Examining the economic impacts of caregiving among families of children of medical complexity: A qualitative study to inform inclusive economic models.

BACKGROUND: Children with medical complexity (CMC) represent a heterogeneous group of children with multiple, chronic healthcare conditions. Caregivers of CMC experience a high intensity of caregiving that is often variable, extends across several networks of care, and often lasts for the entirety of the child's life. The economic impacts of caregiving are yet understudied in the CMC context. There have been recognized limitations to the sole use of quantitative methods when developing economic models of disease because they lack direct caregiver voice and context of caregiving activities and existing methods have been noted to be ableist. OBJECTIVE: The purpose of this study was to explore the economic spillover impacts of caregiving among families of CMC using their own words and perspectives with the intent of expanding caregiver-centered perspectives when developing economic models. METHODS: This study was a secondary analysis of a qualitative study that was conducted to examine family management practices among caregivers of CMC and their social networks. Caregivers of CMC were recruited through a Pediatric Complex Care clinic at an academic medical center in the mid-Atlantic region, USA. This study used inductive qualitative descriptive methods and the use of a template to define features of the person impacted and to define the economic construct as either a direct or indirect/spillover cost. RESULTS: Twenty caregivers were included in this study. Perspectives from the caregivers of CMC revealed several key themes: (1) time investment in caregiving - impacting the primary caregivers; (2) physical and mental health impacts - impacting the child themselves, siblings, and the primary caregivers; (3) impacts to leisure activities and self-care - impacting the child themselves, siblings, and the primary caregivers; (4) impacts to the social network/social capital. CONCLUSIONS: The themes described can be operationalized into inclusive family-centered models that represent the impacts of caregiving in the context of the family units of CMC. The use of qualitative methods to expand our development of quantitative economic models can be adapted to other populations where caregivers are involved in care. Caregivers can and should have an active voice in preference-based assessments that are operationalized in economic contexts to make them more inclusive. CLINICALTRIAL: n/a. INTERNATIONAL REGISTERED REPORT: RR2-10.2196/14810.

Prospective validation of clinical deterioration predictive models prior to intensive care unit transfer among patients admitted to acute care cardiology wards.

Objective. Very few predictive models have been externally validated in a prospective cohort following the implementation of an artificial intelligence analytic system. This type of real-world validation is critically important due to the risk of data drift, or changes in data definitions or clinical practices over time, that could impact model performance in contemporaneous real-world cohorts. In this work, we report the model performance of a predictive analytics tool developed before COVID-19 and demonstrate model performance during the COVID-19 pandemic.Approach. The analytic system (CoMETⓇ, Nihon Kohden Digital Health Solutions LLC, Irvine, CA) was implemented in a randomized controlled trial that enrolled 10 422 patient visits in a 1:1 display-on display-off design. The CoMET scores were calculated for all patients but only displayed in the display-on arm. Only the control/display-off group is reported here because the scores could not alter care patterns.Main results.Of the 5184 visits in the display-off arm, 311 experienced clinical deterioration and care escalation, resulting in transfer to the intensive care unit, primarily due to respiratory distress. The model performance of CoMET was assessed based on areas under the receiver operating characteristic curve, which ranged from 0.725 to 0.737.Significance.The models were well-calibrated, and there were dynamic increases in the model scores in the hours preceding the clinical deterioration events. A hypothetical alerting strategy based on a rise in score and duration of the rise would have had good performance, with a positive predictive value more than 10-fold the event rate. We conclude that predictive statistical models developed five years before study initiation had good model performance despite the passage of time and the impact of the COVID-19 pandemic.

Designing for caregiving networks: a case study of primary caregivers of children with medical complexity.

