# trends/ 

This folder contains files with daily, weekly, and monthly data shown across time. Note that these trend data are published by date of event, not by date of report. The Health Department recommends against interpreting daily changes to these files as one day’s worth of data, due to the difference between date of event and date of report.

## Files 

### caserate-by-modzcta.csv 

This file contains the rate of cases per 100,000 people, stratified by week and three different geographies: citywide, borough, and modified ZIP Code Tabulation Area (MODZCTA). The level of geography is indicated following the underscore (_) in each column heading. Please see the technical notes for a [description of MODZCTA](https://github.com/nychealth/coronavirus-data#geography-zip-codes-and-zctas).

People with COVID-19 are categorized based on the date of diagnosis, and are aggregated by full-weeks starting each Sunday and ending on Saturday. For example, a person who was diagnosed with COVID-19 on Monday, October 12, 2020 would be categorized as diagnosed during the week ending October 17, 2020. Note that sum of counts in this file may not match values in citywide tables because of records with missing geographic information.

The rate of cases per 100,000 people is suppressed for a specific geography when the count of deaths is greater than 0 and less than 5 due to imprecise and unreliable estimates and also to protect patient confidentiality. 

Note that sum of counts in this file may not match values in citywide tables because of:
* Records with missing geographic information
* Cells that are suppressed due to imprecise and unreliable estimates or for the protection of patient confidentiality

### cases-by-day.csv  

This file contains citywide and borough-specific daily counts of probable and confirmed cases. Cases are aggregated by the date of diagnosis. To address variation in the number of cases diagnosed per day, we have included a 7-day average (mean). This is calculated as the average of number of cases on that day and the previous 6 days.

This file includes data since February 29, 2020 based on the date that the Health Department classifies as the start of the COVID-19 outbreak in NYC (i.e., date of the first laboratory-confirmed case). 

Indicators include: 

| Variable name | Definition | Timeframe | 
|--------------------------------|---------------------------------------------------------------|---------------------------------| 
| DATE_OF_INTEREST | Date of diagnosis | | 
| CASE_COUNT | Count of confirmed cases citywide | Day |  
| PROBABLE_CASE_COUNT | Count of probable cases citywide | Day |  
| CASE_COUNT_7DAY_AVG | 7-day average of count of confirmed cases citywide | Current day and previous 6 days | 
| ALL_CASE_COUNT_7DAY_AVG | 7-day average of count cases citywide | Current day and previous 6 days | 
| BX_CASE_COUNT | Count of confirmed cases in the Bronx | Day | 
| BX_PROBABLE_CASE_COUNT | Count of probable cases in the Bronx | Day |   
| BX_CASE_COUNT_7DAY_AVG | 7-day average of count of confirmed cases in the Bronx | Current day and previous 6 days | 
| BX_ALL_CASE_COUNT_7DAY_AVG | 7-day average of count of confirmed and probable cases in the Bronx | Current day and previous 6 days | 
| BK_CASE_COUNT | Count of confirmed cases in Brooklyn | Day |     
| BK_PROBABLE_CASE_COUNT | Count of probable cases in Brooklyn | Day |  
| BK_CASE_COUNT_7DAY_AVG | 7-day average of count of confirmed cases in Brooklyn | Current day and previous 6 days |
| BK_ALL_CASE_COUNT_7DAY_AVG | 7-day average of count of confirmed and probable cases in Brooklyn | Current day and previous 6 days | 
| MN_CASE_COUNT | Count of confirmed cases in Manhattan | Day | 
| MN_PROBABLE_CASE_COUNT | Count of probable cases in Manhattan | Day |  
| MN_CASE_COUNT_7DAY_AVG | 7-day average of count of confirmed cases in Manhattan | Current day and previous 6 days | 
| MN_ALL_CASE_COUNT_7DAY_AVG | 7-day average of count of confirmed and probable cases in Manhattan | Current day and previous 6 days |
| QN_CASE_COUNT | Count of confirmed cases in Queens | Day | 
| QN_PROBABLE_CASE_COUNT | Count of probable cases in Queens | Day | 
| QN_CASE_COUNT_7DAY_AVG | 7-day average of count of confirmed cases in Queens | Current day and previous 6 days | 
| QN_ALL_CASE_COUNT_7DAY_AVG | 7-day average of count of confirmed and probable cases in Queens | Current day and previous 6 days |
| SI_CASE_COUNT | Count of confirmed cases in Staten Island | Day |  
| SI_PROBABLE_CASE_COUNT | Count of probable cases in Staten Island | Day | 
| SI_CASE_COUNT_7DAY_AVG | 7-day average of count of confirmed cases in Staten Island | Current day and previous 6 days | 
| SI_ALL_CASE_COUNT_7DAY_AVG | 7-day average of count of confirmed and probable cases in Staten Island | Current day and previous 6 days |
| INCOMPLETE | Used for display purposes only | | 

