Coronavirus Disease 2019 Severity and Risk of Subsequent Cardiovascular Events.

BACKGROUND: Little is known about the relationship between coronavirus disease 2019 (COVID-19) severity and subsequent risk of experiencing a cardiovascular event (CVE) after COVID-19 recovery. We evaluated this relationship in a large cohort of United States adults. METHODS: Using a claims database, we performed a retrospective cohort study of adults diagnosed with COVID-19 between 1 April 2020 and 31 May 2021. We evaluated the association between COVID-19 severity and risk of CVE >30 days after COVID-19 diagnosis using inverse probability of treatment-weighted competing risks regression. Severity was based on level of care required for COVID-19 treatment: intensive care unit (ICU) admission, non-ICU hospitalization, or outpatient care only. RESULTS: A total of 1 357 518 COVID-19 patients were included (2% ICU, 3% non-ICU hospitalization, and 95% outpatient only). Compared to outpatients, there was an increased risk of any CVE for patients requiring ICU admission (adjusted hazard ratio [aHR], 1.80 [95% confidence interval {CI}, 1.71-1.89]) or non-ICU hospitalization (aHR, 1.28 [95% CI, 1.24-1.33]). Risk of subsequent hospitalization for CVE was even higher (aHRs, 3.47 [95% CI, 3.20-3.76] for ICU and 1.96 [95% CI, 1.85-2.09] for non-ICU hospitalized vs outpatient only). CONCLUSIONS: COVID-19 patients hospitalized or requiring critical care had a significantly higher risk of experiencing and being hospitalized for post-COVID-19 CVE than patients with milder COVID-19 who were managed solely in the outpatient setting, even after adjusting for differences between these groups. These findings underscore the continued importance of preventing severe acute respiratory syndrome coronavirus 2 infection from progressing to severe illness to reduce potential long-term cardiovascular complications.

Utilization of nonguideline concordant antibiotic treatment following acute otitis media in children in the United States.

PURPOSE: Acute otitis media (AOM) is a common indication for antibiotics in children. We sought to characterize the frequency of nonguideline concordant antibiotic therapy for AOM in the United States, by agent and duration. METHODS: Using national administrative claims data (2016-2019), we identified children aged 6 months to 17 years with an oral antibiotic dispensed within 3 days of a new diagnosis of suppurative AOM. Use of nonguideline concordant agents and durations, defined based on national treatment guidelines, were summarized by age, race, rurality, region, and insurance type. Subsequent oral antibiotic dispensing within the year after AOM diagnosis was also evaluated. We created sunburst diagrams to visualize longitudinal patterns of within-person antibiotic utilization for AOM, by agent and duration. RESULTS: We identified 789 424 eligible commercially-insured and 502 239 medicaid-insured children. Among commercially insured children, 35% received nonguideline concordant agents for AOM, including cefdinir (16%), amoxicillin-clavulanate (12%), and azithromycin (7%). Fewer children age <2 years received a nonguideline concordant initial agent (27%) compared to age ≥6 years (41%). More children age <2 years received three or more antibiotics over the following year (34% vs. 3% for children age ≥6 years). The most common treatment duration was 10 days for all ages; treatment duration for the initial antibiotic was nonguideline concordant for 95% and 89% of children age 2-5 years and ≥6 years, respectively. Patterns were similar for medicaid-insured children. CONCLUSIONS: Nonguideline concordant antibiotic use is common when treating AOM in children, including use of broad-spectrum agents and longer-than-recommended antibiotic durations.

Comparative Effectiveness of High-Dose Versus Standard-Dose Influenza Vaccine Among Patients Receiving Maintenance Hemodialysis.

