Hospital Use of a Web-Based Clinical Knowledge Support System and In-Training Examination Performance Among Postgraduate Resident Physicians in Japan: Nationwide Observational Study.

Background: The relationship between educational outcomes and the use of web-based clinical knowledge support systems in teaching hospitals remains unknown in Japan. A previous study on this topic could have been affected by recall bias because of the use of a self-reported questionnaire. Objective: We aimed to explore the relationship between the use of the Wolters Kluwer UpToDate clinical knowledge support system in teaching hospitals and residents' General Medicine In-Training Examination (GM-ITE) scores. In this study, we objectively evaluated the relationship between the total number of UpToDate hospital use logs and the GM-ITE scores. Methods: This nationwide cross-sectional study included postgraduate year-1 and -2 residents who had taken the examination in the 2020 academic year. Hospital-level information was obtained from published web pages, and UpToDate hospital use logs were provided by Wolters Kluwer. We evaluated the relationship between the total number of UpToDate hospital use logs and residents' GM-ITE scores. We analyzed 215 teaching hospitals with at least 5 GM-ITE examinees and hospital use logs from 2017 to 2019. Results: The study population consisted of 3013 residents from 215 teaching hospitals with at least 5 GM-ITE examinees and web-based resource use log data from 2017 to 2019. High-use hospital residents had significantly higher GM-ITE scores than low-use hospital residents (mean 26.9, SD 2.0 vs mean 26.2, SD 2.3; P=.009; Cohen d=0.35, 95% CI 0.08-0.62). The GM-ITE scores were significantly correlated with the total number of hospital use logs (Pearson r=0.28; P<.001). The multilevel analysis revealed a positive association between the total number of logs divided by the number of hospital physicians and the GM-ITE scores (estimated coefficient=0.36, 95% CI 0.14-0.59; P=.001). Conclusions: The findings suggest that the development of residents' clinical reasoning abilities through UpToDate is associated with high GM-ITE scores. Thus, higher use of UpToDate may lead physicians and residents in high-use hospitals to increase the implementation of evidence-based medicine, leading to high educational outcomes.

Development, validation and visualization of a web-based nomogram for predicting risk of new-onset diabetes after percutaneous coronary intervention.

Simple and practical tools for screening high-risk new-onset diabetes after percutaneous coronary intervention (PCI) (NODAP) are urgently needed to improve post-PCI prognosis. We aimed to evaluate the risk factors for NODAP and develop an online prediction tool using conventional variables based on a multicenter database. China evidence-based Chinese medicine database consisted of 249, 987 patients from 4 hospitals in mainland China. Patients ≥ 18 years with implanted coronary stents for acute coronary syndromes and did not have diabetes before PCI were enrolled in this study. According to the occurrence of new-onset diabetes mellitus after PCI, the patients were divided into NODAP and Non-NODAP. After least absolute shrinkage and selection operator regression and logistic regression, the model features were selected and then the nomogram was developed and plotted. Model performance was evaluated by the receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test and decision curve analysis. The nomogram was also externally validated at a different hospital. Subsequently, we developed an online visualization tool and a corresponding risk stratification system to predict the risk of developing NODAP after PCI based on the model. A total of 2698 patients after PCI (1255 NODAP and 1443 non-NODAP) were included in the final analysis based on the multicenter database. Five predictors were identified after screening: fasting plasma glucose, low-density lipoprotein cholesterol, hypertension, family history of diabetes and use of diuretics. And then we developed a web-based nomogram ( https://mr.cscps.com.cn/wscoringtool/index.html ) incorporating the above conventional factors for predicting patients at high risk for NODAP. The nomogram showed good discrimination, calibration and clinical utility and could accurately stratify patients into different NODAP risks. We developed a simple and practical web-based nomogram based on multicenter database to screen for NODAP risk, which can assist clinicians in accurately identifying patients at high risk of NODAP and developing post-PCI management strategies to improved patient prognosis.

A Personalized and Interactive Web-Based Advance Care Planning Intervention for Older Adults (Koda Health): Pilot Feasibility Study.