OBJECTIVE: The study aimed to characterize the experiences of primary caregivers of children with medical complexity (CMC) in engaging with other members of the child's caregiving network, thereby informing the design of health information technology (IT) for the caregiving network. Caregiving networks include friends, family, community members, and other trusted individuals who provide resources, information, health, or childcare. MATERIALS AND METHODS: We performed a secondary analysis of two qualitative studies. Primary studies conducted semi-structured interviews (n = 50) with family caregivers of CMC. Interviews were held in the Midwest (n = 30) and the mid-Atlantic region (n = 20). Interviews were transcribed verbatim for thematic analysis. Emergent themes were mapped to implications for the design of future health IT. RESULTS: Thematic analysis identified 8 themes characterizing a wide range of primary caregivers' experiences in constructing, managing, and ensuring high-quality care delivery across the caregiving network. DISCUSSION: Findings evidence a critical need to create flexible and customizable tools designed to support hiring/training processes, coordinating daily care across the caregiving network, communicating changing needs and care updates across the caregiving network, and creating contingency plans for instances where caregivers are unavailable to provide care to the CMC. Informaticists should additionally design accessible platforms that allow primary caregivers to connect with and learn from other caregivers while minimizing exposure to sensitive or emotional content as indicated by the user. CONCLUSION: This article contributes to the design of health IT for CMC caregiving networks by uncovering previously underrecognized needs and experiences of CMC primary caregivers and drawing direct connections to design implications.

External Validation of a Digital Pathology-based Multimodal Artificial Intelligence Architecture in the NRG/RTOG 9902 Phase 3 Trial.

BACKGROUND: Accurate risk stratification is critical to guide management decisions in localized prostate cancer (PCa). Previously, we had developed and validated a multimodal artificial intelligence (MMAI) model generated from digital histopathology and clinical features. Here, we externally validate this model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial. OBJECTIVE: To externally validate the MMAI model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial. DESIGN, SETTING, AND PARTICIPANTS: Our validation cohort included 318 localized high-risk PCa patients from NRG/RTOG 9902 with available histopathology (337 [85%] of the 397 patients enrolled into the trial had available slides, of which 19 [5.6%] failed due to poor image quality). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Two previously locked prognostic MMAI models were validated for their intended endpoint: distant metastasis (DM) and PCa-specific mortality (PCSM). Individual clinical factors and the number of National Comprehensive Cancer Network (NCCN) high-risk features served as comparators. Subdistribution hazard ratio (sHR) was reported per standard deviation increase of the score with corresponding 95% confidence interval (CI) using Fine-Gray or Cox proportional hazards models. RESULTS AND LIMITATIONS: The DM and PCSM MMAI algorithms were significantly and independently associated with the risk of DM (sHR [95% CI] = 2.33 [1.60-3.38], p < 0.001) and PCSM, respectively (sHR [95% CI] = 3.54 [2.38-5.28], p < 0.001) when compared against other prognostic clinical factors and NCCN high-risk features. The lower 75% of patients by DM MMAI had estimated 5- and 10-yr DM rates of 4% and 7%, and the highest quartile had average 5- and 10-yr DM rates of 19% and 32%, respectively (p < 0.001). Similar results were observed for the PCSM MMAI algorithm. CONCLUSIONS: We externally validated the prognostic ability of MMAI models previously developed among men with localized high-risk disease. MMAI prognostic models further risk stratify beyond the clinical and pathological variables for DM and PCSM in a population of men already at a high risk for disease progression. This study provides evidence for consistent validation of our deep learning MMAI models to improve prognostication and enable more informed decision-making for patient care. PATIENT SUMMARY: This paper presents a novel approach using images from pathology slides along with clinical variables to validate artificial intelligence (computer-generated) prognostic models. When implemented, clinicians can offer a more personalized and tailored prognostic discussion for men with localized prostate cancer.

A Comparison of Hospice Care Utilization Between Rural and Urban Children in Appalachia: A Geographic Information Systems Analysis.

