Real-World Evidence for Assessing Pharmaceutical Treatments in the Context of COVID-19

Major cardiovascular events under biologic psoriasis therapies: a 19-year real-world analysis of FAERS data

Objective

Over the years when biologic psoriasis therapies (TNF inhibitors, IL-12/23 inhibitors, IL-23 inhibitors, and IL-17 inhibitors) have been used in psoriasis patients, reports of major cardiovascular events (MACEs) have emerged. This study aims to investigate the association between MACEs and biologic psoriasis therapies by using information reported to the US Food and Drug Administration Adverse Event Reporting System (FAERS).

Methods

FAERS data (January 2004 to December 2022) were reviewed. For each drug-event pair, the proportional reporting ratio (PRR) and the multi-item gamma Poisson shrinker (MGPS) algorithms were used to identify drug-adverse event associations.

Results

We filtered the query for indication and identified 173,330 reports with psoriasis indication in FAERS throughout the analyzed time frame. MACEs occurred in 4,206 patients treated with biologics. All the four biological classes had an elevated and similar reporting rates for MACEs relative to other alternative psoriasis treatments (PRR from 2.10 to 4.26; EB05 from 1.15 to 2.45). The descending order of association was IL-12/23 inhibitors>IL-17 inhibitors>IL-23 inhibitors>TNF inhibitors. The signal strength for myocardial infarction (PRR, 2.86; χ2, 296.27; EBGM 05, 1.13) was stronger than that for stroke, cardiac fatality, and death. All the biological classes demonstrated a little higher EBGM 05 score≥1 for the MACEs in patients aged 45-64 years. The time-to-onset of MACEs was calculated with a median of 228 days.

Conclusions

Analysis of adverse event reports in the FAERS reflects the potential risk of MACEs associated with the real-world use of biological therapies in comparison to other alternative psoriasis treatments. Future long-term and well-designed studies are needed to further our knowledge regarding the cardiovascular safety profile of these agents.

Can Weight of Evidence, Quantitative Bias, and Bounding Methods Evaluate Robustness of Real-world Evidence for Regulator and Health Technology Assessment Decisions on Medical Interventions?

PURPOSE

High-quality evidence is crucial for health care intervention decision-making. These decisions frequently use nonrandomized data, which can be more vulnerable to biases than randomized trials. Accordingly, methods to quantify biases and weigh available evidence could elucidate the robustness of findings, giving regulators more confidence in making approval and reimbursement decisions.

METHODS

We conducted an integrative literature review to identify methods for determining probability of causation, evaluating weight of evidence, and conducting quantitative bias analysis as related to health care interventions. Eligible studies were published from 2012 to 2021, applicable to pharmacoepidemiology, and presented a method that met our objective.

FINDINGS

Twenty-two eligible studies were classified into 4 categories: (1) quantitative bias analysis; (2) weight of evidence methods; (3) Bayesian networks; and (4) miscellaneous. All of the methods have strengths, limitations, and situations in which they are more well suited than others. Some methods seem to lend themselves more to applications of health care evidence on medical interventions than others.

IMPLICATIONS

To provide robust evidence for and improve confidence in regulatory or reimbursement decisions, we recommend applying multiple methods to triangulate associations of medical interventions, accounting for biases in different ways. This approach could lead to well-defined robustness assessments of study findings and appropriate science-driven decisions by regulators and payers for public health.

Totality of evidence of the effectiveness of repurposed therapies for COVID-19: Can we use real-world studies alongside randomized controlled trials?

