IMPORTANCE: Little is known about changes in marijuana use and cannabis use disorder (CUD) after recreational marijuana legalization (RML). OBJECTIVES: To examine the associations between RML ...enactment and changes in marijuana use, frequent use, and CUD in the United States from 2008 to 2016. DESIGN, SETTING, AND PARTICIPANTS: This survey study used repeated cross-sectional survey data from the National Survey on Drug Use and Health (2008-2016) conducted in the United States among participants in the age groups of 12 to 17, 18 to 25, and 26 years or older. INTERVENTIONS: Multilevel logistic regression models were fit to obtain estimates of before-vs-after changes in marijuana use among respondents in states enacting RML compared to changes in other states. MAIN OUTCOMES AND MEASURES: Self-reported past-month marijuana use, past-month frequent marijuana use, past-month frequent use among past-month users, past-year CUD, and past-year CUD among past-year users. RESULTS: The study included 505 796 respondents consisting of 51.51% females and 77.24% participants 26 years or older. Among the total, 65.43% were white, 11.90% black, 15.36% Hispanic, and 7.31% of other race/ethnicity. Among respondents aged 12 to 17 years, past-year CUD increased from 2.18% to 2.72% after RML enactment, a 25% higher increase than that for the same age group in states that did not enact RML (odds ratio OR, 1.25; 95% CI, 1.01-1.55). Among past-year marijuana users in this age group, CUD increased from 22.80% to 27.20% (OR, 1.27; 95% CI, 1.01-1.59). Unmeasured confounders would need to be more prevalent in RML states and increase the risk of cannabis use by 1.08 to 1.11 times to explain observed results, indicating results that are sensitive to omitted variables. No associations were found among the respondents aged 18 to 25 years. Among respondents 26 years or older, past-month marijuana use after RML enactment increased from 5.65% to 7.10% (OR, 1.28; 95% CI, 1.16-1.40), past-month frequent use from 2.13% to 2.62% (OR, 1.24; 95% CI, 1.08-1.41), and past-year CUD from 0.90% to 1.23% (OR, 1.36; 95% CI, 1.08-1.71); these results were more robust to unmeasured confounding. Among marijuana users in this age group, past-month frequent marijuana use and past-year CUD did not increase after RML enactment. CONCLUSIONS AND RELEVANCE: This study’s findings suggest that although marijuana legalization advanced social justice goals, the small post-RML increase in risk for CUD among respondents aged 12 to 17 years and increased frequent use and CUD among adults 26 years or older in this study are a potential public health concern. To undertake prevention efforts, further studies are warranted to assess how these increases occur and to identify subpopulations that may be especially vulnerable.
The evolving nature of the opioid epidemic and continued increases in overdose deaths highlight a need for fundamental change in the collection and use of surveillance data to link them to ...implementation of effective service, treatment, and prevention approaches. Yet at present, the quality and timeliness of US surveillance data often limits data-driven approaches. We review current information needs, summarize limitations of existing data, propose complementary surveillance resources, and provide examples of promising approaches designed to meet the needs of data end-users. We conclude that there is a need for an approach that focuses on the needs of data end-users, such as public health systems leaders, policy makers, public, nonprofit and prepaid healthcare systems, and other systems, such as the justice system. Such an approach, which may require investments in new infrastructure, should prioritize improvements in data timeliness, sample representativeness, database linkage, and increased flexibility to adapt to shifts in the environment, while preserving the privacy of survey participants. Use of simulations, distributed research and data networks, alternative data sources, such as wastewater or digital data collection and use of blockchain technology, are some of promising avenues toward an improved and more user-centered surveillance system.
