The leading causes of death among the white population of the United States are cardiovascular diseases and cancer. Cardiovascular diseases and cancer accounted for a combined **** percent of all deaths among this population in 2023. In 2020 and 2021, COVID-19 was the third leading cause of death among white people but was the eighth leading cause in 2023. Disparities in causes of death In the United States, there exist disparities in the leading causes of death based on race and ethnicity. For example, chronic liver disease and cirrhosis is the ***** leading cause of death among the white population and the ******* among the Hispanic population but is not among the ten leading causes for Black people. On the other hand, homicide is the ******leading cause of death among the Black population but is not among the 10 leading causes for whites or Hispanics. However, cardiovascular diseases and cancer by far account for the highest share of deaths for every race and ethnicity. Diseases of despair The American Indian and Alaska Native population in the United States has the highest rates of death from suicide, drug overdose, and alcohol. Together, these three behavior-related conditions are often referred to as diseases of despair. Asians have by far the lowest rates of death due to drug overdose and alcohol, as well as slightly lower rates of suicide.

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. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. 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. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

Context

"The U.S. has now passed the grim milestone of 150,000 coronavirus deaths with Califoria, Florida and Texas all recently setting single-day records for deaths from the pandemic. On July 29, one American was dying from Covid-19 every minute with the total number of infections approaching 4.4 million. Studies have found that men are dying at nearly twice the rate of women in the U.S. while the pandemic is proving especially devastating for black Americans who are dying at nearly three times the rate of white people." https://www.statista.com/chart/22430/coronavirus-deaths-by-race-in-the-us/

Content

"That's according to The COVID Tracking Project who state that 30,648 black lives have been lost to the coronavirus to date, accounting for 23 percent of all U.S. deaths where race is known. The deaths were broken down by race or ethnicity with 74 black Americans dying per 100,000 people compared to 30 white Americans per 100,000 people as of July 30, 2020."

Acknowledgements

Niall McCarthy, Data Journalist https://www.statista.com/chart/22430/coronavirus-deaths-by-race-in-the-us/ Photo United Nations COVID-19 Response on Unsplash

Inspiration

Covid-19

In 2024, white Americans remained the largest racial group in the United States, numbering just over 254 million. Black Americans followed at nearly 47 million, with Asians totaling around 23 million. Hispanic residents, of any race, constituted the nation’s largest ethnic minority. Despite falling fertility, the U.S. population continues to edge upward and is expected to reach 342 million in 2025. International migrations driving population growth The United States’s population growth now hinges on immigration. Fertility rates have long been in decline, falling well below the replacement rate of 2.1. On the other hand, international migration stepped in to add some 2.8 million new arrivals to the national total that year. Changing demographics and migration patterns Looking ahead, the U.S. population is projected to grow increasingly diverse. By 2060, the Hispanic population is expected to grow to 27 percent of the total population. Likewise, African Americans will remain the largest racial minority at just under 15 percent.

This dataset is no longer being updated as of 5/11/2023. It is being retained on the Open Data Portal for its potential historical interest.

This table displays the number of COVID-19 deaths among Cambridge residents by race and ethnicity. The count reflects total deaths among Cambridge COVID-19 cases.

The rate column shows the rate of COVID-19 deaths among Cambridge residents by race and ethnicity. The rates in this chart were calculated by dividing the total number of deaths among Cambridge COVID-19 cases for each racial or ethnic category by the total number of Cambridge residents in that racial or ethnic category, and multiplying by 10,000. The rates are considered “crude rates” because they are not age-adjusted. Population data are from the U.S. Census Bureau’s 2014–2018 American Community Survey estimates and may differ from actual population counts.

Of note:

This chart reflects the time period of March 25 (first known Cambridge death) through present.

It is important to note that race and ethnicity data are collected and reported by multiple entities and may or may not reflect self-reporting by the individual case. The Cambridge Public Health Department (CPHD) is actively reaching out to cases to collect this information. Due to these efforts, race and ethnicity information have been confirmed for over 80% of Cambridge cases, as of June 2020.