Note that sum of counts in this file may not match values in citywide tables because of records with missing geographic information.

### covid-like-illness-by-boro.csv

This file includes the rate of visits to NYC emergency departments (ED) per 100,000 people, and rates of subsequent admissions to the hospital through the ED, for influenza-like illness, pneumonia, or COVID-like illness (defined as having the ICD-10-CM code (U07.1) for COVID-19 disease documented). Data are stratified by date of visit, age groups, and patient borough of residence. Please see additional details about the Health Department's [syndromic surveillance system](https://github.com/nychealth/coronavirus-data#syndromic-surveillance) in the key technical notes section.  

The data in this file go back to February 1, 2020, to provide a view of the early days of the COVID-19 outbreak in NYC.

Indicators include: 

| Variable name | Definition | Timeframe | 
|---------------------|---------------|----------------| 
| Date | Date of ED visit | |    
| Borough | Borough of residence | |    
| Admit_0_4 | Rate of hospital admissions among people aged 0-4 years per 100,000 people by borough of residence | Day | 
| Admit_5_12 | Rate of hospital admissions among people aged 5-12 years per 100,000 people by borough of residence | Day | 
| Admit_13_17 | Rate of hospital admissions among people aged 13-17 years per 100,000 people by borough of residence | Day | 
| Admit_18_24 | Rate of hospital admissions among people aged 18-24 years per 100,000 people by borough of residence | Day | 
| Admit_25_34| Rate of hospital admissions among people aged 25-34 years per 100,000 people by borough of residence | Day | 
| Admit_35_44| Rate of hospital admissions among people aged 35-44 years per 100,000 people by borough of residence | Day | 
| Admit_45_54 | Rate of hospital admissions among people aged 45-54 years per 100,000 people by borough of residence | Day | 
| Admit_55_64 | Rate of hospital admissions among people aged 55-64 years per 100,000 people by borough of residence | Day | 
| Admit_65_74 | Rate of hospital admissions among people aged 65-74 years per 100,000 people by borough of residence | Day | 
| Admit_75 | Rate of hospital admissions among people aged 75+ years per 100,000 people by borough of residence | Day | 
| Admit_All_ages | Rate of hospital admissions among people of all ages per 100,000 people by borough of residence | Day |    			           
| Visit_0_4 | Rate of ED visits among people aged 0-4 years per 100,000 people by borough of residence | Day | 
| Visit_5_12| Rate of ED visits among people aged 5-12 years per 100,000 people by borough of residence | Day | 
| Visit_13_17 | Rate of ED visits among people aged 13-17 years per 100,000 people by borough of residence | Day | 
| Visit_18_24 | Rate of ED visits among people aged 18-24 years per 100,000 people by borough of residence | Day | 
| Visit_25_34 | Rate of ED visits among people aged 25-34 years per 100,000 people by borough of residence | Day | 
| Visit_35_44 | Rate of ED visits among people aged 35-44 years per 100,000 people by borough of residence | Day | 
| Visit_45_54 | Rate of ED visits among people aged 45-54 years per 100,000 people by borough of residence | Day | 
| Visit_55_64 | Rate of ED visits among people aged 55-64 years per 100,000 people by borough of residence | Day | 
| Visit_65_74 | Rate of ED visits among people aged 65-74 years per 100,000 people by borough of residence | Day | 
| Visit_75 | Rate of ED visits among people aged 75+ years per 100,000 people by borough of residence | Day | 
| Visit_All_ages | Rate of ED visits among people of all ages per 100,000 people by borough of residence | Day |

