Patterns of Alcohol and Drug Screening in Trauma Patients: Understanding Housing Status as a Determinant of Care

Patterns of Alcohol and Drug Screening in Trauma Patients: Understanding Housing Status as a Determinant of Care | 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 Research Article Patterns of Alcohol and Drug Screening in Trauma Patients: Understanding Housing Status as a Determinant of Care John Beckerle, Vlada Stark, Bertille Assoumou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8810083/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background: Traumatic injury remains a leading cause of morbidity and mortality in the United States, with substance use and social determinants of health playing important roles in injury risk and clinical outcomes. People experiencing homelessness are disproportionately exposed to trauma and are screened for alcohol and drugs at higher rates than housed patients, raising questions about whether differences in substance positivity reflect true variation in exposure or disparities in screening practices. This study examines patterns of alcohol and drug screening and positivity among housed and unhoused trauma patients, with particular attention to polysubstance use. Methods: We conducted a retrospective cross-sectional analysis of adult trauma patients included in the 2021 National Trauma Data Bank. Alcohol and drug screening within the first 24 hours of hospital encounter were examined, along with screening results and the presence of multiple concurrent drug positives. Homelessness was the primary exposure of interest. Multivariable logistic regression models were used to assess the association between housing status and screening practices as well as positive alcohol, drug, and polydrug results, adjusting for age, sex, race, ethnicity, injury severity, Glasgow Coma Scale score, physical and behavioral comorbidities. Results: Among 1,000,269 adult trauma patients, 9,466 (0.9%) were unhoused. Unhoused patients were significantly more likely to be screened for alcohol and drugs than housed patients. After adjustment, homelessness remained the strongest predictor of screening for both alcohol and drugs. Among those screened, homelessness was associated with markedly higher odds of positive drug and polydrug results, the association with positive blood alcohol concentration was modest despite substantially higher screening rates. Injury severity and lower Glasgow Coma Scale scores were also associated with increased screening and positivity. Conclusions: Unhoused trauma patients experience substantially higher rates of alcohol and drug screening and higher odds of drug and polysubstance positivity compared with housed patients. These findings suggest that housing status strongly shapes diagnostic practices in trauma care and may influence the interpretation of substance use epidemiology. Standardized, non-stigmatizing screening approaches that are paired with appropriate clinical and social interventions are needed to ensure equitable trauma care. Trauma social determinants of health homelessness drugs alcohol Background Traumatic injury remains a leading cause of morbidity and mortality in the United States (US), with recent decades marked by a sustained rise in trauma-related deaths driven in part by interpersonal violence and substance-related mechanisms [ 1 ]. Social determinants of health, particularly housing instability, play a critical role in shaping both trauma risk and clinical outcomes following injury. People experiencing homelessness are disproportionately exposed to environmental hazards, interpersonal violence, and untreated medical and psychiatric conditions, all of which increase the likelihood of traumatic injury and complicate recovery [ 2 , 3 ]. As a result, trauma systems increasingly function as de facto safety nets for unhoused populations, often managing patients with complex medical and social needs that extend beyond the acute injury itself [ 2 , 4 ]. Substance use is highly prevalent among people experiencing homelessness and represents an important, though methodologically complex, contributor to trauma risk. Epidemiologic studies have demonstrated elevated rates of alcohol and drug exposure in this population, with polysubstance use being common [ 5 ]. However, the clinical interpretation of substance use screening results in trauma settings is not straightforward. Prior work has shown that urine drug screen (UDS) positivity does not consistently predict perioperative complications or mortality and may reflect a combination of chronic use, acute intoxication, and medications administered after hospital admission [ 6 ]. These findings underscore the limitations of interpreting single-substance detection as a direct proxy for pre-injury substance use or trauma-related risk. At the systems level, alcohol and drug screening practices in trauma care are highly variable. National analyses have demonstrated substantial institutional variation in screening adoption, with fewer than one-third of trauma patients screened overall [ 7 ]. Importantly, people experiencing homelessness are screened more frequently than housed patients, which complicates interpretation of higher test positivity rates and raises concern for structural and institutional bias in diagnostic practices [ 4 , 7 , 8 ]. Prior studies have also shown that unhoused trauma patients are more likely to be admitted to the hospital, experience longer lengths of stay, and utilize intensive care resources at higher rates, even after accounting for injury severity, highlighting the influence of social context and discharge safety concerns on clinical decision-making [ 4 , 7 , 8 ]. Despite the well-documented overlap between homelessness, substance use, and traumatic injury, limited research has examined whether differences in substance positivity among unhoused trauma patients reflect true variation in pre-injury exposure or disparities in screening practices shaped by injury severity, clinician perception, and institutional norms. Existing studies have largely focused on single substances or opioid-related use, leaving an important gap in understanding polysubstance patterns and their relationship to housing status within trauma systems [ 9 , 10 ]. Using data from the 2021 National Trauma Data Bank, the present study compares rates of alcohol and drug screening among housed and unhoused trauma patients to better characterize how housing status intersects with screening practices and substance use patterns in contemporary trauma care. Methods Study Design and Population This retrospective cross-sectional study used data from the 2021 National Trauma Data Bank (NTDB), made available by the American College of Surgeons (ACS) and its Trauma Quality Programs (TQP) data. The ACS TQP includes data from participating hospitals in the US and Canada that adhere to the National Trauma Data Standard, which aims to set standards for clinical data elements that are important for trauma care. Patient demographic, clinical, injury, and hospital data are extracted without protected health information. The NTDB does not include data on costs, laboratory values, readmissions, or long-term outcomes [ 11 ]. The Institutional Review Board for the University of Nevada, Las Vegas determined that this study is exempt from the need for informed consent due to the deidentified nature of the dataset. Variables Description Dependent Variables The outcomes we were interested in were the frequency of alcohol and drug screenings, screening results and the presence of multiple drugs detected concurrently, which we deemed polydrug use. The NTDB reports screening for alcohol and 13 types of drugs performed within the first 24 hours of hospital presentation. Screening status is recorded as a binary variable indicating whether a patient was screened for alcohol and/or drugs. Among screened patients, results are reported as a numeric ethanol level for alcohol, and a binary result for each class of drugs. The 13 drug classes included in screening were opioids, amphetamine, barbiturates, benzodiazepine, cocaine, methamphetamine, ecstasy, methadone, oxycodone, phencyclidine, tricyclic antidepressants (excluded from this study), cannabinoids, and others [ 11 ]. For each patient, the number of positive drug classes was calculated; 2 or more drug classes were considered polydrug use. A blood alcohol concentration (BAC) ≥ 0.08% (0.08 g/dL) was considered positive for this study. Independent Variables Homelessness was the primary risk factor of interest, used to examine disparities in screening practices. Homelessness, as defined by the NTDB, refers to individuals lacking permanent housing, including those living in transitional housing or temporary living quarters. Homelessness is determined by NTDB data abstractors using a patient’s registered postal code. In accordance with protected health information regulations, postal codes are not available to researchers. Covariates Covariates included in multivariate modeling include age, sex, race, ethnicity, injury severity score (ISS), total Glasgow Coma Scale (GCS), and presence of physical and behavioral comorbidities [ 12 ]. These variables were compiled for both housed and unhoused patients. The NTDB collects race and ethnicity data from self-report or report by family members and records race and ethnicity as separate variables. Categories included Hispanic and non-Hispanic for ethnicity, and Black, White, Asian, Pacific Islander (which we combined into AAPI) and other for race. Comorbidities are assigned by ICD-10 comorbidity codes. Physical comorbidities include conditions such as heart disease, diabetes, hypertension, liver disease, and malignant neoplasms. Behavioral comorbidities include bipolar disorder, major depressive disorder, posttraumatic stress disorder, and substance use disorder. Statistical Analysis Chi-square tests of independence were used to assess significant differences between housed and unhoused patients. Missing values for any demographic information were included in a descriptive statistics table for clarity. Univariate logistic regression models were used to estimate the unadjusted odds ratios for the association between homelessness on alcohol and drug screening, and the results of these screens. Multivariate logistic