Assessing Exposure-Response Trends Using the Disease Risk Score

Standardization by a disease risk score (DRS) may be preferable to weighting on the exposure propensity score if the exposure is difficult to model (1), relatively novel (i.e., newly emerging or rapidly-evolving), or extremely rare (2, 3). For exposures with more than two levels, methods are lacking for a DRS-based approach. We present an approach to estimate trends in standardized risk ratios (RRs) based on a regression model that uses a DRS

Surface Normal Deconvolution: Photometric Stereo for Optically Thick Translucent Objects

Computer Vision – ECCV 2014 13th European Conference, Zurich, Switzerland, September 6-12, 2014,This paper presents a photometric stereo method that works for optically thick translucent objects exhibiting subsurface scattering. Our method is built upon the previous studies showing that subsurface scattering is approximated as convolution with a blurring kernel. We extend this observation and show that the original surface normal convolved with the scattering kernel corresponds to the blurred surface normal that can be obtained by a conventional photometric stereo technique. Based on this observation, we cast the photometric stereo problem for optically thick translucent objects as a deconvolution problem, and develop a method to recover accurate surface normals. Experimental results of both synthetic and real-world scenes show the effectiveness of the proposed method

Marginal Structural Models for Risk or Prevalence Ratios for a Point Exposure Using a Disease Risk Score

The disease risk score is a summary score that can be used to control for confounding with a potentially large set of covariates. While less widely used than the exposure propensity score, the disease risk score approach might be useful for novel or unusual exposures, when treatment indications or exposure patterns are rapidly changing, or when more is known about the nature of how covariates cause disease than is known about factors influencing propensity for the exposure of interest. Focusing on the simple case of a binary point exposure, we describe a marginal structural model for estimation of risk (or prevalence) ratios. The proposed model incorporates the disease risk score as an offset in a regression model, and it yields an estimate of a standardized risk ratio where the target population is the exposed group. Simulations are used to illustrate the approach, and an empirical example is provided. Confounder control based on the proposed method might be a useful alternative to approaches based on the exposure propensity score, or as a complement to them

Reducing Bias Due to Exposure Measurement Error Using Disease Risk Scores

Suppose that an investigator wants to estimate an association between a continuous exposure variable and an outcome, adjusting for a set of confounders. If the exposure variable suffers classical measurement error, in which the measured exposures are distributed with independent error around the true exposure, then an estimate of the covariate-Adjusted exposure-outcome association may be biased. We propose an approach to estimate a marginal exposure-outcome association in the setting of classical exposure measurement error using a disease score-based approach to standardization to the exposed sample. First, we show that the proposed marginal estimate of the exposure-outcome association will suffer less bias due to classical measurement error than the covariate-conditional estimate of association when the covariates are predictors of exposure. Second, we show that if an exposure validation study is available with which to assess exposure measurement error, then the proposed marginal estimate of the exposure-outcome association can be corrected for measurement error more efficiently than the covariate-conditional estimate of association. We illustrate both of these points using simulations and an empirical example using data from the Orinda Longitudinal Study of Myopia (California, 1989-2001)

Standardizing Discrete-Time Hazard Ratios with a Disease Risk Score

The disease risk score (DRS) is a summary score that is a function of a potentially large set of covariates. The DRS can be used to control for confounding by the covariates that went into estimation of the DRS and obtain a standardized estimate of an exposure's effect on disease. However, to date, literature on the DRS has not addressed analyses that focus on estimation of survival or hazard functions, which are common in epidemiologic analyses of cohort data. Here, we propose a method for standardization of hazard ratios using the DRS in longitudinal analyses of the association between a binary exposure and an outcome. This approach to handling a potentially large set of covariates through a model-based approach to standardization may provide a useful tool for cohort analyses of hazard ratios and may be particularly well-suited to settings where an exposure propensity score is difficult to model. Simulations are used in this paper to illustrate the approach, and an empirical example is provided

A Bespoke Instrumental Variable Approach to Correction for Exposure Measurement Error

