Individuals; Tax filers and dependants by total income, sex and age groups (final T1 Family File; T1FF).

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 molecular diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

Percent positivity is calculated as the number of positive tests among community residents conducted during the 14 days divided by the total number of positive and negative tests among community residents during the same period. If someone was tested more than once during that 14 day period, then those multiple test results (regardless of whether they were positive or negative) are included in the calculation.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Data suppression is applied when the rate is <5 cases per 100,000 or if there are <5 cases within the town. Information on why data suppression rules are applied can be found online here: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/suppression.htm

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

This dataset includes a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020.

Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

Analysis of ‘COVID-19 case rate per 100,000 population and percent test positivity in the last 7 days by town - ARCHIVE’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ceb31b99-df28-4d47-bfc9-dd3ab1896172 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

This dataset includes a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020.

Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

--- Original source retains full ownership of the source dataset ---

This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are based on national threshold values, regardless of selected geography; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% national income threshold. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

Distribution of employment income of individuals by sex and work activity, Canada, provinces and selected census metropolitan areas, annual.

Monthly COVID-19 death rates per 100,000 population stratified by age group, race/ethnicity, sex, and region, with race/ethnicity by age group and age group by race/ethnicity double stratification

Rates of confirmed COVID-19 in Ottawa Wards, excluding LTC and RH cases, and number of cases in LTCH and RH in Ottawa Wards. Data are provided for all cases (i.e. cumulative), cases reported within 30 days of the data pull (i.e. last 30 days), and cases reported within 14 days of the data pull (i.e. last 14 days). Based on the most up to date information available at 2pm from the COVID-19 Ottawa Database (The COD) on the day prior to publication.Rates of confirmed COVID-19 in Ottawa Wards, excluding LTC and RH cases, and number of cases in LTCH and RH in Ottawa Wards. Data are provided for all cases (i.e. cumulative), cases reported within 30 days of the data pull (i.e. last 30 days), and cases reported within 14 days of the data pull (i.e. last 14 days). Based on the most up to date information available at 2pm from the COVID-19 Ottawa Database (The COD) on the day prior to publication. You can see the map on Ottawa Public Health's website.Accuracy: Points of consideration for interpretation of the data:Data extracted by Ottawa Public Health at 2pm from the COVID-19 Ottawa Database (The COD) on May 12th, 2020. The COD is a dynamic disease reporting system that allow for continuous updates of case information. These data are a snapshot in time, reflect the most accurate information that OPH has at the time of reporting, and the numbers may differ from other sources. Cases are assigned to Ward geography based on their postal code and Statistics’ Canada’s enhanced postal code conversion file (PCCF+) released in January 2020. Most postal codes have multiple geographic coordinates linked to them. Thus, when available, postal codes were attributed to a XY coordinates based on the Single Link Identifier provided by Statistics’ Canada’s PCCF+. Otherwise, postal codes that fall within the municipal boundaries but whose SLI doesn’t, were attributed to the first XY coordinates within Ottawa listed in the PCCF+. For this reason, results for rural areas should be interpreted with caution as attribution to XY coordinates is less likely to be based on an SLI and rural postal codes typically encompass a much greater surface area than urban postal codes (e.i. greater variability in geographic attribution, less precision in geographic attribution). Population estimates are based on the 2016 Census. Rates calculated from very low case numbers are unstable and should be interpreted with caution. Low case counts have very wide 95% confidence intervals, which are the lower and upper limit within which the true rate lies 95% of the time. A narrow confidence interval leads to a more precise estimate and a wider confidence interval leads to a less precise estimate. In other words, rates calculated from very low case numbers fluctuate so much that we cannot use them to compare different areas or make predictions over time.Update Frequency: Biweekly Attributes:Ward Number – numberWard Name – textCumulative rate (per 100 000 population), excluding cases linked to outbreaks in LTCH and RH – cumulative number of residents with confirmed COVID-19 in a Ward, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardCumulative number of cases, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward, excluding cases linked to outbreaks in LTCH and RHCumulative number of cases linked to outbreaks in LTCH and RH - Number of residents with confirmed COVID-19 linked to an outbreak in a long-term care home or retirement home by WardRate (per 100 000 population) in the last 30 days, excluding cases linked to outbreaks in LTCH and RH –number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardNumber of cases in the last 30 days, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding cases linked to outbreaks in LTCH and RHNumber of cases in the last 30 days linked to outbreaks in LTCH and RH - Number of residents with confirmed COVID-19, reported in the 30 days prior to the data pull, linked to an outbreak in a long-term care home or retirement home by WardRate (per 100 000 population) in the last 14 days, excluding cases linked to outbreaks in LTCH and RH –number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardNumber of cases in the last 14 days, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding cases linked to outbreaks in LTCH and RHContact: OPH Epidemiology Team

This table provides the age-standardized mortality rates per 100,000 population, for the three selected causes of death and all causes combined for both the local geographic area and Alberta for the most recent three-year period available. The three selected causes of death are Circulatory System, Neoplasms and External Causes (Injury). Age standardization is a technique applied to make rates comparable across groups with different age distributions. A simple rate is defined as the number of people with a particular condition divided by the whole population. An age-standardized rate is defined as the number of people with a condition divided by the population within each age group. Standardizing (adjusting) the rate across age groups allows a more accurate comparison between populations that have different age structures. Age standardization is typically done when comparing rates across time periods, different geographic areas, and or population sub-groups (e.g. ethnic group). This indicator dataset contains information at both Local Geographic Area (for example, Lacombe, Red Deer - North, Calgary - West Bow, etc.) and Alberta levels. Local geographic area refers to 132 geographic areas created by Alberta Health (AH) and Alberta Health Services (AHS) based on census boundaries. This table is the part of "Alberta Health Primary Health Care - Community Profiles" report published March 2015

A dataset listing the richest zip codes in New Jersey per the most current US Census data, including information on rank and average income.

Cumulation of the weekly release of COVID-19 data for Maricopa County by City. Includes COVID Case Rate per 100k population as viewed on the Maricopa County School Reopening Dashboard map by week. For more information about the data, visit: https://www.maricopa.gov/5594/School-Metrics.

Cumulation of the weekly release of COVID-19 data for Maricopa County by Elementary School District. Includes COVID Case Rate per 100k population as viewed on the Maricopa County School Reopening Dashboard map by week. For more information about the data, visit: https://www.maricopa.gov/5594/School-Metrics.

A dataset listing Wisconsin cities by population for 2024.

Cumulation of the weekly release of COVID-19 data for Maricopa County by High School District. Includes COVID Case Rate per 100k population as viewed on the Maricopa County School Reopening Dashboard map by week. For more information about the data, visit: https://www.maricopa.gov/5594/School-Metrics.

Cumulation of the weekly release of COVID-19 data for Maricopa County by Zip Code. Includes COVID Case Rate per 100k population as viewed on the Maricopa County School Reopening Dashboard map by week. For more information about the data, visit: https://www.maricopa.gov/5594/School-Metrics.