regression models were used to estimate adjusted odds ratios to more closely examine the relationship between homelessness and screening outcomes while controlling for potential confounders. We conducted multivariate analysis for five outcomes: alcohol screening, drugs screening, positive screenings for alcohol, drug and polydrug use. All statistical analyses were performed in R4.4.0. Statistical significance was set at P < .05. For regression models, all continuous variables were categorized into discrete intervals: age intervals were 18–34, 35–49, 50–64, and 65 or older; GCS categories were 15 − 13, 12 − 9, and 8 − 3; ISS categories were mild (1–8), moderate (9–15), and severe (16 or higher). Results Descriptive Analysis The total sample size of the 2021 NTDB data set is 1,209,097 patients. After removing those under the age of 18 years or with a missing age, 1,000,269 patients were included in the present study. Of these, 9,466 (0.9%) were unhoused. In the housed cohort, 587,009 (59.2%) were male whereas 7,845 (82.9%) of the unhoused patients were male. Unhoused patients were less likely to be elderly (65 or older) (7.2% vs. 39.6%, p<0.001). Although White people made up the majority of the patient population in both groups, there was a higher prevalence of Hispanic and Black patients in the unhoused cohort (2,024 [21.4%] and 2,118 [22.4%] vs. 116,723 [11.8%] and 148,753 [15.0%], p<0.001). Medicaid and self-pay were the most common forms of primary payment for unhoused patients (4,570 [48.3%] and 2,030 [21.3%], p<0.001) while the majority of housed patients used either Medicare or private insurance (345,416 [34.9%] and 312,470 [31.5%] respectively). Importantly, unhoused patients had higher instances of severe ISS (1,978 [20.9%] vs. 167,179 [16.9%], p<0.001) and lowest GCS (under 9) on admission (801 [8.5%] vs. 55,549 [5.6%] compared to housed patients. The presence of any physical or behavioral comorbidity was similar between both groups. Table 1 presents a summary of patient characteristics. Table 1. Patient Characteristics by Housing Status in the 2021 National Trauma Data Bank Dataset Patient Characteristics Housed n (%) Unhoused n (%) p Value* All 990,803 (99.1) 9,466 (0.9) <0.001 Sex <0.001 Male 587,009 (59.2) 7,845 (82.9) Female 394,977 (39.8) 1,533 (16.1) Missing 271 (0.0) 2 (0.0) Age (years) <0.001 18-34 248,377 (25.1) 2,910 (30.7) 35-49 163,305 (16.5) 3,110 (32.9) 50-64 186,446 (18.8) 2,760 (29.2) 65+ 392,675 (39.6) 686 (7.2) Race <0.001 White 715,233 (72.2) 5,506 (58.2) Black 148,753 (15.0) 2,118 (22.4) AAPI 23,652 (2.4) 206 (2.2) Other/Missing 103,165 (10.4) 1,636 (17.3) Ethnicity Hispanic Non-Hispanic Missing Insurance 116,723 (11.8) 839,178 (84.7) 34,902 (3.5) 2,024 (21.4) 7,080 (74.8) 362 (3.8) <0.001 <0.001 Medicaid 151,935 (15.3) 4,570 (48.3) Medicare 345,416 (34.9) 909 (9.6) Other Government 31,467 (3.2) 452 (4.7) Private/Commercial 312,470 (31.5) 1,256 (13.2) Self-Pay 104,952 (10.6) 2,030 (21.3) Other 22,215 (2.2) 143 (1.5) Not Billed (for any reason) 2,363 (0.2) 18 (0.2) Missing Data Comorbidity Any None ISS Mild (1-8) Moderate (9-15) Severe (≥16) Missing GCS 13-15 9-12 3-8 Missing 19,985 (2.0) 536,160 (54.1) 454,643 (45.9) 456,982 (46.1) 364,460 (36.8) 167,179 (16.9) 2,182 (0.2) 859,321 (86.7) 21,606 (2.2) 55,549 (5.6) 54,327 (5.5) 88 (0.9) 5,059 (53.4) 4,407 (46.6) 4,639 (49.0) 2,925 (29.8) 1,978 (20.9) 24 (0.3) 7,772 (82.1) 533 (5.6) 801 (8.5) 360 (3.8) 0.20 <0.001 <0.001 AAPI: Asian American or Pacific Islander ISS: Injury Severity Score GCS: Glasgow Coma Scale * determined by Chi-square tests of independence Alcohol screening was conducted for 7,135 unhoused patients (75.4%) and 462,134 housed patients (46.6%) while drug screening was performed for 5,733 (60.6%) and 312,212 (31.5%) patients, respectively. Of those screened, 1,926 (27.0%) unhoused and 100,525 (21.8%) housed patients tested positive for alcohol, whereas 4,270 (74.4%) unhoused and 140,041 (44.9%) housed patients tested positive for drugs; polydrug use was detected in 2,424 (42.3%) and 54,435 (17.4%) patients, respectively. Univariate Analysis In univariate logistic regression analysis, unhoused patients had more than three times the odds of getting screened on admission for drugs and alcohol compared to housed patients (OR: 3.34, 95% CI: 3.20, 3.48 and OR: 3.50, 95% CI: 3.34, 3.67, respectively). Of those screened, the unhoused were around five times as likely to test positive for drug and polydrug use (OR: 4.99, 95% CI: 4.79, 5.20 and OR: 5.92, 95% CI: 5.65, 6.21), but moderately more likely to test positive for alcohol (OR: 1.37, 95% CI: 1.30, 1.44), despite being screened more ( Table 2 ). Table 2. Univariate Analysis for Effects of Homelessness on Screening Results Outcome Unadjusted Odds Ratio (95% CI) Screened for Drugs 3.34 (3.20, 3.48) Screened for Alcohol 3.50 (3.34, 3.67) Tested Positive for Drugs Tested Positive for Multiple Drugs (Polydrug) 4.99 (4.79, 5.20) 5.92 (5.65, 6.21) Tested Positive for Alcohol 1.37 (1.30, 1.44) CI: Confidence Interval Multivariate analysis When accounting for confounders, homelessness remained the strongest determinant of whether a patient was screened for substance use. This was true for both alcohol and drug screenings, with unhoused patients having more than twice the odds of being screened (aOR: 2.37, 95% CI: 2.25, 2.49 vs. aOR: 2.42, 95% CI: 2.31, 2.53, respectively). Black patients were consistently screened more than white patients for alcohol (aOR: 1.16, 95% CI: 1.14, 1.17) and drugs (aOR: 1.12, 95% CI: 1.10,1.13); Hispanic patients were more likely to be screened for alcohol (aOR 1.21, 95% CI: 1.19, 1.23 ) and drugs (aOR: 1.24, 95% CI: 1.22, 1.26) than non-Hispanics. Female trauma patients were less likely to be screened for alcohol (aOR: 0.68, 95% CI: 0.67, 0.69) and drugs (aOR: 0.76, 95% CI: 0.76, 0.77), compared to male patients. Patients with moderate and severe ISS were screened more for alcohol, (aOR 1.17, 95% CI: 1.16, 1.18 and aOR: 2.03, 95% CI: 2.00, 2.06, respectively), but less for drugs (aOR: 0.90, 95% CI: 0.89, 0.92 and aOR 0.88, 95% CI: 0.86, 0.90, respectively). Patients with a GCS of 9-12 and 8 or lower were screened more than patients with a GCS of 13-15 for both alcohol and drugs, respectively (aOR: 2.31, 95% CI: 2.23, 2.38 and aOR: 2.46, 95% CI: 2.39, 2.53 vs. aOR: 1.21, 95% CI: 1.18, 1.23 and aOR: 1.60, 95% CI: 1.57, 1.63). Increasing age was associated with progressively lower odds of substance use screening, with the lowest odds among the elderly for both alcohol and drug screenings (aOR: 0.36, 95% CI: 0.36, 0.37 and aOR: 0.43, 95% CI: 0.42, 0.43, respectively). (Table 3) . Table 3. Multivariate Analysis of Patient Characteristics Affecting Alcohol and Drug Screening in Trauma Patients Patient Characteristics Alcohol aOR (95% CI) Drug aOR (95% CI) Housing Status Housed Homelessness [REF] 2.37 (2.25, 2.49) [REF] 2.42 (2.31, 2.53) Race White Black AAPI Other [REF] 1.16 (1.14, 1.17) 1.44 (1.40, 1.48) 1.12 (1.10, 1.13) [REF] 1.12 (1.10, 1.13) 0.89 (0.86, 0.92) 1.00 (0.98, 1.02) Ethnicity Non-Hispanic Hispanic [REF] 1.21 (1.19, 1.23) [REF] 1.24 (1.22, 1.26) Sex Male Female ISS Mild (1-8) Moderate (9-15) Severe (≥16) [REF] 0.68 (0.67, 0.69) [REF] 1.17 (1.16, 1.18) 2.03 (2.00, 2.06) [REF] 0.76 (0.76, 0.77) [REF] 0.90 (0.89, 0.92) 0.88 (0.86, 0.90) GCS 13-15 9-12 3-8 Age 18-34 35-49 50-64 65 and older [REF] 2.31 (2.23, 2.38) 1.21 (1.18, 1.23) [REF] 0.90 (0.88, 0.91) 0.71 (0.70, 0.72) 0.36 (0.36, 0.37) [REF] 2.46 (2.39, 2.53) 1.60 (1.57, 1.63) [REF] 0.97 (0.96, 0.98) 0.79 (0.78, 0.80) 0.43 (0.42, 0.43) Comorbidity Physical Behavioral 0.99 (0.98, 1.00) 0.95 (0.94, 0.96) 1.06 (1.05, 1.08) 0.97 (0.96, 0.98) aOR: Adjusted Odds Ratio CI: Confidence Interval AAPI: Asian American or Pacific Islander ISS: Injury Severity Score GCS: Glasgow Coma Scale Homelessness was associated with a three- and a fourfold increase in the odds of positive drug and polydrug screening results respectively (aOR: 3.38, 95% CI: 3.23, 3.53 vs. 4.06, 95% CI: 3.86, 4.27). However, the effect of homelessness on positive alcohol screenings was much smaller, though still statistically significant (aOR 1.07, 95% CI: 1.01, 1.13). Black race was associated with higher odds of positive tests for alcohol, drugs and polydrug use (aOR: 1.09, 95% CI: 1.07, 1.11; aOR: 1.35, 95% CI: 1.33, 1.37; and aOR: 1.12, 95% CI: 1.10, 1.15, respectively). Hispanic ethnicity was not significantly associated with alcohol or drug screening positivity, but was associated with lower odds of polydrug use (aOR: 0.88, 95% CI: 0.86, 0.91). Female patients had lower odds of testing positive for alcohol and drugs (aOR: 0.73 95% CI: 0.72, 0.74 and aOR: 0.72, 95% CI: 0.71, 0.73) as well as polydrug use (aOR: 0.74 95% CI: 0.72, 0.75). Higher ISS were associated with lower odds of positive alcohol screenings for both moderate and severe injuries (aOR: 0.90, 95% CI: 0.89, 0.92 and aOR: 0.88, 95% CI: 0.86, 0.90 respectively), and higher odds of positive drug screenings (aOR: 1.33, 95% CI: 1.31, 1.35 and aOR: 1.67, 95% CI: 1.64, 1.70 respectively). This pattern was also observed for polydrug use (aOR: 1.34, 95% CI: 1.31, 1.37 and aOR: 1.51, 95% CI: 1.48, 1.55). GCS scores of 3-8 had increased odds of positive alcohol test (aOR: 1.63, 95% CI: 1.58, 1.66) more so than for drug (aOR: 1.38, 95% CI: 1.35, 1.41) or polydrug (aOR: 1.26, 95% CI: 1.22, 1.30). GCS scores of 9-12 were also associated with higher odds of positive screens for drug (aOR: 2.11, 95% CI: 2.04, 2.18) and polydrug (aOR: 1.95, 95% CI: 1.86, 2.04) than alcohol (aOR: 1.70, 95% CI: 1.64, 1.76). Elderly patients (over age 65) had the lowest risk of a positive alcohol (aOR: 0.35, 95% CI: 0.35, 0.36), drug (aOR: 0.13, 95% CI: 0.13, 0.13), or polydrug screen (aOR: 0.09, 95% CI: 0.09, 0.09). ( Table 4 ). Table 4. Multivariate Analysis of Patient Characteristics Associated with Positive Alcohol and Drug Screening Results in Trauma Patients Patient Characteristics Alcohol aOR (95% CI) Drug aOR (95% CI) Polydrug aOR (95% CI) Housing Status Housed Homelessness [REF] 1.07 (1.01, 1.13) [REF] 3.38 (3.23, 3.53) [REF] 4.06 (3.86, 4.27) Race White Black AAPI Other/Missing [REF] 1.09 (1.07, 1.11) 0.57 (0.54, 0.61) 1.24 (1.21, 1.27) [REF] 1.35 (1.33, 1.37) 0.50 (0.47, 0.52) 0.93 (0.91, 0.95) [REF] 1.12 (1.10, 1.15) 0.49 (0.46, 0.53) 0.92 (0.89, 0.95) Ethnicity Not