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Women's health care has evolved significantly since it was first acknowledged as an integral part of internal medicine training more than two decades ago. To update and clarify core competencies in sex- and gender-based women's health for general internists, the Society of General Internal Medicine (SGIM) Women and Medicine Commission prepared the following Position Paper, approved by the SGIM council in 2023. Competencies were developed using several sources, including the 2021 Accreditation Council for Graduate Medical Education Program Requirements for Internal Medicine and the 2023 American Board of Internal Medicine Certification Examination Blueprint. These competencies are relevant to the care of patients who identify as women, as well as gender-diverse individuals to whom these principles apply. They align with pivotal advances in women's health and acknowledge the changing context of patients' lives, reaffirming the role of general internal medicine physicians in providing comprehensive care to women.

BackgroundPrimary care practices are responding to calls to incorporate patients' social risk factors, such as housing, food, and economic insecurity, into clinical care. Healthcare likely relies on the expertise and resources of community-based organizations to improve patients' social conditions, yet little is known about the referral process.ObjectiveTo characterize referrals to community-based organizations by primary care practices.DesignQualitative study using semi-structured interviews with healthcare administrators responsible for social care efforts in their organization.ParticipantsAdministrators at 50 diverse US healthcare organizations with efforts to address patients' social risks.Main measuresApproaches used in primary care to implement social needs referral to community-based organizations.ResultsInterviewed administrators reported that social needs referrals were an essential element in their social care activities. Administrators described the ideal referral programs as placing limited burden on care teams, providing patients with customized referrals, and facilitating closed-loop referrals. We identified three key challenges organizations experience when trying to implement the ideal referrals program: (1) developing and maintaining resources lists; (2) aligning referrals with patient needs; and (3) measuring the efficacy of referrals. Collectively, these challenges led to organizations relying on staff to manually develop and update resource lists and, in most cases, provide patients with generic referrals. Administrators not only hoped that referral platforms may help overcome some of these barriers, but also reported implementation challenges with platforms including inconsistent buy-in and use across staff; integration with electronic health records; management and prioritization of resources; and alignment with other organizations in their market.Conclusion and relevanceReferrals to community-based organizations were used in primary care to improve patients' social conditions, but despite strong motivations, interviewees reported challenges providing tailored and up-to-date information to patients.

As part of an investment by the Bill & Melinda Gates Foundation to support the Government of Bihar to improve reproductive, maternal, newborn and child health and nutrition (RMNCHN) statewide, BBC Media Action implemented multiple communication tools to support front-line worker (FLW) outreach. We analyse the impacts of a package of mHealth audio messaging and paper-based job aids used by FLWs during government-sponsored village health, sanitation and nutrition days (VHSNDs) on knowledge and practices of childbearing women across the RMNCHN continuum of care. Data from two surveys collected between July and September 2016 were analysed using logistic regression to compare health-related knowledge and behaviours between women who had been exposed at VHSNDs to the mHealth GupShup Potli (GSP) audio recordings or interpersonal communication (IPC) tools versus those who were unexposed. Exposure to GSP recordings (n=2608) was associated with improved knowledge across all continuum-of-care domains, as well as improved health-related behaviours in some domains. The odds of having taken iron-folic acid (IFA) tablets were significantly higher in exposed women (OR 1.5, 95% CI 1.1 to 2.2), as was contraceptive use (OR 2.0, 95% CI 1.2 to 3.2). There were no differences in birth preparedness or complementary feeding practices between groups. Exposure to IPC paper-based tools (n=2002) was associated with a twofold increased odds of IFA consumption (OR 2.3, 95% CI 1.7 to 3.2) and contraceptive use (OR 1.8, 95% CI 1.2 to 2.8). Women exposed to both tools were generally at least twice as likely to subsequently discuss the messages with others. BBC Media Action's mHealth audio messaging job aids and paper-based IPC tools were associated with improved knowledge and practices of women who were exposed to them across multiple domains, suggesting their important potential for improving health outcomes for beneficiaries at scale in low-resource settings. NCT02726230.

BackgroundBrucellosis, as a serious zoonotic infectious disease, has been recognized as a re-emerging disease in the developing countries worldwide. In china, the incidence of brucellosis is increasing each year, seriously threatening the health of humans as well as animal populations. Despite a quite number of diagnostic methods currently being used for brucellosis, innovative technologies are still needed for its rapid and accurate diagnosis, especially in area where traditional diagnostic is unavailable.Methodology/principal findingsIn this study, a total of 22 B cell linear epitopes were predicted from five Brucella outer membrane proteins (OMPs) using an immunoinformatic approach. These epitopes were then chemically synthesized, and with the method of indirect ELISA (iELISA), each of them displayed a certain degree of capability in identifying human brucellosis positive sera. Subsequently, a fusion protein consisting of the 22 predicted epitopes was prokaryotically expressed and used as diagnostic antigen in a newly established brucellosis testing method, nano-ZnO modified paper-based ELISA (nano-p-ELISA). According to the verifying test using a collection of sera collected from brucellosis and non-brucellosis patients, the sensitivity and specificity of multiepitope based nano-p-ELISA were 92.38% and 98.35% respectively. The positive predictive value was 98.26% and the negative predictive value was 91.67%. The multiepitope based fusion protein also displayed significantly higher specificity than Brucella lipopolysaccharide (LPS) antigen.ConclusionsB cell epitopes are important candidates for serologically testing brucellosis. Multiepitope fusion protein based nano-p-ELISA displayed significantly sensitivity and specificity compared to Brucella LPS antigen. The strategy applied in this study will be helpful to develop rapid and accurate diagnostic method for brucellosis in human as well as animal populations.

The use of misidentified and contaminated cell lines continues to be a problem in biomedical research. Research Resource Identifiers (RRIDs) should reduce the prevalence of misidentified and contaminated cell lines in the literature by alerting researchers to cell lines that are on the list of problematic cell lines, which is maintained by the International Cell Line Authentication Committee (ICLAC) and the Cellosaurus database. To test this assertion, we text-mined the methods sections of about two million papers in PubMed Central, identifying 305,161 unique cell-line names in 150,459 articles. We estimate that 8.6% of these cell lines were on the list of problematic cell lines, whereas only 3.3% of the cell lines in the 634 papers that included RRIDs were on the problematic list. This suggests that the use of RRIDs is associated with a lower reported use of problematic cell lines.

Building on long-term benefits of early intervention (Paper 2 of this Series) and increasing commitment to early childhood development (Paper 1 of this Series), scaled up support for the youngest children is essential to improving health, human capital, and wellbeing across the life course. In this third paper, new analyses show that the burden of poor development is higher than estimated, taking into account additional risk factors. National programmes are needed. Greater political prioritisation is core to scale-up, as are policies that afford families time and financial resources to provide nurturing care for young children. Effective and feasible programmes to support early child development are now available. All sectors, particularly education, and social and child protection, must play a role to meet the holistic needs of young children. However, health provides a critical starting point for scaling up, given its reach to pregnant women, families, and young children. Starting at conception, interventions to promote nurturing care can feasibly build on existing health and nutrition services at limited additional cost. Failure to scale up has severe personal and social consequences. Children at elevated risk for compromised development due to stunting and poverty are likely to forgo about a quarter of average adult income per year, and the cost of inaction to gross domestic product can be double what some countries currently spend on health. Services and interventions to support early childhood development are essential to realising the vision of the Sustainable Development Goals.

