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CGC pdf. CGS pdf. CGP pdf. Warning: Homeport recommends using Microsoft Edge. Unsupported browsers; such as Chrome, Firefox or Safari, may erroneously say credentials are invalid or display an incompatibility warning. If this happens please contact the NMC via chat, email or phone. Live Chat. Website Feedback. Program Survey. Sample Marine Random Testing Letter. Sample Military Random Testing Letter. Sample Pre-Employment Testing Letter. Drug Testing Requirements. Drug Testing FAQs. Need to find an Approved Drug Testing Facility? Use your favorite search engine to find a local DOT drug testing location. Disclaimer: This directory is not maintained by the federal government and is provided to mariners as a convenience for informational purposes only. Mariners are advised to contact the testing facility of their choice directly to ensure it meets their needs as well as all Coast Guard requirements. The Coast Guard does not recommend or endorsement any provider. In order to meet drug testing requirements, you must choose and provide one of the below options. Option Two: Provide a letter attesting to participation in random drug testing programs. Marine Employers Drug Testing Guide. This option requires you to provide the results of an approved drug test. This test MUST be the following:. However, it is useful in ensuring that all information needed is provided. We will also accept letters or a Federal Chain of Custody form as long as all information above is provided. This option requires you to provide a letter attesting to participation in a random drug testing program. The letter must be an original and:. We will accept letters from valid consortiums to meet this requirement. We will NOT accept drug testing letters from a Union attesting to participation in a random drug testing program. This option requires you to provide a letter attesting to pre-employment drug testing. An official website of the United States government Here's how you know. Official websites use. Department of Defense organization in the United States. Share sensitive information only on official, secure websites. Skip to main content Press Enter. Do not complete the form in the web browser. Do you want to proceed to Homeport Application Status? Do you want to proceed to Homeport Credential Verification? Do you want to proceed to Sea Service Renewal Calculator? NMC Home. Apply for a Merchant Mariner Credential. Apply for a Medical Certificate. Check your application status. Get your records. How did we do? Let us know below. Drug Testing. Important Information. Option One: Provide the results of an approved drug test. Option Three: Provide a letter attesting to pre-employment drug testing. Option One This option requires you to provide the results of an approved drug test. Option Two This option requires you to provide a letter attesting to participation in a random drug testing program. Signed by the appropriate personnel We will accept letters from valid consortiums to meet this requirement. Option Three This option requires you to provide a letter attesting to pre-employment drug testing. Signed by a company official.

In Puerto Rico, growing opioid crisis adds to island's post-hurricane issues

How can I buy cocaine online in Mayaguez

Official websites use. Share sensitive information only on official, secure websites. We compared BACs for the , , and crash cases drawn from the U. National Roadside Survey. For both sober and drinking drivers, being positive for a drug was found to increase the risk of being fatally injured. When the drug-positive variable was separated into marijuana and other drugs, only the latter was found to contribute significantly to crash risk. In all cases, the contribution of drugs other than alcohol to crash risk was significantly lower than that produced by alcohol. Although overall, drugs contribute to crash risk regardless of the presence of alcohol, such a contribution is much lower than that by alcohol. The lower contribution of drugs other than alcohol to crash risk relative to that of alcohol suggests caution in focusing too much on drugged driving, potentially diverting scarce resources from curbing drunk driving. A body of work ranging from experimental studies Dobbs, ; Moskowitz and Wilkinson, to epidemiological studies Asbridge et al. In response to this growing concern, several countries e. Stimulated in part by these results, the U. Seventeen states currently have such laws Voas et al. A key requirement for designing efficient drugged-driving laws is the accurate determination of the marginal increase in crash risk associated with drugged driving relative to the crash risk associated with the absence of drugs. Based on a comparison of data from injured drivers entering medical facilities with data collected from drivers stopped at road surveys, the DRUID project found an increase in the RR of injury and death among drivers testing positive for drugs Bernhoft, ; Hargutt et al. In the United States, no study comparing crash data with roadside data has yet been reported. We believe that this article constitutes the first U. The strategy of estimating RR by comparing drivers in fatal crashes with roadside survey drivers has been applied in the past to the analysis of alcohol-related crash risk Voas et al. However, absent from those reports is the contribution to crash risk of drugs either alone or associated with alcohol. We now have an opportunity to address that gap with the inclusion of drug testing in the NRS and the improved reporting of drug tests on fatally injured drivers in the U. Hingson et al. Relevant to this effort, they found evidence suggesting that the contribution of these drugs to crash risk may vary, depending on the presence of alcohol. The overall analytical strategy pursued by this effort follows that of Zador et al. Zador et al. Measures of crash exposure were obtained from the NRS Lacey et al. Drivers were randomly selected