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Few studies of the concordance between patient self-report and medical record data have examined how concordance varies with patient characteristics, and results of such studies have been mixed. Given discrepancies in the quality of care received across patient cohorts, it is important to understand the degree to which concordance metrics are robust across patient characteristics. We hypothesized that concordance between ambulatory medical record and patient survey data varies by patient demographic characteristics, especially education, income, and race/ethnicity. We present the results of bivariate and multivariate analyses including data from 1,270 patients with at least one of: diabetes, ischemic heart disease, asthma or COPD, or low back pain sampled from 39 West Coast medical organizations. We present total agreement, kappa, and survey sensitivity and specificity, stratified by patient demographic and health status characteristics. We conducted logistic regressions to test the impact of patient demographic characteristics, domain of medical care, and health status on these three measures of concordance. Survey sensitivity varied significantly by race/ethnicity in bivariate analyses, but this effect was erased in multivariate analyses. Our findings do not support the hypothesis that patient education, income, or race/ethnicity have an independent effect on concordance when controlling for other factors. However, concordance varied significantly by patient health status. The medical record and patient self-report do not measure quality comparably across patient cohorts. We recommend continued efforts to improve survey data collection across different patient populations and to improve the quality of clinical data. © Springer Science+Business Media, LLC 2006.

Background: The validity of quality of care assessments relies upon data quality, yet little is known about the relative completeness and validity of data sources for evaluating the quality of care.Objectives: We evaluated concordance between ambulatory medical record and patient survey data. Levels of concordance, variations by type of item, sources of disagreement between data sources, and implications for quality of care assessment efforts are discussed.Design and Subjects: This was an observational study that included 1270 patients sampled from 39 West Coast medical organizations with at least 1 of the following: diabetes, ischemic heart disease, asthma or chronic obstructive pulmonary disease, or low back pain.Measures: Items from both data sources were grouped into 4 conceptual domains: diagnosis, clinical services delivered, counseling and referral, and medication use. We present total agreement, kappa, sensitivity, and specificity at the item and domain-levels and for all items combined.Results: We found good concordance between survey and medical records overall, but there was substantial variation within and across domains. The worst concordance was in the counseling and referrals domain, the best in the medication use domain. Patients were able to report with good sensitivity on memorable items.Conclusions: Quality ratings are likely to vary in differing directions, depending on the data source used. The most appropriate data source for analyses of components of and overall quality of care must be considered in light of study objectives and resources. We recommend data collection from multiple sources to most accurately portray the patient and provider experience of medical care. [ABSTRACT FROM AUTHOR]

Understanding what matters to people with Parkinson's and their family is essential to derive relevant clinical outcome measures and guide clinical care. The purpose of this study was to explore what is important to people with Parkinson's disease vs. family over time. A qualitative content-analysis of online survey data collected by Parkinson's UK was conducted to identify types and frequencies of important symptoms and impacts of Parkinson's for people with the disease vs. family of people with Parkinson's. Independent T-tests were used to identify significance of between group differences for patients vs. family at < 2, 2-5, 6-10, 11-20, > 20-year durations. ANOVA was used to assess for within group differences by disease duration. We found that symptom priority changed significantly over time with longer disease duration. Tremor was reported less often later on, whereas mobility, dyskinesias, gait and speech/communication symptoms gained priority. In general, patients identified movement-related symptoms (e.g., walking, bradykinesia) as the most bothersome at all durations while family more strongly prioritized the physical and psychosocial impacts of disease (e.g., mobility, safety, interpersonal interactions, independence, and family impact). We conclude that important differences exist between family and patient perspectives of what matters and change over time with longer duration of disease., (© 2024. The Author(s).)

Prevalence estimates of Parkinson's disease (PD)-the fastest-growing neurodegenerative disease-are generally underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating the gap between patient symptoms and diagnosis. Early-stage PD patients ( n = 82) and age-matched comparison participants ( n = 50) completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) random forest classification model (AUC = 0.92) that performed well across environmental and platform contexts. These findings provide robust support for further exploration of consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance.

Introduction: Digital health technologies (DHTs) have the potential to alleviate challenges experienced in clinical trials through more objective, naturalistic, and frequent assessments of functioning. However, implementation of DHTs come with their own challenges, including acceptability and ease of use for study participants. In addition to acceptability, it is also important to understand device proficiency in the general population and within patient populations who may be asked to use DHTs for extended periods of time. We thus aimed to provide an overview of participant feedback on acceptability of DHTs, including body-worn sensors used in the clinic and a mobile application used at-home, used throughout the duration of the Wearable Assessments in the Clinic and at Home in Parkinson's Disease (WATCH-PD) study, an observational, longitudinal study looking at disease progression in early Parkinson's Disease (PD)., Methods: 82 participants with PD and 50 control participants were enrolled at 17 sites throughout the United States and followed for 12 months. We assessed participants' general device proficiency at baseline, using the Mobile Device Proficiency Questionnaire (MDPQ). The mean MDPQ score at Baseline did not significantly differ between PD patients and healthy controls (20.6 [2.91] vs 21.5 [2.94], p = .10)., Results: Questionnaire results demonstrated that participants had generally positive views on the comfort and use of the digital technologies throughout the duration of the study, regardless of group., Discussion: This is the first study to evaluate patient feedback and impressions of using technology in a longitudinal observational study in early Parkinson's Disease. Results demonstrate device proficiency and acceptability of various DHTs in people with Parkinson's does not differ from that of neurologically healthy older adults, and, overall, participants had a favorable view of the DHTs deployed in the WATCH-PD study., Competing Interests: 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., (© 2024 Kangarloo, Latzman, Adams, Dorsey, Kostrzebski, Severson, Anderson, Horak, Stephenson and Cosman.)

Digital measures may provide objective, sensitive, real-world measures of disease progression in Parkinson's disease (PD). However, multicenter longitudinal assessments of such measures are few. We recently demonstrated that baseline assessments of gait, tremor, finger tapping, and speech from a commercially available smartwatch, smartphone, and research-grade wearable sensors differed significantly between 82 individuals with early, untreated PD and 50 age-matched controls. Here, we evaluated the longitudinal change in these assessments over 12 months in a multicenter observational study using a generalized additive model, which permitted flexible modeling of at-home data. All measurements were included until participants started medications for PD. Over one year, individuals with early PD experienced significant declines in several measures of gait, an increase in the proportion of day with tremor, modest changes in speech, and few changes in psychomotor function. As measured by the smartwatch, the average (SD) arm swing in-clinic decreased from 25.9 (15.3) degrees at baseline to 19.9 degrees (13.7) at month 12 (P = 0.004). The proportion of awake time an individual with early PD had tremor increased from 19.3% (18.0%) to 25.6% (21.4%; P < 0.001). Activity, as measured by the number of steps taken per day, decreased from 3052 (1306) steps per day to 2331 (2010; P = 0.16), but this analysis was restricted to 10 participants due to the exclusion of those that had started PD medications and lost the data. The change of these digital measures over 12 months was generally larger than the corresponding change in individual items on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale but not greater than the change in the overall scale. Successful implementation of digital measures in future clinical trials will require improvements in study conduct, especially data capture. Nonetheless, gait and tremor measures derived from a commercially available smartwatch and smartphone hold promise for assessing the efficacy of therapeutics in early PD., (© 2024. The Author(s).)