Long driving times from hospice providers to patients lead to poor quality of care, which may exacerbate in rural and highly isolated areas of Appalachia. This study aimed to investigate geographic patterns of pediatric hospice care across Appalachia. Using person-level Medicaid claims of 1,788 pediatric hospice enrollees who resided in the Appalachian Region between 2011 and 2013. A database of boundaries of Appalachian counties, postal addresses of hospices, and population-weighted county centroids of residences of hospice enrollees driving times from the nearest hospices were calculated. A choropleth map was created to visualize rural/urban differences in receiving hospice care. The average driving time from hospice to child residence was 28 minutes (SD = 26). The longest driving time was in Eastern Kentucky-126 minutes (SD = 32), and the shortest was in South Carolina-11 min (SD = 9.1). The most significant differences in driving times between rural and urban counties were found in Virginia 28 (SD = 7.5) and 5 minutes (SD = 0), respectively, Tennessee-43 (SD = 28) and 8 minutes (SD = 7), respectively; and West Virginia-49 (SD = 30) and 12 minutes (SD = 4), respectively. Many pediatric hospice patients reside in isolated counties with long driving times from the nearest hospices. State-level policies should be developed to reduce driving times from hospice providers.

Geographic Information Systems Utilization in Pediatric End-of-Life Research: A Scoping Review.

Currently, little is known about how geographic information systems (GIS) has been utilized to study end-of-life care in pediatric populations. The purpose of this review was to collect and examine the existing evidence on how GIS methods have been used in pediatric end-of-life research over the last 20 years. Scoping review method was used to summarize existing evidence and inform research methods and clinical practice was used. The Preferred Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA) was utilized. The search resulted in a final set of 17 articles. Most studies created maps for data visualization and used ArcGIS as the primary software for analysis. The scoping review revealed that GIS methodology has been limited to mapping, but that there is a significant opportunity to expand the use of this methodology for pediatric end-of-life care research.

The Use of Medical Crowdfunding to Mitigate the Personal Costs of Serious Chronic Illness: Scoping Review.

BACKGROUND: Persons diagnosed with serious chronic illnesses and their caretakers experience multiple types of financial costs that strain their income and generate financial distress. Many turn to medical crowdfunding (MCF) to mitigate the harms of these costs on their health and quality of life. OBJECTIVE: This scoping review aims to summarize the research on MCF for persons diagnosed with serious chronic illness regarding study designs and methods; the responsible conduct of research practices; and study foci as they relate to stress, stress appraisals, and the coping processes. METHODS: This review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Eligible studies were conducted in countries designated as high income by the World Bank and focused on beneficiaries diagnosed with serious chronic illness. The findings of the included studies were summarized as they related to the key concepts in a conceptual framework derived from an established stress, appraisal, and coping framework and a conceptual model of financial toxicity in pediatric oncology. RESULTS: Overall, 26 studies were eligible for inclusion in the review. The main findings included a lack of integration of qualitative and quantitative approaches and the inconsistent reporting of the responsible conduct of research practices. The included studies focused on financial stressors that contributed to financial burden, such as out-of-pocket payments of medical bills, basic living expenses, medical travel expenses, and lost income owing to illness-related work disruptions. Few studies addressed stress appraisals as threatening or the adequacy of available financial resources. When mentioned, appraisals related to the global financial struggle during the COVID-19 pandemic or the capacity of social network members to donate funds. The consequences of MCF included the receipt of 3 forms of social support (tangible, informational, and emotional), privacy loss, embarrassment, and the propagation of scientifically unsupported information. Studies found that friends and family tended to manage MCF campaigns. Although most of the studies (21/26, 81%) focused on monetary outcomes, a few (5/26, 19%) concentrated on peoples' experiences with MCF. CONCLUSIONS: The identified methodological gaps highlight the need for more robust and reproducible approaches to using the copious data available on public MCF platforms. The integration of quantitative and qualitative methods will allow for nuanced explorations of the MCF experience. A more consistent elaboration of strategies to promote the responsible conduct of research is warranted to minimize risk to populations that are vulnerable and express concerns regarding the loss of privacy. Finally, an examination of the unanticipated consequences of MCF is critical for the development of future interventions to optimize existing supports while providing needed supports, financial and nonfinancial, that are lacking.

Disseminating for impact: creating curriculum activities for translational dissemination for the clinical and translational research workforce.

