A study on the deception of the tobacco industry

An old advertisement for lucky strike cigarettes. Organizations worried about climate change have long drawn comparisons between the petroleum and tobacco industries, arguing that each has minimized public health damages of its products to operate unchecked. Some have urged federal regulators to prosecute oil companies under racketeering charges, as the Department of Justice did in in a case against Philip Morris and other major tobacco brands.

A study on the deception of the tobacco industry

The paper used a time-series analysis to compare the monthly rate of heart attack deaths prior to the smoking ban to the rate after the ban was implemented. The baseline period was January through July The implementation period was August through December Adjustments and analyses were performed using the Autoregressive Integrated Moving Average with exogenous variables ARIMAX method modelled by environmental variables and atmospheric pollutants to evaluate the effect of smoking ban law in mortality and hospital admission rate.

They concluded that there was an The investigators attributed the observed decline in heart attack deaths to a reduction in secondhand smoke exposure, citing evidence that just 30 minutes of exposure to secondhand smoke can cause a heart attack.

The Rest of the Story To demonstrate the blatant bias in the reporting of the study results, simply take a look yourself at the actual data from the study. The smoking ban went into effect in August You can easily see from the figure that inthere was a striking increase in the number of heart attack deaths, which reached an all-time high for the study period.

Somehow, it appears that all this fancy modeling yielded a completely spurious result. This is why I teach my students to always start out by looking at the actual data.

When you put the data into a fancy statistical model, strange things can happen. You always need to make sure that the results of a statistical model are consistent with what you are observing visually when you look at the data.

If there is a major inconsistency, as in this case, then you must suspect that something is wrong: It is also possible that the data are wrong. But clearly, something is wrong here. Here, an examination of the actual data reveals that there is absolutely no basis to conclude that the smoking ban resulted in an But why did nobody see this?

It's difficult to believe that the authors didn't see it, the reviewers didn't see it, and the journal editorial team didn't see it. This should in fact be the first thing that everybody looks at.

A study on the deception of the tobacco industry

Even if you just look at the data in Table 2 without plotting it out, it is immediately apparent that there was a striking increase in heart attack deaths inwiping out the possibility that the smoking ban led to a large and sustained decline in heart attack deaths through It appears that either nobody looked at the actual data or that they looked but ignored it.

Either way, this demonstrates a severe bias on the part of the investigators, reviewers, and editorial team. Had the study found no effect of a smoking ban, you can rest assured that everyone would have scoured over the paper for hours, trying to find some explanation for why the results came out "wrong.

Finally, it is critical to mention that even if the paper had found a decline in heart attack deaths inthis would not justify the conclusion that the smoking ban caused a decrease in heart attacks. The critical and fatal methodological flaw of this paper is that there is no comparison group.

It is very possible that heart attack death rates were declining during the study period anyway, even in the absence of smoking bans. We actually know this to be the case from abundant international data. To conclude that the smoking ban had an effect on heart attacks, one would need to first control for secular trends in heart attack mortality that were occurring anyway, independent of the smoking ban.

The paper could easily have done this by including some comparison group -- such as a nearby city, the county, the state, or the country. But there needs to be some control for secular trends. Without a comparison group, this study is as good as worthless. When the tobacco industry used to put out studies like this to show that smoking bans cause massive losses of revenue for restaurants, we attacked them for conducting time series analyses without using an appropriate control group.Tobacco marketing makes cigarette smoking and tobacco use seem to be a normal and important part of everyday behavior.

5–7 Tobacco industry denormalization (TID) strategies and messages advance tobacco control by countering marketing strategies and revealing tobacco industry deception.

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CSs have the potential to act as a TID message. Unlike most editing & proofreading services, we edit for everything: grammar, spelling, punctuation, idea flow, sentence structure, & more.

Get started now! New Study Proves Tobacco Industry Deception Continues March 14, For decades the tobacco industry deceived the American people into believing light and mild cigarettes were somehow better for their health than regular cigarettes.

New Study Proves Tobacco Industry Deception Continues March 14, For decades the tobacco industry deceived the American people into believing light and mild cigarettes were somehow better for their health than regular cigarettes. Video: The History of the Tobacco Industry The use of tobacco was once thought to be a healthy lifestyle choice, but by the early 20th century, doubts were cast on this positive image.

Learn about the history of the tobacco industry and how it handled the changing image of tobacco. For a movement that has devoted so much attention to attacking the tobacco industry for its deception and scientific dishonesty, I believe that we need to adhere to the highest standards of honesty and transparency in our scientific reporting.

The Rest of the Story: Tobacco and Alcohol News Analysis and Commentary: November