In a time of global uncertainty, understanding the psychological health of the American public is imperative. There are no current data on anxiety trends among adults in the United States (US) over ...time. This study aimed to investigate prevalence of anxiety among US adults from 2008 to 2018.
Data from the National Survey on Drug Use and Health (NSDUH), which is an annual, cross-sectional survey on substance use and mental health in the US, were analyzed in 2020. Prevalence of past-month anxiety was estimated among those ages ≥18, by survey year from 2008 to 2018. Time trends were tested using logistic regression.
Anxiety increased from 5.12% in 2008 to 6.68% in 2018 (p < 0.0001) among adult Americans. Stratification by age revealed the most notable increase from 7.97% to 14.66% among respondents 18–25 years old (p < 0.001), which was a more rapid increase than among 26–34 and 35–49 year olds (differential time trend p < 0.001). Anxiety did not significantly increase among those ages 50 and older. Anxiety increased more rapidly among those never married and with some college education, relative to their respective counterparts. Apart from age, marital status and education, anxiety increased consistently among sociodemographic groups.
Anxiety is increasing among adults under age 50 in the US, with more rapid increase among young adults. To prepare for a healthier adulthood and given direct and indirect (via 24/7 media) exposure to anxiety-provoking world events, prophylactic measures that can bolster healthy coping responses and/or treatment seeking seem warranted on a broad scale.
•Anxiety increased from 2008 to 2018 among American adults.•Nearly 7% of adults and 15% of young adults reported anxiety in 2018.•Anxiety increased most rapidly among young adults ages 18–25 years old.•Anxiety did not significantly increase among individuals 50 years old and older.
Objectives To examine the relationship between nonmedical use of prescription opioids and heroin initiation from childhood to young adulthood, and to test whether certain ages, racial/ethnic, and ...income groups were at higher risk for this transition. Study design Among a nationally representative sample of US adolescents assessed in the 2004-2011 National Surveys on Drug Use and Health cross-sectional surveys (n = 223 534 respondents aged 12-21 years), discrete-time hazard models were used to estimate the age-specific hazards of heroin initiation associated with prior history of nonmedical use of prescription opioids. Interactions were estimated between prior history of nonmedical use of prescription opioids and age of nonmedical use of prescription opioid initiation, race/ethnicity, and income. Results A prior history of nonmedical use of prescription opioids was strongly associated with heroin initiation (hazard ratio 13.12, 95% CI 10.73, 16.04). Those initiating nonmedical use of prescription opioids at ages 10-12 years had the highest risk of transitioning to heroin use; the association did not vary by race/ethnicity or income group. Conclusions Prior use of nonmedical use of prescription opioids is a strong predictor of heroin use onset in adolescence and young adulthood, regardless of the user's race/ethnicity or income group. Primary prevention of nonmedical use of prescription opioids in late childhood may prevent the onset of more severe types of drug use such as heroin at later ages. Moreover, because the peak period of heroin initiation occurs at ages 17-18 years, secondary efforts to prevent heroin use may be most effective if they focus on young adolescents who already initiated nonmedical use of prescription opioids.
Highlights • Marijuana use increased in adults 26+ after enactment of medical marijuana laws (MML) • Among adults 26+ perceived availability of marijuana increased after MML were enacted • Perceived ...availability was associated with marijuana use
Prescription drug monitoring programs (PDMPs) are a key component of the president's Prescription Drug Abuse Prevention Plan to prevent opioid overdoses in the United States.
To examine whether PDMP ...implementation is associated with changes in nonfatal and fatal overdoses; identify features of programs differentially associated with those outcomes; and investigate any potential unintended consequences of the programs.
Eligible publications from MEDLINE, Current Contents Connect (Clarivate Analytics), Science Citation Index (Clarivate Analytics), Social Sciences Citation Index (Clarivate Analytics), and ProQuest Dissertations indexed through 27 December 2017 and additional studies from reference lists.
Observational studies (published in English) from U.S. states that examined an association between PDMP implementation and nonfatal or fatal overdoses.
2 investigators independently extracted data from and rated the risk of bias (ROB) of studies by using established criteria. Consensus determinations involving all investigators were used to grade strength of evidence for each intervention.
