Tag Archives: health

screening tests and invasive medical procedures

I recently read an article on Forbes about a forthcoming JAMA article about screening test uncertainty and invasive medical procedures [Link]. In the study [Study Link], the researchers gave 727 men hypothetical PSA scores for screening for prostate cancer. The control group was not given a PSA result. Those who not in the control group were given one of three outcomes, leading to four groups.

  1. Normal PSA test
  2. No PSA test (control group) – no uncertainty
  3. Inconclusive PSA test – high uncertainty
  4. Elevated PSA test

Men in each group were asked if they would pursue a biopsy, an invasive and expensive medical procedure compared to the PSA test. Groups 2 and 3 are similar in that they don’t have conclusive of cancer. However, group 3 has more uncertainty.

Probability of patients who opted for a biopsy:

  1. Normal PSA test – 13%
  2. No PSA test (control group) – 25%
  3. Inconclusive PSA test – 40%
  4. Elevated PSA test – 62%

The issue here is that an inconclusive test gives the same information as doing no test at all, yet those with an inconclusive test want to get a biopsy at a higher rate. In the study, the biopsies are requested by the patients, but in real life, doctors often turn inconclusive tests into expensive and invasive medical procedures.

I had a few other reactions to the issues associated with screening for disease.

With limited resources, inexpensive screening in theory helps to keep health care costs down. A cheap test is supposed to be used to weed out some of the population that does not need more invasive screening test. If disease cannot be ruled out, it may make some sense to retest. Of course, the issue here is that biopsies are chosen at different rates for different inconclusive patients. The results of this study suggests that screening for disease starts a process that is hard to turn off – screening could result in more men being biopsied instead of fewer. This isn’t just an issue with PSA tests.

I also found it interesting that 13% of the population apparently refused to be weeded out.

Humans do not do a good job of figuring out how to effectively use resources to screen for and manage disease. And as we see in this study, humans do not always make better decisions with better information. This is why we need good models of disease and its treatment. With those models in place, we can explore how to effectively target limited healthcare resources at the patients who most need them.

This is a growing area for operations research – identifying how to make good decisions at the patient level for understanding when to do more testing and when to take a wait-and-see approach. For slow-growing cancers such as prostate cancer, a wait-and-see approach may be a good one most of the time (disclaimer: for what it’s worth, I’m not a medical doctor). Waiting and retesting was not an option considered in the study, and maybe the results would be different. A wait-and-see approach is often used for other types of cancers, such as for pre-cancerous legions that could lead to cervical cancer.

For more reading about screening policies, inaccurate screening tests, and unnecessary treatment, read this NY Times article about breast cancer: [Link] Breast cancer can be quite aggressive, especially when it affects younger people, and it is common. I’ll admit that delaying or avoiding mammography is hard to fathom. The article highlights how aggressive mammography policies have led to the discovery of more cancerous and pre-cancerous legions (and thus cancer survivors) but it has not led to higher cancer survival rates. Other issues such as self-exams are also discussed.

I’m looking forward to learning about the latest research in this area at the INFORMS Healthcare Conference in Chicago this summer [Link]. I hope to see many of you there.

 


The numbers behind health care – for a lay audience

In October, This American Life produced two interesting episodes about health interventions and evidence-based medicine that I am just listening to now. These This American Life shows seem fresh and interesting, largely because they try to discuss health insurance/care issues from a quantitative, evidence-based perspective, even though cite few numbers in the episodes.  To illustrate this point, part of the first episode is even titled “Every CAT scan has nine lives,” referring to the side effects of over-using advanced medical techniques such as CAT scans.

The More is Less episode is particularly interesting for OR folks.  It starts with a twenty-minute discussion of some of the cost and effectiveness problems with health care.  The episode steps through one of the problems that grappled the medical community from the 1970s: geographic disparities in hysterectomies and other medical procedures in Maine and Vermont.  This baffled the doctors, since the disparities in hysterectomies across the state could not be explained by demographics, age, religion, or other factors, as was initially suspected.  One of the doctors who performed some of the analysis (Wennberg) concluded that 70% of women would receive hysterectomies in some communities. Maine doctors concluded that disparities were in part based on the doctors choosing to over-perform certain surgeries rather than the patients asking for procedures.

The episode continues to discuss why performing more medical procedures leads to more side effects and potential destruction in some cases, including the PSA tests for prostate cancer, thus exploring the tradeoff between cost and effectiveness.  These issues have been in the news quite a bit lately, particularly with the chance in mammogram screening recommendations.  These discussions in the news have included too much pandering and too little math and analysis, for the most part.   I’ve struggled to find good resources for a lay audience that address the numbers, so this episode was much appreciated.  I’ll leave the rest of the episode a mystery, so you can listen to it yourself.  The second episode (Someone Else’s Money) is not nearly as good, but examines health insurance in more detail.

