Furthermore, a significant relationship was found between GCS scores, sex, blood alcohol levels, and a history of substance abuse at the time of presentation in the ED. Older participants were found to have higher GCS scores, indicating a less serious brain injury. Study participants who had higher blood alcohol levels were found to have lower GCS scores, indicating a more serious brain injury. Age and higher blood alcohol levels were found to be associated, with higher blood alcohol levels noted in younger patients. A linear regression showed that the presence of THC was associated with lower GCS scores, which is a predictor of TBI severity. However, that finding was not statistically significant. Alternatively, being male, having elevated blood alcohol levels, having other drugs present on admission, and a history of substance abuse were all found to have a significant influence on GCS scores and TBI severity, with GCS scores being lower for all four variables, implying a more serious TBI. To effectively determine the relationship between the presence of THC and TBI severity, better data, or datasets, are needed. Perhaps, the American College of Surgeons can be empowered to employ strategies to acquire more consistent data as it pertains to drugs, so that clean data is abstracted and inputted, and clean data is analyzed and then interpreted. Another important implication to consider is the inclusion of different variables and outcome measures that can help provide a better dataset for analysis. This can include diagnostic tests such as CTs can findings, or mortality. However, the NTDB does not provide such data, therefore, the NTDB itself may not be the most ideal database to use to answer the research question posed. As expected,ebb and flow table and supported by other research studies, elevated blood alcohol levels, being male, presence of other drugs, and a diagnoses of substance abuse were found to have an influence on GCS scores.
This confirms that these variables need to be considered in the context of TBI research. While the presence of THC initially did show a hypothesized relationship to GCS score , the relationship became insignificant when adjusted for all the other covariates variables. As noted in the discussion section, and in the context of such large percentages of missing data in this study, validity of findings, such as THC prevalence rate in this TBI population, should be cautiously interpreted for all the included hypothesized explanatory variables. Further research with datasets that are larger and more complete are needed to fully understand and examine the relationship between marijuana and TBI severity. This study importantly underscores the need for better data to enable better research regarding the relationship between marijuana and TBI severity. This secondary analysis is based on data from a larger pilot, which aimed to measure the efficacy of an 8-week CM trial in treating MUD through behavioral and neuroimaging outcomes. Ethics approval for the larger pilot study, “Contingency Management, Neuroplasticity and Methamphetamines Abuse in South Africa” was obtained by the University of Cape Town’s Human Research Ethics Committee as well as the University of California, Los Angeles’ Institutional Review Board . Data were collected in Cape Town, South Africa, from August 2016 to January 2017. We report on how our sample size was determined, in addition to describing any data exclusions, manipulations, and all measures used in the study. The pilot trial aimed to select approximately 30 participants with MUD, in line with the pilot nature of the study containing a neuroimaging component, which is in line with samples sizes reported in neuroimaging studies of substance use disorder participants within a treatment setting . A total of 269 individuals were recruited via drug rehabilitation centers and then screened, of which 33 participants were eligible and consented to partake in the 8-week CM trial, in addition to completing a computerized risk-taking task and various self-report measures.
One participant was excluded due to missing baseline computerized decision-making task data, resulting in a final analytic sample size of 32. Participants were eligible to participate in the study if they were between the ages of 18 and 45 years, if they met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria for current and primary MUD as assessed using the Structured Clinical Interview for DSM-5 , were able to attend several visits over a 2-week baseline period to complete screening tools, be available for CM pilot visits over a further 8 weeks, and tested positive for methamphetamine during the baseline phase. Exclusion criteria included meeting DSM-5 criteria for a substance other than MA, excluding secondary tobacco, marijuana, or methaqualone use disorder. Other psychiatric comorbidities were excluded for, including schizophrenia spectrum disorder, bipolar and related disorders, obsessive-compulsive related disorder, as well as depressive and anxiety disorder not induced by MUD. Currently receiving treatment for a substance other than MA, requiring inpatient treatment and/or current use of psychoactive medication, and scoring a subthreshold score on the Wechsler Abbreviated Scale of Intelligence was excluded for. Additional exclusion criteria included chronic physical or neurological illness, previous head injury, Human Immunodeficiency Virus -seropositive status based on pin-prick test, left-handedness, and exclusion criteria relating to the neuroimaging component of the study included current pregnancy, claustrophobia, pacemaker, or presence of any metal in the body.Participants attended thrice weekly clinic visits over an 8-week period to undergo drug urine testing, where urine collection was supervised and verified through urine cup temperature-sensitive strips. Urine was assessed for the presence of MA using radioimmunoassay strips , which detects MA in urine over the prior 48–73 hr. Participants were rewarded with cash vouchers for MA-negative urine tests, where the value of each subsequent cash voucher was incrementally increased by ZAR12.50 with sustained abstinence, starting at ZAR25 . A total of 24 cash vouchers could be obtained, worth a maximum of ZAR4850 . At every visit, abstinence was defined as either confirmed, with a MA-negative urine test, or as a relapse, with either a MA-positive urine test or missing test. A missing test was defined as an unattended scheduled visit with no attempt by the participant to reschedule it.
