**Transcript of Cyberseminar**

**HERC Health Economics Cyberseminar**

**Challenges measuring healthcare costs attributable to an individual chronic condition**

**Presenter: Steve Zeliadt, PhD MPH**

**October 16, 2013**

*This is an unedited transcript of this session. As such, it may contain omissions or errors due to sound quality or misinterpretation. For clarification or verification of any points in the transcript, please refer to the audio version posted at www.hsrd.research.va.gov/cyberseminars/catalog-archive.cfm or contact herc@va.gov.*

Moderator:     We are pleased to have Steve Zeliadt present for us today. Steve is a core investigator at the Seattle VA. He is also a research assistant professor in the Department of Health Services at the University of Washington. He actively conducts research there with collaborators at the Health Promotion and Research Center and the Urology Outcomes Research Collaborative. He is also an affiliate investigator at the Fred Hutchison Cancer Research Center and Group Health Research Institute. His research interests involve helping patients and providers make individualized and informed decisions about cancer care. He received his Ph.D. in Health Services and MPH from the University of Washington. Today he will be talking about challenges in measuring healthcare costs attributable to individual chronic conditions. Welcome Steve. 

Steve Zeliadt:     Thank you Jean. I wanted to notify everyone when I talked to the operator that there might be some problems in the VANTS line. Heidi assures us that if that happens, she will solve it. If everything goes mute, someone will know about it and we will figure out a back-up plan. 

Thank you guys very much for joining me today on this call. This work is coming out of a grant that we had. Andrew Zhou, who is the biostatistician chair who runs VASA for the VA, looked at all kinds of different approaches to modeling healthcare costs, really focusing on non-parametric approaches. I was involved in that grant, looking at how we interpret what comes out of those models. If you are interested in the very complex non-parametric methods, we can talk about that. That is not the focus of today’s call. Today’s call is really talking about thinking carefully about interpreting all the coefficients we get, especially when we are focusing on attributable costs in individual chronic diseases. 

For today, I have a few goals for you guys. I teach this class to the health economics and Ph.D. students at the University of Washington. We do spend a lot of time looking at all the different types of cost models to run. We do not spend as much time talking about interpreting those coefficients. When it comes time for some of the exams and the students are writing out their answers, they write things that do not make a lot of sense. The inspiration for this session today is to talk, focusing on that part. 

What I want you guys to do is really think carefully about this issue of causalities. We think about causality a lot in disease outcomes and health outcomes. We do not think about it so much in cost outcomes. Almost always, we present our findings about cost in terms of causality. A little bit of today is an issue of semantics and some of it is philosophical approaches. This is issue of causality and cost is really important. I want you to think really carefully and critically about that. 

I am going to go over some of the different approaches that have been used to estimate attributable costs. I hope that at the end of the day you will know what those methods are and think about how you can adopt those or choose one of those methods for some work that you might want to do. What I am focusing on too is really understanding what the challenges are with this. When you do want to say diabetes costs X, how do you go about that? What are some of the challenges of that? 

I want all of you to be a little bit skeptical at the end of this session, when you read something that says heart disease costs $100 billion dollars or X-disease costs these billions of dollars. We are all guilty of this. We are going to talk a little bit about that later. Be a little skeptical and you will see why, in making these sorts of causal and large statements. 

I wanted to introduce you all to something, or reintroduce you all to something, that you are all familiar with. That is the general causal framework. When we think about this in terms of smoking and lung cancer, it makes a lot of sense. We really want to understand how much lung cancer is caused by smoking. If we got rid of smoking, how much lung cancer incidence would go down? When we think about causality, that is the type of question we are looking at. We can think about that. It makes sense in some circumstances. It is very intuitive. We are looking at fractures and calcium deficiencies. 

We can look at cost and we can look at utilization in more single event episodes of care, such as pregnancy. It is pretty easy to add up the cost of pregnancy and say that if this person had not been pregnant, the cost would be different. We can make a pretty strong causal association between that exposure and that type of utilization or cost. It is not easy to do. It is not easy to look at the causality for smoking and lung cancer. This is part of the goal. What we are looking for here is how much cost there is associated with heart disease or how much cost is associated with and due to having a diagnosis of diabetes.  

In reality, this is really complex. There is a whole series of measured and unmeasured confounders. We will talk a little bit about that, but keep that in mind. When you try to approach this problem and you are bringing the data that you have to bear to this problem, that you are going to try to account for all these different factors. The underlying causal framework is looking at how much this disease or this disease process is causing is cost. 

I should not be so hard on the Ph.D. students, but these are quite a few examples of what investigators, individuals, the Institute of Medicine and everyone is guilty of, when they imply causality and cost. Here are some examples. This first one is a very commonly cited paper that is looking at the cost of cancer. I do not have the actual investigator’s link to this to protect identities and not cause any guilt. These are the types of examples of what we are trying to get out of these models and the data that we have available. 

