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Background The U.S. Army suicide attempt rate increased sharply during the wars in Iraq and Afghanistan. Risk may vary according to occupation, which significantly influences the stressors that soldiers experience. Methods Using administrative data from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS), we identified person-month records for all active duty Regular Army enlisted soldiers who had a medically documented suicide attempt from 2004 through 2009 ( n  = 9650) and an equal-probability sample of control person-months ( n  = 153,528). Logistic regression analyses examined the association of combat occupation (combat arms [CA], special forces [SF], combat medic [CM]) with suicide attempt, adjusting for socio-demographics, service-related characteristics, and prior mental health diagnosis. Results In adjusted models, the odds of attempting suicide were higher in CA (OR = 1.2 [95% CI: 1.1–1.2]) and CM (OR = 1.4 [95% CI: 1.3–1.5]), but lower in SF (OR = 0.3 [95% CI: 0.2–0.5]) compared to all other occupations. CA and CM had higher odds of suicide attempt than other occupations if never deployed (ORs = 1.1–1.5) or previously deployed (ORs = 1.2–1.3), but not when currently deployed. Occupation was associated with suicide attempt in the first ten years of service, but not beyond. In the first year of service, primarily a time of training, CM had higher odds of suicide attempt than both CA (OR = 1.4 [95% CI: 1.2–1.6]) and other occupations (OR = 1.5 [95% CI: 1.3–1.7]). Discrete-time hazard functions revealed that these occupations had distinct patterns of monthly risk during the first year of service. Conclusions Military occupation can inform the understanding suicide attempt risk among soldiers.

The Institute for Social Research (ISR) at the University of Michigan successfully led an intensive, long-term, in-person survey for the Moving to Opportunity (MTO) for Fair Housing demonstration final impacts evaluation (Sanbonmatsu et al, 2011), achieving final effective response rates (ERRs) of 89.6 percent among MTO adults and 88.7 percent among youth, well above what response rates of surveys with comparable low-income populations have accomplished. A variety of survey field strategies ISR employed—careful staff selection, strategic use of financial incentives, and close collaboration between ISR and the National Bureau of Economic Research—all contributed to these high ERRs. The high costs associated with achieving high ERRs for in-person surveys like that employed in MTO raises questions about added value. Costs per survey interview nearly quadrupled during the last 4 fielding months. This extra investment increased the MTO adult survey ERR by only about 3.2 percentage points. A reanalysis of intention-to-treat estimates on selected outcomes suggests the merits of such an investment. If survey fielding had stopped at an 81-percent ERR for adults, we would have falsely concluded that MTO had no effect on two of four key health outcomes, that MTO had no effect on female youth mental health, and that MTO increased female youth idleness.

Incentives are known to increase response rates in mail surveys, and although they are increasingl being used in face-to-face and telephone surveys, there is much less information about their effects in those surveys, which differ radically in the demands they place on respondents. In this article, we analyze all the studies we have been able to locate that experimen with incentives in surveys done in person or by telephone in order to answer four questions: Do incentives improve response rates and does the effect vary by mode of interviewing? Are prepaid incentives more effective than promised incentives? Is money more effective than a gift? What is the effect of interview burden on the effectiveness of incentives?

The 2013 US Veterans Administration/Department of Defense Clinical Practice Guidelines (VA/DoD CPG) require comprehensive suicide risk assessments for VA/DoD patients with mental disorders but provide minimal guidance on how to carry out these assessments. Given that clinician-based assessments are not known to be strong predictors of suicide, we investigated whether a precision medicine model using administrative data after outpatient mental health specialty visits could be developed to predict suicides among outpatients. We focused on male nondeployed Regular US Army soldiers because they account for the vast majority of such suicides. Four machine learning classifiers (naive Bayes, random forests, support vector regression and elastic net penalized regression) were explored. Of the Army suicides in 2004–2009, 41.5% occurred among 12.0% of soldiers seen as outpatient by mental health specialists, with risk especially high within 26 weeks of visits. An elastic net classifier with 10–14 predictors optimized sensitivity (45.6% of suicide deaths occurring after the 15% of visits with highest predicted risk). Good model stability was found for a model using 2004–2007 data to predict 2008–2009 suicides, although stability decreased in a model using 2008–2009 data to predict 2010–2012 suicides. The 5% of visits with highest risk included only 0.1% of soldiers (1047.1 suicides/100 000 person-years in the 5 weeks after the visit). This is a high enough concentration of risk to have implications for targeting preventive interventions. An even better model might be developed in the future by including the enriched information on clinician-evaluated suicide risk mandated by the VA/DoD CPG to be recorded.