Where a MA-negative urine test was followed by a MA-positive test, the voucher for the next MA-negative urine sample was reset to ZAR25 . A “rapid reset” rule was applied to sustain motivation, which allowed a participant to return to their highest received voucher value after providing three consecutive MA-negative urine tests. In addition to testing for MA, participants were randomly tested on a weekly basis for barbiturates, cocaine, opiates, and cannabis. However, vouchers were exclusively contingent on MA nonusage. Spending during the CM trial was defined as the expenditure of a voucher on non-drug rewards following a MA-negative test, which was documented through retrieval of voucher expenditure receipts. At baseline, participants completed a sociodemographic questionnaire, the SCID-5, WASI, and Addiction Severity Index , as well as providing usage history of MA and other substances. Participants were administered the Psychology Experiment Building Language 0.14 computerized version of the Iowa Gambling Task at baseline, which has been demonstrated to capture deficits in real-world decision-making under conditions of uncertain reward and loss outcomes . The IGT has been extensively utilized to demonstrate risky decision-making among various clinical populations, including participants who use substances and those with lesions of the ventromedial prefrontal cortex . The IGT was designed to assess the extent to which individuals can learn to switch from short-term to greater long-term gains, what will be referred in this article as the “IGT Magnitude Effect,” as the metric has, in part, much to do with the size of rewards and losses. Lower “IGT Magnitude Effect” scores are more reflective of a riskier, maladaptive strategy that prioritizes immediate, large short-term rewards over long-term gains, whereas in contrast,flood table higher “IGT Magnitude Effect” scores typically illustrate a greater tendency to avoid short term rewards for larger long-term gains. Decision-making on the IGT can also be driven by the frequency with which rewards and losses are presented , where the preference for more frequent rewards will be referred to as the “IGT Frequency effect.” The IGT consists of four virtual decks, decks A, B, C, and D, each associated with a unique reward and loss probability, where participants were instructed to select decks over a series of 100 trials in a time unconstrained, quiet, and distraction-free room, after participants successfully passed a Snellen chart test for visual competence. The “IGT Magnitude Effect” is measured by a greater selection of riskier decks, A and B, where the score was calculated as the sum of deck selections –, where lower scores exhibit the “IGT Magnitude Effect.” In contrast, the “IGT Frequency effect” was demonstrated by a greater selection of decks associated with more frequency rewards, namely B and D, and was calculated as the sum of deck selections –, where a higher score reflects the “IGT Frequency effect.” Given that the objective of the IGT is to obtain a net positive payout, to promote optimal performance, participants received a flat rate of ZAR25 if they obtained an overall positive net payout following 100 trials.Participants were first described according to various sociodemographic and socioeconomic factors, including gender, ethnicity, age, education, employment, household income, and broad intellectual function. Participants were also described according to substance use characteristics, such as MA use quantity, history, and severity of use, in addition to use of other substances. Data were summarized using frequencies and percentages, for categorical variables, and median and interquartile range , for continuous variables.
A series of time-lagged counting process Cox Proportional Hazards models, computing standard errors using the grouped jacknife method, were conducted to assess whether baseline decision-making tendencies were associated with spending at future visits and whether these baseline decision-making tendencies and spending at visits were associated with future abstinence. Models controlled for recent and cumulative earnings, recent and cumulative expenditure, as well as baseline household income. Specific to Aim , adjusted models were first run, controlling only for recent and cumulative expenditure and baseline household income, and were then rerun to incorporate recent and cumulative earnings . All data analyses were conducted in R , using the survival package, version 3.2–13 . Hypotheses and methods were registered on the Open Science Framework . Participants were predominately males of mixed ancestry , with a median of 34 years old. Most participants were unemployed , had completed a median of 11 years of education, and obtained a median WASI score of 89. Participants used a median of 1 g of MA per day over a 12-year period, and 18 of participants used methaqualone and/or cannabis as a secondary substance/s alongside MA . During the trial, MA was the only substance detected in the urine of participants except for Tetrahydrocannabinol , which was detected in three participants.Overall, 30 out of the 32 participants provided all 24 urine samples , whereas one participant provided 18 urine samples and another provided 10 out of the 24 required urine samples before dropping out. Thirty-two percent of the total possible number of vouchers that could be received were missing due to MA-positive urine samples. Only “IGT Frequency Effect” was significantly associated with greater probability of spending at the current visit after controlling for last and cumulative earnings and expenditure . Obtaining a preceding MA-negative sample and greater cumulative past expenditure significantly predicted greater odds of spending at the current visit. In contrast, there was no association between “The Magnitude Effect” and spending. A higher baseline “IGT Magnitude Effect” score and recent voucher spending were linked to significantly greater odds of remaining abstinent at the current visit . In contrast, baseline “IGT Frequency Effect” was not associated with abstinence at the current visit. In both “IGT Magnitude and Frequency Effect” models, the most recent purchase significantly increased the likelihood of being abstinent at the current visit, whereas cumulative expenditure did not. Moreover, a higher baseline household income decreased the likelihood of being abstinent at the current visit. After incorporating the impact of prior abstinence in predicting current abstinence, recent expenditure no longer predicted current abstinence. In this secondary analysis, spending of CM rewards was associated with a higher chance of obtaining abstinence at the future visit, as was a baseline tendency to avoid short term rewards for larger long-term gains on the IGT. This spending result is consistent with findings from Krishnamurti et al. and Ling Murtaugh et al. , even after controlling for decision-making tendency.