DOHMH COVID-19 Antibody-by-Modified ZIP Code Tabulation Area

6qs8-44kiuncategorised15 columns

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by modified ZIP Code Tabulation Area (ZCTA) of residence. Modified ZCTA reflects the first non-missing address within NYC for each person reported with an antibody test result. This unit of geography is similar to ZIP codes but combines census blocks with smaller populations to allow more stable estimates of population size for rate calculation. It can be challenging to map data that are reported by ZIP Code. A ZIP Code doesn’t refer to an area, but rather a collection of points that make up a mail delivery route. Furthermore, there are some buildings that have their own ZIP Code, and some non-residential areas with ZIP Codes. To deal with the challenges of ZIP Codes, the Health Department uses ZCTAs which solidify ZIP codes into units of area. Often, data reported by ZIP code are actually mapped by ZCTA. The ZCTA geography was developed by the U.S. Census Bureau. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-modzcta.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders) Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis wi

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Quality

C77.8 / 100

From a 177-row sample across 15 columns.

completeness100/ 25% weight

consistency57/ 20% weight

accuracy99/ 20% weight

timeliness24/ 15% weight

usability90/ 20% weight

· No obvious temporal column — limits time-series joins
· Has coordinates — directly mappable, and joinable to other NYC datasets

Related datasets

Geography

Mappable — Socrata point column `point`.

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Sample rows

modzcta_first	neighborhood_name	label	lat	lon	num_peop_test	num_peop_pos	percent_positive	test_rate	point	:@computed_region_efsh_h5xi	:@computed_region_f5dn_yrer
10464	City Island	10464	40.872311	-73.804653	1432	396	0.277	33415.82	40.87231, -73.80465	11273	44
11367

Columns

10 queryable · 10 profiled

The machine field names SoQL needs, with the publisher's own description of each.

Field	Type	Description	Null	Values
`modzcta_first`	text	Modified ZIP Code Tabulation Are (ZCTA) of patient residence	0%	`112031001611415`
`neighborhood_name`	text	Neighborhood name of the MODZCTA	0%	`Hell's Kitchen/Midtown M…Financial DistrictLenox Hill/Upper East Si…`
`label`	text	—	0%	`103101120310016`
`lat`	float	—	0%	`40.70415340.5766640.599333`

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