The present longitudinal study, for 12 years, followed a group of young adults, examining (1) whether/how victimization in childhood increased the likelihood of heavy drinking; (2) whether depression mediated the strain–heavy drinking relationship; and (3) whether/how relationships among strain, depression, and heavy drinking differed across two gender groups. Data came from the National Longitudinal Survey of Youth 1997 cohort, dating 2004–2015 . We linked consumption of 5+ drinks (during the month prior) to four discrete measures of violent victimization, to one measure of stressful events, and to depression. We needed to consider repeat measures of the same variables over time, so we used generalized estimating equations (GEE) to analyze data. Depression was found to increase heavy drinking uniformly. Empirical evidence confirmed that in the strain–heavy drinking relationship, depression plays a minor mediating role. Gender moderated heavy drinking’s associations. Specifically, bullying in childhood raised risk for female respondents. The current strain was associated with a higher risk of heavy drinking among male respondents. Childhood victimization, as well as current life stress, play an important role in depression and heavy drinking. Future research should focus on the development of specific, targeted care to reduce heavy drinking’s harm and promote equity among Americans. Most often defined as four or more drinks (for women) and five or more drinks (for men) in a relatively short window of time , heavy dRecent research into heavy drinking behavior has elucidated nuances related to gender and race and still other statuses, often operating in tandem, that further refines our understanding of the complex interplay of social structure and lived experience. Heavy drinking occurs among all racial groups, with a general understanding that it is more common among male and White adolescents and young adults ,16. TherThree theoretical frameworks, the self-medication hypothesis, general strain theory, and the stress-process model, each posit overlapping elements of a stress and substance use coping relationship. Each shares a central tenet that stressful events in life can affect levels of substance use, although pathways differ among each. Rather than being seen as entirely competing perspectives on explaining heavy drinking behavior among adolescents, instead, these perspectives together help frame relevant predictors of adolescent heavy drinking, such as objective  and subjective stress/strain , mental health symptomatology, and a presence or lack of social support.Khantzian’s ,29 self-Agnew’s general strain theory ,36,37 waPearlin’s ,43 stresVictimization’s relationship to increased risk of heavy drinking is documented in the literature ,45,46, mRich empirical evidence associates stressful and straining events with heavy drinking as an outcome in the short- and long-term ,55,57,58direct forms of childhood victimization, being bullied, and one vicarious form of childhood victimization, witnessing the shooting of a person, functioned as a mechanism linking victimization and adult heavy drinking and depression. (2) We further hypothesized depression mediates any relationship between childhood victimization and heavy drinking as is outlined in the criminological and mental health frameworks of stress-process, self-medication, and general strain. (3) Because we posited that a victim’s age when victimized might be associated with differential impacts of victimization, we measured both of our childhood victimization variables twice: when respondents were preadolescents age 11 or younger and when they were 12–18. Moreover, we assigned a time factor to childhood victimization, namely, before or after age 12 (but no later than age 18). (4) We could find limited empirical evidence of gender’s possible association with heavy drinking among individuals who experienced direct or vicarious victimization during preadolescence or adolescence [The present study aimed at identifying a social mechanism that might connect childhood victimization to adulthood heavy drinking and depression using a framework including elements of the self-medication hypothesis, general strain theory, and the stress-process model. These perspectives suggest a general trajectory of higher risk of engaging in substance use, including heavy drinking, in the presence of current or former stressful events or strain, mental health symptomatology, and a lack of social support. Our prospective longitudinal design and multiple measures of respondents’ heavy drinking as they aged allowed the study to track the impact of childhood victimization across a decade. The data collected in 2004, 2006, 2008, 2010, and 2015 described respondents as they reached ages anywhere from 19 to 36. That data meant we could take time into consideration as we observed what happened to effects of childhood victimization as adulthood progressed . We hypothesized (1) lescence ,65 warraThe National Longitudinal Survey of Youth 1997 (NLSY97), funded by the Bureau of Labor Statistics, provided data for the present longitudinal study. The Ohio State University had begun the survey as an annual data collection, making it biennial after 2011. The survey sought to capture individuals’ life experiences, notably, those involving family, crime, health, and substance abuse, over time. Its national representative sample comprised civilian, non-institutionalized people born between 1980 and 1984, who responded to items posed by an interviewer using computer-assisted personal interview (CAPI) technology, either in person or by phone. Respondents chose to be interviewed in English or in Spanish. A supplemental survey oversampling Black and Hispanic/Latino youth (born 1980–1984) was also included in the full sample of NLSY97. In 1997, the researchers interviewed 8984 persons age 12–18. Other descriptions of NLSY97 are included in previous research ,66.Our outcome variable, heavy drinking in the past month, was measured during 5 NLSY97 survey years between 2004 and 2015 (inclusive). We created for each respondent a longitudinal record that linked data from the 5 survey years. From each such record, we derived a number of person-waves, which provided our units of analysis; we applied the discrete-time method to analyze the data . Within We measured the time-varying outcome variable, heavy drinking in the past month, for the NLSY97 survey years 2004, 2006, 2008, 2010, and 2015. Data from only these years made up the longitudinal records in our final sample, via which we measured respondent likelihood of recent heavy drinking. The measure reflected NLSY97 respondents’ reports of how often , in the 30 days preceding interview, they had consumed 5 or more drinks. A 1, indicating heavy drinking, was assigned to a respondent reporting consumption of 5 or more drinks on at least 1 day of the 30; the 0 assigned to respondents not reporting such consumption constituted the reference.We employed 12 independent variables: gender, race/ethnicity, depression, recent stressful events, income-to-poverty ratio, education, age, marital status, parent’s education, bullying victimization before age 12, bullying victimization at age 12–18, seeing the shooting of a person before age 12, and seeing the shooting of a person at age 12–18. Level of depression, income-to-poverty ratio, education, age, and marital status were treated as time-varying variables; all others were considered time-invariant. Depression was measured with a 5-item index asking how often, in the 30 days preceding survey, a respondent had felt nervous; calm or peaceful; down or blue; happy; and depressed. The index’s offered responses ranged from 1  to 4 (none of the time); reversed coding allowed higher index scores to indicate higher levels of depression. The index’s overall reliability is indicated by alpha scores ranging from 0.78–0.81 across the 5 survey years.We used an index of 7 types of recent stressful events to describe respondents’ experience over the previous 5 years of data collection in 2002, 2007, and 2013. The 7 included death of a close relative; becoming the victim of a violent crime; hospitalization of a member of one’s household; incarceration of a member of one’s household; a household member’s becoming unemployed; one’s parents divorcing; and one’s family becoming homeless. Index scores ranged from 0 to 7; a relatively high number indicated the experience of relatively numerous stressful events.Concerning education, the variable high school completion indicated possession of a high school diploma or GED, and the variable college completion indicated possession of an associate or baccalaureate degree; receipt of neither diploma nor degree provided the reference. At initial interviews conducted in 1997, NLSY97 respondents were ages 12–18. We used respondent age in 1997 to construct our variable age, which we measured in each of the 5 selected survey years. For each respondent, we recorded a continuous measure of household income-to-poverty ratio for the 12 months preceding each of the 5 interviews. For each, we measured marital status dichotomously, a 1 indicating being married, 0 otherwise.We treated as time-invariant and measured dichotomously the variables bullying victimization before age 12, bullying victimization age 12–18, seeing the shooting of a person before age 12, and seeing the shooting of a person age 12–18. We assigned a 1 to indicate the presence of any of these forms of victimization, a 0 to indicate absence (the reference). We measured gender dichotomously, 1 indicating male, 0 female. We employed two dichotomous race/ethnicity measures, with 1 indicating a respondent self-reporting Hispanic ethnicity or non-Hispanic Black ethnicity and 0 indicating self-report of non-Hispanic White ethnicity. Parent’s education was a time-invariant continuous variable stating the highest level of education attained by parents or, where mother’s level differed from father’s by that parent having the most formal education. Offered responses for this measure ranged from 0 (no education) to 7 . Additionally, we employed dichotomous time factors describing each interview wave represented in our data: 2006, 2008, 2010, and 2015 (the 2004 survey year provided the reference).In light of our use of repeated measurements, we employed generalized estimating equations (GEE) to analyze the longitudinal data, estimating autocorrelations and autoregressive correlations using STATA  . The anaConcerning our heavy drinking outcome, data from 6303 respondents  drove the results of the final multivariate analysis. Of the 6303, self-reported White respondents constituted 51.4%, self-reported Black respondents were 25.3%, and self-reported Hispanic respondents were 20.6%. For the sample as a whole, bullying victimization was more pervasive than the vicarious