from the traffic flow at those sites and recruited for participation in the survey Lacey et al. As in previous roadside surveys, the NRS collected information on the drivers' age, gender, race, ethnicity, and BAC. New to the NRS was the collection of an oral-fluid sample that was submitted for laboratory analysis to determine the presence of drugs other than alcohol. The outcome of both analyses suggested that the main reason for refusing participation in the oral-fluid component of the NRS was the additional time required for survey completion, a finding that reduces our concerns about sampling bias in the exposure database Lacey et al. Drugs tested for in the U. For crash data, we used the FARS. The descriptions of each fatal crash reported in the FARS characterize in detail the features of the crash and the vehicles involved. In this study, we used driver records having known drug-test results and BACs. Information about drug use from deceased drivers was obtained by using either blood or urine samples or both. Only driver records having known drug-test results and BACs were used. To allow for a meaningful merging of databases, only a selected number of FARS records was used in this study. First, we selected crash data only from states that were surveyed in the NRS. Unlike Voas et al. The inclusion of one more year allowed for a larger sample size and, therefore, more power to account for the stricter data selection process we applied to this effort. Second, we considered only states that routinely test fatally injured drivers for drugs other than alcohol. As suggested by Hingson and colleagues, by restricting our study to states that routinely test for drugs other than alcohol, we avoided much of the laboratory-based variation caused by data originated after occasional, court-mandated analyses. Third, to further match the conditions of the crash-exposure database the NRS , we considered only the FARS records of drivers of four-wheeled passenger vehicles who were between 16 and 97 years old and who had been involved in crashes that occurred a on Fridays between 9 a. Census projections. As in Zador et al. The variable time of the day had a value of 1 to indicate daytime Friday between 9 p. Drug results were codified into a single binary variable indicating the presence or absence of any drug other than alcohol. We also conducted separate analyses for marijuana. The BAC was entered as a continuous variable in the models, although it was rescaled by multiplying the actual BAC by 1, to avoid large coefficient estimates associated with BAC in the logistic models we denoted the rescaled BAC by breath alcohol concentration. Last, despite relying only on states that routinely test their fatally injured drivers for drugs other than alcohol, the likelihood of finding drug-positive drivers may vary from state to state because of some unaccounted laboratory differences across states. The variable Y ik takes the value of 1 if the i th driver record is a fatal crash from the k th state, elsewhere zero. The OR for two drivers from state k i. That is, we limit the discussion to scenarios in which, for instance, drug-positive and drug-negative drivers come from the same state. This assumption leads to the same OR calculation as in the ordinary logistic regression i. Key to this effort is the examination of the Dual Drug x Alcohol interaction. If significant, that interaction would indicate that the contribution of drugs other than alcohol to crash risk varies depending on the driver being alcohol positive or not. If positive, we could subsequently estimate how much crash risk increases when drug consumption is added to alcohol consumption. Table 2 lists the number of drivers in the databases by state. Table 2 shows variation among the states in their contribution to the exposure and crash files, which provides support for the decision to adjust risk estimates by state-based differences. Table 3 lists the percentage of drivers who were drug positive and the percentage of drivers who tested positive for each of five drug categories. These differences seem to suggest that cannabinols and stimulants are contributors to fatal crash risk. The marginal and joint prevalence of alcohol and other drugs are shown in Table 4. There are more alcohol-positive drivers among crashes Blood alcohol concentration and drug results for fatally injured drivers from to U. We conducted a separate analysis of the RR for drugs other than alcohol for sober drivers. In Model 2, the presence of drugs other than marijuana was found to be associated with an increase in fatal crash risk regardless of the driver's BAC. The presence of marijuana, however, did not contribute to fatal crash risk. Not shown in Table 5 is the impact of including the time of day in the models. Models 1 and 2 only differ on how the drug variable is built: two levels in Model 1 positive for any drug, drug negative and three levels in Model 2 positive for marijuana, positive for drugs other than marijuana, and drug negative ;. Coefficient and SE denote the estimated coefficient and its standard error, respectively. The corresponding ORs are provided in Table 6. When the drug-positive variable is partitioned into positive for marijuana and positive for drugs other than marijuana Model 2 , only drivers positive for drugs other than marijuana had the odds of being involved in a fatal crash significantly different from one, being almost twice that of drug-negative drivers, irrespective of the drivers' BACs. Odds ratios relative to drug negative drivers. Odds ratios for drugs are estimated from coefficients shown in Table 5 sober drivers only and Table 6 all drivers. As shown in Tables 5 and 6 , Models 1 and 2 differ only on how the drug variable is built: two levels in Model 1 positive for any drug, drug negative and three levels in Model 2 positive for marijuana, positive for drugs other than marijuana, and drug negative. Odds ratios for BAC are obtained only from coefficients in