I am Dr. Shmuel Prints, an Internal Medicine specialist for over 30 years. However, my route in medicine was a bit unusual. It included 4 years of public health studies, followed by a period where I took advantage of the knowledge acquired in statistics and epidemiology for research purposes.
Therefore, I could not ignore the epidemiological aspect of rare diseases, which is not usually the focus of physicians and researchers. I believe that some problems that are concerning the medical world regarding the rare diseases field, can get unexpected solutions from epidemiology. In the next series of blogs I will explain my opinion.
Rare Diseases definition:
Rare diseases, also known as orphan diseases, are defined by most authorities based on their low frequency in the population. Its threshold varies from country to country, yet the principle stays the same.
When we estimate disease frequency in epidemiology, we use two measures: incidence & prevalence. Since incidence relates only to new cases in the population in a certain period, it does not reflect the burden of chronical illnesses, including a majority of rare diseases.
As opposed to that, prevalence relates to the proportion of the people in the population who have a disease during a certain time interval (periodic prevalence). It relates to both new cases of the diseases and to patients that were diagnosed prior to that period.
It is the basis used to predict and plan the resources required for diagnosing and treating these patients. On top of that, prevalence also affects economic considerations amongst pharmaceutical manufacturers about developing new orphan drugs.
Point prevalence is the proportion of the population ill with a certain disease at a 'point' in time. Therefore, it includes all previous cases who still have the medical condition and are still members of the population in the moment. This measure is the most important for physicians, because it gives the probability that the patient has a certain disease in a point in time.
Prevalence is the criteria that is mostly used to define orphan diseases in most health systems. To explain more about how the prevalence is calculated, I will use an example from Principles of Epidemiology in Public Health Practice, third Edition:
New Cases of an Illness from October 1, 2004–September 30, 2005
According to the graph, there are 10 new cases of illness over about 15 months in a population of 20 people. Each horizontal line represents one person. The down arrow indicates the date of onset of illness. The solid line represents the duration of illness. The up arrow and the cross represent the date of recovery and date of death, respectively.
- Calculating the period prevalence
The numerator of period prevalence includes anyone who was ill any time during the period. In this graph, the first 10 patients were all ill at some time during the period.
Period prevalence = (10 ⁄ 20) × 100 = 50.0%
This rate reflects the burden of the disease in the population, and according to this data, policies of health systems are made. However, from the physician's point of view, the point prevalence is even more interesting.
- Calculating the point prevalence
Point prevalence is the number of patients ill on the date divided by the population on that date. On April 1, seven patients (patient number 1, 4, 5, 7, 9, and 10) were ill, and 2 passed away.
Point prevalence = (7 ⁄ 18) × 100 = 38.89%
Apart from evaluating the prevalence of the disease, point prevalence also estimates the probability that a patient might have this disease in that certain time.
So, when we are talking about the disease's prevalence, we estimate the relation between numerator and the denominator. In this kind of fracture:
All new and pre-existing cases during a given period / Population during the same time × 10 n*
* The value of 10 n is usually 10,000, 100,000, or even 1,000,000 for rare attributes.
To be considered in the nominator, the patient needs to be documented as diagnosed with a certain illness. In the case of common illnesses, that is a short-term process. For example, to determine that a patient has diabetes, it is enough to find twice that a patient's blood level is above 125 mg/dl in a blood test taken after an overnight fast. As opposed to that, diagnosing rare disease patients can take several years. Some disorders are so rare that the patient stays undiagnosed all their lives. Shire survey in 2013 found that the average time required to recognize orphan illnesses is 4.8 years! During all that period, named the "diagnostic odyssey", the patient is misdiagnosed and therefore is not considered in the prevalence calculation.
In conclusion, when calculating their real prevalence, some rare diseases are actually not so rare. The difference between rare diseases and illnesses that are more common but difficult-to-solve, is not conclusive. From the physician's point of view these are all medical mysteries that require an unusual effort to diagnose them. For that, I have developed NDC Medicine online platform- an innovative diagnostic tool for physicians that will aid them to end the diagnostic odyssey of their patients quickly and accurately using smarts.
- Next blog: Why an accelerated diagnostic process is the key to solving many problems rare diseases patients face