### covid-like-illness.csv 

covid-like-illness.csv was previously called syndromic_data.csv. 

This file includes the rate of visits to NYC EDs per 100,000 people, and rates of subsequent admissions to the hospital through the ED, for influenza-like illness, pneumonia, or COVID-like illness (defined as having the ICD-10-CM code (U07.1) for COVID-19 disease documented). Data are stratified by date of visit and age group. Please see additional details about the Health Department's [syndromic surveillance system](https://github.com/nychealth/coronavirus-data#syndromic-surveillance) in the key technical notes section.

The data in this file go back to February 1, 2020, to provide a view of the early days of the COVID-19 outbreak in NYC.

Indicators include: 

| Variable name | Definition | Timeframe | 
|----------------|------------------------------------------------------------------------------|-----------| 
| Date | Date of ED visit | |    
| Admit_0_4 | Rate of hospital admissions among people aged 0-4 years per 100,000 people | Day | 
| Admit_5_12 | Rate of hospital admissions among people aged 5-12 years per 100,000 people | Day | 
| Admit_13_17 | Rate of hospital admissions among people aged 13-17 years per 100,000 people | Day | 
| Admit_18_24 | Rate of hospital admissions among people aged 18-24 years per 100,000 people | Day | 
| Admit_25_34 | Rate of hospital admissions among people aged 25-34 years per 100,000 people | Day | 
| Admit_35_44 | Rate of hospital admissions among people aged 35-44 years per 100,000 people | Day | 
| Admit_45_54 | Rate of hospital admissions among people aged 45-54 years per 100,000 people | Day | 
| Admit_55_64 | Rate of hospital admissions among people aged 55-64 years per 100,000 people | Day | 
| Admit_65_74  | Rate of hospital admissions among people aged 65-74 years per 100,000 people | Day | 
| Admit_75 | Rate of hospital admissions among people aged 75+ years per 100,000 people | Day | 
| Admit_All_ages | Rate of hospital admissions among people of all ages per 100,000 people | Day |                                   
| Visit_0_4 | Rate of ED visits among people aged 0-4 years per 100,000 people | Day | 
| Visit_5_12 | Rate of ED visits among people aged 5-12 years per 100,000 people | Day | 
| Visit_13_17 | Rate of ED visits among people aged 13-17 years per 100,000 people | Day | 
| Visit_18_24 | Rate of ED visits among people aged 18-24 years per 100,000 people | Day | 
| Visit_25_34 | Rate of ED visits among people aged 25-34 years per 100,000 people | Day | 
| Visit_35_44 | Rate of ED visits among people aged 35-44 years per 100,000 people | Day | 
| Visit_45_54 | Rate of ED visits among people aged 45-54 years per 100,000 people | Day | 
| Visit_55_64 | Rate of ED visits among people aged 55-64 years per 100,000 people | Day | 
| Visit_65_74 | Rate of ED visits among people aged 65-74 years per 100,000 people | Day | 
| Visit_75 | Rate of ED visits among people aged 75+ years per 100,000 people | Day | 
| Visit_All_ages | Rate of ED visits among people of all ages per 100,000 people | Day | 
  
### data-by-day.csv  

This file contains the same data as cases-by-day.csv, hosp-by-day.csv, and deaths-by-day.csv, but combined for display purposes.
   
