Digital phenotyping of depression during pregnancy using self-report data.

BACKGROUND: Depression is a common pregnancy complication yet is often under-detected and, subsequently, undertreated. Data collected through mobile health tools may be used to support the identification of depression symptoms in pregnancy. METHODS: An observational cohort study of 2062 pregnancies collected self-reports of patient history, mood, pregnancy-specific symptoms, and written language using a prenatal support app. These app inputs were used to model depression risk in subsequent 30- and 60-day periods throughout pregnancy. A selective inference lasso modeling approach examined the individual and additive value of each type of patient-reported app input. RESULTS: Depression models ranged in predictive power (AUC value of 0.64-0.83), depending on the type of inputs. The most predictive model included personal history, daily mood, and acute pregnancy-related symptoms (e.g., severe vomiting, cramping). Across models, daily mood was the strongest indicator of depression symptoms in the following month. Models that retained natural language inputs typically improved predictive accuracy and offered insight into the lived context associated with experiencing depression. LIMITATIONS: Our findings are not generalizable beyond a digitally literate patient population that is self-motivated to report data during pregnancy. CONCLUSIONS: Simple patient reported data, including sparse language, shared directly via digital tools may support earlier depression symptom identification and a more nuanced understanding of depression context.

A Model for Engaging Citizen Scientists: A Community-Partnered Research Collaboration to Address Inequities for Black Birthing People.

PURPOSE: Co-creation of a citizen-science research initiative with a collaborative team of community members and university-based scientists to address regional disparities in maternal and fetal health outcomes for Black birthing people. DESCRIPTION: Citizen scientist-led projects, where community members actively contribute to each discovery step, from setting a research agenda to collecting data and disseminating results, can extend community participatory research initiatives and help reconceptualize traditional research processes. The Pregnancy Collaborative is a citizen-science research initiative and one of nine scientific committees of The Pittsburgh Study-a longitudinal, community-partnered study designed to bring together collaborators to improve child thriving. ASSESSMENT: Ten community members and five university-based scientists participated during all phases of developing a citizen-scientist collaboration over an initial two-and-a-half-year period. Phases include forming the Pregnancy Collaborative and group research ethics training; co-creating a research agenda grounded in shared principles; and community-partnered data collection, analysis, and dissemination. These phases produced three key co-designed products: (1) a mission and vision statement of the Pregnancy Collaborative, (2) a Collaborative-endorsed research agenda, and (3) a citizen-scientist-executed research survey. CONCLUSION: Lessons learned from the formation of the Pregnancy Collaborative highlight the importance of equitable power distribution through bidirectional knowledge sharing and by centering intellectual effort, lived experience, and tools and resources of those affected by health inequities. Using a citizen science approach to co-designing and executing research helps us move maternal health inequity work from "research on" to "research with."

Predicting first time depression onset in pregnancy: applying machine learning methods to patient-reported data.

PURPOSE: To develop a machine learning algorithm, using patient-reported data from early pregnancy, to predict later onset of first time moderate-to-severe depression. METHODS: A sample of 944 U.S. patient participants from a larger longitudinal observational cohortused a prenatal support mobile app from September 2019 to April 2022. Participants self-reported clinical and social risk factors during first trimester initiation of app use and completed voluntary depression screenings in each trimester. Several machine learning algorithms were applied to self-reported data, including a novel algorithm for causal discovery. Training and test datasets were built from a randomized 80/20 data split. Models were evaluated on their predictive accuracy and their simplicity (i.e., fewest variables required for prediction). RESULTS: Among participants, 78% identified as white with an average age of 30 [IQR 26-34]; 61% had income ≥ $50,000; 70% had a college degree or higher; and 49% were nulliparous. All models accurately predicted first time moderate-severe depression using first trimester baseline data (AUC 0.74-0.89, sensitivity 0.35-0.81, specificity 0.78-0.95). Several predictors were common across models, including anxiety history, partnered status, psychosocial factors, and pregnancy-specific stressors. The optimal model used only 14 (26%) of the possible variables and had excellent accuracy (AUC = 0.89, sensitivity = 0.81, specificity = 0.83). When food insecurity reports were included among a subset of participants, demographics, including race and income, dropped out and the model became more accurate (AUC = 0.93) and simpler (9 variables). CONCLUSION: A relatively small amount of self-report data produced a highly predictive model of first time depression among pregnant individuals.

