Red meat & mortality & the usual bad science
The media lit up on the evening of Monday March 12th as a press release was issued about an article in the Archives of Internal Medicine published that day.
The BBC were among the first to pick up the story and the story was featured extensively on BBC Breakfast TV and Radio 4 on Tuesday 13th March. Interestingly, John Humphries asked the pertinent question of science reporter Tom Feilden “We’re all going to die – let’s accept that. So what does this lower risk mean?” Tom couldn’t answer the question. He replied “It’s very difficult to unpick these statistics – these numbers are used as bald headlines.” Quite so!
So let us try to unpick the data and see what this article is all about:
At the outset we must highlight the error that this, and every similar study, makes. All that a study like this can even hope to achieve is to suggest a relationship between two things. To then leap from an observed association to causation or risk is ignorant and erroneous. This article makes this mistake – as has every other study I have reviewed demonising red or processed meat over the past year such as this or this.
The studies used in this article
There have been two large studies in America where people have been asked to record dietary intake, smoking, activity, weight and many other factors over a long period of time. The data from these two studies has been analysed retrospectively to look for patterns. This was not a study designed to review meat consumption over a period of time – some data just happens to be available and it has been reviewed to make headlines about meat consumption.
The two studies are the Health Professionals Follow-up Study (1986-2008) (abbreviated to HPFS) involving 49,934 men and the Nurses’ Health Study (1980-2008) (abbreviated to NHS) involving 92,468 women. A number of participants from these two studies were excluded in this meat review. After excluding people with cardiovascular disease (CVD) or cancer at the start of the study and excluding people whose dietary responses were incomplete, this article proceeded to review data from 37,698 men in the HPFS and 83,644 women in the NHS. Diet was assessed by validated food frequency questionnaires and updated every 4 years.
The dietary questionnaire offered 9 possible responses for meat consumption, ranging from “never or less than once per month” to “6 or more times per day.”
Unprocessed red meat was assumed to be “beef, pork, or lamb as main dish” (pork was queried separately beginning in 1990), “hamburger,” and “beef, pork, or lamb as a sandwich or mixed dish.” The standard serving size was 85 g (3 oz) for unprocessed red meat. As this was an American study, the great American hamburger has been included in unprocessed meat – it is of course as processed as meat can be. Hamburgers account for approximately half of American ‘beef’ consumption[i] and should be categorised as processed meat. If someone has had a beef sandwich or a pork kebab or a lamb curry – this has also been deemed unprocessed meat. Hardly what Paleo types would call real meat!
Processed red meat included “bacon” (2 slices, 13g), “hot dogs” (one, 45g), and “sausage, salami, bologna, and other processed red meats” (1 piece, 28g).
The Data – Table 1
Table 1 (http://archinte.ama-assn.org/cgi/content/full/archinternmed.2011.2287) has the raw (baseline) data for the two studies separately categorised into quintiles for total red meat consumption (processed and unprocessed meat lumped together). The five quintiles take the lowest fifth consumption of red meat and then the next lowest and then the middle of the five groups then the second highest and then the highest. Table 1 is age standardised (to remove the impact of any age differentials between the different five groups of red & processed meat consumption) and it then lists other characteristics of the five groups.
Here is where the first problem emerges. As you can see for yourself in Table 1, Q1 is the lowest red & processed meat intake and Q5 is the highest. There are many other variables that correlate to the groups Q1 to Q5 – this is for the HPFS – the top part of Table 1:
- Physical activity, as measured by hours of metabolic equivalent tasks, falls from 27.5 in Q1 to 22.7 in Q2 to 20.2 in Q3 to 18.8 in Q4 to 17.2 in Q5. As red & processed meat consumption increases, so exercise falls. Could lack of exercise impact mortality?
- Body Mass Index – the average BMI for Q1 was 24.7; the average BMI for Q2 was 25.3; for Q3 it was 25.5; for Q4 it was 25.7 and for Q5 it was 26. As red & processed meat consumption increases, so does BMI. Could BMI impact mortality?
- Smoking – the percentage of people in Q1 who smoke was 5%; in Q2 it was 7.3%; in Q3 9.8%; in Q4 11.3% and 14.5% in Q5. As red & processed meat consumption increases, so does smoking – the top quintile virtually three times higher than the lowest. Could smoking impact mortality?