ObjectiveElectronic health records (EHRs) are widely adopted, but the time demands of EHR use on ophthalmology trainees are not well understood. This study evaluated ophthalmology trainee time spent on clinical activities in an outpatient clinic undergoing EHR implementation.DesignProspective, manual time-motion observations of ophthalmology trainees in 2018.ParticipantsEleven ophthalmology residents and fellows observed during 156 patient encounters.MethodsProspective time-motion study of ophthalmology trainees 2 weeks before and 6 weeks after EHR implementation in an academic ophthalmology department. Manual time-motion observations were conducted for 11 ophthalmology trainees in 6 subspecialty clinics during 156 patient encounters. Time spent documenting, examining, and talking with patients were recorded. Factors influencing time requirements were evaluated using linear mixed effects models.Main outcome measuresTotal time spent by ophthalmology residents and fellows per patient, time spent on documentation, examination, and talking with patients.ResultsSeven ophthalmology residents and four ophthalmology fellows with mean (standard deviation) postgraduate year of 3.7 (1.2) were observed during 156 patient encounters. Using paper charts, mean total time spent on each patient was 11.6 (6.5) minutes, with 5.4 (3.5) minutes spent documenting (48%). After EHR implementation, mean total time spent on each patient was 11.8 (6.9) minutes, with 6.8 (4.7) minutes spent documenting (57%). Total time expenditure per patient did not significantly change after EHR implementation (+0.17 minutes, 95% confidence interval [CI] for difference in means: -2.78, 2.45; p = 0.90). Documentation time did not change significantly after EHR implementation in absolute terms (+1.42 minutes, 95% CI: -3.13, 0.29; p = 0.10), but was significantly greater as a proportion of total time (48% on paper to 57% on EHR; +9%, 95% CI: 2.17, 15.83; p = 0.011).ConclusionTotal time spent per patient and absolute time spent on documentation was not significantly different whether ophthalmology trainees used paper charts or the recently implemented EHR. Percentage of total time spent on documentation increased significantly with early EHR use. Evaluating EHR impact on ophthalmology trainees may improve understanding of how trainees learn to use the EHR and may shed light on strategies to address trainee burnout.

Engagement is an important component in the advancement of gene-drive vector control research programs as developers look to transition the technology from the laboratory to the field. As research advances and engagement surrounding this novel technology is put into practice, knowledge can be gained from practical experiences and applications in the field. A relationship-based model (RBM) provides a framework for end-user development of engagement programs and strategies. The model places end users at the center of the engagement decision-making processes rather than as recipients of predetermined strategies, methods, and definitions. Successful RBM application for healthcare delivery has previously been demonstrated, and the University of California Malaria Initiative (UCMI) has applied this model to its gene-drive program in the Democratic Republic of São Tomé and Príncipe. The model emphasizes the importance of local leadership in the planning, development, and implementation of all phases of project engagement. The primary aim of this paper is to translate the model from paper to practice and provide a transparent description, using practical examples, of the UCMI program implementation of RBM at its field site. End-user development of the UCMI engagement program provides a unique approach to the development of ethical, transparent, and effective engagement strategies for malaria control programs. This paper may also serve as a reference and example for projects looking to establish an engagement program model that integrates end-user groups in the decision-making processes surrounding engagement.

UnlabelledThis position paper reviews how the National Bone Health Alliance (NBHA) will execute a project to help assure health professionals of the clinical utility of bone turnover markers; the current clinical approaches concerning osteoporosis and the status and use of bone turnover markers in the USA; the rationale for focusing this effort around two specific bone turnover markers; the need to standardize bone marker sample collection procedures, reference ranges, and bone turnover marker assays in clinical laboratories; and the importance of harmonization for future research of bone turnover markers.IntroductionOsteoporosis is a major global health problem, with the prevalence and incidence of osteoporosis for at-risk populations estimated to be 44 million Americans. The potential of bone markers as an additional tool for health care professionals to improve patient outcomes and impact morbidity and mortality is crucial in providing better health care and addressing rising health care costs. This need to advance the field of bone turnover markers has been recognized by a number of organizations, including the International Osteoporosis Foundation (IOF), National Osteoporosis Foundation, International Federation of Clinical Chemistry, and Laboratory Medicine (IFCC), and the NBHA.MethodsThis position paper elucidates how this project will standardize bone turnover marker sample collection procedures in the USA, establish a USA reference range for one bone formation (serum procollagen type I N propeptide, s-PINP) and one bone resorption (serum C-terminal telopeptide of type I collagen, s-CTX) marker, and standardize bone turnover marker assays used in clinical laboratories. This effort will allow clinicians from the USA to have confidence in their use of bone turnover markers to help monitor osteoporosis treatment and assess future fracture risk. This project builds on the recommendations of the IOF/IFCC Bone Marker Standards Working Group by developing USA reference standards for s-PINP and s-CTX, the markers identified as most promising for use as reference markers.ResultsThe goals of this project will be realized through the NBHA and will include its governmental, academic, for-profit, and non-profit sector stakeholders as well as major academic and commercial laboratories. Upon completion, a parallel effort will be pursued to make bone turnover marker measurements reliable and accepted by all health care professionals for facilitating treatment decisions and ultimately be reimbursed by all health insurance payers.ConclusionsSuccessful completion of this project will help assure health professionals from the USA of the clinical utility of bone turnover markers and ties in with the parallel effort of the IOF/IFCC to develop worldwide bone turnover reference ranges.

The StayWell at Home intervention, a 60-day text-messaging program based on Cognitive Behavioral Therapy (CBT) principles, was developed to help adults cope with the adverse effects of the global pandemic. Participants in StayWell at Home were found to show reduced depressive and anxiety symptoms after participation. However, it remains unclear whether the intervention improved mood and which intervention components were most effective at improving user mood during the pandemic. Thus, utilizing a micro-randomized trial (MRT) design, we examined two intervention components to inform the mechanisms of action that improve mood: 1) text messages delivering CBT-informed coping strategies (i.e., behavioral activation, other coping skills, or social support); 2) time at which messages were sent. Data from two independent trials of StayWell are included in this paper. The first trial included 303 adults aged 18 or older, and the second included 266 adults aged 18 or older. Participants were recruited via online platforms (e.g., Facebook ads) and partnerships with community-based agencies aiming to reach diverse populations, including low-income individuals and people of color. The results of this paper indicate that participating in the program improved and sustained self-reported mood ratings among participants. We did not find significant differences between the type of message delivered and mood ratings. On the other hand, the results from Phase 1 indicated that delivering any type of message in the 3 pm-6 pm time window improved mood significantly over sending a message in the 9 am-12 pm time window. The StayWell at Home program increases in mood ratings appeared more pronounced during the first two to three weeks of the intervention and were maintained for the remainder of the study period. The current paper provides evidence that low-burden text-message interventions may effectively address behavioral health concerns among diverse communities.

In 2019, the National Health Interview survey found that nearly 59% of adults reported pain some, most, or every day in the past 3 months, with 39% reporting back pain, making back pain the most prevalent source of pain, and a significant issue among adults. Often, identifying a direct, treatable cause for back pain is challenging, especially as it is often attributed to complex, multifaceted issues involving biological, psychological, and social components. Due to the difficulty in treating the true cause of chronic low back pain (cLBP), an over-reliance on opioid pain medications among cLBP patients has developed, which is associated with increased prevalence of opioid use disorder and increased risk of death. To combat the rise of opioid-related deaths, the National Institutes of Health (NIH) initiated the Helping to End Addiction Long-TermSM (HEAL) initiative, whose goal is to address the causes and treatment of opioid use disorder while also seeking to better understand, diagnose, and treat chronic pain. The NIH Back Pain Consortium (BACPAC) Research Program, a network of 14 funded entities, was launched as a part of the HEAL initiative to help address limitations surrounding the diagnosis and treatment of cLBP. This paper provides an overview of the BACPAC research program's goals and overall structure, and describes the harmonization efforts across the consortium, define its research agenda, and develop a collaborative project which utilizes the strengths of the network. The purpose of this paper is to serve as a blueprint for other consortia tasked with the advancement of pain related science.