Rapid and robust strategies to evaluate the efficacy and effectiveness of novel and existing pharmacotherapeutic interventions (repurposed treatments) in future pandemics are required. Observational "real-world studies" (RWS) can report more quickly than randomized controlled trials (RCTs) and would have value were they to yield reliable results. Both RCTs and RWS were deployed during the coronavirus disease 2019 (COVID-19) pandemic. Comparing results between them offers a unique opportunity to determine the potential value and contribution of each. A learning review of these parallel evidence channels in COVID-19, based on quantitative modeling, can help improve speed and reliability in the evaluation of repurposed therapeutics in a future pandemic. Analysis of all-cause mortality data from 249 observational RWS and RCTs across eight treatment regimens for COVID-19 showed that RWS yield more heterogeneous results, and generally overestimate the effect size subsequently seen in RCTs. This is explained in part by a few study factors: the presence of RWS that are imbalanced for age, gender, and disease severity, and those reporting mortality at 2 weeks or less. Smaller studies of either type contributed negligibly. Analysis of evidence generated sequentially during the pandemic indicated that larger RCTs drive our ability to make conclusive decisions regarding clinical benefit of each treatment, with limited inference drawn from RWS. These results suggest that when evaluating therapies in future pandemics, (1) large RCTs, especially platform studies, be deployed early; (2) any RWS should be large and should have adequate matching of known confounders and long follow-up; (3) reporting standards and data standards for primary endpoints, explanatory factors, and key subgroups should be improved; in addition, (4) appropriate incentives should be in place to enable access to patient-level data; and (5) an overall aggregate view of all available results should be available at any given time.

Use of Open Claims vs Closed Claims in Health Outcomes Research

Background: Closed claims are frequently used in outcomes research studies. Lately, the availability of open claims has increased the possibility of obtaining information faster and on a larger scale. However, because of the possibility of missing claims and duplications, these data sets have not been highly utilized in medical research. Objective: To compare frequently used healthcare utilization measures between closed claims and open claims to analyze if the possibility of missing claims in open claims data creates a downward bias in the estimates. Methods: We identified 18 different diseases using 2022 data from 2 closed claims data sets (MarketScan® and PharMetrics® Plus) and 1 open claims database (Kythera). After applying an algorithm that removes possible duplications from open claims data, we compared healthcare utilizations such as inpatient, emergency department, and outpatient use and length of stay among these 3 data sets. We applied standardized differences to compare the medians for each outcome. Results: The sample size of the open claims data sets was 10 to 65 times larger than closed claims data sets depending on disease type. For each disease, the estimates of healthcare utilization were similar between the open claims and closed claims data. The difference was statistically insignificant. Conclusions: Open claims data with a bigger sample size and more current available information provide essential advantages for healthcare outcomes research studies. Therefore, especially for new medications and rare diseases, open claims data can provide information much earlier than closed claims, which usually have a time lag of 6 to 8 months.

Contributions of cost-effectiveness analyses (CEA) to influenza vaccination policy for older adults in Europe

This review describes the importance of economic evaluations and real-world evidence (RWE) for the assessment of enhanced influenza vaccines for older adults in Europe. Individuals ≥65 years of age are at increased risk of severe influenza outcomes and many countries in Europe recommend enhanced vaccines for this population to mitigate immunosenescence. Some National Immunization Technical Advisory Groups (NITAGs) may preferentially recommend a specific enhanced vaccine, necessitating comparative economic evaluation and estimation of relative vaccine effectiveness between enhanced vaccine options in the absence of direct head-to-head efficacy data. Distinct approaches to economic modeling and cost-effectiveness analysis (CEA) guide national vaccination policies in Europe, including how underlying data, such as RWE, are used in these models. RWE is an important evidence source for input into CEA models based on disease factors (e.g., antigenic shift and seasonal variation) and practical factors (e.g., limitations of performing multiple randomized clinical trials to capture seasonal variation; the need to obtain relevant patient-oriented, real-world endpoints, such as hospitalizations). CEA is considered crucial to vaccine assessment among certain countries in Europe, but further harmonization of economic evaluations, including the use of RWE, across NITAGs in Europe may be of benefit, alongside standardized approaches for vaccine appraisal. In the future, more countries may use RWE as an input in CEA models to support NITAG recommendations for enhanced influenza vaccines in older populations, especially considering the value of RWE for the assessment of influenza epidemiology and vaccine effectiveness as stated by the World Health Organization, and the availability of a broad RWE base for certain enhanced vaccines.