- Diabetes – the percentage of people in Q1 and Q2 with diabetes was 2%; in Q3 it was 2.2%; in Q4 2.4% and 3.5% in Q5. As red & processed meat consumption increases, so does diabetes. Could diabetes impact mortality?
- The interesting one was cholesterol. 14.8% of Q1 were recorded as having high cholesterol; 11.1% of Q2; 9.7% of Q3; 9% of Q4 and 7.9% of Q5. So, as red & processed meat consumption increases, cholesterol recorded as high fell. Could low cholesterol impact mortality? Given the protective nature of life vital cholesterol and the repair role that it plays in the body, it is highly likely that high cholesterol is protective against cancer and heart disease. Quite the opposite of what we have been led to believe in the interests of statin and plant-sterol-injected-low-fat spread profitability.
- Total calorie intake – the average daily calorie intake for Q1 was 1,659; the average daily calorie intake for Q2 was 1,752; for Q3 it was 1,886; for Q4 it was 2,091 and it was 2,396 for Q5. As red & processed meat consumption increases, so does calorie intake. Could calorie intake impact mortality?
- Alcohol intake – in Q1 an average 8.4 grams of alcohol were consumed daily; in Q2 this was 10.7; in Q3 it was 11.2; in Q4 it was 12.4 and 13.4 grams of alcohol were consumed daily in Q5. As red & processed meat consumption increases, so does alcohol intake. Could alcohol intake impact mortality?
The Nurses Health Study showed exactly the same correlations – the numbers were slightly different but the trends were the same. As red and processed meat consumption increased so exercise and high cholesterol fell; BMI, smoking, diabetes, calorie intake and alcohol intake all increased.
Table 2 looks at all mortality (I will stay at the all mortality level – the study does not stand up to scrutiny at this level so there is no point looking at cardiovascular (CVD) mortality vs. cancer mortality).
Table 2
Table 2 presents mortality data per quintile. The high level numbers are that:
- The HPFS covered 758,524 person years and there were 8,926 deaths in total: 2,716 attributed to CVD and 3,073 to cancer.
- The NHS covered 2,199,892 person years and there were 15,000 deaths in total: 3,194 attributed to CVD and 6,391 to cancer.
- The two studies combined, therefore, covered 2,958,416 person years and there were 23,926 deaths in total: 5,910 attributed to CVD and 9,464 to cancer.
The first point to make, therefore, is that the overall death rate was very small:
- In the HPFS, in 758,524 person years the overall death rate was 1.18% and the CVD death rate was 0.36% and the cancer death rate was 0.41%. Over a 22 year period, just over one in a hundred members of the study died.
- In the NHS, in 2,199,892 person years the overall death rate was 0.68% and the CVD death rate was 0.15% and the cancer death rate was 0.29%. Over a 28 year period, approximately one out of 150 members of the study died.
- In the two studies combined, in 2,958,416 person years the overall death rate was 0.81% and the CVD death rate was 0.2% and the cancer death rate was 0.32%. In the combined studies, fewer than one person in one hundred died in a 28 year period.
Table 2 is then supposed to have adjusted for all the other factors noted under the analysis of Table 1. The article says that the multivariate analysis adjusted for energy intake, age, BMI, race, smoking, alcohol intake and physical activity level. However, I don’t see how this can have been done – certainly not satisfactorily.
In Table 2 the raw data for deaths per person years for each quintile is presented. I have done a raw ratio (marked Z) on these numbers to show the following:
Health Professionals Follow up Study
| | Q1 | Q2 | Q3 | Q4 | Q5 | TOTAL | |||
| Total meat | Deaths | 1,713 | 1,610 | 1,679 | 1,794 | 2,130 | 8,926 | ||
| person yrs | 151,212 | 152,120 | 151,558 | 152,318 | 151,315 | 758,524 | |||
| Death Rate (Z) | 1.13 | 1.06 | 1.11 | 1.18 | 1.41 | ||||
| Multivariate (*) | 1.00 | 1.12 | 1.21 | 1.25 | 1.37 | 1.14 | |||
| Unprocessed | Deaths | 1,855 | 1,722 | 1,535 | 1,819 | 1,995 | 8,926 | ||
| person yrs | 150,676 | 149,097 | 154,352 | 150,925 | 153,474 | 758,524 | |||
| Death Rate (Z) | 1.23 | 1.15 | 0.99 | 1.21 | 1.30 | ||||
| Multivariate (*) | 1.00 | 1.11 | 1.14 | 1.20 | 1.29 | 1.17 | |||
| Processed | Deaths | 1,917 | 1,395 | 1,661 | 1,717 | 2,236 | 8,926 | ||
| person yrs | 171,619 | 131,069 | 152,481 | 152,128 | 151,227 | 758,524 | |||
| Death Rate (Z) | 1.12 | 1.06 | 1.09 | 1.13 | 1.48 | ||||
| Multivariate (*) | 1.00 | 1.06 | 1.15 | 1.18 | 1.27 | 1.18 | |||
Above, I have simply taken the raw number of deaths for each quintile over person years and then calculated this as a ratio. The Multivariate line is the one presented in Table 2 of the article. It is the alleged comparison between the five quintiles – using quintile 1 as the base of 1.00 and relating the other quintiles to this base number. This multivariate line is supposed to have adjusted for the fact that exercise and cholesterol went down and BMI, smoking, diabetes, calorie intake and alcohol intake all increased alongside red and processed meat consumption. It is supposed to have removed all those correlations to isolate meat consumption alone.
