In 2023, around 4.7 percent of persons with a Black or African American ethnicity claimed to have heavy alcohol use in the past month. Heavy use refers to five or more drinks on the same occasion on each of five or more days in the last 30 days. This statistic displays the percentage of persons in the U.S. aged 12 or older who had current, binge, and heavy alcohol use in the past month, by race/ethnicity, in 2023.

In 2022, just under four in ten Medicaid/CHIP enrollees were White, non-Hispanic. In comparison, roughly three-quarters of Medicare beneficiaries were White. The Affordable Care Act (ACA) Medicaid expansion in 2014, has helped reduce racial disparities in access to healthcare in the United States.

Medicaid eligibility

Medicaid provides health coverage to certain low-income individuals, families, children, pregnant women, the elderly, and persons with disabilities. Each state has its own Medicaid eligibility criteria in accordance with federal guidelines. As a result, Medicaid eligibility and benefits differ widely from state to state. Medicaid expansion provision under the Affordable Care Act (ACA) allows states to provide coverage for low-income adults by expanding eligibility for Medicaid to 138 percent of the federal poverty line (FPL).

Medicaid coverage gap

Uninsured individuals who live in states that have chosen not to expand Medicaid under the Affordable Care Act (ACA) are referred to as being in the Medicaid coverage gap. As of January 2021, 12 states have not adopted the Medicaid expansion provision under the Affordable Care Act (ACA). More than two million uninsured adults fall into this coverage gap, and among them, more than 60 percent are people of color.

Multivariable logistic regression analysis of the association between risk factors in persons living with HIV in Florida and data sources on TB diagnosis.

A. SUMMARY This dataset includes San Francisco COVID-19 tests by race/ethnicity and by date. This dataset represents the daily count of tests collected, and the breakdown of test results (positive, negative, or indeterminate). Tests in this dataset include all those collected from persons who listed San Francisco as their home address at the time of testing. It also includes tests that were collected by San Francisco providers for persons who were missing a locating address. This dataset does not include tests for residents listing a locating address outside of San Francisco, even if they were tested in San Francisco.

The data were de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected). If a person tested multiple times on the same date, only one test is included from that date. When there are multiple tests on the same date, a positive result, if one exists, will always be selected as the record for the person. If a PCR and antigen test are taken on the same day, the PCR test will supersede. If a person tests multiple times on the same day and the results are all the same (e.g. all negative or all positive) then the first test done is selected as the record for the person.

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco.

When a person gets tested for COVID-19, they may be asked to report information about themselves. One piece of information that might be requested is a person's race and ethnicity. These data are often incomplete in the laboratory and provider reports of the test results sent to the health department. The data can be missing or incomplete for several possible reasons:

• The person was not asked about their race and ethnicity.
• The person was asked, but refused to answer.
• The person answered, but the testing provider did not include the person's answers in the reports.
• The testing provider reported the person's answers in a format that could not be used by the health department.

For any of these reasons, a person's race/ethnicity will be recorded in the dataset as “Unknown.”

B. NOTE ON RACE/ETHNICITY The different values for Race/Ethnicity in this dataset are "Asian;" "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" "White;" "Multi-racial;" "Other;" and “Unknown."

The Race/Ethnicity categorization increases data clarity by emulating the methodology used by the U.S. Census in the American Community Survey. Specifically, persons who identify as "Asian," "Black or African American," "American Indian or Alaska Native," "Native Hawaiian or Other Pacific Islander," "White," "Multi-racial," or "Other" do NOT include any person who identified as Hispanic/Latino at any time in their testing reports that either (1) identified them as SF residents or (2) as someone who tested without a locating address by an SF provider. All persons across all races who identify as Hispanic/Latino are recorded as “"Hispanic or Latino/a, all races." This categorization increases data accuracy by correcting the way “Other” persons were counted. Previously, when a person reported “Other” for Race/Ethnicity, they would be recorded “Unknown.” Under the new categorization, they are counted as “Other” and are distinct from “Unknown.”

