Forensic Medicine

ICD-10-CM codes infrequently used to document human trafficking in 2019 Nationwide Emergency Department Sample

a b s t r a c t

Introduction: People who experience human trafficking (HT) visit emergency departments (ED). The Interna- tional Classification of Diseases, Clinical Modification introduced codes to document HT in June 2018. The aim of this study is to identify characteristics of ED patients who experienced forced labor or sexual exploitation as a documented external cause of morbidity in US visits.

Methods: Nationally representative surveillance based on patient visits to 989 hospital-owned EDs in the Nation- wide Emergency Department Sample in 2019 became available in 2021. Eight ICD-10-CM codes to classify HT as an external cause of morbidity were combined into one HT variable for analysis in 2021-2022.

Results: A weighted count of 517 of 33.1 million ED visits (0.0016%) documented HT as an external cause of mor- bidity. Of them, sexual exploitation (71.6%) was documented more frequently than labor exploitation (28.4%). Most HT-related codes were visits by females (87.3%) from large metropolitan areas, and identified as white. Approximately 40% of visits were from ZIP codes with a median Household income less than $48,000 annually. Relative to all other ED visits, patients with HT as an external cause of morbidity had higher odds of being female (OR = 6.54, 95% CI:3.59, 11.92) and being a minor (OR = 1.76, 95% CI:1.02, 3.04).

Conclusion: HT was rarely documented as an external cause of morbidity in 989 hospitals’ ED visits from a nation- ally Representative sample in 2019. Documentation of recently added HT ICD-10-CM codes does not appear to have been implemented sufficiently to yield an unbiasED representation of those who experienced HT and pre- sented in the ED. Efforts to enhance the utility of ICD-10-CM HT codes for surveillance and documentation must first address ED personnel training on identification and response to HT. In doing so, ED personnel also need to address Ethical concerns (e.g. stigma, confidentiality, risk of Patient harm) and allow for informed consent among trafficked patients in order to be scaled up responsibly.

(C) 2022

  1. Introduction

Human trafficking (HT) is defined by United States (US) federal leg- islation as the recruitment, harboring, transport, provision or obtaining of a person for labor or sexual exploitation via force, fraud, or coercion [1]. Demonstrating force, fraud, or coercion is not required for the sexual exploitation of children under age 18 [1]. Prevalence of HT has been dif- ficult to identify, given the hidden and misunderstood nature of the crime. Data from emergency departments (ED) may be an important

* Corresponding author.

E-mail addresses: [email protected] (N. Dell), [email protected] (E. Koegler), [email protected] (K.J. Holzer), [email protected] (M.G. Vaughn), [email protected] (C. Bitter), [email protected] (R.K. Price).

data source for informing HT surveillance efforts, as HT survivors are often seen in EDs during episodes of trafficking [2-4]. However, invest- ment in HT surveillance and monitoring is still at a nascent stage in healthcare settings.

Efforts to establish protocols to address human trafficking in healthcare settings, educate health practitioners, and screen for human trafficking among patients have expanded over the last decade [5-7]. Research and protocol development to identify and treat HT vic- tims have been most active in emergency medicine in the US [8], thus recent developments in the ED provide a glimpse toward further diffu- sion into other specialties. Evidence-based tools to screen for trafficking in healthcare settings are emerging, especially in EDs [9-13]. Psycho- metric studies offer insight into estimates of the prevalence of human trafficking among urban ED patients [10,11,13]. Increasing efforts to

https://doi.org/10.1016/j.ajem.2022.11.017

0735-6757/(C) 2022

implement healthcare HT protocols, healthcare training, and validated Screening tools, coupled with emerging prevalence estimates of HT in the ED, provide a foundation for HT surveillance in healthcare settings. Successful HT surveillance and monitoring in healthcare settings will require developing empirically based knowledge training, screening, identification, classification, and code documentation [14,15]. Surveil- lance efforts may be aided by the recent development of codes to docu- ment HT, which were first introduced in the International Classification of Diseases, Clinical Modification (ICD-10-CM) in June 2018 [16]. The first full year of HT codes from nationally representative 2019 data in the Nationwide Emergency Department Sample (NEDS) became avail- able through the Agency for Healthcare Research and Quality (AHRQ) in September 2021. The primary objective of this study is to identify characteristics of ED patients who experienced HT, whether forced labor or sexual exploitation, as a documented external cause of morbid- ity. The secondary objective was to explore the use of HT-related Z codes, or HT documented as a factor influencing health status and con- tact with health services. Understanding the use of ICD-10 CM codes to document victims of trafficking can inform current human trafficking

training, screening, and protocols in healthcare settings.