RATIONALE & OBJECTIVE: Studies of patients on maintenance dialysis therapy suggest that standard-dose influenza vaccine (SDV) may not prevent influenza-related outcomes. Little is known about the comparative effectiveness of SDV versus high-dose influenza vaccine (HDV) in this population. STUDY DESIGN: Cohort study using data from the US Renal Data System. SETTING & PARTICIPANTS: 507,552 adults undergoing in-center maintenance hemodialysis between the 2010 to 2011 and 2014 to 2015 influenza seasons. EXPOSURES: SDV and HDV. OUTCOMES: All-cause mortality, hospitalization due to influenza or pneumonia, and influenza-like illness during the influenza season. ANALYTIC APPROACH: Patients were eligible for inclusion in multiple yearly cohorts; thus, our unit of analysis was the influenza patient-season. To examine the relationship between vaccine dose and effectiveness outcomes, we estimated risk differences and risk ratios using propensity score weighting of Kaplan-Meier functions, accounting for a wide range of patient- and facility-level characteristics. For nonmortality outcomes, we used competing-risk methods to account for the high mortality rate in the dialysis population. RESULTS: Within 225,215 influenza patient-seasons among adults 65 years and older, 97.4% received SDV and 2.6% received HDV. We observed similar risk estimates for HDV and SDV recipients for mortality (risk difference, -0.08%; 95% CI, -0.85% to 0.80%), hospitalization due to influenza or pneumonia (risk difference, 0.15%; 95% CI, -0.69% to 0.93%), and influenza-like illness (risk difference, 0.00%; 95% CI, -1.50% to 1.08%). Our findings were similar among adults younger than 65 years, as well as within other subgroups defined by influenza season, age group, dialysis vintage, month of influenza vaccination, and vaccine valence. LIMITATIONS: Residual confounding and outcome misclassification. CONCLUSIONS: The HDV does not appear to provide additional protection beyond the SDV against all-cause mortality or influenza-related outcomes for adults undergoing hemodialysis. The additional cost and side effects associated with HDV should be considered when offering this vaccine. Future studies of HDV and other influenza vaccine strategies are warranted.

Who are we missing? Underrepresentation of data sources used for pharmacoepidemiology research in the United States.

PURPOSE: Research using healthcare databases often includes patients frequently excluded from clinical trials; yet it is not known whether commonly used data represents the overall population or specific sub-populations of interest. We aimed to examine population representativeness from data sources in recent research studies in the United States (US). METHODS: We identified data sources from abstracts accepted to the 34th International Conference on Pharmacoepidemiology & Therapeutic Risk Management. The final sample included research studies using ≥1 data source from the US. We classified data sources broadly as claims, linkage, electronic health records (EHR), survey, distributed data network, and other. Studies using claims and EHRs were further classified into more specific categories, including special populations of interest (eg, children). RESULTS: We identified 356 abstracts. The majority used claims data (n = 201, 56.5%), followed by data linkages (n = 46, 12.9%), and EHR data (n = 39, 11.0%). Among EHR studies, most (n = 16, 41.0%) came from network data sources (eg, Kaiser Permanente). Almost half (49.4%) of claims-based studies used commercial claims data sources, followed by Medicare (22.1%), Medicaid (11.3%), and Medicare Supplemental (6.1%). Only 15% of studies included children in the study population (n = 53), with 8% focused on a pediatric topic (n = 27). CONCLUSIONS: We find that certain populations in the US are under-represented in pharmacoepidemiology, particularly Medicaid enrollees and children. Researchers should strive to utilize data sources that may be more representative of the US population, particularly vulnerable populations.

Using negative control outcomes to assess the comparability of treatment groups among women with osteoporosis in the United States.

PURPOSE: In contrast to randomized clinical trials, comparative safety and effectiveness assessments of osteoporosis medications in clinical practice may be subject to confounding by indication. We used negative control outcomes to detect residual confounding when comparing osteoporosis medications. METHODS: Using MarketScan Commercial and Supplemental claims, we identified women aged ≥55 years who initiated an oral bisphosphonate (BP) (risedronate, alendronate, or ibandronate), denosumab (an injected biologic), or intravenous zoledronic acid (ZA) from October 1, 2010 to September 30, 2015. Women with Paget's disease or cancer were excluded. We compared individual oral BPs to each other, denosumab to ZA, denosumab to oral BPs, and ZA to oral BPs, with respect to 11 negative control outcomes identified by subject matter experts. We estimated the 12-month cumulative risk difference (RD) using inverse probability of treatment and censoring weights. RESULTS: Among 148 587 women, most initiated alendronate (57%), followed by ibandronate (12%), ZA (11%), risedronate (10%), and denosumab (10%). Compared with denosumab, patients initiating ZA had similar risks of all negative control outcomes. Compared with oral BPs, patients initiating denosumab had a higher risk of a wellness visit (RD = 1.2%, 95% CI: 0.4, 1.9) and a lower risk of receiving herpes zoster vaccine (RD = -0.6%, 95% CI: -1.1, -0.2). Comparing ZA with oral BP initiators resulted in two outcomes with positive associations. CONCLUSIONS: Caution is warranted when comparing injectable vs oral osteoporosis medications, given the potential for unmeasured confounding. Evaluating negative control outcomes could be a standard validity check prior to conducting comparative studies.

