Austin recently talked about how universal coverage, although a stated goal of many health care systems, is rarely fully achieved. This coincided nicely with recent news that the ACA has significantly reduced the number of uninsured in the United States.
However, many people still do remain uninsured. Who are they? The Urban Institute Health Policy Center has a new report which can help us answer that question.
Mary Clare Carley leaves home each morning with a gallon water jug and carries it wherever she goes to stay hydrated.
The 34-year-old teacher doesn’t remember when she last drank water from a tap. Instead she buys Crystal Springs or Great Value distilled water at supermarkets for $1 or less. “If I don’t have my gallon of water, I just feel incomplete,’’ said Ms. Carley, seated at an Atlanta food court with her giant water bottle.
Despite obvious drawbacks—the plastic and the extra cost for something essentially free out of the tap—thirst for bottled water just keeps growing. U.S. bottled water volume rose 7% last year. That puts it on track to outsell soda by 2017, according to forecasts by industry tracker Beverage Marketing Corp. Nestlé SA, whose water brands include Pure Life and Poland Spring, sold more water than Dr Pepper Snapple Group Inc. sold soda last year, making Nestlé—not Dr Pepper—the No. 3 company in the U.S. for nonalcoholic beverages, according to Beverage Digest.
You may remember Nestle from my Upshot piece where I noted that they funded a number of those studies showing everyone is dehydrated. Sigh:
But water is so hot that most consumers buy it despite the environmental drawbacks.
Meanwhile, dozens of smaller, high-end specialty-water brands with names like Real Water, People Water and HappyWater have begun flooding the market. They are backed by investors of all types who are trying to create higher margins with new bottle designs, exotic minerals and elaborate tales of provenance. Eternal’s label faces inward so shoppers view it through water, filtered by limestone, quartzite and sandstone from the Allegheny Mountains. Karma Wellness Water’s cap injects seven vitamins when you’re ready to drink. Other startups pitch birch water, maple water and cactus water.
All you’re doing is making expensive urine. And buying something that you can get for almost free in your homes. But go ahead. Keep on saying stuff like this:
Debra Ann Stokes recently stopped at Whole Foods in Atlanta to stock up on bottles of alkaline water, which has a high pH level that proponents say can neutralize acids and help the body absorb nutrients. “This goes down much different, smoother,’’ said Ms. Stokes, a 64-year-old belly-dance instructor, grabbing six one-liter bottles of Alkalife Ten.
There are people forcing themselves to drink so much water that they’re concerned with how smoothly it’s going down? Baffling.
Either Medicare Advantage is doing something amazing or data limitations are skewing our view of it. I imagine most people’s priors will drive them to interpret the findings of Bruce Landon and colleagues in one of either of those two ways.
Medicare Advantage (MA) plans have greater flexibility than traditional Medicare (TM). MA can offer more benefits, selectively contract with providers, impose utilization controls (like referral requirements), and implement care coordination programs without large regulatory burdens or new acts of Congress. MA plans must also be responsive to the market, which should provide incentives for higher quality and greater efficiency.
Put it all together and, in theory at least, MA should outperform TM in efficiency and quality. But does it?
Most studies fail to convince one way or the other because researchers are not permitted the same degree of access to MA data as that for TM. For the latter, full claims over many years are available* (though quality measures not derived from claims data are not). For the former, some aggregate measures of utilization provided by plans are usually all we get, and when we get them, they’re not over many years. (However, more quality measures are available from MA plans.)
Comparing MA to TM is like trying to compare two houses, one of which you can live in, the other of which you can only observe through a few keyholes.
In 2006 and 2007 (and only 2006 and 2007), the Centers for Medicare and Medicaid Services (CMS) offered a glimpse of MA through a new keyhole: relative resource use (RRU) data. These plan-level data measure utilization with standardized prices, which removes geographic and MA- or TM-specific price differences. They do so for diabetic patients in both years and those with cardiovascular disease in 2007 only. They are also stratified by age, sex, diabetes type (1 or 2), cardiovascular disease (acute myocardial infarction, congestive heart failure, angina, or coronary artery disease), and the presence or absence of at least one major comorbidity.
Individual-level Healthcare Effectiveness Data and Information Set (HEDIS) data for MA plans—which measure quality of ambulatory care—are also available for those years (and many others). Using 2007 RRU data to measure efficiency and HEDIS to measure quality, Landon et al. constructed similar resource use and quality metrics for a 20% random sample of TM beneficiaries. Quality metrics included, for diabetics, A1C testing in the current year and a diabetic retinal exam in the current or prior year; for both diabetics and patients with cardiovascular disease, LDL cholesterol testing in the current year. These quality metrics are only applicable to and computed for 65 to 75 year olds.