Introduction: Studies have found that sodium-glucose cotransporter 2 inhibitors (SGLT2) and glucagon-like peptide 1 receptor agonists (GLP1) have cardiovascular benefits for patients with type 2 diabetes (DM2) and atherosclerotic cardiovascular disease (ASCVD), chronic kidney disease (CKD), or heart failure (HF). The literature does not provide evidence specifically for patients with these conditions who are adding one of these medicines to two glucose-lowering medications (ie, as "third-step" therapy). We explored the effects of different third-step medications on cardiovascular outcomes in patients with diabetes and these comorbid conditions. Specifically, we compared third-step SGLT2 or GLP1 to third-step dipeptidyl peptidase-4 inhibitors (DPP4), insulin, or thiazolidinediones (TZD)., Research Design and Methods: We assembled a retrospective cohort of adults at five Kaiser Permanente sites with DM2 and ASCVD, CKD, or HF, initiating third-step treatment between 2016 and 2020. Propensity score weighted Poisson models were used to calculate adjusted rate ratios (ARRs) for all-cause mortality, incident major adverse cardiovascular event (MACE), and incident HF hospitalization in patients initiating SGLT2 or GLP1 compared with DPP4, insulin, or TZD., Results: We identified 27 542 patients initiating third-step treatment with one or more of these conditions (19 958 with ASCVD, 14 577 with CKD, and 3919 with HF). ARRs for GLP1 and SGLT2 versus DPP4, insulin, and TZD in the patient subgroups ranged between 0.22 and 0.55 for all-cause mortality, 0.38 and 0.81 for MACE, and 0.46 and 1.05 for HF hospitalization. Many ARRs were statistically significant, and all significant ARRs showed a benefit (ARR <1) for GLP1 or SGLT2 when compared with DPP4, insulin, or TZD., Conclusions: Third-step SGLT2 and GLP1 are generally associated with a benefit for these outcomes in these patient groups when compared with third-step DPP4, insulin, or TZD. Our results add to evidence of a cardiovascular benefit of SGLT2 and GLP1 and could inform clinical guidelines for choosing third-step diabetes treatment., Competing Interests: Competing interests: JPM provided paid consulting services to an expert panel with the American Academy of HIV Medicine. All other authors declare no competing interests., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Digital health technologies show promise for improving the measurement of Parkinson's disease in clinical research and trials. However, it is not clear whether digital measures demonstrate enhanced sensitivity to disease progression compared to traditional measurement approaches., Methods: To this end, we develop a wearable sensor-based digital algorithm for deriving features of upper and lower-body bradykinesia and evaluate the sensitivity of digital measures to 1-year longitudinal progression using data from the WATCH-PD study, a multicenter, observational digital assessment study in participants with early, untreated Parkinson's disease. In total, 82 early, untreated Parkinson's disease participants and 50 age-matched controls were recruited and took part in a variety of motor tasks over the course of a 12-month period while wearing body-worn inertial sensors. We establish clinical validity of sensor-based digital measures by investigating convergent validity with appropriate clinical constructs, known groups validity by distinguishing patients from healthy volunteers, and test-retest reliability by comparing measurements between visits., Results: We demonstrate clinical validity of the digital measures, and importantly, superior sensitivity of digital measures for distinguishing 1-year longitudinal change in early-stage PD relative to corresponding clinical constructs., Conclusions: Our results demonstrate the potential of digital health technologies to enhance sensitivity to disease progression relative to existing measurement standards and may constitute the basis for use as drug development tools in clinical research., (© 2024. The Author(s).)

Background: Speech changes are an early symptom of Huntington disease (HD) and may occur prior to other motor and cognitive symptoms. Assessment of HD commonly uses clinician-rated outcome measures, which can be limited by observer variability and episodic administration. Speech symptoms are well suited for evaluation by digital measures which can enable sensitive, frequent, passive, and remote administration., Methods: We collected audio recordings using an external microphone of 36 (18 HD, 7 prodromal HD, and 11 control) participants completing passage reading, counting forward, and counting backwards speech tasks. Motor and cognitive assessments were also administered. Features including pausing, pitch, and accuracy were automatically extracted from recordings using the BioDigit Speech software and compared between the three groups. Speech features were also analyzed by the Unified Huntington Disease Rating Scale (UHDRS) dysarthria score. Random forest machine learning models were implemented to predict clinical status and clinical scores from speech features., Results: Significant differences in pausing, intelligibility, and accuracy features were observed between HD, prodromal HD, and control groups for the passage reading task (e.g., p  < 0.001 with Cohen'd = -2 between HD and control groups for pause ratio). A few parameters were significantly different between the HD and control groups for the counting forward and backwards speech tasks. A random forest classifier predicted clinical status from speech tasks with a balanced accuracy of 73% and an AUC of 0.92. Random forest regressors predicted clinical outcomes from speech features with mean absolute error ranging from 2.43-9.64 for UHDRS total functional capacity, motor and dysarthria scores, and explained variance ranging from 14 to 65%. Montreal Cognitive Assessment scores were predicted with mean absolute error of 2.3 and explained variance of 30%., Conclusion: Speech data have the potential to be a valuable digital measure of HD progression, and can also enable remote, frequent disease assessment in prodromal HD and HD. Clinical status and disease severity were predicted from extracted speech features using random forest machine learning models. Speech measurements could be leveraged as sensitive marker of clinical onset and disease progression in future clinical trials., Competing Interests: AN, RM, and AV were employed by BioSensics. The remaining 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. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Nunes, Pawlik, Mishra, Waddell, Coffey, Tarolli, Schneider, Dorsey, Vaziri and Adams.)

Importance: Variability in intervention participation within care management programs can complicate standard analysis strategies., Objective: To evaluate whether care management was associated with reduced hospital readmissions among individuals with higher participation probabilities., Design, Setting, and Participants: A total of 800 hospitalized patients aged 18 years and older were randomized as part of the Health Care Hotspotting randomized clinical trial, which was conducted in Camden, New Jersey, from June 2014 to September 2017. Data were collected through October 2018. In this new analysis performed between April 6, 2022, and April 23, 2023, the distillation method was applied to account for variable intervention participation. A gradient-boosting machine learning model produced predicted probabilities of engaged participation using baseline covariates only. Predicted probabilities were used to trim both intervention and control populations in an equivalent manner, and intervention effects were reevaluated within study population subsets that were increasingly concentrated with patients having higher participation probabilities. Patients had 2 or more hospitalizations in the 6-month preenrollment period and documented evidence of chronic illness and social complexity., Intervention: Multidisciplinary teams provided services to patients in the intervention arm for a mean 120 days after hospital discharge. Patients in the control group received usual postdischarge care., Main Outcomes and Measures: Hospital readmission rates and counts 30, 90, and 180 days postdischarge., Results: Of 800 eligible patients, 782 had complete discharge information and were included in this analysis (mean [SD] age, 56.6 [12.7] years; 395 [50.5%] female). In the intent-to-treat analysis, the unadjusted 180-day readmission rate for treatment and control groups was 60.1% vs 61.7% (adjusted odds ratio, 0.95; 95% CI, 0.71-1.28; P = .73) and the mean (SD) number of 180-day readmissions was 1.45 (1.89) vs 1.48 (1.94) (adjusted incidence rate ratio, 0.99, 95% CI, 0.88-1.12; P = .86). Among the population with the highest participation probabilities, the mean (SD) 180-day readmission count was 1.22 (1.74) vs 1.57 (1.74) and the incidence rate ratio attained statistical significance (adjusted incidence rate ratio, 0.74; 95% CI, 0.56-0.99; P = .045). Adjusted odds ratios and adjusted incidence rate ratios for 30- and 90-day outcomes reached statistical significance after population distillation., Conclusions and Relevance: This secondary analysis of a randomized clinical trial found that care management was associated with reduced readmissions among patients with higher participation probabilities, suggesting that program operation could be improved by addressing barriers to participation and refining inclusion criteria to identify patients most likely to benefit., Trial Registration: ClinicalTrials.gov Identifier: NCT02090426.

