Incidence rates of thrombosis with thrombocytopenia syndrome (TTS) among adults in United States commercial and Medicare claims databases, 2017-2020.

BACKGROUND: Increased risk of thrombosis with thrombocytopenia syndrome (TTS) following adenovirus vector-based COVID-19 vaccinations has been identified in passive surveillance systems. TTS incidence rates (IRs) in the United States (U.S.) are needed to contextualize reports following COVID-19 vaccination. METHODS: We estimated annual and monthly IRs of overall TTS, common site TTS, and unusual site TTS for adults aged 18-64 years in Carelon Research and MarketScan commercial claims (2017-Oct 2020), CVS Health and Optum commercial claims (2019-Oct 2020), and adults aged ≥ 65 years using CMS Medicare claims (2019-Oct 2020); IRs were stratified by age, sex, and race/ethnicity (CMS Medicare). RESULTS: Across data sources, annual IRs for overall TTS were similar between Jan-Dec 2019 and Jan-Oct 2020. Rates were higher in Medicare (IRs: 370.72 and 365.63 per 100,000 person-years for 2019 and 2020, respectively) than commercial data sources (MarketScan IRs: 24.21 and 24.06 per 100,000 person-years; Optum IRs: 32.60 and 31.29 per 100,000 person-years; Carelon Research IRs: 24.46 and 26.16 per 100,000 person-years; CVS Health IRs: 30.31 and 30.25 per 100,000 person-years). Across years and databases, common site TTS IRs increased with age and were higher among males. Among adults aged ≥ 65 years, the common site TTS IR was highest among non-Hispanic black adults. Annual unusual site TTS IRs ranged between 2.02 and 3.04 (commercial) and 12.49 (Medicare) per 100,000 person-years for Jan-Dec 2019; IRs ranged between 1.53 and 2.67 (commercial) and 11.57 (Medicare) per 100,000 person-years for Jan-Oct 2020. Unusual site TTS IRs were higher in males and increased with age in commercial data sources; among adults aged ≥ 65 years, IRs decreased with age and were highest among non-Hispanic American Indian/Alaska native adults. CONCLUSION: TTS IRs were generally similar across years, higher for males, and increased with age. These rates may contribute to surveillance of post-vaccination TTS.

Development and Validation of an Algorithm for Thrombosis with Thrombocytopenia Syndrome (TTS) at Unusual Sites.

Introduction: Thrombosis with thrombocytopenia syndrome (TTS) has been reported following receipt of adenoviral vector-based COVID-19 vaccines. However, no validation studies evaluating the accuracy of International Classification of Diseases-10-Clinical Modification (ICD-10-CM)-based algorithm for unusual site TTS are available in the published literature. Methods: The purpose of this study was to assess the performance of clinical coding to 1) leverage literature review and clinical input to develop an ICD-10-CM-based algorithm to identify unusual site TTS as a composite outcome and 2) validate the algorithm against the Brighton Collaboration's interim case definition using laboratory, pathology, and imaging reports in an academic health network electronic health record (EHR) within the US Food and Drug Administration (FDA) Biologics Effectiveness and Safety (BEST) Initiative. Validation of up to 50 cases per thrombosis site was conducted, with positive predictive values (PPV) and 95% confidence intervals (95% CI) calculated using pathology or imaging results as the gold standard. Results: The algorithm identified 278 unusual site TTS cases, of which 117 (42.1%) were selected for validation. In both the algorithm-identified and validation cohorts, over 60% of patients were 56 years or older. The positive predictive value (PPV) for unusual site TTS was 76.1% (95% CI 67.2-83.2%) and at least 80% for all but one individual thrombosis diagnosis code. PPV for thrombocytopenia was 98.3% (95% CI 92.1-99.5%). Discussion: This study represents the first report of a validated ICD-10-CM-based algorithm for unusual site TTS. A validation effort found that the algorithm performed at an intermediate-to-high PPV, suggesting that the algorithm can be used in observational studies including active surveillance of COVID-19 vaccines and other medical products.

Immune globulin usage trends in commercially insured and Medicare populations, 2009-2019.

