Friday, March 5, 2021

Interpreting Cancer Statistics


Cancer statistics are used for a variety of purposes. Researchers and cancer organizations (such as the National Cancer Institute) use statistics to track cancer trends. For example, if the incidence of any type of cancer is seen to be increasing across several years, researchers will want to discover why and what can be done about it. Scientists also use statistics to determine how well a particular prevention or treatment method may be working.

When it comes to the general public and cancer patients, the usefulness of cancer statistics depends on how they are interpreted and used. It has been widely reported that the lifetime risk of developing breast cancer is one in eight — a frightening thought for women who misinterpret that statistic to mean that at any time, they have a one in eight chance of having breast cancer. The actual chance of developing breast cancer changes throughout a woman’s life, so that a 20-year-old woman has a current risk of only one in 2,500 of developing the disease within the next 10 years, and a 50-year-old woman has a current risk of about one in 39. Heredity, ethnicity, reproductive history, lifestyle factors and other risk factors all contribute to an individual’s risk. So cancer statistics are useful when used for broad perspective, but not for an individual situation.

The following definitions can help you make sense of the sometimes confusing statistical terminology used when discussing cancer and its outcomes.

Incidence describes the number of new cases of cancer developed by a specific population group within a set period of time — usually one year. For example, the total 2005 incidence of testicular cancer was about 8,000 men. Incidence rate is the number of new cases in a population. The incidence usually is expressed in terms of the number of cases per 100,000 people. For example, the incidence rate for testicular cancer in the United States is approximately four new cases per 100,000 men, often stated simply as four per 100,000.

Prevalence is the total number of people with cancer or with a particular risk factor for cancer at a particular moment in time in the entire population. For large groups of people, prevalence is estimated by collecting information from a smaller subset of people and then extrapolating that information to the general population. For example, by collecting DNA information from breast cancer patients, scientists have estimated that the prevalence of the BRCA-1 gene in the total population is between 0.04% and 0.2%, meaning that much less than 1% of the total population has this breast cancer-susceptibility gene.

Morbidity is a state of illness. For instance, it often is said that smoking is a major cause of morbidity in the U.S.

Mortality means pertaining to death. Mortality rate is the number of people in a population group who die of cancer within a set period of time, usually one year. A cancer mortality rate usually is expressed in terms of deaths per 100,000 people. For example, the mortality rate for stomach cancer in the U.S. in 1930 was 28 (28 deaths per 100,000 people), but dropped to four by 1992, meaning that only four people out of every 100,000 in the U.S. died of stomach cancer in 1992.

Prognosis is the prediction or estimation of the course and outcome of the disease, usually including the chances for recovery. While physicians may base a prognosis on statistical precedents, each individual is different, with actual outcomes affected by many factors, including the patient’s age and general health, the type and stage of cancer and the effectiveness of the particular treatment used. Therefore, while a prognosis may be helpful for explaining the seriousness of a disorder or for guiding treatment decisions, it cannot be used to predict disease outcomes for an individual.

Survival rate is the measure of the number of people who develop cancer and survive over a period of time. Scientists commonly use 5-year survival as the standard statistical basis for defining when a cancer has been successfully treated.

The 5-year survival rate includes anyone who is living 5 years after a cancer diagnosis. This includes those who are cured, those in remission and those who still have cancer and are undergoing treatment. For example, when colorectal cancers are detected early, the 5-year survival rate is 92%, meaning that 92% of all colorectal cancer patients live at least 5 years after diagnosis if the cancer is detected early.

The overall 5-year survival rate measures everyone who has ever been diagnosed with a particular cancer equally, which may lead to distorted statistics. For example, a 90-year-old man and a 30-year-old man who have the same cancer will be grouped together. The 90-year-old may die of other causes within the 5-year period due to normal life expectancy, and this can skew the data. A more statistically accurate view of survival is the relative 5-year survival rate, which compares a cancer patients’ survival rate with the survival rate of the general population, taking into account differences in age, gender, race and other factors. In this case, the 30-year-old and the 90-year-old would be treated as statistically different.

Risk refers to the chance that an individual will contract a disease. High risk is when the chance of developing cancer is greater than the chance for the general population. For example, people who smoke have a high risk of developing lung cancer compared with people who don’t smoke.

Risk factor is anything that has been identified as increasing a person’s chance of getting a disease. These can be controllable or uncontrollable, personal or environmental. For example, risk factors for developing breast cancer include having a hereditary predisposition to the disease (uncontrollable) and taking estrogen-containing hormones for more than 10 years after menopause.

Relative risk is a measure of how much a particular risk factor increases the risk of development of a specific cancer. For example, the risk for developing ovarian cancer increases by 300% for a woman with a close family history of the disease compared to a woman without a family history. In this example, the relative risk of developing ovarian cancer is three for those with a family history, meaning they have three times the risk.

Attributable risk is a measure of how much of the total incidence of disease is caused by that risk factor. For example, even though the relative risk of developing breast cancer for a woman with the BRCA-1 gene is high, most cases of breast cancer are not caused by the BRCA-1 gene since the prevalance of the BRCA-1 gene is low.

Lifetime risk is the probability of developing or dying of cancer sometime during one’s lifetime. A person has a lifetime risk of two in five of developing cancer, meaning that for every five people in the population, two eventually will develop cancer. The lifetime risk of dying of cancer in one in five.

Medically trained in the UK. Writes on the subjects of injuries, healthcare and medicine. Contact me

Science-Based Reasons To Practice Gratitude

What if we aren’t doing the things science teaches us really can bring happiness? “The problem is not...

Lowering Bad Cholesterol for Good

Last month, I discussed atherosclerosis and the risk factors for coronary artery disease . Cholesterol is near the top of the...

51 Tips for Going Plant-Based

Learn about animals and factory farms. Know the truth behind what goes into dairy/eggs and meat. If you don’t like veggies,...

Colon Cleanse Questions, Answered!

We are exposed to the various toxins and ...

Wheat Allergy? Don’t Eat These Foods!

Wheat allergies are one of the most common types of food allergies that women get. Although many adults suffer...

Sun Exposure: Precautions and Protection

A golden, bronze tan is often considered a status symbol. Perhaps this supports the idea that people who have time to lie in...