Dataset Information

Risk and Protective Factors of Current Opioid Use Among Youth Living on or Near American Indian Reservations: An Application of Machine Learning.

ABSTRACT: Opioid use among youth, particularly among American Indian (AI) youth, is rising, resulting in a large number of accidental overdoses and deaths. In order to develop effective prevention strategies, we need to use exploratory data analysis to identify previously unknown predictors of opioid use among youth living on or near reservations. The present study is an application of Machine Learning, a type of exploratory data analysis, to the Our Youth, Our Future epidemiological survey (N = 6482) to determine salient risk and protective factors for past 30-day opioid use. The Machine Learning algorithm identified 11 salient risk and protective factors. Importantly, highest risk was conferred for those reporting recent cocaine use, having ever tried a narcotic other than heroin, and identifying as American Indian. Protective factors included never having tried opioids other than heroin, infrequent binge drinking, having fewer friends pressuring you to use illicit drugs, initiating alcohol use at a later age, and being older. This model explained 61% of the variance in the training sample and, on average, 24% of the variance in the bootstrapped samples. Taken together, this model identifies known predictors of 30-day opioid use, for example, recent substance use, as well as unknown predictors including being AI, Snapchat use, and peer encouragement for use. Notably, recent cocaine use was a more salient predictor of recent opioid use than lifetime opioid use.

SUBMITTER: Prince MA

PROVIDER: S-EPMC8386181 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Risk and Protective Factors of Current Opioid Use Among Youth Living on or Near American Indian Reservations: An Application of Machine Learning.

Prince Mark A MA Conner Bradley T BT Davis Samuel R SR Swaim Randall C RC Stanley Linda R LR

Translational issues in psychological science 20200601 2

Opioid use among youth, particularly among American Indian (AI) youth, is rising, resulting in a large number of accidental overdoses and deaths. In order to develop effective prevention strategies, we need to use exploratory data analysis to identify previously unknown predictors of opioid use among youth living on or near reservations. The present study is an application of Machine Learning, a type of exploratory data analysis, to the Our Youth, Our Future epidemiological survey (<i>N</i> = 64 ...[more]

PMID: 34447859

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Project description:IntroductionMany causes of cancer related morbidity and mortality can be traced back to childhood behaviors. The culmination of cancer related risk and protective factors impacting the health and wellbeing of American Indian youth is unknown. The aim of this Mixed Studies Review was to identify cancer related risk and protective factors among American Indian youth. Results will be shared with Tribal communities to inform surveillance efforts.MethodsA Mixed Studies Review process was deemed most appropriate for the search process and data collection. Seven databases were included in the search along with 3 databases that were hand searched. Google Scholar and Google power searching were also conducted. Covidence was utilized for abstract and full-text review. Out of 1,512 articles, 75 articles were included for review and data from each article was sorted out into the levels of the social ecological model.ResultsAfter extracting significant cancer related risk and protective factors from the 75 relevant articles, cancer related themes were identified at the individual, relationship (family and non-family), community, institutional, and cultural levels of the social ecological model. It was observed that the risk and protective factor profile for substance use spanned all levels of the social ecological model, whereas physical health-diet indicators and sexual health indicators did not. Most articles (n = 58, 77%) focused on substance use-related risk and protective factors.DiscussionThe method that was used for this study can be utilized by other professionals researching risk and protective factors impacting the health and wellbeing of American Indian youth for a multitude of health outcomes. Tribal communities will be able to use the results from our literature review to inform the creation of a community specific data collection tool focused on cancer related risk and protective factors. Upon completion of the overarching research, results will be shared with the community and can be used to inform ongoing surveillance efforts, influence priorities for intervention and education work, and inform the management of resources. The continuation of community informed and driven research with Tribal communities is essential to the health and wellbeing of Tribal Nations as community grounded research is limited.

Project description:BackgroundThis study is built on a long-standing research partnership between the Johns Hopkins Center for American Indian Health and the White Mountain Apache Tribe to identify effective interventions to prevent suicide and promote resilience among American Indian (AI) youth. The work is founded on a tribally-mandated, community-based suicide surveillance system with case management by local community mental health specialists (CMHSs) who strive to connect at-risk youth to treatment and brief, adjunctive interventions piloted in past research.MethodsOur primary aim is to evaluate which brief interventions, alone or in combination, have the greater effect on suicide ideation (primary outcome) and resilience (secondary outcome) among AI youth ages 10-24 ascertained for suicide-related behaviors by the tribal surveillance system. We are using a Sequential Multiple Assignment Randomized Trial with stratified assignment based on age and suicidal-behavior type, and randomizing N = 304 youth. Brief interventions are delivered by AI CMHSs, or by Elders with CMHS support, and include: 1) New Hope, an evidence-based intervention to reduce immediate suicide risk through safety planning, emotion regulation skills, and facilitated care connections; and 2) Elders' Resilience, a culturally-grounded intervention to promote resilience through connectedness, self-esteem and cultural identity/values. The control condition is Optimized Case Management, which all study participants receive. We hypothesize that youth who receive: a) New Hope vs. Optimized Case Management will have significant reductions in suicide ideation; b) Elders' Resilience vs. Optimized Case Management will have significant gains in resilience; c) New Hope followed by Elders' Resilience will have the largest improvements on suicide ideation and resilience; and d) Optimized Case Management will have the weakest effects of all groups. Our secondary aim will examine mediators and moderators of treatment effectiveness and sequencing.DiscussionDue to heterogeneity of suicide risk/protective factors among AI youth, not all youth require the same types of interventions. Generating evidence for what works, when it works, and for whom is paramount to AI youth suicide prevention efforts, where rates are currently high and resources are limited. Employing Native paraprofessionals is a means of task-shifting psychoeducation, culturally competent patient support and continuity of care.Trial registrationClinical Trials NCT03543865, June 1, 2018.

Dataset Information

Risk and Protective Factors of Current Opioid Use Among Youth Living on or Near American Indian Reservations: An Application of Machine Learning.

Publications

Risk and Protective Factors of Current Opioid Use Among Youth Living on or Near American Indian Reservations: An Application of Machine Learning.

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