For most years between 1603 and 1680, plague was responsible for less than one percent of all deaths in London. However, when epidemics did break out they could often be responsible for more than half of all deaths in the city during those years, even going as high as 86 percent in 1603. This was the highest share of deaths due to plague in London in the given time period, although the final epidemic shown in the graph is remembered as the most devastating, taking almost 70,000 lives during the Great Plague of London in 1665.

The Black Death was the largest and deadliest pandemic of Yersinia pestis recorded in human history, and likely the most infamous individual pandemic ever documented. The plague originated in the Eurasian Steppes, before moving with Mongol hordes to the Black Sea, where it was then brought by Italian merchants to the Mediterranean. From here, the Black Death then spread to almost all corners of Europe, the Middle East, and North Africa. While it was never endemic to these regions, it was constantly re-introduced via trade routes from Asia (such as the Silk Road), and plague was present in Western Europe until the seventeenth century, and the other regions until the nineteenth century. Impact on Europe In Europe, the major port cities and metropolitan areas were hit the hardest. The plague spread through south-western Europe, following the arrival of Italian galleys in Sicily, Genoa, Venice, and Marseilles, at the beginning of 1347. It is claimed that Venice, Florence, and Siena lost up to two thirds of their total population during epidemic's peak, while London, which was hit in 1348, is said to have lost at least half of its population. The plague then made its way around the west of Europe, and arrived in Germany and Scandinavia in 1348, before travelling along the Baltic coast to Russia by 1351 (although data relating to the death tolls east of Germany is scarce). Some areas of Europe remained untouched by the plague for decades; for example, plague did not arrive in Iceland until 1402, however it swept across the island with devastating effect, causing the population to drop from 120,000 to 40,000 within two years. Reliability While the Black Death affected three continents, there is little recorded evidence of its impact outside of Southern or Western Europe. In Europe, however, many sources conflict and contrast with one another, often giving death tolls exceeding the estimated population at the time (such as London, where the death toll is said to be three times larger than the total population). Therefore, the precise death tolls remain uncertain, and any figures given should be treated tentatively.

The Third Plague Epidemic began in the mid-1800s in Yunnan, China, (an area that is still a natural reservoir for the Yersinia pestis bacteria) and had a huge death toll across Asia in the next century. While plague was confined to the Yunnan region for some decades, the mass displacement and social upheaval caused by the Taiping Rebellion saw millions flee the area , bringing the disease to other parts of the country. A plague epidemic then emerged in British-controlled Hong Kong in 1894, where merchants then unknowingly transported infected rats to other parts of the empire along various trade routes. Arrival in Bombay The first Indian cases were reported in Bombay (present-day Mumbai), and the Bombay Presidency suffered more losses than any other region in India (although there were some individual years where the state of Punjab reported more deaths). As with most disease or famine outbreaks in the region, the British authorities were slow to react, and their eventual response was in many ways too late. In some cases authorities even facilitated the spread of the disease; with multiple accounts of the military forcing healthy people into quarantine camps, evicting and burning homes of the afflicted, or by using such excessive force that the public would refuse medical help. Spread in India Lack of understanding among the Indian public was also to their own detriment. Some religions in India forbid the killing of rats, while some people simply refused to acknowledge that they were sick. As the plague in Bombay spiraled out of control, many fled to other parts of the country, taking the plague with them. It is estimated that there were over one million deaths in India by 1902, and almost one million further deaths in 1903 alone. The first four months of 1904 also saw over half a million deaths, almost matching the entire total for 1902. Plague would remain endemic to India for the next few decades, and there are varying reports of up to 10 or 12 million total plague deaths in this time. The public health measures taken to combat the plague in the early 20th century would mark the beginnings of India's public health system, and some of the quarantine measures put in place by the colonial government were even used in 2020 during the outbreak of the COVID-19 pandemic.

