Behind the Scenes: Facilitators and Barriers to Developing State Scarce Resource Allocation Plans for the COVID-19 Pandemic.

BACKGROUND: In response to COVID-19, many states revised, developed, or attempted to develop plans to allocate scarce critical care resources in the event that crisis standards of care were triggered. No prior analysis has assessed this plan development process, including whether plans were successfully adopted. RESEARCH QUESTION: How did states develop or revise scarce resource allocation plans during the COVID-19 pandemic, and what were the barriers and facilitators to their development and adoption at the state level? STUDY DESIGN AND METHODS: Plan authors and state leaders completed a semi-structured interview February to September 2022. Interview transcripts were qualitatively analyzed for themes related to plan development and adoption according to the principles of grounded theory. RESULTS: Thirty-six participants from 34 states completed an interview, from states distributed across all US regions. Among participants' states with plans that existed prior to 2020 (n = 24), 17 were revised and adopted in response to COVID-19. Six states wrote a plan de novo, with the remaining states failing to develop or adopt a plan. Thirteen states continued to revise their plans in response to disability or aging bias complaints or to respond to evolving needs. Many participants expressed that urgency in the early days of the pandemic prevented an ideal development process. Facilitators of successful plan development and adoption include: coordination or support from the state department of health and existing relationships with key community partners, including aging and disability rights groups and minoritized communities. Barriers include lack of perceived political interest in a plan and development during a public health emergency. INTERPRETATION: To avoid repeating mistakes from the early days of the COVID-19 response, states should develop or revise plans with community engagement and consider maintaining a standing committee with diverse membership and content expertise to periodically review plans and advise state officials on pandemic preparedness.

Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900 to 2050.

Based on an extensive model intercomparison, we assessed trends in biodiversity and ecosystem services from historical reconstructions and future scenarios of land-use and climate change. During the 20th century, biodiversity declined globally by 2 to 11%, as estimated by a range of indicators. Provisioning ecosystem services increased several fold, and regulating services decreased moderately. Going forward, policies toward sustainability have the potential to slow biodiversity loss resulting from land-use change and the demand for provisioning services while reducing or reversing declines in regulating services. However, negative impacts on biodiversity due to climate change appear poised to increase, particularly in the higher-emissions scenarios. Our assessment identifies remaining modeling uncertainties but also robustly shows that renewed policy efforts are needed to meet the goals of the Convention on Biological Diversity.

Managing differential performance of polygenic risk scores across groups: Real-world experience of the eMERGE Network.

The differential performance of polygenic risk scores (PRSs) by group is one of the major ethical barriers to their clinical use. It is also one of the main practical challenges for any implementation effort. The social repercussions of how people are grouped in PRS research must be considered in communications with research participants, including return of results. Here, we outline the decisions faced and choices made by a large multi-site clinical implementation study returning PRSs to diverse participants in handling this issue of differential performance. Our approach to managing the complexities associated with the differential performance of PRSs serves as a case study that can help future implementers of PRSs to plot an anticipatory course in response to this issue.

Qualitative Analysis of Decision to Pursue Electrical Brain Stimulation by Patients With Drug-Resistant Epilepsy and Their Caregivers.

Background and Objectives: To understand why patients with drug-resistant epilepsy (DRE) pursue invasive electrical brain stimulation (EBS). Methods: We interviewed patients with DRE (n = 20) and their caregivers about their experiences in pursuing EBS approximately 1 year post device implant. Inductive analysis was applied to identify key motivating factors. Results: The cohort included participants aged from teens to 50s with deep brain stimulation, vagus nerve stimulation, responsive neurostimulation, and chronic subthreshold cortical stimulation. Patients' motivations included (1) improved quality of life (2) intolerability of antiseizure medications, (3) desperation, and (4) patient-family dynamics. Both patients and caregivers described a desire to alleviate burdens of the other. Patient apprehensions about EBS focused on invasiveness and the presence of electrodes in the brain. Previous experiences with invasive monitoring and the ability to see hardware in person during clinical visits influenced patients' comfort in proceeding with EBS. Despite realistic expectations for modest and delayed benefits, patients held out hope for an exceptionally positive outcome. Discussion: Our findings describe the motivations and decision-making process for patients with DRE who pursue invasive EBS. Patients balance feelings of desperation, personal goals, frustration with medication side effects, fears about surgery, and potential pressure from concerned caregivers. These factors together with the sense that patients have exhausted therapeutic alternatives may explain the limited decisional ambivalence observed in this cohort. These themes highlight opportunities for epilepsy care teams to support patient decision-making processes.