Benefits, Harms, and Stakeholder Perspectives Regarding Opioid Therapy for Pain in Individuals With Metastatic Cancer: Protocol for a Descriptive Cohort Study.

BACKGROUND: Opioids are a key component of pain management among patients with metastatic cancer pain. However, the evidence base available to guide opioid-related decision-making in individuals with advanced cancer is limited. Patients with advanced cancer or cancer that is unlikely to be cured frequently experience pain. Opioids are a key component of pain management among patients with metastatic cancer pain. Many individuals with advanced cancer are now living long enough to experience opioid-related harm. Emerging evidence from chronic noncancer pain literature suggests that longer-term opioid therapy may have limited benefits for pain and function, and opioid-related harms are also a major concern. However, whether these benefits and harms of opioids apply to patients with cancer-related pain is unknown. OBJECTIVE: This manuscript outlines the protocol for the "Opioid Therapy for Pain in Individuals With Metastatic Cancer: The Benefits, Harms, and Stakeholder Perspectives (BEST) Study." The study aims to better understand opioid decision-making in patients with advanced cancer, along with opioid benefits and harms, through prospective examination of patients' pain experiences and opioid side effects and understanding the decision-making by patients, care partners, and clinicians. METHODS: This is a multicenter, prospective cohort study that aims to enroll 630 patients with advanced cancer, 20 care partners, and 20 clinicians (670 total participants). Patient participants must have an advanced solid cancer diagnosis, defined by the American Cancer Society as cancer that is unlikely to be cured. We will recruit patient participants within 12 weeks after diagnosis so that we can understand opioid benefits, harms, and perspectives on opioid decision-making throughout the course of their advanced cancer (up to 2 years). We will also specifically elicit information regarding long-term opioid use (ie, opioids for ≥90 consecutive days) and exclude patients on long-term opioid therapy before an advanced cancer diagnosis. Lived-experience perspectives related to opioid use in those with advanced cancer will be captured by qualitative interviews with a subset of patients, clinicians, and care partners. Our data collection will be grounded in a behavioral decision research approach that will allow us to develop future interventions to inform opioid-related decision-making for patients with metastatic cancer. RESULTS: Data collection began in October 2022 and is anticipated to end by November 2024. CONCLUSIONS: Upon successful execution of our study protocol, we anticipate the development of a comprehensive evidence base on opioid therapy in individuals with advanced cancer guided by the behavioral decision research framework. The information gained from this study will be used to guide interventions to facilitate opioid decisions among patients, clinicians, and care partners. Given the limited evidence base about opioid therapy in people with cancer, we envision this study will have significant real-world implications for cancer-related pain management and opioid-related clinical decision-making. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54953.

Decision-making and Treatment Preferences Surrounding Bacteriuria in Nonpregnant Women: A Qualitative Investigation.

INTRODUCTION AND HYPOTHESIS: Best practices suggest nontreatment for asymptomatic bacteriuria in a nonpregnant population, yet there is little literature on patient preference or understanding of asymptomatic bacteriuria treatment. We hypothesize that there might be core factors that affect antibiotic preferences and care-seeking decisions for urinary tract infection and asymptomatic bacteriuria in a postmenopausal population. METHODS: We performed semi-structured interviews with postmenopausal individuals who had been previously treated for at least one patient-reported urinary tract infection. Interviews covered a discussion about their approach to seeking antibiotics for management and knowledge/preferences for asymptomatic bacteriuria management. Two authors independently coded the interviews and identified a set of symptom-related knowledge and experiences that relate to care-seeking and treatment preferences. We then graphically represented a mental model of antibiotic-seeking practices as an influence diagram, illustrating how knowledge and values affect preferences for care. RESULTS: We performed 30 interviews of participants with a mean age of 69.4 (SD 6.4). Among participants, there were four core factors that influence antibiotic seeking for bacteriuria. Participants noted concern for sequelae from untreated bacteria as their primary motivation, but also noted past experiences, information sources, and testing results as themes that affected their mental model surrounding bacteriuria treatment. CONCLUSIONS: The cognitive approach to care-seeking and treatment preference for bacteriuria is influenced by a few central factors. An improved ability to allay concerns either by provider discussions or educational materials are necessary to bridge the gap from the existence of evidence-based guidelines to patient and provider comfort with adherence to these guidelines.

