Population-scale patient safety data reveal inequalities in adverse events before and during COVID-19 pandemic
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Abstract
Adverse patient safety events were associated with 110 thousand deaths in the U.S. alone in 2019. The COVID-19 pandemic has further challenged the ability of healthcare systems to ensure safe medication use, and its effects on patient safety remain unknown. Here, we investigate negative outcomes associated with medication use before and during the pandemic. Using a dataset of 10,443,476 reports involving 3,624 drugs and 19,193 adverse events, we develop an algorithmic approach to analyze the pandemic’s impact on the incidence of drug safety events by evaluating disproportional reporting relative to the pre-pandemic time, quantifying unexpected trends in clinical outcomes, and adjusting for drug interference. Among 64 adverse events identified by our analyses, we find 54 have increased incidence rates during the pandemic, even though adverse event reporting decreased by 4.4% overall. We find clinically relevant differences in drug safety outcomes between demographic groups. Compared to male patients, women report 47.0% more distinct adverse events whose occurrence significantly increased during the pandemic relative to pre-pandemic levels. Out of 53 adverse events with a pre-pandemic gender gap, 33 have an increased gender gap during the pandemic. While musculoskeletal and metabolic side effects are disproportionately enriched in women during the pandemic, immune-related adverse events are enriched only in men. We also find the number of adverse events with an increased reporting ratio is higher in adults (by 16.8%) than in older patients (adjusted for population size). Our findings have implications for safe medication use and tie the variation in adverse events to patients that may be disproportionately affected by preventable inequities during a public health emergency.
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SciScore for 10.1101/2021.01.17.21249988: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There is an important limitation to consider in interpreting our findings. The patient safety dataset comprises voluntarily submitted reports that are not necessarily representative of prevalence rates of adverse drug reactions [58]. Further, the pandemic has likely affected reporting rates, which can vary across adverse …
SciScore for 10.1101/2021.01.17.21249988: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There is an important limitation to consider in interpreting our findings. The patient safety dataset comprises voluntarily submitted reports that are not necessarily representative of prevalence rates of adverse drug reactions [58]. Further, the pandemic has likely affected reporting rates, which can vary across adverse events and time [9]. Interestingly, the total number of adverse event reports decreased in 2020 relative to 2019. Nevertheless, we find that adverse events whose reporting frequency has changed relative to pre-pandemic levels (Figure 1b) tend to be reported considerably more often than expected based on historical data (Figure 1c). This observation, together with abundant research on clinically relevant insights extracted from patient safety datasets [59–64], further strengthen confidence in our key findings. Our algorithmic approach can identify differential reporting patterns in patient cohorts formed as a function of gender, age, adverse events, and drugs. With additional information on medical and non-medical patient characteristics such as race and ethnicity, the approach is suitable for systematic safety surveillance to pinpoint individuals at high risk for safety events based on risk-altering interactions. We also present a new resource of adverse drug effects and drug-event associations for use in pharmacoepidemiology and public health policy to inform medication use in diverse populations. We expect this algorithmic approach to enable comparison of t...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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