Unbiased Analysis of Today's Healthcare Issues

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Quotation of the Day

“Lottery: A tax on people who are bad at math.” Ambrose Bierce, HT: The Browser.

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Friday Links

Progress on risk-based agreements. ACOs increase cost? P4P fail with gym memberships. The importance of reading. “Super Bowl of lung cancer immunotherapy” Apple Watch health data leads to murder conviction. plus The Spring is Here! HWR.

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Healthcare Economist on Vacation (+ Links)

The Healthcare Economist will be on vacation with blog posts resuming March 26.  I will be in Peru.  To learn more about the Peruvian health care system, read this post. For those of you in Healthcare Economist withdrawal, below are some end of the week links to tide you over until I get back. 3D […]

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Weekend Links

Benefits of smoking bans. Service-level selection. Workplace wellness selection. The end of cervical cancer? The drawbacks of mental health reform.

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How restrictive value assessments affect R&D and innovation

Health technology assessment (HTA) is growing in popularity. Already widely entrenched in Europe, in the U.S. value frameworks are being used to measure the cost effectiveness of different therapies.  Some of these frameworks, however, take a narrow view of societal benefits and value to include components of value to patients or society such as caregiver […]

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Monday Links

Practice makes perfect. CON fail. Love conquers all but nicotine. Growing popularity of the welfare state. “Fairness” is not always how things get done.

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Links

P4P in the NFL. Cancer screening blood test. Some progress towards sharing clinical trial data Separating conjoined twins. Kicking the can down the road is very bipartisan

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Weekend Links

Words matter. 2017: Most drug approvals in 21 years. Digital medicine comes to your gut. “raising the level of discussion around value in health care” Spanish flu myths.

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Top Health Economics Stories of 2017

What were the top stories at the intersection of health and economics stories in 2017?  Here is the Healthcare Economist’s take. Obamacare repeal. One of the top stories clearly must be the on-going debate around the repeal of the Affordable Care Act (ACA, a.k.a. Obamacare).  Although the ACA was not fully repealed, the most recent Republican […]

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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.

Source:

  • 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).
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