Abstract
Context
In the United States, nearly 80 % of the adult population reported lifetime alcohol use, with 50 % of those reporting alcohol consumption within the past 30 days in 2019. The expense of excess alcohol intake was estimated to have an annual associated healthcare cost of $28 billion, and there was greater than $221 billion in additional costs due to the detrimental effects of excess alcohol intake on productivity and societal setbacks over the last year. Alcohol use disorder (AUD) provides a major barrier for patients seeking medical treatment, because AUD is consistently regarded as one of the most stigmatized disorders globally. Provider-based discrimination toward patients with AUD may lead to providing a lower quality of care.
Objectives
Our objective was to assess whether patients with a history of AUD and/or positive blood alcohol content (BAC+) affect emergency department (ED) wait times. We hypothesized that patients presenting to the ED with AUD+/BAC+ would have longer wait times. Secondarily, we investigated the impacts of sociodemographics within these analyses.
Methods
We conducted a cross-sectional analysis of the 2019–2021 National Hospital Ambulatory Medical Care Survey (NHAMCS). Individuals’ primary diagnosis had to be of musculoskeletal origin based on ICD-10 codes starting with ‘S’ for skeletal or bodily injuries or ‘M’ for diagnoses related to musculoskeletal or connective tissue conditions. Wait time was quantified from time of entry into the triage system to the time patients were seen by the first provider. We included data points with or without a recorded history of alcohol misuse or dependence (AUD+/−) in their chart and those with a positive or negative blood alcohol content (BAC+/−).
Results
ED wait times among individuals presenting with musculoskeletal injuries with a current history of AUD presenting with BAC- at the time of triage were not significantly different from those without a history of AUD. Individuals who were BAC+ at the time of triage had shorter wait times regardless of AUD history – and only AUD-/BAC+ had shorter wait times. Our binary regression and adjusted models showed that individuals who were AUD-/BAC+ had a significantly shorter wait time (minimum −18.43, standard error [SE]=1.92, t=−9.59, p<0.001; SE=2.97; t=−5.62, p<0.001) compared to individuals who were AUD-/BAC- respectively. Those who were AUD+/BAC+ also had shorter wait times compared to AUD-/BAC− (min=−11.11, SE=4.05; t=−2.75, p=0.006).
Conclusions
Overall, our study showed no significant difference in ED wait times between individuals with and without a history of AUD – indicating that AUD history does not delay being seen. Shorter wait times for those entering the ED BAC+ may be due to their immediate need for treatment due to toxicity or alcohol withdrawal syndrome, having more severe injuries, or harm prevention.
In the United States, nearly 4 in 5 adults reported lifetime alcohol use, with half of those reporting alcohol consumption within the past 30 days in 2019 [1]. The expense of excess alcohol intake was estimated to have an annual associated healthcare cost of $28 billion, and it had greater than $221 billion in additional costs due to its detrimental effects on productivity and societal setbacks over the last year [2]. Excess alcohol consumption carries a major risk for overall disease burden, with alcohol use being one of the top three worldwide contributors to chronic diseases including cardiovascular disease and cancer [3], 4]. As of 2021, 29.5 million people in the United States ages 12 and older have been diagnosed with alcohol use disorder (AUD), an increase from 15.7 million reported cases in 2015 [5], 6]. As a result of the implications that alcohol has on the healthcare system, coupled with the increasing prevalence of AUD, healthcare providers must anticipate alcohol-associated admissions due to increased chronic disease and premature mortality [7]. These admissions can, unfortunately, be impacted by the attitudes of individual healthcare workers, leading to poor patient-provider communication and low-quality healthcare for patients with AUDs and substance use disorders (SUDs) [8].
AUD provides a major barrier for patients seeking medical treatment due to the systemic stigmatization felt by patients with AUD, as AUD is consistently regarded as one of the most stigmatized disorders globally [9], 10]. Provider-based discrimination toward patients with AUD may lead to an improper diagnosis, thus generating distrust between provider and patient, resulting in a potentially lower quality of care [8]. The emergency department (ED) is the most utilized point of entry into the healthcare system for patients experiencing a traumatic injury and one of the most common admission sites for patients with AUD. Upon entry to a healthcare facility, blood alcohol content (BAC) is universally screened for those entering with an injury [11]. Those seeking care for any type of injury are more likely to be BAC-positive (BAC+) than those without an injury [12].