Hispanic Hispanic [REF] 1.01 (0.99, 1.02) [REF] 0.99 (0.97, 1.01) [REF] 0.88 (0.86, 0.91) Sex Male Female ISS Mild (1-8) Moderate (9-15) Severe (≥16) [REF] 0.73 (0.72, 0.74) [REF] 0.90 (0.89, 0.92) 0.88 (0.86, 0.90) [REF] 0.72 (0.71, 0.73) [REF] 1.33 (1.31, 1.35) 1.67 (1.64, 1.70) [REF] 0.74 (0.72, 0.75) [REF] 1.34 (1.31, 1.37) 1.51 (1.48, 1.55) GCS 13-15 9-12 3-8 Age 18-34 35-49 50-64 65 and older Comorbidity Physical Behavioral [REF] 1.70 (1.64, 1.76) 1.63 (1.58, 1.66) [REF] 1.08 (1.06, 1.10) 0.95 (0.93, 0.97) 0.35 (0.35, 0.36) 1.00 (0.98,1.02) 1.01 (0.99,1.03) [REF] 2.11 (2.04, 2.18) 1.38 (1.35, 1.41) [REF] 0.87 (0.86, 0.88) 0.54 (0.53, 0.54) 0.13 (0.13, 0.13) 1.06 (1.05, 1.08) 0.97 (0.96, 0.99) [REF] 1.95 (1.86, 2.04) 1.26 (1.22, 1.30) [REF] 1.04 (1.02, 1.07) 0.55 (0.54, 0.56) 0.09 (0.09, 0.09) 1.07 (1.04, 1.09) 0.98 (0.96, 1.00) aOR: Adjusted Odds Ratio CI: Confidence Interval AAPI: Asian American or Pacific Islander ISS: Injury Severity Score GCS: Glasgow Coma Scale Discussion To our knowledge, this is the first NTDB study to examine factors contributing to disparities in substance use screening between housed and unhoused trauma patients. Consistent with other studies, there are notable disparities in alcohol and drug screening among housed and unhoused trauma patients [ 7 , 12 ]. Homelessness and a GCS of 9–12 were the strongest determinants for whether a patient was screened for alcohol or drugs. Importantly, of those screened, homelessness was the single strongest predictor of drug and polydrug positivity. However, the association between homelessness and a positive alcohol screening was modest, despite patients being screened for alcohol two-to-threefold more frequently. This contrast likely reflects differences in detection windows and clinical workflows: alcohol intoxication is transient and more uniformly screened in trauma settings, whereas drug screening captures longer-term exposure and may be influenced by selective testing practices. Consequently, alcohol positivity may be less sensitive to social context than drug or polydrug detection. Housing status influences both the patients’ injury risk and their hospital care. Consistent with other studies, we found that unhoused trauma patients presented with more severe injuries than housed patients [ 7 – 10 ]. Yet injury severity alone did not fully explain the disparities in screening practices. Higher ISS was associated with more alcohol screening and less UDS, likely reflecting practical differences in screening modalities. Screening modalities were also influenced by demographic factors. Compared to housed patients, unhoused patients were more often younger, male, Black, and/or Hispanic, characteristics associated with higher screening rates. Despite being screened more, many of these demographic characteristics did not alter rates of positive screenings. Hispanic patients, in particular, did not test positive for any substance more than other groups, and had lower odds of polydrug positivity. Our findings support broader epidemiologic evidence of high rates of polydrug exposure among people experiencing homelessness [ 5 ]. Prior work has shown that urine drug screening may reflect medications administered after hospital admission and thus may not always indicate pre-injury substance use [ 6 ]. Polydrug positivity may therefore function as a more stable epidemiologic marker of pre-injury substance exposure than single-substance results, which can be contaminated by in-hospital medications. In trauma datasets where toxicology timing is imprecise, prioritizing polydrug patterns may improve interpretability when examining substance-related injury risk across socially marginalized populations. These findings also suggest that social context, clinician perception, and institutional norms play an important role in shaping diagnostic practices. The disproportionate screening of unhoused patients likely reflects a multifactorial relationship among provider perception, institutional norms, and visible social determinants of health. Such selective testing may reinforce stigma and the assumption that substance use is ubiquitous among unhoused individuals. Homelessness magnifies structural barriers in care, including longer hospital stays and delayed discharges, which may heighten clinician awareness and contribute to higher testing rates [ 6 ]. Even after adjusting for demographics and injury characteristics, housing status remained a strong independent predictor of alcohol and drug screening, highlighting the potential role of stigma in driving disparities in alcohol screening and positivity. Selective screening carries implications for trauma care. Conflating visible social vulnerability with presumed substance use may influence clinical documentation, risk stratification, and discharge planning in ways that extend beyond toxicology itself, potentially shaping downstream care and patient–provider interactions. Without standardized, injury-based screening criteria, over screening of unhoused patients may distort substance-use epidemiology and reinforce stigmatizing perceptions. The findings highlight the need for standardized, injury-based criteria for substance screening in trauma settings rather than reliance on social heuristics. Additionally, screening should be paired with appropriate interventions such as brief counseling, social work engagement, or harm-reduction referral. Trauma systems increasingly function as safety nets for socially marginalized populations, and equitable care requires that screening approaches minimize bias while ensuring that substance use identification is paired with appropriate intervention rather than punitive and stigmatizing responses [ 1 , 7 – 9 ]. This study is subject to several limitations, including institutional variability, test timing after admission, and the binary housing variable, which may underestimate unstable living conditions. The institutions included in the NTDB dataset primarily represent level I and II trauma centers and may not be representative of all trauma centers [ 13 ]. There is the possibility of testing bias because most trauma patients are not receiving substance screening. Additionally, UDS may detect medications administered after hospital admission, particularly opioids used for acute pain management. Despite these limitations, this large, multicenter dataset provides valuable insight into social disparities in trauma-related substance use screening. Conclusion Unhoused trauma patients were significantly more likely to be screened and to test positive for alcohol, drugs, and polydrug use compared with housed patients. While unhoused trauma patients were screened at substantially higher rates, differences in alcohol positivity were modest compared with marked disparities in drug and polydrug detection. Disparities in screening and positivity reflect both social determinants of health and institutional practices. Standardized, equitable screening approaches are necessary to minimize bias, accurately capture substance-use patterns, and ensure that identification is paired with meaningful clinical and social support rather than reinforcing stigma. Abbreviations US United States ACS American College of Surgeons NTDB National Trauma Data Bank TQP Trauma Quality Programs AAPI Asian and Pacific Islander ISS Injury Severity Score GCS Glascow Coma Scale BAC Blood Alcohol Concentration UDS Urine Drug Screening aOR Adjusted Odds Ratio CI Confidence Interval Declarations Ethics approval and consent to participate: The Institutional Review Board for the University of Nevada, Las Vegas approved this study, and written informed consent was waived due to the deidentified nature of the dataset. All methods were performed in accordance with the ethical standards in the Declaration of Helsinki and its later amendments or comparable ethical standards. Consent for publication: not applicable Funding: The authors received no funding for this study. Author Contribution Conceptualization and data acquisition, JB; methodology, JB & BA; data curation and recoding, JB & BA; formal analysis and interpretation, JB; writing-original draft preparation, JB & VS; writing-review and editing, JB, VS & BA. All authors have read and agreed to the published version of the manuscript. Acknowledgements: not applicable Data Availability The data that support the findings of this study are available from the American College of Surgeons, but restrictions apply to the availability of these data, which was used under license for this study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the American College of Surgeons. References Rhee P, Holcomb JB, Zangbar B. Evolving Epidemiology of Increasing Trauma Deaths in the United States (2000–2020). Ann Surg. 2025;281(6):976–81. 10.1097/SLA.0000000000006668 . Miller JP, O’ Reilly GM, Mackelprang JL, Mitra B. Trauma in adults experiencing homelessness. Injury. 2020;51(4):897–905. 10.1016/j.injury.2020.02.086 . US Department of Housing and Urban Development. The 2024 Annual Homelessness Assessment Report (AHAR) to Congress. Published online December 2024. Accessed March 2. 2025. https://www.huduser.gov/portal/sites/default/files/pdf/2024-AHAR-Part-1.pdf Schaffer KB, Wang J, Nasrallah FS, et al. Disparities in triage and management of the homeless and the elderly trauma patient. Injury Epidemiol. 2020;7(1):39. 10.1186/s40621-020-00262-1 . Cawley CL, Kanzaria HK, Kushel M, Raven MC, Zevin B. Mortality Among People Experiencing Homelessness in San Francisco 2016–2018. J Gen Intern Med. 2022;37(4):990–1. 10.1007/s11606-021-06769-7 . Culhane JT, Freeman CA. J Emerg Trauma Shock. 2020;13(4):279–85. 10.4103/JETS.JETS_141_19 . Silver CM, Thomas AC, Reddy S, et al. Morbidity and Length of Stay After Injury Among People Experiencing Homelessness in North America. JAMA Netw Open. 2024;7(2):e240795. 10.1001/jamanetworkopen.2024.0795 . Silver CM໿ T, AC໿ R, S໿, et al. Injury patterns and hospital admission after trauma among people experiencing homelessness. ໿ JAMA Netw Open. 2023;6(6):e2320862. 10.1001/jamanetworkopen.2023.20862 . Khadka S, Bardes JM, Al-Mamun MA. Injury Epidemiol. 2023;10(54). 10.1186/s40621-023-00459-0 . Rook JM, Spurrier RG, Russell CJ, et al. Disparities in Screening for Substance Use Among Injured Adolescents. JAMA Netw Open. 2024;7(10):e2436371–2436371. 