Race/Ethnicity Category Definitions: “White” indicates “White, not of Hispanic origin.” “Black” indicates “Black, not of Hispanic origin.” “Hispanic” refers to a person having Hispanic origin. A person having Hispanic origin may be of any race. “Asian” indicates “Asian, not of Hispanic origin.” To protect individual privacy, a category is suppressed when it has one to four people. Categories with zero cases are reported as zero. "Other" indicates multiple races, another race that is not listed above, and cases who have reported nationality in lieu of a race category recognized by the US Census. Population data are from the U.S. Census Bureau’s 2014–2018 American Community Survey estimates and may differ from actual population counts. "Other" also includes a small number of people who identify as Native American or Native Hawaiian/Pacific islander. Because the count for Native Americans or Native Hawaiian/Pacific Islanders is currently < 5 people, these categories have been combined with “Other” to protect individual privacy.

Note: Note: Starting October 10th, 2025 this dataset is deprecated and is no longer being updated. As of April 27, 2023 updates changed from daily to weekly.

Summary The cumulative number of confirmed COVID-19 deaths among Maryland residents by race and ethnicity: African American; White; Hispanic; Asian; Other; Unknown.

Description The MD COVID-19 - Confirmed Deaths by Race and Ethnicity Distribution data layer is a collection of the statewide confirmed and probable COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by categories of race and ethnicity. A death is classified as confirmed if the person had a laboratory-confirmed positive COVID-19 test result. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Probable deaths are available from the MD COVID-19 - Probable Deaths by Race and Ethnicity Distribution data layer.

Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Data Source: CA Department of Finance, Demographic Research Unit

Report P-3: Population Projections, California, 2010-2060 (Baseline 2019 Population Projections; Vintage 2020 Release). Sacramento: California. July 2021.

This data biography shares the how, who, what, where, when, and why about this dataset. We, the epidemiology team at Napa County Health and Human Services Agency, Public Health Division, created it to help you understand where the data we analyze and share comes from. If you have any further questions, we can be reached at epidemiology@countyofnapa.org.

Data dashboard featuring this data: Napa County Demographics https://data.countyofnapa.org/stories/s/bu3n-fytj

How was the data collected? Population projections use the following demographic balancing equation: Current Population = Previous Population + (Births - Deaths) +Net Migration

Previous Population: the starting point for the population projection estimates is the 2020 US Census, informed by the Population Estimates Program data.

Births and Deaths: birth and death totals came from the California Department of Public Health, Vital Statistics Branch, which maintains birth and death records for California.

Net Migration: multiple sources of administrative records were used to estimate net migration, including driver’s license address changes, IRS tax return data, Medicare and Medi-Cal enrollment, federal immigration reports, elementary school enrollments, and group quarters population.

Who was included and excluded from the data? Previous Population: The goal of the US Census is to reflect all populations residing in a given geographic area. Results of two analyses done by the US Census Bureau showed that the 2020 Census total population counts were consistent with recent counts despite the challenges added by the pandemic. However, some populations were undercounted (the Black or African American population, the American Indian or Alaska Native population living on a reservation, the Hispanic or Latino population, and people who reported being of Some Other Race), and some were overcounted (the Non-Hispanic White population and the Asian population). Children, especially children younger than 4, were also undercounted.

Births and Deaths: Birth records include all people who are born in California as well as births to California residents that happened out of state. Death records include people who died while in California, as well as deaths of California residents that occurred out of state. Because birth and death record data comes from a registration process, the demographic information provided may not be accurate or complete.

Net Migration: each of the multiple sources of administrative records that were used to estimate net migration include and exclude different groups. For details about methodology, see https://dof.ca.gov/wp-content/uploads/sites/352/2023/07/Projections_Methodology.pdf.

Where was the data collected?  Data is collected throughout California. This subset of data includes Napa County.

When was the data collected? This subset of Napa County data is from Report P-3: Population Projections, California, 2010-2060 (Baseline 2019 Population Projections; Vintage 2020 Release). Sacramento: California. July 2021.

These 2019 baseline projections incorporate the latest historical population, birth, death, and migration data available as of July 1, 2020. Historical trends from 1990 through 2020 for births, deaths, and migration are examined. County populations by age, sex, and race/ethnicity are projected to 2060.

Why was the data collected?  The population projections were prepared under the mandate of the California Government Code (Cal. Gov't Code § 13073, 13073.5).