We are looking at cancer and saying that it costs $125 billion dollars. Another one is looking at costs. These are using the words incremental costs. This is the incremental cost of care for people with diabetes, as compared to those people without diabetes. This is a very common statement in many articles. Then we look at the proportion, the proportion of the total costs that are attributable to a condition. These are the kinds of examples that we want to talk about. I want you to really think about the causality issue when you are trying to write up and summarize what your findings are in the data that you have available to you. 

Think about this in terms of the counterfactual. In terms of lung cancer and smoking, we think about this pretty intuitively. How much lung cancer would decrease if there were no smoking? If we cured cancer, would we actually save $125 billion? That is what that statement and those causality associations imply. 

I have a poll question here, which is to see how guilty we all are of these types of statements. I want to find out from the audience how many of you have written a paper like this or have done something that says this condition costs this. There are a few other options as well, people who are interested in attributable costs, plan to measure attributable costs but have not done it yet, then people who just want to know more about cost methods and how these things originate so they can use the data and the findings and people who might be on the call who are just curious about this and have not thought yet about attributable costs, so this is a little bit new to them but they are interested in it. 

Okay. The votes are pouring in here. Everything is changing here pretty dramatically. We will see. 

Moderator:     We will give them a few more seconds for things to change around a little bit. We will let you read the results out here. 

Steve Zeliadt:     Can everyone see the results? 

Moderator:     Not yet. I have not broadcast the results. We wonder if we broadcast the results too  early, if it skews the data. I try to hold off on it a little bit. 

Steve Zeliadt:     This is not a very scientific question. 

Moderator:     You never know. It looks like things have leveled off here. I will show the results. If you want to read through them for anyone we have on the phone, that would be fantastic.

Steve Zeliadt:      Okay. I think they are broadcast now. It is interesting. There are quite a few people, about 1/3 or a little under 1/3 who have actually tackled this problem. I know some of you have done this in the VA. There is about 1/3 that are trying to think about doing this and are interested in doing it, but have not done it before. About 1/3 is interested in it, have not done it before, but a few people who definitely want to add those very influential findings to their grant applications. That is the first paragraph of almost every grant that says X-disease costs X-billions of dollars. Okay. Is there a way to make this go away here? There we go. Great.

Okay. I am going to talk about all the different approaches. There are a couple of very simplistic approaches to this. These are not done as commonly as they used to be. They do not really apply very much to the VA. It is important for everyone to be aware of these. What happens is that you are sitting there looking at your data set. You have a whole bunch of medical claims. You have every claim on every patient that was seen in Medicare or insurance plans. You know they had this on this date and on this date they had this hospitalization. On this date, they had this activity. You can look at those and you can find the procedures you are interested in. That is one thing you can do. 

Here we are talking about conditions. What you can do is find all the people who had a diagnosis anywhere in any of their, up to 15 sometimes, diagnosis codes on one of their claims. You add all those together. You find all the claims that had any mention of diabetes and you sum them all together. You say this is the cost of diabetes. This is referred to as the sum-all approach. 

You can imagine that now you do it for diabetes and your colleague down the hall is interested in adding them all up. She does them for depression. Now you have a problem because some people have depression and diabetes on the same claim. One of you is saying this cost is attributable to diabetes. One of you is saying this is attributable to depression. 

An alternative approach is to sum only the primary diagnosis code for each of the claims, hospitalizations or care activities that you have in your data set. You look and you find just the primary diagnosis code. You sum only those together. That is called the sum-primary approach. This approach does not work all that well in the VA because our coding approaches are a little bit different. It might work better in a Medicare or claims system, where coding is really carefully scrutinized. There are some caveats to that as well, because things are likely to be upcoded for maximum utilization. Even if the visit was due to a lesser condition or more minor condition, it might be upcoded to one that would be more likely to be reimbursed. 

On the next slide, I have some data that comes out of MEPS, which is the Medical Expenditure Panel Survey. I do not work very closely with MEPS. The students I teach work with the data. It is a very easily accessible cost data set. It comes out of the National Health Interview Survey. They ask patients to scrutinize their medical expenditures for a whole year. They go and find every bill, every claim, every medical cost that these 25,000 people in the National Health University Survey might have. They look at them really carefully. It includes VA costs. They find patients that have VA. They estimate what the VA paid for costs. They do not actually have a bill for the VA. The VA costs are in there as well. 

They use this data to estimate what the total cost of care in the U.S. is. In 2009, which is where this data set came from, the total cost was about $1.3 trillion. $1,260 billion is what the MEPS data set projected was spent on medical care in the U.S. This data set or attempt of looking at the data comes from the National Heart, Lung and Blood Institute. They are interested in clearly documenting how much diseases they are interested in cost. At the top, they highlight blood diseases, COPD and cardiovascular diseases. In the first column, it sums up to $278 billion. That is about 22% of the $1.3 trillion that was spent in the U.S. in MEPS. 