IMPORTANCE: Because more than one-third of people making nonfatal suicide attempts do not receive mental health treatment, it is essential to extend suicide attempt risk factors beyond high-risk ...clinical populations to the general adult population. OBJECTIVE: To identify future suicide attempt risk factors in the general population using a data-driven machine learning approach including more than 2500 questions from a large, nationally representative survey of US adults. DESIGN, SETTING, AND PARTICIPANTS: Data came from wave 1 (2001 to 2002) and wave 2 (2004 to 2005) of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). NESARC is a face-to-face longitudinal survey conducted with a national representative sample of noninstitutionalized civilian population 18 years and older in the US. The cumulative response rate across both waves was 70.2% resulting in 34 653 wave 2 interviews. A balanced random forest was trained using cross-validation to develop a suicide attempt risk model. Out-of-fold model prediction was used to assess model performance, including the area under the receiver operator curve, sensitivity, and specificity. Survey design and nonresponse weights allowed estimates to be representative of the US civilian population based on the 2000 census. Analyses were performed between May 15, 2019, and June 10, 2020. MAIN OUTCOMES AND MEASURES: Attempted suicide in the 3 years between wave 1 and wave 2 interviews. RESULTS: Of 34 653 participants, 20 089 were female (weighted proportion, 52.1%). The weighted mean (SD) age was 45.1 (17.3) years at wave 1 and 48.2 (17.3) years at wave 2. Attempted suicide during the 3 years between wave 1 and wave 2 interviews was self-reported by 222 of 34 653 participants (0.6%). Using survey questions measured at wave 1, the suicide attempt risk model yielded a cross-validated area under the receiver operator characteristic curve of 0.857 with a sensitivity of 85.3% (95% CI, 79.8-89.7) and a specificity of 73.3% (95% CI, 72.8-73.8) at an optimized threshold. The model identified 1.8% of the US population to be at a 10% or greater risk of suicide attempt. The most important risk factors were 3 questions about previous suicidal ideation or behavior; 3 items from the 12-Item Short Form Health Survey, namely feeling downhearted, doing activities less carefully, or accomplishing less because of emotional problems; younger age; lower educational achievement; and recent financial crisis. CONCLUSIONS AND RELEVANCE: In this study, after searching through more than 2500 survey questions, several well-known risk factors of suicide attempt were confirmed, such as previous suicidal behaviors and ideation, and new risks were identified, including functional impairment resulting from mental disorders and socioeconomic disadvantage. These results may help guide future clinical assessment and the development of new suicide risk scales.
Suicide Risks of Health Care Workers in the US Olfson, Mark; Cosgrove, Candace M; Wall, Melanie M ...
JAMA : the journal of the American Medical Association,
09/2023, Letnik:
330, Številka:
12
Journal Article
Recenzirano
Historically elevated risks of suicide among physicians may have declined in recent decades. Yet there remains a paucity of information concerning suicide risks among other health care workers.
To ...estimate risks of death by suicide among US health care workers.
Cohort study of a nationally representative sample of workers from the 2008 American Community Survey (N = 1 842 000) linked to National Death Index records through December 31, 2019.
Age- and sex-standardized suicide rates were estimated for 6 health care worker groups (physicians, registered nurses, other health care-diagnosing or treating practitioners, health technicians, health care support workers, social/behavioral health workers) and non-health care workers. Cox models estimated hazard ratios (HRs) of suicide for health care workers compared with non-health care workers using adjusted HRs for age, sex, race and ethnicity, marital status, education, and urban or rural residence.
Annual standardized suicide rates per 100 000 persons (median age, 44 IQR, 35-53 years; 32.4% female among physicians to 91.1% among registered nurses) were 21.4 (95% CI, 15.4-27.4) for health care support workers, 16.0 (95% CI, 9.4-22.6) for registered nurses, 15.6 (95% CI, 10.9-20.4) for health technicians, 13.1 (95% CI, 7.9-18.2) for physicians, 10.1 (95% CI, 6.0-14.3) for social/behavioral health workers, 7.6 (95% CI, 3.7-11.5) for other health care-diagnosing or treating practitioners, and 12.6 (95% CI, 12.1-13.1) for non-health care workers. The adjusted hazards of suicide were increased for health care workers overall (adjusted HR, 1.32 95% CI, 1.13-1.54), health care support workers (adjusted HR, 1.81 95% CI, 1.35-2.42), registered nurses (adjusted HR, 1.64 95% CI, 1.21-2.23), and health technicians (adjusted HR, 1.39 95% CI, 1.02-1.89), but adjusted hazards of suicide were not increased for physicians (adjusted HR, 1.11 95% CI, 0.71-1.72), social/behavioral health workers (adjusted HR, 1.14 95% CI, 0.75-1.72), or other health care-diagnosing or treating practitioners (adjusted HR, 0.61 95% CI, 0.36-1.03) compared with non-health care workers (reference).
