A Bayesian nonparametric approach for multiple mediators with applications in mental health studies.

Mediation analysis with contemporaneously observed multiple mediators is a significant area of causal inference. Recent approaches for multiple mediators are often based on parametric models and thus may suffer from model misspecification. Also, much of the existing literature either only allow estimation of the joint mediation effect or estimate the joint mediation effect just as the sum of individual mediator effects, ignoring the interaction among the mediators. In this article, we propose a novel Bayesian nonparametric method that overcomes the two aforementioned drawbacks. We model the joint distribution of the observed data (outcome, mediators, treatment, and confounders) flexibly using an enriched Dirichlet process mixture with three levels. We use standardization (g-computation) to compute all possible mediation effects, including pairwise and all other possible interaction among the mediators. We thoroughly explore our method via simulations and apply our method to a mental health data from Wisconsin Longitudinal Study, where we estimate how the effect of births from unintended pregnancies on later life mental depression (CES-D) among the mothers is mediated through lack of self-acceptance and autonomy, employment instability, lack of social participation, and increased family stress. Our method identified significant individual mediators, along with some significant pairwise effects.

Bayesian semiparametric model for sequential treatment decisions with informative timing.

We develop a Bayesian semiparametric model for the impact of dynamic treatment rules on survival among patients diagnosed with pediatric acute myeloid leukemia (AML). The data consist of a subset of patients enrolled in a phase III clinical trial in which patients move through a sequence of four treatment courses. At each course, they undergo treatment that may or may not include anthracyclines (ACT). While ACT is known to be effective at treating AML, it is also cardiotoxic and can lead to early death for some patients. Our task is to estimate the potential survival probability under hypothetical dynamic ACT treatment strategies, but there are several impediments. First, since ACT is not randomized, its effect on survival is confounded over time. Second, subjects initiate the next course depending on when they recover from the previous course, making timing potentially informative of subsequent treatment and survival. Third, patients may die or drop out before ever completing the full treatment sequence. We develop a generative Bayesian semiparametric model based on Gamma Process priors to address these complexities. At each treatment course, the model captures subjects' transition to subsequent treatment or death in continuous time. G-computation is used to compute a posterior over potential survival probability that is adjusted for time-varying confounding. Using our approach, we estimate the efficacy of hypothetical treatment rules that dynamically modify ACT based on evolving cardiac function.

Marshall Joffe's Contributions to Causal Inference, Biostatistics, and Epidemiology.

We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall's research and their significance for causal inference but also offer a portrayal of Marshall's personal accomplishments and character. Our discussion of Marshall's research notably includes (but is not limited to) handling of posttreatment variables such as noncompliance, employing G-estimation for treatment effects on failure-time outcomes, estimating effects of time-varying exposures subject to time-dependent confounding, and developing a causal framework for case-control studies. We also provide a description of some of Marshall's unpublished work, which is accompanied by a bonus anecdote. We discuss future research directions related to Marshall's research. While Marshall's impact in causal inference and the world outside of it cannot be wholly captured by our words, we hope nonetheless to present some of what he has done for our field and what he has meant to us and to his loved ones.

Place of care in the last three years of life for Medicare beneficiaries.