Leveraging satellite observations to reveal ecological drivers of pest densities across landscapes.

Landscape ecologists have long suggested that pest abundances increase in simplified, monoculture landscapes. However, tests of this theory often fail to predict pest population sizes in real-world agricultural fields. These failures may arise not only from variation in pest ecology, but also from the widespread use of categorical land-use maps that do not adequately characterize habitat-availability for pests. We used 1163 field-year observations of Lygus hesperus (Western Tarnished Plant Bug) densities in California cotton fields to determine whether integrating remotely-sensed metrics of vegetation productivity and phenology into pest models could improve pest abundance analysis and prediction. Because L. hesperus often overwinters in non-crop vegetation, we predicted that pest abundances would peak on farms surrounded by more non-crop vegetation, especially when the non-crop vegetation is initially productive but then dries down early in the year, causing the pest to disperse into cotton fields. We found that the effect of non-crop habitat on pest densities varied across latitudes, with a positive relationship in the north and a negative one in the south. Aligning with our hypotheses, models predicted that L. hesperus densities were 35 times higher on farms surrounded by high versus low productivity non-crop vegetation (EVI area 350 vs. 50) and 2.8 times higher when dormancy occurred earlier versus later in the year (May 15 vs. June 30). Despite these strong and significant effects, we found that integrating these remote-sensing variables into land-use models only marginally improved pest density predictions in cotton compared to models with categorical land cover metrics alone. Together, our work suggests that the remote sensing variables analyzed here can advance our understanding of pest ecology, but not yet substantively increase the accuracy of pest abundance predictions.

Perspectives of Oncologists on the Ethical Implications of Using Artificial Intelligence for Cancer Care.

Importance: Artificial intelligence (AI) tools are rapidly integrating into cancer care. Understanding stakeholder views on ethical issues associated with the implementation of AI in oncology is critical to optimal deployment. Objective: To evaluate oncologists' views on the ethical domains of the use of AI in clinical care, including familiarity, predictions, explainability (the ability to explain how a result was determined), bias, deference, and responsibilities. Design, Setting, and Participants: This cross-sectional, population-based survey study was conducted from November 15, 2022, to July 31, 2023, among 204 US-based oncologists identified using the National Plan & Provider Enumeration System. Main Outcomes and Measures: The primary outcome was response to a question asking whether participants agreed or disagreed that patients need to provide informed consent for AI model use during cancer treatment decisions. Results: Of 387 surveys, 204 were completed (response rate, 52.7%). Participants represented 37 states, 120 (63.7%) identified as male, 128 (62.7%) as non-Hispanic White, and 60 (29.4%) were from academic practices; 95 (46.6%) had received some education on AI use in health care, and 45.3% (92 of 203) reported familiarity with clinical decision models. Most participants (84.8% [173 of 204]) reported that AI-based clinical decision models needed to be explainable by oncologists to be used in the clinic; 23.0% (47 of 204) stated they also needed to be explainable by patients. Patient consent for AI model use during treatment decisions was supported by 81.4% of participants (166 of 204). When presented with a scenario in which an AI decision model selected a different treatment regimen than the oncologist planned to recommend, the most common response was to present both options and let the patient decide (36.8% [75 of 204]); respondents from academic settings were more likely than those from other settings to let the patient decide (OR, 2.56; 95% CI, 1.19-5.51). Most respondents (90.7% [185 of 204]) reported that AI developers were responsible for the medico-legal problems associated with AI use. Some agreed that this responsibility was shared by physicians (47.1% [96 of 204]) or hospitals (43.1% [88 of 204]). Finally, most respondents (76.5% [156 of 204]) agreed that oncologists should protect patients from biased AI tools, but only 27.9% (57 of 204) were confident in their ability to identify poorly representative AI models. Conclusions and Relevance: In this cross-sectional survey study, few oncologists reported that patients needed to understand AI models, but most agreed that patients should consent to their use, and many tasked patients with choosing between physician- and AI-recommended treatment regimens. These findings suggest that the implementation of AI in oncology must include rigorous assessments of its effect on care decisions as well as decisional responsibility when problems related to AI use arise.