victimization of seeing a shooting: While 20.1% reported being bullied before age 12 and 15.2% reported being bullied at 12–18 years of age, just 10.5% reported seeing a shooting before age 12, and 9.7% reported seeing a shooting at between 12 and 18 years of age. Compared to females, males in our study were significantly more likely to have been subjected to bullying victimization before age 12, as well as to seeing a shooting both before age 12 and between the ages of 12 and 18.t-tests, we identified significant differences across gender groups for, respectively, the categorical and continuous variables. Across the 5 survey years, we considered, 44.4% of male respondents and 25.6% of female respondents reported consuming at least 5 drinks on at least one day in the preceding 30 days. For each independent variable except age and survey year, we observed statistically significant male–female differences.Female respondents in our study were significantly more likely to report heavy drinking if they experienced bullying victimization before age 12; if they were White (rather than Black); if they held an associate’s or baccalaureate degree (versus lacked a high school diploma); if their income was relatively high; if they were unmarried; or if they had a relatively well-educated parent(s). Model 2 indicated that for the females in our sample, reporting a relatively high level of depression was significantly associated with heavy drinking. Comparing Model 1 to Model 2, most coefficients did not exhibit substantial change; an exception was a slight change for the variable bullying victimization before 12. We obtained significant Wald chi-squares for all four of our models, signifying that the four represented significant improvements over the null models in the explanation of heavy drinking.Using the procedure detailed under “Data Analysis” above, we evaluated gender’s moderating role in heavy drinking’s associations with the included independent variables. Five significant gender differences demonstrate that gender moderated heavy drinking’s associations. First, bullying victimization before age 12 reduced heavy drinking among males in our study, but it increased heavy drinking among females, according to our data analysis results. Second, Black respondents’ relatively lower likelihood of heavy drinking, compared to Whites, was more pronounced among females compared to males. Third, in comparison with White respondents, we found Hispanic males to be at lower risk of heavy drinking versus White males, but heavy drinking risk for Hispanic females was about the same as that for White females. Fourth, among all females in our study, having an associate’s or baccalaureate degree was associated with a relatively high likelihood of heavy drinking, to a degree surpassing the association found for males in the study. Finally, our study found heavy drinking to be much less likely among married respondents of either gender versus unmarried respondents of either gender, and this marriage–heavy drinking relationship was much more pronounced.Plainly, heavy drinking is a risky health behavior associated with many negative outcomes across the lifespan ranging from structural changes in the brain to criminal-legal interventions in adolescence and adulthood ,45,69. OFirst, we acknowledge the outsized role played by victimization in patterning heavy drinking outcomes. Our study measured both direct and indirect victimization occurring in childhood and in adolescence, a total of four possible victimization measures early in the lifespan and their effects over time. This is an improvement to cross-sectional literature with more static measurements of victimization. With the control of more proximate traumatic and stressful events as indicated theoretically ,70, the Second, our findings contribute to the theoretical knowledge of heavy drinking behavior. While the theoretical perspectives guiding this study have differences, a core of stressful or straining events potentially leading to coping through substance use in the absence of social support or other protective factors provides opportunities for interpretation across health and criminological disciplines. As the self-medication hypothesis suggests, substance use behavior may be a feature of coping , and resThe final theoretical framework, the stress-process model, has a deeper understanding of the stressors and symptomology associated with negative coping ,82,83. AThird, the interpolation of gender alongside other social statuses enriches our understanding of the nuanced social structure elements leading to or protecting against heavy drinking as an outcome . Our finThe picture becomes more complex when we interpolate race and gender, however. Our findings indicate that African American respondents are at significantly lower risk than their White counterparts, and Hispanic men are also at a significantly lower risk of heavy drinking. The interaction effect of race and gender is relevant, as the protective effect of race was weaker for Black males than Black females. The protective effect, however, is not found for Hispanic females, and again the results indicate an interactive effect among these two critical social status variables. The literature acknowledges African Americans generally have more risk factors for heavy drinking with lower rates of heavy drinking  than thoFourth, prevention efforts working at multiple levels must include modifying norms and beliefs about drinking behavior , includiThe current study extends the literature on victimization’s long-term consequences, with direct victimization in childhood exerting an effect on heavy drinking later in the lifespan among female respondents and recent stressful events associated with heavy drinking among male respondents. The results suggest that gender remains an important variable to examine in analyses of substance use and victimization-related sequelae. Specific differences among gender groups in our findings suggest early childhood adverse experiences are powerful predictors of later heavy drinking for girls and women, while they are also more protected by their own education and parental education against heavy drinking. This suggests an educational pipeline generating capital for women, but not men, in a way that protects them from heavy drinking. Race, socioeconomic status, and other important statuses, alongside gender, may play substantial roles in structuring heavy drinking outcomes; these should be investigated thoroughly in order to promote health equity among Americans. Our study demonstrated several strengths, primarily related to the multiple measures of victimization, occurring at different points in the lifespan, and these effects across time. We were further able to draw from both public health and criminology theory to better explain the roles of victimization, depression, and heavy drinking behavior and how it differs for male and female gender groups. We also unearthed several gender-specific differences in the risk and protective factors associated with heavy drinking, which can enrich the already-deployed programming, helping make it more relevant to community audiences.Our study had a few limitations to note. First, our measures of victimization, both direct and indirect, are imperfect. Victimization, particularly vicarious or indirect victimization, is not a well-studied concept empirically, and large, nationally representative surveys have only a few indicators for use. The inclusion of additional indicators in these large surveys would help better elucidate these further. Next, while longitudinal, limitations in the data collection did not allow us a perfect year-for-year measurement of each indicator. While the gaps in collection years are small, additional years of data may paint a clearer picture of the link between victimization in childhood and later heavy drinking. Next, a host of negative affective states are relevant to discussions of criminal coping, including substance use. While depression has demonstrated some support, other affective states such as anxiety or anger may also be leading to heavy drinking behavior and are in need of substantial investigation to better plan successful programming that will reach broad audiences and reduce the harm of heavy drinking.


Since the COVID-19 pandemic onset, telemedicine has increased exponentially across numerous outpatient departments and specialties. Qualitative studies examining clinician telemedicine perspectives during the pandemic identified challenges with physical examination, workflow concerns, burnout, and reduced personal connection with patients. However, these studies only included a relatively small number of physicians or were limited to a single specialty, and few assessed perspectives on integrating trainees into workflows, an important area to address to support the clinical learning environment. As telemedicine use continues, it is necessary to understand a range of clinician perspectives.This study aims to survey pediatric and adult medicine clinicians at the University of Chicago Medical Center to understand their telemedicine benefits and barriers, workflow impacts, and training and support needs.In July 2020, we conducted an observational cross-sectional study of University of Chicago Medical Center faculty and advanced practice providers in the Department of Medicine (DOM) and Department of Pediatrics (DOP).P=.02), with no differences in the number of half-days per week providing direct outpatient care or supervising trainees. Further, clinicians who conducted a higher percentage of their telemedicine by video were less likely to feel overwhelmed (P=.02), with no difference in reported burnout. Female clinicians were “more overwhelmed” with video visits compared to males . Clinicians 50 years or older were “less overwhelmed” than those younger than 50 years . Those who received more video visit training modalities  were less likely to feel overwhelmed by the conversion to video visits (P=.007) or burnt out (P=.009). In addition, those reporting a higher ability to technically navigate a video visit were also less likely to feel overwhelmed by video visits (P=.02) or burnt out (P=.001). The top telemedicine barriers were patient-related: lack of technology access, lack of skill, and reluctance. Training needs to be focused on integrating learners into workflows. Open-ended responses highlighted a need for increased support staff. Overall, more than half “enjoyed conducting video visits”  and wanted to continue using video visits in the future .The overall response rate was 39% ; most respondents were physicians . One-third took longer to prepare for  and conduct  video visits compared to in-person visits. Male clinicians reported conducting a higher percentage of telemedicine visits by video than their female counterparts (Despite positive telemedicine experiences, more support to facilitate video visits for patients and clinicians is needed. Further, clinicians need additional training on trainee education and integration into workflows. Further work is needed to better understand why gender and age differences exist. In conclusion, interventions to address clinician and patient barriers, and enhance clinician training are needed to support telemedicine’s durability. Telemedicine uses electronic communications and software, like video and telephone visits, to deliver remote clinical services to patients . PositivSince the COVID-19 pandemic onset, telemedicine has increased exponentially across numerous outpatient departments and specialties ,7. CliniOur study aims to survey pediatric and adult medicine clinicians at the University of Chicago Medical Center (UCMC) to understand perspectives on telemedicine benefits and barriers, workflow impacts, and training and support needs. Capturing clinician perceptions in various outpatient departments and specialties is critical to improving the clinician and patient telemedicine experience and to ensuring successful integration and durability of virtual encounters .UCMC is a large urban academic health system and affiliated care network that provides tertiary care in the South Side of Chicago. As background, telemedicine was used for outpatient primary care at UCMC in the Department of Medicine (DOM) and Department of Pediatrics (DOP) whenever possible beginning March 15, 2020, to provide safe and socially distanced care. The total number of UCMC ambulatory visits at this time dropped substantially, with ambulatory visits falling to 23% of visit volumes when compared to the same week in the fiscal year (FY) 2019 . After aWe developed a 54-question survey  to captuIn July 2020, 517 UCMC physicians and advanced practice providers  in the DOM (n=325) and DOP (n=192) were invited via email to participate in the survey. The email was sent by UCMC leadership and the study investigators (MAA and WWL). Data was collected and managed using REDcap  . The surt tests assessed differences in outcomes among groups of interest. Ordinal logistic regressions examined associations between ordinal outcomes and explanatory variables of interest. Significance was defined as a two-sided P value less than .05.REDcap data was exported to Stata 16 (Stata Corp)  for statOpen-ended question responses were collectively pooled and read. Content analysis identified unique response themes, and representative quotations were identified to build a picture of clinicians’ collective experiences and video visit needs .P<.001). Most clinicians were aged 30 to 59 years , and 65% (130/200) were female. More female clinicians were also younger . There were no significant differences by department (DOM vs DOP) or clinician age in terms of the number of half-days per week spent providing direct outpatient care, supervising trainees, or the percentage of telemedicine visits they personally conducted by video in the past week (P=.02), there were no significant differences in number of half-days per week spent providing direct outpatient care or supervising trainees.The overall response rate was 39% . Respondent demographics are displayed in ast week . AlthougP<.001). There were no differences in training across gender, age, or departments.Most clinicians received some video visit training on technical issues , and fewer received telemedicine communication practice training  and telephone visits .P=.003), and the ability to share decisions with patients  was better over video compared to telephone. DOP clinicians were also more likely to report that their ability to share decisions with patients was as good or better via video compared to in-person visits .Despite the virtual nature of the visit, most of the 200 clinicians  felt they could promote shared decision making during video visits as well as they could in in-person visits. Half  felt they could better promote shared decision making during video visits compared to telephone visits. Just over half  felt they could personally connect as well or better with patients during video visits compared to in-person visits, with 66% (n=131) reporting they connected better with patients over video than over telephone. Although there were no differences across gender or age, DOP clinicians were more likely to report personal connection  and telephone visits . Most  felt patient trust in their diagnosis over video was similar compared to in person, whereas about half  felt patient trust over video was similar compared to telephone. Just over one-third  felt patient trust was better over video compared to telephone. Finally, nearly two-thirds agreed that being able to visualize a patient’s home environment  and being able to have patient companions join the video visit  added valuable insight into their patients’ lives. There were no differences across gender, age, or departments in these areas.The top three most commonly cited barriers from the 200 clinicians to conducting video visits were not clinician-specific barriers but rather patient related, including patient lack of technical knowledge , lack of patient access to necessary technology for a video visit , and patient reluctance to have a video visit  during telehealth visits cited “concerns about integrating them into video visit workflows” and “uncertainty about documentation rules” as the top two barriers both when working with residents and fellows  and medical students  but not by gender . Of note, clinicians younger than 50 years  also reported higher levels of burnout compared to those 50 years or older .In the survey, participants were asked to self-report their perceived level of burnout. Overall, 81 clinicians reported burnout, with significant differences between departments . Although there was no overall difference between clinician age and feeling more overwhelmed with video visits, clinicians 50 years or older felt significantly less overwhelmed  than those younger than 50 years . Differences in feeling overwhelmed by video visits were not seen across departments.Participants were also asked whether converting in-person visits to video made them feel less, similarly, or more overwhelmed. Overall, only 28% (n=56) of the 200 clinicians felt more overwhelmed, with nearly half of clinicians  feeling similarly and 27% (n=53) feeling less overwhelmed. Notably, a higher proportion of female clinicians  than males  reported feeling more overwhelmed (P=.007) or burnt out (P=.009). Those reporting a higher ability to technically navigate a video visit were also less likely to feel overwhelmed by video visits (P=.02) or burnt out (P=.001). Further, clinicians who conducted a higher percentage of their telemedicine by video were less likely to feel overwhelmed (P=.02); however, there was no difference in reported burnout. There were no gender, age, or departmental differences in training or self-reported ability. Interestingly, there were also no significant differences in feeling burnt out or overwhelmed by the switch to video visits and the number of either personal or supervising teaching attending clinic sessions per week or by the type of virtual visits their trainees had .With respect to training, clinicians who received a greater number of video visit training modalities  were less likely to feel overwhelmed by the conversion to video visits (P<.001). There were no differences across gender, age, or department in these areas.Overall, more than half of the 200 clinicians  enjoyed conducting video visits, and 69% (n=137) reported “the benefits of video visits outweighed the negatives.” Most wanted to continue using video visits , which was higher than the fraction of clinicians  who wanted to continue using telephone visits  occurred , with the next largest group citing patient service representatives  or medical assistants  as their primary support resource. The top three video visit training needs reported were guidance on performing an exam , billing , and technical aspects  in As virtual visits continue to comprise an important and increasingly prevalent form of health care delivery, it is important to understand the clinician experience and how they perceive video and telephone visits compared to in-person visits. Most clinicians enjoyed conducting video visits and felt that the connection they had with patients was similar to in-person visits. However, it is important to note that one-third of clinicians reported video visits took longer to prepare, conduct, and document compared to in-person visits. Prior to the COVID-19 pandemic, most of our clinicians had never conducted virtual visits. The overnight conversion to telemedicine required rapid adjustments to a new technology and the creation of new workflows. Further, with in-person visits, a medical assistant often starts the visit for the clinician, documenting intake questions and administering screening tools such as depression assessments, gathering background information such as interim hospitalizations and emergency room visits, and verifying information such as their medications, preferred pharmacy, and allergies.roomed just like in a regular visit, and the lack of external visit support may have led to increased clinician burden and therefore increased time to prepare and document virtual visits. Additionally, we found clinicians who had more video visit training and higher self-rated technical knowledge were less likely to feel overwhelmed or burnt out. The longer time needed to prepare and document virtual visits could be due to the need for more training and increased familiarity with technology. As clinicians become more comfortable with virtual visits and new clinical support is implemented, providers should be resurveyed on whether they feel that telehealth visits take more time and what, if any, training needs they continue to have.At the start of the transition to telemedicine, most medical assistants were not assisting clinicians with these visit duties, and the burden of that additional workload and documentation fell to clinicians. Having conducted our study, the need to provide clinician visit support in the virtual setting much like that of the in-person setting became clear. Many clinicians stated that they needed more help supporting virtual visits so that patients could be Prior to the pandemic, electronic documentation demands on clinicians were already high with clinicians spending more than one half of their workday, nearly 6 hours, interacting with the electronic health record (EHR) during and after clinic hours, 1 to 2 hours of which was during their personal time each night , an actiDespite one-third of clinicians reporting it took longer to prepare, conduct, and document telemedicine visits, we found no significant differences in burnout or