Background: Advance care planning (ACP) is a process that involves patients expressing their personal goals, values, and future medical care preferences. Digital applications may help facilitate this process, though their use in older adults has not been adequately studied. Objective: This pilot study aimed to evaluate the reach, adoption, and usability of Koda Health, a web-based patient-facing ACP platform, among older adults. Methods: Older adults (aged 50 years and older) who had an active Epic MyChart account at an academic health care system in North Carolina were recruited to participate. A total of 2850 electronic invitations were sent through MyChart accounts with an embedded hyperlink to the Koda platform. Participants who agreed to participate were asked to complete pre- and posttest surveys before and after navigating through the Koda Health platform. Primary outcomes were reach, adoption, and System Usability Scale (SUS) scores. Exploratory outcomes included ACP knowledge and readiness. Results: A total of 161 participants enrolled in the study and created an account on the platform (age: mean 63, SD 9.3 years), with 80% (129/161) of these participants going on to complete all steps of the intervention, thereby generating an advance directive. Participants reported minimal difficulty in using the Koda platform, with an overall SUS score of 76.2. Additionally, knowledge of ACP (eg, mean increase from 3.2 to 4.2 on 5-point scale; P<.001) and readiness (eg, mean increase from 2.6 to 3.2 on readiness to discuss ACP with health care provider; P<.001) significantly increased from before to after the intervention. Conclusions: This study demonstrated that the Koda Health platform is feasible, had above-average usability, and improved ACP documentation of preferences in older adults. Our findings indicate that web-based health tools like Koda may help older individuals learn about and feel more comfortable with ACP while potentially facilitating greater engagement in care planning.

Co-production of an online research and resource platform for improving the health of young people-The hype project.

Mental health conditions tend to go unrecognised and untreated in adolescence, and therefore it is crucial to improve the health and social outcomes for these individuals through age and culturally appropriate interventions. This paper aims firstly to describe the development and implementation of the HYPE project platform (a research and resource platform co-designed and co-produced with young people). The second aim is to describe the characteristics of participants who engaged with the platform and an embedded pilot online survey. Participatory action research approach was used to address objectives of the HYPE project. Data were analysed to: (1) help improve access to health and social services, (2) guide provision of information of online resources and (3) deliver complementary community-based events/activities to promote mental health and to ultimately prevent mental health issues. Pilot and main phases of the HYPE project demonstrated the capacity and feasibility for such a platform to reach local, national, and international populations. Analyses demonstrated that the platform was particularly relevant for young females with pre-existing health difficulties. Some of the barriers to involving young people in research and help-seeking are discussed.

Companies inadvertently fund online misinformation despite consumer backlash.

The financial motivation to earn advertising revenue has been widely conjectured to be pivotal for the production of online misinformation1-4. Research aimed at mitigating misinformation has so far focused on interventions at the user level5-8, with little emphasis on how the supply of misinformation can itself be countered. Here we show how online misinformation is largely financed by advertising, examine how financing misinformation affects the companies involved, and outline interventions for reducing the financing of misinformation. First, we find that advertising on websites that publish misinformation is pervasive for companies across several industries and is amplified by digital advertising platforms that algorithmically distribute advertising across the web. Using an information-provision experiment9, we find that companies that advertise on websites that publish misinformation can face substantial backlash from their consumers. To examine why misinformation continues to be monetized despite the potential backlash for the advertisers involved, we survey decision-makers at companies. We find that most decision-makers are unaware that their companies' advertising appears on misinformation websites but have a strong preference to avoid doing so. Moreover, those who are unaware and uncertain about their company's role in financing misinformation increase their demand for a platform-based solution to reduce monetizing misinformation when informed about how platforms amplify advertising placement on misinformation websites. We identify low-cost, scalable information-based interventions to reduce the financial incentive to misinform and counter the supply of misinformation online.

Development of a web-based patient decision aid for myopia laser correction method.