Parent Perceptions of Telemedicine for Acute Pediatric Respiratory Tract Infections: Sequential Mixed Methods Study.

BACKGROUND: Since 2020, parents have had increasing opportunities to use telemedicine for their children, but how parents decide whether to use telemedicine for acute pediatric care relative to alternative sites of care is not clear. One of the most common reasons parents seek acute care for their children is for acute respiratory tract infections (ARTIs). OBJECTIVE: This study aims to examine parental expectations of care via telemedicine for pediatric ARTIs, contrasting expectations of care delivered via primary care telemedicine and direct-to-consumer (DTC) telemedicine. METHODS: We performed a sequential mixed methods analysis to examine how parents assess telemedicine for their children's acute care. We used ARTIs as a case study for examining parent perceptions of telemedicine. First, we analyzed semistructured interviews focused on parent responses about the use of telemedicine. Each factor discussed by parents was coded to reflect whether parents indicated it incentivized or disincentivized their preferences for telemedicine versus in-person care. Results were organized by a 7-dimension framework of parental health care seeking that was generated previously, which included dimensions related to care sites (expected access, affordability, clinical quality, and site quality) and dimensions related to child or family factors (perceived illness severity, perceived child susceptibility, and parent self-efficacy). Second, we analyzed responses to a national survey, which inquired about parental expectations of primary care telemedicine, commercial DTC telemedicine, and 3 in-person sites of care (primary care, urgent care, and emergency department) across 21 factors identified through prior qualitative work. To assess whether parents had different expectations of different telemedicine models, we compared survey responses for primary care telemedicine and commercial DTC telemedicine using weighted logistic regression. RESULTS: Interview participants (n=40) described factors affecting their perceptions of telemedicine as a care modality for pediatric ARTIs. Generally, factors aligned with access and affordability (eg, decreased wait time and lower out-of-pocket cost) were discussed as potential incentives for telemedicine use, while factors aligned with perceived illness severity, child susceptibility, and clinician quality (eg, trustworthiness) were discussed as potential disincentives for telemedicine use. In survey responses (n=1206), primary care and commercial DTC telemedicine were rated similarly on items related to expected accessibility and affordability. In contrast, on items related to expected quality of care, primary care telemedicine was viewed similarly to in-person primary care, while commercial DTC telemedicine was rated lower. For example, 69.7% (weighted; 842/1197) of respondents anticipated their children would be comfortable and cooperative with primary care telemedicine versus 49.7% (weighted; 584/1193) with commercial DTC telemedicine (P<.001). CONCLUSIONS: In a mixed methods analysis focused on telemedicine for ARTIs, parents expressed more concerns about telemedicine quality in commercial DTC models compared with primary care-based telemedicine. These results could help health systems better design telemedicine initiatives to support family-centered care.

Collection and Analysis of Adherence Information for Software as a Medical Device Clinical Trials: Systematic Review.

