Mesothelioma Aid

How to Interpret Cancer Statistics

There are several different purposes that necessitate the use of cancer statistics. Researchers and cancer organizations (e.g. the National Cancer Institute) make use of these statistics to review cancer trends. For instance, if the occurrence of a specific type of cancer has increased over a certain period, researchers would be keen to determine the causes behind the same and whether it can be controlled. These statistics are also used by scientists to determine the effectiveness of a specific prevention or treatment procedure.

The usefulness of available cancer statistics in relation to cancer patients and the general public depends upon their interpretation and how they are utilized. For instance, many women may misinterpret that their chances of developing breast cancer is one in eight at any given point of time in their lives (based on the widely reported statistic that the lifetime risk of contracting breast cancer is one in eight). However, in reality, the risk of developing breast cancer varies with a woman’s age. In that sense, the current risk of developing breast cancer for a 20 year old woman is only 1 in 2500 in the next 10 years. In comparison, the current risk is 1 in 39 for a 50 year old woman. Factors such as ethnicity, heredity, lifestyle factors and reproductive history contribute to the overall risks faced by an individual. This implies that cancer statistics offer more value when used for evaluating a broader perspective as opposed to an individual situation.

Described below are some statistical terms and phrases that are often used when discussing issues related to cancer and its outcomes.

Incidence refers to the total number of new occurrences of a particular type of cancer reported within a specific population group in a specified time period (usually one year). For instance, the incidence of testicular cancer for the year 2005 was around 8,000. The incidence rate describes the total number of new cases reported in a given population group. This rate is generally described in terms of the total number of cases reported for every 100,000 individuals. For example, the current incidence rate pertaining to testicular cancer is around 4 new cases for every 100,000 people in the United States. This is often described simply as 4 per 100,000.

Prevalence refers to the total number of individuals with a specific type of cancer or cancer risk in the entire population at a given point of time. When large groups are involved, prevalence is first calculated by gathering data from a smaller group of people. The information derived thereon is then projected onto the general population. For instance, based on data (DNA information) gathered from patients diagnosed with breast cancer, scientists have been able to determine the prevalence of the BRCA – 1 gene as applicable for the entire population. Ranging from 0.04% to 0.2%, it implies that less than 1% of the entire population may have this susceptibility gene (relevant to breast cancer). Click here for more on use of prevalence stats to evaluate risk.

Morbidity refers to a state of illness. For instance, it has been widely reported that smoking is one of the major factors contributing to morbidity in the United States.

Mortality means death related. The mortality rate expresses the total number of individuals who die of a particular type of cancer within a population group in a specified time frame (usually one year). This rate is generally described in terms of total deaths per 100,000 individuals. For instance, the mortality rate pertaining to stomach cancer in 1930 was 28 (28 deaths for every 100,000 individuals) in the United States. By 1992, this rate dropped to 4, which implies that only 4 individuals out of every 100,000 died due to stomach cancer in 1992 in the U.S.

Prognosis involves the estimation or prediction of the course of the disease and its outcome. It may also include an estimation of the probable chances for recovery. Although doctors make a prognosis based on available cancer statistics, the actual outcome may vary from patient to patient depending on factors such as the patient’s age and overall health, the form of cancer and its extent (stage), and the effectiveness of the specific treatment protocol being used. As such, a prognosis may prove more beneficial when used for describing the seriousness of the disorder or for making critical decisions related to treatment. It may not be as useful when it comes to predicting the outcome of a disease in case of an individual.

Survival rate is used to describe the total number of people who develop a particular type of cancer and survive for a specific time period. As a statistical standard, scientists generally use a 5-year survival period to define the time it takes for the successful treatment of cancer.

The 5-year survival rate refers to the total number of individuals who may be living for 5 years following a cancer diagnosis. This figure includes patients who were cured, those who are in remission and those still with cancer and receiving treatment. For instance, when colorectal cancer is detected in its early stages, the 5-year survival rate comes around 92%. It implies that 92% of all patients with colorectal cancer live for at least 5 years following an early detection.

The overall 5-year survival rate includes people of all ages who may have been diagnosed with a particular type of cancer. This can lead to distorted and unreliable statistics. For instance, an 80-year old man and a 40-year old man will both be included for calculating this rate. This can skew the statistical results since the 80-year old man can die of other causes as well in the 5-year time period. To get a more accurate estimate of survival, researchers use the relative 5-year survival rate. In this approach, the cancer patient’s survival rate is compared to that of the general population, after taking into account factors such as age, gender and race. Here, the 80-year old and the 40-year old would belong to different statistical groups.

Risk refers to the probability that an individual will develop a particular disease. The term “high risk” is used when an individual’s chance of developing cancer is more than that of the general population. For instance, the risk of developing lung cancer is more for smokers than non-smokers.

Risk factors can include anything that has been known to increase an individual’s chances of contracting a disease. Some of these may be controllable whereas others may be unavoidable. It may also relate to personal or environmental factors. For instance, risk factors associated with breast cancer include a hereditary predisposition to cancer (uncontrollable) and taking hormones (that contain estrogen) for more than 10 years following menopause.

Relative risk provides an estimate of how much a specific risk factor contributes to the potential development of a particular type of cancer. For instance, risk associated with the potential onset of ovarian cancer increases by around 300% for women who have a family history of the disease in comparison to women who may not have any such family history. The relative risk in the above example is three, which implies that women with a family history of this disease face three times the risk.

Attributable risk provides an estimate of how much out of the total incidence of cancer is attributable to a specific risk factor. For instance, even when the relative risk of contracting breast cancer is high for women with the BRCA-1 gene, majority of breast cancer cases are not attributable to the BRCA-1 gene since the prevalence of this gene is low.

Lifetime risk measures the chances of developing cancer or dying from it at any stage of one’s lifetime. The lifetime risk of developing cancer is 2 in 5 for any individual, which implies that 2 out of every five people will eventually develop cancer. In comparison, the lifetime risk of death due to cancer is 1 in 5.