The climate of a region is characterized by its usual weather conditions, seasons, and patterns in extreme weather events such as heat waves, droughts, and hurricanes. New York City (NYC) faces public health risks from extreme weather events such as heat waves and coastal storms. With climate change, the severity of some of these risks will increase. A 2013 New York City Panel on Climate Change (NPCC) report predicts that by the 2020s, average summer temperatures in New York City will increase by 2.0°F to 3.0°F compared to a 1971-2000 baseline. Heat waves will become more frequent, intense, and longer in duration. Rainfall and precipitation are expected to increase, together with another 4-8 inches of sea level rise and increased probability of coastal storms with flooding (NPCC, Climate Risk Information, 2013).

Weather and Health

Even without long-term changes in climate, New Yorkers face weather-related risks today. Heat and humidity cause serious illness and death every summer. In fact, more Americans die from heat waves in a typical year than all other natural disasters combined, both in direct mortality from heat stress (hyperthermia, also known as heat stroke) and by way of increases in the rates of deaths from natural causes due to the exacerbation of other medical conditions. While far less common, coastal storms can also have serious consequences, including long-term mental health impacts. In 2012, Hurricane Sandy caused extensive flooding and damage in New York City, and resulted in at least 44 deaths.

Power outages can also occur during heat waves and coastal storms. Potential health impacts include gastrointestinal illness due to food spoilage, carbon monoxide poisoning from the use of generators, and illness and death among the medically frail due to mechanical equipment failure, and inability to obtain medical care, or extreme exertion (such as having to climb many flights of stairs in high-rise buildings due to elevator outages). Lack of air conditioning or home heat may also result in heat- and cold-related illnesses.

About the Data and Indicators

The Tracking Portal provides data on counts and rates of death, hospital admission, and treatment in an emergency department for heat stress. While these cases can be readily identified with hyperthermia listed as a diagnosis or cause of death, it is important to note that they reflect only a portion of the overall health impact of extreme heat. Increases in natural-cause hospitalization and death rates during heat waves (from cardiovascular disease, diabetes, and renal disease for example) may be estimated using statistical methods.

The Tracking Portal also provides counts and rates of hospital admission and treatment in an emergency department for cold stress. As with heat stress data, is important to note that cold stress data reflect only a portion of the overall health impact, and does not account for potential increases in hospital visits for other conditions that can be exacerbated by cold exposure (such as cardiovascular and respiratory disease for example).

Prevention

Air conditioning is the most important way to prevent heat stress and death on hot days.

• Those most vulnerable to heat stress include older adults and people with health conditions including cardiovascular disease, diabetes, mental illness, or cognitive impairments.
• Set air conditioners to 78°F or low-cool to provide comfort while not wasting energy.
• Check on family, friends, and neighbors at-risk and assist them in staying cool and well hydrated.

Living without heat or being outdoors in cold weather for a prolonged time can be dangerous.

• Stay indoors as much as possible. When outdoors, wear layers of warm clothing and cover exposed skin.
• If your home lacks heat, get to a warm place if you can and wear warm clothes. Report loss of heat or hot water to property managers immediately, and call 311.
• Seniors should take extra care outdoors to avoid slips and falls from icy conditions.
• Protect yourself and your family from carbon monoxide (CO) poisoning:

• Make sure your home has a working smoke alarm and CO detector.
• If a CO detector goes off in your home, immediately open a nearby window, go outside for fresh air and call 911.
• NEVER run a vehicle inside a garage or against a snowbank. Clear snow from your car’s tailpipe before running the engine.

• Check on neighbors, friends, and relatives.

For more information on preparing for weather hazards– including heat, cold, coastal storms and power outages – visit the DOHMH extreme weather website.

Key Messages

About 80% of heat stroke victims in New York City are overcome by excessively hot conditions in their own homes, as indoor temperatures in the urban environment may be even hotter than outdoor temperatures when an air conditioner is not available or not in use. Although heat-related mortality rates are lower than in historical periods before electricity and air conditioning, it is possible that rates may rise as a result of climate change, depending on the extent of adaptation. In 2011, a Health Department survey estimated that 25% of New York City adults still do not have or rarely use air conditioning during hot summer weather, many of whom may be at increased risk of heat stress due to age or poor health.

People most likely to be exposed to dangerous cold include those who lack shelter, work outdoors and/or live in homes with malfunctioning or inadequate heat. Seniors, infants, people with chronic cardiovascular or lung conditions, people using alcohol or drugs and people with cognitive impairments or mental health conditions are at increased risk.

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.