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Although there have been quality improvements in nursing home care over time, most elderly (including my own grandmother) dread the thought of entering a nursing home. According to a Mattimore et al. (J Am Geriatrics Soc 1997) study only 7% of patients surveyed were “very willing” to live permanently in a nursing home, while 26% were “very unwilling” and 30% would “rather die.”
A New York Times (“Rethinking Old Age“) gives an illustrative anecdote
“Nursing home priorities are matters like avoiding bedsores and maintaining weight — important goals, but they are means, not ends. [One patient] left an airy apartment she furnished herself for a small beige hospital-like room with a stranger for a roommate. Her belongings were stripped down to what she could fit into the one cupboard and shelf they gave her. Basic matters, like when she goes to bed, wakes up, dresses, and eats were put under the rigid schedule of institutional life. Her main activities have become bingo, movies, and other forms of group entertainment. Is it any wonder most people dread nursing homes?”
The Green House Project is trying to change these attitudes through an innovative way of structuring elderly care. The program was developed by Dr. William Thomas and based on the Eden Alternative model. The Green House methodology does not only focus on medical quality measures, but quality of life measures as well such as reducing boredom and loneliness. The NYT article continues to explain that the Green House Project utilizes
“…houses for no more than 10 residents, equipped with a kitchen and living room at its center, not a nurse’s station, and personal furnishings. The bedrooms are private. Residents help one another with cooking and other work as they are able. Staff members provide not just nursing care but also mentoring for engaging in daily life, even for Alzheimer’s patients. And the homes meet all federal safety guidelines and work within state-reimbursement levels”
Kane, Lum, Cutler, Degenholtz and Yu (J Am Geriatrics Soc June 2007) find that controlling for baseline characteristics, Green House residents reported significantly higher quality of life ratings than those in a traditional nursing home owned by the same parent company.
I have two concerns regarding the Green House. The first is cost. While the Green House certainly seems like an improvement over traditional nursing homes, one may ask if they are generally affordable or if the cost if prohibitive. Secondly, the Green House model may work best with the relatively healthy elderly. Will this model be able to function for more severely ill patients?
Pay-for-performance (P4P) is supposed to improve the health care quality for all. One may not be surprised if it were the case that more affluent, more educated individuals benefit most from P4P and thus existing health care disparities may increase. Author Lawrence Casalino and Arthur Elster, however, posit that P4P may actually reduce the quality of care that the poorest and sickest individuals experience. Why would this be? Here are some examples from their paper “Will P4P and quality reporting affect health care disparities“:
- Reduction of physician income in poor, minority communities: The authors claim that doctors in poor communities will have less money for the IT, staffing and training needed to improve quality scores. When the doctors serving low-income households make less money, the supply of these doctors will inevitably decrease. Also, those doctors who decide to continue serving low-income communities may receive low P4P scores if patient compliance with physician recommendations is lower in poor communities. The low compliance rates could be due to language barriers, lack of education or lack of understanding of the doctor’s orders.
- “Teaching to the Test“: Doctors may preform services which evaluators deem important, but which in reality are less important to patient health in these under-served communities. “For example, with a relatively uneducated diabetic patient who speaks poor English, the physician might focus on making sure the patient has a hemoglobin A1c test (because this is measured) but not on the time-consuming task of explaining to the patient how to control his or her diabetes and blood pressure (because control of these things is not measured).”
- Avoiding ‘problem’ patients: If there is no risk adjustment measure in the P4P compensation schemes, physicians who have healthier patients will score higher on all measures. Thus, physicians may have an incentive to avoid the specific patients they are supposed to help the most: the sickest. The authors write: “It is often stated, without evidence being cited, that patients’ characteristics do not affect physicians’ scores on process measures and therefore that process measures, unlike outcome measures, need not be adjusted for patients’ health status or socioeconomic status. To practicing physicians, this might seem implausible. For example, it should be easier for physicians who practice in affluent areas such as Marin County, California, than for those who practice in poor areas of Oakland to achieve higher rates of screening mammography: Their patients are more likely to be wealthy, well-educated, well-insured women who are aware of the benefits of mammograms, are more likely to request them, and are more able to travel to obtain them. “
- Casalino and Elster (2006) “Will pay-for-performance and quality reporting affect health care disparities” Health Affairs, 26, no. 3 (2007): w405-w414
Today is Memorial Day. A day to remember all individuals around the world who gave their life during a time of war. I have never served in the military, so today I will defer to the Pen and Sworld blog written by retired U.S. Navy Commander Jeff Huber. In his post “My Memorial Day,” Mr. Huber states that Memorial Day is a time to respect the sacrifice of generations of soldiers throughout the years, but is not “…about glorifying wars, past, present and future.”