feeling overwhelmed by the conversion from in-person to video and clinicians’ personal or teaching attending workload. This may be partly due to the fact that our survey period was relatively early on in the course of the pandemic, and although data showing burnout is increasing , this maFurther, COVID-19 significantly increased the challenges of work-life balance for clinicians with children . School-Additionally, the finding that clinicians who had more video visit training and, perhaps consequently, a higher self-rated technical facility with video visits were less likely to feel overwhelmed or burnt out by transitioning to video visits underscores the importance of clinician familiarity and efficiency with technology as a key driver in their experience. Studies examining EHR use support this finding and suggest that enhanced education and training can improve clinician technical proficiency, self-reported efficiency, and satisfaction, which could eventually have an effect on burnout .proportion of telemedicine visits conducted by video, it is possible that these individuals also conducted a higher amount of visits by video by the time they took our survey. Perhaps this group of clinicians had become more adept at conducting video visits and therefore felt less overwhelmed moving their clinics to virtual because of their skill, as previously mentioned. However, it also may be that video visits are for some reason less stressful to conduct compared to telephone visits, perhaps because communication and assessment is easier with the added visual benefit of video. That said, further study in this area is needed.We also found that clinicians who conducted a higher percentage of their telemedicine visits by video were less overwhelmed. Although we know this variable refers to the Additionally, we saw differences between groups in regard to the burden of telemedicine and potential for subsequent burnout. For instance, women reported being more overwhelmed with video visits compared to men. This may be attributed to females conducting a lower percentage of telemedicine visits by video, which was shown to be associated with feeling overwhelmed, as previously mentioned. It is unclear why female clinicians were less likely to conduct video visits despite similar clinical and teaching workloads; however, given female clinicians were younger and thus more junior, they may have opted for fewer video and more telephone sessions. Further, prior to COVID-19, female physicians spent significantly more time on household activities and childcare than their male counterparts, which was likely exacerbated by the closing of schools, day cares, etc during the pandemic -28. TherOur study also found that older clinicians (>50 years) reported being less overwhelmed than younger clinicians with the addition of video visits to their practice, despite having a similar personal and teaching clinic workload as their younger counterparts, and that younger clinicians had higher burnout at baseline compared to older clinicians. Of note, other studies have similarly found older clinicians generally experienced greater well-being and lower levels of stress compared to younger clinicians during the pandemic ,32. AlthAlthough our surveyed clinicians found they could still promote patient-centered care through virtual visits, we found notable differences between pediatric and adult medicine clinicians in these areas. In particular, pediatric clinicians found telephone visits less beneficial for connecting with and making shared decisions with their patients. Pediatric patients are often not participating verbally in the visit themselves, but rather the child’s parent or guardian; therefore, the added benefit of visually observing and connecting with the child through the camera may be more important on the pediatric side. That said, pediatric clinicians reported higher confidence compared to adult clinicians that they could share decision making with their patients over video compared to in-person visits. This reinforces the idea that, although virtual visits are still useful for pediatric patients, video visits may allow for more patient-centered techniques compared to telephone visits.Although clinicians recognized the need for ongoing training for themselves, the top three telemedicine barriers clinicians encountered were not clinician-centered barriers such as inadequate staff support. Instead, the top three barriers identified were their patient’s barriers: access to technology, technical literacy and knowledge, and overall reluctance toward video visits. These findings have several important implications for patient care for telemedicine to be a successful means of providing care for all patients, not only technically savvy or resource-rich patients. Our findings underscore the need to better understand and minimize potential disparities with respect to the digital divide or the gap between persons who have and do not have access to new forms of information technology . An earlThis finding is particularly troubling, as telemedicine was a lifeline for many to access needed clinical care during the pandemic. Telemedicine exposed inequities related to the digital divide for many of our South Side Chicago patients, and in response to this study and the knowledge that our clinician experience and success with telemedicine was critically dependent on our patient’s ability to access and use technology, we developed patient-facing materials to help patients prepare for and navigate virtual visits, including high-tech  and lower-tech  means. We have also started a qualitative study in response to understand our patients’ telemedicine experiences  and willThere are several important limitations of our study to note. First, our study is limited to one institution, situated in a largely underserved area. To increase generalizability, our survey was cross departmental, including representation from our affiliate care partners who practice in nonacademic and community-based settings. Additionally, it is possible that clinician responses were influenced by the specific telemedicine platform used at UCMC; other organizations may have different experiences based on other platforms. It is important to note, however, that our survey questions broadly targeted aspects of the clinician virtual visit experience without reference to the specific telemedicine platform used. Finally, we did not directly survey patients during this time and all identified barriers, challenges, and perceptions of telemedicine in this study are based solely on the clinician experience. This underscores the need to elicit these perceptions directly from patients to better understand their challenges and perceived benefits of telemedicine.In conclusion, this is the first study to elicit perspectives on telemedicine from a wide range of faculty from the departments of medicine and pediatrics. Clinicians identified barriers to implementation, challenges to incorporating trainee education, and training needs that should be addressed to improve the telemedicine experience. Overall, it is encouraging that clinicians enjoy video visits and can connect with their patients similarly to in-person visits. However, it is concerning that for a third of clinicians, video visits took longer to prepare, conduct, and document. To support clinician wellness, institutions must more completely understand and support clinician needs. Regarding trainee education, training is needed to help clinician educators successfully integrate students and house staff into virtual workflows, assess learner telemedicine performance, and structure virtual clinic feedback. Most importantly, the top three barriers to successful telemedicine implementation identified by clinicians are patient barriers, highlighting the need to better understand patient perceptions toward video and telephone visits, and proactively address barriers that contribute to the digital divide. It is critical to address each of these needs to support the durability of telemedicine visits as a way to complement and augment the care patients receive in person and to ensure that both clinician and patient experiences are efficient, positive, and patient-centered.


The secondary use of structured electronic medical record (sEMR) data has become a challenge due to the diversity, sparsity, and high dimensionality of the data representation. Constructing an effective representation for sEMR data is becoming more and more crucial for subsequent data applications.We aimed to apply the embedding technique used in the natural language processing domain for the sEMR data representation and to explore the feasibility and superiority of the embedding-based feature and patient representations in clinical application.The entire training corpus consisted of records of 104,752 hospitalized patients with 13,757 medical concepts of disease diagnoses, physical examinations and procedures, laboratory tests, medications, etc. Each medical concept was embedded into a 200-dimensional real number vector using the Skip-gram algorithm with some adaptive changes from shuffling the medical concepts in a record 20 times. The average of vectors for all medical concepts in a patient record represented the patient. For embedding-based feature representation evaluation, we used the cosine similarities among the medical concept vectors to capture the latent clinical associations among the medical concepts. We further conducted a clustering analysis on stroke patients to evaluate and compare the embedding-based patient representations. The Hopkins statistic, Silhouette index (SI), and Davies-Bouldin index were used for the unsupervised evaluation, and the precision, recall, and F1 score were used for the supervised evaluation.The dimension of patient representation was reduced from 13,757 to 200 using the embedding-based representation. The average cosine similarity of the selected disease (subarachnoid hemorrhage) and its 15 clinically relevant medical concepts was 0.973. Stroke patients were clustered into two clusters with the highest SI (0.852). Clustering analyses conducted on patients with the embedding representations showed higher applicability (Hopkins statistic 0.931), higher aggregation (SI 0.862), and lower dispersion (Davies-Bouldin index 0.551) than those conducted on patients with reference representation methods. The clustering solutions for patients with the embedding-based representation achieved the highest F1 scores of 0.944 and 0.717 for two clusters.The feature-level embedding-based representations can reflect the potential clinical associations among medical concepts effectively. The patient-level embedding-based representation is easy to use as continuous input to standard machine learning algorithms and can bring performance improvements. It is expected that the