Even in 2021, bubonic plague continues to exist in nature, and there are generally a few thousand human cases per year. Going back to the beginning of the 20th century, it is estimated that there were roughly one million cases per year in 1907. Within two decades, this number had fallen below one fifth of this level to 170,000 cases per year in the 1920s, and in the 1940s it was just over 20,000 per year. By the mid-20th century, it had fallen below 5,000 cases per year, but the rapid decrease in cases observed in the first half of the 1900s did not continue through the second half of the century. Even in 2019, there was one case of plague recorded in the United States. How infection occurs Yersinia pestis is the bacteria that causes the plague virus, and it is most commonly spread by rats and their fleas. The disease survives by fleas infecting rats, which in turn infect other fleas; the majority of rats survive the disease, which facilitates its spread; this is known as the "enzootic cycle ". Interestingly, the disease is usually fatal for the fleas, as it blocks their "stomachs" and causes them to starve; as the fleas get hungrier, they attempt to feed on more hosts, spreading the disease more rapidly. When the rats die, the parasitic fleas then search for a new host, which means that other animals (particularly mammals) are susceptible to this virus. While rat fleas can not survive on other hosts for very long, they can infect other (including human) fleas with the virus. The most common way for humans to contract the plague however, is when a rat flea bites its human host; during this process the flea simultaneously regurgitates Yersinia pestis bacteria into the wound, and this causes bubonic plague. Humans can then spread the disease among one another by coming into contact with the infected tissue or fluids of an infected person, or from the transfer of fleas. Continued existence of the plague Plague is extremely difficult to eradicate in nature, as rodent communities in the wild provide natural reservoirs for the disease to spread. In previous centuries, rats had much more frequent contact with humans for a variety of reasons; houses were more often made of wood (which made infestations easier), public spaces were much dirtier, and the presence of rats was tolerated more. As the understanding of epidemiology grew in the 20th century, this greatly reduced the frequency of plague in human populations. Unlike human diseases such as smallpox, which was eradicated through vaccination and other medical advancements, basic sanitation and the extermination of rats have been the driving force behind the decline of plague.

Legend: Year: the demography of the 6 "years of plague" is in bold characters and the column immediately at the left indicates, for each epidemic, the demography of the previous year. Mortality rate: crude mortality rate (as per thousand) evaluated for the heads of households; reflects the global damage of an epidemic S1 Fig. Survivors: number of surviving heads of households after exclusion of those not corresponding to individuals S2 Text. Single deaths: number of households with one reported death, whether or not of the head of household. Multiple deaths: number of deaths in the households where the concomitant death of several persons is reported S4 Text. Total deaths: total number of deaths taken into account for analysis (sum of lines 4 and 5). Death rate: ratio between the number of reported deaths and the sum of reported deaths and survivors (as percent); does not reflect the mortality of the year, but allows comparisons between groups of individuals or areas during the same year. Grouped deaths: households where multiple deaths took place, or households contiguous or separated by a single survivor in the register.Demography of the "years of plague" and of the previous years.

Legend: Line 1: Grouped death clusters (and Dijon). Line 2: total number of households (deaths and survivors). Line 3: number of deaths (grouped and non-grouped). Line 4: result of the chi-square comparing the cluster to the rest of the city. Line 5: death rate as percent. Column 2: pooled data from the 3 clusters of higher grouped death relative risk. Column 3: data restricted to the larger cluster of higher grouped death relative risk (included in the previous column). Column 4: pooled data from the 2 clusters of lower grouped death relative risk. Column 5: data for the whole city. For each cluster, data of the control (rest of the city) can be computed by subtraction from the data of the whole city.Deaths in the mortality-based clusters in 1400.