BACKGROUND: The rapid growth of digital health apps has necessitated new regulatory approaches to ensure compliance with safety and effectiveness standards. Nonadherence and heterogeneous user engagement with digital health apps can lead to trial estimates that overestimate or underestimate an app's effectiveness. However, there are no current standards for how researchers should measure adherence or address the risk of bias imposed by nonadherence through efficacy analyses. OBJECTIVE: This systematic review aims to address 2 critical questions regarding clinical trials of software as a medical device (SaMD) apps: How well do researchers report adherence and engagement metrics for studies of effectiveness and efficacy? and What efficacy analyses do researchers use to account for nonadherence and how appropriate are their methods? METHODS: We searched the Food and Drug Administration's registration database for registrations of repeated-use, patient-facing SaMD therapeutics. For each such registration, we searched ClinicalTrials.gov, company websites, and MEDLINE for the corresponding clinical trial and study articles through March 2022. Adherence and engagement data were summarized for each of the 24 identified articles, corresponding to 10 SaMD therapeutics. Each article was analyzed with a framework developed using the Cochrane risk-of-bias questions to estimate the potential effects of imperfect adherence on SaMD effectiveness. This review, funded by the Richard King Mellon Foundation, is registered on the Open Science Framework. RESULTS: We found that although most articles (23/24, 96%) reported collecting information about SaMD therapeutic engagement, of the 20 articles for apps with prescribed use, only 9 (45%) reported adherence information across all aspects of prescribed use: 15 (75%) reported metrics for the initiation of therapeutic use, 16 (80%) reported metrics reporting adherence between the initiation and discontinuation of the therapeutic (implementation), and 4 (20%) reported the discontinuation of the therapeutic (persistence). The articles varied in the reported metrics. For trials that reported adherence or engagement, there were 4 definitions of initiation, 8 definitions of implementation, and 4 definitions of persistence. All articles studying a therapeutic with a prescribed use reported effectiveness estimates that might have been affected by nonadherence; only a few (2/20, 10%) used methods appropriate to evaluate efficacy. CONCLUSIONS: This review identifies 5 areas for improving future SaMD trials and studies: use consistent metrics for reporting adherence, use reliable adherence metrics, preregister analyses for observational studies, use less biased efficacy analysis methods, and fully report statistical methods and assumptions.

Indirect Identification of Perinatal Psychosocial Risks from Natural Language.

During the perinatal period, psychosocial health risks, including depression and intimate partner violence, are associated with serious adverse health outcomes for birth parents and children. To appropriately intervene, healthcare professionals must first identify those at risk, yet stigma often prevents people from directly disclosing the information needed to prompt an assessment. In this research we use short diary entries to indirectly elicit information that could indicate psychosocial risks, then examine patterns that emerge in the language of those at risk. We find that diary entries exhibit consistent themes, extracted using topic modeling, and emotional perspective, drawn from dictionary-informed sentiment features. Using these features, we use regularized regression to predict screening measures for depression and psychological aggression by an intimate partner. Journal text entries quantified through topic models and sentiment features show promise for depression prediction, corresponding with self-reported screening measures almost as well as closed-form questions. Text-based features are less useful in predicting intimate partner violence, but topic models generate themes that align with known risk correlates. The indirect features uncovered in this research could aid in the detection and analysis of stigmatized risks.

Using natural language from a smartphone pregnancy app to identify maternal depression.

Depression is highly prevalent in pregnancy, yet it often goes undiagnosed and untreated. Language can be an indicator of psychological well-being. This longitudinal, observational cohort study of 1,274 pregnancies examined written language shared in a prenatal smartphone app. Natural language feature of text entered in the app (e.g. in a journaling feature) throughout the course of participants' pregnancies were used to model subsequent depression symptoms. Language features were predictive of incident depression symptoms in a 30-day window (AUROC = 0.72) and offer insights into topics most salient in the writing of individuals experiencing those symptoms. When natural language inputs were combined with self-reported current mood, a stronger predictive model was produced (AUROC = 0.84). Pregnancy apps are a promising way to illuminate experiences contributing to depression symptoms. Even sparse language and simple patient-reports collected directly from these tools may support earlier, more nuanced depression symptom identification.

Parent Care-Seeking Decisions for Pediatric Acute Respiratory Tract Infections in the United States: A Mental Models Approach.

