The Trauma Quality Programs research database housed in the NTDB for the year 2107 is the time frame for this study. Though initially the researcher intended to include data from 2013-2017, data from years other than 2017 had to be excluded. In effort to standardize the type of data collected by local, regional, and state trauma registries, the NTDB designs a National Trauma Data Standard Data Dictionary that is designed to establish a national standard for the collection of trauma registry data while also providing the operational definitions for the NTDB. In summary, the NTDS provides the exact standards for trauma registry data submitted to the NTDB. Prior to the 2017 data dictionary, trauma registry programs had limited selections regarding data related to drug use. The options provided by the NTDB registry only included whether drug use was present and whether it was confirmed by a test or by prescription. It did not allow the trauma data abstractor to specifically identify the type of drug found. In 2017, the data dictionary was revised to include a drug screening category that aimed at recording the first positive drug screen result within 24 hours after the first hospital encounter. Typically, in trauma hospitals reporting to NTDB and within the context of trauma, acquisition of a urine and blood drug and alcohol screen is standard expectation of practice. It then provided a list of 15 options for the abstractor to choose from. Because it was impossible to isolate cannabinoid use in earlier data sets, the researcher was only able to use the 2017 NTDB data set, which at the beginning of the study was the latest available data set by the NTDB. As of February 13th, 2021 the 2018 NTDB data set was not available. All the trauma data used in this study are organized by an element INC_KEY, which is a designated unique identifier for each record. The designated unique identifier INC_KEY expresses a unique clinical visit/episode by an individual at a participating trauma center. It is important to consider that an individual could have been included/counted more than once in the registry because of more than one traumatic event within the year. The Participant Use File Trauma data set contained all the demographic, environmental,commercial vertical farming and clinical data information. However, it did not identify or delineate TBI cases as such. Therefore, a separate data set that contained ICD 10 Diagnosis Codes had to be utilized to identify TBI cases which then could be used to create a merged data set that is complete.

The 2017 PUF Trauma data set was uploaded to SPSS version 25 on September 10th, 2020. The PUF Trauma data set included a total of 997,970 unique identifier cases. A frequency analysis was performed to ensure no duplicate cases were found . The PUF Trauma data set included 328 unique variables. Next, the PUF ICD-10 Diagnosis data set was uploaded and examined. The PUF ICD Diagnosis data set is organized via the same INC_KEY identifiers. The PUF ICD Diagnosis data set included 3 variables: ICD CM diagnosis code, ICD CM diagnoses code Blank Inappropriate Values and ICD Clinical Modification version. This data set was used to distinguish TBI cases from cases related to other traumas such as pneumothorax, liver laceration or femur fractures.Additionally, the selection of TBI related ICD 10 codes was corroborated by examining a list of codes found in existing studies on TBI which validated the inclusion of the specifically identified TBI codes in this study. Though these other studies included ICD 10 Diagnosis codes related to concussion injuries , these codes were excluded from this study as the researcher was only interested in identifying cases with either a moderate or severe TBI and concussions are designated as mild TBI. The following codes were ultimately selected: S02.0xx ; S02.1 ; S06.1 ; S02.19XD ;S06.2 ; S06.30 ; S06.31 ; S06.32 ; S06.33 ; S09.X . Next, PUF ICD 10 Diagnosis codes were regrouped into the following categories via numerical representation. ICD 10 Diagnosis code S02.0xx was grouped into group 3683-3687; S02.1 into group 3688; S02.19XD into group 3738; S06.1 into group 4008-4025; S06.2 into groups 4026-4045; S06.3, S06.31, S06.32, and S06.33 into groups 4046-4095; S09.X into groups 4310-4311. A missing value analysis for the ICD 10 Diagnosis code variable revealed no missing values. A new variable titled ‘TBI” was created in the PUF ICD-10 Diagnosis data set where if a TBI related ICD 10 code was assigned, the value ‘1’ was given. If not, it was assigned a value of ‘0’. A frequency analysis on the ‘TBI’ variable was then done to determine the number of TBI codes which were found to be 131,518.The final data set to be used in the analysis consisted of 15 variables not including the cases themselves: sex, age in years, race , ethnicity, alcohol screen result, total GCS, cannabinoids , positive for drugs , comorbid condition currently receiving chemotherapy, comorbid condition disseminated cancer, comorbid condition mental/personality disorder, comorbid condition substance abuse disorder, comorbid condition alcohol use disorder , crash intrusion and motorcycle crash.