To control for geographic variation in service delivery and quality and demographic differences between MA and TM, the authors weighted the TM sample such that it matched their MA sample demographically within each zip code. This also controls for zip code level socioeconomic differences across the two samples.
On average, RRU was about 20 percentage points lower for MA than TM. Lower utilization was observed in MA across both disease types and service categories (inpatient, surgery and procedures, evaluation and management). However, as shown in the figure below, for newer (entered the program in 2006 or 2007), smaller (<25,000 enrollees), and for-profit HMO or PPO MA contracts,** RRU was higher in MA than TM for inpatient care.
The chart below combines resource use and a composite of diabetes care quality for MA HMOs vs TM. (A chart with similar patterns for cardiovascular disease is provided in the paper’s appendix.) The former is on the horizontal axis (low spending to the left, high to the right). The latter is on the vertical axis (low spending downward, high upward). Each data point (circle or triangle) is the difference between a specific HMO contract and TM. Larger symbols are for larger contracts (>25,000 members); triangles for new contracts, circles for older ones; blue for nonprofit and purple for for-profit.
By and large, MA HMOs use fewer resources and provide better quality, though this is more often the case for larger, established ones (relatively more big circles in the upper left and relatively more small triangles in the lower right). (The authors did not include a similar analysis of PPOs. They wrote me that most PPOs were small, new, and for profit, and there were many fewer of them than HMOs in 2007.)
The authors point out several limitations of the analysis:
It only considered a few aspects of quality, as constrained by data availability.
It is possible MA plans experienced favorable selection in the time period assessed, even within disease type, and even controlling for demographics, comorbidities, and socioeconomic status, to the extent the authors could and did.
The data are quite old, from 2007; that’s the latest year available.
To these, we should add:
Only two disease types were considered, again because of data limitations.
It is possible MA plans upcoded relative to TM such that the MA cohort appeared relatively sicker, which after adjustments, might make resource use look relatively lower.
Beneficiaries with “concomitant specified dominant medical conditions including active cancer, end-stage renal disease, human immunodeficiency virus/AIDS, and organ transplants” were not included in RRUs and, hence, excluded from the analysis. It’s possible MA plans provide disproportionately inefficient or poor care to such beneficiaries.
The analysis included all ages above 65 but the contract HEDIS quality measures used in the analysis are only applicable and computed for ages up to 75, so it’s possible there are some offsetting quality differences for older enrollees.
Within zip code socioeconomic differences could not be controlled for.
PPOs were excluded from the quality/efficiency analysis (the figure just above).
Even if the results accurately depict the efficiency and quality of MA, relative to TM, it must be emphasized that MA plans were paid well above their costs in 2007 and are still paid above them today, though not by as much. In other words, whatever their efficiency, taxpayers are not benefiting; whatever their quality, that comes at a higher price.
Still, either MA is doing something amazing—broadly providing substantially better care with less utilization, which is something few initiatives in TM have ever been able to do—or the results are (or are in part) artifacts of analytic limitations. The best way to decide which is to do more research with more complete data. Until we’re offered more than selected glimpses through keyholes at MA, we may never get the chance to do that.
It made no difference. When, two years later, we published a book on medical myths that once again debunked the idea that we need eight glasses of water a day, I thought it would persuade people to stop worrying. I was wrong again.
Many people believe that the source of this myth was a 1945 Food and Nutrition Board recommendation that said people need about 2.5 liters of water a day. But they ignored the sentence that followed closely behind. It read, “Most of this quantity is contained in prepared foods.”
Water is present in fruits and vegetables. It’s in juice, it’s in beer, it’s even in tea and coffee. Before anyone writes me to tell me that coffee is going to dehydrate you, research shows that’s not true either.
Although I recommended water as the best beverage to consume, it’s certainly not your only source of hydration. You don’t have to consume all the water you need through drinks. You also don’t need to worry so much about never feeling thirsty. The human body is finely tuned to signal you to drink long before you are actually dehydrated.
A significant number of advertisers and news media reports are trying to convince you otherwise. The number of people who carry around water each day seems to be larger every year. Bottled water sales continue to increase.
