Background

Sub-optimal HbA1c control is a driver of disparities in diabetes outcomes among Hispanic patients. Differences in medication adherence may underlie racial/ethnic differences in HbA1c level.

Objective

To examine the relationship between medication adherence and disparities in HbA1c level among Hispanic patients, relative to other racial/ethnic groups, obtaining care in the University of California Health System (UC Health).

Design

This study used clinical, administrative, and prescription dispensing data (January-December 2021) derived from the electronic health records of 5 Academic Medical Centers in UC Health, and linear regression models (LRMs) to conduct a cross-sectional analysis of the association between medication adherence, race/ethnicity, and HbA1c level. Adjusted LRMs were run with and without the measure of medication adherence to assess this relationship.

Participants

Patients with a UC Health primary care physician (PCP), with ≥ 1 PCP visit within the last 3 years, ages 18-75, reporting Asian, Hispanic, or White race/ethnicity, and who had ≥ 2 encounters with an ICD diagnosis of diabetes or had a prescription for a diabetes medication within the last 2 years, as of 12/31/21 (N = 27, 542; Asian = 6253, Hispanic = 7216, White = 14,073).

Main measures

Our measure of medication adherence was the proportion of days covered (PDC) for diabetes medications in 2021. Our outcome was the most recent HbA1c value.

Key results

In the LRM excluding the PDC, Hispanic ethnicity was positively associated with HbA1c level (β = 0.31, p =  < 0.001). In the LRM model including PDC, PDC was negatively associated with HbA1c level (β =  - 0.18, p =  < 0.001). However, the positive relationship between Hispanic ethnicity and HbA1c level did not change (β = 0.31, p =  < 0.001).

Conclusions

The findings of this study suggest that the relationship between Hispanic ethnicity, HbA1c level, and factors outside of medication adherence should be explored among primary care patients receiving care in Academic Medical Centers.

Objective

Using the newly created University of California (UC) Health Data Warehouse, we present the first study to analyze antihyperglycemic treatment utilization across the five large UC academic health systems (Davis, Irvine, Los Angeles, San Diego, and San Francisco).

Research design and methods

This retrospective analysis used deidentified electronic health records (EHRs; 2014-2019) including 97,231 patients with type 2 diabetes from 1,003 UC-affiliated clinical settings. Significant differences between health systems and individual providers were identified using binomial probabilities with cohort matching.

Results

Our analysis reveals statistically different treatment utilization patterns not only between health systems but also among individual providers within health systems. We identified 21 differences among health systems and 29 differences among individual providers within these health systems, with respect to treatment intensifications within existing guidelines on top of either metformin monotherapy or dual therapy with metformin and a sulfonylurea. Next, we identified variation for medications within the same class (e.g., glipizide vs. glyburide among sulfonylureas), with 33 differences among health systems and 86 among individual providers. Finally, we identified 2 health systems and 55 individual providers who more frequently used medications with known cardioprotective benefits for patients with high cardiovascular disease risk, but also 1 health system and 8 providers who prescribed such medications less frequently for these patients.

Conclusions

Our study used cohort-matching techniques to highlight real-world variation in care between health systems and individual providers. This demonstrates the power of EHRs to quantify differences in treatment utilization, a necessary step toward standardizing precision care for large populations.

Objectives

We hypothesised that severe asthmatics taking a statin drug, in addition to inhaled corticosteroids/long-acting β-agonist inhaler therapy, would have better asthma symptom control and improved lung function compared to their controls.

Study design

A retrospective, cross-sectional study of 165 patients with severe asthma seen from 2001-2008. Hierarchical linear and logistic regression models were used for modelling fitting.

Setting

University of California, Davis Medical Center (Sacramento, California, USA). Academic, single-centre, severe asthma subspecialty clinic.

Participants

612 screened, 223 eligible and 165 adult patients were included in the final study (N=165; 31 statin users and 134 non-users).

Primary and secondary outcome measures

The primary endpoint was asthma control as measured by the Asthma Control Test (ACT). The secondary endpoints included lung function, symptoms and the need for corticosteroid burst and peripheral eosinophil count.

Results

At baseline, statin users compared to non-users were older, had lower lung function (FEV1% predicted, FEV1, forced vital capacity and FEF25-75%) and had a higher prevalence of comorbid conditions. Statin use was associated with more aspirin and ipratropium inhaler use than in non-users. Patients in both groups were obese (body mass index ≥ 30). Statin users had better asthma symptom control compared to non-users (higher adjusted mean ACT score by 2.2±0.94 points, p<0.02). Median statin use was for 1 year. There were no statistically significant differences in lung function, corticosteroid or rescue bronchodilator use or peripheral eosinophilia between the two groups.

Conclusions

In our severe asthma referral population, statin users already taking inhaled controller therapy achieved better asthma control compared to non-users. The implications of this study is that patients with severe asthma could potentially benefit from added statin treatment. Because our study population was on average obese, the obese severe asthmatic may be a viable asthma subphenotype for further studies. Prospective randomised clinical trials evaluating the safety and efficacy of statins in severe asthma are warranted.