Using administrative healthcare data to evaluate drug repurposing opportunities for cancer: the possibility of using beta-blockers to treat breast cancer

Introduction: Cancer registries and hospital electronic medical records are commonly used to investigate drug repurposing candidates for cancer. However, administrative data are often more accessible than data from cancer registries and medical records. Therefore, we evaluated if administrative data could be used to evaluate drug repurposing for cancer by conducting an example study on the association between beta-blocker use and breast cancer mortality. Methods: A retrospective cohort study of women aged ≥50 years with incident breast cancer was conducted using a linked dataset with statewide hospital admission data and nationwide medication claims data. Women receiving beta blockers and first-line anti-hypertensives prior to and at diagnosis were compared. Breast cancer molecular subtypes and metastasis status were inferred by algorithms from commonly prescribed breast cancer antineoplastics and hospitalization diagnosis codes, respectively. Subdistribution hazard ratios (sHR) and corresponding 95% confidence intervals (CIs) for breast cancer mortality were estimated using Fine and Gray's competing risk models adjusted for age, Charlson comorbidity index, congestive heart failure, myocardial infraction, molecular subtype, presence of metastasis at diagnosis, and breast cancer surgery. Results: 2,758 women were hospitalized for incident breast cancer. 604 received beta-blockers and 1,387 received first-line antihypertensives. In total, 154 breast cancer deaths were identified over a median follow-up time of 2.7 years. We found no significant association between use of any beta-blocker and breast-cancer mortality (sHR 0.86, 95%CI 0.58-1.28), or when stratified by beta-blocker type (non-selective, sHR 0.42, 95%CI 0.14-1.25; selective, sHR 0.95, 95%CI 0.63-1.43). Results were not significant when stratified by molecular subtypes (e.g., triple negative breast cancer (TNBC), any beta blocker, sHR 0.16, 95%CI 0.02-1.51). Discussion: It is possible to use administrative data to explore drug repurposing opportunities. Although non-significant, an indication of an association was found for the TNBC subtype, which aligns with previous studies using registry data. Future studies with larger sample size, longer follow-up are required to confirm the association, and linkage to clinical data sources are required to validate our methodologies.

Real-World Evidence in Cost-Effectiveness Analysis of Enhanced Influenza Vaccines in Adults ≥ 65 Years of Age: Literature Review and Expert Opinion

Influenza vaccination can benefit most populations, including adults ≥ 65 years of age, who are at greater risk of influenza-related complications. In many countries, enhanced vaccines, such as adjuvanted, high-dose, and recombinant trivalent/quadrivalent influenza vaccines (aTIV/aQIV, HD-TIV/HD-QIV, and QIVr, respectively), are recommended in older populations to provide higher immunogenicity and increased relative vaccine efficacy/effectiveness (rVE) than standard-dose vaccines. This review explores how efficacy and effectiveness data from randomized controlled trials and real-world evidence (RWE) are used in economic evaluations. Findings from published cost-effectiveness analyses (CEA) on enhanced influenza vaccines for older adults are summarized, and the assumptions and approaches used in these CEA are assessed alongside discussion of the importance of RWE in CEA. Results from many CEA showed that adjuvanted and high-dose enhanced vaccines were cost-effective compared with standard vaccines, and that differences in rVE estimates and acquisition price may drive differences in cost-effectiveness estimates between enhanced vaccines. Overall, RWE and CEA provide clinical and economic rationale for enhanced vaccine use in people ≥ 65 years of age, an at-risk population with substantial burden of disease. Countries that consider RWE when making vaccine recommendations have preferentially recommended aTIV/aQIV, as well as HD-TIV/HD-QIV and QIVr, to protect older individuals.

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

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.

Short-term safety of COVID-19 mRNA vaccines with respect to all-cause mortality in the older population in Norway

BACKGROUND

There have been concerns about COVID-19 vaccination safety among frail older individuals. We investigated the relationship between COVID-19 mRNA vaccination and mortality among individuals aged ≥ 70 years and whether mortality varies across four groups of health services used.

