Insulin for Persons Already on Metformin: A Population Health Perspective

As most population health providers know, diabetes guidelines tend to focus on shorter-term or "intermediate" outcomes, such as average blood sugar levels or A1c levels.  That's because these short-term measures are surrogates for "long term" outcomes, such as blindness and kidney disease.

Two inconvenient facts have complicated the focus on intermediate outcomes:  

1) Once a threshold has been achieved, lower short-term blood glucose control doesn't necessarily lead to better long term outcomes;

2) The side effects of drugs - that otherwise work quite well at achieving short-term blood glucose control - may outweigh any long-term advantages. 

And now a just-published research study from JAMA raises the possibility that insulin has additional long-term side-effects.

According to diabetes mellitus treatment guidelines from organizations like the American Diabetes Association, the first medication option for Type 2 diabetes should be metformin.  If that doesn't work, the ADA suggests that there are several options for a second drug, including one of several sulfonylureas (glyburide, glipizide or glimepiride) or insulin. 

Sulfonylureas are pills, but have a reputation for not leading to the same level of diabetes control as insulin.  Unfortunately, while it's a more potent means of blood glucose control, insulin has to be injected.

Further details on the methodology are below.* Basically, Veterans Affairs electronic records were "mined" to find thousands of persons with diabetes who were using metformin and then had to start either insulin or a sulfonylurea.  Propensity scoring was then used to create two otherwise similar cohorts of patients and neutralize the impact of the diabetes control and disease burden.

2436 patients on metformin and insulin were compared to 12,180 patients on metformin and a sulfonylurea. 

After a median of 50 months of observation, the risk of a heart atttack, stroke or death from all causes was 43 per 1000 person-years in the insulin group vs. 33 in the sulfonylurea group.  That difference was statistically significant.  When deaths alone were examined, there was likewise an increased number in the insulin group (34 per 1000 person years) vs. the sulfonylurea group (23 per 100 person years).

The Population Health Blog's take:

This study raises the possibility that, among persons with diabetes on metformin, insulin is associated with an increased absolute risk of about 1 per 100 person years (10 per thousand person years, or one person out of a hundred persons followed for one year) of heart attack, stroke or death vs. the sulfonylurea pill.  Yikes.

Before we ban insulin in this population, however, the PHB is reminded that this was an observational study.  As an accompanying editorial points out, propensity scoring is not perfect and other unmeasured and confounding factors in the population could be biasing the results.  Short of a randomized clinical trial, there are other databases that could be mined the same way.  That includes those of the population health vendors, who also have a stake in risk stratification and long-term follow-up.

In the course of coaching persons with diabetes on metformin who are considering insulin, the additional risk of heart attack, stroke or death should be raised.  While the study above isn't perfect, the possibility is something that health care consumers need to weigh.

++++++++++++++++++++++

*Methodology:

Veterans 18 years and older who.....

1) were followed for at least two years with provider visits every 6 months,

 2) who had been placed on metformin and regularly used it between 2001 and 2008,

3) had one year of records prior to the first prescription for metformin and

4) were not on dialysis or in hospice

Once a vet filled a prescription for either insulin (long acting, premixed or short/long acting) or a sulfonylurea (glyburide, glipizide or glimepiride) and continued it for 6 months, their records became eligible for the study.  Patient records were excluded if there was no follow-up for six months, if the meformin was stopped for 3 months or a third diabetic drug was prescribed.

52% (approximately 92,000) of the 178,000 vets on metformin did not use another medicine.  Most were men (95%) and white (70%).  2948 were started on insulin and 39,990 started a sulfonylurea. The persons placed on insulin had, on average, worse diabetes control (A1c 8.5% vs. 7.5%) and a higher disease burden.

More Big Insights on Big Data

Given the data, what are her chances of
getting breast cancer?

Unable to sate its big appetite for big data insights, the Population Health Blog glommed onto the New England Journal's just-published article on "Learning from Big Data."