Objectives

We hypothesised that severe asthmatics taking a statin drug, in addition to inhaled corticosteroids/long-acting β-agonist inhaler therapy, would have better asthma symptom control and improved lung function compared to their controls.

Study design

A retrospective, cross-sectional study of 165 patients with severe asthma seen from 2001-2008. Hierarchical linear and logistic regression models were used for modelling fitting.

Setting

University of California, Davis Medical Center (Sacramento, California, USA). Academic, single-centre, severe asthma subspecialty clinic.

Participants

612 screened, 223 eligible and 165 adult patients were included in the final study (N=165; 31 statin users and 134 non-users).

Primary and secondary outcome measures

The primary endpoint was asthma control as measured by the Asthma Control Test (ACT). The secondary endpoints included lung function, symptoms and the need for corticosteroid burst and peripheral eosinophil count.

Results

At baseline, statin users compared to non-users were older, had lower lung function (FEV1% predicted, FEV1, forced vital capacity and FEF25-75%) and had a higher prevalence of comorbid conditions. Statin use was associated with more aspirin and ipratropium inhaler use than in non-users. Patients in both groups were obese (body mass index ≥ 30). Statin users had better asthma symptom control compared to non-users (higher adjusted mean ACT score by 2.2±0.94 points, p<0.02). Median statin use was for 1 year. There were no statistically significant differences in lung function, corticosteroid or rescue bronchodilator use or peripheral eosinophilia between the two groups.

Conclusions

In our severe asthma referral population, statin users already taking inhaled controller therapy achieved better asthma control compared to non-users. The implications of this study is that patients with severe asthma could potentially benefit from added statin treatment. Because our study population was on average obese, the obese severe asthmatic may be a viable asthma subphenotype for further studies. Prospective randomised clinical trials evaluating the safety and efficacy of statins in severe asthma are warranted.

Background

Real-world performance of COVID-19 diagnostic tests under Emergency Use Authorization (EUA) must be assessed. We describe overall trends in the performance of serology tests in the context of real-world implementation.

Methods

Six health systems estimated the odds of seropositivity and positive percent agreement (PPA) of serology test among people with confirmed SARS-CoV-2 infection by molecular test. In each dataset, we present the odds ratio and PPA, overall and by key clinical, demographic, and practice parameters.

Results

A total of 15,615 people were observed to have at least one serology test 14-90 days after a positive molecular test for SARS-CoV-2. We observed higher PPA in Hispanic (PPA range: 79-96%) compared to non-Hispanic (60-89%) patients; in those presenting with at least one COVID-19 related symptom (69-93%) as compared to no such symptoms (63-91%); and in inpatient (70-97%) and emergency department (93-99%) compared to outpatient (63-92%) settings across datasets. PPA was highest in those with diabetes (75-94%) and kidney disease (83-95%); and lowest in those with auto-immune conditions or who are immunocompromised (56-93%). The odds ratios (OR) for seropositivity were higher in Hispanics compared to non-Hispanics (OR range: 2.59-3.86), patients with diabetes (1.49-1.56), and obesity (1.63-2.23); and lower in those with immunocompromised or autoimmune conditions (0.25-0.70), as compared to those without those comorbidities. In a subset of three datasets with robust information on serology test name, seven tests were used, two of which were used in multiple settings and met the EUA requirement of PPA ≥87%. Tests performed similarly across datasets.

Conclusion

Although the EUA requirement was not consistently met, more investigation is needed to understand how serology and molecular tests are used, including indication and protocol fidelity. Improved data interoperability of test and clinical/demographic data are needed to enable rapid assessment of the real-world performance of in vitro diagnostic tests.

Background Real-world performance of COVID-19 diagnostic tests under Emergency Use Authorization (EUA) must be assessed. We describe overall trends in the performance of serology tests in the context of real-world implementation. Methods Six health systems estimated the odds of seropositivity and positive percent agreement (PPA) of serology test among people with confirmed SARS-CoV-2 infection by molecular test. In each dataset, we present the odds ratio and PPA, overall and by key clinical, demographic, and practice parameters. Results A total of 15,615 people were observed to have at least one serology test 14–90 days after a positive molecular test for SARS-CoV-2. We observed higher PPA in Hispanic (PPA range: 79–96%) compared to non-Hispanic (60–89%) patients; in those presenting with at least one COVID-19 related symptom (69–93%) as compared to no such symptoms (63–91%); and in inpatient (70–97%) and emergency department (93–99%) compared to outpatient (63–92%) settings across datasets. PPA was highest in those with diabetes (75–94%) and kidney disease (83–95%); and lowest in those with auto-immune conditions or who are immunocompromised (56–93%). The odds ratios (OR) for seropositivity were higher in Hispanics compared to non-Hispanics (OR range: 2.59–3.86), patients with diabetes (1.49–1.56), and obesity (1.63–2.23); and lower in those with immunocompromised or autoimmune conditions (0.25–0.70), as compared to those without those comorbidities. In a subset of three datasets with robust information on serology test name, seven tests were used, two of which were used in multiple settings and met the EUA requirement of PPA ≥87%. Tests performed similarly across datasets. Conclusion Although the EUA requirement was not consistently met, more investigation is needed to understand how serology and molecular tests are used, including indication and protocol fidelity. Improved data interoperability of test and clinical/demographic data are needed to enable rapid assessment of the real-world performance of in vitro diagnostic tests.