 ### deathrate-by-modzcta.csv 

This file contains the rate of deaths per 100,000 people, stratified by month and three different geographies: citywide, borough, and MODZCTA. The level of geography is indicated following the underscore (_) in each column heading. Please see the technical notes for a [description of MODZCTA](https://github.com/nychealth/coronavirus-data#geography-zip-codes-and-zctas).

Deaths are aggregated by the date of death. This file is updated on the third Thursday each month with data through the end of the previous month to address delays in reporting.

The rate of deaths per 100,000 people is suppressed for a specific geography when the count of deaths is greater than 0 and less than 5 due to imprecise and unreliable estimates and also to protect patient confidentiality. 

Note that sum of counts in this file may not match values in citywide tables because of:
* Records with missing geographic information
* Cells that are suppressed due to imprecise and unreliable estimates or for the protection of patient confidentiality
   
### deaths-by-day.csv  

This file contains citywide and borough-specific daily counts of total deaths. Deaths are aggregated by the date of death. To address variation in the number of cases diagnosed per day, we have included a 7-day average (mean). This is calculated as the average of number of deaths on that day and the previous 6 days.

This file includes data since February 29, 2020 based on the date that the Health Department classifies as the start of the COVID-19 outbreak in NYC (date of the first laboratory-confirmed case).  

Indicators include: 

| Variable name | Definition | Timeframe | 
|--------------------------------|---------------------------------------------------------------|---------------------------------| 
| DATE_OF_INTEREST | Date of death | |  
| DEATH_COUNT | Count of total deaths citywide | Day | 
| DEATH_COUNT_7DAY_AVG | 7-day average of count of total deaths citywide | Current day and previous 6 days | 
| BX_DEATH_COUNT | Count of total deaths in the Bronx | Day | 
| BX_DEATH_COUNT_7DAY_AVG | 7-day average of count of total deaths in the Bronx | Current day and previous 6 days | 
| BK_DEATH_COUNT | Count of total deaths in Brooklyn | Day |  
| BK_DEATH_COUNT_7DAY_AVG | 7-day average of count of total deaths in Brooklyn  | Current day and previous 6 days | 
| MN_DEATH_COUNT | Count of total deaths in Manhattan | Day |  
| MN_DEATH_COUNT_7DAY_AVG | 7-day average of count of total deaths in Manhattan | Current day and previous 6 days |
| QN_DEATH_COUNT | Count of total deaths in Queens | Day |
| QN_DEATH_COUNT_7DAY_AVG | 7-day average of count of total deaths in Queens | Current day and previous 6 days | 
| SI_DEATH_COUNT | Count of total deaths in Staten Island | Day |  
| SI_DEATH_COUNT_7DAY_AVG | 7-day average of count of total deaths in Staten Island | Current day and previous 6 days | 
| INCOMPLETE | Used for display purposes only | | 

Note that sum of counts in this file may not match values in citywide tables because of records with missing geographic information.

### hosp-by-day.csv  

This file contains citywide and borough-specific daily counts of hospitalizations. Hospitalizations are aggregated by the date of admission. To address variation in the number of cases who are hospitalized per day, we have included a 7-day average (mean). This is calculated as the average of number of hospitalizations on that day and the previous 6 days.

This file includes data since February 29, 2020 based on the date that the Health Department classifies as the start of the COVID-19 outbreak in NYC (date of the first laboratory-confirmed case). 

