Predictors of Asylum Approval: Insights from a Decade of Forensic Medical Evaluations at a Student-Run Clinic

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Forensic medical evaluations (FMEs) support these claims and improve case outcomes. This study investigates which demographic, traumatic, and medical factors predict asylum approval within a student-run asylum clinic. Methods We performed a retrospective study of clinic FMEs (2014–2024). Of 289 completed evaluations, 154 cases with known outcomes were analyzed. The outcome variable was asylum granted (1) vs not granted (0). 29 binary predictors (10 demographic, 10 trauma, 9 medical) were abstracted from affidavits. We conducted univariate and multivariable logistic regressions. Results were compared with national data from Physicians for Human Rights. Results 57.1% cases were granted asylum, exceeding the national average of 39.3%. In univariate analyses, higher approval odds were observed with lacerations, scars, burns, any psychiatric diagnosis, PTSD, MDD, kidnapping, torture, income, having children, and having a physical and psychological FME conducted (p < 0.05). Gait abnormality was inversely associated. In the multivariable model, kidnapping, lacerations, and income remained associated with approval; genital mutilation also showed a large positive association. Conclusion A decade of data from a student-run asylum clinic confirmed the importance of FMEs. Findings suggest the medical content of FMEs, especially clear, well-documented injuries that match the client’s story and select trauma exposures (e.g., kidnapping), carries strong evidentiary weight, while demographic indicators (income) may also play a role. Results support maintaining capacity for both physical and psychological FMEs and funding for student-run clinics. Larger multi-site studies are needed to validate. asylum medicine refugee health forensic medical evaluation student-run clinic Figures Figure 1 Background Increases in the primary drivers of displacement—armed conflict, violence against civilians, attacks on healthcare, and climate-related resource disputes—have caused a global uptick of asylum seekers [ 1 ]. At the end of 2024, there were 8.4 million asylum seekers, doubling since the last decade [ 2 ]. The US is the world's leading recipient of asylum applications, with 3.4 million open cases as of July 2025 [ 3 ]. Asylum is a protection granted to foreign nationals in or arriving to the US who meet the international law definition of a “refugee”. Asylum seekers must undergo a multi-stage, lengthy screening process to prove that they are unable or unwilling to return to their home country because of a “well-founded fear of persecution” on account of their race, religion, nationality, political group, or membership in a social group [ 4 ] [ 5 ]. Asylum seekers face significant challenges in substantiating their claims of trauma and persecution. Previous research highlights the extensive exposure of asylum seekers to violence: asylum seekers report experiencing between 11 and 13 distinct traumatic events [ 6 , 7 ]; human rights violations and traumatic losses are reported by 90% and 83%, respectively [ 6 ]. Despite this high prevalence, experiences of traumatic stress are neither necessary nor sufficient grounds for asylum without documentation. Many asylum seekers rely solely on their personal accounts to support their claims, as they typically flee life-threatening situations in their home countries with minimal supporting documents. A forensic medical evaluation (FME) documents facts relevant to an applicant’s history of torture, ill-treatment, or persecution; conducts a focused physical and psychological assessment to identify trauma-consistent findings; and assesses the consistency between the reported history and examination results [ 8 ]. FMEs are well-documented as a valuable resource for asylum seekers, providing essential evidence to corroborate accounts of trauma and persecution in asylum proceedings [ 9 – 11 ]. In a retrospective analysis between 2000–2004, 89% of cases in which asylum seekers received an evaluation from a clinician resulted in a grant of asylum, compared to the national average of 37.5% over the same period [ 9 ]. In a follow-up study by the same organization, between 2008–2018, 60.2% of their applicants with FMEs were granted asylum compared to the national asylum grant rate of 42.4% [ 10 ]. Beyond demonstrating higher grant rates among asylum applicants who receive an FME, previous work has begun to examine which specific elements documented in FMEs are important for approval. For example, younger age, LGB sexual orientation, being from the African continent, and experiencing sexual violence were factors found to have higher odds of having a positive case outcome. Being South American, experiencing gang violence, and being detained at the time of the evaluation had decreased odds of a positive outcome [ 10 ]. Building on this literature, our study moves beyond the binary presence of an FME to disaggregate and test item-level factors recorded in affidavits. We expand the set of examined variables to include demographic, trauma-related, and medical domains to assess which components of FMEs carry the strongest association with asylum approval. Given their demonstrated importance, FMEs for asylum cases in the US have gained prominence and recognition in the late 20th and early 21st centuries. In 1989, Physicians for Human Rights (PHR) launched an Asylum Network to connect lawyers and asylum seekers with medical professionals who could provide evaluations. The Istanbul Protocol was created in 1999 as a guideline for these evaluations and updated in 2022 [ 12 ]. Recognizing the importance and increased need for FMEs, medical schools across the US have supported the development of student-led human rights medical clinics that serve asylum seekers. At least 19 such clinics exist, having completed more than 1,600 forensic evaluations together [ 8 , 13 ]. Our medical school’s asylum clinic was founded in 2010 to expand access to pro bono forensic evaluations. Our clinic is a member of PHR’s Asylum Network and receives cases through PHR as well as our law school’s Immigrants’ Rights Clinic and various private law offices. As a student-run free clinic, our clinic faces the same challenges documented by other clinics, including clinician and volunteer shortages, non-standardized documentation needs, and lack of funds and manpower [ 14 , 15 ]. While previous studies have looked at challenges, caseloads, and student perspectives while serving asylum seekers, there are currently no data available on case outcomes from a student-run asylum clinic. The purpose of this study is to report case outcomes and data from a student-run asylum clinic, comparing it with available data from the national PHR Asylum Network, and to add to previous literature on which demographic, trauma-related, and medical factors impact asylum approval. Methods We conducted a retrospective study of FMEs performed by a single, student-run asylum clinic from 2014 to 2024. This protocol was approved by our school’s Institutional Review Board (IRB-AAAS0586) and conducted in accordance with the Declaration of Helsinki, as revised in 2024 [ 16 ]. Study data were captured and managed using REDCap (Research Electronic Data Capture) tool hosted at our institution. Across the study period, 289 FMEs were completed; of these, 154 cases were included for our outcome analyses. Cases were eligible if: (1) the evaluator’s affidavit was completed and submitted with the applicant’s petition for asylum; and (2) the outcome of the case was known. Case outcomes were gleaned from legal records and categorized as asylum granted, denied, dismissed, administratively closed, or other action (Fig. 1 ). Out of 289 completed FMEs from 2014 to 2024, 135 cases did not meet the inclusion criteria. 7 cases were excluded due to the affidavits not yet being ready. 128 cases were excluded for having unknown or pending case outcomes. Demographic, trauma-related, and medical data were extracted from evaluator-completed REDCap forms at the time of the FME. Referring attorneys were advised to notify the clinic of outcomes for record-keeping. However, because proceedings can last years and staff turnover/hand-offs are common in legal offices, some outcomes were not reported. For cases with unknown outcomes that had a reported Alien Number, publicly available records in the Executive Office for Immigration Review Automated Case Information System (EOIR ACIS; https://acis.eoir.justice.gov/en/ ) were queried to confirm dispositions. Researchers conducted follow-up emails with referring counsel for any outcomes that remained unknown; non-responses were classified as undetermined and not included in analyses. Measures The outcome variable was asylum status, coded 1 = granted and 0 = not granted (denial, dismissal, administrative closure, other forms of relief). Twenty-nine binary predictors—10 demographic, 10 trauma-related, and 9 medical—were included. Demographic variables were: age < 18; cisgender man; LGBTQ + identity; White race; interpreter use; detention status; marital status; living children; direct family in the US; and reported income. Trauma exposures were: gang, political, police, ethnic, domestic, and sexual violence; genital mutilation; kidnapping; trafficking; and torture. Medical findings included: diagnosis of any psychiatric condition (including major depressive disorder (MDD), post-traumatic stress disorder (PTSD), generalized anxiety disorder, persistent depressive disorder, acute stress