As noted previously, "big data" is the use of statistical associations ("predictors") in a) large and b) disparate data sets  to gain insights at the individual level ("outcomes"). For example, a physician could know the likelihood - based on demographic, clinical and economic inputs - that a particular patient won't fill a prescription. As an other example, the PHB spouse could know the likelihood - based on prior active-passive behaviors, incentives and maternal upbringing - the likelihood, despite numerous reminders, that her husband will "forget" to take out the trash.

It's important to recall that big data is not about causality. Just because living in a certain zip code is an independent predictor of obesity (for example) doesn't mean living in [insert name of town] causes residents to be fat. Big data is "agnostic" about the cause, but that doesn't mean Big Data Architects (BDAs) can't use the information.

According to the author, the road from the promise to the reality of big data will be lined with:

1. generalizability, or being confident that the populations used in big data studies are similar to the populations where their lessons are being applied. Propensity matching or scoring is a good step in that direction;

2. automation, so that multiple questions can be answered simultaneously by many users;

3. "data refreshes," so that associations can be retested on repeated basis as new data come on line;

4. ease-of-use, so that even an orthopedist could use the software and understand the outputs.*

Politically, we'll also need to get

5. the owners of data warehouses - including the electronic health record vendors and insurers - to agree on either a) common data formats or b) methods that allow for the interpretation of data regardless of the format. An example of the latter the use of an order, entry or insurance claim for supplemental oxygen therapy as a marker of poor health status.

6) a resolution of our absolutist privacy "impasse.""De-identification" of patients' information makes it possible, but never guaranteed, to keep personal health information secure.

*okay, the New England Journal author didn't poke fun at the orthopedists by saying that, but the PHB couldn't resist. By the way, one way to do this would be to have the outputs be in pictures.

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The Veterans Administration Scandal: Implications for Health Reform and A Call for Clinical Research Into the Reported Death Rate

As the Population Health Blog understands it, dozens of veterans died while waiting for outpatient appointments at the Phoenix Veterans Administration (VA) Hospital.  Approximately 1500 vets were assigned to an "off-the-books" waiting list that made the clinics' official waiting times appear shorter than they really were. Because waiting times are an important feature of health care quality, the VA was probably holding its local administrators responsible for routinely measuring and reporting them up the chain of command.  If reports are true, instead of using their increased budgetary resources to provide more care, the Phoenix bureaucrats allegedly responded by gaming the system.

And the scandal is flourishing.  Investigations suggest other VA hospitals may have also adopted the same wait-list legerdemain.  A senior D.C. official resigned fast-tracked his already scheduled retirement. The VA Inspector General's investigation prejudgment is that none of the deaths can be attributed to delays in care. You can't make this stuff up.

"Good grief!" says the PHB.  Numerous articles like this, this and this had convinced lay writers, impressive policy wonks and countless physicians that this version of government run health care was not only the greatest thing since the invention of Medicare, but a model for U.S. health care reform.

Not any more.

That's why the implications of this extend far beyond a huge stain on the VA's reputation.  Once again, taxpayers are witnessing another failure of big government. While this has nothing to do with Obamacare, voters have another reason to doubt Washington's ability to competently deliver on its health care promises.

In the meantime, the PHB offers the VA plutocrats one approach to figuring out if the waiting lists were associated with higher death rates.  It's possible, thinks the PHB, to use propensity score matching within the VA's much-admired electronic health record system to retrospectively create a cohort of patients that were similar in every way except for being on the wait list.  A similar death rate in that group - demonstrated by unbiased scientists outside the control of the VA - would go a long way toward reassuring all of us that this debacle was limited to customer service.      

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More JAMA Drama: The Medical Home Reduces Costs, But Only For High Risk Patients

A medical home
candidate?

Just when the Population Health Blog decided to take a break from all the JAMA drama, along comes this study "Medical Homes and Cost and Utilization Among High-Risk Patients" that was just published in American Journal of Managed Care (AJMC).

It cannot resist.