Where can I learn more about this data? https://dof.ca.gov/Forecasting/Demographics/Projections/ https://dof.ca.gov/wp-content/uploads/sites/352/Forecasting/Demographics/Documents/P3_Dictionary.txt https://dof.ca.gov/wp-content/uploads/sites/352/2023/07/Projections_Methodology.pdf

In 2023, 690 black and pardo Brazilians were killed by security agents in Rio de Janeiro, Brazil. Compared to the 71 whites who died in the same circumstances, the number of black civilians killed in that state was almost ten times greater. In the state of Bahia, the disparity was even greater, with 1,321 blacks killed by police compared to 71 whites.

Content

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

About this dataset

Context

The dataset tabulates the population of Dead Lake township by race. It includes the population of Dead Lake township across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Dead Lake township across relevant racial categories.

Key observations

The percent distribution of Dead Lake township population by race (across all racial categories recognized by the U.S. Census Bureau): 95.91% are white, 0.63% are some other race and 3.46% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Dead Lake township
• Population: The population of the racial category (excluding ethnicity) in the Dead Lake township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Dead Lake township total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Dead Lake township Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Dead Lake township by race. It includes the distribution of the Non-Hispanic population of Dead Lake township across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Dead Lake township across relevant racial categories.

Key observations

Of the Non-Hispanic population in Dead Lake township, the largest racial group is White alone with a population of 602 (95.86% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Dead Lake township
• Population: The population of the racial category (for Non-Hispanic) in the Dead Lake township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Dead Lake township total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Dead Lake township Population by Race & Ethnicity. You can refer the same here

Context

COVID-19 Cases and Deaths by Race/Ethnicity

Content

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The age-adjusted rates are directly standardized using the 2018 ASRH Connecticut population estimate denominators (available here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Annual-State--County-Population-with-Demographics).

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age-adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

This dataset will be updated on a daily basis. Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differs from the timestamp in DPH's daily PDF reports.

Acknowledgements

Thanks to catalog.data.gov.

BackgroundCOVID-19 has had a disproportionate impact on racial and ethnic minorities compared to White people. Studies have not sufficiently examined how sex and age interact with race/ethnicity, and potentially shape COVID-19 outcomes. We sought to examine disparities in COVID-19 outcomes by race, sex and age over time, leveraging data from Michigan, the only state whose Department of Health and Human Services (DHSS) publishes cross-sectional race, sex and age data on COVID-19.MethodsThis is an observational study using publicly available COVID-19 data (weekly cases, deaths, and vaccinations) from August 31 2020 to June 9 2021. Outcomes for descriptive analysis were age-standardized COVID-19 incidence and mortality rates, case-fatality rates by race, sex, and age, and within-gender and within-race incidence rate ratios and mortality rate ratios. We used descriptive statistics and linear regressions with age, race, and sex as independent variables.ResultsThe within-sex Black-White racial gap in COVID-19 incidence and mortality decreased at a similar rate among men and women but the remained wider among men. As of June 2021, compared to White people, incidence was lower among Asian American and Pacific Islander people by 2644 cases per 100,000 people and higher among Black people by 1464 cases per 100,000 people. Mortality was higher among those aged 60 or greater by 743.6 deaths per 100,000 people vs those 0–39. The interaction between race and age was significant between Black race and age 60 or greater, with an additional 708.5 deaths per 100,000 people vs White people aged 60 or greater. Black people had a higher case fatality rate than White people.ConclusionCOVID-19 incidence, mortality and vaccination patterns varied over time by race, age and sex. Black-White disparities decreased over time, with a larger effect on Black men, and Older Black people were particularly more vulnerable to COVID-19 in terms of mortality. Considering different individual characteristics such as age may further help elucidate the mechanisms behind racial and gender health disparities.

In 2020, there were a total of 384,536 deaths in the United States caused by COVID-19. White, non-Hispanics accounted for 232,555 COVID deaths that year. This statistic shows the total number of deaths due to COVID-19 in the United States in 2020, 2021, and 2022, by race/ethnicity.

