Cancer is the general name for a group of more than 100 diseases in which cells in a part of the body begin to grow out of control.

In New York City cancer kills nearly 13,000 residents every year, with smoking as the leading cause. One of every two men, and one of every three women, will be diagnosed with cancer at some time in their life. In women, breast is the most common type of cancer diagnosed. In men, prostate is the most common type of cancer diagnosed. For both men and women, lung cancers are the leading causes of death from cancer.

Cancer occurs at all ages, but the risk increases with age. Most cancers develop slowly in people. Cancers with known environmental causes usually appear five to 40 years after exposure to a carcinogen. This long latency period is one of the reasons it is difficult to determine what causes cancer.

Cancer and the Environment

Cancers have different causes and risk factors. Factors related to the occurrence of many types of cancer include age, sex, race, family history, smoking, diet, exercise and exposure to certain chemicals. Cancers often take a long time (decades) to develop, so it can be hard to single out a particular exposure as having a definite link to cancer.

Environmental exposures that may increase the risk of cancer include asbestos and other inhalable fibers; ionizing radiation and chemicals found in the home, workplace, water, food and air.

About the Data and Indicators

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. This site contains data on cancers that were selected based on concern that environmental exposures could play a role in their development.

Prevention

To reduce the risk of developing cancer or to detect cancer early:

• Quit smoking, encourage others to quit, and avoid exposure to secondhand smoke. Call 311 for resources to help you quit.
• Use sun protection consistently to prevent skin cancers.
• Get screened at appropriate ages for cervical, breast and colon and rectal cancer.

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.