This approach relies very heavily on the sum-primary diagnosis claims. They find each claim, each procedure and each activity that was found in MEPS, and the cost associated with that gets associated with just one condition. That is how they total this. 

There are a couple of things to note about this. In the first slide, we talked about cancer costing $125 billion dollars. From the MEPS estimate, using this approach, they have a much lower cost of $86 billion. One of the inspirations and reasons for this session today is to understand what is going on here. It is a little bit like the Republicans and Democrats coming together and saying they are talking about the exact same thing, but they have two very different prices attached to it. On one hand, investigators looked at Medicare claims and used data from Medicare. They extrapolated it to younger populations as well, to come up with the $125 billion cost estimate. In the MEPS data set, they estimate that cancer cost at $86 billion. You will note the National Heart, Lung and Blood Institute likes the MEPS data, because it highlights how much the costs are for the diseases they are interested in, compared to other diseases.

There are a couple of other costs associated on this slide that I really do not want to talk too much about. The NHLBI also estimates the indirect costs of mortality. They have an approach for estimating when people die from these diseases, at what age and how many years of life are lost to that. They put in some dollar amounts for those. That is a little bit beyond what we are talking about today. That is what that other column is for. 

Moving on from looking at the claims data that you might have available, there are some approaches that use more of an accounting approach. This is what I want to focus a lot on today. One thing that many people do is this first approach. They find the people with the conditions. They find somebody with diabetes. They just add up all the costs that person had over a year. That person might have had $10,000 on average for cost. Then the cost for diabetes is $10,000 on average. That is a total cost approach. It is pretty commonly done. There is the same problem that we had before when your colleague down the hall finds somebody who had diabetes and depression. Now they are saying this person also has depression that cost $10,000 a year. 

What we want to do is move to a more attributable cost or net cost approach. This is very commonly done using matching. This was pioneered using Medicare data in the cancer setting. What would happen is that they would find a cohort of patients with cancer. They would find a matched cohort of patients without cancer. Usually they match on very few things. That might not be such a bad idea in the cancer scenario, because cancer is perceived as a generally random process. They would find 20,000 people on Medicare that had breast cancer. They would find 20,000 women who were matched on the same age, same geographic region and same race, and look at those. They would take those two different cohorts. What they would do is sum together all the cost for all the people with breast cancer. Then they would sum together all the cost for all the people who looked just like those women, but did not have breast cancer. They would end up with the net or attributable cost. 

This is the source of that $125 billion cost estimate. This is a pretty common approach. You can imagine there might be some problems with this. If you are looking at lung cancer, which is definitely associated with smoking, the population that you find when you find a lung cancer population have probably been smoking for 30 or 40 years. You probably cannot easily go and find a matched cohort of the same people who have been smoking for 30 or 40 years who do not have lung cancer. The people in your cohort of lung cancer cases have many other things they might have. They have been smoking for 30 years. Their heart disease risks might be higher. Their diabetes risk might be higher, compared to the general population. This approach does not directly account for that. 

An alternative approach is more of a regression approach. I have a very simple cost model here. You can imagine that this model would be very complicated. For the sake of today, for simplistic purposes, just think about looking at total cost for your whole population. You could take the whole VA population. You could take the whole Medicare population. You could put in an indicator for them having breast cancer, lung cancer or diabetes. You could put in all the confounders that you can possibly measure in that data set. Then you look at your coefficient for that condition you have. You interpret that as being the cost associated with having lung cancer or breast cancer. 

I want to highlight some of the challenges of these data sets and approaches by walking through an example, using some modeling work that we did in the grant with Andrew Zhou. What we did is have a sample of about 1 million VA users. We had all of their costs for 2008, for FY 2008. We used DSS costs. There was a lot of cleaning that went into those DSS costs. They are all geographically price adjusted. The total for that 20% sample over that fiscal year was about $7 billion. That is going to be an important amount to keep in mind. We were interested in many different chronic conditions. We went back and forth about which diseases and which conditions we should look at. We ultimately went with the conditions that were in the CMS Chronic Conditions Warehouse, as well as several other conditions that we picked based on VA priority conditions. I have a list of these in a second. What we did is find all the people in the VA using a series of diagnosis codes, based on clinical classification software. 

Here are the 31 conditions. There are a couple of conditions that we included that were of interest to research groups. BPH is probably not a priority condition. It is an interesting condition to look at, because it is not a very severe condition. Most of these chronic conditions are pretty severe. Some of them, like BPH or benign prostatic hyperplasia and erectile dysfunction, were of interest to the research groups that we have here. They were not as severe as some of the other conditions. That is interesting on the findings, as you will see here in a little bit. We also included some other conditions that are a rare in VA but were VA priority conditions, including spinal cord injury and TBI. 