Relative to non-health care workers, registered nurses, health technicians, and health care support workers in the US were at increased risk of suicide. New programmatic efforts are needed to protect the mental health of these US health care workers.
A Population-Level Approach to Suicide Prevention Blanco, Carlos; Wall, Melanie M; Olfson, Mark
JAMA : the journal of the American Medical Association,
06/2021, Letnik:
325, Številka:
23
Journal Article
Recenzirano
The recent signing into law of the $1.9 trillion American Rescue Plan aims to improve the financial security of millions of people with low and middle incomes in the US through extending unemployment ...benefits, providing direct payments, funding emergency paid leave, and several other measures. The epidemiologic literature connecting financial crises to elevated risk of suicidal behavior raises the possibility that by improving financial security at a population level, the new legislation might help reverse a 2-decades-long gradual national increase in suicide.
IMPORTANCE: Over the last 25 years, illicit cannabis use and cannabis use disorders have increased among US adults, and 28 states have passed medical marijuana laws (MML). Little is known about MML ...and adult illicit cannabis use or cannabis use disorders considered over time. OBJECTIVE: To present national data on state MML and degree of change in the prevalence of cannabis use and disorders. DESIGN, PARTICIPANTS, AND SETTING: Differences in the degree of change between those living in MML states and other states were examined using 3 cross-sectional US adult surveys: the National Longitudinal Alcohol Epidemiologic Survey (NLAES; 1991-1992), the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC; 2001-2002), and the National Epidemiologic Survey on Alcohol and Related Conditions–III (NESARC-III; 2012-2013). Early-MML states passed MML between NLAES and NESARC (“earlier period”). Late-MML states passed MML between NESARC and NESARC-III (“later period”). MAIN OUTCOMES AND MEASURES: Past-year illicit cannabis use and DSM-IV cannabis use disorder. RESULTS: Overall, from 1991-1992 to 2012-2013, illicit cannabis use increased significantly more in states that passed MML than in other states (1.4–percentage point more; SE, 0.5; P = .004), as did cannabis use disorders (0.7–percentage point more; SE, 0.3; P = .03). In the earlier period, illicit cannabis use and disorders decreased similarly in non-MML states and in California (where prevalence was much higher to start with). In contrast, in remaining early-MML states, the prevalence of use and disorders increased. Remaining early-MML and non-MML states differed significantly for use (by 2.5 percentage points; SE, 0.9; P = .004) and disorder (1.1 percentage points; SE, 0.5; P = .02). In the later period, illicit use increased by the following percentage points: never-MML states, 3.5 (SE, 0.5); California, 5.3 (SE, 1.0); Colorado, 7.0 (SE, 1.6); other early-MML states, 2.6 (SE, 0.9); and late-MML states, 5.1 (SE, 0.8). Compared with never-MML states, increases in use were significantly greater in late-MML states (1.6–percentage point more; SE, 0.6; P = .01), California (1.8–percentage point more; SE, 0.9; P = .04), and Colorado (3.5–percentage point more; SE, 1.5; P = .03). Increases in cannabis use disorder, which was less prevalent, were smaller but followed similar patterns descriptively, with change greater than never-MML states in California (1.0–percentage point more; SE, 0.5; P = .06) and Colorado (1.6–percentage point more; SE, 0.8; P = .04). CONCLUSIONS AND RELEVANCE: Medical marijuana laws appear to have contributed to increased prevalence of illicit cannabis use and cannabis use disorders. State-specific policy changes may also have played a role. While medical marijuana may help some, cannabis-related health consequences associated with changes in state marijuana laws should receive consideration by health care professionals and the public.
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