Accommodating detection limits of multiple exposures in environmental mixture analyses: an overview of statistical approaches.

BACKGROUND: Identifying the impact of environmental mixtures on human health is an important topic. However, such studies face challenges when exposure measurements lie below limit of detection (LOD). While various approaches for accommodating a single exposure subject to LOD have been used, their impact on mixture analysis has not been thoroughly investigated. Our study aims to understand the impact of five popular LOD accommodation approaches on mixture analysis results with multiple exposures subject to LOD, including omitting subjects with any exposures below LOD (complete case analysis); single imputations by LOD/ 2 , and by estimates from a censored accelerated failure time (AFT) model; and multiple imputation (MI) with or without truncation based on LOD. METHODS: In extensive simulation studies with high-dimensional and highly correlated exposures and a continuous health outcome, we examined the performance of each LOD approach on three mixture analysis methods: elastic net regression, weighted quantile sum regression (WQS) and Bayesian kernel machine regression (BKMR). We further analyzed data from the National Health and Nutrition Examination Survey (NHANES) on how persistent organic pollutants (POPs) influenced leukocyte telomere length (LTL). RESULTS: Complete case analysis was inefficient and could result in severe bias for some mixture methods. Imputation by LOD/ 2 showed unstable performance across mixture methods. Conventional MI was associated with consistent mild biases, which can be reduced by using a truncated distribution for imputation. Estimating censored values by AFT models had a minimal impact on the results. In the NHANES analysis, imputation by LOD/ 2 , truncated MI and censored AFT models performed similarly, with a positive overall effect of POPs on LTL while PCB126, PCB169 and furan 2,3,4,7,8-pncdf being the most important exposures. CONCLUSIONS: Our study favored using truncated MI and censored AFT models to accommodate values below LOD for the stability of downstream mixture analysis.

Partial-linear single-index transformation models with censored data.

In studies with time-to-event outcomes, multiple, inter-correlated, and time-varying covariates are commonly observed. It is of great interest to model their joint effects by allowing a flexible functional form and to delineate their relative contributions to survival risk. A class of semiparametric transformation (ST) models offers flexible specifications of the intensity function and can be a general framework to accommodate nonlinear covariate effects. In this paper, we propose a partial-linear single-index (PLSI) transformation model that reduces the dimensionality of multiple covariates into a single index and provides interpretable estimates of the covariate effects. We develop an iterative algorithm using the regression spline technique to model the nonparametric single-index function for possibly nonlinear joint effects, followed by nonparametric maximum likelihood estimation. We also propose a nonparametric testing procedure to formally examine the linearity of covariate effects. We conduct Monte Carlo simulation studies to compare the PLSI transformation model with the standard ST model and apply it to NYU Langone Health de-identified electronic health record data on COVID-19 hospitalized patients' mortality and a Veteran's Administration lung cancer trial.

National Assessment of Opportunities for Improvement in Preventable Trauma Deaths: A Mixed-Methods Study.

Trauma is a significant public health issue worldwide, particularly affecting economically active age groups. Quality management of trauma care at the national level is crucial to improve outcomes of major trauma. In Korea, a biennial nationwide survey on preventable trauma death rate is conducted. Based on the survey results, we analyzed opportunities for improving the trauma treatment process. Expert panels reviewed records of 8282 and 8482 trauma-related deaths in 2017 and 2019, respectively, identifying 258 and 160 cases in each year as preventable deaths. Opportunities for improvement were categorized into prehospital, interhospital, and hospital stages. Hemorrhage was the primary cause of death, followed by sepsis/multiorgan failure and central nervous system injury. Delayed hemostatic procedures and transfusions were common areas for improvement in hospital stage. Interhospital transfers experienced significant delays in arrival time. This study emphasizes the need to enhance trauma care by refining treatment techniques, centralizing patients in specialized facilities, and implementing comprehensive reviews and performance improvements throughout the patient transfer system. The findings offer valuable insights for addressing trauma care improvement from both clinical and systemic perspectives.

Goodness-of-fit two-phase sampling designs for time-to-event outcomes: a simulation study based on New York University Women's Health Study for breast cancer.

