this paper, we present an end-to-end imputation and mortality prediction model, named bidirectional coupled and Gumbel subset network (BiCGSN), for mortality prediction with such irregularly multivariate time series. Our proposed model (BiCGSN) uses a recurrent network to learn the temporal dependencies (intra-time series couplings) and uses a Gumbel selector on multi-head attention to obtain the relationship between the variables (inter-time series couplings) in the forward and backward directions. Then the learned bidirectional inter-and intra-time series couplings are fused to impute missing values for further mortality prediction. We evaluate our model on PhysioNet2012 and COVID-19 datasets to imputation and predict mortality. Experiments show that BiCGSN obtains the AUC 0.869 and 0.911 on two real-world datasets respectively and outperforms all the baselines. © 2022 IEEE.

Fostering Energy Resilience in the Rural Thai Power System—A Case Study in Nakhon Phanom

With rising electricity demand, heavy reliance on imports, and recent economic downturns due to the negative impact of the COVID-19 pandemic, supply chain bottlenecks, and the Russian invasion of Ukraine, Thailand is suffering severely from energy resilience risks. The government has therefore set a goal of decentralizing energy production through small-scale distributed renewable energy systems. To support their design and the planning process, we simulate multiple scenarios with wind turbines, photovoltaic systems, and battery storage for a model community in rural Nakhon Phanom, Thailand. Using the software NESSI4D, we evaluate and discuss their impact on energy resilience by considering environmental sustainability, economic attractiveness, and independence from the central power grid. To fill the gap of missing data on energy demand, we synthesize high-resolution load profiles from the Thailand Vietnam Socio-Economic Panel. We conclude that distributed photovoltaic systems with additional battery storage are only suitable to promote energy resilience if the government provides appropriate financial incentives. Considering temporal variations and local conditions, as well as a participatory decision-making process, are crucial for the long-term success of energy projects. Our advice to decision-makers is to design policies and regulatory support that are aligned with the preferences and needs of target communities.

An Assessment of the Missing Data Imputation Techniques for COVID-19 Data

In medical domain, the accuracy of the data supplied is critical. Missing values, on the other hand, are a typical occurrence in this sector for a variety of reasons. Most current science concentrates on establishing novel data imputation procedures, but more research on conducting a comprehensive review of existing algorithms is highly desired. Authors have evaluated the performance of four mostly adopted data imputation techniques, i.e., MICE, EM, mean, and KNN on a real-world dataset of COVID-19. KNN is an imputation approach that, according to the findings of the studies, is expected to be a good fit for dealing with missing data in the healthcare industry. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Impact of Nonrandom Missingness in Surveillance Data for Population-Level Summaries: Simulation Study.

BACKGROUND: Surveillance data are essential public health resources for guiding policy and allocation of human and capital resources. These data often consist of large collections of information based on nonrandom sample designs. Population estimates based on such data may be impacted by the underlying sample distribution compared to the true population of interest. In this study, we simulate a population of interest and allow response rates to vary in nonrandom ways to illustrate and measure the effect this has on population-based estimates of an important public health policy outcome. OBJECTIVE: The aim of this study was to illustrate the effect of nonrandom missingness on population-based survey sample estimation. METHODS: We simulated a population of respondents answering a survey question about their satisfaction with their community's policy regarding vaccination mandates for government personnel. We allowed response rates to differ between the generally satisfied and dissatisfied and considered the effect of common efforts to control for potential bias such as sampling weights, sample size inflation, and hypothesis tests for determining missingness at random. We compared these conditions via mean squared errors and sampling variability to characterize the bias in estimation arising under these different approaches. RESULTS: Sample estimates present clear and quantifiable bias, even in the most favorable response profile. On a 5-point Likert scale, nonrandom missingness resulted in errors averaging to almost a full point away from the truth. Efforts to mitigate bias through sample size inflation and sampling weights have negligible effects on the overall results. Additionally, hypothesis testing for departures from random missingness rarely detect the nonrandom missingness across the widest range of response profiles considered. CONCLUSIONS: Our results suggest that assuming surveillance data are missing at random during analysis could provide estimates that are widely different from what we might see in the whole population. Policy decisions based on such potentially biased estimates could be devastating in terms of community disengagement and health disparities. Alternative approaches to analysis that move away from broad generalization of a mismeasured population at risk are necessary to identify the marginalized groups, where overall response may be very different from those observed in measured respondents.

From Illegal Waste Dumps to Beneficial Resources Using Drone Technology and Advanced Data Analysis Tools: A Feasibility Study

In a resource-constrained world, there is ongoing concern over the exploitation and potential future shortage of Earth’s natural resources. In this paper, we present the results of two pilot studies in which we used drone technology with spatial mapping tools and environmental and economic analysis to map illegal waste sites. Besides the technical feasibility, we aimed at understanding the benefits, costs, and tradeoffs of extracting the materials stocked therein, transforming illegal waste sites into valuable resources. The innovation of our work is reflected in the integration of existing technologies for aerial mapping and economic\environmental assessment methodologies for promoting a local circular economy. The pilot results suggest that it is feasible to identify valuable materials left on the ground in the form of unattended, illegally disposed waste. Our initial national estimates for the illegal waste cleanup based on the pilot results suggest that the treatment cost in Israel can be reduced by 58 million USD and even reach zero, with the potential to generate up to 82.8 million USD profits. Finally, we link our results to the Sustainable Development Goals framework and suggest how mapping and implementing the recycling potential can promote achieving some of the goals. Our work provides missing data that the state, local authorities, contractors, and companies that monitor and manage waste and recycled raw materials may find useful.