disorder, and others); diagnosis of MDD; diagnosis of PTSD; visible scars; lacerations; burns; gait abnormality; neurological impairment; and physical evidence of female genital mutilation. Three additional binary indicators captured whether a psychological, physical, and/or gynecological FME was performed. Candidate variables were included if (1) they were collected when the clinic’s research registry was established and (2) they could be meaningfully binarized. Demographic factors with multiple categories were dichotomized only when a well-established advantage or disadvantage existed. For example, race was included because it has been well documented that being of the White race offers privileges in the US[ 17 , 18 ]. Race was therefore coded as White = 1 and all other races = 0. Gender was binarized in the same manner, with cisgender men = 1 and all other genders = 0. Clients’ country of origin was excluded due to its high cardinality and lack of a clear binary split. Traumatic and medical factors were coded as 1 if affirmed and 0 if denied by the client, or not queried by the clinician. All factors could be asked and determined in both physical and psychological evaluations, but it was up to clinicians’ discretion to omit questions deemed irrelevant. Analysis All analyses were conducted in R (R version 4.2.2, http://www.r-project.org ). Descriptive statistics were used to summarize the data. We compared each binary predictor (present vs absent) with the primary outcome (asylum granted vs not granted) using two-sided Fisher’s exact tests (α = 0.05). All gynecological evaluations also received physical evaluations, so completion of a gynecological evaluation was not included as a separate variable in the logistical analysis. We ran univariate regression, including all 29 factors and 2 evaluation type variables. In multivariable regression, the medical factor evidence of female genital mutilation was dropped because of a high variance inflation factor (VIF) with the traumatic experience of genital mutilation. The variables for completion of psychological and physical evaluation were also excluded due to their redundancy and high multicollinearity. Results were compared to corresponding PHR data. Results Case Characteristics and Outcomes In total, 154 case outcomes were known. 88 (57.1%) of these cases were granted asylum. Of the cases that were not granted asylum, 13 (8.4%) were denied asylum, 25 (16.2%) were dismissed, 4 (2.6%) were administratively closed, and 24 (15.6%) were closed due to other reasons (including withholding of removal and voluntary departure). 136 (88.3%) cases received psychological evaluations, 72 (46.8%) cases received physical evaluations, and 4 (2.6%) received gynecological evaluations. 69.4% of cases with physical evaluation received a grant of asylum, compared to 46.3% without physical evaluation, while 58.8% of cases with psychological evaluation had a positive outcome, compared to 44.4% without psychological evaluation. Full counts are found in Table 1 . Table 1 Counts of predictors present by asylum outcome and Fisher’s exact test p-values. Total Asylum Granted Asylum not Granted Variable n % n % n % p-val Physical Evaluation 72 46.8 50 69.4 22 30.6 0.005*** Psychological Evaluation 136 88.3 80 58.8 56 41.2 0.313 Gynecological Evaluation 4 2.6 3 75.0 1 25.0 0.64 Demographic Income 52 33.8 39 75.0 13 25.0 0.002** Has Children 73 47.4 48 65.8 25 34.2 0.05* Married 48 31.2 33 68.8 15 31.3 0.06 White Race 23 14.9 10 43.5 13 56.5 0.175 Interpreter Use 111 72.1 61 55.0 50 45.0 0.47 Cisgender Man 83 53.9 50 60.2 33 39.8 0.42 Family in US 76 49.4 41 53.9 35 46.1 0.52 Under 18 24 15.6 12 50.0 12 50.0 0.5 LGBTQ 22 14.3 12 54.5 10 45.5 0.82 Detained 46 29.9 27 58.7 19 41.3 0.86 Trauma Experience Kidnapping 26 16.9 24 92.3 2 7.7 0.00005*** Torture 37 24.0 29 78.4 8 21.6 0.004** Ethnic Violence 26 16.9 19 73.1 7 26.9 0.08 Sexual Violence 57 37.0 37 64.9 20 35.1 0.18 Police Violence 37 24.0 25 67.6 12 32.4 0.18 Genital Mutilation 7 4.5 6 85.7 1 14.3 0.24 Trafficked 5 3.2 4 80.0 1 20.0 0.39 Political Violence 43 27.9 27 62.8 16 37.2 0.47 Gang Violence 57 37.0 32 56.1 25 43.9 0.867 Domestic Violence 65 42.2 37 56.9 28 43.1 1 Medical Lacerations 37 24.0 33 89.2 4 10.8 0.000003*** MDD 106 68.8 72 67.9 34 32.1 0.00009*** Scars 68 44.2 50 73.5 18 26.5 0.0003*** PTSD 126 81.8 81 64.3 45 35.7 0.0002*** Burns 19 12.3 17 89.5 2 10.5 0.002** Psychiatric Diagnosis 129 83.8 79 61.2 50 38.8 0.03* Gait Abnormalities 16 10.4 5 31.3 11 68.8 0.03* Evidence of FGM 5 3.2 4 80.0 1 20.0 0.39 Neurological Impairments 15 9.7 9 60.0 6 40.0 1 For each binary predictor (present = 1 vs absent = 0), we report the number of cases granted and not granted asylum, with two-sided p-values from Fisher’s exact test (*p < 0.05, **p < 0.01, ***p < 0.001). Correlates of Asylum Approval In univariate analysis, having lacerations, diagnosis of MDD, visible scars, diagnosis of PTSD, being kidnapped, having a physical evaluation completed, having an income, having a psychological evaluation completed, being tortured, having burns, having any psychological diagnosis, and having children were associated with significantly increased odds of being granted asylum. In addition, being married approached significance. Of the factors with increased odds, 6 were medical, 2 were trauma, and 2 were demographic factors. Gait abnormalities showed decreased odds of asylum approval (Table 2 ). In multivariable analysis, being kidnapped, having lacerations, and having an income remained associated with significantly increased odds of asylum approval. Experiencing genital mutilation was associated with extremely high odds of asylum approval, which was not observed in univariate analysis. Experiencing ethnic violence neared significant levels. Gait abnormality remained associated with decreased odds (Table 3 ). Table 2 Univariate logistic regression for correlates of asylum approval. Odds ratios (OR) and 95% confidence intervals from separate logistic regressions of asylum approval on each binary predictor (present = 1 vs absent = 0). Variables are organized under demographic, trauma experience, and medical, as well as 2 evaluation type variables. Asterisks denote statistical significant as reported in the sheet (*p < 0.05, **p < 0.01, ***p < 0.001). Variable OR Lower CI Upper CI p-val df SE Physical Evaluation 3.11 1.59 6.23 0.001** 152 0.35 Psychological Evaluation 4.34 1.75 11.86 0.002** 152 0.48 Demographic Income 3.24 1.58 6.99 0.002** 152 0.38 Has Children 1.97 1.03 3.81 0.04* 152 0.33 Married 2.04 1.01 4.27 0.05 152 0.37 White Race 0.52 0.21 1.27 0.16 152 0.46 Interpreter Use 0.72 0.35 1.48 0.38 152 0.37 Cisgender Man 1.32 0.69 2.51 0.40 152 0.33 Family in US 0.77 0.41 1.46 0.43 152 0.33 Under 18 0.71 0.29 1.71 0.44 152 0.45 LGBTQ 0.88 0.36 2.23 0.79 152 0.46 Detained 1.09 0.55 2.22 0.80 152 0.36 Trauma Experience Kidnapping 12.00 3.37 76.63 0.001** 152 0.76 Torture 3.56 1.56 8.95 0.004** 152 0.44 Ethnic Violence 2.32 0.95 6.29 0.08 152 0.48 Sexual Violence 1.67 0.86 3.31 0.14 152 0.34 Police Violence 1.79 0.83 4.00 0.14 152 0.40 Genital Mutilation 4.76 0.79 91.01 0.15 152 1.09 Trafficked 3.10 0.44 61.33 0.32 152 1.13 Political Violence 1.38 0.68 2.89 0.38 152 0.37 Gang Violence 0.94 0.48 1.82 0.85 152 0.34 Domestic Violence 0.98 0.52 1.88 0.96 152 0.33 Medical Lacerations 9.30 3.43 32.66 7.01e-05*** 152 0.56 MDD 4.24 2.08 8.93 9.66e-05*** 152 0.37 Scars 3.51 1.79 7.10 0.0003*** 152 0.35 PTSD 5.40 2.22 14.62 0.0003*** 152 0.47 Burns 7.66 2.09 49.52 0.008** 152 0.77 Psychiatric Diagnosis 2.81 1.17 7.10 0.02* 152 0.45 Gait Abnormalities 0.30 0.09 0.88 0.03* 152 0.57 Evidence of FGM 3.10 0.44 61.33 0.32 152 1.13 Neurological Impairments 1.14 0.39 3.56 0.81 152 0.55 Table 3 Multivariable logistic regression of correlates for asylum approval. Variable aOR Lower CI Upper CI p-val df SE Demographic Income 3.22 1.05 10.75 0.05* 125 0.59 Has Children 1.09 0.26 4.49 0.90 125 0.72 Married 1.18 0.30 4.88 0.81 125 0.70 White Race 0.67 0.15 2.81 0.59 125 0.74 Interpreter Use 0.60 0.19 1.74 0.35 125 0.55 Cisgender Man 0.94 0.24 3.65 0.93 125 0.69 Family in US 0.76 0.25 2.22 0.61 125 0.55 Under 18 1.92 0.43 8.93 0.39 125 0.76 LGBTQ 0.92 0.18 4.38 0.92 125 0.80 Detained 0.54 0.18 1.56 0.26 125 0.54 Trauma Experience Kidnapping 18.57 3.19 188.50 0.004** 125 1.01 Torture 1.54 0.35 6.80 0.56 125 0.74 Ethnic Violence 4.32 1.01 21.87 0.06 125 0.78 Sexual Violence 1.21 0.33 4.53 0.77 125 0.66 Police Violence 0.53 0.09 2.82 0.46 125 0.86 Genital Mutilation 107.03 2.32 12428.81 0.03* 125 2.17 Trafficked 0.12 0.01 3.45 0.15 125 1.47 Political Violence 0.65 0.12 3.45 0.61 125 0.85 Gang Violence 0.81 0.28 2.34 0.70 125 0.54 Domestic Violence 0.42 0.12 1.34 0.15 125 0.61 Medical Lacerations 8.80 1.49 70.44 0.02* 125 0.97 MDD 2.21 0.74 7.05 0.16 125 0.57 Scars 1.15 0.28 4.79 0.85 125 0.72 PTSD 6.60 0.85 151.52 0.12 125 1.22 Burns 1.87 0.22 21.30 0.58 125 1.14 Psychiatric Diagnosis 1.70 0.07 21.14 0.70 125 1.36 Gait Abnormalities 0.10 0.01 0.63 0.03* 125 1.05 Neurological Impairments 2.09 0.28 18.19 0.48 125 1.05 Adjusted odds ratios (aOR) and 95% confidence intervals from separate logistic regressions of asylum approval on each binary predictor (present = 1 vs absent = 0). Variables are organized under demographic, trauma experience, and medical categories. Evaluation-type variables and physical evidence of female genital mutilation were removed. Asterisks denote statistical significant as reported in the sheet (*p < 0.05, **p < 0.01, ***p < 0.001). Discussion The purpose of our study was to report grant rates and determine correlates of asylum approval for cases undergoing physical and/or psychological FMEs at a student-run asylum clinic. For 154 asylum cases at CHRIA (2014–2024), an overall asylum grant rate of 57.1% was found. The US national average was 39.3% during the same period[ 19 , 20 ]. The difference between asylum approval at our clinic and the national average was 17.8%, exactly equal to what PHR found for their cases versus the national average between 2008 and 2018 (60.2% vs. 42.4%) 1 . 