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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. [ABSTRACT FROM AUTHOR]

Background: Alaska Native and American Indian people (AN/AIs) have a high incidence of colorectal cancer (CRC) and CRC-related mortality. Screening can prevent death from CRC, but screening rates are low in racially and ethnically diverse populations. The authors conducted a randomized controlled trial using text messaging to increase CRC screening among unscreened AN/AIs in a tribal health care system in Anchorage, Alaska.Methods: The intervention entailed up to 3 text messages sent 1 month apart. The authors randomized 2386 AN/AIs aged 40 to 75 years who were eligible for CRC screening to the intervention or usual-care control conditions. Screening status was ascertained from electronic health records 3 months and 6 months after the last text message. Hazard ratios (HRs) were estimated to evaluate the effectiveness of the intervention, stratified by age and sex.Results: The intervention increased CRC screening for AN/AIs aged 50 to 75 years (HR, 1.42; 95% confidence interval [95% CI], 0.97-2.09) and aged 40 to 49 years (HR, 1.24; 95% CI, 0.95-1.62). Within both age groups, the HRs were higher for women (HR, 1.69 [95% CI, 1.02-2.80] and HR, 1.37 [95% CI, 1.01-1.88]) compared with men (HR, 1.09 [95% CI, 0.59-1.99] and HR, 0.90 [95% CI, 0.54-1.53]). Interaction analysis yielded P values of .55 and .09, respectively, for age and sex.Conclusions: A simple text messaging intervention was found to increase CRC screening rates in AN/AIs, a group with high CRC morbidity and mortality. Text messaging may be a cost-effective means of reducing CRC screening disparities in AN/AIs and other populations. Cancer 2017;123:1382-1389. © 2016 American Cancer Society. [ABSTRACT FROM AUTHOR]

Alaska Native and American Indian people (AN/AIs) are disproportionately affected by suicide. Within a large AN/AI health service organization, demographic, clinical, and service utilization factors were compared between those with a suicide-related health visit and those without. Cases had higher odds of a behavioral health diagnosis, treatment for an injury, behavioral health specialty care visits, and opioid medication dispensation in the year prior to a suicide-related visit compared to gender-, age-, and residence- (urban versus rural) matched controls. Odds of a suicide-related visit were lower among those with private insurance and those with non-primary care ambulatory clinic visits. [ABSTRACT FROM AUTHOR]

Background: Fifty percent of people living with dementia are undiagnosed. The electronic health record (EHR) Risk of Alzheimer's and Dementia Assessment Rule (eRADAR) was developed to identify older adults at risk of having undiagnosed dementia using routinely collected clinical data. We aimed to externally validate eRADAR in two real‐world healthcare systems, including examining performance over time and by race/ethnicity. Methods: External validation was conducted in Kaiser Permanente Washington (KPWA), an integrated health system providing insurance coverage and medical care, and three primary care practices at University of California San Francisco Health (UCSF), an academic medical system. This retrospective cohort study included 688,599 person‐years from 129,315 KPWA members and 42,371 person‐years from 12,567 UCSF patients aged 65 years or older without prior EHR documentation of dementia diagnosis or medication. Analyses validated performance of eRADAR scores, calculated annually from EHR data (including vital signs, diagnoses, medications, and utilization in the prior 2 years), for predicting EHR documentation of incident dementia diagnosis within 12 months. Results: 7,631 dementia diagnoses were observed at KPWA (11.1 per 1,000 person‐years) and 189 at UCSF (4.5 per 1,000 person‐years). AUC was 0.84 (95% confidence interval: 0.84‐0.85) at KPWA and 0.78 (0.74‐0.81) at UCSF. Using the 90th percentile as the cutpoint, sensitivity was 54% (53‐56%) at KPWA and 45% (38‐52%) at UCSF. All performance measures were similar over time, including across the transition from International Classification of Diseases, version 9 (ICD‐9) to ICD‐10 codes, and across racial/ethnic groups. Conclusions: eRADAR showed strong external validity for detecting undiagnosed dementia in two health systems with different patient populations and differential availability of external health care data for risk calculations. In this study, eRADAR demonstrated generalizability from a research sample to real‐world clinical populations, transportability across health systems, robustness to temporal changes in health care, and similar performance across racial/ethnic groups. [ABSTRACT FROM AUTHOR]