scholarly journals Wisconsin healthcare utilization cost among American Indians/Alaskan Natives with and without ADRD

2021 ◽  
Vol 17 (S10) ◽  
Author(s):  
Adrienne L Johnson ◽  
Derek L Norton ◽  
Marlon P Mundt ◽  
Elaina Seep ◽  
Mary F Wyman ◽  
...  
Keyword(s):  
Healthcare Utilization ◽  
American Indians ◽  
Alaskan Natives ◽  
Utilization Cost

Suicide as a Public Health Burden

2019 ◽  
pp. 207-220
Author(s):  
Holly C. Wilcox ◽  
Diana Clarke ◽  
Adrienne Grzenda ◽  
Stephanie G. Smith ◽  
William W. Eaton
Keyword(s):  
American Indians ◽  
Military Personnel ◽  
The United States ◽  
Sociological Perspective ◽  
Public Health Burden ◽  
Alaskan Natives ◽  
Public Figures ◽  
Bisexual And Transgender ◽  
Prevention Techniques ◽  
The 19Th Century

This chapter reviews the burden associated with suicide in the United States and around the world as a leading cause of death. There are many factors associated with higher risk for suicide, and there are a range of conceptual approaches to understanding it, including the sociological perspective, originally proposed by Durkheim in the 19th century and elaborated in many ways since then. American Indians and Alaskan Natives; lesbian, gay, bisexual and transgender individuals; middle-aged men and women; and military personnel and veterans have higher than expected rates of suicide. Suicide sometimes occurs in imitation of public figures who take their own life, and the manner of journalistic reporting can affect the size of the imitative response in the population. Future directions for suicide prevention should focus on the need for data linkage and upstream approaches to identify novel predictors, evaluate the effectiveness of prevention techniques, and expand on prevention paradigms.

Predicting Rehabilitation Success Rate Trends Among Ethnic Minorities Served by State Vocational Rehabilitation Agencies: A National Time Series Forecast Model Demonstration Study

2017 ◽  
Vol 31 (3) ◽  
pp. 158-173
Author(s):  
Corey L. Moore ◽  
Ningning Wang ◽  
Janique Tynez Washington
Keyword(s):  
African Americans ◽  
Vocational Rehabilitation ◽  
Success Rate ◽  
American Indians ◽  
Predictive Accuracy ◽  
Arima Model ◽  
Pacific Islanders ◽  
Native Hawaiians ◽  
Fiscal Year ◽  
Alaskan Natives

Purpose: This study assessed and demonstrated the efficacy of two select empirical forecast models (i.e., autoregressive integrated moving average [ARIMA] model vs. grey model [GM]) in accurately predicting state vocational rehabilitation agency (SVRA) rehabilitation success rate trends across six different racial and ethnic population cohorts (i.e., Blacks or African Americans, non-Latino Whites, American Indians or Alaskan Natives, Asians, Native Hawaiians or other Pacific Islanders, and Latinos). Methods: Eleven years of Rehabilitation Services Administration (RSA)-911 case record data (fiscal year [FY] 2004 through 2014) on SVRA employment outcomes were extracted and entered into the ARIMA model and GM to test their predictive performance. Results: The GM was demonstrated to be superior to the ARIMA model in predictive accuracy performance. Remarkably, although the GM (1, 1) 3-year frequency curve projection simulation results (FY 2015–2017) showed a slight upward trajectory in the number of successfully rehabilitated Latinos compared to baseline FY 2014 actual numbers, more drastic downward trajectories were projected for Blacks or African Americans, non-Latino Whites, American Indians or Alaskan Natives, Asians, and Native Hawaiians or other Pacific Islanders. Conclusions: The GM represents a demonstrably capable and promising forecasting tool that could be useful to SVRA leaders, policy makers, advocates, and researchers in simulating predictions that inform future policy initiatives, influence strategic plan development, and help guide the state of the science on future research and development foci. Additional multiple comprehensive demonstration trials, nonetheless, are needed to either confirm or refute the GM’s veracity in national and state predictive accuracy and curve fitting performance.

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