Drilling Down into Alzheimer’s: A Market-Level Analysis

Introduction to Market-Level Analysis

Torch Insight is designed to help evaluate and analyze healthcare markets and organizations. There are countless analyses that can be performed, focusing on demographics, financials, performance, quality measures and many, many other pieces of information. For this demonstration, I will focus on a chronic condition that is pervasive across America: Alzheimer’s disease. The goal is to show one approach to performing a preliminary market-level analysis of a disease. 

Alzheimer’s Disease

Alzheimer’s disease is the most common form of dementia that affects over 5 million Americans, the vast majority over the age of 65. It’s a progressive illness that causes mental decline including causing difficulty with thinking, reasoning and remembering. As a country, America spends hundreds of billions of dollars a year on direct care.

Market-Level Analysis of Alzheimer’s

The impact of the disease is not uniform around the country. Using Torch Insight, we can start to visualize the differences. Torch Insight has both market-level data and organizational-level data (such as information about physicians, hospitals, insurance companies, etc.). For this example, I’m focusing on the market-level prevalence of Alzheimer’s.

Since Alzheimer’s primarily affects those that are on Medicare (those aged 65 and older or those that with a disability – including Alzheimer’s disease – that qualifies them for supplemental security income), Medicare data is a great resource to assess the prevalence of the disease. Medicare is a government-backed health insurance program and collects massive amounts of information based on enrollment information and the claims it pays on behalf of beneficiaries. Some of this data is then released in a variety of public use files (PUFs) and limited use files. The prevalence of Alzheimer’s in the Medicare population comes from the 2015 Medicare Geographic Variation PUF, which is prepared by the Centers for Medicare & Medicaid Services (CMS). This file provides a variety of pre-tabulated fields, including data at the state- and county-level.  While I’m demonstrating this data based on the pre-tabulated data, we’ve also calculated it directly from Medicare claims data.

In the following map you see the percent of Medicare beneficiaries that have Alzheimer’s or a related disorder, by state.  The map is interactive and you can zoom, scroll and hover over the individual states to see values.

You can see that the percent of the Medicare population with these dementia-related illnesses ranges from a low of around 7% to a high of around 12%.  For states that are providing services for these patients, either through social services or through Medicaid for those that are dually eligible for Medicare and Medicaid, the prevalence can make a significant impact to the state budget.

Asking questions about Alzheimer’s

When I start to study a topic, I like to look at the data and start to ask questions, and the map is a great way to do this. In this example, after I looked at the distribution of the prevalence of the disease, a few questions occurred to me:

(1) The disease visually appears to be more prevalent in states with larger populations (Florida, Texas, New York, California, etc.) and I wonder if there’s a correlation?

(2) I know that Alzheimer’s is an expensive disease, so I wonder if an increased prevalence of Alzheimer’s is associated with higher Medicare costs?

(3) I wonder if the variation at the state level is similar to the variation at smaller geographies.

Alzheimer’s correlation with population

The first two questions will require a little more data. Luckily, Torch Insight has hundreds of pre-built, healthcare-related variables so I can select a few that are interesting.

Rather than overlaying the variables, I generally prefer to work with the raw data, so I exported the file and brought the variables into Stata.  It turns out that Alzheimer’s is positively correlated with absolute population size (corr=0.4467, p=.001), and I can plot it:

The R2 is nearly 0.2, which, is modestly explanatory for a single variable looking at a linear relationship.  There are a few states that are outliers with population, so I transformed the population and took the log of it and the R2 went up to 0.31 – decently explanatory.


This means that not only are states with higher populations more likely to have more total Alzheimer’s patients, but they are more likely to have a higher prevalence of Alzheimer’s patients.

Alzheimer’s and Medicare Costs

When comparing costs, it’s important to think through what costs are important. Medicare provides a variety of cost numbers in the PUF file, including total and per-beneficiary costs, as well as costs that are standardized (meaning they adjust for different prices that are paid in different regions of the country) and risk-adjusted. Since risk-adjusted costs account for the prevalence of different diseases, I will use state-average, standardized annual costs per Medicare beneficiary. This time the correlation is really strong (corr=0.781,p<.0001).

State-level market-analysis

The only outlier with low costs and high prevalence of Alzheimer’s is Hawaii, which may partly be explained by how costs are standardized with Hawaii. I can confidently say that a high prevalence of Alzheimer’s disease within a state is associated with higher total Medicare costs.

Alzheimer’s at the County Level

While state-level analyses are interesting, it’s often valuable to drill down to a smaller geographic level. Torch Insight includes states, hospital referral regions, metropolitan areas, congressional districts and counties as options to dig deeper within the visual interface.  I just selected the state with the highest prevalence of Alzheimer’s – Florida – and included the county-level map.

The prevalence of the disease ranges from 7% (not much higher than the best states) to 21% in Miami-Dade county. Not only is that a huge difference, but the highest prevalence is in the most populous county, which drives up the state average. Similar to the previous analyses, the prevalence of the disease at the county-level in Florida is strongly associated with population (corr=0.655, p<.0001) and costs (corr=0.627, p<.0001).