METHODS

In this nationwide cohort study, we included 688,152 individuals aged ≥ 70 years at the start of the Norwegian vaccination campaign (December 27, 2020). We collected individual-level data from theNorwegian Emergency Preparedness Register for COVID-19. Vaccinated and unvaccinated individuals were matched (1:1 ratio) on the date of vaccination based on sociodemographic and clinical characteristics. The main outcome was all-cause mortality during 21 days after first dose of COVID-19 mRNA vaccination. Kaplan-Meier survival functions were estimated for the vaccinated and unvaccinated groups. We used Cox proportional-hazards regression to estimate hazard ratios (HRs) of death between vaccinated and unvaccinated individuals, with associated 95% confidence intervals (CIs), overall and by use of health services (none, home-based, short- and long-term nursing homes) and age group.

RESULTS

Between December 27, 2020, and March 31, 2021, 420,771 older individuals (61.1%) were vaccinated against COVID-19. The Kaplan-Meier estimates based on the matched study sample showed a small absolute risk difference in all-cause mortality between vaccinated and unvaccinated individuals, with a lower mortality in the vaccinated group (overall HR 0.28 [95% CI: 0.24-0.31]). Similar results were obtained in analyses stratified by use of health services and age group.

CONCLUSION

We found no evidence of increased short-term mortality among vaccinated individuals in the older population after matching on sociodemographic and clinical characteristics affecting vaccination and mortality.

Noninterventional studies in the COVID-19 era: methodological considerations for study design and analysis

The global COVID-19 pandemic has generated enormous morbidity and mortality, as well as large health system disruptions including changes in use of prescription medications, outpatient encounters, emergency department admissions, and hospitalizations. These pandemic-related disruptions are reflected in real-world data derived from electronic medical records, administrative claims, disease or medication registries, and mobile devices. We discuss how pandemic-related disruptions in healthcare utilization may impact the conduct of noninterventional studies designed to characterize the utilization and estimate the effects of medical interventions on health-related outcomes. Using hypothetical studies, we highlight consequences that the pandemic may have on study design elements including participant selection and ascertainment of exposures, outcomes, and covariates. We discuss the implications of these pandemic-related disruptions on possible threats to external validity (participant selection) and internal validity (for example, confounding, selection bias, missing data bias). These concerns may be amplified in populations disproportionately impacted by COVID-19, such as racial/ethnic minorities, rural residents, or people experiencing poverty. We propose a general framework for researchers to carefully consider during the design and analysis of noninterventional studies that use real-world data from the COVID-19 era.

Challenges in evaluating treatments for COVID-19: The case of in-hospital anticoagulant use and the risk of adverse outcomes

Anticoagulants are a potential treatment for the thrombotic complications resulting from COVID-19. We aimed to determine the association between anticoagulant use and adverse outcomes among hospitalized patients with COVID-19. We used data from the COVID-19 International Collaborative Research Project in South Korea from January to June 2020. We defined exposure using an intention-to-treat approach, with person-time classified as use or non-use of anticoagulants at cohort entry, and a time-varying approach. The primary outcome was all-cause, in-hospital mortality; the secondary outcome was a composite including respiratory outcomes, cardiovascular outcomes, venous thromboembolism, major bleeding, and intensive care unit admission. Cox proportional hazards models estimated adjusted hazard ratios (HRs) of the outcomes comparing use versus non-use of anticoagulants. Our cohort included 2,677 hospitalized COVID-19 patients, of whom 24 received anticoagulants at cohort entry. Users were older and had more comorbidities. The crude incidence rate (per 1,000 person-days) of mortality was 5.83 (95% CI: 2.80, 10.72) among anticoagulant users and 1.36 (95% CI: 1.14, 1.59) for non-users. Crude rates of the composite outcome were 3.20 (95% CI: 1.04, 7.47) and 1.80 (95% CI: 1.54, 2.08), respectively. Adjusted HRs for mortality (HR: 1.12, 95% CI: 0.48, 2.64) and the composite outcome (HR: 0.79, 95% CI: 0.28, 2.18) were inconclusive. Although our study was not able to draw conclusions on anticoagulant effectiveness for COVID-19 outcomes, these results can contribute to future knowledge syntheses of this important question. Our study demonstrated that the dynamic pandemic environment may have important implications for observational studies of COVID-19 treatment effectiveness.

