Accepting that obesity rates anywhere went up anywhere from 4x to 9x from 1900-1960 (i.e. from 1.5%-3% to 13.4%), I still think we have to explain the “elbow” in the obesity data starting in 1976-80. It really does look “steady around 10%” in the 1960-1976 era, with an abrupt change in 1976. If we’d continued to increase our obesity rates at the rate of 1960-74, we’d have less than 20% obesity today rather than the 43% obesity rate we actually experience. I think that is the phenomenon SMTM is talking about, and I think it’s worth emphasizing.
I think the relevant fact is that, based on the available data, it appears that average BMI increased relatively linearly and smoothly throughout the 20th century. Since BMI is approximately normally distributed (though skewed right), the seemingly sudden increase in the proportion of people obese is not surprising: it’s a simple consequence of the mathematics of normal distributions.
In other words, the smooth increase in mean BMI coupled with a normal distribution over BMI in the population at any particular point in time explains away the observation that there was an abrupt change centered around roughly 1980. It is not necessary to posit a separate, further variable that increased rapidly after 1980. The existing data most plausibly supports the simple interpretation that the environmental factors that underlie the obesity epidemic have changed relatively gradually over time, with no large breaks.
I think it might be a good idea to try to visualize what’s going on here, to fully understand why you can get a fairly sharp break in the proportion of the population exceeding a threshold even as the variable mean is going up gradually over time. Unfortunately, the best tool I have right now is this simple widget from Desmos that I just built that shows you what fraction of a normal distribution lies above a threshold (t). You can slide the mean variable (u) up and see the effect it has on the fraction exceeding t.
Let’s get a clearer illustration of your point. Here’s a graph of the fraction of a normally distributed population above an arbitrary threshold as the population mean varies, ending when the population mean equals the threshold. In obesity terms, we start with a normally distributed population of BMI, and increase the average BMI linearly over time, ending when the average person is obese, and determine at each timepoint what fraction of the population is obese (so 50% obesity at the end).
The graph above has 4 timepoints roughly evenly spaced by the decade from 1960-2000, so here’s what the graph above looks like if we extract four evenly spaced timepoints to kind of simulate the spacing between the data points:
Here’s the actual data for reference:
Glancing back and forth, I keep changing my mind about whether or not I think the messy empirical data is close enough to the prediction from the normal distribution to accept your conclusion, or whether that elbow feature around 1976-80 seems compelling.
This does seem like the crux of the argument here—do we see a sharp change in 1976-80 or don’t we? At this point I feel like I could go either way.
>Glancing back and forth, I keep changing my mind about whether or not I think the messy empirical data is close enough to the prediction from the normal distribution to accept your conclusion, or whether that elbow feature around 1976-80 seems compelling.
I realize you two had a long discussion about this, but my few cents: This kind of situation (eyeballing is not enough to resolve which of two models fit the data better) is exactly the kind of situation for which a concept of statistical inference is very useful.
I’m a bit too busy right now to present a computation, but my first idea would be to gather the data and run a simple “bootstrappy” simulation: 1) Get the original data set. 2) Generate k = 1 … N simulated samples x^k = [x^k_1, … x^k_t] form a normal distribution with linearly increasing mean mu(t) = mu + c * t at time points t= 1960 … 2018, where c and variance are as in “linear increase hypothesis”. 3) How many of simulated replicate time series have an elbow at 1980 that is equally or more extreme than observed in the data? (One could do this not too informal way by fitting a piece-wise regression model with break at t = 2018 to reach replicate time series, and computing if the two slope estimates differ by a predetermined threshold, such as the estimates recovered by fitting the same piece-wise model in the real data).
This is slightly ad hoc, and there are probably fancier statistical methods for this kind of test, or you could fits some kind of Bayesian model, but I’d think such computational exercise would be illustrative.
I think this is a great idea—I’m also too busy to do this right now and not equipped with that skillset, but I would read your work with interest if you chose to carry this out.