Family, twin, and adoption studies have documented a strong genetic basis for ADHD/HKD, but these studies do not identify specific genes linked to the disorder. Molecular genetic studies can identify allelic variations of specific genes that are functionally associated with ADHD/HKD, and dopamine genes have been the initial candidates based on the site of action of the stimulants drugs, which for a half century have provided the primary pharmacological treatment for ADHD/HKD. Two candidate dopamine genes have been investigated and reported to be associated with ADHD/HKD: the dopamine transporter (DAT1) gene [Cook et al., American Journal of Human Genetics 1995;56:993-998, Gill et al., Molecular Psychiatry 1997;2:311-313] and the dopamine receptor D4 (DRD4) gene [LaHoste et al., Molecular Psychiatry 1996;1:121-124: Smalley et al., 1998;3:427-430; Swanson et al., Molecular Psychiatry 1998;3:38-41]. Speculative hypotheses [Swanson and Castellanos, NIH Consensus Development Conference: Diagnosis and Treatment of Attention Deficit Hyperactivity Disorder, November 1998. p. 37-42] have suggested that specific alleles of these dopamine genes may alter dopamine transmission in the neural networks implicated in ADHD/HKD (e.g. that the 10-repeat allele of the DAT1 gene may be associated with hyperactive re-uptake of dopamine or that the 7-repeat allele of the DRD4 gene may be associated with a subsensitive postsynaptic receptor). These and other variants of the dopamine hypothesis of ADHD will be discussed.

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, dynamic scheduling of follow-ups, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients’ well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present a comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process.

The profound impact of food on health necessitates advanced nutrition-oriented food recommendation services. Conventional methods often lack the crucial elements of personalization, explainability, and interactivity. While Large Language Models (LLMs) bring interpretability and explainability, their standalone use falls short of achieving true personalization. In this paper, we introduce ChatDiet, a novel LLM-powered framework designed specifically for personalized nutrition-oriented food recommendation chatbots. ChatDiet integrates personal and population models, complemented by an orchestrator, to seamlessly retrieve and process pertinent information. The personal model leverages causal discovery and inference techniques to assess personalized nutritional effects for a specific user, whereas the population model provides generalized information on food nutritional content. The orchestrator retrieves, synergizes and delivers the output of both models to the LLM, providing tailored food recommendations designed to support targeted health outcomes. The result is a dynamic delivery of personalized and explainable food recommendations, tailored to individual user preferences. Our evaluation of ChatDiet includes a compelling case study, where we establish a causal personal model to estimate individual nutrition effects. Our assessments, including a food recommendation test showcasing a 92% effectiveness rate, coupled with illustrative dialogue examples, underscore ChatDiet's strengths in explainability, personalization, and interactivity.

ObjectiveBlockchain has emerged as a potential data-sharing structure in healthcare because of its decentralization, immutability, and traceability. However, its use in the biomedical domain is yet to be investigated comprehensively, especially from the aspects of implementation and evaluation, by existing blockchain literature reviews. To address this, our review assesses blockchain applications implemented in practice and evaluated with quantitative metrics.Materials and methodsThis systematic review adapts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to review biomedical blockchain papers published by August 2023 from 3 databases. Blockchain application, implementation, and evaluation metrics were collected and summarized.ResultsFollowing screening, 11 articles were included in this review. Articles spanned a range of biomedical applications including COVID-19 medical data sharing, decentralized internet of things (IoT) data storage, clinical trial management, biomedical certificate storage, electronic health record (EHR) data sharing, and distributed predictive model generation. Only one article demonstrated blockchain deployment at a medical facility.DiscussionEthereum was the most common blockchain platform. All but one implementation was developed with private network permissions. Also, 8 articles contained storage speed metrics and 6 contained query speed metrics. However, inconsistencies in presented metrics and the small number of articles included limit technological comparisons with each other.ConclusionWhile blockchain demonstrates feasibility for adoption in healthcare, it is not as popular as currently existing technologies for biomedical data management. Addressing implementation and evaluation factors will better showcase blockchain's practical benefits, enabling blockchain to have a significant impact on the health sector.

Multilevel interventions (MLIs) are appropriate to reduce health disparities among Indigenous peoples because of their ability to address these communities' diverse histories, dynamics, cultures, politics, and environments. Intervention science has highlighted the importance of context-sensitive MLIs in Indigenous communities that can prioritize Indigenous and local knowledge systems and emphasize the collective versus the individual. This paradigm shift away from individual-level focus interventions to community-level focus interventions underscores the need for community engagement and diverse partnerships in MLI design, implementation, and evaluation. In this paper, we discuss three case studies addressing how Indigenous partners collaborated with researchers in each stage of the design, implementation, and evaluation of MLIs to reduce health disparities impacting their communities. We highlight the following: (1) collaborations with multiple, diverse tribal partners to carry out MLIs which require iterative, consistent conversations over time; (2) inclusion of qualitative and Indigenous research methods in MLIs as a way to honor Indigenous and local knowledge systems as well as a way to understand a health disparity phenomenon in a community; and (3) relationship building, maintenance, and mutual respect among MLI partners to reconcile past research abuses, prevent extractive research practices, decolonize research processes, and generate co-created knowledge between Indigenous and academic communities.

IntroductionBehavioral health emergencies (BHEs) are a common patient encounter for emergency medical services (EMS) clinicians and other first responders, in particular law enforcement (LE) officers. It is critical for EMS clinicians to have management strategies for BHEs, yet relatively little information exists on best practices. In 2016, the Los Angeles County EMS Agency's Commission initiated a comprehensive evaluation of the 9-1-1 response for BHEs and developed a plan for improving the quality of care and safety for patients and first responders.MethodsA Behavioral Health Initiative Committee was assembled with broad representation from EMS, LE, health agencies, and the public. Committee objectives included: 1) produce a process map of the BHE response from the time of a 9-1-1 call to patient arrival at transport destination, 2) identify and describe the different agencies that respond, 3) describe the critical decision points in the EMS and LE field responses, 4) acquire data that quantitatively and/or qualitatively describe the services available, and 5) recommend interventions for system performance improvement.ResultsThe committee generated comprehensive process maps for the prehospital response to BHEs, articulated principles for evaluation, and described key observations of the current system including: 9-1-1 dispatch criteria are variable and often defaults to a LE response, the LE response inadvertently criminalizes BHEs, EMS field treatment protocols for BHEs (and especially agitated patients) are limited, substance use disorder treatment lacks integration, destination options differ by transporting agency, and receiving facilities' capabilities to address BHEs are variable. Recommendations for performance improvement interventions and initial implementation steps included: standardize dispatch protocols, shift away from a LE primary response, augment EMS treatment protocols for BHEs and the management of agitation, develop alternate destination for EMS transport.ConclusionThis paper describes a comprehensive performance improvement initiative in LAC-EMSA's 9-1-1 response to BHEs. The initiative included a thorough current state analysis, followed by future state mapping and the implementation of interventions to reduce LE as the primary responder when an EMS response is often warranted, and to improve EMS protocols and access to resources for BHEs.