The Plague of Justinian was an outbreak of bubonic plague that ravaged the Mediterranean and its surrounding area, between 541 and 767CE. It was likely the first major outbreak of bubonic plague in Europe, and possibly the earliest pandemic to have been recorded reliably and with relative accuracy. Contemporary scholars described the symptoms and effects of the disease in detail, and these matched descriptions of the Black Death and Third Pandemic, leading most historians to believe that this was bubonic plague. It was also assumed that the plague originated in sub-Saharan Africa, before making its way along the Nile to Egypt, and then across the Mediterranean to Constantinople. In 2013, scientists were able to confirm that Justinian's Plague was in fact Yersinia pestis (the bacteria which causes bubonic plague), and recent theories suggest that the plague originated in the Eurasian Steppes, where the Black Death and Third Pandemic are also thought to have originated from, and that it was brought to Europe by the Hunnic Tribes of the sixth century. Plague of Justinian The pandemic itself takes its name from Emperor Justinian I, who ruled the Byzantine Empire (or Eastern Roman Empire) at the time of the outbreak, and who actually contracted the disease (although he survived). Reports suggest that Constantinople was the hardest hit city during the pandemic, and saw upwards of five thousand deaths per day during the most severe months. There are a multitude of sources with differing estimates for the plague's death toll, with most ranging between 25 and 100 million. Until recently, scholars assumed that the plague killed between one third and 40 percent of the world's population, with populations in infected regions declining by up to 25 percent in early years, and up to 60 percent over two centuries. The plague was felt strongest during the initial outbreak in Constantinople, however it remained in Europe for over two centuries, with the last reported cases in 767. Pre-2019 sources vary in their estimates, with some suggesting that up to half of the world's population died in the pandemic, while others state that it was just a quarter of the Mediterranean or European population; however most of them agree that the death toll was in the tens of millions. Historians have also argued about the plague's role in the fall of the Roman Empire, with opinions ranging from "fundamental" to "coincidental", although new evidence is more aligned with the latter theories. Challenging theories As with the recent studies which propose a different origin for the disease, one study conducted by researchers in Princeton and Jerusalem calls into question the accuracy of the death tolls estimated by historians in the 19th and 20th centuries. In 2019, L. Mordechai and M. Eisenberg published a series of papers suggesting that, although the plague devastated Constantinople, it did not have the same impact as the Black Death. The researchers argue that modern historians have taken a maximalist approach to the death tolls of the pandemic, and have applied the same models of distribution to Justinian's Plague as they believe occurred during the Black Death; however there is little evidence to support this. They examine the content and number of contemporary texts, as well archaeological, agricultural and genetic evidence which shows that the plague did spread across Europe, but did not seem to cause the same societal upheaval as the Black Death. It is likely that there will be further investigation into this outbreak in the following years, which may shed more light on the scale of this pandemic.

This data includes counts of deaths by race (total, Black, white) by age grouping and cause of death by Census Tract aggregated over a five-year period (2014-18). Data extracted from Pennsylvania's Electronic Death Registry System (EDRS) with the following disclaimer: "These data were provided by the Pennsylvania Department of Health. The Department specifically disclaims responsibility for any analyses, interpretations, or conclusions." Census tract of residence was determined using address-level data. Records were excluded from analysis if address was missing or unmatched to a census tract (≈1% records). Census tracts starting with 980x.xx, 981x.xx, and 982x.xx were also excluded due to a geocoding error. For cause of death by census tract, counts were assessed using census tract by age and race; records were excluded if age, race, or location data were missing. Census-tract level counts < 5 are censored and displayed as NULL. Census-tract-level counts may not equal county-level counts when summed due to censored data or missing data.

The CDC WONDER Mortality - Underlying Cause of Death online database is a county-level national mortality and population database spanning the years since 1979 -2008. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., Census region, Census division, state, and county), age group (including infant age groups), race (years 1979-1998: White, Black, and Other; years 1999-2008: American Indian or Alaska Native, Asian or Pacific Islander, Black or African American, and White), Hispanic origin (years 1979-1998: not available; years 1999-present: Hispanic or Latino, not Hispanic or Latino, Not Stated), gender, year of death, and underlying cause of death (years 1979-1998: 4-digit ICD-9 code and 72 cause-of-death recode; years 1999-present: 4-digit ICD-10 codes and 113 cause-of-death recode, as well as the Injury Mortality matrix classification for Intent and Mechanism), and urbanization level of residence (2006 NCHS urban-rural classification scheme for counties). The Compressed Mortality data are produced by the National Center for Health Statistics.