Podcast Links:  More is Less and Someone Else’s Money.

Link: The Numbers Guy (WSJ) on Mammogram Math is also an interesting read.

Have you found any good references on lay explanations of the numbers behind health care and health insurance?


one more link

Yesterday, I posted a list of links that I shared with my class this semester.  I left one off.  I have quite a few pre-med students, so I like to talk about medical applications when necessary.  I shared a brief history of evidence-based medicine with my students, as conveniently summarized in a Business Week article.  The article is about David Eddy, the medical doctor turned operations researcher who transformed how medicine is practiced by using more math.  The article begs for more OR to be applied to health care applications:

The human brain, Eddy explains, needs help to make sense of patients who have combinations of diseases, and of the complex probabilities involved in each.

The article describes many of the challenges in the medical domain as well as some of the benefits of using advanced analytical methods for approaching medicine.

[Eddy's] PhD thesis made front-page news in 1980 by overturning the guidelines of the time. It showed that annual chest X-rays and yearly Pap smears for women at low risk of cervical cancer were a waste of resources, and it won the most prestigious award in the field of operations research, the Frederick W. Lanchester prize. Based on his results, the American Cancer Society changed its guidelines.

The recent changes in how we screen for prostate cancer and breast cancer are part of Eddy’s legacy.  They are controversial, but few medical treatments have been proven in clinical trials (the article estimates this could be as low as 20%).

[Link]


OR and H1N1

This is the second of three posts about the INFORMS Annual Meeting.

I enjoyed a talk by Dr. Richard Larson of MIT about the timely topic of H1N1 and operations research.  I tuned out much of the alarmist news prior to the conference (to keep my sanity) and instead adopted a rigorous handwashing regimen.  Larson’s talk highlighted the many opportunities for addressing H1N1 issues using operations research, including:

  • Queuing for vaccinations.
  • Reneging on vaccinations (some health care workers are refusing required vaccinations).
  • Timing the vaccinations (before the prevalence peaks) is important for reducing risks, since youths are particularly susceptible to dying from H1N1..
  • Locating facilities to manage surge capacity when the epidemic hits.
  • Correctly diagnosing and isolating cases of H1N1.
  • Supply chains for vaccinations.

Larson and his collaborator Dr. Stan Finkelstein takes a different kind of focus, looking at personal choices, such as hand washing, coughing into sleeves, avoiding handshakes, and avoiding crowds.  They examine this issue through non-pharmaceutical interventions.  Someone infected with H1N1 infects about 1.5 people in the next 24 hours (on average).  This value is the mean of a random variable, which depends on personal choices (like handwashing).  He examines the conditions under which the average number of infections decreases below 1.0, when the virus essentially dies out (Similar to my reasoning on vampire populations).

Finkelstein, a medical doctor, discussed some of the policy results.  Initial reports suggested that H1N1 has a fatality rate of about 50% (Spanish flu has a FR of 3%).  After an initial panic, flu fatigue set in.  And the first wave of H1N1 resemble seasonal rather than pandemic flu.  But after the recent panicking, many of us simply have not been motivated to improve our personal choices to reduce H1N1 transmission.  Case in point, elbow bumping pictured below (instead of hand shaking) did not catch on at the conference as I had hoped. And the anti-bacterial hand gel was not located in useful places at the conference, so I used my own personal stash of anti-bacterial lotion after shaking hands.

I hope some of this research is used to lessen the impact of H1N1 this year before I am transformed into a germ-a-phobe.

Link:  Flu101@MIT

Karima Nigmatulina, after successfully defending the first PhD thesis on our flu research project, bumps congratulatory elbows with advisor Richard Larson as Anna Teytelman looks on. 	 	 CESF Venn CESF embraces problems operating at the Venn diagram intersection of ‘traditional engineering,’ management (broadly interpreted) and social science.

Karima Nigmatulina, after successfully defending the first PhD thesis on our flu research project, bumps congratulatory elbows with advisor Richard Larson.


OR *can* reduce health disparities

After attending the second day of the Virginia Health Equity Conference, I have been convinced that public health is a function of much, much more than just medical treatment.  Paula Braveman provided some figures in her keynote that indicate that the mortality and incidence rates of many infectious diseases drastically reduced before effective medical interventions were introduced.  I had heard this before, but a picture is worth a 1000 words.  I tracked down a few of the images online (see below).   Public health initiatives such as housing, ventilation, improved sewage systems, and education improved health outcomes much more than medical interventions that were later introduced.  This reflects the fact that there are many good proxies for public health that reflect infrastructure, networks, and transportation, all of which are things that we like to evaluate in operations research models.