OBJECTIVE: To understand US parent health care-seeking decisions in the context of multiple in-person and telehealth care options. As the health care landscape evolves, new research is needed to explain how parents now decide when and where to seek acute pediatric health care. METHODS: We applied a mental models approach, focusing on the archetypal example of care-seeking for pediatric acute respiratory tract infections (ARTIs), by first reviewing pediatric ARTI guidelines with 16 health care professionals to inform 40 subsequent semi-structured interviews with parents of young children in 2021. Interviews were qualitatively coded using thematic analysis, with code frequency and co-occurrence informing the final influence model of parent health care-seeking decisions. RESULTS: Parent interviewees identified 33 decisional factors which were synthesized into seven dimensions influencing care-seeking decisions: perceived illness severity, perceived child susceptibility, parental self-efficacy, expected accessibility of care, expected affordability of care, expected quality of clinician, and expected quality of site. The first three dimensions (perceived severity, perceived susceptibility, parental self-efficacy) influenced an initial decision about whether to seek care, while all seven factors influenced a subsequent decision about where to seek care (eg, in-person primary care, primary care-based telehealth, urgent care, direct-to-consumer telehealth). Uncertainty was present within many dimensions (eg, severity, access, quality) indicating potential targets to support parent decision-making processes and optimize care-seeking behaviors. CONCLUSIONS: A mental models approach identified dimensions influencing parent choice to seek care and choice of care site for children with ARTIs, suggesting targets to advance family-centered practice and policy.

Parent-Reported Use of Pediatric Primary Care Telemedicine: Survey Study.

BACKGROUND: Telemedicine delivered from primary care practices became widely available for children during the COVID-19 pandemic. OBJECTIVE: Focusing on children with a usual source of care, we aimed to examine factors associated with use of primary care telemedicine. METHODS: In February 2022, we surveyed parents of children aged ≤17 years on the AmeriSpeak panel, a probability-based panel of representative US households, about their children's telemedicine use. We first compared sociodemographic factors among respondents who did and did not report a usual source of care for their children. Among those reporting a usual source of care, we used Rao-Scott F tests to examine factors associated with parent-reported use versus nonuse of primary care telemedicine for their children. RESULTS: Of 1206 respondents, 1054 reported a usual source of care for their children. Of these respondents, 301 of 1054 (weighted percentage 28%) reported primary care telemedicine visits for their children. Factors associated with primary care telemedicine use versus nonuse included having a child with a chronic medical condition (87/301, weighted percentage 27% vs 113/753, 15%, respectively; P=.002), metropolitan residence (262/301, weighted percentage 88% vs 598/753, 78%, respectively; P=.004), greater internet connectivity concerns (60/301, weighted percentage 24% vs 116/753, 16%, respectively; P=.05), and greater health literacy (285/301, weighted percentage 96% vs 693/753, 91%, respectively; P=.005). CONCLUSIONS: In a national sample of respondents with a usual source of care for their children, approximately one-quarter reported use of primary care telemedicine for their children as of 2022. Equitable access to primary care telemedicine may be enhanced by promoting access to primary care, sustaining payment for primary care telemedicine, addressing barriers in nonmetropolitan practices, and designing for lower health-literacy populations.

The Role of Confidence and Knowledge in Intentions to (Not) Seek Care for Hypertension: Evidence From a National Survey.