  1. Methods

The present study uses data from the 2019 NEDS to explore the use of recently developed ICD-10-CM codes to document forced labor and sexual exploitation in ED settings.

    1. Data and sample

The NEDS is part of the Healthcare Cost and Utilization Project dis- tributed by AHRQ. It is comprised of a 20% stratified sample of hospital owned EDs in the US. In 2019, 989 EDs from 40 states and the District of Columbia were represented and contained information from 33.1 million ED visits [17]. Further information on the NEDS sampling design is available from ahrq.gov. [17]

    1. Measures
      1. Human trafficking as a cause of morbidity and factors influencing health status

Table 1 specifies a total of 12 ICD-10-CM codes to document HT. Eight codes classify HT as an external cause of morbidity (T code) and allow the provider to specify whether the form of exploitation was ei- ther labor or sexual, whether the patient is a child or adult, and whether the exploitation is suspected or confirmed. All T-codes documenting HT as an external cause of morbidity were combined into a single HT

Table 1

ICD-10-CM codes documenting human trafficking. Code Description

T Other and unspecified effects of external cause of morbidity

T74.51 Adult forced sexual exploitation, confirmed T74.52 Child sexual exploitation, confirmed T74.61 Adult forced labor exploitation, confirmed T74.62 Child forced labor exploitation, confirmed T76.51 Adult forced sexual exploitation, suspected T76.52 Child sexual exploitation, suspected

T76.61 Adult forced labor exploitation, suspected T76.62 Child forced labor exploitation, suspected

Z Factors influencing health status and contact with health services

Z04.81 Encounter for examination and observation of victim following forced sexual exploitation

Z04.82 Encounter for examination and observation of victim following forced labor exploitation

Z62.813 Personal history of forced labor or sexual exploitation in childhood Z91.42 Personal history of forced labor or sexual exploitation

variable, with a weighted count of 517 encounters (SE = 66). All esti- mates were weighted to account for the NEDS complex sampling design.

Four codes document a personal history of HT or document an en- counter following forced sexual or labor exploitation (Z code). These codes include documented history of trafficking (N = 513, SE = 83), history of child trafficking (N = 274, SE = 34), and a documented en- counter for examination and observation following forced labor (N = 23, SE = 10) or sexual exploitation (N = 162, SE = 34). Codes documenting a history of HT or a childhood history of HT were com- bined into a single variable flagging any history of HT.

      1. Demographic variables

Available patient-related variables that we examined included age, sex, race/ethnicity, Median household income in patient’s ZIP code, the patient’s primary payer, and the patient’s urban-rural status. Char- acteristics of the hospital setting include region of the country, teaching status, and trauma center designation. Whether the patient was admit- ted on a weekend and the outcome of the ED visit (e.g., treated and re- leased, admitted, or died in the ED) were also assessed.

    1. Analysis

Descriptive statistics are first presented to assess the socio- demographic characteristics of patients with suspected or confirmed forced labor or sexual exploitation as the external cause of morbidity (T codes). Next, multivariate logistic regression was conducted to exam- ine correlates of patient-level characteristics of ED visits associated with HT as an external cause of morbidity (T codes) compared to all other ED visits in the NEDS sample. Finally, descriptive statistics were summa- rized on characteristics of patients with Z codes documenting a history of forced labor or sexual exploitation. Descriptive statistics related to en- counter data are not presented, per NEDS data use requirements, due to the low number of cases and to protect patient confidentiality. Analyses were conducted using Stata 16.1 [18].