10.1001/jamanetworkopen.2024.36371 . American College of Surgeons: National Trauma Data Standard (NTDS). Accessed December 2. 2024. https://www.tn.gov/content/dam/tn/health/events/Natl%20Trauma%20Data%20Standard%20Data%20Dictionary%202021.pdf Elkbuli A, Dowd B, Flores R, Boneva D, Hai S, Mckenney M. Alcohol and Drug Testing in the National Trauma Data Bank: Does it Matter? Journal of Emergencies, Trauma, and Shock 12(2):p 97, Apr–Jun 2019. | 10.4103/JETS.JETS_106_18 Hashmi ZG, Kaji AH, Nathens AB. JAMA Surg. 2018;153(9):852–3. 10.1001/jamasurg.2018.0483 . Practical Guide to Surgical Data Sets: National Trauma Data Bank (NTDB). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Apr, 2026 Reviews received at journal 06 Apr, 2026 Reviews received at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers invited by journal 12 Feb, 2026 Editor assigned by journal 12 Feb, 2026 Submission checks completed at journal 12 Feb, 2026 First submitted to journal 06 Feb, 2026 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-8810083","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589160275,"identity":"1f10f987-c095-4912-a182-852956bbfc7c","order_by":0,"name":"John Beckerle","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIie3QMUvDQBTA8XcE7HKQ9Qkav8ILB7alXyah0C6niyAZxAYEXQ66ph9DAs6Rg7rkA9wgKBQ6ZchYQYoxSp0u6iZ4/+XB4/3gOACX6w9GAB4AR+4DsOJr+b7tJoNgPwX4DYFEUPFT0u9drag+wDg3J4Xm8BjQg76tIRnFqYUM1VLEGcf4zpxGDVkLKidnCOXUSshIT/OWSLrPQMeLVBKya20nT5WnXxuSZx9ktphXYsO2HcRwb9x8siCUVNSgIx/lMbLUToZqIkLFMcCyagjpcI7r80G0nAob6ff0Cl/UJfdvZPgcJfpozx/npr4YHdpIG1O7d37OqOu8bfPthcvlcv3n3gAEdVsR5SSi6wAAAABJRU5ErkJggg==","orcid":"","institution":"University of Nevada, Las Vegas","correspondingAuthor":true,"prefix":"","firstName":"John","middleName":"","lastName":"Beckerle","suffix":""},{"id":589160276,"identity":"b86898e4-fd7d-4877-8e5c-4339a2579024","order_by":1,"name":"Vlada Stark","email":"","orcid":"","institution":"University of Nevada, Las Vegas","correspondingAuthor":false,"prefix":"","firstName":"Vlada","middleName":"","lastName":"Stark","suffix":""},{"id":589160280,"identity":"5e966cf7-cdbc-4e47-a6bf-dbe05d201493","order_by":2,"name":"Bertille Assoumou","email":"","orcid":"","institution":"University of Nevada, Las Vegas","correspondingAuthor":false,"prefix":"","firstName":"Bertille","middleName":"","lastName":"Assoumou","suffix":""}],"badges":[],"createdAt":"2026-02-06 18:24:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8810083/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8810083/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102746901,"identity":"4b753f2c-b4e9-4927-82ef-0fd1b167bb0b","added_by":"auto","created_at":"2026-02-16 09:02:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":683336,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8810083/v1/75e5140c-7290-4d1b-93a4-a22a0e62f2d4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Patterns of Alcohol and Drug Screening in Trauma Patients: Understanding Housing Status as a Determinant of Care","fulltext":[{"header":"Background","content":"\u003cp\u003eTraumatic injury remains a leading cause of morbidity and mortality in the United States (US), with recent decades marked by a sustained rise in trauma-related deaths driven in part by interpersonal violence and substance-related mechanisms [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Social determinants of health, particularly housing instability, play a critical role in shaping both trauma risk and clinical outcomes following injury. People experiencing homelessness are disproportionately exposed to environmental hazards, interpersonal violence, and untreated medical and psychiatric conditions, all of which increase the likelihood of traumatic injury and complicate recovery [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As a result, trauma systems increasingly function as de facto safety nets for unhoused populations, often managing patients with complex medical and social needs that extend beyond the acute injury itself [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSubstance use is highly prevalent among people experiencing homelessness and represents an important, though methodologically complex, contributor to trauma risk. Epidemiologic studies have demonstrated elevated rates of alcohol and drug exposure in this population, with polysubstance use being common [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the clinical interpretation of substance use screening results in trauma settings is not straightforward. Prior work has shown that urine drug screen (UDS) positivity does not consistently predict perioperative complications or mortality and may reflect a combination of chronic use, acute intoxication, and medications administered after hospital admission [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These findings underscore the limitations of interpreting single-substance detection as a direct proxy for pre-injury substance use or trauma-related risk.\u003c/p\u003e \u003cp\u003eAt the systems level, alcohol and drug screening practices in trauma care are highly variable. National analyses have demonstrated substantial institutional variation in screening adoption, with fewer than one-third of trauma patients screened overall [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Importantly, people experiencing homelessness are screened more frequently than housed patients, which complicates interpretation of higher test positivity rates and raises concern for structural and institutional bias in diagnostic practices [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Prior studies have also shown that unhoused trauma patients are more likely to be admitted to the hospital, experience longer lengths of stay, and utilize intensive care resources at higher rates, even after accounting for injury severity, highlighting the influence of social context and discharge safety concerns on clinical decision-making [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the well-documented overlap between homelessness, substance use, and traumatic injury, limited research has examined whether differences in substance positivity among unhoused trauma patients reflect true variation in pre-injury exposure or disparities in screening practices shaped by injury severity, clinician perception, and institutional norms. Existing studies have largely focused on single substances or opioid-related use, leaving an important gap in understanding polysubstance patterns and their relationship to housing status within trauma systems [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Using data from the 2021 National Trauma Data Bank, the present study compares rates of alcohol and drug screening among housed and unhoused trauma patients to better characterize how housing status intersects with screening practices and substance use patterns in contemporary trauma care.\u003c/p\u003e"},{"header":"Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eThis retrospective cross-sectional study used data from the 2021 National Trauma Data Bank (NTDB), made available by the American College of Surgeons (ACS) and its Trauma Quality Programs (TQP) data. The ACS TQP includes data from participating hospitals in the US and Canada that adhere to the National Trauma Data Standard, which aims to set standards for clinical data elements that are important for trauma care. Patient demographic, clinical, injury, and hospital data are extracted without protected health information. The NTDB does not include data on costs, laboratory values, readmissions, or long-term outcomes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The Institutional Review Board for the University of Nevada, Las Vegas determined that this study is exempt from the need for informed consent due to the deidentified nature of the dataset.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables Description\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDependent Variables\u003c/h2\u003e \u003cp\u003eThe outcomes we were interested in were the frequency of alcohol and drug screenings, screening results and the presence of multiple drugs detected concurrently, which we deemed polydrug use. The NTDB reports screening for alcohol and 13 types of drugs performed within the first 24 hours of hospital presentation. Screening status is recorded as a binary variable indicating whether a patient was screened for alcohol and/or drugs. Among screened patients, results are reported as a numeric ethanol level for alcohol, and a binary result for each class of drugs. The 13 drug classes included in screening were opioids, amphetamine, barbiturates, benzodiazepine, cocaine, methamphetamine, ecstasy, methadone, oxycodone, phencyclidine, tricyclic antidepressants (excluded from this study), cannabinoids, and others [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For each patient, the number of positive drug classes was calculated; 2 or more drug classes were considered polydrug use. A blood alcohol concentration (BAC)\u0026thinsp;\u0026ge;\u0026thinsp;0.08% (0.08 g/dL) was considered positive for this study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIndependent Variables\u003c/h3\u003e\n\u003cp\u003eHomelessness was the primary risk factor of interest, used to examine disparities in screening practices. Homelessness, as defined by the NTDB, refers to individuals lacking permanent housing, including those living in transitional housing or temporary living quarters. Homelessness is determined by NTDB data abstractors using a patient\u0026rsquo;s registered postal code. In accordance with protected health information regulations, postal codes are not available to researchers.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eCovariates included in multivariate modeling include age, sex, race, ethnicity, injury severity score (ISS), total Glasgow Coma Scale (GCS), and presence of physical and behavioral comorbidities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These variables were compiled for both housed and unhoused patients. The NTDB collects race and ethnicity data from self-report or report by family members and records race and ethnicity as separate variables. Categories included Hispanic and non-Hispanic for ethnicity, and Black, White, Asian, Pacific Islander (which we combined into AAPI) and other for race. Comorbidities are assigned by ICD-10 comorbidity codes. Physical comorbidities include conditions such as heart disease, diabetes, hypertension, liver disease, and malignant neoplasms. Behavioral comorbidities include bipolar disorder, major depressive disorder, posttraumatic stress disorder, and substance use disorder.