Exploring Suicidal Ideation Predictors in U.S. Adults with Depression: The Roles of Demographics and Vitamin D in a Clinical Study

Exploring Suicidal Ideation Predictors in U.S. Adults with Depression: The Roles of Demographics and Vitamin D in a Clinical Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF comment Exploring Suicidal Ideation Predictors in U.S. Adults with Depression: The Roles of Demographics and Vitamin D in a Clinical Study Hong Guo, Lu Dai, Jian Zhan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7074898/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Identifying predictors of suicidal ideation in adults with depression is crucial for developing preventive strategies. However, comprehensive studies that integrate demographic and vitamin D (VID) factors are limited. This study aimed to investigate potential predictors of suicidal ideation among U.S. adults with depression, with a particular focus on demographic and VID indicators. Additionally, a predictive model was developed via logistic regression combined with nomogram analysis. Methods A clinical prediction framework was developed utilizing multivariable logistic regression to assess the associations between suicidal ideation and variables such as sex, age, race, military service history, education level, marital status, household size, income‒poverty ratio, and VID concentrations (VD2 and VD3). The model's performance was assessed through receiver operating characteristic (ROC) curve analysis in both the training and validation cohorts. Results VID was significantly, albeit mildly, associated with suicidal ideation (OR 0.99, 95% CI 0.99–1.00; p = 0.033). In contrast, age and other demographic variables, including race, marital status, and household size, did not achieve statistical significance. Receiver operating characteristic (ROC) curve evaluation revealed moderate discriminative ability, with an area under the curve (AUC) of 0.636 (95% CI: 0.587–0.686) in the training cohort and 0.619 (95% CI: 0.517–0.720) in the validation cohort, suggesting acceptable generalizability. Conclusion VID concentrations may serve as a significant predictive factor for suicidal ideation among depressed adults in the United States, whereas demographic and socioeconomic factors exhibit limited predictive value. suicidal ideation depression vitamin D logistic regression predictive model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 INTRODUCTION Depression is a debilitating mental health disorder affecting approximately 21 million adults in the United States, contributing significantly to morbidity, mortality, and healthcare expenditures[ 1 ]. Among its most severe sequelae is suicidal ideation (SI), a critical precursor to suicide attempts and completed suicide[ 2 ]. Recent data indicate that suicide remains a leading cause of death nationwide, with over 48,000 annual fatalities[ 3 ], highlighting the urgent need for early risk stratification and targeted interventions. Demographic and socioeconomic disparities profoundly influence SI risk. The prevalence of depression is greater in women than in men, but male patients with depression are more likely to commit suicide, which is associated with factors such as substance use, anger, and risk-taking[ 4 ]. Compared with other age cohorts, middle-aged adults have elevated suicide rates, which is potentially linked to cumulative life stressors and socioeconomic instability[ 5 ]. Racial and ethnic minorities, particularly Black and Hispanic populations, face systemic barriers to accessing mental healthcare, exacerbating depressive symptoms and SI [ 5 ]. Military veterans, another high-risk group, experience suicide rates that are 1.5 times higher than those of nonveterans, driven by trauma exposure, posttraumatic stress disorder, and insufficient support systems [ 6 ]. Socioeconomic factors further compound vulnerability: low educational attainment, divorce or widowhood, household isolation, and income‒poverty ratios below the federal threshold are strongly associated with severe depression and SI[ 5 ]. Emerging evidence implicates biological mechanisms in SI pathogenesis, with vitamin D deficiency emerging as a modifiable risk factor. Vitamin D (specifically D2 and D3 isoforms) modulates serotonin synthesis and neuroinflammation—pathways dysregulated in depression[ 7 ]. Meta-analytic data confirm that low serum vitamin D levels correlate with increased SI severity, potentially through disrupted hypothalamic‒pituitary‒adrenal (HPA) axis function and heightened proinflammatory states [ 8 , 9 ]. Despite these advances, the interplay between vitamin D deficiency and sociodemographic determinants remains underexplored in large-scale clinical cohorts. To address this gap, this study employs logistic regression and nomogram-based modeling to integrate multidimensional predictors—spanning demographic, socioeconomic, and biological domains—in a nationally representative sample of U.S. adults with depression. Nomograms offer clinically actionable tools for visualizing individual SI risk, enhancing personalized intervention strategies[ 10 ]. By elucidating synergistic effects (e.g., vitamin D deficiency amplified by poverty or racial disparities), this work aims to advance precision psychiatry and reduce the preventable burden of suicide. MATERIALS AND METHODS Patient Data All study data were derived from the open-access database of the NHANES, which comprises a series of nationally representative cohort investigations established to track the health status of the U.S. population. Data acquisition involved three primary methods: in-home interviews, physical assessments conducted at mobile examination centers, and laboratory analyses. Ethical approval for this research was granted by the NCHS Ethics Review Board, with all participants providing informed consent before participation[11]. For more comprehensive information about the NHANES initiative, interested readers may refer to its official website (https://www.cdc.gov/nchs/nhanes/). This investigation drew upon data from a nationally representative NHANES cohort covering the period from August 2021 to August 2023, with an initial inclusion of 11,933 participants. Participant eligibility was refined through the application of specific exclusion criteria: (1) individuals younger than 18 years of age; (2) incomplete PHQ-9 dataset; (3) absence of 25-hydroxyvitamin D measurement data; and (4) nondepressed participants. Total PHQ-9 scores ≥10 are indicative of major depressive disorder (MDD), with high diagnostic sensitivity and specificity for MDD, as corroborated by multiple studies[12]. Consequently, PHQ-9 scores below 10 (indicating no depressive symptoms) were excluded from the current analysis. After these screening procedures, a final sample of 684 participants was enrolled in the study (Figure 1). Data collection Study data were derived from the open-access NHANES database, covering the period from August 2021 to August 2023. The collected demographic and clinical variables included sex, age, race, country of birth, military service history, educational level, marital status, household size, the family income‒poverty ratio, and measurements of 25-hydroxyvitamin D2 (VD2) and 25-hydroxyvitamin D3 (VD3) levels. Vitamin D (VID) is expressed as the sum of VD2 and VD3. Outcome data from the DPQ-090 group were also retrieved. Ethical supervision was conducted by the National Center for Health Statistics Ethics Review Board, which guaranteed adherence to U.S. federal regulations and emphasized the protection of participants' rights and welfare throughout the research process. Suicidal ideation Widely adopted as a depression screening instrument, the PHQ-9 assesses the frequency of depressive symptoms experienced in the previous two weeks[13]. This tool is composed of 9 items and uses a 0--3 scoring scale to quantify the occurrence of symptoms. Suicidal ideation (SI) was evaluated via the ninth item of the PHQ-9. This item specifically inquires: "Over the past two weeks, how frequently have you been troubled by thoughts of being better off dead or of harming yourself?" Participants who scored ≥1 on this item were categorized as exhibiting suicidal ideation, which is consistent with established criteria[14]. 