The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses.

BACKGROUND: When job demand exceeds job resources, burnout occurs. Burnout in healthcare workers extends beyond negatively affecting their functioning and physical and mental health; it also has been associated with poor medical outcomes for patients. Data-driven technology holds promise for the prediction of occupational burnout before it occurs. Early warning signs of burnout would facilitate preemptive institutional responses for preventing individual, organizational, and public health consequences of occupational burnout. This protocol describes the design and methodology for the decentralized Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) Study. This study aims to develop predictive models of occupational burnout and estimate burnout-associated costs using consumer-grade wearable smartwatches and systems-level data. METHODS: A total of 360 registered nurses (RNs) will be recruited in 3 cohorts. These cohorts will serve as training, testing, and validation datasets for developing predictive models. Subjects will consent to one year of participation, including the daily use of a commodity smartwatch that collects heart rate, step count, and sleep data. Subjects will also complete online baseline and quarterly surveys assessing psychological, workplace, and sociodemographic factors. Routine administrative systems-level data on nursing care outcomes will be abstracted weekly. DISCUSSION: The BROWNIE study was designed to be decentralized and asynchronous to minimize any additional burden on RNs and to ensure that night shift RNs would have equal accessibility to study resources and procedures. The protocol employs novel engagement strategies with participants to maintain compliance and reduce attrition to address the historical challenges of research using wearable devices. TRIAL REGISTRATION: NCT05481138.

Unusual case of bullous emphysema with superimposed pneumonia.

Bullous emphysema is a chronic obstructive pulmonary disease (COPD) that results from chronic inflammation of the lung parenchyma leading to alveolar destruction. Etiology includes tobacco smoking and alpha-1 antitrypsin deficiency. In this article, we present a rare case of bullous emphysema in a nonsmoker with no genetic predisposition or social risk factors presenting with productive cough, fatigue, and shortness of breath. The patient was diagnosed with bullous emphysema with superimposed pneumonia based on clinical and radiological findings. The patients acute complaints were treated successfully with antibiotics, supplemental oxygen, systemic steroids, and, nebulizer treatments. With this case report the authors highlight an unusual presentation of pneumonia in a patient with underlying bullous emphysema. Environmental exposure is often overlooked and the outcomes cannot be turned to favor without a comprehensive approach in patient management from history and physical to deciding the right treatment and follow-up protocols.

Mapping the planet's critical areas for biodiversity and nature's contributions to people.

Meeting global commitments to conservation, climate, and sustainable development requires consideration of synergies and tradeoffs among targets. We evaluate the spatial congruence of ecosystems providing globally high levels of nature's contributions to people, biodiversity, and areas with high development potential across several sectors. We find that conserving approximately half of global land area through protection or sustainable management could provide 90% of the current levels of ten of nature's contributions to people and meet minimum representation targets for 26,709 terrestrial vertebrate species. This finding supports recent commitments by national governments under the Global Biodiversity Framework to conserve at least 30% of global lands and waters, and proposals to conserve half of the Earth. More than one-third of areas required for conserving nature's contributions to people and species are also highly suitable for agriculture, renewable energy, oil and gas, mining, or urban expansion. This indicates potential conflicts among conservation, climate and development goals.

