Every year in New York City, workers are injured on the job or become ill as a result of exposure to health hazards at work. However, most work-related illnesses and injuries are preventable –  safer work environments can be achieved when workplace hazards are understood and appropriate prevention measures are put in place.

Data on work-related injuries and illness are used to show how often these preventable events occur and to identify  factors that place workers at risk. These data are also used to design, target and evaluate prevention strategies and regulations that aim to keep workers safe and healthy.

Work, Workplaces and Health

High-quality work benefits physical and mental health in a variety of ways. In addition to economic benefits, work can be a source of identity and pride, an avenue for social participation and a way to learn new skills. Individuals who work have lower rates of illness and live longer than those who are unemployed. On the other hand, unfavorable work environments expose workers to serious health hazards which regularly result in injury, illness, disability and death.

Although many factors contribute to worker health, such as workplace design, organization, worker characteristics and culture, it is the employer’s responsibility to provide safe and healthful workplaces for their workers.

Preventing Workplace Injury and Disease

Both workers and employers play important roles in keeping workplaces safe and healthy.

Employers should:

• Demonstrate and communicate an ongoing commitment to safety and wellbeing.
• Eliminate known hazards if possible and design work processes and environments to minimize exposure.
• Ask workers or their representatives what safety and health issues are most important to them and act on their concerns.
• Provide safety and health education and resources to all workers.

Workers should:

• Communicate concerns about potential hazards to their employer in a timely manner.
• Follow established work safety practices, such as wearing recommended personal protective equipment and following safety protocols.

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.