BACKGROUND: In the context of healthcare centered on the patient, Patient Decision Aids (PtDAs) acts as an essential instrument, promoting shared decision-making (SDM). Considering the prevalent occurrence of myopia, the objective of this study is to furnish exhaustive and easily comprehensible information to assist patients in making well-informed decisions about their options for myopia laser correction. METHOD: The research team developed a decision guide for myopia patients considering laser correction, aiming to facilitate informed decisions. The study followed the first four stages of the IPDAS process model: "scope/scoping," "design," "prototype development," and "alpha testing." Ten semi-structured interviews with patients (n = 6) and corneal specialist ophthalmologists (n = 4) were conducted to understand the challenges in selecting a laser correction method. Online meetings with 4 corneal specialists were held to discuss challenging cases. A comparison table of harms and benefits was created. The initial prototype was developed and uploaded on the internet portal. User feedback on software and text aspects was incorporated into the final web software, which was reviewed by a health education expert for user-friendliness and effectiveness. RESULT: Educational needs assessment revealed concerns such as pain, daily life activities, return to work, the potential need for glasses ('number return'), eye prescription stability, and possible complications. These shaped the decision aid tool's content. Expert consensus was achieved in several areas, with some items added or extended. In areas lacking consensus, comments were added for clarity. Five clients assessed the web app (PDAIN), rating it 46/50 in user-centricity, 47/50 in usability, and 45/50 in accuracy and reliability, totaling 138/150. Post-piloting, software errors were documented and rectified. During the trial phase, five myopic users interacted with the software, leading to modifications. User feedback indicated the tool effectively enhanced understanding and influenced decision-making. CONCLUSION: PDAIN, serves as a facilitative tool in the process of selecting a corneal laser correction method for myopic patients. It enabling Nearsighted patients to make informed decisions.

Analysis of rural broadband adoption dynamics: A theory-driven agent-based model.

Demand for broadband internet has far outpaced its availability. In addition, the "new normal" imposed by the COVID-19 pandemic has further disadvantaged unserved and underserved areas. To address this challenge, federal and state agencies are funding internet service providers (ISPs) to deploy broadband infrastructure in these areas. To support goals to provide broadband service to as many people as possible as quickly as possible, policymakers and ISPs may benefit from better tools to predict take rates and formulate effective strategies to increase the adoption of high-speed internet. However, there is typically insufficient data available to understand consumer attitudes. We propose using an agent-based model grounded in the Theory of Planned Behavior, a behavioral theory that explains the consumer's decision-making process. The model simulates residential broadband adoption by capturing the effect of market competition, broadband service attributes, and consumer characteristics. We demonstrate the effectiveness of this type of tool via a use case in Missouri to show how simulation results can inform predictions of broadband adoption. In the model, broadband take rates increase as the presence of existing internet users in the area increases and price decreases. With further development, this type of simulation can guide decision-making for infrastructure and digital literacy investment based on demand as well as support the design of market subsidies that aim to reduce the digital divide.

Threatening language detection from Urdu data with deep sequential model.

The Urdu language is spoken and written on different social media platforms like Twitter, WhatsApp, Facebook, and YouTube. However, due to the lack of Urdu Language Processing (ULP) libraries, it is quite challenging to identify threats from textual and sequential data on the social media provided in Urdu. Therefore, it is required to preprocess the Urdu data as efficiently as English by creating different stemming and data cleaning libraries for Urdu data. Different lexical and machine learning-based techniques are introduced in the literature, but all of these are limited to the unavailability of online Urdu vocabulary. This research has introduced Urdu language vocabulary, including a stop words list and a stemming dictionary to preprocess Urdu data as efficiently as English. This reduced the input size of the Urdu language sentences and removed redundant and noisy information. Finally, a deep sequential model based on Long Short-Term Memory (LSTM) units is trained on the efficiently preprocessed, evaluated, and tested. Our proposed methodology resulted in good prediction performance, i.e., an accuracy of 82%, which is greater than the existing methods.

Feasibility of Web-Based Single-Session Empowered Relief in Patients With Chronic Pain Taking Methadone or Buprenorphine: Protocol for a Single-Arm Trial.

