What do cancer patients value when making treatment decisions?

Clearly choosing treatments that extend expected survival is important, but survival expectations are not the only factor that matters to cancer patients. A 2017 NCCN policy report–based on the findings from a working group–identifies a number of factors: Patients, for example, may view high-value care as any combination of trust, transparency, and effective communication with…

Do HDHPs save money?

This is the question that Zhang et al. (2017) attempt to answer using data form people who switched to a high-deductible health plan (HDHP) compared to those who stayed in the same plan.  They found: After enrollment in HDHPs, 28 percent of enrollees changed physicians for office visits (compared to 19 percent in the Traditional…

How to pay for cures

Do you want to cure cancer?   Diabetes?  HIV?  Imagine if there was a single pill for each of these diseases that one could take to cure the diseases.  That would be a clear clinical advance, it would save lives, and also save money form reducing the cost of treating these disease. Can we expect to…

Will CVS buy Aetna for $66 billion?

CVS is in talks to buy Aetna for $66 billion.  This would be a merger between one of the largest pharmacy benefits managers (PBM) in the country and the third largest insurer. A successful deal could push millions of Aetna’s members toward CVS’s retail pharmacies, walk-in Minute Clinics, and services such as home visits for…

How much is your life worth?

According to the Environmental Protection Agency, the answer is $10 million.  Other agencies place use a somewhat lower number.  The Food and Drug administration pegs the value at $9.5 million and the Department of Agriculture places the value at $8.9 million. Technically, what these agencies are calculating are the value of a statistical life (VSL). …

Improving catchment area definitions when measuring quality of care

Oftentimes, we want to measure the quality of care of a give hospital or health care system. The easiest way of doing this is to measure the quality of care received by patients who go to that hospital. These patients, however, may attend multiple hospitals during they year. Further, if quality of care includes avoiding hospitalizations, we need to identify not only patients who had a hospital admission but patients who were at risk of going to that hospital if a preventable admission occurred.

One way to model quality of care is to use catchment areas. Catchment areas are typically aggregations of geographic units. For instance, hospital service areas (HSAs) are aggregations of ZIP codes. However, previous research has shown that HSA-based catchment areas only capturing 50% to 80% of hospital admissions for their given population. One could use larger geographic regions—such as hospital referral regions (HRRs)—but then one is susceptible to assigning patients to hospitals over which they are unlikely to have responsibility for their care.

My previous research on the hospital wage index (see here and here) proposed assigning a weighting of the geographic units While that approach aimed to measure geographic variation in wages where data was available by geography rather than by person, an interesting paper by Falster, Jorn and Leyland (2017) proposes a different approach using individual patient data and a methodology known as multiple-membership multi-level model multi-level.

To explain this model, consider first a standard approach whereby where I people are clustered within J hospitals or HSAs. Yij is the outcome, xpi are the regression parameters for P person-level variables, and xqj are the
regression parameters for Q hospital-level variables. This multilevel model captures the effects of clustering by allowing both regression parameters and error terms to exist at different hierarchical levels.

A multiple-membership multilevel model extends this approach by allowing a weighted structure for each of the hospital-level components as follows:

Faster and co-authors apply this model to date on preventable hospitalizations in NSW Australia using weighted hospital service area networks (weighted-HSANs). The authors contend that:

Between-hospital variation in rates of preventable hospitalization
was more than two times greater when modeled using weighted-HSANs rather
than HSAs. Use of weighted-HSANs permitted identification of small hospitals with
particularly high rates of admission and influenced performance ranking of hospitals,
particularly those with a broadly distributed patient base

While this approach is a significant improvement for an academic setting, it is problematic to operationalize in terms of quality improvement. In order to improve quality, hospitals need clear rules regarding the patients to which it is attributed. While the authors compellingly argue that multiple-membership multilevel models do a better job mof measuring quality retrospectively than would be the case using HSAs alone, operationalizing the use of weighted HSANs in practice would be more difficult due to the model complexity. Nevertheless, this approach clearly highlights the challenges of using HAS-based catchment areas to measure quality of care.


  • Falster, Michael O., Louisa R. Jorm, and Alastair H. Leyland. “Using Weighted Hospital Service Area Networks to Explore Variation in Preventable Hospitalization.” Health Services Research (2017).