25-hydroxyvitamin D2 and 25-hydroxyvitamin D3 Serum concentrations of 25-hydroxyvitamin D2 (VD2) and 25-hydroxyvitamin D3 (VD3) (measured in nmol/L) were measured via high-performance liquid chromatography‒tandem mass spectrometry (HPLC‒MS‒MS)[15]. Vitamin D (VID) is expressed as the sum of VD2 and VD3. The comprehensive methodology for this analytical procedure has been extensively delineated in other scholarly publications. Venous blood samples were collected from participants aged ≥1 year by certified phlebotomists, with the majority of assays conducted across 35 laboratories nationwide[15]. Covariates The study collected demographic variables, including age; birth country (categorized as U.S. state/D.C. born or other regions); household size (classified as 1–6 members or ≥7 members); racial/ethnic group (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Other Race); educational attainment (below 9th grade, 9th–11th grade, high school graduate, some college/associate degree, college graduate or higher); marital status (married/cohabiting, widowed/divorced/separated, never married); and the income‒to-poverty ratio[16]. Statistical analysis The NHANES-derived dataset underwent random partitioning into training and validation cohorts with an 8:2 allocation, followed by baseline variable comparison. Nonnormally distributed variables are reported as medians (interquartile ranges) and descriptive statistics. For univariate analysis, categorical variables were assessed via the chi-square test or Fisher's exact test, whereas continuous variables were analyzed via Student's t test or the Mann‒Whitney U test[16]. Within the training cohort, least absolute shrinkage and selection operator (LASSO) logistic regression was used in multivariate analysis to detect independent risk factors and develop a predictive nomogram specific to the DPQ-090 group. Model performance was evaluated through receiver operating characteristic (ROC) curves and calibration plots, where the area under the ROC curve (AUC) ranged from 0.5 (no discriminatory ability) to 1 (perfect discrimination). Similarly, decision curve analysis (DCA) was conducted to define the net benefit threshold for predictions. A p value of less than 0.05 was considered statistically significant, and all analyses were performed with R software (version 4.2.2) and MSTATA (www.mstata.com). RESULTS Patient characteristics The baseline demographic and clinical features of the study cohorts are detailed in Table 1 . The training cohort consisted of 547 participants, with the internal test cohort encompassing 137 individuals. The training cohort included 651 participants, with the internal test cohort containing 434 individuals. No significant differences in sociodemographic variables, including age, race, military history, educational attainment, marital status, total number of household members, or the family income‒poverty ratio, were detected between the two groups. Similarly, clinical measures, including VID (73 ± 37 vs. 73 ± 35, p = 0.882), VD2 (7 ± 21 vs. 8 ± 20, p = 0.847), and VD3 (66 ± 37 vs. 65 ± 35, p = 0.796), demonstrated no statistically significant differences between the training and internal test cohorts. A notable difference emerged in the country of birth distribution (p = 0.027), with a greater percentage of participants born in the 50 US states or Washington in the internal test cohort (91.2%) than in the training cohort (83.7%). Collectively, baseline characteristics demonstrated good balance across cohorts, with country of birth being the sole variable exhibiting significant disparity, indicating that the study groups were sufficiently comparable for predictive modeling applications. Univariate analyses were performed to compare parameters across distinct outcome groups, and the results are presented in Table 2 . Table 1 Patient demographics and baseline characteristics Characteristic Cohort p value Training Cohort N = 547 Internal Test Cohort N = 137 Gender, n (%) 0.377 1 Male 210 (38.4%) 47 (34.3%) Female 337 (61.6%) 90 (65.7%) Age, Mean ± SD 48 ± 19 45 ± 19 0.200 2 Race, n (%) 0.411 1 Mexican American 49 (9.0%) 10 (7.3%) Other Hispanic 62 (11.3%) 11 (8.0%) Non-Hispanic White 298 (54.5%) 87 (63.5%) Non-Hispanic Black 70 (12.8%) 16 (11.7%) Other Race - Including Multi-Racial 68 (12.4%) 13 (9.5%) Country of birth, n (%) 0.027 1 Born in 50 US states or Washington 458 (83.7%) 125 (91.2%) Others 89 (16.3%) 12 (8.8%) Served Armed, n (%) 0.840 1 Yes 45 (8.2%) 12 (8.8%) No 502 (91.8%) 125 (91.2%) Education level, n (%) 0.270 1 College graduate or above 130 (23.8%) 23 (16.8%) Some college or AA degree 189 (34.6%) 59 (43.1%) High school graduate/GED or equivalent 138 (25.2%) 33 (24.1%) 9-11th grade 58 (10.6%) 16 (11.7%) Less than 9th grade 32 (5.9%) 6 (4.4%) Marital status, n (%) 0.268 1 Married/Living with partner 187 (34.2%) 56 (40.9%) Widowed/Divorced/Separated 153 (28.0%) 38 (27.7%) Never married 207 (37.8%) 43 (31.4%) Total number of people in the Household, n (%) 0.772 3 1 163 (29.8%) 41 (29.9%) 2 164 (30.0%) 33 (24.1%) 3 93 (17.0%) 28 (20.4%) 4 65 (11.9%) 17 (12.4%) 5 36 (6.6%) 9 (6.6%) 6 10 (1.8%) 3 (2.2%) ≥ 7 16 (2.9%) 6 (4.4%) Ratio of family income to poverty, Mean ± SD 2.25 ± 1.49 2.23 ± 1.43 0.881 2 VID, Mean ± SD 73 ± 37 73 ± 35 0.882 2 VD2, Mean ± SD 7 ± 21 8 ± 20 0.847 2 VD3, Mean ± SD 66 ± 37 65 ± 35 0.796 2 1 Pearson's Chi-squared test 2 Welch Two Sample t test 3 Fisher's exact test Table 2 Patient demographics and baseline characteristics Characteristic Cohort (Training Cohort), N = 547 Cohort (Internal Test Cohort), N = 137 DPQ090-group (No idea of suicide) N = 380 DPQ090-group (Idea of suicide) N = 167 p value 1 DPQ090-group (No idea of suicide) N = 93 DPQ090-group (Idea of suicide) N = 44 p value 2 Gender, n (%) 0.351 0.132 Male 141 (37.1%) 69 (41.3%) 28 (30.1%) 19 (43.2%) Female 239 (62.9%) 98 (58.7%) 65 (69.9%) 25 (56.8%) Age, Mean ± SD 49 ± 18 44 ± 20 0.006 48 ± 18 41 ± 20 0.056 Race, n (%) 0.255 0.050 Mexican American 33 (8.7%) 16 (9.6%) 3 (3.2%) 7 (15.9%) Other Hispanic 36 (9.5%) 26 (15.6%) 6 (6.5%) 5 (11.4%) Non-Hispanic White 209 (55.0%) 89 (53.3%) 65 (69.9%) 22 (50.0%) Non-Hispanic Black 51 (13.4%) 19 (11.4%) 10 (10.8%) 6 (13.6%) Other Race - Including Multi-Racial 51 (13.4%) 17 (10.2%) 9 (9.7%) 4 (9.1%) Country of birth, n (%) 0.143 0.751 Born in 50 US states or Washington 324 (85.3%) 134 (80.2%) 84 (90.3%) 41 (93.2%) Others 56 (14.7%) 33 (19.8%) 9 (9.7%) 3 (6.8%) Served Armed, n (%) 0.207 0.751 Yes 35 (9.2%) 10 (6.0%) 9 (9.7%) 3 (6.8%) No 345 (90.8%) 157 (94.0%) 84 (90.3%) 41 (93.2%) Education level, n (%) 0.577 > 0.999 College graduate or above 92 (24.2%) 38 (22.8%) 16 (17.2%) 7 (15.9%) Some college or AA degree 136 (35.8%) 53 (31.7%) 40 (43.0%) 19 (43.2%) High school graduate/GED or equivalent 96 (25.3%) 42 (25.1%) 22 (23.7%) 11 (25.0%) 9-11th grade 36 (9.5%) 22 (13.2%) 11 (11.8%) 5 (11.4%) Less than 9th grade 20 (5.3%) 12 (7.2%) 4 (4.3%) 2 (4.5%) Marital status, n (%) 0.013 0.402 Married/Living with partner 143 (37.6%) 44 (26.3%) 41 (44.1%) 15 (34.1%) Widowed/Divorced/Separated 107 (28.2%) 46 (27.5%) 26 (28.0%) 12 (27.3%) Never married 130 (34.2%) 77 (46.1%) 26 (28.0%) 17 (38.6%) Total number of people in the Household, n (%) 0.678 1 111 (29.2%) 52 (31.1%) 28 (30.1%) 13 (29.5%) 2 122 (32.1%) 42 (25.1%) 24 (25.8%) 9 (20.5%) 3 60 (15.8%) 33 (19.8%) 18 (19.4%) 10 (22.7%) 4 48 (12.6%) 17 (10.2%) 10 (10.8%) 7 (15.9%) 5 25 (6.6%) 11 (6.6%) 8 (8.6%) 1 (2.3%) 6 6 (1.6%) 4 (2.4%) 2 (2.2%) 1 (2.3%) ≥ 7 8 (2.1%) 8 (4.8%) 3 (3.2%) 3 (6.8%) Ratio of family income to poverty, Mean ± SD 2.26 ± 1.50 2.22 ± 1.46 0.785 2.29 ± 1.46 2.09 ± 1.37 0.426 VID, Mean ± SD 76 ± 39 66 ± 33 0.002 79 ± 31 58 ± 39 0.003 VD2, Mean ± SD 8 ± 22 6 ± 17 0.534 9 ± 24 5 ± 8 0.139 VD3, Mean ± SD 69 ± 38 59 ± 33 0.004 70 ± 32 54 ± 39 0.016 1 Pearson's Chi-squared test; Welch Two Sample t test 2 Pearson's Chi-squared test; Welch Two Sample t test; Fisher's exact test Predictive Model The initial predictive model encompassed a set of candidate variables, including Age, Race, Military History, Educational Attainment, Marital Status, Household Size, Family Income-to-Poverty Ratio, VID, VD2, and VD3. LASSO regression analysis, which was conducted on the training cohort, reduced these variables to five critical predictors. The corresponding regression coefficient trajectories are depicted in Fig. 2 , which also presents the cross-validation error plot of the LASSO model. The optimally regularized minimal model—determined at the point where cross-validated error reached within one standard deviation of the minimum value—contained five variables. Fourteen clinical features were selected to construct a clinical model after LASSO regression and 10-fold cross-validation (Fig. 3 ). The following variables were selected by LASSO regression (λ = 0.0279729413424682) and can be used in the subsequent modeling analysis: age, race, marital status, total number of people in the household, and VID. Figure 4 shows that ROC analysis of the aforementioned variables produced AUC values greater than 0.5. Receiver operating characteristic (ROC) curve evaluation revealed that all investigated variables displayed moderate predictive capacity for the outcome event (DPQ090 group), with area under the curve (AUC) values ranging between 0.550 and 0.583. Notably, VID exhibited the strongest discriminative power (AUC = 0.583, 95% CI: 0.533–0.633), followed closely by age (AUC = 0.578, 95% CI: 0.524–0.632) and marital status (AUC = 0.574, 95% CI: 0.526–0.623). The total number of household members showed intermediate predictive utility (AUC = 0.562, 95% CI: 0.511–0.613), whereas Race presented the lowest discriminative ability (AUC = 0.550, 95% CI: 0.502–0.598). The finalized logistic regression model incorporated five independent predictive variables (Age, Race, Marital status, Total household members, and VID), which were translated into a user-friendly nomogram. This nomogram is visually depicted in Fig. 5 and accessible via the provided online platform ( https://guohongdp.shinyapps.io/dynnomapp/ ). Multivariate logistic regression modeling conducted on the training cohort indicated that VID was significantly correlated with the outcome (OR 0.99, 95% CI 0.99–1.00; p = 0.033), whereas age was not significantly associated with the outcome (OR 0.99, 95% CI 0.98–1.01; p = 0.258). Among the racial groups, other Hispanic (OR 1.91, 95% CI 0.84–4.33), non-Hispanic White (OR 1.27, 95% CI 0.63–2.57), non-Hispanic Black (OR 0.89, 95% CI 0.39–2.04), and other race (OR 0.79, 95% CI 0.34–1.84) groups showed no significant differences compared with the Mexican American reference group (all p > 0.05). Marital status comparisons revealed nonsignificant trends for widowed/divorced/separated (OR 1.38, 95% CI 0.81–2.36) and never married (OR 1.54, 95% CI 0.88–2.70) relative to married/living with partners (both p > 0.05). Compared with single-person households, household size categories (2–6 + persons) showed no significant associations (all p > 0.05), with ORs ranging from 0.72–2.00 (Table 3 ). Table 3 Results of multivariate logistic regression for the training cohort Characteristic N Event N OR 95% CI p value Age 547 167 0.99 0.98, 1.01 0.258 