Most studies focus on the stigmatization of patients with AUD in the setting of seeking treatment for the disorder; however, few studies have evaluated AUD as a comorbidity while seeking treatment in EDs for unrelated injuries. Given the increased likelihood of ED staff experiencing individuals in the ED with AUD or a BAC+ test, and the potential stigma that exists for the condition, identifying that potential barriers to individuals seeking care in the ED may improve overall hospital care outcomes. Thus, the primary objective of our study was to evaluate the potential effects of having an AUD diagnosis on wait times during admission for diseases of the musculoskeletal system and injuries of external causes in EDs utilizing the National Hospital Ambulatory Medical Care Survey (NHAMCS) We hypothesize that those with AUD+/BAC+ will have longer wait times. Secondarily, we will investigate the impacts of sociodemographics – age, race, income, and metropolitan statistical area status – within these analyses. Addressing these barriers through actionable changes can result in better wait times, earlier treatment, and improved outcomes in patients with AUD seeking care in the ED.
Methods
We conducted a cross-sectional analysis of the 2019–2021 NHAMCS. This dataset is a nationwide collection on the utilization of ambulatory care services in noninstitutional and short-stay EDs in the United States. Federal, military, and Veterans Administration hospitals were excluded from these surveys. Each ED is randomly assigned a 4-week reporting period in which there is a random collection of patient data and characteristics. Each hospital was trained in data collection, was verified for eligibility, and aided in creating a collection plan prior to participation in the survey. This study was not submitted for ethics review to an Institutional Review Board oversight because it did not meet the regulatory definition of human subject research as defined in 45 CFR 46.102(e) of the Department of Health and Human Services’ Code of Federal Regulations.
Inclusion criteria
For individuals to be included in our analysis, they had to be within three levels of triage need – emergent, urgent, or semi-urgent. These are levels determined upon admission into the ED based on the seriousness of the injury and perceived urgency of care. By choosing these top three levels of triage, we looked to exclude injuries that were not serious enough to warrant immediate care in the ED. Further, individuals must have had their primary diagnosis be of skeletal, muscular, or external injuries based on ICD-10 codes starting with ‘S’ (skeletal or bodily injuries) or ‘M’ (diagnoses related to musculoskeletal or connective tissue conditions) Individuals not within these levels of triage, presenting with a primary diagnosis other than S or M, or having missing data points for either of these, were excluded from the analyses.
Wait times
We included data for wait time for patients for all of the patients meeting the inclusion criteria. Wait times in NHAMCS are collected as a continuous variable in minutes. Wait times were quantified from the time the patient was entered into the triage system for the ED, to the time they were seen by the first provider, whether that was a physician, physician’s assistant, or Advanced Practice Registered Nurse (APRN). Individuals who had missing or incomplete data points for wait times were excluded from the analyses.
Alcohol use disorder
We included data points of patients with or without a recorded history of alcohol misuse or dependence (AUD+/−) in their chart as well as those with a BAC+/−. In NHAMCS, a positive data point for an AUD diagnosis was included as the patient having been previously diagnosed with an AUD, by an appropriate provider, and with that diagnosis existing in their health record. A BAC+ was indicated, upon entry, by the patient having tested positive for any alcohol in their system to the ED, because testing all incoming patients to the ED is protocol. Individuals who had missing or incomplete data points for AUD or BAC were excluded from the analyses.
Also included in the analysis were ethnoracial groupings that were provided in the NHAMCS. These categories were as follows: White, Black/African American, Asian, American Indian/Alaska Native (AI/AN), Hispanic, and ‘Other races not listed’. Additionally, patient sex, whether the ED was located in a rural or urban setting, and whether the ED had a residency training program were also categories.
Statistical analysis
We employed the survey design and sampling weights, provided by NHAMCS after adjusting for multiple data cycles. First, we reported the overall sample size and population estimate of the sample. Next, we recorded the sociodemographics of the sample overall and by AUD/BAC status. After calculating the mean wait times for each sociodemographic of our sample, we assessed the differences in these wait times utilizing binary and multivariable linear regression models. The regression models and mean wait times were bootstrapped (2000 replications) due to unbalanced sample sizes, as performed in other studies [13].