  1. Results
    1. Human trafficking as a cause of morbidity and demographic composi- tion of patients

In the first full year of being introduced into the ICD-10-CM, a weighted count of 517 out of 33.1 million ED visits (0.0016%) had HT as an external cause of morbidity (Table 2). Among patients with docu- mented HT as an external cause of morbidity, sex trafficking was docu- mented more often than labor trafficking HT was more frequently coded as “confirmed” rather than “suspected.” Most patients with docu- mented HT were female, and identified as White. A high percentage of HT patients either received Medicaid (41%) or were self-pay/other (41%); and from large metropolitan areas. Approximately 40% of pa- tients lived in ZIP codes with a median household income less than

$48,000 annually. Approximately three-fourths of patients who visited the ED for HT were treated in and discharged directly from the ED. Nearly 50% of visits occurred in the southern region of the US; and nearly four out of five HT-related visits occurred in metropolitan teach- ing hospitals.

    1. Correlates of patient characteristics associated with HT as a cause of morbidity

Patient-level correlates associated with HT are presented in Table 3. Relative to all other ED visits, patients with HT as an external cause of morbidity (T codes) had higher odds of being female relative to male (OR = 6.54, 95% CI: 3.59, 11.92) and being a minor compared to aged 18 years or older (OR = 1.76, 95% CI: 1.02, 3.04). Patients with docu- mented HT had greater odds of receiving Medicaid (OR = 3.37, 95%CI:

Table 2 Emergency department visits related to human trafficking as a cause of morbidity (T codes combined) compared to other visits.

Variable

Weighted Count

(N = 517, SE = 66)

Weighted% (95% CI)

Sex Trafficking

370

71.61 (59.57, 81.20)

Labor Trafficking

147

28.39 (18.80, 40.43)

Suspected

184

35.55 (26.68, 45.53)

Confirmed

333

64.45 (54.47, 73.32)

Female

451

87.31 (79.74, 92.32)

Minor (< 18)

159

30.81 (18.45, 46.72)

Payer Medicare

36

7.08 (3.49, 13.83)

Medicaid

214

41.34 (31.55, 51.88)

Private Insurance

55

10.61 (5.95, 18.20)

Self-pay, no charge, other

212

40.97 (31.57, 51.09)

Urbanicity

Large metropolitan (>= 1 million)

291

57.16 (46.33, 67.35)

Small metropolitan (50,000-999,999)

172

33.74 (25.11, 43.61)

Non-metropolitan (< 50,000)

46

9.10 (4.32, 18.17)

Race/Ethnicity

Non-Hispanic White

235

52.07 (41.42, 62.54)

Non-Hispanic Black

114

25.38 (16.46, 37.01)

Hispanic, any race

75

16.58 (9.43, 27.49)

Non-Hispanic Other

27

5.97 (2.74, 12.52)

Median Household Income

< $48,000

198

40.53 (29.64, 52.45)

$48,000 – $60,999

139

28.45 (19.74, 39.14)

$61,000 – $81,999

109

22.36 (15.37, 31.35)

>= $82,000

42

8.66 (4.60, 15.69)

Weekend Admission

146

28.28 (21.22, 36.59)

ED Outcome

Treated and released

382

73.82 (61.62, 83.20)

Admitted/Transferred

135

26.18 (16.80, 38.38)

Died in ED

0

0

Hospital Region Northeast

42

8.11 (3.17, 19.22)

Midwest

132

25.57 (17.47, 35.79)

South

244

47.22 (34.96, 59.83)

West

99

19.10 (12.11, 28.79)

Hospital Trauma Center Not a trauma center

125

24.11 (15.89, 34.83)

Trauma center level I

237

45.94 (33.68, 58.70)

Trauma center level II

103

20.00 (13.36, 28.84)

Trauma center level III

51

9.95 (4.34, 21.23)

hospital teaching status Metropolitan non-teaching

75

14.48 (8.79, 22.92)

Metropolitan teaching

411

79.52 (69.08, 87.09)

Non-metropolitan

31

6.00 (2.05, 16.35)

Codes T74.5 (forced sexual exploitation, confirmed); T74.6 (forced labor exploitation, con- firmed); T76.5 (forced sexual exploitation, suspected); T76.6 (forced labor exploitation, suspected).

1.59, 7.15), or being self-pay/no charge (OR = 8.51, 95% CI: 3.94, 18.39) relative to having private insurance. Living in a non-metropolitan area was associated with lower odds of documented HT (OR = 0.37, 95% CI: 0.14, 0.97). Race/ethnicity and median household income based on patient’s ZIP code were not significantly associated with HT as an exter- nal cause of morbidity.

