Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities

Choi, Daejin; Sumner, Steven A.; Holland, Kristin M.; Draper, John; Murphy, Sean; Bowen, Daniel; Zwald, Marissa; Wang, Jing; Law, Royal; Taylor, Jordan; Konjeti, Chaitanya; Choudhury, Munmun De

doi:10.1001/jamanetworkopen.2020.30932

Cited by 25 publications

(29 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, prior research has indicated that it is still prudent to attempt to combine information from these alternative signals with traditional sources to improve both performance and reliability of the model. 14 Although this approach to predicting opioid overdose deaths demonstrated promise, much additional work remains. Although we validated our approach over 2 separate years, future prospective validation is an ongoing need.…”

Section: Limitationsmentioning

confidence: 99%

Estimating Weekly National Opioid Overdose Deaths in Near Real Time Using Multiple Proxy Data Sources

et al. 2022

Self Cite

View full text Add to dashboard Cite

IMPORTANCEOpioid overdose is a leading public health problem in the United States; however, national data on overdose deaths are delayed by several months or more. OBJECTIVES To build and validate a statistical model for estimating national opioid overdose deaths in near real time. DESIGN, SETTING, AND PARTICIPANTS In this cross-sectional study, signals from 5 overdoserelated, proxy data sources encompassing health, law enforcement, and online data from 2014 to 2019 in the US were combined using a LASSO (least absolute shrinkage and selection operator) regression model, and weekly predictions of opioid overdose deaths were made for 2018 and 2019 to validate model performance. Results were also compared with those from a baseline SARIMA (seasonal autoregressive integrated moving average) model, one of the most used approaches to forecasting injury mortality. EXPOSURES Time series data from 2014 to 2019 on emergency department visits for opioid overdose from the National Syndromic Surveillance Program, data on the volume of heroin and synthetic opioids circulating in illicit markets via the National Forensic Laboratory Information System, data on the search volume for heroin and synthetic opioids on Google, and data on post volume on heroin and synthetic opioids on Twitter and Reddit were used to train and validate prediction models of opioid overdose deaths. MAIN OUTCOMES AND MEASURES Model-based predictions of weekly opioid overdose deaths in the United States were made for 2018 and 2019 and compared with actual observed opioid overdose deaths from the National Vital Statistics System. RESULTSStatistical models using the 5 real-time proxy data sources estimated the national opioid overdose death rate for 2018 and 2019 with an error of 1.01% and −1.05%, respectively. When considering the accuracy of weekly predictions, the machine learning-based approach possessed a mean error in its weekly estimates (root mean squared error) of 60.3 overdose deaths for 2018 (compared with 310.2 overdose deaths for the SARIMA model) and 67.2 overdose deaths for 2019 (compared with 83.3 overdose deaths for the SARIMA model). CONCLUSIONS AND RELEVANCEResults of this serial cross-sectional study suggest that proxy administrative data sources can be used to estimate national opioid overdose mortality trends to provide a more timely understanding of this public health problem.

show abstract

Section: Limitationsmentioning

confidence: 99%

Estimating Weekly National Opioid Overdose Deaths in Near Real Time Using Multiple Proxy Data Sources

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some helplines specialize in particular issues such as suicide, children or violence against women. Suicide helplines, for example, have been shown to reduce suicide rates 13 , and call volumes of suicide prevention helplines have been shown to relate to the incidence of actual suicides 14 .…”

mentioning

confidence: 99%

Mental health concerns during the COVID-19 pandemic as revealed by helpline calls

Brülhart

Klotzbücher

Lalive

et al. 2021

Nature

139

View full text Add to dashboard Cite

“…Lifeline have been used to predict the incidence of actual suicides, e.g. by [12]. As a helpline focused on suicide, however, the Lifeline does not allow us to track changes in the composition of mental health problems.…”

Section: Resultsmentioning

confidence: 99%

Distress and Anxiety during the COVID-19 Pandemic as Revealed through Helpline Calls

Brülhart

Klotzbücher

Lalive

et al. 2021

Preprint

View full text Add to dashboard Cite

The COVID-19 crisis affects mental health. We use helpline calls to quantify this effect. Helpline calls offer a real-time measure of ‘revealed’ anxiety across a range of topics. We have collected data on 7 million calls from from 19 countries. We find that calls peaked at 35% above pre-pandemic levels, six weeks after the initial outbreak. The increase was mainly driven by fear (including fear of infection), loneliness, and, later in the pandemic, physical-health concerns. Relationship issues, economic problems, violence, and suicidal ideation, however, were less prevalent than before the pandemic. This pattern is apparent both during the first wave and during subsequent waves of the pandemic. Issues directly tied to COVID-19 therefore seem to have substituted rather than exacerbated underlying anxieties. Conditional on infection rates, suicide-related calls increased when containment policies became more stringent and decreased when income support was extended. This implies that financial relief can allay the distress triggered by lockdown measures.

show abstract

Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities

Cited by 25 publications

References 40 publications

Estimating Weekly National Opioid Overdose Deaths in Near Real Time Using Multiple Proxy Data Sources

Estimating Weekly National Opioid Overdose Deaths in Near Real Time Using Multiple Proxy Data Sources

Mental health concerns during the COVID-19 pandemic as revealed by helpline calls

Distress and Anxiety during the COVID-19 Pandemic as Revealed through Helpline Calls

Contact Info

Product

Resources

About