embedding-based representation will be helpful in a wide range of secondary uses of sEMR data. The past decade has witnessed an explosion in the amount of digital information stored in electronic medical records (EMRs), which contain massive quantities of information on the clinical history of patients. The wide secondary use of this information for various clinical applications has become a prevalent trend , helpingAs we all know, the method of data representation is becoming more and more crucial for the performance of data applications ,13. ReceHowever, there are still challenges in the representation of structured EMR (sEMR) data containing high-dimensional and diverse features. Such features as demographic characteristics, disease diagnoses, physical examinations and procedures, and laboratory tests may have discrete or continuous values, making it difficult to reveal the latent relations among them. Moreover, it is difficult to make full use of every available feature  due to the unavoidable missing values. It is worth exploring how to deal with the patient records with features that are unequal in length.Therefore, in this study, we leveraged a distributed embedding technique originated in natural language processing (NLP), the Skip-gram algorithm, with several adaptive changes to obtain effective representations from the sEMR data. The feature representation was evaluated by the dimension reduction visualization method and feature correlation analysis method. We further conducted clustering analyses on patients expressed with the proposed representations to evaluate the representation scheme. We aimed to explore the feasibility and superiority of the embedding-based representations in data mining tasks for sEMR data.The sEMR data of 144,375 hospital admissions for 104,752 patients were collected from Xuanwu Hospital, Capital Medical University, Beijing, China, between January 2014 and December 2016. Patients’ features were grouped into seven major categories: demographic characteristics, hospital admission and discharge, utilization of medical resources, disease diagnoses , examination and procedures undergone , laboratory tests, and medications  or 3 classes  according to the clinical laboratory test references. Other continuous features were grouped into 4 percentile bins (quartiles), each containing one-fourth of all samples  was the size of the training context , tw and t+jw denoted the target word and the jth neighboring words before or after the target word in the training context window, v represented the d-dimensional (d was set to 200 in this study) real number vector of the word, and W  was the total number of words in the corpus.where k medical concepts for k features would have k real number vectors. The average of these vectors was considered the embedding-based representation for the patient.Unlike a natural language sentence with a relatively fixed word order, a medical concept’s location in a record was appointed manually. It was difficult to assume that the more relevant the concepts were, the closer they were located in a record. Therefore, medical concepts relevant to the target concept might not appear in the training context window in Equation (1). To reduce the impact of the concept sequence on the Skip-gram algorithm, we used the shuffling mechanism  to rearrThe feature representation was first evaluated visually by mapping the d-dimensional real number vector space into a two-dimensional space using the t-distributed stochastic neighbor embedding (t-SNE) algorithm ,20. We uThe embedding-based feature representation was then evaluated on how it could capture the latent association among features. We identified the 10 closest medical concepts from each of the diagnosis, laboratory test, physical examination and procedure, medication, and other feature categories in the low-dimensional embedding space for two index diagnosis concepts: subarachnoid hemorrhage (SAH) and occlusion and stenosis of middle cerebral artery (OSMCA). The similarities between the index diagnosis concepts and others were measured by cosine similarity, which was suitable for numerical vectors.The distributed embedding technique had the advantage of revealing the potential relevance among samples , and theWe conducted k-means clustering analyses on the stroke patients, using cosine distance for the embedding-based representations, Jaccard distance  for the As shown in In the k-means clustering analyses, the optimal k was determined to be 2, where the corresponding SI value was the highest when k changed from 2 to 15 in each of the representation schemes  patients, of whom 92.2% (range 85.5%-95.7%) had a primary diagnosis of IS. Cluster 2 contained an average of 1363 (range 528-2018) patients, of whom 63.1% (range 51.2%-74.5%) had a primary diagnosis of HS. Therefore, we used IS as the label of patients in cluster 1 and HS as the label of patients in cluster 2. Among the embedding-based representations, the representation trained with the shuffled full corpus reached the greatest F1 scores of 0.944 and 0.717 for clusters 1 and 2, respectively . In thisP<.001), cost per hospital stay , and LOS . Patients in cluster 2 occupied more medical resources than those in cluster 1 concerning the ventilator  and intensive care unit . This might partially be linked to the fact that patients in cluster 2 usually also had such acute diseases as pneumonia , while patients in cluster 1 had chronic diseases like paralysis . Table S3 in Between clusters 1 and 2 of stroke patients represented by the embedding vectors learned from shuffled full corpus, there were significant differences in mortality rate (45/6922, 0.65% vs 91/1310, 6.95%, Representation for structured medical data is critical for data mining tasks in the medical domain ,5,6,14. The distributed embedding representations have the merits of low dimensionality and the capability for revealing the latent relationship among the represented objects . Thus, tIn this study, we borrowed the idea from this originally text-oriented technique and applied it to sEMR data with diverse patient features. We embedded each medical concept into a low-dimensional real number vector using the Skip-gram algorithm. Both the visualized and quantitative analyses showed that the embedding-based feature representation provided a relatively clear understanding of the associations and connections among the medical concepts, which were consistent with medical knowledge and clinical practice. On the other hand, clustering solutions on patients represented with embedding vectors showed a better clustering nature than those expressed with multi-hot vectors. The embedding-based representation showed advantages in dimension reduction and in the convenience of numerical computation and association mining in this study.An informative representation was usually derived from different modalities and medical data sources, such as cross-sectional and longitudinal data, and quantitative indexes and narrative notes. In this study, demographic characteristics, diagnoses, physical examinations and procedures, laboratory tests, medications, and hospital admission and discharge were all brought into the feature representation learning. A particular and unavoidable characteristic of laboratory tests was that patients might take different laboratory test items according to the need for diagnosis and treatment. This must result in lots of missing values for laboratory tests. The joint use of the discretization of continuous values and the Skip-gram algorithm solved the problem, making all the available features to be fully used. Clustering analyses showed that patients represented by embedding vectors were more likely to cluster together than those represented in the original form, where about three-quarters of laboratory tests were dropped due to missing values. It may partially attribute to the inclusion of all the features and discretization of the continuous features.When using the Skip-gram algorithm for representation learning for sEMR data, several adaptive changes had been made. First, we applied the shuffling mechanism when building the training corpus to reduce the impact of the concept order on the coverage of the training context. Glicksberg et al  randomlyIn the Skip-gram algorithm, the range of training context was also crucial to the algorithm performance. For the same reason as for using a shuffling method, we set the window size to the maximum to have the training context covered the most neighboring concepts. However, there was no outstanding performance improvement in the clustering task. The finding was consistent with other studies ,35 that Our study had some limitations. First, we did not use time-oriented patient records with critical importance for evaluating the patient course and prognosis. The history of medical events may affect future medical events; these medical sequence data are crucial for clinical diagnosis and treatment. Rich time-oriented data, including time-series features in an inpatient record and the temporality between multiple inpatient records, were used for learning patient representations by some algorithms targeted at sequence data, such as recurrent neural network , time-awIn this study, we applied an embedding technique in learning the patient representation from sEMR data with different types of clinical features. With the original Skip-gram algorithm’s adaptive changes, the embedding-based representations could somehow reflect the potential associations among features and patients. The performance improvement in a clinically meaningful clustering task suggested the proposed patient representation’s effectiveness and efficiency. It is expected that the embedding-based representation will be helpful in a wide range of secondary uses of EMR data.