Of 2661 records, 17 articles met the inclusion criteria. These articles examined PDMP implementation only (n = 8), program features only (n = 2), PDMP implementation and program features (n = 5), PDMP implementation with mandated provider review combined with pain clinic laws (n = 1), and PDMP robustness (n = 1). Evidence from 3 studies was insufficient to draw conclusions regarding an association between PDMP implementation and nonfatal overdoses. Low-strength evidence from 10 studies suggested a reduction in fatal overdoses with PDMP implementation. Program features associated with a decrease in overdose deaths included mandatory provider review, provider authorization to access PDMP data, frequency of reports, and monitoring of nonscheduled drugs. Three of 6 studies found an increase in heroin overdoses after PDMP implementation.
Few studies, high ROB, and heterogeneous analytic methods and outcome measurement.
Evidence that PDMP implementation either increases or decreases nonfatal or fatal overdoses is largely insufficient, as is evidence regarding positive associations between specific administrative features and successful programs. Some evidence showed unintended consequences. Research is needed to identify a set of "best practices" and complementary initiatives to address these consequences.
National Institute on Drug Abuse and Bureau of Justice Assistance.
Aims
To estimate the impact of recreational and medical cannabis laws (RCL, MCL) on the use of cannabis and cigarettes in the United States.
Design
A difference‐in‐difference approach was applied to ...data from the 2004–17 National Survey on Drug Use and Health (NSDUH).
Setting
United States.
Participants
Nationally representative cross‐sectional survey of Americans aged 12 years and older (combined analytical sample for 2004–17, n = 783 663).
Measurements
Data on past‐month use of (1) cigarettes and (2) cannabis were used to classify respondents into four groups: cigarette and cannabis co‐use, cigarette‐only use, cannabis‐only use or no cigarette or cannabis use. State of residence was measured by self‐report. MCL/RCL status came from state government websites.
Findings
Difference‐in‐difference analyses suggest that MCL was associated with an increase in cigarette–cannabis co‐use overall adjusted odds ratio (aOR) = 1.09; 95% confidence interval (CI) = 1.02–1.16, with the greatest increases among those aged 50 years and above (aOR = 1.60; CI = 1.39–1.84), married (aOR = 1.19; CI = 1.07–1.31), non‐Hispanic (NH) black (aOR = 1.14; CI = 1.02–1.07) and with a college degree or above (aOR = 1.15; CI = 1.06–1.24). MCL was associated with increases in cigarette‐only use among those aged 50 years and above (aOR = 1.07; CI = 1.01–1.14) and NH black (aOR = 1.16; CI = 1.06–1.27) and increases in cannabis‐only use among those aged 50 years and above (aOR = 1.24; CI = 1.07–1.44) and widowed/divorced/separated (aOR = 1.18; CI = 1.01–1.37). RCL was associated with an increase in cannabis‐only use overall (aOR = 1.21; 95% CI = 1.09–1.34), a decline in cigarette‐only use overall (aOR = 0.89; 95% CI = 0.81–0.97) and increases in co‐use among those who were married (aOR = 1.24; CI = 1.02–1.50) and aged 50 years and above (aOR = 1.37; CI = 1.03–1.84).
Conclusions
Recreational and medical cannabis legalization have had a varying impact on the use, and co‐use, of cannabis and cigarettes in the United States.
Background and Aims
Cannabis use among parents may be increasing with legalization, but perception of associated risk has declined. The study investigated the association between cannabis ...legalization and cannabis use among adults with children in the home over time in the United States (US).
Design
A difference‐in‐difference approach was applied to public and restricted‐use data from the 2004–2017 National Survey on Drug Use and Health (NSDUH), an annual cross‐sectional survey.
Setting
A representative sample of the United States.
Participants/Cases
Respondents ages 18+ with children living in the home drawn from the NSDUH (n = 287,624), which is administered to non‐institutionalized civilians in the 50 states and District of Columbia.
Measurements
Exposures were year and state‐level cannabis policy in state of residence annually. Outcomes were past‐30‐day cannabis use and daily cannabis use. Sociodemographic variables included age, gender, marital status, annual family income, race/ethnicity, educational attainment, and strength of state‐level tobacco control.
Findings
In 2017, past‐month cannabis use (11.9%, 9.3%, and 6.1%) and daily cannabis use (4.2%, 3.2%, and 2.3%) were more common in states with recreational marijuana laws (RML), followed by states with medical marijuana laws (MML) and without legal cannabis use, respectively. RML and MML were associated with significantly higher prevalence of past‐month cannabis use (adjusted odds ratio AOR = 1.28, 95% confidence interval CI = 1.12–1.46; AOR = 1.12, 95% CI = 1.03–1.22) and daily cannabis use (AOR = 1.25, 95% CI = 1.03–1.51; AOR = 1.16, 95% CI = 1.02–1.32), respectively. The impact of MML was particularly salient among adults ages 50+ and the highest income and education subgroups.