17.8% demonstrates a decrease from the difference originally published by PHR on data from 2000–2004 of 89% compared to the national average of 37.5%. Several factors likely explain why the margin is smaller today. First, the “novelty effect” of FMEs has diminished as affidavits have become more common, narrowing the comparative advantage they once conferred. Additionally, the study period of the first study (2000–2004) had a lower national benchmark, with the post-2001 period (2002–2004) having especially low grant rates [ 21 ]. Despite the decrease in the difference with the national comparison rate from the early 2000s, our findings reinforce the enduring value of FMEs and demonstrate that a student-run clinic can achieve outcomes comparable to a leading national network, underscoring the role of academic, pro bono programs in enhancing resources for asylum seekers. Both having a physical FME (OR = 3.1, p = 0.001) and psychological FME (OR = 4.3, p = 0.002) showed increased odds of asylum approval. This was also found by PHR from 2008 to 2018, that physical (OR = 7.04, p = 0.04) and psychological (OR = 4.91, p = 0.09) evaluations were both associated with positive outcomes [ 10 ]. Our odds ratios were likely lower due to a smaller sample size. These findings indicate that both physical and psychological FMEs are essential, complementary components of evidence in asylum adjudication. Physical FMEs provide objective, injury-consistent documentation, while psychological FMEs can contextualize symptoms and demeanor as a sequela of trauma rather than credibility deficits. Clinics and referral networks should prioritize capacity for both modalities to strengthen outcomes. In this cohort, the univariate model served to screen a broad set of factors to identify signals worth testing in an adjusted framework. The significant factors clustered in the medical domain (lacerations, burns, scars, MDD, PTSD, any psychiatric diagnosis), alongside two trauma exposures (kidnapping and torture) and two demographic factors (having an income and having children). The high amount of significant medical factors underscores that FMEs add value primarily by translating trauma histories into clinically documented physical and psychological findings. Among trauma variables, the increased odds of asylum approval after experiencing torture align with the legal frameworks that afford heightened protections when torture is credibly established [ 22 ][ 23 ]. For demographics, income may serve as a proxy for integration potential often discussed in adjudicative reasoning, while having children may cue humanitarian considerations [ 24 , 25 ]. In the multivariable model, kidnapping, lacerations, and income continued to hold significantly increased odds of asylum approval, while gait abnormality remained inversely associated. The latter may reflect nonspecificity (many non-persecution etiologies), sparse counts, and measurement limits, making it less probative than discrete lesion-level findings. Genital mutilation showed a high association with asylum approval; the adjusted effect is large but imprecise with a wide confidence interval due to the low number of cases. Several univariate factors lost statistical significance after adjustment, an expected consequence of controlling for confounding, multicollinearity, and mediating pathways (e.g., torture → PTSD/MDD → adjudicator perceptions). Overall, these patterns suggest that the medical component of FMEs—both physical and psychological—carries the greatest independent evidentiary weight, while certain trauma experiences and demographics provide complementary context. This pattern aligns with prior reports that injury-mechanism-timeline consistency drives adjudicator confidence [ 26 , 27 ]. In comparing our multivariable results with those from PHR’s analysis, we note four factors assessed in both models: detention at time of evaluation, gang violence, sexual violence, and LGB identity. PHR reported that detention status (OR = 0.48, p = 0.002) and gang violence (OR = 0.54, p = 0.001) were both less likely to be granted a positive case outcome compared to a negative case outcome. Sexual violence (OR = 1.80, p = 0.003) and LGB identity (OR = 2.11, p = 0.005) were more likely to be granted a positive case outcome compared to a negative case outcome. While none of these four factors reached statistical significance in our dataset, detention status, gang violence, and sexual violence all trended in the same direction as PHR, with odds ratios of 0.54 (vs 0.48), 0.81 (vs 0.54), and 1.21 (vs 1.80), respectively. Genital mutilation, conceptually related to sexual violence, was significantly associated with asylum approval. LGBTQ identity had a high, non-significant p-value in our analysis (p = 0.92). Several methodological differences likely account for our lack of significance. PHR categorized case outcomes into positive, negative, and other, and ran two comparisons: positive versus negative and positive versus other. Our analysis utilized a narrower endpoint, categorizing outcomes into asylum granted versus all other outcomes. Our narrower positive endpoint and smaller sample size possibly led to smaller effects and lower power. The directional concordance across shared predictors with PHR is reassuring and suggests underlying consistency despite our attenuated effects. Asylum literature supports a mechanism in which FMEs translate trauma histories into forensically legible medical and psychological evidence that courts can weigh against legal standards (credibility, consistency, severity) [ 9 , 10 ]. Physical FMEs appear especially persuasive when injury patterns are medically consistent with the alleged mechanism and timeline [ 28 , 29 ]. Psychological FMEs are likewise critical when they explicitly link trauma to current symptom patterns (including memory disturbances that might be otherwise misread as credibility gaps) [ 30 ]. Translating these findings into tangible practice guidelines, we propose that clinics should expand capacity for both physical and psychological FMEs and implement a standardized workflow with checklists for high-yield findings (lacerations, burns, torture sequelae). When resources are limited, a triage system that reserves scarce physical-exam slots for applicants with plausible visible injuries or kidnapping and torture histories, while guaranteeing universal access to psychological FMEs that contextualize memory and affect. Student-run clinics are critical access points that expand FME capacity while training the next generation of clinicians. We advocate for institutions, funders, and policymakers to commit sustained support towards expanding and stabilizing student-run asylum clinics. Limitations Outcomes were only known for 53.3% of completed cases with potentially non-random missing data. Cases with unknown outcomes may reflect particularly delayed adjudication or lack of communication from lawyers, neither of which would necessarily occur randomly, introducing selection bias if loss to follow-up correlated with lowered grant probability. Some predictors had a small observation size, yielding imprecise or extreme odds ratios. Our model lacked some covariates, which have been previously mentioned to be associated with asylum outcomes in the literature, including the type of legal representation and country of origin, which could have residual confounding [ 21 ]. Finally, our findings were collected from a single student-run clinic, which may limit generalizability. Declarations This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We disclose no financial or non-financial interests that are directly or indirectly related to the work submitted for publication. New Contribution to the Literature Prior work on student-run asylum clinics has focused on program management, caseloads, and medical-student perspectives, but has not reported case outcomes. This study presents asylum outcomes from a decade of cases at a student-run asylum clinic (2014–2024) and benchmarks those outcomes against national data from the PHR Asylum Network. We extend the literature by estimating univariate and multivariable associations between asylum grants and a comprehensive set of demographic, trauma-related, and medical factors derived from FME affidavits. Methodologically, we introduce a registry-based, binary-coded analytic framework that can be replicated by other clinics. Footnotes 1-Note: Atkinson et al. 2021 reported all positive outcomes of asylum cases, including asylum approval, withholding of removal, convention against torture, etc., at 81.6%. However, to compare directly with our statistics, we calculated their rate of solely asylum approval from within their positive outcomes measure. Author Contribution S.X. wrote the main manuscript text and prepared tables 1-3. C.K. prepared figure 1. All authors reviewed the manuscript. Acknowledgement The authors thank Andrew Shin, Anusha Mudigonda, Carolin Bao, Grace Xu, Jacques Calixte, Kathryn Tian, Ken Kaplan, Maxine Mackie, Minerva Nong, Nobel Zhou, Ramzi Elased, Rishi Dasgupta, Shariq Jumanji, Suuba Demby, Vanessa Anderson, Zaid Bustami, and all other current and past members of the Columbia Human Rights Initiative Asylum Clinic (CHRIA). In addition, we sincerely thank the many CHRIA clinicians who volunteered to conduct these forensic medical evaluations. Data Availability The data that support the findings of this study are available from Columbia Human Rights Initiative Asylum Clinic (CHRIA), but restrictions apply to the availability of these data, which were used under licence for the current study and so are not publicly available. The data are, however, available from the authors upon reasonable request and with the permission of Columbia Human Rights Initiative Asylum Clinic (CHRIA). References Council DR. 