scholarly journals Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248360
Author(s):  
Wei-Hsuan Lo-Ciganic ◽  
Julie M. Donohue ◽  
Eric G. Hulsey ◽  
Susan Barnes ◽  
Yuan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Criminal Justice ◽  
Human Services ◽  
Claims Data ◽  
Integrated Model ◽  
Medical Examiner ◽  
Opioid Overdose ◽  
C Statistic ◽  
The Social ◽  
Medicaid Beneficiaries

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples. We measured 290 potential predictors (239 derived from Medicaid claims data) in 30-day periods, beginning with the first observed Medicaid enrollment date during the study period. Using a gradient boosting machine, we predicted a composite outcome (i.e., fatal or nonfatal opioid overdose constructed using medical examiner and claims data) in the subsequent month. We compared prediction performance between a Medicaid claims only model to one integrating human services and criminal justice data with Medicaid claims (i.e., integrated model) using several metrics (e.g., C-statistic, number needed to evaluate [NNE] to identify one overdose). Beneficiaries were stratified into risk-score decile subgroups. The samples (training = 79,087, testing = 79,086, validation = 79,086) had similar characteristics (age = 38±18 years, female = 56%, white = 48%, having at least one overdose = 1.7% during study period). Using the validation sample, the integrated model slightly improved on the Medicaid claims only model (C-statistic = 0.885; 95%CI = 0.877–0.892 vs. C-statistic = 0.871; 95%CI = 0.863–0.878), with small corresponding improvements in the NNE and positive predictive value. Nine of the top 30 most important predictors in the integrated model were human services and criminal justice variables. Using the integrated model, approximately 70% of individuals with overdoses were members of the top risk decile (overdose rates in the subsequent month = 47/10,000 beneficiaries). Few individuals in the bottom 9 deciles had overdose episodes (0-12/10,000). Machine-learning algorithms integrating claims and social service and criminal justice data modestly improved opioid overdose prediction among Medicaid beneficiaries for a large U.S. county heavily affected by the opioid crisis.

scholarly journals What health records data are required for accurate prediction of suicidal behavior?

2019 ◽  
Vol 26 (12) ◽  
pp. 1458-1465 ◽  
Author(s):  
Gregory E Simon ◽  
Susan M Shortreed ◽  
Eric Johnson ◽  
Rebecca C Rossom ◽  
Frances L Lynch ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Suicide Attempt ◽  
Suicidal Behavior ◽  
Claims Data ◽  
Learning Models ◽  
Health Records ◽  
C Statistic ◽  
Insurance Claims Data ◽  
Machine Learning Models

Abstract Objective The study sought to evaluate how availability of different types of health records data affect the accuracy of machine learning models predicting suicidal behavior. Materials and Methods Records from 7 large health systems identified 19 061 056 outpatient visits to mental health specialty or general medical providers between 2009 and 2015. Machine learning models (logistic regression with penalized LASSO [least absolute shrinkage and selection operator] variable selection) were developed to predict suicide death (n = 1240) or probable suicide attempt (n = 24 133) in the following 90 days. Base models were used only historical insurance claims data and were then augmented with data regarding sociodemographic characteristics (race, ethnicity, and neighborhood characteristics), past patient-reported outcome questionnaires from electronic health records, and data (diagnoses and questionnaires) recorded during the visit. Results For prediction of any attempt following mental health specialty visits, a model limited to historical insurance claims data performed approximately as well (C-statistic 0.843) as a model using all available data (C-statistic 0.850). For prediction of suicide attempt following a general medical visit, addition of data recorded during the visit yielded a meaningful improvement over a model using all data up to the prior day (C-statistic 0.853 vs 0.838). Discussion Results may not generalize to setting with less comprehensive data or different patterns of care. Even the poorest-performing models were superior to brief self-report questionnaires or traditional clinical assessment. Conclusions Implementation of suicide risk prediction models in mental health specialty settings may be less technically demanding than expected. In general medical settings, however, delivery of optimal risk predictions at the point of care may require more sophisticated informatics capability.

scholarly journals A scoping review of factors that influence opioid overdose prevention for justice-involved populations

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Christine E. Grella ◽  
Erika Ostlie ◽  
Christy K. Scott ◽  
Michael L. Dennis ◽  
John Carnevale ◽  
...  
Keyword(s):  
Criminal Justice ◽  
Drug Use ◽  
Perceived Risk ◽  
Opioid Overdose ◽  
Overdose Prevention ◽  
Risk Of Death ◽  
Prevention Interventions ◽  
Implementation And Dissemination ◽  
Injection Use ◽  
Release From Prison

