As of 2023, across 70 measures assessing health and healthcare in the U.S., the Black, AI/AN, and Hispanic populations fare worse than the White population. The racial/ethnic disparity was largest comparing Black and White populations. The Black population fared worse than the White population across 55 health and healthcare measures, while they only fared better than the White population for 12 of them.

On the other hand, the Asian population did not fare worse than White people across most examined measures. Nonetheless, these measures cover aspects of health coverage, access, and use; health status, outcomes, and behaviors; and social determinants of health, yet more is needed to provide the full scope of healthcare disparities.

Racial disparities arise across many vital areas of American life, including employment, health, and interpersonal treatment. For example, 1 in 3 Black children live in poverty (vs. 1 in 9 White children) and on average, Black Americans live 4 fewer years than White Americans. Which disparity is more likely to spark reduction efforts? We find that highlighting disparities in health-related (vs. economic) outcomes spurs greater social media engagement and support for disparity-mitigating policy. Further, reading about racial health disparities elicits greater support for action (e.g., protesting) than economic or belonging-based disparities. This occurs, in part, because people view health disparities as violating morally-sacred values which enhances perceived injustice. This work elucidates which manifestations of racial inequality are most likely to prompt Americans to action., The data from Studies 1a, 1b, 3, 4a, and 4b were collected via online platfroms (i.e., Mturk.com, Prolific Academic, and NORC’s AmeriSpeak Panel). All analyses were run in R with the R code provided (title: Health_Disparities_Syntax.R)., , # Highlighting Health Consequences of Racial Disparities Sparks Support for Action

There are a total of 5 datasets available (Studies 1a, 1b, 3, 4a, 4b) each collected by the researchers from online survey platforms. All data files are .sav files. We recommed using SPSS or RStudio to work with the data. We provide our code using RStudio and a codebook with the name of all variables in each dataset.

Description of the data and file structure

Study 1a and Study 1b utilized a within-subjects experimental design (S1a: N=191; S1b, preregistered: N=337, 50% White participants, 50% Black participants) where samples of U.S. citizens recruited from MTurk.com and Prolific Academic read nine examples of racial disparities, three each from the domains of health, economics, and belonging. After each example, participants reported whether the disparity was unjust and fair (reverse-coded; 2-items averaged to create a perceived injustice scale). Participants also indicated their agreement (1=s...

PIONEER: The impact of ethnicity and multi-morbidity on COVID-related outcomes; a primary care supplemented hospitalised dataset Dataset number 3.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 65million cases and more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) and death. Evidence suggests that older patients, those from some ethnic minority groups and those with multiple long-term health conditions have worse outcomes. This secondary care COVID dataset contains granular demographic and morbidity data, supplemented from primary care records, to add to the understanding of patient factors on disease outcomes.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 and 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. UHB has cared for >5000 COVID admissions to date.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – May 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes but also primary care records and clinic letters. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT, MRI, ultrasound).

Available supplementary data: Health data preceding and following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

This statistic shows the results of a survey conducted in the United States in March 2017, by ethnicity. U.S. adults were asked if they had ever heard about e-health. Only five percent of Black or African American respondents knew what e-health was, compared to 11 percent of Asian respondents.

This statistic shows the percentage of U.S. adults that have received treatment for a mental health condition as of April 2018, by ethnicity. According to the results, among those who identified as Hispanic, 14 percent said they are currently in treatment for a mental health issue.

IntroductionEnsuring high-quality race and ethnicity data within the electronic health record (EHR) and across linked systems, such as patient registries, is necessary to achieving the goal of inclusion of racial and ethnic minorities in scientific research and detecting disparities associated with race and ethnicity. The project goal was to improve race and ethnicity data completion within the Pediatric Rheumatology Care Outcomes Improvement Network and assess impact of improved data completion on conclusions drawn from the registry.MethodsThis is a mixed-methods quality improvement study that consisted of five parts, as follows: (1) Identifying baseline missing race and ethnicity data, (2) Surveying current collection and entry, (3) Completing data through audit and feedback cycles, (4) Assessing the impact on outcome measures, and (5) Conducting participant interviews and thematic analysis.ResultsAcross six participating centers, 29% of the patients were missing data on race and 31% were missing data on ethnicity. Of patients missing data, most patients were missing both race and ethnicity. Rates of missingness varied by data entry method (electronic vs. manual). Recovered data had a higher percentage of patients with Other race or Hispanic/Latino ethnicity compared with patients with non-missing race and ethnicity data at baseline. Black patients had a significantly higher odds ratio of having a clinical juvenile arthritis disease activity score (cJADAS10) of ≥5 at first follow-up compared with White patients. There was no significant change in odds ratio of cJADAS10 ≥5 for race and ethnicity after data completion. Patients missing race and ethnicity were more likely to be missing cJADAS values, which may affect the ability to detect changes in odds ratio of cJADAS ≥5 after completion.ConclusionsAbout one-third of the patients in a pediatric rheumatology registry were missing race and ethnicity data. After three audit and feedback cycles, centers decreased missing data by 94%, primarily via data recovery from the EHR. In this sample, completion of missing data did not change the findings related to differential outcomes by race. Recovered data were not uniformly distributed compared with those with non-missing race and ethnicity data at baseline, suggesting that differences in outcomes after completing race and ethnicity data may be seen with larger sample sizes.

