Stream bioassessments rely on taxonomic composition at sites compared with natural, reference conditions. We developed and tested an observed/expected (O/E) predictive model of taxonomic completeness and an index of compositional dissimilarity (BC index) for Central Appalachian streams using combined macroinvertebrate datasets from riffle habitats in West Virginia (WV) and Kentucky (KY). A total of 102 reference sites were used to calibrate the O/E model, which was then applied to assess over 1,200 sites sampled over a 10-year period. Using an all subsets discriminant function analysis (DFA) procedure, we tested combinations of 14 predictor variables that produced DF and O/E models of varying performance. We selected the most precise model using a probability of capture at >0.5 (O/E₀.₅, SD = 0.159); this model was constructed with only three simple predictor variables--Julian day, latitude, and whether a site was in ecoregion 69a. We evaluated O/E and BC indices between reference and test sites and compared their response to regional stressors, including coal mining, residential development, and acid deposition. The Central Appalachian O/E and BC indices both showed excellent discriminatory power and were significantly correlated to a variety of regional stressors; in some instances, the BC index was slightly more sensitive and responsive than the O/E₀.₅ model. These indices can be used to supplement existing bioassessment tools crucial to detecting and diagnosing stream impacts in the Central Appalachian region of WV and KY.
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J Cannabis Res
December 2024
University of Kentucky, Lexington, US.
Purpose: We conducted this study to assess cannabis use rates in the state of Kentucky relative to socioeconomic, demographic, and geographic factors, as well as reasons for use and modes of use, before the legal medical marijuana market commences in 2025.
Methods: We pooled Kentucky Behavioral Risk Factor Surveillance System (BRFSS) data for 2020-2021 and used weighted responses for all analyses. We estimated current cannabis use (at least once in the past 30 days), and heavy use (at least 20 of the past 30 days) prevalence rates for Appalachian, Delta, and Central geographic regions of Kentucky.
J Health Care Poor Underserved
November 2024
Central Appalachia's coal fields are the site of health disparities influenced by social determinants of health including poverty and isolation, compounded by transportation barriers to health care. In this study, we conducted two surveys among patients at a rural federally qualified health center (FQHC) to evaluate the health and financial ramifications of transportation barriers to primary care. Our findings indicate that patients facing transportation barriers rely disproportionately on emergency department services or hospitalization.
View Article and Find Full Text PDFInt J Environ Res Public Health
September 2024
School of Behavioral and Brain Sciences, Ponce Health Sciences University, Ponce, PR 00716, USA.
Background: Mental health in Puerto Rico is a complex and multifaceted issue that has been shaped by the island's unique history, culture, and political status. Recent challenges, including disasters, economic hardships, and political turmoil, have significantly affected the mental well-being of the population, coupled with the limitations in the accessibility of mental health services. Thus, Puerto Rico has fewer mental health professionals per capita than any other state or territory in the United States.
View Article and Find Full Text PDFJ Prim Care Community Health
September 2024
West Virginia Alliance for Creative Health Solutions, Culloden, WV, USA.
Introduction/objectives: Since 2015, the rise in e-cigarette use among youth has concerned public health authorities. After peaking in 2019, usage rates have declined but remain high. In 2023, 10% of high school and 4.
View Article and Find Full Text PDFMem Cognit
November 2024
Medical Faculty Mannheim/Heidelberg University, Central Institute of Mental Health, Mannheim, Germany.
Individual differences in working memory capacity (WMC) are correlated with long-term memory (LTM) differences. Whether this is because high-WMC individuals encode more effectively, resulting in better LTM storage, or because they better retrieve information from LTM is debated. In two experiments, we used Bayesian-hierarchical multinomial modeling to correlate participant-level storage and retrieval processes from LTM recall to WMC abilities estimated from operation and symmetry complex span tasks.
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