Race Mexican American 49 16 — — Other Hispanic 62 26 1.91 0.84, 4.33 0.122 Non-Hispanic White 298 89 1.27 0.63, 2.57 0.500 Non-Hispanic Black 70 19 0.89 0.39, 2.04 0.782 Other Race - Including Multi-Racial 68 17 0.79 0.34, 1.84 0.592 Marital status Married/Living with partner 187 44 — — Widowed/Divorced/Separated 153 46 1.38 0.81, 2.36 0.238 Never married 207 77 1.54 0.88, 2.70 0.127 Total number of people in the Household 1 163 52 — — 2 164 42 0.87 0.51, 1.50 0.619 3 93 33 1.11 0.62, 2.00 0.721 4 65 17 0.72 0.35, 1.48 0.377 5 36 11 0.94 0.41, 2.17 0.890 6 10 4 1.22 0.30, 4.94 0.785 ≥ 7 16 8 2.00 0.66, 6.01 0.218 VID 547 167 0.99 0.99, 1.00 0.033 Abbreviations: CI = Confidence Interval, OR = Odds Ratio Figure 6 shows the area under the curve (AUC) values of the model across distinct cohorts. Receiver operating characteristic (ROC) curve evaluation revealed that the predictive model exhibited an AUC of 0.636 (95% CI: 0.587–0.686) in the training cohort, indicating moderate discriminative ability for the outcome of the DPQ090 group. In the validation cohort, the model maintained comparable performance, with an AUC of 0.619 (95% CI: 0.517–0.720), suggesting reasonable generalizability of the predictive model across different datasets. The overlapping confidence intervals between the two cohorts imply that the model's performance was consistent, although the wider confidence interval in the validation cohort reflects the reduced sample size and consequently greater uncertainty in the estimate. Figure 7 presents the calibration curves of the nomogram across different cohorts, revealing strong consistency between the observed and predicted DPQ090-group outcomes. Validation analyses confirmed that the original nomogram retained its applicability in the validation datasets, with the model's calibration curve closely approximating the ideal reference line. This alignment indicates robust agreement between the predicted results and the empirical observations. Decision Curve Analysis The DCA curves related to the nomogram are shown in Fig. 8 . When clinicians use a nomogram, a high-risk threshold probability implies a greater likelihood of significant predictive inaccuracies due to critical limitations in diagnosis and decision-making. Notably, this study demonstrated that the nomogram confers substantial net clinical benefits, as evidenced by its DCA curve results. DISCUSSION This study investigated potential predictors of suicidal ideation among adult patients with depression in the United States, focusing on demographic, socioeconomic, and vitamin D-related factors. Multivariate logistic regression revealed a statistically significant correlation between vitamin D levels and suicidal ideation, although the magnitude of this association remained relatively modest (OR: 0.99). This finding is consistent with growing evidence suggesting a neuroprotective role for vitamin D in mood regulation and the mitigation of suicidal tendencies[7]. The inverse relationship observed between vitamin D status and suicidal ideation may be explained through various biological mechanisms, including its roles in neurotrophic biosynthesis, inflammation regulation, and serotonin homeostasis [9]. However, the modest effect size suggests that vitamin D is likely one of several contributing factors rather than a primary predictor of suicidal ideation in individuals with depression. In contrast to certain previous studies[17], our analysis did not find a significant association between age and suicidal ideation. This discrepancy may be attributed to differences in the study populations or the relatively narrow age range of our sample. Although our findings of nonsignificant racial differences in the risk of suicidal ideation contradict reports of higher rates among specific minority groups[18], they align with research suggesting that racial disparities in suicide risk may diminish when controlling for socioeconomic factors [19]. Additionally, our results indicated that marital status and household size were not significant predictors of suicidal ideation, which contradicts some earlier findings [20] but may reflect the complex interaction between social determinants and the severity of depression. The model displayed moderate discriminative ability, as evidenced by AUC metrics of 0.636 in the training dataset and 0.619 in the validation dataset. These results suggest that the predictors included in the model offer limited clinical utility, with a significant portion of the variance in predictive power remaining unexplained. This level of performance is consistent with other suicide prediction models applied to psychiatric populations, as noted by Belsher et al., highlighting the persistent challenges associated with accurately forecasting suicidal behavior[21]. The similarity in performance between the training and validation datasets suggests that the model possesses a degree of generalizability; however, the moderate AUC values indicate potential areas for improving predictive accuracy. Overall, these findings underscore the complex, multifactorial nature of suicidal ideation and the limitations of existing prediction models, which predominantly rely on demographic and clinical variables[22]. From a clinical perspective, identifying vitamin D as a modifiable risk factor has potential for its use in adjuvant therapeutic strategies. While vitamin D supplementation alone is unlikely to substantially reduce suicide risk, it may enhance a comprehensive prevention approach when integrated with other interventions[23]. The model's limited predictive performance underscores the importance of comprehensive clinical evaluations rather than reliance on isolated demographic or biological markers. Clinicians should remain vigilant for signs of suicidal ideation in all patients with depression, regardless of their demographic characteristics[24]. Interpretation of these findings has several limitations. The cross-sectional study design inherently limits the ability to infer causal relationships between vitamin D status and suicidal ideation. Moreover, dependence on self-reported outcome measures may introduce measurement bias, whereas single-time assessments of vitamin D levels may not adequately capture long-term status[25]. The external validity of the findings is further constrained by the unique demographic features of the U.S. sample, and the exclusion of critical clinical variables, such as depression severity and psychiatric comorbidities, limits the analytical depth[26]. Furthermore, the relatively small effect sizes observed imply that the clinical significance of these findings may be limited. Future research should prioritize longitudinal studies examining the relationship between vitamin D levels and suicidal behavior, utilizing repeated measurements of both variables. Investigating potential effect modifiers, such as seasonality and geographic location, could elucidate the nature of this association[27]. The development of more comprehensive predictive models that integrate clinical, psychological, and biological factors may enhance risk stratification[28]. Furthermore, interventional trials evaluating the effect of vitamin D supplementation on suicide risk in populations with deficiency might yield definitive evidence regarding this modifiable determinant[29]. Conclusion This investigation demonstrated VID levels as a notable predictive indicator of SI within the U.S. population of depressed adults while underscoring the restricted predictive capacity of demographic variables in this specific scenario. These results not only provide a foundation for improving risk assessment instruments but also support the development of targeted preventive measures aimed at mitigating suicide risk in this at-risk group. Declarations Acknowledgments I appreciate my consistent self, my family, my colleagues, and the editors and reviewers! Author Contributions T he tasks of acquiring and analyzing data, drafting figures and tables, and composing the original manuscript were carried out by Hong Guo and Lu Dai. The manuscript was reviewed and edited by Hong Guo and Lu Dai, with revisions and refinements made by Hong Guo, Lu Dai, and Jian Zhan. Each author contributed to the article and agreed to the version that was submitted. Funding No funding was received for this research. The data available for this study can be found on the National Health and Nutrition Examination Survey website: https://www.cdc.gov/nchs/nhanes/index.htm. The data used were all publicly available. Ethics approval The authors did not perform any studies with human or animal subjects for this article. Consent for publication Not applicable. Conflict of interest The authors report no conflicts of interest. References Statistics N. 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The relationship between body roundness index (BRI) and suicidal ideation: evidence from NHANES 2013–2018. BMC Psychiatry. 2025;25(1):395. 10.1186/s12888-025-06834-z . Park LT, Zarate CA. Jr. Depression in the Primary Care Setting. N Engl J Med. 2019;380(6):559–68. 10.1056/NEJMcp1712493 . Zhang X, Yang Q, Huang J, Lin H, Luo N, Tang H. Association of the newly proposed dietary index for gut microbiota and depression: the mediation effect of phenotypic age and body mass index. Eur Arch Psychiatry Clin NeuroSci. 2025;275(4):1037–48. Li K, Lyu H, Zhang L, Ma S, Wang K, Fu Y, et al. Association between dietary patterns and suicide ideation among depressed adults: Insights from NHANES 2007–2020. J Affect Disord. 2025;377(null):235–44. Chen Z, Qiu X, Wang Q, Wu J, Li M, Niu W. Serum vitamin D and obesity among US adolescents, NHANES 2011–2018. Front Pead. 2024;12:1334139. 