BackgroundIn a setting such as Nepal with malnutrition and persistent poor maternal and infant health outcomes, developing interventions to improve the nutrition of preconception and pregnant women is essential.ObjectiveThe objectives of this paper are to describe the full design process of an intervention for newly married women, their husbands, and mothers-in-law to improve maternal nutrition and gender norms, and findings from the feasibility and acceptability pilot.MethodsIn this paper we describe the three phases of the design of an intervention in rural Nepal. We first conducted a mixed methods formative phase which included in depth interviews with newly married women, their husbands and mothers-in-law (N=60) and a longitudinal study for 18 months with 200 newly married women. We then designed of a household level, group, intervention, in close partnership with community members. Finally, we conducted a pilot intervention with 90 participants and collected both pre/post survey data and in-depth qualitative interviews with a subset (N= 30). All participants from all phases of the study lived in Nawalparasi district of Nepal. Qualitative data was analyzed using a thematic analysis, with inductive and deductive themes and quantitative data was analyzed using descriptive statistics.ResultsOur formative work highlighted lack of awareness about nutrition, and how women eating last, limited mobility, household and community inequitable gender norms and poor household-level communication contributed to low quality diets. Thus we designed Sumadhur, an intervention that brought groups of households (newly married wife, husband, and mother-in-law) together weekly for four months to strengthen relationships and gain knowledge through interactive content. We found Sumadhur to be highly feasible and acceptable by all respondents, with most (83%) attending 80% of sessions or more and 99% reporting that they would like it to continue. Pre/post surveys showed a decrease in the proportion of women eating last and increase in knowledge about nutrition in preconception and pregnancy. Qualitative interviews suggested that respondents felt it made large impacts on their lives, in terms of strengthening relationships and trust, understanding each other, and changing behaviors.ConclusionsWe show how a designing an intervention in close partnership with the target recipients and local stakeholders can lead to an intervention that is able to target complicated and culturally held practices and beliefs, positively benefit health and wellbeing, and that is very well received.Trial registrationClinicalTrials.gov NCT04383847 , registered 05/12/2020.

Bipolar disorder is heterogeneous in phenomenology, illness trajectory, and response to treatment. Despite evidence for the efficacy of multimodal-ity interventions, the majority of persons affected by this disorder do not achieve and sustain full syndromal recovery. It is eagerly anticipated that combining datasets across various information sources (e.g., hierarchical "multi-omic" measures, electronic health records), analyzed using advanced computational methods (e.g., machine learning), will inform future diagnosis and treatment selection. In the interim, identifying clinically meaningful subgroups of persons with the disorder having differential response to specific treatments at point-of-care is an empirical priority. This paper endeavours to synthesize salient domains in the clinical characterization of the adult patient with bipolar disorder, with the overarching aim to improve health outcomes by informing patient management and treatment considerations. Extant data indicate that characterizing select domains in bipolar disorder provides actionable information and guides shared decision making. For example, it is robustly established that the presence of mixed features - especially during depressive episodes - and of physical and psychiatric comorbidities informs illness trajectory, response to treatment, and suicide risk. In addition, early environmental exposures (e.g., sexual and physical abuse, emotional neglect) are highly associated with more complicated illness presentations, inviting the need for developmentally-oriented and integrated treatment approaches. There have been significant advances in validating subtypes of bipolar disorder (e.g., bipolar I vs. II disorder), particularly in regard to pharmacological interventions. As with other severe mental disorders, social functioning, interpersonal/family relationships and internalized stigma are domains highly relevant to relapse risk, health outcomes, and quality of life. The elevated standardized mortality ratio for completed suicide and suicidal behaviour in bipolar disorder invites the need for characterization of this domain in all patients. The framework of this paper is to describe all the above salient domains, providing a synthesis of extant literature and recommendations for decision support tools and clinical metrics that can be implemented at point-of-care.

Maternity services cannot be postponed due to the nature of this service, however, the pandemic resulted in wide-ranging and significant changes to working practices and services. This paper aims to describe UK midwives' experiences of working during the COVID-19 pandemic. This study forms part of a larger multiple phase research project using a cross-sectional design based on an online survey. The online survey used validated psychometric tools to measure work-related quality of life, wellbeing, coping, and burnout as well as open-ended questions to further understand the experiences of staff working during the pandemic. This paper reports the qualitative data collected from the open-ended questions. The qualitative data were subjected to thematic analysis and the four main themes that emerged were 'relentless stress/pressure', 'reconfiguration of services', 'protection of self and others', and 'workforce challenges'. The key conclusions were that midwives experienced a reduction in quality of working life and significant stress throughout the pandemic due to a range of factors including staffing shortages, restrictions placed on women's partners, changes to services and management support, all of which compounded workforce pressures that existed prior to the pandemic. This research recommends consultation of front-line midwives in relation to possible changes in practice and workforce planning in preparation for crises such as a pandemic and to ensure equitable and supportive management with access to practical and psychological support.

AimThrough a review of philosophical and theoretical constructs, this paper offers insight and guidance as to ways in which nurse leaders may operationalize advocacy and an adherence to nursing's core ethical values.BackgroundThe US health care system works in opposition to core nursing values. Nurse leaders are obliged to advocate for the preservation of ethical care delivery.EvaluationThis paper draws upon the philosophies of Fromm, Foucault, and Deleuze and Guattari to critically review the functions of nurse leaders within a capitalist paradigm.Key issueKey emergent issues in the paper include health care and capitalism and the nurse leader's obligations towards advocacy.ConclusionThe nurse leader acts as parrhèsia in viewing truth telling as a duty critical to improving the lives of patients. Ramifications of the decisions by those in power have even greater impact in institutions that serve those with little to no political agency.Implications for nursing managementThe nurse leader has a freedom and platform that their patients do not and must take the courageous risk of choosing to speak. This paper serves as a call to action for nurse leaders to urgently address the current state of US health outcomes.

This paper defines a policy-relevant nationwide composite index to identify communities disproportionately impacted by environmental, energy, and climate injustices in the United States. We review existing vulnerability indicators and indices to assess the tradeoffs of different design parameters, including variable selection, geographic unit, dimensionality reduction, weighting, and aggregation methods. From this methodological review, we create the first nationwide, census tract-level index of cumulative burden that includes energy-relevant indicators alongside climate, social, environmental, and economic indicators, and is flexible to the inclusion of additional data sources. We provide a summary of the sources of inputs used to develop a definition for “disadvantaged communities” that can be used to prioritize energy investments. We discuss use-cases for this index including the implementation of the Justice40 Initiative, which calls for 40 % of certain federal clean energy benefits to flow to disadvantaged communities in the United States. We use our results to examine historic allocations of federal energy investments and show that communities that we identify as disadvantaged received about 37 % fewer funds per capita than non-disadvantaged communities.

ObjectiveThe Mexican government has pursued multiple initiatives to improve healthcare coverage and financial protection. Yet, out-of-pocket health spending and use of private sector providers in Mexico remains high. In this paper, we sought to describe the characteristics of public and private healthcare users, describe recent visit quality across provider types, and to assess whether perceiving the public healthcare sector as poor quality is associated with private health sector use.Methods and findingsWe analyzed the cross-sectional People's Voice Survey conducted from December 2022 to January 2023. We used Chi-square tests to compare contextual, individual, and need-for-care factors and ratings of most recent visits between users of public (social security and other public providers) and private sector providers (stand-alone private providers and providers adjacent to pharmacies). We used a multivariable Poisson regression model to assess associations between low ratings of public healthcare sources and the use of private care. Among the 811 respondents with a healthcare visit in the past year, 31.2% used private sources. Private healthcare users were more educated and had higher incomes than public healthcare users. Quality of most recent visit was rated more highly in private providers (70.2% rating the visit as excellent or very good for stand-alone private providers and 54.3% for pharmacy-adjacent doctors) compared to social security (41.6%) and other public providers (46.6%). Those who perceived public health institutions as low quality had a higher probability of seeking private healthcare.ConclusionUsers rated public care visits poorly relative to private care; at the population level, perceptions of poor quality care may drive private care use and hence out-of-pocket costs. Improving public healthcare quality is necessary to ensure universal health coverage.

