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

Amplification of Bias Due to Exposure Measurement Error

Observational epidemiologic studies typically face challenges of exposure measurement error and confounding. Consider an observational study of the association between a continuous exposure and an outcome, where the exposure variable of primary interest suffers from classical measurement error (i.e., the measured exposures are distributed around the true exposure with independent error). In the absence of exposure measurement error, it is widely recognized that one should control for confounders of the association of interest to obtain an unbiased estimate of the effect of that exposure on the outcome of interest. However, here we show that, in the presence of classical exposure measurement error, the net bias in an estimate of the association of interest may increase upon adjustment for confounders. We offer an analytical expression for calculating the change in net bias in an estimate of the association of interest upon adjustment for a confounder in the presence of classical exposure measurement error, and we illustrate this problem using simulations

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

Hurricane flooding and acute gastrointestinal illness in North Carolina

Hurricanes often flood homes and industries, spreading pathogens. Contact with pathogen-contaminated water can result in diarrhea, vomiting, and/or nausea, known collectively as acute gastrointestinal illness (AGI). Hurricanes Matthew and Florence caused record-breaking flooding in North Carolina (NC) in October 2016 and September 2018, respectively. To examine the relationship between hurricane flooding and AGI in NC, we first calculated the percent of each ZIP code flooded after Hurricanes Matthew and Florence. Rates of all-cause AGI emergency department (ED) visits were calculated from NC's ED surveillance system data. Using controlled interrupted time series, we compared AGI ED visit rates during the three weeks after each hurricane in ZIP codes with a third or more of their area flooded to the predicted rates had these hurricanes not occurred, based on AGI 2016–2019 ED trends, and controlling for AGI ED visit rates in unflooded areas. We examined alternative case definitions (bacterial AGI) and effect measure modification by race and age. We observed an 11% increase (rate ratio (RR): 1.11, 95% CI: 1.00, 1.23) in AGI ED visit rates after Hurricanes Matthew and Florence. This effect was particularly strong among American Indian patients and patients aged 65 years and older after Florence and elevated among Black patients for both hurricanes. Florence's effect was more consistent than Matthew's effect, possibly because little rain preceded Florence and heavy rain preceded Matthew. When restricted to bacterial AGI, we found an 85% (RR: 1.85, 95% CI: 1.37, 2.34) increase in AGI ED visit rate after Florence, but no increase after Matthew. Hurricane flooding is associated with an increase in AGI ED visit rate, although the strength of effect may depend on total storm rainfall or antecedent rainfall. American Indians and Black people—historically pushed to less desirable, flood-prone land—may be at higher risk for AGI after storms