The main dependent variables were three different AUDIT-C cut-off scores for at-risk drinking: (a) 5 for both genders as recommended by Public Health England, (b) 7 for females and 8 for males (cut-off for students and young people) and (c) 8 for both genders . Among the independent variables were gender and age, motivations for home drinking using the Home Drinking Assessment Scale (HDAS), purchasing patterns, context of drinking and health and wellbeing. The predictors following hierarchical logistic regressions were for (a) purchasing alcohol online or at a supermarket and emotional HDAS scores, (b) purchasing alcohol online or at a supermarket and for parties, drinking alone and with other members of the household and emotional and practical reason HDAS scores, (c) as for b with the addition that men were more likely to be at-risk drinkers. At-risk drinking in the pandemic was explained by motivational reasons, purchasing patterns and situational factors.During the COVID-19 pandemic, alcohol consumption was largely confined to drinking in the home. There has been little research examining variables associated with risk in home drinking. The study employed an online survey of ( Pubs, bars and restaurants closed and re-opened on 4 July 2020. This provided a naturalistic opportunity to examine alcohol consumption in a domestic setting, predominantly at home. Stevely et al. (2021) [“General population survey data indicate there has been a rise in the proportions of both non-drinkers and higher risk drinkers, and that similar proportions of people are drinking more than before and less than before”.Economic evidence suggests a shift in beverage consumption during the initial months of the lockdown period. The Institute of Alcohol Studies (IAS)  in the UThere has been an update from Public Health England . When coBell and Britton , in a 20n = 488) [n = 411). On this occasion, the significant predictors of at-risk drinking were drinking at home every day, purchasing alcohol as part of weekly shopping and preferring to drink at home.There has been work that has considered the motivations underpinning home drinking. Qualitative findings  suggest n = 488) , using tn = 488) , to measn = 488)  aimed ton = 488) . The sign = 488) , Canfieln = 488)  surveyedn = 5401) Dutch university students [n = 3720) as a result of their alcohol consumption concluded that the AUDIT-C cut-off for both genders should be 8 [There is a shorter version of the AUDIT, this is the AUDIT-C , that usstudents , a cut-ould be 8 .This study examines predictors of at-risk drinking using different cut-off points in a general population sample who completed an online survey. The focus of the study is on reasons for drinking at home, alcohol purchasing patterns, sociodemographic factors and health and well-being. It provides greater data concerning the relationship between at-risk drinking and consumption of alcohol at home during the COVID-19 lockdown period.n = 48,000), mainly constructed to encourage participation in cognitive psychology projects. To be included, participants take a series of face recognition tests, and on completion, can optionally consent to be contacted for future research projects conducted by the current third author, his students or other staff at the university. The key defining feature of the database is that participants tend to have far better than average face and voice recognition ability, e.g., [Participants were invited from the University of Greenwich Face and Voice Recognition volunteer participant worldwide database .The following types of demographic data were collected: gender, age, ethnicity, highest education level and gross income. There were also a series of questions relating to whether alcohol consumption changed during the lockdown period and patterns of home drinking  and alcohol purchasing . The AUDIT-C uses the first three consumption items of the Alcohol Use Disorders Identification Test (AUDIT) . The thrn = 26,946) [n = 222) [Participants were given pictorial representations of drinks and were told that a drink (1 unit) equates to half a UK pint of beer (approximately 500 ml), 175 ml of normal-strength wine and one UK measure of spirits (25 ml). Higgins Biddle and Babor  recommen 26,946)  and moren = 222) .Foster et al.  describeThese items were originally derived from Foster and Canfield . Five quA question was added concerning whether there was a change in alcohol consumption compared to prior the lockdown. Less than before was scored 1, the same 2 and more than before 3.A series of questions were asked concerning purchasing alcohol before and after the lockdown period. The areas of enquiry were purchasing alcohol at a supermarket, at an off licence , for parties and online. Participants were asked to indicate the level of agreement with whether they engaged in purchasing behaviour. Not applicable was scored 0, and thereafter there was a five-point scale: strongly agree (1)–strongly disagree (5).Mental health and well-being were assessed by the Warwick and Edinburgh Mental Health and Well-being Scale (WEMWBS) . This isp ≤ 0.05 in any of the three univariate analyses were entered into final regression model as independent variables. These were three hierarchical logistic regressions based on the findings of Foster and Canfield [Initially, descriptive data were presented. Thereafter, in order to construct final regression models, all the study variables were tested against all three cut-offs using a univariate analysis. Pearson’s product moment correlation co-efficient was computed to test for multicollinearity. If there was an association beyond the 0.8 level, in accordance with a large effect , then fuCanfield . The depn = 1128) are shown in n = 648, 57.4%) being educated to an undergraduate level or above. Over half of the sample of those who replied had monthly incomes of less than £2000. The mean AUDIT-C score was 6.93 (SD = 2.40). This is above the cut-off recommended by Public Health England [n = 794, 69.6%) of the participants were either drinking the same or less than before the lockdown period. The mean amount of money spent per month on alcohol during the lockdown was £24.6 (SD = 255.6). The WEMWBS scores were at the bottom range of average mental health and well-being.One thousand six hundred and twenty-eight participants clicked on the survey link, and of these, 138 did not start the survey and 312 did not complete it. A further fifty were excluded as they did not complete one of the main study measures, the HDAS. The characteristics of the sample , followed by, in descending order, not having to drink and drive , not feeling comfortable drinking out , because they had children and could not go out  and lastly, not being able to smoke in licensed premises . With regards to the emotional reasons subscale, convenience was the most endorsed motivation , followed by relaxation  and drinking at home because it was safer than going out. . The statistics for the HDAS stand-alone item “I prefer to drink at home rather than a pub or restaurant” were .n = 1119) . These were subdivided into the following three items: watching TV , watching or playing computer games or other screen activities.  and lastly, reading a book or a newspaper . There were also two questions  relating to whom participants drank with during the lockdown period. Drinking with other members of the household was more common than drinking alone.In addition to the HDAS questions, five questions asked about activities conducted whilst consuming alcohol during the lockdown period. They were scored identically to the HDAS items, so that lower scores indicated higher levels of endorsement. The statistics for the “with food items” were having a barbeque in the garden (with members of the same household)  and with meals . The “with entertainment” items were endorsed less often than the items with food variable were. The statistics for “with entertainment” were   . n = 151, 49.0%)  . All other between-gender differences were p > 0.4.The mean AUDIT-C score was 6.93, (SD = 2.40) . Men hadp > 0.5). Women more likely to have a postgraduate qualification  . Men were more likely to have higher salaries (p = 0.004) with  men having salaries £2000 or more compared to the equivalent figures for women .There were no between-gender differences in age (p > 0.244) in any of these variables. Similarly, there were no gender differences in the typical weekly spend (£) on alcohol either before (p = 0.09) or during (p = 0.584) lockdown.There were a number of questions concerning whether drinking had decreased, remained the same or increased during the lockdown period. There were no between-gender differences (p > 0.4 on both occasions). There were no gender differences for the “with food” variable (p = 0.263); however, men were more likely to consume alcohol along with entertainment  ((−) t = 2.47, p = 0.014).With regards to motivations for drinking (HDAS), there were no significant between-gender differences for both subscales  . During the lockdown period, the gender difference was no longer significant (p = 0.079). Prior to lockdown, there was no between-gender difference (p = 0.313), but during lockdown men were more likely to purchase alcohol as part of normal household shopping  (t= (−) 2.69, p = 0.007). There was no difference in the probability of men or women purchasing alcohol online either before or during the lockdown period (p > 0.2 on both occasions). There was no gender difference in drinking with members of the household (p = 0.678), but men were more likely to drink alone during the lockdown  (t= (−) 2.52, p = 0.012)Women were more likely to state that purchasing for parties prior to the lockdown was the main reason for them purchasing alcohol to be consumed outside of pubs and bars (two-tailed independent sample t-test) (men: p > 0.5) in WEMBWS scores.Finally, there were no gender differences . Prior to the lockdown, the most common purchasing motivation was special occasions such as parties. During the lockdown period, this was less frequent, as indicated by higher scores. This was a significant change (t= (−) 24.72, p < 0.001). The next most prevalent purchasing pattern prior to the lockdown was as part of normal household shopping. This was more prevalent during lockdown. The within-group difference was significant .This data are is presented in n = 1107)  .Purchasing alcohol in an off licence before the lockdown was infrequent. Data could not be collected relating to this during lockdown due to restrictions on the opening of such outlets. Purchasing alcohol online prior to the lockdown was infrequent and was significantly more popular during lockdown (p > 0.6), and educational level (p > 0.2). Changes in the amount spent on alcohol during the lockdown compared to before lockdown (p > 0.1), “with meals” (p > 0.19) or WEMBS scores (p > 0.1) were also not significant at any point. No further data are reported related to these variables.Two demographic variables were not related to any of the designated cut-off points, these were age  participants who were AUDIT-C-positive using the Public Health England recommended cut-off. The relationships with the cut-off score and gender (p = 0.235) and household status (p = 0.197) were not statistically significant. The association with income was significant with 86% of the sample who had a monthly salary of £2000 or more reaching the cut-off score for an at-risk drinker . The relationships with either drinking with members of the household (p > 0.9) or drinking alone (p > 0.35) were not significant.There were  and being AUDIT-C-positive . The same was true for those who spent more on alcohol during than lockdown than before ; they were AUDIT-C-positive . All purchasing patterns during lockdown were associated with at-risk drinking at a p < 0.001 level. The most common purchasing pattern was firstly, at a supermarket , secondly, for