Age-Based Trends in Behavioral Risk Factors and Chronic Disease Outcomes Using a Large Non-Institutionalized Sample of Adults

Introduction

Understanding age trends in behavioral risk factors and chronic disease outcomes are important for accurately assessing and modeling temporal effects of risk factors on chronic disease outcomes.

Methods

The Behavioral Risk Factor Surveillance System is a state-based cross-sectional survey. Its objective is to collect state-specific data regarding preventive health practices, risk behaviors, injuries, chronic and infectious diseases. Data are collected in all 50 states, the District of Columbia, Guam, and Puerto Rico. We used data from approximately 906,100 adults aged 18 years or older who were interviewed in 2014 and 2015.

We examined age trends in smoking, binge drinking, depression, obesity, heart disease, stroke, skin cancer, and other cancer. To estimate age trends, we fit both unadjusted and multivariable adjusted logistic regression models using the complex survey design. The multivariable models adjusted for age, sex, race/ethnicity, education, employment status, and marital status. Finally, we examined whether age-based trends in 4 chronic conditions (heart disease, stroke, skin cancer, and other cancer) varied by depression status, a known predictor of subsequent chronic conditions.

Results

For all 8 outcomes, the adjusted effects for age, sex, race/ethnicity, education, employment status, and marital status were statistically significant. For the age effects, all the adjusted non-linear quadratic age effects were statistically significant. Smoking, depression and obesity had similar age curves with lower prevalence rates among the youngest adults that peaked in prevalence between 35 and 54 years of age and declined among those 55 years or older. Binge drinking started at its highest prevalence at 18-24 years and dropped dramatically with age. For the other four outcomes (heart disease, stroke, skin cancer, and other cancer) they all increased in prevalence exponentially by age. When analyses were stratified by depression status, there were significant differences in the adjusted age effects for depressed [OR 1.06, (95% CI 1.05 - 1.08)] and not depressed individuals [OR=1.12, (95% CI 1.11 - 1.13)], respectively, for only the other cancer outcome.

Discussion

In this study, there were substantial differences in the age-based trends for all 8 outcomes examined. Understanding age trends in both behavioral risk-factors and chronic condition health outcomes provides valuable information for understanding the potential impacts of chronic disease prevention efforts and the time periods during the life course in which the need for interventions may be greatest.