Dataset Information

Validating a predictive algorithm for suicide risk with Alaska Native populations.

ABSTRACT:

Introduction

The American Indian/Alaska Native (AI/AN) suicide rate in Alaska is twice the state rate and four times the U.S. rate. Healthcare systems need innovative methods of suicide risk detection. The Mental Health Research Network (MHRN) developed suicide risk prediction algorithms in a general U.S.

Patient population

Methods

We applied MHRN predictors and regression coefficients to electronic health records of AI/AN patients aged ≥13 years with behavioral health diagnoses and primary care visits between October 1, 2016, and March 30, 2018. Logistic regression assessed model accuracy for predicting and stratifying risk for suicide attempt within 90 days after a visit. We compared expected to observed risk and assessed model performance characteristics.

Results

10,864 patients made 47,413 primary care visits. Suicide attempt occurred after 589 (1.2%) visits. Visits in the top 5% of predicted risk accounted for 40% of actual attempts. Among visits in the top 0.5% of predicted risk, 25.1% were followed by suicide attempt. The best fitting model had an AUC of 0.826 (95% CI: 0.809-0.843).

Conclusions

The MHRN model accurately predicted suicide attempts among AI/AN patients. Future work should develop clinical and operational guidance for effective implementation of the model with this population.

SUBMITTER: Shaw JL

PROVIDER: S-EPMC9378560 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Validating a predictive algorithm for suicide risk with Alaska Native populations.

Shaw Jennifer L JL Beans Julie A JA Noonan Carolyn C Smith Julia J JJ Mosley Mike M Lillie Kate M KM Avey Jaedon P JP Ziebell Rebecca R Simon Gregory G

Suicide & life-threatening behavior 20220315 4

<h4>Introduction</h4>The American Indian/Alaska Native (AI/AN) suicide rate in Alaska is twice the state rate and four times the U.S. rate. Healthcare systems need innovative methods of suicide risk detection. The Mental Health Research Network (MHRN) developed suicide risk prediction algorithms in a general U.S.<h4>Patient population</h4><h4>Methods</h4>We applied MHRN predictors and regression coefficients to electronic health records of AI/AN patients aged ≥13 years with behavioral health dia ...[more]

PMID: 35293010

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Project description:BackgroundAmerican Indian/Alaska Native (AI/AN) populations have been disproportionately affected by chronic respiratory diseases for reasons incompletely understood. Past research into disease disparity using population-based surveys mostly focused on state-specific factors. The present study investigates the independent contributions of AI/AN racial status and other socioeconomic/demographic variables to chronic respiratory disease disparity in an 11-state region with historically high AI/AN representation. Using data from the Behavioral Risk Factor Surveillance System (BRFSS) spanning years 2011-2018, this work provides an updated assessment of disease disparity and potential determinants of respiratory health in AI/AN populations.MethodsThis cross-sectional study used data from the BRFSS survey, 2011-2018. The study population included AI/AN and non-Hispanic white individuals resident in 11 states with increased proportion of AI/AN individuals. The yearly number of respondents averaged 75,029 (62878-87,350) which included approximately 5% AI/AN respondents (4.5-6.3%). We compared the yearly adjusted prevalence for chronic respiratory disease, where disease status was defined by self-reported history of having asthma and/or chronic obstructive pulmonary disease (COPD). Multivariable logistic regression was performed to determine if being AI/AN was independently associated with chronic respiratory disease. Covariates included demographic (age, sex), socioeconomic (marital status, education level, annual household income), and behavioral (smoking, weight morbidity) variables.ResultsThe AI/AN population consistently displayed higher adjusted prevalence of chronic respiratory disease compared to the non-Hispanic white population. However, the AI/AN race/ethnicity characteristic was not independently associated with chronic respiratory disease (OR, 0.93; 95% CI, 0.79-1.10 in 2017). In contrast, indicators of low socioeconomic status such as annual household income of <$10,000 (OR, 2.02; 95% CI, 1.64-2.49 in 2017) and having less than high school education (OR, 1.37; 95% CI, 1.16-1.63 in 2017) were positively associated with disease. These trends persisted for all years analyzed.ConclusionsThis study highlighted that AI/AN socioeconomic burdens are key determinants of chronic respiratory disease, in addition to well-established risk factors such as smoking and weight morbidity. Disease disparity experienced by the AI/AN population is therefore likely a symptom of disproportionate socioeconomic challenges they face. Further promotion of public health and social service efforts may be able to improve AI/AN health and decrease this disease disparity.

Dataset Information

Validating a predictive algorithm for suicide risk with Alaska Native populations.

Introduction

Patient population

Methods

Results

Conclusions

Publications

Validating a predictive algorithm for suicide risk with Alaska Native populations.

Similar Datasets

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