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Scalable Screening and Treatment Response Monitoring for Perinatal Depression in Low- and Middle-Income Countries
Common perinatal mental disorders such as anxiety and depression are a public health concern in low- and middle-income countries. Several tools exist for screening and monitoring treatment responses, which have frequently been tested globally in clinical and research settings. However, these tools a...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8297354/ https://www.ncbi.nlm.nih.gov/pubmed/34206237 http://dx.doi.org/10.3390/ijerph18136693 |
Sumario: | Common perinatal mental disorders such as anxiety and depression are a public health concern in low- and middle-income countries. Several tools exist for screening and monitoring treatment responses, which have frequently been tested globally in clinical and research settings. However, these tools are relatively long and not practical for integration into routine data systems in most settings. This study aims to address this gap by considering three short tools: The Community Informant Detection Tool (CIDT) for the identification of women at risk, the 4-item Patient Health Questionnaire (PHQ-4) for screening women at high-risk, and the 4-item Hamilton Depression Rating Scale (HAMD-4) for measuring treatment responses. Studies in rural Pakistan showed that the CIDT offered a valid and reliable key-informant approach for the detection of perinatal depression by utilizing a network of peers and local health workers, yielding a sensitivity of 97.5% and specificity of 82.4%. The PHQ-4 had excellent psychometric properties to screen women with perinatal depression through trained community health workers, with a sensitivity of 93.4% and specificity of 91.70%. The HAMD-4 provided a good model fit and unidimensional construct for assessing intervention responses. These short, reliable, and valid tools are scalable and expected to reduce training, administrative and human resource costs to health systems. |
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