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1.
Pharmacoepidemiology and Drug Safety ; 31:407-407, 2022.
Article in English | Web of Science | ID: covidwho-2083955
2.
Value in Health ; 25(1):S255, 2022.
Article in English | EMBASE | ID: covidwho-1650255

ABSTRACT

Objectives: Patients with the coronavirus disease 2019 (COVID-19) have an increased risk of thrombotic and cardiac events. We assessed the incidence and trends in selected thrombotic and cardiac events among hospitalized COVID-19 patients from a large, geographically diverse US electronic health records (EHR) database. Methods: This retrospective study analyzed Optum® de-identified COVID-19 EHR dataset to identify patients hospitalized between 03/01/2020 and 10/31/2020 with a COVID-19 diagnosis code. Patients were members of an integrated delivery network with ≥1 encounter during the 12 months prior to admission. Events of interest, including acute coronary syndrome (ACS), venous thromboembolism (VTE), stroke and myocarditis, were identified by diagnosis codes during hospitalization. Patients with any such events in their 12-month baseline were excluded. A composite event was defined by the occurrence of any of these events. Events were reported as proportions, in total as well as by month of admission and age group. Results: Among 25,574 hospitalized COVID-19 patients, the median age was 60 years (IQR: 44-72), and 54.5% were female. The composite event occurred in 3,052 (11.9%) patients (ACS=6.4%, VTE=4.1%, stroke=2.4%, myocarditis=0.6%) and its incidence increased with age (≤19=4.6%, 20-49=6.2%, 50-59=11.7%, 60-69=13.4%, ≥70=17.3%). In the youngest age group, myocarditis (2.2%) and VTE (1.7%) contributed to the majority of events, while in the oldest, ACS (10.9%), VTE (4.2%) and stroke (4.1%) were observed most often. Patients admitted during March had the highest incidence (20.6%), which decreased to 9.1% by July and remained steady through October (9.6%). The percentage decline in the composite event incidence during the study period was higher in younger versus older patients (≤19=77.0% 20-49=61.9%, 50-59=66.0%, 60-69=58.6%, ≥70=44.1%). Conclusions: The incidence of the composite event was highest among patients hospitalized early in the pandemic. The composite event occurred in almost 5% patients ≤19 years old and this group experienced the largest decline during the study period.

3.
Value in Health ; 23:S569-S569, 2020.
Article in English | PMC | ID: covidwho-1386137

ABSTRACT

Objectives: The SARS-CoV-2 pandemic has had unprecedented clinical and economic effects worldwide, with global efforts to develop a protective vaccine. Mass vaccination strategies have been employed to reduce seasonal influenza outbreaks. We identified published economic models assessing influenza vaccination strategies to determine which key economic modeling components could be useful for future models in support of a SARS-CoV-2 vaccine. Methods: Economic models published between January 2009 and June 2020 were identified by searching Medline (through National Library of Medicine’s PubMed) and GoogleScholar. The search strategy combined terminology for influenza vaccines with terminology for economic models (e.g., cost-effectiveness, cost-benefit, cost-consequence, decision trees, Markov). Results: 1,154 records were screened for inclusion;21 publications were included in the analysis. Sixteen identified models employed a Markov cohort or patient-level simulation approach, while 18 models incorporated quality-of-life and 13 incorporated a societal perspective;typical health states included uninfected, incubating, asymptomatic, symptomatic+isolated, symptomatic+circulating, partially immune, and dead. Common clinical outcomes included avoidance of virus cases, hospitalizations, and deaths;economic outcomes included cost savings associated with reduced hospitalizations, increased quality-of-life, and productivity gains. All but 1 model considered transmission rate, vaccination rate, vaccine efficacy, and rate of complications as key model drivers, whereas only 3 models considered vaccination plus broader public health strategies. Only 7 models incorporated herd immunity, citing uncertainty around availability and efficacy. While all models considered age and risk stratifications, no models assessed the implications of vaccination across different racial and ethnic groups. Conclusions: Future economic modeling in support of a SARS-CoV-2 vaccine should incorporate the components of existing model frameworks for influenza vaccination strategies as well as account for differences specific to SAR-CoV-2 including broader public health strategies (e.g., face masks, social distancing) and racial disparities. Without these elements, future models may fail to accurately capture the potential benefits of a SARS-CoV-2 vaccine.

4.
Pharmacoepidemiology and Drug Safety ; 30:205-205, 2021.
Article in English | Web of Science | ID: covidwho-1381794
5.
Pharmacoepidemiology and Drug Safety ; 30:366-367, 2021.
Article in English | Web of Science | ID: covidwho-1381689
7.
Value in Health ; 23:S404-S404, 2020.
Article in English | Web of Science | ID: covidwho-1098256
8.
Value in Health ; 23:S569-S569, 2020.
Article in English | Web of Science | ID: covidwho-1097688
9.
Value in Health ; 23:S573, 2020.
Article in English | EMBASE | ID: covidwho-988626

ABSTRACT

Objectives: Various comorbidities have been found to be associated with the severity of novel coronavirus disease 2019 (COVID-19), but data on their healthcare resource implications are limited. This study sought to characterize length of stay (LOS) and charges associated with COVID-19 related inpatient (IP) visits by selected baseline comorbidities using a large, multi-hospital US database. Methods: The Premier Healthcare Database COVID-19 from 2019/01/01 through 2020/05/24 was used to identify patients with an IP visit including a COVID-19 diagnosis and with ≥1 encounter for any reason during the 12 months (baseline) prior to the IP visit start (index). The outcomes were LOS and total charges (medical and medication) associated with the index IP visit. The baseline comorbidities included coronary artery disease, chronic kidney disease (CKD), chronic lung disease (CLD), diabetes, liver disease and immune-suppressing diseases, along with the total number of these conditions. Continuous variables were summarized using the mean, median and standard deviation (SD) and categorial variables using percentages. Results: The study included 10,948 patients (mean age=63.6 years;females=50.7%) with 40.9% having none of the baseline comorbidities, 28.5% with one, 16.7% with two, 9.4% with three and 4.4% with four or more. The most prevalent comorbidities were diabetes (31.1%), CKD (23.2%) and CLD (22.3%). The mean LOS was 8.6 days (median=7.0, SD=8.1) and the median of total charges was $44,123 (mean=$75,917, SD=$103,651). The longest mean LOS was for patients with immune suppressing diseases (10.1 days) and the highest median charges was for patients with liver disease ($56,778). Both mean LOS and median charges increased with greater number of comorbidities (zero=8.1, $37,445;one=8.7, $44,098;two=9.0, $51,642;three=9.4, $55,943;four or more=10.1, $62,257). Conclusions: Our findings show that LOS and charges associated with COVID-19 related IP visits increase with the number of selected comorbidities. This is consistent with other reports showing these conditions also increase mortality.

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