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journal contribution
posted on 2023-06-01, 12:00 authored by Michelle R. Lent, Karen L. Dugosh, Hannah Callahan, Emily Hurstak, Kimberly Mazur

Data capture and statistical approach

All study data were collected and managed using REDCap

(Research Electronic Data Capture) hosted at the

University of Pennsylvania [15]. Descriptive statistics

were calculated to characterize the sample at study

entry and to generate prevalence rates for lifetime suicidal

ideation and attempts. Next, a series of bivariate

correlations were calculated between each of the binary

suicide-related response variables (i.e., lifetime ideation,

lifetime attempts) and clinical and demographic factors

that may be associated with suicidality (age, education

completed [years], gender identity [male/female with

additional responses added for non-binary, etc.], race

[White, Black, Other], ethnicity, years of heroin use and

other opioid use, lifetime history of depression or anxiety

[yes/no], physical and/or sexual abuse [yes/no], presence

of a chronic pain-related condition [yes/no], parole/

probation [yes/no], days worked [past 30 days], and

lifetime years of daily use of more than one substance

[including alcohol]). Finally, a series of logistic regression

analyses were performed to predict each of the suicide related binary response variables.


Grant ID

Pennsylvania Department of Health: SAP #4100083338; PCORI: OBOT-2018C2-13158

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