Abstract Background There is a high risk of death from opioid overdose following release from prison. Efforts to develop and implement overdose prevention programs for justice-involved populations have increased in recent years. An understanding of the gaps in knowledge on prevention interventions is needed to accelerate development, implementation, and dissemination of effective strategies. Methods A systematic search process identified 43 published papers addressing opioid overdose prevention in criminal justice settings or among justice-involved populations from 2010 to February 2020. Cross-cutting themes were identified, coded and qualitatively analyzed. Results Papers were coded into five categories: acceptability (n = 8), accessibility (n = 4), effectiveness (n = 5), feasibility (n = 7), and participant overdose risk (n = 19). Common themes were: (1) Acceptability of naloxone is associated with injection drug use, overdose history, and perceived risk within the situational context; (2) Accessibility of naloxone is a function of the interface between corrections and community; (3) Evaluations of overdose prevention interventions are few, but generally show increases in knowledge or reductions in opioid overdose; (4) Coordinated efforts are needed to implement prevention interventions, address logistical challenges, and develop linkages between corrections and community providers; (5) Overdose is highest immediately following release from prison or jail, often preceded by service-system interactions, and associated with drug-use severity, injection use, and mental health disorders, as well as risks in the post-release environment. Conclusion Study findings can inform the development of overdose prevention interventions that target justice-involved individuals and policies to support their implementation across criminal justice and community-based service systems.

Predicting Opioid Overdose Readmission and Opioid Use Disorder with Machine Learning

2020 ◽  
Author(s):  
Sarah McDougall ◽  
Priyanka Annapureddy ◽  
Praveen Madiraju ◽  
Nicole Fumo ◽  
Stephen Hargarten
Keyword(s):  
Machine Learning ◽  
Opioid Use Disorder ◽  
Opioid Overdose ◽  
Opioid Use

Norfolk’s “Model Prison Community”: Howard Belding Gill and the Social Process of Prison Reform

2021 ◽  
Vol 101 (2) ◽  
pp. 127-146
Author(s):  
Matthew DelSesto
Keyword(s):  
Criminal Justice ◽  
Social Process ◽  
Prison Reform ◽  
Criminal Justice Reform ◽  
Spatial Design ◽  
The Social ◽  
Public Consciousness ◽  
Prison Community ◽  
The Relationship

This article explores the social process of criminal justice reform, from Howard Belding Gill’s 1927 appointment as the first superintendent of the Norfolk Prison Colony to his dramatic State House hearing and dismissal in 1934. In order to understand the social and spatial design of Norfolk’s “model prison community,” this article reviews Gills’ tenure as superintendent through administrative documents, newspaper reports, and his writings on criminal justice reform. Particular attention is given to the relationship between correctional administration and public consciousness. Concluding insights are offered on the possible lessons from Norfolk Prison Colony for contemporary reform efforts.

scholarly journals Linguistic drivers of misinformation diffusion on social media during the COVID-19 pandemic

2021 ◽  
Author(s):  
Giandomenico Di Domenico ◽  
Annamaria Tuan ◽  
Marco Visentin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
New Technologies ◽  
Fake News ◽  
Conspiracy Theories ◽  
Social Media Platform ◽  
The Social ◽  
Media Platform ◽  
Textual Cues ◽  
Twitter Users

AbstractIn the wake of the COVID-19 pandemic, unprecedent amounts of fake news and hoax spread on social media. In particular, conspiracy theories argued on the effect of specific new technologies like 5G and misinformation tarnished the reputation of brands like Huawei. Language plays a crucial role in understanding the motivational determinants of social media users in sharing misinformation, as people extract meaning from information based on their discursive resources and their skillset. In this paper, we analyze textual and non-textual cues from a panel of 4923 tweets containing the hashtags #5G and #Huawei during the first week of May 2020, when several countries were still adopting lockdown measures, to determine whether or not a tweet is retweeted and, if so, how much it is retweeted. Overall, through traditional logistic regression and machine learning, we found different effects of the textual and non-textual cues on the retweeting of a tweet and on its ability to accumulate retweets. In particular, the presence of misinformation plays an interesting role in spreading the tweet on the network. More importantly, the relative influence of the cues suggests that Twitter users actually read a tweet but not necessarily they understand or critically evaluate it before deciding to share it on the social media platform.

