Abstract
Introduction:
Early intervention for patients at risk for Posttraumatic Stress Disorder (PTSD) relies upon the ability to engage and follow trauma-exposed patients. Recent requirements by the American College of Surgeons Committee on Trauma (College) have mandated screening and referral for patients with high levels of risk for the development of PTSD or depression. Investigations that assess factors associated with engaging and following physically injured patients may be essential in assessing outcomes related to screening, intervention, and referral.
Methods:
This investigation was a secondary analysis of data collected as part of a United States level I trauma center site randomized clinical trial. All 635 patients were ages ≥18 and had high PTSD symptom levels (i.e., DSM-IV PTSD Checklist score ≥35) at the time of the baseline trauma center admission. Baseline technology use, demographic, and injury characteristics were collected for patients who were followed up with over the course of the year after physical injury. Regression analyses were used to assess the associations between technology use, demographic and injury characteristics, and the attainment of follow-up outcome assessments.
Results:
Thirty-one percent of participants were missing one or more 3-, 6- or 12-month follow-up outcome assessments. Increased risk of missing one or more outcome assessments was associated with younger age (18–30 versus ≥55 Relative Risks [RR] = 1.78, 95 % Confidence Interval [CI] = 1.09, 2.91), lack of cell phone (RR = 1.32, 95 % CI = 1.01, 1.72), no internet access (RR = 1.47, 95 % CI = 1.01, 2.16), public versus private insurance (RR = 1.47, 95 % CI = 1.12, 1.92), having no chronic medical comorbidities (≥4 versus none, RR = 0.28, 95 % CI = 0.20, 0.39), and worse pre-injury mental health function (RR = 0.99, 95 % CI = 0.98, 0.99).
Conclusions:
This multisite investigation suggests that younger and publicly insured and/or uninsured patients with barriers to cell phone and internet access may be particularly vulnerable to lapses in trauma center follow-up. Clinical research informing trauma center-based screening, intervention, and referral procedures could productively explore strategies for patients at risk for not engaging and adhering to follow-up care and outcome assessments.
Keywords: Injury, Follow-up, Posttraumatic stress disorder, Age, Cell phone, Internet, Insurance Status
Introduction
Traumatic physical injuries are highly prevalent and have become a major public health concern worldwide [1]. Approximately 1.5–2.5 million individuals are so severely injured each year in the United States that they require inpatient hospital admissions [2–4].
Almost one-fourth of hospitalized US patients are at risk for developing Posttraumatic Stress Disorder (PTSD) symptoms in the 12 months post-injury; PTSD is one of a number of mental health conditions that can develop after physical injury trauma [5–8]. Recent requirements by the American College of Surgeons Committee on Trauma (College) now mandate screening and referral procedures for patients at high risk for the development of PTSD [9]. Some, but not all, PTSD early intervention studies have demonstrated the ability to reduce PTSD symptom levels longitudinally over the course of the months and years following an injury [10–13].
A key element of conducting successful post-injury investigations, whether they be randomized clinical trials or prospective cohort studies, is the ability to engage physically injured trauma survivors in long-term follow-up. Prior large-scale prospective cohort studies have identified injured patient characteristics that are associated with lack of follow-up attainment [5,12,14–17]; these characteristics include younger age, racial and ethnic minority group membership, intentional injury, being male, less educational attainment, being unemployed, and public or self-pay insurance status. Large-scale randomized clinical trials involving general physical trauma and orthopedic trauma patients have demonstrated increased loss of follow-up attainment for patients who are younger than 30 years of age, male, and substance-using [18]. Clinical trials have demonstrated difficulty in follow-up, introducing threats to trial internal validity [5,19,20]. A small number of studies have focused specifically on enhancing follow-up among injury survivors recruited into prospective cohort and clinical trial investigations [18,21–23].
Technological innovation has been posited as a means of assisting longitudinal follow-up after injury [21,24,25]. It has been suggested that mHealth, mobile devices connected to health care, can aid in enhancing follow-up attainment through the passive collection of longitudinal data over time [24]. Social media platforms may be a possible means to follow, track, and locate injury survivors after acute care visits [21,26,27]. Beyond longitudinal follow-up attainment, technological innovations have also been proposed as a broad-reach intervention delivery strategy for acute care patient populations [24,25,28,29]. The literature review, however, revealed few investigations that have integrated assessments of technology use and capacity into post-injury follow-up assessments.
The current secondary analysis examined the interplay between clinical and demographic characteristics and patient technology use to assess rates of follow-up attainment among patients enrolled in a national US PTSD clinical intervention trial. The study team hypothesized the clinical and demographic characteristics that have been previously associated with follow-up difficulties (e.g., younger age), as well as lack of access to technological innovation, would be independently associated with loss to follow-up.
