US Army Soldiers Research Articles

Estimating Treatment Effect Heterogeneity in Psychiatry: A Review and Tutorial With Causal Forests.

Flexible machine learning tools are increasingly used to estimate heterogeneous treatment effects. This paper gives an accessible tutorial demonstrating the use of the causal forest algorithm, available in the R package grf. We start with a brief non-technical overview of treatment effect estimation methods, focusing on estimation in observational studies; the same techniques can also be applied in experimental studies. We then discuss the logic of estimating heterogeneous effects using the extension of the random forest algorithm implemented in grf. Finally, we illustrate causal forest by conducting a secondary analysis on the extent to which individual differences in resilience to high combat stress can be measured among US Army soldiers deploying to Afghanistan based on information about these soldiers available prior to deployment. We illustrate simple and interpretable exercises for model selection and evaluation, including targeting operator characteristics curves, Qini curves, area-under-the-curve summaries, and best linear projections. A replication script with simulated data is available at https://github.com/grf-labs/grf/tree/master/experiments/ijmpr.

Insomnia and sleep apnea in the entire population of US Army soldiers: Associations with deployment and combat exposure 2010-2019, a retrospective cohort investigation

Insomnia and sleep apnea in the entire population of US Army soldiers: Associations with deployment and combat exposure 2010-2019, a retrospective cohort investigation

Predicting suicide attempts among US Army soldiers using information available at the time of periodic health assessments

Predicting suicide attempts among US Army soldiers using information available at the time of periodic health assessments

Prospective associations of alcohol and drug misuse with suicidal behaviors among US Army soldiers who have left active service.

This study examines the prospective associations of alcohol and drug misuse with suicidal behaviors among service members who have left active duty. We also evaluate potential moderating effects of other risk factors and whether substance misuse signals increased risk of transitioning from thinking about to attempting suicide. US Army veterans and deactivated reservists (N=6,811) completed surveys in 2016-2018 (T1) and 2018-2019 (T2). Weights-adjusted logistic regression was used to estimate the associations of binge drinking, smoking/vaping, cannabis use, prescription drug abuse, illicit drug use, alcohol use disorder (AUD), and drug use disorder (DUD) at T1 with suicide ideation, plan, and attempt at T2. Interaction models tested for moderation of these associations by sex, depression, and recency of separation/deactivation. Suicide attempt models were also fit in the subgroup with ideation at T1 (n=1,527). In models controlling for socio-demographic characteristics and prior suicidality, binge drinking, cannabis use, prescription drug abuse, illicit drug use, and AUD were associated with subsequent suicidal ideation (AORs=1.42-2.60, ps<.01). Binge drinking, AUD, and DUD were associated with subsequent suicide plan (AORs=1.23-1.95, ps<.05). None of the substance use variables had a main effect on suicide attempt; however, interaction models suggested certain types of drug use predicted attempts among those without depression. Additionally, the effects of smoking/vaping and AUD differed by sex. Substance misuse did not predict the transition from ideation to attempt. Alcohol and drug misuse are associated with subsequent suicidal behaviors in this population. Awareness of differences across sex and depression status may inform suicide risk assessment.

A prediction model for differential resilience to the effects of combat-related stressors in US army soldiers.

To develop a composite score for differential resilience to effects of combat-related stressors (CRS) on persistent DSM-IV post-traumatic stress disorder (PTSD) among US Army combat arms soldiers using survey data collected before deployment. A sample of n=2542 US Army combat arms soldiers completed a survey shortly before deployment to Afghanistan and then again two to three and 8-9months after redeployment. Retrospective self-reports were obtained about CRS. Precision treatment methods were used to determine whether differential resilience to persistent PTSD in the follow-up surveys could be developed from pre-deployment survey data in a 60% training sample and validated in a 40% test sample. 40.8% of respondents experienced high CRS and 5.4% developed persistent PTSD. Significant test sample heterogeneity was found in resilience (t=2.1, p=0.032), with average treatment effect (ATE) of high CRS in the 20% least resilient soldiers of 17.1% (SE=5.5%) compared to ATE=3.8% (SE=1.2%) in the remaining 80%. The most important predictors involved recent and lifetime pre-deployment distress disorders. A reliable pre-deployment resilience score can be constructed to predict variation in the effects of high CRS on persistent PTSD among combat arms soldiers. Such a score could be used to target preventive interventions to reduce PTSD or other resilience-related outcomes.