BACKGROUND: Sub-cohort sampling designs such as a case-cohort study play a key role in studying biomarker-disease associations due to their cost effectiveness. Time-to-event outcome is often the focus in cohort studies, and the research goal is to assess the association between the event risk and risk factors. In this paper, we propose a novel goodness-of-fit two-phase sampling design for time-to-event outcomes when some covariates (e.g., biomarkers) can only be measured on a subgroup of study subjects. METHODS: Assuming that an external model, which can be the well-established risk models such as the Gail model for breast cancer, Gleason score for prostate cancer, and Framingham risk models for heart diseases, or built from preliminary data, is available to relate the outcome and complete covariates, we propose to oversample subjects with worse goodness-of-fit (GOF) based on an external survival model and time-to-event. With the cases and controls sampled using the GOF two-phase design, the inverse sampling probability weighting method is used to estimate the log hazard ratio of both incomplete and complete covariates. We conducted extensive simulations to evaluate the efficiency gain of our proposed GOF two-phase sampling designs over case-cohort study designs. RESULTS: Through extensive simulations based on a dataset from the New York University Women's Health Study, we showed that the proposed GOF two-phase sampling designs were unbiased and generally had higher efficiency compared to the standard case-cohort study designs. CONCLUSION: In cohort studies with rare outcomes, an important design question is how to select informative subjects to reduce sampling costs while maintaining statistical efficiency. Our proposed goodness-of-fit two-phase design provides efficient alternatives to standard case-cohort designs for assessing the association between time-to-event outcome and risk factors. This method is conveniently implemented in standard software.

Trauma system establishment and outcome improvement: a retrospective national cohort study in South Korea.

BACKGROUND: Trauma is a major cause of mortality, disability, and health care costs worldwide. The establishment of a trauma system is known to solve these problems, but few studies have objectively evaluated the impact of a trauma system on outcomes. Since 2012, South Korea has established a national trauma system based on the implementation of 17 regional trauma centers nationwide and the improvement of the prehospital transfer system. This study aimed to measure the changes in performance and outcome according to the established national trauma system. MATERIAL AND METHODS: In this national cohort-based, retrospective follow-up observational study, the authors calculated the preventable trauma death rate (PTDR) by conducting a multipanel review of patients who died in 2015, 2017, and 2019. Furthermore, the authors constructed a risk-adjusted mortality prediction model of 4 767 876 patients between 2015 and 2019 using the extended-International Classification of Disease Injury Severity Scores to compare outcomes. RESULTS: The PTDR was lower in 2019 than in 2015 (15.7 vs. 30.5, P <0.001) and 2017 (15.7 vs. 19.9%, P <0.001) representing 1247 additional lives saved in 2019 compared to that in 2015. In the risk-adjusted model, total trauma mortality was highest in 2015 at 0.56%, followed by that in 2016 and 2017 (0.50%), 2018 (0.51%), and 2019 (0.48%), revealing a significant decrease in mortality over the years ( P <0.001 for trend), representing nearly 800 additional lives saved. The number of deaths for more severe patients with a probability of survival less than 0.25 significantly decreased from 81.50% in 2015 to 66.17% in 2019 ( P <0.001). CONCLUSIONS: The authors observed a significant reduction in the PTDR and risk-adjusted trauma mortality in the 5-year follow-up since 2015 when the national trauma system was established. These findings could serve as a model for low-income and middle-income countries, where trauma systems are not yet established.

National Follow-up Survey of Preventable Trauma Death Rate in Korea.

BACKGROUND: The preventable trauma death rate survey is a basic tool for the quality management of trauma treatment because it is a method that can intuitively evaluate the level of national trauma treatment. We conducted this study as a national biennial follow-up survey project and report the results of the review of the 2019 trauma death data in Korea. METHODS: From January 1, 2019 to December 31, 2019, of a total of 8,482 trauma deaths throughout the country, 1,692 were sampled from 279 emergency medical institutions in Korea. All cases were evaluated for preventability of death and opportunities for improvement using a multidisciplinary panel review approach. RESULTS: The preventable trauma death rate was estimated to be 15.7%. Of these, 3.1% were judged definitive preventable deaths, and 12.7% were potentially preventable deaths. The odds ratio for preventable traumatic death was 2.56 times higher in transferred patients compared to that of patients who visited the final hospital directly. The group that died 1 hour after the accident had a statistically significantly higher probability of preventable death than that of the group that died within 1 hour after the accident. CONCLUSION: The preventable trauma death rate for trauma deaths in 2019 was 15.7%, which was 4.2%p lower than that in 2017. To improve the quality of trauma treatment, the transfer of severe trauma patients to trauma centers should be more focused.

