Mediators of the Association Between Socioeconomic Status and Survival After Out-of-Hospital Cardiac Arrest: A Systematic Review.

Low socioeconomic status (SES) is associated with poor outcomes after out-of-hospital cardiac arrest (OHCA). Patient characteristics, care processes, and other contextual factors may mediate the association between SES and survival after OHCA. Interventions that target these mediating factors may reduce disparities in OHCA outcomes across the socioeconomic spectrum. This systematic review identified and quantified mediators of the SES-survival after OHCA association. Electronic databases (MEDLINE, Embase, PubMed, Web of Science) and grey literature sources were searched from inception to July or August 2023. Observational studies of OHCA patients that conducted mediation analyses to evaluate potential mediators of the association between SES (defined by income, education, occupation, or a composite index) and survival outcomes were included. A total of 10 studies were included in this review. Income (n = 9), education (n = 4), occupation (n = 1), and composite indices (n = 1) were used to define SES. The proportion of OHCA cases that had bystander involvement, presented with an initial shockable rhythm, and survived to hospital discharge or 30 days increased with higher SES. Common mediators of the SES-survival association that were evaluated included initial rhythm (n = 6), emergency medical services response time (n = 5), and bystander cardiopulmonary resuscitation (n = 4). Initial rhythm was the most important mediator of this association, with a median percent excess risk explained of 37.4% (range 28.6%-40.0%; n = 5; 1 study reported no mediation) and mediation proportion of 41.8% (n = 1). To mitigate socioeconomic disparities in outcomes after OHCA, interventions should target potentially modifiable mediators, such as initial rhythm, which may involve improving bystander awareness of OHCA and the need for prompt resuscitation.

Long-term Trajectories of Physical Function Decline in Women With and Without Cancer.

Importance: Patients with cancer experience acute declines in physical function, hypothesized to reflect accelerated aging driven by cancer-related symptoms and effects of cancer therapies. No study has examined long-term trajectories of physical function by cancer site, stage, or treatment compared with cancer-free controls. Objective: Examine trajectories of physical function a decade before and after cancer diagnosis among older survivors and cancer-free controls. Design, Setting, and Participants: This prospective cohort study enrolled patients from 1993 to 1998 and followed up until December 2020. The Women's Health Initiative, a diverse cohort of postmenopausal women, included 9203 incident cancers (5989 breast, 1352 colorectal, 960 endometrial, and 902 lung) matched to up to 5 controls (n = 45 358) on age/year of enrollment and study arm. Exposures: Cancer diagnosis (site, stage, and treatment) via Medicare and medical records. Main Outcomes and Measures: Trajectories of self-reported physical function (RAND Short Form 36 [RAND-36] scale; range: 0-100, higher scores indicate superior physical function) estimated from linear mixed effects models with slope changes at diagnosis and 1-year after diagnosis. Results: This study included 9203 women with cancer and 45 358 matched controls. For the women with cancer, the mean (SD) age at diagnosis was 73.0 (7.6) years. Prediagnosis, physical function declines of survivors with local cancers were similar to controls; after diagnosis, survivors experienced accelerated declines relative to controls, whose scores declined 1 to 2 points per year. Short-term declines in the year following diagnosis were most severe in women with regional disease (eg, -5.3 [95% CI, -6.4 to -4.3] points per year in regional vs -2.8 [95% CI, -3.4 to -2.3] for local breast cancer) or who received systemic therapy (eg, for local endometrial cancer, -7.9 [95% CI, -12.2 to -3.6] points per year with any chemotherapy; -3.1 [95% CI, -6.0 to -0.3] with radiation therapy alone; and -2.6 [95% CI, -4.2 to -1.0] with neither, respectively). While rates of physical function decline slowed in the later postdiagnosis period (eg, women with regional colorectal cancer declined -4.3 [95% CI, -5.9 to -2.6] points per year in the year following diagnosis vs -1.4 [95% CI, -1.7 to -1.0] points per year in the decade thereafter), survivors had estimated physical function significantly below that of age-matched controls 5 years after diagnosis. Conclusions and Relevance: In this prospective cohort study, survivors of cancer experienced accelerated declines in physical function after diagnosis, and physical function remained below that of age-matched controls even years later. Patients with cancer may benefit from supportive interventions to preserve physical functioning.

Monte Carlo Simulation Approaches for Quantitative Bias Analysis: A Tutorial.

Quantitative bias analysis can be used to empirically assess how far study estimates are from the truth (i.e., an estimate that is free of bias). These methods can be used to explore the potential impact of confounding bias, selection bias (collider stratification bias), and information bias. Quantitative bias analysis includes methods that can be used to check the robustness of study findings to multiple types of bias and methods that use simulation studies to generate data and understand the hypothetical impact of specific types of bias in a simulated data set. In this article, we review 2 strategies for quantitative bias analysis: 1) traditional probabilistic quantitative bias analysis and 2) quantitative bias analysis with generated data. An important difference between the 2 strategies relates to the type of data (real vs. generated data) used in the analysis. Monte Carlo simulations are used in both approaches, but the simulation process is used for different purposes in each. For both approaches, we outline and describe the steps required to carry out the quantitative bias analysis and also present a bias-analysis tutorial demonstrating how both approaches can be applied in the context of an analysis for selection bias. Our goal is to highlight the utility of quantitative bias analysis for practicing epidemiologists and increase the use of these methods in the epidemiologic literature.

Can Survival Bias Explain the Age Attenuation of Racial Inequalities in Stroke Incidence?: A Simulation Study.

BACKGROUND: In middle age, stroke incidence is higher among black than white Americans. For unknown reasons, this inequality decreases and reverses with age. We conducted simulations to evaluate whether selective survival could account for observed age patterning of black-white stroke inequalities. METHODS: We simulated birth cohorts of 20,000 blacks and 20,000 whites with survival distributions based on US life tables for the 1919-1921 birth cohort. We generated stroke incidence rates for ages 45-94 years using Reasons for Geographic and Racial Disparities in Stroke (REGARDS) study rates for whites and setting the effect of black race on stroke to incidence rate difference (IRD) = 20/10,000 person-years at all ages, the inequality observed at younger ages in REGARDS. We compared observed age-specific stroke incidence across scenarios, varying effects of U, representing unobserved factors influencing mortality and stroke risk. RESULTS: Despite a constant adverse effect of black race on stroke risk, the observed black-white inequality in stroke incidence attenuated at older age. When the hazard ratio for U on stroke was 1.5 for both blacks and whites, but U only directly influenced mortality for blacks (hazard ratio for U on mortality =1.5 for blacks; 1.0 for whites), stroke incidence rates in late life were lower among blacks (average observed IRD = -43/10,000 person-years at ages 85-94 years versus causal IRD = 20/10,000 person-years) and mirrored patterns observed in REGARDS. CONCLUSIONS: A relatively moderate unmeasured common cause of stroke and survival could fully account for observed age attenuation of racial inequalities in stroke.