Overtreated, excess care

"Overtreated", "Overdiagnosed", and "Overdosed",... these are the terms I have used in one of seminars several years ago. By comparing the health care system between the United States and the China, you could easily think of these terms, especially when I heard the new medical conditions "ADHD - Attention-Deficit Hyperactivity Disorder", "M-IBS - Mixed Irritable Bowel Syndrome", "Chronic Fatigure Syndrome (CFS)", "fibromyhalia"; when I saw the images how many pills a patient took regularly.

Driven by the NPR interview with Shannon Brownlee (Are Today's Hospital Patients "Overtreated"?), I went to the local library to borrow her book "Overtreated: why to much medicine is making us sicker and poorer". I enjoyed very much in reading this book.

I intended to write a blog about this book, then found that many people had already expressed their opinion about this book. See Book Reviewer's comments from Amazon

Even though this book was written two years ago (in 2007), the arguments, the facts, the reasoning described in this book is very much relevant to the situation today (when the debate on the health care reform heats up). Below is a list of chapters:

• One: Too Much Medicine
• Two: The Most Dangerous Place
• Three: Your Local Hospital
• Four: Broken Hearts
• Five: The Desperate Cure
• Six: The Limits of Seeing
• Seven: The Persuaders
• Eight: Money, Drugs, and Lies (my favorite chapter)
  
• Nine: The Doctor Isn't In
• Ten: Less is More

Instead of going to detail, I would just cite some sentences from the book:

• "Doctors have a saying: Never get admitted to a teaching hospital in July, because that's when all the new interns arrive fresh from medical schools."
• "As research would show over the coming decades, stunningly little of what physicians do has ever been examined scientifically, and when many treatments and procedures have been put to the test, they have turned out to cause more harm than good."
• "Every patient admitted to a hospital risks being hurt or even killed by the very people who wish to help her."
• "Even as the number of [medical] imaging tests [X-ray, CT, MRI] is going up, numerous studies suggest that all those pictures are not nearly as effective at improving diagnosis as many doctors--and patients--tend to think."
• "The drug company representative, or drug rep, usually [is] a handsome young man or shapely young woman who has been recruited more for his or her good looks and outgoing personality than for his or her aptitude for science or medicine."
• "Among drug reps the unofficial name for thought leaders who work for multiple companies is 'drug whores'"
• "The more specialists involved in your health, the more likely it is that you will suffer from a medical error, that you will be given care you don't need and be harmed by it."
• "The Institute of Medicine estimates that only 4 percent of treatments and tests are backed up by strong scientific evidence; more than half have very weak evidence or none."
• "In the view of Richard Horton, a British physician and editor of the prestigious medical journal the Lancet, 'Journals have devolved into information-laundering operations for the pharmaceutical industry'"
• Says John abramson "The primary mission of medical research has been transformed. It used to be all about gathering information to improve health. Now clinical research is aimed at gathering information that will maximize return on investment"
  

Further readings:

• Are Americans Seeing Their Doctors Too Often? by Shannon Brownlee
• Overtreated blog by Shannon Brownlee
• Getting Well: It's About Time by Melinda Beck "Americans spend billions of dollars a year to treat things that would go away on their own."
• HARLOT plc: an amalgamation of the World's Two Oldest Professions by David Sackett and Andrew Oxman. BMJ
• Overdosed America: The Broken Promise of American Medicine by John Abramson

Reporting pregnancies during clinical trials

Unless a clinical trial is designed for the pregnancy women, the typical clinical trial will exclude the females with pregnancy and lactating. In either the inclusion or exclusion criteria, there will be one criterion related to the exclusion of female subjects with pregnancy. The wording for inclusion or exclusion criteria varies. Here are some examples:

Inclusion criteria:

"Women of childbearing age must have a negative pregnancy test and must useadequate contraception during the treatment phase of the study and for 9months afterwards. Women who wish to breast feed are not eligible for thestudy"