Restriction of Pharmacoepidemiologic Cohorts to Initiators of Medications in Unrelated Preventive Drug Classes to Reduce Confounding by Frailty in Older Adults.

Nonexperimental studies of the effectiveness of seasonal influenza vaccine in older adults have found 40%-60% reductions in all-cause mortality associated with vaccination, potentially due to confounding by frailty. We restricted our cohort to initiators of medications in preventive drug classes (statins, antiglaucoma drugs, and ß blockers) as an approach to reducing confounding by frailty by excluding frail older adults who would not initiate use of these drugs. Using a random 20% sample of US Medicare beneficiaries, we framed our study as a series of nonrandomized "trials" comparing vaccinated beneficiaries with unvaccinated beneficiaries who had an outpatient health-care visit during the 5 influenza seasons occurring in 2010-2015. We pooled data across trials and used standardized-mortality-ratio-weighted Cox proportional hazards models to estimate the association between influenza vaccination and all-cause mortality before influenza season, expecting a null association. Weighted hazard ratios among preventive drug initiators were generally closer to the null than those in the nonrestricted cohort. Restriction of the study population to statin initiators with an uncensored approach resulted in a weighted hazard ratio of 1.00 (95% confidence interval: 0.84, 1.19), and several other hazard ratios were above 0.95. Restricting the cohort to initiators of medications in preventive drug classes can reduce confounding by frailty in this setting, but further work is required to determine the most appropriate criteria to use.

Assessing Residual Bias in Estimating Influenza Vaccine Effectiveness: Comparison of High-dose Versus Standard-dose Vaccines.

BACKGROUND: Estimating influenza vaccine effectiveness using an unvaccinated comparison group may result in biased effect estimates. OBJECTIVES: To explore the reduction of confounding bias in an active comparison of high-dose versus standard-dose influenza vaccines, as compared with vaccinated versus unvaccinated comparisons. METHODS: Using Medicare data from the United States end-stage renal disease program (2009-2013), we compared the risk of all-cause mortality among recipients of high-dose vaccine (HDV) versus standard-dose vaccine (SDV), HDV versus no vaccine, and SDV versus no vaccine. To quantify confounding bias, analyses were restricted to the preinfluenza season, when the protective effect of vaccination should not yet be observed. We estimated the standardized mortality ratio-weighted cumulative incidence functions using Kaplan-Meier methods and calculated risk ratios (RRs) and risk differences between groups. RESULTS: Among 350,921 eligible patients contributing 825,642 unique patient preinfluenza seasons, 0.8% received HDV, 70.5% received SDV, and 28.7% remained unvaccinated. Comparisons with unvaccinated patients yielded spurious decreases in mortality risk during the preinfluenza period, for HDV versus none [RR, 0.60; 95% confidence interval (CI), 0.51-0.70)] and SDV versus none (RR, 0.72; 95% CI, 0.70-0.75). The effect estimate was attenuated in the HDV versus SDV comparison (RR, 0.89; 95% CI, 0.77-1.03). Estimates on the absolute scale followed a similar pattern. CONCLUSIONS: The HDV versus SDV comparison yielded less-biased estimates of the all-cause mortality before influenza season compared to those with nonuser comparison groups. Vaccine effectiveness and safety researchers should consider the active comparator design to reduce bias due to differences in underlying health status between vaccinated and unvaccinated individuals.

Controlling confounding by frailty when estimating influenza vaccine effectiveness using predictors of dependency in activities of daily living.

