A recent study by Arabian et al. revealed the presence of inaccuracy and variability between hospitals, specifically in the areas of data coding and injury severity scoring. Additionally, the type of registry software a hospital utilizes can report injury severity scores differently . This too, renders data subject to informational bias. Information bias is due to inaccurate or incorrect recording of individual data points. When continuous variables are involved, it is called measurement error; when categorical variables are involved, it is called misclassification . In this study, the potential for information bias is mostly due to 1) incomplete data documented in the medical record, or 2) inaccurate entry into the hospital trauma database by hospital staff. Missing data will be analyzed in terms of potential effect for both the independent and dependent variable . While the database captures marijuana exposure through the first recorded positive drug screen within the first 24 hours after first hospital encounter, it is recognized that at times patients will not be screened, even if they have been exposed to marijuana. Marijuana presence can be detected in the urine up to 3-5 days from exposure in infrequent users; marijuana can be detected up to 30 days for chronic users . Therefore, patients could potentially have a positive marijuana toxicology screen even though they may not have ingested marijuana the day of the event. A positive marijuana urine toxicology screen indicates the probability of prior use, not immediate use. This is an important limitation to note. In clinical practice,drying room the determination for a toxicology screen is often symptomology, so it is reasonable to assume that patients who have ingested marijuana a week prior to the event date may not exhibit the expected symptomology.
Unlike other observational cohort studies, the potential of recall bias is minimal due to the availability of an objective marker to measure the independent variable, namely, the presence of marijuana. The presence of marijuana is captured from the hospital lab urinalysis results and is recorded as present within 24 hours after the first hospital encounter. Similarly, the data entered to measure the GCS score is also captured objectively through a numeric recorded score found in the medical record. See analysis section for how this type of bias will be addressed. The final sample size for this study involved 7,875 total unique cases. Those cases represent individuals who sustained a moderate or severe TBI in the NTDB database. Of the 997,970 total cases for 2017, there was a total of 32,896 cases that were identified as having sustained some form of traumatic brain injury, ranging from a concussion to severe injury, using the ICD 10 Diagnosis codes listed below . Of the 32,896 cases, 25,021 were identified as having a concussion diagnosis, and were ultimately excluded from the final sample size. This was because mild concussion diagnosis was found to suffer from large underestimates in documented incidence . A World Health Organization systematic review of mild TBI found that up to 90% of overall TBIs was mild in nature. The WHO has also estimated a yearly incidence of mild TBI anywhere from 100-600 per 100,000 cases, 0.1 to 0.6 respectively . Furthermore, up to 40% of individuals who sustain a mild TBI, or concussion do not seek the attention of a physician . Another study found that 57% of veterans who had returned from Iraq and/or Afghanistan, and had sustained a possible TBI, were not evaluated or seen by a physician . According to the WHO and CDC reports, these numbers may still not represent the actual incidence of TBI worldwide. Furthermore, the data suggests that individuals with a mild TBI for the most part do not go and seek medical attention, and this study focuses on individuals who sustain a moderate or severe TBI as those individuals suffer life-long devastatingly debilitating effects and are the targets of public health initiatives and injury prevention measures.
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, 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,how to trim cannabis 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. The way this was done was first the researcher identified TBI related ICD 10 CM diagnosis codes by visiting the ICD 10 Data website at www.icd10data.com and searching for all head injury related codes. 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 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. 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. When age was missing, mean total GCS was 14.77 compared to 13.21. This is not a large difference, indicating that data may be indeed missing at random. When alcohol screen result is missing, the mean age is 28.86 compared to 42.64 when alcohol screen result is present. This indicate that the data may not be missing completely at random it is important to consider that in the alcohol screen result variable, there is a large percentage of missing values. Additionally, since this data set includes patients ages 16 years and younger, it may be that clinicians are not drawing alcohol levels. This can lead to the fact that the values that are missing when these two variables are cross-tabulated, may not be missing at random. Finally, it is important to note that unlike in questionnaires or surveys, these trauma patients are not asked for an alcohol screen result, rather they are tested by the retrieval of a blood sample. Therefore, it is not the patient themselves that chooses to respond or not, rather, it is the hospital system that contributes to whether the data is missing. Data for alcohol screen result may be missing due to lack of time to retrieve the blood sample as can be found when patients present to the ER in traumatic full arrest. Alternatively, the sample may have been drawn but not sent to lab, or sent to lab but not reported by lab, or reported by lab not recorded by the nurse.