It is time for another poll. Before we get into this, how many of you want to pick what you think would be the most expensive condition for the VA? There are a couple of options there for people who want to indicate do not know or that there might be some other condition we have not picked that is really the most expensive condition for the VA. 

Moderator:     This is a very long poll. You can scroll down for further options. 

Steve Zeliadt:     Yes Heidi. Thank you for putting all those options in there. 

Moderator:     It was really cut and paste. It was really fast and not a problem. I think it is really cool to be able to do this large of a poll in here. 

[Background Noise]

Steve Zeliadt:     I see a few people’s priorities, diseases and conditions they work on probably influencing this. It is very interesting to think about this issue and what is happening. I know the answer. I know a few of you guys know the answer as well. I will give it a couple more seconds here. 

Moderator:     Let me know when you are ready to broadcast these results. 

Steve Zeliadt:     Okay. I think people have stopped entering things. Let’s go ahead and broadcast the results. We will see how it is going to look when you broadcast them. 

Moderator:     It is up right now. It does not change what you see. It just changes what the audience sees. 

Steve Zeliadt:     Okay. There is quite a range of what people picked here. The top contenders are chronic kidney disease. That had about 9%. Heart failure is 12%. Depression is 9%. Diabetes with complications I think is the winner so far, with 17%. Hypertension is 6%. Spinal cord injury is 7%. Okay, it is very interesting. I will not tell you the answer. We are going to go through the data a little bit here. Part of the challenge is that I do not think there really is a very easy answer to this question. 

Here is what we found in terms of prevalence of these conditions. Among our million veteran population two of the most common conditions, diabetes without complications was quite common and diabetes with complications was about 7%. The most common of the conditions we picked was hypertension. Keep in mind that there is a lot of contention about using diagnosis codes for measuring disease prevalence and true disease burden. Not all 31 conditions are listed here. I know that many times you are limited to the data that you have to work with. It is easy for many of other investigators and data sets to use diagnosis codes. We relied on that here. 

It is also important to point out that almost everyone had at least one of these 31 conditions. Only 16% of the population did not have any of these 31 conditions. They could be incredibly healthy or they could have a condition that was not in our list of 31. 

Here is the total cost, the average total cost. This is when we look at all the people that have alcohol dependence, arthritis or BPH. This is what the mean cost was. Overall, the average cost was about $7,000 per VA user. For those who did not have any of the 31 conditions, that were either very healthy or have a different condition, they had about $1,800 in cost. 

Some of the costs that were the highest average total costs were chronic kidney disease at about $24,000 per VA user that had that condition and spinal cord injury, which was about $60,000. Even though spinal cord injury was really rare, there are only 1,900 subjects in this 20% sample, the total average cost of the subjects was quite high. 

Here are the findings when we do our attributable cost approaches for each of these different conditions. For alcohol dependence, we found all the patients that had alcohol dependence and we found a matched cohort of the same age, geographic region, race and gender who did not have alcohol dependence. We did not match on any other disease indicators. They could be different. All they had to do was look like the same age, same gender, same race and same region as somebody who had the condition of interest. We did an actual 5 to 1 match. For each disease cohort or each condition cohort, we found five subjects that matched to that subject. Then we took the total cost for the people in the cohort with the condition of interest and we took the total cost for the cohort without the condition of interest. That net cost is what is in this column, in the matching column. 

Under Regression 1, we fit very simple regression models for each of the conditions. We fit 31 regression models. Each model had age, comorbidity and a lot what we had available for patient and demographic characteristics, but only one condition of interest. Each model in the Regression 1 column had just that one condition of interest. In the Regression M multi-condition model, we fit one single model with all 31 conditions there. Those are the beta coefficients that we got, and the dollar scale. We actually used OLS, which performed very well in this large sample, compared to some non-parametric and other approaches we were testing out. That is another story. 

I will walk you through this a little bit. For some of these conditions that were really expensive, spinal cord injury on the bottom, the total average cost per subject was about $60,000. Because of the matched cohort and because spinal cord injury patients tend to be a little bit younger, the matched cohort did not cost very much. The net that is attributable to spinal cord injury is interpreted as $52,000. When we use the other approaches, the regression model with just a single indicator for spinal cord injury or a multi-variant model, we get fairly substantially different results. This is why I said it is not particularly easy to find out what the cost is. 

Another condition I wanted to point out is hypertension. Hypertension is the most common condition that we had in our set of 31. Using a matching approach, the cost associated or attributable to hypertension could be interpreted as $3,500. Under a single regression model, there are very similar findings. That is about $3,500 as well. When we started adjusting for or including potentially along the causal pathway other conditions, such as heart disease, diabetes or other conditions that might be correlated with it, they start to absorb some of the cost. The cost that is now attributable to hypertension is only $660. 