If a person records their race/ethnicity as “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other” for their first COVID-19 test, then this data will not change—even if a different race/ethnicity is reported for this person for any future COVID-19 test. There are two exceptions to this rule. The first exception is if a person’s race/ethnicity value is reported as “Unknown” on their first test and then on a subsequent test they report “Asian;” "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" or "White”, then this subsequent reported race/ethnicity will overwrite the previous recording of “Unknown”. If a person has only ever selected “Unknown” as their race/ethnicity, then it will be recorded as “Unknown.” This change provides more specific and actionable data on who is tested in San Francisco.

The second exception is if a person ever marks “Hispanic or Latino/a, all races” for race/ethnicity then this choice will always overwrite any previous or future response. This is because it is an overarching category that can include any and all other races and is mutually exclusive with the other responses.

A person's race/ethnicity will be recorded as “Multi-racial” if they select two or more values among the following choices: “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other.” If a person selects a combination of two or more race/ethnicity answers that includes “Hispanic or Latino/a, all races” then they will still be recorded as “Hispanic or Latino/a, all races”—not as “Multi-racial.”

C. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information.

D. UPDATE PROCESS Updates automatically at 5:00AM Pacific Time each day. Redundant runs are scheduled at 7:00AM and 9:00AM in case of pipeline failure.

E. HOW TO USE THIS DATASET San Francisco population estimates for race/ethnicity can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24, 2020 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a user can analyze this data by sorting or filtering by the "specimen_collection_date" field.

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. When there are fewer than 20 positives tests for a given race/ethnicity and time period, the positivity rate is not calculated for the public tracker because rates of small test counts are less reliable.

Calculating Testing Rates: To calculate the testing rate per 10,000 residents, divide the total number of tests collected (positive, negative, and indeterminate results) for the specified race/ethnicity by the total number of residents who identify as that race/ethnicity (according to the 2016-2020 American Community Survey (ACS) population estimate), then multiply by 10,000. When there are fewer than 20 total tests for a given race/ethnicity and time period, the testing rate is not calculated for the public tracker because rates of small test counts are less reliable.

Read more about how this data is updated and validated daily: https://sf.gov/information/covid-19-data-questions

F. CHANGE LOG

• 1/12/2024 - This dataset will stop updating as of 1/12/2024
• 6/21/2023 - A small number of additional COVID-19 testing records were released as part of our ongoing data cleaning efforts. An update to the race or ethnicity designation among a subset of testing records was simultaneously released.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 2/10/2022 - race/ethnicity categorization was changed. See section NOTE ON RACE/ETHNICITY for additional information.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

Median Household Income, Race/Ethnicity, New Mexico, 2014-2018

Total Households = 775,651

ALL HOUSEHOLDERS $ 48,059

Black or African American Householder $ 38,490

American Indian and Alaska Native Householder $ 33,552

Asian Householder $ 65,019

Non-Hispanic White Householder $ 58,181

Hispanic or Latino Householder $ 40,641

Native Hawaiian and Other Pacific Islander Householder $ 47,311

Some Other Race Householder $ 35,625

2 or More Races Householder $ 46,036

SEE ALSO Race/Ethnicity with Lowest Median Income: https://nmcdc.maps.arcgis.com/home/webmap/viewer.html?webmap=0ed46c1e58034bf583e7afc99fcd6a5c This map shows which race/ethnicity group has the highest median income, by tract, county and state. For each group showing a median income figure, the highest median income determines the color used on the map.The map shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. The map uses the latest available Census ACS data from the ACS Median Household Income Variables - Boundaries ready-to-use layer in the ArcGIS Living Atlas.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available. The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use web map can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.

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

PLEASE NOTE: This is an index of a historical collection that contains words and phrases that may be offensive or harmful to individuals investigating these records. In order to preserve the objectivity and historical accuracy of the index, State Archives staff took what would today be considered archaic and offensive descriptions concerning race, ethnicity, and gender directly from the original court papers. For more information on appropriate description, please consult the Diversity Style Guide and Archives for Black Lives in Philadelphia: Anti-Racist Description Resources.