This summer’s rash of stories was inspired by a recent study in the American Journal of Public Health. Researchers used data from the National Health and Nutrition Examination Survey from 2009 to 2012 to examine 4,134 children ages 6 to 19. Specifically, they calculated their mean urine osmolality, which is a measure of urine concentration. The higher the value, the more concentrated the urine.
They found that more than half of children had a urine osmolality of 800 mOsm/kg or higher. They also found that children who drank eight ounces or more of water a day had, on average, a urine osmolality about 8 mOsm less than those who didn’t.
So if you define “dehydration” as a urine osmolality of 800 mOsm/kg or higher, the findings of this study are really concerning. This article did. The problem is that most clinicians don’t.
I’m a pediatrician, and I can tell you that I have rarely, if ever, used urine osmolality as the means by which I decide if a child is dehydrated. When I asked colleagues, none thought 800 mOsm/kg was the value at which they’d be concerned. And in a web search, most sources I found thought values up to 1,200 mOsm/kg were still in the physiologically normal range and that children varied more than adults. None declared that 800 mOsm/kg was where we’d consider children to be dehydrated.
In other words, there’s very little reason to believe that children who have a spot urine measurement of 800 mOsm/kg should be worried. In fact, back in 2002, a study was published in the Journal of Pediatrics, one that was more exploratory in nature than a look for dehydration, and it found that boys in Germany had an average urine osmolality of 844 mOsm/kg. Thethird-to-last paragraph in the paper recounted a huge number of studies from all over the world finding average urine mOsm/kg in children ranging from 392 mOsm/kg in Kenya to 964 in Sweden.
That hasn’t stopped more recent studies from continuing to use the 800 mOsm/kg standard to declare huge numbers of children to be dehydrated. A 2012 study in the Annals of Nutrition and Metabolism used it to declare that almost two-thirds of French children weren’t getting enough water. Another in the journal Public Health Nutrition used it to declare that almost two-thirds of children in Los Angeles and New York City weren’t getting enough water. The first study was funded by Nestlé Waters; the second by Nestec, a Nestlé subsidiary.
It’s possible that there are children who need to be better hydrated. But at some point, we are at risk of calling an ordinary healthy condition a disease. When two-thirds of healthy children, year after year, are found to have a laboratory value that you are labeling “abnormal,” it may be the definition, and not their health, that is off.
None of this has slowed the tidal push for more water. It has even been part of Michelle Obama’s “Drink Up” campaign. In 2013, Sam Kass, then a White House nutritional policy adviser, declared “40 percent of Americans drink less than half of the recommended amount of water daily.”
There is no formal recommendation for a daily amount of water people need. That amount obviously differs by what people eat, where they live, how big they are and what they are doing. But as people in this country live longer than ever before, and have arguably freer access to beverages than at almost any time in human history, it’s just not true that we’re all dehydrated.
Several weeks ago, I made a substantial change to how and when I do various work tasks and non-work activities. It seems to have not only boosted my productivity, but also improved my mental health (both completely subjective and N=1). FWIW, I thought I’d share.
Before the change, here’s roughly how I spent my weekdays and how I felt about it:*
~5:30AM-6:30AM: Catch up on email, news, Twitter. If time permitted (which was rare), also write blog posts.
~6:30AM-8:00AM: Cycle among podcasts, email, news, Twitter on my commute. If time permitted (which was rare), also read. Already there’s a problem here. Writing and reading were the things I felt I should get to, but clearly I was permitting other tasks to take priority in the early morning hours. This made me feel bad and immediately “behind.”
~8:00AM-4:00PM: Cycle among email, news, Twitter, and various work tasks. This caused me to feel generally scatter brained and unfocused. I didn’t like it, but felt I needed to “keep up.” I had alerts for email going on my computer and phone, which encouraged me to switch to it whenever someone sent me an email. Why I should let someone else dictate my work flow never really crossed my mind … until recently.
~4:00PM-9:00PM: Cycle among email, news, Twitter, family stuff. If time permitted (which was rare until after 8PM), try to write blog posts. Basically more of the same scatter brained nonsense.
Not surprisingly, this is generally dumb behavior for two reasons. First, I was trying to cycle too quickly among tasks, which is inefficient and made me feel (and, I think, caused me to be) relatively unproductive and unfocused. Second, there was zero attempt to match times of day to tasks for which my brain is best suited. I was not happy, but I didn’t really recognize it until recently.
I changed all this, so my days are now closer to:
~5:30AM-6:30AM: Write blog posts because this is the absolute best time for me to write. It’s what my brain wants to do. If no topic is available, I read papers.
