That’s right, once again the DMCB knows that your fresh issue of PHMJ is languishing on your to-do list, laying in your in-box and going unread in your must-read list. But you also know you can count on the DMCB to give you a handy summary of the contents so you can a) pick and choose which articles warrant greater scrutiny over lunch or while dealing with some other biological need and b) quote confidently from even the articles that don’t deserve your attention to the amazement of your jealous colleagues.
Sarah Beaton, Scott Robinson Ann Von Worley, Herbert Davis, Audra Boscoe, Rami Ben-Joseph, and Lynn Okamoto: Cardiometabolic risk and health care utilization and cost for Hispanic and non-Hispanic women. The Lovelace Foundation, with help from United Biosource and Sanofi-Aventis, mined their 4211-person osteoporosis data base because the weight and height information obtained in the course of DEXA scanning enabled a calculation of body mass index (BMI). They then went on to combine the BMI with the available lab and blood pressure data (winnowing the number down to 2578 persons) to assess the cardiometabolic risk (CMR) of continuously enrolled Caucasians and Hispanic women. Even if six different definitions of elevated CMR were used, a statistically significant 66% of the Hispanics versus 52% of the Caucasians fulfilled criteria. Having elevated CMR was also associated with increased healthcare utilization and cost. Yet, being Hispanic was also associated with lower utilization and cost. Nothing new here: cultural and socioeconomic factors have been long known to drive Hispanics toward obesity and impede access to healthcare. While this was a convenience sample and may not be generalizable, however, the 2/3 prevalence rate in this population speaks once again to the pressing need to address a crushing public health crisis in a vulnerable group of citizens. Kudos also for showing how claims and registry data can be used by health plans on behalf of science and public health.
Joseph Couto, Martha Romney, Harry Leider, Smiriti Sharma and Neil Goldfarb: High rates of inappropriate drug use in the chronic pain population. Ameritox is a company that deals with a lot of pee. Like, 938,000 samples’ worth. And they used every single one of them to test for the presence of prescribed opiates as well as other illicit substances. These were urine drug tests ordered by physicians in the course of follow-up for patients being treated for chronic pain. 11% test positive for illegal drugs, 29% tested positive for a non-prescribed medication, 38% did not have detectable prescribed drug (indicating they could be diverting the drug to friends or for sale) and 15% had lower than expected levels of their medication. While these categories were non-exclusive, the bottom line is that a whopping 75% had one or more problems. Even if this speaks to a population with special needs, the DMCB is not that surprised. The authors point out correctly note that docs only order urine tests when they suspect something is wrong, so maybe the real news is that they’re wrong 25% of the time. The DMCB is also unsure of the positive and negative predictive value of Ameritox’s testing, because their testing algorithms are proprietary and not reported. Conservatively assuming that a typical urine submission is maybe 3 ccs, that’s about 2.8 million ccs of pee or 2820 liters or about 700 gallons of the stuff. Now that is dedication to science.
Alex Harris, Thomas Bowe, John Finney and Keith Humphreys: HEDIS initiation and engagement quality measures of substance use disorder care: Impact of setting and health care specialty. The VA really likes HEDIS because they spend a lot of time thinking about it and they’re good at it. This is amply on display in this paper from the Palo Alto Center for Health Care Evaluation in their examination of the HEDIS metrics for ‘initiation’ and ‘engagement’ measures for over 320,000 vets meeting criteria for substance abuse disorder. How and where (for example, inpatient setting vs. a specialty clinic vs. a primary care setting) vets got treated made a big difference on whether HEDIS defined quality was met, but the DMCB was overwhelmed by the permutations of how patients flowed through the system. Even worse, the authors then applied a Markov model to game how greater substance-abuse specialty involvement can increase the VA’s HEDIS rates. No tables but there are two mind-numbing flow diagrams. Avoid this one unless you like to also think how HEDIS defines the denominator population or believe the VA really has any lessons for the rest of the world, especially when it comes to the management of substance abuse.
Sandra Adams, Albert Crawford, Rajiv Rimal, Joyce Lee, Laura Janneck, Christopher Sciamanna: The effects of a computer-tailored message on secondary prevention in type 2 diabetes: A randomized trial. Think sending targeted computer generated message-reminders to persons with diabetes just before a doc appointment is a neat slam dunk? You may need to think about that again after reading this assiduously conducted randomized clinical trial from Jefferson, Miriam Hospital, Brown University, Hopkins and last but not least Penn State (Class of ’81 - Go Joe!) involving 203 persons with type 2 diabetes mellitus. Depending on a baseline assessment of blood pressure, lipids or blood glucose control, participants got either nothing, a positive message (lower you blood pressure and protect your eyes), or a negative message (you could go blind). Interviews following the physician appointment by research assistants unaware of the group assignment showed no difference in medication changes or needed testing. Of course, there may have been too few patients to yield up a statistically significant difference, but even then, it would have been small. The authors correctly wonder if the intervention itself was weak (it only happened once), suffered from not being personalized to each patient’s special circumstances and relied too much on patient self-report. The DMCB also thinks that single interventions are less effective unless they’re combined with other interventions, i.e., 'synergy.' Would this work if integrated with nurse-based care management or a medical home?
Jared Puterman and David Alter: The application of disease management to clinical trial designs. Be of good cheer disease management-ites! These researchers from York University and the University of Toronto read every big cardiology randomized clinical trial from the New England Journal of Medicine, JAMA and The Lancet and over three years, over time from year to year, the intervention and control groups had greater degrees of disease management present. The DMCB interprets this to mean that if you’re going to assess the impact of a new drug or device, you need to gauge it in an environment of population-based care support. In other words, this is becoming the standard of care. Another way of thinking about this: if a drug or device has benefit, it may mean that that benefit depends on the presence of disease management support. The only problem is that the authors used the unfamiliar “American Heart Association taxonomy” to define disease management and only small minority (3.4%) had all “eight elements.” 11% had four. The good news here is that even the academicians are recognizing the importance of disease management, even if they aren’t aware of it and are being slow-pokes about it.
Ronald Deprez, Amy Kinner, Peter Millard, LeeAnn Baggott, Jean Mellet and Jia Ling Loo: Improving quality of care for patients with chronic obstructive pulmonary disease. This is a pre-post study involving 18 inland Maine primary care clinics that agreed to go through a 3 year collaborative-style intervention aimed at helping docs do a better job of managing patients with COPD. In addition to attending three workshops, the participants were given flow sheets to better help them document various processes of care. Charts were reviewed at baseline (N=584) and afterwards (N=626). As you might predict, spirometry rates, tobacco use documentation, vaccinations, counseling and referrals all went up. The authors correctly point out this study has all the problems associated with a pre-post design, including regression to the mean or bias from other factors that could have influenced the observed results. In addition, the authors pointed out that they didn’t know if their outcomes were thanks to the docs doing a better job of documenting what was happening anyway. Maybe they were itching to use that brand new spirometry machine. Last but not least, the DMCB agrees with the authors that it’s possible that once the collaborative is over, the docs may go back to their old patterns of care. This is a good article to learn about the severe limitations of a pre-post study.Plus, there's a editorial 'Health Is Not Enough' from Ben Leedle of Healthways about their Gallup-Healthways Well-Being Index. He argues that it gives additional insights about the health status of populations that are not captured by the usual metrics.