BACKGROUND: Most older adults prefer aging in place; however, patients with advanced illness often need institutional care. Understanding place of care trajectory patterns may inform patient-centered care planning and health policy decisions. The purpose of this study was to characterize place of care trajectories during the last three years of life. METHODS: Linked administrative, claims, and assessment data were analyzed for a 10% random sample cohort of US Medicare beneficiaries who died in 2018, aged fifty or older, and continuously enrolled in Medicare during their last five years of life. A group-based trajectory modeling approach was used to classify beneficiaries based on the proportion of days of institutional care (hospital inpatient or skilled nursing facility) and skilled home care (home health care and home hospice) used in each quarter of the last three years of life. Associations between group membership and sociodemographic and clinical predictors were evaluated. RESULTS: The analytic cohort included 199,828 Medicare beneficiaries. Nine place of care trajectory groups were identified, which were categorized into three clusters: home, skilled home care, and institutional care. Over half (59%) of the beneficiaries were in the home cluster, spending their last three years mostly at home, with skilled home care and institutional care use concentrated in the final quarter of life. One-quarter (27%) of beneficiaries were in the skilled home care cluster, with heavy use of skilled home health care and home hospice; the remaining 14% were in the institutional cluster, with heavy use of nursing home and inpatient care. Factors associated with both the skilled home care and institutional care clusters were female sex, Black race, a diagnosis of dementia, and Medicaid insurance. Extended use of skilled home care was more prevalent in southern states, and extended institutional care was more prevalent in midwestern states. CONCLUSIONS: This study identified distinct patterns of place of care trajectories that varied in the timing and duration of institutional and skilled home care use during the last three years of life. Clinical, socioregional, and health policy factors influenced where patients received care. Our findings can help to inform personal and societal care planning.

Addressing positivity violations in causal effect estimation using Gaussian process priors.

In observational studies, causal inference relies on several key identifying assumptions. One identifiability condition is the positivity assumption, which requires the probability of treatment be bounded away from 0 and 1. That is, for every covariate combination, it should be possible to observe both treated and control subjects the covariate distributions should overlap between treatment arms. If the positivity assumption is violated, population-level causal inference necessarily involves some extrapolation. Ideally, a greater amount of uncertainty about the causal effect estimate should be reflected in such situations. With that goal in mind, we construct a Gaussian process model for estimating treatment effects in the presence of practical violations of positivity. Advantages of our method include minimal distributional assumptions, a cohesive model for estimating treatment effects, and more uncertainty associated with areas in the covariate space where there is less overlap. We assess the performance of our approach with respect to bias and efficiency using simulation studies. The method is then applied to a study of critically ill female patients to examine the effect of undergoing right heart catheterization.

Decreased Emergency Cholecystectomy and Case Fatality Rate, Not Explained by Expansion of Medicaid.

INTRODUCTION: Population data on longitudinal trends for cholecystectomies and their outcomes are scarce. We evaluated the incidence and case fatality rate of emergency and ambulatory cholecystectomies in New Jersey (NJ) and whether the Medicaid expansion changed trends. MATERIALS AND METHODS: A retrospective population cohort design was used to study the incidence of cholecystectomies and their case fatality rate from 2009 to 2018. Using linear and logistic regression we explored the trends of incidence and the odds of case fatality after versus before the January 1, 2014 Medicaid expansion. RESULTS: Overall, 93,423 emergency cholecystectomies were performed, with 644 fatalities; 87,239 ambulatory cholecystectomies were performed, with fewer than 10 fatalities. The 2009 to 2018 annual incidence of emergency cholecystectomies dropped markedly from 114.8 to 77.5 per 100,000 NJ population (P < 0.0001); ambulatory cholecystectomies increased from 93.5 to 95.6 per 100,000 (P = 0.053). The incidence of emergency cholecystectomies dropped more after than before Medicaid expansion (P < 0.0001). The odds ratio for case fatality among those undergoing emergency cholecystectomies after versus before expansion was 0.85 (95% CI, 0.72-0.99). This decrease in case fatality, apparent only in those over age 65, was not explained by the addition of Medicaid. CONCLUSIONS: A marked decrease in the incidence of emergency cholecystectomies occurred after Medicaid expansion, which was not accounted for by a minimal increase in the incidence of ambulatory cholecystectomies. Case fatality from emergency cholecystectomy decreased over time due to factors other than Medicaid. Further work is needed to reconcile these findings with the previously reported lack of decrease in overall gallstone disease mortality in NJ.

Respiratory Microbiome Disruption and Risk for Ventilator-Associated Lower Respiratory Tract Infection.

