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Tags: Links
A statistician’s wife gives birth to twins. Excitedly, he calls everyone to share the good news. When he tells the church they say, ‘That’s terrific! Bring them down to church this Sunday, and we’ll baptize them!’
’Uh, let’s just baptize one of them,’ says the statistician. ‘We can keep the other one as a control”.
Tags: Joke
Currently 68.0% as of 5:56pm PT, 7 Apr 2011. See more updated figures here.
What others are saying:
Tags: Government Shutdown
Its has been well established that medical errors are a serious problem in modern health care. One study found that medical errors kill between 50k and 100k annually and another found that 2.9% of people who enter the hospital are actually harmed by the care they receive. The most recent issue of Health Affairs, however, has found that not only is there still much work to be done, but the scale of the problem may be even larger than previously anticipated.
Some of the key findings from the press release include the following:
Most people think medical care is supposed to improve your health, not diminish it. Reducing medical errors may be the single most important issue to improve medical quality now and in the future.
Sources:
Check out the latest edition of the CoR at moneyblog. There are contributions from a number valued “regulars” as information on risks specific to New Zealand. It even includes quoatations suchs as the following:
“Every noble acquisition is attended with its risks; he who fears to encounter the one must not expect to obtain the other” – Metastasio
Tags: CoR
Cost effectiveness and quality analysis of the treatment of cancer has long been a goal of health services researchers. In particular, researchers aim to determine whether various treatments provide cost-effective methods to improve longevity and quality. Physicians, however, use different treatments depending on the patient’s cancer stage. Although most cost-effectiveness researchers want to take into account patient cancer stage in their analyses, these data are not available in many administrative data files, such as the Medicare claims files.
To overcome this problem, recent studies have examined how to develop accurate algorithms to account for cancer stage in studies using claims data. A paper by Cooper et al. has provided an initial attempt to accomplish this feat, but a more recent paper by Smith et al. 2010 offers an alternative. Today, I will review the Smith paper.
Methods
The initial study population consisted of 150,764 women (age ≥ 65 years) diagnosed with breast cancer between 1992 and 2002 identified through Surveillance Epidemiology and End Results (SEER)-Medicare. From this population, the following cohorts were excluded beneficiaries characterized by:
To determine the cancer stage, physicians typically use the following heuristic:
This spreadsheet explains the cancer stage classification according to the American Joint Committee on Cancer (AJCC).
The study relied on demographic, tumor, and treatment characteristics to identify the cancer stage. One of the key variables in the breast cancer algorithm was axillary lymph node involvement. This spreadsheet also lists all the covariates included in the prediction algorithm.
To test the accuracy of the algorithm, the authors relied on four metrics: sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The authors calibrated the model on a baseline sample of the SEER data and tested the accuracy using a validation sample.
One drawback of the Smith et al. algorithm is that it requires both retrospective and prospective data for up to 1 year prior to and 1 year after the date of diagnosis. Further, patients have to be continually enrolled in Medicare FFS for the algorithm to work properly. Those who join a Medicare Advantage plan are dropped from the sample.
Conclusion
The authors claimed the following results:
“A claims-based algorithm was utilized to predict breast cancer stage, and was particularly successful when used to identify early stage disease. These prediction equations may be applied in future studies of breast cancer patients, substantially improving the utility of claims-based studies in this group. This method may similarly be employed to develop algorithms permitting claims-based epidemiologic studies of patients with other cancers.”
Source:
Tags: Algorithm, Breast Cancer, Claims, IOM
Previous research by the team at Dartmouth Atlas found that there is significant regional variation in spending and disease diagnosis frequency, but this variation is not correlated with higher quality. However, regional variation may be overstated. According to Zuckerman et al. (2010):
“Unadjusted Medicare spending per beneficiary was 52% higher in geographic regions in the highest spending quintile than in regions in the lowest quintile. After adjustment for demographic and baseline health characteristics and changes in health status, the difference in spending between the highest and lowest quintiles was reduced to 33%. Health status accounted for 29% of the unadjusted geographic difference in per-beneficiary spending; additional adjustment for area-level dif ferences in the supply of medical resources did not further reduce the observed differences between the top and bottom quintiles.”
The authors used 2000-2002 data from the MCBS to draw these conclusions. They controlled for age, sex, race, urban/rural, self-reported health status, smoking status, BMI, previous diagnoses of diabetes or hypertension, family income, supplementary insurance coverage, and area-level supply of medical resources. The analysis was conducted at the HRR level.
If regional variation is spending is mostly due to disease burden rather than practice patterns, efforts to change practice patterns at the region level may prove fruitless.
Tags: Dartmouth Atlas, Health Status, Spending
Poor Need More Financial Services than Rich
April 6, 2011 in Books, Financial | No comments
Why would that be the case? The answer is risk. Poor people (especially in developing countries) have a larger share of their assets at risk due to theft, a family illness, or funeral. Thus, cash flow management is vital for survival for the poor. The Enlightened Economist discusses a book called Portfolios of the Poor where Daryl Collins and three co-authors examine how the poor in India, Bangladesh and South Africa manage their money. The data the authors use is unique: weekly diaries kept by researchers who tracked these individuals and families. They found the following:
“The first surprise is the extent to which poor people – living on or about the $2 a day threshold, or about 40% of the world’s population – use a wide variety of financial services, although many of them are informal. The reason is that such low incomes are typically extremely variable and so there is a more intense need than in the case of better-off people for financial management. Poor people have a greater demand for financial intermediation. Less surprising, perhaps, are the facts that poor people face much greater risks including the risk of theft or unanticipated life emergencies, and that their typical transaction is very small indeed…
The typical researchers’ focus on balance sheets will show that the kind of people who kept diaries for this research will see little change in their (tiny) level of assets from one year end to the next, but there will have been, relatively speaking, huge flows of cash in and out in between. Another insight is that a year is far too long a time horizon for financial decision making – and this includes assessing loan rates using APRs. Most of the time poor borrowers will want a loan for a very short period and may regard the (high) interest rate as a fee for a service.”
Thus usurious interest rates may not be so high in levels if paid back quickly. In other words, the high interest rate translate into a small interest payment for short-term loans, and thus the interest may just cover lenders’ high transaction cost of making such a short term loan.
Tags: Financial Services, interest rates, Poor, Portfolios of the Poor, usury