Improving the nation’s access to ocular health care is important, given that visual impairment (VI) is associated with a variety of functional and health outcomes, including increased risk for mortality.^1-14 Visual impairment has significant negative effects on physical and psychosocial health,^13,15-40 but the specific mechanisms and pathways by which VI increases overall mortality remain unclear.

Structural equation modeling (SEM) is a modeling framework that encompasses many types of statistical models and can accommodate a variety of estimation and testing methods. SEM has been used primarily in social sciences but is increasingly used in epidemiology, public health, and the medical sciences. SEM provides many advantages for the analysis of survey and clinical data, including the ability to model latent constructs that may not be directly observable. Another major feature is simultaneous estimation of parameters in systems of equations that may include mediated relationships, correlated dependent variables, and in some instances feedback relationships. SEM allows for the specification of theoretically holistic models because multiple and varied relationships may be estimated together in the same model. SEM has recently expanded by adding generalized linear modeling capabilities that include the simultaneous estimation of parameters of different functional form for outcomes with different distributions in the same model. Therefore, mortality modeling and other relevant health outcomes may be evaluated. Random effects estimation using latent variables has been advanced in the SEM literature and software. In addition, SEM software has increased estimation options. Therefore, modern SEM is quite general and includes model types frequently used by health researchers, including generalized linear modeling, mixed effects linear modeling, and population average modeling. This article does not present any new information. It is meant as an introduction to SEM and its uses in ocular and other health research.

Introduction: Over the past decade the United States (US) has seen a decrease in advanced cancer diagnoses. There has also been an increase in the number of cancer survivors returning to work. Cancer screening behaviors among survivors may play an important role in their return-to-work process. Adherence to a post-treatment cancer screening protocol increases early detection of secondary tumors and reduces potentially limiting side-effects. We compared screening trends among all cancer survivors, working survivors, and the general population over the last decade. Materials and Methods: Trends in adherence to recommended screening were analyzed by site-specific cancer. We used the Healthy People goals as a measure of desired adherence. We selected participants 18+ years from 1997 to 2010 National Health Interview Survey for years where detailed cancer screening information was available. Using the recommendations of the American Cancer Society as a guide, we assessed adherence to cancer screening across the decade. There were 174,393 participants. Analyses included 7,528 working cancer survivors representing 3.8 million US workers, and 119,374 adults representing more than 100 million working Americans with no cancer history. Results: The US population met the Healthy People 2010 goal for colorectal screening, but declined in all other recommended cancer screening. Cancer survivors met and maintained the HP2010 goal for all, except cervical cancer screening. Survivors had higher screening rates than the general population. Among survivors, white-collar and service occupations had higher screening rates than blue-collar survivors. Conclusion: Cancer survivors report higher screening rates than the general population. Nevertheless, national screening rates are lower than desired, and disparities exist by cancer history and occupation. Understanding existing disparities, and the impact of cancer screening on survivors is crucial as the number of working survivors increases.