BACKGROUND: Ventilator-associated lower respiratory tract infection (VA-LRTI) is common among critically ill patients and has been associated with increased morbidity and mortality. In acute critical illness, respiratory microbiome disruption indices (MDIs) have been shown to predict risk for VA-LRTI, but their utility beyond the first days of critical illness is unknown. We sought to characterize how MDIs previously shown to predict VA-LRTI at initiation of mechanical ventilation change with prolonged mechanical ventilation, and if they remain associated with VA-LRTI risk. METHODS: We developed a cohort of 83 subjects admitted to a long-term acute care hospital due to their prolonged dependence on mechanical ventilation; performed dense, longitudinal sampling of the lower respiratory tract, collecting 1066 specimens; and characterized the lower respiratory microbiome by 16S rRNA sequencing as well as total bacterial abundance by 16S rRNA quantitative polymerase chain reaction. RESULTS: Cross-sectional MDIs, including low Shannon diversity and high total bacterial abundance, were associated with risk for VA-LRTI, but associations had wide posterior credible intervals. Persistent lower respiratory microbiome disruption showed a more robust association with VA-LRTI risk, with each day of (base e) Shannon diversity <2.0 associated with a VA-LRTI odds ratio of 1.36 (95% credible interval, 1.10-1.72). The observed association was consistent across multiple clinical definitions of VA-LRTI. CONCLUSIONS: Cross-sectional MDIs have limited ability to discriminate VA-LRTI risk during prolonged mechanical ventilation, but persistent lower respiratory tract microbiome disruption, best characterized by consecutive days with low Shannon diversity, may identify a population at high risk for infection and may help target infection-prevention interventions.

The Future of Causal Inference.

The past several decades have seen exponential growth in causal inference approaches and their applications. In this commentary, we provide our top-10 list of emerging and exciting areas of research in causal inference. These include methods for high-dimensional data and precision medicine, causal machine learning, causal discovery, and others. These methods are not meant to be an exhaustive list; instead, we hope that this list will serve as a springboard for stimulating the development of new research.

Associations between neighborhood disinvestment and breast cancer outcomes within a populous state registry.

BACKGROUND: Breast cancer (BrCa) outcomes vary by social environmental factors, but the role of built-environment factors is understudied. The authors investigated associations between environmental physical disorder-indicators of residential disrepair and disinvestment-and BrCa tumor prognostic factors (stage at diagnosis, tumor grade, triple-negative [negative for estrogen receptor, progesterone receptor, and HER2 receptor] BrCa) and survival within a large state cancer registry linkage. METHODS: Data on sociodemographic, tumor, and vital status were derived from adult women who had invasive BrCa diagnosed from 2008 to 2017 ascertained from the New Jersey State Cancer Registry. Physical disorder was assessed through virtual neighborhood audits of 23,276 locations across New Jersey, and a personalized measure for the residential address of each woman with BrCa was estimated using universal kriging. Continuous covariates were z scored (mean ± standard deviation [SD], 0 ± 1) to reduce collinearity. Logistic regression models of tumor factors and accelerated failure time models of survival time to BrCa-specific death were built to investigate associations with physical disorder adjusted for covariates (with follow-up through 2019). RESULTS: There were 3637 BrCa-specific deaths among 40,963 women with a median follow-up of 5.3 years. In adjusted models, a 1-SD increase in physical disorder was associated with higher odds of late-stage BrCa (odds ratio, 1.09; 95% confidence interval, 1.02-1.15). Physical disorder was not associated with tumor grade or triple-negative tumors. A 1-SD increase in physical disorder was associated with a 10.5% shorter survival time (95% confidence interval, 6.1%-14.6%) only among women who had early stage BrCa. CONCLUSIONS: Physical disorder is associated with worse tumor prognostic factors and survival among women who have BrCa diagnosed at an early stage.

Dichotomy between the humoral and cellular responses elicited by mRNA and adenoviral vector vaccines against SARS-CoV-2.