BACKGROUND: Longitudinal patterns of immune globulins (IG) use have not been described in large populations. Understanding IG usage is important given potential supply limitations impacting individuals for whom IG is the sole life-saving/health-preserving therapy. The study describes US IG utilization patterns from 2009 to 2019. STUDY DESIGN AND METHODS: Using IBM MarketScan commercial and Medicare claims data, we examined four metrics overall and by condition-specific categories during 2009-2019: (1) IG administrations per 100,000 person-years, (2) IG recipients per 100,000 enrollees, (3) average annual administrations per recipient, and (4) average annual dose per recipient. RESULTS: In the commercial and Medicare populations respectively: IG administrations per 100,000 person-years increased by 120% (213-470) and 144% (692-1693); IG recipients per 100,000 enrollees grew by 71% (24-42) and 102% (89-179); average annual administrations per recipient rose by 28% (8-10) and 19% (8-9); and average annual dose (grams) per recipient increased by 29% (384-497) and 34% (317-426). IG administrations associated with immunodeficiency (per 100,000 person-years) increased by 154% (from 127 to 321) and 176% (from 365 to 1007). Autoimmune and neurologic conditions were associated with higher annual average administrations and dose than other conditions. DISCUSSION: IG use increased, coinciding with a growth in the IG recipient population in the United States. Several conditions contributed to the trend, with the largest increase observed among immunodeficient individuals. Future investigations should assess changes in the demand for IVIG by disease state or indication and consider treatment effectiveness.

A seasonality-adjusted sequential test for vaccine safety surveillance.

Post-market active safety monitoring is important for the timely capture of safety signals associated with exposure to a new vaccine or drug. The group sequential analysis is a common method employed in safety surveillance. Specifically, it compares the post-vaccination incidence of adverse event (AE) in a vaccinated population with a pre-specified reference level by sequentially conducting hypothesis testing during the surveillance. When the number of AEs is "too high", a safety signal is identified. If the null hypothesis is never rejected, the vaccine is considered safe. Such an approach does not account for either the variation in determining the reference risk from a control population or the seasonality effect. Furthermore, not rejecting the null could be due to a lack of power and cannot always be interpreted as proof of safety. In this paper, we proposed a new group sequential test procedure fully accounting for both seasonality and variation from the historical controls. More importantly, we proposed to construct a confidence interval for the relative AE risk between the exposed and control groups at the end of the study, which can be used to quantify the safety of the vaccine. The proposed method is illustrated via real-data examples on anaphylaxis and examined by extensive simulation studies.

Background rates of adverse events of special interest for COVID-19 vaccine safety monitoring in the United States, 2019-2020.

BACKGROUND: The U.S. Food and Drug Administration (FDA) Biologics Effectiveness and Safety (BEST) Initiative conducts active surveillance of adverse events of special interest (AESI) after COVID-19 vaccination. Historical incidence rates (IRs) of AESI are comparators to evaluate safety. METHODS: We estimated IRs of 17 AESI in six administrative claims databases from January 1, 2019, to December 11, 2020: Medicare claims for adults ≥ 65 years and commercial claims (Blue Health Intelligence®, CVS Health, HealthCore Integrated Research Database, IBM® MarketScan® Commercial Database, Optum pre-adjudicated claims) for adults < 65 years. IRs were estimated by sex, age, race/ethnicity (Medicare), and nursing home residency (Medicare) in 2019 and for specific periods in 2020. RESULTS: The study included >100 million enrollees annually. In 2019, rates of most AESI increased with age. However, compared with commercially insured adults, Medicare enrollees had lower IRs of anaphylaxis (11 vs 12-19 per 100,000 person-years), appendicitis (80 vs 117-155), and narcolepsy (38 vs 41-53). Rates were higher in males than females for most AESI across databases and varied by race/ethnicity and nursing home status (Medicare). Acute myocardial infarction (Medicare) and anaphylaxis (all databases) IRs varied by season. IRs of most AESI were lower during March-May 2020 compared with March-May 2019 but returned to pre-pandemic levels after May 2020. However, rates of Bell's palsy, Guillain-Barré syndrome, narcolepsy, and hemorrhagic/non-hemorrhagic stroke remained lower in multiple databases after May 2020, whereas some AESI (e.g., disseminated intravascular coagulation) exhibited higher rates after May 2020 compared with 2019. CONCLUSION: AESI background rates varied by database and demographics and fluctuated in March-December 2020, but most returned to pre-pandemic levels after May 2020. It is critical to standardize demographics and consider seasonal and other trends when comparing historical rates with post-vaccination AESI rates in the same database to evaluate COVID-19 vaccine safety.

Assessment of performance characteristics of COVID-19 ICD-10-CM diagnosis code U07.1 using SARS-CoV-2 nucleic acid amplification test results.