This statistic shows the results of a survey conducted in the United States in March 2017, by ethnicity. U.S. adults were asked if they were interested in the topic of e-health. About 39 percent of Asian respondents indicated that they were very interested in the topic of e-health, compared to 24 percent of White or Caucasian respondents.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the 2020 County Health Rankings page about Life Expectancy:"Life Expectancy is an AverageLife Expectancy measures the average number of years from birth a person can expect to live, according to the current mortality experience (age-specific death rates) of the population. Life Expectancy takes into account the number of deaths in a given time period and the average number of people at risk of dying during that period, allowing us to compare data across counties with different population sizes.Life Expectancy is Age-AdjustedAge is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. Life Expectancy is age-adjusted in order to fairly compare counties with differing age structures.What Deaths Count Toward Life Expectancy?Deaths are counted in the county where the individual lived. So, even if an individual dies in a car crash on the other side of the state, that death is attributed to his/her home county.Some Data are SuppressedA missing value is reported for counties with fewer than 5,000 population-years-at-risk in the time frame.Measure LimitationsLife Expectancy includes mortality of all age groups in a population instead of focusing just on premature deaths and thus can be dominated by deaths of the elderly.[1] This could draw attention to areas with higher mortality rates among the oldest segment of the population, where there may be little that can be done to change chronic health problems that have developed over many years. However, this captures the burden of chronic disease in a population better than premature death measures.[2]Furthermore, the calculation of life expectancy is complex and not easy to communicate. Methodologically, it can produce misleading results caused by hidden differences in age structure, is sensitive to infant and child mortality, and tends to be overestimated in small populations."Click on the map to see a breakdown by race/ethnicity in the pop-up: Full details about this measureThere are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.Data from County Health Rankings 2020 (in this layer and referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World

This data is compiled by the Cook County Department of Public Health using data from the Illinois Department of Public Health Vital Statistics. It includes the annual number of live births, and birth related outcomes and characteristics. Further analysis is available by birth mother's age group, race/ethnicity, and place/district of residence for all births in suburban Cook County. Also included is data related to infant mortality. Table of Contents and other information can be found at http://opendocs.cookcountyil.gov/docs/Birth_Table_Of_Contents_Data_Portal_fyn8-c3rk.pdf. Note: * Counts suppressed for events between 1 and 4, - Rates not calculated for events less than 20

According to a 2021 health care systems ranking among selected high-income countries, the United States came last in the overall ranking of its health care system performance. The overall ranking was based on five performance categories, including access to care, care process, administrative efficiency, equity, and health care outcomes. For the category equity, which takes into account income-related disparities in the health system, the U.S. was ranked last again, while Australia took first place. Other disparities of ethnicity, gender, or geography were not included. This statistic present the health care equity rankings of the United States' health care system compared to ten other high-income countries in 2021.

This statistic shows the results of a survey conducted in the United States in March 2017, by gender. U.S. adults were asked if they would consider using an app for health emergencies. According to the results, 9 percent of Asian and Hispanic American or Latino respondents said that they use an app for health emergencies regularly.

The dataset comprised individuals from four race and ethnicity classes: Other, White, Hispanic, Black.

The Hispanic EPESE provides data on risk factors for mortality and morbidity in Mexican Americans in order to contrast how these factors operate differently in non-Hispanic White Americans, African Americans, and other major ethnic groups. The Wave 8 dataset comprises the seventh follow-up of the baseline Hispanic EPESE (HISPANIC ESTABLISHED POPULATIONS FOR THE EPIDEMIOLOGIC STUDIES OF THE ELDERLY, 1993-1994: [ARIZONA, CALIFORNIA, COLORADO, NEW MEXICO, AND TEXAS] [ICPSR 2851]). The baseline Hispanic EPESE collected data on a representative sample of community-dwelling Mexican Americans, aged 65 years and older, residing in the five southwestern states of Arizona, California, Colorado, New Mexico, and Texas. The public-use data cover demographic characteristics (age, sex, marital status), height, weight, BMI, social and physical functioning, chronic conditions, related health problems, health habits, self-reported use of hospital and nursing home services, and depression. Subsequent follow-ups provide a cross-sectional examination of the predictors of mortality, changes in health outcomes, and institutionalization, and other changes in living arrangements, as well as changes in life situations and quality of life issues. During this 8th Wave, 2012-2013, re-interviews were conducted either in person or by proxy, with 452 of the original respondents. This Wave also includes 292 re-interviews from the additional sample of Mexican Americans aged 75 years and over with higher average-levels of education than those of the surviving cohort who were added in Wave 5, increasing the total number of respondents to 744.