I admit that the data is a bit fuzzy and hard to interpret. But ultimately, we’ve basically reached the point at which it’s hard to tell whether the data supports an abrupt shift, which to me indicates that, even if we find such a shift, it’s not going to be that large. The data could very well support a minor acceleration around 1980 (indeed I think this is fairly likely, from looking at the other data).
On the one hand, that means there are some highly interesting questions to explore about what happened around 1980! But on the other hand, I think the data is essentially conclusive in its rejection of the idea that the obesity epidemic began in about 1980, which is what I interpreted SMTM to be saying.
But ultimately, we’ve basically the point at which it’s hard to tell whether the data supports an abrupt shift, which to me indicates that, even if we find such a shift, it’s not going to be that large. The data could very well support a minor acceleration around 1980 (indeed I think this is fairly likely, from looking at the other data).
I don’t think this is the right way to look at it.
Conditional on accepting that there are two distinct linear regimes, the first linear regime from at least 1960 to 1976-80 is growing about 3x more slowly than the one from 1976-80 and on. I think that a tripling of the rate at which the obese population increases is a perfectly fine definition of “the start of the obesity epidemic.”
I want to be quite clear that I’m not defending SMTM or saying that they are correct—I am just trying to make sure that we are representing accurately the underlying data and the specific claims being made. I see myself as a self-appointed referee here, not a player in the game.
Conditional on accepting that there are two distinct linear regimes, the first linear regime from at least 1960 to 1976-80 is growing about 3x more slowly than the one from 1976-80 and on.
But we have data going back to the late 19th century, and it demonstrates that weight was increasing smoothly, and at a moderately fast rate, before 1960. That is a crucial piece of evidence. It shows that whatever happened in about 1980 could have simply been a minor part of a longer-term trend. I don’t see why we would call that the “start” of the obesity epidemic.
But we have data going back to the late 19th century, and it demonstrates that weight was increasing smoothly, and at a moderately fast rate, before 1960. That is a crucial piece of evidence. It shows that whatever happened in about 1980 could have simply been a minor part of a longer-term trend. I don’t see why we would call that the “start” of the obesity epidemic.
An obesity epidemic is an epidemic of obesity, not of weight gain, and conventionally refers both to an increase in the rate of obesity increase and to the sheer amount of obesity in the population. It wouldn’t surprise me if the trajectory looked something like this:
Late 19th century-earlyish 20th century: increasing gains in agricultural productivity and infrastructure among other things ensure adequate food supply, increasing the population’s weight to a steady state where everybody has enough to eat. This leads to a big increase in % obesity, but from a very low base rate. We don’t call this an “obesity epidemic” because most of what is happening is malnourished people getting enough to eat, even though the abundant food is causing a 3x to 8x fold change in obesity from like 1.5-3% to ~10-13%.
Mid 20th century: a steady state in which everybody has enough to eat, a constant increase in obesity but not enough to ring alarm bells
1974-1980: some event (contamination, whatever) disrupts the equilibrium leading to a new regime in which % obesity rises 3x faster than it was during the mid 20th century equilibrium era.
Here’s how I see your and Natalia’s hypothesis vs. SMTM’s hypothesis. Both seem mechanistically plausible and roughly in accordance with the data, depending on which graphs you pick. So it’s not that I see an overwhelming case for one or the other, just that SMTM’s hypothesis implies there’s “one weird trick” to not have rampant obesity and that we ought to figure out what it is, which is a “big if true” idea that motivates investigation.
I agree that whatever happened in ~1980 could have been a minor part of a longer-term trend, but if it’s not, if there was some contamination that put us on a very different trajectory into raging obesity rather than modest constant increases oevr time, then that is a pretty important thing to check out and we’d definitely call it the start of the obesity epidemic.
I agree that whatever happened in ~1980 could have been a minor part of a longer-term trend, but if it’s not, if there was some contamination that put us on a very different trajectory into raging obesity
I agree that there’s still some plausible thing that happened in 1980 that was different from the previous trend. There could be, and probably are, multiple causes of the trend of increasing weight over time. And as one trend loses steam, another could have taken over. To the extent that that’s what you’re saying, I agree.