Results
After excluding nonresponses, our sample consisted of 6,558 patients who fell into the criteria of the top three levels of ED triage who presented with M or S ICD-10 codes, representing approximately 18.4 million ED visits between 2019 and 2021 after applying sampling weights. Our sample consisted of a majority of individuals identifying as White (n=3,339, 66.7 %), followed by Black/African American (1,041, 19.6 %) and Hispanic (454, 10.4 %; Table 1). There were slightly more males than females (51.0 and 49.0 %, respectively). A large majority were from metropolitan areas (5,032, 83.5 %). Further, 5,032 (83.5 %) of patients presented at EDs with a residency program. Among those, 180 presented with a history of AUD – 35 of whom presented with a BAC+ upon admission. Among those without a previous history of alcohol misuse (5,738, 96.9 %), 92 presented with a BAC+.
Demographics of the overall sample and by AUD/BAC status.
| AUD/BAC status | AUD-/BAC- | AUD+/BAC- | AUD-/BAC+ | AUD+/BAC+ | Total | Design-based X2 |
|---|---|---|---|---|---|---|
| No., % | No., % | No., % | No., % | No., % | Value, P | |
| Race/ethnicity | ||||||
| White | 3,174 (94.44) | 73 (1.96) | 61 (2.55) | 31 (1.05) | 3,339 (66.74) | 0.91, 0.51 |
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| Black/African American | 1,002 (96.61) | 21 (1.72) | 13 (1.17) | 5 (0.50) | 1,041 (19.58) | |
| Asian | 109 (94.60) | 0 (0.00) | 2 (5.06) | 1 (0.34) | 112 (1.83) | |
| American Indian/Alaska native | 43 (90.80) | 5 (8.28) | 0 (0.00) | 1 (0.92) | 49 (0.73) | |
| Hispanic (white) | 434 (95.53) | 8 (1.39) | 9 (2.22) | 3 (0.86) | 454 (10.35) | |
| Other/multiracial | 48 (97.02) | 2 (2.98) | 0 (0.00) | 0 (0.00) | 50 (0.77) | |
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| Sex | ||||||
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| Female | 2,788 (95.83) | 40 (1.38) | 42 (2.212) | 13 (0.57) | 2,883 (48.99) | 3.38, 0.021 |
| Male | 2,858 (94.38) | 95 (2.66) | 50 (1.827) | 32 (1.13) | 3,035 (51.01) | |
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| Triage level | ||||||
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| Emergent | 469 (78.26) | 31 (4.26) | 49 (13.26) | 21 (4.22) | 570 (9.67) | 53.00, <0.0001 |
| Urgent | 2,334 (94.82) | 73 (2.61) | 36 (1.609) | 22 (0.96) | 2,465 (41.20) | |
| Semi-urgent | 2,843 (98.63) | 31 (1.12) | 7 (0.144) | 2 (0.11) | 2,883 (49.13) | |
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| Urbanicity | ||||||
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| MSA | 4,792 (94.98) | 114 (1.85) | 88 (2.309) | 38 (0.86) | 5,032 (83.46) | 2.59, 0.09 |
| Non-MSA | 854 (95.68) | 21 (2.98) | 4 (0.5331) | 7 (0.81) | 886 (16.54) | |
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| ED has a residency program | ||||||
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| Yes | 1,822 (95.14) | 59 (2.63) | 32 (1.44) | 18 (0.79) | 1,931 (29.16) | 1.06, 0.36 |
| No | 3,511 (95.08) | 67 (1.81) | 55 (2.19) | 25 (0.92) | 3,658 (70.84) | |
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AUD, alcohol use disorder; BAC, blood alcohol content; ED, emergency department; MSA, metropolitan statistical area, and whether the hospital has a residency training program.