The Litchfield County Court African Americans and Native Americans Collection is an artificial collection consisting of photocopies of cases involving persons of African descent and indigenous people from the Files and Papers by Subject series of Litchfield County Court records. This collection was created in order to highlight the lives and experiences of underrepresented groups in early America, and make them more easily accessible to researchers.

Collection Overview

The collection consists of records of 188 court cases involving either African Americans or Native Americans. A careful search of the Files for the Litchfield County Court discovered 165 on African Americans and 23 on Native Americans, about one third of the total that was found in Files for the New London County Court for the period up to the American Revolution. A couple of reasons exist for this vast difference in numbers. First, Litchfield County was organized much later than New London, one of Connecticut's four original counties. New London was the home of four of seven recognized tribes, was a trading center, and an area of much greater wealth. Second, minority population in the New London County region has been tracked and tabulated by Barbara Brown and James Rose in Black Roots of Southeastern Connecticut.¹ Although this valuable work does not include all of Negro or Indian background, it provides a wonderful starting point and it has proven to be of some assistance in tracking down minorities in Litchfield County. In most instances, however, identification is based upon language in the documents and knowledge of surnames or first names.² Neither surname nor first name provides an invariably reliable guide so it is possible that some minorities have been missed and some persons included that are erroneous.

In thirteen of 188 court cases, the person of African or Native American background cannot be identified even by first name. He or she is noted as "my Negro," a slave girl, or an Indian. In twenty-three lawsuits, a person with a first name is identified as a Negro, as an Indian in two other cases, and Mulatto in one. In the remaining 151 cases, a least one African American or Native American is identified by complete name.³ Thirteen surnames recur in three or more cases.⁴ A total of seventy surnames, some with more than one spelling, are represented in the records.

The Jacklin surname appears most frequently represented in the records. Seven different Jacklins are found in eighteen cases, two for debt and the remaining sixteen for more serious crimes like assault, breach of peace, keeping a bawdy house, and trespass.⁵ Ten cases concern Cuff Kingsbury of Canaan between 1808 and 1812, all involving debts against Kingsbury and the attempts of plaintiffs to secure writs of execution against him. Cyrus, Daniel, Ebenezer, Jude, Luke, Martin, Nathaniel, Pomp, Titus, and William Freeman are found in nine cases, some for debt, others for theft, and one concerning a petition to appoint a guardian for aged and incompetent Titus Freeman.⁶ Six persons with the surname Caesar are found in seven court cases.

Sixty-one of 188 cases concern debt.⁷ Litchfield County minorities were plaintiffs in only about ten of these lawsuits, half debt by book and half debt by note. The largest single category of court proceedings concern cases of crimes against person or property. They include assault (32 cases), theft (30), breach of peace (5), and breaking out of jail (1). In cases of assault, the Negro or Indian was the perpetrator in about two thirds of the cases and victim in one third. In State v. Alexander Kelson, the defendant was accused of assaulting Eunice Mawwee.⁸ Minority defendants in assault cases included Daniel K. Boham, William Cable, Prince Comyns, Adonijah Coxel, Homer Dolphin, Jack Jacklin, Pompey Lepean, John Mawwee, Zack Negro, and Jarvis Phillips. One breach of peace case, State v. Frederic Way, the defendant, "a transient Indian man," was accused of breach of the peace for threatening Jonathan Rossetter and the family of Samuel Wilson of Harwinton.⁹

In cases of theft, African Americans appeared as defendants in 27 of 30 cases, the only exceptions being two instances in which Negroes were illegally seized by whites and the case of State v. William Pratt of Salisbury. The State charged Pratt with stealing $35 from the house of George Ceasor.¹⁰ More typical, however, are such cases as State v. Prince Cummins for the theft of a dining room table and State v. Nathaniel Freeman for the theft of clothes.¹¹