10.3389/fped.2024.1334139 . Wu S, Teng Y, Lan Y, Wang M, Zhang T, Wang D, et al. 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Prediction Models for Suicide Attempts and Deaths: A Systematic Review and Simulation. JAMA Psychiatry. 2019;76(6):642–51. 10.1001/jamapsychiatry.2019.0174 . Franklin JC, Ribeiro JD, Fox KR, Bentley KH, Kleiman EM, Huang X, et al. Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychol Bull. 2017;143(2):187–232. 10.1037/bul0000084 . Umhau JC, George DT, Heaney RP, Lewis MD, Ursano RJ, Heilig M, et al. Low vitamin D status and suicide: a case–control study of active duty military service members. PLoS ONE. 2013;8(1):e51543. 10.1371/journal.pone.0051543 . Zalsman G, Hawton K, Wasserman D, van Heeringen K, Arensman E, Sarchiapone M, et al. Evidence-based national suicide prevention taskforce in Europe: A consensus position paper. Eur neuropsychopharmacology: J Eur Coll Neuropsychopharmacol. 2017;27(4):418–21. 10.1016/j.euroneuro.2017.01.012 . Dionisie V, Gaman MA, Anghele C, Manea MC, Puiu MG, Stanescu S, II, et al. Vitamin D and depression in adults: A systematic review. Biomolecules Biomed. 2025. 10.17305/bb.2025.12331 . Too LS, Spittal MJ, Bugeja L, Reifels L, Butterworth P, Pirkis J. The association between mental disorders and suicide: A systematic review and meta-analysis of record linkage studies. J Affect Disord. 2019;259:302–13. 10.1016/j.jad.2019.08.054 . Park JI, Yang JC, Won Park T, Chung SK. Is serum 25-hydroxyvitamin D associated with depressive symptoms and suicidal ideation in Korean adults? Int J Psychiatry Med. 2016;51(1):31–46. Walsh CG, Ribeiro JD, Franklin JC. Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning. J Child Psychol Psychiatry Allied Discip. 2018;59(12):1261–70. 10.1111/jcpp.12916 . Shaffer JA, Edmondson D, Wasson LT, Falzon L, Homma K, Ezeokoli N, et al. Vitamin D supplementation for depressive symptoms: a systematic review and meta-analysis of randomized controlled trials. Psychosom Med. 2014;76(3):190–6. 10.1097/psy.0000000000000044 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7074898","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"comment","associatedPublications":[],"authors":[{"id":513469253,"identity":"5ff2ff51-dfbd-4080-8fb5-a588fb1b091d","order_by":0,"name":"Hong Guo","email":"","orcid":"","institution":"The Second Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Guo","suffix":""},{"id":513469254,"identity":"daf2d7be-c6ba-4bbe-97db-7e5c083ba335","order_by":1,"name":"Lu Dai","email":"","orcid":"","institution":"The Second Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Dai","suffix":""},{"id":513469255,"identity":"c2a12dde-cbc8-45f0-bb2e-d131cf835e8c","order_by":2,"name":"Jian Zhan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYDACCTBpw8PP3kC0FmYQmSYj2XOANC2HbQxuOBCpQz66//Br3rbzPAw3GBg/fMwhQovhncNs1jxnbvMwzm5glpy5jRgtM5LZjHkqbvMwyxxgY+YlXovBOR42iQQitchLJDM/5qk4wMNDtBYDiWQzxjlnknkkeA42E+cX+RmJjz+8bbOztz/efPDDR6JsOcDAJsUDZjI2EKEeZEsDA/PHH8SpHQWjYBSMgpEKAAFrMspMjRMcAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Zunyi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhan","suffix":""}],"badges":[],"createdAt":"2025-07-08 12:38:16","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7074898/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7074898/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91136314,"identity":"851c8cba-2f24-46bc-98e4-425234aa444f","added_by":"auto","created_at":"2025-09-12 03:34:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81877,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant inclusion.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7074898/v1/e72d478b138731c8988f43f6.png"},{"id":91136892,"identity":"72ec234c-98cd-4183-88ef-284a791fc8ab","added_by":"auto","created_at":"2025-09-12 03:50:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":98447,"visible":true,"origin":"","legend":"\u003cp\u003eA. Tenfold cross-validation was applied to select the most suitable feature via the LASSO regression model (λ = 0.0279729413424682). B. Plot of the LASSO regression coefficient profiles (λ = 0.0279729413424682).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7074898/v1/584f775baf560ccda6d68e8e.png"},{"id":91136760,"identity":"12b7258d-7f34-4db1-9d19-4ddf570d3082","added_by":"auto","created_at":"2025-09-12 03:42:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":118486,"visible":true,"origin":"","legend":"\u003cp\u003eLasso-Selected Predictors and Corresponding Coefficients. Only variables with nonzero coefficients after Lasso selection are shown.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7074898/v1/b6f7878c5539cd134ca482d2.png"},{"id":91136311,"identity":"9c51dd4d-52e6-43ae-a2d1-dfb323c71dae","added_by":"auto","created_at":"2025-09-12 03:34:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":77994,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of ROC curves for individual predictive variables. Receiver operating characteristic (ROC) curves for individual predictors assessed via univariate logistic regression.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7074898/v1/c6170d135410aa206ec05715.png"},{"id":91136893,"identity":"41f32847-5a11-4203-a734-cddff054950d","added_by":"auto","created_at":"2025-09-12 03:50:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":63142,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram prediction model\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7074898/v1/79e964bf79ec207bcf48e65b.png"},{"id":91136762,"identity":"70f1dce6-767d-4adb-956e-65ea754e5c85","added_by":"auto","created_at":"2025-09-12 03:42:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":61759,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve of the prediction model in the different cohorts.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7074898/v1/105ad664f7e343ff5b417298.png"},{"id":91136763,"identity":"16b2497d-ad24-42d4-92aa-1fd9fb2aba71","added_by":"auto","created_at":"2025-09-12 03:42:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":67273,"visible":true,"origin":"","legend":"\u003cp\u003eA. Calibration curve of the nomogram prediction model for the training cohort; B. Calibration curve of the nomogram prediction model for the internal test cohort.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7074898/v1/5738f72e9218fc0902bdb2d0.png"},{"id":91136316,"identity":"97d8e3a8-c97c-4ee2-a6e6-6f3d1c3bba65","added_by":"auto","created_at":"2025-09-12 03:34:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":51477,"visible":true,"origin":"","legend":"\u003cp\u003eA. Decision curve analysis of the nomogram of the training cohort. B. Decision curve analysis of the nomogram of the internal test cohort.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7074898/v1/1346fff1cc0f034a0dc255df.png"},{"id":91137518,"identity":"04f2897a-4f25-4123-aaac-ce6dcf4c7582","added_by":"auto","created_at":"2025-09-12 03:58:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1915504,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7074898/v1/abd805d8-c8fd-4e80-94a0-2e3828f9b34d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Suicidal Ideation Predictors in U.S. Adults with Depression: The Roles of Demographics and Vitamin D in a Clinical Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDepression is a debilitating mental health disorder affecting approximately 21\u0026nbsp;million adults in the United States, contributing significantly to morbidity, mortality, and healthcare expenditures[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among its most severe sequelae is suicidal ideation (SI), a critical precursor to suicide attempts and completed suicide[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recent data indicate that suicide remains a leading cause of death nationwide, with over 48,000 annual fatalities[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], highlighting the urgent need for early risk stratification and targeted interventions.\u003c/p\u003e\u003cp\u003eDemographic and socioeconomic disparities profoundly influence SI risk. The prevalence of depression is greater in women than in men, but male patients with depression are more likely to commit suicide, which is associated with factors such as substance use, anger, and risk-taking[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Compared with other age cohorts, middle-aged adults have elevated suicide rates, which is potentially linked to cumulative life stressors and socioeconomic instability[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Racial and ethnic minorities, particularly Black and Hispanic populations, face systemic barriers to accessing mental healthcare, exacerbating depressive symptoms and SI [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Military veterans, another high-risk group, experience suicide rates that are 1.5 times higher than those of nonveterans, driven by trauma exposure, posttraumatic stress disorder, and insufficient support systems [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Socioeconomic factors further compound vulnerability: low educational attainment, divorce or widowhood, household isolation, and income‒poverty ratios below the federal threshold are strongly associated with severe depression and SI[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEmerging evidence implicates biological mechanisms in SI pathogenesis, with vitamin D deficiency emerging as a modifiable risk factor. Vitamin D (specifically D2 and D3 isoforms) modulates serotonin synthesis and neuroinflammation\u0026mdash;pathways dysregulated in depression[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Meta-analytic data confirm that low serum vitamin D levels correlate with increased SI severity, potentially through disrupted hypothalamic‒pituitary‒adrenal (HPA) axis function and heightened proinflammatory states [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite these advances, the interplay between vitamin D deficiency and sociodemographic determinants remains underexplored in large-scale clinical cohorts.