Familial Hypercholesterolemia in the Electronic Medical Records and Genomics Network: Prevalence, Penetrance, Cardiovascular Risk, and Outcomes After Return of Results.

BACKGROUND: The implications of secondary findings detected in large-scale sequencing projects remain uncertain. We assessed prevalence and penetrance of pathogenic familial hypercholesterolemia (FH) variants, their association with coronary heart disease (CHD), and 1-year outcomes following return of results in phase III of the electronic medical records and genomics network. METHODS: Adult participants (n=18 544) at 7 sites were enrolled in a prospective cohort study to assess the clinical impact of returning results from targeted sequencing of 68 actionable genes, including LDLR, APOB, and PCSK9. FH variant prevalence and penetrance (defined as low-density lipoprotein cholesterol >155 mg/dL) were estimated after excluding participants enrolled on the basis of hypercholesterolemia. Multivariable logistic regression was used to estimate the odds of CHD compared to age- and sex-matched controls without FH-associated variants. Process (eg, referral to a specialist or ordering new tests), intermediate (eg, new diagnosis of FH), and clinical (eg, treatment modification) outcomes within 1 year after return of results were ascertained by electronic health record review. RESULTS: The prevalence of FH-associated pathogenic variants was 1 in 188 (69 of 13,019 unselected participants). Penetrance was 87.5%. The presence of an FH variant was associated with CHD (odds ratio, 3.02 [2.00-4.53]) and premature CHD (odds ratio, 3.68 [2.34-5.78]). At least 1 outcome occurred in 92% of participants; 44% received a new diagnosis of FH and 26% had treatment modified following return of results. CONCLUSIONS: In a multisite cohort of electronic health record-linked biobanks, monogenic FH was prevalent, penetrant, and associated with presence of CHD. Nearly half of participants with an FH-associated variant received a new diagnosis of FH and a quarter had treatment modified after return of results. These results highlight the potential utility of sequencing electronic health record-linked biobanks to detect FH.

Treating addiction with deep brain stimulation: Ethical and legal considerations.

BACKGROUND: The use of neuromodulation in the treatment of psychiatric conditions is controversial despite its lengthy history. This particularly applies to the use of invasive neuromodulation, such as deep brain stimulation (DBS), to treat substance use disorder (SUD) due to the considerable risks of the procedures. However, given the advances in DBS research and the shortcomings of current treatment modalities for addiction, off-label use and clinical trials are being implemented for the management of treatment-refractory patients. METHODS: Here we conduct an ethical and legal analysis of DBS for SUD, referencing the four foundational principles of medical ethics and key legal concepts. RESULTS: There are major concerns related to the capacity of a SUD patient to provide informed consent, as well as the risks and benefits of DBS compared to traditional treatment methods. In addition to ethical concerns, we explore potential legal issues that may arise from DBS in the treatment of addiction. These include the potential mandate of these procedures in the context of the criminalization of substance use, and the issue of familial consent in the decision-making process. Given the paucity of relevant clinical guidelines or legal cases, general medico-legal principles serve as the reference in making decisions about the responsible use of DBS as a treatment for addiction. CONCLUSIONS: Given the rapidly increasing evidence for DBS as a treatment for SUD, it is an urgent imperative to consider the relevant key ethical and legal issues. Incorporating IDEAL (Idea, Development, Exploration, Assessment, Long-term follow-up) framework into future research in DBS is recommended to evaluate patient safety and ethical perspectives. With the broad criminalization of SUD across the globe, legal coercion of DBS is not impossible, especially if proven to be effective to treat SUD. It is advised for stakeholders to urgently consider incorporating DBS-related drug policies so that the potential benefits of DBS within the rights of people with SUD are not hindered by the lack of clinical guidance and legislations.

