Nothing you have pasted, nothing in those studies tell me where the chart got its cannabis mortality figures from or how they calculate them.

So you haven’t read them. And still with this inane sealioning, purposefully ignoring what I keep repeating.

You know smoking is the most popular way of using cannabis. You know smoking causes cancers. You also know these mortality figures have “drug-related” mortality in them, and that is specifically said to be from, among other things, lung cancer.

So stomp your foot all your want but you are wrong and this childish bullshit is making you lose a whole lot of respect you’ve gained on Lemmy.

Methods Study design The analysis was undertaken in a two-stage process. The choice of harm criteria was made during a special meeting in 2009 of the UK Advisory Council on the Misuse of Drugs (ACMD), which was convened for this purpose. At this meeting, from first principles and with the MCDA approach, members identified 16 harm criteria (figure 1). Nine relate to the harms that a drug produces in the individual and seven to the harms to others both in the UK and overseas. These harms are clustered into five subgroups representing physical, psychological, and social harms. The extent of individual harm is shown by the criteria listed as to users, whereas most criteria listed as to others take account indirectly of the numbers of users. An ACMD report explains the process of developing this model.

Fig 1

n June, 2010, a meeting under the auspices of the Independent Scientific Committee on Drugs (ISCD)—a new organisation of drug experts independent of government interference—was convened to develop the MCDA model and assess scores for 20 representative drugs that are relevant to the UK and which span the range of potential harms and extent of use. The expert group was formed from the ISCD expert committee plus two external experts with specialist knowledge of legal highs (webappendix). Their experience was extensive, spanning both personal and social aspects of drug harm, and many had substantial research expertise in addiction. All provided independent advice and no conflicts of interest were declared. The meeting’s facilitator was an independent specialist in decision analysis modelling. He applied methods and techniques that enable groups to work effectively as a team, enhancing their capability to perform,7 thereby improving the accuracy of individual judgments. The group scored each drug on each harm criterion in an open discussion and then assessed the relative importance of the criteria within each cluster and across clusters. They also reviewed the criteria and the definitions developed by the ACMD. This method resulted in a common unit of harm across all the criteria, from which a new analysis of relative drugs harms was achieved. Very slight revisions of the definitions were adopted, and panel 1 shows the final version.

Panel 1

Evaluation criteria and their definitions

Drug-specific mortality

Intrinsic lethality of the drug expressed as ratio of lethal dose and standard dose (for adults)

Drug-related mortality

The extent to which life is shortened by the use of the drug (excludes drug-specific mortality)—eg, road traffic accidents, lung cancers, HIV, suicide

Drug-specific damag…

(I won’t list the rest of the panel because no relation to the matter at hand and you can still look it up yourself, which you’ve been lying about.)

Scoring of the drugs on the criteria

Drugs were scored with points out of 100, with 100 assigned to the most harmful drug on a specific criterion. Zero indicated no harm. Weighting subsequently compares the drugs that scored 100 across all the criteria, thereby expressing the judgment that some drugs scoring 100 are more harmful than others.

In scaling of the drugs, care is needed to ensure that each successive point on the scale represents equal increments of harm. Thus, if a drug is scored at 50, then it should be half as harmful as the drug that scored 100. Because zero represents no harm, this scale can be regarded as a ratio scale, which helps with interpretation of weighted averages of several scales. The group scored the drugs on all the criteria during the decision conference. Consistency checking is an essential part of proper scoring, since it helps to minimise bias in the scores and encourages realism in scoring. Even more important is the discussion of the group, since scores are often changed from those originally suggested as participants share their different experiences and revise their views. Both during scoring and after all drugs have been scored on a criterion, it is important to look at the relativities of the scores to see whether there are any obvious discrepancies.

Weighting of the criteria

Some criteria are more important expressions of harm than are others. More precision is needed, within the context of MCDA, to enable the assessment of weights on the criteria. To ensure that assessed weights are meaningful, the concept of swing weighting is applied. The purpose of weighting in MCDA is to ensure that the units of harm on the different preference scales are equivalent, thus enabling weighted scores to be compared and combined across the criteria. Weights are, essentially, scale factors.

MCDA distinguishes between facts and value judgments about the facts. On the one hand, harm expresses a level of damage. Value, on the other hand, indicates how much that level of damage matters in a particular context. Because context can affect assessments of value, one set of criterion weights for a particular context might not be satisfactory for decision making in another context. It follows then, that two stages have to be considered. First, the added harm going from no harm to the level of harm represented by a score of 100 should be considered—ie, a straightforward assessment of a difference in harm. The next step is to think about how much that difference in harm matters in a specific context. The question posed to the group in comparing the swing in harm from 0 to 100 on one scale with the swing from 0 to 100 on another scale was: “How big is the difference in harm and how much do you care about that difference?”

During the decision conference participants assessed weights within each cluster of criteria. The criterion within a cluster judged to be associated with the largest swing weight was assigned an arbitrary score of 100. Then, each swing on the remaining criteria in the cluster was judged by the group compared with the 100 score, in terms of a ratio. For example, in the cluster of four criteria under the category physical harm to users, the swing weight for drug-related mortality was judged to be the largest difference of the four, so it was given a weight of 100. The group judged the next largest swing in harm to be in drug-specific mortality, which was 80% as great as for drug-related mortality, so it was given a weight of 80. Thus, the computer multiplied the scores for all the drugs on the drug-related mortality scale by 0·8, with the result that the weighted harm of heroin on this scale became 80 as compared with heroin’s score of 100 on drug-specific mortality. Next, the 100-weighted swings in each cluster were compared with each other, with the most harmful drug on the most harmful criterion to users compared with the most harmful drug on the most harmful criterion to others. The result of assessing these weights was that the units of harm on all scales were equated. A final normalisation preserved the ratios of all weights, but ensured that the weights on the criteria summed to 1·0. The weighting process enabled harm scores to be combined within any grouping simply by adding their weighted scores. Dodgson and colleagues3 provide further guidance on swing weighting. Scores and weights were input to the Hiview computer program, which calculated the weighted scores, provided displays of the results, and enabled sensitivity analyses to be done.

Figure 4 shows the contributions that the part scores make on each criterion to the total score of each drug. Alcohol, with an overall score of 72, was judged to be most harmful, followed by heroin at 55, then crack cocaine with a score of 54. Only eight drugs scored, overall, 20 points or more. Drug-specific mortality was a substantial contributor to five of the drugs (alcohol, heroin, γ hydroxybutyric acid [GHB], methadone, and butane), whereas economic cost contributed heavily to alcohol, heroin, tobacco, and cannabis.

We also investigated drug-specific mortality estimates in studies of human beings.13 These estimates show a strong correlation with the group input scores: the mean fatality statistics from 2003 to 2007 for five substances (heroin, cocaine, amfetamines, MDMA/ecstasy, and cannabis) show correlations with the ISCD lethality scores of 0·98 and 0·99, for which the substances recorded on the death certificates were among other mentions or sole mentions, respectively.

So just like I’ve said FROM THE START, the mortality comes from drug-related disease, like lung cancer, and drug-specific mortality comes from dying in a car crash with the coroner reporting cannabis AMONG other substances, which will still make it count towards the stat, while not having had an effect on the crash compared to the others. But no. You sit there claiming that I haven’t understood and that these studies somehow claim that people are dying of cannabis-overdoses. Which you’ve asked explicitly several times over, despite me trying to explain this to you in the simplest way possible.