"Females must be of non-childbearing potential. Women of non-childbearing potential are defined as those who have no uterus, ligation of the fallopian tubes, or permanent cessation of ovarian function due to ovarian failure or surgical removal of the ovaries. Documentation of surgical procedure or physical examination is required for subjects who have had a hysterectomy or tubal ligation. In the absence of such documentation, a urine pregnancy test is required for inclusion into the study. A woman is also presumed to be infertile due to natural causes if she has been amenorrheic for greater than 12 months and has an FSH greater than 40 IU/L"

Exclusion criteria:
"Pregnant or nursing (lactating) women, where pregnancy is defined as the state of a female after conception and until the termination of gestation, confirmed by a positive hCG laboratory test (>= 5 mIU/mL) "

"Pregnant or breast-feeding patients. Women of childbearing potential must have
a negative pregnancy test performed within seven days prior to the start of study
drug. Both men and women enrolled in this trial must use adequate birth control"

The females that are childbearing potential are typically allowed to be enrolled in the clinical trials as long as they are willing to practice a highly effective method of contraception (oral, injectable or implanted hormonal methods of contraception, placement of an intrauterine device [IUD] or intrauterine system [IUS] condom or occlusive cap with spermicidal foam/gel/film/cream/suppository, male sterilization, or true abstinence) throughout the study.

However, it is not uncommon to have female subjects who become pregnant during a clinical investigation. In such instances, should the pregnancy be reported as AE or SAE?

The answer depends on the outcome of the pregnancy (either on mother side or on fetus side).

Pregnancy occurring during a patient’s participation in a clinical trial, although not
typically considered an SAE, must be notified to the sponsor within the same timelines as an
SAE (within one working day) on a Pregnancy Monitoring Form. The
outcome of a pregnancy should be followed up carefully and any abnormal outcome
of the mother or the child should be reported. This also applies to pregnancies
following the administration of the investigational product to the father prior to
sexual intercourse.

Based on the outcome and the timing of the delivery, the pregnancies during the clinical trial can be categoried into the followings:
Female is study participant and becomes pregnant during study participation:

1. Normal outcome before end of study
2. Abnormal outcome before end of study
3. Normal outcome after end of study
4. Abnormal outcome after end of study

Female is partner of study participation and becomes pregnant during study:

5. Normal outcome before or after end of study

6. Abnormal outcome before or after end of study

In all of these situations, the Pregnancy Monitoring Form should always be filled out. However, only for situation #2, a SAE needs to be reported.

Since the typical clinical trials do not include the pregnancy women, the potential impact of the drug on pregnancy women is not obtained during the pre-market studies. A lot of drug labels contain a statement in the contradiction section about the pregancy women. Drug exposure could also have impact on fetus - a term called 'Teratogenicity'. Teratogenicity refers to the capability of a drug to cause fetal abnormalities when administered to the pregnant mother. One of the best-known examples of such a drug- induced birth defect is the Thalidomide disaster. The drug was prescribed on a wide scale to pregnant mothers to ease the anxiety associated with it. The large-scale consumption of the drug resulted in children born with seal like limbs, often referred to as phocomelia. The drug was banned for prescription in 1961.

The potential impact of the drug on pregnancy could be obtained from observational studies - pregnancy registries. Refer to FDA's website about "General Information about Pregnancy Exposure Registries".

In ICH E2D "Post-Approval Safety Data Management: Definitions and Standards for Expedited Reporting", the following paragraph is stated:

"5.4.1 Pregnancy Exposure
MAHs (market authorization holders) are expected to follow up all pregnancy reports from healthcare professionals or consumers where the embryo/foetus could have been exposed to one of its medicinal products. When an active substance, or one of its metabolites, has a long half-life, this should be taken into account when considering whether a foetus could have been exposed (e.g., if medicinal products taken before the gestational period should be considered). "

A sample pregnancy registry form can be found from GSK website.