    1. Human trafficking as a factor influencing health status and contact with health services

Table 4 presents characteristics of encounters documenting any his- tory of forced labor or sexual exploitation (Z91.42) or history of labor or sexual exploitation in childhood (Z62.813). Z codes to document patient history of HT (N = 787, SE = 119) were used more often than codes documenting HT as an external cause of morbidity (T codes) Personal

Table 3

Patient-level correlates associated with human trafficking as an external cause of mor- bidity in emergency departments.

Variable

Odds Ratio (95% CI)

Female

6.54 (3.59, 11.92)

Minor (< 18)

1.76 (1.02, 3.04)

Payer

Medicare

0.91 (0.35, 2.35)

Medicaid

3.37 (1.59, 7.15)

Private insurance

(reference)

Self-pay, No charge, Other

8.51 (3.94, 18.39)

Urbanicity

Large metropolitan (reference)

(reference)

Small metropolitan

0.82 (0.50, 1.36)

Non-metropolitan

0.37 (0.14, 0.97)

Race/Ethnicity

White

(reference)

Black

0.77 (0.43, 1.39)

Hispanic

0.65 (0.32, 1.33)

Other

0.64 (0.26, 1.58)

Median Household Income in Patient’s ZIP

< $48,000

(reference)

$48,000 – $60,999

0.78 (0.43, 1.41)

$61,000 – $81,999

0.78 (0.40, 1.53)

>= $82,000

0.53 (0.23, 1.22)

history of trafficking (N = 513, SE = 83) was more frequently applied than personal history of child trafficking (N = 274, SE = 34). A higher percentage of visits with a documented history of HT were included patients identified as female, White, from large metropolitan areas, and receiving care at a level 1 trauma center.

  1. Discussion

In the first full year after being introduced into the ICD-10-CM, codes for human trafficking were rarely applied, whether as an external cause of morbidity or as part of the patient’s personal history. Sex trafficking was more frequently documented relative to labor trafficking. In both the sample of patients with HT as an external cause of morbidity, and in comparison, to the full sample of ED visits, females in large metropol- itan areas who received Medicaid or self-pay/no charge were more fre- quently documented. Although less than a third of patients with HT as an external cause of morbidity were minors, minors were more likely to be identified as having experienced HT compared to the full sample. Documentation of recently added HT ICD-10-CM codes does not appear to have been implemented sufficiently to yield an unbiased representa- tion of those who experienced HT and presented in the ED.

The results of the present study do not depict generalizable preva- lence estimates of HT in EDs as the use of HT ICD-10-CM codes was not systematic nor has training been scaled to such a level. Still, descrip- tive studies of HT ICD-10-CM codes from the PHIS database of pediatric patients and TriNetX database of health care organizations found 0.005% and 0.0043% encounters respectively with trafficked patients [19,20], compared to our NEDS database findings of 0.0016% of encounters in EDs with trafficked patients. In all three studies, patients were infre- quently identified as having experienced trafficking. However, increased identification in the pediatric dataset is consistent with our finding that pediatric patients in our study were more likely to be identified compared to adult patients.

More rigorous studies focused on HT prevalence have identified

higher rates of HT from 1.1% in randomly selected participants [11] to up to 12.3% among high-risk participants [10]. Increased documentation of sex trafficking in ICD-10-CM HT codes may be due to there being sev- eral screening tools to identify sex trafficking in the ED, but only one ED screening tool focusing on both sex and labor trafficking, which identi- fied more male patients and more patients who experienced labor

Table 4

Emergency department encounters in 2019 documenting a history of human trafficking (Z codes).

