Objective: The aim of the study is to investigate clinically meaningful, secondary outcomes in a randomized trial of a culturally adapted contingency management (CM) intervention for alcohol use in 3 diverse American Indian and Alaska Native communities.
Methods: Three American Indian and Alaska Native communities located in the Northern Plains, Alaska, and the Inland Northwest were partnering sites. A total of 158 individuals were randomized to either a 12-week CM intervention or a noncontingent (NC) control group. The CM group received reinforcers for providing alcohol-negative ethyl glucuronide (EtG < 150 ng/mL) urine samples, while the NC group received reinforcers unconditionally. Outcomes included EtG as a continuous measure (range, 0-2,000 ng/mL), EtG > 499 ng/mL (a measure of higher levels of recent alcohol use), longest duration of abstinence, and time-to-first alcohol-positive EtG during the trial. Generalized estimating equations along with Cox proportional hazard and negative binomial regressions were used.
Results: Participants randomized to the CM group had lower mean EtG levels (-241.9 ng/mL; 95% confidence interval [CI], -379.0 to -104.8 ng/mL) and 45.7% lower odds (95% CI, 0.31 to 0.95) of providing an EtG sample indicating higher levels of alcohol use during the intervention. Longest duration of abstinence was 43% longer for the CM group than the NC group (95% CI, 1.0 to 1.9). Risk of time-to-first drink during treatment did not differ significantly.
Conclusions: These secondary outcome analyses provide evidence that CM is associated with reductions in alcohol use and longer durations of abstinence (as assessed by EtG), both clinically meaningful endpoints and analyses that differ from the primary study outcome.
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http://dx.doi.org/10.1097/ADM.0000000000001116 | DOI Listing |
Sci Rep
January 2025
Department of Computer Science, Faculty of Computers and Information, Suez University, P. O. Box 43221, Suez, Egypt.
Diabetes is a long-term condition characterized by elevated blood sugar levels. It can lead to a variety of complex disorders such as stroke, renal failure, and heart attack. Diabetes requires the most machine learning help to diagnose diabetes illness at an early stage, as it cannot be treated and adds significant complications to our health-care system.
View Article and Find Full Text PDFAm Heart J Plus
January 2025
Department of Cardiac Electrophysiology, University of California Davis Health, Sacramento, CA, USA.
Background: Stroke associated with atrial fibrillation (AF) is a significant cause of mortality. This study analyzed demographic trends and disparities in mortality rates due to stroke in AF patients aged ≥25 years.
Methods: A retrospective analysis was conducted to acquire death data using the Centers for Disease Control and Prevention database from 1999 to 2020.
Cureus
December 2024
Community Medicine, Dr. D. Y. Patil Medical College, Hospital and Research Centre, Dr. D. Y. Patil Vidyapeeth, Pune, IND.
This review article examines the life and medical contributions of Dr. Ida Sophia Scudder (1870-1960), a pioneering American physician and missionary who significantly impacted healthcare in India. Born into a family of medical missionaries, Scudder initially resisted following in her family's footsteps.
View Article and Find Full Text PDFEnviron Res Health
March 2025
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, United States of America.
North Carolina (NC) ranks third among US states in both hog production and hurricanes. NC's hogs are housed in concentrated animal feeding operations (CAFOs) in the eastern, hurricane-prone part of the state. Hurricanes can inundate hog waste lagoons, transporting fecal bacteria that may cause acute gastrointestinal illness (AGI).
View Article and Find Full Text PDFNPP Digit Psychiatry Neurosci
January 2025
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA.
Reinforcement learning studies propose that decision-making is guided by a tradeoff between computationally cheaper model-free (habitual) control and costly model-based (goal-directed) control. Greater model-based control is typically used under highly rewarding conditions to minimize risk and maximize gain. Although prior studies have shown impairments in sensitivity to reward value in individuals with frequent alcohol use, it is unclear how these individuals arbitrate between model-free and model-based control based on the magnitude of reward incentives.
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