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eChi-square tests of independence were used to assess significant differences between housed and unhoused patients. Missing values for any demographic information were included in a descriptive statistics table for clarity. Univariate logistic regression models were used to estimate the unadjusted odds ratios for the association between homelessness on alcohol and drug screening, and the results of these screens. Multivariate logistic regression models were used to estimate adjusted odds ratios to more closely examine the relationship between homelessness and screening outcomes while controlling for potential confounders. We conducted multivariate analysis for five outcomes: alcohol screening, drugs screening, positive screenings for alcohol, drug and polydrug use. All statistical analyses were performed in R4.4.0. Statistical significance was set at P \u0026lt; .05.\u003c/p\u003e \u003cp\u003eFor regression models, all continuous variables were categorized into discrete intervals: age intervals were 18\u0026ndash;34, 35\u0026ndash;49, 50\u0026ndash;64, and 65 or older; GCS categories were 15\u0026thinsp;\u0026minus;\u0026thinsp;13, 12\u0026thinsp;\u0026minus;\u0026thinsp;9, and 8\u0026thinsp;\u0026minus;\u0026thinsp;3; ISS categories were mild (1\u0026ndash;8), moderate (9\u0026ndash;15), and severe (16 or higher).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe total sample size of the 2021 NTDB data set is 1,209,097 patients. After removing those under the age of 18 years or with a missing age, 1,000,269 patients were included in the present study. Of these, 9,466 (0.9%) were unhoused. In the housed cohort, 587,009 (59.2%) were male whereas 7,845 (82.9%) of the unhoused patients were male. Unhoused patients were less likely to be elderly (65 or older) (7.2% vs. 39.6%, p\u0026lt;0.001). Although White people made up the majority of the patient population in both groups, there was a higher prevalence of Hispanic and Black patients in the unhoused cohort (2,024 [21.4%] and 2,118 [22.4%] vs. 116,723 [11.8%] and 148,753 [15.0%], p\u0026lt;0.001). Medicaid and self-pay were the most common forms of primary payment for unhoused patients (4,570 [48.3%] and 2,030 [21.3%], p\u0026lt;0.001) while the majority of housed patients used either Medicare or private insurance (345,416 [34.9%] and 312,470 [31.5%] respectively).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImportantly, unhoused patients had higher instances of severe ISS (1,978 [20.9%] vs. 167,179 [16.9%], p\u0026lt;0.001) and lowest GCS (under 9) on admission (801 [8.5%] vs. 55,549 [5.6%] compared to housed patients. The presence of any physical or behavioral comorbidity was similar between both groups. \u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003epresents a summary of patient characteristics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Patient Characteristics by Housing Status in the 2021 National Trauma Data Bank Dataset\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"593\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003ePatient Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eHoused\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eUnhoused\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ep Value*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e990,803 (99.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e9,466 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e587,009 (59.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e7,845 (82.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e394,977 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1,533 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e271 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e18-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e248,377 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2,910 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e35-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e163,305 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3,110 (32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e50-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e186,446 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2,760 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e392,675 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e686 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e715,233 (72.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e5,506 (58.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e148,753 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2,118 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eAAPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e23,652 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e206 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eOther/Missing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e103,165 (10.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1,636 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003cp\u003eNon-Hispanic\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003cp\u003eInsurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e116,723 (11.8) 839,178 (84.7) 34,902 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2,024 (21.4)\u003c/p\u003e\n \u003cp\u003e7,080 (74.8)\u003c/p\u003e\n \u003cp\u003e362 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e151,935 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e4,570 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e345,416 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e909 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eOther Government\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e31,467 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e452 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003ePrivate/Commercial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e312,470 (31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1,256 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eSelf-Pay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e104,952 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2,030 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e22,215 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e143 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eNot Billed (for any reason)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2,363 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e18 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eMissing Data\u003c/p\u003e\n \u003cp\u003eComorbidity\u003c/p\u003e\n \u003cp\u003eAny\u003c/p\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003cp\u003eISS\u003c/p\u003e\n \u003cp\u003eMild (1-8)\u003c/p\u003e\n \u003cp\u003eModerate (9-15)\u003c/p\u003e\n \u003cp\u003eSevere (\u0026ge;16)\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003cp\u003eGCS\u003c/p\u003e\n \u003cp\u003e13-15\u003c/p\u003e\n \u003cp\u003e9-12\u003c/p\u003e\n \u003cp\u003e3-8\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e19,985 (2.0)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e536,160 (54.1)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e454,643 (45.9)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e456,982 (46.1) 364,460 (36.8)\u003c/p\u003e\n \u003cp\u003e167,179 (16.9)\u003c/p\u003e\n \u003cp\u003e2,182 (0.2)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e859,321 (86.7) 21,606 (2.2) 55,549 (5.6) 54,327 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e88 (0.9)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5,059 (53.4)\u003c/p\u003e\n \u003cp\u003e4,407 (46.6)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4,639 (49.0)\u003c/p\u003e\n \u003cp\u003e2,925 (29.8)\u003c/p\u003e\n \u003cp\u003e1,978 (20.9)\u003c/p\u003e\n \u003cp\u003e24 (0.3)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7,772 (82.1)\u003c/p\u003e\n \u003cp\u003e533 (5.6)\u003c/p\u003e\n \u003cp\u003e801 (8.5)\u003c/p\u003e\n \u003cp\u003e360 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eAAPI: Asian American or Pacific Islander\u003c/p\u003e\n \u003cp\u003eISS: Injury Severity Score\u003c/p\u003e\n \u003cp\u003eGCS: Glasgow Coma Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;* determined by Chi-square tests of independence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAlcohol screening was conducted for 7,135 unhoused patients (75.4%) and 462,134 housed patients (46.6%) while drug screening was performed for 5,733 (60.6%) and 312,212 (31.5%) patients, respectively. Of those screened, 1,926 (27.0%) unhoused and 100,525 (21.8%) housed patients tested positive for alcohol, whereas 4,270 (74.4%) unhoused and 140,041 (44.9%) housed patients tested positive for drugs; polydrug use was detected in 2,424 (42.3%) and 54,435 (17.4%) patients, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn univariate logistic regression analysis, unhoused patients had more than three times the odds of getting screened on admission for drugs and alcohol compared to housed patients (OR: 3.34, 95% CI: 3.20, 3.48 and OR: 3.50, 95% CI: 3.34, 3.67, respectively). Of those screened, the unhoused were around five times as likely to test positive for drug and polydrug use (OR: 4.99, 95% CI: 4.79, 5.20 and OR: 5.92, 95% CI: 5.65, 6.21), but moderately more likely to test positive for alcohol (OR: 1.37, 95% CI: 1.30, 1.44), despite being screened more (\u003cstrong\u003eTable 2\u003c/strong\u003e). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Univariate Analysis for Effects of Homelessness on Screening Results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"396\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eUnadjusted Odds Ratio (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003eScreened for Drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e3.34 (3.20, 3.