Variable

Weighted Count

(N = 787, SE = 119)

% (95% CI)

Female

713

90.71 (84.56, 94.57)

Minor (< 18)

181

23.02 (11.78, 40.11)

Payer

Medicare

79

10.03 (5.78, 16.83)

Medicaid

530

54.61 (44.71, 64.16)

Private insurance

92

11.64 (7.11, 18.47)

Self-pay, No charge, Other

187

23.72 (15.54, 34.46)

Urbanicity

Large metropolitan

462

59.26 (46.29, 71.06)

Small metropolitan

257

33.01 (22.44, 45.63)

Non-metropolitan

60

7.73 (4.25, 13.65)

Race/Ethnicity White

388

55.51 (43.76, 66.68)

Black

186

26.63 (16.04, 40.82)

Hispanic

90

12.86 (8.23, 19.53)

Other

35

0.5 (2.31, 10.45)

Median Household Income

< $48,000

280

36.40 (27.37, 46.50)

$48,000 – $60,999

205

26.63 (18.95, 36.04)

$61,000 – $81,999

206

26.75 (16.63, 40.07)

>= $82,000

79

10.22 (6.13, 16.55)

Weekend admission

200

25.45 (20.43, 31.22)

ED Outcome

Treated and released

311

39.53 (26.28, 54.51)

Admit/Transfer

476

60.47 (45.49, 73.72)

Died

0

0

Hospital Region

Northeast 96 12.17 (7.33, 19.52)

Midwest

201

25.54 (15.07, 39.87)

South

381

48.49 (34.11, 63.14)

West

109

13.80 (6.77, 26.11)

Hospital Trauma Center

Not a trauma center

187

23.78 (15.72, 34.30)

Trauma level I

428

54.44 (40.42, 67.78)

Trauma level IIa

75

9.60 (5.24, 16.94)

Trauma level III

96

12.18 (6.35, 22.10)

Hospital Teaching Status Metropolitan non-teaching

86

10.99 (5.68, 20.18)

Metropolitan teaching

659

83.79 (73.77, 90.48)

Non-metropolitan

41

5.22 (2.28, 11.52)

a Includes collapsed level I & II categories.

trafficking compared to sex trafficking [11]. ICD-10-CM identification of increased sex exploitation among women may be attributable to in- creased research focused on sex trafficking and/or potential health care provider bias. In one study, the overwhelming majority of patients who screened positive for sex trafficking were adult females (92%) and all likely HT patients were female [13]. Similarly, increased ICD-10-CM HT identification of minors for sex trafficking may be attributable to the first validated HT screening tool being specifically for minors [21] and other minor focused efforts [10]. In one study of minors, all identi- fied patients who experienced sex trafficking were girls, however only four boys were screened among 108 high-risk patients [21]. In another study to identify minor sex trafficking, 88.5% of identified patients were female, with the highest percentage identifying as White LAtino (42%) [10]. Further examination of race and ethnicity data among patients identified as having experienced HT in EDs shows more diverse distri- bution than our results from the 2019 ICD-10-CM HT codes. For exam- ple, in one study of minor patients who experienced sex trafficking, 72% were African American [21]. The racial and ethnic backgrounds of patients identified in the ICD-10-CM HT codes are more consistent with what was reported among adult patients who experienced all forms of HT. [11] Existing ED HT screening tools and prevalence studies

have not reported income or Medicaid status, thus we cannot compare how our income findings relate to HT patient characteristics in other studies.

Several practitioner scholars leading academic efforts to identify HT in EDs initiated the work in metropolitan areas and very likely in teach- ing hospitals. This may explain why ICD-10-CM HT codes in the present study were more likely to have identified patients who experienced HT in metropolitan settings and teaching hospitals. Documented academic efforts, which involved extensive training among ED staff to screen pa- tients, have largely occurred in the South, specifically Texas [11,22] and Atlanta [13,21] but also the Northeast [11]. Effective healthcare worker training is critical for successful HT surveillance and monitoring. A sys- tematic review of educational interventions for healthcare providers demonstrated that training increases HT awareness, knowledge, and confidence to identify at-risk patients [23]. HT ED training interventions have targeted all staff that interacts with patients, delivered in myriad formats [24]. Notably, even brief 20-min trainings administered to ED clinical staff increase confidence to identify and care for patients who experience HT. [13,25] More intensive training, with the purpose of screening tool validation, suggests success in identifying most, if not all, cases of trafficking [26]. Education to identify most patients who have experienced HT is ideal for scaling up effective national HT surveillance.