But I’m still not sure I agree with the thrust of what you’re saying. I maintain that the data doesn’t strongly support the theory that there was a major, abrupt change around 1980. Given the relative size of the change, it seems more plausible to me that whatever happened in 1980 was simply the continuation and slight acceleration of the pre-1980 trend.
I think most of the plausibility behind thinking that 1980 is special has to do with the size of the change that we’re talking about. And to be a bit blunt, I suspect you’re implicitly exaggerating the size of this change as evidenced by how you drew it in your chart (although I know the chart was drawn to illustrate a point, not necessarily to show the exact sizes of the relevant changes).
However, I’m not very confident that there isn’t something big going on here, and at this point I’m not sure whether the disagreement we have is mostly semantic. To me, it kind of seems like this argument might just be two people arguing about whether an effect found in the data is “big”, and their entire disagreement just comes down to what they’d consider “big”.
I maintain that the data doesn’t strongly support the theory that there was a major, abrupt change around 1980. Given the relative size of the change, it seems more plausible to me that whatever happened in 1980 was simply the continuation and slightly acceleration of the pre-1980 trend.
I think our problem fundamentally with the graph in question is that we have just 5 timepoints to base our perception of the shape of the trend during this era and our data cuts off at a crucial moment. Again, what you call a “slight” acceleration appears to me as a tripling of the obesity increase rate which I do not consider slight.
Where I think our semantics are a little off is that we’re most centrally debating the existence of a “contamination epidemic” that happens to result in obesity. We can both agree there was some obesity growing at a slower rate prior to 1980, and much more obesity growing at a faster rate after 1980. The question is whether this graph is evidence for a contamination epidemic, against, or neutral. My visual inspection makes me think that, in isolation, the graph better fits the SMTM hypothesis than your hypothesis, updating my priors somewhat toward the existence of a “contamination epidemic,” but that’s just one piece of data in the context of a much larger argument. I would be entirely open to revising my opinion contingent on more fine-grained data or on a better method to distinguish how well the data we do have fits the “shifting normal” vs. “two linear regimes” models.
ETA: I misinterpreted the above comment. I thought they were talking about the data, rather than the specific graph. See discussion below.
My visual inspection makes me think that, in isolation, the graph better fits the SMTM hypothesis than your hypothesis
And I’m quite confused by that, because of the chart below (and the other ones for different demographic groups). I am not saying that this single fact proves much in isolation. It doesn’t disprove SMTM, for sure. But when I read your qualitative description of the shift that we’re supposed to find in this data, and then compare it to what I see in the chart, I just don’t get the sense that you’re describing it well. Honestly I don’t know what’s going on here.
[ETA: note that the x-axis is the cohort birth year. And it’s the output of a statistical model created using NCHS data collected between 1959 and 2006. And also, this model uses the same data as the other chart.]
To be super clear, I am exclusively considering the one graph that I started this comment chain with and am not making any other claims whatsoever about the rest of the data. What I specifically said is that in isolation, the graph we have been discussing better fits the SMTM hypothesis than your hypothesis. Bringing in a separate graph that you think better supports your hypothesis than SMTM’s has zero bearing on the claim that I made, which is exclusively and entirely about the one graph we have been discussing. This new comment with this new graph reads to me as changing the subject, not making a rebuttal.
This might seem unreasonable, but I think it’s extremely important that we be able to see truly what a specific piece of evidence tells us in isolation. We should not let other pieces of evidence distort what we see. We should form our synthetic interpretation on the basis of truly seeing each individual piece for what it is, and then building up our interpretation from there. When I look individually at the one graph we’ve been discussing and don’t consider the rest, I see an abrupt change between two different linear paths more than I see a smooth exponential increase.
Note: I deleted and re-posted this comment since I felt it was missing key context and I was misinterpreting you previously.
