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To simulate a future, warmer climate, we subjected subarctic dwarf shrub heath to 5°C direct soil warming for five consecutive growing seasons (1993-1997). Supplemental air warming treatments were imposed on warmed soil by plastic tents in 1994 and open-top chambers in 1995. Plant responses to warming were assessed by changes in: 1) shrub phenology, 2) current-year aboveground biomass in the dominant shrubs (Empetrum hermaphroditum, Vaccinium myrtillus, V. uliginosum and V. vitis-idaea), and 3) vascular and nonvascular plant cover. We estimated warming effects on soil nitrogen (N) availability by in situ buried bag incubation of soils. Soil warming stimulated soil N cycling and shrub growth and development in the short term (2-3 yr). In the second year, net N mineralization rates doubled in warmed soil (4.3 kg N ha-1 season-1 in untreated soil vs 9.2 kg ha-1 season-1). Greater N availability likely contributed to the observed 62% increase in current-year growth of V. myrtillus, the dominant deciduous shrub. In the third year, soil and air warming increased shoot production by > 80% in the evergreen shrubs V. vitis-idaea and E. hermaphroditum. Soil warming had no detectable effects on plant growth or soil N cycling in the fifth year, suggesting that the long-term response may be less dramatic than short-term changes. Past fertilization studies in arctic and subarctic tundra reported an increase in the abundance of graminoids. Despite enhanced soil N mineralization in the second year, we found that warming had little effect on plant community composition after five years. Even in an extreme climate warming scenario, it appears that subarctic soils mineralize an order of magnitude less N than was applied in fertilization experiments. High-dose fertilization studies provide insight into controls on plant communities, but do not accurately simulate increases in N availability predicted for a warmer climate.

The overarching aim of this 3-study research project is to understand the mechanisms through which regional social and economic factors impact the utilization of health services among individuals living in resource-poor communities. In this dissertation, we tested our hypotheses among populations of patients living in rural Michigan (studies 1 & 2) and rural New York (study 3). Studies 1 & 2 focused on access to routine primary medical care, while study 3 examined the interplay between primary and specialty medical care using one of the most complex and resource-intensive diagnosis as an example (cancer).In recent years, there has been a rapid movement toward multi-level interventions and evaluating multi-level risk factors for healthcare utilization. Despite this shift, we are just beginning to understand how place matters for health and healthcare access, let alone how place may impact individual-level determinants of health. Rural/urban disparities in health outcomes and health indicators are increasing, and there is some evidence that this is grounded in differences in socioeconomic status and physician shortages; however, among rural populations, there is scant evidence about the variation in risk factors by the degree of rurality, county-level infrastructure, and community attributes.Implementation of cancer survivorship care has largely failed and is highly variable as implemented. There are gaps in understanding the drivers of variation in rural areas at the system/community-level and the provider-level. Therefore, we want to better understand the interplay between the community-level factors and individuals’ social determinants of health and provider-level barriers to providing survivorship care, including population density/distance to a metro area (SA1) and county-level infrastructure and community attributes (SA2). In addition, we aimed to improve the provision of cancer survivorship care by addressing the stakeholders’ needs, engaging diverse groups of community, patient, and caregiver stakeholders, and applying a formal implementation framework and introducing novel implementation strategies through the application of multidisciplinary team science methods (SA3).We used existing data sources to address these aims. We evaluated the relationship between socio-demographic characteristics and primary care utilization in the Upper Peninsula of Michigan to determine whether social demographic risk factors for poor healthcare utilization varied by county infrastructure and community attributes.In SA1, we used the 2017 Health Survey of Upper Peninsula Adults, linked with the USDA ERS Rural-Urban Continuum Codes (RUCCs) to understand the impact of population density and proximity to a metro area on individual-level risk factors for healthcare access. In SA2, we used the 2017 Health Survey of Upper Peninsula Adults as well as County Health Rankings data from a collaboration between The University of Wisconsin Population Health Institute and the Robert Wood Johnson Foundation.The County Health Rankings (CHR) provide a measure of county infrastructure and community attributes. In SA2, we linked individual-level Health Survey data to corresponding CHR quartiles based on respondents’ residence to explore whether social demographic risk factors for poor healthcare utilization varied by county infrastructure and community social and economic attributes. We assessed the relationships between sociodemographic characteristics and utilization using Generalized Linear Models with a logit link for logistic regression, using GEE to account for clustering at the county level. We calculated interaction terms assessing effect modification, after adjusting for general health status.The proximity of residence to an urban area and population size have been critical metrics in determining access to cancer services and are a significant predictor of the cost of care.1-3 Our results suggest that the relationship between patient characteristics and healthcare access varies by location of the patients’ residence. The location of residence has the potential to compound (low education in regions geographically remote to services) and offset (high income and high education in high-income counties) the individual impacts. Population size and proximity to an urban center should be considered when aiming to understand sociodemographic factors associated with healthcare utilization. Individual-level determinants may have different degrees of importance in regions that are closer or farther from urban centers (SA1).Similarly, where infrastructure is lacking, community attributes are poor, and social and family support is lacking, poverty is significantly associated with low healthcare utilization. In contrast, where infrastructure is strong, and social, familial, and community-level economic attributes are strong, there are no differences in income categories. High-income respondents living in any county may be able to overcome some of the community-level barriers to healthcare access (SA2).Some risk factors for poor utilization are different depending on community infrastructure; public health programs should be tailored to local community needs. Promoting the use of formal teamwork principles and training among rural providers may be an efficient strategy to facilitate the implementation of innovative models of care in accordance with best practices and clinical guidelines. Formal implementation strategies have the potential to improve program adaptation, minimize implementation failures, and improve long-term program sustainability. Tailoring of implementation strategies to the unique characteristics of the program being implemented and the population it is intended to serve (e.g., rural cancer patients and providers) is a critical factor affecting implementation success (SA3).Context matters when evaluating the determinants of healthcare utilization and planning for the implementation of new programming and intervention strategies. Further research is necessary to determine what components of community infrastructure is beneficial or detrimental for patients seeking to access care, and how to overcome barriers imposed by geographic distance and sparse population densities.6.1 SA1: To evaluate whether the association between individual social demographic characteristics and utilization of primary care is affected by rurality and specific domains of rurality among rural residents of the Upper Peninsula of MichiganHypothesis: The relationship between individuals’ social and demographic factors and primary care utilization varies by degree of rurality and county-level indicators of community well-being (potential effect modification) 6.2 SA2: To evaluate whether the association between individual characteristics and utilization of primary care is affected by place-based characteristics Hypothesis: The relationship between individuals’ social determinants of health and primary care utilization varies by county (potential effect modification).6.3 SA3: To evaluate the mechanisms of and barriers to implementation of a new health IT innovation using a comprehensive implementation frameworka. Hypothesis 1: Identifying and engaging relevant stakeholders will be central in developing a feasible and acceptable intervention for its intended usersb. Hypothesis 2: Implementation of a sustainable innovation for resource-poor communities can be maximized by the use of an implementation science framework at each step of implementation and dissemination.