BACKGROUND: Hypertension (high blood pressure) is a modifiable risk factor for cardiovascular disease. However, patients may lack confidence in their understanding of what constitutes normal/healthy blood pressure, potentially affecting intentions to seek necessary care. The American Heart Association defines normal/healthy blood pressure as <120/80 mm Hg, with a 130/80 mm Hg threshold for hypertension diagnosis. METHODS: Our US sample (N = 6,592) included 1,342 adults with hypertension alone and 795 with hypertension and relevant comorbidities (heart disease, kidney disease, and diabetes mellitus). We assessed confidence in understanding blood pressure numbers, knowledge of thresholds for normal/healthy blood pressure ("normal or healthy blood pressure is below . . ."; counting 120-130/80 mm Hg as correct), and intentions to seek care for randomly assigned blood pressure readings of 142/91 (stage 2 hypertension), 132/69 (stage 1 hypertension), or 118/78 mm Hg (normal/healthy blood pressure). RESULTS: Among nonhypertensive participants, 55% expressed confidence in their understanding of blood pressure numbers, but only 36% knew the upper thresholds for normal/healthy blood pressure. Among participants with hypertension alone, 78% were confident while 47% were knowledgeable. Among participants with hypertension and comorbidities, 81% were confident and 40% were knowledgeable. Participants who were confident (v. not) were more likely to express intentions to act on stage 2 hypertension readings but less likely to express intentions to act on stage 1 readings, even after adjustment for knowledge, hypertension diagnosis, and sociodemographics. LIMITATIONS: Confidence, knowledge, and intentions were each measured with 1 question. CONCLUSIONS: Independent of knowledge, confidence was associated with greater willingness to act on stage 2 hypertension readings but reduced willingness to act on stage 1 hypertension readings. Interventions aiming to improve hypertension care-seeking behavior should improve confidence in accurate knowledge. HIGHLIGHTS: Hypertension or high blood pressure is a major risk factor for heart disease.Most Americans do not know that normal/healthy blood pressure levels are ≤120/80 mm Hg, yet they are confident that they know this information.Inappropriate confidence in understanding of blood pressure numbers undermines intentions to seek care for stage 1 hypertension blood pressure readings.

Decision-making tendencies and voucher spending independently support abstinence within contingency management for methamphetamine use disorder.

Decision-making tendencies and spending within cash voucher-based interventions have individually been shown to be related to future abstinence among participants with methamphetamine use disorder (MUD), but less is known of their independent contributions. This study of participants in a contingency management (CM) trial investigated whether decision-making and spending were each associated with future abstinence. Thirty-two outpatients with MUD, predominately male (68%) and mixed ancestry (94%) with a median age of 34 years, participated in an 8-week cash voucher-based CM pilot trial. Prior to commencing the trial, participants completed a computerized Iowa Gambling Task (IGT) to measure decision-making preferences for more frequent rewards and longer term gains of greater magnitude. Spending and abstinence of participants were tracked over the duration of the trial. In a secondary analysis, time-lagged counting process Cox Proportional Hazard models were conducted. Baseline decision-making, characterized by a preference for frequent rewards, was associated with a greater likelihood of future spending, Hazard Ratio; HR = 1.13 [1.06: 1.21]. Avoidance of short-term rewards to realize longer term, higher magnitude rewards, and spending at the prior visit were each associated with abstinence on the trial, HR = 1.12 [1.03: 1.22] and HR = 1.32 [1.08: 1.61], respectively. Controlling for decision-making, spending, and cumulative abstinence, prior abstinence remained the largest predictor of future abstinence, HR = 3.85 [2.88: 5.16]. Decision-making tendencies and spending are correlated yet independently associated with abstinence reinforcement in CM. Findings highlight the opportunity for behavioral treatment programs to tailor program structures to individual-specific characteristics. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Perceptions and knowledge of COVID-19 vaccine safety and efficacy among vaccinated and non-vaccinated obstetric healthcare workers.

The COVID-19 vaccines are highly effective in preventing COVID-19 illness; however, pregnant people were not included in the original COVID-19 vaccine trials, with resultant conflicting recommendations from health organizations regarding vaccinations for this high-risk population. Pregnant and lactating healthcare workers (HCWs), along with people planning a pregnancy, identified as "obstetric HCWs" in our study, were among the first to make decisions regarding vaccinating themselves against COVID-19. Given that HCWs are key sources of information and access to vaccinations, this study was conducted to understand the perceptions and knowledge of obstetric HCWs regarding the COVID-19 vaccine. An electronic survey to HCWs at a tertiary care institution in Pittsburgh, PA identified 83 obstetric HCWs, of which 65 (78.3%) received at least one dose of the either the Pfizer or Moderna COVID-19 vaccine, and 18 (21.7%) had not received any doses of vaccine. Pregnancy status influenced more people not to receive than to receive the vaccine. We found that both vaccinated and non-vaccinated obstetric HCWs had accurate knowledge regarding the COVID-19 vaccine. However, compared to non-vaccinated obstetric HCWs, vaccinated obstetric HCWs tended to endorse beliefs regarding herd immunity, believed they had a higher chance of acquiring COVID-19, and felt that the COVID-19 vaccine was safe for fetuses and people who were pregnant, lactating, breastfeeding, or planning a pregnancy. This study offers insight into obstetric individuals' perceptions and knowledge of the COVID-19 vaccine, and highlights areas where additional education and outreach may help obstetric individuals make informed decisions on receiving the COVID-19 vaccine.

