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Granular analysis of pathways to care and durations of untreated psychosis: A marginal delay model

OBJECTIVE: An extensive international literature demonstrates that understanding pathways to care (PTC) is essential for efforts to reduce community Duration of Untreated Psychosis (DUP). However, knowledge from these studies is difficult to translate to new settings. We present a novel approach to...

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Autores principales: Mathis, Walter S., Ferrara, Maria, Burke, Shadie, Hyun, Emily, Li, Fangyong, Zhou, Bin, Cahill, John, Kline, Emily R., Keshavan, Matcheri S., Srihari, Vinod H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9725156/
https://www.ncbi.nlm.nih.gov/pubmed/36472968
http://dx.doi.org/10.1371/journal.pone.0270234
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author Mathis, Walter S.
Ferrara, Maria
Burke, Shadie
Hyun, Emily
Li, Fangyong
Zhou, Bin
Cahill, John
Kline, Emily R.
Keshavan, Matcheri S.
Srihari, Vinod H.
author_facet Mathis, Walter S.
Ferrara, Maria
Burke, Shadie
Hyun, Emily
Li, Fangyong
Zhou, Bin
Cahill, John
Kline, Emily R.
Keshavan, Matcheri S.
Srihari, Vinod H.
author_sort Mathis, Walter S.
collection PubMed
description OBJECTIVE: An extensive international literature demonstrates that understanding pathways to care (PTC) is essential for efforts to reduce community Duration of Untreated Psychosis (DUP). However, knowledge from these studies is difficult to translate to new settings. We present a novel approach to characterize and analyze PTC and demonstrate its value for the design and implementation of early detection efforts. METHODS: Type and date of every encounter, or node, along the PTC were encoded for 156 participants enrolled in the clinic for Specialized Treatment Early in Psychosis (STEP), within the context of an early detection campaign. Marginal-delay, or the portion of overall delay attributable to a specific node, was computed as the number of days between the start dates of contiguous nodes on the PTC. Sources of delay within the network of care were quantified and patient characteristic (sex, age, race, income, insurance, living, education, employment, and function) influences on such delays were analyzed via bivariate and mixed model testing. RESULTS: The period from psychosis onset to antipsychotic prescription was significantly longer (52 vs. 20.5 days, [p = 0.004]), involved more interactions (3 vs. 1 nodes, [p<0.001]), and was predominated by encounters with non-clinical nodes while the period from antipsychotic to STEP enrollment was shorter and predominated by clinical nodes. Outpatient programs were the greatest contributor of marginal delays on both before antipsychotic prescription (median [IQR] of 36.5 [1.3–132.8] days) and (median [IQR] of 56 [15–210.5] days). Sharper functional declines in the year before enrollment correlated significantly with longer DUP (p<0.001), while those with higher functioning moved significantly faster through nodes (p<0.001). No other associations were found with patient characteristics and PTCs. CONCLUSIONS: The conceptual model and analytic approach outlined in this study give first episode services tools to measure, analyze, and inform strategies to reduce untreated psychosis.
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spelling pubmed-97251562022-12-07 Granular analysis of pathways to care and durations of untreated psychosis: A marginal delay model Mathis, Walter S. Ferrara, Maria Burke, Shadie Hyun, Emily Li, Fangyong Zhou, Bin Cahill, John Kline, Emily R. Keshavan, Matcheri S. Srihari, Vinod H. PLoS One Research Article OBJECTIVE: An extensive international literature demonstrates that understanding pathways to care (PTC) is essential for efforts to reduce community Duration of Untreated Psychosis (DUP). However, knowledge from these studies is difficult to translate to new settings. We present a novel approach to characterize and analyze PTC and demonstrate its value for the design and implementation of early detection efforts. METHODS: Type and date of every encounter, or node, along the PTC were encoded for 156 participants enrolled in the clinic for Specialized Treatment Early in Psychosis (STEP), within the context of an early detection campaign. Marginal-delay, or the portion of overall delay attributable to a specific node, was computed as the number of days between the start dates of contiguous nodes on the PTC. Sources of delay within the network of care were quantified and patient characteristic (sex, age, race, income, insurance, living, education, employment, and function) influences on such delays were analyzed via bivariate and mixed model testing. RESULTS: The period from psychosis onset to antipsychotic prescription was significantly longer (52 vs. 20.5 days, [p = 0.004]), involved more interactions (3 vs. 1 nodes, [p<0.001]), and was predominated by encounters with non-clinical nodes while the period from antipsychotic to STEP enrollment was shorter and predominated by clinical nodes. Outpatient programs were the greatest contributor of marginal delays on both before antipsychotic prescription (median [IQR] of 36.5 [1.3–132.8] days) and (median [IQR] of 56 [15–210.5] days). Sharper functional declines in the year before enrollment correlated significantly with longer DUP (p<0.001), while those with higher functioning moved significantly faster through nodes (p<0.001). No other associations were found with patient characteristics and PTCs. CONCLUSIONS: The conceptual model and analytic approach outlined in this study give first episode services tools to measure, analyze, and inform strategies to reduce untreated psychosis. Public Library of Science 2022-12-06 /pmc/articles/PMC9725156/ /pubmed/36472968 http://dx.doi.org/10.1371/journal.pone.0270234 Text en © 2022 Mathis et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mathis, Walter S.
Ferrara, Maria
Burke, Shadie
Hyun, Emily
Li, Fangyong
Zhou, Bin
Cahill, John
Kline, Emily R.
Keshavan, Matcheri S.
Srihari, Vinod H.
Granular analysis of pathways to care and durations of untreated psychosis: A marginal delay model
title Granular analysis of pathways to care and durations of untreated psychosis: A marginal delay model
title_full Granular analysis of pathways to care and durations of untreated psychosis: A marginal delay model
title_fullStr Granular analysis of pathways to care and durations of untreated psychosis: A marginal delay model
title_full_unstemmed Granular analysis of pathways to care and durations of untreated psychosis: A marginal delay model
title_short Granular analysis of pathways to care and durations of untreated psychosis: A marginal delay model
title_sort granular analysis of pathways to care and durations of untreated psychosis: a marginal delay model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9725156/
https://www.ncbi.nlm.nih.gov/pubmed/36472968
http://dx.doi.org/10.1371/journal.pone.0270234
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