Mr. Huber ends his post with three quotations from Dwight Eisenhower:
- “Every gun that is made, every warship launched, every rocket fired, signifies in the final sense a theft from those who hunger and are not fed, those who are cold and are not clothed.”
- “I hate war as only a soldier who has lived it can, only as one who has seen its brutality, its futility, its stupidity. “
- “I think that people want peace so much that one of these days government had better get out of their way and let them have it. “
What is life like for prisoners in Guantánamo Bay? What (if any) due process do they receive?
The docu-drama movie titled Road to Guantánamo, answers these questions by examining the incarceration of the Tipton Three. The film follows the three British Muslim youths from Pakistan to Afghanistan to Cuba. The three spent over two years in American confinement until they were spent to the UK in March 2004, and subsequently release without any charges. Watching the experience of the Tipton Three has only strengthened my belief in the importance of civil rights.
For more information see:
Accoding to a Canadian study, infants can tell the difference between two languages without hearing the spoken words. To read more on the study, see today’s Washington Post (“Babies Can Discern Languages…“).
Según una investigación canadiense, los infantes se pueden distinguir entre dos idiomas sin oÃr las palabras en voz alta.  Para más información acerca de la investigación, vea a La Razón (âLos bebés son capaces de distinguir un idioma a los cuatro mesesâ?).
The 2005 Deficit Reduction Act aimed to reduce Medicaid entitlements as one means to reduce the national deficit. One portion of the bill changed eligibility rules for the elderly to qualify for Medicaid payment of nursing home (NH) expenses. In the May edition of the Journal of Health Economics, authors David Grabowski and Jonathan Gruber estimate whether or not restricting (or expanding) Medicaid eligibility rules significantly impact NH utilization.
Who pays for nursing homes?
There are generally 4 institutions which pay for nursing home (NH) care.
- Medicare. Medicare pays 100% of the first 20 days of NH stays after a hospitalization, and a portion of NH costs between days 21-100 after a hospitalization.
- Long-term care (LTC) insurance. Private LTC insurance is purchased at younger ages and protects against the risk of needing NH care. LTC insurance is still fairly rare; only about 4% of NH expenditures are paid by private LTC insurance plans.
- Out of pocket payments.
- Medicaid. Medicaid will generally pay for NH care if the individual meets certain income and asset requirements. Generally, the individual needs to qualify for Supplemental Security Income (SSI) or be in a state which allows the individual to “spend down” income in order to qualify for Medicaid.
Analysis
The authors seek to see how loosening Medicaid restrictions will impact NH usage. Expanding Medicaid income eligibility requirements, of course, will mechanically make more individuals eligible for Medicaid, but it may also make Medicaid more attractive since one does not need to “spend down” such a large portion of their assets or income. Loosening Medicaid restrictions can also affects supply. Higher Medicaid reimbursement rates will likely increase the percentage of patients who are Medicaid patients (vs. private insurance or out-of-pocket patients), but may also increase the total number of nursing home patients.
Complications
The analysis does have some complications. Certificate-of-need (CON) laws restrict the ability of NH supply to adjust to changes in NH demand. Also, the population affected by changes in eligibility are the middle class; poor individuals (always eligible) and rich individuals (never eligible) are not impacted by marginal changes in eligibility requirements. One also has to worry that Medicaid NH patients receive worse care than private insurance NH patients, but Grabowski et al. (NBER 2006) shows that this is not the case. Finally, the availability of care given by family or friends is not measured in this study and would likely impact NH utilization.
Data and Results
The data used is the National Long-Term Care Survey (NLTCS) from 1982, 1984, 1989, 1994 and 1999. The data have information on individual demographics, health, as well as income and asset levels to determine Medicaid eligibility. The econometric specification uses a probit regression with time, marriage, and state dummies included.