There has been an increased focus on the practices associated with dissemination for the translation of research to clinical practice and ultimately, policy. Simultaneously, there has been attention placed on the role of the clinical research workforce in supporting optimal dissemination efforts for impact and societal benefit. Curriculums focused on education opportunities for dissemination for translational scientists have been under-reported. The Translational Science Benefits Model (TSBM) is a framework that has been developed to support assessment of clinical and translational research outcomes that measure impact (both in the clinical and community setting) beyond traditional citations in academic journals/bibliometric activities. The TSBM framework outlines more than 30 different facets of impact and can provide a basis for operationalizing broad impacts of research for translational and clinical scientists. Engagement science offers methods and modalities to work with individual stakeholders, and collaborators in a team science model, and engagement with external scholars and society. This article will describe the use of the TSBM framework and engagement science strategies to develop a translational dissemination framework with novel components for evaluation of dissemination and implementation activities. We propose using the translational dissemination framework to guide the development of an educational curriculum for the clinical research workforce. We outline the educational domains and proposed evaluation criteria essential in implementing this innovative translational dissemination educational content for the clinical and translational research workforce.

Establishing the Need for Anticipatory Symptom Guidance and Networked Models of Disease in Adaptive Family Management Among Children With Medical Complexity: Qualitative Study.

BACKGROUND: Caregivers of children with medical complexity navigate complex family management tasks for their child both in the hospital and home-based setting. The roles and relationships of members of their social network and the dynamic evolution of these family management tasks have been underexamined. OBJECTIVE: The purpose of this study was to explore the structures and processes of family management among caregivers of children with medical complexity, with a focus on the underlying dynamic nature of family management practices and the role of members of their social network. METHODS: This study used a qualitative approach to interview caregivers of children with medical complexity and members of their social network. Caregivers of children with medical complexity were recruited through an academic Children's Hospital Complex Care Clinic in the mid-Atlantic region and interviewed over a period of 1 to 3 days. Responses were analyzed using constructivist grounded theory and situational analysis to construct a new conceptual model. Only caregiver responses are reported here. RESULTS: In total, 20 caregivers were included in this analysis. Caregiver perspectives revealed the contextual processes that allowed for practices of family management within the setting of rapidly evolving symptoms and health concerns. The dynamic and adaptive nature of this process is a key underlying action supporting this novel conceptual model. The central themes underpinning the adaptive family management model include symptom cues, ongoing surveillance, information gathering, and acute on chronic health concerns. The model also highlights facilitators and threats to successful family management among children with medical complexity and the networked relationship among the structures and processes. CONCLUSIONS: The adaptive family management model provides a basis for further quantitative operationalization and study. Previously described self- or family management frameworks do not account for the underlying dynamic nature of the disease trajectory and the developmental stage progression of the child or adolescent, and our work extends existing work. For future work, there is a defined role for technology-enhanced personalized approaches to home-based monitoring. Due to the disparities caregivers and the children in this population already experience, technology-enhanced approaches must be built alongside key stakeholders with an equity orientation to technology co-development. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/14810.

Ryan White HIV/AIDS Part B and AIDS Drug Assistance Programs during COVID-19: safety net public health programs' challenges and innovations.

Introduction: We characterized the challenges and innovations of states' Ryan White HIV/AIDS Program (RWHAP) Part B programs, including AIDS Drug Assistance Programs (ADAPs), during the COVID-19 pandemic. In the United States, these are important safety net programs for HIV healthcare, providing essential medical and support services, and medications, to people with HIV with low incomes who are uninsured/underinsured. Methods: Data were collected via the 2021-2022 NASTAD National RWHAP Part B and ADAP Monitoring Project Report, a cross-sectional survey of state, district, and territorial programs through a mixed method study design. For quantitative data, we used descriptive statistics. Qualitative responses were coded and analyzed using content analysis. Results: Forty-seven RWHAP Part B and ADAPs responded (92% response rate). The majority of respondents reported that maintaining client eligibility (78%) and working remotely (70%) were the most challenging aspects of the pandemic, particularly in regards to implementing new telehealth and e-certification platforms. In response to COVID-19, programs introduced enrollment "grace periods" (19%), bolstered client outreach (11%), allowed more than a 30 day supply of medications (79%), and supported medication home delivery for clients (80%). Discussion: Despite the challenges of the COVID-19 pandemic, RWHAP Part B and ADAPs implemented several operational innovations in order to continue providing essential medicines and services. Other public health programs may adopt similar innovations, including digital innovations, for greater public health benefit. Future studies should assess the retention of policy innovations over time, their impact on the individual client level satisfaction or health outcomes, and what factors may improve the acceptability of telehealth and e-certification platforms.