What is interesting about this problem and this approach, and this is why one of the goals for today is to use extreme caution when interpreting the cost attributable or due to these different conditions, is that you get findings that do not make a lot of sense. I will remind you that the total cost, what DSF believes the VA spent in total healthcare costs for this million veteran cohort, was $7 billion. When we look at the attributable cost due to these different conditions, we get some pretty big numbers. 

We say alcohol dependence was associated with using the matching approach and was $625 million in cost to the VA, due to alcohol dependence. Using a multi-variant and multiple condition model, we say that alcohol dependence might be costing the VA $269 million. Some of the more expensive conditions here are COPD. That could be costing the VA nearly $1 billion using a matching approach and only half that using a multi-variant modeling approach. 

You can see a little bit of the challenge that people run into. They are focused on, and most of these papers and the literature are focused on, one condition, the cost of arthritis, the cost of diabetes, the cost of COPD, the cost of cavities, the cost of obesity, the cost of one single condition. Especially in the veteran population when there are multiple overlapping and co-occurring conditions, trying to parse out and attribute the cost to any individual condition becomes extremely challenging. 

On the next slide I want to point out that in using this approach, even with the multi-variant modeling regression approach, we come up with some very nonsensical findings. We would say that if we added all 32 conditions together, and this is excluding any other conditions and also keeping in mind that the patients with these conditions, alcohol dependence or diabetes, might actually be receiving care for something that is not related to their alcohol dependence, arthritis or prostate cancer. We sum all those together and we get a figure of $7 billion. That is more than the $7 billion the VA actually spent. Keep in mind that the costs for the patients that do not have any of these 31 conditions are not even in that figure at all. They are not represented in that $7 billion. Clearly, we are overestimating or exaggerating the cost of these individual conditions using any of these approaches. Using a matching approach or an individual regression approach severely overestimates these costs. 

I guess we should try to answer the question of what the disease is that is costing the VA the most. This has been observed in a couple of different data sets in the VA. The VA actually spends quite a bit on renal failure and patients with chronic kidney disease. Here we have it, using the most conservative approach here, about $600 million among this cohort. Psychoses, which very few people mentioned, is associated with quite a few costs in the VA. Heidi, is it possible to go back and look at the poll results? 

Moderator:     Yes. This is for the second poll? 

Steve Zeliadt:     Yes. 

Moderator:     There you go. 

Steve Zeliadt:     Psychoses was very low. A few people indicated chronic kidney disease as one of the big burdens for the VA. Thanks Heidi. Okay. 

I want to digest these findings with you a little bit and say that this is not a unique problem with VA data or with any kind of data. Any kind of study that you find where they are estimating the cost of pain or cost of an individual condition is really out of scale when you look at the total cost that is being spent. There was a recent example. We use this in the class I teach, of how much pain costs using the MEPS data. They found that pain cost $300 billion, which is about 1/3 or less than 1/3 of these total U.S. healthcare budget. That does not seem to make a lot of sense. 

This next point here is about whether it is appropriate to conclude that chronic kidney disease is the most expensive condition for the VA. That is a bit of a philosophical issue. We can talk about that in the discussion here. I am going to put that out there for you guys to highlight. 

What I want you to take home a little bit from this, in looking at those results, is understanding how some of these coexisting conditions interfere with each other in trying to find attributable cost. When you think about heart disease and depression and you are trying to find the cost of heart disease and depression, and you have a patient that has both, is whether the heart disease is causing the depression, so you ignore depression cost and you attribute only the cost of heart disease. Is it the other way around? Is depression causing the heart disease and you can ignore the cost of heart disease and attribute those to depression? That is not a very clear association. You can see the challenge. You need to understand the causal pathways and the causal directions that these interacting and co-occurring diseases are having. 

This was very obvious in looking at the hypertension example. When you start to put in the downstream diseases that are associated with hypertension that are more expensive, the cost of hypertension itself goes away. Do you then conclude that hypertension is not very costly, or not? It is kind of a philosophical consideration here. 

I want to highlight a few things for people to move forward and not be left with these unsatisfying results about the costs of different conditions. One thing to keep in mind is that you should think about what your goal is with your cost model. If you are trying to figure out the cost of what diabetes is, think about it in a slightly different way. Maybe it is thinking about treating these patients in this approach with diabetes cost at this amount, versus treating these patients with diabetes in this other approach. Be clear about what the cost question or cost goal is that you are trying to answer. That will help you get out of this problem a little bit. 