Anthropogenic nitrogen deposition to the forests of Northeastern America may change both forest species composition and productivity, by altering soil N availability and the ratio of ammonium to nitrate available. N deposition was simulated in forest and shadehouse studies to investigate changes in seedling growth due to different N availability, and ratio of ammonium to nitrate, and possible interactions between N deposition and seedling light environment for four species of birch, Betula populifolia, B. papyrifera, B. lenta and B. alleghaniensis. The species responded differentially, as B. papyrifera seedlings grew larger given either ammonium or nitrate, B. papyrifera and B. lenta responded to nitrate, and B. alleghaniensis responded to neither. In high light, different forms of N deposition may lead to changes in the species composition of the seedling community. Under shaded conditions, nitrate increased the growth of B. lenta but not B. alleghaniensis, which could also change community species composition. The thesis also investigates whether B. lenta seedlings can maximize growth by foraging selectively for nitrate or ammonium, and the effect of the ratio of ammonium to nitrate. Root architecture was examined, to ask whether root architecture is adapted to maximize efficiency of uptake of nutrients locally, and the effect of integration of patches of different N type. Spatial distribution of ammonium and nitrate in soils affected seedling growth; those offered ammonium and nitrate homogeneously grew larger than those offered a heterogeneous patch of nitrate and a patch of ammonium, although all seedlings took up a similar ratio of ammonium and nitrate. Lower growth in the heterogeneous treatment may reflect the additional cost of searching for nitrate separately from ammonium, compared with the homogeneous choice plants that encountered both together. As nitrate-rich deposition continues to fall, B. lenta seedlings will encounter nitrate more readily, and grow faster, like the seedlings offered a homogeneous choice of ammonium nitrate. Ultimately, they may have to search for scarce ammonium in a patchy environment. The exploitation of appropriate patches may be facilitated by changes in root architecture that increase the soil volume searched, and increase the likelihood of encountering new ammonium rich patches.

Long-term immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the identification of T-cell epitopes affecting host immunogenicity. In this computational study, we explored the CD8 + epitope diversity estimated in 27 of the most common HLA-A and HLA-B alleles, representing most of the United States population. Analysis of 16 SARS-CoV-2 variants [B.1, Alpha (B.1.1.7), five Delta (AY.100, AY.25, AY.3, AY.3.1, AY.44), and nine Omicron (BA.1, BA.1.1, BA.2, BA.4, BA.5, BQ.1, BQ.1.1, XBB.1, XBB.1.5)] in analyzed MHC class I alleles revealed that SARS-CoV-2 CD8 + epitope conservation was estimated at 87.6%–96.5% in spike (S), 92.5%–99.6% in membrane (M), and 94.6%–99% in nucleocapsid (N). As the virus mutated, an increasing proportion of S epitopes experienced reduced predicted binding affinity: 70% of Omicron BQ.1-XBB.1.5 S epitopes experienced decreased predicted binding, as compared with ~3% and ~15% in the earlier strains Delta AY.100–AY.44 and Omicron BA.1–BA.5, respectively. Additionally, we identified several novel candidate HLA alleles that may be more susceptible to severe disease, notably HLA-A*32:01 , HLA-A*26:01 , and HLA-B*53:01 , and relatively protected from disease, such as HLA-A*31:01 , HLA-B*40:01 , HLA-B*44:03 , and HLA-B*57:01. Our findings support the hypothesis that viral genetic variation affecting CD8 T-cell epitope immunogenicity contributes to determining the clinical severity of acute COVID-19. Achieving long-term COVID-19 immunity will require an understanding of the relationship between T cells, SARS-CoV-2 variants, and host MHC class I genetics. This project is one of the first to explore the SARS-CoV-2 CD8 + epitope diversity that putatively impacts much of the United States population.