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Manual on the Effective Investigation and Documentation of Torture and Other Cruel, Inhuman or Degrading Treatment or Punishment (2022 edition) [Internet]. OHCHR. [cited 2025 Sep 4]. Available from: https://www.ohchr.org/en/publications/policy-and-methodological-publications/istanbul-protocol-manual-effective-0 Sharp MB, Milewski AR, Lamneck C, McKenzie K. Evaluating the impact of student-run asylum clinics in the US from 2016–2018. Health Hum Rights. 2019;21:309–23. Gu F, Chu E, Milewski A, Taleghani S, Maju M, Kuhn R, et al. Challenges in founding and developing medical school student-run asylum clinics. J Immigr Minor Health. 2021;23:179–83. Milewski AR, Ackerman KS, Pilato TC, Shah PD, Kalman TP. Challenges for students in the creation, growth, and management of an academic, student-run asylum clinic. J Hum Rights Pract. 2021;13:456–70. WMA Declaration of Helsinki – Ethical Principles for Medical Research. Involving Human Participants [Internet]. [cited 2025 Aug 28]. Available from: https://www.wma.net/policies-post/wma-declaration-of-helsinki/ Bell D. White superiority in America: Its legal legacy, its economic costs. Villanova law Rev. 1988;33:767. Jones CP, Truman B, Elam-Evans LD, Jones CA, Jones C, Jiles R, et al. Using socially assigned race to probe white advantages in health status. Ethn Dis. 2008;18:496–504. Asylum Process in Immigration Courts and Selected Trends [Internet]. Available from: https://www.congress.gov/crs-product/R47504 Asylum Grant Rates. Decline by a Third [Internet]. [cited 2025 Aug 22]. Available from: https://tracreports.org/reports/751/ Rottman AJ, Fariss CJ, Poe SC. The path to asylum in the US and the determinants for who gets in and why. Int Migr Rev. 2009;43:3–34. Convention against Torture and Other Cruel. Inhuman or Degrading Treatment or Punishment [Internet]. OHCHR. [cited 2025 Aug 29]. Available from: https://www.ohchr.org/en/instruments-mechanisms/instruments/convention-against-torture-and-other-cruel-inhuman-or-degrading 8 CFR 208.16. -- Withholding of removal under section 241(b)(3)(B) of the Act and withholding of removal under the Convention Against Torture [Internet]. [cited 2025 Aug 29]. Available from: https://www.ecfr.gov/current/title-8/chapter-I/subchapter-B/part-208/subpart-A/section-208.16 Guidelines on International Protection No. 8: Child Asylum Claims under Articles 1(A)2 and 1(F) of the 1951 Convention and/or 1967 Protocol relating to the Status of Refugees, 22 December 2009 [Internet]. UNHCR US. UNHCR US -; [cited 2025 Aug 29]. Available from: https://www.unhcr.org/us/media/guidelines-international-protection-no-8-child-asylum-claims-under-articles-1-2-and-1-f-1951 ORR Unaccompanied Children Bureau Policy Guide. Section 6 [Internet]. [cited 2025 Aug 29]. Available from: https://acf.gov/orr/policy-guidance/unaccompanied-children-program-policy-guide-section-6 Plesons M, Hullfish H, Joshi P, Symes S, Saxena A. A decade’s experience performing forensic medical and psychologic evaluations for pediatric asylum seekers in the United States [Internet]. bioRxiv. 2023. Available from: http://dx.doi.org/10.1101/2023.09.10.23295337 Keten A, Nicolakis J, Abacı R, Lale A. An evaluation within the context of the Istanbul Protocol of the medico-legal examinations of Turkish detainees during the recent state of emergency in Turkey. J Law Med. 2022;29:254–9. Revital Arbel MD, Benninga Z. The Istanbul Protocol (Manual on the Effective Investigation and Documentation of Torture and Other Cruel, Inhuman or Degrading Treatment or Punishment): Implementation and Education in Israel. Isr Med Assoc J [Internet]. 2001;16. Available from: https://www.academia.edu/download/78737967/38020.pdf Iacopino V, Haar RJ, Heisler M, Lin J, Fincancı ŞK, Esdaile C, et al. Istanbul Protocol 2022 empowers health professionals to end torture. Lancet. 2022;400:143–5. Green AS, Ruchman SG, Birhanu B, Wu S, Katz CL, Singer EK, et al. Immigration judges’ perceptionsof telephonic and in-person forensic mental health evaluations. J Am Acad Psychiatry Law. 2022;50:240–51. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Feb, 2026 Read the published version in Journal of Immigrant and Minority Health → Version 1 posted Editorial decision: Revision requested 12 Dec, 2025 Reviews received at journal 25 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 02 Nov, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviewers invited by journal 13 Oct, 2025 Editor assigned by journal 20 Sep, 2025 Submission checks completed at journal 20 Sep, 2025 First submitted to journal 16 Sep, 2025 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. 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11:40:27","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129526,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7634084/v1/0978bf379b42fef0636c8547.html"},{"id":92802095,"identity":"eed523c9-0fa5-4008-8a66-e90b0ab63ca9","added_by":"auto","created_at":"2025-10-05 11:40:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56462,"visible":true,"origin":"","legend":"\u003cp\u003eCase selection and categorization.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7634084/v1/fb0e2048f794cffee03e521d.png"},{"id":102234435,"identity":"52a7a5ff-3fb9-4a71-8b64-c1cbe233d6de","added_by":"auto","created_at":"2026-02-09 16:11:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1107610,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7634084/v1/ec7a7fab-518f-41ad-b296-4171171ac50a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictors of Asylum Approval: Insights from a Decade of Forensic Medical Evaluations at a Student-Run Clinic","fulltext":[{"header":"Background","content":"\u003cp\u003eIncreases in the primary drivers of displacement\u0026mdash;armed conflict, violence against civilians, attacks on healthcare, and climate-related resource disputes\u0026mdash;have caused a global uptick of asylum seekers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. At the end of 2024, there were 8.4\u0026nbsp;million asylum seekers, doubling since the last decade [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The US is the world's leading recipient of asylum applications, with 3.4\u0026nbsp;million open cases as of July 2025 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Asylum is a protection granted to foreign nationals in or arriving to the US who meet the international law definition of a \u0026ldquo;refugee\u0026rdquo;. Asylum seekers must undergo a multi-stage, lengthy screening process to prove that they are unable or unwilling to return to their home country because of a \u0026ldquo;well-founded fear of persecution\u0026rdquo; on account of their race, religion, nationality, political group, or membership in a social group [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAsylum seekers face significant challenges in substantiating their claims of trauma and persecution. Previous research highlights the extensive exposure of asylum seekers to violence: asylum seekers report experiencing between 11 and 13 distinct traumatic events [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]; human rights violations and traumatic losses are reported by 90% and 83%, respectively [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite this high prevalence, experiences of traumatic stress are neither necessary nor sufficient grounds for asylum without documentation. Many asylum seekers rely solely on their personal accounts to support their claims, as they typically flee life-threatening situations in their home countries with minimal supporting documents.\u003c/p\u003e\u003cp\u003eA forensic medical evaluation (FME) documents facts relevant to an applicant\u0026rsquo;s history of torture, ill-treatment, or persecution; conducts a focused physical and psychological assessment to identify trauma-consistent findings; and assesses the consistency between the reported history and examination results [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. FMEs are well-documented as a valuable resource for asylum seekers, providing essential evidence to corroborate accounts of trauma and persecution in asylum proceedings [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In a retrospective analysis between 2000\u0026ndash;2004, 89% of cases in which asylum seekers received an evaluation from a clinician resulted in a grant of asylum, compared to the national average of 37.5% over the same period [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In a follow-up study by the same organization, between 2008\u0026ndash;2018, 60.2% of their applicants with FMEs were granted asylum compared to the national asylum grant rate of 42.4% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBeyond demonstrating higher grant rates among asylum applicants who receive an FME, previous work has begun to examine which specific elements documented in FMEs are important for approval. For example, younger age, LGB sexual orientation, being from the African continent, and experiencing sexual violence were factors found to have higher odds of having a positive case outcome. Being South American, experiencing gang violence, and being detained at the time of the evaluation had decreased odds of a positive outcome [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Building on this literature, our study moves beyond the binary presence of an FME to disaggregate and test item-level factors recorded in affidavits. We expand the set of examined variables to include demographic, trauma-related, and medical domains to assess which components of FMEs carry the strongest association with asylum approval.