As readers will recall, the offending JAMA article described how a large three year-long Patient Centered Medical Home (PCMH) multi-payer pilot involving approximately 64,000 patients failed to reduce health care costs or increase quality. The pilot program was called the "Chronic Care Initiative" (CCI), and was the brainchild of then Governor Rendell's reform-minded "Prescription for Pennsylvania."

In the AMJC study, 6940 "intervention" patients with a) at least 3 months of primary care physician follow-up, plus b) at least 6 months of assignment to one of the medical home practices were retrospectively compared to 6940 similar "control" patients from a single non-participating practice. The control patients were matched using "DxCG" risk adjustment software* that was combined with propensity matching.

Pediatric practices were excluded, as were outlier patients with more than $100,000 in medical expenses.

In addition to looking at those patients, the top 10% of risk DxCG patients from the medical home (654 patients) were compared to matched high-risk non-medical home practices (734 patients). 

The analysis was complicated by the later attainment of NCQA medical home recognition among some clinics that were taking some of the control patients.  This limited the pool of patients in the 3rd year to just over a thousand in both arms, and just over 100 patients in the high risk groups.

Results?

There was no difference in the evolution of health care costs among all patients included in the analysis.  This confirmed the JAMA drama.

But......

For the top 10% high-risk patients, there were reductions of 61, 48 and 94 hospitalizations per thousand over each of the three years study. This was accompanied by a difference of the per member per month (PMPM) inpatient costs of $115 and $62 in years 1 and 2.  While there was also an increase in outpatient specialist visits, the downward change in inpatient utilization drove the difference in combined overall costs in years 1 and 2 of $107 and $75 PMPM. 

All these differences were statistically significant.  The 3rd year was not because there were too few patients to achieve statistical significance.

While the study was retrospective, the matching methodology is credible enough for the peer reviewers of AJMC and for the PHB. Using control patients from just one clinic is problematic, but no study is perfect. 

Which brings us to the punchlines:

1. Two years ago, the prescient Population Health Blog described how modern Ver. 2.0 "disease" (better described as "population") health management can financially succeed.  It said that one key ingredient is risk segmenting the population and targeting services at the highest risk patients. This AJMC article says it was right.  Most patients won't benefit, but vulnerable patients will.  They are the PCMH's customer.

2. The AMJC article also comports with an accompanying JAMA editorial that is discussed here.  As the PHB quoted, the JAMA drama....

".... has done a great service for the advocates of the Patient Centered Medical Home by effectively ending promotion of this care model as a generic, low-level, unselective approach to health care delivery for all.  The next critical phase of PCMH development should focus on its strategic deployment for the care of high-utilization patients...."

* This uses "linear additive formulas obtained from ordinary least squares regression to combine expenses associated with clinical groups and demographic factors to generate predictions." Wasn't that easy?

The Persistence of Disease Management: It's Not Going Away......

We're all aware of the past criticisms of "disease management." According to the critics, these for-profit vendors were in collusion with commercial insurers, relying robo-calls to blanket unsuspecting patients with dubious advice. Their claims of "outcomes" were based on flawed research that was never intended to be science; it was really intended to market their wares.  

But suppose the Disease Management Care Blog alerted you to:

1. A company that had developed a patient registry to identify at-risk patients who had not received an evidence-based care recommendation? Software created mailings to those patients that not only informed them of the recommendation but offered them a toll-free number to call if there were questions. Patients who remained non-compliant were then called by coordinators, who made three attempts to contact the patient and assist in any scheduling needs. If necessary, a nurse was available to telephonically engage patients and develop alternative care options.

If you think that sounds like typical vendor-driven telephonic disease management, you'd be right.  You'd also be describing an approach to care that was studied by Group Health Cooperative using their electronic record, medical assistants and nurses.  When it was applied to colon cancer screening, a randomized study revealed each additional level of support progressively resulted in statistically significant screening rates.

Or how about.......

