Contrasting cumulative risk and multiple individual risk models of the relationship between Adverse Childhood Experiences (ACEs) and adult health outcomes (original) (raw)
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Impact of adverse childhood experiences on quality-adjusted life expectancy in the U.S. population
Child Abuse & Neglect, 2020
Background: Adverse childhood experiences (ACEs) adversely impact morbidity and mortality. Objective: To quantify burden of disease associated with ACEs among U.S. adults by estimating quality-adjusted life expectancy (QALE) according to number of ACEs reported. Participants and setting: Data from respondents' adverse experiences occurring before age 18 were collected in nine states through the 2011 and 2012 Behavioral Risk Factor Surveillance System (BRFSS). Methods: We estimated health-related quality of life (HRQOL) scores from BRFSS data. We constructed life tables from the Compressed Mortality Files to calculate QALE, a generalization of life expectancy that weights expected years of life lived with the HRQOL score, according to number of ACEs. Results: The QALE for an 18-year-old person reporting 0, 1-2, and 3+ ACEs was 55.1, 53.4, and 45.6 years, respectively. Reporting 3+ ACEs was associated with a 9.5-year decrease (17%) in QALE. The adverse impact of ACEs are present according to age, gender, and race/ethnicity subgroups. The impact of 3+ ACEs on QALE was nearly 3-fold greater for women than men (13.2 vs. 4.7-year decrease). By contrast, an 18-year-old reporting 1-2 ACEs experienced a small decrease in QALE (1.7 years). Conclusions: Reporting 3+ ACEs led to a significant burden of disease, as assessed by QALE loss, to a similar degree as many other well-established behavioral risk factors and chronic conditions. Providers and policymakers should focus on efforts to prevent ACEs, initiate early detection of and interventions to minimize the impact of an ACE, and reduce the likelihood of engaging in maladaptive risky behaviors.
Cumulative Risk, Cumulative Outcome: A 20-Year Longitudinal Study
PloS one, 2015
Cumulative risk (CR) models provide some of the most robust findings in the developmental literature, predicting numerous and varied outcomes. Typically, however, these outcomes are predicted one at a time, across different samples, using concurrent designs, longitudinal designs of short duration, or retrospective designs. We predicted that a single CR index, applied within a single sample, would prospectively predict diverse outcomes, i.e., depression, intelligence, school dropout, arrest, smoking, and physical disease from childhood to adulthood. Further, we predicted that number of risk factors would predict number of adverse outcomes (cumulative outcome; CO). We also predicted that early CR (assessed at age 5/6) explains variance in CO above and beyond that explained by subsequent risk (assessed at ages 12/13 and 19/20). The sample consisted of 284 individuals, 48% of whom were diagnosed with a speech/language disorder. Cumulative risk, assessed at 5/6-, 12/13-, and 19/20-years-...
Developing and validating risk prediction models in an individual participant data meta-analysis
BMC Medical Research Methodology, 2014
Background Risk prediction models estimate the risk of developing future outcomes for individuals based on one or more underlying characteristics (predictors). We review how researchers develop and validate risk prediction models within an individual participant data (IPD) meta-analysis, in order to assess the feasibility and conduct of the approach. Methods A qualitative review of the aims, methodology, and reporting in 15 articles that developed a risk prediction model using IPD from multiple studies. Results The IPD approach offers many opportunities but methodological challenges exist, including: unavailability of requested IPD, missing patient data and predictors, and between-study heterogeneity in methods of measurement, outcome definitions and predictor effects. Most articles develop their model using IPD from all available studies and perform only an internal validation (on the same set of data). Ten of the 15 articles did not allow for any study differences in baseline risk...
Annals of Epidemiology, 2002
In cohort studies of common outcomes, odds ratios (ORs) may seriously overestimate the true effect of an exposure on the outcome of interest (as measured by the risk ratio [RR]). Since few study designs require ORs (most frequently, case-control studies), their popularity is due to the widespread use of logistic regression. Because ORs are used to approximate RRs so frequently, methods have been published in the general medical literature describing how to convert ORs to RRs; however, these methods may produce inaccurate confidence intervals (CIs). The authors explore the use of binomial regression as an alternative technique to directly estimate RRs and associated CIs in cohort studies of common outcomes. METHODS: Using actual study data, the authors describe how to perform binomial regression using the SAS System for Windows, a statistical analysis program widely used by US health researchers. RESULTS: In a sample data set, the OR for the exposure of interest overestimated the RR more than twofold. The 95% CIs for the OR and converted RR were wider than for the directly estimated RR. CONCLUSIONS: The authors argue that for cohort studies, the use of logistic regression should be sharply curtailed, and that instead, binomial regression be used to directly estimate RRs and associated CIs.
Risk adjustment and outcome research. Part I
Journal of Cardiovascular Medicine, 2006
Objective The increasing demand for comparative evaluation of outcomes requires the development and diffusion of epidemiologic research, the ability to correctly formulate hypotheses, to conduct analyses and to interpret the results. The purpose of this paper is to provide a detailed but easy-reading review of epidemiologic methods to compare healthcare outcomes, particularly riskadjustment methods. Methods The paper is divided into three parts. Part I describes confounding in observational studies, the ways confounding is identified and controlled (propensity adjustment and risk adjustment), and the methods for constructing the severity measures in risk-adjustment procedures. Conclusions It is becoming increasingly important for policy makers and planners to identify which factors may improve or worsen the effectiveness of treatments and services and to compare the performances of providers. Politicians, managers, epidemiologists, and clinicians should make their decisions based on the validity and precision of study results, by using the best scientific knowledge available. The statistical methods described in this review cannot measure 'reality' as it 'truly' is, but can produce 'images' of it, defining limits and uncertainties in terms of validity and precision. Studies that use credible risk-adjustment strategies are more likely to yield reliable and applicable findings.
Assessing Risk Prediction Models Using Individual Participant Data From Multiple Studies
American Journal of Epidemiology, 2014
Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from casecontrol studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous.
Nephrology Dialysis Transplantation, 2013
Regression models are being used to quantify the effect of an exposure on an outcome, while adjusting for potential confounders. While the type of regression model to be used is determined by the nature of the outcome variable, e.g. linear regression has to be applied for continuous outcome variables, all regression models can handle any kind of exposure variables. However, some fundamentals of representation of the exposure in a regression model and also some potential pitfalls have to be kept in mind in order to obtain meaningful interpretation of results. The objective of this educational paper was to illustrate these fundamentals and pitfalls, using various multiple regression models applied to data from a hypothetical cohort of 3000 patients with chronic kidney disease. In particular, we illustrate how to represent different types of exposure variables (binary, categorical with two or more categories and continuous), and how to interpret the regression coefficients in linear, logistic and Cox models. We also discuss the linearity assumption in these models, and show how wrongly assuming linearity may produce biased results and how flexible modelling using spline functions may provide better estimates.