Barriers of Acceptance to Hospice Care: a Randomized Vignette-Based Experiment.

BACKGROUND: The per diem financial structure of hospice care may lead agencies to consider patient-level factors when weighing admissions. OBJECTIVE: To investigate if treatment cost, disease complexity, and diagnosis are associated with hospice willingness to accept patients. DESIGN: In this 2019 online survey study, individuals involved in hospice admissions decisions were randomized to view one of six hypothetical patient vignettes: "high-cost, high-complexity," "low-cost, high-complexity," and "low-cost, low-complexity" within two diseases: heart failure and cystic fibrosis. Vignettes included demographics, prognoses, goals, and medications with costs. Respondents indicated their perceived likelihood of acceptance to their hospice; if likelihood was <100%, respondents were asked the barriers to acceptance. We used bivariate tests to examine associations between demographic, clinical, and organizational factors and likelihood of acceptance. PARTICIPANTS: Individuals involved in hospice admissions decisions MAIN MEASURES: Likelihood of acceptance to hospice care KEY RESULTS: N=495 (76% female, 53% age 45-64). Likelihoods of acceptance in cystic fibrosis were 79.8% (high-cost, high-complexity), 92.4% (low-cost, high-complexity), and 91.5% (low-cost, low-complexity), and in heart failure were 65.9% (high-cost, high-complexity), 87.3% (low-cost, high-complexity), and 96.6% (low-cost, low-complexity). For both heart failure and cystic fibrosis, respondents were less likely to accept the high-cost, high-complexity patient than the low-cost, high-complexity patient (65.9% vs. 87.3%, 79.8% vs. 92.4%, both p<0.001). For heart failure, respondents were less likely to accept the low-cost, high-complexity patient than the low-cost, low-complexity patient (87.3% vs. 96.6%, p=0.004). Treatment cost was the most common barrier for 5 of 6 vignettes. CONCLUSIONS: This study suggests that patients receiving expensive and/or complex treatments for palliation may have difficulty accessing hospice.

Using implementation science frameworks to translate and adapt a pregnancy app for an emerging Latino community.

BACKGROUND: Digital mobile health (mHealth) applications are a popular form of prenatal education and care delivery in the U.S.; yet there are few Spanish language options for native speakers. Furthermore, existing applications do not consider cultural differences and disparities in healthcare access, including those specific to emerging Latino communities. OBJECTIVE: To adapt and translate an English-language pregnancy mobile health app to meet the language and cultural needs of Spanish-speaking Latino immigrants living in the United States. METHODS: We use a multi-step process, grounded in implementation science frameworks, to adapt and translate the contents of an existing pregnancy app. Interviews with stakeholders (n = 12) who advocate for the needs of pregnant individuals in an emerging Latino community were used to identify domains of possible disparities in access to prenatal care. We then conducted semi-structured interviews with peripartum Spanish-speaking Latino users (n = 14) to understand their perspectives within those domains. We identified a list of topics to create educational material for the modified app and implemented a systematic translation approach to ensure that the new version was acceptable for immigrants from different countries in Latin America. RESULTS: The interviews with stakeholders revealed seven critical domains that need to be addressed in an adapted prenatal app: language and communication, financial concerns, social support, immigration status, cultural differences, healthcare navigation, and connection to population-specific community resources that offer Spanish language services. The interviews with peripartum Spanish-speaking Latino women informed how the existing content in the app could be adjusted or built upon to address these issues, including providing information on accessing care offered in their native language and community support. Finally, we used a systematic approach to translate the existing application and create new content. CONCLUSION: This work illustrates a process to adapt an mHealth pregnancy app to the needs of an emerging Latino community, by incorporating culturally sensitive Spanish language content while focusing on addressing existing health disparities.

