Journal Article > CommentaryFull Text
Lancet Diabetes Endocrinol. 2019 August 1; DOI:10.1016/S2213-8587(19)30197-4.
Kehlenbrink S, Jaacks LM, Perone SA, Ansbro É, Ashbourne E, et al.
Lancet Diabetes Endocrinol. 2019 August 1; DOI:10.1016/S2213-8587(19)30197-4.
Conference Material > Slide Presentation
Wilson JM, Chowdhury F, Hassan S, Harriss E, Alves F, et al.
MSF Scientific Day International 2024. 2024 May 16; DOI:10.57740/R7W2C8dil
Conference Material > Abstract
Wilson JM, Chowdhury F, Hassan S, Harriss E, Alves F, et al.
MSF Scientific Day International 2024. 2024 May 16; DOI:10.57740/9SthRqK
INTRODUCTION
Visceral leishmaniasis (VL) is a neglected tropical disease prevalent in populations affected by poverty, war, and famine. Without effective treatment, death is the norm. Prognostic models, as used by Médecins Sans Frontières (MSF) in East Africa, are used to identify high-risk patients for intensive management, including hospital admission, treatment with liposomal amphotericin B, broad-spectrum antibiotics, and blood transfusions. We provide a comprehensive and objective resource for policymakers, healthcare providers, and investigators, by identifying, summarising, and appraising the available prognostic models predicting clinical outcomes in patients with VL.
METHODS
We performed a systematic review of published studies that developed, validated, or updated models predicting future clinical outcomes in patients diagnosed with VL. We searched five bibliographic databases (Ovid Embase, Ovid MEDLINE, Web of Science Core Collection, SciELO, and LILACS) on March 1, 2023, for papers published from database inception, with no language restriction. Screening, data extraction, and risk of bias assessment were performed in duplicate. This study is registered with PROSPERO (ID: CRD42023417226).
RESULTS
Eight prognostic model studies, published between 2003 and 2021, were identified describing 12 prognostic model developments and 19 external validations. Nine models were developed in Brazil and three in East Africa by MSF investigators (two developed in South Sudan and one in Ethiopia). In-hospital mortality was the outcome for all but two Brazilian models, which predicted registry-reported mortality. Three models were developed exclusively in adolescents or children. Risk of bias was assessed as high for all model evaluations. Model overfitting due to small sample sizes, leading to optimistic model performance measures and exaggerated risk estimates, was identified for all but one model development. Only half of the presented risk scores were reproducible by following the authors’ methodology.
CONCLUSION
A poorly developed model can result in inaccurate risk estimation, potentially leading to harmful and inequitable decision making. With half of all risk scores incorrectly calculated, and a high risk of bias identified across all model evaluations, caution must be exercised when using these models to guide patient management. In the first systematic review of VL prognostic models, we show that no models predicted treatment failure and relapse, and despite South Asia representing the highest VL burden before 2010, no models were developed in this population. These represent important evidence gaps, which should be prioritised when developing new models. Using the Infectious Diseases Data Observatory repository of VL individual patient data from clinical trials, we are currently building a prognostic model for VL relapse in South Asia, which we hope to serve the ongoing elimination campaign.
Visceral leishmaniasis (VL) is a neglected tropical disease prevalent in populations affected by poverty, war, and famine. Without effective treatment, death is the norm. Prognostic models, as used by Médecins Sans Frontières (MSF) in East Africa, are used to identify high-risk patients for intensive management, including hospital admission, treatment with liposomal amphotericin B, broad-spectrum antibiotics, and blood transfusions. We provide a comprehensive and objective resource for policymakers, healthcare providers, and investigators, by identifying, summarising, and appraising the available prognostic models predicting clinical outcomes in patients with VL.
METHODS
We performed a systematic review of published studies that developed, validated, or updated models predicting future clinical outcomes in patients diagnosed with VL. We searched five bibliographic databases (Ovid Embase, Ovid MEDLINE, Web of Science Core Collection, SciELO, and LILACS) on March 1, 2023, for papers published from database inception, with no language restriction. Screening, data extraction, and risk of bias assessment were performed in duplicate. This study is registered with PROSPERO (ID: CRD42023417226).
RESULTS
Eight prognostic model studies, published between 2003 and 2021, were identified describing 12 prognostic model developments and 19 external validations. Nine models were developed in Brazil and three in East Africa by MSF investigators (two developed in South Sudan and one in Ethiopia). In-hospital mortality was the outcome for all but two Brazilian models, which predicted registry-reported mortality. Three models were developed exclusively in adolescents or children. Risk of bias was assessed as high for all model evaluations. Model overfitting due to small sample sizes, leading to optimistic model performance measures and exaggerated risk estimates, was identified for all but one model development. Only half of the presented risk scores were reproducible by following the authors’ methodology.