Conversations About Opioids: Impact of the Opioid Overdose Epidemic on Social Interactions for People Who Live With Chronic Pain

2021 ◽  
pp. 104973232110030
Author(s):  
Lise Dassieu ◽  
Angela Heino ◽  
Élise Develay ◽  
Jean-Luc Kaboré ◽  
M. Gabrielle Pagé ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Media Coverage ◽  
Opioid Overdose ◽  
Opioid Use ◽  
Negative Effects ◽  
Self Advocacy ◽  
Social Lives ◽  
The Social ◽  
The Impact ◽  
Depth Interviews

The objective of this study was to understand the impact of the opioid overdose epidemic on the social lives of people suffering from chronic pain, focusing on interactions within their personal and professional circles. The study was based on 22 in-depth interviews with people living with chronic pain in Canada. Using thematic analysis, we documented three main impacts of the opioid overdose epidemic: (a) increased worries of people in pain and their families regarding the dangers of opioids; (b) prejudices, stigma, and discrimination faced during conversations about opioids; and (c) stigma management attempts, which include self-advocacy and concealment of opioid use. This study represents important knowledge advancement on how people manage stigma and communicate about chronic disease during everyday life interactions. By showing negative effects of the epidemic’s media coverage on the social experiences of people with chronic pain, we underscore needs for destigmatizing approaches in public communication regarding opioids.

scholarly journals All-cause Dementia Prediction by Machine Learning: The Health, Aging, and Body Composition Study

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 487-487
Author(s):  
Chenkai Wu ◽  
Xurui Jin
Keyword(s):  
Machine Learning ◽  
Body Composition ◽  
Risk Prediction ◽  
Risk Score ◽  
Body Composition Study ◽  
C Statistic ◽  
Dementia Risk ◽  
Health Aging ◽  
Composition Study ◽  
Traditional Approaches

Abstract There are several shortcomings of the currently available risk prediction models for dementia. We developed a risk prediction model for dementia using machine-learning approach and compared its performance with traditional approaches. Data were from the Health, Aging, and Body Composition Study, comprising 3,075 older adults (at least 70 years). Dementia was defined as (1) use of a prescribed dementia medication, (2) adjudicated dementia diagnosis, or (3) a race-stratified cognitive decline>1.5 SDs from the baseline mean. We selected 275 predictors collected from questionnaires, imaging data, performance testing, and biospecimen. We used random survival forest (RSF) to build the full model and rank the importance of predictors. Subsequently, we built parsimonious models with top-20 predictors using RSF and Cox regression. A dementia risk score was developed using top-ranked variables. We used the C-statistic for performance evaluation. Over a median of 11.4 years of follow-up, 659 dementias (21.4%) occurred. The RSF model (both including all and top-20 variables) showed a higher C-statistic than the regression model. Digit symbol score, physical performance battery, finger tapping score, weight change since age 50, serum adiponectin, and APOE genotype were the top-6 variables. We created a dementia risk score (0-10) using the top-6 variables. A 1-unit increase in the risk score was associated with an 8% higher risk of dementia. The risk score demonstrated good discrimination (C-statistic=0.75). Machine learning methods offered improvement over traditional approaches in predicting dementia. The risk prediction score derived from a parsimonious model had good prediction performance.

scholarly journals Overview of PROMISE

2020 ◽  
pp. 216514342098045
Author(s):  
David E. Emenheiser ◽  
Corinne Weidenthal ◽  
Selete Avoke ◽  
Marlene Simon-Burroughs
Keyword(s):  
Career Development ◽  
Social Security ◽  
Human Services ◽  
Technical Assistance ◽  
Department Of Education ◽  
Social Security Administration ◽  
Special Issue ◽  
Health And Human Services ◽  
The Social ◽  
The U.S

Promoting the Readiness of Minors in Supplemental Security Income (PROMISE), a study of 13,444 randomly assigned youth and their families, includes six model demonstration projects and a technical assistance center funded through the U.S. Department of Education and a national evaluation of the model demonstration projects funded through the Social Security Administration. The Departments of Labor and Health and Human Services and the Executive Office of the President partnered with the Department of Education and Social Security Administration to develop and monitor the PROMISE initiative. This article provides an overview of PROMISE as the introduction to this special issue of Career Development and Transition for Exceptional Individuals.

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