Another thing you might want to consider is looking at more than just the 31 conditions. If you parse everything out into all the different possible disease groups, you might have a little bit more precision. This is a little bit challenging. There are over 14,000 ICD-9 codes. You can group those into 285 diagnostic groups. There is still quite a bit of challenge here. Some work that we are trying to do, and we have not gotten the models to fit very well, is to apply game theory to this approach. You start with your $7 billion or your total budget. You use game theory to try to allocate, in the best possible scenario, what each condition actually costs. 

What you need to make this work very well is to observe all the universe of conditions, or all the universe of possible players that were in the equation. This is a little bit of a challenge, especially if you rely on coding or diagnostic group coding issues. I have not concluded that this is an ideal solution to this, but this is definitely a promising and potentially interesting approach. 

The final thing I would like to leave you guys with here, in terms of thinking about a way to move forward, is to be really clear about what causal pathways and causal associations you are trying to model. If you are really interested in the relationship of depression and cost and how it influences different disease processes, then be very explicit about measuring how depression is associated with that disease process and then how that is related to cost. 

One thing that is pretty important to comment on is that a lot of the data we have been working with here, are looking at and is being used in claims data are cross sectional. When you want to move to issues of causality, I strongly encourage you to think about looking at longitudinal or time series data. This way, you can look at disease trajectories over a time horizon and drill down a little bit more on causal associations. 

Here is an example in cancer. Now we have additional information, more than the fact that they happen to have a diagnosis code for cancer. We actually have a date when the disease was diagnosed. You can pretty clearly assume that all the costs prior to that diagnosis date were not due to the treatment of cancer. Then you can look and scrutinize the cost following that diagnosis date and become a little bit more precise in what is truly due to treating and managing the diagnosis of cancer. 

This is becoming a little bit more readily available because of the longitudinal data and disease registries that are now being amassed. Think about it. If you are trying to answer this question, this might be a better approach. Think about what you are trying to answer. You are trying to answer if this person did not have this condition of interest, if we could take this disease away from that patient, what their otherwise natural cost trajectory would be. 

I have an example here, of how we did this in prostate cancer. I do not want to spend too much time. I want to save a lot of time for questions and discussion. Really briefly, what we have observed when we tried the matching approach here is that the cost for the people we were matching to were going up. They were quite a bit higher than the cost among the people who have prostate cancer in the period right before they were diagnosed. Ideally, if your matching is consistent, then these costs should be identical to each other. Everything before they were diagnosed with cancer should have the exact same cost and then only after they were diagnosed with cancer, would the costs increase due to cancer. 

What we found was that it was not actually true. It actually became much more severe over time. What was going on a little bit was that PSA screening was becoming more and more common. The people that were being diagnosed with prostate cancer were a healthy screening population. Their costs were much lower on average, than the general population. 

We will skip to the discussion here. I want to start to open it up for questions. People can start writing some questions here. I hope that after seeing after this data and thinking about this, that you guys will go away and no longer write the cost of heart disease is X. You will write things that are a little bit more appropriate for the data that you have looked and the models you have fit, which are that the expenditures we observed among these people that had this condition were X, or even better, include the counterfactual that the expenditures among these people with this condition were this much above the expected costs, and how you calculate the expected cost is still a matter of debate. You calculate those expected costs and how much they were for people without that condition. 

Okay. I hope that there are some ideas out there for how we can do better. 

Moderator:     There are a couple questions here in the queue. I wanted to start out by asking this. You had talked about different methodological approaches in terms of measuring cause. I wonder whether people have done any sort of qualitative work by looking in medical records to see what happened during the encounter and how much of it went for treating one condition versus another. Are you aware of any kind of research that has looked at that? 

Steve Zeliadt:      There is definitely interest in doing that. Those are more micro-costing approaches. They can be qualitative or they can be semi-quantitative as well. They do require a lot of personnel time and a lot of effort to go and look at those costs. At the end of the day, they require some subjective decision about whether or not this cost was due to this condition or due to that condition. There is some work, quite a bit of work, doing that. 

There was a very interesting article in the Harvard Business Review a couple of years ago by Michael Porter. He highlights doing this for episodes of care and for chronic diseases. He highlights that you should, going back to the pregnancy example, not just looking at pregnancy which is a very easy episode of care to measure, but taking your date of the onset of your diabetic patient and following him or her until his or her diabetes is resolved, or the condition is resolved. This could be a very long time. You are really trying to figure out all of the different costs and care components that go into that. That is an approach. It is not an easy approach or a very feasible approach. That is definitely one way of trying to answer this question in a little bit less biased or more appropriate way. 

Moderator:     Okay, thanks. The next question asked what model from game theory you are using or investigating. 

Steve Zeliadt:     We are trying to fit Chappee values to these. We are trying to take all the players and treat them based on the date that the ICD-9 code popped up. The challenge is that we do not know how many incremental units. We could do it among the total population. I think we saw that some people in our group had 29 conditions. We could play each person off every other person. We could play each person in all of their claims. If a person came in and had 170 visits in that year, we could play all their claims off all the claims for everyone else. It is a little bit challenging, especially with the VA data, where the coding of all the diagnosis codes is not as reliable as claims data. 