A covariate-adjusted estimate of an exposure-outcome association may be biased if the exposure variable suffers measurement error. We propose an approach to correct for exposure measurement error in a covariate-adjusted estimate of the association between a continuous exposure variable and outcome of interest. Our proposed approach requires data for a reference population in which the exposure was a priori set to some known level (e.g., 0, and is therefore unexposed); however, our approach does not require an exposure validation study or replicate measures of exposure, which are typically needed when addressing bias due to exposure measurement error. A key condition for this method, which we refer to as "partial population exchangeability," requires that the association between a measured covariate and outcome in the reference population equals the association between that covariate and outcome in the target population in the absence of exposure. We illustrate the approach using simulations and an example

Potential Predictors of Injury Among Pre-Professional Ballet and Contemporary Dancers

Injuries occur frequently among ballet and contemporary dancers. However, limited literature exists on injuries to pre-professional dancers in the USA. The goals of this study were to 1. provide a descriptive epidemiology of the incidence of musculoskeletal injuries in an adolescent and young adult dance population and 2. identify parsimonious regression models that could be potentially used to predict injury incidence. The study was based at the University of North Carolina School of the Arts (UNCSA) from Fall 2009 to Spring 2015. An injury was defined as any event that caused a dancer to be seen at the UNCSA Student Health Services and caused the dancer to modify or curtail dance activity for at least 1 day. Injury rate ratios (IRRs) were calculated using negative binomial generalized estimating equations. Models predicting injury rates were built using forward selection, stratified by sex. Among 480 dancers, 1,014 injuries were sustained. Most injuries were to the lower extremity and the result of overuse. There were differences in upper extremity, lower extremity, and traumatic injury rates by demographic subgroups. Among females, the most parsimonious predictive model for injury rates included a self-reported history of depression, age at time of injury, and number of injuries sustained at UNCSA prior to the semester of current injury. Among males, the most parsimonious model was a univariate model with family history of alcohol or drug problems. Strategies for traumatic injury prevention among dancers should be both sex- and style-specific. No differences were observed in overuse injury rates by sex or style, suggesting that generic overuse prevention strategies may not need to be guided by these factors. It is concluded that strategies can be implemented to reduce and mitigate the consequences of injuries if not the injuries themselves

Estimating Associations Between Annual Concentrations of Particulate Matter and Mortality in the United States, Using Data Linkage and Bayesian Maximum Entropy

Background: Exposure to fine particulate matter (PM2.5) is an established risk factor for human mortality. However, previous US studies have been limited to select cities or regions or to population subsets (e.g., older adults). Methods: Here, we demonstrate how to use the novel geostatistical method Bayesian maximum entropy to obtain estimates of PM2.5 concentrations in all contiguous US counties, 2000–2016. We then demonstrate how one could use these estimates in a traditional epidemiologic analysis examining the association between PM2.5 and rates of all-cause, cardiovascular, respiratory, and (as a negative control outcome) accidental mortality. Results: We estimated that, for a 1 log(μg/m3) increase in PM2.5 concentration, the conditional all-cause mortality incidence rate ratio (IRR) was 1.029 (95% confidence interval [CI]: 1.006, 1.053). This implies that the rate of all-cause mortality at 10 µg/m3 would be 1.020 times the rate at 5 µg/m3. IRRs were larger for cardiovascular mortality than for all-cause mortality in all gender and race–ethnicity groups. We observed larger IRRs for all-cause, nonaccidental, and respiratory mortality in Black non-Hispanic Americans than White non-Hispanic Americans. However, our negative control analysis indicated the possibility for unmeasured confounding. Conclusion: We used a novel method that allowed us to estimate PM2.5 concentrations in all contiguous US counties and obtained estimates of the association between PM2.5 and mortality comparable to previous studies. Our analysis provides one example of how Bayesian maximum entropy could be used in epidemiologic analyses; future work could explore other ways to use this approach to inform important public health questions