What I specifically said is that in isolation, the graph we have been discussing better fits the SMTM hypothesis than your hypothesis. Bringing in a separate graph that you think better supports your hypothesis than SMTM’s has zero bearing on the claim that I made, which is exclusively and entirely about the one graph we have been discussing. This new comment with this new graph reads to me as changing the subject, not making a rebuttal.
To be clear, I said I was “confused”, not that I disagreed because of additional evidence. But I did misinterpret what you were saying, ultimately. So, let me try again.
I don’t know how to reconcile any differences between the chart I brought up and the chart you mentioned. Note that they’re drawn from the same data, so in theory my chart is showing is simply how BMI moves through time, whereas yours is showing the same thing but under a different transformation; namely, it shows the proportion of people who are above a certain BMI threshold.
My rough guess is, and my initial implicit assumption was, that the two charts are simply consistent and the original point I made about shifting a normal distribution still holds, and the apparent shift is mostly an illusion, with the caveat that there’s a slight acceleration (as shown in the chart I brought up). The reason why I brought up the chart is because I assumed you agreed that they were consistent, but simply thought that the acceleration in BMI was large. I wanted to say: “I don’t agree. See how it looks when you just follow BMI through time. There’s barely any acceleration!”
No worries! I am approaching this debate in a collaborative spirit. I may have been misunderstanding you as well.
What I see when I examine the second graph you have shown, again in isolation, is that it does indeed look very much like the results of the “shifted normal” model you described earlier. Or rather, of that process happening twice, with a sort of temporary tapering off around 1940. Although if I’m understanding right, the earlier pre-60s part is pure extrapolation. This graph clearly fits your and Natalia’s hypothesis and not SMTM’s, we see nothing of particular significance around 1980.
As you say, the next question becomes how to decide which to put more weight on. Do we like the statistical heft of Komlos and Brabec, or do we think they’re just using fancy statistics to erase a crucial feature of the raw data? I don’t know how to arbitrate that question. But I would be sympathetic to an interpreter who said they were convinced by the sophisticated statistical model and viewed the apparent “elbow” in the raw data as more likely an artifact than a real feature of the true trend, and I’m sure Komlos and Brabec know much better than me what they’re about.
My way of proceeding would be to say “looking at the raw data, it does look like there’s a sharp change around 1980, but that might be an artifact. Looking at a sophisticated curve-fitting model of similar data, that feature vanishes. We might put 80% weight on the sophisticated modeling and 20% on the raw data, and note that the raw data itself isn’t so incompatible with a “shifted normal” interpretation, maybe 70⁄30.” Overall, I’m inclined to put maybe 85% credence in the “shifted normal” interpretation in which there was no big event in obesity around 1980, and 15% credence that a real ” elbow feature” is being obscured by the statistical smoothing.
I think that, when you cite that chart, it’s useful for readers if you point out that it’s the output of a statistical model created using NCHS data collected between 1959 and 2006.
Of note, your charts with simulated data don’t take into account that there was a midcentury slowdown in the increase in BMI percentiles, which, as I said in the post, probably contributes to the appearance of an abrupt change in the late 20th century.
I think the relevant fact is that, based on the available data, it appears that average BMI increased relatively linearly and smoothly throughout the 20th century. Since BMI is approximately normally distributed (though skewed right), the seemingly sudden increase in the proportion of people obese is not surprising: it’s a simple consequence of the mathematics of normal distributions.
In other words, the smooth increase in mean BMI coupled with a normal distribution over BMI in the population at any particular point in time explains away the observation that there was an abrupt change centered around roughly 1980. It is not necessary to posit a separate, further variable that increased rapidly after 1980. The existing data most plausibly supports the simple interpretation that the environmental factors that underlie the obesity epidemic have changed relatively gradually over time, with no large breaks.
I think it might be a good idea to try to visualize what’s going on here, to fully understand why you can get a fairly sharp break in the proportion of the population exceeding a threshold even as the variable mean is going up gradually over time. Unfortunately, the best tool I have right now is this simple widget from Desmos that I just built that shows you what fraction of a normal distribution lies above a threshold (t). You can slide the mean variable (u) up and see the effect it has on the fraction exceeding t.