\u003c/p\u003e\u003cp\u003eGiven their demonstrated importance, FMEs for asylum cases in the US have gained prominence and recognition in the late 20th and early 21st centuries. In 1989, Physicians for Human Rights (PHR) launched an Asylum Network to connect lawyers and asylum seekers with medical professionals who could provide evaluations. The Istanbul Protocol was created in 1999 as a guideline for these evaluations and updated in 2022 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recognizing the importance and increased need for FMEs, medical schools across the US have supported the development of student-led human rights medical clinics that serve asylum seekers. At least 19 such clinics exist, having completed more than 1,600 forensic evaluations together [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Our medical school\u0026rsquo;s asylum clinic was founded in 2010 to expand access to \u003cem\u003epro bono\u003c/em\u003e forensic evaluations. Our clinic is a member of PHR\u0026rsquo;s Asylum Network and receives cases through PHR as well as our law school\u0026rsquo;s Immigrants\u0026rsquo; Rights Clinic and various private law offices. As a student-run free clinic, our clinic faces the same challenges documented by other clinics, including clinician and volunteer shortages, non-standardized documentation needs, and lack of funds and manpower [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile previous studies have looked at challenges, caseloads, and student perspectives while serving asylum seekers, there are currently no data available on case outcomes from a student-run asylum clinic. The purpose of this study is to report case outcomes and data from a student-run asylum clinic, comparing it with available data from the national PHR Asylum Network, and to add to previous literature on which demographic, trauma-related, and medical factors impact asylum approval.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe conducted a retrospective study of FMEs performed by a single, student-run asylum clinic from 2014 to 2024. This protocol was approved by our school\u0026rsquo;s Institutional Review Board (IRB-AAAS0586) and conducted in accordance with the Declaration of Helsinki, as revised in 2024 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Study data were captured and managed using REDCap (Research Electronic Data Capture) tool hosted at our institution. Across the study period, 289 FMEs were completed; of these, 154 cases were included for our outcome analyses. Cases were eligible if: (1) the evaluator\u0026rsquo;s affidavit was completed and submitted with the applicant\u0026rsquo;s petition for asylum; and (2) the outcome of the case was known. Case outcomes were gleaned from legal records and categorized as asylum granted, denied, dismissed, administratively closed, or other action (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOut of 289 completed FMEs from 2014 to 2024, 135 cases did not meet the inclusion criteria. 7 cases were excluded due to the affidavits not yet being ready. 128 cases were excluded for having unknown or pending case outcomes.\u003c/p\u003e\u003cp\u003eDemographic, trauma-related, and medical data were extracted from evaluator-completed REDCap forms at the time of the FME. Referring attorneys were advised to notify the clinic of outcomes for record-keeping. However, because proceedings can last years and staff turnover/hand-offs are common in legal offices, some outcomes were not reported. For cases with unknown outcomes that had a reported Alien Number, publicly available records in the Executive Office for Immigration Review Automated Case Information System (EOIR ACIS; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://acis.eoir.justice.gov/en/\u003c/span\u003e\u003cspan address=\"https://acis.eoir.justice.gov/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e were queried to confirm dispositions. Researchers conducted follow-up emails with referring counsel for any outcomes that remained unknown; non-responses were classified as undetermined and not included in analyses.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMeasures\u003c/h2\u003e\u003cp\u003eThe outcome variable was asylum status, coded 1\u0026thinsp;=\u0026thinsp;granted and 0\u0026thinsp;=\u0026thinsp;not granted (denial, dismissal, administrative closure, other forms of relief). Twenty-nine binary predictors\u0026mdash;10 demographic, 10 trauma-related, and 9 medical\u0026mdash;were included. Demographic variables were: age\u0026thinsp;\u0026lt;\u0026thinsp;18; cisgender man; LGBTQ\u0026thinsp;+\u0026thinsp;identity; White race; interpreter use; detention status; marital status; living children; direct family in the US; and reported income. Trauma exposures were: gang, political, police, ethnic, domestic, and sexual violence; genital mutilation; kidnapping; trafficking; and torture. Medical findings included: diagnosis of any psychiatric condition (including major depressive disorder (MDD), post-traumatic stress disorder (PTSD), generalized anxiety disorder, persistent depressive disorder, acute stress disorder, and others); diagnosis of MDD; diagnosis of PTSD; visible scars; lacerations; burns; gait abnormality; neurological impairment; and physical evidence of female genital mutilation. Three additional binary indicators captured whether a psychological, physical, and/or gynecological FME was performed. Candidate variables were included if (1) they were collected when the clinic\u0026rsquo;s research registry was established and (2) they could be meaningfully binarized. Demographic factors with multiple categories were dichotomized only when a well-established advantage or disadvantage existed. For example, race was included because it has been well documented that being of the White race offers privileges in the US[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Race was therefore coded as White\u0026thinsp;=\u0026thinsp;1 and all other races\u0026thinsp;=\u0026thinsp;0. Gender was binarized in the same manner, with cisgender men\u0026thinsp;=\u0026thinsp;1 and all other genders\u0026thinsp;=\u0026thinsp;0. Clients\u0026rsquo; country of origin was excluded due to its high cardinality and lack of a clear binary split. Traumatic and medical factors were coded as 1 if affirmed and 0 if denied by the client, or not queried by the clinician. All factors could be asked and determined in both physical and psychological evaluations, but it was up to clinicians\u0026rsquo; discretion to omit questions deemed irrelevant.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAnalysis\u003c/h3\u003e\n\u003cp\u003eAll analyses were conducted in R (R version 4.2.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Descriptive statistics were used to summarize the data. We compared each binary predictor (present vs absent) with the primary outcome (asylum granted vs not granted) using two-sided Fisher\u0026rsquo;s exact tests (α\u0026thinsp;=\u0026thinsp;0.05). All gynecological evaluations also received physical evaluations, so completion of a gynecological evaluation was not included as a separate variable in the logistical analysis. We ran univariate regression, including all 29 factors and 2 evaluation type variables. In multivariable regression, the medical factor evidence of female genital mutilation was dropped because of a high variance inflation factor (VIF) with the traumatic experience of genital mutilation. The variables for completion of psychological and physical evaluation were also excluded due to their redundancy and high multicollinearity. Results were compared to corresponding PHR data.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eCase Characteristics and Outcomes\u003c/h2\u003e\u003cp\u003eIn total, 154 case outcomes were known. 88 (57.1%) of these cases were granted asylum. Of the cases that were not granted asylum, 13 (8.4%) were denied asylum, 25 (16.2%) were dismissed, 4 (2.6%) were administratively closed, and 24 (15.6%) were closed due to other reasons (including withholding of removal and voluntary departure). 136 (88.3%) cases received psychological evaluations, 72 (46.8%) cases received physical evaluations, and 4 (2.6%) received gynecological evaluations. 69.4% of cases with physical evaluation received a grant of asylum, compared to 46.3% without physical evaluation, while 58.8% of cases with psychological evaluation had a positive outcome, compared to 44.4% without psychological evaluation. Full counts are found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCounts of predictors present by asylum outcome and Fisher\u0026rsquo;s exact test p-values.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eAsylum Granted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eAsylum not Granted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep-val\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical Evaluation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e69.