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003eScreened for Alcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e3.50 (3.34, 3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003eTested Positive for Drugs\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTested Positive for Multiple Drugs (Polydrug)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e4.99 (4.79, 5.20)\u003c/p\u003e\n \u003cp\u003e5.92 (5.65, 6.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003eTested Positive for Alcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.37 (1.30, 1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003e\u0026nbsp;CI: Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen accounting for confounders, homelessness remained the strongest determinant of whether a patient was screened for substance use. This was true for both alcohol and drug screenings, with unhoused patients having more than twice the odds of being screened (aOR: 2.37, 95% CI: 2.25, 2.49 vs. aOR: 2.42, 95% CI: 2.31, 2.53, respectively). Black patients were consistently screened more than white patients for alcohol (aOR: 1.16, 95% CI: 1.14, 1.17) and drugs (aOR: 1.12, 95% CI: 1.10,1.13); Hispanic patients were more likely to be screened for alcohol (aOR 1.21, 95% CI: 1.19, 1.23 ) and drugs (aOR: 1.24, 95% CI: 1.22, 1.26) than non-Hispanics. Female trauma patients were less likely to be screened for alcohol (aOR: 0.68, 95% CI: 0.67, 0.69) and drugs (aOR: 0.76, 95% CI: 0.76, 0.77), compared to male patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatients with moderate and severe ISS were screened more for alcohol, (aOR 1.17, 95% CI: 1.16, 1.18 and aOR: 2.03, 95% CI: 2.00, 2.06, respectively), but less for drugs (aOR: 0.90, 95% CI: 0.89, 0.92 and aOR 0.88, 95% CI: 0.86, 0.90, respectively). Patients with a GCS of 9-12 and 8 or lower were screened more than patients with a GCS of 13-15 for both alcohol and drugs, respectively (aOR: 2.31, 95% CI: 2.23, 2.38 and aOR: 2.46, 95% CI: 2.39, 2.53 vs. aOR: 1.21, 95% CI: 1.18, 1.23 and aOR: 1.60, 95% CI: 1.57, 1.63). Increasing age was associated with progressively lower odds of substance use screening, with the lowest odds among the elderly for both alcohol and drug screenings (aOR: 0.36, 95% CI: 0.36, 0.37 and aOR: 0.43, 95% CI: 0.42, 0.43, respectively). \u003cstrong\u003e(Table 3)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eMultivariate Analysis of Patient Characteristics Affecting Alcohol and Drug Screening in Trauma Patients\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"532\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003ePatient Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003cp\u003eaOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eDrug\u003c/p\u003e\n \u003cp\u003eaOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eHousing Status\u003c/p\u003e\n \u003cp\u003eHoused\u003c/p\u003e\n \u003cp\u003eHomelessness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e2.37 (2.25, 2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e2.42 (2.31, 2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp; Race\u003c/p\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003cp\u003eAAPI\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e1.16 (1.14, 1.17)\u003c/p\u003e\n \u003cp\u003e1.44 (1.40, 1.48)\u003c/p\u003e\n \u003cp\u003e1.12 (1.10, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e1.12 (1.10, 1.13)\u003c/p\u003e\n \u003cp\u003e0.89 (0.86, 0.92)\u003c/p\u003e\n \u003cp\u003e1.00 (0.98, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003cp\u003eNon-Hispanic\u003c/p\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e1.21 (1.19, 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e1.24 (1.22, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eISS\u003c/p\u003e\n \u003cp\u003eMild (1-8)\u003c/p\u003e\n \u003cp\u003eModerate (9-15)\u003c/p\u003e\n \u003cp\u003eSevere (\u0026ge;16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e0.68 (0.67, 0.69)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e1.17 (1.16, 1.18)\u003c/p\u003e\n \u003cp\u003e2.03 (2.00, 2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e0.76 (0.76, 0.77)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e0.90 (0.89, 0.92)\u003c/p\u003e\n \u003cp\u003e0.88 (0.86, 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eGCS\u003c/p\u003e\n \u003cp\u003e13-15\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9-12\u003c/p\u003e\n \u003cp\u003e3-8\u003c/p\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003e18-34\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35-49\u003c/p\u003e\n \u003cp\u003e50-64\u003c/p\u003e\n \u003cp\u003e65 and older\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e2.31 (2.23, 2.38)\u003c/p\u003e\n \u003cp\u003e1.21 (1.18, 1.23)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; [REF]\u003c/p\u003e\n \u003cp\u003e0.90 (0.88, 0.91)\u003c/p\u003e\n \u003cp\u003e0.71 (0.70, 0.72)\u003c/p\u003e\n \u003cp\u003e0.36 (0.36, 0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[REF]\u003c/p\u003e\n \u003cp\u003e2.46 (2.39, 2.53)\u003c/p\u003e\n \u003cp\u003e1.60 (1.57, 1.63)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e0.97 (0.96, 0.98)\u003c/p\u003e\n \u003cp\u003e0.79 (0.78, 0.80)\u003c/p\u003e\n \u003cp\u003e0.43 (0.42, 0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eComorbidity\u003c/p\u003e\n \u003cp\u003ePhysical\u003c/p\u003e\n \u003cp\u003eBehavioral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.99 (0.98, 1.00)\u003c/p\u003e\n \u003cp\u003e0.95 (0.94, 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.06 (1.05, 1.08)\u003c/p\u003e\n \u003cp\u003e0.97 (0.96, 0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eaOR: Adjusted Odds Ratio\u003c/p\u003e\n \u003cp\u003eCI: Confidence Interval\u003c/p\u003e\n \u003cp\u003eAAPI: Asian American or Pacific Islander\u003c/p\u003e\n \u003cp\u003eISS: Injury Severity Score\u003c/p\u003e\n \u003cp\u003eGCS: Glasgow Coma Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHomelessness was associated with a three- and a fourfold increase in the odds of positive drug and polydrug screening results respectively (aOR: 3.38, 95% CI: 3.23, 3.53 vs. 4.06, 95% CI: 3.86, 4.27). However, the effect of homelessness on positive alcohol screenings was much smaller, though still statistically significant (aOR 1.07, 95% CI: 1.01, 1.13). Black race was associated with higher odds of positive tests for alcohol, drugs and polydrug use (aOR: 1.09, 95% CI: 1.07, 1.11; aOR: 1.35, 95% CI: 1.33, 1.37; and aOR: 1.12, 95% CI: 1.10, 1.15, respectively). Hispanic ethnicity was not significantly associated with alcohol or drug screening positivity, but was associated with lower odds of polydrug use (aOR: 0.88, 95% CI: 0.86, 0.91). Female patients had lower odds of testing positive for alcohol and drugs (aOR: 0.73 95% CI: 0.72, 0.74 and aOR: 0.72, 95% CI: 0.71, 0.73) as well as polydrug use (aOR: 0.74 95% CI: 0.72, 0.75).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHigher ISS were associated with lower odds of positive alcohol screenings for both moderate and severe injuries (aOR: 0.90, 95% CI: 0.89, 0.92 and aOR: 0.88, 95% CI: 0.86, 0.90 respectively), and higher odds of positive drug screenings (aOR: 1.33, 95% CI: 1.31, 1.35 and aOR: 1.67, 95% CI: 1.64, 1.70 respectively). This pattern was also observed for polydrug use (aOR: 1.34, 95% CI: 1.31, 1.37 and aOR: 1.51, 95% CI: 1.48, 1.55).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGCS scores of 3-8 had increased odds of positive alcohol test (aOR: 1.63, 95% CI: 1.58, 1.66) more so than for drug (aOR: 1.38, 95% CI: 1.35, 1.41) or polydrug (aOR: 1.26, 95% CI: 1.22, 1.30). GCS scores of 9-12 were also associated with higher odds of positive screens for drug (aOR: 2.11, 95% CI: 2.04, 2.18) and polydrug (aOR: 1.95, 95% CI: 1.86, 2.04) than alcohol (aOR: 1.70, 95% CI: 1.64, 1.76). Elderly patients (over age 65) had the lowest risk of a positive alcohol (aOR: 0.35, 95% CI: 0.35, 0.36), drug (aOR: 0.13, 95% CI: 0.13, 0.13), or polydrug screen (aOR: 0.09, 95% CI: 0.09, 0.09). (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Multivariate Analysis of Patient Characteristics Associated with Positive Alcohol and Drug Screening Results in Trauma Patients\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"654\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 197px;\"\u003e\n \u003cp\u003ePatient Characteristics \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003cp\u003eaOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eDrug\u003c/p\u003e\n \u003cp\u003eaOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003ePolydrug\u003c/p\u003e\n \u003cp\u003eaOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 197px;\"\u003e\n \u003cp\u003eHousing Status\u003c/p\u003e\n \u003cp\u003eHoused\u003c/p\u003e\n \u003cp\u003eHomelessness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e1.07 (1.01, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e3.38 (3.23, 3.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e4.06 (3.86, 4.