Let’s get a clearer illustration of your point. Here’s a graph of the fraction of a normally distributed population above an arbitrary threshold as the population mean varies, ending when the population mean equals the threshold. In obesity terms, we start with a normally distributed population of BMI, and increase the average BMI linearly over time, ending when the average person is obese, and determine at each timepoint what fraction of the population is obese (so 50% obesity at the end).
The graph above has 4 timepoints roughly evenly spaced by the decade from 1960-2000, so here’s what the graph above looks like if we extract four evenly spaced timepoints to kind of simulate the spacing between the data points:
Here’s the actual data for reference:
Glancing back and forth, I keep changing my mind about whether or not I think the messy empirical data is close enough to the prediction from the normal distribution to accept your conclusion, or whether that elbow feature around 1976-80 seems compelling.
This does seem like the crux of the argument here—do we see a sharp change in 1976-80 or don’t we? At this point I feel like I could go either way.
>Glancing back and forth, I keep changing my mind about whether or not I think the messy empirical data is close enough to the prediction from the normal distribution to accept your conclusion, or whether that elbow feature around 1976-80 seems compelling.
I realize you two had a long discussion about this, but my few cents: This kind of situation (eyeballing is not enough to resolve which of two models fit the data better) is exactly the kind of situation for which a concept of statistical inference is very useful.
I’m a bit too busy right now to present a computation, but my first idea would be to gather the data and run a simple “bootstrappy” simulation: 1) Get the original data set. 2) Generate k = 1 … N simulated samples x^k = [x^k_1, … x^k_t] form a normal distribution with linearly increasing mean mu(t) = mu + c * t at time points t= 1960 … 2018, where c and variance are as in “linear increase hypothesis”. 3) How many of simulated replicate time series have an elbow at 1980 that is equally or more extreme than observed in the data? (One could do this not too informal way by fitting a piece-wise regression model with break at t = 2018 to reach replicate time series, and computing if the two slope estimates differ by a predetermined threshold, such as the estimates recovered by fitting the same piece-wise model in the real data).
This is slightly ad hoc, and there are probably fancier statistical methods for this kind of test, or you could fits some kind of Bayesian model, but I’d think such computational exercise would be illustrative.
I think this is a great idea—I’m also too busy to do this right now and not equipped with that skillset, but I would read your work with interest if you chose to carry this out.
I admit that the data is a bit fuzzy and hard to interpret. But ultimately, we’ve basically reached the point at which it’s hard to tell whether the data supports an abrupt shift, which to me indicates that, even if we find such a shift, it’s not going to be that large. The data could very well support a minor acceleration around 1980 (indeed I think this is fairly likely, from looking at the other data).
On the one hand, that means there are some highly interesting questions to explore about what happened around 1980! But on the other hand, I think the data is essentially conclusive in its rejection of the idea that the obesity epidemic began in about 1980, which is what I interpreted SMTM to be saying.
I don’t think this is the right way to look at it.
Conditional on accepting that there are two distinct linear regimes, the first linear regime from at least 1960 to 1976-80 is growing about 3x more slowly than the one from 1976-80 and on. I think that a tripling of the rate at which the obese population increases is a perfectly fine definition of “the start of the obesity epidemic.”
I want to be quite clear that I’m not defending SMTM or saying that they are correct—I am just trying to make sure that we are representing accurately the underlying data and the specific claims being made. I see myself as a self-appointed referee here, not a player in the game.
But we have data going back to the late 19th century, and it demonstrates that weight was increasing smoothly, and at a moderately fast rate, before 1960. That is a crucial piece of evidence. It shows that whatever happened in about 1980 could have simply been a minor part of a longer-term trend. I don’t see why we would call that the “start” of the obesity epidemic.