\u003c/p\u003e\u003cp\u003eTo address this gap, this study employs logistic regression and nomogram-based modeling to integrate multidimensional predictors\u0026mdash;spanning demographic, socioeconomic, and biological domains\u0026mdash;in a nationally representative sample of U.S. adults with depression. Nomograms offer clinically actionable tools for visualizing individual SI risk, enhancing personalized intervention strategies[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. By elucidating synergistic effects (e.g., vitamin D deficiency amplified by poverty or racial disparities), this work aims to advance precision psychiatry and reduce the preventable burden of suicide.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003ePatient Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll study data were derived from the open-access database of the NHANES, which comprises a series of nationally representative cohort investigations established to track the health status of the U.S. population. Data acquisition involved three primary methods: in-home interviews, physical assessments conducted at mobile examination centers, and laboratory analyses. Ethical approval for this research was granted by the NCHS Ethics Review Board, with all participants providing informed consent before participation[11]. For more comprehensive information about the NHANES initiative, interested readers may refer to its official website (https://www.cdc.gov/nchs/nhanes/). This investigation drew upon data from a nationally representative NHANES cohort covering the period from August 2021 to August 2023, with an initial inclusion of 11,933 participants. Participant eligibility was refined through the application of specific exclusion criteria: (1) individuals younger than 18 years of age; (2) incomplete PHQ-9 dataset; (3) absence of 25-hydroxyvitamin D measurement data; and (4) nondepressed participants. Total PHQ-9 scores \u0026ge;10 are indicative of major depressive disorder (MDD), with high diagnostic sensitivity and specificity for MDD, as corroborated by multiple studies[12]. Consequently, PHQ-9 scores below 10 (indicating no depressive symptoms) were excluded from the current analysis. After these screening procedures, a final sample of 684 participants was enrolled in the study (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy data were derived from the open-access NHANES database, covering the period from August 2021 to August 2023. The collected demographic and clinical variables included sex, age, race, country of birth, military service history, educational level, marital status, household size, the family income‒poverty ratio, and measurements of 25-hydroxyvitamin D2 (VD2) and 25-hydroxyvitamin D3 (VD3) levels. Vitamin D (VID) is expressed as the sum of VD2 and VD3. Outcome data from the DPQ-090 group were also retrieved. Ethical supervision was conducted by the National Center for Health Statistics Ethics Review Board, which guaranteed adherence to U.S. federal regulations and emphasized the protection of participants\u0026apos; rights and welfare throughout the research process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSuicidal ideation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWidely adopted as a depression screening instrument, the PHQ-9 assesses the frequency of depressive symptoms experienced in the previous two weeks[13]. This tool is composed of 9 items and uses a 0--3 scoring scale to quantify the occurrence of symptoms.\u0026nbsp;Suicidal ideation (SI) was evaluated via the ninth item of the PHQ-9. This item specifically inquires: \u0026quot;Over the past two weeks, how frequently have you been troubled by thoughts of being better off dead or of harming yourself?\u0026quot; Participants who scored \u0026ge;1 on this item were categorized as exhibiting suicidal ideation, which is consistent with established criteria[14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e25-hydroxyvitamin D2 and 25-hydroxyvitamin D3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum concentrations of 25-hydroxyvitamin D2 (VD2) and 25-hydroxyvitamin D3 (VD3) (measured in nmol/L) were measured via high-performance liquid chromatography‒tandem mass spectrometry (HPLC‒MS‒MS)[15]. Vitamin D (VID) is expressed as the sum of VD2 and VD3. The comprehensive methodology for this analytical procedure has been extensively delineated in other scholarly publications. Venous blood samples were collected from participants aged \u0026ge;1 year by certified phlebotomists, with the majority of assays conducted across 35 laboratories nationwide[15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study collected demographic variables, including age; birth country (categorized as U.S. state/D.C. born or other regions); household size (classified as 1\u0026ndash;6 members or \u0026ge;7 members); racial/ethnic group (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Other Race); educational attainment (below 9th grade, 9th\u0026ndash;11th grade, high school graduate, some college/associate degree, college graduate or higher); marital status (married/cohabiting, widowed/divorced/separated, never married); and the income‒to-poverty ratio[16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES-derived dataset underwent random partitioning into training and validation cohorts with an 8:2 allocation, followed by baseline variable comparison. Nonnormally distributed variables are reported as medians (interquartile ranges) and descriptive statistics. For univariate analysis, categorical variables were assessed via the chi-square test or Fisher\u0026apos;s exact test, whereas continuous variables were analyzed via Student\u0026apos;s t test or the Mann‒Whitney U test[16]. Within the training cohort, least absolute shrinkage and selection operator (LASSO) logistic regression was used in multivariate analysis to detect independent risk factors and develop a predictive nomogram specific to the DPQ-090 group. Model performance was evaluated through receiver operating characteristic (ROC) curves and calibration plots, where the area under the ROC curve (AUC) ranged from 0.5 (no discriminatory ability) to 1 (perfect discrimination). Similarly, decision curve analysis (DCA) was conducted to define the net benefit threshold for predictions. A p value of less than 0.05 was considered statistically significant, and all analyses were performed with R software (version 4.2.2) and MSTATA (www.mstata.com).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003ePatient\u003c/b\u003e characteristics\u003c/p\u003e\u003cp\u003eThe baseline demographic and clinical features of the study cohorts are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The training cohort consisted of 547 participants, with the internal test cohort encompassing 137 individuals. The training cohort included 651 participants, with the internal test cohort containing 434 individuals. No significant differences in sociodemographic variables, including age, race, military history, educational attainment, marital status, total number of household members, or the family income‒poverty ratio, were detected between the two groups. Similarly, clinical measures, including VID (73\u0026thinsp;\u0026plusmn;\u0026thinsp;37 vs. 73\u0026thinsp;\u0026plusmn;\u0026thinsp;35, p\u0026thinsp;=\u0026thinsp;0.882), VD2 (7\u0026thinsp;\u0026plusmn;\u0026thinsp;21 vs. 8\u0026thinsp;\u0026plusmn;\u0026thinsp;20, p\u0026thinsp;=\u0026thinsp;0.847), and VD3 (66\u0026thinsp;\u0026plusmn;\u0026thinsp;37 vs. 65\u0026thinsp;\u0026plusmn;\u0026thinsp;35, p\u0026thinsp;=\u0026thinsp;0.796), demonstrated no statistically significant differences between the training and internal test cohorts. A notable difference emerged in the country of birth distribution (p\u0026thinsp;=\u0026thinsp;0.027), with a greater percentage of participants born in the 50 US states or Washington in the internal test cohort (91.2%) than in the training cohort (83.7%). Collectively, baseline characteristics demonstrated good balance across cohorts, with country of birth being the sole variable exhibiting significant disparity, indicating that the study groups were sufficiently comparable for predictive modeling applications. Univariate analyses were performed to compare parameters across distinct outcome groups, and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient demographics and baseline characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraining Cohort \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;547\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInternal Test Cohort \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;137\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.377\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e210 (38.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (34.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e337 (61.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90 (65.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.200\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.411\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (7.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (11.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (8.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e298 (54.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87 (63.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (12.