Another major category of lawsuits revolves around the subject of slaves as property. The number and percentage of such cases is much lower than that for New London County due to the fact that the county was only organized one generation before the American Revolution and the weaker grip the institution of slavery had in that county. The cases may be characterized as conversion to own use (4), fraudulent contract (3), fraudulent sale (3), runaways (3), illegal enslavement (2), and trespass (2).¹² The Litchfield County Court in April 1765 heard George Catling v. Moses Willcocks, a case in which Willcocks was accused of converting a slave girl and household goods to his own use.¹³ In the 1774 fraudulent contract case of Josiah Willoughby v. Elisha Bigelow, the plaintiff accused Bigelow of lying about York Negro's age and condition. Willoughby stated that York Negro was twenty years older that he was reputed to be, was blind in one eye, and "very intemperate in the use of Speretuous Lickor." He sued to recover the purchase price of £45, the court agreed, and the defendant appealed.¹⁴ Cash Africa sued Deborah Marsh of Litchfield in 1777 for illegal enslavement. He claimed that he was unlawfully seized with force and arms and compelled to labor for the defendant for three years.¹⁵ In another case, David Buckingham v. Jonathan Prindle, the defendant was accused of persuading Jack Adolphus to run away from his master. The plaintiff claimed that Adolphus was about twenty years old and bound to service until age twenty-five, when he would be freed under terms of Connecticut's gradual emancipation law.¹⁶

Other subjects found in Litchfield County Minorities include defamation, gambling, keeping a bawdy house, and lascivious carriage. The defamation cases all included the charge of sexual intercourse with an Indian or Negro. In one such case, Henry S. Atwood v. Norman Atwood, both of Watertown, the defendant defamed and slandered the plaintiff by charging that he was "guilty of the crime of fornication or adultery with [a] Black or Negro woman," the wife of Peter Deming.¹⁷ Three cases, two from 1814 and one from 1821, accuse several Negroes accuse Harry Fitch, Polly Gorley, Violet Jacklin, Betsy Mead, and Jack Peck alias Jacklin, of running houses of ill repute.¹⁸

The records on African Americans and Native Americans from Litchfield County are relatively sparse, but they do provide some indication of the difficulties encountered by minorities in white society. They also provide some useful genealogical data on a handful of families in northwestern Connecticut.

1. Barbara W. Brown and James M. Rose, joint authors, Black Roots in Southeastern Connecticut, 1650-1900 (Detroit: Gale Research Co., 1980).
2. The court cases often identify minorities by the words Negro, mulatto, colored, or Indian.
3. Two or more African Americans or Native Americans are found in 27 lawsuits, but a maximum of two people are included in the Litchfield County Minorities database.
4. Surnames with spelling variations: Boston (3), Botsford (4), Caesar (7), Coxel (3), Freedom (3), Freeman (9), Gauson (5), Jacklin (17), Kingsbury (10), Leopen (4), Mawwee (5), Quomenor (4), and Smith (3).
5. George, Harvey, Isaac, Jack, Philip, Violet, and William Jacklin. Also included is Jack Peck, alias Jack Jacklin.
6. For the last case, see Conservators and Guardians, Box 2, folder 42.
7. Fifty-seven suits for debt, the vast majority of which a minority was plaintiff or defendant, and four concerning writs of execution to recover debt owed.
8. Dec. 1836, Box 3, folder 16.
9. Sep. 1796, Box 3, folder 6.
10. David King v. Stephen Walton, Mar. 1791, Box 1, folder 17;Simon Mitchel v. Edward Hinman, Dec. 1793, Box 1, folder 18; State v. William Pratt, Oct. 1848, Box 2, folder 37.
11. Apr. 1828, Box 2, folder 23; Oct. 1837, Box 2, folder 29.
12. Three additional conversion cases concern livestock and hay.
13. Apr. 1765, Box 1, folder 5.
14. Dec. 1774, Box 1, folder 9.
15. Sep. 1777, Box 1, folder 9.
16. Dec. 1813, Box 1, folder 49.
17. Dec. 1814, Box 2, folder 2.
18. Sep. 1814, Box 2, folder 3, Sep. 1814, Box 2, folder 4; Sep. 1821, Box 2, folder 15.