Patients and methods
Design overview
The investigation was a secondary study and analysis of data embedded within a larger pragmatic randomized trial designed to assess the impact of a collaborative care intervention on PTSD symptoms [11]. The Trauma Survivors Outcomes and Support (TSOS) pragmatic trial was orchestrated by the study team’s data coordinating center, located at University of Washington’s Harborview Medical Center, in close collaboration with the National Institutes of Health (NIH) Health Care Systems Research Collaboratory [30,31]. The Western Institutional Review Board approved the protocol prior to study initiation and informed consent was obtained for participation in the study. Sites recruited into the study constituted a representative subsample of all US Level I trauma centers [30]. Recruitment for the trial began in January of 2016, and 12-month patient study follow-up ended in November of 2019. Patients were assessed at baseline as inpatients and again at 3-, 6- and 12-months after injury; the 12-month injury timepoint constituted the final study outcome assessment.
Patient inclusion/exclusion criteria
Survivors of intentional and unintentional injuries ≥18 years of age were included in the investigation. Prisoners and non-English-speaking patients were excluded. Patients whose index injury was a self-inflicted injury/suicide attempt or who were psychotic and required immediate psychiatric treatment were also excluded from the trial. Patients were required to provide two pieces of contact information to ensure adequate follow-up rates.
Electronic health record (EHR) PTSD screen
The study team had previously developed a 10-domain electronic health record (EHR) screen to detect patients at risk for the development of PTSD [32]. Patients identified by EHR evaluation as high risk for PTSD, with a score of ≥3 domains positive, were then formally screened for study entry with the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) [33] PTSD Checklist (PCL-C), anchored to the acute injury event [29,34,35]. Patients scoring ≥35 on the PCL-C, indicating that they had symptoms of PTSD that were at least moderate, were followed longitudinally over the course of the year after the index injury admission.
Patient-reported outcome assessments
The study team attempted to contact all patients at 3-, 6- and 12-months after the index injury admissions. Participants were compensated $ 20 for the baseline, $ 30 for the 3-month, $ 35 for the 6-month, and $ 50 for completing the 12-month assessment, for a total of up to $ 135. The follow-up interviews contained questions assessing violence risk behaviors, mental health symptoms such as PTSD, physical functioning, and health services utilization. All measures had been previously administered in prior study team trials [30,34,36]. Individual assessments used in this investigation are described below.
Technology Use and Capacity:
Patients were asked a series of questions at baseline and follow-up regarding their technology use and capacity. At baseline, patients were asked: “Do you have a cell phone? Do you text message? Do you have access to internet? Do you have an email address?” At 3-, 6- and 12-month assessments, the same questions were asked as well as the following few questions to examine the concept of cell phone volatility [37]: “Since the last study interview have you used the internet? Since the last study interview have you used email? Since the last study interview have you used Social Networking Sites (e.g., Facebook, Twitter, etc.…)? Since the last study interview have you used text messaging?” Also, a series of questions were asked to assess the use of technology in contact with medical providers: “Since the last study interview have you been in contact with an actual health care provider or mental health provider via phone (i.e., had a phone call with a provider)? Since the last study interview have you been in contact with an actual health care provider or mental health provider via text message? Since the last study interview have you been in contact with an actual health care provider or mental health provider via email? Since the last study interview have you been in contact with an actual health care provider or mental health provider via an internet website?”
PTSD Symptoms:
The PCL-C was used to assess the symptoms of posttraumatic stress disorder [29,34,35]. At baseline during the index hospitalization, patients were asked to rate their symptoms since the injury event, and the 3-, 6- and 12-month interviews queried patients about their symptoms over the month prior to the interview. Prior investigations, including studies with injured patients, have established the psychometric equivalence of the DSM-IV and Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) versions of the PCL-C [33,38–40]. Because the study team had validated the 10-domain EHR screen with the PCL-C for DSM-IV and DSM-IV Clinician Administered PTSD Scale (CAPS) [32], all primary assessments were performed with the DSM-IV version of the PCL-C.
Other Questionnaire Items:
At baseline, single items were used to assess patients’ pre-injury gun ownership and carriage of a weapon other than a firearm (e.g., knife, club). The 9-item Patient Health Questionnaire (PHQ-9) brief depression severity measure was used to assess depressive symptoms [41]. The Alcohol Use Disorder Identification Test (AUDIT) was used to assess alcohol use problems [34,42]. The Medical Outcomes Study Short Form Physical Components Summary Score (MOS SF PCS) SF-12 was used to assess physical function in the month prior to the injury admission [34,43]. The Medical Outcomes Study Short Form Mental Components Summary Score (MOS SF MCS) SF-12 was used to assess mental health function in the month prior to the injury admission [34,43]. The trauma history screen from the National Comorbidity Survey was used to assess pre-injury lifetime trauma exposures (e.g., combat, physical assaults, motor vehicle crashes) that occurred before the index injury admission [30,44]. Questionnaire items also assessed pre-injury substance use, number of medical conditions and health service utilization, as well as demographic characteristics (e. g., marital status) [30]. Patient age was taken from the EHR and then was corroborated during the baseline interview.