Indicators include: 

| Variable name | Definition | Timeframe | 
|--------------------------------|---------------------------------------------------------------|---------------------------------| 
| DATE_OF_INTEREST | Date of admission | |  
| HOSPITALIZED_COUNT | Count of hospitalized cases citywide | Day | 
| HOSP_COUNT_7DAY_AVG | 7-day average of count of hospitalized cases citywide | Current day and previous 6 days | 
| BX_HOSPITALIZED_COUNT | Count of hospitalized cases in the Bronx | Day | 
| BX_HOSPITALIZED_COUNT_7DAY_AVG | 7-day average of count of hospitalized cases in the Bronx | Current day and previous 6 days | 
| BK_HOSPITALIZED_COUNT | Count of hospitalized cases in Brooklyn | Day |  
| BK_HOSPITALIZED_COUNT_7DAY_AVG | 7-day average of count of hospitalized cases in Brooklyn | Current day and previous 6 days | 
| MN_HOSPITALIZED_COUNT | Count of hospitalized cases in Manhattan | Day |  
| MN_HOSPITALIZED_COUNT_7DAY_AVG | 7-day average of count of hospitalized cases in Manhattan | Current day and previous 6 days | 
| QN_HOSPITALIZED_COUNT | Count of hospitalized cases in Queens | Day |  
| QN_HOSPITALIZED_COUNT_7DAY_AVG | 7-day average of count of hospitalized cases in Queens | Current day and previous 6 days | 
| SI_HOSPITALIZED_COUNT | Count of hospitalized cases in Staten Island | Day |  
| SI_HOSPITALIZED_COUNT_7DAY_AVG | 7-day average of count of hospitalized cases in Staten Island | Current day and previous 6 days | 
| INCOMPLETE | Used for display purposes only | | 

Note that sum of counts in this file may not match values in citywide tables because of records with missing geographic information.
  
### hosprate-by-modzcta.csv 

This file contains the rate of hospitalized cases per 100,000 people, stratified by month and three different geographies: citywide, borough, and MODZCTA. The level of geography is indicated following the underscore (_) in each column heading. Please see the technical notes for a description of MODZCTA ([Geography: Zip codes and ZCTAs](https://github.com/nychealth/coronavirus-data#geography-zip-codes-and-zctas)).

Hospitalizations are aggregated by the date of admission. This file is updated on the third Thursday each month with data through the end of the previous month to address delays in reporting.

The rate of hospitalized cases per 100,000 people is suppressed for a specific geography when the count of hospitalized deaths is greater than 0 and less than 5 due to imprecise and unreliable estimates. 

Note that sum of counts in this file may not match values in citywide tables because of:
* Records with missing geographic information
* Cells that are suppressed due to imprecise and unreliable estimates

### weekly-case-rate-age.csv 

This file contains the rate of cases per 100,000 people, stratified by week and age group. 

People with COVID-19 are categorized based on the date of diagnosis, and are aggregated by full-weeks starting each Sunday and ending on Saturday. For example, a person who was diagnosed with COVID-19 on Monday, June 21, 2021 would be categorized as diagnosed during the week ending June 26, 2021. Rates are suppressed if the count of the numerator is between 1 and 4, to prevent the display of unreliable rates.

Indicators include: 

| Variable name | Definition | Timeframe |  
| --------------|------------|-----------| 
| WEEK_ENDING | Week-ending date | | 
| AGE_ALL_AGES | Rate of cases per 100,000 among people of all ages | Full week preceding the week-ending date | 
| AGE_0_4 | Rate of cases per 100,000 among people aged 0-4 years | Full week preceding the week-ending date | 
| AGE_5_12 | Rate of cases per 100,000 among people aged 5-12 years | Full week preceding the week-ending date | 
| AGE_13_17 | Rate of cases per 100,000 among people aged 13-17 years | Full week preceding the week-ending date | 
| AGE_18_24 | Rate of cases per 100,000 among people aged 18-24 years | Full week preceding the week-ending date | 
| AGE_25_34 | Rate of cases per 100,000 among people aged 25-34 years | Full week preceding the week-ending date | 
| AGE_35_44 | Rate of cases per 100,000 among people aged 35-44 years | Full week preceding the week-ending date | 
| AGE_45_54 | Rate of cases per 100,000 among people aged 45-54 years | Full week preceding the week-ending date | 
| AGE_55_64 | Rate of cases per 100,000 among people aged 55-64 years | Full week preceding the week-ending date | 
| AGE_65_74 | Rate of cases per 100,000 among people aged 65-74 years | Full week preceding the week-ending date | 
| AGE_75UP | Rate of cases per 100,000 among people aged 75+ years | Full week preceding the week-ending date | 

### weekly-case-rate-race.csv

This file contains the rate of cases per 100,000 people, stratified by week and race/ethnicity group. Rates are age-adjusted according to [the US 2000 standard population](https://www.cdc.gov/nchs/data/statnt/statnt20.pdf).