Conclusions
Among adults with children living in the home, cannabis use appears to be more common in US states with legalized cannabis use compared with states with no legal cannabis use. Recreational legalization appears to increase use among adults with children in the home broadly across nearly all sociodemographic groups, whereas the effect of legalization for medical use is heterogeneous by age and socioeconomic status.
Twenty-three states and the District of Columbia have passed laws implementing medical marijuana programs. The nineteen programs that were in operation as of October 2014 collectively had over one ...million participants. All states (including D.C.) with medical marijuana laws require physicians directly or indirectly to authorize the use of marijuana at their discretion, yet little is known about how medical marijuana programs vary regarding adherence to basic principles of medical practice and associated rates of enrollment. To explore this, we analyzed marijuana programs according to seven components of traditional medical care and pharmaceutical regulation. We then examined enrollment rates, while controlling for potentially confounding state characteristics. We found that fourteen of the twenty-four programs were nonmedical and collectively enrolled 99.4 percent of participants nationwide, with enrollment rates twenty times greater than programs deemed to be "medicalized." Policy makers implementing or amending medical marijuana programs should consider the powerful relationship between less regulation and greater enrollment. Researchers should consider variations across programs when assessing programs' population-level effects.
To assess the association between medical marijuana laws (MMLs) and the odds of a positive opioid test, an indicator for prior use.
We analyzed 1999-2013 Fatality Analysis Reporting System (FARS) ...data from 18 states that tested for alcohol and other drugs in at least 80% of drivers who died within 1 hour of crashing (n = 68 394). Within-state and between-state comparisons assessed opioid positivity among drivers crashing in states with an operational MML (i.e., allowances for home cultivation or active dispensaries) versus drivers crashing in states before a future MML was operational.
State-specific estimates indicated a reduction in opioid positivity for most states after implementation of an operational MML, although none of these estimates were significant. When we combined states, we observed no significant overall association (odds ratio OR = 0.79; 95% confidence interval CI = 0.61, 1.03). However, age-stratified analyses indicated a significant reduction in opioid positivity for drivers aged 21 to 40 years (OR = 0.50; 95% CI = 0.37, 0.67; interaction P < .001).
Operational MMLs are associated with reductions in opioid positivity among 21- to 40-year-old fatally injured drivers and may reduce opioid use and overdose.
Most information on the relationship between medical cannabis laws (MCL) and the risk for opioid overdose fatality has been based on studies with ecological designs. To contribute additional ...information, we used a novel case-control design and individual-level data from national surveys to assess whether state medical cannabis laws were associated with reduced risk of fatal opioid overdose between 2000-2011.
Data from participants surveyed in the National Health Interview Survey (NHIS) between 1986-2011 were included. For those sampled between 1986-2009, detailed mortality follow-up data were available from the National Death Index up to 12/31/2011. Opioid overdose decedents (n = 791) were classified as cases. Between 2000-2011, all cases arising in a given year were matched to adult controls who were surveyed the same year and eligible for mortality follow-up (n = 723,920). The distribution of exposure to state MCL was contrasted between cases and controls, providing an approximation of the rate ratio of fatal opioid overdose associated with MCLs. Due to a NHIS sample redesign, we stratified analysis using timeframes before and after 2005.
Overall, compared to controls, cases were more likely to be male, middle-aged, non-Hispanic White, separated/divorced; less educated, and have a family income below the poverty threshold. No overall association between state MCLs and the rate of opioid overdose was observed between 2000-2005 (aOR = 1.22, 95% CI: 0.83-1.79) or between 2006-2011 (aOR = 0.87, 95% CI: 0.60-1.25). No significant difference between sampling timeframes was observed (ratio of aOR's = 0.71, 95% CI: 0.49-1.01).
We found no overall protective relationship between state MCLs and opioid overdose. Future research with more recent mortality data and more refined cannabis policy classifications would be useful. The importance of the study is two-fold. First, the findings provide an additional source of information countering claims of a protective effect of MCLs on opioid overdoses, suggesting that other solutions to the opioid overdose crisis are needed. Second, the study offers a potentially useful design to answer important population-level public health questions.
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