Perspectives On Medical Research
Volume 5, 1995
Aping Science
A Critical Analysis of Research at the Yerkes
Regional Primate Research Center
Public Health Research for the Future
A. The Importance of Understanding Disease Causation
Historically, medical researchers have sought to identify disease causes--those
environmental agents that change a person from a healthy state to one of disease.
Obviously, such information is vital in preventing disease and developing rational
treatment approaches. Nevertheless, over the past 20 years, medical researchers,
teachers, and practitioners have all revealed a reduced interest in causation.1 Rather
than look for specific causative agents, the tendency today is to identify
a multitude of "risk factors" associated with disease. For example,
at least 246 separate risk factors for coronary artery disease have been identified,
including cigarette smoking, elevated plasma cholesterol, obesity, type A personality,
Jewish religion, baldness, sense of exhaustion in college, slow beard growth,
and psychiatric disorders.2 Cardiovascular researchers are recognizing
the limits of this approach, because many "risk factors" do not cause
cardiovascular disease but, rather, represent mere associations,3 and
many "risk factors" are unavoidable and therefore not amenable to
medical or social intervention.
Another problem with contemporary research strategies is that scientists have focussed primarily on risk factors related to lifestyle, tending to overlook specific causative agents in the environment. For example, researchers have generally seen coronary artery disease as a consequence of elevated blood levels of certain fats and fat-protein compounds, usually a consequence of poor diet. Based on this theory, cardiologists have tried to remove, displace, or bypass plaques of presumed fatty deposits. However, clinicians have been frustrated by a persistent problem: dividing cells in the arterial wall that repeatedly narrow the arteries after interventions to widen them. For much of the 20th century, cardiovascular researchers had interpreted the smooth-muscle cell masses bulging into patients' arteries as inflammatory scar tissue. However, growing evidence has pointed to benign smooth muscle cell tumors as the main cause of renewed blockage of angioplastied arteries.4-6 It appears that human atherosclerotic plaques are composed primarily of benign tumors that may, secondarily, accumulate fatty deposits.
The group of scientists who had shown clearly by 1968 that chemicals
in cigarette smoke cause both lung tumors and atherosclerosis7 subsequently
showed that diagnostic X-rays also cause coronary artery disease,8 indicating
that coronary artery disease resulted from DNA damage. Indeed, when scientists
began to look more closely at the composition of human atherosclerotic
plaques lining the arterial wall, they found benign tumors comprised
of cells of monoclonal origin.4,5 Increasingly, researchers
have found that the chronic, degenerative diseases responsible for most
morbidity and mortality in Western nations result from environmental
mutagens (that damage DNA).9 Nevertheless, the careful retrospective
analyses needed to elucidate the sources of DNA damage from environmental
mutagens has rarely been funded by the NIH. The NIH primarily funds human,
animal, and cell experiments to determine the relative importance of
different "risk factors." However, the very nature of experimentation
involves inducing a condition on an artificial, experimental system.
While certain experiments can yield some kinds of information about anatomy,
physiology, and pathology, they cannot determine the underlying causes of
naturally occurring diseases through a process that merely simulates the
disease in humans or animals. Experimental conditions may resemble naturally
occurring human diseases, but invariably they exhibit fundamental biological
differences in etiology, clinical presentation, and natural history.
In contrast, human population studies are devoid of artifact, because
they study actual human disease in human beings. Researchers can identify
disease causation by studying disease incidence in populations at high
and low risk for suspected disease-causing agents. While such research
is often difficult and time-consuming, it is the only way to reliably
determine human disease causation.
B. Problems With Current NIH Research Resources
Former lipid researcher Edward Ahrens has decried the "crisis in clinical
research" --the shifting of funding priorities from learning about human
diseases by direct observation of human patients to experiments on animals,
cells, and human volunteer subjects.10 Only 7.4% of new NIH grants
in 1987 were directed to patient-oriented research; a survey of 1990-1991 grants
revealed that only 4.5% were basic research involving human subjects.11 Many
of these grants were experimental studies, indicating that the federal investment
in direct human clinical observational study is minimal.
