Changes in perceived stress and food or housing insecurity associated with COVID-19 in doctor of pharmacy students: A pre- and current- COVID-19 survey.

Background: The novel coronavirus 2019 (COVID-19) pandemic impacted everyday life for most individuals, including students. Unique COVID-19 stressors among students may include virtual learning, mental stress, and being socially distanced from classmates. Studies examining the impact of COVID-19 on stress and lifestyle changes among pharmacy students are limited. Objective: The primary purpose of this study was to compare stress and food or housing insecurity changes associated with COVID-19 in U.S. Doctor of Pharmacy (PharmD) students pre-COVID vs. during-COVID. Methods: A 23-item survey was administered via Qualtrics® to multiple PharmD programs across the U.S. in pre-COVID-19 (spring 2019) and during-COVID-19 (spring 2021). Participants were recruited via e-mail. The survey included questions related to demographics, lifestyle (sleep, exercise, work hours, extracurricular activities), and food and housing insecurities. The survey also included a validated instrument to measure stress (Cohen-Perceived Stress Scale). Results from 2021 were compared to a similar national survey serendipitously administered prior to COVID-19 in Spring 2019. Results: Pre- and COVID-19 analytical cohorts included 278 and 138 participants, respectively. While pre-COVID-19 students were slightly older (29.9 ± 4.7 vs. 27.7 ± 4.2, p ≤0.001), relative to COVID-19 students, other demographic factors were similar. No significant difference was observed in reported stress levels (PSS = 20.0 ± 6.3 vs. 19.7 ± 6.2, p = 0.610) between time periods. Significant differences in food (53.2% vs. 51.4%, p = 0.731) and housing (45.0% vs. 47.1%, p = 0.680) insecurity were also not seen. Conclusions: These findings highlight that PharmD students' perceived stress and food and housing insecurities due to COVID-19 may have been minimal. Additional studies on pharmacy students should be conducted to validate these results. These results may help inform policymakers and stakeholders during the early stages of any future pandemics.

Multiple comparisons: To compare or not to compare, that is the question.

Researchers attempt to minimize Type-I errors (concluding there is a relationship between variables, when there in fact, isn't one) in their experiments by exerting control over the p-value thresholds or alpha level. If a statistical test is conducted only once in a study, it is indeed possible for the researcher to maintain control, so that the likelihood of a Type-I error is equal to or less than the significance (p-value) level. When making multiple comparisons in a study, however, the likelihood of making a Type-I error can dramatically increase. When conducting multiple comparisons, researchers frequently attempt to control for the increased risk of Type-I errors by making adjustments to their alpha level or significance threshold level. The Bonferroni adjustment is the most common of these types of adjustment. However, these, often rigid adjustments, are not without risk and are often applied arbitrarily. The objective of this review is to provide a balanced commentary on the advantages and disadvantages of making adjustments when undertaking multiple comparisons. A summary discussion of familiar- and experiment-wise error is also presented. Lastly, advice on when researchers should consider making adjustments in p-value thresholds and when they should be avoided, is provided.

A narrative review of using prescription drug databases for comorbidity adjustment: A less effective remedy or a prescription for improved model fit?

BACKGROUND: The use of claims data for identifying comorbid conditions in patients for research purposes has been widely explored. Traditional measures of comorbid adjustment included diagnostic data (e.g., ICD-9-CM or ICD-10-CM codes), with the Charlson and Elixhauser methodology being the two most common approaches. Prescription data has also been explored for use in comorbidity adjustment, however early methodologies were disappointing when compared to diagnostic measures. OBJECTIVE: The objective of this methodological review is to compare results from newer studies using prescription-based data with more traditional diagnostic measures. METHODS: A review of studies found on PubMed, Medline, Embase or CINAHL published between January 1990 and December 2020 using prescription data for comorbidity adjustment. A total of 50 studies using prescription drug measures for comorbidity adjustment were found. CONCLUSIONS: Newer prescription-based measures show promise fitting models, as measured by predictive ability, for research, especially when the primary outcomes are utilization or drug expenditure rather than diagnostic measures. More traditional diagnostic-based measures still appear most appropriate if the primary outcome is mortality or inpatient readmissions.