BACKGROUND: Protection from severe disease and hospitalization by SARS-CoV-2 vaccination has been amply demonstrated by real-world data. However, the rapidly evolving pandemic raises new concerns. One pertains efficacy of adenoviral vector-based vaccines, particularly the single-dose Ad26.COV2.S, relative to mRNA vaccines. MAIN BODY: We investigated the immunogenicity of Ad26.COV2.S and mRNA vaccines in 33 subjects vaccinated with either vaccine class 5 months earlier on average. After controlling for the time since vaccination, Spike-binding antibody and neutralizing antibody levels were higher in the mRNA-vaccinated subjects, while no significant differences in antigen-specific B cell and T cell responses were observed between the two groups. CONCLUSIONS: A dichotomy exists between the humoral and cellular responses elicited by the two vaccine classes. Testing only for humoral responses to compare the durability of SARS-CoV-2 vaccine-induced responses, as typically performed for public health and research purposes, is insufficient.

Risk of HCC With Hepatitis B Viremia Among HIV/HBV-Coinfected Persons in North America.

BACKGROUND AND AIMS: Chronic HBV is the predominant cause of HCC worldwide. Although HBV coinfection is common in HIV, the determinants of HCC in HIV/HBV coinfection are poorly characterized. We examined the predictors of HCC in a multicohort study of individuals coinfected with HIV/HBV. APPROACH AND RESULTS: We included persons coinfected with HIV/HBV within 22 cohorts of the North American AIDS Cohort Collaboration on Research and Design (1995-2016). First occurrence of HCC was verified by medical record review and/or cancer registry. We used multivariable Cox regression to determine adjusted HRs (aHRs [95% CIs]) of factors assessed at cohort entry (age, sex, race, body mass index), ever during observation (heavy alcohol use, HCV), or time-updated (HIV RNA, CD4+ percentage, diabetes mellitus, HBV DNA). Among 8,354 individuals coinfected with HIV/HBV (median age, 43 years; 93% male; 52.4% non-White), 115 HCC cases were diagnosed over 65,392 person-years (incidence rate, 1.8 [95% CI, 1.5-2.1] events/1,000 person-years). Risk factors for HCC included age 40-49 years (aHR, 1.97 [1.22-3.17]), age ≥50 years (aHR, 2.55 [1.49-4.35]), HCV coinfection (aHR, 1.61 [1.07-2.40]), and heavy alcohol use (aHR, 1.52 [1.04-2.23]), while time-updated HIV RNA >500 copies/mL (aHR, 0.90 [0.56-1.43]) and time-updated CD4+ percentage <14% (aHR, 1.03 [0.56-1.90]) were not. The risk of HCC was increased with time-updated HBV DNA >200 IU/mL (aHR, 2.22 [1.42-3.47]) and was higher with each 1.0 log10 IU/mL increase in time-updated HBV DNA (aHR, 1.18 [1.05-1.34]). HBV suppression with HBV-active antiretroviral therapy (ART) for ≥1 year significantly reduced HCC risk (aHR, 0.42 [0.24-0.73]). CONCLUSION: Individuals coinfected with HIV/HBV on ART with detectable HBV viremia remain at risk for HCC. To gain maximal benefit from ART for HCC prevention, sustained HBV suppression is necessary.

Community- Versus Health Care Organization-Based Approaches to Expanding At-Home COVID-19 Testing in Black and Latino Communities, New Jersey, 2021.

At-home COVID-19 testing offers convenience and safety advantages. We evaluated at-home testing in Black and Latino communities through an intervention comparing community-based organization (CBO) and health care organization (HCO) outreach. From May through December 2021, 1100 participants were recruited, 94% through CBOs. The odds of COVID-19 test requests and completions were significantly higher in the HCO arm. The results showed disparities in test requests and completions related to age, race, language, insurance, comorbidities, and pandemic-related challenges. Despite the popularity of at-home testing, barriers exist in underresourced communities. (Am J Public Health. 2022;112(S9):S918-S922. https://doi.org/10.2105/AJPH.2022.306989).