Trauma registry data
Medical record data from the 25 sites’ trauma registries were used to derive injury severity scores and injury mechanism. Other clinical characteristics, including length of hospital and intensive care unit stays and insurance status were also obtained from trauma registries. Insurance status was treated as a categorical variable (i.e., private insurance versus public insurance/uninsured status).
Statistical analysis
The study team first compared the baseline clinical, demographic, and injury characteristics of patients who missed one or more follow-up assessments at the 3, 6, and 12 time points versus patients who completed all follow-up assessments. Next, the study team compared the cell phone instability (i.e., change in either cell phone number or device) of patients who missed one or more follow-up assessments versus patients who completed all follow-up assessments. Comparisons were also made for internet, email, text message, and social networking use, as well as the use of these technological methods of contacting health care providers were assessed for patients with and without missing follow-up assessments. The association between individual patient cell phone turnover and change in number (i.e., cell phone volatility) and follow-up attainment was also assessed.
Next, the associations between demographic, clinical and injury characteristics and missing one or more follow-up assessments were ascertained. Baseline demographic, injury and clinical characteristics including age groupings (i.e., 18–30, 31–54, ≥55 years of age), gender, race, ethnicity, insurance status, employment status, educational status, marital status, intentional versus unintentional injury, injury severity, injury type, prior hospitalizations, ICU stays, current tobacco use, substance use, psychiatric diagnosis, and medical co-morbidities, as well as cell phone, text message, email, and internet technology usage were entered into a Poisson regression model with robust standard error using a stepwise backwards elimination procedure. [45] Variables with significant independent associations with missing any follow-up assessments at the P <0.05 level were retained in the final models that accounted for the clustering of characteristics across sites. Statistical analyses were performed using SAS Software Version 9.4 (SAS Institute Inc., Cary, NC, USA) and SPSS version 25 (SPSS Software IBM).
Results
Of the 635 patients followed longitudinally in the trial, 201 (31.7 %) did not complete one or more follow-up assessments. The demographic and clinical characteristics of patients who missed one or more follow-up assessments versus patients who completed all follow-up assessments are presented in Table 1.
Table 1.
Baseline Patient Characteristics (N = 635).
| No. Patients (%) | |||
|---|---|---|---|
| Follow-up Complete (n = 434) | Missing any follow-up (n = 201) | P-value | |
| Basic Demographic | |||
| Age Categories (years) | <0.0001 | ||
| 18–30 | 126 (29.2) | 89 (45.2) | |
| 31–54 | 227 (52.5) | 91 (46.2) | |
| 55+ | 79 (18.3) | 17 (8.6) | |
| Male gender | 213 (49.1) | 114 (56.7) | 0.08 |
| Race | 0.81 | ||
| White | 220 (50.7) | 95 (47.3) | |
| Black | 150 (34.6) | 68 (33.8) | |
| American Indian | 11 (2.5) | 4 (2.0) | |
| Asian/PI | 5 (1.2) | 3 (1.5) | |
| Other | 48 (11.1) | 31 (15.4) | |
| Hispanic | 0.26 | ||
| No | 369(85.2) | 161(80.9) | |
| Yes | 64(14.8) | 38(19.1) | |
| Insurance | 0.002 | ||
| Private | 153 (35.3) | 44 (21.9) | |
| Public/None | 281 (64.7) | 157 (78.1) | |
| Marital Status | 0.06 | ||
| Married/living with a partner | 133 (30.7) | 45 (22.4) | |
| Other | 300 (69.3) | 156 (77.6) | |
| Employed prior to injury | 263 (60.9) | 113 (56.8) | 0.33 |
| Level of education | 0.03 | ||
| High school or less | 285 (66.1) | 152 (75.6) | |
| At least some college | 146 (33.9) | 49 (24.4) | |
| No. of comorbid conditions | <0.0001 | ||
| 0 | 95 (21.9) | 97 (54.2) | |
| 1 | 88 (20.3) | 29 (16.2) | |
| 2 | 66 (15.2) | 25 (14.0) | |
| 3 | 49 (11.3) | 8 (4.5) | |
| ≥4 | 136 (31.3) | 20 (11.2) | |
| Baseline Symptoms | |||
| PCL-4 total score, mean (SD) | 51.9 (12.0) | 52.3 (11.7) | 0.9 |
| PHQ-9 total score, mean (SD) | 13.9 (6.0) | 14.4 (5.4) | 0.34 |