A key limitation of this research is that one cannot know the veracity of reports documented in the ICD-10-CM codes. Clinicians may have ap- plied inconsistent protocols, had variable or no training, and utilized non-validated screening tools. The 2019 NEDS data were collected prior to the publication of the first validated HT screening tool for adults in 2021. It is not clear if physicians or nurses apply codes or how they determine confirmed versus suspected trafficking. The data reported re- flect patient visits and not necessarily individual patients. Therefore, some patients may be counted more than once based on readmission. Still, the cases identified by ICD-10-CM HT codes are likely an under- count of all patients who experienced HT, even within institutions that have implemented HT protocols, education, and screening, which have not yet been scaled up. Although 2019 NEDS includes data related to Patient sex, patient gender identity is not recorded in this dataset. The findings in the current study appear to reflect a general bias in the mis- understanding of trafficking, that white women are exploited for the purpose of sex. This is a misrepresentation of diverse victims who may be at greater risk for exploitation due to oppression and discrimination. Finally, it must be clear that the findings of this study do not adequately characterize potential victims of HT in EDs given the limited use of ICD- 10-CM HT codes coupled with the unknown size of the true population of HT victims who presented at EDs in this sample.

Surveillance efforts in the ED are just beginning and as such these findings set a foundation for future surveillance. If ICD-10-CM HT codes are applied more systematically, they have the potential to inform trends in identification, treatment, and referral over time. Efforts to date are laudable, but are not without controversy given considerable ethical implications of attempting to apply HT ICD-10-CM codes at scale. Codes pose challenges for patient concerns (e.g., discrimination, stigma, confi- dentiality), clinical practice (e.g., lack of specialized knowledge and time), and organizational adoption (e.g., lack of protocols and time reimbursement) [27]. Codes may be viewed by non-healthcare practi- tioners, including traffickers/abusers, in online portals, visit summaries, billing/explanation of benefits, or inadvertently during an appointment that can put the patient at risk, thus patients should be aware of codes to make informed decisions about their sensitive information [14]. HT ICD-10-CM codes can be implemented safely by building staff and organizational capacity, ensuring ethical clinical practice in alignment with mandated reporting, and safeguarding electronic health records (EHR) by masking sensitive data [27]. Medical documentation may be used for future legal proceedings. Therefore, clinicians should only re- cord medically relevant facts with supporting details from the physical exam; documenting all signs of abuse with dated pictures/drawings

[8]. A provider can document “suspected HT” if patient denies HT or consent for pictures [8]. A balance needs to be struck between documenting HT for patient care, resources, surveillance, and from risk of others accessing private information that can follow a patient in- definitely. patient autonomy and informed consent should be priori- tized. If scaled up, ICD-10-CM HT codes can make a large impact in the identification and support of victims, which can be tracked over time.

To allow for ongoing surveillance of HT in EDs and to enhance the

utility of ICD-10-CM HT codes we have several recommendations. Ef- forts to create and implement HT protocols in the ED, train ED staff to identify HT in teaching and non-teaching hospitals, and follow up with patients should be scaled up responsibly with particular attention to the diverse nature of human trafficking experiences and demograph- ics. Current levels of HT identification using ICD-10-CM codes are not acceptable as a surveillance mechanism and may do more harm than good. Changes to policy may be required along with associated funding to responsibly implement changes. Patients should be aware of any use of ICD-10-CM HT codes in their record, the risks and consequences of having a code in their record, and understand how information in their record is used [14]. Facilities that choose to implement ICD-10- CM HT codes for enhanced surveillance require partnerships with payers to protect patient confidentiality [27]. Validated short screening tools are now available to screen adult ED patients for sex and labor traf- ficking and minor ED patients for sex trafficking. A similar tool is needed to screen for child labor trafficking. Future studies should monitor how ICD-10-CM codes are applied each year and identify any ethical conse- quences of applying such codes to patients. With the emergence of val- idated HT screening tools, ED protocols and evidence-based HT training, screening efforts can be scaled up to identify and treat victims over time more effectively.

The American College of Emergency Physicians Policy Statement on Human Trafficking recommends training and education of emergency physicians, trainees and other ED personnel and supports further re- search on best practices for recognition and intervention. Barriers to coding potentially stigmatizing information in the medical record in- clude lack of recognition, diagnostic uncertainty, fear of medicolegal li- ability, and patient preference regarding reporting [28]. Appropriate coding of HT may improve with time as more clinicians are trained to recognize potential HT victims and more become aware of the new code. Best practice alerts integrated into the electronic medical record have been shown to increase identification and testing for child abuse, HIV, and Hepatitis C [29-32], and have been successfully implemented to screen for child sex trafficking [33]. An alert could be triggered for pa- tients presenting with a high-risk chief complaint or other identified risk factors for HT. Validation of the utility of EHR alert in facilities with less experience evaluating for HT would be required.