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (11.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (12.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCountry of birth, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.027\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBorn in 50 US states or Washington\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e458 (83.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125 (91.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89 (16.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (8.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eServed Armed, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.840\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (8.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e502 (91.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125 (91.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.270\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege graduate or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (23.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (16.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSome college or AA degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e189 (34.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (43.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school graduate/GED or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138 (25.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (24.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9-11th grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58 (10.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (11.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 9th grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (4.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.268\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/Living with partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e187 (34.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (40.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153 (28.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (27.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e207 (37.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (31.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal number of people in the Household, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.772\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e163 (29.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (29.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164 (30.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (24.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (17.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (20.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (11.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (12.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (6.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (6.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (4.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRatio of family income to poverty, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.881\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVID, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73\u0026thinsp;\u0026plusmn;\u0026thinsp;37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73\u0026thinsp;\u0026plusmn;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.882\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVD2, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.847\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVD3, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66\u0026thinsp;\u0026plusmn;\u0026thinsp;37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65\u0026thinsp;\u0026plusmn;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.796\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003ePearson's Chi-squared test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eWelch Two Sample t test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eFisher's exact test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient demographics and baseline characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eCohort (Training Cohort), N\u0026thinsp;=\u0026thinsp;547\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eCohort (Internal Test Cohort), N\u0026thinsp;=\u0026thinsp;137\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDPQ090-group (No idea of suicide) \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;380\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDPQ090-group (Idea of suicide) \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;167\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep value\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDPQ090-group (No idea of suicide) \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;93\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDPQ090-group (Idea of suicide) \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;44\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141 (37.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (41.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28 (30.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19 (43.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e239 (62.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (58.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65 (69.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25 (56.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (3.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7 (15.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (15.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (6.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e209 (55.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89 (53.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65 (69.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (13.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (10.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6 (13.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (13.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (10.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4 (9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCountry of birth, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBorn in 50 US states or Washington\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e324 (85.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134 (80.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84 (90.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41 (93.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (14.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (19.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (6.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eServed Armed, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (9.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (6.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e345 (90.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e157 (94.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84 (90.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41 (93.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege graduate or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (24.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (22.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (17.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7 (15.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSome college or AA degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136 (35.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (31.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40 (43.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19 (43.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school graduate/GED or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96 (25.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (25.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (23.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11 (25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9-11th grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (13.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (11.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 9th grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (7.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.402\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/Living with partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e143 (37.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (26.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41 (44.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (34.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107 (28.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (27.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26 (28.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12 (27.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (34.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (46.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26 (28.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17 (38.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal number of people in the Household, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111 (29.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (31.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28 (30.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13 (29.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122 (32.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (25.