Cataract research using electronic health records

<p>Abstract</p> <p>Background</p> <p>The eMERGE (electronic MEdical Records and Genomics) network, funded by the National Human Genome Research Institute, is a national consortium formed to develop, disseminate, and apply approaches to research that combine DNA biorepositories with electronic health record (EHR) systems for large-scale, high-throughput genetic research. Marshfield Clinic is one of five sites in the eMERGE network and primarily studied: 1) age-related cataract and 2) HDL-cholesterol levels. The purpose of this paper is to describe the approach to electronic evaluation of the epidemiology of cataract using the EHR for a large biobank and to assess previously identified epidemiologic risk factors in cases identified by electronic algorithms.</p> <p>Methods</p> <p>Electronic algorithms were used to select individuals with cataracts in the Personalized Medicine Research Project database. These were analyzed for cataract prevalence, age at cataract, and previously identified risk factors.</p> <p>Results</p> <p>Cataract diagnoses and surgeries, though not type of cataract, were successfully identified using electronic algorithms. Age specific prevalence of both cataract (22% compared to 17.2%) and cataract surgery (11% compared to 5.1%) were higher when compared to the Eye Diseases Prevalence Research Group. The risk factors of age, gender, diabetes, and steroid use were confirmed.</p> <p>Conclusions</p> <p>Using electronic health records can be a viable and efficient tool to identify cataracts for research. However, using retrospective data from this source can be confounded by historical limits on data availability, differences in the utilization of healthcare, and changes in exposures over time.</p

PLoS One

Quantifying the association between lifetime exposures and the risk of developing a chronic disease is a recurrent challenge in epidemiology. Individual exposure trajectories are often heterogeneous and studying their associations with the risk of disease is not straightforward. We propose to use a latent class mixed model (LCMM) to identify profiles (latent classes) of exposure trajectories and estimate their association with the risk of disease. The methodology is applied to study the association between lifetime trajectories of smoking or occupational exposure to asbestos and the risk of lung cancer in males of the ICARE population-based case-control study. Asbestos exposure was assessed using a job exposure matrix. The classes of exposure trajectories were identified using two separate LCMM for smoking and asbestos, and the association between the identified classes and the risk of lung cancer was estimated in a second stage using weighted logistic regression and all subjects. A total of 2026/2610 cases/controls had complete information on both smoking and asbestos exposure, including 1938/1837 cases/controls ever smokers, and 1417/1520 cases/controls ever exposed to asbestos. The LCMM identified four latent classes of smoking trajectories which had different risks of lung cancer, all much stronger than never smokers. The most frequent class had moderate constant intensity over lifetime while the three others had either long-term, distant or recent high intensity. The latter had the strongest risk of lung cancer. We identified five classes of asbestos exposure trajectories which all had higher risk of lung cancer compared to men never occupationally exposed to asbestos, whatever the dose and the timing of exposure. The proposed approach opens new perspectives for the analyses of dose-time-response relationships between protracted exposures and the risk of developing a chronic disease, by providing a complete picture of exposure history in terms of intensity, duration, and timing of exposure

A flexible modeling approach to estimating the component effects of smoking behavior on lung cancer.

OBJECTIVE: Despite the established causal association between cigarette smoking and lung cancer, the relative contributions of age started, duration, years since quitting, and daily amount smoked have not been well characterized. We estimated the contribution of each of these aspects of smoking behavior. STUDY DESIGN AND SETTING: A case-control study was conducted in Montreal on the etiology of lung cancer. There were 640 cases and 938 control subjects for whom lifetime smoking histories were collected. We used generalized additive models, incorporating cubic smoothing splines to model nonlinear effects of various smoking variables. We adopted a multistep approach to deal with the multicollinearity among time-related variables. RESULTS: The main findings are that (1) risk increases independently by daily amount and by duration; (2) among current smokers, lung cancer risk doubles for every 10 cigarettes per day up to 30 to 40 cigarettes per day and tails off thereafter; (3) among ex-smokers, the odds ratio decreases with increasing time since quitting, the rate of decrease being sharper among heavy smokers than among light smokers; and (4) absolute risks demonstrate the dramatic public health benefits of long-term smoking cessation. CONCLUSION: Our results reinforce some previous findings on this issue

Competing risks analyses: objectives and approaches.

Studies in cardiology often record the time to multiple disease events such as death, myocardial infarction, or hospitalization. Competing risks methods allow for the analysis of the time to the first observed event and the type of the first event. They are also relevant if the time to a specific event is of primary interest but competing events may preclude its occurrence or greatly alter the chances to observe it. We give a non-technical overview of competing risks concepts for descriptive and regression analyses. For descriptive statistics, the cumulative incidence function is the most important tool. For regression modelling, we introduce regression models for the cumulative incidence function and the cause-specific hazard function, respectively. We stress the importance of choosing statistical methods that are appropriate if competing risks are present. We also clarify the role of competing risks for the analysis of composite endpoints

Association of long-term exposure to ambient air pollution with retinal neurodegeneration: the prospective alienor study

Chronic exposure to air pollution may have adverse effects on neurodegenerative diseases. Glaucoma, the second leading cause of blindness worldwide, is a neurodegenerative disease of the optic nerve, characterized by progressive thinning of the retinal nerve fiber layer (RNFL). We investigated the relationship of air pollution exposure with longitudinal changes of RNFL thickness in the Alienor study, a population-based cohort of residents of Bordeaux, France, aged 75 years or more. Peripapillary RNFL thickness was measured using optical coherence tomography imaging every 2 years from 2009 to 2020. Measurements were acquired and reviewed by specially trained technicians to control quality. Air pollution exposure (particulate matter </=2.5 mum (PM(2.5)), black carbon (BC), nitrogen dioxide (NO(2))) was estimated at the participants' geocoded residential address using land-use regression models. For each pollutant, the 10-year average of past exposure at first RNFL thickness measurement was estimated. Associations of air pollution exposure with RNFL thickness longitudinal changes were assessed using linear mixed models adjusted for potential confounders, allowing for intra-eye and intra-individual correlation (repeated measurements). The study included 683 participants with at least one RNFL thickness measurement (62% female, mean age 82 years). The average RNFL was 90 mum (SD:14.4) at baseline. Exposure to higher levels of PM(2.5) and BC in the previous 10 years was significantly associated with a faster RNFL thinning during the 11-year follow-up (-0.28 mum/year (95% confidence interval (CI) [-0.44;-0.13]) and -0.26 mum/year (95% CI [-0.40;-0.12]) per interquartile range increment; p < 0.001 for both). The size of the effect was similar to one year of age in the fitted model (-0.36 mum/year). No statistically significant associations were found with NO(2) in the main models. This study evidenced a strong association of chronic exposure to fine particulate matter with retinal neurodegeneration, at air pollution levels below the current recommended thresholds in Europe