We present an artificial intelligence (AI) system to remotely assess the motor performance of individuals with Parkinson's disease (PD). In our study, 250 global participants performed a standardized motor task involving finger-tapping in front of a webcam. To establish the severity of Parkinsonian symptoms based on the finger-tapping task, three expert neurologists independently rated the recorded videos on a scale of 0-4, following the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The inter-rater reliability was excellent, with an intra-class correlation coefficient (ICC) of 0.88. We developed computer algorithms to obtain objective measurements that align with the MDS-UPDRS guideline and are strongly correlated with the neurologists' ratings. Our machine learning model trained on these measures outperformed two MDS-UPDRS certified raters, with a mean absolute error (MAE) of 0.58 points compared to the raters' average MAE of 0.83 points. However, the model performed slightly worse than the expert neurologists (0.53 MAE). The methodology can be replicated for similar motor tasks, providing the possibility of evaluating individuals with PD and other movement disorders remotely, objectively, and in areas with limited access to neurological care., (© 2023. Springer Nature Limited.)

Introduction Insight into the characteristics of populations from which research samples are drawn is essential to understanding the generalizability of research findings. This study characterizes the membership of Kaiser Permanente and compares members to the population of the communities in which they live. Methods This study is a descriptive comparison of population distributions for Kaiser Permanente members vs the general population within counties in which Kaiser Permanente operates. Kaiser Permanente data on demographics, membership, geographically linked census data, and chronic condition prevalence were compared with community data drawn from the US Census and the Behavioral Risk Factor Surveillance System. Results Overall, Kaiser Permanente members were older (50% aged 40 or older compared to 45.8% of the general population) and more likely to be female (51.8% vs 50.5% of the general population). Distribution by race and ethnicity was similar for all Regions combined but varied somewhat within Regions. Distribution by neighborhood-linked income, education, and social vulnerability was similar between Kaiser Permanente and the community. Prevalence of 6 of 7 chronic conditions was higher in the community than in Kaiser Permanente, with differences ranging from 0.5% for depression to 7.7% for hyperlipidemia. Conclusion The demographic characteristics of Kaiser Permanente members are similar to the general population within each of the Kaiser Permanente Regions. Overall, the size and diversity of the Kaiser Permanente membership offers an effective platform for research. This approach to comparing health system members with the larger community provides valuable context for interpreting real-world evidence, including understanding the generalizability of research and of measures of system performance., Competing Interests: Conflicts of InterestAll authors are employed by Kaiser Permanente. The authors declare no other conflicts of interest.

Vulnerable populations are those who experience disparity at a disproportionate rate. For this article, specific vulnerable populations of interest include people who experience intellectual or developmental disorders, mental illness, or substance misuse. Vulnerable populations are some of the most stigmatized populations in our society. Research shows that vulnerable populations receive less empathic care than general health care populations, resulting in reduced quality of care and disparities in health outcomes. Empathy, a necessary health care competency, is associated with improved patient outcomes, enhanced job satisfaction, and increased retention and resilience across health care professions. However, there is no current standard for how empathy is taught, assessed, or sustained. Even when empathy education is implemented in healthcare professions curricula, research has demonstrated that it appears to erode with experience and time. In addition, the COVID-19 pandemic has exacerbated inequities in health care systems, with consequences for both patients and providers. There is an urgent need to develop efficacious training in empathy across health care professions to foster and sustain a robust workforce and improve health care experiences and outcomes., (Copyright © 2023 American Association of Colleges of Pharmacy. Published by Elsevier Inc. All rights reserved.)

Digital health technologies can provide continuous monitoring and objective, real-world measures of Parkinson's disease (PD), but have primarily been evaluated in small, single-site studies. In this 12-month, multicenter observational study, we evaluated whether a smartwatch and smartphone application could measure features of early PD. 82 individuals with early, untreated PD and 50 age-matched controls wore research-grade sensors, a smartwatch, and a smartphone while performing standardized assessments in the clinic. At home, participants wore the smartwatch for seven days after each clinic visit and completed motor, speech and cognitive tasks on the smartphone every other week. Features derived from the devices, particularly arm swing, the proportion of time with tremor, and finger tapping, differed significantly between individuals with early PD and age-matched controls and had variable correlation with traditional assessments. Longitudinal assessments will inform the value of these digital measures for use in future clinical trials., (© 2023. The Author(s).)

An in vitro model of human ovarian follicles would greatly benefit the study of female reproduction. Ovarian development requires the combination of germ cells and several types of somatic cells. Among these, granulosa cells play a key role in follicle formation and support for oogenesis. Whereas efficient protocols exist for generating human primordial germ cell-like cells (hPGCLCs) from human induced pluripotent stem cells (hiPSCs), a method of generating granulosa cells has been elusive. Here, we report that simultaneous overexpression of two transcription factors (TFs) can direct the differentiation of hiPSCs to granulosa-like cells. We elucidate the regulatory effects of several granulosa-related TFs and establish that overexpression of NR5A1 and either RUNX1 or RUNX2 is sufficient to generate granulosa-like cells. Our granulosa-like cells have transcriptomes similar to human fetal ovarian cells and recapitulate key ovarian phenotypes including follicle formation and steroidogenesis. When aggregated with hPGCLCs, our cells form ovary-like organoids (ovaroids) and support hPGCLC development from the premigratory to the gonadal stage as measured by induction of DAZL expression. This model system will provide unique opportunities for studying human ovarian biology and may enable the development of therapies for female reproductive health., Competing Interests: MP, MK, GB, VK, ET, RK, TS is listed as an inventor for US Provisional Application No. 63/326,640, entitled 'Methods and Compositions for Producing Granulosa-Like Cells', CK is listed as an inventor for US Provisional Application No. 63/326,640, entitled 'Methods and Compositions for Producing Granulosa-Like Cells'. CK is the VP of Cell Engineering of Gameto, Inc, PF, JA, RS, ED No competing interests declared, PC is listed an an inventor for US Provisional Application No. 63/326,640, entitled 'Methods and Compositions for Producing Granulosa-Like Cells'. PC is a co-founder and scientific advisor to Gameto, Inc, GC is listed as an inventor for US Provisional Application No. 63/326,640, entitled 'Methods and Compositions for Producing Granulosa-Like Cells'. GMC serves on the scientific advisory board of Gameto, Inc, Colossal Biosciences, and GCTx, (© 2023, Pierson Smela, Kramme et al.)

