Bridging the gaps in pediatric complex healthcare: the case for home nursing care among children with medical complexity.

BACKGROUND: Children with medical complexity (CMC) comprise < 1% of the pediatric population, but account for nearly one-third of healthcare expenditures. Further, while CMC account for up to 80% of pediatric inpatient hospital costs, only 2% of Medicaid spending is attributed to home healthcare. As a result, the current health system heavily relies on family caregivers to fill existing care gaps. This study aimed to: (1) examine factors associated with hospital admissions among CMC and (2) contextualize the potential for home nursing care to improve outcomes among CMC and their families in South Carolina (SC). METHODS: This mixed-methods study was conducted among CMC, their family caregivers, and physicians in SC. Electronic health records data from a primary care clinic within a large health system (7/1/2022-6/30/2023) was analyzed. Logistic regression examined factors associated with hospitalizations among CMC. In-depth interviews (N = 15) were conducted among physicians and caregivers of CMC statewide. Patient-level quantitative data is triangulated with conceptual findings from interviews. RESULTS: Overall, 39.87% of CMC experienced ≥ 1 hospitalization in the past 12 months. CMC with higher hospitalization risk were dependent on respiratory or neurological/neuromuscular medical devices, not non-Hispanic White, and demonstrated higher healthcare utilization. Interview findings contextualized efforts to reduce hospitalizations, and suggested adaptations related to capacity and willingness to provide complex care for CMC and their families. CONCLUSIONS: Findings may inform multi-level solutions for accessible, high-quality home nursing care among CMC and their families. Providers may learn from caregivers' insight to emphasize family-centered care practices, acknowledging time and financial constraints while optimizing the quality of medical care provided in the home.

An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: a Covid-19 case study.

Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.

Examining outpatient occupational therapy utilization among children and youth with special health care needs in South Carolina: A structural equation model of individual- and community-level factors.

BACKGROUND: Children and youth with special health care needs (CYSHCN) comprise 23 % of the pediatric population in South Carolina (SC), compared to state prevalences of 13.6 %-24 % nationwide. While occupational therapy (OT) is critical to maximizing CYSHCN's participation in daily activities, there are significant disparities in rehabilitation access in the Southeast region. OBJECTIVE: This study examines associations between patient-and community-level factors and outpatient OT utilization. METHODS: This study analyzes OT utilization data among CYSHCN (N = 1913) in a health system in SC from 07/01/2022-06/31/2023, merged with Child Opportunity Index 2.0 zip-code level social drivers of health (SDOH) data. SDOH are non-medical factors that affect individual's long-term health, with estimates ranging from "very low" to "very high" opportunity. Structural equation modeling was conducted to understand complex associations between observed and latent variables in a real-world context. RESULTS: CYSHCN were diagnosed with congenital (40.77 %), developmental (37.87 %), and neurological/neuromuscular conditions (21.36 %). CYSHCN who were non-Hispanic Black or Hispanic and were hospitalized were associated with living in lower opportunity areas. CYSHCN with private or military/other insurance/self-pay were associated with living in higher opportunity areas than Medicaid-insured CYSHCN. CYSHCN who were female and ≥12 years demonstrated lower OT utilization. CYSHCN who participated in speech and/or physical therapy demonstrated higher OT utilization. OT utilization increased with each increase in SDOH. CONCLUSION: With these findings, healthcare providers may consider accessibility barriers, including transportation, when referring CYSHCN to OT services. Further research is needed to examine the impact of household-level SDOH on OT access across SC.

Identification of factors associated with opioid-related and hepatitis C virus-related hospitalisations at the ZIP code area level in the USA: an ecological and modelling study.

BACKGROUND: Opioid overdose and related diseases remain a growing public health crisis in the USA. Identifying sociostructural and other contextual factors associated with adverse health outcomes is needed to improve prediction models to inform policy and interventions. We aimed to identify high-risk communities for targeted delivery of screening and prevention interventions for opioid use disorder and hepatitis C virus (HCV). METHODS: In this ecological and modelling study, we fit mixed-effects negative binomial regression models to identify factors associated with, and predict, opioid-related and HCV-related hospitalisations for ZIP code tabulation areas (ZCTAs) in South Carolina, USA. All individuals aged 18 years or older living in South Carolina from Jan 1, 2016, to Dec 31, 2021, were included. Data on opioid-related and HCV-related hospitalisations, as well as data on additional individual-level variables, were collected from medical claims records, which were obtained from the South Carolina Revenue and Fiscal Affairs Office. Demographic and socioeconomic variables were obtained from the United States Census Bureau (American Community Survey, 2021) with additional structural health-care barrier data obtained from South Carolina's Center for Rural and Primary Health Care, and the American Hospital Directory. FINDINGS: Between Jan 1, 2016, and Dec 31, 2021, 41 691 individuals were hospitalised for opioid misuse and 26 860 were hospitalised for HCV. There were a median of 80 (IQR 24-213) opioid-related hospitalisations and 61 (21-196) HCV-related hospitalisations per ZCTA. A standard deviation increase in ZCTA-level uninsured rate (relative risk 1·24 [95% CI 1·17-1·31]), poverty rate (1·24 [1·17-1·31]), mortality (1·18 [1·12-1·25]), and social vulnerability index (1·17 [1·10-1·24]) was significantly associated with increased combined opioid-related and HCV-related hospitalisation rates. A standard deviation increase in ZCTA-level income (0·79 [0·75-0·84]) and unemployment rate (0·87 [0·82-0·93]) was significantly associated with decreased combined opioid-related and HCV-related hospitalisations. Using 2016-20 hospitalisations as training data, our models predicted ZCTA-level opioid-related hospitalisations in 2021 with a median of 80·4% (IQR 66·8-91·1) accuracy and HCV-related hospitalisations in 2021 with a median of 75·2% (61·2-87·7) accuracy. Several underserved high-risk ZCTAs were identified for delivery of targeted interventions. INTERPRETATION: Our results suggest that individuals from economically disadvantaged and medically under-resourced communities are more likely to have an opioid-related or HCV-related hospitalisation. In conjunction with hospitalisation forecasts, our results could be used to identify and prioritise high-risk, underserved communities for delivery of field-level interventions. FUNDING: South Carolina Center for Rural and Primary Healthcare, National Institute on Drug Abuse, and National Library of Medicine.