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp;Race\u003c/p\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003cp\u003eAAPI\u003c/p\u003e\n \u003cp\u003eOther/Missing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e1.09 (1.07, 1.11)\u003c/p\u003e\n \u003cp\u003e0.57 (0.54, 0.61)\u003c/p\u003e\n \u003cp\u003e1.24 (1.21, 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e1.35 (1.33, 1.37)\u003c/p\u003e\n \u003cp\u003e0.50 (0.47, 0.52)\u003c/p\u003e\n \u003cp\u003e0.93 (0.91, 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e1.12 (1.10, 1.15)\u003c/p\u003e\n \u003cp\u003e0.49 (0.46, 0.53)\u003c/p\u003e\n \u003cp\u003e0.92 (0.89, 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Ethnicity\u003c/p\u003e\n \u003cp\u003eNot Hispanic\u003c/p\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e1.01 (0.99, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e0.99 (0.97, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e0.88 (0.86, 0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Sex\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;ISS\u003c/p\u003e\n \u003cp\u003eMild (1-8)\u003c/p\u003e\n \u003cp\u003eModerate (9-15)\u003c/p\u003e\n \u003cp\u003eSevere (\u0026ge;16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e0.73 (0.72, 0.74)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e0.90 (0.89, 0.92)\u003c/p\u003e\n \u003cp\u003e0.88 (0.86, 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[REF]\u003c/p\u003e\n \u003cp\u003e0.72 (0.71, 0.73)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e1.33 (1.31, 1.35)\u003c/p\u003e\n \u003cp\u003e1.67 (1.64, 1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e0.74 (0.72, 0.75)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[REF]\u003c/p\u003e\n \u003cp\u003e1.34 (1.31, 1.37)\u003c/p\u003e\n \u003cp\u003e1.51 (1.48, 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp;GCS\u003c/p\u003e\n \u003cp\u003e13-15\u003c/p\u003e\n \u003cp\u003e9-12\u003c/p\u003e\n \u003cp\u003e3-8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Age\u003c/p\u003e\n \u003cp\u003e18-34\u003c/p\u003e\n \u003cp\u003e35-49\u003c/p\u003e\n \u003cp\u003e50-64\u003c/p\u003e\n \u003cp\u003e65 and older\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Comorbidity\u003c/p\u003e\n \u003cp\u003ePhysical\u003c/p\u003e\n \u003cp\u003eBehavioral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e1.70 (1.64, 1.76)\u003c/p\u003e\n \u003cp\u003e1.63 (1.58, 1.66)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e1.08 (1.06, 1.10)\u003c/p\u003e\n \u003cp\u003e0.95 (0.93, 0.97)\u003c/p\u003e\n \u003cp\u003e0.35 (0.35, 0.36)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.00 (0.98,1.02)\u003c/p\u003e\n \u003cp\u003e1.01 (0.99,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e2.11 (2.04, 2.18)\u003c/p\u003e\n \u003cp\u003e1.38 (1.35, 1.41)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e0.87 (0.86, 0.88)\u003c/p\u003e\n \u003cp\u003e0.54 (0.53, 0.54)\u003c/p\u003e\n \u003cp\u003e0.13 (0.13, 0.13)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.06 (1.05, 1.08)\u003c/p\u003e\n \u003cp\u003e0.97 (0.96, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e1.95 (1.86, 2.04)\u003c/p\u003e\n \u003cp\u003e1.26 (1.22, 1.30)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[REF]\u003c/p\u003e\n \u003cp\u003e1.04 (1.02, 1.07)\u003c/p\u003e\n \u003cp\u003e0.55 (0.54, 0.56)\u003c/p\u003e\n \u003cp\u003e0.09 (0.09, 0.09)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.07 (1.04, 1.09)\u003c/p\u003e\n \u003cp\u003e0.98 (0.96, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 197px;\"\u003e\n \u003cp\u003eaOR: Adjusted Odds Ratio\u003c/p\u003e\n \u003cp\u003eCI: Confidence Interval\u003c/p\u003e\n \u003cp\u003eAAPI: Asian American or Pacific Islander\u003c/p\u003e\n \u003cp\u003eISS: Injury Severity Score\u003c/p\u003e\n \u003cp\u003eGCS: Glasgow Coma Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first NTDB study to examine factors contributing to disparities in substance use screening between housed and unhoused trauma patients. Consistent with other studies, there are notable disparities in alcohol and drug screening among housed and unhoused trauma patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Homelessness and a GCS of 9\u0026ndash;12 were the strongest determinants for whether a patient was screened for alcohol or drugs. Importantly, of those screened, homelessness was the single strongest predictor of drug and polydrug positivity. However, the association between homelessness and a positive alcohol screening was modest, despite patients being screened for alcohol two-to-threefold more frequently. This contrast likely reflects differences in detection windows and clinical workflows: alcohol intoxication is transient and more uniformly screened in trauma settings, whereas drug screening captures longer-term exposure and may be influenced by selective testing practices. Consequently, alcohol positivity may be less sensitive to social context than drug or polydrug detection.\u003c/p\u003e \u003cp\u003eHousing status influences both the patients\u0026rsquo; injury risk and their hospital care. Consistent with other studies, we found that unhoused trauma patients presented with more severe injuries than housed patients [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Yet injury severity alone did not fully explain the disparities in screening practices. Higher ISS was associated with more alcohol screening and less UDS, likely reflecting practical differences in screening modalities. Screening modalities were also influenced by demographic factors. Compared to housed patients, unhoused patients were more often younger, male, Black, and/or Hispanic, characteristics associated with higher screening rates. Despite being screened more, many of these demographic characteristics did not alter rates of positive screenings. Hispanic patients, in particular, did not test positive for any substance more than other groups, and had lower odds of polydrug positivity.\u003c/p\u003e \u003cp\u003eOur findings support broader epidemiologic evidence of high rates of polydrug exposure among people experiencing homelessness [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Prior work has shown that urine drug screening may reflect medications administered after hospital admission and thus may not always indicate pre-injury substance use [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Polydrug positivity may therefore function as a more stable epidemiologic marker of pre-injury substance exposure than single-substance results, which can be contaminated by in-hospital medications. In trauma datasets where toxicology timing is imprecise, prioritizing polydrug patterns may improve interpretability when examining substance-related injury risk across socially marginalized populations.\u003c/p\u003e \u003cp\u003eThese findings also suggest that social context, clinician perception, and institutional norms play an important role in shaping diagnostic practices. The disproportionate screening of unhoused patients likely reflects a multifactorial relationship among provider perception, institutional norms, and visible social determinants of health. Such selective testing may reinforce stigma and the assumption that substance use is ubiquitous among unhoused individuals. Homelessness magnifies structural barriers in care, including longer hospital stays and delayed discharges, which may heighten clinician awareness and contribute to higher testing rates [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Even after adjusting for demographics and injury characteristics, housing status remained a strong independent predictor of alcohol and drug screening, highlighting the potential role of stigma in driving disparities in alcohol screening and positivity.\u003c/p\u003e \u003cp\u003eSelective screening carries implications for trauma care. Conflating visible social vulnerability with presumed substance use may influence clinical documentation, risk stratification, and discharge planning in ways that extend beyond toxicology itself, potentially shaping downstream care and patient\u0026ndash;provider interactions. Without standardized, injury-based screening criteria, over screening of unhoused patients may distort substance-use epidemiology and reinforce stigmatizing perceptions.\u003c/p\u003e \u003cp\u003eThe findings highlight the need for standardized, injury-based criteria for substance screening in trauma settings rather than reliance on social heuristics. Additionally, screening should be paired with appropriate interventions such as brief counseling, social work engagement, or harm-reduction referral. Trauma systems increasingly function as safety nets for socially marginalized populations, and equitable care requires that screening approaches minimize bias while ensuring that substance use identification is paired with appropriate intervention rather than punitive and stigmatizing responses [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study is subject to several limitations, including institutional variability, test timing after admission, and the binary housing variable, which may underestimate unstable living conditions. The institutions included in the NTDB dataset primarily represent level I and II trauma centers and may not be representative of all trauma centers [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. There is the possibility of testing bias because most trauma patients are not receiving substance screening. Additionally, UDS may detect medications administered after hospital admission, particularly opioids used for acute pain management. Despite these limitations, this large, multicenter dataset provides valuable insight into social disparities in trauma-related substance use screening.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUnhoused trauma patients were significantly more likely to be screened and to test positive for alcohol, drugs, and polydrug use compared with housed patients. While unhoused trauma patients were screened at substantially higher rates, differences in alcohol positivity were modest compared with marked disparities in drug and polydrug detection. Disparities in screening and positivity reflect both social determinants of health and institutional practices. Standardized, equitable screening approaches are necessary to minimize bias, accurately capture substance-use patterns, and ensure that identification is paired with meaningful clinical and social support rather than reinforcing stigma.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican College of Surgeons\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNTDB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Trauma Data Bank\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTQP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTrauma Quality Programs\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAAPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAsian and Pacific Islander\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInjury Severity Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlascow Coma Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood Alcohol Concentration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUrine Drug Screening\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eaOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003e The Institutional Review Board for the University of Nevada, Las Vegas approved this study, and written informed consent was waived due to the deidentified nature of the dataset. All methods were performed in accordance with the ethical standards in the Declaration of Helsinki and its later amendments or comparable ethical standards.