COVID-19, Mental Health, and Chronic Illnesses: A Syndemic Perspective.

BACKGROUND: The COVID-19 pandemic is an epidemiological and psychological crisis; what it does to the body is quite well known by now, and more research is underway, but the syndemic impact of COVID-19 and mental health on underlying chronic illnesses among the general population is not completely understood. METHODS: We carried out a literature review to identify the potential impact of COVID-19 and related mental health issues on underlying comorbidities that could affect the overall health of the population. RESULTS: Many available studies have highlighted the impact of COVID-19 on mental health only, but how complex their interaction is in patients with comorbidities and COVID-19, the absolute risks, and how they connect with the interrelated risks in the general population, remain unknown. The COVID-19 pandemic can be recognized as a syndemic due to; synergistic interactions among different diseases and other health conditions, increasing overall illness burden, emergence, spread, and interactions between infectious zoonotic diseases leading to new infectious zoonotic diseases; this is together with social and health interactions leading to increased risks in vulnerable populations and exacerbating clustering of multiple diseases. CONCLUSION: There is a need to develop evidence to support appropriate and effective interventions for the overall improvement of health and psychosocial wellbeing of at-risk populations during this pandemic. The syndemic framework is an important framework that can be used to investigate and examine the potential benefits and impact of codesigning COVID-19/non-communicable diseases (NCDs)/mental health programming services which can tackle these epidemics concurrently.

Suicide Ideation within and outside the Perinatal Period: An Exploration of Interpersonal Factors from a Maternal Cohort in Rural Pakistan to Improve Intervention Targeting.

OBJECTIVE: Suicide accounts for substantial mortality in low-resourced settings and contributes to nearly 20% of maternal deaths. In Asia, interpersonal conflict is a salient factor that contributes to suicidal thoughts and actions, yet limited research has been done to explore the type and timing of such conflicts and a woman's accompanying social support. Identifying such risk factors can inform improved efforts to identify who to target for psychosocial interventions. METHODS: Using the Bachpan Cohort study of mothers in Pakistan (n = 1154), we examined the prevalence and interpersonal influences on SI within the past two weeks of pregnancy and then at 3, 6, and 24 months after birth. Using hierarchical mixed effects models, we explored the separate and combined associations of interpersonal factors [e.g., social support, interpersonal conflict, isolation, and past year intimate partner violence (IPV)] on SI at each timepoint. RESULTS: SI prevalence was highest in pregnancy (12.2%) and dropped to 5% throughout two years postpartum. The interpersonal conflict was independently associated with increased odds of SI in pregnancy and 24 months postpartum. IPV was associated with increased SI in pregnancy and 24 months postpartum. Isolation was not associated with SI at any timepoint. Perceived social support remained a robust independent factor associated with reduced SI at all timepoints. CONCLUSION: In addition to screening and deploying interventions for perinatal women with depression, targeting interventions for those who also experience interpersonal conflict, including intimate partner violence, may significantly reduce suicidal thoughts and related sequelae. Social support is a viable and potentially powerful target to reduce the burden of suicide among women.HIGHLIGHTSSuicidal ideation prevalence was higher in pregnancy compared to postpartum.Perceived social support was independently associated with reduced suicidal ideation.Interventions addressing suicide must attend to women's family and social context.

Machine Learning Methods for Predicting Postpartum Depression: Scoping Review.

BACKGROUND: Machine learning (ML) offers vigorous statistical and probabilistic techniques that can successfully predict certain clinical conditions using large volumes of data. A review of ML and big data research analytics in maternal depression is pertinent and timely, given the rapid technological developments in recent years. OBJECTIVE: This study aims to synthesize the literature on ML and big data analytics for maternal mental health, particularly the prediction of postpartum depression (PPD). METHODS: We used a scoping review methodology using the Arksey and O'Malley framework to rapidly map research activity in ML for predicting PPD. Two independent researchers searched PsycINFO, PubMed, IEEE Xplore, and the ACM Digital Library in September 2020 to identify relevant publications in the past 12 years. Data were extracted from the articles' ML model, data type, and study results. RESULTS: A total of 14 studies were identified. All studies reported the use of supervised learning techniques to predict PPD. Support vector machine and random forest were the most commonly used algorithms in addition to Naive Bayes, regression, artificial neural network, decision trees, and XGBoost (Extreme Gradient Boosting). There was considerable heterogeneity in the best-performing ML algorithm across the selected studies. The area under the receiver operating characteristic curve values reported for different algorithms were support vector machine (range 0.78-0.86), random forest method (0.88), XGBoost (0.80), and logistic regression (0.93). CONCLUSIONS: ML algorithms can analyze larger data sets and perform more advanced computations, which can significantly improve the detection of PPD at an early stage. Further clinical research collaborations are required to fine-tune ML algorithms for prediction and treatment. ML might become part of evidence-based practice in addition to clinical knowledge and existing research evidence.

