Risk assessment and clinical implications of COVID-19 in multiple myeloma patients: A systematic review and meta-analysis.

INTRODUCTION: Patients with multiple myeloma (MM) face heightened infection susceptibility, particularly severe risks from COVID-19. This study, the first systematic review in its domain, seeks to assess the impacts of COVID-19 on MM patients. METHOD: Adhering to PRISMA guidelines and PROSPERO registration (ID: CRD42023407784), this study conducted an exhaustive literature search from January 1, 2020, to April 12, 2024, using specified search terms in major databases (PubMed, EMBASE, and Web of Science). Quality assessment utilized the JBI Critical checklist, while publication bias was assessed using Egger's test and funnel plot. The leave-one-out sensitivity analyses were performed to assess the robustness of the results by excluding one study at a time to identify studies with a high risk of bias or those that significantly influenced the overall effect size. Data synthesis involved fitting a random-effects model and estimating meta-regression coefficients. RESULTS: A total of 14 studies, encompassing a sample size of 3214 yielded pooled estimates indicating a hospitalization rate of 53% (95% CI: 40.81, 65.93) with considerable heterogeneity across studies (I2 = 99%). The ICU admission rate was 17% (95% CI: 11.74, 21.37), also with significant heterogeneity (I2 = 94%). The pooled mortality rate was 22% (95% CI: 15.33, 28.93), showing high heterogeneity (I2 = 97%). The pooled survival rate stood at 78% (95% CI: 71.07, 84.67), again exhibiting substantial heterogeneity (I2 = 97%). Subgroup analysis and meta-regression highlighted that study types, demographic factors, and patient comorbidities significantly contributed to the observed outcome heterogeneity, revealing distinct patterns. Mortality rates increased by 15% for participants with a median age above 67 years. ICU admission rates were positively correlated with obesity, with a 20% increase for groups with at least 19% obesity. Mortality rates rose by 33% for the group of patients with at least 19% obesity, while survival rates decreased by 33% in the same group. CONCLUSION: Our meta-analysis sheds light on diverse COVID-19 outcomes in multiple myeloma. Heterogeneity underscores complexities, and study types, demographics, and co-morbidities significantly influence results, emphasizing the nuanced interplay of factors.

Depression and health-related quality of life of patients with type 2 diabetes attending tertiary level hospitals in Dhaka, Bangladesh.

INTRODUCTION: Type 2 diabetes mellitus (T2DM) and depression are closely linked. People with T2DM are at increased risk of developing depression and vice versa. T2DM and depression comorbid conditions adversely affect Health-Related Quality of Life (HRQOL) and management of T2DM. In this study, we assessed depression and HRQOL among patients with T2DM in Dhaka, Bangladesh. METHODS: A cross-sectional study was conducted in two tertiary-level hospitals in Dhaka, Bangladesh. Data were collected from 318 patients with T2DM. A set of standard tools, PHQ-9 (for assessing depression) and EuroQol-5D-5L (for assessing the HRQOL), were used. Statistical analyses, including Chi-square and Fisher's exact tests, Wilcoxon (Mann-Whitney), and Spearman's correlation coefficient tests, were performed using SPSS (v.20). RESULTS: The majority of the patients (58%) were females, with a mean age (standard deviation) of 52 ± 10 years, and 74% of patients lived in urban areas. The prevalence of depression was 62% (PHQ-9 score ≥ 5). Over three-quarters (76%) reported problems in the anxiety/ depression dimension of EQ-5D, followed by pain/discomfort (74%), mobility (40%), self-care (36%), and usual activities (33%). The depression and T2DM comorbid condition were associated with all the five dimensions of EQ-5D (χ2 statistics with df = 1 was 52.33, 51.13, 52.67, 21.61, 7.92 for mobility, self-care, usual activities, pain/discomfort, and anxiety/ depression dimensions respectively, p- < 0.01). The mean EQ-5D index (0.53 vs. 0.75) and the mean EQ-5D VAS (65 vs. 76) both showed lower values in T2DM patients with depression compared to T2DM patients without depression (Wilcoxon test, p- < 0.001). CONCLUSIONS: We conclude that the majority of the patients with T2DM had comorbid conditions, and the HRQOL was negatively affected by comorbid depression in T2DM patients. This suggests the importance of timely screening, diagnosis, treatment, and follow-up of comorbid depression in T2DM patients to improve overall health and QOL.

Machine learning approaches for predicting suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic: A cross-sectional study.

Psychological and behavioral stress has increased enormously during Coronavirus Disease 2019 (COVID-19) pandemic. However, early prediction and intervention to address psychological distress and suicidal behaviors are crucial to prevent suicide-related deaths. This study aimed to develop a machine algorithm to predict suicidal behaviors and identify essential predictors of suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic. An anonymous online survey was conducted among university students in Bangladesh from June 1 to June 30, 2022. A total of 2391 university students completed and submitted the questionnaires. Five different Machine Learning models (MLMs) were applied to develop a suitable algorithm for predicting suicidal behaviors among university students. In predicting suicidal behaviors, the most crucial background and demographic features were relationship status, friendly environment in the family, family income, family type, and sex. In addition, features related to the impact of the COVID-19 pandemic were identified as job loss, economic loss, and loss of family/relatives due to COVID-19. Moreover, factors related to mental health include depression, anxiety, stress, and insomnia. The performance evaluation and comparison of the MLM showed that all models behaved consistently and were comparable in predicting suicidal risk. However, the Support Vector Machine was the best and most consistent performing model among all MLMs in terms of accuracy (79%), Kappa (0.59), receiver operating characteristic (0.89), sensitivity (0.81), and specificity (0.81). Support Vector Machine is the best-performing model for predicting suicidal risks among university students in Bangladesh and can help in designing appropriate and timely suicide prevention interventions.

Determinants of health seeking behavior for chronic non-communicable diseases and related out-of-pocket expenditure: results from a cross-sectional survey in northern Bangladesh.

BACKGROUND: In spite of high prevalence rates, little is known about health seeking and related expenditure for chronic non-communicable diseases in low-income countries. We assessed relevant patterns of health seeking and related out-of-pocket expenditure in Bangladesh. METHODS: We used data from a household survey of 2500 households conducted in 2013 in Rangpur district. We employed multinomial logistic regression to assess factors associated with health seeking choices (no care or self-care, semi-qualified professional care, and qualified professional care). We used descriptive statistics (5% trimmed mean and range, median) to assess related patterns of out-of-pocket expenditure (including only direct costs). RESULTS: Eight hundred sixty-six (12.5%) out of 6958 individuals reported at least one chronic non-communicable disease. Of these 866 individuals, 139 (16%) sought no care or self-care, 364 (42%) sought semi-qualified care, and 363 (42%) sought qualified care. Multivariate analysis confirmed that the following factors increased the likelihood of seeking qualified care: a higher education, a major chronic non-communicable disease, a higher socio-economic status, a lower proportion of chronic household patients, and a shorter distance between a household and a sub-district public referral health facility. Seven hundred fifty-four (87 %) individuals reported out-of-pocket expenditure, with drugs absorbing the largest portion (85%) of total expenditure. On average, qualified care seekers encountered the highest out-of-pocket expenditure, followed by those who sought semi-qualified care and no care, or self-care. CONCLUSION: Our study reveals insufficiencies in health provision for chronic conditions, with more than half of all affected people still not seeking qualified care, and the majority still encountering considerable out-of-pocket expenditure. This calls for urgent measures to secure better access to care and financial protection.