Legend: Line 1: Spatial entity. Line 2: total number of households (deaths and survivors). Line 3: number of deaths (grouped and non-grouped). Line 4: result of the chi-square comparing the group with the rest of the city. Line 5: death rate as percent. Column 2: data from the higher death relative risk cluster. Column 3: data from the higher grouped death relative risk cluster (mostly included in the previous one). Column 4: pooled data from the intramural parts of the northeastern Saint-Nicolas and Saint-Michel parishes (that largely corresponded to the higher death risk cluster of column 2). Column 5: data from the southwestern lower grouped death relative risk cluster. Column 6: data for the whole city. For each group, data of the control (rest of the city) can be computed by subtraction from the data of the whole city.Excess deaths in the northeast of intramural Dijon in 1428.

As early as 1319, allegations of well-poisoning had been levelled at leper communities in Europe, in an attempt to demonize and ostracize this group in society. In France and Spain in 1321, the "leper's plot" developed into a widespread conspiracy, claiming that leper communities were acting on the orders of the Jews or Spanish Moors, poisoning water supplies in an attempt to spread disease among Christians. Under royal decrees, many lepers were then tortured into confessing to these acts, and were subsequently burnt at the stake (although this was often carried out by vigilante mobs before it could be done by the courts). After the initial hysteria in 1321, the involvement of lepers was quickly dismissed, and a papal bull was introduced to grant protection to leper communities in France; this however did not dispel the myths surrounding the Jews' involvement in the conspiracy, and the issue emerged again a few decades later. Why the Jews were blamed When the bubonic plague made its way to Europe, many were eager to find a scapegoat on whom they could blame their misfortune. The "well-poisoning" accusations were quickly raised again against Jewish communities in France and Spain, and also across the German states. Historians point to several reasons why Jews were blamed for the Black Death; many Jews lived in separate communities and did not use the same common wells, and Jewish religious practices promote bathing and hand-washing; both of these factors meant that the plague spread differently and at a different rate among Jews than it did among the general population. Modern historians also point to the fact that Jews were often moneylenders, and their debtors often used the plague as an opportunity to expunge their debts; Holy Roman Emperor Charles IV also forfeited the property of Jews who were killed in the pogroms, giving further impetus to these mobs. Anti-Jewish pogroms The first reported pogroms took place in Toulon in 1348, before the violence then spread across the rest of Western Europe. Over the next three years, hundreds of Jewish communities were attacked and exterminated, with the majority taking place in the German states. A number of larger communities, such as those in Cologne and Mainz, were destroyed completely, resulting in the deaths and forced conversions of thousands of Jews. Pope Clement VI introduced two papal bulls in 1348, which granted the church's protection to Europe's Jews. He also urged the clergy and nobility to take measures that protected Jews in their local areas, although most sources show that authorities were apathetic or complicit in the actions of the mobs. There is even evidence that authorities orchestrated several of the pogroms, such as in Strasbourg, where authorities led the city's Jewish community to a newly-built house outside the city, but when they arrived, any Jews who refused to convert to Christianity were then burned alive inside the house. Legacy Many of the sources present different versions of events, with death tolls ranging from one hundred to several thousand in some cases, while some sources also claim that Jews set fire to their own homes rather than convert. It is now impossible to confirm the exact sequence of events, or the actual number of deaths resulting from these pogroms, however, the limited sources available do provide a brief foundation for the modern understanding of medieval anti-Semitism and the destruction inflicted upon the Jews during the plague. It is also important to note that these pogroms were not unique to the Black Death's outbreak, and there is evidence of numerous massacres of Jewish communities in the centuries that followed. The demographic impact of the massacres was that there was a mass exodus of Jews from west to Eastern Europe, to countries such as Poland (where they were actually welcomed by authorities). The consequences of this demographic shift would be most felt six centuries later, when millions of Jews across Eastern Europe were exterminated at the hands of the Nazi regime during the Holocaust.

The leading causes of death in Massachusetts are cancer, heart disease, unintentional injury, stroke, and chronic lower respiratory disease. These mortality rates tend to be higher for people of color; and Black residents have a higher premature mortality rate overall and Asian residents have a higher rate of mortality due to stroke.