To develop an evidence-based compassion fatigue program and evaluate its impact on nurse-reported burnout, secondary traumatic stress, and compassion satisfaction, as well as correlated factors of resilience and coping behaviors. The quality improvement pilot program was conducted with 59 nurses on a 20-bed subspecialty pediatric oncology unit at the St. Jude Children's Research Hospital in Memphis, Tennessee. Validated measures of compassion fatigue and satisfaction (Professional Quality of Life Scale V [ProQOLV]), coping (Brief COPE), and resilience (Connor-Davidson Resilience Scale-2) were evaluated preprogram and at two, four, and six months postprogram, with resilience and coping style measured at baseline and at six months postprogram. Secondary traumatic stress scores significantly improved from baseline to four months. Select coping characteristics were significantly correlated with ProQOLV subscale scores. Ongoing organizational support and intervention can reduce compassion fatigue and foster compassion satisfaction among pediatric oncology nurses.

Long COVID remains poorly characterized at the genomic level. The primary aim of this study was to examine the relationship between viral sequences and the incidence of Long COVID at a tertiary care center in Louisiana between April 2020 and December 2022. A secondary aim was analysis of the Spike protein to identify conserved regions for multivalent vaccine targets. To estimate Long COVID incidence across variants, we linked 4789 SARS-CoV-2 sequences to 3090 de-identified patient electronic health record information. The base population was defined as any patient with an International Classification of Diseases-10-Clinical Modification COVID-19 diagnosis code (U07.1) based definitions of Long COVID presentation developed by the N3C consortium. 1,554 patients (1,536 Long COVID-negative) met Long COVID definitions, with 56.3% being female, 36.1% self-reported as African American, 5.5% self-reported as Hispanic/Latino, and 54.5% had received at least one vaccine dose 14 days prior to SARS-CoV-2 collection. Long COVID-positive patients were older (mean age 43.1 years) than negative patients (35.9 years; = 0.0054) and were more likely to be female ( = 0.0001). Among unvaccinated patients, those with Long COVID were significantly younger than their vaccinated counterparts ( < 0.00001). Long COVID incidence varied by PANGO lineage, ranging between 14% in AY.13 to 67.8% in B.1.1.7. Analysis of spike protein diversity revealed eight conserved amino acid regions (Shannon entropy < 0.43), representing potential targets for vaccine design. Long COVID rates across thousands of annotated SARS-CoV-2 sequences revealed lineage-specific risk and conserved epitopes for future interventions.

Background In healthcare change interventions, on-the-ground learning about the implementation process is often lost because of a primary focus on outcome improvements. This paper describes the Learning Evaluation, a methodological approach that blends quality improvement and implementation research methods to study healthcare innovations. Methods Learning Evaluation is an approach to multi-organization assessment. Qualitative and quantitative data are collected to conduct real-time assessment of implementation processes while also assessing changes in context, facilitating quality improvement using run charts and audit and feedback, and generating transportable lessons. Five principles are the foundation of this approach: (1) gather data to describe changes made by healthcare organizations and how changes are implemented; (2) collect process and outcome data relevant to healthcare organizations and to the research team; (3) assess multi-level contextual factors that affect implementation, process, outcome, and transportability; (4) assist healthcare organizations in using data for continuous quality improvement; and (5) operationalize common measurement strategies to generate transportable results. Results Learning Evaluation principles are applied across organizations by the following: (1) establishing a detailed understanding of the baseline implementation plan; (2) identifying target populations and tracking relevant process measures; (3) collecting and analyzing real-time quantitative and qualitative data on important contextual factors; (4) synthesizing data and emerging findings and sharing with stakeholders on an ongoing basis; and (5) harmonizing and fostering learning from process and outcome data. Application to a multi-site program focused on primary care and behavioral health integration shows the feasibility and utility of Learning Evaluation for generating real-time insights into evolving implementation processes. Conclusions Learning Evaluation generates systematic and rigorous cross-organizational findings about implementing healthcare innovations while also enhancing organizational capacity and accelerating translation of findings by facilitating continuous learning within individual sites. Researchers evaluating change initiatives and healthcare organizations implementing improvement initiatives may benefit from a Learning Evaluation approach.