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Being obese is worse than smoking. Do you agree? How do you measure costs?

This discussion is about how to correctly assess costs and benefits of smoking versus obesity to society, and where to get reliable data sources, and how to properly clean data. It is also about healthcare policies and the new health insurance regulations, sometimes referred to as Obamacare.

To decide whether smoking is more dangerous than obesity, how would you proceed? How do you compute all the costs:

  • Sick days,
  • Extra healthcare expenditures,
  • Increased health insurance costs for everyone including employers,
  • Increased costs to fly or drive a car (more fuel required),
  • Need to provide larger more expensive seats in theaters and restaurants and thus accomodate fewer patrons,
  • Depletion of food sources at a faster rate,
  • Time spent on research to fight obesity rather than on constructive projects,
  • Second-hand smoke,
  • Fires caused by cigarettes,
  • Fire alarms mandatory in new houses for everybody (a side effect of smoking)
  • How do you compute costs over lifetime, as $1,000 in 2012 is worth less than $1,000 in 2030 (need for econometric models)? And how do you factor in the fact that health expenditures costs grow even faster than regular inflation (although in my opinion it will violently deflate in a few years)?

Potential benefits of smoking and obesity:

Some will argue that those seeking self-destruction would do so even if cigarettes and big fat hamburgers were illegal, possibly using illegal products instead, which would be even worse (remember the prohibition and the alcohol black market). Also, as cigarette smokers die before retirement, they save tons of money to social security. They also allow scientists do research on many different cancers and heart problems - research that would otherwise be more limited without all the smokers and the obese. Smoking also provide huge taxes to state governments, and cigarette manufacturing employs thousands of workers.

And some would even say that smoking is a natural selection process that put some downward pressures on over-population, due to shorter lifetimes of smokers. Because it is self-inflicted, it is not as bad as famines, wars, diseases, or road accidents.

Health insurance issues:

Finally, a question about health insurance. Should an insurer ask how many hamburgers per week you eat, to determine your premium? We already ask for cigarettes (by why not for alcohol or illegal drugs?) And what about smokers who switch to tar-free cigarettes (e-cigarettes) or people eating fat-free hamburgers?

To summarize, how to gather and blend all the data, analyse it, incorporate financial gains - not just losses associated with these bad habits - and come up with strategies to improve the situation - strategies that involve multiple, competing government agencies? And how do you measure improvement (lift)?

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These kinds of analyses are commonly prepared in what is called Health Economics or Pharmaco-economics - and have names like "cost effectiveness", "cost utility" or "cost consequences".  This is a question that also fits into the paradigm , outlined on the Health  Care plan approved by Congress as "comparative effectiveness research" (CER), though for your example above, one is more interested in the benefits, that accrue due to reducing obesity and reducing or stopping smoking.  The "outcome" of fundamental interest, is how long people live or how much life (survival) increases by reducing obesity or stopping  smoking. The question as posed is too broad. It can be refocussed and useful answers prepared by considering an intervention, that reduces obesity (such as an exercise program, or diet pills, etc.) and an invention that leads someone to reduce or stop smoking.    These citations (I don't have the papers in full, look at "cost effectiveness") http://professional.diabetes.org/News_Display.aspx?TYP=9&CID=83452 http://www.mendeley.com/research/cost-effectiveness-smoking-cessati...  Implicit in these cost effectiveness arguments is a "Quality adjusted life year" (QALY), And the QALY captures or can capture all the direct and indirect costs (expenses as in the original question).  One useful, way to think about a QALY is with the hypothetical question (and its variations): If one could have 10 years of life at 1/2 of ones best possible state of health, or 5 years at a perfect state of health, which would one prefer?  The variations change the years of life and the "state of health".  These models include "discount factors" which address the time value of money (i.e. how much is a dollar ten years from now worth today?) To the  question how do you measure improvement, use a "QALY" http://www.medicine.ox.ac.uk/bandolier/booth/glossary/QALY.html   These sort of calculations of QALY's are routinely used by organizations such as "NICE" (National Institute for Clinical Effectiveness ) http://www.nice.org.uk/newsroom/features/measuringeffectivenessandc...

Both obesity and smoking are self-inflicted health threats.  The people involved need to change their behavior.  Behavior change is hard.


One of the problems in trying to motivate these people is that most of them are aware that not everyone who is overweight or who smokes has serious health problems.  These people often rationalize their unhealthy habits by pointing to other people who smoke or eat too much and seem to be healthy and happy.

An interesting project for predictive analytics would be to analyze a large database of health and behavioral information (and hopefully some genetic markers) to determine which people are seriously at risk from smoking or over eating.  This type of information might be very helpful in convincing people who are at serious risk to quit smoking and/or push themselves away from the dinner table before they have consumed everything in sight.  If we could eliminate some of the uncertainty about the eventual outcome, most rational people would find a way to improve their behavior.