The objective of this study is to assess the associations of race/ethnicity and severe housing problems with COVID-19 death rates in the US throughout the first three waves of the COVID-19 pandemic in the US. We conducted a cross-sectional study using a negative binomial regression model to estimate factors associated with COVID-19 deaths in 3063 US counties between March 2020 and July 2021 by wave and pooled across all three waves. In Wave 1, counties with larger percentages of Black, Hispanic, American Indian and Alaska Native (AIAN), and Asian American and Pacific Islander (AAPI) residents experienced a greater risk of deaths per 100,000 residents of +22.82 (95% CI 15.09, 30.56), +7.50 (95% CI 1.74, 13.26), +13.52 (95% CI 8.07, 18.98), and +5.02 (95% CI 0.92, 9.12), respectively, relative to counties with larger White populations. By Wave 3, however, the mortality gap declined considerably in counties with large Black, AIAN and AAPI populations: +10.38 (95% CI 4.44, 16.32), +7.14 (95% CI 1.14, 13.15), and +3.72 (95% CI 0.81, 6.63), respectively. In contrast, the gap increased for counties with a large Hispanic population: +13 (95% CI 8.81, 17.20). Housing problems were an important predictor of COVID-19 deaths. However, while housing problems were associated with increased COVID-19 mortality in Wave 1, by Wave 3, they contributed to magnified mortality in counties with large racial/ethnic minority groups. Our study revealed that focusing on a wave-by-wave analysis is critical to better understand how the associations of race/ethnicity and housing conditions with deaths evolved throughout the first three COVID-19 waves in the US. COVID-19 mortality initially took hold in areas characterized by large racial/ethnic minority populations and poor housing conditions. Over time, as the virus spread to predominantly White counties, these disparities decreased substantially but remained sizable.

Each Maryland County's number of infant deaths and infant mortality rates by race in 2012 and 2013. Includes: a) Number of Infant Deaths of All Races, 2012, b) Number of Infant Deaths of All Races, 2013, c) Infant Mortality Rate of All Races Per 1,000 Live Births, 2012, d) Infant Mortality Rate of All Races Per 1,000 Live Births, 2013, e) White Infant Deaths, 2012, f) White Infant Deaths, 2013, g) White Infant Mortality Rate Per 1,000 Live Births 2012, h) White Infant Mortality Rate Per 1,000 Live Births 2013, i) Black Infant Deaths, 2012, j) Black Infant Deaths, 2013, k) Black Infant Mortality Rate Per 1,000 Live Births 2012, l) Black Infant Mortality Rate Per 1,000 Live Births 2013, m) Number of Infant Deaths All Races from 2004-2008, n) Number of Infant Deaths All Races from 2009-2013, o) Average Infant Mortality Rate of All Races from 2004-2008, p) Average Infant Mortality Rate of All Races from 2009-2013, q) Percent Change of Infant Deaths. Values = Rates based on <5 deaths are not presented since rates based on small numbers are statistically unreliable.

This project contains a city-level panel dataset of deaths-by-cause from the U.S. Census Bureau for the years 1915 to 1938, annually, as reported in the publication “Mortality Statistics.” For some cities, the data is available separately for white and non-white deaths. This data is based on transcripts of death certificates received by the Census Bureau from certain areas of the country called “registration areas.” In 1918, the data covers an estimated population of 82,091,523, or 77.8% total estimated population of the United States, and includes 30 states, the District of Columbia, and 27 cities in nonregistration states. States and cities are added over time, so the panel is not complete. When data is reported based on white and non-white deaths, the majority (95%+) are Blacks (1918, page 11).

Age-adjusted rate of death (all causes) by sex, race/ethnicity, age; trends. Source: Santa Clara County Public Health Department, VRBIS, 2007-2016. Data as of 05/26/2017; U.S. Census Bureau; 2010 Census, Tables PCT12, PCT12H, PCT12I, PCT12J, PCT12K, PCT12L, PCT12M; generated by Baath M.; using American FactFinder; Accessed June 20, 2017. METADATA:Notes (String): Lists table title, notes and sourcesYear (Numeric): Year of dataCategory (String): Lists the category representing the data: Santa Clara County is for total population, sex: Male and Female, race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only); age categories as follows: child age groups: <1, 1 to 4, 5 to 11, 12 to 17; youth age groups: 10 to 19, 20 to 24; age groups 1: 0 to 17, 18 to 64, 65+; age groups 2: <1, 1 to 4, 5 to 14, 15 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, 65 to 74, 75 to 84, 85+; United StatesRate per 100,000 people (Numeric): Rate of deaths by all causes. Rates for age groups are reported as age-specific rates per 100,000 people. All other rates are age-adjusted rates per 100,000 people.