Determinants of environmental styrene exposure in Gulf coast residents

Background: In a previous study of exposure to oil-related chemicals in Gulf coast residents, we measured blood levels of volatile organic compounds. Levels of styrene were substantially elevated compared to a nationally representative sample. We sought to identify factors contributing to these levels, given the opportunities for styrene exposure in this community. Methods: We measured blood styrene levels in 667 Gulf coast residents and compared participants’ levels of blood styrene to a nationally representative sample. We assessed personal and environmental predictors of blood styrene levels using linear regression and predicted the risk of elevated blood styrene (defined as above the National Health and Nutrition Examination Survey 95th percentile) using modified Poisson regression. We assessed exposure to styrene using questionnaire data on recent exposure opportunities and leveraged existing databases to assign ambient styrene exposure based on geocoded residential location. Results: These Gulf coast residents were 4–6 times as likely as the nationally representative sample to have elevated blood styrene levels. The change in styrene (log ng/mL) was 0.42 (95% CI: 0.34, 0.51) for smoking, 0.34 (0.09, 0.59) for time spent in vehicles and 1.10 (0.31, 1.89) for boats, and −0.41 (−0.73, −0.10) for fall/winter blood draws. Residential proximity to industrial styrene emissions did not predict blood styrene levels. Ambient styrene predicted elevated blood styrene in subgroups. Conclusions: Personal predictors of increasing blood styrene levels included smoking, vehicle emissions, and housing characteristics. There was a suggestive association between ambient and blood styrene. Our measures of increased regional exposure opportunity do not fully explain the observed elevated blood styrene levels in this population

Patterns of Children’s Blood Lead Screening and Blood Lead Levels in North Carolina, 2011–2018—Who Is Tested, Who Is Missed?

BACKGROUND: No safe level of lead in blood has been identified. Blood lead testing is required for children on Medicaid, but it is at the discretion of providers and parents for others. Elevated blood lead levels (EBLLs) cannot be identified in children who are not tested. OBJECTIVES: The aims of this research were to identify determinants of lead testing and EBLLs among North Carolina children and estimate the number of additional children with EBLLs among those not tested. METHODS: We linked geocoded North Carolina birth certificates from 2011–2016 to 2010 U.S. Census data and North Carolina blood lead test results from 2011–2018. We estimated the probability of being screened for lead and created inverse probability (IP) of testing weights. We evaluated the risk of an EBLL of ≥3 lg=dL at <30 months of age, conditional on characteristics at birth, using generalized linear models and then applied IP weights to account for missing blood lead results among unscreened children. We estimated the number of additional children with EBLLs of all North Carolina children using the IP-weighted population and bootstrapping to produce 95% credible intervals (CrI). RESULTS: Mothers of the 63.5% of children (402,002 of 633,159) linked to a blood lead test result were disproportionately young, Hispanic, Black, American Indian, or on Medicaid. In full models, maternal age ≤20 y [risk ratio ðRRÞ = 1:10; 95% confidence interval (CI): 1.13, 1.20] or smoking (RR = 1.14; 95% CI: 1.12, 1.17); proximity to a major roadway (RR = 1.10; 95% CI: 1.05, 1.15); proximity to a lead-releasing Toxics Release Inventory site (RR = 1.08; 95% CI: 1.03, 1.14) or a National Emissions Inventory site (RR = 1.11; 95% CI: 1.07, 1.14); and living in neighborhoods with more housing built before 1950 (RR = 1.10; 95% CI: 1.05, 1.14) or before 1940 (RR = 1.18; 95% CI: 1.11, 1.25) or more vacant housing (RR = 1.14; 95% CI: 1.11, 1.17) were associated with an increased risk of EBLL, whereas overlap with a public water service system was associated with a decreased risk of EBLL (RR = 0.85; 95% CI: 0.83, 0.87). Children of Black mothers were no more likely than children of White mothers to have EBLLs (RR = 0.98; 95% CI: 0.96, 1.01). Complete blood lead screening in 2011–2018 may have identified an additional 17,543 (95% CrI: 17,462, 17,650) children with EBLLs ≥3 lg=dL. DISCUSSION: Our results indicate that current North Carolina lead screening strategies fail to identify over 30% (17,543 of 57,398) of children with subclinical lead poisoning and that accounting for characteristics at birth alters the conclusions about racial disparities in children’s EBLLs