Dynamic Metabolic Risk Profiling of World Trade Center Lung Disease: A Longitudinal Cohort Study.

Rationale: Metabolic syndrome (MetSyn) increases the risk of World Trade Center (WTC) lung injury (LI). However, the temporal relationship of MetSyn, exposure intensity, and lung dysfunction is not well understood. Objective: To model the association of longitudinal MetSyn characteristics with WTC lung disease to define modifiable risk. Methods: Firefighters, for whom consent was obtained (N = 5,738), were active duty on September 11, 2001 (9/11). WTC-LI (n = 1,475; FEV1% predicted

Empirical evaluation of sub-cohort sampling designs for risk prediction modeling.

Sub-cohort sampling designs, such as nested case-control (NCC) and case-cohort (CC) studies, have been widely used to estimate biomarker-disease associations because of their cost effectiveness. These designs have been well studied and shown to maintain relatively high efficiency compared to full-cohort designs, but their performance of building risk prediction models has been less studied. Moreover, sub-cohort sampling designs often use matching (or stratifying) to further control for confounders or to reduce measurement error. Their predictive performance depends on both the design and matching procedures. Based on a dataset from the NYU Women's Health Study (NYUWHS), we performed Monte Carlo simulations to systematically evaluate risk prediction performance under NCC, CC, and full-cohort studies. Our simulations demonstrate that sub-cohort sampling designs can have predictive accuracy (i.e. discrimination and calibration) similar to that of the full-cohort design, but could be sensitive to the matching procedure used. Our results suggest that researchers can have the option of performing NCC and CC studies with huge potential benefits in cost and resources, but need to pay particular attention to the matching procedure when developing a risk prediction model in biomarker studies.

A family of partial-linear single-index models for analyzing complex environmental exposures with continuous, categorical, time-to-event, and longitudinal health outcomes.

BACKGROUND: Statistical methods to study the joint effects of environmental factors are of great importance to understand the impact of correlated exposures that may act synergistically or antagonistically on health outcomes. This study proposes a family of statistical models under a unified partial-linear single-index (PLSI) modeling framework, to assess the joint effects of environmental factors for continuous, categorical, time-to-event, and longitudinal outcomes. All PLSI models consist of a linear combination of exposures into a single index for practical interpretability of relative direction and importance, and a nonparametric link function for modeling flexibility. METHODS: We presented PLSI linear regression and PLSI quantile regression for continuous outcome, PLSI generalized linear regression for categorical outcome, PLSI proportional hazards model for time-to-event outcome, and PLSI mixed-effects model for longitudinal outcome. These models were demonstrated using a dataset of 800 subjects from NHANES 2003-2004 survey including 8 environmental factors. Serum triglyceride concentration was analyzed as a continuous outcome and then dichotomized as a binary outcome. Simulations were conducted to demonstrate the PLSI proportional hazards model and PLSI mixed-effects model. The performance of PLSI models was compared with their counterpart parametric models. RESULTS: PLSI linear, quantile, and logistic regressions showed similar results that the 8 environmental factors had both positive and negative associations with triglycerides, with a-Tocopherol having the most positive and trans-b-carotene having the most negative association. For the time-to-event and longitudinal settings, simulations showed that PLSI models could correctly identify directions and relative importance for the 8 environmental factors. Compared with parametric models, PLSI models got similar results when the link function was close to linear, but clearly outperformed in simulations with nonlinear effects. CONCLUSIONS: We presented a unified family of PLSI models to assess the joint effects of exposures on four commonly-used types of outcomes in environmental research, and demonstrated their modeling flexibility and effectiveness, especially for studying environmental factors with mixed directional effects and/or nonlinear effects. Our study has expanded the analytical toolbox for investigating the complex effects of environmental factors. A practical contribution also included a coherent algorithm for all proposed PLSI models with R codes available.

