The impact of medical and recreational marijuana laws on opioid prescribing in employer-sponsored health insurance.
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ABSTRACT:
SUBMITTER: Wen J
PROVIDER: S-EPMC8237244 | biostudies-literature | 2021 May
REPOSITORIES: biostudies-literature
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The impact of medical and recreational marijuana laws on opioid prescribing in employer-sponsored health insurance.
Wen Jiebing J Wen Hefei H Butler J S JS Talbert Jeffery C JC
Health economics 20210223 5
Using data from Truven Health MarketScan Commercial Claims and Encounters Database between 2009 and 2015, we studied the effects of medical and recreational marijuana laws on opioid prescribing in employer-sponsored health insurance. We used a differences-in-differences (DD) approach and found that the implementation of medical marijuana laws (MMLs) and recreational marijuana laws (RMLs) reduced morphine milligram equivalents per enrollee by 7% and 13%, respectively. The reduction associated wit ...[more]
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| S-EPMC2614003 | biostudies-literature
Project description:ImportanceOverprescribing of opioids is considered a major driving force behind the opioid epidemic in the United States. Marijuana is one of the potential nonopioid alternatives that can relieve pain at a relatively lower risk of addiction and virtually no risk of overdose. Marijuana liberalization, including medical and adult-use marijuana laws, has made marijuana available to more Americans.ObjectiveTo examine the association of state implementation of medical and adult-use marijuana laws with opioid prescribing rates and spending among Medicaid enrollees.Design, setting, and participantsThis cross-sectional study used a quasi-experimental difference-in-differences design comparing opioid prescribing trends between states that started to implement medical and adult-use marijuana laws between 2011 and 2016 and the remaining states. This population-based study across the United States included all Medicaid fee-for-service and managed care enrollees, a high-risk population for chronic pain, opioid use disorder, and opioid overdose.ExposuresState implementation of medical and adult-use marijuana laws from 2011 to 2016.Main outcomes and measuresOpioid prescribing rate, measured as the number of opioid prescriptions covered by Medicaid on a quarterly, per-1000-Medicaid-enrollee basis.ResultsState implementation of medical marijuana laws was associated with a 5.88% lower rate of opioid prescribing (95% CI, -11.55% to approximately -0.21%). Moreover, the implementation of adult-use marijuana laws, which all occurred in states with existing medical marijuana laws, was associated with a 6.38% lower rate of opioid prescribing (95% CI, -12.20% to approximately -0.56%).Conclusions and relevanceThe potential of marijuana liberalization to reduce the use and consequences of prescription opioids among Medicaid enrollees deserves consideration during the policy discussions about marijuana reform and the opioid epidemic.
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Project description:ImportanceDirect costs of substance use disorders (SUDs) in the United States are incurred primarily among the working-age population. Quantifying the medical cost of SUDs in the employer-sponsored insurance (ESI) population can improve understanding of how SUDs are affecting workplaces and inform decision-making on the value of prevention strategies.ObjectiveTo estimate the annual attributable medical cost of SUDs in the ESI population from the health care payer perspective.Design, setting, and participantsIn this economic evaluation, Merative MarketScan 2018 databases were weighted to represent the non-Medicare eligible ESI population. Regression and mathematical modeling of medical expenditures controlled for insurance enrollee demographic, clinical, and insurance factors to compare enrollees with and without an SUD diagnosis to identify the annual attributable medical cost of SUDs. Data analysis was conducted from January to March 2022.ExposuresInternational Statistical Classification of Diseases, Tenth Revision, Clinical Modification SUD diagnoses on inpatient or outpatient medical records according to Clinical Classifications Software categories (alcohol-, cannabis-, hallucinogen-, inhalant-, opioid-, sedative-, stimulant-, and other substance-related disorders).Main outcomes and measuresAnnual SUD medical cost in the ESI population overall and by substance type (eg, alcohol). Number of enrollees with an SUD diagnosis and the annual mean cost per affected enrollee of SUD diagnosis (any and by substance type) are also reported.ResultsAmong 162 million ESI enrollees, 2.3 million (1.4%) had an SUD diagnosis in 2018. The regression analysis sample included 210 225 individuals with an SUD diagnosis (121 357 [57.7%] male individuals; 68 325 [32.5%] aged 25-44 years) and 1 049 539 individuals with no SUD diagnosis. The mean annual medical cost attributable to SUD diagnosis per affected enrollee was $15 640 (95% CI, $15 340-$15 940), and the total annual medical cost in the ESI population was $35.3 billion (2018 USD). Alcohol use disorder ($10.2 billion) and opioid use disorder ($7.3 billion) were the most costly.Conclusions and relevanceIn this economic evaluation of medical expenditures in the ESI population, the per-person and total medical costs of SUDs were substantial. Strategies to support employees and their health insurance dependents to prevent and treat SUDs can be considered in terms of potentially offsetting the existing high medical cost of SUDs. Medical expenditures for SUDs represent the minimum direct cost that employers and health insurers face because not all people with SUDs have a diagnosis, and costs related to absenteeism, presenteeism, job retention, and mortality are not addressed.
