Assessing Disparities in COVID-19 Testing Using National COVID Cohort Collaborative

Lyu, Jinyan; Cui, Wanting; Finkelstein, Joseph

doi:10.3233/shti220726

Cited by 4 publications

(2 citation statements)

References 3 publications

(3 reference statements)

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“…Additional geospatial analysis revealed stark regional differences in Paxlovid uptake (Figure 2B), with Utah, Mid-Atlantic, and Northwest regions displaying the greatest uptake while portions of the Lower Midwest and Southeast showing limited adoption. These values, however, are not necessarily representative of the entire US population, since N3C is largely skewed towards urban regions 22 . The geographic bias of our study population is represented in the Supplement eFigure 2.…”

Section: Resultsmentioning

confidence: 99%

Paxlovid (nirmatrelvir/ritonavir) effectiveness against hospitalization and death in N3C: A target trial emulation study

Hansen

Makkar

Sahner

et al. 2023

Preprint

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Importance: COVID-19 has placed a monumental burden on the health care system globally. Although no longer a public health emergency, there is still a pressing need for effective treatments to prevent hospitalization and death. Paxlovid (nirmatrelvir/ritonavir) is a promising and potentially effective antiviral that has received emergency use authorization by the U.S. FDA. Objective: Determine real world effectiveness of Paxlovid nationwide and investigate disparities between treated and untreated eligible patients. Design/Setting/Participants: Population-based cohort study emulating a target trial, using inverse probability weighted models to balance treated and untreated groups on baseline confounders. Participants were patients with a SARS-CoV-2 positive test or diagnosis (index) date between December 2021 and February 2023 selected from the National COVID Cohort Collaborative (N3C) database who were eligible for Paxlovid treatment. Namely, adults with at least one risk factor for severe COVID-19 illness, no contraindicated medical conditions, not using one or more strictly contraindicated medications, and not hospitalized within three days of index. From this cohort we identified patients who were treated with Paxlovid within 5 days of positive test or diagnosis (n = 98,060) and patients who either did not receive Paxlovid or were treated outside the 5-day window (n = 913,079 never treated; n = 1,771 treated after 5 days). Exposures: Treatment with Paxlovid within 5 days of positive COVID-19 test or diagnosis. Main Outcomes and Measures: Hospitalization and death in the 28 days following COVID-19 index date. Results: A total of 1,012,910 COVID-19 positive patients at risk for severe COVID-19 were included, 9.7% of whom were treated with Paxlovid. Uptake varied widely by geographic region and timing, with top adoption areas near 50% and bottom near 0%. Adoption increased rapidly after EUA, reaching steady state by 6/2022. Participants who were treated with Paxlovid had a 26% (RR, 0.742; 95% CI, 0.689-0.812) reduction in hospitalization risk and 73% (RR, 0.269, 95% CI, 0.179-0.370) reduction in mortality risk in the 28 days following COVID-19 index date. Conclusions/Relevance: Paxlovid is effective in preventing hospitalization and death in at-risk COVID-19 patients. These results were robust to a large number of sensitivity considerations.

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Section: Resultsmentioning

confidence: 99%

Paxlovid (nirmatrelvir/ritonavir) effectiveness against hospitalization and death in N3C: A target trial emulation study

Hansen

Makkar

Sahner

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…To enable COVID-19 research driven by data acquired across the United States, the National Center for Advancing Translational Sciences (NCATS) supported the creation of the National COVID Cohort Collaborative (N3C), a centralized repository of EHR-sourced data currently including over 9 million patients from 69 sites representing 49 out of 50 states that can be leveraged to study potential treatments and evaluate standards of care and best practices for COVID-19 in a real-world setting [5]. Compared to census data, N3C data have been shown to be more racially diverse, though biased towards urban as opposed to rural areas [6]. N3C aggregates and harmonizes EHR data across clinical organizations in the United States and supports data from both harmonized and unharmonized common data models (CDMs) including ACT, OMOP, PCORnet, and TriNetX, with OMOP version 5.3.1 being the target data model into which others are converted.…”

Section: Introductionmentioning

confidence: 99%

Data quality considerations for evaluating COVID-19 treatments using real world data: learnings from the National COVID Cohort Collaborative (N3C)

Sidky

Young

Girvin

et al. 2023

BMC Med Res Methodol

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Background Multi-institution electronic health records (EHR) are a rich source of real world data (RWD) for generating real world evidence (RWE) regarding the utilization, benefits and harms of medical interventions. They provide access to clinical data from large pooled patient populations in addition to laboratory measurements unavailable in insurance claims-based data. However, secondary use of these data for research requires specialized knowledge and careful evaluation of data quality and completeness. We discuss data quality assessments undertaken during the conduct of prep-to-research, focusing on the investigation of treatment safety and effectiveness. Methods Using the National COVID Cohort Collaborative (N3C) enclave, we defined a patient population using criteria typical in non-interventional inpatient drug effectiveness studies. We present the challenges encountered when constructing this dataset, beginning with an examination of data quality across data partners. We then discuss the methods and best practices used to operationalize several important study elements: exposure to treatment, baseline health comorbidities, and key outcomes of interest. Results We share our experiences and lessons learned when working with heterogeneous EHR data from over 65 healthcare institutions and 4 common data models. We discuss six key areas of data variability and quality. (1) The specific EHR data elements captured from a site can vary depending on source data model and practice. (2) Data missingness remains a significant issue. (3) Drug exposures can be recorded at different levels and may not contain route of administration or dosage information. (4) Reconstruction of continuous drug exposure intervals may not always be possible. (5) EHR discontinuity is a major concern for capturing history of prior treatment and comorbidities. Lastly, (6) access to EHR data alone limits the potential outcomes which can be used in studies. Conclusions The creation of large scale centralized multi-site EHR databases such as N3C enables a wide range of research aimed at better understanding treatments and health impacts of many conditions including COVID-19. As with all observational research, it is important that research teams engage with appropriate domain experts to understand the data in order to define research questions that are both clinically important and feasible to address using these real world data.

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The lapse of federal COVID-19 funding: implications for health equity

et al. 2023

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Assessing Disparities in COVID-19 Testing Using National COVID Cohort Collaborative

Cited by 4 publications

References 3 publications

Paxlovid (nirmatrelvir/ritonavir) effectiveness against hospitalization and death in N3C: A target trial emulation study

Paxlovid (nirmatrelvir/ritonavir) effectiveness against hospitalization and death in N3C: A target trial emulation study

Data quality considerations for evaluating COVID-19 treatments using real world data: learnings from the National COVID Cohort Collaborative (N3C)

The lapse of federal COVID-19 funding: implications for health equity

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