a special occasion such as a party  and lastly, online .Drinking more than before lockdown was associated with the at-risk drinking (p < 0.001); practical reasons  (t = (−) 4.02, df = 1088, p < 0.001).Both HDAS subscales were related with the at-risk drinking cut-offs. The statistics were as follows: emotional reasons  (t = (−) 9.19, df = 1088, n = 908, mean = 8.41, SD = 3.94)   (lower scores indicate greater endorsement). “Drinking whilst reading a book or newspaper” had the strongest relationship to at-risk drinking   . “Playing computer games or other screen activities” was also significant, albeit at a slightly lower level   . Both were inverse relationships. The probability level of the “Watching TV” variable was (p = 0.064).There was an inverse relationship in the “with entertainment” variable  participants who were AUDIT-C-positive at this cut-off point. Once more, there were no significant associations between this cut-off and gender (p = 0.839) or income (p = 0.063).There were  . The same was true for those who spent more on alcohol during than lockdown than before. One hundred and eighty-six (75.0%) participants who spent more on alcohol during lockdown than previously were AUDIT-C-positive .Drinking more than before lockdown was associated with the at-risk cut-off, (p < 0.001 level. The hierarchy was as before (in descending order): “at a supermarket”  , for special occasions such as parties   and finally, online  .Once more, all three of the purchasing category variables were associated with being AUDIT-C+ at a p < 0.001.) Practical reasons   (t= (−) 1.96, df = 1079, p = 0.050) The relationships “with entertainment” and with meals were not significant (p > 0.5).Both HDAS subscales were related with the at-risk drinking cut-offs, though the relationship with emotional reasons had the strongest relationship. The statistics were as follows: emotional reasons   ( t= (−) 9.03, df = 1079, p < 0.001) and drinking alone   (p = 0.020).There was a positive significant relationship with both drinking with members of the household    participants who were at-risk drinkers according to this cut-off value. On this occasion, men were more likely to be at-risk drinkers; men , women  (Men: Women) . Income was also significantly related to at-risk drinking . Those with a salary per month of more than £2000  were more likely to be at-risk drinkers than were those with a lower salary .There were  . The same was true for those who spent more on alcohol during than lockdown than before  .Drinking more than before lockdown was associated with the at-risk cut-off, (p < 0.001 level. The hierarchy was as follows: (in descending order): (a) at a supermarket , (b) for special occasions such as parties  and (c) online .Once again, all the purchasing variables were significant at a p = 0.020) and drinking alone   (p = 0.008).There was a positive significant relationship with both drinking with members of the household    who were AUDIT-C-positive. The statistics were as follows: emotional reasons   (t= (−) 7.94, df = 1088, p < 0.001); practical reasons   (t= (−) 2.42, df = 1079, p < 0.016). The association between this cut-off point and “with entertainment” was of borderline statistical significance (p = 0.054).Both HDAS subscales were related with the at-risk drinking cut-offs, though the relationship with emotional reasons had the strongest relationship. There were  . These wThe final cut-off was 8 for both genders, which has been established as a cut-off for adults seeking online help as a consequence of their drinking . On thisIn the current study, gender was only significant at the highest cut-off point when men were more likely to be at-risk drinkers. This suggests that when the opportunities to drink in pubs and bars are curtailed, there is little gender difference in alcohol consumption patterns at lower at-risk cut-offs. The fact that men are drinking at a higher level is consistent with much research , althougThe relationship between age and the study variables  was very weak. There were however some significant findings. Older age was related with better health and well-being skills and a greater likelihood of purchasing alcohol for parties during lockdown. Older age was also associated with endorsing emotional reasons for drinking at home and not selecting practical reasons. The findings in our study concerning gender and age are consistent with Spanish data collected during the lockdown period .n = 57,807) [None of the measures in the study that could approximate to social class, income and highest education level were associated with any of the at-risk cut-offs. This is contrary to findings from the Alcohol Toolkit Study  , which f 57,807)  collecte 57,807)  found thEmotional reasons for drinking at home was a significant predictor at all three cut-off points. This means that convenience, relaxation and drinking at home because it is safer than going out were drivers for at-risk drinking at all levels. In contrast, practical reasons was not a significant factor until the two highest cut-offs and was in a different direction to emotional reasons. This means that as at-risk drinking increases, issues such as cost, drinking and driving, not feeling comfortable going out, child care and smoking are weak explanations of at-risk drinking.Purchasing alcohol online and at a supermarket was a significant factor at all three cut-off points, whereas purchasing alcohol for parties was only significant at the two higher cut-offs. This indicates that this could be a marker for higher at-risk drinking both during and outside of lockdown. Drinking alone and as part of a household were not significant at the lowest cut-off, but both variables were significant at higher cut offs. All these suggest that as drinking opportunities increase, then the likelihood of at-risk drinking increases also.Men had higher AUDIT-C scores, but gender was not significant until both genders had AUDIT-C scores of 8. This does suggest that further work needs to be carried out to establish whether the behaviours that have been associated with at-risk drinking during the lockdown period are maintained when pub/bar and restaurant drinking return to pre-lockdown patterns. Then, it could be beneficial to revisit the AUDIT-C cut-offs for each gender. One possibility could be to establish a base at-risk cut-off and a higher one indicative of higher-risk drinking. This could then be a prompt to offer referral to lower-threshold treatment services. These findings suggest the cut-off for higher-risk drinking would be 8 for women.The COVID-19 lockdown period was exceptional; however, these findings indicate that home drinking has become a habitual driver of alcohol consumption. This may be an important consideration for both treatment providers and policymakers, albeit a longitudinal study with a follow-up period of one year or more would be required to see if these changes have become entrenched. We suggest that the HDAS (9 items) is routinely integrated into research that is examining the trajectory of both at-risk and dependent drinkers. The findings are of import as women with this age profile (mean age of 45) have received little research focus in alcohol studies. The sample was highly educated, but 45% of the women had a net income more than £2000 per month. They confirm that home drinking is a driver of at-risk drinking. Eighty-five per cent of the sample reported that they were at-risk drinkers when the recommended Public Health England limits were applied, and nearly half of the sample were at-risk drinkers when a limit for student and young people was used. On both occasions, there was no between gender differences. It was only when the cut-off for seeking online help was tested (AUDIT-C scores of 8 for both genders) that males were more likely to be at-risk drinkers.Cost was the most frequently motivation for drinking at home. Scotland introduced a policy of minimum unit pricing (MUP) in May 2018, and Wales followed suit in March 2020. The Republic of Ireland will introduce a similar policy in January 2022. This means that in Scotland and Wales, the minimum unit price for a unit of alcohol is 50 p. Robinson et al.  found thDespite the rise of home drinking, it has received comparatively little research attention. The current research employed a relatively large participant sample, and most of the sample are women (mean age of the sample 45+). The analysis has included an investigation of the association between home drinking and health and wellbeing. However, there are some limitations that should be acknowledged. Firstly, this is a cross-sectional study, and no data were collected before the lockdown period, so no conclusions concerning causation can be inferred. Data were not collected concerning employment status or whether individuals were working during the lockdown period, either at home or outside the home, and these may have had an impact on the study findings. Secondly, this is an internet survey, and whilst these are increasing common, despite the sample size it is not possible to draw conclusions about representativeness of the sample. Asking questions concerning alcohol is regarded as sensitive, and women are more likely to respond to online surveys than men are . Even thOn the other hand, it should be acknowledged that this proclivity to display high drinking patterns may be a property of the specific sample of participants recruited who are interested in face recognition research and mainly possess far better than average face recognition ability. The subset of participants invited who completed the survey may have found the topic of alcohol use during lockdown particularly salient, and their results may not be representative of the rest of the database, let alone the wider population. Nevertheless, public health research of this type rarely assesses participants with this demographic profile, and follow-up research is required to determine if lockdown has established a pattern of at-risk drinking in a demographic not previously identified as such.Notwithstanding that a lockdown is not a typical drinking culture, this paper confirms that home drinking is an integral part of the current drinking culture. A large proportion of the sample were women, and the mean age was 45. Over eighty percent of the sample were at-risk drinkers using current Public Health England recommended cut-offs, and nearly half of the sample were at-risk drinkers when a cut-off used for young people and student was used. The predictors of at-risk drinking at home were a combination of motivational reasons, purchasing patterns and situational factors. The Home Drinking Assessment Scale has produced some valuable data, and future researchers should consider including it to understand how home drinking contributes to a trajectory of harmful, hazardous and dependent drinking.