My goal with this was not to explain what’s driving the spread of Alzheimer’s Disease and other forms of dementia or to do any sort of a causal assessment. Hopefully, you were able to see the power of being able to quickly combine market-level characteristics and see how you can dig into the variation that exists. Hopefully, also, this raises questions in your mind about Alzheimer’s disease – it certainly does for me and there are multiple avenues of further inquiry I could perform (How does its prevalence relate to other chronic diseases? What other diseases are as predictive of total Medicare costs? Why might there be a higher prevalence of Alzheimer’s disease in higher population areas and larger cities? And many more). The joy of data exploration is that there is always more to learn and Torch Insight can accelerate that learning.




Understanding Healthcare Markets: Introduction to the Torch Insight Blog

Torch Insight Blog

Blogs solve a couple of really important challenges: (1) quick dissemination of information to a broad audience and (2) a lasting reference source.  Often, the quickest way to share information is to speak to someone or send an email, but that doesn’t go to a broad audience, while lasting references, like books, take a long time to get to the market.  The Torch Insight Blog exists to quickly share interesting data-driven findings and conclusions about healthcare markets that can be referenced by anyone who is interested in healthcare.

As the inaugural blog, I thought I would share a little bit about the history of Torch Insight and explain what it is and why we built it.  In the future I will focus more on interesting quantitative findings and commentary around what’s happening in the healthcare system.

A Brief History of Torch Insight

Since its inception, Leavitt Partners has sought to help clients understand how the healthcare system is changing and create strategic plans.  A key to understanding the system is accurate data about the state of healthcare.  Back in 2011, when I first started doing quantitative analysis for clients, I learned that there was no available data set that brought in comprehensive data about the healthcare system and linked it together in a meaningful fashion.  To understand a healthcare market you need to know who is in a market and how the different organizations and stakeholders interact, so we began a process of linking together disparate databases to provide a comprehensive view of healthcare markets.  During these early years we identified many different data sources and also began collecting some of our own data, particularly our ACO tracking data, and figured out how to bring the data sources together for each project we did.

After five years of manually connecting databases, we recognized that there was an opportunity to standardize the process of linking data and that some of the customized work that we then performed could be automated.  So, in the summer of 2016 we kicked off the development of the tool that would become Torch Insight.  The process was relatively straightforward:

  1. Take the knowledge we had gained about which data sources were needed to provide a 360-degree perspective of a healthcare market and build out the process and system to keep hundreds of disparate data sources updated, linked and accessible.
  2. Build a platform to access the data, visualize answers and gain insights about market-level relationships.
  3. Develop algorithms and analytic approaches to derive meaningful insights from the data that are not available anywhere else.

Working from this plan, we have taken Leavitt Partners’ expertise of data and markets and built a platform that allows users to explore the relationships between healthcare providers, payers and populations.  While there are definite use cases where clients are already using the platform, the amazing part of an interlinked platform is the ability to come up with novel uses for it on a daily basis.

Healthcare Markets

While healthcare is a 3.3 trillion dollar industry, it’s far from a uniform industry with each market having developed from its own little accident of history where thousands of disparate decisions and strategies have turned the market into what it is today.

Each market has its own makeup of providers, payers and patients.  For example, some markets are dominated by a single not-for-profit health system that primarily employs many of the physicians, while others have many competing health systems or independent physician practices or a high number of for-profit systems, or countless other dynamics.  There are no national health systems, like we see with national chain restaurants or retail stores, and each organization has its own structure.

Torch Insight shows how these markets are structured and also brings in relationships between providers so you can see which hospitals are working with which physicians and value-based arrangements, like ACOs.

It also shows how patients interact with the system and create visualizations to show where patients naturally move between providers, provides financial and enrollment data for health insurers, population demographics (including a wealth of disease metrics), competition metrics, and tools for visualizing and exploring the relationships between each of these factors.

One of my favorite things to do is to overlay different factors to start to see how markets have formed.  This could be as simple as overlaying disease burden with the location of different types of healthcare facilities, or integrating dozens of different variables into a composite metric which tracks some interesting facet of the market.  Since the data can always be exported on demand, it doesn’t take much effort to take the raw numbers and run some statistical tests on them to enhance certainty about conclusions.

Torch Insight Map of Healthcare Markets
Map showing pinpoints overlaid on demographic data

Analysis of Healthcare Markets

My objective with this blog is to show how Torch Insight can help answer questions about healthcare markets.  I’ll do this by applying the data to questions that are relevant to current issues, answering questions that I have, and highlighting interesting points that I discover.  Some of the posts will focus on using the tool and showing practical examples of how to answer a question, but others will focus more on showing what I find when I take the data out of the tool and look at it in a statistical package.  At heart, I’m a data guy and a researcher which means I like to ask, and answer, questions with data.  I hope you’ll join me on this process as we explore the American healthcare system.