People with COVID-19 are categorized based on the date of diagnosis, and are aggregated by full-weeks starting each Sunday and ending on Saturday. For example, a person who was diagnosed with COVID-19 on Monday, June 21, 2021 would be categorized as diagnosed during the week ending June 26, 2021. Rates are suppressed if the count of the numerator is between 1 and 4, to prevent the display of unreliable rates.

Indicators include: 

| Variable name | Definition | Timeframe |  
| --------------|------------|-----------| 
| WEEK_ENDING | Week-ending date | | 
| ALL_RACE_ETHNICITY | Rate of cases per 100,000 among people of all race/ethnicity groups | Full week preceding the week-ending date | 
| ASIAN_PACIFIC_ISLANDER | Rate of cases per 100,000 among people reported as Asian or Pacific Islander | Full week preceding the week-ending date | 
| BLACK_AFRICAN_AMERICAN | Rate of cases per 100,000 among people reported as Black or African-American | Full week preceding the week-ending date | 
| HISPANIC_LATINO | Rate of cases per 100,000 among people reported as Hispanic/Latino | Full week preceding the week-ending date | 
| WHITE | Rate of cases per 100,000 among people reported as White | Full week preceding the week-ending date | 

Data on people identified as American Indian/Alaska Native, two or more races, or certain other races/ethnicities not listed are not included in this table. The Hispanic/Latino category includes people of any race. Race/ethnicity information is most complete for people who have been hospitalized or have died. There are much less data currently available on race/ethnicity for people who have not been hospitalized.

Differences in health outcomes among racial and ethnic groups are due to long-term institutional and personal biases against people of color. There is no evidence that these health inequities are due to personal traits. Lasting racism and an inequitable distribution of resources needed for wellness cause these health inequities. These resources include quality jobs, housing, health care and food, among others. The greater impact of the COVID-19 pandemic on people of color shows how these inequities influence health outcomes.

### weekly-death-rate-age.csv 

This file contains the rate of deaths per 100,000 people, stratified by week and age group. 

People with COVID-19 are categorized based on the date of death, and are aggregated by full-weeks starting each Sunday and ending on Saturday. For example, a person who died on Monday, June 21, 2021 would be categorized as having died during the week ending June 26, 2021. Rates are suppressed if the count of the numerator is between 1 and 4, to prevent the display of unreliable rates.

Indicators include: 

| Variable name | Definition | Timeframe |  
| --------------|------------|-----------| 
| WEEK_ENDING | Week-ending date | | 
| AGE_ALL_AGES | Rate of deaths per 100,000 among people of all ages | Full week preceding the week-ending date | 
| AGE_0_4 | Rate of deaths per 100,000 among people aged 0-4 years | Full week preceding the week-ending date | 
| AGE_5_12 | Rate of deaths per 100,000 among people aged 5-12 years | Full week preceding the week-ending date | 
| AGE_13_17 | Rate of deaths per 100,000 among people aged 13-17 years | Full week preceding the week-ending date | 
| AGE_18_24 | Rate of deaths per 100,000 among people aged 18-24 years | Full week preceding the week-ending date | 
| AGE_25_34 | Rate of deaths per 100,000 among people aged 25-34 years | Full week preceding the week-ending date | 
| AGE_35_44 | Rate of deaths per 100,000 among people aged 35-44 years | Full week preceding the week-ending date | 
| AGE_45_54 | Rate of deaths per 100,000 among people aged 45-54 years | Full week preceding the week-ending date | 
| AGE_55_64 | Rate of deaths per 100,000 among people aged 55-64 years | Full week preceding the week-ending date | 
| AGE_65_74 | Rate of deaths per 100,000 among people aged 65-74 years | Full week preceding the week-ending date | 
| AGE_75UP | Rate of deaths per 100,000 among people aged 75+ years | Full week preceding the week-ending date | 