An obesity epidemic is an epidemic of obesity, not of weight gain, and conventionally refers both to an increase in the rate of obesity increase and to the sheer amount of obesity in the population. It wouldn’t surprise me if the trajectory looked something like this:
Late 19th century-earlyish 20th century: increasing gains in agricultural productivity and infrastructure among other things ensure adequate food supply, increasing the population’s weight to a steady state where everybody has enough to eat. This leads to a big increase in % obesity, but from a very low base rate. We don’t call this an “obesity epidemic” because most of what is happening is malnourished people getting enough to eat, even though the abundant food is causing a 3x to 8x fold change in obesity from like 1.5-3% to ~10-13%.
Mid 20th century: a steady state in which everybody has enough to eat, a constant increase in obesity but not enough to ring alarm bells
1974-1980: some event (contamination, whatever) disrupts the equilibrium leading to a new regime in which % obesity rises 3x faster than it was during the mid 20th century equilibrium era.
Here’s how I see your and Natalia’s hypothesis vs. SMTM’s hypothesis. Both seem mechanistically plausible and roughly in accordance with the data, depending on which graphs you pick. So it’s not that I see an overwhelming case for one or the other, just that SMTM’s hypothesis implies there’s “one weird trick” to not have rampant obesity and that we ought to figure out what it is, which is a “big if true” idea that motivates investigation.
I agree that whatever happened in ~1980 could have been a minor part of a longer-term trend, but if it’s not, if there was some contamination that put us on a very different trajectory into raging obesity rather than modest constant increases oevr time, then that is a pretty important thing to check out and we’d definitely call it the start of the obesity epidemic.
I agree that there’s still some plausible thing that happened in 1980 that was different from the previous trend. There could be, and probably are, multiple causes of the trend of increasing weight over time. And as one trend loses steam, another could have taken over. To the extent that that’s what you’re saying, I agree.
But I’m still not sure I agree with the thrust of what you’re saying. I maintain that the data doesn’t strongly support the theory that there was a major, abrupt change around 1980. Given the relative size of the change, it seems more plausible to me that whatever happened in 1980 was simply the continuation and slight acceleration of the pre-1980 trend.
I think most of the plausibility behind thinking that 1980 is special has to do with the size of the change that we’re talking about. And to be a bit blunt, I suspect you’re implicitly exaggerating the size of this change as evidenced by how you drew it in your chart (although I know the chart was drawn to illustrate a point, not necessarily to show the exact sizes of the relevant changes).
However, I’m not very confident that there isn’t something big going on here, and at this point I’m not sure whether the disagreement we have is mostly semantic. To me, it kind of seems like this argument might just be two people arguing about whether an effect found in the data is “big”, and their entire disagreement just comes down to what they’d consider “big”.
I think our problem fundamentally with the graph in question is that we have just 5 timepoints to base our perception of the shape of the trend during this era and our data cuts off at a crucial moment. Again, what you call a “slight” acceleration appears to me as a tripling of the obesity increase rate which I do not consider slight.
Where I think our semantics are a little off is that we’re most centrally debating the existence of a “contamination epidemic” that happens to result in obesity. We can both agree there was some obesity growing at a slower rate prior to 1980, and much more obesity growing at a faster rate after 1980. The question is whether this graph is evidence for a contamination epidemic, against, or neutral. My visual inspection makes me think that, in isolation, the graph better fits the SMTM hypothesis than your hypothesis, updating my priors somewhat toward the existence of a “contamination epidemic,” but that’s just one piece of data in the context of a much larger argument. I would be entirely open to revising my opinion contingent on more fine-grained data or on a better method to distinguish how well the data we do have fits the “shifting normal” vs. “two linear regimes” models.
ETA: I misinterpreted the above comment. I thought they were talking about the data, rather than the specific graph. See discussion below.
And I’m quite confused by that, because of the chart below (and the other ones for different demographic groups). I am not saying that this single fact proves much in isolation. It doesn’t disprove SMTM, for sure. But when I read your qualitative description of the shift that we’re supposed to find in this data, and then compare it to what I see in the chart, I just don’t get the sense that you’re describing it well. Honestly I don’t know what’s going on here.