Examining the Impact of Polygenic Risk Information in Primary Care.

BACKGROUND: Polygenic risk testing examines variation across multiple genes to estimate a risk score for a particular disease, including risk scores for many common, chronic health conditions. Although polygenic risk information (PRI) may be a promising tool for enhancing preventive counseling and facilitating early identification of disease, its potential impact on primary-care encounters and disease prevention efforts has not been well characterized. METHODS: We conducted in-depth, semi-structured interviews of patients to assess their understandings of PRI and their beliefs about its relevance to disease prevention. RESULTS: We completed interviews with 19 participants. Participants described enthusiasm for the generation of PRI and recognized its utility for disease prevention. Participants also described the value of PRI as limited if not corroborated by non-genetic risk factors. Finally, participants noted that PRI, by itself, would be insufficient as a trigger for initiating many preventive interventions. CONCLUSION: PRI has the potential to become an important tool in primary care. However, patient views about PRI as well as the complexities of disease prevention in the primary care context may limit the impact of PRI on disease prevention.

Ethical Aspects of Prodromal Synucleinopathy Prognostic Counseling.

Alpha-synucleinopathies can be identified in their prodromal phase, raising several ethical issues. In this review, we first provide definitions of prodromal α-synucleinopathies and discuss the importance of distinguishing between prodromes and risk factors. Next, we discuss the implications of a diagnosis of prodromal α-synucleinopathy and considerations regarding prognostic counseling in both clinical and research settings. We review available data on patient preferences regarding disclosure as well as providers' perspectives. We examine the pros and cons of disclosing a diagnosis of prodromal α-synucleinopathy, taking into consideration the differences between clinical and research settings. Asking about willingness to know in clinical and research settings and the shared decision-making process applied to prognostic counseling is discussed. Concerning research settings, ethical aspects regarding clinical trials are addressed. Availability of direct-to-consumer technologies will likely lead to novel contexts requiring prognostic counseling, and future neuroprotective or neuromodulating treatments may require further considerations on the timing, role, and importance of prognostic counseling. Recommendations on how to address ethical gaps should be a priority for patients, medical professional societies, and research workgroups. Ethical issues must be considered as an integral part of the overall clinical and research approach to prodromal synucleinopathies.

Mapping the planet's critical natural assets.

Sustaining the organisms, ecosystems and processes that underpin human wellbeing is necessary to achieve sustainable development. Here we define critical natural assets as the natural and semi-natural ecosystems that provide 90% of the total current magnitude of 14 types of nature's contributions to people (NCP), and we map the global locations of these critical natural assets at 2 km resolution. Critical natural assets for maintaining local-scale NCP (12 of the 14 NCP) account for 30% of total global land area and 24% of national territorial waters, while 44% of land area is required to also maintain two global-scale NCP (carbon storage and moisture recycling). These areas overlap substantially with cultural diversity (areas containing 96% of global languages) and biodiversity (covering area requirements for 73% of birds and 66% of mammals). At least 87% of the world's population live in the areas benefitting from critical natural assets for local-scale NCP, while only 16% live on the lands containing these assets. Many of the NCP mapped here are left out of international agreements focused on conserving species or mitigating climate change, yet this analysis shows that explicitly prioritizing critical natural assets and the NCP they provide could simultaneously advance development, climate and conservation goals.

Patient values and preferences regarding prognostic counseling in isolated REM sleep behavior disorder.

