Translating Evidence for Early Intervention in Psychosis (TRANSLATE): Protocol for Evaluating the Implementation of Early Intervention in Psychosis in Low and Lower-Middle Income Countries (LMICs).
12 affiliations
- 1Faculty of Medicine and Health Sciences, Keele University, Newcastle-under-Lyme, UK.
- 2Rheumatology and Musculoskeletal Sciences, Nuffield Department of Orthopaedics, University of Oxford, Oxford, UK.
- 3Institute of Family Medicine, Khyber Medical University, Peshawar, Pakistan.
- 4Psychiatry Department, Lady Reading Hospital, Peshawar, Pakistan.
- 5Institute of Public Mental Health & Behavioural Sciences, Khyber Medical University, Peshawar, Pakistan.
- 6Department & Organization Office of Research, Innovation, and Commercialization (ORIC), Khyber Medical University Peshawar, Peshawar, Pakistan.
- 7Department of Psychiatry and Behavioural Sciences, Faisalabad Medical University, Faisalabad, Pakistan.
- 8Department of Psychiatry and Behavioural Sciences, Ayub Medical Teaching Institute, Abbottabad, Pakistan.
- 9Balochistan, Institute of Psychiatry and Behavioural Science, Quetta, Pakistan.
- 10Medicine and Allied Specialties Liaquat University of Medical and Health Sciences, Jamshoro, Pakistan.
- 11Institute for Research and Development in Health and Social Care, Battaramulla, Sri Lanka.
- 12Karapitiya Teaching Hospital, Galle, Sri Lanka.
Abstract
Background
In high-income countries, early intervention in psychosis (EIP) services provide treatment for first episode psychosis (FEP) as early as possible, proven to effectively improve health outcomes. These services rarely exist in low- and lower-middle-income countries (LMICs). The TRANSLATE study aims to evaluate the implementation of the EIP services using the selected indicators and to develop a clinical tool to predict the risk of Treatment-Resistant Schizophrenia (TRS) in patients with FEP.Methods
This is an observational implementation research study conducted across five sites in Pakistan and one in Sri Lanka, recruiting 670 FEP patients. The evaluation of the implementation of the service will include key clinical and process outcomes such as disengagement from services, rates of remission, the level of functioning, quality of life, psychopathology and physical health. Required data will be collected over a 1-year period at baseline and at 1, 3, 6 and 12-month intervals. A descriptive analysis of baseline data and service utilisation data will be conducted, stratified by site. Probabilities of disengagement, remission and process evaluation outcomes will be modelled using logistic regression models adjusting for duration of untreated psychosis, baseline symptom severity and geographical location. Selected key clinical outcomes will also be analysed and presented descriptively. To develop a clinical model prediction model for TRS, a penalised multivariable logistic regression model will be fitted to estimate the one-year risk of TRS following an FEP diagnosis, incorporating up to 15 predictor variables identified within the study cohort. Model performance will be assessed using calibration plots and the C-statistics for discrimination. Internal validation will be carried out by bootstrapping and the optimism-adjusted calibration slope will be used as a global shrinkage factor to account for overfitting, with the model intercept re-estimated to ensure calibration-in-the-large.Discussion
This will be the first large study that will evaluate the implementation of EIP service in a LMIC country setting. The findings of this study will inform the adoption and scaling-up of EIP services in routine clinical practice. The clinical prediction model for the early detection of TRS will help initiate effective treatments for TRS at the earliest possible stage and potentially reduce the significant burden of disease associated with TRS.Trial registration
ClinicalTrials.gov identifier: ISRCTN16495294.
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BibTeX
@article{farooq2026translating,
title = {{Translating Evidence for Early Intervention in Psychosis (TRANSLATE): Protocol for Evaluating the Implementation of Early Intervention in Psychosis in Low and Lower-Middle Income Countries (LMICs).}},
author = {Saeed Farooq and Nishani Fonseka and Nalinda Wellappuli and Abbie Milner and Saima Sheikh and Abdul Jalil Khan and Mian Mukhtar-Ul-Haq Azeemi and Muhammad Firaz Khan and Laila Noushad and Imtiaz Ahmad Dogar and Aftab Alam Khan and Hazrat Ali Khan and Moin Ahmed Ansari and Athula Sumathipala and Rumi Ruben and Ivonne Solis-Trapala},
journal = {Early intervention in psychiatry},
year = {2026},
month = oct,
doi = {10.1111/eip.70243},
url = {https://europepmc.org/article/MED/42905631},
note = {CC BY},
}
RIS
TY - JOUR
TI - Translating Evidence for Early Intervention in Psychosis (TRANSLATE): Protocol for Evaluating the Implementation of Early Intervention in Psychosis in Low and Lower-Middle Income Countries (LMICs).
AU - Saeed Farooq
AU - Nishani Fonseka
AU - Nalinda Wellappuli
AU - Abbie Milner
AU - Saima Sheikh
AU - Abdul Jalil Khan
AU - Mian Mukhtar-Ul-Haq Azeemi
AU - Muhammad Firaz Khan
AU - Laila Noushad
AU - Imtiaz Ahmad Dogar
AU - Aftab Alam Khan
AU - Hazrat Ali Khan
AU - Moin Ahmed Ansari
AU - Athula Sumathipala
AU - Rumi Ruben
AU - Ivonne Solis-Trapala
PY - 2026
DA - 2026/10/01
JO - Early intervention in psychiatry
DO - 10.1111/eip.70243
UR - https://europepmc.org/article/MED/42905631
AB - Background In high-income countries, early intervention in psychosis (EIP) services provide treatment for first episode psychosis (FEP) as early as possible, proven to effectively improve health outcomes. These services rarely exist in low- and lower-middle-income countries (LMICs). The TRANSLATE study aims to evaluate the implementation of the EIP services using the selected indicators and to develop a clinical tool to predict the risk of Treatment-Resistant Schizophrenia (TRS) in patients with FEP.Methods This is an observational implementation research study conducted across five sites in Pakistan and one in Sri Lanka, recruiting 670 FEP patients. The evaluation of the implementation of the service will include key clinical and process outcomes such as disengagement from services, rates of remission, the level of functioning, quality of life, psychopathology and physical health. Required data will be collected over a 1-year period at baseline and at 1, 3, 6 and 12-month intervals. A descriptive analysis of baseline data and service utilisation data will be conducted, stratified by site. Probabilities of disengagement, remission and process evaluation outcomes will be modelled using logistic regression models adjusting for duration of untreated psychosis, baseline symptom severity and geographical location. Selected key clinical outcomes will also be analysed and presented descriptively. To develop a clinical model prediction model for TRS, a penalised multivariable logistic regression model will be fitted to estimate the one-year risk of TRS following an FEP diagnosis, incorporating up to 15 predictor variables identified within the study cohort. Model performance will be assessed using calibration plots and the C-statistics for discrimination. Internal validation will be carried out by bootstrapping and the optimism-adjusted calibration slope will be used as a global shrinkage factor to account for overfitting, with the model intercept re-estimated to ensure calibration-in-the-large.Discussion This will be the first large study that will evaluate the implementation of EIP service in a LMIC country setting. The findings of this study will inform the adoption and scaling-up of EIP services in routine clinical practice. The clinical prediction model for the early detection of TRS will help initiate effective treatments for TRS at the earliest possible stage and potentially reduce the significant burden of disease associated with TRS.Trial registration ClinicalTrials.gov identifier: ISRCTN16495294.
SN - 1751-7885
SN - 1751-7893
ER -