C. Prototypes of Scientific Research Resources for the Future
1. A National Numbering System
In order to advance public health effectively, Americans must recognize that
we are all subjects of ongoing, unplanned "experiments." These "experiments" reveal
the real human health implications of contemporary life, with all its sociological,
psychological, toxicological, and physiological stressors. For example, careful
population studies can determine the dangers of radiation and other mutagens
(that initiate the genetic damage responsible for tumor formation and birth
defects). Over the past few decades, Americans have been exposed to diagnostic
X-rays and to a wide range of chemical pollutants that damage cells' genetic
material. Identifying who gets cancer, birth defects, and other manifestations
of genetic damage, and then analyzing cellular and molecular characteristics
of those individuals is the best (perhaps the only) way to determine how these
mutagens affect humans.12 The question for Americans is not whether
they are experimental subjects, but whether they wish to know the results of
the unplanned experiments in which they participate.
Recognizing this, Sweden has initiated a massive public health research
project. The Swedish government assigns each newborn infant a tracking
number for recording all medically relevant life events of that person
and his/her parents. For example, in studies to investigate hazards to
the developing fetus, researchers can access information about the parents’ diseases,
occupations, and exposures to mutagens (such as cigarette smoke). The
primary concern of this numbering system is its potential invasion of
privacy. American health officials would, therefore, need to find a balance
between individual rights to privacy and improved public health. Without
such a tracking system, American public health research will remain incomplete,
often uninterpretable, and consequently frequently erroneous.13
2. Deep Mathematical Models
A team of biostatisticians at Roswell Park Memorial Institute for Cancer Research
(RPMI) pioneered new ways of modelling human diseases in the 1960s and 1970s.
Irwin Bross, Leslie Blumenson, and colleagues developed the first "deep
mathematical models" of human disease,14 which describe the
complex chain of events that occur in the human body, from initiating molecular
events to full-blown diseases. Unlike laboratory-based models, deep models
utilize data from human patients during life and at autopsy. For example, Bross
and Blumenson's deep mathematical model of breast cancer used data from two
large-scale human breast cancer chemotherapy trials involving 2,000 women at
RPMI.14 In attempting to explain the data in terms of the growth
of a clone of tumor cells in the breast and their eventual spread through the
body, Bross and Blumenson identified at least two distinct forms of breast
cancer. One is a fast-growing, fast-spreading variety that tends to occur in
women under 50; the other is a relatively slow-growing tumor of older women.15,16 While
these two tumor types have different clinical features, they look identical
microscopically. Treatments and screening methods that construe all breast
tumors as identical cannot possibly lower mortality significantly, because
many tumors will be treated inappropriately. Only by tailoring treatments to
the specific subtypes of breast cancer can substantial progress be made. Bross
and Blumenson’s model, therefore, had profound implications for breast
cancer treatment and prevention strategies.
3. Medical Technology Assessment Research: Number-Crunching Versus "Metatechnology"
Health services research includes assessing cost effectiveness of medical technologies
and healthcare delivery systems. Many argue that the efficacy of all medical
services should be assessed through randomized controlled clinical trials (RCTs).
Such trials have been useful in determining whether surgical interventions
are more effective than medical therapy in lowering mortality from chronic
diseases. However, RCTs have not resolved controversies about services for
which risk/benefit considerations are complex. Screening mammography for women
under age 50 is a case in point. In the 1970s, widespread mammography screening
of asymptomatic women under age 50 was sponsored by the NCI based on RCTs showing
mortality reduction in women over 50. While RCTs have consistently failed to
show any benefit (and some have shown danger) from screening mammography for
women under age 50,17,18 the NCI still encourages routine
mammograms in these women.