Early Real-World Data to Assess Benefits and Risks of COVID-19 Vaccines: A Systematic Review of Methods

Since the authorization of the first COVID-19 vaccines in December 2020, multiple studies using real-world data (RWD) have been published to assess their effectiveness/safety profile. This systematic review aimed to characterize the methods and outcomes of studies using RWD for assessment of COVID-19 vaccines, four months after vaccine approval. MEDLINE and EMBASE were searched to identify published studies until 6 May 2021. Two independent researchers selected relevant publications and extracted data from included studies. The risk of bias was assessed using New-Castle Ottawa tools. After screening 1086 studies, 15 were included. Out of the 15 studies, 12 (80%) followed a cohort design, 8 (53%) were based on USA data, 7 (47%) assessed health care professionals, and 14 articles (93%) assessed the BNT162b2 vaccine. Data sources included institutional databases, electronic health records, and patient-generated data. The primary endpoint mainly described was SARS-CoV-2-infection. Hospitalization and mortality were assessed in 2 studies. For the comparability domain, six studies (40%) had a high risk of bias. A few months after the beginning of COVID-19 vaccination, Real-world Evidence (RWE) provided timely safety surveillance and comparative effectiveness with findings that showed similar findings to Randomized control trial (RCT). Most of the initiatives assessed BNT162b2 and were conducted in the USA and used healthcare workers’ data.

ISPE-Endorsed Guidance in Using Electronic Health Records for Comparative Effectiveness Research in COVID-19: Opportunities and Trade-Offs

As the scientific research community along with healthcare professionals and decision makers around the world fight tirelessly against the coronavirus disease 2019 (COVID-19) pandemic, the need for comparative effectiveness research (CER) on preventive and therapeutic interventions for COVID-19 is immense. Randomized controlled trials markedly under-represent the frail and complex patients seen in routine care, and they do not typically have data on long-term treatment effects. The increasing availability of electronic health records (EHRs) for clinical research offers the opportunity to generate timely real-world evidence reflective of routine care for optimal management of COVID-19. However, there are many potential threats to the validity of CER based on EHR data that are not originally generated for research purposes. To ensure unbiased and robust results, we need high-quality healthcare databases, rigorous study designs, and proper implementation of appropriate statistical methods. We aimed to describe opportunities and challenges in EHR-based CER for COVID-19-related questions and to introduce best practices in pharmacoepidemiology to minimize potential biases. We structured our discussion into the following topics: (1) study population identification based on exposure status; (2) ascertainment of outcomes; (3) common biases and potential solutions; and (iv) data operational challenges specific to COVID-19 CER using EHRs. We provide structured guidance for the proper conduct and appraisal of drug and vaccine effectiveness and safety research using EHR data for the pandemic. This paper is endorsed by the International Society for Pharmacoepidemiology (ISPE).

Assessment of A new Strategy to Prevent Prescribing Errors Involving COVID-19 Patients in Community Pharmacies