The rate of deaths per 100,000 people is suppressed for a specific age group when the corresponding count of deaths is between 1 and 4 due to imprecise and unreliable estimates and also to protect patient confidentiality.

### weekly-death-rate-race.csv 

This file contains the rate of deaths per 100,000 people, stratified by week and race/ethnicity group. Rates are age-adjusted according to [the US 2000 standard population](https://www.cdc.gov/nchs/data/statnt/statnt20.pdf).

People with COVID-19 are categorized based on the date of death, and are aggregated by full-weeks starting each Sunday and ending on Saturday. For example, a person who died on Monday, June 21, 2021 would be categorized as having died during the week ending June 26, 2021. Rates are suppressed if the count of the numerator is between 1 and 4, to prevent the display of unreliable rates.

Indicators include: 

| Variable name | Definition | Timeframe |  
| --------------|------------|-----------| 
| WEEK_ENDING | Week-ending date | | 
| ALL_RACE_ETHNICITY | Rate of deaths per 100,000 among people of all race/ethnicity groups | Full week preceding the week-ending date | 
| ASIAN_PACIFIC_ISLANDER | Rate of deaths per 100,000 among people reported as Asian or Pacific Islander | Full week preceding the week-ending date | 
| BLACK_AFRICAN_AMERICAN | Rate of deaths per 100,000 among people reported as Black or African-American | Full week preceding the week-ending date | 
| HISPANIC_LATINO | Rate of deaths per 100,000 among people reported as Hispanic/Latino | Full week preceding the week-ending date | 
| WHITE | Rate of deaths per 100,000 among people reported as White | Full week preceding the week-ending date | 

The rate of deaths per 100,000 people is suppressed for a specific race/ethnicity group when the corresponding count of deaths is between 1 and 4 due to imprecise and unreliable estimates and also to protect patient confidentiality.

Data on people identified as American Indian/Alaska Native, two or more races, or certain other races/ethnicities not listed are not included in this table. The Hispanic/Latino category includes people of any race. Race/ethnicity information is most complete for people who have been hospitalized or have died. There are much less data currently available on race/ethnicity for people who have not been hospitalized.

Note that sum of counts in this file may not match citywide values because of records with missing demographic information.

Differences in health outcomes among racial and ethnic groups are due to long-term institutional and personal biases against people of color. There is no evidence that these health inequities are due to personal traits. Lasting racism and an inequitable distribution of resources needed for wellness cause these health inequities. These resources include quality jobs, housing, health care and food, among others. The greater impact of the COVID-19 pandemic on people of color shows how these inequities influence health outcomes.

### weekly-hosp-rate-age.csv 

This file contains the rate of hospitalizations per 100,000 people, stratified by week and age group. 

People with COVID-19 are categorized based on the date of admission, and are aggregated by full-weeks starting each Sunday and ending on Saturday. For example, a person who was admitted to the hospital on Monday, June 21, 2021 would be categorized as hospitalized during the week ending June 26, 2021. Rates are suppressed if the count of the numerator is between 1 and 4, to prevent the display of unreliable rates.

