Changes in State Technical Assistance Priorities and Block Grant Funds for Addiction After ACA Implementation.

Am J Public Health

Amanda J. Abraham is with the School of Public and International Affairs, University of Georgia, Athens. Bikki Tran Smith, Colleen M. Grogan, and Harold A. Pollack are with the School of Social Service Administration, University of Chicago, Chicago, IL. Christina M. Andrews is with the College of Social Work, University of South Carolina, Columbia. Clifford S. Bersamira is with the Myron B. Thompson School of Social Work, University of Hawai'i at Mānoa, Honolulu. Peter D. Friedmann is with the University of Massachusetts Medical School Baystate, Springfield.

Published: June 2019

To assess states' provision of technical assistance and allocation of block grants for treatment, prevention, and outreach after the expansion of health insurance coverage for addiction treatment in the United States under the Affordable Care Act (ACA). We used 2 waves of survey data collected from Single State Agencies in 2014 and 2017 as part of the National Drug Abuse Treatment System Survey. The percentage of states providing technical assistance for cross-sector collaboration and workforce development increased. States also shifted funds from outpatient to residential treatment services. However, resources for opioid use disorder medications changed little. Subanalyses indicated that technical assistance priorities and allocation of funds for treatment services differed between Medicaid expansion and nonexpansion states. The ACA's infusion of new public and private funds enabled states to reallocate funds to residential services, which are not as likely to be covered by health insurance. The limited allocation of block grant funds for effective opioid medications is concerning in light of the opioid crisis, especially in states that did not implement the ACA's Medicaid expansion.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6507974PMC
http://dx.doi.org/10.2105/AJPH.2019.305052DOI Listing

Publication Analysis

Top Keywords

technical assistance
16
assistance priorities
8
block grant
8
grant funds
8
allocation block
8
health insurance
8
treatment services
8
medicaid expansion
8
funds
6
states
6

Similar Publications

Natural honey is enriched with essential and beneficial nutrients. This study aimed to investigate the melliferous flora microscopic techniques and assess the biochemical properties of honey. Flavonoid and phenolic contents in honey samples were analyzed via colorimetric and Folin-Ciocalteu methods and the alpha-amylase, reducing power, and minerals using Pull's and spectroscopy methods.

View Article and Find Full Text PDF

Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.

View Article and Find Full Text PDF

Accurate estimation of the soil resilient modulus (M) is essential for designing and monitoring pavements. However, experimental methods tend to be time-consuming and costly; regression equations and constitutive models usually have limited applications, while the predictive accuracy of some machine learning studies still has room for improvement. To forecast M efficiently and accurately, a new model named black-winged kite algorithm-extreme gradient boosting (BKA-XGBOOST) is proposed.

View Article and Find Full Text PDF

The Epstein-Barr virus (EBV) is widespread and has been related to a variety of malignancies as well as infectious mononucleosis. Despite the lack of a vaccination, antiviral medications offer some therapy alternatives. The EBV BZLF1 gene significantly impacts viral replication and infection severity.

View Article and Find Full Text PDF

Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-consuming and error-prone. Automatic detection and segmentation can assist radiologists in these tasks. This work explores the automated detection and segmentation of brain metastases (BMs) in longitudinal MRIs.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!