Abstract
Suicide rates among American Indian and Alaska Native (AI/AN) young people are significantly higher than other ethnic groups in the United States. Not only are there great differences when comparing AI/AN rates and those of other Americans, some tribal groups have very low rates of suicide while other Native communities have much higher rates. Despite this obvious variability, there is little research to help understand the factors associated with these differences. The current study considers the correlates of suicidal behavior in one rural Alaska Native region that suffers disproportionately from suicide. The analysis describes suicide behavior between the years 2001–2009, and considers the characteristics associated with both suicide deaths and nonfatal suicidal behavior. In multivariate analyses we identified gender, method of suicide and history of previous attempt as significant predictors of fatal suicide behavior, similar to results obtained from analyses on the same community's data from the previous decade. This descriptive study can offer some insights to shape prevention efforts in this and other rural, tribal communities.
Notes
Note. Numbers may not sum to totals due to missing data.
*Alaskan Native includes any report of Alaskan Native race/ethnicity.
+Limited to population aged 15 and older (n = 510).
**Limited to population aged 16 and older (n = 481).
αLimited to population aged 25 and older (n = 203).
Note. *p-values from Chi-squared tests for categorical variables.
N may not sum up to 548, 510 & 38 due to missing data.
**Categories are not mutually exclusive (i.e., percentages may sum to more than 100%).
+ p-values fom Chi-squared tests comparing none versus any.
† Other includes father in jail, unknown, unspecified, other, etc.
≡Other includes laceration/stabbing, drug ingestion, hanging, drowning, etc.
∼Other includes abandoned building, bridge, water, other, unspecified, etc.
Note. *p-values from logistic regression models adjusted for age and sex.
**p-values from multivariable logistic regression models adjusted for all other factors in the column.