Cancer Screening and Treatment Delays During the COVID-19 Pandemic and the Role of Health Literacy in Care Re-engagement: Findings from an NCI-Designated Comprehensive Cancer Center sample.

The COVID-19 pandemic has led to numerous delays in cancer-related care and cancer-specific screening, but the extent is not fully understood. For those that experience a delay or disruption in care, health related self-management is required to re-engage in care pathways and the role of health literacy in this pathway has not been explored. The purpose of this analysis is to (1) report the frequency of self-reported delays in cancer treatment and preventative screening services at an academic, NCI-designated center during the COVID-19 pandemic and (2) investigate cancer-related care and screening delays among those with adequate and limited health literacy. A cross-sectional survey was administered from an NCI-designated Cancer Center with a rural catchment area during November 2020 through March 2021. A total of 1,533 participants completed the survey, and nearly 19 percent of participants were categorized as having limited health literacy. Twenty percent of those with a cancer diagnosis reported a delay in cancer-related care; and 23-30% of the sample reported a delay in cancer screening. In general, the proportions of delays among those with adequate and limited health literacy were similar with the exception of colorectal cancer screening. There was also a notable difference in the ability to re-engage in cervical cancer screening among those with adequate and limited health literacy. Thus, there is a role for those engaged in cancer-related education and outreach to offer additional navigation resources for those at risk to cancer-related care and screening disruptions. Future study is warranted to investigate the role of health literacy on cancer care engagement.

Data Science and Precision Oncology Nursing: Creating an Analytic Ecosystem to Support Personalized Supportive Care across the Trajectory of Illness.

OBJECTIVES: The authors' objective is to present an overarching framework of an analytic ecosystem using diverse data domains and data science approaches that can be used and implemented across the cancer continuum. Analytic ecosystems can improve quality practices and offer enhanced anticipatory guidance in the era of precision oncology nursing. DATA SOURCES: Published scientific articles supporting the development of a novel framework with a case exemplar to provide applied examples of current barriers in data integration and use. CONCLUSION: The combination of diverse data sets and data science analytic approaches has the potential to extend precision oncology nursing research and practice. Integration of this framework can be implemented within a learning health system where models can update as new data become available across the continuum of the cancer care trajectory. To date, data science approaches have been underused in extending personalized toxicity assessments, precision supportive care, and enhancing end-of-life care practices. IMPLICATIONS FOR NURSING PRACTICE: Nurses and nurse scientists have a unique role in the convergence of data science applications to support precision oncology across the trajectory of illness. Nurses also have specific expertise in supportive care needs that have been dramatically underrepresented in existing data science approaches thus far. They also have a role in centering the patient and family perspectives and needs as these frameworks and analytic capabilities evolve.

Decision Analytic Modeling for Global Clinical Trial Planning: A Case for HIV-Positive Patients at High Risk for Mycobacterium tuberculosis Sepsis in Uganda.

Sepsis is a significant cause of mortality among people living with human immunodeficiency virus (HIV) in sub-Saharan Africa. In the planning period prior to the start of a large multi-country clinical trial studying the efficacy of the immediate empiric addition of anti-tuberculosis therapy to standard-of-care antibiotics for sepsis in people living with HIV, we used decision analysis to assess the costs and potential health outcome impacts of the clinical trial design based on preliminary data and epidemiological parameter estimates. The purpose of this analysis was to highlight this approach as a case example where decision analysis can estimate the cost effectiveness of a proposed clinical trial design. In this case, we estimated the impact of immediate empiric anti-tuberculosis (TB) therapy versus the diagnosis-dependent standard of care using three different TB diagnostics: urine TB-LAM, sputum Xpert-MTB/RIF, and the combination of LAM/Xpert. We constructed decision analytic models comparing the two treatment strategies for each of the three diagnostic approaches. Immediate empiric-therapy demonstrated favorable cost-effectiveness compared with all three diagnosis-dependent standard of care models. In our methodological case exemplar, the proposed randomized clinical trial intervention demonstrated the most favorable outcome within this decision simulation framework. Applying the principles of decision analysis and economic evaluation can have significant impacts on study design and clinical trial planning.