PURPOSE: To improve control of confounding by frailty when estimating the effect of influenza vaccination on all-cause mortality by controlling for a published set of claims-based predictors of dependency in activities of daily living (ADL). METHODS: Using Medicare claims data, a cohort of beneficiaries >65 years of age was followed from September 1, 2007, to April 12, 2008, with covariates assessed in the 6 months before follow-up. We estimated Cox proportional hazards models of all-cause mortality, with influenza vaccination as a time-varying exposure. We controlled for common demographics, comorbidities, and health care utilization variables and then added 20 ADL dependency predictors. To gauge residual confounding, we estimated pre-influenza season hazard ratios (HRs) between September 1, 2007 and January 5, 2008, which should be 1.0 in the absence of bias. RESULTS: A cohort of 2 235 140 beneficiaries was created, with a median follow-up of 224 days. Overall, 52% were vaccinated and 4% died during follow-up. During the pre-influenza season period, controlling for demographics, comorbidities, and health care use resulted in a HR of 0.66 (0.64, 0.67). Adding the ADL dependency predictors moved the HR to 0.68 (0.67, 0.70). Controlling for demographics and ADL dependency predictors alone resulted in a HR of 0.68 (0.66, 0.70). CONCLUSIONS: Results were consistent with those in the literature, with significant uncontrolled confounding after adjustment for demographics, comorbidities, and health care use. Adding ADL dependency predictors moved HRs slightly closer to the null. Of the comorbidities, health care use variables, and ADL dependency predictors, the last set reduced confounding most. However, substantial uncontrolled confounding remained.

Controlling Time-Dependent Confounding by Health Status and Frailty: Restriction Versus Statistical Adjustment.

Nonexperimental studies of preventive interventions are often biased because of the healthy-user effect and, in frail populations, because of confounding by functional status. Bias is evident when estimating influenza vaccine effectiveness, even after adjustment for claims-based indicators of illness. We explored bias reduction methods while estimating vaccine effectiveness in a cohort of adult hemodialysis patients. Using the United States Renal Data System and linked data from a commercial dialysis provider, we estimated vaccine effectiveness using a Cox proportional hazards marginal structural model of all-cause mortality before and during 3 influenza seasons in 2005/2006 through 2007/2008. To improve confounding control, we added frailty indicators to the model, measured time-varying confounders at different time intervals, and restricted the sample in multiple ways. Crude and baseline-adjusted marginal structural models remained strongly biased. Restricting to a healthier population removed some unmeasured confounding; however, this reduced the sample size, resulting in wide confidence intervals. We estimated an influenza vaccine effectiveness of 9% (hazard ratio = 0.91, 95% confidence interval: 0.72, 1.15) when bias was minimized through cohort restriction. In this study, the healthy-user bias could not be controlled through statistical adjustment; however, sample restriction reduced much of the bias.

Evolving methods for inference in the presence of healthy worker survivor bias.

Healthy worker survivor bias may occur in occupational studies due to the tendency for unhealthy individuals to leave work earlier, and consequently accrue less exposure, compared with their healthier counterparts. If occupational data are not analyzed using appropriate methods, this bias can result in attenuation or even reversal of the estimated effects of exposures on health outcomes. Recent advances in computing power, coupled with state-of-the-art statistical methods, have greatly increased the ability of analysts to control healthy worker survivor bias. However, these methods have not been widely adopted by occupational epidemiologists. We update the seminal review by Arrighi and Hertz-Picciotto (Epidemiology.1994; 5: 186-196) of the sources and methods to control healthy worker survivor bias. In our update, we discuss methodologic advances since the publication of that review, notably with a consideration of how directed acyclic graphs can inform the choice of appropriate analytic methods. We summarize and discuss methods for addressing this bias, including recent work applying g-methods to account for employment status as a time-varying covariate affected by prior exposure. In the presence of healthy worker survivor bias, g-methods have advantages for estimating less biased parameters that have direct policy implications and are clearly communicated to decision-makers.

Occupational radon exposure and lung cancer mortality: estimating intervention effects using the parametric g-formula.

