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      Sociodemographic Characteristics Associated With and Prevalence and Frequency of Cannabis Use Among Adults in the US

      research-article
      , PhD 1 , , , PhD 2 , , PhD, MPH 3 , 4 , 5 , , MD, MPH 1 , 6 , 7
      JAMA Network Open
      American Medical Association

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          Key Points

          Question

          What are the sociodemographic characteristics of adults who engage in high-frequency cannabis use?

          Findings

          In this survey study including 387 157 US adults residing in 21 states conducted in 2016 through 2019, young, male, Black, and Native American individuals and individuals with low educational attainment and income were more likely to engage in higher frequency cannabis use.

          Meaning

          Higher-frequency use among these populations may warrant more attention from policymakers and public health officials in the form of screening, risk stratification, and treatment given the known and emerging adverse health effects of cannabis.

          Abstract

          This survey study of 21 US states and 2 US territories examines social and demographic characteristics associated with the frequency of cannabis use among adults.

          Abstract

          Importance

          Cannabis use has increased, but there are few studies on frequent and daily cannabis use among US adults. Individuals who engage in higher frequency use may suffer more health consequences.

          Objective

          To examine frequency of cannabis use and associated factors among US adults.

          Design, Setting, and Participants

          This survey study included data from 21 US states and 2 US territories reported in the Behavioral Risk Factor Surveillance System surveys from 2016 to 2019. Cross-sectional data on US adults ages 18 years and older were used to estimate demographic, socioeconomic, and behavioral risk factors for cannabis use, taking into account the survey strata and sampling weights for the 4 years of combined data. Using a multivariable ordinal logistic analysis, the association of demographic, socioeconomic status, and behavioral risk factors with past month cannabis frequency were examined.

          Exposures

          Sociodemographic characteristic, ie, age, gender, race and ethnicity, educational attainment, employment status, and annual household income.

          Main Outcomes and Measures

          Ordinal categorization of number of days of cannabis use in the past 30 days in terms of nonuse, infrequent use (1-5 days), frequent use (6-29 days), and daily use.

          Results

          Among the 387 179 respondents, 58 009 (27.9%) were ages 18 to 34 years, 186 923 (50.3%) were ages 35 to 64 years, and 142 225 (21.8%) were age 65 years or older (mean [SD] age, 48.3 [0.1] years). The sample included 28 345 (9.8%) Black, 36 697 (22.6%) Hispanic, and 292 210 (57.3%) White respondents. Smoking was the most common form of cannabis use. The frequency of cannabis use varied significantly by age, gender, race, marital status, education, and employment. Higher frequency cannabis use was associated with younger age (ages 18-34 years: adjusted odds ratio [aOR], 4.12; 95% CI, 3.63-4.68; ages 35-64 years: aOR, 2.22; 95% CI, 1.98-2.49), Black (aOR, 1.46; 95% CI, 1.33-1.71) and Native American (aOR, 1.25; 95% CI, 1.04-1.52) race, and less educational attainment (high school or less: aOR, 1.09; 95% CI, 1.02-1.17; some college: aOR, 1.27; 95% CI, 1.19-1.35). Being married (aOR, 0.54; 95% CI, 0.51-0.58) or identifying as Asian (aOR, 0.60; 95% CI, 0.51-0.71) or Hispanic (aOR, 0.71; 95% CI, 0.65-0.77) was associated with lower-frequency cannabis use after accounting for other baseline factors.

          Conclusions and Relevance

          This nationally based study found that higher-frequency cannabis use is more common among young and racial minority populations, as well as respondents with low socioeconomic status. Given the known and emerging negative health effects of cannabis use, more attention may need to be paid to high-frequency use among underserved populations in the form of screening, risk stratification, and treatment.

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          Most cited references87

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          Adverse Health Effects of Marijuana Use

          New England Journal of Medicine, 370(23), 2219-2227
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            How many imputations are really needed? Some practical clarifications of multiple imputation theory.

            Multiple imputation (MI) and full information maximum likelihood (FIML) are the two most common approaches to missing data analysis. In theory, MI and FIML are equivalent when identical models are tested using the same variables, and when m, the number of imputations performed with MI, approaches infinity. However, it is important to know how many imputations are necessary before MI and FIML are sufficiently equivalent in ways that are important to prevention scientists. MI theory suggests that small values of m, even on the order of three to five imputations, yield excellent results. Previous guidelines for sufficient m are based on relative efficiency, which involves the fraction of missing information (gamma) for the parameter being estimated, and m. In the present study, we used a Monte Carlo simulation to test MI models across several scenarios in which gamma and m were varied. Standard errors and p-values for the regression coefficient of interest varied as a function of m, but not at the same rate as relative efficiency. Most importantly, statistical power for small effect sizes diminished as m became smaller, and the rate of this power falloff was much greater than predicted by changes in relative efficiency. Based our findings, we recommend that researchers using MI should perform many more imputations than previously considered sufficient. These recommendations are based on gamma, and take into consideration one's tolerance for a preventable power falloff (compared to FIML) due to using too few imputations.
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              AmeliaII: A Program for Missing Data

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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                30 November 2021
                November 2021
                30 November 2021
                : 4
                : 11
                : e2136571
                Affiliations
                [1 ]formerly of Center for Tobacco Control Research & Education, University of California, San Francisco
                [2 ]Center for Tobacco Control Research & Education, University of California, San Francisco
                [3 ]Department of Psychiatry & Behavioral Sciences, University of California, San Francisco
                [4 ]Division of Geriatrics, Department of Medicine, University of California, San Francisco
                [5 ]Section of Mental Health Services, San Francisco Veterans Affairs Medical Center, San Francisco, California
                [6 ]Division of Internal Medicine, Department of Medicine, University of California, San Francisco
                [7 ]Section of General Internal Medicine, San Francisco Veterans Affairs Medical Center, San Francisco, California
                Author notes
                Article Information
                Accepted for Publication: October 4, 2021.
                Published: November 30, 2021. doi:10.1001/jamanetworkopen.2021.36571
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Jeffers AM et al. JAMA Network Open.
                Corresponding Author: Abra M. Jeffers, PhD, The Mongan Institute, Massachusetts General Hospital, 100 Cambridge St, Ste 1600, Boston, MA 02114 ( amjeffers@ 123456mgh.harvard.edu ).
                Author Contributions : Dr Jeffers had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: All authors.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: Jeffers, Keyhani.
                Critical revision of the manuscript for important intellectual content: Glantz, Byers, Keyhani.
                Statistical analysis: Jeffers, Glantz, Byers.
                Supervision: Keyhani.
                Conflict of Interest Disclosures: None reported.
                Funding/Support: Dr Jeffers was supported by National Cancer Institute grant No. T32 CA113710. Dr Keyhani was supported by National Institute on Aging grant No. R01AG068678-01A1.
                Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Meeting Presentations: This study was presented at the American Heart Association Epidemiological and Prevention Conference; May 20-21, 2021; virtual conference.
                Article
                zoi211032
                10.1001/jamanetworkopen.2021.36571
                8634054
                34846523
                f0feccfd-987b-4835-84c1-91ba96edb989
                Copyright 2021 Jeffers AM et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 6 July 2021
                : 4 October 2021
                Categories
                Research
                Original Investigation
                Online Only
                Substance Use and Addiction

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