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e30.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.005***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychological Evaluation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e41.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGynecological Evaluation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDemographic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.002**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHas Children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e34.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.05*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e68.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e31.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e56.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterpreter Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e45.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCisgender Man\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e39.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily in US\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e53.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e46.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnder 18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGBTQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e54.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e45.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDetained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e41.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTrauma Experience\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidnapping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e92.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00005***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTorture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e78.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e21.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.004**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnic Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e73.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e26.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSexual Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e64.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e35.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolice Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e32.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenital Mutilation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e85.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrafficked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e62.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e37.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGang Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e43.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDomestic Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e43.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedical\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLacerations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000003***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e32.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00009***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e73.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e26.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0003***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePTSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e64.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e35.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0002***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBurns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.002**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychiatric Diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e83.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e61.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e38.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.03*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGait Abnormalities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e68.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.03*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvidence of FGM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeurological Impairments\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e40.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor each binary predictor (present\u0026thinsp;=\u0026thinsp;1 vs absent\u0026thinsp;=\u0026thinsp;0), we report the number of cases granted and not granted asylum, with two-sided p-values from Fisher\u0026rsquo;s exact test (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCorrelates of Asylum Approval\u003c/h3\u003e\n\u003cp\u003eIn univariate analysis, having lacerations, diagnosis of MDD, visible scars, diagnosis of PTSD, being kidnapped, having a physical evaluation completed, having an income, having a psychological evaluation completed, being tortured, having burns, having any psychological diagnosis, and having children were associated with significantly increased odds of being granted asylum. In addition, being married approached significance. Of the factors with increased odds, 6 were medical, 2 were trauma, and 2 were demographic factors. Gait abnormalities showed decreased odds of asylum approval (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In multivariable analysis, being kidnapped, having lacerations, and having an income remained associated with significantly increased odds of asylum approval. Experiencing genital mutilation was associated with extremely high odds of asylum approval, which was not observed in univariate analysis. Experiencing ethnic violence neared significant levels. Gait abnormality remained associated with decreased odds (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate logistic regression for correlates of asylum approval. Odds ratios (OR) and 95% confidence intervals from separate logistic regressions of asylum approval on each binary predictor (present\u0026thinsp;=\u0026thinsp;1 vs absent\u0026thinsp;=\u0026thinsp;0). Variables are organized under demographic, trauma experience, and medical, as well as 2 evaluation type variables. Asterisks denote statistical significant as reported in the sheet (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUpper CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-val\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical Evaluation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychological Evaluation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDemographic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHas Children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterpreter Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCisgender Man\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily in US\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnder 18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGBTQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDetained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTrauma Experience\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidnapping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTorture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnic Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSexual Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolice Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenital Mutilation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrafficked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGang Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDomestic Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedical\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLacerations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.01e-05***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.66e-05***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0003***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePTSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0003***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBurns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychiatric Diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGait Abnormalities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvidence of FGM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeurological Impairments\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable logistic regression of correlates for asylum approval.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUpper CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-val\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHas Children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterpreter Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCisgender Man\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily in US\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnder 18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGBTQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDetained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTrauma Experience\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidnapping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e188.