Prevalence of Anxiety in University Students during the COVID-19 Pandemic: A Systematic Review.

There is a dearth of evidence synthesis on the prevalence of anxiety among university students even though the risk of psychological disorders among this population is quite high. We conducted a quantitative systematic review to estimate the global prevalence of anxiety among university students during the COVID-19 pandemic. A systematic search for cross-sectional studies on PubMed, Scopus, and PsycINFO, using PRISMA guidelines, was conducted from September 2020 to February 2021. A total of 36 studies were included, using a random-effects model to calculate the pooled proportion of anxiety. A meta-analysis of the prevalence estimate of anxiety yielded a summary prevalence of 41% (95% CI = 0.34-0.49), with statistically significant evidence of between-study heterogeneity (Q = 80801.97, I2 = 100%, p ≤ 0.0001). A subgroup analysis reported anxiety prevalence in Asia as 33% (95% CI:0.25-0.43), the prevalence of anxiety in Europe as 51% (95% CI: 0.44-0.59), and the highest prevalence of anxiety in the USA as 56% (95% CI: 0.44-0.67). A subgroup gender-based analysis reported the prevalence of anxiety in females as 43% (95% CI:0.29-0.58) compared to males with an anxiety prevalence of 39% (95% CI:0.29-0.50). University students seem to have a high prevalence of anxiety, indicating an increased mental health burden during this pandemic.

Criterion-related validity and reliability of the Urdu version of the patient health questionnaire in a sample of community-based pregnant women in Pakistan.

BACKGROUND: Depression is one of the most prevalent, yet unrecognized but treatable mental disorders in low and middle income countries (LMICs). In such locations, screening tools that are easy-to-administer, valid, and reliable are needed to assist in detecting symptoms of depression. The Patient Health Questionnaire (PHQ-9) is one of the most widely used depression screeners. However, its applicability to community-based settings of Pakistan is limited by the lack of studies examining its validity and reliability in such settings. The current study aimed to demonstrate the criterion-related validity and internal reliability of the Urdu version of the PHQ-9 in a sample of community-based pregnant women in Pakistan compared to a diagnostic clinical interview, the Structured Clinical Interview for DSM disorders (SCID), using data from a depression treatment cluster randomized trial in rural Pakistan. METHODS: Pregnant women in a rural, low income sub-district in Pakistan were approached between October 2014 and February 2016 and, after providing informed consent, screened for depression using the Urdu version of the PHQ-9, with a cutoff of ≥10 used to indicate significant depressive symptoms. Following the PHQ-9, the diagnostic module for current major depressive episode of the SCID was administered. We examined the psychometric properties of PHQ-9 compared to SCID as a gold standard, using sensitivity, specificity, and negative and positive predictive value to measure the criterion-related validity of the PHQ-9 as an indicator of symptoms of depression. We computed area under the receiver operating characteristic curve to determine diagnostic accuracy, and used Cronbach's alpha to assess internal reliability. RESULTS: A total of 1,731 women in their third trimester of pregnancy were assessed for major depressive disorder. Of these women, 572 (33%) met the cutoff for significant depressive symptoms on PHQ-9, and 454 (26%) were assessed positive for depression using the SCID. The sensitivity and specificity of PHQ-9 at a cutoff of ≥10 was 94.7% and 88.9%, respectively. The positive and negative predictive values were 75.2% and 97.9%, respectively; and the area under the curve was 0.959. Internal reliability, as measured by Cronbach's alpha, was 0.844. DISCUSSION: Valid and reliable screening tools to assist in detecting symptoms of depressive disorder are needed in low income settings where depressive disorders are highly prevalent. The Urdu version of the PHQ-9 has not been previously validated against a well-known assessment of depression in a community setting among pregnant women in Pakistan. This study demonstrates that the Urdu version of the PHQ-9 has acceptable criterion-related validity and reliability for screening for depressive symptoms in Pakistan among community-based pregnant women; and when the recommended cut-off score of ≥10 is used it can also serve as an accurate screening tool for major depressive disorder.