Determinants and Dynamics of SARS-CoV-2 Infection in a Diverse Population: 6-Month Evaluation of a Prospective Cohort Study.

BACKGROUND: We studied risk factors, antibodies, and symptoms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a diverse, ambulatory population. METHODS: A prospective cohort (n = 831) previously undiagnosed with SARS-CoV-2 infection underwent serial testing (SARS-CoV-2 polymerase chain reaction, immunoglobulin G [IgG]) for 6 months. RESULTS: Ninety-three participants (11.2%) tested SARS-CoV-2-positive: 14 (15.1%) asymptomatic, 24 (25.8%) severely symptomatic. Healthcare workers (n = 548) were more likely to become infected (14.2% vs 5.3%; adjusted odds ratio, 2.1; 95% confidence interval, 1.4-3.3) and severely symptomatic (29.5% vs 6.7%). IgG antibodies were detected after 79% of asymptomatic infections, 89% with mild-moderate symptoms, and 96% with severe symptoms. IgG trajectories after asymptomatic infections (slow increases) differed from symptomatic infections (early peaks within 2 months). Most participants (92%) had persistent IgG responses (median 171 days). In multivariable models, IgG titers were positively associated with symptom severity, certain comorbidities, and hospital work. Dyspnea and neurologic changes (including altered smell/taste) lasted ≥ 120 days in ≥ 10% of affected participants. Prolonged symptoms (frequently more severe) corresponded to higher antibody levels. CONCLUSIONS: In a prospective, ethnically diverse cohort, symptom severity correlated with the magnitude and trajectory of IgG production. Symptoms frequently persisted for many months after infection.Clinical Trials Registration. NCT04336215.

Novel Risk Factors for Progression of Diabetic and Nondiabetic CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study.

RATIONALE & OBJECTIVE: Identification of novel risk factors for chronic kidney disease (CKD) progression may inform mechanistic investigations and improve identification of high-risk subgroups. The current study aimed to characterize CKD progression across levels of numerous risk factors and identify independent risk factors for CKD progression among those with and without diabetes. STUDY DESIGN: The Chronic Renal Insufficiency Cohort (CRIC) Study is a prospective cohort study of adults with CKD conducted at 7 US clinical centers. SETTING & PARTICIPANTS: Participants (N=3,379) had up to 12.3 years of follow-up; 47% had diabetes. PREDICTORS: 30 risk factors for CKD progression across sociodemographic, behavioral, clinical, and biochemical domains at baseline. OUTCOMES: Study outcomes were estimated glomerular filtration rate (eGFR) slope and the composite of halving of eGFR or initiation of kidney replacement therapy. ANALYTICAL APPROACH: Stepwise selection of independent risk factors was performed stratified by diabetes status using linear mixed-effects and Cox proportional hazards models. RESULTS: Among those without and with diabetes, respectively, mean eGFR slope was-1.4±3.3 and-2.7±4.7mL/min/1.73m2 per year. Among participants with diabetes, multivariable-adjusted hazard of the composite outcome was approximately 2-fold or greater with higher levels of the inflammatory chemokine CXCL12, the cardiac marker N-terminal pro-B-type natriuretic peptide (NT-proBNP), and the kidney injury marker urinary neutrophil gelatinase-associated lipocalin (NGAL). Among those without diabetes, low serum bicarbonate and higher high-sensitivity troponin T, NT-proBNP, and urinary NGAL levels were all significantly associated with a 1.5-fold or greater rate of the composite outcome. LIMITATIONS: The observational study design precludes causal inference. CONCLUSIONS: Strong associations for cardiac markers, plasma CXCL12, and urinary NGAL are comparable to that of systolic blood pressure≥140mm Hg, a well-established risk factor for CKD progression. This warrants further investigation into the potential mechanisms that these markers indicate and opportunities to use them to improve risk stratification.

