The HADS includes data from 38 Army/DoD administrative data systems.26 (link) (See eTable 2 at http://www.armystarrs.org/publications ) Troister et al.,27 (link) in a comprehensive review of 8 published studies of predictors of civilian post-hospital suicides, found five replicated classes of predictors: (i) socio-demographics (the most consistent being male gender and recent job loss); (ii) history of prior suicidal behaviors; (iii) quality of care (e.g., low continuity of care); (iv) time since hospital discharge (inversely related to suicide risk); and (v) other psychopathological risk factors (the most consistent being non-affective psychosis, mood disorders, and multiple comorbid psychiatric disorders). More recent studies found similar predictors.17 (link),28 (link),29 (link) We extracted HADS variables operationalizing these predictors and added Army career variables found to predict military suicides,19 (link)–22 (link) unit variables, criminal justice variables (violent crime victimization-perpetration), and measures of registered weapons. Importantly, all predictors other than those involving the hospitalization were defined as of the month before hospitalization, while predicted suicides were in the 12 months after hospital discharge.
We cast a wide net in extracting HADS measures of the predictor constructs. For example, we distinguished 23 categories of psychiatric diagnoses defined largely by aggregated ICD-9-CM codes (e.g., ADHD/learning disorders [ICD-9-CM 314.0-315.9]), 8 additional categories of behavioral stressors (e.g., marital problems, other stressors/adversities, suicidal ideation and self-damaging behavior), and summary measures of any prior admission diagnoses, admission count variables, and parallel outpatient variables (eTable 1 at http://www.armystarrs.org/publications ).We also included NDC psychotropic medication codes collapsed into 15 categories (e.g., antianxiety antidepressant, antipsychotic) and 25 sub-categories (e.g., SSRI, SNRI, TCA) based on the First Databank (FDB) Enhanced Therapeutic Classification System™ (http://www.fdbhealth.com ) (eTable 3 at http://www.armystarrs.org/publications ). A total of 421 individual variables were constructed (eTable 4 at http://www.armystarrs.org/publications ).
As the HADS data systems were not developed for research, there was more missing-inconsistent data in some (e.g., socio-demographic) component datasets than in research datasets. However, as HADS datasets are updated monthly, missing values typically appeared in earlier and/or later months, allowing nearest neighbor imputations. Remaining missing values were resolved using randomly selected multiple imputations.30 Inconsistencies were reconciled using rational imputations (e.g., a soldier classified female one month but male others was recoded male).
We cast a wide net in extracting HADS measures of the predictor constructs. For example, we distinguished 23 categories of psychiatric diagnoses defined largely by aggregated ICD-9-CM codes (e.g., ADHD/learning disorders [ICD-9-CM 314.0-315.9]), 8 additional categories of behavioral stressors (e.g., marital problems, other stressors/adversities, suicidal ideation and self-damaging behavior), and summary measures of any prior admission diagnoses, admission count variables, and parallel outpatient variables (
As the HADS data systems were not developed for research, there was more missing-inconsistent data in some (e.g., socio-demographic) component datasets than in research datasets. However, as HADS datasets are updated monthly, missing values typically appeared in earlier and/or later months, allowing nearest neighbor imputations. Remaining missing values were resolved using randomly selected multiple imputations.30 Inconsistencies were reconciled using rational imputations (e.g., a soldier classified female one month but male others was recoded male).