Legend: Line 1: Mortality-based clusters (or Dijon). Line 2: Tax level group (Low: low-tax payers in 1399; Non-low: heads of households who were not low-tax payers in 1399). Line 3: total number of households (deaths and survivors) in 1400. Line 4: number of deaths in 1400. Line 5: result of the chi-square comparing the low-tax payers and the non-low-taxpayers. Line 6: death rates as percent. Columns 2 & 3: pooled data from the 3 clusters of higher grouped death relative risk in 1400. Columns 4 & 5: pooled data from the 2 clusters of lower grouped death relative risk in 1400. Columns 6 & 7: data for the whole city.Deaths (in 1400) of low-tax payers and non-low-tax payers of 1399.

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.

BackgroundAccording to one USA Renal Data System report, 57% of end-stage renal disease (ESRD) cases are attributed to hypertensive and diabetic nephropathy. Yet, trends in hypertension related ESRD mortality rates in adults ≥ 35 years of age have not been studied.ObjectivesThe aim of this retrospective study was to analyze the different trends hypertension related ESRD death rates among adults in the United States.MethodsDeath records from the CDC (Centers for Disease Control and Prevention Wide-Ranging OnLine Data for Epidemiologic Research) database were analyzed from 1999 to 2020 for hypertension related ESRD mortality in adults ≥ 35 years of age. Age-Adjusted mortality rates (AAMRs) per 100,000 persons and annual percent change (APC) were calculated and stratified by year, sex, race/ethnicity, place of death, and geographic location.ResultsHypertension-related ESRD caused a total of 721,511 deaths among adults (aged ≥ 35 years) between 1999 and 2020. The overall AAMR for hypertension related ESRD deaths in adults was 9.70 in 1999 and increased all the way up to 43.7 in 2020 (APC: 9.02; 95% CI: 8.19-11.04). Men had consistently higher AAMRs than woman during the analyzed years from 1999 (AAMR men: 10.8 vs women: 9) to 2020 (AAMR men: 52.2 vs women: 37.2). Overall AAMRs were highest in Non-Hispanic (NH) Black or African American patients (45.7), followed by NH American Indian or Alaska Natives (24.7), Hispanic or Latinos (23.4), NH Asian or Pacific Islanders (19.3), and NH White patients (15.4). Region-wise analysis also showed significant variations in AAMRs (overall AAMR: West 21.2; South: 21; Midwest: 18.3; Northeast: 14.2). Metropolitan areas had slightly higher AAMRs (19.1) than nonmetropolitan areas (19). States with AAMRs in 90th percentile: District of Columbia, Oklahoma, Mississippi, Tennessee, Texas, and South Carolina, had roughly double rates compared to states in 10th percentile.ConclusionsOverall hypertension related ESRD AAMRs among adults were seen to increase in almost all stratified data. The groups associated with the highest death rates were NH Black or African Americans, men, and populations in the West and metropolitan areas of the United States. Strategies and policies targeting these at-risk groups are required to control the rising hypertension related ESRD mortality.

THIS DATASET WAS LAST UPDATED AT 7:11 AM EASTERN ON DEC. 1

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

Legend: Line 1: Cluster (or Dijon). Line 2: total number of households (deaths and survivors). Line 3: number of high-tax payers. Line 4: result of the chi-square test comparing the proportions of high-tax payers within and outside the cluster. Line 5: number of low-tax payers. Line 6: result of the chi-square test comparing the proportions of low-tax payers within and outside the cluster. Line 7: number of survivors. Line 8: number of exempted for poverty. Line 9: number of exempted by privilege. Line10: number of taxpayers. Line 11: percent of high-tax payers among the taxpayers. Line 12: percent of low-tax payers among the taxpayers. Line 13: mean tax per taxpayer. Line 14: results of the Student's t test comparing the mean tax level of the cluster to that of the rest of the city. Columns 2–5: data from the clusters. Column 6: data for the whole city. ND: not done. For each cluster, data of the control (rest of the city) can be computed by subtraction from the data of the whole city.Characteristics in the tax level-based clusters.