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24 (25.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60 (15.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (19.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18 (19.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10 (22.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48 (12.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (10.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (10.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7 (15.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (6.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (6.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (3.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (6.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRatio of family income to poverty, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVID, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76\u0026thinsp;\u0026plusmn;\u0026thinsp;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66\u0026thinsp;\u0026plusmn;\u0026thinsp;33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e79\u0026thinsp;\u0026plusmn;\u0026thinsp;31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e58\u0026thinsp;\u0026plusmn;\u0026thinsp;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVD2, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u0026thinsp;\u0026plusmn;\u0026thinsp;22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u0026thinsp;\u0026plusmn;\u0026thinsp;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVD3, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69\u0026thinsp;\u0026plusmn;\u0026thinsp;38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59\u0026thinsp;\u0026plusmn;\u0026thinsp;33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70\u0026thinsp;\u0026plusmn;\u0026thinsp;32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54\u0026thinsp;\u0026plusmn;\u0026thinsp;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003ePearson's Chi-squared test; Welch Two Sample t test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e2\u003c/sup\u003ePearson's Chi-squared test; Welch Two Sample t test; Fisher's exact test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePredictive Model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe initial predictive model encompassed a set of candidate variables, including Age, Race, Military History, Educational Attainment, Marital Status, Household Size, Family Income-to-Poverty Ratio, VID, VD2, and VD3. LASSO regression analysis, which was conducted on the training cohort, reduced these variables to five critical predictors. The corresponding regression coefficient trajectories are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which also presents the cross-validation error plot of the LASSO model. The optimally regularized minimal model\u0026mdash;determined at the point where cross-validated error reached within one standard deviation of the minimum value\u0026mdash;contained five variables.\u003c/p\u003e\u003cp\u003eFourteen clinical features were selected to construct a clinical model after LASSO regression and 10-fold cross-validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The following variables were selected by LASSO regression (λ\u0026thinsp;=\u0026thinsp;0.0279729413424682) and can be used in the subsequent modeling analysis: age, race, marital status, total number of people in the household, and VID.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that ROC analysis of the aforementioned variables produced AUC values greater than 0.5. Receiver operating characteristic (ROC) curve evaluation revealed that all investigated variables displayed moderate predictive capacity for the outcome event (DPQ090 group), with area under the curve (AUC) values ranging between 0.550 and 0.583. Notably, VID exhibited the strongest discriminative power (AUC\u0026thinsp;=\u0026thinsp;0.583, 95% CI: 0.533\u0026ndash;0.633), followed closely by age (AUC\u0026thinsp;=\u0026thinsp;0.578, 95% CI: 0.524\u0026ndash;0.632) and marital status (AUC\u0026thinsp;=\u0026thinsp;0.574, 95% CI: 0.526\u0026ndash;0.623). The total number of household members showed intermediate predictive utility (AUC\u0026thinsp;=\u0026thinsp;0.562, 95% CI: 0.511\u0026ndash;0.613), whereas Race presented the lowest discriminative ability (AUC\u0026thinsp;=\u0026thinsp;0.550, 95% CI: 0.502\u0026ndash;0.598).\u003c/p\u003e\u003cp\u003eThe finalized logistic regression model incorporated five independent predictive variables (Age, Race, Marital status, Total household members, and VID), which were translated into a user-friendly nomogram. This nomogram is visually depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and accessible via the provided online platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://guohongdp.shinyapps.io/dynnomapp/\u003c/span\u003e\u003cspan address=\"https://guohongdp.shinyapps.io/dynnomapp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMultivariate logistic regression modeling conducted on the training cohort indicated that VID was significantly correlated with the outcome (OR 0.99, 95% CI 0.99\u0026ndash;1.00; p\u0026thinsp;=\u0026thinsp;0.033), whereas age was not significantly associated with the outcome (OR 0.99, 95% CI 0.98\u0026ndash;1.01; p\u0026thinsp;=\u0026thinsp;0.258). Among the racial groups, other Hispanic (OR 1.91, 95% CI 0.84\u0026ndash;4.33), non-Hispanic White (OR 1.27, 95% CI 0.63\u0026ndash;2.57), non-Hispanic Black (OR 0.89, 95% CI 0.39\u0026ndash;2.04), and other race (OR 0.79, 95% CI 0.34\u0026ndash;1.84) groups showed no significant differences compared with the Mexican American reference group (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Marital status comparisons revealed nonsignificant trends for widowed/divorced/separated (OR 1.38, 95% CI 0.81\u0026ndash;2.36) and never married (OR 1.54, 95% CI 0.88\u0026ndash;2.70) relative to married/living with partners (both p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Compared with single-person households, household size categories (2\u0026ndash;6\u0026thinsp;+\u0026thinsp;persons) showed no significant associations (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), with ORs ranging from 0.72\u0026ndash;2.00 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of multivariate logistic regression for the training cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEvent N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.98, 1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.84, 4.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.63, 2.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.39, 2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.34, 1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/Living with partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.81, 2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.88, 2.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal number of people in the Household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.51, 1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.619\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.62, 2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.35, 1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.41, 2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.30, 4.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.66, 6.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99, 1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: CI\u0026thinsp;=\u0026thinsp;Confidence Interval, OR\u0026thinsp;=\u0026thinsp;Odds Ratio\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the area under the curve (AUC) values of the model across distinct cohorts. Receiver operating characteristic (ROC) curve evaluation revealed that the predictive model exhibited an AUC of 0.636 (95% CI: 0.587\u0026ndash;0.686) in the training cohort, indicating moderate discriminative ability for the outcome of the DPQ090 group. In the validation cohort, the model maintained comparable performance, with an AUC of 0.619 (95% CI: 0.517\u0026ndash;0.720), suggesting reasonable generalizability of the predictive model across different datasets. The overlapping confidence intervals between the two cohorts imply that the model's performance was consistent, although the wider confidence interval in the validation cohort reflects the reduced sample size and consequently greater uncertainty in the estimate.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the calibration curves of the nomogram across different cohorts, revealing strong consistency between the observed and predicted DPQ090-group outcomes. Validation analyses confirmed that the original nomogram retained its applicability in the validation datasets, with the model's calibration curve closely approximating the ideal reference line. This alignment indicates robust agreement between the predicted results and the empirical observations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDecision Curve Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe DCA curves related to the nomogram are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. When clinicians use a nomogram, a high-risk threshold probability implies a greater likelihood of significant predictive inaccuracies due to critical limitations in diagnosis and decision-making. Notably, this study demonstrated that the nomogram confers substantial net clinical benefits, as evidenced by its DCA curve results.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study investigated potential predictors of suicidal ideation among adult patients with depression in the United States, focusing on demographic, socioeconomic, and vitamin D-related factors.