BACKGROUND: Traditional regression analysis techniques used to estimate associations between occupational radon exposure and lung cancer focus on estimating the effect of cumulative radon exposure on lung cancer. In contrast, public health interventions are typically based on regulating radon concentration rather than workers' cumulative exposure. Estimating the effect of cumulative occupational exposure on lung cancer may be difficult in situations vulnerable to the healthy worker survivor bias. METHODS: Workers in the Colorado Plateau Uranium Miners cohort (n = 4,134) entered the study between 1950 and 1964 and were followed for lung cancer mortality through 2005. We use the parametric g-formula to compare the observed lung cancer mortality to the potential lung cancer mortality had each of 3 policies to limit monthly radon exposure been in place throughout follow-up. RESULTS: There were 617 lung cancer deaths over 135,275 person-years of follow-up. With no intervention on radon exposure, estimated lung cancer mortality by age 90 was 16%. Lung cancer mortality was reduced for all interventions considered, and larger reductions in lung cancer mortality were seen for interventions with lower monthly radon exposure limits. The most stringent guideline, the Mine Safety and Health Administration standard of 0.33 working-level months, reduced lung cancer mortality from 16% to 10% (risk ratio = 0.67 [95% confidence interval = 0.61 to 0.73]). CONCLUSIONS: This work illustrates the utility of the parametric g-formula for estimating the effects of policies regarding occupational exposures, particularly in situations vulnerable to the healthy worker survivor bias.

Increased disease severity during COVID-19 related hospitalization in black non-hispanic, hispanic and medicaid-insured young children.

Background: The COVID-19 pandemic has disproportionately affected marginalized groups in the United States. Although most children have mild or asymptomatic COVID-19, some experience severe disease and long-term complications. However, few studies have examined health disparities in severe COVID-19 outcomes among US children. Objective: To examine disparities in the clinical outcomes of infants and children aged <5 years hospitalized with COVID-19 by race/ethnicity and payer status. Methods: Children aged <5 years hospitalized with an admission diagnosis of COVID-19 (April 2021-February 2023) were selected from the PINC AI™ Healthcare Database. Hospital outcomes included length of stay (LOS), intensive care unit (ICU) admission, oxygen supplementation, invasive mechanical ventilation (IMV), and prolonged duration of each outcome. Multivariable logistic regression models compared hospitalization outcomes by race/ethnicity and payer status. Results: Among 10,190 children (mean age: 0.9 years, 56.5% male, 66.7% Medicaid-insured), race/ethnicity was distributed as follows: White non-Hispanic (35.1%), Hispanic (any or Unknown race; 28.3%), Black non-Hispanic (15.2%), Other race/ethnicity (8.9%) and Unknown (12.5%). Payer status varied by race/ethnicity. White non-Hispanic children had the highest proportion with commercial insurance (42.9%) while other racial/ethnic groups ranged between 13.8% to 26.1%. Black non-Hispanic children had the highest proportion with Medicaid (82.3%) followed by Hispanic children (76.9%). Black non-Hispanic children had higher odds of prolonged outcomes: LOS (adjusted odds ratio [aOR] = 1.20, 95% confidence interval [CI]:1.05-1.38), ICU days (aOR = 1.44, 95% CI: 1.07-1.93), and IMV days (aOR = 1.80, 95% CI: 1.09-2.97) compared to White non-Hispanic children. Similar patterns were observed for Hispanic and children of Other race/ethnicity. Medicaid-insured and children with other insurance had higher odds of prolonged LOS and oxygen days than commercially insured patients. Conclusion: There were disparities in clinical outcomes of COVID-19 by race/ethnicity and insurance type, particularly for prolonged-duration outcomes. Further research is required to fully comprehend the causes and consequences of these disparities and develop strategies to reduce them while ensuring equitable healthcare delivery.

Using a data-driven approach to define post-COVID conditions in US electronic health record data.

OBJECTIVE: To create a data-driven definition of post-COVID conditions (PCC) by directly measure changes in symptomatology before and after a first COVID episode. MATERIALS AND METHODS: Retrospective cohort study using Optum® de-identified Electronic Health Record (EHR) dataset from the United States of persons of any age April 2020-September 2021. For each person with COVID (ICD-10-CM U07.1 "COVID-19" or positive test result), we selected up to 3 comparators. The final COVID symptom score was computed as the sum of new diagnoses weighted by each diagnosis' ratio of incidence in COVID group relative to comparator group. For the subset of COVID cases diagnosed in September 2021, we compared the incidence of PCC using our data-driven definition with ICD-10-CM code U09.9 "Post-COVID Conditions", first available in the US October 2021. RESULTS: The final cohort contained 588,611 people with COVID, with mean age of 48 years and 38% male. Our definition identified 20% of persons developed PCC in follow-up. PCC incidence increased with age: (7.8% of persons aged 0-17, 17.3% aged 18-64, and 33.3% aged 65+) and did not change over time (20.0% among persons diagnosed with COVID in 2020 versus 20.3% in 2021). For cases diagnosed in September 2021, our definition identified 19.0% with PCC in follow-up as compared to 2.9% with U09.9 code in follow-up. CONCLUSION: Symptom and U09.9 code-based definitions alone captured different populations. Maximal capture may consider a combined approach, particularly before the availability and routine utilization of specific ICD-10 codes and with the lack consensus-based definitions on the syndrome.