2. A major insurer that decided to use its claims data to identify its own "best practices" without waiting for any published evidence-based studies?  Since "strict experimental conditions cannot always be met," shortcuts like time-series analyses" and "propensity score approaches" will be used to "blur" the lines between feedback and evaluation, as well as the lines between provider and insurer?

If you think that sounds like an commercial insurer muscling into health care delivery while using quasi-experimental research shortcuts, you'd be right.  You'd also be describing how Medicare's Innovation Center is borrowing from the disease management industry's approach.  It's all here.

Lastly, there's......

3. A major insurer that decided to NOT to pay primary care sites enough fee-for-service or capitation, preventing them from hiring nurses who could provide coordinated care.  The insurer instead hired its own nurses and "embedded" them in the primary care sites while linking additional monthly payments of approximately $5 to pay-for-performance metrics.

If you think that sounds like a step away from the usual Patient Centered Medical Home, you'd be wrong.  In this instance, having the embedded nurses did not get in the way of the sites achieving PPC-PCMH recognition. What's more, compared to usual care in a prospective randomized study that was underwritten by the medical-home fans at the Commonwealth Fund, the embedded nurse approach resulted in better hypertension care, breast cancer screening and fewer emergency room visits.

Alas, disease management: to paraphrase The Bard, a rose still smells as sweet by any other name, especially if it's used by Group Health, CMS and the Commonwealth Fund.

The Electronic Health Record (EHR) On-Line Portal Increases Hospitalization Rates



"Hi doc! I used my on-line
portal to make an appointment!"

Hey there Accountable Care Organization executive.

You're probably willing to continue to commit millions of dollars toward an electronic health record (EHR) coupled to an online patient portal.  That's because you've been told by your leadership team that electronic consumer empowerment, patient-provider communication and the substitution of efficient two-way messaging for costly face-to-face visits will increase quality, reduce expenses, generate shared savings and guarantee that your life-sized portrait will be prominently displayed in your flagship hospital's lobby.

Well, after you've read a just-published JAMA research study by Ted Palen, Colleen Ross, David Powers and Stanley Xu, you may want to tell your administrative assistant to cancel that appointment with the portrait artist.

The article's title is Association of Online Patient Access to Clinicians and Medical Records With Use of Clinical Services.

How the study was done:

Kaiser Permanente Colorado added "MyHealthManager" (MHM) to their EHR in May 2006. MHM allows patients to view tests, records, problem lists as well as care plans, schedule appointments, request refills and message their doctors. By June of 2009, over 375,000 Kaiser patients had signed up for MHM. Of those, about 45% had used the system at least once.  Of this number, Kaiser researchers pulled the records of 44,321 persons who had been continuously enrolled in the Kaiser system for at least two years. 

This group was retrospectively matched to a control group of Kaiser patients who had not signed up for MHM.  The authors did this through "propensity matching." This found a similar number of patients, based on age, gender, race, number of chronic illnesses and baseline office visits who, using logistic regression analytics, appeared to be the type of patient who would otherwise sign up for MHM.

The results:

Compared to non-MHM patients, the MHM experienced an increase in hospitalization rates (20 per thousand patients) and emergency room visits (11 per thousand).  In other words, for every hundred patients, the on-line portal seemed to lead to 2 extra hospitalizations and 1 extra ER visit. Both differences were statistically significant.

There were also increases in the number of office visits (.7 per patient per year), telephone calls (.3 per patient per year) and after-hour clinic visits (18.7 per thousand patients per year).

Caveats:

The authors correctly point out that this study is not perfect.  Retrospective propensity matching is not as good as a randomized clinical trial; it's possible that the patients who self-selected for MHM were already realtively more interested in or likely to increase their use of health care services.  Results at Kaiser may not apply elsewhere.

Implications:

Despite the limitations, this study should be a wake-up call for those who believe EHR portals is a savings panacea.  By increasing access to on-line services, physicians and patients may paradoxically use the system to address concerns that otherwise wouldn't come to medical attention.  In other words, the EHR portal exacerbates the classic health care economics problem of supplier-induced demand.

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