Objective: Malnutrition is a severe complication in Systemic Sclerosis (SSc) and it is associated with significant mortality. Notwithstanding, there is no defined screening or clinical pathway for patients, which is hampering effective management and limiting the opportunity for early intervention. Here we aim to identify a combined index predictive of malnutrition at 12 months using clinical data and specific serum adipokines.Methods: This was an international, multicentre observational study involving 159 SSc patients in two independent discovery (n = 98) and validation (n = 61) cohorts. Besides routine clinical and serum data at baseline and 12 months, Malnutrition Universal Screening Tool (MUST) score and serum concentration of leptin and adiponectin were measured for each participant at baseline. The endpoint of malnutrition was defined according to European Society of Clinical Nutrition and Metabolism (ESPEN) recommendation. Significant parameters from univariate analysis were tested in logistic regression analysis to identify the predictive index of malnutrition in the derivation cohort.Results: The onset of malnutrition at 12 months correlated with adiponectin, leptin and their ratio (A/L), MUST, clinical subset, disease duration, Scl70 and Forced Vital Capaciy (FVC). Logistic regression analysis defined the formula: −2.13 + (A/L*0.45) + (Scl70*0.28) as the best PREdictor of MAlnutrition in SSc (PREMASS) . PREMASS < −1.46 had a positive predictive value (PPV) > 62% and negative predictive value (NPV) > 97% for malnutrition at 12 months.Conclusion: PREMASS is a feasible index which has shown very good performance in two independent cohorts for predicting malnutrition at 12 months in SSc. The implementation of PREMASS could aid both in clinical management and clinical trial stratification/enrichment to target malnutrition in SSc. Systemic sclerosis (SSc) is a rare autoimmune disease characterized by microvascular injury, immune dysregulation and fibroblasts activation, leading to progressive skin and multi-organ fibrosis with potentially life-threatening complications . SystemiRecently, the European Society of Clinical Nutrition and Metabolism (ESPEN), aiming at helping clinicians to document clinically relevant malnutrition by employing a clear definition, appointed a consensus group to provide criteria for the diagnosis of malnutrition .2 for subjects <70 years of age, and a BMI <22 kg/m2 for subjects ≥70 years older, combined with either a >5% weight loss over the last 3 months to cover for acute illnesses, or a >10% weight loss of habitual weight, independent of time, in order to include and be relevant for chronic conditions  and Padova (n = 50). An additional validation cohort of 70 SSc consecutive patients was enrolled within the observational study: Stratification for Risk of Progression in SSc (STRIKE–SSc) at the Scleroderma Research Program of the University of Leeds. Subjects engaged in weight loss program (n = 2) and malnourished according to the ESPEN criteria (n = 19) were excluded. Thus, the derivation cohort comprised 159 SSc patients .The discovery cohort comprised 110 consecutive patients with SSc, all fulfilling the EULAR/ACR 2013 criteria , and refEach patient underwent clinical history and physical examination, and provided sociodemographic information. All patients were evaluated at study entry and at 12 months follow up visit.This observational study was conducted following approval by the ethical committee of the University of Messina (prot. 15–15), University of Padova (prot. 2015–32) and the University of Leeds .2), at baseline and at 12 months. Subjects were divided in the following groups: normal weight (BMI > 18.6 <24.9) and overweight/obese (BMI ≥ 25). Weight loss was calculated at 12 months in each patient and a ≥10% weight loss of baseline weight was used as outcome for the definition of malnutrition in combination with a BMI <20 kg/m2 or <22 kg/m2 if older than 70 years at 12 months, as defined by the ESPEN consensus  provides a score based on the following parameters: body mass index (BMI): >20.0 = 0, 18.5–20 = 1, <18.5 = 2; weight loss score (unplanned weight loss in the past 3–6 months): <5% = 0; 5%−10% = 1: >10% = 2. The MUST also adds a score of 2 if there has been or is likely to be no nutritional intake for the next 5 days or more.These scores are summed for the total score, conferring the degree of the risk for malnutrition: a score of 0 corresponds to low risk, 1 is moderate, ≥2 is high .Adiponectin and leptin serum levels were measured using anonymized bar-coded serum samples at baseline and at 12 months. All the tested compounds were quantitatively measured using an enzyme-linked immunosorbent assay kit  in the discovery cohort and with a bead-based multiplexed immunoassay  in the validation cohort.Disease duration was calculated from the onset of the first non-RP symptom. Clinical subset of disease was defined according to Le Roy et al. . Forced 2 for subjects <70 years of age, and a BMI <22 kg/m2 for subjects aged 70 years and older, and a weight loss ≥10% of baseline weight at 12 months or a BMI ≤ 18.5  curves were constructed using library (pROC). The area under the receiver operating characteristic curve (AUC) was estimated and a 95% CI determined using bootstrap resamples. AUCs were compared using a bootstrap significance test with the significance of differences between bootstrap AUCs assessed using a normal approximation. Data-derived optimum cut-points were selected by optimizing the sensitivity and the specificity. Where the predictive ability of a combination of variables was being assessed, multivariable logistic regression models were constructed and fitted probability values used in the ROC analysis as described. Analysis was undertaken using SPSS (version 24) for statistical computing.One hundred fifty-nine consecutive SSc patients, 98 from the discovery cohort, and 61 from the validation cohort, participated in the study. Clinical summary of the SSc patients is shown in p < 0.001) while MUST increased in both cohorts. Accordingly, serum levels of leptin significantly decreased (p < 0.001) while adiponectin and adiponectin to leptin ratio (A/L) increased significantly . Next, we evaluated the distributi on of adiponectin and leptin according to BMI clustering in both discovery and validation cohorts and we found a similar distribution for both adiponectin and leptin across different BMI clusters  at 12 months in both cohorts. The number of progressors was 15 (15.3%) in the discovery cohort and 10 (16.3%) in the validation cohort. Thus, 25 SSc patients among 159 (15.7%) met the end point of becoming malnourished at 12 months . FurtherNext, we aimed to identify the discriminants associated with the risk of developing malnutrition in the discovery cohort by univariate analysis. Odds ratios with relative confidence intervals and statistical significance are shown in Next we combined both discovery and validation cohorts in a single cohort, hereafter defined derivation cohort. Univariate analysis for derivation cohort, reported in The factors deemed significant in univariate analysis were analyzed by receiver operating curves (ROC) adopting progression to malnutrition as state variable. The best performing factors are shown in These 9 factors were further fitted in a logistic regression model adopting progression to malnutrition as binary outcome . The model was restricted to A/L and Scl70 as the 2 most significant variables according to the number of events (25 progressors).*0.45) + (Scl70*0.28). A PREMASS score < -1.46, identified at maximizing for specificity and sensitivity, showed in the derivation cohort an AUC of 96% with 90% sensitivity [95% CI: 79–100] and 88% specificity (95% CI:83–93) for malnutrition at 12 months with an overall 62% positive predictive value (95% CI: 48–76) and 97% negative predictive value (95% CI: 95–100)  .After dividing the derivation cohort according to the PREMASS cut-off (−1.46), we noted that, for patients with a score < -1.46, the PREMASS formula had a 62% PPV for becoming malnourished at 12 months, while for those having a score >-1.46 the formula had a 97% NPV .Malnutrition is an important severe clinical outcome in SSc, associated with significant mortality and to date remains poorly predictable with the available tools. The clear definition of malnutrition for complex populations, such as SSc, and the appropriate application of general tools, such as MUST, for its prediction remains unsatisfactory. Hence the identification of a tool to stratify patients with SSc for the risk of developing malnutrition would be particularly useful. Our longitudinal data suggest that in a population with 7 years average disease duration, 15.7% of SSc patients experience malnutrition at 12 months, according to the most updated classification of the ESPEN consensus in the absence of a SSc specific definition of malnutrition. A few reports have explored the value of the Malnutrition Universal Screening Tool (MUST) –13, and In fact, as already reported in other studies , we deciIndependently of the cause of malnutrition, it is known that weight loss is associated with perturbances in the serum concentration of adipokines, the most studied of which are adiponectin and leptin, both in SSc and other conditions such as anorexia or diabetes , 24.Therefore, we set out to measure adiponectin and leptin in our two SSc cohorts and test their value in predicting future malnutrition. Despite the limitation of testing adipokines serum levels with slightly different techniques, their concentrations were comparable between our discovery and validation cohorts. Most importantly, the ratio between the two neutralized the putative differences in concentration given by the two methodologies.We noted that both adiponectin and leptin inversely correlated to BMI in SSc patients, as already reported . FurtherIndeed, we noted that the increase in A/L after 12 months is due to an opposite change in adiponectin and leptin levels after 12 months in our SSc cohort. Previous studies suggest that adiponectin and leptin levels change in response to inflammatory cytokines, that can alter their levels over time . This haNext, we aimed at identifying all the factors associated with malnutrition in order to build a composite score for the prediction of malnutrition. Our analysis showed that A/L and MUST were significantly associated with malnutrition. Additionally, malnourished SSc patients had the diffuse form of the disease, with higher mRSS and lower FVC, a shorter disease duration and were more frequently Scl70 positive. Among these, Scl70 positivity had the most relevant significance and it was included in our analysis, in association with A/L, for the identification of the most performing index. Scl70 positivity has been already associated with capillary rarefaction and digital ulcers development, severe skin involvement and ILD and also poorer prognosis in SSc patients, and our data further extends the relevance of these antibodies in the management and prediction of SSc-comorbidities .Other circulating biomarkers have been studied in systemic sclerosis, such as prealbumin or hemoglobin, but there are no clear evidence that any of these factor are predictors of future malnutrition, and they could be considered markers of active malnutrition instead , 12, 50.Our study has some limitations: the index has been validated to predict malnutrition at 12 months and longer longitudinal studies are needed to assess its validity for long-term changes. Furthermore, no additional circulating biomarkers of malnutrition were tested nor healthy participants were enrolled, thus not allowing us to assess the differences in adipokines serum levels in comparison to the general population. In addition, our cohorts correspond to consecutive patients under treatment with vasodilators and/or immunosuppressants , so the influence of different therapeutic regimens on adipokines levels might be difficult to ascertain. Lastly the absolute concentration of adiponectin and leptin could vary slightly among techniques, nevertheless the ratio between adiponectin and leptin could overcome this latter issue.Using BMI as a metric for malnutrition has many well-known limitations and indeed, waist circumference measurement would be a better alternative. Nonetheless, the use of skin-dependent methods are not applicable in SSc research due to the intrinsic features of the disease.n , University of Padova: prot. 2015-32 and University of Leeds: NHS REC Approval number (STRIKE REC 15/NE/0211). The patients/participants provided their written informed consent to participate in this study.GB, FD, and WR: concept and design of the study. EP, AB, GP, DS, NI, EZ, GA, AF, and MW: data collection, data analysis, and interpretation. FS, MB, PE, SG, AS, and FC: manuscript preparation. All authors revised and approved the manuscript to be published.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.