Predicting Suicides Among US Army Soldiers After Leaving Active Service

The suicide rate of military servicemembers increases sharply after returning to civilian life. Identifying high-risk servicemembers before they leave service could help target preventive interventions. To develop a model based on administrative data for regular US Army soldiers that can predict suicides 1 to 120 months after leaving active service. In this prognostic study, a consolidated administrative database was created for all regular US Army soldiers who left service from 2010 through 2019. Machine learning models were trained to predict suicides over the next 1 to 120 months in a random 70% training sample. Validation was implemented in the remaining 30%. Data were analyzed from March 2023 through March 2024. The outcome was suicide in the National Death Index. Predictors came from administrative records available before leaving service on sociodemographics, Army career characteristics, psychopathologic risk factors, indicators of physical health, social networks and supports, and stressors. Of the 800 579 soldiers in the cohort (84.9% male; median [IQR] age at discharge, 26 [23-33] years), 2084 suicides had occurred as of December 31, 2019 (51.6 per 100 000 person-years). A lasso model assuming consistent slopes over time discriminated as well over all but the shortest risk horizons as more complex stacked generalization ensemble machine learning models. Test sample area under the receiver operating characteristic curve ranged from 0.87 (SE = 0.06) for suicides in the first month after leaving service to 0.72 (SE = 0.003) for suicides over 120 months. The 10% of soldiers with highest predicted risk accounted for between 30.7% (SE = 1.8) and 46.6% (SE = 6.6) of all suicides across horizons. Calibration was for the most part better for the lasso model than the super learner model (both estimated over 120-month horizons.) Net benefit of a model-informed prevention strategy was positive compared with intervene-with-all or intervene-with-none strategies over a range of plausible intervention thresholds. Sociodemographics, Army career characteristics, and psychopathologic risk factors were the most important classes of predictors. These results demonstrated that a model based on administrative variables available at the time of leaving active Army service can predict suicides with meaningful accuracy over the subsequent decade. However, final determination of cost-effectiveness would require information beyond the scope of this report about intervention content, costs, and effects over relevant horizons in relation to the monetary value placed on preventing suicides.

Relative Strength and Physical Performance in US Army Male and Female Soldiers.

Grier, T, Benedict, T, Mahlmann, O, and Canham-Chervak, M. Relative Strength and Physical Performance in U.S. Army Male and Female Soldiers. J Strength Cond Res 38(8): 1479-1485, 2024-In occupations with high physical demands, strength relative to bodymass is an important measure as it signifies an individual's ability to control and move their body mass through space. The purpose of this investigation was to examine physical characteristics, training, and performance based on different magnitudes of relative strength. Subjects were 1,806 male and 319 female US Army soldiers. Sex, age, height, body mass, and physical training data were obtained by an electronic survey. Physical performance was measured by the Army Combat Fitness Test (ACFT), which includes a three-repetition maximum deadlift, standing power throw, hand release push-up, sprint-drag-carry, leg tuck, and two-mile run. The relative body mass deadlifted was calculated and categorized into 4 groups for men and 3 groups for women. An analysis of variance and post hoc least significant difference test were used to evaluate the differences in relative strength and physical performance. Overall, 33% of men deadlifted ≥1.5 times their body mass, while 30% of women deadlifted ≥1.25 times their body mass. Men and women deadlifting the highest percentage of their body mass (≥1.5 times for men and ≥1.25 times for women) outperformed those with lower relative strength within their own sex in all 6 ACFT events. In 4 of the 6 ACFT events, women who deadlifted ≥1.25 times their body mass had similar performance compared with men deadlifting 1 to 1.24 times their body mass and outperformed men deadlifting <1 times their body mass. Greater strength relative to body mass was associated with higher physical performance.

Day-to-day reliability of basal heart rate and short-term and ultra short-term heart rate variability assessment by the Equivital eq02+ LifeMonitor in US Army soldiers

IntroductionThe present study determined the (1) day-to-day reliability of basal heart rate (HR) and HR variability (HRV) measured by the Equivital eq02+ LifeMonitor and (2) agreement of ultra short-term HRV...

Prevalence of Pain With Movement in Active Duty US Army Soldiers.