If a record of interest is found, and a reproduction of the original record is desired, you may submit a request via <a

Sensitivity, specificity, positive and negative predictive values and agreement of self-reported TB compared to TB based on medical records of PLWH in Florida.

Flannel Shirts Market Outlook 2032

The global flannel shirts market size was valued at USD XX Billion in 2023 and is likely to reach USD XX Billion by 2032, expanding at a CAGR of XX% during 2024 – 2032. The market growth is attributed to the growing innovation in fabric technology and the increasing consumer interest in casual and comfortable clothing.



Increasing consumer interest in casual and comfortable clothing is projected to boost the global flannel shirts market. Flannel shirts are made from soft woven fabric, typically wool, cotton, or synthetic fiber, which makes them highly comfortable and attractive, encouraging consumers to adopt them. Therefore, the rising preference for casual clothing is propelling the market.





The demand for flannel shirts is rapidly rising among people, especially among the young generation and working population, as they help them to keep up with the latest fashion trends. Additionally, the rising disposable income, particularly in developing economies, contributes to market expansion, as high income encourages people to invest heavily in their wardrobes. Moreover, flannel shirts offer comfort and a stylish feel, encouraging people to adopt them.



Growing investment in R&D activities for advancements in fabric technology is projected to drive the market. Companies in the market are heavily investing to offer superior quality flannel shirts by developing fabrics that are durable, easy to care for, and adaptable, to different weather conditions.

Impact of Artificial Intelligence (AI) on the Flannel Shirts

<span lang="EN-US" style="

In 2023, approximately ******** percent of the Hispanic population in the United States did not have health insurance, a historical low since 2010. In 2023, the national average was *** percent. White Americans had a below-average rate of just *** percent, whereas *** percent of Black Americans had no health insurance.Impact of the Affordable Care ActThe Affordable Care Act (ACA), also known as Obamacare, was enacted in March 2010, which expanded the Medicaid program, made affordable health insurance available to more people and aimed to lower health care costs by supporting innovative medical care delivery methods. Though it was enacted in 2010, the full effects of it weren’t seen until 2013, when government-run insurance marketplaces such as HealthCare.gov were opened. The number of Americans without health insurance fell significantly between 2010 and 2015, but began to rise again after 2016. What caused the change?The Tax Cuts and Jobs Act of 2017 has played a role in decreasing the number of Americans with health insurance, because the individual mandate was repealed. The aim of the individual mandate (part of the ACA) was to ensure that all Americans had health coverage and thus spread the costs over the young, old, sick and healthy by imposing a large tax fine on those without coverage.

From the sixteenth to the mid-nineteenth centuries, Portuguese and Brazilian traders were responsible for transporting the highest volume of slaves during the transatlantic slave trade. It is estimated that, of the 12.5 million African slaves captured during this time, more than 5.8 million were transported in ships that sailed under the Portuguese and, later, Brazilian flags. British traders transported the second-highest volume of slaves across the Atlantic, totaling at almost 3.3 million; over 2.5 million of these were transported in the 18th century, which was the highest volume of slaves transported by one nation in one century.

Factors associated with TB according to self-reports and medical records among 655 persons living with HIV in Florida.

Despite comprising of a smaller share of the U.S. population than African Americans or Hispanics, the most represented non-white U.S. CEOs were of an Asian background. They made up 55 percent of CEO positions at Fortune 500 and S&P 500 companies in 2024. By comparison, 11 percent of CEOs at the time were African American. The rise of environmental, social, and corporate governance (ESG) Investments in ESG have risen dramatically over last few years. In November 2023 there were approximately 480 billion U.S. dollars in ESG ETF assets worldwide, compared to 16 billion U.S. dollars in 2015. ESG measures were put in place to encourage companies to act responsibly, with the leading reason for ESG investing stated to be brand and reputation according to managers and asset owners. Gender diversity With the general acceptance of ESG in larger companies, there has still been a significant employment gap of women working in senior positions. For example, the share of women working as a partner or principal at EY, one of the largest accounting firms in the world, was just only 28 percent in 2023.

