ArticleJournal of substance use and addiction treatment2024
Methadone treatment utilization and overdose trends among Medicaid beneficiaries in New Jersey before and during the COVID-19 pandemic.
Article in Journal of substance use and addiction treatment, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
introductionThe COVID-19 pandemic disrupted the traditional mode of methadone maintenance treatment (MMT) delivery through the imposition of lockdowns and social distancing measures. In response, policy makers granted flexibilities to providers delivering MMT to change their practices to maintain patient participation while accommodating the measures imposed to prevent the spread of COVID-19. This study examines the utilization of MMT and overdoses of patients receiving MMT during the COVID-19 pandemic in one mid-Atlantic state. MATERIALS AND
methodsWe analyzed Medicaid claims data for 2018-2020, calculating weekly trends for starts, discontinuations, and medically-treated overdoses for beneficiaries receiving MMT who had been continuously enrolled in Medicaid for the previous 12 months, to account for changes in the composition of the Medicaid population following the COVID-19 public health emergency (PHE). We completed data analyses from January to June 2022.
resultsWe observed countervailing trends in new starts, which experienced an immediate, non-significant dip of -22.47 per 100,000 Medicaid beneficiaries (95%CI, -50.99 to 6.04) at the outset of the pandemic followed by an increasing upward trend of 1.41 per 100,000 beneficiaries per week (95%CI, 0.37 to 2.46), and in discontinuations, which also experienced an immediate dip of -3.23 per 1000 MMT enrollees (95%CI, -4.49 to -1.97) followed by an increasing upward trend of 0.14 per 1000 MMT enrollees per week (95%CI, 0.09 to 0.19). The net result of these shifts was a stable, slowly increasing rate of MMT treatment of 0.02 % per week before and after the PHE. We also found no statistically significant association of the PHE with medically-treated overdoses among beneficiaries enrolled in MMT (trend change = 0.02 overdoses per 10,000 MMT enrollees, 95%CI, -0.05 to 0.09).
conclusionsNew Jersey achieved overall stability in MMT treatment prevalence following the pandemic's onset, while some changes in treatment dynamics took place. This outcome may reflect that the extensive flexibilities granted to providers of MMT by the state and federal government successfully maintained access to MMT for Medicaid beneficiaries through the pandemic without increasing risk of medically-treated overdose. These findings should inform policy makers developing the post-COVID-19 legal and regulatory landscape.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.