~6:30AM-8:00AM: Catch up on email, news, Twitter, after which, read. Podcasts are reserved for the walking part of my commute during which none of those are optimal.
~8:00AM-4:00PM: Segment into a long, morning work time (~2.5 hours) and another afternoon work time of the same length during which I do just one work task at a time, to completion or the end of the time period, whichever comes first. Compress all email, news, Twitter checking into a midday and afternoon check. Turn off all alerts. No dings. No vibrations. Nothing. Try to schedule meetings and calls during the remaining times. In other words, protect some large chunks for focused work. Also, only respond to emails requiring responses. Save the rest for later or delete. Do a lot of deleting without even reading. Unsubscribe from lots of stuff. Create filters to trash for the unsubscribe-able.
~4:00PM-9:00PM: Read on my commute (or podcasts while walking). Then family stuff until 8PM or so, at which point just deal with emails I’d put off and easy, brainless home and work administrative tasks. (We all have lots of this crap.) Read if time permits. Don’t do any other work. This is the time of day during which writing is much harder. I’m tired. So doing the stuff with low cognitive demands now is optimal.
Having done this for a few weeks, I feel dramatically less scatter brained (more focused). I think I’m getting more done more efficiently, and I’m happier. I no longer go through the day thinking I need to get to this writing task or read that paper. I get to what I can get to when it makes most sense. I don’t worry about anything else but maintaining the discipline of my schedule. (Sometimes meetings and other demands intervene, but at least I’m not causing additional interruption through bad behavior anymore.)
It’s no longer about what I have to get to, it’s about simply doing the thing right now that I have dedicated right now for. When I find my mind drifting toward “you’ve got other stuff you have to get to” I push that thought away and keep focusing on the task at hand. When my writing time or my allocated chunk of work time is over, I put the job away, and do the next thing I’m supposed to do. It’s the difference between managing the time (good) vs. juggling the tasks (bad).
For me, it just works. Your mileage may vary.
* I also made a big weekend change, which amounts to not checking email/news/Twitter except once in the morning and once at night.
IMPORTANCE Epidemiological evidence suggests that physical activity benefits cognition, but results from randomized trials are limited and mixed.
OBJECTIVE To determine whether a 24-month physical activity program results in better cognitive function, lower risk of mild cognitive impairment (MCI) or dementia, or both, compared with a health education program.
DESIGN, SETTING, AND PARTICIPANTS A randomized clinical trial, the Lifestyle Interventions and Independence for Elders (LIFE) study, enrolled 1635 community-living participants at 8 US centers from February 2010 until December 2011. Participants were sedentary adults aged 70 to 89 years who were at risk for mobility disability but able to walk 400 m.
INTERVENTIONS A structured, moderate-intensity physical activity program (n = 818) that included walking, resistance training, and flexibility exercises or a health education program (n = 817) of educational workshops and upper-extremity stretching.
MAIN OUTCOMES AND MEASURES Prespecified secondary outcomes of the LIFE study included cognitive function measured by the Digit Symbol Coding (DSC) task subtest of the Wechsler Adult Intelligence Scale (score range: 0-133; higher scores indicate better function) and the revised Hopkins Verbal Learning Test (HVLT-R; 12-item word list recall task) assessed in 1476 participants (90.3%). Tertiary outcomes included global and executive cognitive function and incident MCI or dementia at 24 months.
We have lots of epidemiologic evidence that physical activity is associated with better cognition. But that could be reverse correlation. We need an RCT. This is it.
Researchers randomized more than 1600 elderly people who were sedentary to (1) a structured, moderate intensity physical activity program, including walking, resistance training, and flexibility exercises, or (2) a control health education program of workshops and upper-extremity stretching.
They measured a number of cognitive outcomes, including the Digit Symbol Coding (DSC) task subtest of the Wechsler Adult Intelligence Scale and the revised Hopkins Verbal Learning Test. They also measured global and executive cognitive function and dementia two years after the intervention.
At 2 years, the scored of the cognititve tests were no different between the groups. On a 133 point scale, the mean scores of the DSC were 46.26 in the physical activity group and 46.28 in the control group. The HVLT (which could score up to 12) was 7.22 in the physical activity group and 7.25 in the control group.
There were also no significant differences in the measures of global or executive cognitive function, nor any in the diagnosis of cognitive impairment or dementia.
IMPORTANCE Observational data have suggested that high dietary intake of saturated fat and low intake of vegetables may be associated with increased risk of Alzheimer disease.
