Pharmaceutical products, including active pharmaceutical ingredients and inactive ingredients such as packaging materials, have raised significant concerns due to their persistent input and potential threats to human and environmental health. Discourse on reducing pharmaceutical waste and subsequent pollution is often limited, as information about the toxicity of pharmaceuticals in humans is yet to be fully established. Nevertheless, there is growing awareness about ecotoxicity, and efforts to curb pharmaceutical pollution in the European Union (EU), United States (US), and Canada have emerged along with waste disposal and treatment procedures, as well as growing concerns about impacts on human and animal health, such as through antimicrobial resistance. Yet, the outcomes of such endeavors are often disparate and involve multiple agencies, organizations, and departments with little evidence of cooperation, collaboration, or oversight. Environmental health disparities occur when communities exposed to a combination of poor environmental quality and social inequities experience more sickness and disease than wealthier, less polluted communities. In this paper, we discuss pharmaceutical environmental pollution in the context of health disparities and examine policies across the US, EU, and Canada in minimizing environmental pollution.

Background: The COVID-19 pandemic has caused innumerable changes to all aspects of human life and behavior, including academic life. This study describes the development of a COVID-19 Knowledge, Attitudes, and Practices (COVKAP) Survey among U.S. student pharmacists. The survey was administered at Doctor of Pharmacy programs in three states—Tennessee, Ohio, and Pennsylvania. Methods: The COVKAP survey—an online cross-sectional survey—was distributed to U.S. student pharmacists enrolled in three different colleges of pharmacy in three states during the fall semester of 2020. The survey was developed using literature review and Dillman’s recommendations for survey design. The COVKAP survey consisted of 23 closed and Likert-scale questions, and three open-ended questions. The research team conducted descriptive and inductive thematic analyses on the quantitative and qualitative data, respectively using SPSS (v27) and Dedoose® software. Results: A total of 421 responses were received. Respondents were predominantly female (72%) and White (79%). The average age of respondents was 23.4 years. The qualitative analysis revealed three themes: (1) Wellbeing and mental health struggles; (2) Being part of the decision-making process; (3) Necessity of adequate protection measures. Conclusions: Preliminary study findings indicate that student pharmacists’ concerns and the challenges that they face in their academic pursuits are largely similar across three states and inform about the importance of recognizing and mitigating the impact of widespread disruption in education. This disruption provides an opportunity for pharmacy academia to examine practices and methods that can be improved upon to help students become successful practitioners.

Implementing a federal-level program among a diverse subset of patients is challenging and requires concerted efforts from health care providers and support from the institution. The Medicare drug benefit task force at the institution assumed responsibility for all pharmacy activities related to Medicare Part D and achieved its goals in education, outreach, and operations. This resulted in continued access to pharmacy services and prescribed medications for patients.

Progressive optic neuropathies such as glaucoma are major causes of blindness globally. Multiple sources of subjectivity and analytical challenges are often encountered by the clinicians in the process of early diagnosis and clinical management of these diseases. In glaucoma, the structural damage is often characterized by neuroretinal rim (NRR) thinning of the optic nerve head, and other clinical parameters. Optical coherence tomography (OCT) is a popular and quantitative eye imaging platform for precise and reproducible measurement of such parameters in the clinic. Baseline structural heterogeneity in the eyes can play a key role in the progression of optic neuropathies, and thus present challenges to clinical decision-making. To address this, large and diverse normative OCT databases with mathematically precise description of phenotypes can help with early detection and characterization of the different phenotypes that are encountered in the clinic. In this study, we generated a new large dataset of OCT generated high-resolution circular data on NRR phenotypes, along with other clinical covariates, of nearly 4,000 healthy eyes as part of a well-established clinical cohort (LVPEI-GLEAMS) of Asian Indian participants. In this study, we (1) generated high-resolution circular OCT measurements of NRR thickness in a given eye, (2) introduced CIFU, a new computational pipeline for CIrcular FUnctional data modeling and analysis that is demonstrated using the OCT dataset, and (3) addressed the disparity of representation of the Asian Indian population in normative OCT databases. We demonstrated CIFU by unsupervised circular functional clustering of the OCT NRR data, meta-clustering to characterize the clustering output using clinical covariates, and presenting a circular visualization of the results. Upon stratification by age, we identified a healthy NRR phenotype cluster in the age group 40-49 years with predictive potential for glaucoma.

Progressive optic neuropathies such as glaucoma are major causes of blindness globally. Multiple sources of subjectivity and analytical challenges are often encountered by clinicians in the process of early diagnosis and clinical management of these diseases. In glaucoma, the structural damage is often characterized by neuroretinal rim (NRR) thinning of the optic nerve head, and other clinical parameters. Baseline structural heterogeneity in the eyes can play a key role in the progression of optic neuropathies, and present challenges to clinical decision-making. We generated a dataset of Optical Coherence Tomography (OCT) based high-resolution circular measurements on NRR phenotypes, along with other clinical covariates, of 3973 healthy eyes as part of an established clinical cohort of Asian Indian participants. We introduced CIFU, a new computational pipeline for CIrcular FUnctional data modeling and analysis. We demonstrated CIFU by unsupervised circular functional clustering of the OCT NRR data, followed by meta-clustering to characterize the clusters using clinical covariates, and presented a circular visualization of the results. Upon stratification by age, we identified a healthy NRR phenotype cluster in the age group 40–49 years with predictive potential for glaucoma. Our dataset also addresses the disparity of representation of this particular population in normative OCT databases.