Declaration of Competing Interest

None of the authors have personal, commercial, or financial conflicts of interests to disclose.

Acknowledgements

This research did not receive any specific grant from funding agen- cies in the public, commercial, or not-for-profit sectors.

References

  1. Victims of Trafficking and Violence Protection Act of 2000 (P.L. 106-386), reauthorized by the Trafficking Victims Protection Reauthorization Act (TVPRA) of 2003 (P.L. 108-193), the TVPRA of 2005 (P.L.109 164), and the William Wilberforce Trafficking Victims Protection Reauthorization Act (WW-TVPA) of 2008 (P.L. 110-457), the TVPRA of 2013 (P.L. 113-4), the JVTA of 2015 (P.L.114-22); 2015..
  2. Baldwin SB, Eisenman DP, Sayles JN, Ryan G, Chuang KS. Identification of human trafficking victims in health care settings. Health & Hum Rts. 2011;13(1):E36-49.
  3. Chisolm-Straker M, Baldwin S, Gaigbe-Togbe B, Ndukwe N, Johnson PN, Richardson LD. Health care and human trafficking: we are seeing the unseen. J Health Care Poor Underserved. 2016;27(3):1220-33.
  4. Lederer L, Wetzel C. The health consequences of sex trafficking and their implica- tions for identifying victims in healthcare facilities. Ann Health Law. 2014;23:61-91.
  5. Marcinkowski B, Caggiula A, Tran BN, Tran QK, Pourmand A. Sex trafficking screen- ing and intervention in the emergency department: a scoping review. J Am Coll Emerg Physicians Open. 2022;3(1):E12638.
  6. McDow J, Dols JD. Implementation of a human trafficking screening protocol. J Nurse

Pract. 2017;17(3):339-43.