[ETA: note that the x-axis is the cohort birth year. And it’s the output of a statistical model created using NCHS data collected between 1959 and 2006. And also, this model uses the same data as the other chart.]
To be super clear, I am exclusively considering the one graph that I started this comment chain with and am not making any other claims whatsoever about the rest of the data. What I specifically said is that in isolation, the graph we have been discussing better fits the SMTM hypothesis than your hypothesis. Bringing in a separate graph that you think better supports your hypothesis than SMTM’s has zero bearing on the claim that I made, which is exclusively and entirely about the one graph we have been discussing. This new comment with this new graph reads to me as changing the subject, not making a rebuttal.
This might seem unreasonable, but I think it’s extremely important that we be able to see truly what a specific piece of evidence tells us in isolation. We should not let other pieces of evidence distort what we see. We should form our synthetic interpretation on the basis of truly seeing each individual piece for what it is, and then building up our interpretation from there. When I look individually at the one graph we’ve been discussing and don’t consider the rest, I see an abrupt change between two different linear paths more than I see a smooth exponential increase.
Note: I deleted and re-posted this comment since I felt it was missing key context and I was misinterpreting you previously.
To be clear, I said I was “confused”, not that I disagreed because of additional evidence. But I did misinterpret what you were saying, ultimately. So, let me try again.
I don’t know how to reconcile any differences between the chart I brought up and the chart you mentioned. Note that they’re drawn from the same data, so in theory my chart is showing is simply how BMI moves through time, whereas yours is showing the same thing but under a different transformation; namely, it shows the proportion of people who are above a certain BMI threshold.
My rough guess is, and my initial implicit assumption was, that the two charts are simply consistent and the original point I made about shifting a normal distribution still holds, and the apparent shift is mostly an illusion, with the caveat that there’s a slight acceleration (as shown in the chart I brought up). The reason why I brought up the chart is because I assumed you agreed that they were consistent, but simply thought that the acceleration in BMI was large. I wanted to say: “I don’t agree. See how it looks when you just follow BMI through time. There’s barely any acceleration!”
Sorry about the misinterpretation.
No worries! I am approaching this debate in a collaborative spirit. I may have been misunderstanding you as well.
What I see when I examine the second graph you have shown, again in isolation, is that it does indeed look very much like the results of the “shifted normal” model you described earlier. Or rather, of that process happening twice, with a sort of temporary tapering off around 1940. Although if I’m understanding right, the earlier pre-60s part is pure extrapolation. This graph clearly fits your and Natalia’s hypothesis and not SMTM’s, we see nothing of particular significance around 1980.
As you say, the next question becomes how to decide which to put more weight on. Do we like the statistical heft of Komlos and Brabec, or do we think they’re just using fancy statistics to erase a crucial feature of the raw data? I don’t know how to arbitrate that question. But I would be sympathetic to an interpreter who said they were convinced by the sophisticated statistical model and viewed the apparent “elbow” in the raw data as more likely an artifact than a real feature of the true trend, and I’m sure Komlos and Brabec know much better than me what they’re about.
My way of proceeding would be to say “looking at the raw data, it does look like there’s a sharp change around 1980, but that might be an artifact. Looking at a sophisticated curve-fitting model of similar data, that feature vanishes. We might put 80% weight on the sophisticated modeling and 20% on the raw data, and note that the raw data itself isn’t so incompatible with a “shifted normal” interpretation, maybe 70⁄30.” Overall, I’m inclined to put maybe 85% credence in the “shifted normal” interpretation in which there was no big event in obesity around 1980, and 15% credence that a real ” elbow feature” is being obscured by the statistical smoothing.
I think that, when you cite that chart, it’s useful for readers if you point out that it’s the output of a statistical model created using NCHS data collected between 1959 and 2006.
OK, I’ll add that to my comment.
Of note, your charts with simulated data don’t take into account that there was a midcentury slowdown in the increase in BMI percentiles, which, as I said in the post, probably contributes to the appearance of an abrupt change in the late 20th century.