Background As diagnostic tests for COVID-19 were broadly deployed under Emergency Use Authorization, there emerged a need to understand the real-world utilization and performance of serological testing across the United States. Methods Six health systems contributed electronic health records and/or claims data, jointly developed a master protocol, and used it to execute the analysis in parallel. We used descriptive statistics to examine demographic, clinical, and geographic characteristics of serology testing among patients with RNA positive for SARS-CoV-2. Results Across datasets, we observed 930,669 individuals with positive RNA for SARS-CoV-2. Of these, 35,806 (4%) were serotested within 90 days; 15% of which occurred <14 days from the RNA positive test. The proportion of people with a history of cardiovascular disease, obesity, chronic lung, or kidney disease; or presenting with shortness of breath or pneumonia appeared higher among those serotested compared to those who were not. Even in a population of people with active infection, race/ethnicity data were largely missing (>30%) in some datasets—limiting our ability to examine differences in serological testing by race. In datasets where race/ethnicity information was available, we observed a greater distribution of White individuals among those serotested; however, the time between RNA and serology tests appeared shorter in Black compared to White individuals. Test manufacturer data was available in half of the datasets contributing to the analysis. Conclusion Our results inform the underlying context of serotesting during the first year of the COVID-19 pandemic and differences observed between claims and EHR data sources–a critical first step to understanding the real-world accuracy of serological tests. Incomplete reporting of race/ethnicity data and a limited ability to link test manufacturer data, lab results, and clinical data challenge the ability to assess the real-world performance of SARS-CoV-2 tests in different contexts and the overall U.S. response to current and future disease pandemics.

Background

As diagnostic tests for COVID-19 were broadly deployed under Emergency Use Authorization, there emerged a need to understand the real-world utilization and performance of serological testing across the United States.

Methods

Six health systems contributed electronic health records and/or claims data, jointly developed a master protocol, and used it to execute the analysis in parallel. We used descriptive statistics to examine demographic, clinical, and geographic characteristics of serology testing among patients with RNA positive for SARS-CoV-2.

Results

Across datasets, we observed 930,669 individuals with positive RNA for SARS-CoV-2. Of these, 35,806 (4%) were serotested within 90 days; 15% of which occurred <14 days from the RNA positive test. The proportion of people with a history of cardiovascular disease, obesity, chronic lung, or kidney disease; or presenting with shortness of breath or pneumonia appeared higher among those serotested compared to those who were not. Even in a population of people with active infection, race/ethnicity data were largely missing (>30%) in some datasets-limiting our ability to examine differences in serological testing by race. In datasets where race/ethnicity information was available, we observed a greater distribution of White individuals among those serotested; however, the time between RNA and serology tests appeared shorter in Black compared to White individuals. Test manufacturer data was available in half of the datasets contributing to the analysis.

Conclusion

Our results inform the underlying context of serotesting during the first year of the COVID-19 pandemic and differences observed between claims and EHR data sources-a critical first step to understanding the real-world accuracy of serological tests. Incomplete reporting of race/ethnicity data and a limited ability to link test manufacturer data, lab results, and clinical data challenge the ability to assess the real-world performance of SARS-CoV-2 tests in different contexts and the overall U.S. response to current and future disease pandemics.

This article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administration's 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses.

Background

Adverse events of special interest (AESIs) were pre-specified to be monitored for the COVID-19 vaccines. Some AESIs are not only associated with the vaccines, but with COVID-19. Our aim was to characterise the incidence rates of AESIs following SARS-CoV-2 infection in patients and compare these to historical rates in the general population.

Methods

A multi-national cohort study with data from primary care, electronic health records, and insurance claims mapped to a common data model. This study's evidence was collected between Jan 1, 2017 and the conclusion of each database (which ranged from Jul 2020 to May 2022). The 16 pre-specified prevalent AESIs were: acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, deep vein thrombosis, disseminated intravascular coagulation, encephalomyelitis, Guillain- Barré syndrome, haemorrhagic stroke, non-haemorrhagic stroke, immune thrombocytopenia, myocarditis/pericarditis, narcolepsy, pulmonary embolism, transverse myelitis, and thrombosis with thrombocytopenia. Age-sex standardised incidence rate ratios (SIR) were estimated to compare post-COVID-19 to pre-pandemic rates in each of the databases.

Findings

Substantial heterogeneity by age was seen for AESI rates, with some clearly increasing with age but others following the opposite trend. Similarly, differences were also observed across databases for same health outcome and age-sex strata. All studied AESIs appeared consistently more common in the post-COVID-19 compared to the historical cohorts, with related meta-analytic SIRs ranging from 1.32 (1.05 to 1.66) for narcolepsy to 11.70 (10.10 to 13.70) for pulmonary embolism.

Interpretation

Our findings suggest all AESIs are more common after COVID-19 than in the general population. Thromboembolic events were particularly common, and over 10-fold more so. More research is needed to contextualise post-COVID-19 complications in the longer term.

Funding

None.