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1.
Med Care ; 58(4): 307-313, 2020 04.
Article in English | MEDLINE | ID: mdl-31914105

ABSTRACT

OBJECTIVES: This study tested the impacts of peer specialists on housing stability, substance abuse, and mental health status for previously homeless Veterans with cooccurring mental health issues and substance abuse. METHODS: Veterans living in the US Housing and Urban Development-Veterans Administration Supported Housing (HUD-VASH) program were randomized to peer specialist services that worked independently from HUD-VASH case managers (ie, not part of a case manager/peer specialist dyad) and to treatment as usual that included case management services. Peer specialist services were community-based, using a structured curriculum for recovery with up to 40 weekly sessions. Standardized self-report measures were collected at 3 timepoints. The intent-to-treat analysis tested treatment effects using a generalized additive mixed-effects model that allows for different nonlinear relationships between outcomes and time for treatment and control groups. A secondary analysis was conducted for Veterans who received services from peer specialists that were adherent to the intervention protocol. RESULTS: Treated Veterans did not spend more days in housing compared with control Veterans during any part of the study at the 95% level of confidence. Veterans assigned to protocol adherent peer specialists showed greater housing stability between about 400 and 800 days postbaseline. Neither analysis detected significant effects for the behavioral health measures. CONCLUSIONS: Some impact of peer specialist services was found for housing stability but not for behavioral health problems. Future studies may need more sensitive measures for early steps in recovery and may need longer time frames to effectively impact this highly challenged population.


Subject(s)
Case Management , Health Status , Mental Disorders/therapy , Peer Group , Public Housing/statistics & numerical data , Substance-Related Disorders/therapy , Veterans/psychology , Female , Ill-Housed Persons/psychology , Humans , Intention to Treat Analysis , Male , Mental Disorders/complications , Middle Aged , Substance-Related Disorders/complications , United States
2.
Diabetes Technol Ther ; 13(12): 1241-8, 2011 Dec.
Article in English | MEDLINE | ID: mdl-21932986

ABSTRACT

BACKGROUND: Several metrics of glucose variability have been proposed to date, but an integrated approach that provides a complete and consistent assessment of glycemic variation is missing. As a consequence, and because of the tedious coding necessary during quantification, most investigators and clinicians have not yet adopted the use of multiple glucose variability metrics to evaluate glycemic variation. METHODS: We compiled the most extensively used statistical techniques and glucose variability metrics, with adjustable hyper- and hypoglycemic limits and metric parameters, to create a user-friendly Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation (CGM-GUIDE©). In addition, we introduce and demonstrate a novel transition density profile that emphasizes the dynamics of transitions between defined glucose states. RESULTS: Our combined dashboard of numerical statistics and graphical plots support the task of providing an integrated approach to describing glycemic variability. We integrated existing metrics, such as SD, area under the curve, and mean amplitude of glycemic excursion, with novel metrics such as the slopes across critical transitions and the transition density profile to assess the severity and frequency of glucose transitions per day as they move between critical glycemic zones. CONCLUSIONS: By presenting the above-mentioned metrics and graphics in a concise aggregate format, CGM-GUIDE provides an easy to use tool to compare quantitative measures of glucose variability. This tool can be used by researchers and clinicians to develop new algorithms of insulin delivery for patients with diabetes and to better explore the link between glucose variability and chronic diabetes complications.


Subject(s)
Blood Glucose Self-Monitoring/methods , Blood Glucose/metabolism , Data Interpretation, Statistical , Diabetes Mellitus, Type 1/metabolism , Blood Glucose/analysis , Blood Glucose Self-Monitoring/standards , Diabetes Mellitus, Type 1/blood , Humans , Nomograms
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