A Bayesian nonparametric model for zero-inflated outcomes: Prediction, clustering, and causal estimation.

Researchers are often interested in predicting outcomes, detecting distinct subgroups of their data, or estimating causal treatment effects. Pathological data distributions that exhibit skewness and zero-inflation complicate these tasks-requiring highly flexible, data-adaptive modeling. In this paper, we present a multipurpose Bayesian nonparametric model for continuous, zero-inflated outcomes that simultaneously predicts structural zeros, captures skewness, and clusters patients with similar joint data distributions. The flexibility of our approach yields predictions that capture the joint data distribution better than commonly used zero-inflated methods. Moreover, we demonstrate that our model can be coherently incorporated into a standardization procedure for computing causal effect estimates that are robust to such data pathologies. Uncertainty at all levels of this model flow through to the causal effect estimates of interest-allowing easy point estimation, interval estimation, and posterior predictive checks verifying positivity, a required causal identification assumption. Our simulation results show point estimates to have low bias and interval estimates to have close to nominal coverage under complicated data settings. Under simpler settings, these results hold while incurring lower efficiency loss than comparator methods. We use our proposed method to analyze zero-inflated inpatient medical costs among endometrial cancer patients receiving either chemotherapy or radiation therapy in the SEER-Medicare database.

A practical introduction to Bayesian estimation of causal effects: Parametric and nonparametric approaches.

Substantial advances in Bayesian methods for causal inference have been made in recent years. We provide an introduction to Bayesian inference for causal effects for practicing statisticians who have some familiarity with Bayesian models and would like an overview of what it can add to causal estimation in practical settings. In the paper, we demonstrate how priors can induce shrinkage and sparsity in parametric models and be used to perform probabilistic sensitivity analyses around causal assumptions. We provide an overview of nonparametric Bayesian estimation and survey their applications in the causal inference literature. Inference in the point-treatment and time-varying treatment settings are considered. For the latter, we explore both static and dynamic treatment regimes. Throughout, we illustrate implementation using off-the-shelf open source software. We hope to leave the reader with implementation-level knowledge of Bayesian causal inference using both parametric and nonparametric models. All synthetic examples and code used in the paper are publicly available on a companion GitHub repository.

Measured and accounted-for confounding in pharmacoepidemiologic studies: Some thoughts for practitioners.

BACKGROUND: Valid causal inference from observational pharmacoepidemiologic studies relies on adequately adjusting for confounding. AIMS: The goal of this article is to provide clarity and guidance on issues related to confounding and provide motivation for using more flexible models for causal inference in pharmacoepidemiology. MATERIALS & METHODS: In this article we elucidate two important components of making valid inference from observational data: measuring the necessary set of variables at the design/data collection phase (measured confounding) and properly accounting for confounding at the modeling/analysis phase (accounted-for confounding). For the latter concept, we contrast parametric modeling approaches, which are susceptible to model misspecification bias, with data adaptive approaches. DISCUSSION: Both measuring and properly accounting for confounding is critical to obtaining valid causal inference from pharmacoepidemiology studies. Carefully thought out DAGs, based on subject matter knowledge, can help to better identify confounders and confounding. Even when confounding has been adequately measured, mis-specified models may lead to unaccounted for confounding and increasing the sample size often does not help. We recommend modern analytic techniques such as flexible data adaptive approaches that do not rely on strong parametric assumptions. Further, sensitivity analyses and other modern bounding approaches are recommended to account for the effects of unmeasured confounding. CONCLUSION: Confounding must be considered at both the design and analysis stages of a study. DAGs and data adaptive approaches can help.

Core concepts in pharmacoepidemiology: Violations of the positivity assumption in the causal analysis of observational data: Consequences and statistical approaches.

