Short communicationAge trends in rates of substance use disorders across ages 18–90: Differences by gender and race/ethnicity
Introduction
Substance use disorders (SUDs) contribute to considerable morbidity and mortality, including premature mortality, infectious disease, and comorbid mental health conditions, as well as societal costs from lost productivity, health care costs, and crime (Center for Behavioral Health Statistics and Quality, 2015, Degenhardt and Hall, 2012). These disorders are not distributed evenly across the population; instead, prevalence varies across age, and by gender and race/ethnicity. A new method, the time-varying effect model (TVEM) can be used to understand age-varying differences in SUDs, and to estimate periods at which health disparities are more pronounced. In this study, we used TVEM to estimate prevalence of four SUDs (alcohol use disorder, tobacco use disorder, cannabis/marijuana use disorder, and opioid use disorder) across ages 18–90 by gender and race/ethnicity in a nationally representative U.S. sample.
In the US, 14% of individuals meet criteria for alcohol use disorder (AUD; Grant et al., 2015a, Grant et al., 2015b), 13% for tobacco use disorder (TUD; Falk et al., 2006); 4% for cannabis use disorder (CUD; Hasin et al., 2015), 1% for opioid use disorder (OUD; Center for Behavioral Health Statistics and Quality, 2015, Saha et al., 2016). Men generally report higher rates of substance use disorders than women (Grant et al., 2015a,b; Schulte et al.,2009). Racial/ethnic differences in SUDs vary by drug. Recent data indicates AUDs are lower among Black and Latino compared to White adults (Grant et al., 2015a, Grant et al., 2015b), and tobacco use and nicotine dependence is lower among Black and Hispanic compared to White individuals (Hu et al., 2006, SAMHSA, 2015, Thomas et al., 2016). White and Black adults have similar rates of past year CUD, while Latinos have lower rates (Hasin et al., 2015), and OUDs are lower among Black compared to White and Latino adults (SAMHSA, 2015).
However, disparities in SUDs are likely not consistent across the lifespan. Age-varying gender and racial/ethnic differences in substance use have been documented in adolescence and young adulthood (Chen and Jacobson, 2012, Kandel et al., 2011). For example, a racial/ethnic crossover effect has been found such that Black adolescents have lower rates of use compared to White adolescents; however, in young adulthood this difference reverses such that rates are higher among Black individuals compared to White individuals (Chen and Kandel, 2002, Geronimus et al., 1993, Kandel et al., 2011, Ensminger et al., 2016). However, less is known about age-varying differences in SUDs, or how disparities in SUDs may extend or weaken for midlife or older adults. Such information can be used to ensure programs target the most at-risk groups of individuals at particular ages or periods of risk.
A new method, the time-varying effect model (TVEM; Tan et al., 2012) can flexibly estimate when disparities are greatest and when crossovers occur. Because TVEM allows for estimation of curves that do not require a specified parametric form, it can identify precise periods of change, such as ages when differences by race/ethnicity are significant. When nationally representative data and weights are used in TVEM, analyses can provide precise estimates of age-varying trends for particular population subgroups. TVEM has been used to understand gender and racial/ethnic differences in substance use among adolescents and young adults (Evans-Polce et al., 2014). The current study applies TVEM to a nationally representative sample of U.S. adults to examine age-varying disparities in SUDs by gender and race/ethnicity across the adult lifespan (ages 18–90).
Section snippets
Study population and design
This study used data from the National Epidemiologic Survey of Alcohol and Related Conditions–III (NESARC-III), a nationally representative, cross-sectional study of the non-institutionalized adult population in the US collected in 2012–2013 (Grant et al., 2014, Grant et al., 2015a, Grant et al., 2015b). Participants were recruited through a multi-stage sampling plan, with oversamples of ethnic minority respondents. The overall response rate was 60%. The final sample contained 36,309
Results
Estimated prevalences of SUDs by age and gender are presented in Fig. 1. Men had higher prevalence of AUD, TUD and CUD compared to women at most ages. Prevalence of AUD peaked in the twenties (32% for men at age 25 and 24% for women at age 22) and then decreased steadily by age, with very few men or women reporting AUD past age 75. TUD peaked in the mid-twenties for both men (32% at age 27) and women (23% at age 26); rates then decreased until around age 38, increased until about age 46, and
Discussion
This study applied an innovative statistical approach to examine rates of SUDs by gender and race/ethnicity among individuals ages 18–90. Results by gender are generally consistent with prior research (Grant et al., 2015a, Grant et al., 2015b; Grant et al., 2015a, Grant et al., 2015b; Grant et al., 2015a,b; Schulte et al., 2009) showing higher rates of substance use and SUDs among men compared to women. In general, rates of SUDs decreased with older ages for both groups, and men had higher
Conclusions
This study provides recent, national age trends in the prevalence of SUDs across adulthood by gender and race/ethnicity. Results show higher rates of SUDs for men compared to women for most substances, and that a racial/ethnic crossover occurs, with rates higher for Black compared to White participants at later ages. Findings suggest the importance of early prevention and treatments for all subgroups, but that Black adults at older ages may also be at increased risk. In addition, this paper
Contributors
Sara Vasilenko contributed to the design of the study, conducted analyses, wrote the initial draft of the methods and results sections, and created figures. Stephanie Lanza contributed to the design of the study and provided feedback on analyses. Rebecca-Evans Polce contributed to the design of the study, provided feedback on analyses, and wrote the initial draft of the introduction. All three authors read and provided feedback on the manuscript and approved its content.
Role of funding sources
This manuscript was prepared using a limited access dataset obtained from the National Institute on Alcohol Abuse and Alcoholism and does not reflect the opinions or view of NIAAA or the U.S. Government. This research and the authors were supported by grants R01-DA039854, P50-DA039838, and R01 DA037902 from the National Institute on Drug Abuse (NIDA). The content is solely the responsibility of the authors and does not necessarily represent the official views of NIDA, NIAAA, or the National
Conflict of interest
No conflict declared.
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