STUDY OBJECTIVES: Isolated REM sleep behavior disorder (iRBD) carries a high lifetime risk for phenoconversion to a defined neurodegenerative disease (NDD) including Parkinson disease, dementia with Lewy bodies, and multiple system atrophy. We aimed to examine iRBD patient values and preferences regarding prognostic counseling. METHODS: One hundred thirteen iRBD patient participants enrolled in the Mayo Clinic iRBD Patient Registry were sent an email survey concerning their values and preferences concerning NDD prognostic counseling and their experiences following diagnosis with iRBD. RESULTS: Of 81 respondents (71.7% response rate), the majority were men (74.0%) with an average age of 65.7 (±9.7) years. Responses indicated a strong preference toward receiving prognostic information about possible future NDD development. 92.5% of respondents felt knowledge concerning personal NDD risk was important, while 87.6% indicated prognostic discussions were important to maintaining trust in their physician. 95.7% indicated a desire for more information, while only 4.3% desired less information regarding their NDD prognostic risk. Most respondents strongly agreed that prognostic information was important to discuss with their family and friends and inform future life planning, and most expressed interest in learning more about future neuroprotective therapies and symptomatic treatments for parkinsonism and dementia. CONCLUSIONS: Most iRBD patients indicated strong preferences for disclosure of NDD prognostic risk and indicated that prognostic information was important for family discussions and future life planning. Future broader surveys and qualitative studies of clinic-based and ultimately community dwelling iRBD patients' values and preferences are needed to guide appropriately tailored and individualized prognostic counseling approaches following iRBD diagnosis.

Assessing socioeconomic bias in machine learning algorithms in health care: a case study of the HOUSES index.

OBJECTIVE: Artificial intelligence (AI) models may propagate harmful biases in performance and hence negatively affect the underserved. We aimed to assess the degree to which data quality of electronic health records (EHRs) affected by inequities related to low socioeconomic status (SES), results in differential performance of AI models across SES. MATERIALS AND METHODS: This study utilized existing machine learning models for predicting asthma exacerbation in children with asthma. We compared balanced error rate (BER) against different SES levels measured by HOUsing-based SocioEconomic Status measure (HOUSES) index. As a possible mechanism for differential performance, we also compared incompleteness of EHR information relevant to asthma care by SES. RESULTS: Asthmatic children with lower SES had larger BER than those with higher SES (eg, ratio = 1.35 for HOUSES Q1 vs Q2-Q4) and had a higher proportion of missing information relevant to asthma care (eg, 41% vs 24% for missing asthma severity and 12% vs 9.8% for undiagnosed asthma despite meeting asthma criteria). DISCUSSION: Our study suggests that lower SES is associated with worse predictive model performance. It also highlights the potential role of incomplete EHR data in this differential performance and suggests a way to mitigate this bias. CONCLUSION: The HOUSES index allows AI researchers to assess bias in predictive model performance by SES. Although our case study was based on a small sample size and a single-site study, the study results highlight a potential strategy for identifying bias by using an innovative SES measure.

Implementation of preemptive DNA sequence-based pharmacogenomics testing across a large academic medical center: The Mayo-Baylor RIGHT 10K Study.

PURPOSE: The Mayo-Baylor RIGHT 10K Study enabled preemptive, sequence-based pharmacogenomics (PGx)-driven drug prescribing practices in routine clinical care within a large cohort. We also generated the tools and resources necessary for clinical PGx implementation and identified challenges that need to be overcome. Furthermore, we measured the frequency of both common genetic variation for which clinical guidelines already exist and rare variation that could be detected by DNA sequencing, rather than genotyping. METHODS: Targeted oligonucleotide-capture sequencing of 77 pharmacogenes was performed using DNA from 10,077 consented Mayo Clinic Biobank volunteers. The resulting predicted drug response-related phenotypes for 13 genes, including CYP2D6 and HLA, affecting 21 drug-gene pairs, were deposited preemptively in the Mayo electronic health record. RESULTS: For the 13 pharmacogenes of interest, the genomes of 79% of participants carried clinically actionable variants in 3 or more genes, and DNA sequencing identified an average of 3.3 additional conservatively predicted deleterious variants that would not have been evident using genotyping. CONCLUSION: Implementation of preemptive rather than reactive and sequence-based rather than genotype-based PGx prescribing revealed nearly universal patient applicability and required integrated institution-wide resources to fully realize individualized drug therapy and to show more efficient use of health care resources.