Afghan mental health and psychosocial well-being: thematic review of four decades of research and interventions.

BACKGROUND: Four decades of war, political upheaval, economic deprivation and forced displacement have profoundly affected both in-country and refugee Afghan populations. AIMS: We reviewed literature on mental health and psychosocial well-being, to assess the current evidence and describe mental healthcare systems, including government programmes and community-based interventions. METHOD: In 2022, we conducted a systematic search in Google Scholar, PTSDpubs, PubMed and PsycINFO, and a hand search of grey literature (N = 214 papers). We identified the main factors driving the epidemiology of mental health problems, culturally salient understandings of psychological distress, coping strategies and help-seeking behaviours, and interventions for mental health and psychosocial support. RESULTS: Mental health problems and psychological distress show higher risks for women, ethnic minorities, people with disabilities and youth. Issues of suicidality and drug use are emerging problems that are understudied. Afghans use specific vocabulary to convey psychological distress, drawing on culturally relevant concepts of body-mind relationships. Coping strategies are largely embedded in one's faith and family. Over the past two decades, concerted efforts were made to integrate mental health into the nation's healthcare system, train cadres of psychosocial counsellors, and develop community-based psychosocial initiatives with the help of non-governmental organisations. A small but growing body of research is emerging around psychological interventions adapted to Afghan contexts and culture. CONCLUSIONS: We make four recommendations to promote health equity and sustainable systems of care. Interventions must build cultural relevance, invest in community-based psychosocial support and evidence-based psychological interventions, maintain core mental health services at logical points of access and foster integrated systems of care.

Association of Machine Learning-Based Assessment of Tumor-Infiltrating Lymphocytes on Standard Histologic Images With Outcomes of Immunotherapy in Patients With NSCLC.

Importance: Currently, predictive biomarkers for response to immune checkpoint inhibitor (ICI) therapy in lung cancer are limited. Identifying such biomarkers would be useful to refine patient selection and guide precision therapy. Objective: To develop a machine-learning (ML)-based tumor-infiltrating lymphocytes (TILs) scoring approach, and to evaluate TIL association with clinical outcomes in patients with advanced non-small cell lung cancer (NSCLC). Design, Setting, and Participants: This multicenter retrospective discovery-validation cohort study included 685 ICI-treated patients with NSCLC with median follow-up of 38.1 and 43.3 months for the discovery (n = 446) and validation (n = 239) cohorts, respectively. Patients were treated between February 2014 and September 2021. We developed an ML automated method to count tumor, stroma, and TIL cells in whole-slide hematoxylin-eosin-stained images of NSCLC tumors. Tumor mutational burden (TMB) and programmed death ligand-1 (PD-L1) expression were assessed separately, and clinical response to ICI therapy was determined by medical record review. Data analysis was performed from June 2021 to April 2022. Exposures: All patients received anti-PD-(L)1 monotherapy. Main Outcomes and Measures: Objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were determined by blinded medical record review. The area under curve (AUC) of TIL levels, TMB, and PD-L1 in predicting ICI response were calculated using ORR. Results: Overall, there were 248 (56%) women in the discovery cohort and 97 (41%) in the validation cohort. In a multivariable analysis, high TIL level (≥250 cells/mm2) was independently associated with ICI response in both the discovery (PFS: HR, 0.71; P = .006; OS: HR, 0.74; P = .03) and validation (PFS: HR = 0.80; P = .01; OS: HR = 0.75; P = .001) cohorts. Survival benefit was seen in both first- and subsequent-line ICI treatments in patients with NSCLC. In the discovery cohort, the combined models of TILs/PD-L1 or TMB/PD-L1 had additional specificity in differentiating ICI responders compared with PD-L1 alone. In the PD-L1 negative (<1%) subgroup, TIL levels had superior classification accuracy for ICI response (AUC = 0.77) compared with TMB (AUC = 0.65). Conclusions and Relevance: In these cohorts, TIL levels were robustly and independently associated with response to ICI treatment. Patient TIL assessment is relatively easily incorporated into the workflow of pathology laboratories at minimal additional cost, and may enhance precision therapy.