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003enot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThe authors received no funding for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization and data acquisition, JB; methodology, JB \u0026amp; BA; data curation and recoding, JB \u0026amp; BA; formal analysis and interpretation, JB; writing-original draft preparation, JB \u0026amp; VS; writing-review and editing, JB, VS \u0026amp; BA. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003enot applicable\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the American College of Surgeons, but restrictions apply to the availability of these data, which was used under license for this study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the American College of Surgeons.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRhee P, Holcomb JB, Zangbar B. Evolving Epidemiology of Increasing Trauma Deaths in the United States (2000\u0026ndash;2020). Ann Surg. 2025;281(6):976\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/SLA.0000000000006668\u003c/span\u003e\u003cspan address=\"10.1097/SLA.0000000000006668\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller JP, O\u0026rsquo; Reilly GM, Mackelprang JL, Mitra B. Trauma in adults experiencing homelessness. Injury. 2020;51(4):897\u0026ndash;905. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.injury.2020.02.086\u003c/span\u003e\u003cspan address=\"10.1016/j.injury.2020.02.086\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUS Department of Housing and Urban Development. The 2024 Annual Homelessness Assessment Report (AHAR) to Congress. Published online December 2024. Accessed March 2. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.huduser.gov/portal/sites/default/files/pdf/2024-AHAR-Part-1.pdf\u003c/span\u003e\u003cspan address=\"https://www.huduser.gov/portal/sites/default/files/pdf/2024-AHAR-Part-1.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchaffer KB, Wang J, Nasrallah FS, et al. Disparities in triage and management of the homeless and the elderly trauma patient. Injury Epidemiol. 2020;7(1):39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40621-020-00262-1\u003c/span\u003e\u003cspan address=\"10.1186/s40621-020-00262-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCawley CL, Kanzaria HK, Kushel M, Raven MC, Zevin B. Mortality Among People Experiencing Homelessness in San Francisco 2016\u0026ndash;2018. J Gen Intern Med. 2022;37(4):990\u0026ndash;1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11606-021-06769-7\u003c/span\u003e\u003cspan address=\"10.1007/s11606-021-06769-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCulhane JT, Freeman CA. J Emerg Trauma Shock. 2020;13(4):279\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/JETS.JETS_141_19\u003c/span\u003e\u003cspan address=\"10.4103/JETS.JETS_141_19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilver CM, Thomas AC, Reddy S, et al. Morbidity and Length of Stay After Injury Among People Experiencing Homelessness in North America. JAMA Netw Open. 2024;7(2):e240795. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2024.0795\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2024.0795\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilver CM໿ T, AC໿ R, S໿, et al. Injury patterns and hospital admission after trauma among people experiencing homelessness. ໿ JAMA Netw Open. 2023;6(6):e2320862. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2023.20862\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2023.20862\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhadka S, Bardes JM, Al-Mamun MA. Injury Epidemiol. 2023;10(54). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40621-023-00459-0\u003c/span\u003e\u003cspan address=\"10.1186/s40621-023-00459-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRook JM, Spurrier RG, Russell CJ, et al. Disparities in Screening for Substance Use Among Injured Adolescents. JAMA Netw Open. 2024;7(10):e2436371\u0026ndash;2436371. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2024.36371\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2024.36371\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican College of Surgeons: National Trauma Data Standard (NTDS). Accessed December 2. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.tn.gov/content/dam/tn/health/events/Natl%20Trauma%20Data%20Standard%20Data%20Dictionary%202021.pdf\u003c/span\u003e\u003cspan address=\"https://www.tn.gov/content/dam/tn/health/events/Natl%20Trauma%20Data%20Standard%20Data%20Dictionary%202021.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElkbuli A, Dowd B, Flores R, Boneva D, Hai S, Mckenney M. Alcohol and Drug Testing in the National Trauma Data Bank: Does it Matter? Journal of Emergencies, Trauma, and Shock 12(2):p 97, Apr\u0026ndash;Jun 2019. | \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/JETS.JETS_106_18\u003c/span\u003e\u003cspan address=\"10.4103/JETS.JETS_106_18\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHashmi ZG, Kaji AH, Nathens AB. JAMA Surg. 2018;153(9):852\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamasurg.2018.0483\u003c/span\u003e\u003cspan address=\"10.1001/jamasurg.2018.0483\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Practical Guide to Surgical Data Sets: National Trauma Data Bank (NTDB).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"injury-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"inep","sideBox":"Learn more about [Injury Epidemiology](https://injepijournal.biomedcentral.com)","snPcode":"40621","submissionUrl":"https://submission.nature.com/new-submission/40621/3","title":"Injury Epidemiology","twitterHandle":"@InjuryEpi","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Trauma, social determinants of health, homelessness, drugs, alcohol","lastPublishedDoi":"10.21203/rs.3.rs-8810083/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8810083/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eTraumatic injury remains a leading cause of morbidity and mortality in the United States, with substance use and social determinants of health playing important roles in injury risk and clinical outcomes. People experiencing homelessness are disproportionately exposed to trauma and are screened for alcohol and drugs at higher rates than housed patients, raising questions about whether differences in substance positivity reflect true variation in exposure or disparities in screening practices. This study examines patterns of alcohol and drug screening and positivity among housed and unhoused trauma patients, with particular attention to polysubstance use.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cross-sectional analysis of adult trauma patients included in the 2021 National Trauma Data Bank. Alcohol and drug screening within the first 24 hours of hospital encounter were examined, along with screening results and the presence of multiple concurrent drug positives. Homelessness was the primary exposure of interest. Multivariable logistic regression models were used to assess the association between housing status and screening practices as well as positive alcohol, drug, and polydrug results, adjusting for age, sex, race, ethnicity, injury severity, Glasgow Coma Scale score, physical and behavioral comorbidities.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eAmong 1,000,269 adult trauma patients, 9,466 (0.9%) were unhoused. Unhoused patients were significantly more likely to be screened for alcohol and drugs than housed patients. After adjustment, homelessness remained the strongest predictor of screening for both alcohol and drugs. Among those screened, homelessness was associated with markedly higher odds of positive drug and polydrug results, the association with positive blood alcohol concentration was modest despite substantially higher screening rates. Injury severity and lower Glasgow Coma Scale scores were also associated with increased screening and positivity.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eUnhoused trauma patients experience substantially higher rates of alcohol and drug screening and higher odds of drug and polysubstance positivity compared with housed patients. These findings suggest that housing status strongly shapes diagnostic practices in trauma care and may influence the interpretation of substance use epidemiology. Standardized, non-stigmatizing screening approaches that are paired with appropriate clinical and social interventions are needed to ensure equitable trauma care.\u003c/p\u003e","manuscriptTitle":"Patterns of Alcohol and Drug Screening in Trauma Patients: Understanding Housing Status as a Determinant of Care","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 16:27:24","doi":"10.21203/rs.3.rs-8810083/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-07T17:28:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T01:02:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-16T22:35:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234462195318992660849244064311243596906","date":"2026-03-16T18:03:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154507741310224050584218184639823149147","date":"2026-02-18T14:52:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T17:41:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-12T14:01:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-12T13:57:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Injury Epidemiology","date":"2026-02-06T18:12:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"injury-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"inep","sideBox":"Learn more about [Injury Epidemiology](https://injepijournal.biomedcentral.com)","snPcode":"40621","submissionUrl":"https://submission.nature.com/new-submission/40621/3","title":"Injury Epidemiology","twitterHandle":"@InjuryEpi","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a1558a06-6a37-426f-b908-b084d6d0e9ba","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T02:39:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 16:27:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8810083","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8810083","identity":"rs-8810083","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}