The new data set contained 324 total variables. The variables present were identified as subsets of the following categories: work-related injury, patients occupational industry, patient’s occupation, ICD 10 primary external cause, ICD 10 place of injury code, ICD 10 additional External cause code, protective devices, child specific restraint, airbag deployment variables, report of physical abuse, investigation of physical abuse, caregiver at discharge, transport modes, initial emergency service system vital signs , time to EMS response, time from dispatch to ED/hospital, interfacility transfer, pre-hospital cardiac arrest, trauma center criteria for admission, vehicular/pedestrian or other risk, mechanism of injury , total time between ED/hospital arrive and ED discharge, systolic blood pressure, pulse rate, temperature, respiratory rate and assistance, pulse oximetry, supplemental oxygen, height, weight, primary method of payment, signs of life, emergency room disposition, hospital discharge disposition, comorbid conditions , total intensive care unit length of stay, total ventilator days, length of stay , hospital complications, procedural interventions, medications administered, blood transfusions, withdrawal of life support, facility level, year of discharge, ISS, and AIS derived ISS. Variables that would not be included in the final analysis were removed. Example of variables removed were ventilator days, length of stay and blood transfusions. Some of the variables that incorporated more than one value, such as race, ethnicity, alcohol screen result and drugs, were concatenated to form new variables. A description of how each variable was dealt with is delineated below. This was done to facilitate the analysis of more than one categorical variable to be treated as one. In SPSS the missing values analysis module provides two different methods to analyze missing data, the first is the Expectation-Maximization method and the second is the Regression Imputation method . Expectation-Maximization provides statistical estimates such as estimated means, covariances and correlations. The Regression Imputation method is dependent on the Expectation-Maximization method to fill in the missing values using predicted values from a regression of one variable on another within the analysis . Both analyses were performed to assess any patterns of missing values. A missing value analysis was conducted. This analysis produces a univariate statistics table showing the total number of cases within each variable,commercial vertical farming systems the mean and standard deviations, the missing counts and percentages and the number of extremes. It is here that the extent of missing data can be observed and identified.A separate-variance t Test table is displayed by SPSS as part of the missing value analysis. This table can help identify variables whose pattern of missing values may be influencing the quantitative variables.

When age is missing, the mean alcohol screen result is .0031 compared to .0652 when age is present. This large difference in mean alcohol screen result scores when age is present indicates that the data missing is not missing at random. However, it is important to consider that these differences cannot be solely attributed to the patient’s provision of information, as these are all clinical tests performed by hospital personnel. If data is missing, it is most likely due to the reasons mentioned above, and not necessarily because the patient was choosing to withhold information. The cross tabulations of categorical variables versus indicator variables table shows similar information to that found in the separate-variance t test table. This table provides information that can help determine whether there are differences in missing data among different categories. Males were found to have a documented value in alcohol screen 30.4% compared to 19.3% in females. This may indicate that there are differences in missing values among males and females. Similarly, males were found to have a documented THC result 28.4% of the time compared to females at 22.1% of the time. This indicates that the data is missing at random. Differences were smaller between males and females for the variables of total GCS and ethnicity, with males having a documented result for total GCS 94% of the times compared to 93.1% for females. Ethnicity was documented for 93.2% of the times with male participants and 92.9% for females. The small difference indicates that the data is not missing at random. For the variable of race, no drastic differences were noted between ethnicity, and THC Combo. However, the variable of alcohol screen result was found to be largely different in the American Indian group when compared to the other groups . Looking at ethnicity, non-Hispanic patients had a value for alcohol screen result 27.5% of the time compared to 21.4% of the time for Hispanic or Latino patients. Non-Hispanic patients had a THC value documented 26% of the time compared to 23.3% of the time in Hispanic or Latino patients. Total GCS was present in 93.8% of the time in the non-Hispanic group compared to 94.7% of the time for Hispanic or Latino group. This shows that data missing amongst these variables can be attributed to chance. When considering the cross tabulation for THC Combo, or THC presence, it was found that patients who had a negative test for THC were more likely to have missing data for alcohol result when compared to those who tested positive. For those who tested negative, 55.8% had a value reported for alcohol screen result compared to 86.5% for those who tested positive. This aligns with the clinical scenario in that patients who had a blood sample drawn to test for substances had a higher chance of testing positive than those who did not get a blood sample drawn, as all substances are tested using the same sample and sample time. If a patient was having blood drawn to test for alcohol, they were also likely to be tested for other substances. The results were similar when looking at all the positive for drugs table. Patients who tested negative for all other substances were more likely to have missing data for alcohol screen result when compared to those who had a positive test. For those who tested negative, 53.8% of the time there was a value documented for alcohol compared to 83.7% of the time in the presence of a positive substance test. This supports the idea that data for THC Combo may be missing if alcohol screen result is missing, which indicates that the missing values for THC may not be missing completely at random.When patterns in SPSS are requested, a bar chart displaying the percentage of cases for each pattern is tabulated. The bar chart seen below in Table 13 shows that almost 40% of the cases in the dataset have Pattern 40, and the missing value patterns chart, as seen in Table 12, shows that this is the pattern for cases with a missing value on alcohol screen result and THC Combo. Pattern 49 represents cases with a missing value on age, alcohol screen result and THC combo. The bar chart shows that almost 15% of the cases in the dataset have Pattern 1, and the missing value patterns charts shows that this is the pattern for cases with no missing values. Pattern 28 represents cases with a missing value on THC combo. .