BackgroundHIV or sexually transmitted infections remain a significant public health concern in the United States, with adolescents affected disproportionately. Adolescents engage in HIV/STI risk behaviors, including drug use and condomless sex, which increase the risk for HIV/STIs. At-risk adolescents, many of whom are racial minorities, experience HIV/STI disparities. Although at-risk adolescents are disproportionately affected by HIV/STI risk behaviors and infections and although the Centers for Disease Control and Prevention recommends routine HIV/STI testing for adolescents, relatively few adolescents report having ever been tested for HIV/STI. With expected increases in health clinic visits as a result of the Affordable Care Act combined with technological advances, health clinics and mobile health (mHealth), including apps, provide innovative contexts and tools to engage at-risk adolescents in HIV/STI prevention programs. Yet, there is a dearth of efficacious mHealth interventions in health clinics to prevent and reduce both condomless sex and drug use and increase HIV/STI testing for at-risk adolescents.ObjectiveTo address this gap in knowledge, we developed a theory-driven, culturally congruent mHealth intervention (hereon referred to as S4E [Storytelling 4 Empowerment]) that has demonstrated feasibility and acceptability in a clinical setting. The next step is to examine the preliminary efficacy of S4E on adolescent HIV/STI testing and risk behaviors. This goal will be accomplished by 2 aims: the first aim is to develop a cross-platform and universal version of S4E. The cross-platform and universal version of S4E will be compatible with both iOS and Android operating systems and multiple mobile devices, aimed at providing adolescents with ongoing access to the intervention once they leave the clinic, and the second aim is to evaluate the preliminary efficacy of S4E, relative to usual care control condition, in preventing or reducing drug use and condomless sex and increasing HIV/STI testing in a clinical sample of at-risk adolescents aged 14-21 years living in Southeast Michigan.MethodsIn this study, 100 adolescents recruited from a youth-centered community health clinic will be randomized via blocked randomization with random sequences of block sizes to one of the 2 conditions: S4E mHealth intervention or usual care. Theory-driven and culturally congruent, S4E is an mHealth adaptation of face-to-face storytelling for empowerment, which is registered with the Substance Abuse and Mental Health Services Administration's National Registry of Evidence-Based Programs and Practices.ResultsThis paper describes the protocol of our study. The recruitment began on May 1, 2018. This study was registered on December 11, 2017, in ClinicalTrials.gov. All participants have been recruited. Data analysis will be complete by the end of March 2024, with study findings available by December 2024.ConclusionsThis study has the potential to improve public health by preventing HIV/STI and substance use disorders.Trial registrationClinicalTrials.gov NCT03368456; https://clinicaltrials.gov/study/NCT03368456.International registered report identifier (irrid)DERR1-10.2196/47216.

BackgroundHow we research eating disorder (ED) recovery impacts what we know (perceive as fact) about it. Traditionally, research has focused more on the "what" of recovery (e.g., establishing criteria for recovery, reaching consensus definitions) than the "how" of recovery research (e.g., type of methodologies, triangulation of perspectives). In this paper we aim to provide an overview of the ED field's current perspectives on recovery, discuss how our methodologies shape what is known about recovery, and suggest a broadening of our methodological "toolkits" in order to form a more complete picture of recovery. BODY: This paper examines commonly used methodologies in research, and explores how incorporating different perspectives can add to our understanding of the recovery process. To do this, we (1) provide an overview of commonly used methodologies (quantitative, qualitative), (2) consider their benefits and limitations, (3) explore newer approaches, including mixed-methods, creative methods (e.g., Photovoice, digital storytelling), and multi-methods (e.g., quantitative, qualitative, creative methods, psycho/physiological, behavioral, laboratory, online observations), and (4) suggest that broadening our methodological "toolkits" could spur more nuanced and specific insights about ED recoveries. We propose a potential future research model that would ideally have a multi-methods design, incorporate different perspectives (e.g., expanding recruitment of diverse participants, including supportive others, in study co-creation), and a longitudinal course (e.g., capturing cognitive and emotional recovery, which often comes after physical). In this way, we hope to move the field towards different, more comprehensive, perspectives on ED recovery.ConclusionOur current perspectives on studying ED recovery leave critical gaps in our knowledge about the process. The traditional research methodologies impact our conceptualization of recovery definitions, and in turn limit our understanding of the phenomenon. We suggest that we expand our range of methodologies, perspectives, and timeframes in research, in order to form a more complete picture of what is possible in recovery; the multiple aspects of an individual's life that can improve, the greater number of people who can recover than previously believed, and the reaffirmation of hope that, even after decades, individuals can begin, and successfully continue, their ED recovery process.

BackgroundIn Peru, a majority of individuals bypass primary care facilities even for routine services. Efforts to strengthen primary care must be informed by understanding of current practice. We conducted a time motion assessment in primary care facilities in Lima with the goals of assessing the feasibility of this method in an urban health care setting in Latin America and of providing policy makers with empirical evidence on the use of health care provider time in primary care.MethodsThis cross-sectional continuous observation time motion study took place from July - September 2019. We used two-stage sampling to draw a sample of shifts for doctors, nurses, and midwives in primary health facilities and applied the Work Observation Method by Activity Timing tool to capture type and duration of provider activities over a 6-h shift. We summarized time spent on patient care, paper and electronic record-keeping, and non-work (personal and inactive) activities across provider cadres. Observations are weighted by inverse probability of selection.ResultsTwo hundred seventy-five providers were sampled from 60 facilities; 20% could not be observed due to provider absence (2% schedule error, 8% schedule change, 10% failure to appear). One hundred seventy-four of the 220 identified providers consented (79.1%) and were observed for a total of 898 h of provider time comprising 30,312 unique tasks. Outpatient shifts included substantial time on patient interaction (110, 82, and 130 min for doctors, nurses, and midwives respectively) and on paper records (132, 97, and 141 min) on average. Across all shifts, 1 in 6 h was spent inactive or on personal activities. Two thirds of midwives used computers compared to half of nurses and one third of doctors.ConclusionsThe time motion study is a feasible method to capture primary care operations in Latin American countries and inform health system strengthening. In the case of Lima, absenteeism undermines health worker availability in primary care facilities, and inactive time further erodes health workforce availability. Productive time is divided between patient-facing activities and a substantial burden of paper-based record keeping for clinical and administrative purposes. Electronic health records remain incompletely integrated within routine care, particularly beyond midwifery.

The incorporation of omics approaches into symptom science research can provide researchers with information about the molecular mechanisms that underlie symptoms. Most of the omics analyses in symptom science have used a single omics approach. Therefore, these analyses are limited by the information contained within a specific omics domain (e.g., genomics and inherited variations, transcriptomics and gene function). A multi-staged data-integrated multi-omics (MS-DIMO) analysis integrates multiple types of omics data in a single study. With this integration, a MS-DIMO analysis can provide a more comprehensive picture of the complex biological mechanisms that underlie symptoms. The results of a MS-DIMO analysis can be used to refine mechanistic hypotheses and/or discover therapeutic targets for specific symptoms. The purposes of this paper are to: (1) describe a MS-DIMO analysis using "Symptom X" as an example; (2) discuss a number of challenges associated with specific omics analyses and how a MS-DIMO analysis can address them; (3) describe the various orders of omics data that can be used in a MS-DIMO analysis; (4) describe omics analysis tools; and (5) review case exemplars of MS-DIMO analyses in symptom science. This paper provides information on how a MS-DIMO analysis can strengthen symptom science research through the prioritization of functional genes and biological processes associated with a specific symptom.

This paper examines factors associated with intimate partner violence (IPV) among newly married women in Nepal, and how IPV was affected by food insecurity and COVID-19. Given evidence that food insecurity is associated with IPV and COVID-19, we explored whether increased food insecurity during COVID-19 is associated with changes in IPV. We used data from a cohort study of 200 newly married women aged 18-25 years, interviewed five times over two years at 6-month intervals (02/2018-07/2020), including after COVID-19-associated lockdowns. Bivariate analysis and mixed-effects logistic regression models were used to examine the association between selected risk factors and recent IPV. IPV increased from 24.5% at baseline to 49.2% before COVID-19 and to 80.4% after COVID-19. After adjusting for covariates, we find that both COVID-19 (OR = 2.93, 95% CI 1.07-8.02) and food insecurity (OR = 7.12, 95% CI 4.04-12.56) are associated with increased odds of IPV, and IPV increased more for food-insecure women post COVID-19 (compared to non-food insecure), but this was not statistically significant (confidence interval 0.76-8.69, p-value = 0.131). Young, newly married women experience high rates of IPV that increase with time in marriage, and COVID-19 has exacerbated this, especially for food-insecure women in the present sample. Along with enforcement of laws against IPV, our results suggest that special attention needs to be paid to women during a crisis time like the current COVID-19 pandemic, especially those who experience other household stressors.