STOP!!!!!!!!!  PLEASE BE CAREFUL!  You are talking about human beings, here first, not just statistics.  

I say that because after 30 years of being treated by doctors as just another obesity "statistic"  I had a doctor stop and actually take closer look.  And he found I had Hashimoto's (thyroid autoimmune disease)  and was hypothyroid.  And my thyroid was swiss cheese.  It was so full of cysts.  And the doctors end up removing my thyroid only to find cancer.  

The thyroid hormone is the signal for every cell in your body to produce the proteins to run your metabolism.  Not everyone has a healthy metabolism.

I had suffered by this disease continuously facing stupid logic such like  "Because it is self-inflicted, it is not as bad as famines, wars, diseases, or road accidents."   Excuse me, many famines are caused by governments not taking care of their people.  Wars are caused by oh people.  How many road accidents are caused by stupid decisions like being distracted while driving.  You can't do that logic.  It doesn't work.  Stop and think about it from all sides.  

To many doctors believe the stats more then their patients because it's easier. No matter that it's killing their patients.  Another 5 years of bad doctors and I would have been dead.    

As Data Specialists we need to look at more then just a few numbers.  What is your goal?   If you don't like smokers or over weight people its easy to make the data look like something it isn't.  

Did you know that hypothyroidism can cause obesity, high blood pressure, diabetes, high cholesterol?   Which is totally against what the data science suggests.  But the hard core science says other wise.   So who is right?  The science.  The statistics is just one tool in the toolbox to find the answers.  You need to keep that in mind.  And you can't generalize like this when it comes to people because you don't know all the different factors like environments, genetics, and social influences.   

PLEASE REMEMBER YOUR STATS CAN KILL if your not careful.

And if you don't think so.  I have had a doctor even blow raspberries at me because the statistics said other wise to my experience and my 30 years of living with this disease and having cancer didn't mean a thing him.  Needless to say I walked out on that doctor.    You want to know flaw in the system.  The doctor still got paid for the full appointment.  Because he billed my insurance.  And the insurance never contacted me on how the doctor was.   I was so upset I didn't want to talk to anybody or trust anyone for awhile.  And until you live with this disease you can say jack about how you would feel or deal with anything like that.  

We need to make the point that not all people are obese by choice (I don't know enough about smoking).   You can't tell from looking at someone if its self inflicted or not.   We need to keep that in mind.  

Your first paragraph is not a correct statement.  Be careful.

  


Peter W Frey said:

Both obesity and smoking are self-inflicted health threats.  The people involved need to change their behavior.  Behavior change is hard.


One of the problems in trying to motivate these people is that most of them are aware that not everyone who is overweight or who smokes has serious health problems.  These people often rationalize their unhealthy habits by pointing to other people who smoke or eat too much and seem to be healthy and happy.

An interesting project for predictive analytics would be to analyze a large database of health and behavioral information (and hopefully some genetic markers) to determine which people are seriously at risk from smoking or over eating.  This type of information might be very helpful in convincing people who are at serious risk to quit smoking and/or push themselves away from the dinner table before they have consumed everything in sight.  If we could eliminate some of the uncertainty about the eventual outcome, most rational people would find a way to improve their behavior.

Not everyone wants to finance someone's else health problems via mandatory health insurance, especially if it's a result of drug abuse, eating too many hamburgers, rock climbing, driving a car, skiing or dangerous sports. 

PS. 

And much is spent on injured athletes and for their care for the rest of their lives?  Oh but that's healthy.   Yet I see more athletes at the doctors then I do the obese.  I think people are being prejudice and using statistics to make it ok to mistreat people because they don't have to feel responsible to have any compassion.



Amy Hays said:

STOP!!!!!!!!!  PLEASE BE CAREFUL!  You are talking about human beings, here first, not just statistics.  

I say that because after 30 years of being treated by doctors as just another obesity "statistic"  I had a doctor stop and actually take closer look.  And he found I had Hashimoto's (thyroid autoimmune disease)  and was hypothyroid.  And my thyroid was swiss cheese.  It was so full of cysts.  And the doctors end up removing my thyroid only to find cancer.  

The thyroid hormone is the signal for every cell in your body to produce the proteins to run your metabolism.  Not everyone has a healthy metabolism.

I had suffered by this disease continuously facing stupid logic such like  "Because it is self-inflicted, it is not as bad as famines, wars, diseases, or road accidents."   Excuse me, many famines are caused by governments not taking care of their people.  Wars are caused by oh people.  How many road accidents are caused by stupid decisions like being distracted while driving.  You can't do that logic.  It doesn't work.  Stop and think about it from all sides.  

To many doctors believe the stats more then their patients because it's easier. No matter that it's killing their patients.  Another 5 years of bad doctors and I would have been dead.    