The murder of George Floyd by police in May 2020 sparked international protests and brought unparalleled levels of attention to the Black Lives Matter movement. As we show, his death set record levels of activity and amplification on Twitter, prompted the saddest day in the platform's history, and caused his name to appear among the ten most frequently used phrases in a day, where he is the only individual to have ever received that level of attention who was not known to the public earlier that same week. Importantly, we find that the Black Lives Matter movement's rhetorical strategy to connect and repeat the names of past Black victims of police violence-foregrounding racial injustice as an ongoing pattern rather than a singular event-was exceptionally effective following George Floyd's death: attention given to him extended to over 185 prior Black victims, more than other past moments in the movement's history. We contextualize this rising tide of attention among 12 years of racial justice activism on Twitter, demonstrating how activists and allies have used attention and amplification as a recurring tactic to lift and memorialize the names of Black victims of police violence. Our results show how the Black Lives Matter movement uses social media to center past instances of police violence at an unprecedented scale and speed, while still advancing the racial justice movement's longstanding goal to "say their names.", Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: To assess whether escalating to high-dose corticosteroids or anakinra compared with continuing low-dose corticosteroids reduced mortality in patients with severe COVID-19 whose respiratory function deteriorated while receiving dexamethasone 6 mg daily., Methods: We conducted a retrospective cohort study between March 1 to December 31, 2020, of hospitalized patients with confirmed COVID-19 pneumonia. In-hospital death was analyzed using logistic regression with inverse probability of treatment weighting of receiving anakinra, high-dose corticosteroid (dexamethasone >10 mg daily), or remaining on low-dose corticosteroids on the day of first respiratory deterioration., Results: We analyzed 6671 patients whose respiratory status deteriorated while receiving dexamethasone 6 mg daily for COVID-19 pneumonia, of whom 6265 stayed on low-dose corticosteroids, 232 were escalated to high-dose corticosteroids, and 174 to anakinra in addition to corticosteroids. The propensity score-adjusted odds of death were higher in the anakinra (odds ratio [OR] 1.76; 95% CI 1.13-2.72) and high-dose corticosteroid groups (OR 1.53; 95% CI 1.14-2.07) compared with those who continued low-dose corticosteroids on the day of respiratory deterioration. The odds of hospital-acquired infections were also higher in the anakinra (OR 2.00; 95% CI 1.28-3.11) and high-dose corticosteroid groups (OR 1.43; 95% CI 1.00-2.04) compared with low-dose corticosteroid group., Conclusion: Our findings do not support escalating patients with COVID-19 pneumonia who deteriorate on low-dose corticosteroids to high-dose corticosteroids or anakinra., Competing Interests: Declaration of competing interest KB has received research support unrelated to this study from Moderna/Pfizer. LCM has received research support unrelated to this study from Boehringer Ingelheim. GJE has received research support unrelated to this study from Astra Zeneca. The other authors have no conflict of interest., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Adoption of new digital measures for clinical trials and practice has been hindered by lack of actionable qualitative data demonstrating relevance of these metrics to people with Parkinson's disease., Objective: This study evaluated of relevance of WATCH-PD digital measures to monitoring meaningful symptoms and impacts of early Parkinson's disease from the patient perspective., Methods: Participants with early Parkinson's disease (N = 40) completed surveys and 1:1 online-interviews. Interviews combined: 1) symptom mapping to delineate meaningful symptoms/impacts of disease, 2) cognitive interviewing to assess content validity of digital measures, and 3) mapping of digital measures back to personal symptoms to assess relevance from the patient perspective. Content analysis and descriptive techniques were used to analyze data., Results: Participants perceived mapping as deeply engaging, with 39/40 reporting improved ability to communicate important symptoms and relevance of measures. Most measures (9/10) were rated relevant by both cognitive interviewing (70-92.5%) and mapping (80-100%). Two measures related to actively bothersome symptoms for more than 80% of participants (Tremor, Shape rotation). Tasks were generally deemed relevant if they met three participant context criteria: 1) understanding what the task measured, 2) believing it targeted an important symptom of PD (past, present, or future), and 3) believing the task was a good test of that important symptom. Participants did not require that a task relate to active symptoms or "real" life to be relevant., Conclusion: Digital measures of tremor and hand dexterity were rated most relevant in early PD. Use of mapping enabled precise quantification of qualitative data for more rigorous evaluation of new measures.

Background: Patient perspectives on meaningful symptoms and impacts in early Parkinson's disease (PD) are lacking and are urgently needed to clarify priority areas for monitoring, management, and new therapies., Objective: To examine experiences of people with early-stage PD, systematically describe meaningful symptoms and impacts, and determine which are most bothersome or important., Methods: Forty adults with early PD who participated in a study evaluating smartwatch and smartphone digital measures (WATCH-PD study) completed online interviews with symptom mapping to hierarchically delineate symptoms and impacts of disease from "Most bothersome" to "Not present," and to identify which of these were viewed as most important and why. Individual symptom maps were coded for types, frequencies, and bothersomeness of symptoms and their impacts, with thematic analysis of narratives to explore perceptions., Results: The three most bothersome and important symptoms were tremor, fine motor difficulties, and slow movements. Symptoms had the greatest impact on sleep, job functioning, exercise, communication, relationships, and self-concept- commonly expressed as a sense of being limited by PD. Thematically, most bothersome symptoms were those that were personally limiting with broadest negative impact on well-being and activities. However, symptoms could be important to patients even when not present or limiting (e.g., speech, cognition)., Conclusion: Meaningful symptoms of early PD can include symptoms that are present or anticipated future symptoms that are important to the individual. Systematic assessment of meaningful symptoms should aim to assess the extent to which symptoms are personally important, present, bothersome, and limiting.

Objective: To introduce a novel analytical approach for randomized controlled trials that are underpowered because of low participant enrollment or engagement., Data Sources: Reanalysis of data for 805 patients randomized as part of a pilot complex care intervention in 2015-2016 in a large delivery system. In the pilot randomized trial, only 64.6% of patients assigned to the intervention group participated., Study Design: A case study and simulation. The "Distillation Method" capitalizes on the frequently observed correlation between the probability of subjects' participation or engagement in the intervention and the magnitude of benefit they experience. The novel method involves three stages: first, it uses baseline covariates to generate predicted probabilities of participation. Next, these are used to produce nested subsamples of the randomized intervention and control groups that are more concentrated with subjects who were likely to participate/engage. Finally, for the outcomes of interest, standard statistical methods are used to re-evaluate intervention effectiveness in these concentrated subsets., Data Extraction Methods: We assembled secondary data on patients who were randomized to the pilot intervention for one year prior to randomization and two follow-up years. Data included program enrollment status, membership data, demographics, utilization, costs, and clinical data., Principal Findings: Using baseline covariates only, Generalized Boosted Regression Models predicting program enrollment performed well (AUC 0.884). We then distilled the full randomized sample to increasing levels of concentration and reanalyzed program outcomes. We found statistically significant differences in outpatient utilization and emergency department utilization (both follow-up years), and in total costs (follow-up year two only) at select levels of population concentration., Conclusions: By offering an internally valid analytic framework, the Distillation Method can increase the power to detect effects by redefining the estimand to subpopulations with higher enrollment probabilities and stronger average treatment effects while maintaining the original randomization., (© 2022 The Authors. Health Services Research published by Wiley Periodicals LLC on behalf of Health Research and Educational Trust.)