Enrollment in the Zoster Eye Disease Study.

PURPOSE: To present the results of a survey of the Zoster Eye Disease Study (ZEDS) investigators regarding barriers to the enrollment of study participants and approaches to overcome them. METHODS: ZEDS is a multicenter randomized clinical trial supported by the National Eye Institute to determine whether prolonged suppressive valacyclovir reduces the complications of herpes zoster ophthalmicus (HZO), relative to placebo. Enrollment of study participants is currently far below expectations. An institutional review board-approved anonymous internet survey was conducted of ZEDS investigators to study their experiences and opinions regarding barriers to enrollment and various approaches to overcome them. RESULTS: The overall survey response rate was 54% (79/145). Only 29% (23/79) agreed that it is easy to enroll study participants. Regarding patient barriers, 69% (55/79) agreed that HZO patients want to be treated with antiviral medication and 69% (54/78) agreed that HZO patients on antivirals do not want to be randomized. Regarding personal barriers facing investigators, 91% (72/79) agreed that antivirals are effective and 100% that the research questions ZEDS is designed to answer are very important. Fewer than 30% of respondents believed that steps taken to increase enrollment have been very helpful. Over half (54%, 42/78) believed that advertising on social media would be moderately or very effective. CONCLUSIONS: Belief among ZEDS investigators that antivirals are effective, and the preference of patients to be treated with antivirals rather than be randomized in ZEDS, are major barriers to enrollment. New approaches to overcoming barriers are necessary to develop an evidence-based standard of care for treatment of HZO.

Change in Waist Circumference With Continuous Use of a Smart Belt: An Observational Study.

BACKGROUND: Health insurers and policymakers are trying to prevent and reduce cardiovascular diseases due to obesity. A smart belt that monitors activity and waist circumference is a new concept for conquering obesity and may be a promising new strategy for health insurers and policymakers. OBJECTIVE: This preliminary study evaluated whether the use of a smart belt was associated with a decrease in waist circumference. METHODS: In the manufacturer's database, there were data on a total of 427 men at baseline. A total of 223, 81, and 27 users kept using the smart belt for 4, 8, and 12 weeks, respectively. Paired t tests and repeated measures analysis of variance (ANOVA) were used to identify the change in waist circumference at specified time intervals (at 4, 8, and 12 weeks). In addition, a linear mixed model was used to incorporate all users' waist circumference data at each time point. Preexisting data on waist circumference and self-reported demographics were obtained from the manufacturer of the smart belt (WELT Corporation, South Korea). RESULTS: Compared with baseline, the waist circumference (cm) decreased significantly at all time points: -0.270 for week 4, -0.761 for week 8, and -1.972 for week 12 (all P<.01). Although each paired t test had a different sample size because of loss to follow-up, the differences between baseline and each subsequent week increased. Equal continuous reduction in waist circumference was observed with the ANOVA and mixed model analysis (beta=-0.158 every week). CONCLUSIONS: The smart belt is a newly developed, wearable device that measures real-time steps, sedentary time, and waist circumference. In this study, we showed that wearing the smart belt was associated with reducing waist circumference over 12 weeks. This direct-to-consumer smart health device may contribute toward reducing the risk of obesity and related conditions and controlling increasing health costs for health insurers.

A Comparison of Spatial Pattern Detection Methods for Major Cancer Mortality in Korea.

Spatial pattern detection can be a useful tool for understanding the geographical distribution of cancer mortality. This study compared the results from several special analysis methods to examine the geographical variations in Korean major cancer mortality during 2010 to 2013. The spatial scan statistic and Tango's method were used to detect spatial clusters with unusually high or low rates. The local indicator of spatial association was used for cluster detection with accounting for spatial autocorrelation among adjacent areas. A nonparametric approach was used to identify local districts with significantly higher or lower rates, compared to the average national rate. The different methods revealed similar results in the overall patterns, but slightly different specific results. These techniques can provide complementary information and should be used based on the distinguishing features of each method. It may be better to use several different methods for these analyses rather than a single method.