The great majority of cases are represented by these four patterns. It is important to note that patterns 21, 51, 43, 45, and 53 are considerably smaller than the first four patterns, and they are similar in size. This means that the patterns of missingness across the variables is somewhat consistent, and that no dominant pattern to the missingness is readily seen. Based on this extensive analysis, it was determined that variables total GCS, alcohol screen result and THC Combo are not missing completely at random.When missing values in each variable account for less than 5%, those values can be missing at random and list wise deletion can be performed relatively safely is appropriate to do. This holds true for all the variables except for THC Combo, positive for drugs, alcohol screen result, age in years, ethnicity and total GCS. These variables, three quantitative and three categorical, were found to have greater than 5% missing values. On observation of the missing value analysis, it was observed that most cases had these two variables as missing, perhaps suggesting a relationship, or an effect. Furthermore, the Little’s MCAR test revealed that missing data may not be missing completely at random. Deleting cases with missing values can reduce the statistical power of the analysis and result in biased outcomes and estimates. Therefore, the use of multiple imputation is appropriate for this dataset and this study. Another method in SPSS that can be utilized is the Replacing Missing Values method. The Linear Interpolation method will be utilized. The Linear Interpolation method is a simple statistical method used by SPSS which estimates the value of one variable from the value of another and using regression methods to find the line of best fit. Using the Replacing Missing Values method in this study will help solve the problem of bias and ensure that power is not decreased because a large majority of the sample size will be preserved. It is important to consider the implications associated with imputing or replacing missing data. Multiple imputation or missing value replacement analyses will avoid bias only if enough variables predictive of missing values are included in the replacement method. If variables that may be predictive of the estimates are not included in the model,horticultural vertical farming for example the effect of age on alcohol result, replacement computation will underestimate these associations and bias the final analysis.
Therefore, it is preferrable to include as many predictive variables as possible in the model when either imputation or replacing missing value methods are utilized.Replacing missing values was utilized to minimize the many problems associated with missing data. The absence of data reduces statistical power and can also lead to bias in the estimation of parameters and analyses. Finally, missing data can diminish the representatives of the sample size and cases . It is important to consider that though replacing or imputing data is a common approach to the problem of missing data, it still does not allow analyses of actual data that is provided by actual participants, or in this case, data entered by abstractors and hospital registry systems. In gaining a larger sample size, and perhaps a more representative sample, confidence is lost that actual responses provided are those analyzed. It is important to note that methods used to account for missing data only provide researchers with the best estimated guess of what actual data may have been had it been documented in the first place. It is this ideology that influenced the decision to include some of the variables with missing data to be multiply imputed. Though multiple imputation process was utilized, it presented a complication in terms of the number of iterations and the subsequent analysis. 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 variables selected for missing value replacement were age and alcohol screen result. 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, 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. The first aim of this study was to identify the prevalence of THC in a purposive sample of TBI patients. In this study, it was found that 27.7% of study participants tested negative for THC, and 6.2% of study participants had tested positive for THC on presentation to the emergency department. An overwhelmingly large percentage of the data was attributed as missing, 66% to be exact. This large percentage of missing data makes it difficult to have confidence in the 6% prevalence rate found in this study. National surveys on drug use and health have documented an increase in individual daily marijuana use over the last 5 years, with almost 22 million users each month in the United States . Federally, marijuana use remains illegal in the United States, however, in 2017, the year corresponding to the data of this study, 29 states had legalized marijuana for medical use, and 8 states for recreational use.
A recent study has found that marijuana use tends to be higher in states that have legalized its use compared to marijuana use in the United States overall . As a result, it is difficult to have confidence in the low prevalence rate found in this study. Another important consideration to make regarding the large percentage of missing data is the scarcity of studies investigating marijuana use and prevalence in TBI patients. As noted earlier in the literature review, only one study, by Nguyen et al. ,indoor agriculture vertical farming investigated the effects of THC presence on mortality in patients who had sustained a TBI, and they reported a prevalence rate of 18.4%. However, Nguyen’s et al. study involved a 3-year retrospective review of data obtained from a local hospital-based database, which can perhaps help explain their higher prevalence rate. The availability of a larger sample size because of 3 years’ worth of data may have contributed to that study’s higher prevalence rates. A recent publication has already noted areas of improvement necessary for the NTDB to improve data quality and completeness . It is important to note that the dataset used for this study reflects only one year worth of data, from 2017. At the start of this research study, the last dataset available for use was from 2017; datasets from 2018 and onward had not yet been released. Therefore, establishing previous prevalence rates for comparison, from the NTDB, could not be calculated because the presence of THC was never abstracted nor documented in earlier NTDB databases established before 2017. Finally, it is imperative to consider what happens at the bedside, or the clinical setting, when trying to understand why there is a large percentage of missing data when it comes to the presence of THC. When it comes to the care of the trauma patient, it is a common expectation amongst trauma centers, that a urine drug screen would be completed on every trauma patient presenting the emergency department. Despite this, drug screens are often either not obtained, not resulted, or not documented by the clinical team. At times, clinicians may simply forget to draw a screen and send it to the lab. This commonly occurs in patients who do not receive a foley catheter, a practice that is now encouraged in hospitals. As a result, patients may take a while to urinate, often doing so in the absence of the trauma nurse, or later in another unit or when under the care of a non-trauma nurse who then simply forgets to collect the sample. At times, the sample may be collected, but the result was never documented in the medical record. All these clinical factors can also contribute to the missing data by simply not including it in the medical record, and ultimately not making it into the trauma registry itself. When examining the differences between the group of participants with THC and those without and the influence on TBI severity, it was noted the group of participants who tested positive for THC had worsened GCS scores compared to those who tested negative for THC on presentation to the emergency department. The findings were significant, indicating that individuals who were positive for THC had a worsened neurological status as evidenced by lower GCS scores than those who tested negative. This finding is different than findings reported in the study by Nguyen et al. , which examined the relationship between the presence of THC and mortality after TBI. Their study only focused on mortality after TBI and not TBI severity. Based on toxicology test results, participants who tested positive for THC had a significantly higher number of males. Additionally, participants in the group that tested negative for THC were significantly older than participants who tested positive. This is supported by the literature, which indicates that men are more likely than women to use marijuana, as well as almost all other types of drugs . Individuals 18-29 years of age were the largest group of marijuana uses in the US in 2019 . Marijuana use dropped among older age groups, with seniors the least likely to use marijuana . No differences were noted in Non-Hispanic versus Hispanic groups regarding marijuana use. Marijuana use was higher in the American Indian and Black participants when compared to all other race groups. Participants who identified as ‘other’ had a greater proportion of testing negative compared to all other race groups. Marijuana use disorder was greatest among African Americans compared to other race/ethnicities .