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.

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*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.

Image from Wikipedia

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.      

Image from Wikipedia