As Data Specialists we need to look at more then just a few numbers.  What is your goal?   If you don't like smokers or over weight people its easy to make the data look like something it isn't.  

Did you know that hypothyroidism can cause obesity, high blood pressure, diabetes, high cholesterol?   Which is totally against what the data science suggests.  But the hard core science says other wise.   So who is right?  The science.  The statistics is just one tool in the toolbox to find the answers.  You need to keep that in mind.  And you can't generalize like this when it comes to people because you don't know all the different factors like environments, genetics, and social influences.   

PLEASE REMEMBER YOUR STATS CAN KILL if your not careful.

And if you don't think so.  I have had a doctor even blow raspberries at me because the statistics said other wise to my experience and my 30 years of living with this disease and having cancer didn't mean a thing him.  Needless to say I walked out on that doctor.    You want to know flaw in the system.  The doctor still got paid for the full appointment.  Because he billed my insurance.  And the insurance never contacted me on how the doctor was.   I was so upset I didn't want to talk to anybody or trust anyone for awhile.  And until you live with this disease you can say jack about how you would feel or deal with anything like that.  

Agreed.  But it is not just a financial issue either.   When it affects the quality of care I pay for, much more is at stake.   

One thing that is rarely talked about is the basic costs of health care.  Why is it so expensive?  Greed, frivolous lawsuits, educational costs, and so on.   But corporations cause a lot of it.   When people have to pay no matter what, and they need it to survive, like in the government there is little feedback in the system.  So when there is something that isn't working there is waste. lots of it.  The bigger they are the harder they fall.  We have had a few examples of market crashes lately because companies got to greedy.   They thought they could get away with it because people had to have houses to live in. Or they were to big to fail.  But they need to fail to learn what they were doing wrong.   The problems we have seen in other markets persists in the health care system.   Instead of creating a new one they should fix the old.  Find where the inefficiencies are and not blame the nature of the human condition which is its source of business.  

Vincent Granville said:

Not everyone wants to finance someone's else health problems via mandatory health insurance, especially if it's a result of drug abuse, eating too many hamburgers, rock climbing, driving a car, skiing or dangerous sports. 

Vincent,  I like what you do.  And in no way think bad of you or what you do.  I appreciate it.   But I think your approach is wrong on this article.  Your asking for an opinion of what the issue is, then asking how to prove it?  That is always possible depending on how you look at the data and what you use to prove your opinion.  This is dangerous.

Science dictates you have an hypothesis and then you test that hypothesis with controls.  Then use the statistics to analyze that data to prove or disprove your hypothesis.  And right or wrong you accept the out come without manipulating the information.

Taking large amounts of data to prove an hypothesis is not a good idea.  You can paint it however you want it to be.   Your hypothesis is not proved or disproved.    Only trends can be shown.  And this is a problem with a lot of data now days because people miss use those trends as fact.  When in reality they are only hypothesis and were never properly tested.  Just preliminary trending was done.   And when it's released to the public or used by corporations they often forget that.

  

  These arguments about obesity are the same I have had with my doctors over the years.  And at the root of why I didn't get the treatment years sooner.  And it nearly cost me my life.  This why I am so vocal on this topic.  I am not meaning to offend anyone.  And I apologize if I do.  I hope to open some thinking in the way mine has been opened by living through thyroid disease and cancer the last few years.   

Amy,  Your experience with doctors is a real world example of a science trained professional not understaning that he/she applies a model (the doctors diagnosis process) and just like any model it has false negatives , false positives, true positives and true negatives.  Different doctors exhibit different ROC curves with respect to how good their diagnostice model performs.


There is no certainty. But models and statistics can be used to identify what to target. Unfortunately the uncertainty means that the model may predict that you have high risk of some condition, but it may get it wrong for a number of cases it considers highly likely. The number needed to treat is the number you need to treat before you get to one patient who really has the condition.

You should blame your physician for his inaccuracy, don't blame the whole of science.  Controlled experiments, the core of scientific method, have contributed a great deal to the progress of mankind.

Taking large amounts of data to explore and test an hypothesis is perfectly acceptable.  That is what we do with data mining and machine learning techniques.  That someone then posts something that you find offensive as a means of provoking a discussion is unfortunate but you should beware making claims about scientific method that are indefensible.

Mike, I don't blame the science but the people that misuse the data. If they actually tested an hypothesis, but many times they are not testing it. They are seeing trends and taking that to fact.

It's not the science at all. It is the misuse of it. That's why I say we need to be careful.

Believe me I have a complete and humbling appreciation for the fact that I would be dead right now if it weren't for science. And it keeps me alive every day.

Amy, Absolutely. Understanding uncertainty and the costs associated with decision making under uncertainty is what is required.

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