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTorture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnic Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSexual Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolice Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenital Mutilation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e107.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12428.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrafficked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGang Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDomestic Violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedical\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLacerations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePTSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e151.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBurns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychiatric Diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGait Abnormalities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeurological Impairments\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAdjusted odds ratios (aOR) and 95% confidence intervals from separate logistic regressions of asylum approval on each binary predictor (present\u0026thinsp;=\u0026thinsp;1 vs absent\u0026thinsp;=\u0026thinsp;0). Variables are organized under demographic, trauma experience, and medical categories. Evaluation-type variables and physical evidence of female genital mutilation were removed. Asterisks denote statistical significant as reported in the sheet (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe purpose of our study was to report grant rates and determine correlates of asylum approval for cases undergoing physical and/or psychological FMEs at a student-run asylum clinic. For 154 asylum cases at CHRIA (2014\u0026ndash;2024), an overall asylum grant rate of 57.1% was found. The US national average was 39.3% during the same period[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The difference between asylum approval at our clinic and the national average was 17.8%, exactly equal to what PHR found for their cases versus the national average between 2008 and 2018 (60.2% vs. 42.4%)\u003csub\u003e1\u003c/sub\u003e. 17.8% demonstrates a decrease from the difference originally published by PHR on data from 2000\u0026ndash;2004 of 89% compared to the national average of 37.5%. Several factors likely explain why the margin is smaller today. First, the \u0026ldquo;novelty effect\u0026rdquo; of FMEs has diminished as affidavits have become more common, narrowing the comparative advantage they once conferred. Additionally, the study period of the first study (2000\u0026ndash;2004) had a lower national benchmark, with the post-2001 period (2002\u0026ndash;2004) having especially low grant rates [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Despite the decrease in the difference with the national comparison rate from the early 2000s, our findings reinforce the enduring value of FMEs and demonstrate that a student-run clinic can achieve outcomes comparable to a leading national network, underscoring the role of academic, \u003cem\u003epro bono\u003c/em\u003e programs in enhancing resources for asylum seekers.\u003c/p\u003e\u003cp\u003eBoth having a physical FME (OR\u0026thinsp;=\u0026thinsp;3.1, p\u0026thinsp;=\u0026thinsp;0.001) and psychological FME (OR\u0026thinsp;=\u0026thinsp;4.3, p\u0026thinsp;=\u0026thinsp;0.002) showed increased odds of asylum approval. This was also found by PHR from 2008 to 2018, that physical (OR\u0026thinsp;=\u0026thinsp;7.04, p\u0026thinsp;=\u0026thinsp;0.04) and psychological (OR\u0026thinsp;=\u0026thinsp;4.91, p\u0026thinsp;=\u0026thinsp;0.09) evaluations were both associated with positive outcomes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Our odds ratios were likely lower due to a smaller sample size. These findings indicate that both physical and psychological FMEs are essential, complementary components of evidence in asylum adjudication. Physical FMEs provide objective, injury-consistent documentation, while psychological FMEs can contextualize symptoms and demeanor as a sequela of trauma rather than credibility deficits. Clinics and referral networks should prioritize capacity for both modalities to strengthen outcomes.\u003c/p\u003e\u003cp\u003eIn this cohort, the univariate model served to screen a broad set of factors to identify signals worth testing in an adjusted framework. The significant factors clustered in the medical domain (lacerations, burns, scars, MDD, PTSD, any psychiatric diagnosis), alongside two trauma exposures (kidnapping and torture) and two demographic factors (having an income and having children). The high amount of significant medical factors underscores that FMEs add value primarily by translating trauma histories into clinically documented physical and psychological findings. Among trauma variables, the increased odds of asylum approval after experiencing torture align with the legal frameworks that afford heightened protections when torture is credibly established [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e][\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For demographics, income may serve as a proxy for integration potential often discussed in adjudicative reasoning, while having children may cue humanitarian considerations [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In the multivariable model, kidnapping, lacerations, and income continued to hold significantly increased odds of asylum approval, while gait abnormality remained inversely associated. The latter may reflect nonspecificity (many non-persecution etiologies), sparse counts, and measurement limits, making it less probative than discrete lesion-level findings. Genital mutilation showed a high association with asylum approval; the adjusted effect is large but imprecise with a wide confidence interval due to the low number of cases. Several univariate factors lost statistical significance after adjustment, an expected consequence of controlling for confounding, multicollinearity, and mediating pathways (e.g., torture \u0026rarr; PTSD/MDD \u0026rarr; adjudicator perceptions). Overall, these patterns suggest that the medical component of FMEs\u0026mdash;both physical and psychological\u0026mdash;carries the greatest independent evidentiary weight, while certain trauma experiences and demographics provide complementary context. This pattern aligns with prior reports that injury-mechanism-timeline consistency drives adjudicator confidence [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn comparing our multivariable results with those from PHR\u0026rsquo;s analysis, we note four factors assessed in both models: detention at time of evaluation, gang violence, sexual violence, and LGB identity. PHR reported that detention status (OR\u0026thinsp;=\u0026thinsp;0.48, p\u0026thinsp;=\u0026thinsp;0.002) and gang violence (OR\u0026thinsp;=\u0026thinsp;0.54, p\u0026thinsp;=\u0026thinsp;0.001) were both less likely to be granted a positive case outcome compared to a negative case outcome. Sexual violence (OR\u0026thinsp;=\u0026thinsp;1.80, p\u0026thinsp;=\u0026thinsp;0.003) and LGB identity (OR\u0026thinsp;=\u0026thinsp;2.11, p\u0026thinsp;=\u0026thinsp;0.005) were more likely to be granted a positive case outcome compared to a negative case outcome. While none of these four factors reached statistical significance in our dataset, detention status, gang violence, and sexual violence all trended in the same direction as PHR, with odds ratios of 0.54 (vs 0.48), 0.81 (vs 0.54), and 1.21 (vs 1.80), respectively. Genital mutilation, conceptually related to sexual violence, was significantly associated with asylum approval. LGBTQ identity had a high, non-significant p-value in our analysis (p\u0026thinsp;=\u0026thinsp;0.92). Several methodological differences likely account for our lack of significance. PHR categorized case outcomes into positive, negative, and other, and ran two comparisons: positive versus negative and positive versus other. Our analysis utilized a narrower endpoint, categorizing outcomes into asylum granted versus all other outcomes. Our narrower positive endpoint and smaller sample size possibly led to smaller effects and lower power. The directional concordance across shared predictors with PHR is reassuring and suggests underlying consistency despite our attenuated effects.\u003c/p\u003e\u003cp\u003eAsylum literature supports a mechanism in which FMEs translate trauma histories into forensically legible medical and psychological evidence that courts can weigh against legal standards (credibility, consistency, severity) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Physical FMEs appear especially persuasive when injury patterns are medically consistent with the alleged mechanism and timeline [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Psychological FMEs are likewise critical when they explicitly link trauma to current symptom patterns (including memory disturbances that might be otherwise misread as credibility gaps) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Translating these findings into tangible practice guidelines, we propose that clinics should expand capacity for both physical and psychological FMEs and implement a standardized workflow with checklists for high-yield findings (lacerations, burns, torture sequelae). When resources are limited, a triage system that reserves scarce physical-exam slots for applicants with plausible visible injuries or kidnapping and torture histories, while guaranteeing universal access to psychological FMEs that contextualize memory and affect. Student-run clinics are critical access points that expand FME capacity while training the next generation of clinicians. We advocate for institutions, funders, and policymakers to commit sustained support towards expanding and stabilizing student-run asylum clinics.