BACKGROUND: Chronic pain affects tens of millions of US adults and continues to rise in prevalence. Nonpharmacologic behavioral pain treatments are greatly needed and yet are often inaccessible, particularly in settings where medication prescribing is prioritized. OBJECTIVE: This study aims to test the feasibility of a live-instructor, web-based 1-session pain relief skills class in an underserved and potentially at-risk population: people with chronic pain prescribed methadone or buprenorphine either solely for pain or for comorbid opioid use disorder (OUD). METHODS: This is a national, prospective, single-arm, uncontrolled feasibility trial. The trial is untethered from medical care; to enhance participants' willingness to join the study, no medical records or drug-monitoring records are accessed. At least 45 participants will be recruited from outpatient pain clinics and from an existing research database of individuals who have chronic pain and are taking methadone or buprenorphine. Patient-reported measures will be collected at 6 time points (baseline, immediately post treatment, 2 weeks, and months 1-3) via a web-based platform, paper, or phone formats to include individuals with limited internet or computer access and low literacy skills. At baseline, participants complete demographic questions and 13 study measures (Treatment Expectations, Body Pain Map, Medication Use, Pain Catastrophizing Scale [PCS], Patient-Reported Outcomes Measurement Information System [PROMIS] Measures, and Opioid Craving Scale). Immediately post treatment, a treatment satisfaction and acceptability measure is administered on a 0 (very dissatisfied) to 10 (completely satisfied) scale, with 3 of these items being the primary outcome (perceived usefulness, participant satisfaction, and likelihood of using the skills). At each remaining time point, the participants complete all study measures minus treatment expectations and satisfaction. Participants who do not have current OUD will be assessed for historical OUD, with presence of OUD (yes or no), and history of OUD (yes or no) reported separately. Feasibility threshold is set as an overall group treatment satisfaction rating of 8 of 10. In-depth qualitative interviews will be conducted with about 10 participants to obtain additional data on patient perceptions, satisfactions, needs, and wants. To assess preliminary efficacy, we will examine changes in pain catastrophizing, pain intensity, pain bothersomeness, sleep disturbance, pain interference, depression, anxiety, physical function, global impression of change, and opioid craving at 1 month post treatment. RESULTS: This project opened to enrollment in September 2021 and completed the recruitment in October 2023. The data collection was completed in February 2024. Results are expected to be published in late 2024. CONCLUSIONS: Results from this trial will inform the feasibility and preliminary efficacy of Empowered Relief in this population and will inform the design of a future randomized controlled trial testing web-based Empowered Relief in chronic pain and comorbid OUD. TRIAL REGISTRATION: ClinicalTrials.gov NCT05057988; https://clinicaltrials.gov/study/NCT05057988. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/53784.

Using machine learning algorithms and techniques for defining the impact of affective temperament types, content search and activities on the internet on the development of problematic internet use in adolescents' population.

Background: By using algorithms and Machine Learning - ML techniques, the aim of this research was to determine the impact of the following factors on the development of Problematic Internet Use (PIU): sociodemographic factors, the intensity of using the Internet, different contents accessed on the Internet by adolescents, adolescents' online activities, life habits and different affective temperament types. Methods: Sample included 2,113 adolescents. The following instruments were used: questionnaire about: socio-demographic characteristics, intensity of the Internet use, content categories and online activities on the Internet; Facebook (FB) usage and life habits; The Internet Use Disorder Scale (IUDS). Based on their scores on the scale, subjects were divided into two groups - with or without PIU; Temperament Evaluation of Memphis, Pisa, Paris, and San Diego scale for adolescents (A-TEMPS-A). Results: Various ML classification models on our data set were trained. Binary classification models were created (class-label attribute was PIU value). Models hyperparameters were optimized using grid search method and models were validated using k-fold cross-validation technique. Random forest was the model with the best overall results and the time spent on FB and the cyclothymic temperament were variables of highest importance for these model. We also applied the ML techniques Lasso and ElasticNet. The three most important variables for the development of PIU with both techniques were: cyclothymic temperament, the longer use of the Internet and the desire to use the Internet more than at present time. Group of variables having a protective effect (regarding the prevention of the development of PIU) was found with both techniques. The three most important were: achievement, search for contents related to art and culture and hyperthymic temperament. Next, 34 important variables that explain 0.76% of variance were detected using the genetic algorithms. Finally, the binary classification model (with or without PIU) with the best characteristics was trained using artificial neural network. Conclusion: Variables related to the temporal determinants of Internet usage, cyclothymic temperament, the desire for increased Internet usage, anxious and irritable temperament, on line gaming, pornography, and some variables related to FB usage consistently appear as important variables for the development of PIU.

Evaluating the web as a source of information for patients with chest wall deformities: insights into engagement and disparities.