Bias from questionnaire invitation and response in COVID-19 research: an example using ALSPAC.

Background: Longitudinal studies are crucial for identifying potential risk factors for infection with, and consequences of, COVID-19, but relationships can be biased if they are associated with invitation and response to data collection. We describe factors relating to questionnaire invitation and response in COVID-19 questionnaire data collection in a multigenerational birth cohort (the Avon Longitudinal Study of Parents and Children, ALSPAC). Methods: We analysed online questionnaires completed between the beginning of the pandemic and easing of the first UK lockdown by participants with valid email addresses who had not actively disengaged from the study. We assessed associations of pre-pandemic sociodemographic, behavioural, anthropometric and health-related factors with: i) being sent a questionnaire; ii) returning a questionnaire; and iii) item response (for specific questions). Analyses were conducted in three cohorts: the index children born in the early 1990s (now young adults; 41 variables assessed), their mothers (35 variables) and the mothers' partners (27 variables). Results: Of 14,849 young adults, 41% were sent a questionnaire, of whom 57% returned one. Item response was >95%. In this cohort, 78% of factors were associated with being sent a questionnaire, 56% with returning one, and, as an example of item response, 20% with keyworker status response. For instance, children from mothers educated to degree-level had greater odds of being sent a questionnaire (OR=5.59; 95% CI=4.87-6.41), returning one (OR=1.60; 95% CI=1.31-1.95), and responding to items (e.g., keyworker status OR=1.65; 95% CI=0.88-3.04), relative to children from mothers with fewer qualifications. Invitation and response rates and associations were similar in all cohorts. Conclusions: These results highlight the importance of considering potential biases due to non-response when using longitudinal studies in COVID-19 research and interpreting results. We recommend researchers report response rates and factors associated with invitation and response in all COVID-19 observational research studies, which can inform sensitivity analyses.

A hybrid return to baseline imputation method to incorporate MAR and MNAR dropout missingness.

Missing data are inevitable in longitudinal clinical trials due to intercurrent events (ICEs) such as treatment interruption or premature discontinuation for different reasons. Missing at random (MAR) assumption is usually unverifiable and sensitivity analyses are often requested under missing not at random (MNAR) assumption. Return to baseline (RTB) imputation is a commonly used MNAR method. In practice, not all dropout missingness can be assumed MNAR. For example, missingness or dropouts due to COVID-19 can be reasonably assumed MAR. Therefore, traditional RTB is not applicable when there is both MAR and MNAR dropout missingness. Here we propose a hybrid strategy for RTB imputation which can handle missing data due to MAR and MNAR dropouts at the same time. Standard multiple imputation approach is proposed and an analytic likelihood based approach is derived to improve efficiency.

A model for simultaneous evaluation of NO2, O3, and PM10 pollution in urban and rural areas: handling incomplete data sets with multivariate curve resolution analysis

A powerful methodology, based on the multivariate curve resolution alternating least squares (MCR-ALS) method with quadrilinearity constraints, is proposed to handle complex and incomplete four-way atmospheric data sets, providing concise results that are easy to interpret. Changes in air quality by nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM10) in eight sampling stations located in the Barcelona metropolitan area and other parts of Catalonia during the COVID-19 lockdown period (2020) with respect to previous years (2018 and 2019), are investigated using such methodology. The MCR-ALS simultaneous analysis of the three contaminants among the eight stations and for the 3 years allows the evaluation of potential correlations among the pollutants, even when having missing data blocks. Correlated profiles are shown by NO2 and PM10 due to similar pollution sources (traffic and industry), evidencing a decrease in 2019 and 2020 due to traffic restriction policies and the COVID-19 lockdown period, especially noticeable in the most transited urban areas (i.e., Vall d'Hebron, Granollers and Gràcia). The O3 evidences an opposed interannual trend, showing higher amounts in 2019 and 2020 with respect to 2018 due to the decreased titration effect, more significant in rural areas (Begur) and in the control site (Obserbatori Fabra).

Multiple imputation to minimise bias from missing stage information in estimates of early cancer diagnosis in England: a population-based study.

INTRODUCTION: Monitoring early diagnosis is a priority of cancer policy in England. Information on stage has not always been available for a large proportion of patients, however, which may bias temporal comparisons. We previously estimated that early-stage diagnosis of colorectal cancer rose from 32% to 44% during 2008-2013, using multiple imputation. Here we examine the underlying assumptions of multiple imputation for missing stage using the same dataset. METHODS: Individually-linked cancer registration, Hospital Episode Statistics (HES), and audit data were examined. Six imputation models including different interaction terms, post-diagnosis treatment, and survival information were assessed, and comparisons drawn with the a priori optimal model. Models were further tested by setting stage values to missing for some patients under one plausible mechanism, then comparing actual and imputed stage distributions for these patients. Finally, a pattern-mixture sensitivity analysis was conducted. RESULTS: Data from 196,511 colorectal patients were analysed, with 39.2% missing stage. Inclusion of survival time increased the accuracy of imputation: the odds ratio for change in early-stage diagnosis during 2008-2013 was 1.7 (95% CI: 1.6, 1.7) with survival to 1 year included, compared to 1.9 (95% CI 1.9-2.0) with no survival information. Imputation estimates of stage were accurate in one plausible simulation. Pattern-mixture analyses indicated our previous analysis conclusions would only change materially if stage were misclassified for 20% of the patients who had it categorised as late. INTERPRETATION: Multiple imputation models can substantially reduce bias from missing stage, but data on patient's one-year survival should be included for highest accuracy.