| SF12 PCS, mean (SD) | 49.0 (9.9) | 50.4 (9.2) | 0.07 |
| SF12 MCS, mean (SD) | 45.6 (13.2) | 43.2 (13.7) | 0.03 |
| AUDIT total score, mean (SD) | 7.3 (8.6) | 7.3 (8.7) | 0.98 |
| Pre-injury self-report drug (opioid/stimulant) use | 104 (24.0) | 57 (28.6) | 0.31 |
| Tobacco use | 240 (55.3) | 116 (57.7) | 0.59 |
| psychiatric diagnosis | 179 (41.2) | 67 (33.3) | 0.08 |
| Substance use diagnosis/positive BAC on admission | 268 (61.8) | 129 (64.2) | 0.61 |
| Injury Related | |||
| Injury severity score, mean (SD) | 0.08 | ||
| 0–8 | 88 (21.7) | 49 (30.2) | |
| 9–15 | 152 (37.4) | 45 (27.8) | |
| ≥16 | 166 (40.9) | 68 (42.0) | |
| TBI | 0.1 | ||
| None | 266 (65.5) | 122 (75.3) | |
| Mild | 78 (19.2) | 23 (14.2) | |
| Moderate/Severe | 62 (15.3) | 17 (10.5) | |
| Days in hospital, mean (SD) | 13.3 (13.1) | 11.8 (11.7) | 0.1 |
| ICU admission | 258 (59.5) | 119 (59.2) | 0.95 |
| Prior inpatient hospitalization | 184 (42.4) | 65 (32.3) | 0.01 |
| No. of serious prior traumas | 0.81 | ||
| 0 | 29 (6.7) | 9 (8.1) | |
| 1–3 | 164 (37.8) | 40 (36.0) | |
| 4–6 | 137 (31.6) | 33 (29.7) | |
| 7–8 | 57 (13.1) | 11 (9.9) | |
| ≥9 | 47 (10.8) | 18 (16.2) | |
| Intentional injury | 144 (33.2) | 94 (46.8) | 0.01 |
| Treatment Condition | |||
| Control | 258 (59.5) | 112 (55.7) | |
| Intervention | 176 (40.5) | 89 (44.3) | |
| Technology | |||
| Cell phone | 380 (88.0) | 156 (78.8) | 0.002 |
| Text message | 370 (86.5) | 161 (82.6) | 0.22 |
| Internet access | 379 (87.5) | 163 (83.6) | 0.25 |
| 351 (81.6) | 147 (75.8) | 0.1 | |
PCL-4 = PTSD Checklist 4, DSM-IV. PHQ-9 = 9-Item Patient Health Questionnaire. SF-12 PCS = Medical Outcomes Study 12-Item Short Form, physical components summary. SF-12 MCS = Medical Outcomes Study 12-Item Short Form, mental components summary. AUDIT = Alcohol Use Disorders Identifications Test. BAC = Blood Alcohol Concentration. TBI = Traumatic Brain Injury. ICU = Intensive Care Unit.
Although patients who missed one or more follow-up assessments were observed to exhibit greater cell phone instability compared to patients who completed all follow-up assessments, these comparisons did not attain statistical significance (Table 2). Patients who missed one or more follow-up assessments were significantly more likely at baseline to report not using the internet, text, or social networking sites (Table 3). Similarly, patients who missed one or more follow-up assessments were less likely to contact health care providers via phone, text, email, or internet (Table 3).
Table 2.
Phone Use and Instability.
| No. Patients (%) | |||
|---|---|---|---|
| Follow-up Complete (n = 434) | Missing any follow-up (n = 108) | P-value | |
| Any Change of physical phone (0–12 months) | 215 (49.5) | 59 (55.1) | 0.25 |
| Any change of phone number (0–12 months) | 176 (40.6) | 53 (49.1) | 0.16 |
| Any change of physical phone/phone number (0–12 months) | 240 (55.3) | 65 (60.2) | 0.35 |
Table 3.
Technology Use and Follow-Up.
| No. Patients (%) | |||
|---|---|---|---|
| Follow-up Complete (n = 434) | Missing any follow-up (n = 108) | P-value | |
| Internet use | 381 (87.8) | 88 (80.0) | 0.08 |
| Email use | 354 (85.1) | 79 (80.6) | 0.36 |
| Text messaging | 406 (93.6) | 97 (88.2) | 0.06 |
| Use of social networking sites | 347 (83.4) | 74 (75.5) | 0.08 |
| Provider contact via phone | 295 (68.0) | 49 (44.6) | 0.001 |
| Provider contact via text message | 125 (29.8) | 18 (17.8) | 0.01 |
| Provider contact via email | 130 (32.6) | 14 (16.3) | 0.004 |
| Provider contact via internet | 117 (28.3) | 13 (13.8) | 0.001 |
Poisson regression identified baseline variables that were independently associated with lack of follow-up attainment (Table 4). Patients between the ages of 18–30 were significantly more likely than those over the age of 55 to miss at least one follow-up assessment. Lack of cell phone and internet were also significantly associated with lack of follow-up. Public or self-pay insurance status, no medical comorbidities, and lower MCS scale scores were also associated with greater risk of lack of follow-up attainment. Of note, intervention versus control group status was not associated with significant differential follow-up attainment.