Moderator:     Okay. The next questions ask if in estimating the costs of obesity in those with ESRD, whether you need to control hypertension and diabetes that are common in ESRD. They are using cross sectional data. 

Steve Zeliadt:     Do you need to control for hypertension and diabetes? If you include hypertension in your model, it depends on where it is in the causal pathway. If you think that end stage renal disease is causing hypertension, and you want to take that out of the model as a potential cost component, you would include hypertension. 

If you think that patients have hypertension and that might be what is going on and it is an independent disease process in end stage renal disease, then you could also put that in your model and let hypertension absorb some of the cost. 

Neither of those is exactly right for all of your patients. It is tricky. It is very tricky. There are some ways of looking at the mediating associations, so that we can talk about a mediator. If you think that all the cost or some portion of the cost that the patient is incurring is because end stage renal diseases is having a direct effect on cost and it is having a mediating effect going through hypertension and then hypertension is leading to cost, there are some approaches you can do for mediating approaches, looking at the mediating effect. 

I hope that you can say safely at the end of the day here is how much patients with end stage renal disease cost the VA. You cannot really conclude, especially from cross sectional data, that it is only because of their end stage renal disease that they cost that much, and they would have cost $10,000 less if they did not have that. 

Moderator:     Great. Okay. The next question asks what counterfactual data is available for non-VA populations. 

Steve Zeliadt:      Counterfactual data for non-VA populations? The counterfactual data is in the data set that you have. You just have to be very clear about what that counterfactual question is. If you are looking in the lung cancer question, you might find people that smoke for 30 years but did not develop lung cancer. What are their costs, compared to people who smoked for 30 years and did develop lung cancer? If you are only interested in the cost of lung cancer, that would then answer the question. If they did not develop lung cancer, they might still have hypertension and cardiovascular disease, or heart failure. They are still incurring a lot of cost. It is just the incremental additional cost due to only lung cancer. You have to be very clear about what it is that you care about in terms of the counterfactual. Think about whether you can find that counterfactual population in your data. 

Moderator:     All right. The two examples you had talked about, MEPS and VA, have counterfactual is actually different. MEPS is everybody, whether or not you had any healthcare, whereas in the VA we only look at whether you showed up for care or not. It is a little bit different, right? 

Steve Zeliadt:     Right. They are all users. There are some people who show up for very little care. They are not not completely known to the VA. The VA, in terms of the counterfactual, are the people who do not show up and use VA services at all, are an important counterfactual or important population. If they are not going to use VA services or Medicare services, they are going to use their private insurance. That is different. You might not be used in that population. 

Much of it does come down to the data that you have available. We were really looking at, for people who have end stage renal disease, all of the people who use a lot of healthcare and look like those patients. They do not have end stage renal disease. 

Moderator:     Okay. The next question asks if we were interested in comparing total costs between two groups with very different survival rates, for examples individuals with and without a condition with a high mortality rate, how do we account for or adjust for the differences in survival when comparing costs? 

Steve Zeliadt:     That is a big topic. It depends on what the question is that you are trying to answer. There is a little bit of a debate of whether or not there are some approaches you can use that use Kaplan-Meier survival estimators, and you apply that weight to the population, so you estimate if they died in a short period of time. You are going to weight their early costs. You get what their costs might have been over that full period of time for the people you do not have observed cost data for. 

It depends on if you are the payer and they die. They die, so you do not need to worry about the costs that are not observed because they died. It comes down to what the question is that you are asking. If you truly have a censoring problem because people are dying from your data and you want to know what their costs of care would be because of the censoring problem. There are some approaches. KSMA estimator approaches are one of those. 

One example that I can think of the Net Trial. It was lung volume reduction surgery compared to medication treatment for emphysema. They wanted to know in a very theoretical basis what the different costs are for these different conditions. Some people who were in the surgical group died because of surgery. There was some censoring. They wanted to extrapolate what the ten-year costs or lifetime costs would be for these two different treatment arms. In that setting, they applied some of these KSMA estimators to account for that censoring issue. 

Moderator:     Okay. We only have about three minutes left. There are another four or five questions. Can you stay on an extra couple of minutes to answer these questions? 

Steve Zeliadt:     Sure. 

Moderator:     Okay. This person asked if they had mentioned asking whether Medication X is helping to reduce costs among people with a disease, if can we use the regression base [inaud.] cost technique. 