Objectives: To assess whether high- compared with low-dose corticosteroids started upon hospitalization reduce mortality in patients with severe COVID-19 pneumonia or in subgroups stratified by severity of respiratory impairment on admission., Methods: We conducted a retrospective cohort study of patients with confirmed SARS-CoV-2 infection who required oxygen supplementation upon hospitalization between March 1 and December 31, 2020. In-hospital death was analyzed using logistic regression with inverse probability of treatment weighting of receiving low- or high-dose corticosteroid (dexamethasone 6-10 mg daily or >10-20 mg daily or other corticosteroid equivalents)., Results: We analyzed 13,366 patients who received low-dose and 948 who received high-dose corticosteroids, of whom 31.3% and 40.4% had severe respiratory impairment (>15 l/min of oxygen or mechanical ventilation) upon admission, respectively. There were no differences in the propensity score-adjusted odds of death (odds ratio 1.17, 95% CI 0.72-1.90) or infections (odds ratio 0.70, 95% CI 0.44-1.11) for patients who received high-dose compared with low-dose corticosteroids, beginning on the day of admission. No significant differences in subgroups stratified by severity of respiratory impairment were found., Conclusion: Initiating high-dose compared with low-dose corticosteroids among newly hospitalized patients with COVID-19 pneumonia did not improve survival. However, benefit of high-dose corticosteroids in specific subgroups cannot be excluded., Competing Interests: Declaration of competing interest KB has received research support unrelated to this study from Moderna/Pfizer. LCM has received research support unrelated to this study from Boehringer Ingelheim. The other authors have no conflict of interest., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Although there are many interventions to support caregivers, SMS text messaging has not been used widely., Objective: In this paper, we aimed to describe development of the Department of Veterans Affairs (VA) Annie Stress Management SMS text messaging protocol for caregivers of veterans, its pilot test, and subsequent national rollout., Methods: The stress management protocol was developed with text messages focusing on education, motivation, and stress-alleviating activities based on the Resources for Enhancing All Caregivers Health (REACH) VA caregiver intervention. This protocol was then tested in a pilot study. On the basis of the pilot study results, a national rollout of the protocol was executed and evaluated. Caregivers were referred from VA facilities nationally for the pilot and national rollout. Pilot caregivers were interviewed by telephone; national rollout caregivers were sent a web-based evaluation link at 6 months. For both evaluations, questions were scored on a Likert scale ranging from completely disagree to completely agree. For both the pilot and national rollout, quantitative data were analyzed with frequencies and means; themes were identified from open-ended qualitative responses., Results: Of the 22 caregivers in the pilot study, 18 (82%) provided follow-up data. On a 5-point scale, they reported text messages had been useful in managing stress (mean score 3.8, SD 1.1), helping them take care of themselves (mean score 3.7, SD 1.3), and making them feel cared for (mean score 4.1, SD 1.7). Texts were easy to read (mean score 4.5, SD 1.2), did not come at awkward times (mean score 2.2, SD 1.4), were not confusing (mean score 1.1, SD 0.2), and did not cause problems in responding (mean score 1.9, 1.1); however, 83% (15/18) of caregivers did not want to request an activity when stressed. Consequently, the national protocol did not require caregivers to respond. In the national rollout, 22.17% (781/3522) of the eligible caregivers answered the web-based survey and reported that the messages had been useful in managing stress (mean score 4.3, SD 0.8), helping them take care of themselves (mean score 4.3, SD 0.8) and loved ones (mean score 4.2, SD 0.8), and making them feel cared for (mean score 4.5, SD 0.8). Almost two-thirds (509/778, 65.4%) of the participants tried all or most of the strategies. A total of 5 themes were identified. The messages were appreciated, helped with self-care, and made them feel less alone, looking on Annie as a friend. The caregivers reported that the messages were on target and came when they were most needed and did not want them to stop. This success has led to four additional caregiver texting protocols: bereavement, dementia behaviors and stress management, (posttraumatic stress disorder) PTSD behaviors, and taking care of you, with 7274 caregivers enrolled as of February 2022., Conclusions: Caregivers reported the messages made them feel cared for and more confident. SMS text messaging, which is incorporated into clinical settings and health care systems, may represent a low-cost way to provide useful and meaningful support to caregivers., (©Jennifer Lynn Martindale-Adams, Carolyn Davis Clark, Jessica Roxy Martin, Charles Richard Henderson, Linda Olivia Nichols. Originally published in Journal of Participatory Medicine (https://jopm.jmir.org), 17.10.2022.)

Smartphones and wearables are widely recognised as the foundation for novel Digital Health Technologies (DHTs) for the clinical assessment of Parkinson's disease. Yet, only limited progress has been made towards their regulatory acceptability as effective drug development tools. A key barrier in achieving this goal relates to the influence of a wide range of sources of variability (SoVs) introduced by measurement processes incorporating DHTs, on their ability to detect relevant changes to PD. This paper introduces a conceptual framework to assist clinical research teams investigating a specific Concept of Interest within a particular Context of Use, to identify, characterise, and when possible, mitigate the influence of SoVs. We illustrate how this conceptual framework can be applied in practice through specific examples, including two data-driven case studies., (© 2022. The Author(s).)

Importance: Opioid addiction or dependency is a serious crisis in the US that affects public health as well as social and economic welfare. The State of California passed Assembly Bill (AB) 2760 in 2018 that mandates the coprescription of naloxone and opioids for patients with a high overdose risk., Objective: To assess whether the AB 2760-based electronic prompts were associated with increased naloxone orders for opioid users and reduced opioid prescribing when integrated into the practitioner workflow., Design, Setting, and Participants: This cohort study used interrupted time series mixed models to evaluate data obtained from the regional integrated health care system Kaiser Permanente Southern California (KPSC) from January 1, 2018, to December 31, 2019. Clinician participants were continuously employed at KPSC during the study period and ordered an opioid analgesic for eligible patients in 2018. Patient participants were KPSC members aged 18 years or older who received an opioid analgesic prescription during the study period. A series of AB 2760-based electronic prompts were integrated into the KPSC electronic health record system on December 27, 2018. The prompts are triggered or activated when 1 or more opioid prescribing conditions, defined in the AB 2760, are met at outpatient visits. Data were analyzed from January 8, 2021, to September 15, 2021., Exposures: Assembly Bill 2760-based electronic prompts for outpatient opioid prescriptions in the electronic health record system., Main Outcomes and Measures: Primary outcomes were changes in outpatient naloxone order rates among patients who were prescribed opioids and changes in outpatient opioid prescribing rates. Secondary outcomes were total morphine milligram equivalents (MMEs) ordered per prescriber-month, prompts-targeted objectives, and unintended consequences. Risk for opioid abuse among 3 types of patients was also assessed., Results: The 6515 eligible clinicians (mean [SD] age, 45.9 [9.43] years; 3604 men [55.3%]) included in the study served 500 711 unique patients in 1 903 289 outpatient encounters (mean [SD] age, 60.4 [15.67] years; 1 121 004 women [58.9%]) in which an opioid analgesic was prescribed. Naloxone order rate increased from 2.0% in December 2018 to 13.2% in January 2019 and then continued to increase to 27.1% in December 2019. Outpatient opioid prescribing rates decreased by 15.1% (rate ratio [RR], 0.85; 95% CI, 0.83-0.87) per prescriber-month when the electronic prompts were implemented. The postimplementation trend increased by 0.7% per prescriber-month (RR, 1.01; 95% CI, 1.01-1.01); the overall trend was still decreasing. The total MMEs per prescriber-month decreased by 7.8% (RR, 0.92; 95% CI, 0.89-0.96) after implementation of the prompts. The postimplementation trend tapered off. Other safe opioid prescribing measures also improved after implementation (decreases in concomitant muscle relaxants orders [RR, 0.94; 95% CI, 0.89-1.00], initial [RR, 0.86; 0.83-0.89] and renewal [RR, 0.65; 95% CI, 0.62-0.69] opioid orders, and long-term high-dose orders [RR, 0.96; 95% CI, 0.94-0.98])., Conclusions and Relevance: This study found an association between implementation of AB 2760-based prompts and increased naloxone order rate; improved opioid prescribing measures (ie, decreased concomitant muscle relaxants orders, initial and renewal opioid orders, and long-term high-dose orders), except monthly median MMEs; and reduced opioid prescribing. The findings suggest that opioid overdose risks can be mitigated by encouraging safe prescribing habits.