Table 4.
Characteristics Associated with Missing Any Follow-Up.
| Variable | Relative Risk | 95 % Confidence Interval | P |
|---|---|---|---|
| Public Insurance Status (reference is private) | 1.47 | 1.12, 1.92 | 0.01 |
| SF-12 MCS Scale Score (per point) | 0.99 | 0.98, 0.99 | 0.001 |
| Medical Comorbidities (vs. none) | |||
| 1 | 0.54 | 0.37, 0.78 | 0.001 |
| 2 | 0.58 | 0.39, 0.86 | 0.01 |
| 3 | 0.34 | 0.20, 0.59 | 0.0001 |
| ≥4 | 0.28 | 0.20, 0.39 | <0.0001 |
| Does not have Cell Phone | 1.32 | 1.01, 1.72 | 0.04 |
| Does not have Internet | 1.47 | 1.01, 2.16 | 0.05 |
| Age Groups (vs. 55 and older) | |||
| 18–30 | 1.78 | 1.09, 2.91 | 0.02 |
| 31–54 | 1.54 | 0.97, 2.45 | 0.07 |
Discussion
This US national investigation examined patient characteristics associated with lack of follow-up attainment in a multisite level I trauma center-based randomized clinical trial of a preventive intervention for physically injured patients at risk of developing PTSD. The investigation found that a number of patient demographic and clinical characteristics were associated with lack of follow-up attainment, including younger age, public versus private insurance, fewer chronic medical comorbidities, and worse pre-injury mental health function. Prior prospective cohort and randomized clinical trial investigations have described these and similar patient characteristics being associated with lack of follow-up attainment [5,12,14–17].
A novel finding was that lower levels of patient technology use and capacity were independently associated with lack of follow-up attainment. Patients who, at the baseline trauma center index assessment, endorsed lacking a cell phone were significantly less likely to attain follow-up. Similarly, at baseline, patients who endorsed not having internet access demonstrated significantly less follow-up attainment.
Beyond follow-up attainment, this investigation also examined patterns of patient technology use. As with prior study team investigations, a substantial group of patients, over 50 %, demonstrated turnover in their original cell phone number and physical phone. However, cell phone volatility was not significantly associated with lack of follow-up attainment. These findings corroborate and extend the observation from Kelly et al., who documented cell phone volatility rates of 81 % over the course of the six months post-injury [37].
Of interest, diminished general mental health function pre-injury, as assessed with the SF MOS MCS, but not elevated PTSD symptoms in the immediate aftermath of the injury was associated with lack of follow-up attainment. This may indicate that enduring depressive and anxiety symptoms that predate the physical injury may have a greater impact on follow-up attainment than acute symptoms in the immediate aftermath of the trauma event [11]. Also of note, the current investigation utilized the DSM-IV PTSD Checklist. A key difference between the DSM-IV and DSM-5 PTSD Checklists is that the DSM-5 includes novel items that assess negative alterations in cognition and mood. Future investigations could explore the associations between the use of the DSM-5 PTSD Checklist and follow-up attainment.
This present investigation has limitations. Observations regarding characteristics associated with lack of follow-up attainment occurred in the context of a randomized clinical trial; although intervention and control group status was not associated with differential follow-up, future prospective cohort studies could include individual technology capacity and use in assessments of follow-up attainment. Future investigations could productively focus on the mechanisms by which technology use may enhance retention and engagement in clinical injury investigations. Also, observations of cell phone volatility were occurring concurrently with observations of follow-up attainment. Finally, rates of follow-up attainment described in this current research protocol had the advantage of having dedicated staff for study follow-up, which may underestimate follow-up rates of routine clinical practice, where staff time may be more limited, and patients may not be motivated by financial follow-up incentives.