Indicators include: 

| Variable name | Definition | Timeframe |  
| --------------|------------|-----------| 
| WEEK_ENDING | Week-ending date | | 
| AGE_ALL_AGES | Rate of hospitalized cases per 100,000 among people of all ages | Full week preceding the week-ending date | 
| AGE_0_4 | Rate of hospitalized cases per 100,000 among people aged 0-4 years | Full week preceding the week-ending date | 
| AGE_5_12 | Rate of hospitalized cases per 100,000 among people aged 5-12 years | Full week preceding the week-ending date | 
| AGE_13_17 | Rate of hospitalized cases per 100,000 among people aged 13-17 years | Full week preceding the week-ending date | 
| AGE_18_24 | Rate of hospitalized cases per 100,000 among people aged 18-24 years | Full week preceding the week-ending date | 
| AGE_25_34 | Rate of hospitalized cases per 100,000 among people aged 25-34 years | Full week preceding the week-ending date | 
| AGE_35_44 | Rate of hospitalized cases per 100,000 among people aged 35-44 years | Full week preceding the week-ending date | 
| AGE_45_54 | Rate of hospitalized cases per 100,000 among people aged 45-54 years | Full week preceding the week-ending date | 
| AGE_55_64 | Rate of hospitalized cases per 100,000 among people aged 55-64 years | Full week preceding the week-ending date | 
| AGE_65_74 | Rate of hospitalized cases per 100,000 among people aged 65-74 years | Full week preceding the week-ending date | 
| AGE_75UP | Rate of hospitalized cases per 100,000 among people aged 75+ years | Full week preceding the week-ending date | 

The rate of hospitalized cases per 100,000 people is suppressed for a specific age group when the count of hospitalized deaths is between 1 and 4 due to imprecise and unreliable estimates.

### weekly-hosp-rate-race.csv 

This file contains the rate of hospitalizations per 100,000 people, stratified by week and race/ethnicity group. Rates are age-adjusted according to [the US 2000 standard population](https://www.cdc.gov/nchs/data/statnt/statnt20.pdf).

People with COVID-19 are categorized based on the date of admission, and are aggregated by full-weeks starting each Sunday and ending on Saturday. For example, a person who was admitted to the hospital on Monday, June 21, 2021 would be categorized as hospitalized during the week ending June 26, 2021. Rates are suppressed if the count of the numerator is between 1 and 4, to prevent the display of unreliable rates.

Indicators include: 

| Variable name | Definition | Timeframe |  
| --------------|------------|-----------| 
| WEEK_ENDING | Week-ending date | | 
| ALL_RACE_ETHNICITY | Rate of hospitalized cases per 100,000 among people of all race/ethnicity groups | Full week preceding the week-ending date | 
| ASIAN_PACIFIC_ISLANDER | Rate of hospitalized cases per 100,000 among people reported as Asian or Pacific Islander | Full week preceding the week-ending date | 
| BLACK_AFRICAN_AMERICAN | Rate of hospitalized cases per 100,000 among people reported as Black or African-American | Full week preceding the week-ending date | 
| HISPANIC_LATINO | Rate of hospitalized cases per 100,000 among people reported as Hispanic/Latino | Full week preceding the week-ending date | 
| WHITE | Rate of hospitalized cases per 100,000 among people reported as White | Full week preceding the week-ending date | 

The rate of hospitalized cases per 100,000 people is suppressed for a specific race/ethnicity group when the count of hospitalized deaths is between 1 and 4 due to imprecise and unreliable estimates.

Data on people identified as American Indian/Alaska Native, two or more races, or certain other races/ethnicities not listed are not included in this table. The Hispanic/Latino category includes people of any race. Race/ethnicity information is most complete for people who have been hospitalized or have died. There are much less data currently available on race/ethnicity for people who have not been hospitalized.

Note that sum of counts in this file may not match citywide values because of records with missing demographic information.

Differences in health outcomes among racial and ethnic groups are due to long-term institutional and personal biases against people of color. There is no evidence that these health inequities are due to personal traits. Lasting racism and an inequitable distribution of resources needed for wellness cause these health inequities. These resources include quality jobs, housing, health care and food, among others. The greater impact of the COVID-19 pandemic on people of color shows how these inequities influence health outcomes.