Trajectory analysis of rhinitis in a birth cohort from lower-income New York City neighborhoods.

BACKGROUND: Rhinitis is a prevalent, chronic nasal condition associated with asthma. However, its developmental trajectories remain poorly characterized. OBJECTIVE: We sought to describe the course of rhinitis from infancy to adolescence and the association between identified phenotypes, asthma-related symptoms, and physician-diagnosed asthma. METHODS: We collected rhinitis data from questionnaires repeated across 22 time points among 688 children from infancy to age 11 years and used latent class mixed modeling (LCMM) to identify phenotypes. Once children were between ages 5 and 12, a study physician determined asthma diagnosis. We collected information on the following asthma symptoms: any wheeze, exercise-induced wheeze, nighttime coughing, and emergency department visits. For each, we used LCMM to identify symptom phenotypes. Using logistic regression, we described the association between rhinitis phenotype and asthma diagnosis and each symptom overall and stratified by atopic predisposition and sex. RESULTS: LCMM identified 5 rhinitis trajectory groups: never/infrequent; transient; late onset, infrequent; late onset, frequent; and persistent. LCMM identified 2 trajectories for each symptom, classified as frequent and never/infrequent. Participants with persistent and late onset, frequent phenotypes were more likely to be diagnosed with asthma and to have the frequent phenotype for all symptoms (P < .01). We identified interaction between seroatopy and rhinitis phenotype for physician-diagnosed asthma (P = .04) and exercise-induced wheeze (P = .08). Severe seroatopy was more common among children with late onset, frequent and persistent rhinitis, with nearly 25% of these 2 groups exhibiting sensitivity to 4 or 5 of the 5 allergens tested. CONCLUSIONS: In this prospective, population-based birth cohort, persistent and late onset, frequent rhinitis phenotypes were associated with increased risk of asthma diagnosis and symptoms during adolescence.

Historical Redlining, Social Determinants of Health, and Stroke Prevalence in Communities in New York City.

Importance: Historical redlining was a discriminatory housing policy that placed financial services beyond the reach of residents in inner-city communities. The extent of the impact of this discriminatory policy on contemporary health outcomes remains to be elucidated. Objective: To evaluate the associations among historical redlining, social determinants of health (SDOH), and contemporary community-level stroke prevalence in New York City. Design, Setting, and Participants: An ecological, retrospective, cross-sectional study was conducted using New York City data from January 1, 2014, to December 31, 2018. Data from the population-based sample were aggregated on the census tract level. Quantile regression analysis and a quantile regression forests machine learning model were used to determine the significance and overall weight of redlining in relation to other SDOH on stroke prevalence. Data were analyzed from November 5, 2021, to January 31, 2022. Exposures: Social determinants of health included race and ethnicity, median household income, poverty, low educational attainment, language barrier, uninsurance rate, social cohesion, and residence in an area with a shortage of health care professionals. Other covariates included median age and prevalence of diabetes, hypertension, smoking, and hyperlipidemia. Weighted scores for historical redlining (ie, the discriminatory housing policy in effect from 1934 to 1968) were computed using the mean proportion of original redlined territories overlapped on 2010 census tract boundaries in New York City. Main Outcomes and Measures: Stroke prevalence was collected from the Centers for Disease Control and Prevention 500 Cities Project for adults 18 years and older from 2014 to 2018. Results: A total of 2117 census tracts were included in the analysis. After adjusting for SDOH and other relevant covariates, the historical redlining score was independently associated with a higher community-level stroke prevalence (odds ratio [OR], 1.02 [95% CI, 1.02-1.05]; P < .001). Social determinants of health that were positively associated with stroke prevalence included educational attainment (OR, 1.01 [95% CI, 1.01-1.01]; P < .001), poverty (OR, 1.01 [95% CI, 1.01-1.01]; P < .001), language barrier (OR, 1.00 [95% CI, 1.00-1.00]; P < .001), and health care professionals shortage (OR, 1.02 [95% CI, 1.00-1.04]; P = .03). Conclusions and Relevance: This cross-sectional study found that historical redlining was associated with modern-day stroke prevalence in New York City independently of contemporary SDOH and community prevalence of some relevant cardiovascular risk factors.

A FLEXIBLE SENSITIVITY ANALYSIS APPROACH FOR UNMEASURED CONFOUNDING WITH MULTIPLE TREATMENTS AND A BINARY OUTCOME WITH APPLICATION TO SEER-MEDICARE LUNG CANCER DATA.

In the absence of a randomized experiment, a key assumption for drawing causal inference about treatment effects is the ignorable treatment assignment. Violations of the ignorability assumption may lead to biased treatment effect estimates. Sensitivity analysis helps gauge how causal conclusions will be altered in response to the potential magnitude of departure from the ignorability assumption. However, sensitivity analysis approaches for unmeasured confounding in the context of multiple treatments and binary outcomes are scarce. We propose a flexible Monte Carlo sensitivity analysis approach for causal inference in such settings. We first derive the general form of the bias introduced by unmeasured confounding, with emphasis on theoretical properties uniquely relevant to multiple treatments. We then propose methods to encode the impact of unmeasured confounding on potential outcomes and adjust the estimates of causal effects in which the presumed unmeasured confounding is removed. Our proposed methods embed nested multiple imputation within the Bayesian framework, which allow for seamless integration of the uncertainty about the values of the sensitivity parameters and the sampling variability, as well as use of the Bayesian Additive Regression Trees for modeling flexibility. Expansive simulations validate our methods and gain insight into sensitivity analysis with multiple treatments. We use the SEER-Medicare data to demonstrate sensitivity analysis using three treatments for early stage non-small cell lung cancer. The methods developed in this work are readily available in the R package SAMTx.