The Food and Drug Administration's Biologics Effectiveness and Safety Initiative conducts active surveillance to protect public health during the coronavirus disease 2019 (COVID-19) pandemic. This study evaluated performance of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis code U07.1 in identifying COVID-19 cases in claims compared with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid amplification test results in linked electronic health records (EHRs). Care episodes in three populations were defined using COVID-19-related diagnoses (population 1), SARS-CoV-2 nucleic acid amplification test procedures (population 2), and all-cause hospitalizations (population 3) in two linked claims-EHR databases: IBM® MarketScan® Explorys® Claims-EMR Data Set (commercial) and OneFlorida Data Trust linked Medicaid-EHR. Positive and negative predictive values were calculated. Respectively, populations 1, 2, and 3 included 26,686, 26,095, and 2,564 episodes (commercial) and 29,117, 23,412, and 9,629 episodes (Florida Medicaid). The positive predictive value was >80% and the negative predictive value was >95% in each population, with the highest positive predictive value in population 3 (commercial: 91.9%; Medicaid: 93.1%). Findings did not vary substantially by patient age. Positive predictive values in populations 1 and 2 fluctuated during April-June 2020. They then stabilized in the commercial but not the Medicaid population. Negative predictive values were consistent over time in all populations and databases. Our findings indicate that U07.1 has high performance in identifying COVID-19 cases and noncases in claims databases. Performance may vary across populations and periods.

Vaccine exposure during pregnancy among privately and publicly insured women in the United States, 2016-2018.

BACKGROUND: Vaccine use during pregnancy affects maternal and infant health. Many women do not receive vaccines recommended during pregnancy; conversely, inadvertent exposure to vaccines contraindicated or not recommended during pregnancy may occur. We assessed exposure to two recommended vaccines and two vaccines not recommended during pregnancy among privately and Medicaid-insured women in the United States. METHODS: This study includes a retrospective cohort of pregnancies in women aged 12-55 years resulting in live birth, spontaneous abortion, or stillbirth identified in the IBM® MarketScan® Commercial, Blue Health Intelligence® (BHI®) Commercial, and IBM MarketScan Multi-State Medicaid Databases from August 1, 2016, to December 31, 2018. Gestational age at vaccination was determined using a validated algorithm. We examined vaccines (1) recommended by the Centers for Disease Control and Prevention Advisory Committee on Immunization Practices (ACIP) (tetanus, diphtheria, and acellular pertussis [Tdap]; inactivated influenza) and (2) not recommended (human papillomavirus [HPV]) or contraindicated (measles, mumps, and rubella [MMR]). RESULTS: We identified 496,771 (MarketScan Commercial), 858,961 (BHI), and 289,573 (MarketScan Medicaid) pregnancies (approximately 75% aged 20-34 years). Across these three databases, 52.1%, 50.3%, and 31.3% of pregnancies, respectively, received Tdap, most often at a gestational age of 28 weeks, and influenza vaccination occurred in 32.1%, 30.8%, and 18.0% of pregnancies, respectively. HPV vaccination occurred in < 0.2% of pregnancies, mostly in the first trimester among women aged 12-19 years, and MMR was administered in < 0.1% of pregnancies. Use of other contraindicated vaccines per ACIP (e.g., varicella, live attenuated influenza) was rare. CONCLUSION: Maternal vaccination with ACIP-recommended vaccines was suboptimal among privately and Medicaid-insured patients, with lower vaccination coverage among Medicaid-insured pregnancies than their privately insured counterparts. Inadvertent exposure to contraindicated vaccines during pregnancy was rare. This study evaluated only vaccinations reimbursed among insured populations and may have limited generalizability to uninsured populations.

Validating Claims-Based Algorithms Determining Pregnancy Outcomes and Gestational Age Using a Linked Claims-Electronic Medical Record Database.

INTRODUCTION: Pregnancy outcome identification and precise estimates of gestational age (GA) are critical in drug safety studies of pregnant women. Validated pregnancy outcome algorithms based on the International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS) have not previously been published. METHODS: We developed algorithms to classify pregnancy outcomes and estimate GA using ICD-10-CM/PCS and service codes on claims in the 2016-2018 IBM® MarketScan® Explorys® Claims-EMR Data Set and compared the results with ob-gyn adjudication of electronic medical records (EMRs). Obstetric services were grouped into episodes using hierarchical and spacing requirements. GA was based on evidence with the highest clinical accuracy. Among pregnancies with obstetric EMRs, 100 full-term live births (FTBs), 100 preterm live births (PTBs), 100 spontaneous abortions (SAs), and 24 stillbirths were selected for review. Physicians adjudicated cases using Global Alignment of Immunization safety Assessment in pregnancy (GAIA) definitions applied to structured EMRs. RESULTS: The claims-based algorithms identified 34,204 pregnancies, of which 9.9% had obstetric EMRs. Of sampled pregnancies, 92 FTBs, 93 PTBs, 75 SAs, and 24 stillbirths were adjudicated. Among these pregnancies, the percent agreement was 97.8%, 62.4%, 100.0%, and 70.8% for FTBs, PTBs, SAs, and stillbirths, respectively. The percent agreement on GA within 7 and 28 days, respectively, was 85.9% and 100.0% for FTBs, 81.7% and 98.9% for PTBs, 61.3% and 94.7% for SAs, and 66.7% and 79.2% for stillbirths. CONCLUSIONS: The pregnancy outcome algorithms had high agreement with physician adjudication of EMRs and may inform post-market maternal safety surveillance.

