In order to estimate the need for different types of services in a healthcare market, it's important to understand the characteristics of a patient population in the region. This chapter explained how to view the distribution of disease among different populations or between different areas. Descriptive epidemiology deals with questions ofwho, where, and whenin the distribution or occurrence of disease... and even in the occurrence of health behaviors.

Complete Case Study 5.2. Explain your answers in full sentences (i.e. do not just answer "yes" or "no").

CASE STUDY 5.2: Example of a Cross-Sectional Study

In May 2016, Beckman et al. reported in the American Journal of Epidemiology the results of the Bay Area Solvent Study (BASS)- a cross-sectional study of 835 automotive repair workers in the San Fransisco Bay Area of California.

Previously, a case series report and a small follow-up study had suggested a potential link between occupational exposure to hexane in aerosol solvents used in auto repair shops and peripheral neuropathy and blue-yellow color vision defects. color vision defects in humans can be hereditary or acquired after birth; Hereditary color vision defects result from missing or abnormal photoreceptors, while acquired color vision defects result from damage to the retina or the optic nerve. Blue-green hereditary color vision defects are somewhat more common (10% of men and 0.5% of women) than blue-yellow hereditary defects, which are quite rare (1 in 1 million people) and are associated with age, alcoholism, eye diseases like glaucoma and cataract, and exposure to solvents such as hexane.

The study participants were 835 males younger than 60 years of age still living in the San Francisco Bay Area who had worked as auto repair mechanics at any time during the period from 1989 to 2000 when hexane was being used in solvent automobile cleaners. The 835 participants in the study were recruited from 1,765 men found to be eligible after contacting via telephone or postal service all the individuals in a pool of 2,848 potentially eligible workers identified through union records. Between 2008 and 2011, the participants visited a clinic through individual appointments, where they responded to a questionnaire and were tested for color vision. The questionnaire obtained demographic, clinical, and work history information, including the frequency and duration of work that involved the use of specific solvent products. To aid their memory, study participants were shown pictures of solvent products that had been available during the last 20 years. Based on self-reported work history, the study estimated cumulative exposure to hexane. Color vision in the study participants was tested through a laboratory test (Lanthony desaturated D-15 panel test).

Out of the 835 participants, 81 were excluded fom the final analysis because of missing hexane exposure date (n=18), having congenital color vision defects (n= 5), or inadequate color vision assessment (n=10). For the analysis of blue-yellow color vision defects, 65 participants who had nonspecific or red-green color vision defects were also excluded. Thus, cross-sectional data from 689 individuals were analyzed to explore a possible association between cumulative exposure to solvents and blue-yellow color vision defects in automotive mechanics.

The results of the Lanthony desaturated D-15 panel test for blue-yellow color vision defects were treated as binary ( yes, no) variables. Different levels of cumulative exposure to solvents such as hexane and acetone were estimated based on reported frequency and duration of tasks, types, and brands of solvents used, and quantity of solvents used in various tasks. Because age was found to be strongly associated with color vision defects as well as the level of cumulative exposure, it was treated as a confounding variable in all statistical analytic models. Because color vision deteriorates after age 50, age-stratified analyses were carried out to reduce bias resulting from color vision defects occurring because of age. The investigation used log-binomial regression models to estimate the prevalence ratios for the risk of color vision defects by different levels of exposure to nonhexane, hexane, and hexane-acetone, combination solvents. Statistical models were adjusted for potentially confounding variables such as race, smoking, alcohol consumption, and previous history of concussion and head trauma. TABLE 5.4 shows partial results of statistical analysis.

Questions:

Question 1. In statistical analysis, what was the issue related to age? How did the researchers address that issue?

Question 2. Why was statistical analysis adjusted for smoking, alcohol consumption, and previous history of concussion and head trauma?

Question 3. In table 5.4, what do the prevalence ratios for nonhexane solvent exposure in All Participants and Participants age 50 years indicate? Do these ratios provide conclusive evidence of an association between blue-yellow color defects and exposure to nonhexane solvents?

Question 4. In table 5.4, what is the difference between the prevalence ratios for exposure to nonhexane and hexane solvents? What does that difference suggest?

TABLE 5.4: Categorical Exposure Response Relationship Between Blue-Yello Vision Defects and Cumulative Exposure to Nonhexane and Hexane Solvents Among Participants Completing the Lanthony Desaturated D-15 Panel Test

All Participants Participants Age 50 Years

Model and Exposure Category No. of Cases Pr* 95% Cl- No. of Cases PR* 95% Cl-

Nonhexane Solvents

Exposure, mg/m3-year

0-670.9 155 1.0 Reference 60 1.0 Reference

>670.9- 1,364.3 29 1.25 0.81-1.92 12 1.75 0.89-3.46

>1,364.3- 2,470.7 39 1.16 0.75-1.78 18 1.98 0.98-4.02

> 2,470.7 40 1.31 0.86-2.00 17 2.17 1.03-4.56

Age 47 1.04 1.02-1.07 13 1.02 0.96-1.08

Hexane Solvents

Exposure, mg/m3-year

Unexposed to hexane 155 1.0 Reference 60 1.0 Reference

>0-33.7 81 0.73 0.51- 1.04 25 0.77 0.41- 1.42

0.76-2.1

>33.7 33 0.94 0.68- 1.29 13 1.26 0.76- 2.11

Age 41 1.05 1.02- 1.07 22 1.04 0.99-1.10