My death rate line (Z) should therefore have all the other variables included and the multivariate line should have excluded all the other variables. The multivariate line should therefore be substantially below my death rate line (Z) for every quintile and it isn’t. Indeed the raw data for deaths per person years shows that the death rate was lower in Q2 and Q3 than Q1 for total meat, unprocessed meat and processed meat. Look at unprocessed meat (not withstanding that this includes hamburgers and other junk that it shouldn’t) – the death rate in quintile 3 (Q3) is 0.99 vs 1.23 for Q1. As meat consumption increases from Q1 to Q2 and Q1 to Q3, so the death rate falls. Only in Q4 and Q5 does this reverse and it is in these quintiles that we saw the highest levels of BMI, smoking, low activity, high calorie intake, high alcohol intake and so on and these have clearly not been adequately allowed for.
The nurses study shows exactly the same pattern. The death rate falls in Q2 and Q3 vs. Q1 in all cases. In fact even Q4 is lower than Q1 in all meat groups. Only Q5 is higher than Q1 on my ratio of raw data and this is with none of the smoking, exercise, weight, diabetes, alcohol having been allowed for.
Nurses Health Study
| Q1 | Q2 | Q3 | Q4 | Q5 | |||||||||
| Total meat | Deaths | 2,946 | 2,759 | 2,658 | 2,872 | 3,765 | 15,000 | ||||||
| person yrs | 438,326 | 442,134 | 439,712 | 440,329 | 439,391 | 2,199,892 | |||||||
| Death rate (Z) | 0.67% | 0.62% | 0.60% | 0.65% | 0.86% | ||||||||
| Multivariate (*) | 1.00 | 1.08 | 1.11 | 1.18 | 1.24 | 1.11 | |||||||
| Unprocessed | Deaths | 3,079 | 2,885 | 2,545 | 2,709 | 3,782 | 15,000 | ||||||
| person yrs | 441,041 | 441,207 | 439,306 | 431,097 | 447,240 | 2,199,891 | |||||||
| Death rate (Z) | 0.70% | 0.65% | 0.58% | 0.63% | 0.85% | ||||||||
| Multivariate (*) | 1.00 | 1.07 | 1.07 | 1.10 | 1.19 | 1.10 | |||||||
| Processed | Deaths | 3,076 | 2,799 | 2,778 | 2,814 | 3,533 | 15,000 | ||||||
| person yrs | 442,594 | 420,403 | 455,365 | 441,369 | 440,161 | 2,199,892 | |||||||
| Death rate (Z) | 0.69% | 0.67% | 0.61% | 0.64% | 0.80% | ||||||||
| Multivariate (*) | 1.00 | 1.04 | 1.08 | 1.14 | 1.20 | 1.21 | |||||||
The headline of the article
The key passage in the press release that attracted all the headlines was this:
“Unprocessed and processed red meat intakes were associated with an increased risk of total, CVD, and cancer mortality in men and women in the age-adjusted and fully adjusted models. When treating red meat intake as a continuous variable, the elevated risk of total mortality in the pooled analysis for a 1-serving-per-day increase was 12% for total red meat, 13% for unprocessed red meat, and 20% for processed red meat.”