The killing of Tyre Nichols in January 2023 by Memphis Police Officers has reignited debates about police brutality in the United States. Between 2013 and 2024, over 1,000 people have been killed by police in every year. Some of the most infamous examples include the murder of George Floyd in May 2020, and the shooting of Breonna Taylor earlier that year. Within this time period, the most people killed by police in the United States was in 2023, at 1,353 people. Police Violence in the U.S. Police violence is defined as any instance where a police officer’s use of force results in a civilian’s death, regardless of whether it is considered justified by the law. While many people killed by police in the U.S. were shot, other causes of death have included tasers, vehicles, and physical restraints or beatings. In the United States, the rate of police shootings is much higher for Black Americans than it is for any other ethnicity and recent incidents of police killing unarmed Black men and women in the United States have led to widespread protests against police brutality, particularly towards communities of color. America’s Persistent Police Problem Despite increasing visibility surrounding police violence in recent years, police killings have continued to occur in the United States at a consistently high rate. In comparison to other countries, police in the U.S. have killed people at a rate three times higher than police in Canada, and 60 times the rate of police in England. While U.S. police have killed people in almost all 50 states, as well as the District of Columbia, New Mexico was reported to have the highest rate of people killed by the police in the United States, with 8.03 people per million inhabitants killed by police.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

In light of the rapid socio-economic development in this era, it is necessary to make data on household expenditure and income available, as well as the relationship between those statistics and various variables with direct or indirect impact. Therefore, most of the countries are nowadays keen to periodically carry-out Household Expenditure and Income surveys. Given the continuous changes in spending patterns, income levels and prices, as well as in population both internal and external migration, it was now mandatory to update data for household income and expenditure over time. The main objective of the survey is to obtain detailed data on HH income and expenditure, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, it was well considered that the sample should be representative on the sub-district level. Hence, the data collected through the survey would also enable to achieve the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index. 2- Study the consumer expenditure pattern prevailing in the society and the impact of demograohic and socio-economic variables on those patterns. 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators. 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it. 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector. 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor chracteristics as well as drawing poverty maps.. 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

This survey was carried-out for a sample of 12678 households distributed on urban and rural areas in all the Kingdom governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size has been uniformly selected, and in the second stage, a systematic approach guaranteing a representative sample of all sub-districts (Qada) has been applied.

Mode of data collection

Face-to-face [f2f]

Research instrument

List of survey questionnaires:

(1) General Form (2) Expenditure on food commodities Form (3) Expenditure on non-food commodities Form

Cleaning operations

Raw Data

The design and implementation of this survey procedures are: 1. Sample design and selection. 2. Design of forms/questionnaires, guidelines to assist in filling out the questionnaires, and preparing instruction manuals. 3. Design the tables template to be used for the dissemination of the survey results. 4. Preparation of the fieldwork phase including printing forms/questionnaires, instruction manuals, data collection instructions, data checking instructions and codebooks. 5. Selection and training of survey staff to collect data and run required data checkings. 6. Preparation and implementation of the pretest phase for the survey designed to test and develop forms/questionnaires, instructions and software programs required for data processing and production of survey results. 7. Data collection. 8. Data checking and coding. 9. Data entry. 10. Data cleaning using data validation programs. 11. Data accuracy and consistency checks. 12. Data tabulation and preliminary results. 13. Preparation of the final report and dissemination of final results.

