These basic summary statistics will be calculated for continuous variables and binary categorical variables . Continuous variables will be plotted to assess for normality; tests to assess for normality will include kurtosis and skewness. If data is normally distributed, then parametric statistics will be utilized. If data is not normally distributed, then non-parametric statistics will be utilized. Frequency distributions, including numbers and percentages, will be generated for each of the categorical variables/correlates; scatter plots will be created so that outliers can be identified. All correlate variables presented in table 6 will be examined; all the variables but one are categorical variables. Categorical variables will be mapped against presence of marijuana exposure and TBI severity to determine if significant differences are present across each of the categories. Tests to determine significant differences across categories include chi-square test or Fisher’s exact test based on the data. Variables that are identified as significant will be used as covariates in the adjusted prevalence rates. The variable of age is a continuous variable. The literature suggests that the relationship between age and drug exposure is not linear so we will test this relationship in this study. For this study a bar plot graph plotting age against marijuana exposure will be used to determine if a linear relationship exists. If there is not a linear relationship, the variable will be categorized. Prior to the adjusted prevalence analysis,vertical greenhouse farming these covariates will be examined for multi-collinearity.For Aim 3, the objective is to determine the relationship between marijuana exposure at the time of injury, the mechanism of injury, and TBI severity.
The null hypothesis is that a relationship between marijuana at the time of injury, the mechanism of injury, and severity of TBI does not exist. As illustrated in the conceptual framework , mechanism of injury is considered a mediating variable; it potentially mediates the relationship between marijuana exposure at time of injury and TBI severity . First an estimate of the effect between marijuana exposure and TBI severity will be obtained without the mediator variable of mechanism of injury. To test for mediation, several regression analyses will be conducted that include the mediator variable and significance of the coefficients will be examined in each step to assess for direct and indirect effects. First, I will test for a direct relationship between marijuana exposure and TBI severity. Assuming there is a significant relationship between the two variables, I will then conduct an analysis to determine if marijuana exposure affects mechanism of injury. Assuming there is a significant effect, I will then conduct an analysis to determine if mechanism of injury affects TBI severity, and whether the mediation effect is complete or partial . To determine if the mediation effect is statistically significant I will use either the Sobel test or bootstrapping methods . All analyses will be conducted unadjusted and then adjusted for covariates and confounders identified a priori and via aim 2 . The analyses will use logistic regression modeling because the dependent variable, TBI severity, is a dichotomous variable with only two choices, moderate or severe TBI. While TBI severity can be considered a continuous variable if using the number scoring of the GCS scale, a binary variable will be used as it is easier to interpret for clinicians using a numerical score: clinicians treat not on subtle degrees of TBI severity, but whether it is a moderate or severe one based on GCS threshold cut-offs.
Dummy variables will be used to input non-binary categorical variables into the analysis. However, with the predicted large sample size, and understanding the potentially significant confounding effects of certain variables such as other drugs, I hope to create binary variables for each drug listed in the NTDB database . But if this is unable to be done another approach would be to code all drug use into 3 categories: a value of 0 assigned for ‘no drug use’, a value of 1 for ‘stimulants’ only . Observational studies offer valuable methods for studying various problems within healthcare where other study design methods, such as randomized controlled designs , may not be feasible or even unethical. High quality observational studies can render invaluable and credible results that positively impact healthcare when studying clinically relevant topics in patient populations of interest to practicing clinicians. Despite this, observational studies can be subject to a few potential problems within the design and analytical phases rendering results highly compromised. Potential problems that will be encountered in this study design are selection bias, information bias and confounding. Possible countermeasures to address these problems will be discussed in this section. A potential problem regarding selection bias is present in the current study. The target study population is comprised of a purposive sample of patients registered in the NTDB. The NTDB is a centralized national trauma registry developed by the American College of Surgeons with the largest repository of trauma related data and metrics reported by 65% of trauma centers across the U.S. and Canada. The main advantage to utilizing such a registry for this study is that it constitutes the largest trauma database in the U.S. Furthermore, the NTDB allows for risk-adjusted analyses which can be important when evaluating outcomes in trauma . Despite its incredible potential in informing trauma related research, the selection of participants from the NTDB is not without its own biases. The reporting of data into the NTDB is done on a voluntary basis by participating trauma centers, rendering a convenience sample that may not be representative of all trauma patients, and may also not be representative of all trauma centers across the U.S. . This creates the problem of selection bias.
Furthermore, the NTDB is subject to the limitations of selection bias is that it includes a larger number of trauma centers with typically more severely injured patients potentially under representing patients with milder traumatic injuries and injury scores . Additionally, patients who may be traumatically injured and who are not admitted to a participating trauma center will not be included in the NTDB, nor will trauma patients who died on scene before being transported. Another consideration to note is that participating hospitals may differ in their criteria of which patients to include in the database, specifically patients who are dead on arrival or those who die in the Emergency Department . This discrepancy in inclusion and exclusion criteria between hospitals regarding specific injuries makes representative comparisons potentially difficult. Lastly, it is important to mention that large databases such as the NTDB are subject to missing data or disparate data. This is often due a multitude of factors, a few of which various demographic data points,agricultural vertical farming test results and other key information, such as procedures, that may not be documented in the health record and therefore omitted in the database . Missing data often contributes to information bias; however, it can also contribute to selection bias because one of the methods in dealing with missing data is excluding participants for which data is missing thereby creating potential selection bias. Missing data may undermine the ability to make valid inferences, therefore, steps will be taken throughout the design and operational stages and methods within this study to avoid or minimize missing data. Methods to reduce information bias that can lead to selection bias will be discussed in the analysis section of this paper. Due to the methods by which data are collected and inputted into the NTDB, potential problems are encountered in terms of data accuracy. Under reporting of variables obtained from the NTDB has often been noted as a problem due to the reliability of data extraction by participating hospitals . The data is self-reported and often inputted by staff dedicated to data collection. A major variance between participating hospitals is that hospitals with more resources are more likely to have dedicated staff to data collection. This can lead to informational bias in those hospitals that are more compliant in reporting data metrics when compared to others that are not. For example, hospital data registries that have incomplete data on complications may appear to deliver better care than hospitals that consistently record all complications. 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 mis-classification . 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 exposure is identified through the presence of Cannabinoid in a urine toxicology screen. 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, 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.