The mean wait times were 28.94 min (SE=0.91) for the AUD-/BAC- group, 29.22 min (SE=7.52) for the AUD+/BAC- group, 10.51 min (SE=1.70) for the AUD-/BAC+ group, and 17.82 min (SE=3.97) for the AUD+/BAC+ group (Table 2). The mean wait times were 27.22 min (SE=1.14) for individuals identified as White, 27.77 min (SE=1.61) for Black/African American, 32.79 min (SE=8.93) for Asian, 41.44 min (SE=14.99) for AI/AN, 31.72 min (SE=3.86) for Hispanic, and 18.14 min (SE=3.83) for Other/multiracial. The mean wait time for individuals listed as males was 27.07 min (SE=1.26) and for females was 29.90 min (SE=1.36). EDs with a residency program reported a mean wait time of 30.32 min (SE=1.56), whereas those without a residency program reported a mean wait time of 27.87 min (SE=1.12).
Average ED wait times and regression analysis by AUD/BAC status and sociodemographic factors.
| Wait time, mins | Binary model | Adjusted model | |||
|---|---|---|---|---|---|
| M, SE | Coef, SE | t, P | Coef, SE | t, P | |
| AUD/BAC status | |||||
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| AUD-/BAC- | 28.94 (0.91) | 1 (Ref) | – | 1 (Ref) | – |
| AUD+/BAC- | 29.22 (7.52) | 0.28 (7.56) | 0.04, 0.97 | 3.41 (8.91) | 0.38, 0.70 |
| AUD-/BAC+ | 10.51 (1.70) | −18.43 (1.92) | −9.59, <0.001 | −16.66 (3.08) | −5.40, < 0.001 |
| AUD+/BAC+ | 17.82 (3.97) | −11.11 (4.05) | −2.75, 0.006 | −7.93 (5.01) | −1.58, 0.11 |
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| Race/ethnicity | |||||
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| White | 27.22 (1.14) | 1 (Ref) | – | 1 (Ref) | – |
| Black/African American | 27.77 (1.61) | 0.55 (1.95) | 0.28, 0.78 | 0.40 (2.12) | 0.19, 0.85 |
| Asian | 32.79 (8.93) | 5.57 (9.02) | 0.62, 0.54 | −5.55 (4.41) | −1.26, 0.21 |
| American Indian/Alaska native | 41.44 (14.99) | 14.22 (15.01) | 0.95, 0.34 | 13.76 (15.42) | 0.89, 0.37 |
| Hispanic (white) | 31.72 (3.86) | 4.50 (3.98) | 1.13, 0.26 | 4.39 (4.24) | 1.04, 0.30 |
| Other/multiracial | 18.14 (3.83) | −9.09 (3.97) | −2.29, 0.022 | −8.77 (4.08) | −2.15, 0.032 |
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| Sex | |||||
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| Female | 29.90 (1.36) | 1 (Ref) | – | 1 (Ref) | – |
| Male | 27.07 (1.26) | −2.83 (1.76) | −1.61, 0.11 | −2.39 (1.96) | −1.22, 0.22 |
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| Triage level | |||||
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| Emergent | 24.68 (3.10) | 1 (Ref) | – | 1 (Ref) | – |
| Urgent | 28.62 (1.40) | 3.94 (3.11) | 1.27, 0.21 | 1.86 (3.43) | 0.54, 0.59 |
| Semi-urgent | 29.08 (1.31) | 4.39 (3.11) | 1.41, 0.16 | 2.46 (3.49) | 0.70, 0.48 |
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| Urbanicity | |||||
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| MSA | 29.14 (0.97) | 1 (Ref) | – | 1 (Ref) | – |
| Non-MSA | 24.74 (2.36) | −4.39 (2.54) | −1.73, 0.08 | −2.97 (3.06) | −0.97, 0.33 |
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| ED has a residency program | |||||
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| Yes | 30.32 (1.56) | 1 (Ref) | – | 1 (Ref) | – |
| No | 27.87 (1.12) | −2.45 (1.94) | −1.27, 0.21 | 2.41 (2.20) | 1.10, 0.27 |
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AUD, alcohol use disorder; BAC, blood alcohol content; Coef, coefficient; ED, emergency department; M, mean; MSA, metropolitain statistical area; SE, standard error. Bold values indicate statistical significance (P<.05).