Create maps of U.S. stroke death rates by county. Data can be stratified by age, race/ethnicity, and gender. Visit the CDC/DHDSP Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceMortality data were obtained from the National Vital Statistics System. Bridged-Race Postcensal Population Estimates were obtained from the National Center for Health Statistics. International Classification of Diseases, 10th Revision (ICD-10) codes: I60-I69; underlying cause of death.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC/DHDSP excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.' Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)   RRR: 3 digits represent race/ethnicity     All - Overall     AIA - American Indian and Alaska Native, non-Hispanic     API - Asian and Pacific Islander, non-Hispanic     BLK - Black, non-Hispanic     HIS - Hispanic     WHT - White, non-Hispanic   S: 1 digit represents sex/gender     A - All    F - Female     M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound.  Example: The column 'BLK_M_65UP' displays rates per 100,000 black men aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 100,000 population. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and gender).At least one of the following 3 criteria: At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

In 2020, the death rate for COVID-19 in the United States among Black or African American, non-Hispanics was around 155 per 100,000 population. That year there was a total of 61,401 deaths from COVID-19 among Black or African American, non-Hispanics. This statistic shows the death rate for COVID-19 in the United States in 2020, 2021, and 2022, by race/ethnicity.

BackgroundStudies on the barriers migrant women face when trying to access healthcare services in South Africa have emphasized economic factors, fear of deportation, lack of documentation, language barriers, xenophobia, and discrimination in society and in healthcare institutions as factors explaining migrants’ reluctance to seek healthcare. Our study aims to visualize some of the outcome effects of these barriers by analyzing data on maternal death and comparing the local population and black African migrant women from the South African Development Countries (SADC) living in South Africa. The heightened maternal mortality of black migrant women in South Africa can be associated with the hidden costs of barriers migrants face, including xenophobic attitudes experienced at public healthcare institutions.MethodsOur analysis is based on data on reported causes of death (COD) from the South African Department of Home Affairs (DHA). Statistics South Africa (Stats SA) processed the data further and coded the cause of death (COD) according to the WHO classification of disease, ICD10. The dataset is available on the StatsSA website (http://nesstar.statssa.gov.za:8282/webview/) for research and statistical purposes. The entire dataset consists of over 10 million records and about 50 variables of registered deaths that occurred in the country between 1997 and 2018. For our analysis, we have used data from 2002 to 2015, the years for which information on citizenship is reliably included on the death certificate. Corresponding benchmark data, in which nationality is recorded, exists only for a 10% sample from the population and housing census of 2011. Mid-year population estimates (MYPE) also exist but are not disaggregated by nationality. For this reason, certain estimates of death proportions by nationality will be relative and will not correspond to crude death rates.ResultsThe total number of female deaths recorded from the years 2002 to 2015 in the country was 3740.761. Of these, 99.09% (n = 3,707,003) were deaths of South Africans and 0.91% (n = 33,758) were deaths of SADC women citizens. For maternal mortality, we considered the total number of deaths recorded for women between the ages of 15 and 49 years of age and were 1,530,495 deaths. Of these, deaths due to pregnancy-related causes contributed to approximately 1% of deaths. South African women contributed to 17,228 maternal deaths and SADC women to 467 maternal deaths during the period under study. The odds ratio for this comparison was 2.02. In other words, our findings show the odds of a black migrant woman from a SADC country dying of a maternal death were more than twice that of a South African woman. This result is statistically significant as this odds ratio, 2.02, falls within the 95% confidence interval (1.82–2.22).ConclusionThe study is the first to examine and compare maternal death among two groups of women, women from SADC countries and South Africa, based on Stats SA data available for the years 2002–2015. This analysis allows for a better understanding of the differential impact that social determinants of health have on mortality among black migrant women in South Africa and considers access to healthcare as a determinant of health. As we examined maternal death, we inferred that the heightened mortality among black migrant women in South Africa was associated with various determinants of health, such as xenophobic attitudes of healthcare workers toward foreigners during the study period. The negative attitudes of healthcare workers toward migrants have been reported in the literature and the media. Yet, until now, its long-term impact on the health of the foreign population has not been gaged. While a direct association between the heightened death of migrant populations and xenophobia cannot be established in this study, we hope to offer evidence that supports the need to focus on the heightened vulnerability of black migrant women in South Africa. As we argued here, the heightened maternal mortality among migrant women can be considered hidden barriers in which health inequality and the pervasive effects of xenophobia perpetuate the health disparity of SADC migrants in South Africa.