BACKGROUND: Recent data highlight the internet's pivotal role as the primary information source for patients. In this study, we emulate a patient's/caregiver's quest for online information concerning chest deformities and assess the quality of available information. METHODS: We conducted an internet search using combination of the terms "pectus excavatum," "pectus excavatum surgery," "funnel chest," "pectus excavatum repair" and identified the first 100 relevant websites from the three most popular search engines: Google, Yahoo, and Bing. These websites were evaluated using the modified Ensuring Quality Information for Patients (EQIP) instrument. RESULTS: Of the 300 websites generated, 140 (46.7%) were included in our evaluation after elimination of duplicates, non-English websites, and those targeting medical professionals. The EQIP scores in the final sample ranged from 8 to 32/36, with a median score of 22. Most of the evaluated websites (32.8%) originated from hospitals, yet none met all 36 EQIP criteria. DISCUSSION: None of the evaluated websites pertaining to pectus excavatum achieved a flawless "content quality" score. The diverse array of websites potentially complicates patients' efforts to navigate toward high-quality resources. Barriers in accessing high-quality online patient information may contribute to disparities in referral, patient engagement, treatment satisfaction, and overall quality of life. LEVEL OF EVIDENCE: IV.

Research on the influencing factors of adult learners' intent to use online education platforms based on expectation confirmation theory.

This study addresses the understanding gap concerning the factors that influence the continuous learning intention of adult learners on online education platforms. The uniqueness and significance of this study stem from its dual focus on both platform features, such as service quality, and course features, including perceived interactivity and added value, aspects often overlooked in previous research. Rooted in Expectation Confirmation Theory, the study constructs a comprehensive model to shed light on the complex interplay of these factors. Empirical evidence collected from a survey of 1592 adult learners robustly validates the effectiveness of this model. The findings of the study reveal that platform service quality, perceived interactivity, and perceived added value significantly amplify adult learners' expectation confirmation and perceived usefulness. These elements subsequently enhance learner satisfaction, fostering their ongoing intention to use online education platforms. These insights offer practical guidance for online education providers, emphasizing the necessity to enhance platform service quality and course features to meet adult learners' expectations and perceived usefulness. The study provides valuable perspectives for devising strategies to boost user satisfaction and stimulate continuous usage intention among adult learners in the intensely competitive online education market. This study enriches the literature by uncovering the relationships among platform features, course features, expectation confirmation, perceived usefulness, and continuous usage intention. By proposing a comprehensive model, this study provides a novel theoretical basis for understanding how platform and course features impact adult learners' ongoing intention to use online education platforms, thereby aiding the evolution and refinement of relevant theories.

Effects of mobile Internet use on the health of middle-aged and older adults: evidences from China health and retirement longitudinal study.

The rapid development of digital technology has radically changed people's lives. Simultaneously, as the population is rapidly aging, academic research is focusing on the use of Internet technology to improve middle-aged and older people's health, particularly owing to the popularity of mobile networks, which has further increased the population's accessibility to the Internet. However, related studies have not yet reached a consensus. Herein, empirical analysis of the influence of mobile Internet use on the subjective health and chronic disease status of individuals in their Middle Ages and above was conducted utilizing ordered logit, propensity score matching (PSM), and ordered probit models with data from the 2020 China Health and Retirement Longitudinal Study. The study aimed to provide a theoretical basis and reference for exploring technological advances to empower the development of a healthy Chinese population and to advance the process of healthy aging. The health of middle-aged and older adults mobile Internet users was greatly improved, according to our findings. Further, the use of mobile Internet by these persons resulted in improvements to both their self-assessed health and the state of their chronic diseases. As per the findings of the heterogeneity analysis, the impact of mobile Internet use was shown to be more pronounced on the well-being of middle-aged persons aged 45-60 years compared to those aged ≥ 60 years. Further, the endogeneity test revealed that the PSM model could better eliminate bias in sample selection. The results suggest that the estimates are more robust after eliminating endogeneity, and that failure to disentangle sample selectivity bias would overestimate not only the facilitating effect of mobile Internet use on the self-assessed health impacts of middle-aged and older adults, but also the ameliorating effect of mobile Internet use on the chronic diseases of middle-aged and older adults. The results of the mechanistic analysis suggest that social engagement is an important mediating mechanism between mobile Internet use and the health of middle-aged and older adults. This implies that mobile Internet use increases opportunities for social participation among middle-aged and older adults, thereby improving their health.

MechanoProDB: a web-based database for exploring the mechanical properties of proteins.