Harmonized Data

• The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Agency.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

The rate of fatal police shootings in the United States shows large differences based on ethnicity. Among Black Americans, the rate of fatal police shootings between 2015 and December 2024 stood at 6.1 per million of the population per year, while for white Americans, the rate stood at 2.4 fatal police shootings per million of the population per year. Police brutality in the United States Police brutality is a major issue in the United States, but recently saw a spike in online awareness and protests following the murder of George Floyd, an African American who was killed by a Minneapolis police officer. Just a few months before, Breonna Taylor was fatally shot in her apartment when Louisville police officers forced entry into her apartment. Despite the repeated fatal police shootings across the country, police accountability has not been adequate according to many Americans. A majority of Black Americans thought that police officers were not held accountable for their misconduct, while less than half of White Americans thought the same. Political opinions Not only are there differences in opinion between ethnicities on police brutality, but there are also major differences between political parties. A majority of Democrats in the United States thought that police officers were not held accountable for their misconduct, while a majority of Republicans that they were held accountable. Despite opposing views on police accountability, both Democrats and Republicans agree that police should be required to be trained in nonviolent alternatives to deadly force.

In the United States, Black people have higher rates of gun homicide than White people across all age groups. As of 2022, gun homicide rates were highest among Black people aged between 15 and 24 years, at ***** gun homicides per 100,000 of the population. In comparison, there were only **** gun homicides per 100,000 of the White population within this age range. However, the risk for gun homicide was greatest among all adolescents and adults between the ages of 15 to 44 in that year. The impact of guns on young Americans In the last few years, firearms have become the leading cause of death for American children and teenagers aged one to 19 years old, accounting for more deaths than car crashes and diseases. School shootings also remain on the rise recently, with the U.S. recording ** times as many school shootings than other high-income nations from 2009 to 2018. Black students in particular experience a disproportionately high number of school shootings relative to their population, and K-12 teachers at schools made up mostly of students of color are more likely to report feeling afraid that they or their students would be a victim of attack or harm. The right to bear arms Despite increasingly high rates of gun-related violence, gun ownership remains a significant part of American culture, largely due to the fact that the right to bear arms is written into the U.S. Constitution. Although firearms are the most common murder weapon used in the U.S., accounting for approximately ****** homicides in 2022, almost **** of American households have at least one firearm in their possession. Consequently, it is evident that firearms remain easily accessible nationwide, even though gun laws may vary from state to state. However, the topic of gun control still causes political controversy, as the majority of Republicans agree that it is more important to protect the right of Americans to own guns, while Democrats are more inclined to believe that it is more important to limit gun ownership.

IntroductionBlack/African American men who have sex with other men (BMSM) are disproportionately affected by HIV, experience significant disparities in HIV incidence, and face significant barriers to accessing HIV treatment and care services, including pre-exposure prophylaxis (PrEP). Despite evidence of individual and structural barriers to PrEP use in the Midwest, no review has synthesized this finding to have a holistic view of PrEP uptake and barriers. This review examines patterns of, barriers to, and facilitators of PrEP uptake among BMSM in the Midwest, United States (US).MethodsFive databases (CINAHL Plus, PUBMED, PsycINFO, SCOPUS, and Web of Science) were searched in March 2023. We included studies that focused on BMSM in the Midwestern states; only empirical studies (either quantitative or qualitative or both) were considered. We synthesized the qualitative data and teased out some of the factors inhibiting or facilitating PrEP uptake among BMSM.ResultsWe screened 850 articles, and only 22 (quantitative: 12; qualitative: 8; mixed methods: 2) met our set eligibility criteria. Most of the studies were conducted in Chicago. Most BMSM use oral than injectable PrEP. Uptake of PrEP ranged from 3.0 to 62.8%, and the majority reported a prevalence of less than 15%. The barriers include PrEP awareness, PrEP access, PrEP stigma, side effects, PrEP preference, socioeconomic status, medical insurance and support, partner trust, trust in the health system, and precautions with sexual partners. The identified PrEP facilitators include PrEP use until HIV is eradicated, friend influence, experience with dating men living with HIV, safety, phobia for HIV, disdain for condoms, and power to make decisions.ConclusionOur review summarized patterns of, barriers to, and facilitators of PrEP uptake among BMSM in the Midwest, United States. The low PrEP uptake of BMSM was primarily attributed to mistrust in the health system and low socioeconomic status. Multimodal and multilevel strategies are needed to improve PrEP uptake among BMSM, including improving the marketing of PrEP to BMSM and removing financial barriers to accessing PrEP service.