Heavy regular drinking and binge drinking contribute to a wide range of chronic health problems, including heart disease, diabetes, cancer, stroke, cirrhosis, depression and dementia. Alcohol use is also associated with suicide, homicide, domestic violence, sexually transmitted diseases and injuries. It is one of the leading behavior-related causes of death in New York City (NYC).

This site provides data on heavy alcohol consumption and alcohol outlets in NYC.

Alcohol and the Environment

Greater alcohol outlet density has been associated with a variety of health and social consequences, including:

• Alcohol-related hospitalizations
• Violent crime, assault and homicide
• Child abuse and intimate partner violence
• Liver cirrhosis mortality
• Motor vehicle accidents

Alcohol advertising – at point-of-sale and other locations – permeates the NYC environment. Research suggests that exposing youth to alcohol advertising is associated with increased interest in drinking alcohol.

About the Data and Indicators

Alcohol drinking is measured in the NYC Health Department’s Community Health Surveys. We define heavy drinking as an average of more than two drinks per day for men or more than one drink per day for women. We define binge drinking as five or more drinks on one occasion, and as of 2011, four or more drinks for women. The accuracy of estimates derived from surveys depends in part on participants’ ability to recall and honestly report their behavior.Alcohol outlets data are provided by the New York State Liquor Authority, which licenses establishment to sell alcoholic beverages. Establishments are categorized as service or retail. Service outlets are defined as locations where the patron purchases and consumes the alcohol in the same place (on-premise), such as a bar or restaurant. Retail outlets are defined as locations where the patron purchases the alcohol on-site such as at a grocery or liquor store but must consume the alcohol elsewhere (off-premise).

Prevention

Policies and practices that have been shown to reduce harmful drinking include:

• Enforcing laws prohibiting sales of alcohol to youth and to those already intoxicated.
• Limiting the density of alcohol outlets.

Industry can help to control hazardous drinking behavior by:

• Limiting the use of drink specials or promotions.
• Using standard measures for drinks.
• Maintaining a limit on the number or drinks a person can consume.
• Providing training and education to employees to encourage responsible alcohol sales and consumption.

Health care providers should incorporate universal screening for alcohol problems into medical care settings.

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.