Carbon monoxide (CO) is a poisonous gas that forms when fuel or other materials are burned.  It is invisible and has no odor.

Even short-term exposure to high levels of carbon monoxide can be fatal. Carbon monoxide blocks oxygen transport in the body.  Symptoms of mild carbon monoxide poisoning include headache, nausea, fatigue, dizziness and disorientation. Breathing high concentrations of carbon monoxide can cause somebody to lose consciousness and have permanent neurological injury - or even death.

Rates of unintentional carbon monoxide poisoning have declined over the past three decades.  However, carbon monoxide remains a common cause of life-threatening and fatal poisonings.

About the data

Carbon monoxide poisonings in New York City may be underestimated in these data for several reasons:

• Symptoms of mild to moderate carbon monoxide poisoning may be confused with flu-like illness
• People experiencing these symptoms may not seek medical treatment
• Some health care providers neglect to report poisonings to the Health Department

To help prevent carbon monoxide poisoning:

Owners and landords should:

• Required by NYC law to install and maintain smoke and carbon monoxide detectors
• Required to provide heating for apartments
• Have heating systems inspected before each winter

Residents:

• Call 311 if your landlord doesn’t fix heating problems.
• Test your detectors and replace batteries twice a year.
• Never use a gas stove, oven or other cooking appliance to heat your home.  And never use unvented gas space heaters - they’re illegal.
• Never run an emergency generator indoors or near any enclosed space, even if your power goes out. Keep it outside, far away from doors and windows.

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.