This is what led to the big news story: “adding an extra portion of unprocessed red meat to someone’s daily diet would increase the risk of death by 13%. The figures for processed meat were higher, 20% for overall mortality…”
These numbers come from the bottom lines in Table 2 in the article. The bottom three lines in Table 2 come from the authors of the article combining all deaths in both studies from the multivariate model. They state that, using Q1 as the base line (1.0), the relative results for the other quintiles are as follows and they have added in a final column claimed to be the risk factor for increasing consumption of total, unprocessed or processed meat by one serving a day:
| Q1 | Q2 | Q3 | Q4 | Q5 | Risk factor | ||
| Total meat | 1.00 | 1.10 | 1.15 | 1.21 | 1.30 | 1.12 | |
| Unprocessed | 1.00 | 1.08 | 1.10 | 1.15 | 1.23 | 1.13 | |
| Processed | 1.00 | 1.05 | 1.11 | 1.15 | 1.23 | 1.20 |
The 13% at the end of the unprocessed line is where the 13% headline comes from and the 20% at the end of the processed line is where the 20% comes from. I don’t know precisely how they have come up with these numbers. The corresponding consumption for each quintile was 0.25, 0.61, 0.95, 1.36 and 2.07 servings per day (for the HPFS). I suspect that their model allows them to look at the data for 1 serving vs 2 or half a serving vs one and a half and to compare ratios in this way.
None of this, however, reflects the facts from the raw data that Q2 and Q3 have lower death rates than Q1 in both studies and Q2, Q3 and Q4 are lower than Q1 in the Nurses study.
In summary
There are numerous key problems with this study – I’ll share seven:
1) This study can at best suggest an observed relationship, or association. To make allegations about causation and risk is ignorant and erroneous.
2) The numbers are very small. The overall risk of dying was not even one person in a hundred over a 28 year study. If the death rate is very small, a possible slightly higher death rate in certain circumstances is still very small. It does not warrant a scare-tactic, 13% greater risk of dying headline – this is ‘science’ at its worst.
3) Several other critical variables showed correlation with death rates – lack of activity, low cholesterol, BMI, smoking, diabetes, calorie intake and alcohol intake. These have not been excluded to isolate meat consumption alone. The raw data actually shows deaths rates falling with increased meat consumption up to the third or fourth quintile – and this is before all the other variables have been allowed for. This would suggest that meat consumption has a protective effect while weight, alcohol, calorie intake, exercise and so on are all taking their toll.
4) Several other critical variables were not measured, which would logically correlate with certain meat consumption. Unprocessed meat inexplicably included sandwiches, curries, hamburgers (which come in buns) – has the correlation with bread, margarine, white rice, egg fried rice, poppadoms, burger buns, ketchup, relish or even fizzy drinks been correlated with the death rates? Indeed, Frank Hu, one of the authors of this meat study, is also quoted in today’s paper saying that one soft drink a day raises the risk of heart attacks. It doesn’t of course – it is association at best, just as the meat article is – but one does wonder if that harmful soft drink was the one that just happened to be consumed with the hamburger or the bacon, lettuce and tomato sandwich ‘meal deal’?!
5) Hamburgers and pork sandwiches or lamb curries have been included as unprocessed meat. This is not a study of what real food devotees would consider unprocessed meat therefore. May I suggest that a study of consumers of grass fed ruminants would not deliver the desired headline? The lamb and beef grazing in the fields around me in Wales could not be further in health benefits from the hamburgers in buns and hot dogs in white rolls in fast food America.
6) We are all going to die. We have 100% risk of it in fact. We are not going to increase this risk by 13% or 20% if we have a hamburger and certainly not if we have a grass fed nutrient rich steak. This is headline grabbing egotistical academics doing their worst.
7) As I always consider conflict of interest, it would be remiss of me to end without noting that one of the authors (if not more) is known to be vegetarian and speaks at vegetarian conferences[ii] and the invited ‘peer’ review of the article has been done by none other than the man who claims the credit for having turned ex-President Clinton into a vegan – Dean Ornish.[iii]
All of this nonsense has given me an appetite, so I’m off to get my complete protein and essential fats plus the full range of B vitamins, ample fat soluble vitamins and lashings of iron, phosphorus, magnesium and zinc – also known as grass fed steak!
[i] http://www.meatami.com/ht/a/GetDocumentAction/i/48781
[ii] http://www.vegetariannutrition.org/speakers.html
[iii] http://archinte.ama-assn.org/cgi/content/full/archinternmed.2012.174