Conficence Interval vs. Credible Interval

I recently participated in a project to compare two different ways to do the meta analysis: the traditional way to pool the database directly (sort of the integrated analysis) and the Bayesian approach (prior distribution + likelihood function -> posterior distribution). When we try to compare the results from two different approaches, we run into the issue of comparing 'confidence interval' and 'credible interval'. While these two terms have some similarities, the interpretations are quite different.

The "confidence interval" is a term used by frequentist - I am a frequentist. If we say an estimate has its 90% confidence interval of 35-45, it means that with a large number of repeated samples, 90% of times, the true value of the parameter will fall within the range of 35-45.

The term 'credible interval' is used by Bayesian statisticians and it may also be called 'Bayesian Posterior Interval'. In Bayesian statistics, a credible interval is a posterior probability interval, used for purposes similar to those of confidence intervals in frequentist statistics. Bayesian inference is statistical inference in which probabilities are interpreted not as frequencies or proportions or the like, but rather as degrees of belief. ...

The posterior probability can be calculated by Bayes theorem from the prior probability and the likelihood function. ... In statistics, a confidence interval (CI) is an interval between two numbers, where there is a certain specified level of confidence that a population parameter lies. ... Statistical regularity has motivated the development of the relative frequency concept of probability. ...

For example, a statement such as "following the experiment, a 95% credible interval for the parameter t is 35-45" means that the posterior probability that t lies in the interval from 35 to 45 is 0.9.

A Bayesian credible interval incorporates information from the prior distribution into the estimate, while confidence intervals are based solely on the data.

Like 'confidence interval' vs 'credible interval, there is also 'confidence region' vs 'credible region'.

Here are some links for further reading:

• Credible interval at wikepedia
• Bland and Altman (1998) Bayesians and frequentists. BMJ
• Fisher (1996) Comments on Bayesian and Frequentist Analysis and Interpretation of Clinical Trials. Controlled Clinical Trials
• Credible Interval
• Credible Intervals vs. Confidence Intervals

Placebo and Sham treatment: are they really inactive?

According the ICH guidance E10 (CHOICE OF CONTROL GROUP AND RELATED ISSUES IN CLINICAL TRIALS), "A placebo is a "dummy" treatment that appears as identical as possible to the test treatment with respect to physical characteristics such as color, weight, taste and smell, but that does not contain the test drug."

We typically use the term Placebo, but sometime, the term 'sham treatment' is used. The word 'sham' means something that is a fake or an imitation that purports to be genuine.

In practice, a placebo is often defined as an inactive substance made to appear like a medication or a sham procedure or device imitating a known treatment. Sometimes, the 'inactive' substance used in the clinical trial may not be totally 'inactive'. One example is albumin. On the one hand, the albumin may be treated as inactive substance for Placebo; on the other hand, there are studies to study the effect of albumin in certain diseases. In a study about "the Effectiveness of Intravenous Immune Globulin (10%) for the Treatment of Multifocal Motor Neuropathy", 0.25% human albumin solution was used as Placebo. But there are also plenty of clinical trials to study the efficacy of albumin in sepsis, renal impairment, acute stroke,...

In a lot of publications, the author did not disclose what the placebo is. You can use the same term 'placebo', but the 'placebo' could be sugar pill, saline, albumin,...

Even more...

In article web article by BJ Appelgren titled "The Placebo as Medicine Viewed as Sham, Placebo Itself May Be the Most Significant", the following examples are cited as other type of placebos.

• Having an interaction with a health care provider
• The presence of something symbolic in the encounter such as contact with a person wearing a “white coat,” perceived as a provider of healing

The significance of symbols cannot be measured objectively and, for that reason, is not valued. Researchers also have an additional puzzle when non-treatment causes positive results. Too often, when the effect of symbolism is recognized by conventional medicine, it is removed from a context of positive meaning and denigrated as “being all in the mind,” as if that makes it illusory.