The authors generally find that loosening Medicaid eligibility rules has a limited impact on Medicaid NH usage. Further, the “spend down” rule that many states have adopted also does not significantly affect Medicaid NH utilization. The authors conclude: “we find consistent and clear evidence that nursing home utilization is inelastic with respect to state policies. Thus, the large increase in nursing home expenditures over the past few decades is not likely attributable to increased generosity in state Medicaid payment programs.”
- Grabowski; Gruber (2007) “Moral hazard in nursing home use” JHE, vol 26, issue 3, pp. 560-577.
One of the biggest advances statistical modeling in the last 30 years has been the use of the bootstrap. For those interested in learning about the bootstrap in more detail, a good place to start is an article by UCSD math professor Dimitris N. Politis which I will summarize here. For more detailed information, one may want to look at An Introduction to the Bootstrap by Efron and Tibshirani.
Set-up
Suppose we have n observation of a random variable X. We can group these as a vectors so that X=(X1,…,XN), where each Xi are iid with distribution F. If we want to estimate a parameter θ(F) from the data, we can use a statistic T(X) as an approximation. If we assume that F~Normal, we can use traditional statistics to estimate T(X) as well as the confidence interval around θ(F). If we do not know the distribution of F (which a researcher problem does not in reality), then classical statical theory may be less reliable and a bootstrap methodology may be more robust. Bootstrapping methodology allows the researcher to better estimate F, especially if there is significant skewness to the F distribution.
The bootstrap procedure creates a new sample, by randomly sampling each observation in X with replacement until we have a new vector with N observations. We repeat this B times to create our bootstrap data set. Let’s look at an example..
Example
Pretend we have data on how many push up I have completed each day over a week. I want to estimate the median number of push-ups I do each day. In this sample, N=15 and since we will create ten bootstrap samples, B=10.
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The median of the actual data we have is 22. But we can also calculate the median using a bootstrap methodology. We first randomly choose one of the data points and put it as the first data point of B1 (the bootstrap sample number 1), we then resample with replacement and put another number as the 2nd observation of sample B1. We can see that data points often repeat. For instance in B1 observations X10 repeats twice. We see that the median varies across the 10 bootstrapping samples, but the average value for the median using the bootstrap methodology is 22.8.
We can also calculate the the bootstrap variance (3.36) and standard deviation (1.83). This are calculated according to the formulas:
- Variance: B-1ΣiT(X*i)2 – [B-1ΣiT(X*i)]2
- S.D. = (Var)1/2
Here, T(X*i) is the median for each bootstrap sample i. Since there are 10 bootstrap samples i=1,…,10. To calculate the variance, one simply averages the squared median over the 10 bootstrap samples and then you subtract the squared average median of the 10 samples.
- Politis, Dimitris “Computer-intensive methods in statistical analysis“
There is an interesting article (“Unintended Consequences“) from February 2000 edition of the New England Journal of Medicine in which author Dr. Lawrence Casalino gives his view of how measuring physician quality impacts care. Dr. Casalino cites many unintended consequences of implementing an quality measuring system including 1) more paperwork, 2) the fact that certain quality measures will favor certain organizational frameworks, 3) the misallocation of physician time.
- Casalino (2000) “The Unintended Consequences of Measuring Quality on the Quality of Medical Care” NEJM, Volume 342, no. 7, pp. 519-520.
Globalization and Sushi
May 31, 2007 in Books, Economics - General | Permalink
For those who rant about the evils of globalization, let us examine the Sushi Economy. NPR’s Marketplace discusses The Sushi Economy book with its author Sasha Issenberg. Mr. Issenberg talks about how globalization has made the sushi industry a reality in the modern world. Technological improvements in travel and communication have lead to an increased demand for sushi all over the world; advances in communication technology, supply chain flexibility and decreased transportation costs have made eating sushi affordable from residents living in both Tokyo and Tulsa. Mr. Issenberg also discusses some findings from his book in Boston Magazine (“The one that almost got away“). If you are a sushi aficionado or are simply interested in the effects of globalization, the increased consumption of sushi is an interesting topic.