Cancer begins when cells in a part of the body start to grow out of control. There are many kinds of cancer, but they all start because of out-of-control growth of abnormal cells. Leukemia (cancer of the blood-forming cells) is the most common form of childhood cancer. There are different kinds of childhood leukemia. The most common kinds are acute lymphocytic leukemia and acute myeloid leukemia . Brain and other nervous system cancers are the second most common types of childhood cancers. Of all childhood cancer, leukemias and brain and other nervous system cancers account for about 50% of cases among children less than 15 years old.

This site contains data on total cancers, total leukemias, ALL and AML separately, and brain and other nervous system cancers in children. The data comes from the NYS Cancer Registry.

Childhood Cancer and the Environment

The causes of most childhood cancer are unknown. It is especially difficult to identify environmental factors related to childhood cancers because - like adult cancer - they are likely to be the result of a mix of genetic, environmental, and behavioral causes, and not just one factor by itself. Exposures to the parent, the fetus in the womb, or the child after birth may all play a role. Some risk factors that explain a small percentage of cases include:

• Exposure to high doses of ionizing radiation
• Genetic abnormalities and conditions, including Down Syndrome
• Exposure to chemotherapy drugs known as alkylating agents

About the Data and Indicators

Data are reported over five-year periods because the number of cases and rates for single years can vary considerably, particularly for less common types of cancer.

Counts and rates are calculated based upon residential address at time of diagnosis. Because cancer latencies can be long, the neighborhood of residence at the time of diagnosis may not be the same as the residence at the time an earlier exposure may have occurred.

All cancers diagnosed among NYS residents are required by law to be reported to the NYS Cancer Registry. The registry is considered to have 95% or higher case ascertainment.

The cancer indicators included in this portal were chosen by a National workgroup of the Tracking Network because they are potentially related to environmental exposures, or there is public concern that they may be related.

Prevention

The risk of many adult cancers can be reduced by certain lifestyle changes (such as maintaining a healthy weight or quitting smoking). Less is known about ways to prevent cancer in children. The good news is that there are effective treatments for many childhood cancers. Since the 1970’s, death rates have declined dramatically and survival rates have increased for most childhood cancers.

Key Messages

In NYC, about 18 children out of 100,000 under the age of 20 are likely to develop cancer. Most are leukemias. Acute lymphocytic leukemia (ALL) accounts for about 3 out of 4 leukemia cases. Most of the remaining cases are acute myeloid leukemia (AML). Chronic leukemias are rare in children.

ALL is most common in early childhood, peaking between 2 and 4 years of age. Cases of AML are more spread out across the childhood years, although they occur more commonly during the first 2 years of life and during the teenage years.

Despite its rarity and the major advances in treatment and supportive care, cancer is still the leading cause of death from disease in children younger than 15 years old.

There are many schemes of neighborhood boundaries (or geographies) available for different datasets. These include:

• Citywide
• Borough
• United Hospital Fund 42 (UHF42) and United Hospital Fund 34 (UHF34) neighborhoods
• Community Districts (CD), and, related, Community District Tabulation Areas (CDTA)
• Public Use Microdata Areas (PUMA)
• Neighborhood Tabulation Areas (NTA)

We try to make data available at as fine a geographic resolution as we can. Read more about different geographies at this data story.

It would be convenient if each dataset could be made available at the same geography, but this is not always possible. The neighborhood boundaries available depend on the specifics of the underlying dataset.

When we have multiple years of data, we show the data as trend charts.

Trends for individual neighborhoods tend to be very unstable (or "bouncy"), due to small counts for outcomes in individual neighborhoods. To show clearer trends, we usually aggregate up and show trend data by Borough.

Options on some indicators let you compare this indicator to other similar indicators.

We try to make data available as soon as we can.

Many datasets take a long time to reach us — a "lag" of several years, during which data are gathered from different entities to try to create a complete dataset; data are processed (cleaned, deduplicated, etc), and analyzed. It can take several years to prepare a dataset — so a dataset that is several years old is sometimes the most up-to-date information available. Still, this data continues to show us valuable trends and insights.

We also try to display datasets that have been available for many years. Some datasets (like those that are asked on surveys) are not asked every year of the survey, which means that we cannot provide annual data.

For some datasets, we have to aggregate time periods in order to break down data by borough or neighborhood.

Measures are the numeric ways that a dataset (or indicator) are expressed. Examples of different measures might be: number, percent or rate.

A number tells us how much of an outcome is in a certain area. But a rate (usually expressed as, for example, 40 per 100,000) tells us how much of an outcome there is, relative to the population of the area in question. This lets us compare neighborhoods of different sizes.

On this site, we try to show how environmental factors might be connected to health outcomes. One way we do this is with Correlates. We choose potentially-related datasets and display them on a scatterplot so you can see how these two datasets are correlated.

The Disparities view displays neighborhoods by poverty category to show underlying disparities in the data.

Read more about correlation here.