CONCLUSION
A poorly developed model can result in inaccurate risk estimation, potentially leading to harmful and inequitable decision making. With half of all risk scores incorrectly calculated, and a high risk of bias identified across all model evaluations, caution must be exercised when using these models to guide patient management. In the first systematic review of VL prognostic models, we show that no models predicted treatment failure and relapse, and despite South Asia representing the highest VL burden before 2010, no models were developed in this population. These represent important evidence gaps, which should be prioritised when developing new models. Using the Infectious Diseases Data Observatory repository of VL individual patient data from clinical trials, we are currently building a prognostic model for VL relapse in South Asia, which we hope to serve the ongoing elimination campaign.
Journal Article > CommentaryAbstract Only
J Clin Endocrinol Metab
. 2022 May 27; Volume 107 (Issue 9); e3553-e3561.; DOI:10.1210/clinem/dgac331
Kehlenbrink S, Ansbro É, Besançon S, Hassan S, Roberts B, et al.
J Clin Endocrinol Metab
. 2022 May 27; Volume 107 (Issue 9); e3553-e3561.; DOI:10.1210/clinem/dgac331
Amid the growing global diabetes epidemic, the scale of forced displacement resulting from armed conflict and humanitarian crises is at record-high levels. More than 80% of the displaced population lives in lower- and middle-income countries, which also host 81% of the global population living with diabetes. Most crises are protracted, often lasting decades, and humanitarian aid organizations are providing long-term primary care to both the local and displaced populations. Humanitarian crises are extremely varied in nature and occur in contexts that are diverse and dynamic. The scope of providing diabetes care varies depending on the phase of the crisis. This paper describes key challenges and possible solutions to improving diabetes care in crisis settings. It focuses on (1) ensuring a reliable supply of life preserving medications and diagnostics, (2) restoring and maintaining access to health care, and (3) adapting service design to the context. These challenges are illustrated through case studies in Ukraine, Mali, the Central African Republic, and Jordan.
Journal Article > ResearchFull Text
Comprehensive Psychiatry. 2021 December 20; Volume 113; 152293.; DOI:10.1016/j.comppsych.2021.152293
Djatche JM, Herrington OD, Nzebou D, Galusha D, Boum Y II, et al.
Comprehensive Psychiatry. 2021 December 20; Volume 113; 152293.; DOI:10.1016/j.comppsych.2021.152293
BACKGROUND
Displacement and conflict exposure are known risk factors for mental health conditions. Here, we examine the mental health of youth in a conflict-affected region of Cameroon.
METHODS
Participants were recruited from among beneficiaries of a project conducted by Univers Psy and the United Nations Population Fund in Cameroon's Far North region. Community health workers conducted sensitization campaigns, following which they referred adolescents and young adults who self-identified as having mental health concerns to clinical psychologists. We ultimately conducted chart reviews of 948 of these youth. Univariate analyses using chi-squared tests were used to assess the relationships among demographics, displacement status, and mental health. Logistic regressions were then performed to determine the odds of having a psychiatric disorder based on displacement status.
OUTCOME
Sixty-eight percent of evaluated youth met criteria for a psychiatric disorder. Anxiety disorders were most prevalent at 24.3%, followed by trauma- and stressor-related disorders at 17.0%, and mood disorders at 8.0%. Refugees and IDPs had 0.11 (95% CI 0.06, 0.19) and 0.46 (95% CI 0.29, 0.74) odds, respectively, of any diagnosis compared to the host population. Females had 1.71 (95% CI 1.17, 2.50) odds of an anxiety disorder and 2.18 (95% CI 1.16, 4.10) odds of a mood disorder compared to males.
INTERPRETATION
In a youth sample in Cameroon self-identified as having mental health concerns, this study found high rates of psychiatric illness, particularly anxiety disorders. We found a higher prevalence among host population individuals than among displaced individuals and especially in the female population.
Displacement and conflict exposure are known risk factors for mental health conditions. Here, we examine the mental health of youth in a conflict-affected region of Cameroon.
METHODS
Participants were recruited from among beneficiaries of a project conducted by Univers Psy and the United Nations Population Fund in Cameroon's Far North region. Community health workers conducted sensitization campaigns, following which they referred adolescents and young adults who self-identified as having mental health concerns to clinical psychologists. We ultimately conducted chart reviews of 948 of these youth. Univariate analyses using chi-squared tests were used to assess the relationships among demographics, displacement status, and mental health. Logistic regressions were then performed to determine the odds of having a psychiatric disorder based on displacement status.
OUTCOME
Sixty-eight percent of evaluated youth met criteria for a psychiatric disorder. Anxiety disorders were most prevalent at 24.3%, followed by trauma- and stressor-related disorders at 17.0%, and mood disorders at 8.0%. Refugees and IDPs had 0.11 (95% CI 0.06, 0.19) and 0.46 (95% CI 0.29, 0.74) odds, respectively, of any diagnosis compared to the host population. Females had 1.71 (95% CI 1.17, 2.50) odds of an anxiety disorder and 2.18 (95% CI 1.16, 4.10) odds of a mood disorder compared to males.
INTERPRETATION
In a youth sample in Cameroon self-identified as having mental health concerns, this study found high rates of psychiatric illness, particularly anxiety disorders. We found a higher prevalence among host population individuals than among displaced individuals and especially in the female population.