Social and economic conditions affect people’s health. These conditions (also called “social determinants of health”) are conditions that people may be born into or fall into over the course of their lives.

People tend to be healthier when they have access to:

• Economic and financial stability
• Higher education
• Adequate health care
• Healthy neighborhoods and safe, strong communities

But when people don’t have access to these conditions, they experience worse health outcomes. Discrimination based on race (and other personal or social characteristics) results in disparities in access to resources that protect and maintain health – and thus, results in unfair disparities in health outcomes. This is health inequity – avoidable and unfair health outcomes.

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.