It strikes me that any operations research models that reflect public health would fit in well with community-based OR initiative.  Those who do research in this area might be interested in a new Springer volume.  The call for papers is pasted below (I could not find a link).  Note that I am not affiliated with this effort.

Proposals for manuscripts on the topic of Community-Based Operations Research are being solicited for an edited volume to be published in the Springer International Series in Operations Research and Management Science, as part of the Advancing the State-of-the-Art handbook series.

Community-based operations research (CBOR) is defined as the collection of analytical methods applied to problem domains in which interests of underrepresented, underserved, or vulnerable populations in localized jurisdictions, formal or informal, receive special emphasis, and for which solutions to problems of core concern for daily living must be identified and implemented so as to jointly optimize economic efficiency, social equity, and administrative burdens. This domain was first discussed in a chapter in Tutorials in Operations Research 2007 – OR Tools and Applications: Glimpses of Future Technologies (INFORMS 2007) by Johnson and Smilowitz and subsequently in an article that appeared in the February 2008 issue of OR/MS Today.

As community-oriented operations research, as defined here, is central to the mission of the Section on OR/MS Applied to Public Programs, Service and Needs, submissions from SPPSN members will be especially welcome.

Chapters in this volume can describe current results for a specific research problem, a literature review, or a discussion of the nature of CBOR within the operations research/management science discipline.

Proposals for submissions to this volume should be not more than one page in length, describe the nature of the submission, and clarify if the submission is likely to be based on current or on-going research, a new research project, or synthesize previous findings. Submissions will be peer-reviewed. The deadline for submission proposals is October 16, 2009. The deadline for chapter submissions is February 1, 2010; final drafts of chapter submissions will be sent to the publisher on August 1, 2010.

Manuscript proposals, as well as inquiries regarding further details, should be sent to:  Michael P. Johnson, Ph.D, Department of Public Policy and Public Affairs, University of Massachusetts Boston, Boston, MA 02125-3393

Link to yesterday’s post about the conference.

Mortality rates from infectious disease in the US

Mortality rates from infectious disease in the USMortality rate of whooping cough

Mortality rate of measles

Mortality rate of measles

Mortality rate of tuberculosis
Mortality rate of tuberculosis
Mortality caused by scarlet fever in England and Wales (re-drawn from T Mc Keown, 1976)

Mortality caused by scarlet fever in England and Wales (re-drawn from T Mc Keown, 1976)


can OR reduce health disparities?

I am enjoying the Virginia Health Equity Conference.  It’s hard not to believe that operations research can be used to improve public health.

I particularly enjoyed Dr. Howard Frumkin’s keynote about public health and the built environment. His talk was particularly inspiring for operations researcher. We spend almost all of our time in built environments (home, school, work, transportation, parks), and there is an enormous body of research that suggests that our environment is a strong predictor of our health. Our built environment is the result of a series of decisions, some of which can be the result of good operations research and mathematical modeling. A good built environment offers a wide portfolio of public health benefits. Dr. Frumkin listed several opportunities for operations research modeling to improve our built environment (and in turn, public health), including:
• Investigating where to locate schools (perhaps using decision analysis),
• Improving school bus routes that are also safe (Safe Routes), including walking school bus routes (using network design),
• Improving and analyzing transportation networks in urban sprawl regions, which are plagued with low connectivity and low population density (this leads to a host of problems such as reliance on vehicles, low rates of physical exercise, poor access to emergency medical vehicles, etc.).
• Locating trees and green space in urban areas to “cover” poor neighborhoods.

Dr. Frumkin stressed that many of these decisions are generally not made by public health people (perhaps some can be made by operations researchers).


more to come on health care

I apologize for being offline for the past month. Between illnesses, weddings, and the start of the school year, I cut back on blogging and twitter. I finally feel like I’m back into the swing of things.

I am looking forward to the Virginia Health Equity Conference later this week. Although the conference will mainly be attended by medicine and public health people, I hope to blog about health care during the conference. So keep an eye on the blog.  You can follow the conference twitter feed here.

I have been closely following the national debate surrounding health care and health insurance. Although it seems like the issue is mainly a macro-economics issue, I really do think that OR has a lot to offer, particularly in predicting the costs and benefits of treatment and care. But I see so much misinformation (sometimes well-meaning people with the wrong information, and sometimes more sinister ulterior motives) that I easily get discouraged about having a rational, quantitative discussion using OR methodologies.

Last week, I tried discussing some of the health care issues in my class (probability and statistics for engineers). After introducing Bayes rule, I illustrated why it implies that preventive medicine costs so much, in general. I think the discussion backfired. David Brooks explains it better [podcast link here]–when I try, I come off sounding like a an insensitive monster that wants to deny people health care. Really, I just want good numbers to support the debate so we can make good decisions. I’m not sure what the answer is, because I have no idea what the facts are yet. I hope OR is instrumental in providing the facts.


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