Persons diagnosed with COVID-19 in England in the Clinical Practice Research Datalink (CPRD): a cohort description.

OBJECTIVE: To create case definitions for confirmed COVID-19 diagnoses, COVID-19 vaccination status and three separate definitions of high risk of severe COVID-19, as well as to assess whether the implementation of these definitions in a cohort reflected the sociodemographic and clinical characteristics of COVID-19 epidemiology in England. DESIGN: Retrospective cohort study. SETTING: Electronic healthcare records from primary care (Clinical Practice Research Datalink, CPRD) linked to secondary care data (Hospital Episode Statistics) data covering 24% of the population in England. PARTICIPANTS: 2 271 072 persons aged 1 year and older diagnosed with COVID-19 in CPRD Aurum between 1 August 2020 and 31 January 2022. MAIN OUTCOME MEASURES: Age, sex and regional distribution of COVID-19 cases and COVID-19 vaccine doses received prior to diagnosis were assessed separately for the cohorts of cases identified in primary care and those hospitalised for COVID-19 (primary diagnosis code of ICD-10 U07.1 'COVID-19'). Smoking status, body mass index and Charlson Comorbidity Index were compared for the two cohorts, as well as for three separate definitions of high risk of severe disease used in the UK (National Health Service Highest Risk, PANORAMIC trial eligibility, UK Health Security Agency Clinical Risk prioritisation for vaccination). RESULTS: Compared with national estimates, CPRD case estimates under-represented older adults in both the primary care (age 65-84: 6% in CPRD vs 9% nationally) and hospitalised (31% vs 40%) cohorts, and over-represented people living in regions with the highest median wealth areas of England (20% primary care and 20% hospital admitted cases in South East vs 15% nationally). The majority of non-hospitalised cases and all hospitalised cases had not completed primary series vaccination. In primary care, persons meeting high-risk definitions were older, more often smokers, overweight or obese, and had higher Charlson Comorbidity Index score. CONCLUSIONS: CPRD primary care data are a robust real-world data source and can be used for some COVID-19 research questions, however, limitations of the data availability should be carefully considered. Included in this publication are supplemental files for a total of over 28 000 codes to define each of three definitions of high risk of severe disease.

Definition and measurement of post-COVID-19 conditions in real-world practice: a global systematic literature review.

Post-COVID-19 conditions (PCC) is an umbrella term that encompasses a range of signs, symptoms and conditions present weeks after the acute phase of a SARS-CoV-2 infection. This systematic literature review summarises the heterogeneous methodology used to measure PCC across real-world studies and highlights trends by region, age group, PCC follow-up period and data source. METHODS: Medline, EMBASE and the Cochrane Library were searched and supplemented with conference and grey literature searches. Eligible studies included individuals with (1) PCC or (2) a positive SARS-CoV-2 test or COVID-19 diagnosis who were followed over time. Included studies were published in English between 1 January 2020 and 14 November 2022. FINDINGS: Of 291 publications included, 175 (60%) followed individuals with confirmed COVID-19 over time for PCC and 116 (40%) used a prespecified PCC definition. There was substantial heterogeneity in study design, geography, age group, PCC conditions/symptoms assessed and their classification and duration of follow-up. Among studies using a prespecified PCC definition, author-defined criteria (51%) were more common than criteria recommended by major public health organisations (19%). Measurement periods for PCC outcomes from date of acute COVID-19 test were primarily 3 to <6 months (39.2%), followed by 6 to <12 months (27.5%) and <3 months (22.9%). When classified by organ/system, constitutional-related PCC were the most frequently assessed in adult (86%) and paediatric (87%) populations. Within constitutional symptoms, fatigue was most frequently assessed in adult (91.6%) and paediatric (95.0%) populations, followed by fever/chills (37.9% and 55%, respectively). CONCLUSIONS: PCC definitions are heterogenous across real-world studies, which limits reliable comparisons between studies. However, some similarities were observed in terms of the most frequently measured PCC-associated symptoms/conditions, which may aid clinical management of patients with PCC.CRD42022376111.