The value of adherence information during clinical pharmaceutical trials.

BACKGROUND/AIMS: The quality of the evidence used to evaluate a drug's safety and efficacy depends, in part, on how well participants adhere to the prescribed drug-taking regime. There are multiple approaches to measure adherence in clinical trials, varying in their cost and accuracy. We demonstrate a method for evaluating the cost-effectiveness of common adherence monitoring methods, considering the costs and data quality for drugs that differ in how forgiving they are of nonadherence. METHODS: We propose a simulation approach to estimate the value of evidence about adherence, considering both costs of collection and potential errors in interpreting clinical trial results. We demonstrate the approach with a simulated clinical trial of nitrendipine, a common calcium channel blocker. We consider two trial designs, one using pretrial adherence to "enrich" the trial sample and one without an enrichment strategy. We use scenarios combining high and low values of two key properties of a clinical trial: participant adherence and drug forgiveness. RESULTS: Under the conditions of these simulations, the most cost-effective adherence monitoring approach depends on both trial participant adherence and drug forgiveness. For example, the enrichment strategy is not cost-effective for the base scenario (high forgiveness and high adherence), but is for other scenarios. We also estimate the effects of evaluable patient analysis, a controversial procedure that excludes nonadherent participants from the analyses, after a trial is completed. CONCLUSIONS: Our proposed approach can guide drug regulators and developers in designing efficient clinical trials and assessing the impact of nonadherence on trial results. It can identify cost-effective adherence-monitoring methods, given available knowledge about the methods, drug, and patients' expected adherence.

A Framework for Femtech: Guiding Principles for Developing Digital Reproductive Health Tools in the United States.

The United States has abysmal reproductive health indices that, in part, reflect stark inequities experienced by people of color and those with preexisting medical conditions. The growth of "femtech," or technology-based solutions to women's health issues, in the public and private sectors is promising, yet these solutions are often geared toward health-literate, socioeconomically privileged, and/or relatively healthy white cis-women. In this viewpoint, we propose a set of guiding principles for building technologies that proactively identify and address these critical gaps in health care for people from socially and economically marginalized populations that are capable of pregnancy, as well as people with serious chronic medical conditions. These guiding principles require that such technologies: (1) include community stakeholders in the design, development, and deployment of the technology; (2) are grounded in person-centered frameworks; and (3) address health disparities as a strategy to advance health equity and improve health outcomes.

Physicians' cognitive approach to prognostication after cardiac arrest.

OBJECTIVE: Elucidate how physicians formulate a neurological prognosis after cardiac arrest and compare differences between experts and general providers. METHODS: We performed semi-structured interviews with experts in post-arrest care and general physicians. We created an initial model and interview guide based on professional society guidelines. Two authors independently coded interviews based on this initial model, then identified new topics not included in it. To describe individual physicians' cognitive approach to prognostication, we created a graphical representation. We summarized these individual "mental models" into a single overall model, as well as two models stratified by expertise. RESULTS: We performed 36 interviews (17 experts and 19 generalists), most of whom practice in Europe (23) or North America (12). Participants described their approach to prognosis formulation as complex and iterative, with sequential and repeated data acquisition, interpretation, and prognosis formulation. Eventually, this cycle results in a final prognosis and treatment recommendation. Commonly mentioned factors were diagnostic test performance, time from arrest, patient characteristics. Participants also discussed factors rarely discussed in prognostication research including physician and hospital characteristics. We found no substantial differences between experts and general physicians. CONCLUSION: Physicians' cognitive approach to neurologic prognostication is complex and influenced by many factors, including some rarely considered in current research. Understanding these processes better could inform interventions designed to aid physicians in prognostication.