Berthe Abi Zeid, Leen Farouki, Tanya El Khoury, Abla M Sibai, Carlos F Mendes de Leon, Marwan F Alawieh, Zeinab Ramadan, Sawsan Abdulrahim, Hala Ghattas, Stephen J McCall
{"title":"COVID-19大流行期间黎巴嫩境内叙利亚老年难民心理健康状况不佳的预测:嵌套横断面研究。","authors":"Berthe Abi Zeid, Leen Farouki, Tanya El Khoury, Abla M Sibai, Carlos F Mendes de Leon, Marwan F Alawieh, Zeinab Ramadan, Sawsan Abdulrahim, Hala Ghattas, Stephen J McCall","doi":"10.1136/bmjgh-2024-015069","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>The COVID-19 pandemic has worsened pre-existing vulnerabilities among older Syrian refugees in Lebanon, potentially impacting their mental health. The study aims to describe the evolution of poor mental health over time and to develop and internally validate a prediction model for poor mental health among older Syrian refugees in Lebanon.</p><p><strong>Methods: </strong>This prognostic study used cross-sectional data from a multiwave telephone survey in Lebanon. It was conducted among all Syrian refugees aged 50 years or older from households that received assistance from a humanitarian organisation. Data were collected between 22 September 2020 and 20 January 2021. Poor mental health was defined as a Mental Health Inventory-5 score of 60 or less. The predictors were identified using backwards stepwise logistic regression. The model was internally validated using bootstrapping. The calibration of the model was presented using the calibration slope (C-slope), and the discrimination was presented using the optimised adjusted C-statistic.</p><p><strong>Results: </strong>There were 3229 participants (median age=56 years (IQR=53-62)) and 47.5% were female. The prevalence of poor mental health was 76.7%. Predictors for poor mental health were younger age, food insecurity, water insecurity, lack of legal residency documentation, irregular employment, higher intensity of bodily pain, having debt and having chronic illnesses. The final model demonstrated good discriminative ability (C-statistic: 0.69 (95% CI 0.67 to 0.72)) and calibration (C-slope 0.93 (95%CI 0.82 to 1.07)).</p><p><strong>Conclusion: </strong>Mental health predictors were related to basic needs, rights and financial barriers. These allow humanitarian organisations to identify high-risk individuals, organise interventions and address root causes to boost resilience and well-being among older Syrian refugees in Lebanon.</p>","PeriodicalId":9137,"journal":{"name":"BMJ Global Health","volume":"9 8","pages":""},"PeriodicalIF":7.1000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11367381/pdf/","citationCount":"0","resultStr":"{\"title\":\"Predicting poor mental health among older Syrian refugees in Lebanon during the COVID-19 pandemic: a nested cross-sectional study.\",\"authors\":\"Berthe Abi Zeid, Leen Farouki, Tanya El Khoury, Abla M Sibai, Carlos F Mendes de Leon, Marwan F Alawieh, Zeinab Ramadan, Sawsan Abdulrahim, Hala Ghattas, Stephen J McCall\",\"doi\":\"10.1136/bmjgh-2024-015069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>The COVID-19 pandemic has worsened pre-existing vulnerabilities among older Syrian refugees in Lebanon, potentially impacting their mental health. The study aims to describe the evolution of poor mental health over time and to develop and internally validate a prediction model for poor mental health among older Syrian refugees in Lebanon.</p><p><strong>Methods: </strong>This prognostic study used cross-sectional data from a multiwave telephone survey in Lebanon. It was conducted among all Syrian refugees aged 50 years or older from households that received assistance from a humanitarian organisation. Data were collected between 22 September 2020 and 20 January 2021. Poor mental health was defined as a Mental Health Inventory-5 score of 60 or less. The predictors were identified using backwards stepwise logistic regression. The model was internally validated using bootstrapping. The calibration of the model was presented using the calibration slope (C-slope), and the discrimination was presented using the optimised adjusted C-statistic.</p><p><strong>Results: </strong>There were 3229 participants (median age=56 years (IQR=53-62)) and 47.5% were female. The prevalence of poor mental health was 76.7%. Predictors for poor mental health were younger age, food insecurity, water insecurity, lack of legal residency documentation, irregular employment, higher intensity of bodily pain, having debt and having chronic illnesses. The final model demonstrated good discriminative ability (C-statistic: 0.69 (95% CI 0.67 to 0.72)) and calibration (C-slope 0.93 (95%CI 0.82 to 1.07)).</p><p><strong>Conclusion: </strong>Mental health predictors were related to basic needs, rights and financial barriers. These allow humanitarian organisations to identify high-risk individuals, organise interventions and address root causes to boost resilience and well-being among older Syrian refugees in Lebanon.</p>\",\"PeriodicalId\":9137,\"journal\":{\"name\":\"BMJ Global Health\",\"volume\":\"9 8\",\"pages\":\"\"},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11367381/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMJ Global Health\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1136/bmjgh-2024-015069\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMJ Global Health","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1136/bmjgh-2024-015069","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
Predicting poor mental health among older Syrian refugees in Lebanon during the COVID-19 pandemic: a nested cross-sectional study.
Introduction: The COVID-19 pandemic has worsened pre-existing vulnerabilities among older Syrian refugees in Lebanon, potentially impacting their mental health. The study aims to describe the evolution of poor mental health over time and to develop and internally validate a prediction model for poor mental health among older Syrian refugees in Lebanon.
Methods: This prognostic study used cross-sectional data from a multiwave telephone survey in Lebanon. It was conducted among all Syrian refugees aged 50 years or older from households that received assistance from a humanitarian organisation. Data were collected between 22 September 2020 and 20 January 2021. Poor mental health was defined as a Mental Health Inventory-5 score of 60 or less. The predictors were identified using backwards stepwise logistic regression. The model was internally validated using bootstrapping. The calibration of the model was presented using the calibration slope (C-slope), and the discrimination was presented using the optimised adjusted C-statistic.
Results: There were 3229 participants (median age=56 years (IQR=53-62)) and 47.5% were female. The prevalence of poor mental health was 76.7%. Predictors for poor mental health were younger age, food insecurity, water insecurity, lack of legal residency documentation, irregular employment, higher intensity of bodily pain, having debt and having chronic illnesses. The final model demonstrated good discriminative ability (C-statistic: 0.69 (95% CI 0.67 to 0.72)) and calibration (C-slope 0.93 (95%CI 0.82 to 1.07)).
Conclusion: Mental health predictors were related to basic needs, rights and financial barriers. These allow humanitarian organisations to identify high-risk individuals, organise interventions and address root causes to boost resilience and well-being among older Syrian refugees in Lebanon.
期刊介绍:
BMJ Global Health is an online Open Access journal from BMJ that focuses on publishing high-quality peer-reviewed content pertinent to individuals engaged in global health, including policy makers, funders, researchers, clinicians, and frontline healthcare workers. The journal encompasses all facets of global health, with a special emphasis on submissions addressing underfunded areas such as non-communicable diseases (NCDs). It welcomes research across all study phases and designs, from study protocols to phase I trials to meta-analyses, including small or specialized studies. The journal also encourages opinionated discussions on controversial topics.