Regression analysis
Results from our binary regression showed that compared to individuals with no history of alcohol misuse and were BAC-, we found that individuals who were AUD-/BAC+ had a shorter wait time (−18.43 min, SE=1.92, t=−9.59, p<0.001). Additionally, those who were AUD+/BAC+ had shorter wait times compared to AUD-/BAC- as well (−11.11 min, SE=4.05; t=−2.75, p=0.006). In our adjusted model, only those with AUD-/BAC+ significantly differed from the reference group – having a 16.7 min shorter wait time on average (SE=2.97; t = −5.62, p<0.001) compared to the AUD-/BAC- group.
Discussion
Our findings showed that the ED wait times among individuals presenting with muscular or skeletal injuries with a recorded history of AUD who were BAC- at the time of triage were not significantly different from those without a history of AUD. Our binary regression models show that individuals who were BAC+ upon entry to the ED had shorter wait times regardless of AUD history – and only those who were AUD-/BAC+ had shorter wait times in the adjusted regression models. These results may show that potential stigma toward individuals with AUD within EDs may not apply to wait times.
The shorter wait times among individuals who enter EDs with a BAC+ status may be due to alcohol toxicity or alcohol withdrawal syndrome – which may require immediate attention [14], 15]; however, other factors may also play key roles in this finding. A white paper for the American Academy of Emergency Medicine (AAEM) entitled “Emergency Department Management of Patients With Alcohol Intoxication, Alcohol Withdrawal, and Alcohol Use Disorder,” highlights that individuals entering the ED as BAC+ are often involved in incidents involving serious trauma including motor vehicle collisions, physical violence, and suicide attempts – often requiring prompt attention [15]. Further, a publication by Sarkar et al. [16] entitled “Clinical Practice Guidelines for Assessment and Management of Patients with Substance Intoxication Presenting to the Emergency Department,” states that individuals presenting in EDs who are BAC+ may be being escorted by law enforcement authorities, which would likely require immediate attention. They also state that safety for medical personnel and other patients may be at risk due to increased likelihood of agitation or violence and that prompt treatment may reduce potential harm.
Further, results from our study showed a decrease in wait time in BAC+ patients regardless of triage level, for M and S ICD-10 codes. The lower mean wait times for those with BAC+ admission status, regardless of AUD status or race/ethnicity, shows a correction in ED decision-making compared to a study from Goldfarb et al. [17] to address these patients in a more timely manner Although the reason for the shorter wait times cannot be discerned from our analysis, the progress shown here can be applied in a cross-specialty toward all types of injuries to any potential biases employed by healthcare providers and current practices when dealing with individuals under the influence of alcohol and to ensure that the quality of care does not differ from patient to patient. Although patients who are BAC+ are being attended to in a timelier manner within EDs, this may also reflect healthcare workers potentially trying to remove the potential of risky behavior in the waiting room, regardless of concern for treatment. By prioritizing patients with a BAC+, patients experiencing worse injuries may be delayed in seeing a provider. Therefore, along with BAC, we encourage healthcare workers to consider injury severity, altered mental status, and additional comorbidities to determine patient priority to admission.
Our findings regarding wait times and ethnoracial groupings were contrary to previous research in that within our study there were no significant differences between White, Black, and Hispanic groups. A 10-year study utilizing the NHAMCS from 2006 to 2015 showed that the overall wait time decreased in populations with mental health and SUDs; however, when controlling for AUD and mood disorders, Black patients were reported to have an increased wait time compared to White patients. Conversely, Hispanic patients had shorter wait times than White patients with preexisting anxiety or drug use disorders [18]. A separate study from 2023 utilizing NHAMCS data showed that among individuals presenting to an ED who had been diagnosed with an SUD, Black patients had significantly longer wait times than White patients [17]. Notably, individuals included in the ‘Other/race not listed’ category had significantly shorter wait times compared to White patients in both the binary and adjusted models, whereas AI/AN patients had longer mean wait times – nearly 14 min – although they were not statistically significant, likely due to the small sample size.
