Mental health is a person’s emotional, psychological, and social wellbeing. It is an important part of overall health.

Many factors can contribute to poor mental health. These include chronic disease, poverty, and poor environmental conditions. Many adults with mental illness do not receive the treatment they need.

Mental health and the environment

A person’s environment affects their mental health.

Neighborhood factors like green space, noise, and infrastructure can affect mental health. People who say their neighborhood gives them more opportunities to be physically active are less likely to report poor mental health.

People exposed to poor housing quality (like housing maintenance issues and pests) are more likely to report poor mental health.

Living in high poverty neighborhoods is associated with depression, serious psychological distress, and psychiatric hospitalization.

Experiencing violence is also a risk factor: depression is higher among those who report that their neighborhood is not very safe, and those who have ever been hurt by an intimate partner.

Even climate change is a risk: extreme heat is associated with mood disorders and anxiety, and disasters such as floods and hurricanes result in trauma, posttraumatic stress disorder, and poorer mental health.

How to get help

If you’re struggling, connect with trained counselors at NYC Well. It’s free and confidential. Staff are available 24 hours a day, seven days a week, and can provide counseling and referrals to care in over 200 languages.

• For support, call 888-NYC-WELL (888-692-9355), text “WELL” to 65173 or chat online at nyc.gov/nycwell.
• If you or someone you know is at immediate risk of hurting themselves, or in immediate danger call 911.

You can visit nyc.gov/nycwell and click on the App Library to find apps and online tools to help you manage your health and emotional well-being from home. Or, get more information on coping through COVID-19.

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.