Soldiers must be able to perform a multitude of physically demanding tasks as part of their regular duty, but their physical readiness is often degraded due to pain and musculoskeletal injury (MSKI). The presence of pain with movement has been associated with increased MSKI risk in Soldiers. Improved awareness of the prevalence of painful movements in uninjured Soldiers could help inform Army injury mitigation efforts. The purpose of this study was to identify the prevalence of pain with movement in a population of healthy active duty Soldiers. The Selective Functional Movement Assessment-Top Tier Movements (SFMA-TTM), active range of motion (AROM) of the hips and shoulders, and the elicitation of pain with movement were measured in 268 healthy US Army Soldiers. Descriptive statistics were generated for the number of painful movements for each measure and inferential statistics; independent t-test and one-way independent analysis of variance (ANOVA) were used for analysis of the other measures. Greater than half (59%) of the participants reported pain with at least 1 movement and more than 41% reported pain with 2 or more movements. Soldiers reported a mean of 1.35 painful movements on the SFMA-TTM assessment and a mean of 1.54 painful AROM movements. Pain with functional movement patterns was common across a sample of uninjured Soldiers. The presence of pain with movement warrants further evaluation as it may impact a Soldier's physical performance, risk for future injury, and overall quality of life.

Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia.

The standard of care for military personnel with insomnia is cognitive behavioral therapy for insomnia (CBT-I). However, only a minority seeking insomnia treatment receive CBT-I, and little reliable guidance exists to identify those most likely to respond. As a step toward personalized care, we present results of a machine learning (ML) model to predict CBT-I response. Administrative data were examined for n = 1,449 nondeployed US Army soldiers treated for insomnia with CBT-I who had moderate-severe baseline Insomnia Severity Index (ISI) scores and completed 1 or more follow-up ISIs 6-12 weeks after baseline. An ensemble ML model was developed in a 70% training sample to predict clinically significant ISI improvement (reduction of at least 2 standard deviations on the baseline ISI distribution). Predictors included a wide range of military administrative and baseline clinical variables. Model accuracy was evaluated in the remaining 30% test sample. 19.8% of patients had clinically significant ISI improvement. Model area under the receiver operating characteristic curve (standard error) was 0.60 (0.03). The 20% of test-sample patients with the highest probabilities of improvement were twice as likely to have clinically significant improvement compared with the remaining 80% (36.5% vs 15.7%; χ21 = 9.2, P = .002). Nearly 85% of prediction accuracy was due to 10 variables, the most important of which were baseline insomnia severity and baseline suicidal ideation. Pending replication, the model could be used as part of a patient-centered decision-making process for insomnia treatment. Parallel models will be needed for alternative treatments before such a system is of optimal value. Gabbay FH, Wynn GH, Georg MW, etal. Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia. J Clin Sleep Med. 2024;20(6):921-931.

Uncommon Protein-Coding Variants Associated With Suicide Attempt in a Diverse Sample of U.S. Army Soldiers

Uncommon Protein-Coding Variants Associated With Suicide Attempt in a Diverse Sample of U.S. Army Soldiers

Getting to the Objective: Analyzing Navigation, Automation, and Situation Awareness from a Soldier’s Perspective

Land navigation ability is essential for US Army soldiers, who must use it both when planning routes to an objective and while executing the mission itself. Soldiers use automation to offload the cognitive effort of navigation, but if the system fails soldiers must expend precious time and mental energy reacquiring their location on a map. Despite this risk, soldiers still over-trust automated navigation and lose their location-related situation awareness. Loss of situation awareness leads to an inability to acquire the necessary spatial knowledge required to manually navigate. This paper reviews existing research to examine the effects of automation on situation awareness and a user’s ability to build a cognitive map from a soldier’s perspective. It further identifies issues with current technology and suggests possible directions for future research.

Prospective association of attachment style with suicide attempts among US Army soldiers.

