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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
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Background: Treatment discontinuation within Early Intervention Services (EIS) for psychosis poses a significant challenge to achieving better outcomes in the early stages of psychotic disorders. Prevalence and predictors of early disengagement from EIS located in low- and middle-income countries (LMICs) remain poorly investigated. We aimed to examine the rates and predictors of disengagement from the Ribeirão Preto Early Intervention Program for Psychosis (Ribeirão Preto-EIP) in Brazil.
Methods: We conducted a retrospective cohort study using data from patients referred to the Ribeirão Preto-EIP between January 01, 2015, and December 31, 2018. Exclusion criteria were individuals with a single consultation, a diagnosis other than a psychotic disorder, and documented cases of death.
Results: Our sample comprised 234 patients, with an overall median follow-up time of 14.2 months. Early treatment disengagement was observed in 26.5 % (n=62), with a median time to disengagement of 5.25 months. Univariable analysis identified non-white skin color (HR=2.10, 95 %CI 1.26-3.49), positive THC screening (HR=2.22, 95 %CI 1.23-4.01), and substance-induced psychosis (HR=2.15, 95 %CI 1.10-4.21) as significant predictors. In multivariable analysis, only non-white skin color remained a significant predictor of early disengagement (HR=1.87, 95 %CI 1.08-3.27).
Conclusions: The observed rates of early disengagement in our sample are similar to those reported in wealthy countries, but higher than previously reported for LMICs. Non-white skin color predicted early disengagement in our sample, probably due to social disadvantages. Our data highlights the need for enhanced research elucidating the specific features of EIS in LMICs.
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http://dx.doi.org/10.1016/j.ajp.2024.104119 | DOI Listing |
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