Multilevel service delivery frameworks are approaches to structuring and organizing a spectrum of evidence-based services and supports, focused on assessment, prevention, and intervention designed for the local context. Exemplar frameworks in child mental health include positive behavioral interventions and supports in education, collaborative care in primary care, and systems of care in community mental health settings. Yet, their high-quality implementation has lagged. This work proposes a conceptual foundation for multilevel service delivery frameworks spanning diverse mental health service settings that can inform development of strategic implementation supports. We draw upon the existing literature for three exemplar multilevel service delivery frameworks in different child mental health service settings to (1) identify core components common to each framework, and (2) to highlight prominent implementation determinants that interface with each core component. Six interrelated components of multilevel service delivery frameworks were identified, including, (1) a systems-level approach, (2) data-driven problem solving and decision-making, (3) multiple levels of service intensity using evidence-based practices, (4) cross-linking service sectors, (5) multiple providers working together, including in teams, and (6) built-in implementation strategies that facilitate delivery of the overall model. Implementation determinants that interface with core components were identified at each contextual level. The conceptual foundation provided in this paper has the potential to facilitate cross-sector knowledge sharing, promote generalization across service settings, and provide direction for researchers, system leaders, and implementation intermediaries/practitioners working to strategically support the high-quality implementation of these frameworks.

Anxiety is a common comorbidity for individuals with ASD, and there is some preliminary data about the efficacy of physical exercise to alleviate anxiety. However, we are not aware of any studies that have compared the effects of a physical exercise program on anxiety in underserved children with ASD using a randomized controlled research design. This paper describes a method to evaluate and compare the efficacy of an 8-week physical exercise intervention with a sedentary play intervention to alleviate anxiety in young children with autism spectrum disorders (ASD) from underserved backgrounds. We assessed anxiety and its physical symptoms using the parent-rated Child Behavior Checklist DSM-5 anxiety (CBCL DSM-5) subscale, the child-rated Screen for Childhood Anxiety Related Emotional Disorder (SCARED), the parent-rated Child's Sleep Habits Questionnaire (CSHQ), and salivary cortisol. We also utilized the Physical Activity Questionnaire for Older Children (PAQ-C) to assess physical activity level and identify compounds. Unique components of this study include: •Implementation of novel physical exercise and sedentary play interventions that have been designed for children with ASD.•Recruitment of predominantly underserved and non-English speaking families.

IntroductionNearly 30 000 Mexican women develop breast cancer annually, frequently presenting unmet supportive care needs. In high-income countries, incorporating electronic patient-reported outcomes (ePROs) into cancer care has demonstrated potential for increasing patient-centred care and reducing unmet needs. No such ePRO interventions have been implemented in Mexico. This paper presents the study protocol for designing and evaluating an ePRO digital health application combined with proactive follow-up by nurses.Methods and analysisWe designed a two-component intervention for women receiving breast cancer treatment: a responsive web application for monitoring ePROs and clinical algorithms guiding proactive follow-up by nurses. We will conduct a pilot test of the intervention with 50 patients with breast cancer for 6 weeks to assess feasibility and adjust the application. We will conduct a parallel arm randomised controlled trial assigning 205 patients each to intervention and control in one of Mexico's largest public oncology hospitals. The intervention will be provided for 6 months, with additional 3 months of post-intervention observation. The control group will receive usual healthcare and a list of breast cancer information sources. Women diagnosed with stages I, II or III breast cancer who initiate chemotherapy and/or radiotherapy will be invited to participate. The primary study outcome will be supportive care needs; secondary outcomes include global quality of life and breast symptoms. Information on the outcomes will be obtained through web-based self-administered questionnaires collected at baseline, 1, 3, 6 and 9 months.Ethics and disseminationThe National Research and Ethics Committees of the Mexican Institute of Social Security approved the study (R-2021-785-059). Participants will sign an informed consent form prior to their inclusion. Findings will be disseminated through a policy brief to the local authorities, a webinar for patients, publications in peer-reviewed journals and presentations at national and international conferences.Trial registration numberNCT05925257.

Sleep quality is crucial to both mental and physical well-being. The COVID-19 pandemic, which has notably affected the population's health worldwide, has been shown to deteriorate people's sleep quality. Numerous studies have been conducted to evaluate the impact of the COVID-19 pandemic on sleep efficiency, investigating their relationships using correlation-based methods. These methods merely rely on learning spurious correlation rather than the causal relations among variables. Furthermore, they fail to pinpoint potential sources of bias and mediators and envision counterfactual scenarios, leading to a poor estimation. In this paper, we develop a Causal Machine Learning method, which encompasses causal discovery and causal inference components, to extract the causal relations between the COVID-19 pandemic (treatment variable) and sleep quality (outcome) and estimate the causal treatment effect, respectively. We conducted a wearable-based health monitoring study to collect data, including sleep quality, physical activity, and Heart Rate Variability (HRV) from college students before and after the COVID-19 lockdown in March 2020. Our causal discovery component generates a causal graph and pinpoints mediators in the causal model. We incorporate the strongly contributing mediators (i.e., HRV and physical activity) into our causal inference component to estimate the robust, accurate, and explainable causal effect of the pandemic on sleep quality. Finally, we validate our estimation via three refutation analysis techniques. Our experimental results indicate that the pandemic exacerbates college students' sleep scores by 8%. Our validation results show significant p-values confirming our estimation.

Short Summary: The Letter to the Editors regarding our article was reviewed. The take-home message is that substantively, the authors of the letter are referencing a paper that asks a different research question in a different set of studies. When we ask different questions, we are not surprised when we reach different answers.

BackgroundThere has been increasing interest in cortical microstructure as a complementary and earlier measure of neurodegeneration than macrostructural atrophy, but few papers have related cortical diffusion imaging to post-mortem neuropathology. This study aimed to characterise the associations between the main Alzheimer's disease (AD) neuropathological hallmarks and multiple cortical microstructural measures from in vivo diffusion MRI. Comorbidities and co-pathologies were also investigated.MethodsForty-three autopsy cases (8 cognitively normal, 9 mild cognitive impairment, 26 AD) from the National Alzheimer's Coordinating Center and Alzheimer's Disease Neuroimaging Initiative databases were included. Structural and diffusion MRI scans were analysed to calculate cortical minicolumn-related measures (AngleR, PerpPD+, and ParlPD) and mean diffusivity (MD). Neuropathological hallmarks comprised Thal phase, Braak stage, neuritic plaques, and combined AD neuropathological changes (ADNC-the "ABC score" from NIA-AA recommendations). Regarding comorbidities, relationships between cortical microstructure and severity of white matter rarefaction (WMr), cerebral amyloid angiopathy (CAA), atherosclerosis of the circle of Willis (ACW), and locus coeruleus hypopigmentation (LCh) were investigated. Finally, the effect of coexistent pathologies-Lewy body disease and TAR DNA-binding protein 43 (TDP-43)-on cortical microstructure was assessed.ResultsCortical diffusivity measures were significantly associated with Thal phase, Braak stage, ADNC, and LCh. Thal phase was associated with AngleR in temporal areas, while Braak stage was associated with PerpPD+ in a wide cortical pattern, involving mainly temporal and limbic areas. A similar association was found between ADNC (ABC score) and PerpPD+. LCh was associated with PerpPD+, ParlPD, and MD. Co-existent neuropathologies of Lewy body disease and TDP-43 exhibited significantly reduced AngleR and MD compared to ADNC cases without co-pathology.ConclusionsCortical microstructural diffusion MRI is sensitive to AD neuropathology. The associations with the LCh suggest that cortical diffusion measures may indirectly reflect the severity of locus coeruleus neuron loss, perhaps mediated by the severity of microglial activation and tau spreading across the brain. Recognizing the impact of co-pathologies is important for diagnostic and therapeutic decision-making. Microstructural markers of neurodegeneration, sensitive to the range of histopathological features of amyloid, tau, and monoamine pathology, offer a more complete picture of cortical changes across AD than conventional structural atrophy.