Recommendations and future research
Our initial recommendation for EDs is to continue the active implementation of unbiased decisions toward all patients seeking care in their facility, especially those with traditionally stigmatizing disorders such as AUD. EDs should also work to educate their staff on different ethnic and racial presentations for a BAC+ individual, which could aid in their assessment and selection of triage level. Both the white paper from the AAEM and the Clinical Practice Guidelines referenced previously provide direct guidance on the screening for, identification of, and treatments necessary to help individuals presenting as BAC+ within EDs. The AAEM paper also provides guidance for verbal and pharmaceutical de-escalation methods for agitation and the use of physical restraints if violence becomes an imminent risk. Furthermore, in line with the tenets of osteopathy, osteopathic ED providers should continue to approach patient care by viewing the body as a unit. This should entail more than just treating the physical ailments of the patient. By providing equitable care regardless of past diagnoses, ED providers can support the mental, emotional, and spiritual well-being of patients with AUD. Future research can help identify whether there is a differentiating presentation in those with a BAC+, garnering a shorter wait time, to ensure an equal level of care and attention in the ED. These findings could aid in the assessment and treatment of BAC+ individuals. Additionally, distinctions between an AUD and an SUD should be made and specified while performing an analysis of the NHAMCS data. Focusing on the two separately, as well as in combination, will give valuable insight into the treatment of patients with historically stigmatizing diagnoses.
Limitations
The limitations of our study included the general nature of the ICD-10 diagnosis entries for M & S codes, which does not take into account the severity of the injury, which is partly why we limited the levels of triage included in our analysis. Within the context of the results, due to the nature of alcohol-induced injuries, individuals presenting as BAC+ may have sustained more severe injuries than others in the same triage level, which is a confounding factor unable to be discerned from the data; however, in contrast, patients presenting as BAC+ may be more likely to be placed in incorrect triage levels due to altered presentations. Making the diagnosis of AUD is also contingent on self-reported alcohol intake. This makes identifying AUD patient-dependent, thus potentially skewing the actual number of affected patients due to reporting bias by patients. Further, as a retrospective analysis of ED visits, the researchers had no control over the variables collected or reported and are limited to the data as collected within the NHAMCS. Because some ethnoracial groupings had smaller sample sizes, generalizability among these groups may be limited. Additionally, the findings may not be generalized to federal, military, and Veterans Administration hospitals because they are not included in the NHAMCS. Lastly, because this is a cross-sectional analysis, the findings of the study are correlational, not causal, and should be interpreted appropriately.
Conclusions
Overall, our study showed no significant difference in ED wait times between individuals with and without a history of AUD, although those presenting under the influence of alcohol had shorter wait times. This indicates that the history of AUD does not delay being seen. However, shorter wait times for those entering the ED as BAC+ may be due to their immediate need for treatment due to toxicity or alcohol withdrawal syndrome, having more severe injuries related to intoxication, or the need to be seen promptly due to agitation or aggressive behavior to prevent harm to medical personnel or patients.
Funding source: National Institute of Child Health
Funding source: Human Development
Award Identifier / Grant number: U54HD113173
Funding source: Human Resources Services Administration
Award Identifier / Grant number: U4AMC44250-01-02, PI
Award Identifier / Grant number: R41MC45951
Funding source: National Institute of Justice
Award Identifier / Grant number: 2020-R2-CX-0014
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Research ethics: This study was not submitted for ethics review to an institutional review board oversight because it did not meet the regulatory definition of human subject research as defined in 45 CFR 46.102(e) of the Department of Health and Human Services’ Code of Federal Regulations. This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: None declared.
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Research funding: Dr. Hartwell receives research funding from the National Institute of Child Health and Human Development (U54HD113173; Shreffler), Human Resources Services Administration (U4AMC44250-01-02, PI: Audra Haney; R41MC45951 PI: Hartwell), and previously from the National Institute of Justice (2020-R2-CX-0014 PI: Beaman).
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Data availability: Not applicable.
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Articles in the same Issue
- Frontmatter
- Cardiopulmonary Medicine
- Review Article
- Signs and symptoms of vertebrobasilar insufficiency secondary to atherosclerosis: a systematic review
- General
- Brief Report
- Geographical distribution of osteopathic urology residents and match trends in the United States
- Medical Education
- Original Article
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- Neuromusculoskeletal Medicine (OMT)
- Brief Report
- Osteopathic manipulation to increase lactation quantity: a prospective case series
- Public Health and Primary Care
- Review Article
- Emergency department wait times in concordance with blood alcohol content and subsequent alcohol use disorder
- Clinical Image
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