The mechanical stability of proteins is crucial for biological processes. To understand the mechanical functions of proteins, it is important to know the protein structure and mechanical properties. Protein mechanics is usually investigated through force spectroscopy experiments and simulations that probe the forces required to unfold the protein of interest. While there is a wealth of data in the literature on force spectroscopy experiments and steered molecular dynamics simulations of forced protein unfolding, this information is spread and difficult to access by non-experts. Here, we introduce MechanoProDB, a novel web-based database resource for collecting and mining data obtained from experimental and computational works. MechanoProDB provides a curated repository for a wide range of proteins, including muscle proteins, adhesion molecules and membrane proteins. The database incorporates relevant parameters that provide insights into the mechanical stability of proteins and their conformational stability such as the unfolding forces, energy landscape parameters and contour lengths of unfolding steps. Additionally, it provides intuitive annotations of the unfolding pathways of each protein, allowing users to explore the individual steps during mechanical unfolding. The user-friendly interface of MechanoProDB allows researchers to efficiently navigate, search and download data pertaining to specific protein folds or experimental conditions. Users can visualize protein structures using interactive tools integrated within the database, such as Mol*, and plot available data through integrated plotting tools. To ensure data quality and reliability, we have carefully manually verified and curated the data currently available on MechanoProDB. Furthermore, the database also features an interface that enables users to contribute new data and annotations, promoting community-driven comprehensiveness. The freely available MechanoProDB aims to streamline and accelerate research in the field of mechanobiology and biophysics by offering a unique platform for data sharing and analysis. MechanoProDB is freely available at https://mechanoprodb.ibdm.univ-amu.fr.

Measuring data rot: An analysis of the continued availability of shared data from a Single University.

To determine where data is shared and what data is no longer available, this study analyzed data shared by researchers at a single university. 2166 supplemental data links were harvested from the university's institutional repository and web scraped using R. All links that failed to scrape or could not be tested algorithmically were tested for availability by hand. Trends in data availability by link type, age of publication, and data source were examined for patterns. Results show that researchers shared data in hundreds of places. About two-thirds of links to shared data were in the form of URLs and one-third were DOIs, with several FTP links and links directly to files. A surprising 13.4% of shared URL links pointed to a website homepage rather than a specific record on a website. After testing, 5.4% the 2166 supplemental data links were found to be no longer available. DOIs were the type of shared link that was least likely to disappear with a 1.7% loss, with URL loss at 5.9% averaged over time. Links from older publications were more likely to be unavailable, with a data disappearance rate estimated at 2.6% per year, as well as links to data hosted on journal websites. The results support best practice guidance to share data in a data repository using a permanent identifier.

Reading dies in complexity: Online news consumers prefer simple writing.

Over 30,000 field experiments with The Washington Post and Upworthy showed that readers prefer simpler headlines (e.g., more common words and more readable writing) over more complex ones. A follow-up mechanism experiment showed that readers from the general public paid more attention to, and processed more deeply, the simpler headlines compared to the complex headlines. That is, a signal detection study suggested readers were guided by a simpler-writing heuristic, such that they skipped over relatively complex headlines to focus their attention on the simpler headlines. Notably, a sample of professional writers, including journalists, did not show this pattern, suggesting that those writing the news may read it differently from those consuming it. Simplifying writing can help news outlets compete in the competitive online attention economy, and simple language can make news more approachable to online readers.

Misunderstanding the harms of online misinformation.

The controversy over online misinformation and social media has opened a gap between public discourse and scientific research. Public intellectuals and journalists frequently make sweeping claims about the effects of exposure to false content online that are inconsistent with much of the current empirical evidence. Here we identify three common misperceptions: that average exposure to problematic content is high, that algorithms are largely responsible for this exposure and that social media is a primary cause of broader social problems such as polarization. In our review of behavioural science research on online misinformation, we document a pattern of low exposure to false and inflammatory content that is concentrated among a narrow fringe with strong motivations to seek out such information. In response, we recommend holding platforms accountable for facilitating exposure to false and extreme content in the tails of the distribution, where consumption is highest and the risk of real-world harm is greatest. We also call for increased platform transparency, including collaborations with outside researchers, to better evaluate the effects of online misinformation and the most effective responses to it. Taking these steps is especially important outside the USA and Western Europe, where research and data are scant and harms may be more severe.