Since the dependent variable, total GCS, was not selected for imputation/replacement, it was recommended and deemed appropriate to utilize the Replacing Missing Values function in SPSS to establish estimates for a select group of variables with missing data values. Replacing Missing Values method, a different form of imputation, allows the creation of new variables from existing ones by replacing them with estimates computed with a variety of methods. For this study, the Linear Interpolation method was used. This method utilizes the last valid value before the missing value and the first valid value after the missing value.The variable age was selected due to its effect on traumatic brain injury incidences as well as post TBI outcomes . Additionally, the use of alcohol and other substances is prevalent in young adults with more than half of those who die from overdoses being younger than 50 years of age . The impact of age on TBI, substance abuse and outcomes could not be overlooked, and omitting this large percentage of cases will bias analysis results. The variable of alcohol screen result was also important to replace because of the known impact and association alcohol abuse has on TBI incidence and outcomes. Alcohol and TBI are closely associated, with up to 50% of adults noted to drink more alcohol than recommended prior to their injury,rolling benches hydroponics and ultimately incurring worse outcomes . The variables of total GCS, THC Combo and positive other drugs were not included. Total GCSis the dependent variable, and having estimates instead of actual data seemed conceptually and logically inappropriate.
For being the main predictor variables, both THC Combo and positive other drugs were not included to ascertain a more accurate and true account of the effects they may have on TBI severity. The Replacing Missing Values method yielded 7872 entries for age, with only 3 missing cases. The mean for age in the new dataset with replaced values was 31.19 years with a standard deviation of 26.1 compared to 33.78 years with a standard deviation of 27.3 for the non-replaced dataset. The replacing missing values method yielded 7822 valid entries for alcohol screen result, compared to 2087 entries in the non-replaced dataset. In the new dataset, alcohol screen result had a mean of .03, a standard deviation of .0752, with a minimum value of .00 and a maximum value of .66. The original dataset, with 7875 cases, was used for the missing value replacement method, because as mentioned previously, it is preferrable to include as many predictive variables as possible in the model so that the new replaced/imputed values are indeed best estimates. Once the dataset had the missing variables for age and alcohol screen result replaced, the dataset was then amended to only include participants greater than 16 years of age to meet the inclusion criteria. Once those cases were removed, the final dataset consisted of 4910 unique cases. While patients with a positive THC test had significantly lower GCS scores on admission when compared to patients who did not have THC, or were not known to have THC on admission to the ED. Once other variables, including age, presence of alcohol on admission, sex, presence of other drugs and comorbidities were considered, findings indicated that the presence of THC was indeed associated with lower GCS scores, hence worsened TBI severity, however, the findings were not statistically significant. Age, race, ethnicity, motor vehicle collisions, and motorcycle collisions were also not shown to be independent predictors of TBI severity. Conversely, sex, presence of alcohol on admission, presence of other drugs, and a history of substance abuse were identified as independent predictors of TBI severity.
Being female was associated with higher GCS scores indicating a less severe TBI. Similar to findings in previous studies examining TBI and sex, 67% of the study sample were male, while 32.9% of the sample were female. Gender differences in TBI incidence have been well documented, with men more likely to engage in injury-prone work or high-risk dangerous behavior . Additionally, women are less likely to be involved in a physical altercation than men . Furthermore, gender differences, can influence clinical outcomes between men and women. Research studies have proposed that female steroid hormones may exert some neuroprotective effects through anti-inflammatory and antioxidant processes and may therefore explain why women tend to have better cognitive and functional outcomes after a TBI when compared to men . As expected, this study showed that the presence of alcohol and drugs at the time of injury were independent predictors of lower GCS scores, or otherwise a moderate or more severe TBI. The TBI literature does provide evidence of a close relationship between substance abuse disorder and TBI . Research has identified alcohol use as a common element in individuals with a brain injury. Large percentages of patients who have sustained a TBI have a history of alcohol abuse and drug use, up to 79% and 33% respectively . In another study by Andelic et al. found that 35% of TBI patients were under the influence of alcohol. In this study there was a large percentage of alcohol levels missing, therefore, data was imputed. If in the original data set values were consistently measured and recorded, then findings regarding alcohol presence at presentation would possibly be much higher. Nevertheless, with the imputed values only 23 unique cases did not have an alcohol result. This too, may bias the finding, but like other study findings, this study’s finding showed that when alcohol was present at the time of injury participants had a lower GCS score, hence a more severe TBI indicating a worsened neurological status at presentation. Likewise, patients who were positive for at least one substance/drug were also found to have lower GCS scores and worsened TBI severity.