Biosocial Medicine, with its emphasis on the full integration of the person's biology and biography, proposes a strategy for clinical research and the practice of medicine that is transformative for the care of individual patients. In this paper, we argue that Biology is one component of what makes a person unique, but it does not do so alone. Biography, the lived experience of the person, integrates with biology to create a unique signature for each individual and is the foundational concept on which Biosocial Medicine is based. Biosocial Medicine starts with the premise that the individual patient is the focus of clinical care, and that average results for "ideal" patients in population level research cannot substitute for the "real" patient for whom clinical decisions are needed. The paper begins with a description of the case-based method of clinical reasoning, considers the strengths and limitations of Randomized Controlled Trials and Evidence Based Medicine, reviews the increasing focus on precision medicine and then explores the neglected role of biography as part of a new approach to the tailored care of patients. After a review of the analytical challenges in Biosocial Medicine, the paper concludes by linking the physician's commitment to understanding the patient's biography as a critical element in developing trust with the patient.

Patient-reported outcomes (PROs) are used in clinical trials to provide valuable evidence on the impact of disease and treatment on patients' symptoms, function and quality of life. High-quality PRO data from trials can inform shared decision-making, regulatory and economic analyses and health policy. Recent evidence suggests the PRO content of past trial protocols was often incomplete or unclear, leading to research waste. To address this issue, international, consensus-based, PRO-specific guidelines were developed: the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT)-PRO Extension. The SPIRIT-PRO Extension is a 16-item checklist which aims to improve the content and quality of aspects of clinical trial protocols relating to PRO data collection to minimise research waste, and ultimately better inform patient-centred care. This SPIRIT-PRO explanation and elaboration (E&E) paper provides information to promote understanding and facilitate uptake of the recommended checklist items, including a comprehensive protocol template. For each SPIRIT-PRO item, we provide a detailed description, one or more examples from existing trial protocols and supporting empirical evidence of the item's importance. We recommend this paper and protocol template be used alongside the SPIRIT 2013 and SPIRIT-PRO Extension paper to optimise the transparent development and review of trial protocols with PROs.

The rapid development of automated measurement equipment enables researchers to collect greater quantities of time-resolved data from indoor and outdoor environments. While significant, the interpretation of the resulting data can be a time-consuming effort. This paper introduces an automated process of interpreting PM2.5 time-resolved data and differentiating PM2.5 emissions resulting from indoor and outdoor sources. We use Random Forest (RF), a machine learning approach, to study a dataset of 836 indoor emission events that occurred over a 2-week period in 18 apartments in California. In this paper, we show model development and evaluate its performance as the sample size and source vary. We discuss the characteristics of the dataset that tended to help the source identification and why. For example, we show that data from many events and from different apartments are essential for the model to be suitable for analyzing a new separate dataset. We also show that longitudinal data appear to be more helpful than the time frequency of measurements within a given apartment. We use the resulting RF model to analyze PM2.5 data of an entirely separate dataset collected from 65 new homes in California. The RF model identifies 442 indoor emission events, with only a few misidentifications.

BackgroundMachine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers.ObjectiveWe propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care.ConclusionThe relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.

BackgroundPatient-centered outcomes research seeks to answer patient-centered questions. The process includes varied locations and individuals throughout the care continuum to address individual differences and constraints in implementation and dissemination.ProblemThis paper intends to answer this question: do academic nurses practice what they preach by assisting patient-centered outcomes research and researchers through their engagement with patients, caregivers, and other community stakeholder partners in nursing research?ApproachThis paper provides an overview of how academic nurses in a single institution (the University of Texas Medical Branch at Galveston School of Nursing) began to embrace patient-centered outcomes research.ConclusionWhether academic nurses are practicing what they preach in terms of patient-centered outcomes research remains uncertain. More examples from academia are required to make that determination. Academic nurses worldwide have embarked on a steep learning curve to embrace patient-centered outcomes research. This journey will require patience and a systematic strategy.

Background: As research involving human participants increasingly occurs with the aid of digital tools (e.g., mobile apps, wearable and remote pervasive sensors), the consent content and delivery process is changing. Informed consent documents to participate in research are lengthy and difficult for prospective participants to read and understand. As the consent communication will need to include concepts and procedures unique to digital health research, making that information accessible and meaningful to the prospective participant is critical for consent to be informed. This paper describes a methodology that researchers can apply when developing a consent communication for digital health research. Methods: A consent document approved by a US institutional review board was deconstructed into segments that aligned with federal requirements for informed consent. Three researchers independently revised each segment of text with a goal of achieving a readability score between a 6-8th grade level. The team then consulted with an external readability expert well-versed in revising informed consent documents into "plain language." The resulting text was evaluated using Microsoft Word and Online-Utility accessibility software. The final step involved adding visual images and graphics to complement the text. The Digital Health Checklist consent prototype builder was then used to identify areas where the consent content could be expanded to address four key domains of Access and Usability, Privacy, Risks and Benefits, and Data Management. Results: The approved consent was evaluated at a 12.6 grade reading level, whereas the revised language by our study team received 12.4, 12, and 12.58, respectively. The final consent document synthesized the most readable of the three revised versions and was further revised to include language recommended by the software tool for improving readability, which resulted in a final revised consent readability score of a 9.2 grade level. Moreover, word count was reduced from 6,424 in the original consent to 679 in the rewritten consent form. Conclusion: Utilizing an iterative process to design an accessible informed consent document is a first step in achieving meaningful consent to participate in digital health research. This paper describes how a consent form approved by an institutional review board can be made more accessible to a prospective research participant by improving the document readability score, reducing the word count and assessing alignment with the Digital Health Checklist.

BackgroundWe were tasked by the World Health Organization (WHO) to address the following question: What techniques should be used to diagnose Schistosoma infections in snails and in the water in potential transmission sites? Our goal was to review and evaluate the available literature and provide recommendations and insights for the development of WHO's Guidelines Development Group for schistosomiasis control and elimination.MethodologyWe searched several databases using strings of search terms, searched bibliographies of pertinent papers, and contacted investigators who have made contributions to this field. Our search covered from 1970 to Sept 2020. All papers were considered in a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, and retained papers were grouped by technique and subjected to our GRADE (Grading of Recommendations, Assessment, Development and Evaluations) evidence assessment profile determined in consultation with WHO. We also considered issues of sensitivity, specificity, coverage, cost, robustness, support needs, schistosome species discrimination, and relevant detection limits.Principal findingsOur PRISMA process began with the perusal of 949 articles, of which 158 were retained for data extraction and evaluation. We identified 25 different techniques and for each applied a GRADE assessment considering limitations, inconsistency, imprecision, indirectness, and publication bias. We also provide advantages and disadvantages for each category of techniques.ConclusionsOur GRADE analysis returned an assessment of moderate quality of evidence for environmental DNA (eDNA), qPCR and LAMP (Loop-mediated isothermal amplification). No single ideal diagnostic approach has yet been developed, but considerable recent progress has been made. We note a growing trend to use eDNA techniques to permit more efficient and replicable sampling. qPCR-based protocols for follow-up detection offer a versatile, mature, sensitive, and specific platform for diagnosis though centralized facilities will be required to favor standardization. Droplet digital PCR (ddPCR) can play a complementary role if inhibitors are a concern, or more sensitivity or quantification is needed. Snail collection, followed by shedding, is encouraged to provide specimens for sequence verifications of snails or schistosomes. LAMP or other isothermal detection techniques offer the prospect of less expensive and more distributed network of analysis but may face standardization and verification challenges related to actual sequences amplified. Ability to detect schistosome infections in snails or in the water is needed if control and elimination programs hope to succeed. Any diagnostic techniques used need to be regularly verified by the acquisition of DNA sequences to confirm that the detected targets are of the expected species. Further improvements may be necessary to identify the ideal schistosome or snail sequences to target for amplification. More field testing and standardization will be essential for long-term success.