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eOutcomes were only known for 53.3% of completed cases with potentially non-random missing data. Cases with unknown outcomes may reflect particularly delayed adjudication or lack of communication from lawyers, neither of which would necessarily occur randomly, introducing selection bias if loss to follow-up correlated with lowered grant probability. Some predictors had a small observation size, yielding imprecise or extreme odds ratios. Our model lacked some covariates, which have been previously mentioned to be associated with asylum outcomes in the literature, including the type of legal representation and country of origin, which could have residual confounding [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Finally, our findings were collected from a single student-run clinic, which may limit generalizability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We disclose no financial or non-financial interests that are directly or indirectly related to the work submitted for publication.\u003c/p\u003e\n\u003ch2\u003eNew Contribution to the Literature\u003c/h2\u003e\n\u003cp\u003ePrior work on student-run asylum clinics has focused on program management, caseloads, and medical-student perspectives, but has not reported case outcomes. This study presents asylum outcomes from a decade of cases at a student-run asylum clinic (2014\u0026ndash;2024) and benchmarks those outcomes against national data from the PHR Asylum Network. We extend the literature by estimating univariate and multivariable associations between asylum grants and a comprehensive set of demographic, trauma-related, and medical factors derived from FME affidavits. Methodologically, we introduce a registry-based, binary-coded analytic framework that can be replicated by other clinics.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eFootnotes\u003c/h2\u003e\u003cp\u003e1-Note: Atkinson et al. 2021 reported all positive outcomes of asylum cases, including asylum approval, withholding of removal, convention against torture, etc., at 81.6%. However, to compare directly with our statistics, we calculated their rate of solely asylum approval from within their positive outcomes measure.\u003c/p\u003e\u003c/div\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.X. wrote the main manuscript text and prepared tables 1-3. C.K. prepared figure 1. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank Andrew Shin, Anusha Mudigonda, Carolin Bao, Grace Xu, Jacques Calixte, Kathryn Tian, Ken Kaplan, Maxine Mackie, Minerva Nong, Nobel Zhou, Ramzi Elased, Rishi Dasgupta, Shariq Jumanji, Suuba Demby, Vanessa Anderson, Zaid Bustami, and all other current and past members of the Columbia Human Rights Initiative Asylum Clinic (CHRIA). In addition, we sincerely thank the many CHRIA clinicians who volunteered to conduct these forensic medical evaluations.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from Columbia Human Rights Initiative Asylum Clinic (CHRIA), but restrictions apply to the availability of these data, which were used under licence for the current study and so are not publicly available. The data are, however, available from the authors upon reasonable request and with the permission of Columbia Human Rights Initiative Asylum Clinic (CHRIA).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCouncil DR. Global Displacement Forecast Report 2025: Projections and Analysis. 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUnited Nations High Commissioner for Refugees. Global forced displacement. UNHCR Global Trends. United Nations; 2021. pp. 5\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eImmigration Court Backlog [Internet]. [cited 2025 Aug 22]. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.academia.edu/download/78737967/38020.pdf\u003c/span\u003e\u003cspan address=\"https://www.academia.edu/download/78737967/38020.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIacopino V, Haar RJ, Heisler M, Lin J, Fincancı ŞK, Esdaile C, et al. Istanbul Protocol 2022 empowers health professionals to end torture. Lancet. 2022;400:143\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreen AS, Ruchman SG, Birhanu B, Wu S, Katz CL, Singer EK, et al. Immigration judges\u0026rsquo; perceptionsof telephonic and in-person forensic mental health evaluations. J Am Acad Psychiatry Law. 2022;50:240\u0026ndash;51.\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":"journal-of-immigrant-and-minority-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joih","sideBox":"Learn more about [Journal of Immigrant and Minority Health](http://link.springer.com/journal/10903)","snPcode":"10903","submissionUrl":"https://submission.springernature.com/new-submission/10903/3","title":"Journal of Immigrant and Minority Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"asylum medicine, refugee health, forensic medical evaluation, student-run clinic","lastPublishedDoi":"10.21203/rs.3.rs-7634084/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7634084/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e\u003cp\u003eAsylum seekers face significant challenges in substantiating their claims of trauma and persecution. Forensic medical evaluations (FMEs) support these claims and improve case outcomes. This study investigates which demographic, traumatic, and medical factors predict asylum approval within a student-run asylum clinic.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe performed a retrospective study of clinic FMEs (2014\u0026ndash;2024). Of 289 completed evaluations, 154 cases with known outcomes were analyzed. The outcome variable was asylum granted (1) vs not granted (0). 29 binary predictors (10 demographic, 10 trauma, 9 medical) were abstracted from affidavits. We conducted univariate and multivariable logistic regressions. Results were compared with national data from Physicians for Human Rights.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003e57.1% cases were granted asylum, exceeding the national average of 39.3%. In univariate analyses, higher approval odds were observed with lacerations, scars, burns, any psychiatric diagnosis, PTSD, MDD, kidnapping, torture, income, having children, and having a physical and psychological FME conducted (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Gait abnormality was inversely associated. In the multivariable model, kidnapping, lacerations, and income remained associated with approval; genital mutilation also showed a large positive association.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eA decade of data from a student-run asylum clinic confirmed the importance of FMEs. Findings suggest the medical content of FMEs, especially clear, well-documented injuries that match the client\u0026rsquo;s story and select trauma exposures (e.g., kidnapping), carries strong evidentiary weight, while demographic indicators (income) may also play a role. Results support maintaining capacity for both physical and psychological FMEs and funding for student-run clinics. Larger multi-site studies are needed to validate.\u003c/p\u003e","manuscriptTitle":"Predictors of Asylum Approval: Insights from a Decade of Forensic Medical Evaluations at a Student-Run Clinic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-05 11:40:22","doi":"10.21203/rs.3.rs-7634084/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-12T16:53:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-26T02:05:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-16T19:25:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173035843691613101109132140079291138279","date":"2025-11-02T18:20:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16538328959965301337859000595936052051","date":"2025-10-20T07:46:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-13T18:44:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-20T07:40:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-20T07:39:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Immigrant and Minority Health","date":"2025-09-16T22:43:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-immigrant-and-minority-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joih","sideBox":"Learn more about [Journal of Immigrant and Minority Health](http://link.springer.com/journal/10903)","snPcode":"10903","submissionUrl":"https://submission.springernature.com/new-submission/10903/3","title":"Journal of Immigrant and Minority Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"527b0b21-27ce-41dd-92b2-95f463ec284e","owner":[],"postedDate":"October 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:08:19+00:00","versionOfRecord":{"articleIdentity":"rs-7634084","link":"https://doi.org/10.1007/s10903-026-01860-9","journal":{"identity":"journal-of-immigrant-and-minority-health","isVorOnly":false,"title":"Journal of Immigrant and Minority Health"},"publishedOn":"2026-02-06 15:57:24","publishedOnDateReadable":"February 6th, 2026"},"versionCreatedAt":"2025-10-05 11:40:22","video":"","vorDoi":"10.1007/s10903-026-01860-9","vorDoiUrl":"https://doi.org/10.1007/s10903-026-01860-9","workflowStages":[]},"version":"v1","identity":"rs-7634084","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7634084","identity":"rs-7634084","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}