Insecure attachment styles are associated with retrospectively reported suicide attempts (SAs). It is not known if attachment styles are prospectively associated with medically documented SAs. A representative sample of US Army soldiers entering service (n = 21 772) was surveyed and followed via administrative records for their first 48 months of service. Attachment style (secure, preoccupied, fearful, dismissing) was assessed at baseline. Administrative medical records identified SAs. Discrete-time survival analysis examined associations of attachment style with future SA during service, adjusting for time in service, socio-demographics, service-related variables, and mental health diagnosis (MH-Dx). We examined whether associations of attachment style with SA differed based on sex and MH-Dx. In total, 253 respondents attempted suicide. Endorsed attachment styles included secure (46.8%), preoccupied (9.1%), fearful (15.7%), and dismissing (19.2%). Examined separately, insecure attachment styles were associated with increased odds of SA: preoccupied [OR 2.5 (95% CI 1.7-3.4)], fearful [OR 1.6 (95% CI 1.1-2.3)], dismissing [OR 1.8 (95% CI 1.3-2.6)]. Examining attachment styles simultaneously along with other covariates, preoccupied [OR 1.9 (95% CI 1.4-2.7)] and dismissing [OR 1.7 (95% CI 1.2-2.4)] remained significant. The dismissing attachment and MH-Dx interaction was significant. In stratified analyses, dismissing attachment was associated with SA only among soldiers without MH-Dx. Other interactions were non-significant. Soldiers endorsing any insecure attachment style had elevated SA risk across the first 48 months in service, particularly during the first 12 months. Insecure attachment styles, particularly preoccupied and dismissing, are associated with increased future SA risk among soldiers. Elevated risk is most substantial during first year of service but persists through the first 48 months. Dismissing attachment may indicate risk specifically among soldiers not identified by the mental healthcare system.

Nutrition Knowledge Is Associated With Diet Quality Among US Army Soldiers

Nutrition Knowledge Is Associated With Diet Quality Among US Army Soldiers

Associations of Military-Related Traumatic Brain Injury With New-Onset Mental Health Conditions and Suicide Risk

Research to identify the direct and indirect associations of military-related traumatic brain injury (TBI) with suicide has been complicated by a range of data-related challenges. To identify differences in rates of new-onset mental health conditions (ie, anxiety, mood, posttraumatic stress, adjustment, alcohol use, and substance use disorders) among soldiers with and without a history of military-related TBI and to explore the direct and indirect (through new-onset mental health disorders) associations of TBI with suicide. This retrospective cohort study used data from the Substance Use and Psychological Injury Combat Study (SUPIC) database. Demographic, military, and health data from the Department of Defense within SUPIC were compiled and linked with National Death Index records to identify deaths by suicide. Participants included US Army soldiers who returned from an Afghanistan or Iraq deployment. Data were analyzed from September to December 2022. Military-related TBI. The outcome of interest was suicide. Secondary outcomes were incidence of new-onset mental health conditions. Mediation analyses consisted of accelerated failure time (AFT) models in conjunction with the product of coefficients method. The 6 new-onset mental health diagnosis categories and the 2 or more categories variable were each considered separately as potential mediators; therefore, a total of 14 models plus the overall AFT model estimating the total effect associated with TBI in suicide risk were fit. The study included 860 892 soldiers (320 539 soldiers [37.2%] aged 18-24 at end of index deployment; 766 454 [89.0%] male), with 108 785 soldiers (12.6%) with at least 1 documented TBI on their military health record. Larger increases in mental health diagnoses were observed for all conditions from before to after documented TBI, compared with the matched dates for those without a history of TBI, with increases observed for mood (67.7% vs 37.5%) and substance use (100% vs 14.5%). Time-to-suicide direct effect estimates for soldiers with a history of TBI were similar across mediators. For example, considering new-onset adjustment disorders, time-to-suicide was 16.7% faster (deceleration factor, 0.833; 95% CI, 0.756-0.912) than for soldiers without a history of TBI. Indirect effect estimates of associations with TBI were substantial and varied across mediators. The largest indirect effect estimate was observed through the association with new-onset substance use disorder, with a time to suicide 63.8% faster (deceleration factor, 0.372; 95% CI, 0.322-0.433) for soldiers with a history of TBI. In this longitudinal cohort study of soldiers, rates of new-onset mental health conditions were higher among individuals with a history of TBI compared with those without. Moreover, risk for suicide was both directly and indirectly associated with history of TBI. These findings suggest that increased efforts are needed to conceptualize the accumulation of risk associated with multiple military-related exposures and identify evidence-based interventions that address mechanisms associated with frequently co-occurring conditions.

Predicting Postoperative Injury and Military Discharge Status After Knee Surgery in the US Army.