  1. Stoklosa H, Dawson MB, Williams-Oni F, Rothman EF. A review of US health care in- stitution protocols for the identification and treatment of victims of human traffick- ing. J Hum Traffick. 2017;3(2):116-24.
  2. Shandro J, Chisolm-Straker M, Duber HC, et al. Human trafficking: a guide to identi- fication and approach for the emergency physician. Ann Emerg Med. 2016;68(4): 501-8.
  3. Greenbaum J. Identifying victims of human trafficking in the emergency depart- ment. Clin Pediatr Emerg Med. 2016;17(4):241-8.
  4. Hurst IA, Abdoo DC, Harpin S, Leonard J, Adelgais K. Confidential screening for sex trafficking among minors in a pediatric emergency department. Pediatrics. 2021.; 147(3).
  5. Chisolm-Straker M, Singer E, Strong D, Loo GT, Rothman EF, Clesca C, et al. Validation of a screening tool for labor and sex trafficking among emergency department pa- tients. J Am Coll Emerg Physicians Open. 2021;2(5):e12558.
  6. Mumma BE, Scofield ME, Mendoza LP, Toofan Y, Youngyunpipatkul J, Hernandez B. Screening for victims of sex trafficking in the emergency department: a pilot pro- gram. West J Emerg Med. 2017 Jun;18(4):616-20.
  7. Kaltiso SAO, Greenbaum VJ, Moran TP, Osborne AD, Korniotes J, Marazzi G, et al. Fea- sibility of a screening tool for sex trafficking in an adult emergency department. Acad Emerg Med. 2021;28(12):1399-408.
  8. Greenbaum J, McClure RC, Stare S, Barnes W, Castles CE, Culliton ER, et al. Documenting ICD codes and other sensitive information in electronic health records: guidelines for heathcare professionals who encounter patients with a history of human trafficking or other forms of violence. Intern Centre Miss Expl Child. 2021:1-13. https://cdn.icmec.org/wp-content/uploads/2021/02/Final-ICD- code-documentation-recommendations-Feb-2021.pdf.
  9. Macias-Konstantopoulos WL. Diagnosis codes for human trafficking can help assess incidence, risk factors, and comorbid illness and injury. AMA J Ethics. 2018 Dec 1;20 (12):1143-51.
  10. ICD-10-CM Coding for Human Trafficking. American Hospital Association; April 12, 2022. (Published September 2018). https://www.aha.org/system/files/media/ file/2019/04/AHA-Fact-Sheet-icd-10-code-human-trafficking.updated_1.pdf.
  11. Heathcare Cost and Utilization Project (HCUP). NEDS Description of Data Elements. Agency for Healthcare Research and Quality. (October 2021). NEDS Description of Data Elements; April 12, 2022. (. ahrq.gov).
  12. StataCorp; 2019..
  13. Garg A, Panda PA, Malay S, Slain K. Human trafficking ICD-10 code utilization in pe- diatric Tertiary care centers within the United States. Front Pediatr. 2022:133. https://doi.org/10.3389/fped.2022.818043.
  14. Kerr PL, Bryant G. Use of ICD-10 codes for human trafficking: analysis of data from a large, multisite clinical database in the United States. Public Health Rep. 2022;137 (1_suppl):83S-90S.
  15. Greenbaum VJ, Dodd M, Mccracken C. A short screening tool to identify victims of child sex trafficking in the health care setting. Pediatr Emerg Care. 2018;34(1):33-7.
  16. Dols JD, Beckmann-Mendez D, McDow J, Walker K, Moon MD. Human trafficking victim identification, assessment, and intervention strategies in South Texas emer- gency departments. J Emerg Nurs. 2019;45(6):622-33.
  17. Fraley HE, Aronowitz T, Stoklosa HM. Systematic review of human trafficking educa- tional interventions for health care providers. West J Nurs Res. 2020;42(2):E131-42.
  18. Shadowen C, Beaverson S, Rigby FB. Human trafficking education for emergency department providers. Anti-Trafficking Review. 2021;17:38-55. https://doi.org/10. 14197/atr.201221173.
  19. Donahue S, Schwien M, LaVallee D. Educating emergency department staff on the identification and treatment of human trafficking victims. J Emerg Nurs. 2019;45 (1):16-23.
  20. Chisolm-Straker M, Singer E, Rothman EF, Clesca C, Strong D, Loo GT, et al. Building RAFT: trafficking screening tool derivation and validation methods. Acad Emerg Med. 2020;27(4):297-304.
  21. Greenbaum J, Garrett A, Chon K, Bishop M, Luke J, Stoklosa H. Principles for safe im- plementation of ICD codes for human trafficking. J Law Med Ethics. 2021;49(2): 285-9.
  22. Rudman W, Hart-Hester S, Brown CA, Pittman S, Choo E, Cohn F. Ethical dilemmas in coding domestic violence. J Clin Ethics. 2008 Winter;19(4):353-9.
  23. Rosenthal B, Skrbin J, Fromkin J, Heineman E, McGinn T, Richichi R, et al. Integration of Physical abuse Clinical decision support at 2 general emergency departments. J Am Med Inform Assoc. 2019 Oct 1;26(10):1020-9.
  24. Lin J, Mauntel-Medici C, Heinert S, Baghikar S. Harnessing the power of the elec- tronic medical record to facilitate an opt-out HIV screening program in an urban ac- ademic emergency department. J Public Health Manag Pract. 2017 May/Jun;23(3): 264-8.
  25. Bitter CC, Parmentier M, Subramaniam DS, Byrne L, Buchanan P. An electronic health record alert increases human immunodeficiency virus screening and case identifica- tion in a high-risk emergency department population. Int J STD AIDS. 2022 Jun;33 (7):722-5.
  26. Yeboah-Korang A, Beig MI, Khan MQ, Goldstein JL, Macapinlac DM, Maurer D, et al. Hepatitis C screening in commercially insured U.S. birth-cohort patients: factors

associated with testing and effect of an EMR-based screening alert. J Transl Int Med. 2018 Jun 26;6(2):82-9.

  1. Peterson LJ, Foell R, Lunos S, Heisterkamp B, Greenbaum VJ, Harper NS. Implementa- tion of a screening tool for child sex trafficking among youth presenting to the emer- gency department – a quality improvement initiative. Child Abuse Negl. 2022 Mar; 125:105506.