White-tailed deer Odocoileus virginianus are the most popular big game animal in the United States. Recreational harvest of these animals is a critical tool in population management, as well as an important financial resource for state economies and wildlife agencies. Thus, herd health evaluations can provide information to wildlife managers tasked with developing sustainable harvest practices while monitoring for emergent problems. The purpose of our study was to document causes of illness and natural mortality in New York white-tailed deer submitted for post mortem evaluation. Animals were presented by members of the public and wildlife management personnel due to abnormal behavior or unexplained death. We describe demographic and seasonal associations among gross and histologic evaluation and diagnostic testing. Post mortem examinations were performed on 735 white-tailed deer submitted for necropsy in New York from January 2011 to November 2017. Causes of euthanasia or mortality were classified into nine categories. The most common findings were bacterial infections, trauma not evident at time of collection, and nutritional issues, primarily starvation. Using a multinomial logistic regression model, we looked for associations between the mortality categories and age, sex and season. Compared to the baseline of bacterial deaths, adults were less likely to have died from nutritional and parasitic causes, males were less likely to have died from other causes, and risk of death from nutritional reasons decreased from season to season, with lowest risk in winter. These methods can help wildlife biologists track changes in disease dynamics over time.

Methods Two of the highest priorities, also reflected in the New York State Interagency CWD Risk Minimization Plan, are to detect chronic wasting disease (CWD) in the deer population and document causes of death and disease in white-tailed deer. Standardized criteria for submission in the surveillance program are: 1) live deer behaving abnormally or in poor body condition necessitating humane euthanasia and; 2) deer found dead without an obvious cause of death or found to have some abnormality. DEC may be notified of deer meeting these criteria by members of the public or law enforcement and can submit the animal for necropsy and diagnostic testing. Because the surveillance program specifically excludes deer that died from obvious predation, hunting, and deer-vehicle collisions, animals collected do not represent the New York population as a whole; however, they are valuable for assessing the breadth of diseases affecting wild deer and establishing a standardized baseline for future assessment. A benefit of this program is that these animals can serve as sentinels for emerging diseases. This type of opportunistic surveillance is a widely used method for states to prioritize deer that could be infected by CWD (Joly et al. 2009). Providing a basis for comparison will allow states to refine their surveillance systems to be better informed about white-tailed deer diseases by demo- graphic categories and seasonality.

For the present study, records from deer presented for necropsy through the surveillance program from 2011 to 2017 were compiled to retrospectively evaluate disease occurrence in a subset of the New York deer population. A total of 534 deer out of 735 that died between January 2011 to November 2017 met the criteria for inclusion in the study. Deer that died from obvious, non-natural causes, including deer killed for diagnostic tests (9), forensic studies (102), research (21), hunter killed (49), obvious vehicular trauma and predation (20) were excluded. The study population consisted of 230 females, 169 males, and 135 animals of unknown sex. There were 227 adults, 157 juveniles, 17 neonates, and 133 deer of unknown age. Weight data was available for 215 cases in which full carcasses were submitted.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. Each Maryland County's number of infant deaths and infant mortality rates by race in 2012 and 2013. Includes: a) Number of Infant Deaths of All Races - 2012 - b) Number of Infant Deaths of All Races - 2013 - c) Infant Mortality Rate of All Races Per 1 - 000 Live Births - 2012 - d) Infant Mortality Rate of All Races Per 1 - 000 Live Births - 2013 - e) White Infant Deaths - 2012 - f) White Infant Deaths - 2013 - g) White Infant Mortality Rate Per 1 - 000 Live Births 2012 - h) White Infant Mortality Rate Per 1 - 000 Live Births 2013 - i) Black Infant Deaths - 2012 - j) Black Infant Deaths - 2013 - k) Black Infant Mortality Rate Per 1 - 000 Live Births 2012 - l) Black Infant Mortality Rate Per 1 - 000 Live Births 2013 - m) Number of Infant Deaths All Races from 2004-2008 - n) Number of Infant Deaths All Races from 2009-2013 - o) Average Infant Mortality Rate of All Races from 2004-2008 - p) Average Infant Mortality Rate of All Races from 2009-2013 - q) Percent Change of Infant Deaths. Values = Rates based on <5 deaths are not presented since rates based on small numbers are statistically unreliable. Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/Health/MD_VitalStatistics/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.