OBJECTIVE To test the effects of oral supplementation with nutrients on cognitive function.
DESIGN,SETTING, AND PARTICIPANTS In a double-masked randomized clinical trial
(the Age-Related Eye Disease Study 2 [AREDS2]), retinal specialists in 82 US academic and community medical centers enrolled and observed participants who were at risk for developing late age-related macular degeneration (AMD) from October 2006 to December 2012. In addition to annual eye examinations, several validated cognitive function tests were administered via telephone by trained personnel at baseline and every 2 years during the 5-year study.
INTERVENTIONS Long-chain polyunsaturated fatty acids (LCPUFAs)(1g)and/or lutein
(10 mg)/zeaxanthin (2 mg) vs placebo were tested in a factorial design. All participants were also given varying combinations of vitamins C, E, beta carotene, and zinc.
MAIN OUTCOMES AND MEASURES The main outcome was the yearly change in composite scores determined from a battery of cognitive function tests from baseline. The analyses, which were adjusted for baseline age, sex, race, history of hypertension, education, cognitive score, and depression score, evaluated the differences in the composite score between the treated vs untreated groups. The composite score provided an overall score for the battery, ranging from −22 to 17, with higher scores representing better function.
As with physical activity, there’s lots of observational data linking diet with Alzheimer’s disease. But no RCTs testing nutrients with cognitive changes. This is that RCT.
Using the infrastructure of another trial investigating macular degeneration in elderly people, researchers randomized more than 3700 participants to get long-chain polyunsaturated fatty acids (LCPUFAs), lutein,
(10 mg)/zeaxanthin (2 mg), and/or placebo in a factorial design. They also got varying combinations of vitamins C, E, beta carotene, and zinc.
Then, at baseline and every two years they were assessed. The main outcome of interest was a composite score of a battery of cognitive function tests. The score could range from -22 to 17.
On that scale, the yearly change for those who received supplements was -0.19 for those who got long-chain polyunsaturated fatty acids and -0.18 for those who did not. Needles to say, this was not a significant difference. Those who received lutein/zeaxanthin had a score change of -0.18 versus -0.19 in those who did not. Again, not significant.
Supplemenation with these nutrients had no effect on cognition.
The accompanying editorial offered some hope because other studies have shown benefits to physical activity:
The FINGER trial reported results of a multifaceted intervention that included diet, exercise, cognitive training, and vascular risk monitoring compared with provision of general health advice in participants aged 60 to 77 years who were at risk of developing dementia. At 2 years, the intervention was associated with significant benefits on a comprehensive neuropsychological test battery.
And they’re right. These studies should be added to others, not replace them. I agree with this, too:
It is likely the biggest gains in reducing the overall burden of dementia will be achieved through policy and public health initiatives promoting primary prevention of cognitive decline rather than efforts directed toward individuals who have already developed significant cognitive deficits.
It seems likely that incorporating physical activity, and maybe even diet, into holistic changes earlier in life are likely to do more to improve health and cognitive decline than waiting until problems have already developed later in life.
We asked, you answered. As a Patreon perk, many of our patrons voted for an episode on the dangers of sitting. This has been in the news a lot recently. It’s also the topic of this week’s Healthcare Triage.
Diversification of the physician workforce in the United States remains an ongoing goal,1,2 yet assessments of graduate medical education (GME) diversity, overall and across specialties are lacking. We assessed GME diversity by race, ethnicity, and sex in 2012.
Boom! Let’s get to it.
In 2012, there were 16,835 medical graduates. Just over 48% of them were females. Just over 15% of them were from under-represented minorities. In that year, there were 115,111 trainees in graduate medical education. Just over 46% of them were females, and just under 14% of them were from under-represented minorities. How does that compare with the general population?
Females are getting close. The percentage of females graduating medical school and the percentage of them in the US population are only a few points away. But the percentage of graduates from under-represented minorities is significantly lower than the percentage those minorities make up of the US population. That’s why we call them “under-represented”.
Females were least common in orthopedics (14%) and most common in pediatrics (74%) and OB/GYN (82%). Females comprise a majority of seven specialties – OB/GYN, pediatrics, dermatology, Internal medicine/pediatrics, family medicine, pathology, and psychiatry. Black trainees were most common in OB/GYN (10%) and family medicine (8%), and Hispanic trainees were most common in psychiatry, family medicine, OB/GYN, and pediatrics (all about 9%).
Austin and Aaron are participants in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to amazon.com.