The last decade has seen nearly 20 papers reviewing the totality of the data on saturated fats and cardiovascular outcomes, which, altogether, have demonstrated a lack of rigorous evidence to support continued recommendations either to limit the consumption of saturated fatty acids or to replace them with polyunsaturated fatty acids. These papers were unfortunately not considered by the process leading to the most recent U.S. Dietary Guidelines for Americans, the country's national nutrition policy, which recently reconfirmed its recommendation to limit saturated fats to 10% or less of total energy intake, based on insufficient and inconsistent evidence. Continuation of a cap on saturated fat intake also fails to consider the important effects of the food matrix and the overall dietary pattern in which saturated fatty acids are consumed.

Background: Although studies have described the power imbalance in academic-community partnerships, little has been published describing how community-based participatory research–informed practitioners can change academic institutions to promote more effective community-engaged research. Objectives: This paper describes a university-funded com-munity-based participatory project in which academic researchers and their community partners worked together to articulate, develop and advocate for institutionalizing best practices for equitable partnerships throughout the university. Methods: Findings derive from a collaborative ethnographic process evaluation. Results: The study describes the integral steps proposed to promote equitable community–university research collabo-ration, the process by which these principles and best practice recommendations were developed, and the institutional change outcomes of this process. Conclusions: When universities make even small investments toward promoting and nurturing community-engaged research, the quality of the science can be enhanced to advance health equity and community–university relation-ships can improve, particularly if based on trust, mutual respect, and openness to accomplish a shared vision.

This paper presents a proof of concept for noncontact and noninvasive bladder volume sensing in patients suffering from Neurological Lower Urinary Tract Dysfunction (NLUTD) caused by spinal cord injuries. The study investigates the feasibility of utilizing Frequency Modulated Continuous Wave (FMCW) radar as the underlying sensing modality. An algorithm for spectral data analysis is proposed to map radar measurements to bladder volume and is evaluated through MATLAB simulations. Additionally, time domain simulations of a tissue model are conducted to further assess the proposed spectral data analysis algorithm and demonstrate the feasibility of the sensing modality. The effectiveness of the sensing modality is demonstrated through a proof-of-concept measurement setup using an ex-vivo porcine bladder model, which is incrementally filled with saline solution in 200 ml portions. The results of the proposed spectral data analysis on the measured reflected waveforms reveal a strong correlation between the shift in peak frequency and the bladder volume in the model. These findings provide evidence supporting the potential of FMCW radar as a reliable and noninvasive method for continuous bladder volume monitoring.

Background: The association between cancer and venous thromboembolism (VTE) is well-established with cancer patients accounting for approximately 20% of all VTE incidents. In this paper, we have performed a comparison of machine learning (ML) methods to traditional clinical scoring models for predicting the occurrence of VTE in a cancer patient population, identified important features (clinical biomarkers) for ML model predictions, and examined how different approaches to reducing the number of features used in the model impact model performance. Methods: We have developed an ML pipeline including three separate feature selection processes and applied it to routine patient care data from the electronic health records of 1910 cancer patients at the University of California Davis Medical Center. Results: Our ML-based prediction model achieved an area under the receiver operating characteristic curve of 0.778 ± 0.006 (mean ± SD) when trained on a set of 15 features. This result is comparable with the model performance when trained on all features in our feature pool [0.779 ± 0.006 (mean ± SD) with 29 features]. Our result surpasses the most validated clinical scoring system for VTE risk assessment in cancer patients by 16.1%. We additionally found cancer stage information to be a useful predictor after all performed feature selection processes despite not being used in existing score-based approaches. Conclusion: From these findings, we observe that ML can offer new insights and a significant improvement over the most validated clinical VTE risk scoring systems in cancer patients. The results of this study also allowed us to draw insight into our feature pool and identify the features that could have the most utility in the context of developing an efficient ML classifier. While a model trained on our entire feature pool of 29 features significantly outperformed the traditionally used clinical scoring system, we were able to achieve an equivalent performance using a subset of only 15 features through strategic feature selection methods. These results are encouraging for potential applications of ML to predicting cancer-associated VTE in clinical settings such as in bedside decision support systems where feature availability may be limited.

In this paper, performance of hurdle models in rare events data is improved by modifying their binary component. The rare-event weighted logistic regression model is adopted in place of logistic regression to deal with class imbalance due to rare events. Poisson Hurdle Rare Event Weighted Logistic Regression (REWLR) and Negative Binomial Hurdle (NBH) REWLR are developed as two-part models which use the REWLR model to estimate the probability of a positive count and a Poisson or NB zero-truncated count model to estimate non-zero counts. This research aimed to develop and assess the performance of the Poisson and Negative Binomial (NB) Hurdle Rare Event Weighted Logistic Regression (REWLR) models, applied to simulated data with various degrees of zero inflation and to Nairobi county's maternal mortality data. The study data on maternal mortality were pulled from JPHES. The data contain the number of maternal deaths, which is the outcome variable, and other obstetric and demographic factors recorded in MNCH facilities in Nairobi between October 2021 and January 2022. The models were also fit and evaluated based on simulated data with varying degrees of zero inflation. The obtained results are numerically validated and then discussed from both the mathematical and the maternal mortality perspective. Numerical simulations are also presented to give a more complete representation of the model dynamics. Results obtained suggest that NB Hurdle REWLR is the best performing model for zero inflated count data due to rare events.

Despite growing interest in the health-related consequences of racially discriminatory institutional policies and practices, public health scholars have yet to reach a consensus on how to measure and analyze exposure to institutional racism. The purpose of this paper is to provide an overview of the conceptualization, measurement, and analysis of institutional racism in the context of quantitative research on minority health and health disparities in the United States. We begin by providing definitions of key concepts (e.g., racialization, racism, racial inequity) and describing linkages between these ideas. Next, we discuss the hypothesized mechanisms that link exposure to institutional racism with health. We then provide a framework to advance empirical research on institutional racism and health, informed by a literature review that summarizes measures and analytic approaches used in previous studies. The framework addresses six considerations: (1) policy identification, (2) population of interest, (3) exposure measurement, (4) outcome measurement, (5) study design, and (6) analytic approach. Research utilizing the proposed framework will help inform structural interventions to promote minority health and reduce racial and ethnic health disparities.

Community health workers (CHW) can play an active role in providing integrated HIV and harm reduction services. We used social media to create a virtual network among Vietnamese CHW. This paper reports CHW's social media engagement and the relationships with other work-related indicators. Sixty CHW participated in an intervention for integrated HIV/drug use service delivery. Following two in-person sessions, Facebook groups were established for CHW to share information, seek consultation, and refer patients. CHW's levels of online engagements were tracked for six months and linked to their service provision confidence, interaction with patients and other providers, and job satisfaction. The CHW made 181 posts, which received 557 comments and 1,607 reactions during the six months. Among the 60 CHW, 22 (36.6%) had three or more posts, 19 (31.7%) had one or two posts, and 19 (31.7%) had no post. Comparing the baseline and 6-month follow-up data, we observed that those who posted three or more times showed better service provision confidence (p = 0.0081), more interaction with providers in other settings (p = 0.0071), and higher job satisfaction (p = 0.0268). Our study suggests using social media to engage CHW in virtual communications to improve service provision in communities.