Application of Instrumental Variable Analysis in Pediatric End-of-Life Research: A Case Study.

Instrumental variable analysis (IVA) has been widely used in many fields, including health care, to determine the comparative effectiveness of a treatment, intervention, or policy. However, its application in pediatric end-of-life care research has been limited. This article provides a brief overview of IVA and its assumptions. It illustrates the use of IVA by investigating the comparative effectiveness of concurrent versus standard hospice care for reducing 1-day hospice enrollments. Concurrent hospice care is a relatively recent type of care enabled by the Affordable Care Act in 2010 for children enrolled in the Medicaid program and allows for receiving life-prolonging medical treatment concurrently with hospice care. The IVA was conducted using observational data from 18,152 pediatric patients enrolled in hospice between 2011 and 2013. The results indicated that enrollment in concurrent hospice care reduced 1-day enrollment by 19.3%.

Beyond prediction: Off-target uses of artificial intelligence-based predictive analytics in a learning health system.

Introduction: Artificial-intelligence (AI)-based predictive analytics provide new opportunities to leverage rich sources of continuous data to improve patient care through early warning of the risk of clinical deterioration and improved situational awareness.Part of the success of predictive analytic implementation relies on integration of the analytic within complex clinical workflows. Pharmaceutical interventions have off-target uses where a drug indication has not been formally studied for a different indication but has potential for clinical benefit. An analog has not been described in the context of AI-based predictive analytics, that is, when a predictive analytic has been trained on one outcome of interest but is used for additional applications in clinical practice. Methods: In this manuscript we present three clinical vignettes describing off-target use of AI-based predictive analytics that evolved organically through real-world practice. Results: Off-target uses included:real-time feedback about treatment effectiveness, indication of readiness to discharge, and indication of the acuity of a hospital unit. Conclusion: Such practice fits well with the learning health system goals to continuously integrate data and experience to provide.

Perspectives on Access to Novel Therapeutics Through Clinical Trials Among Adolescents and Young Adults with Advanced Cancer: Implications for Patient-Centered Clinical Trials.

Purpose: Adolescents and young adults (AYA) with advanced cancer have unequal access to and enrollment in clinical trials. Many AYA use online platforms to share their treatment experiences. The purpose of this analysis was to explore how AYA discuss clinical trials and their access to novel therapeutics through their blogs. Methods: We studied illness blogs from 22 AYA (ages 16-38 years old) with advanced cancer who specifically discussed experiences enrolling in a clinical trial. Nearly 500 excerpts were abstracted from their blogs, and we used qualitative descriptive methodology and thematic analysis to explore their longitudinal perspectives. Results: We describe three themes: (1) "Blinded", which represents the uncertainty in treatment pathway and underrepresentation of AYA in clinical trials, (2) "Totally healthy except for the damn cancer", which represents the numerous challenges associated with meeting eligibility criteria and lack of available clinical trials, and (3) "Go ahead and send me the bill!", which represents the precarious financial challenges associated with participating with clinical trials (both direct costs and indirect costs associated with travel, time away from work) as well as the costs of novel therapeutics. Conclusions: By studying AYA online narratives, we can outline several gaps in accessing clinical trials and generate future research priorities. AYA with advanced cancer are known to have aggressive trajectories, and there are opportunities to integrate patient-reported outcomes and supportive care frameworks embedded within clinical trial study design.

Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration.

The idea that we can detect subacute potentially catastrophic illness earlier by using statistical models trained on clinical data is now well-established. We review evidence that supports the role of continuous cardiorespiratory monitoring in these predictive analytics monitoring tools. In particular, we review how continuous ECG monitoring reflects the patient and not the clinician, is less likely to be biased, is unaffected by changes in practice patterns, captures signatures of illnesses that are interpretable by clinicians, and is an underappreciated and underutilized source of detailed information for new mathematical methods to reveal.