Table 6. Because odds ratios based on either Model 1 or Model 2 were very similar, only those based in Model 1 are shown. As expected, crash risk increases with BAC and decreases with age. This finding reproduces once more evidence of the deleterious impact of alcohol on drivers, particularly among the youngest ones. Of interest is the comparison between the ORs for drug-positive and alcohol-positive drivers. For drivers age 35 and older, these differences are reduced to about 3 and 7 times higher, albeit they remain statistically significant. One outcome from this study is that it confirms earlier reports that both alcohol and other drugs do contribute to crash risk. This finding was not surprising given the abundance of previous evidence pointing in that direction Bern-hoft, ; Hargutt et al. Although drugs other than alcohol do contribute to crash risk, we found that such a contribution depends on the type of drug under consideration. Somewhat unexpected was the finding that although marijuana's crude OR indicated a significant contribution to fatal crash risk, once it was adjusted by the presence of alcohol and drivers' demographics, marijuana's OR was no longer significant among either sober or drinking drivers. This finding is somewhat surprising because, as reviewed by Sewell et al. Citing MacDonald and colleagues , Sewell et al. However, our results showing no increase in relative risk for a fatal crash associated with marijuana should be interpreted with caution. Furthermore, the excessive delays in the collection of some biological samples in the FARS file may have reduced the number of marijuana-positive results and diluted the contribution of marijuana to fatal crash risk. Another important outcome from this study is that the contribution of alcohol to crash risk is much larger than that by other drugs. They offer the opportunity not only to apprehend the highest risk drivers but also to identify and intervene with a substantial number of drug-using drivers. Furthermore, our finding that the risk of involvement in a fatal crash at a BAC of. Relevant to extant and proposed alcohol policies is our finding of no statistical interaction between drugs and alcohol in determining the crash risk. Those estimates were crucial to the passing of current per se laws, and our study shows that they were not affected by the failure to include drug data in their analyses. The much higher crash risk of alcohol compared with that of other drugs suggests that in times of limited resources, efforts to curb drugged driving should not reduce our efforts to pass and implement effective alcohol-related laws and policies. As briefly illustrated in the preceding paragraph, perhaps the main limitation of this report resides in the intrinsic complexity of the drugged-driving problem and the near impossibility of capturing it fully. Parent drugs and metabolites vary in their impairing effect depending, among other factors, on their concentration, mechanism of consumption, time since consumption, and individual genotypic and phenotypic characteristics. Such complexity is further exacerbated by variations in the drug-test procedures and standards applied by each state in the FARS database. No publicly available documentation provides comprehensive information on how the drug tests are performed or on which drugs are tested by each of the 50 states. Relevant to this discussion is that it also is reasonable to argue that the contribution of drugs other than alcohol to crash risk differs depending on the severity and therefore type of crash. Therefore, some of the findings reported in this effort may not be reproduced if examined on nonfatal crashes. These limitations, which have hampered previous research efforts using actual traffic data, are also present in this study. Despite these shortcomings, the findings of this study coincide with a growing body of evidence indicating that the contribution of alcohol to crash risk surpasses that of other drugs Bernhoft, ; Hels et al. Alcohol was not only found to be an important contributor to fatal crash risk, but also in keeping with prior research, it was associated with fatal crash risk levels significantly higher than those for other drugs. As noted, this study is the first to use actual traffic data in conjunction with FARS data to estimate the joint contribution of alcohol and other drugs to fatal crash risk in the United States. As such, the findings of this effort are timely and important. By developing information on the relative contribution of alcohol and other drugs to fatal crash risk, this article contributes to the database available to policy makers concerned with the control of drugged driving. One of the most significant pieces of information coming from this article is perhaps the need for future crash studies to focus on individual drugs or drug classes rather than treating all drugs as one. Because of the small percentage of any single drug other than marijuana in the NRS data set, grouping was necessary in this study to provide the power to estimate reliable RRs. However, such a grouping may attribute too much risk to medications necessary for safe driving while clouding the importance of individual drugs with potential serious effects. Thus, the findings of this effort suggest the need to search out larger or more specific databases that would allow us to shift our research focus from a broad treatment of all drugs to more targeted studies. As a library, NLM provides access to scientific literature. J Stud Alcohol Drugs. Find articles by Eduardo Romano. Pedro Torres-Saavedra , Ph. Find articles by Pedro Torres-Saavedra. Robert B Voas , Ph. Find articles by Robert B Voas. John H Lacey , M. Find articles by John H Lacey. Received May 31; Accepted Sep Open in a new tab. Demographic and drug use characteristics associated with fatal crashes, — Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel. Model 1 a positive for any drug.

How can I buy cocaine online in Mayaguez