\u0026nbsp;Multivariate logistic regression revealed a statistically significant correlation between vitamin D levels and suicidal ideation, although the magnitude of this association remained relatively modest (OR: 0.99).\u0026nbsp;This finding is consistent with growing evidence suggesting a neuroprotective role for vitamin D in mood regulation and the mitigation of suicidal tendencies[7]. The inverse relationship observed between vitamin D status and suicidal ideation may be explained through various biological mechanisms, including its roles in neurotrophic biosynthesis, inflammation regulation, and serotonin homeostasis [9]. However, the modest effect size suggests that vitamin D is likely one of several contributing factors rather than a primary predictor of suicidal ideation in individuals with depression.\u003c/p\u003e\n\u003cp\u003eIn contrast to certain previous studies[17], our analysis did not find a significant association between age and suicidal ideation. This discrepancy may be attributed to differences in the study populations or the relatively narrow age range of our sample. Although our findings of nonsignificant racial differences in the risk of suicidal ideation contradict reports of higher rates among specific minority groups[18], they align with research suggesting that racial disparities in suicide risk may diminish when controlling for socioeconomic factors [19]. Additionally, our results indicated that marital status and household size were not significant predictors of suicidal ideation, which contradicts some earlier findings [20] but may reflect the complex interaction between social determinants and the severity of depression.\u003c/p\u003e\n\u003cp\u003eThe model displayed moderate discriminative ability, as evidenced by AUC metrics of 0.636 in the training dataset and 0.619 in the validation dataset.\u0026nbsp;These results suggest that the predictors included in the model offer limited clinical utility, with a significant portion of the variance in predictive power remaining unexplained. This level of performance is consistent with other suicide prediction models applied to psychiatric populations, as noted by Belsher et al., highlighting the persistent challenges associated with accurately forecasting suicidal behavior[21]. The similarity in performance between the training and validation datasets suggests that the model possesses a degree of generalizability; however, the moderate AUC values indicate potential areas for improving predictive accuracy. Overall, these findings underscore the complex, multifactorial nature of suicidal ideation and the limitations of existing prediction models, which predominantly rely on demographic and clinical variables[22].\u003c/p\u003e\n\u003cp\u003eFrom a clinical perspective, identifying vitamin D as a modifiable risk factor has potential for its use in adjuvant therapeutic strategies. While vitamin D supplementation alone is unlikely to substantially reduce suicide risk, it may enhance a comprehensive prevention approach when integrated with other interventions[23]. The model\u0026apos;s limited predictive performance underscores the importance of comprehensive clinical evaluations rather than reliance on isolated demographic or biological markers. Clinicians should remain vigilant for signs of suicidal ideation in all patients with depression, regardless of their demographic characteristics[24].\u003c/p\u003e\n\u003cp\u003eInterpretation of these findings has several limitations. The cross-sectional study design inherently limits the ability to infer causal relationships between vitamin D status and suicidal ideation. Moreover, dependence on self-reported outcome measures may introduce measurement bias, whereas single-time assessments of vitamin D levels may not adequately capture long-term status[25]. The external validity of the findings is further constrained by the unique demographic features of the U.S. sample, and the exclusion of critical clinical variables, such as depression severity and psychiatric comorbidities, limits the analytical depth[26]. Furthermore, the relatively small effect sizes observed imply that the clinical significance of these findings may be limited.\u003c/p\u003e\n\u003cp\u003eFuture research should prioritize longitudinal studies examining the relationship between vitamin D levels and suicidal behavior, utilizing repeated measurements of both variables. Investigating potential effect modifiers, such as seasonality and geographic location, could elucidate the nature of this association[27]. The development of more comprehensive predictive models that integrate clinical, psychological, and biological factors may enhance risk stratification[28]. Furthermore, interventional trials evaluating the effect of vitamin D supplementation on suicide risk in populations with deficiency might yield definitive evidence regarding this modifiable determinant[29].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis investigation demonstrated VID levels as a notable predictive indicator of SI within the U.S. population of depressed adults while underscoring the restricted predictive capacity of demographic variables in this specific scenario. These results not only provide a foundation for improving risk assessment instruments but also support the development of targeted preventive measures aimed at mitigating suicide risk in this at-risk group.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003eI appreciate my consistent self, my family, my colleagues, and the editors and reviewers!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions T\u003c/strong\u003ehe tasks of acquiring and analyzing data, drafting figures and tables, and composing the original manuscript were carried out by Hong Guo and Lu Dai. The manuscript was reviewed and edited by Hong Guo and Lu Dai, with revisions and refinements made by Hong Guo, Lu Dai, and Jian Zhan. Each author contributed to the article and agreed to the version that was submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eNo funding was received for this research.\u003c/p\u003e\n\u003cp\u003eThe data available for this study can be found on the National Health and Nutrition Examination Survey website: https://www.cdc.gov/nchs/nhanes/index.htm. The data used were all publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003eThe authors did not perform any studies with human or animal subjects for this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe authors report no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStatistics N. 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Psychosom Med. 2014;76(3):190\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/psy.0000000000000044\u003c/span\u003e\u003cspan address=\"10.1097/psy.0000000000000044\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"suicidal ideation, depression, vitamin D, logistic regression, predictive model","lastPublishedDoi":"10.21203/rs.3.rs-7074898/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7074898/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eIdentifying predictors of suicidal ideation in adults with depression is crucial for developing preventive strategies. However, comprehensive studies that integrate demographic and vitamin D (VID) factors are limited. This study aimed to investigate potential predictors of suicidal ideation among U.S. adults with depression, with a particular focus on demographic and VID indicators. Additionally, a predictive model was developed via logistic regression combined with nomogram analysis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA clinical prediction framework was developed utilizing multivariable logistic regression to assess the associations between suicidal ideation and variables such as sex, age, race, military service history, education level, marital status, household size, income‒poverty ratio, and VID concentrations (VD2 and VD3). The model's performance was assessed through receiver operating characteristic (ROC) curve analysis in both the training and validation cohorts.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eVID was significantly, albeit mildly, associated with suicidal ideation (OR 0.99, 95% CI 0.99\u0026ndash;1.00; p\u0026thinsp;=\u0026thinsp;0.033). In contrast, age and other demographic variables, including race, marital status, and household size, did not achieve statistical significance. Receiver operating characteristic (ROC) curve evaluation revealed moderate discriminative ability, with an area under the curve (AUC) of 0.636 (95% CI: 0.587\u0026ndash;0.686) in the training cohort and 0.619 (95% CI: 0.517\u0026ndash;0.720) in the validation cohort, suggesting acceptable generalizability.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eVID concentrations may serve as a significant predictive factor for suicidal ideation among depressed adults in the United States, whereas demographic and socioeconomic factors exhibit limited predictive value.\u003c/p\u003e","manuscriptTitle":"Exploring Suicidal Ideation Predictors in U.S. Adults with Depression: The Roles of Demographics and Vitamin D in a Clinical Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 03:34:46","doi":"10.21203/rs.3.rs-7074898/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b503ae89-43f4-42f5-a65d-d6ca21f2d838","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-12T03:34:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-12 03:34:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7074898","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7074898","identity":"rs-7074898","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}