Conclusions
Beyond these considerations, this 25 US level 1 trauma site investigation contributes to the understanding of patient demographic, clinical, and technological characteristics that contribute to patient follow-up retention in randomized clinical trial injury investigations. The investigation corroborates prior studies that have documented factors such as age, socioeconomic status, prior medical history, etc. The investigation presents novel data on individual technology use and capacity, such as cell phone volatility and internet use, as factors that can potentially enhance follow-up attainment following injury hospitalizations. Future prospective cohort and clinical trial investigations could productively examine the interplay between individual demographic and clinical characteristics, and technology use and capacity in the attainment of patient engagement in post-injury clinical research and interventions. The study findings suggest that in the current research and clinical context that focuses exclusively on individual-level technology characteristics, a substantial subpopulation of physically injured trauma survivors may be challenging to follow longitudinally. Future clinical research could productively focus on technology platforms such as emergency department health information exchanges that allow for population-level trauma center patient follow-up across research and clinical contexts [46,47].
Sources of funding
This research was supported within the National Institutes of Health (NIH) Health Care Systems Research Collaboratory by cooperative agreement 1UH2MH106338-01/4UH3MH106338-02 from the NIH Common Fund and by UH3 MH 106338-05S1 from NIMH. Support was also provided by the NIH Common Fund through cooperative agreement U24AT009676 from the Office of Strategic Coordination within the Office of the NIH Director. This research was also supported in part by the Patient-Centered Outcomes Research Institute (PCORI) Award (IHS-2017C1-6151). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, or of PCORI or its Board of Governors.
Footnotes
CRediT authorship contribution statement
Jake Shoyer: Writing – original draft, Writing – review & editing, Conceptualization, Methodology. Kenneth J. Ruggiero: Writing – review & editing, Writing – original draft. Khadija Abu: Writing – review & editing, Writing – original draft. Navneet Birk: Writing – review & editing, Writing – original draft. Cristina Conde: Writing – review & editing, Writing – original draft. Paige Ryan: Writing – review & editing, Writing – original draft. Tanya Knutzen: Writing – review & editing, Writing – original draft. Allison Engstrom: Writing – review & editing, Project administration, Writing – original draft. Joan Russo: Data curation, Formal analysis, Writing – review & editing, Methodology, Writing – original draft. Jin Wang: Formal analysis, Writing – original draft, Writing – review & editing, Methodology. Douglas F Zatzick: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing.
Declaration of competing interest
The authors report no conflicts of interest.
Data sharing
The data is available from the authors by request.
References
- [1].Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global burden of disease study 2019. Lancet. 2020;396 (10258):1204–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [2].Web-based injury statistics query and reporting system (WISQARS) [Online] [Internet]. National Center for Injury Prevention and Control; 2021. [cited August 23, 2023]. Available from: https://wisqars.cdc.gov/reports/?o=NFI&y1=2020&y2=2020&d=0&i=0&m=3000&g=00&s=0&a=ALL&g1=0&g2=199&a1=0]&a2=199&r1=INTENT&r2=NONE&r3=NONE&r4=NONE&adv=true. [Google Scholar]
- [3].Io Medicine. Reducing the burden of injury: advancing prevention and treatment. Washington, DC: National Academy Press; 1999. [PubMed] [Google Scholar]
- [4].Committee on military trauma care’s learning health system and its translation to the civilian sector, board on health sciences policy, populations bothos, division ham, national academies of sciences e, and medicine a national trauma care system: integrating military and civilian trauma systems to achieve zero preventable deaths after injury. In: Berwick D, Downey A, Cornett E, editors. A national trauma care system: integrating military and civilian trauma systems to achieve zero preventable deaths after injury. Washington (DC): National Academies Press (US); 2016. [PubMed] [Google Scholar]
- [5].Zatzick DF, Rivara FP, Nathens AB, Jurkovich GJ, Wang J, Fan MY, et al. A nationwide US study of post-traumatic stress after hospitalization for physical injury. Psychol Med 2007;37(10):1469–80. [DOI] [PubMed] [Google Scholar]
- [6].Shalev A, Liberzon I, Marmar C. Post-traumatic stress disorder. N Engl J Med 2017; 376(25):2459–69. [DOI] [PubMed] [Google Scholar]
- [7].Michaels AJ, Michaels CE, Moon CH, Smith JS, Zimmerman MA, Taheri PA, et al. Posttraumatic stress disorder after injury: impact on general health outcome and early risk assessment. J Trauma 1999;47(3):460–6. discussion 6–7. [DOI] [PubMed] [Google Scholar]
- [8].deRoon-Cassini TA, Hunt JC, Geier TJ, Warren AM, Ruggiero KJ, Scott K, et al. Screening and treating hospitalized trauma survivors for posttraumatic stress disorder and depression. J Trauma Acute Care Surg 2019;87(2):440–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9].American college of surgeons committee on trauma. Resources for optimal care of the injured patient2022.
