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

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 [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.


Weighted Count

(N = 517, SE = 66)

Weighted% (95% CI)

Sex Trafficking


71.61 (59.57, 81.20)

Labor Trafficking


28.39 (18.80, 40.43)



35.55 (26.68, 45.53)



64.45 (54.47, 73.32)



87.31 (79.74, 92.32)

Minor (< 18)


30.81 (18.45, 46.72)

Payer Medicare


7.08 (3.49, 13.83)



41.34 (31.55, 51.88)

Private Insurance


10.61 (5.95, 18.20)

Self-pay, no charge, other


40.97 (31.57, 51.09)


Large metropolitan (>= 1 million)


57.16 (46.33, 67.35)

Small metropolitan (50,000-999,999)


33.74 (25.11, 43.61)

Non-metropolitan (< 50,000)


9.10 (4.32, 18.17)


Non-Hispanic White


52.07 (41.42, 62.54)

Non-Hispanic Black


25.38 (16.46, 37.01)

Hispanic, any race


16.58 (9.43, 27.49)

Non-Hispanic Other


5.97 (2.74, 12.52)

Median Household Income

< $48,000


40.53 (29.64, 52.45)

$48,000 – $60,999


28.45 (19.74, 39.14)

$61,000 – $81,999


22.36 (15.37, 31.35)

>= $82,000


8.66 (4.60, 15.69)

Weekend Admission


28.28 (21.22, 36.59)

ED Outcome

Treated and released


73.82 (61.62, 83.20)



26.18 (16.80, 38.38)

Died in ED



Hospital Region Northeast


8.11 (3.17, 19.22)



25.57 (17.47, 35.79)



47.22 (34.96, 59.83)



19.10 (12.11, 28.79)

Hospital Trauma Center Not a trauma center


24.11 (15.89, 34.83)

Trauma center level I


45.94 (33.68, 58.70)

Trauma center level II


20.00 (13.36, 28.84)

Trauma center level III


9.95 (4.34, 21.23)

hospital teaching status Metropolitan non-teaching


14.48 (8.79, 22.92)

Metropolitan teaching


79.52 (69.08, 87.09)



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.


Odds Ratio (95% CI)


6.54 (3.59, 11.92)

Minor (< 18)

1.76 (1.02, 3.04)



0.91 (0.35, 2.35)


3.37 (1.59, 7.15)

Private insurance


Self-pay, No charge, Other

8.51 (3.94, 18.39)


Large metropolitan (reference)


Small metropolitan

0.82 (0.50, 1.36)


0.37 (0.14, 0.97)





0.77 (0.43, 1.39)


0.65 (0.32, 1.33)


0.64 (0.26, 1.58)

Median Household Income in Patient’s ZIP

< $48,000


$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).


Weighted Count

(N = 787, SE = 119)

% (95% CI)



90.71 (84.56, 94.57)

Minor (< 18)


23.02 (11.78, 40.11)




10.03 (5.78, 16.83)



54.61 (44.71, 64.16)

Private insurance


11.64 (7.11, 18.47)

Self-pay, No charge, Other


23.72 (15.54, 34.46)


Large metropolitan


59.26 (46.29, 71.06)

Small metropolitan


33.01 (22.44, 45.63)



7.73 (4.25, 13.65)

Race/Ethnicity White


55.51 (43.76, 66.68)



26.63 (16.04, 40.82)



12.86 (8.23, 19.53)



0.5 (2.31, 10.45)

Median Household Income

< $48,000


36.40 (27.37, 46.50)

$48,000 – $60,999


26.63 (18.95, 36.04)

$61,000 – $81,999


26.75 (16.63, 40.07)

>= $82,000


10.22 (6.13, 16.55)

Weekend admission


25.45 (20.43, 31.22)

ED Outcome

Treated and released


39.53 (26.28, 54.51)



60.47 (45.49, 73.72)




Hospital Region

Northeast 96 12.17 (7.33, 19.52)



25.54 (15.07, 39.87)



48.49 (34.11, 63.14)



13.80 (6.77, 26.11)

Hospital Trauma Center

Not a trauma center


23.78 (15.72, 34.30)

Trauma level I


54.44 (40.42, 67.78)

Trauma level IIa


9.60 (5.24, 16.94)

Trauma level III


12.18 (6.35, 22.10)

Hospital Teaching Status Metropolitan non-teaching


10.99 (5.68, 20.18)

Metropolitan teaching


83.79 (73.77, 90.48)



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.


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


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