Asthma is a common disease in which the lungs become swollen, making breathing difficult. Symptoms include wheezing, coughing, shortness of breath and tightness in the chest. The exact causes of asthma are not known. Asthma cannot be cured, but it can be controlled.

Asthma is a leading cause of emergency room visits, hospitalizations and missed school days in New York City’s poorest neighborhoods. In NYC, asthma is more common among low-income Black and Latino children.

Asthma and the Environment

Certain things in the environment can trigger an asthma attack or make symptoms worse. Common triggers include:

• Tobacco smoke
• Air pollution, such as particulates, ozone and diesel exhaust
• Dust mites, cockroaches and mold
• Pollen
• Pets

Controlling Asthma

Asthma does not have to prevent you from leading an active and healthy life. You can take charge by having a plan to control asthma, including these strategies for avoiding asthma triggers:

• Check the weather report daily for air pollution level (“air quality index”) and pollen counts. Limit time outside when air pollution levels or pollen counts are high.
• If you smoke, quit. Don’t allow smoking in your home or car.
• Remove clutter and clean regularly to reduce dust levels in your home.
• Work with your landlord to fix leaks promptly to prevent mold. If your landlord fails to fix the problem, call 311.
• Do not allow furry pets on the furniture or in the bedroom.
• Pest proof your home. If building conditions may be attracting pests, contact your landlord. If your landlord does not address your concerns, call 311.

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.