Background: Because COVID-19 patients are vulnerable to prescribing errors (PEs) and adverse drug events, designing and implementing a new approach to prevent prescribing errors (PEs) involving COVID-19 patients has become a priority in pharmacotherapy research. Objectives: To investigate whether using WhatsApp to deliver prescribing error (PE)-related clinical scenarios to community pharmacists could enhance their ability to detect PEs and conduct successful pharmaceutical interventions (PIs). Methods: In this study, 110 community pharmacies were recruited from different regions across Jordan and equally allocated to 2 groups. Over the course of 4 weeks, WhatsApp was used to send PEs-related clinical case scenarios to the active group. The second group was controlled with no clinical scenarios. After completion of the 4-week phase, pharmacists from both groups were asked to document PEs in COVID-19 patients and their interventions using a data-collection form. Results: The incidence of PEs in COVID-19 patients documented in the active group (18.54%) was higher than that reported in the control group (3.09%) (P = .001). Of the 6598 and 968 PIs conducted by participants working in the active and control group pharmacies, 6013 (91.13%) and 651 (67.25%) were accepted, respectively. The proportions of wrong drug (contraindication), wrong drug (unnecessary drug prescribed/no proof of its benefits), and omission of a drug between the active and control groups were 15.30% versus 7.21% (P = .001), 11.85% versus 6.29% (P = .03), and 17.78% versus 10.50% (0.01), respectively. Additionally, the proportions of lethal, serious, and significant errors were 0.74% versus 0.35% (P = .04), 10.52% versus 2.57% (0.002), and 47.88% versus 9.57% (0.001), respectively. Addition of drug therapy interventions (AOR = 0.62; 95% CI, 0.21-0.84) and errors with significant clinical seriousness (AOR = 0.32; 95% CI, 0.16-0.64). Conclusions PEs involving COVID-19 patients in community settings are common and clinically significant. The intervention assessed in this study could be promising for designing a feasible and time-efficient interventional tool to encourage pharmacists' involvement in identifying and correcting PEs in light of COVID-19.

No association of low-dose aspirin with severe COVID-19 in France: A cohort of 31.1 million people without cardiovascular disease

Background

Aspirin at low doses has been reported to be a potential drug candidate to treat or prevent severe coronavirus disease 2019 (COVID‐19).

Objectives

We aimed to explore whether low‐dose aspirin used for primary cardiovascular prevention was associated with a lower risk of severe COVID‐19.

Method

A large cohort of patients without known cardiovascular comorbidities was constructed from the entire French population registered in national health care databases. In total, 31.1 million patients aged ≥40 years, including 1.5 million reimbursed for low‐dose aspirin at least at three time points during the 6 months before the epidemic, were followed until hospitalization with a COVID‐19 diagnosis or intubation/death for hospitalized patients.

Results

Cox models adjusted for age and sex showed a positive association between low‐dose aspirin and the risk of hospitalization (hazard ratio [HR], 1.33; 95% confidence interval (CI), 1.29‐1.37]) or death/intubation (HR, 1.40 [95% CI, 1.33‐1.47]). In fully adjusted models, associations were close to null (HR, 1.03 [95% CI, 1.00‐1.06] and 1.04 [95% CI, 0.98‐1.10], respectively).

Conclusion

There was no evidence for an effect of low‐dose aspirin for primary cardiovascular prevention in reducing severe COVID‐19.

Generating Real-World Evidence on the Quality Use, Benefits and Safety of Medicines in Australia: History, Challenges and a Roadmap for the Future

Australia spends more than $20 billion annually on medicines, delivering significant health benefits for the population. However, inappropriate prescribing and medicine use also result in harm to individuals and populations, and waste of precious health resources. Medication data linked with other routine collections enable evidence generation in pharmacoepidemiology; the science of quantifying the use, effectiveness and safety of medicines in real-world clinical practice. This review details the history of medicines policy and data access in Australia, the strengths of existing data sources, and the infrastructure and governance enabling and impeding evidence generation in the field. Currently, substantial gaps persist with respect to cohesive, contemporary linked data sources supporting quality use of medicines, effectiveness and safety research; exemplified by Australia's limited capacity to contribute to the global effort in real-world studies of vaccine and disease-modifying treatments for COVID-19. We propose a roadmap to bolster the discipline, and population health more broadly, underpinned by a distinct capability governing and streamlining access to linked data assets for accredited researchers. Robust real-world evidence generation requires current data roadblocks to be remedied as a matter of urgency to deliver efficient and equitable health care and improve the health and well-being of all Australians.