Cross-national examination of adolescent suicidal behavior: a pooled and multi-level analysis of 193,484 students from 53 LMIC countries.

INTRODUCTION: Suicide is a leading cause of adolescent mortality worldwide. We aimed to estimate the prevalence and identify individual-level and country-level factors which might explain the variability in suicidal behavior among students in 53 low to middle income countries. METHODS: We used data on adolescents aged 12-16 years from the Global School-based Student Health Surveys from 2009-2016. The suicidal behaviors investigated included suicide ideation, suicidal planning and suicide attempt. The prevalence was estimated for 53 countries, while a multilevel logistic regression analysis (33 countries) was used to investigate the associations of these behaviors with individual and country-level contextual risk factors. The contextual variables included the Gini Coefficient, Gross Domestic Product per capita, pupil-to-teacher ratios, population density, homicide rates, law criminalizing suicide and the night light index. RESULTS: The overall prevalence of suicide ideation, making a plan and suicide attempt were 10.4%, 10.3% and 11.0%, respectively. The highest prevalence rates reported were from the Americas. The strongest risk factors associated with suicidal behavior included anxiety, loneliness, no close friends and the substance abuse. Among the country level variables, the night light index was associated with making a suicide plan and attempting suicide. CONCLUSION: The non-significant country level findings were not entirely surprising given the mixed results from prior studies. Additional knowledge is thus achieved with regard to country level factors associated with suicidal behavior across adolescent populations.

Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes.

BACKGROUND: The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users' self-reported burden of depression. Modern artificial intelligence (AI) methods are commonly employed in analyzing user-generated sentiment on social media. In the forthcoming systematic review, we will examine the content validity of these computer-based health surveillance models with respect to standard diagnostic frameworks. Drawing from a clinical perspective, we will attempt to establish a normative judgment about the strengths of these modern AI applications in the detection of depression. METHODS: We will perform a systematic review of English and German language publications from 2010 to 2020 in PubMed, APA PsychInfo, Science Direct, EMBASE Psych, Google Scholar, and Web of Science. The inclusion criteria span cohort, case-control, cross-sectional studies, randomized controlled studies, in addition to reports on conference proceedings. The systematic review will exclude some gray source materials, specifically editorials, newspaper articles, and blog posts. Our primary outcome is self-reported depression, as expressed on social media. Secondary outcomes will be the types of AI methods used for social media depression screen, and the clinical validation procedures accompanying these methods. In a second step, we will utilize the evidence-strengthening Population, Intervention, Comparison, Outcomes, Study type (PICOS) tool to refine our inclusion and exclusion criteria. Following the independent assessment of the evidence sources by two authors for the risk of bias, the data extraction process will culminate in a thematic synthesis of reviewed studies. DISCUSSION: We present the protocol for a systematic review which will consider all existing literature from peer reviewed publication sources relevant to the primary and secondary outcomes. The completed review will discuss depression as a self-reported health outcome in social media material. We will examine the computational methods, including AI and machine learning techniques which are commonly used for online depression surveillance. Furthermore, we will focus on standard clinical assessments, as indicating content validity, in the design of the algorithms. The methodological quality of the clinical construct of the algorithms will be evaluated with the COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) framework. We conclude the study with a normative judgment about the current application of AI to screen for depression on social media. SYSTEMATIC REVIEW REGISTRATION: International Prospective Register of Systematic Reviews PROSPERO (registration number CRD42020187874).

Digital technology applications for contact tracing: the new promise for COVID-19 and beyond?

Among the most critical strategies in the fight against the Corona Virus Disease (COVID-19) is the rapid tracing and notification of potentially infected persons. Several nations have implemented mobile software applications ("apps") to alert persons exposed to the coronavirus. The expected advantages of this new technology over the traditional method of contact tracing include speed, specificity, and mass reach. Beyond its use for mitigating and containing COVID-19, digital technology can complement or even augment the traditional approach to global health program implementation. However, as with any new system, strong regulatory frameworks are necessary to ensure that individual information is not used for surveillance purposes, and user privacy will be maintained. Having safeguarded this, perhaps the global health community will witness the beginning of a new era of implementing mass health programs through the medium of digital technology.