In the causal analysis of observational data, the positivity assumption requires that all treatments of interest be observed in every patient subgroup. Violations of this assumption are indicated by nonoverlap in the data in the sense that patients with certain covariate combinations are not observed to receive a treatment of interest, which may arise from contraindications to treatment or small sample size. In this paper, we emphasize the importance and implications of this often-overlooked assumption. Further, we elaborate on the challenges nonoverlap poses to estimation and inference and discuss previously proposed methods. We distinguish between structural and practical violations and provide insight into which methods are appropriate for each. To demonstrate alternative approaches and relevant considerations (including how overlap is defined and the target population to which results may be generalized) when addressing positivity violations, we employ an electronic health record-derived data set to assess the effects of metformin on colon cancer recurrence among diabetic patients.

Validation of a claims-based algorithm for identifying non-infectious pneumonitis in patients diagnosed with lung cancer.

PURPOSE: Non-infectious pneumonitis (NIP) is a common complication of treatments for lung cancer. We know of no existing validated algorithm for identifying NIP in claims databases, limiting our ability to understand the morbidity and mortality of this toxicity in real-world data. METHODS: Electronic health records (EHR), cancer registry, and administrative data from a National Cancer Institute-designated comprehensive cancer center were queried for patients diagnosed with lung cancer between 10/01/2015-12/31/2020. Health insurance claims were searched for ICD-10-CM codes that indicate an inpatient or outpatient diagnosis with possible NIP. A 20-code (Algorithm A) and 11-code (Algorithm B) algorithm were tested with and without requiring prescription with corticosteroids. Cases with a diagnosis of possible NIP in the 6 months before their first lung cancer diagnosis were excluded. The algorithms were validated by reviewing the EHR. The positive predictive value (PPV) for each algorithm was computed with 95% confidence intervals (CI). RESULTS: Seventy patients with lung cancer had a diagnosis code compatible with NIP: 36 (51.4%) inpatients and 34 (48.6%) outpatients. The PPV of Algorithm A was 77.1% (95% CI: 65.6-86.3). The PPV of Algorithm B was 86.9% (95% CI: 75.8-94.2). Requiring a documented prescription for a systemic corticosteroid improved the PPV of both Algorithm A and Algorithm B: 92.5% (95% CI: 79.6-98.4) and 100.0% (95% CI: 90.0-100.0), respectively. CONCLUSIONS: This study validated ICD-10-CM and prescription-claims-based definitions of NIP in lung cancer patients. All algorithms have at least reasonable performance. Enriching the algorithm with corticosteroid prescription records results in excellent performance.

Prevalence of SARS-CoV-2 infection in previously undiagnosed health care workers in New Jersey, at the onset of the U.S. COVID-19 pandemic.

BACKGROUND: Healthcare workers (HCW) are presumed to be at increased risk of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection due to occupational exposure to infected patients. However, there has been little epidemiological research to assess these risks. METHODS: We conducted a prospective cohort study of HCW (n = 546) and non-healthcare workers (NHCW; n = 283) with no known prior SARS-CoV-2 infection who were recruited from a large U.S. university and two affiliated university hospitals. In this cross-sectional analysis of data collected at baseline, we examined SARS-CoV-2 infection status (as determined by presence of SARS-CoV-2 RNA in oropharyngeal swabs) by healthcare worker status and role. RESULTS: At baseline, 41 (5.0%) of the participants tested positive for SARS-CoV-2 infection, of whom 14 (34.2%) reported symptoms. The prevalence of SARS-CoV-2 infection was higher among HCW (7.3%) than in NHCW (0.4%), representing a 7.0% greater absolute risk (95% confidence interval for risk difference 4.7, 9.3%). The majority of infected HCW (62.5%) were nurses. Positive tests increased across the two weeks of cohort recruitment in line with rising confirmed cases in the hospitals and surrounding counties. CONCLUSIONS: Overall, our results demonstrate that HCW had a higher prevalence of SARS-CoV-2 infection than NHCW. Continued follow-up of this cohort will enable us to monitor infection rates and examine risk factors for transmission.