Steve Zeliadt:     Yes, I think you could. It depends on making sure you get the right populations in your cohort, so that your coefficient for medication makes sense. It comes down to many of those potential reasons why they might be on that medication, versus the counterfactual population that is not on that medication. Do they have the same disease severity? Have they been managed with that disease for the same amount of time? Are they newly diagnosed? Have they failed other treatments or all those other kinds of issues that really come into play? It is not necessarily that the method is appropriate or inappropriate, but that really thinking about the question you are trying to answer, the data, the populations and the counterfactual that you have to bring to bear is what is most important. 

Moderator:     Okay. The next question asks as far as methods, is OLS better than GLM, or vice versa? 

Steve Zeliadt:     We spend a lot of time dissecting this. Andrew Zhou has worked really hard on some non-parametric modeling approaches. What we found is that OLS, especially in this bigger data set, performed very well, as well as in many of the different cross validation and verification approaches. I am a fan of OLS. It is pretty straightforward and you do not even need to log your costs often. The data that I presented here was based on non-logged costs. We did not have many of zero cost users. All the patients had some costs because they were VA users, at least at some point in that 2008 period. Some had much lower costs than others. 

That is a very complicated question. If you have a huge data set, a million people, I think it is probably okay. There is a huge amount of work on this. Many times GLM models with a nice gamma distribution really fit the distributional parameters of the data set you have. There are some ways you can explore what those distribution parameters are. We teach those to our students. Will Manning has some nice steps you can walk through to look at the data that you have. I think there are a couple of HERC seminars where you guys talk about those.

Moderator:     That is right. 

Steve Zeliadt:     I think that is a topic for some of those, for that. 

Moderator:     We have a whole session that talks about methods to analyze costs. Sometimes you do take the logs of costs to account for a lot of outliers and really high cost patients. Sometimes we do that. Paul Barnett is going to be talking about that in November. 

Another question says that it seems that the issue is more complicated among related chronic conditions, for example, hypertension and chronic kidney disease, versus conditions that are acute and traumatic, such as spinal cord injury. Does this also change the methods that you use? 

Steve Zeliadt:      It is true that the cost estimates do seem to change more dramatically, the more conditions you put in, when they are all correlated with each other or somehow on the same causal pathway, as opposed to conditions that are very independent. If you put in spinal cord injury and erectile dysfunction, that is probably not a good example. If you put in very unrelated conditions, they do not change nearly as dramatically. 

They do still change quite a bit. If you have a very rare condition and you find a very good counterfactual population who had the same risk characteristics and were at risk for that condition, then you might be in safer territory to describe in a cross sectional data set what the attributable costs are. I think that if you really want to say the cost of a condition is due to that condition, looking at it in a longitudinal way is your best approach. 

Moderator:     Okay. There are two last questions here. One question asks for the gamma model, if the coefficient were 1.68 for cost, how would you interpret this coefficient? Cost is a 0.1 dummy variable. 

Steve Zeliadt:     It is 60% higher. That would be it. You can recycle predictions, more predicted probability or cost for your cohort. You can look and see what those costs are. From the gamma model, you can generate actual costs in the dollar scale. 

Moderator:     Sometimes those are more meaningful, if you predict the costs. The last question asks if interactions would be useful in the multi-variant regression model. 

Steve Zeliadt:     That is another complicated question. 

Moderator:     When you have 31 conditions, you can imagine that there will be many interaction terms. 

Steve Zeliadt:     Yes, if you are thinking about interactions with gender and interactions with age, or interactions with the conditions themselves so that people who have diabetes and hypertension might have a different cost trajectory from people who had diabetes without hypertension. That rather goes back to the question of being very clear about your causal pathways and the disease associations you are trying to explore. It is nice if you have longitudinal data where you can look at the onset of these conditions, so you can find a group that had diabetes and did not have preexisting hypertension, and a group that had preexisting hypertension before they were diagnosed with diabetes, then try to sort that out a little bit. Just trying enough interactions for the data that you might have available is a little bit problematic, whereas if you try to fit the data you have or you try to fit populations you can find to answer the questions that are really what you want to answer. 

Moderator:     Okay, great. I appreciate you staying extra, staying over a little bit to answer these extra questions. I thank you for your presentation and raising all these issues around how people present cost around chronic conditions. That is something we are interested in at HERC as well. 

Steve Zeliadt:     It does come down to a little bit of semantics. I feel strongly about this issue, probably more strongly than some people. You can see that even in the cancer world, one group would say cancer costs $86 billion and another group says $125 billion. They are both very respected estimates. They are off by 40%. How do you interpret that? 

Moderator:    Right. Thanks so much Steve. I believe there will be a survey that will pop up. 

Moderator:     Yes, I am just about to close the meeting out and as I do that, there will be a feedback survey that pops up on everyone’s screen. If you could, take a few moments to fill that out. We definitely do read through all of your feedback and do our best to incorporate that into current and upcoming sessions. Thank you everyone for joining us for today’s HSRD cyberseminar. We hope to see you at a future session. Thank you. 

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