Literature DB >> 36129721

Estimating the Prevalence of Substance Use Disorders in the US Using the Benchmark Multiplier Method.

Ramin Mojtabai1,2.   

Abstract

Importance: Prevalence estimates of substance use disorders in the US rely on general population surveys. However, major population groups, such as homeless individuals and institutionalized individuals, are not captured by these surveys, and participants may underreport substance use. Objective: To estimate the prevalence of substance use disorders in the US. Design, Setting, and Participants: The benchmark multiplier method was used to estimate the prevalence of alcohol, cannabis, opioid, and stimulant use disorders based on data from the Transformed Medicaid Statistical Information System (T-MSIS) (the benchmark) and the National Survey on Drug Use and Health (NSDUH) (the multiplier) for 2018 and 2019. T-MSIS collects administrative data on Medicaid beneficiaries 12 years and older with full or comprehensive benefits. NSDUH is a nationally representative annual cross-sectional survey of people 12 years and older. Data were analyzed from February to June 2022. Main Outcomes and Measures: Prevalence of substance use disorders was estimated using the benchmark multiplier method based on T-MSIS and NSDUH data. Confidence intervals for the multiplier method estimates were computed using Monte Carlo simulations. Sensitivity of prevalence estimates to variations in multiplier values was assessed.
Results: This study included Medicaid beneficiaries 12 years and older accessing treatment services in the past year with diagnoses of alcohol (n = 1 017 308 in 2018; n = 1 041 357 in 2019), cannabis (n = 643 737; n = 644 780), opioid (n = 1 406 455; n = 1 575 219), and stimulant (n = 610 858; n = 657 305) use disorders and NSDUH participants with 12-month DSM-IV alcohol (n = 3390 in 2018; n = 3363 in 2019), cannabis (n = 1426; n = 1604), opioid (n = 448; n = 369), and stimulant (n = 545; n = 559) use disorders. The benchmark multiplier prevalence estimates were higher than NSDUH estimates for every type of substance use disorder in both years and in the combined 2018 to 2019 sample: 20.27% (95% CI, 17.04-24.71) vs 5.34% (95% CI, 5.10-5.58), respectively, for alcohol; 7.57% (95% CI, 5.96-9.93) vs 1.68% (95% CI, 1.59-1.79) for cannabis; 3.46% (95% CI, 2.97-4.12) vs 0.68% (0.60-0.78) for opioid; and 1.91% (95% CI, 1.63-2.30) vs 0.85% (95% CI, 0.75-0.96) for stimulant use disorders. In sensitivity analyses, the differences between the benchmark multiplier method and NSDUH estimates persisted over a wide range of potential multiplier values. Conclusions and Relevance: The findings in this study reflect a higher national prevalence of substance use disorders than that represented by NSDUH estimates, suggesting a greater burden of these conditions in the US.

Entities:  

Year:  2022        PMID: 36129721      PMCID: PMC9494265          DOI: 10.1001/jamapsychiatry.2022.2756

Source DB:  PubMed          Journal:  JAMA Psychiatry        ISSN: 2168-622X            Impact factor:   25.911


  33 in total

1.  Incorporating the service multiplier method in respondent-driven sampling surveys to estimate the size of hidden and hard-to-reach populations: case studies from around the world.

Authors:  Lisa G Johnston; Dimitri Prybylski; H Fisher Raymond; Ali Mirzazadeh; Chomnad Manopaiboon; Willi McFarland
Journal:  Sex Transm Dis       Date:  2013-04       Impact factor: 2.830

2.  Assessing bias in community-based prevalence estimates: towards an unduplicated count of problem drinkers and drug users.

Authors:  C Weisner; L Schmidt; T Tam
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3.  Heroin Use Cannot Be Measured Adequately with a General Population Survey.

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4.  Estimating the Prevalence of Opioid use Disorder in the Cincinnati Region using Probabilistic Multiplier Methods and Model Averaging.

Authors:  Peter J Mallow; Nila Sathe; Michael Topmiller; Jennifer Chubinski; Dillon Carr; Roni Christopher
Journal:  J Health Econ Outcomes Res       Date:  2019-04-03

Review 5.  Ambiguous identities of drugs and people: A scoping review of opioid-related stigma.

Authors:  Melissa D McCradden; Denitsa Vasileva; Ani Orchanian-Cheff; Daniel Z Buchman
Journal:  Int J Drug Policy       Date:  2019-10-28

Review 6.  Sources of Error in Substance Use Prevalence Surveys.

Authors:  Timothy P Johnson
Journal:  Int Sch Res Notices       Date:  2014-11-05

7.  Alcohol consumption and associations with sociodemographic and health-related characteristics in Germany: A population survey.

Authors:  Claire Garnett; Sabrina Kastaun; Jamie Brown; Daniel Kotz
Journal:  Addict Behav       Date:  2021-10-19       Impact factor: 3.913

8.  Improved benchmark-multiplier method to estimate the prevalence of ever-injecting drug use in Belgium, 2000-10.

Authors:  Kaatje Bollaerts; Marc Aerts; Andre Sasse
Journal:  Arch Public Health       Date:  2013-05-03

9.  First back-calculation and infection fatality multiplier estimate of the hidden prevalence of COVID-19 in Ireland.

Authors:  Catherine M Comiskey; Anne Snel; Prakashini Banka
Journal:  Eur J Public Health       Date:  2021-07-10       Impact factor: 3.367

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