Similar to findings in studies involving alcohol and brain injury, substance abuse was associated with poorer neuropsychological and functional outcomes . Literature reviews also support this finding, with findings indicating that almost 40% of TBI patients had a positive toxicology screen, or had reported using drugs, with marijuana use accounting for more than half of the drug use . Similar to the large percentage of missing data for alcohol screen, the variable presence of other drugs also had a large percentage of missing data . This is important to consider, as a large percentage of missing data may cause bias. Yet, in this study, even with the large percentage of missing data, the presence of other drugs was found to have a negative influence on TBI severity as indicated by lower GCS scores compared to those who did not have other drugs present on admission. It is important to consider that both alcohol and drug use at the time of injury can confound GCS assessment in trauma patients. Although findings from this study corroborate findings from TBI literature examining substance use, it may be judicious to acquire GCS scores after any intoxicating substances have worn off, perhaps hours or even up to a few days post injury. The GCS score is often assessed numerous times in a trauma patient’s hospital stay, however, the NTDB data set does not include other GCS scores, only the first one on arrival at the hospital. Finally, the large percentage of missing data for both alcohol screen result and presence of other drugs should be considered and addressed. Because blood alcohol and drug measurements in emergency departments are likely biased towards intoxicated and incoherent patients. This can help explain the large percentage of missing data when it comes to these two variables. As mentioned previously, clinicians often will forget to draw a blood sample for alcohol and or drugs,hydro tray and even if they do, these results may not be entered into the medical record or the registry in a timely and accurate manner. These variations in practice create a large proportion of missing data as it relates to alcohol and toxicology screens performed and documented. For purposes of this study, alcohol screen results were imputed, but as helpful as imputation can be to an analysis, it can also misrepresent the actual number of participants with a positive alcohol result thereby biasing the results. Participants with a known history of substance abuse were found to have slightly higher GCS scores when compared to patients who did not. For every participant who had a history and a diagnosis of substance abuse, GCS scores increased by .075 units. Higher GCS scores indicated better neurological function and a less severe TBI. The study by Nguyen et al. and Leskovan et al. explore the relationship between marijuana use, and alcohol, on mortality. The effect of marijuana on TBI severity is far less studied than alcohol, though preclinical studies have shown that the presence of marijuana is associated with some neuroprotective effects, including attenuated cell apoptosis, alleviation of cerebral edema, and improved cerebral blood flow . Further studies are needed to investigate the effects of marijuana on TBI severity alone, not when combined with alcohol or other substances. These findings cannot be discussed without addressing the issue of missing data. Variables that influence GCS scores and TBI severity, such as alcohol screen result, sex, presence of drugs, history of cancer, history of mental and personality disorder, and history of alcohol abuse all had some element of missing data. All the aforementioned variables had less than 6% of the data missing, with some of them having less than 1% missing data . Similarly, history of comorbid conditions all had less than 3% missing data.
The two variables that had a large percentage of data missing were the presence of THC and the presence of other drugs . Despite the missing data, both those variables were found to have a statistically significant influence on GCS scores, hence, TBI severity. Though statistically significant, the validity of those findings should be cautiously interpreted within the context of such large percentage of missing values for these hypothesized explanatory variables. One of the leading causes of injuries resulting in TBI incidence are collision related, such as motor vehicle or motorcycle crashes. Furthermore, almost half of the US states have legalized marijuana for medical use with some states allowing recreational use of marijuana. Therefore, collision type mechanism of injuries was examined to see if there was any mediating influence on TBI severity in the presence of THC. It was determined that motor vehicle collisions did not influence, or mediate, the relationship between THC and TBI severity. However, motorcycle collisions suggested a partial influence on TBI severity. This was an expected result as studies have shown that head injuries are the leading cause of death in fatal motorcycle crashes . It is therefore not surprising to see that GCS scores were reduced when motorcycle collisions were examined for mediating influences on TBI severity in the presence of THC. In one study by Steinemann et al. , THC positivity among road traffic collisions in one US state tripled, with the number of THC positive patients presenting to the highest-level trauma center doubling. However, this data should be interpreted cautiously within the context of such large percentages of missing values for hypothesized explanatory variables. Finally, it is important to note the surprising finding that only 22 participants were found to have been involved in a motor vehicle collision, and only 16 were involved in a motor cycle crash. In the original data set, only 16,324 of 997,970 were involved in a motor vehicle collision, and 12,826 of 997,970 were involved in a motor cycle collision. In 2015, the CDC reported that more than 2.3 million people presented to the emergency department with motor vehicle-related injuries. Because not every single motor vehicle collision warrants a trauma activation or for the patient to be seen by a trauma surgeon, the number represented in the trauma registries would be much less. Hence, this may somewhat explain the lower numbers presented in the 2017 NTDB data set . Several limitations of this study should be noted. Primarily, this study was a retrospective cohort study, therefore it may be missing potentially relevant data. Retrospective cohort studies, though time efficient and cost effective, can be limited due to the nature of data collected. Missing data on several important predictor variables represents another drawback. The patient population in this study was heavily skewed towards moderate and severe TBI patients from one year of available data.