Effects of the COVID-19 Pandemic on Pharmacovigilance Strategy, Systems, and Processes of Large, Medium, and Small Companies: An Industry Survey
Bibliographic record
Abstract
PURPOSE: The COVID-19 pandemic poses an unprecedented threat to global business relationships and dynamics. The pharmacovigilance function of pharmaceutical companies is particularly susceptible to changing external pressures because of its highly structured compliance activities. We conducted an industry-wide survey to provide insights on how the pharmacovigilance function responded to the challenges posed by the pandemic. We compared smaller companies and larger companies regarding impact on portfolios and operational activity metrics. METHODS: , a network of small and medium enterprise (SME) companies, using an online surveying tool during the first quarter of 2021. We collected information on pharmacovigilance activities, including quantitative measures of workload, costs, and key performance indicators, and qualitative data on the effects of the pandemic on product portfolios and operations. FINDINGS: Survey questions were posed to LE (pvnet) network members (n = 12) and SME (pvconnect) network members (n = 18) for the period from January 1 through December 31, 2020. The date of data collection was March 26, 2021. Descriptive median values of parameter metrics included the following: revenue ($28.4 billion for LE companies and $1.6 billion for SME companies), number of products (127 for LE companies and 19 for SME companies), and volume of individual case safety reports (391,000 for LE companies and 13,000 for SME companies). SME companies reported a greater impact on 2 survey categories, remote working and employee well-being, than did LE companies. However, LE companies reported a greater impact than did SME companies on all other survey categories: effect on strategic priorities, shift in product focus, workload changes, changes in sourcing model, effect on case reporting compliance, effect on business continuity, changes in pharmacovigilance technology strategy, impact of interactions with health authorities, effect on resource capacity, and impact on recruitment. IMPLICATIONS: Four major themes emerge from this survey: (1) shift to remote working, (2) recognition of the impact on employee well-being, (3) shift in strategic priorities, and (4) newly recognized aspects of risk mitigation. The COVID-19 pandemic has had a marked effect on every aspect of pharmaceutical companies' pharmacovigilance functions, although the effects appear to be different for LE companies than for SME companies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".