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| S-EPMC5365078 | biostudies-literature
Project description:ObjectiveWe investigate the factors driving the downward trend in employer sponsored health insurance (ESI) coverage between 1999 and 2002 for low- and middle-income workers, and assess their insurance options in the absence of ESI coverage.DataWe use the 1999 and 2002 rounds of the National Survey of America's Families (NSAF), supplemented with ESI premiums from the Medical Expenditure Panel Survey, as well as other state- and county-level data from a variety of sources. The sample includes workers between the ages of 19 and 64.Study designWe first estimate linear probability models of the probability of having an ESI offer and, for those with an offer, the probability of taking up ESI coverage, using two-stage least square regression on the 2002 worker sample. We then use Oaxaca-Blinder regression-based decomposition methods to identify the factors that explain the changes in ESI offer and take-up between 1999 and 2002.Principal findingsWe find that while low-income workers are more likely to be uninsured and are most vulnerable to the loss of ESI coverage, many middle-income workers are also in a precarious position when faced with the loss of ESI coverage. Many low- and middle-income workers have few coverage options in the absence of ESI. This is particularly problematic for low-income workers: only 13 percent have a spouse with an ESI offer and the nongroup premium they face increased at a much higher rate than for middle-income workers. Finally, we find that the drop in ESI offers between 1999 and 2002 was driven largely by changes in nature of the workers' jobs, while the drop in ESI take-up was driven largely by rising ESI premiums.ConclusionsPolicies that shore up the ESI system are important for both low- and middle-income workers, as both are vulnerable to a loss of insurance coverage in the absence of ESI. Over time, the potential coverage options available to low- and middle-income workers in the absence of ESI have narrowed as nongroup premiums have increased. While public coverage has provided some protection from that increase for low-income workers, middle-income workers are much less likely to have access to public protection.
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| S-EPMC6647549 | biostudies-literature
Project description:In states that have passed medical marijuana laws (MMLs), marijuana use (MU) increased after MML enactment among people ages 26 and older, but not among ages 12-25. We examined whether the age-specific impact of MMLs on MU varied by gender. Data were obtained from the 2004-2013 restricted-use National Survey on Drug Use and Health, aggregated at the state level. The exposure was a time-varying indicator of state-level MML (0 = No Law, 1 = Before Law, 2 = After Law). Outcomes included past-month MU prevalence, daily MU prevalence among past-year users (i.e., 300+ days/year), and past-year marijuana use disorder (MUD) prevalence. Linear models tested the state-level MML effect on outcomes by age (12-17, 18-25, 26+) and gender. Models included a state-level random intercept and controlled for time- and state-level covariates. Past-month MU did not increase after enactment of MML in men or women ages 12-25. Among people 26+, past-month MU increased for men from 7.0% before to 8.7% after enactment (+ 1.7%, p < 0.001) and for women from 3.1% before to 4.3% after enactment (+ 1.1%, p = 0.013). Among users 26+, daily MU also increased after enactment in both genders (men 16.3 to 19.1%, + 2.8%, p = 0.014; women 9.2 to 12.7%, + 3.4%, p = 0.003). There were no statistically significant increases in past-year MUD prevalence for any age or gender group after MML enactment. Given the statistically significant increase in daily use among past-year users aged 26+ following enactment, education campaigns should focus on informing the public of the risks associated with regular marijuana use.
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| S-EPMC9441291 | biostudies-literature
Project description:IntroductionIn response to the role of opioid prescribing in the U.S. opioid crisis, states have enacted laws intended to curb high risk opioid prescribing practices. This study assessed the effects of state prescribing cap laws that limit the dose and/or duration of dispensed opioid prescriptions on opioid prescribing patterns and opioid overdose.MethodsWe identified 1,414,908 adults from a large U.S. administrative insurance claims database. Treatment states included 32 states that implemented a prescribing cap law between 2017 and 2019. Comparison states included 16 states and DC without a prescribing cap law by 2019. A difference-in-differences approach with staggered policy adoption was used to assess effects of these laws on opioid analgesic prescribing and opioid overdose.ResultsState opioid prescribing cap laws were not associated with changes in the proportion of people receiving opioid analgesic prescriptions, the dose or duration of opioid prescriptions, or opioid overdose. States with laws that imposed days' supply limits only versus days' supply and dosage limits, as well as with specific law provisions also showed no association with opioid prescribing or opioid overdose outcomes.ConclusionsState opioid prescribing cap laws did not appear to impact outcomes related to opioid analgesic prescribing or opioid overdose. These findings are potentially due to the limited scope of these laws, which often apply only to a subset of opioid prescriptions and include professional judgment exemptions.
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Project description:ObjectiveTo estimate productivity losses and costs and medical costs due to type 2 diabetes (T2D) among employees aged 18-64 years.Research design and methodsUsing 2018-2019 MarketScan databases, we identified employees with T2D or no diabetes among those with records on workplace absences, short-term disability (STD), and long-term disability (LTD). We estimated per capita mean annual time loss attributable to T2D and its associated costs, calculated by multiplying time loss by average hourly wage. We estimated direct medical costs of T2D in total and by service type (inpatient, outpatient, and prescription drugs). We used two-part models (productivity losses and costs and inpatient and drug costs) and generalized linear models (total and outpatient costs) for overall and subgroup analyses by age and sex. All costs were in 2019 U.S. dollars.ResultsEmployees with T2D had 4.2 excess days lost (20.8 vs. 20.3 absences, 6.4 vs. 3.3 STD days, and 1.0 vs. 0.4 LTD days) than those without diabetes. Productivity costs were 13.3% ($680) higher and medical costs were double (total $11,354 vs. $5,101; outpatient $4,558 vs. $2,687, inpatient $3,085 vs. $1,349, prescription drugs $4,182 vs. $1,189) for employees with T2D. Employees aged 18-34 years had higher STD days and outpatient costs. Women had more absences and STD days and higher outpatient costs than men.ConclusionsT2D contributes nearly $7,000 higher annual per capita costs, mostly due to excess medical costs. Our estimates may assist employers to assess potential financial gains from efforts to help workers prevent or better manage T2D.
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