- [10].Shalev AY, Ankri Y, Israeli-Shalev Y, Peleg T, Adessky R, Freedman S. Prevention of posttraumatic stress disorder by early treatment: results from the Jerusalem trauma outreach and prevention study. Arch Gen Psychiatry 2012;69(2):166–76. [DOI] [PubMed] [Google Scholar]
- [11].Zatzick D, Jurkovich G, Heagerty P, Russo J, Darnell D, Parker L, et al. Stepped collaborative care targeting posttraumatic stress disorder symptoms and comorbidity for US trauma care systems: a randomized clinical trial. JAMA Surg 2021;156(5):430–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].Bryant RA, Nickerson A, Creamer M, O’Donnell M, Forbes D, Galatzer-Levy I, et al. Trajectory of post-traumatic stress following traumatic injury: 6-year follow-up. Br J Psychiatry 2015;206(5):417–23. [DOI] [PubMed] [Google Scholar]
- [13].O’Donnell ML, Lau W, Tipping S, Holmes AC, Ellen S, Judson R, et al. Stepped early psychological intervention for posttraumatic stress disorder, other anxiety disorders, and depression following serious injury. J Trauma Stress 2012;25(2): 125–33. [DOI] [PubMed] [Google Scholar]
- [14].McLean SA, Ressler K, Koenen KC, Neylan T, Germine L, Jovanovic T, et al. The AURORA Study: a longitudinal, multimodal library of brain biology and function after traumatic stress exposure. Mol Psychiatry 2020;25(2):283–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [15].Kim H, Cutter GR, George B, Chen Y. Understanding and preventing loss to follow-up: experiences from the spinal cord injury model systems. Top Spinal Cord Inj Rehabil 2018;24(2):97–109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Poulsen E, Lund B, Roos EM. The Danish hip arthroscopy registry: registration completeness and patient characteristics between responders and non-responders. Clin Epidemiol 2020;12:825–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [17].Vos L, Ngan E, Novelo LL, Williams MW, Hammond FM, Walker WC, et al. Predictors of missed follow-up visits in the national traumatic brain injury model systems cohort study. Arch Phys Med Rehabil 2022;103(12):2325–37. [DOI] [PubMed] [Google Scholar]
- [18].Madden K, Scott T, McKay P, Petrisor BA, Jeray KJ, Tanner SL, et al. Predicting and preventing loss to follow-up of adult trauma patients in randomized controlled trials: an example from the FLOW trial. J Bone Joint Surg Am 2017;99(13): 1086–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Soderstrom CA, DiClemente CC, Dischinger PC, Hebel JR, McDuff DR, Auman KM, et al. A controlled trial of brief intervention versus brief advice for at-risk drinking trauma center patients. J Trauma 2007;62(5):1102–11. discussion 11–2. [DOI] [PubMed] [Google Scholar]
- [20].Sommers MS, Dyehouse JM, Howe SR, Fleming M, Fargo JD, Schafer JC. Effectiveness of brief interventions after alcohol-related vehicular injury: a randomized controlled trial. J Trauma 2006;61(3):523–31. discussion 32–3. [DOI] [PubMed] [Google Scholar]
- [21].Bhatia-Lin A, Boon-Dooley A, Roberts MK, Pronai C, Fisher D, Parker L, et al. Ethical and regulatory considerations for using social media platforms to locate and track research participants. Am J Bioeth 2019;19(6):47–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [22].Rajotte E, Fuchs C, Zatzick D. Engaging and following trauma survivors in real world clinical investigations. J Nerv Ment Dis 2003;191(4):265–8. [DOI] [PubMed] [Google Scholar]
- [23].Somerson JS, Bartush KC, Shroff JB, Bhandari M, Zelle BA. Loss to follow-up in orthopaedic clinical trials: a systematic review. Int Orthop 2016;40(11):2213–9. [DOI] [PubMed] [Google Scholar]
- [24].Ranney ML, Stettenbauer EG, Delgado MK, Yao KA, Orchowski LM. Uses of mHealth in injury prevention and control: a critical review. Curr Epidemiol Rep 2022;9(4):273–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [25].Price M, Ruggiero KJ, Ferguson PL, Patel SK, Treiber F, Couillard D, et al. A feasibility pilot study on the use of text messages to track PTSD symptoms after a traumatic injury. Gen Hosp Psychiatry 2014;36(3):249–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].Shaikh HJ, Brodell BJ, Soin SP. Social media perceptions of hip fractures: a two-year cross-sectional analysis. Injury 2023;54(4):1176–9. [DOI] [PubMed] [Google Scholar]
- [27].Giordano V, Kojima KE, Valderrama-Molina CO, Azi ML, Bidolegui F, Pires RE. Adapting non-medical applications for medical use: ethical limits, coverage, and validation. Injury 2023;54(Suppl 3):S74–s80. [DOI] [PubMed] [Google Scholar]