Shifting range limits are predicted for many species as the climate warms. However, the rapid pace of climate change will challenge the natural dispersal capacity of long-lived, sessile organisms such as forest trees. Adaptive responses of populations will, therefore, depend on levels of genetic variation and plasticity for climate-responsive traits, which likely vary across the range due to expansion history and current patterns of selection. Here, we study levels of genetic and plastic variation for phenology and growth traits in populations of red spruce ( Picea rubens ), from the range core to the highly fragmented trailing edge. We measured more than 5000 offspring sampled from three genetically distinct regions (core, margin and edge) grown in three common gardens replicated along a latitudinal gradient. Genetic variation in phenology and growth showed low to moderate heritability and differentiation among regions, suggesting some potential to respond to selection. Phenology traits were highly plastic, but this plasticity was generally neutral or maladaptive in the effect on growth, revealing a potential liability under warmer climates. These results suggest future climate adaptation will depend on the regional availability of genetic variation in red spruce and provide a resource for the design and management of assisted gene flow. This article is part of the theme issue 'Species' ranges in the face of changing environments (Part II)'.

Background: Mental health challenges are thought to affect approximately 10% of the global population each year, with many of those affected going untreated because of the stigma and limited access to services. As social media lowers the barrier for joining difficult conversations and finding supportive groups, Twitter is an open source of language data describing the changing experience of a stigmatized group., Objective: By measuring changes in the conversation around mental health on Twitter, we aim to quantify the hypothesized increase in discussions and awareness of the topic as well as the corresponding reduction in stigma around mental health., Methods: We explored trends in words and phrases related to mental health through a collection of 1-, 2-, and 3-grams parsed from a data stream of approximately 10% of all English tweets from 2010 to 2021. We examined temporal dynamics of mental health language and measured levels of positivity of the messages. Finally, we used the ratio of original tweets to retweets to quantify the fraction of appearances of mental health language that was due to social amplification., Results: We found that the popularity of the phrase mental health increased by nearly two orders of magnitude between 2012 and 2018. We observed that mentions of mental health spiked annually and reliably because of mental health awareness campaigns as well as unpredictably in response to mass shootings, celebrities dying by suicide, and popular fictional television stories portraying suicide. We found that the level of positivity of messages containing mental health, while stable through the growth period, has declined recently. Finally, we observed that since 2015, mentions of mental health have become increasingly due to retweets, suggesting that the stigma associated with the discussion of mental health on Twitter has diminished with time., Conclusions: These results provide useful texture regarding the growing conversation around mental health on Twitter and suggest that more awareness and acceptance has been brought to the topic compared with past years., (©Anne Marie Stupinski, Thayer Alshaabi, Michael V Arnold, Jane Lydia Adams, Joshua R Minot, Matthew Price, Peter Sheridan Dodds, Christopher M Danforth. Originally published in JMIR Mental Health (https://mental.jmir.org), 30.03.2022.)

Sensor data from digital health technologies (DHTs) used in clinical trials provides a valuable source of information, because of the possibility to combine datasets from different studies, to combine it with other data types, and to reuse it multiple times for various purposes. To date, there exist no standards for capturing or storing DHT biosensor data applicable across modalities and disease areas, and which can also capture the clinical trial and environment-specific aspects, so-called metadata. In this perspectives paper, we propose a metadata framework that divides the DHT metadata into metadata that is independent of the therapeutic area or clinical trial design (concept of interest and context of use), and metadata that is dependent on these factors. We demonstrate how this framework can be applied to data collected with different types of DHTs deployed in the WATCH-PD clinical study of Parkinson's disease. This framework provides a means to pre-specify and therefore standardize aspects of the use of DHTs, promoting comparability of DHTs across future studies.

Objective: Contemporary data on the natural history of large abdominal aortic aneurysms (AAAs) in patients undergoing delayed or no repair are lacking. In this study, we examine the impact of large AAA size on the incidence of rupture and mortality., Methods: From a prospectively maintained aneurysm surveillance registry, patients with an unrepaired, large AAA (≥5.5 cm in men and ≥5.0 cm in women) at baseline (ie, index imaging) or who progressed to a large size from 2003 to 2017 were included, with follow-up through March 2020. Outcomes of interest obtained by manual chart review included rupture (confirmed by imaging/autopsy), probable rupture (timing/findings consistent with rupture without more likely cause of death), repair, reasons for either no or delayed (>1 year after diagnosis of large AAA) repair and total mortality. Cumulative incidence of rupture was calculated using a nonparametric cumulative incidence function, accounting for the competing events of death and aneurysm repair and was stratified by patient sex., Results: Of the 3248 eligible patients (mean age, 83.6 ± 9.1 years; 71.2% male; 78.1% white; and 32.0% current smokers), 1423 (43.8%) had large AAAs at index imaging, and 1825 progressed to large AAAs during the follow-up period, with a mean time to qualifying size of 4.3 ± 3.4 years. In total, 2215 (68%) patients underwent repair, of which 332 were delayed >1 year; 1033 (32%) did not undergo repair. The most common reasons for delayed repair were discrepancy in AAA measurement between surgeon and radiologist (34%) and comorbidity (20%), whereas the most common reasons for no repair were patient preference (48%) and comorbidity (30%). Among patients with delayed repair (mean time to repair, 2.6 ± 1.8 years), nine (2.7%) developed symptomatic aneurysms, and an additional 11 (3.3%) ruptured. Of patients with no repair, 94 (9.1%) ruptured. The 3-year cumulative incidence of rupture was 3.4% for initial AAA size 5.0 to 5.4 cm (women only), 2.2% for 5.5 to 6.0 cm, 6.0% for 6.1 to 7.0 cm, and 18.4% for >7.0 cm. Women with AAA size 6.1 to 7.0 cm had a 3-year cumulative incidence of rupture of 12.8% (95% confidence interval, 7.5%-19.6%) compared with 4.5% (95% confidence interval, 3.0%-6.5%) in men (P = .002)., Conclusions: In this large cohort of AAA registry patients over 17 years, annual rupture rates for large AAAs were lower than previously reported, with possible increased risk in women. Further analyses are ongoing to identify those at increased risk for aneurysm rupture and may provide targeted surveillance regimens and improve patient counseling., (Copyright © 2021 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Measuring the specific kind, temporal ordering, diversity, and turnover rate of stories surrounding any given subject is essential to developing a complete reckoning of that subject's historical impact. Here, we use Twitter as a distributed news and opinion aggregation source to identify and track the dynamics of the dominant day-scale stories around Donald Trump, the 45th President of the United States. Working with a data set comprising around 20 billion 1-grams, we first compare each day's 1-gram and 2-gram usage frequencies to those of a year before, to create day- and week-scale timelines for Trump stories for 2016-2021. We measure Trump's narrative control, the extent to which stories have been about Trump or put forward by Trump. We then quantify story turbulence and collective chronopathy-the rate at which a population's stories for a subject seem to change over time. We show that 2017 was the most turbulent overall year for Trump. In 2020, story generation slowed dramatically during the first two major waves of the COVID-19 pandemic, with rapid turnover returning first with the Black Lives Matter protests following George Floyd's murder and then later by events leading up to and following the 2020 US presidential election, including the storming of the US Capitol six days into 2021. Trump story turnover for 2 months during the COVID-19 pandemic was on par with that of 3 days in September 2017. Our methods may be applied to any well-discussed phenomenon, and have potential to enable the computational aspects of journalism, history, and biography., Competing Interests: The authors are grateful for the computing resources provided by the Vermont Advanced Computing Core which was supported in part by NSF award No. OAC-1827314, financial support from the Massachusetts Mutual Life Insurance Company and Google Open Source under the Open-Source Complex Ecosystems And Networks (OCEAN) project. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: Prior studies using aggregated data suggest that better care coordination is associated with higher performance on measures of clinical care process; it is unclear whether this relationship reflects care coordination activities of health plans or physician practices., Objective: Estimate within-plan relationships between beneficiary-reported care coordination measures and HEDIS measures of clinical process for the same individuals., Design: Mixed-effect regression models in cross-sectional data., Participants: 2013 Medicare Advantage CAHPS respondents (n=152,069) with care coordination items linked to independently collected HEDIS data on clinical processes., Main Measures: Care coordination measures assessed follow-up, whether doctors had medical records during visits, whether doctors discussed medicines being taken, how informed doctors seemed about specialist care, and help received with managing care among different providers. HEDIS measures included mammography, colorectal cancer screening, cardiovascular LDL-C screening, controlling blood pressure, 5 diabetes care measures (LDL-C screening, retinal eye exam, nephropathy, blood sugar/HbA1c <9%, LCL-C<100 mg/dL), glaucoma screening in older adults, BMI assessment, osteoporosis management for women with a fracture, and rheumatoid arthritis therapy., Key Results: For 9 of the 13 HEDIS measures, within health plans, beneficiaries who reported better care coordination also received better clinical care (p<0.05) and none of the associations went in the opposite direction; HEDIS differences between those with excellent and poor coordination exceeded 5 percentage points for 7 measures. Nine measures had positive associations (breast cancer screening, colorectal cancer screening, cardiovascular care LDL-C screening, 4 of 5 diabetes care measures, osteoporosis management, and rheumatoid arthritis therapy)., Conclusions: Within health plans, beneficiaries who report better care coordination also received higher-quality clinical care, particularly for care processes that entail organizing patient care activities and sharing information among different healthcare providers. These results extend prior research showing that health plans with better beneficiary-reported care coordination achieved higher HEDIS performance scores., (© 2021. Society of General Internal Medicine.)