The entire Breast Cancer Detection Demonstration Project of the NCI had been based on an assumption that the number of breast cancers detected by mammography and thereby cured far outnumbered those induced by X-ray.19 This may well have been unfounded. Bross and Blumenson20 undertook a thorough analysis of the screening program. Interlinking a mathematical model of the screening process with their "deep mathematical model" of breast cancer, Blumenson and Bross20 estimated that the actual benefit/risk ratio was at best 1/1: (1 cured breast cancer for each induced by the mammography), and possibly much worse. Mammography screening for women under 50 continues to have its advocates, despite the prediction of the deep model of breast cancer that mammography screening could not lower mortality from breast cancer in women under age 50.
Tumors in young women simply grow and spread too quickly to be detected
early enough to affect cure. By the time a young woman's tumor is identified
on X-ray film, it has very likely already spread to a distant organ (metastasized),
and prognosis at this point is poor. In terms of providing direction
for future research, the deep model showed that an effective diagnostic
tool for younger women would need to detect breast tumors at a microscopic
stage, and that it should not utilize carcinogenic ionizing radiation.
Bross refers to deep mathematical models that evaluate risks and benefits
of medical technology as "metatechnology."21
4. Medical Informatics
The overlapping concerns about the shortcomings of healthcare and health research
have spurred development of a new discipline called "medical informatics," which
utilizes sophisticated computer analyses to enhance the value of patient-derived
data.22 For instance, most patient records are written and filed
manually. Consequently, any scientist interested in assessing the quality of
patient care or exploring a medical hypothesis must sort through reams of hand-written
patient records. Computer-based, multimedia records that include free text,
high-resolution images, and sounds (e.g., auscultation) could significantly
expedite what is now a daunting task. While no fully developed computerized
information systems are currently operative, there are a few prototypes.23
Such a system would greatly facilitate important clinical research projects,
as illustrated by the work of John Spratt and co-workers to optimize
breast cancer treatment.24 They found that survival was greater
when the surgery was performed between days 7-20 of the menstrual cycle.
A more accessible database would greatly assist further studies following
up theft research into how the timing of breast cancer surgery affects
prognosis.
5. Specialized Clinical and Autopsy Data Banks
Currently, the primary data collected by the National Center for Health Statistics
(NCHS) consists of mortality rates for the major diseases and injuries. In
order to discharge its Congressional mandate, however, the NCHS should be collecting
and analyzing a wide variety of clinical and autopsy data. Currently, the NCHS
obtains cause of death data from physicians’ death certificates, and
the accuracy of such certificates is questionable because physicians base their
diagnoses on clinical findings, not on autopsy findings.25 In order
to increase the accuracy of national mortality data, several clinical investigators
have advocated that current cause-of-death certificates be based on autopsy
diagnoses which are computerized and collected in a centralized database at
the NCHS.26
In 1977, the Technical Consultant Panel of the United States National
Committee on Vital and Health Statistics issued recommendations for broadening
the data-collection activities of the NCHS.27 Those suggestions,
never followed up, mostly concerned the need for more careful monitoring
of environmental exposures. The World Health Organization has also emphasized
the importance of public health research on pollution hazards, requiring
computerized data banks.28 Such a system benefitted Rosalie
Bertell, who accessed voluminous clinical data to investigate the capacity
of low-level radiation from X-rays to accelerate the aging process.29,30 Based
on her findings, Bertell has contended that the minimum human database
necessary to effectively audit the health of a population includes: 1)
decreasing fertility rate, 2) increasing infant death and birth defect
incidence rate, 3) increasing rates of severe asthmatic or allergic reactions,
4) declining scholastic ability of children, 5) decreasing avenge age
of diagnosis of chronic, life-threatening diseases such as hypertension,
diabetes, and cancer.31 Such use of computerized clinical
and autopsy data, which can be modelled on the Swedish numbering system,
is critical for gathering the public health information needed to identify
causes and means of prevention of disease in America.
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