Vectorborne diseases are diseases that are transmitted from an arthropod (usually a tick or mosquito, etc.) to humans or other animals. Some vectorborne disease agents are maintained primarily through transmission between arthropods and humans (e.g., dengue virus and malaria), while others are maintained between arthropods and animal reservoirs (e.g., Lyme disease and West Nile virus) and humans may become infected incidentally.

Vectorborne Disease and the Environment

Climate is one of many important interacting variables that affect people’s risk for vectorborne diseases in New York City. Warmer winters make it easier for ticks to survive in NYC year-round, while a longer warm season increases the length of time that ticks are active each year. Warming temperatures can lengthen the season in which mosquitoes are out and biting, and accelerate the mosquito life cycle. This can mean more frequent contact between mosquitoes and their hosts, which can facilitate the spread of diseases.

Changing precipitation patterns, with longer periods between rains but heavier rain events, may affect the amount of standing water that mosquitoes have to breed in. Climate change may also alter bird migration patterns or result in changes in deer and small mammal populations that serve as hosts for vectorborne diseases.

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.