Most wearable sensor studies in Parkinson's disease have been conducted in the clinic and thus may not be a true representation of everyday symptoms and symptom variation. Our goal was to measure activity, gait, and tremor using wearable sensors inside and outside the clinic. In this observational study, we assessed motor features using wearable sensors developed by MC10, Inc. Participants wore five sensors, one on each limb and on the trunk, during an in-person clinic visit and for two days thereafter. Using the accelerometer data from the sensors, activity states (lying, sitting, standing, walking) were determined and steps per day were also computed by aggregating over 2 s walking intervals. For non-walking periods, tremor durations were identified that had a characteristic frequency between 3 and 10 Hz. We analyzed data from 17 individuals with Parkinson's disease and 17 age-matched controls over an average 45.4 h of sensor wear. Individuals with Parkinson's walked significantly less (median [inter-quartile range]: 4980 [2835-7163] steps/day) than controls (7367 [5106-8928] steps/day; P = 0.04). Tremor was present for 1.6 [0.4-5.9] hours (median [range]) per day in most-affected hands (MDS-UPDRS 3.17a or 3.17b = 1-4) of individuals with Parkinson's, which was significantly higher than the 0.5 [0.3-2.3] hours per day in less-affected hands (MDS-UPDRS 3.17a or 3.17b = 0). These results, which require replication in larger cohorts, advance our understanding of the manifestations of Parkinson's in real-world settings., (© 2021. The Author(s).)

EXECUTIVE SUMMARY For the American Association of Colleges of Pharmacy (AACP), strategic engagement is critical to the success of colleges and schools of pharmacy in expanding pharmacy and public health practice, meeting programmatic needs, and fulfilling institutional missions. The 2020-2021 Strategic Engagement Standing Committee was charged with identifying effective strategies to leverage the temporary expansion of pharmacist practice capabilities granted during the COVID-19 pandemic for sustained practice. The group was also tasked with looking at ways to partner with the Association of American Medical Colleges (AAMC), our medicine counterparts to develop a plan for collaborating with them to advance interprofessional practice. In this unique year, all standing committees were charged with reading all the reports last year to put President Lin's charges into perspective with the hopes of carrying over the overall theme and work of the previous years committee. Overall, throughout the COVID-19 pandemic, there have been several expansions on the scope of practice for pharmacists and vary by state. We hope to draw out some of those expansions to see how we can build upon efforts to make those permanent., (© 2021 American Association of Colleges of Pharmacy.)

COVID-19 spurred rapid expansion of pharmacy-based point-of-care testing (POCT). This growth was aided, in part, by federal guidance that removed state-level regulatory uncertainty surrounding the ability of pharmacists to administer, interpret, and act on the results of tests. Surveys suggest there is considerable confusion about the legality of these services by state regulators. To ensure the sustainability of POCT services over time, states should consider adopting a standard of care approach to regulation, allowing a flexible framework for practice innovation and expansion over time., (© Individual authors.)

Introduction : Pharmacy-based point-of-care testing has long had the potential to improve patient access to timely care, but adoption has been slowed by financial and regulatory barriers. The COVID-19 pandemic reduced or temporarily eliminated many of the barriers to pharmacy-based testing. This review examines how the changes brought on by may impact pharmacy-based testing after the pandemic. Areas covered : This review searched peer-reviewed, lay, and regulatory literature to explore the implementation of pharmacy-based COVID-19 testing. This includes a review of regulatory and financial changes that removed barriers to testing. Additionally, it reviews the literature related to the growth of pharmacy-based testing. Expert opinion : It is clear that the COVID-19 pandemic created an awareness and opportunity for pharmacy-based point-of-care testing. The changes made in response to the pandemic have the potential to increase the role of pharmacy-based testing, but additional regulatory changes and wider pharmacy adoption are still needed to maximize the value of such services.

Objective: Previous studies of the natural history of abdominal aortic aneurysms (AAAs) have been limited by small cohort sizes or heterogeneous analyses of pooled data. By quickly and efficiently extracting imaging data from the health records, natural language processing (NLP) has the potential to substantially improve how we study and care for patients with AAAs. The aim of the present study was to test the ability of an NLP tool to accurately identify the presence or absence of AAAs and detect the maximal abdominal aortic diameter in a large dataset of imaging study reports., Methods: Relevant imaging study reports (n = 230,660) from 2003 to 2017 were obtained for 32,778 patients followed up in a prospective aneurysm surveillance registry within a large, diverse, integrated healthcare system. A commercially available NLP algorithm was used to assess the presence of AAAs, confirm the absence of AAAs, and extract the maximal diameter of the abdominal aorta, if stated. A blinded expert manual review of 18,000 randomly selected imaging reports was used as the reference standard. The positive predictive value (PPV or precision), sensitivity (recall), and the kappa statistics were calculated., Results: Of the randomly selected 18,000 studies that underwent expert manual review, 48.7% were positive for AAAs. In confirming the presence of an AAA, the interrater reliability of the NLP compared with the expert review showed a kappa value of 0.84 (95% confidence interval [CI], 0.83-0.85), with a PPV of 95% and sensitivity of 88.5%. The NLP algorithm showed similar results for confirming the absence of an AAA, with a kappa of 0.79 (95% CI, 0.799-0.80), PPV of 77.7%, and sensitivity of 91.9%. The kappa, PPV, and sensitivity of the NLP for correctly identifying the maximal aortic diameter was 0.88 (95% CI, 0.87-0.89), 88.8%, and 88.2% respectively., Conclusions: The use of NLP software can accurately analyze large volumes of radiology report data to detect AAA disease and assemble a contemporary aortic diameter-based cohort of patients for longitudinal analysis to guide surveillance, medical management, and operative decision making. It can also potentially be used to identify from the electronic medical records pre- and postoperative AAA patients "lost to follow-up," leverage human resources engaged in the ongoing surveillance of patients with AAAs, and facilitate the construction and implementation of AAA screening programs., (Copyright © 2021 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