Researchers have assessed postoperative injury or disability predictors in the military setting but typically focused on 1 type of surgical procedure at a time, used relatively small sample sizes, or investigated mixed cohorts with civilian populations. To identify the relationship between baseline variables and injury incidence or military discharge status in US Army soldiers after knee surgery. Case-control study; Level of evidence, 3. Data were obtained from a repository containing personnel, performance, and medical records for all active-duty US Army soldiers. Multivariate logistic regressions were used to estimate the effects of numerous variables on postoperative injury or on medical discharge. Variable selection and model validation were conducted using the k-fold method. A total of 7567 soldiers underwent knee surgery between 2017 and 2019. Meniscal procedures were the most common type of surgery (39%), and approximately 71% of the cohort had a postoperative injury. Significant predictors for sustaining a postoperative injury included having a previous nonknee injury (odds ratio [OR], 1.5), female sex (OR, 1.3), and Black race (OR, 1.2). Within 4 years after surgery, 17% of soldiers were discharged from the military because of knee-related disability. Significant predictors for discharge from duty included enlisted rank (OR, 2.3), recent fitness test failure (OR, 1.9), number of previous knee surgeries (OR, 1.7), and having a previous nonknee injury (OR, 1.6). After knee surgery, nearly three-fourths of the soldiers in this cohort sustained a postoperative injury and almost one-fifth of soldiers were medically discharged from the military within 4 years. This study identified variables that indicate statistically increased risk for these postoperative outcomes and highlighted potentially modifiable factors.

Postpartum long-acting reversible contraceptive use among active-duty, female US Army soldiers

Postpartum long-acting reversible contraceptive use among active-duty, female US Army soldiers

Development and Validation of a Machine Learning Prediction Model of Posttraumatic Stress Disorder After Military Deployment

Military deployment involves significant risk for life-threatening experiences that can lead to posttraumatic stress disorder (PTSD). Accurate predeployment prediction of PTSD risk may facilitate the development of targeted intervention strategies to enhance resilience. To develop and validate a machine learning (ML) model to predict postdeployment PTSD. This diagnostic/prognostic study included 4771 soldiers from 3 US Army brigade combat teams who completed assessments between January 9, 2012, and May 1, 2014. Predeployment assessments occurred 1 to 2 months before deployment to Afghanistan, and follow-up assessments occurred approximately 3 and 9 months post deployment. Machine learning models to predict postdeployment PTSD were developed in the first 2 recruited cohorts using as many as 801 predeployment predictors from comprehensive self-report assessments. In the development phase, cross-validated performance metrics and predictor parsimony were considered to select an optimal model. Next, the selected model's performance was evaluated with area under the receiver operating characteristics curve and expected calibration error in a temporally and geographically distinct cohort. Data analyses were performed from August 1 to November 30, 2022. Posttraumatic stress disorder diagnosis was assessed by clinically calibrated self-report measures. Participants were weighted in all analyses to address potential biases related to cohort selection and follow-up nonresponse. This study included 4771 participants (mean [SD] age, 26.9 [6.2] years), 4440 (94.7%) of whom were men. In terms of race and ethnicity, 144 participants (2.8%) identified as American Indian or Alaska Native, 242 (4.8%) as Asian, 556 (13.3%) as Black or African American, 885 (18.3%) as Hispanic, 106 (2.1%) as Native Hawaiian or other Pacific Islander, 3474 (72.2%) as White, and 430 (8.9%) as other or unknown race or ethnicity; participants could identify as of more than 1 race or ethnicity. A total of 746 participants (15.4%) met PTSD criteria post deployment. In the development phase, models had comparable performance (log loss range, 0.372-0.375; area under the curve range, 0.75-0.76). A gradient-boosting machine with 58 core predictors was selected over an elastic net with 196 predictors and a stacked ensemble of ML models with 801 predictors. In the independent test cohort, the gradient-boosting machine had an area under the curve of 0.74 (95% CI, 0.71-0.77) and low expected calibration error of 0.032 (95% CI, 0.020-0.046). Approximately one-third of participants with the highest risk accounted for 62.4% (95% CI, 56.5%-67.9%) of the PTSD cases. Core predictors cut across 17 distinct domains: stressful experiences, social network, substance use, childhood or adolescence, unit experiences, health, injuries, irritability or anger, personality, emotional problems, resilience, treatment, anxiety, attention or concentration, family history, mood, and religion. In this diagnostic/prognostic study of US Army soldiers, an ML model was developed to predict postdeployment PTSD risk with self-reported information collected before deployment. The optimal model showed good performance in a temporally and geographically distinct validation sample. These results indicate that predeployment stratification of PTSD risk is feasible and may facilitate the development of targeted prevention and early intervention strategies.