A Comparative Assessment of Observational Medical Outcomes Partnership and Mini-Sentinel Common Data Models and Analytics: Implications for Active Drug Safety Surveillance.

INTRODUCTION: An often key component to coordinating surveillance activities across distributed networks is the design and implementation of a common data model (CDM). The purpose of this study was to evaluate two drug safety surveillance CDMs from an ecosystem perspective to better understand how differences in CDMs and analytic tools affect usability and interpretation of results. METHODS: Humana claims data from 2007 to 2012 were mapped to Observational Medical Outcomes Partnership (OMOP) and Mini-Sentinel CDMs. Data were described and compared at the patient level by source code and mapped concepts. Study cohort construction and effect estimates were also compared using two different analytical methods--one based on a new user design implementing a high-dimensional propensity score (HDPS) algorithm and the other based on univariate self-controlled case series (SCCS) design--across six established positive drug-outcome pairs to learn how differences in CDMs and analytics influence steps in the database analytic process and results. RESULTS: Claims data for approximately 7.7 million Humana health plan members were transformed into the two CDMs. Three health outcome cohorts and two drug cohorts showed differences in cohort size and constituency between Mini-Sentinel and OMOP CDMs, which was a result of multiple factors. Overall, the implementation of the HDPS procedure on Mini-Sentinel CDM detected more known positive associations than that on OMOP CDM. The SCCS method results were comparable on both CDMs. Differences in the implementation of the HDPS procedure between the two CDMs were identified; analytic model and risk period specification had a significant impact on the performance of the HDPS procedure on OMOP CDM. CONCLUSIONS: Differences were observed between OMOP and Mini-Sentinel CDMs. The analysis of both CDMs at the data model level indicated that such conceptual differences had only a slight but not significant impact on identifying known safety associations. Our results show that differences at the ecosystem level of analyses across the CDMs can lead to strikingly different risk estimations, but this can be primarily attributed to the choices of analytic approach and their implementation in the community-developed analytic tools. The opportunities of using CDMs are clear, but our study shows the need for judicious comparison of analyses across the CDMs. Our work emphasizes the need for ongoing efforts to ensure sustainable transparent platforms to maintain and develop CDMs and associated tools for effective safety surveillance.

Impact of roflumilast on exacerbations of COPD, health care utilization, and costs in a predominantly elderly Medicare Advantage population.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) exacerbations are associated with declining lung function and health-related quality of life, and increased hospitalization and mortality. Clinical trials often poorly represent the elderly and thus have only partial applicability to their clinical care. OBJECTIVE: To compare exacerbations, COPD-related health care utilization (HCU), and costs in a predominantly elderly Medicare COPD population initiated on roflumilast versus those not initiated on roflumilast. METHODS: Deidentified administrative claims data from a large, national payer were utilized. Medicare patients aged 40-89 years with at least one COPD diagnosis from May 1, 2010 to December 31, 2012 were included. Members with at least one roflumilast pharmacy claim (index) were assigned to the roflumilast group and those without were assigned to the non-roflumilast group. Proxy index dates for the non-roflumilast group were randomly assigned for similar distribution of all patients' time at risk. Subjects with at least one pre-index COPD exacerbation had to be continuously enrolled for ≥365 days pre-index and post-index. Unadjusted and adjusted difference-in-difference (DID) analyses contrasted pre-index with post-index changes in exacerbations, HCU, and costs of roflumilast treatment compared with non-roflumilast treatment. RESULTS: A total of 500 roflumilast and 60,145 non-roflumilast patients were included (mean age 69.7 and 72.3 years, respectively; P<0.0001). Unadjusted DID favored roflumilast for all exacerbations, with greater pre-index to post-index reductions in mean per 30-day COPD-related hospitalizations (-0.0182 versus -0.0013, P=0.009), outpatient visits (-0.2500 versus -0.0606, P<0.0001), and COPD-related inpatient costs (-US$141 versus -US$11, P=0.0346) and outpatient costs (-US$31 versus -US$4, P<0.0001). Multivariate analyses identified significantly improved pre-index to post-index COPD-related total costs (P=0.0005) and total exacerbations (P<0.0001) for the roflumilast group versus non-roflumilast group. CONCLUSION: In a predominantly elderly Medicare COPD population, newly initiated roflumilast patients displayed similar or significantly better unadjusted reductions in all exacerbation-related, COPD-related HCU-related, and COPD-related costs outcomes compared with non-roflumilast patients. These analyses also suggest better adjusted COPD-related costs and total exacerbations for roflumilast-initiated patients.