In real time, Twitter strongly imprints world events, popular culture, and the day-to-day, recording an ever-growing compendium of language change. Vitally, and absent from many standard corpora such as books and news archives, Twitter also encodes popularity and spreading through retweets. Here, we describe Storywrangler, an ongoing curation of over 100 billion tweets containing 1 trillion 1-grams from 2008 to 2021. For each day, we break tweets into 1-, 2-, and 3-grams across 100+ languages, generating frequencies for words, hashtags, handles, numerals, symbols, and emojis. We make the dataset available through an interactive time series viewer and as downloadable time series and daily distributions. Although Storywrangler leverages Twitter data, our method of tracking dynamic changes in n -grams can be extended to any temporally evolving corpus. Illustrating the instrument's potential, we present example use cases including social amplification, the sociotechnical dynamics of famous individuals, box office success, and social unrest., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).)

We study collective attention paid towards hurricanes through the lens of n-grams on Twitter, a social media platform with global reach. Using hurricane name mentions as a proxy for awareness, we find that the exogenous temporal dynamics are remarkably similar across storms, but that overall collective attention varies widely even among storms causing comparable deaths and damage. We construct 'hurricane attention maps' and observe that hurricanes causing deaths on (or economic damage to) the continental United States generate substantially more attention in English language tweets than those that do not. We find that a hurricane's Saffir-Simpson wind scale category assignment is strongly associated with the amount of attention it receives. Higher category storms receive higher proportional increases of attention per proportional increases in number of deaths or dollars of damage, than lower category storms. The most damaging and deadly storms of the 2010s, Hurricanes Harvey and Maria, generated the most attention and were remembered the longest, respectively. On average, a category 5 storm receives 4.6 times more attention than a category 1 storm causing the same number of deaths and economic damage., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Author DRD’s commercial affiliation with paid employment. Additionally, we have received funding from a commerical source: Massachusetts Mutual Life Insurance Company and Google. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction: Parkinson's disease (PD) research is hampered by slow, inefficient recruitment and burdensome in-person assessments that may be challenging to conduct in a world affected by COVID-19. Fox Insight is an ongoing prospective clinical research study that enables individuals to participate in clinical research from their own homes by completing online questionnaires. To date, over 45,000 participants with and without PD have enrolled. We sought to validate self-reported PD diagnosis in the Fox Insight cohort, assess the validity of other self-reported health information, and evaluate the willingness of participants to participate in video-based research studies., Methods: Individuals with and without self-reported PD enrolled in Fox Insight were invited to participate in this virtual research study. Participants completed online questionnaires and two virtual visits, during which we conducted standard cognitive and motor assessments. A movement disorder expert determined the most likely diagnosis, which was compared to self-reported diagnosis., Results: A total of 203 participants from 40 U.S. states, 159 with remote clinician-determined PD and 44 without, completed the study (59% male, mean (SD) age 65.7 (9.8)). Level of agreement between self-reported PD diagnosis in Fox Insight and clinician-determined diagnosis was very good ((kappa = 0.85, 95% CI 0.76-0.94). Overall, 97.9% of participants were satisfied with the study, 98.5% were willing to participate in a future observational study with virtual visits, and 76.1% were willing to participate in an interventional trial with virtual visits., Conclusion: Among the Fox Insight cohort, self-reported diagnosis is accurate and interest in virtual research studies is high., (© 2021 Published by Elsevier Ltd.)

Importance: Electronic health records (EHRs) often include default alerts that can influence physician selection of antibiotics, which in turn may be associated with a suboptimal choice of agents and increased antibiotic resistance., Objective: To examine whether removal of a default alert in the EHR to avoid cephalosporin use in patients with penicillin allergies is associated with changes in cephalosporin dispensing or administration in these patients., Design, Setting, and Participants: This retrospective cohort study of a natural experiment included data on patients who had received antibiotic treatment in the hospital or outpatient setting in 2 regions of a large, integrated health system in California from January 1, 2017, to December 31, 2018. Of 4 398 792 patients, 4 206 480 met the eligibility criteria: enrollment in the health system during antibiotic use, availability of complete demographic data, and use of antibiotics outside of the washout period., Interventions or Exposures: Oral or parenteral antibiotics dispensed or administered after removal of an EHR alert to avoid cephalosporin use in patients with a recorded penicillin allergy., Main Outcomes and Measures: Probability that an antibiotic course was a cephalosporin. A multinomial logistic regression model was used to examine the change in rates of cephalosporin use before and after an EHR penicillin allergy alert was removed in 1 of the study regions. Temporal changes in use rates were controlled for by comparing changes in cephalosporin use among patients with or without a penicillin allergy at the site that removed the warning and among patients at a comparison site that retained the warning. Regression models were used to examine adverse events., Results: Of the 4 206 480 patients who met all inclusion criteria, 2 465 849 (58.6%) were women; the mean (SD) age was 40.5 (23.2) years. A total of 10 652 014 antibiotic courses were administered or dispensed, divided approximately evenly between the period before and after removal of the warning. Before removal of an alert in the electronic health record system to avoid prescribing of cephalosporins to patients with a penicillin allergy at 1 of the 2 sites, 58 228 courses of cephalosporins (accounting for 17.9% of all antibiotic use at the site) were used among patients with a penicillin allergy; after removal of the alert, administration or dispensing of cephalosporins increased by 47% compared with cephalosporin administration or dispensing among patients without a penicillin allergy at the same site and patients at the comparison site that retained the warning (ratio of ratios of odds ratios [RROR], 1.47; 95% CI, 1.38-1.56) . No significant differences in anaphylaxis (9 total cases), new allergies (RROR, 1.02; 95% CI, 0.93-1.12), or treatment failures (RROR, 1.02; 95% CI, 0.99-1.05) were found at the course level. No significant differences were found in all-cause mortality (ratio of ratios of rate ratios [RRRR], 1.03; 95% CI, 0.94-1.13), hospital days (RRRR, 1.04; 95% CI, 0.99-1.10), and new infections (Clostridioides difficile: RRRR, 1.02; 95% CI, 0.84-1.22; methicillin-resistant Staphylococcus aureus: RRRR, 0.87; 95% CI, 0.75-1.00; and vancomycin-resistant Enterococcus: RRRR, 0.82; 95% CI, 0.55-1.22) at the patient level., Conclusions and Relevance: In this cohort study, removal of a warning in the electronic health record to avoid cephalosporin use in patients with penicillin allergies was associated with increased administration and dispensing of cephalosporin. This simple and rapidly implementable system-level intervention may be useful for improvement in antibiotic stewardship.