Conceptualizing the Value of Pediatric Concurrent Hospice Care.

Given that pediatric concurrent hospice care has been available for more than a decade, it is appropriate to seek an understanding of the value of this care delivery approach. Value is the cost associated with achieving beneficial health outcomes. In pursuit of this goal, the current literature on pediatric concurrent hospice care was synthesized and used to develop a model to explain its value. Because of its relevance, the Value Assessment Framework was used to conceptualize the value of pediatric concurrent hospice care. This framework gauges the value of a health care service through 2 components: long-term effect and short-term affordability. The framework considers comparative clinical effectiveness, cost-effectiveness, other benefits or disadvantages, contextual considerations, and potential budget impact. Evidence from the literature suggested that the value of concurrent care depended on clinical outcomes evaluated, costs examined, medical services used, care coordinated, context considered, and budget impacted. The literature demonstrated that pediatric concurrent hospice care does offer significant value for children and their families. The conceptual model highlighted the need for a comprehensive approach to assessing value. The model is a useful framework for future research examining the value of concurrent hospice care.

Lagging coverage for mental health services among children and adolescents through home and community-based Medicaid waivers.

PROBLEM: Many states cover mental health home and community-based services (HCBS) for youth through 1915(c) Medicaid HCBS waivers that allow states to waive certain Medicaid eligibility criteria and define high-risk populations based on age, medical condition(s), and disability status. We sought to evaluate how States are covering children and adolescents with mental health needs through 1915(c) waivers compared to other youth waiver populations. METHODS: Data elements were extracted from Medicaid 1915(c) approved waivers applications for all included waivers targeting any pediatric age range through October 31, 2018. Normalization criteria were developed and an aggregate overall coverage score and level of funding per person per waiver were calculated for each waiver. FINDINGS: One hundred and forty-two waivers across 45 states were included in this analysis. Even though there was uniformity in the Medicaid applications, there was great heterogeneity in how waiver eligibility, transition plans, services covered, and wait lists were defined across group classifications. Those with mental health needs (termed serious emotional disturbance) represented 5% of waivers with the least annual funding per person per waiver. CONCLUSIONS: We recommend greater links between public policy, infrastructure, health care providers, and a family-centered approach to extend coverage and scope of services for children and adolescents with mental health needs.

Incremental cost analysis of pediatric hospice care in rural and urban Appalachia.

PURPOSE: Considering growing disparities in health outcomes between rural and urban areas of Appalachia, this study compared the incremental Medicaid costs of pediatric concurrent care (implemented by the Patient Protection and Affordable Care Act) versus standard hospice care. METHODS: Data on 1,788 pediatric hospice patients, from the Appalachian region, collected between 2011 and 2013, were obtained from the Centers for Medicare and Medicaid Services. Incremental per-patient-per-month (PPM) costs of enrollment in concurrent versus standard hospice care were analyzed using multilevel generalized linear models. Increments for analysis were hospice length of stay (LOS). RESULTS: For rural children enrolled in concurrent hospice care, the mean Medicaid cost of hospice care was $3,954 PPPM (95% CI: $3,223-$4,684) versus $1,933 PPPM (95% CI: $1,357-$2,509) for urban. For rural children enrolled in standard hospice care, the mean Medicaid cost was $2,889 PPPM (95% CI: $2,639-$3,139) versus $1,122 PPPM (95% CI: $980-$1,264) for urban. There were no statistically significant differences in Medicaid costs for LOS of 1 day. However, for LOS between 2 and 14 days, concurrent enrollment decreased total costs for urban children (IC = $-236.9 PPPM, 95% CI: $-421-$-53). For LOS of 15 days or more, concurrent care had higher costs compared to standard care, for both rural (IC = $1,399 PPPM, 95% CI: $92-$2,706) and urban children (IC = $1,867 PPPM, 95% CI: $1,172-$2,363). CONCLUSIONS: The findings revealed that Medicaid costs for concurrent hospice care were highest among children in rural Appalachia. Future research on factors of high costs of rural care is needed.