Health Care Resource Utilization and Costs Associated With COVID-19 Among Pediatrics Managed in the Community or Hospital Setting in England: A Population-based Cohort Study.

BACKGROUND: Although COVID-19 morbidity is significantly lower in pediatrics than in adults, the risk of severe COVID-19 may still pose substantial health care resource burden. This study aimed to describe health care resource utilization (HCRU) and costs associated with COVID-19 in pediatrics 1-17 years old in England. METHODS: A population-based retrospective cohort study of pediatrics with COVID-19 using Clinical Practice Research Datalink (CPRD Aurum) primary care data and, where available, linked Hospital Episode Statistics Admitted Patient Care secondary care data. HCRU and associated costs to the National Health Service were stratified by age, risk of severe COVID-19 and immunocompromised status, separately for those with and without hospitalization records (hospitalized cohort: COVID-19 diagnosis August 2020-March 2021; primary care cohort: COVID-19 diagnosis August 2020-January 2022). RESULTS: This study included 564,644 patients in the primary care cohort and 60 in the hospitalized cohort. Primary care consultations were more common in those 1-4 years of age (face-to-face: 4.3%; telephone: 6.0%) compared with those 5-11 (2.0%; 2.1%) and 12-17 years of age (2.2%; 2.5%). In the hospitalized cohort, mean (SD) length of stay was longer [5.0 (5.8) days] among those 12-17 years old (n = 24) than those 1-4 [n = 15; 1.8 (0.9) days] and 5-11 years old [n = 21; 2.8 (2.1) days]. CONCLUSIONS: Most pediatrics diagnosed with COVID-19 were managed in the community. However, hospitalizations were an important driver of HCRU and costs, particularly for those 12-17 years old. Our results may help optimize the management and resource allocation of COVID-19 in this population.

Hospitalization and skilled nursing care are predictors of influenza vaccination among patients on hemodialysis: evidence of confounding by frailty.

BACKGROUND: Observational studies of preventive medications, such as vaccinations, can suffer from the healthy-user bias because vaccinated patients may be healthier than unvaccinated patients. Indicators of health status and frailty suitable for attenuating this bias could be identified in administrative data. OBJECTIVE: To examine the association of baseline variables and time-dependent hospitalization and skilled nursing care with the receipt of influenza vaccination in patients with end-stage renal disease. RESEARCH DESIGN: Observational cohort study using United States Renal Data System files each year from 1999 to 2005. SUBJECTS: Population-based cohorts that included >115,000 adult, hemodialysis patients each year. MEASURES: We estimated hazard ratios for the association of baseline variables and time-dependent hospitalization days and skilled nursing days with influenza vaccination, controlling for demographic and baseline health status variables. RESULTS: Vaccination coverage increased from 47% in 1999 to 60% in 2005. Patients with any length of hospitalization were less likely to be vaccinated, however, the association was stronger in patients with longer stays [15-25 d: hazard ratio=0.64 (95% confidence interval, 0.62-0.65); 26-30 d: 0.40 (0.38-0.42)]. Patients with any length of skilled nursing care of >1 day had similar estimates; these patients were also less likely to be vaccinated [26-30 d: 0.66 (0.64-0.69)]. CONCLUSIONS: Patients with long hospitalizations or skilled nursing stays were less likely to be vaccinated suggesting evidence of the healthy-user effect. These variables could be used to account for bias in studies of preventive services in patients on dialysis.

Controlling for Differential Regression-To-The-Mean via Propensity Scores: A Simulation Study.

