Establishing reliability and validity for mental health screening instruments in resource-constrained settings: Systematic review of the PHQ-9 and key recommendations
•Measure validation in diverse settings is key to account for contextual nuance.•Consider impact of medical comorbidities, administration, language on psychometrics.•Thorough reporting of assessment procedures aids in assessing measure performance. Mental illness is one of the largest contributors t...
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| Published in: | Psychiatry research Vol. 291; p. 113236 |
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| Main Authors: | , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Ireland
Elsevier B.V
01.09.2020
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| Subjects: | |
| ISSN: | 0165-1781, 1872-7123, 1872-7123 |
| Online Access: | Get full text |
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| Summary: | •Measure validation in diverse settings is key to account for contextual nuance.•Consider impact of medical comorbidities, administration, language on psychometrics.•Thorough reporting of assessment procedures aids in assessing measure performance.
Mental illness is one of the largest contributors to the global disease burden. The importance of valid and reliable mental health measures is crucial in order to accurately measure said burden, to capture symptom improvement, and to ensure that symptoms are appropriately identified and quantified. This is of particular importance in low and middle-income countries (LMICs), where the burden of mental illness is relatively high, and there is heterogeneity in linguistic, racial, and ethnic groups. Using the PHQ-9 as an illustrative example, this systematic review aims to provide an overview of existing work and highlight common validation and reporting practices. A systematic review of published literature validating the use of the PHQ-9 in LMICs as indexed in the PubMed and PsychInfo databases was conducted. The review included n = 49 articles (reduced from n = 2,349). This manuscript summarizes these results in terms of the frequency of reporting on important procedures and in regards to the types of reliability and validity measured. Then, building off of the existing literature, we provide key recommendations for measure validation in LMICs, which can be generalized for any type of measure used in a setting in which it was not initially developed. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Undefined-3 Both authors contributed equally CREDIT AUTHOR STATEMENT Haley A. Carroll+: Conceptualization, Methodology, Formal analysis, Investigation, Writing – Original Draft. Kimberly Hook+: Conceptualization, Methodology, Formal analysis, Investigation, Writing – Original Draft. Oscar F. Rojas Perez: Writing – Original Draft. Christy Denckla: Conceptualization, Methodology, Formal analysis, Investigation. Christine Cooper Vince: Conceptualization, Methodology, Formal analysis, Investigation. Senait Ghebrehiwet: Conceptualization, Methodology, Writing – Original Draft. Kanako Ando: Data Curation. Mia Touma: Data Curation. Christina Borba: Supervision. Gregory Fricchione: Supervision. Funding acquisition. David Henderson: Supervision. Funding acquisition. (+both authors contributed equally) |
| ISSN: | 0165-1781 1872-7123 1872-7123 |
| DOI: | 10.1016/j.psychres.2020.113236 |