Financial Impact of Embedded Injury-Prevention Experts in US Army Initial Entry Training.

The US Army embedded injury-prevention experts (IPEs), specifically athletic trainers and strength and conditioning coaches, into initial entry training (IET) to limit musculoskeletal (MSK) conditions and their negative consequences. However, little is known about the financial impact of IPEs. To assess whether IPEs were associated with fewer sunk training costs due to MSK-related early discharges from service. Retrospective cohort study. Database of US Army soldiers' administrative, medical, and readiness records. A total of 198 166 soldiers (age = 20.7 ± 3.2 years, body mass index = 24.4 ± 3.5 kg/m2) who began IET during 2014 to 2017. Early discharge from service was defined as occurring within 6 months of beginning IET. All IET sites employed IPEs from 2011 to 2017, except for 2 sites during April to November 2015. Soldiers who began IET at these 2 sites during these times were categorized as not having IPE exposure. All others were categorized as having IPE exposure. The unadjusted association between IPE access and MSK-related early discharge from service was assessed using logistic regression. Financial impact was assessed by quantifying differences in yearly sunk costs between groups with and those without IPE exposure and subtracting IPE hiring costs. Among 14 094 soldiers without IPE exposure, 2.77% were discharged early for MSK-related reasons. Among 184 072 soldiers with IPE exposure, 1.01% were discharged. Exposure to IPEs was associated with reduced odds of MSK-related early discharge (odds ratio = 0.36, 95% CI = 0.32, 0.40, P < .001) and a decrease in yearly sunk training costs of $11.19 to $20.00 million. Employing IPEs was associated with reduced sunk costs because of fewer soldiers being discharged from service early for MSK-related reasons. Evidence-based recommendations should be developed for guiding policy on the roles and responsibilities of IPEs in the military to reduce negative outcomes from MSK conditions and generate a positive return on investment.

Genetic, environmental, and behavioral correlates of lifetime suicide attempt: Analysis of additive and interactive effects in two cohorts of US Army soldiers

Recently developed measures of genetic liability to suicide attempt may convey unique information regarding an individual’s risk of suicidal behavior. We calculated a polygenic risk score for suicide attempt (SA-PRS) for soldiers of European ancestry who participated in the Army STARRS New Soldier Study (NSS; n = 6573) or Pre/Post Deployment Study (PPDS; n = 4900). Multivariable logistic regression models were fit within each sample to estimate the association of SA-PRS with lifetime suicide attempt (LSA), and to examine whether SA-PRS displayed additive or interactive effects with environmental and behavioral risk/protective factors (lifetime trauma burden, childhood maltreatment, negative urgency impulsivity, social network size, perceived mattering, and dispositional optimism). Age, sex, and within-ancestry variation were included as covariates. Observed prevalence of LSA was 6.3% and 4.2% in the NSS and PPDS samples, respectively. In the NSS model, SA-PRS and environmental/behavioral factors displayed strictly additive effects on odds of LSA. Results indicated an estimated 21% increase in odds of LSA per 1 SD increase in SA-PRS [adjusted odds ratio (AOR; 95% CI) = 1.21 (1.09–1.35)]. In PPDS, the effect of SA-PRS varied by reports of optimism [AOR = 0.85 (0.74–0.98) for SA-PRS x optimism effect]. Individuals reporting low and average optimism had 37% and 16% increased odds of LSA per 1 SD increase in SA-PRS, respectively, whereas SA-PRS was not associated with LSA in those reporting high optimism. Overall, results suggested the SA-PRS had predictive value over and above several environmental and behavioral risk factors for LSA. Moreover, elevated SA-PRS may be more concerning in the presence of environmental and behavioral risk factors (e.g., high trauma burden; low optimism). Given the relatively small effect magnitudes, the cost and incremental benefits of utilizing SA-PRS for risk targeting must also be considered in future work.