Purpose: Regression-to-the-mean (RTM) is a statistical phenomenon that may occur in epidemiologic studies where inclusion in the study cohort is contingent upon experiencing a laboratory/clinical measurement beyond a defined threshold. When differential across treatment groups, RTM could bias the final study estimate. This poses substantial challenges in observational studies that index patients upon experiencing extreme laboratory or clinical values. Our objective was to investigate propensity score-based methods as a tool for mitigating this source of bias via simulation. Methods: We simulated a noninterventional comparative effectiveness study, comparing treatment with romiplostim to standard-of-care therapies for immune thrombocytopenia (ITP), a disease characterized by low platelet counts. Platelet counts were generated from normal distributions according to the underlying ITP severity, a strong confounder of treatment and outcome. Patients were assigned treatment probabilities based upon ITP severity, which created varied levels of differential and non-differential RTM. Treatments were compared via the difference in median platelet counts during 23 weeks of follow-up. We calculated four summary metrics of the platelet counts measured prior to cohort entry and built six propensity score models to adjust for those variables. We adjusted for these summary metrics using inverse probability of treatment weights. Results: Across all simulated scenarios, propensity score adjustment reduced bias and increased precision of the treatment effect estimator. Adjusting for combinations of the summary metrics was most effective at reducing bias. Adjusting for the mean of prior platelet counts or the difference between the cohort-qualifying platelet count and the largest prior count eliminated the most bias when assessed individually. Conclusion: These results suggest that differential RTM could be reasonably addressed by propensity score models with summaries of historical laboratory values. This approach can be easily applied to any comparative effectiveness or safety study, though investigators should carefully consider the best summary metric for their data.

Estimated Effectiveness of Coadministration of the BNT162b2 BA.4/5 COVID-19 Vaccine With Influenza Vaccine.

Importance: No data comparing the estimated effectiveness of coadministering COVID-19 vaccines with seasonal influenza vaccine (SIV) in the community setting exist. Objective: To examine the comparative effectiveness associated with coadministering the BNT162b2 BA.4/5 bivalent mRNA COVID-19 vaccine (BNT162b2-biv [Pfizer BioNTech]) and SIV vs giving each vaccine alone. Design, Setting, and Participants: A retrospective comparative effectiveness study evaluated US adults aged 18 years or older enrolled in commercial health insurance or Medicare Advantage plans and vaccinated with BNT162b2-biv only, SIV only, or both on the same day between August 31, 2022, and January 30, 2023. Individuals with monovalent or another brand of mRNA bivalent COVID-19 vaccine were excluded. Exposure: Same-day coadministration of BNT162b2-biv and SIV; receipt of BNT162b2-biv only (for COVID-19-related outcomes) or SIV only (for influenza-related outcomes) were the comparator groups. For adults aged 65 years or older, only enhanced SIVs were included. Main Outcomes and Measures: COVID-19-related and influenza-related hospitalization, emergency department (ED) or urgent care (UC) encounters, and outpatient visits. Results: Overall, 3 442 996 individuals (57.0% female; mean [SD] age, 65 [16.7] years) were included. A total of 627 735 individuals had BNT162b2-biv and SIV vaccine coadministered, 369 423 had BNT162b2-biv alone, and 2 445 838 had SIV alone. Among those aged 65 years or older (n = 2 210 493; mean [SD] age, 75 [6.7] years; 57.9% female), the coadministration group had a similar incidence of COVID-19-related hospitalization (adjusted hazard ratio [AHR], 1.04; 95% CI, 0.87-1.24) and slightly higher incidence of emergency department or urgent care encounters (AHR, 1.12; 95% CI, 1.02-1.23) and outpatient visits (AHR, 1.06; 95% CI, 1.01-1.11) compared with the BNT162b2-biv-only group. Among individuals aged 18 to 64 years (n = 1 232 503; mean [SD] age, 47 [13.1] years; 55.4% female), the incidence of COVID-19-related outcomes was slightly higher among those who received both vaccines vs BNT162b2-biv alone (AHR point estimate range, 1.14-1.57); however, fewer events overall in this age group resulted in wider CIs. Overall, compared with those who received SIV alone, the coadministration group had a slightly lower incidence of most influenza-related end points (AHR point estimates 0.83-0.93 for those aged ≥65 years vs 0.76-1.08 for those aged 18-64 years). Negative control outcomes suggested residual bias and calibration of COVID-19-related and influenza-related outcomes with negative controls moved all estimates closer to the null, with most CIs crossing 1.00. Conclusions and Relevance: In this study, coadministration of BNT162b2-biv and SIV was associated with generally similar effectiveness in the community setting against COVID-19-related and SIV-related outcomes compared with giving each vaccine alone and may help improve uptake of both vaccines.