- [28].Ruggiero KJ, Davidson TM, Anton MT, Bunnell B, Winkelmann J, Ridings LE, et al. Patient engagement in a technology-enhanced, stepped-care intervention to address the mental health needs of trauma center patients. J Am Coll Surg 2020; 231(2):223–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [29].Zatzick D, O’Connor SS, Russo J, Wang J, Bush N, Love J, et al. Technology-enhanced stepped collaborative care targeting posttraumatic stress disorder and comorbidity after injury: a randomized controlled trial. J Trauma Stress 2015;28 (5):391–400. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [30].Zatzick DF, Russo J, Darnell D, Chambers DA, Palinkas L, Van Eaton E, et al. An effectiveness-implementation hybrid trial study protocol targeting posttraumatic stress disorder and comorbidity. Implement Sci 2016;11:58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [31].NIH. NIH collaboratory Bethesda, MD: National Institutes of Health; 2020. [Available from: https://rethinkingclinicaltrials.org/. [Google Scholar]
- [32].Russo J, Katon W, Zatzick D. The development of a population-based automated screening procedure for PTSD in acutely injured hospitalized trauma survivors. Gen Hosp Psychiatry 2013;35(5):485–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [33].American psychiatric association. American psychiatric association task force on DSM-IV. diagnostic and statistical manual of mental disorders: dsm-iv-tr. 4th editor. Washington, DC: American Psychiatric Association; 2000. p. 943. [Google Scholar]
- [34].Zatzick D, Jurkovich G, Rivara FP, Russo J, Wagner A, Wang J, et al. A randomized stepped care intervention trial targeting posttraumatic stress disorder for surgically hospitalized injury survivors. Ann Surg 2013;257(3):390–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [35].Weathers F, Ford J. Psychometric review of PTSD Checklist (PCL-C, PCL-S, PCL-M, PCL-PR). Stamm BH, editor. Measurement of stress, trauma, and adaptation. Lutherville, MD: The Sidran Press; 1996. p. 250–1. [Google Scholar]
- [36].Zatzick D, Donovan DM, Jurkovich G, Gentilello L, Dunn C, Russo J, et al. Disseminating alcohol screening and brief intervention at trauma centers: a policy-relevant cluster randomized effectiveness trial. Addiction 2014;109(5):754–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [37].Kelly CM, Van Eaton EG, Russo JE, Kelly VC, Jurkovich GJ, Darnell DA, et al. Technology Use, preferences, and capacity in injured patients at risk for posttraumatic stress disorder. Psychiatry 2017;80(3):279–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [38].Hoge CW, Riviere LA, Wilk JE, Herrell RK, Weathers FW. The prevalence of post-traumatic stress disorder (PTSD) in US combat soldiers: a head-to-head comparison of DSM-5 versus DSM-IV-TR symptom criteria with the PTSD checklist. Lancet Psychiatry 2014;1(4):269–77. [DOI] [PubMed] [Google Scholar]
- [39].Rosellini AJ, Stein MB, Colpe LJ, Heeringa SG, Petukhova MV, Sampson NA, et al. Approximating a DSM-5 diagnosis of PTSD using DSM-IV criteria. Depress Anxiety 2015;32(7):493–501. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [40].American psychiatric association. Diagnostic and statistical manual of mental disorders, fifth edition: DSM-5. Arlington, VA: American Psychiatric Association; 2013. Fifth ed. [Google Scholar]
- [41].Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001;16(9):606–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [42].Babor TF, Grant M. From clinical research to secondary prevention: international collaboration in the development of the Alcohol Use Disorders Identification Test. Alcohol Health Res World 1989;13:371–4. [Google Scholar]
- [43].Ware JE, Kosinski M, Keller SD. A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Med Care 1996;34(3): 220–3. [DOI] [PubMed] [Google Scholar]
- [44].Kessler R, Sonnega A, Bromet E, Hughes M, Nelson CB. Posttraumatic stress disorder in the national comorbidity survey. Arch Gen Psychiatry 1995;52(12): 1048–60. [DOI] [PubMed] [Google Scholar]
- [45].Heinze G, Wallisch C, Dunkler D. Variable selection - A review and recommendations for the practicing statistician. Biometrical j Biometrische Zeitschrift 2018;60(3):431–49. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [46].Whiteside LK, Vrablik MC, Russo J, Bulger EM, Nehra D, Moloney K, et al. Leveraging a health information exchange to examine the accuracy of self-report emergency department utilization data among hospitalized injury survivors. Trauma Surg Acute Care Open 2021;6(1):e000550. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [47].Prater L, Bulger E, Maier R, Goldstein E, Thomas P, Russo J, et al. Emergency department and inpatient utilization reductions and cost savings associated with trauma center mental health intervention: results from a 5-year longitudinal randomized clinical trial analysis. Ann Surg 2024;279(1):17–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data is available from the authors by request.