Implementation of a mobile health clinic framework for Hepatitis C virus screening and treatment: a descriptive study.

Background: Although treatment for Hepatitis C Virus (HCV) is effective, individuals face access barriers. The utility of mobile health clinics (MHC), effective mechanisms for providing healthcare to underserved populations, is understudied for HCV-related interventions. We aimed to describe implementation of, and factors associated with, screening and treatment via MHCs. Methods: Clemson Rural Health implemented a novel MHC program to reach and treat populations at-risk for HCV with a focus on care for uninsured individuals. We examined HCV screening and treatment initiation/completion indicators between May 2021 and January 2023. Findings: Among 607 individuals screened across 31 locations, 94 (15.5%) tested positive via antibody and viral load testing. Treatment initiation and completion rates were 49.6% and 86.0%, respectively. Among those screened, the majority were male (57.5%), White (61.3%; Black/Hispanic: 28.2%/7.7%), and without personal vehicle as primary transportation mode (54.4%). Injection drug use (IDU) was 27.2% and uninsured rate was 42.8%. Compared to HCV-negative, those infected included more individuals aged 30-44 (52.1% vs. 36.4%, p = 0.023), male (70.2% vs. 55.2%, p = 0.009), White (78.5% vs. 60.2%, p < 0.0001), without personal vehicle (58.5% vs. 43.5%, p = 0.028), IDU (83.7% vs. 21.0%, p < 0.0001), and uninsured (61.2% vs. 48.8%, p = 0.050). Uninsured rates were higher among those initiating compared to not initiating treatment (74.5% vs. 45.3%, p = 0.004). Interpretation: The MHC framework successfully reaching its target population: at-risk individuals with access barriers to healthcare. The high HCV screening and treatment initiation/completion rates demonstrate the utility of MHCs as effective and acceptable intervention settings among historically difficult-to-treat populations. Funding: Gilead Sciences, Inc., and SC Center for Rural and Primary Healthcare.

Active Learning Pipeline to Identify Candidate Terms for a CDSS Ontology.

Ontology is essential for achieving health information and information technology application interoperability in the biomedical fields and beyond. Traditionally, ontology construction is carried out manually by human domain experts (HDE). Here, we explore an active learning approach to automatically identify candidate terms from publications, with manual verification later as a part of a deep learning model training and learning process. We introduce the overall architecture of the active learning pipeline and present some preliminary results. This work is a critical and complementary component in addition to manually building the ontology, especially during the long-term maintenance stage.

SARS-CoV-2 variant introduction following spring break travel and transmission mitigation strategies.

BACKGROUND: University spring break carries a two-pronged SARS-CoV-2 variant transmission risk. Circulating variants from universities can spread to spring break destinations, and variants from spring break destinations can spread to universities and surrounding communities. Therefore, it is critical to implement SARS-CoV-2 variant surveillance and testing strategies to limit community spread before and after spring break to mitigate virus transmission and facilitate universities safely returning to in-person teaching. METHODS: We examined the SARS-CoV-2 positivity rate and changes in variant lineages before and after the university spring break for two consecutive years. 155 samples were sequenced across four time periods: pre- and post-spring break 2021 and pre- and post-spring break 2022; following whole genome sequencing, samples were assigned clades. The clades were then paired with positivity and testing data from over 50,000 samples. RESULTS: In 2021, the number of variants in the observed population increased from four to nine over spring break, with variants of concern being responsible for most of the cases; Alpha percent composition increased from 22.2% to 56.4%. In 2022, the number of clades in the population increased only from two to three, all of which were Omicron or a sub-lineage of Omicron. However, phylogenetic analysis showed the emergence of distantly related sub-lineages. 2022 saw a greater increase in positivity than 2021, which coincided with a milder mitigation strategy. Analysis of social media data provided insight into student travel destinations and how those travel events may have impacted spread. CONCLUSIONS: We show the role that repetitive testing can play in transmission mitigation, reducing community spread, and maintaining in-person education. We identified that distantly related lineages were brought to the area after spring break travel regardless of the presence of a dominant variant of concern.