In this article, we report findings from an original survey experiment investigating the effects of different framings of disease threats on individual risk perceptions and policy priorities. We analyze responses from 1,946 white and African-American participants in a self-administered, web-based survey in the United States. We sought to investigate the effects of: 1) frames emphasizing disparities in the racial prevalence of disease and 2) frames emphasizing non-normative (blameworthy or stigmatized) behavioral risk factors. We find some evidence that when treated with the first frame, African-Americans are more likely to report higher risk of infection (compared to an African-American control group and to whites receiving the same treatment); and that whites are more likely to report trust in government data (compared to a White control group and to African-Americans receiving the same treatment). Notwithstanding, we find no support for our hypotheses concerning the interactive effects of providing both frames, which was a central motivation for our study. We argue that this may be due to very large differences in risk perception at baseline (which generate limits on possible treatment effects) and the fact that in the context of American race relations, it may not be possible to fully differentiate racialized and stigmatized frames.

This map shows the access to mental health providers in every county and state in the United States according to the 2024 County Health Rankings & Roadmaps data for counties, states, and the nation. It translates the numbers to explain how many additional mental health providers are needed in each county and state. According to the data, in the United States overall there are 319 people per mental health provider in the U.S. The maps clearly illustrate that access to mental health providers varies widely across the country.The data comes from this County Health Rankings 2024 layer. An updated layer is usually published each year, which allows comparisons from year to year. This map contains layers for 2024 and also for 2022 as a comparison.County Health Rankings & Roadmaps (CHR&R), a program of the University of Wisconsin Population Health Institute with support provided by the Robert Wood Johnson Foundation, draws attention to why there are differences in health within and across communities by measuring the health of nearly all counties in the nation. This map's layers contain 2024 CHR&R data for nation, state, and county levels. The CHR&R Annual Data Release is compiled using county-level measures from a variety of national and state data sources. CHR&R provides a snapshot of the health of nearly every county in the nation. A wide range of factors influence how long and how well we live, including: opportunities for education, income, safe housing and the right to shape policies and practices that impact our lives and futures. Health Outcomes tell us how long people live on average within a community, and how people experience physical and mental health in a community. Health Factors represent the things we can improve to support longer and healthier lives. They are indicators of the future health of our communities.Some example measures are:Life ExpectancyAccess to Exercise OpportunitiesUninsuredFlu VaccinationsChildren in PovertySchool Funding AdequacySevere Housing Cost BurdenBroadband AccessTo see a full list of variables, definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details of this layer. For full documentation, visit the Measures page on the CHR&R website. Notable changes in the 2024 CHR&R Annual Data Release:Measures of birth and death now provide more detailed race categories including a separate category for ‘Native Hawaiian or Other Pacific Islander’ and a ‘Two or more races’ category where possible. Find more information on the CHR&R website.Ranks are no longer calculated nor included in the dataset. CHR&R introduced a new graphic to the County Health Snapshots on their website that shows how a county fares relative to other counties in a state and nation. Data Processing:County Health Rankings data and metadata were prepared and formatted for Living Atlas use by the CHR&R team. 2021 U.S. boundaries are used in this dataset for a total of 3,143 counties. Analytic data files can be downloaded from the CHR&R website.

This is a monthly report on publicly funded community services using data from the Community Services Data Set (CSDS) reported in England for January 2019. The CSDS is a patient-level dataset providing information relating to publicly funded community services. These services can include health centres, schools, mental health trusts, and health visiting services. The data collected includes personal and demographic information, diagnoses including long-term conditions and disabilities and care events plus screening activities. It has been developed to help achieve better outcomes for children, young people and adults. It provides data that will be used to commission services in a way that improves health, reduces inequalities, and supports service improvement and clinical quality. Prior to October 2017, the predecessor Children and Young Peoples’ Health Services (CYPHS) Data Set collected data for children and young people aged 0-18. The CSDS superseded the CYPHS data set to allow adult community data to be submitted, expanding the scope of the existing data set by removing the 0-18 age restriction. The structure and content of the CSDS remains the same as the previous CYPHS data set. Further information about the CYPHS and related statistical reports is available in the related links below. References to children and young people covers records submitted for 0-18 year olds and references to adults covers records submitted for those aged over 18. Where analysis for both groups have been combined, this is referred to as all patients. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. They are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. More information about experimental statistics can be found on the UK Statistics Authority website. We hope this information is helpful and would be grateful if you could spare a couple of minutes to complete a short customer satisfaction survey. Please use the survey in the related links to provide us with any feedback or suggestions for improving the report. --------------------------------------------------------------------------------------------------------- We are reviewing our monthly and ad-hoc publications to ensure we are providing outputs that meet customer needs. We would be grateful if you could fill in the survey with your views. This survey will remain open until Friday 28th June 2019. Please take part using the link under the 'Related Links' section below. ---------------------------------------------------------------------------------------------------------

BackgroundMinority ethnic identification between physician and patient can reduce communication and access barriers, improve physician-patient relationship, trust, and health outcomes. Religion influences health beliefs, behaviours, treatment decisions, and outcomes. Ethically contentious dilemmas in treatment decisions are often entangled with religious beliefs. They feature more in medical specialties such as Anaesthesia & Intensive Care, with issues including informed consent for surgery, organ donation, transplant, transfusion, and end-of-life decisions.MethodsWe investigate diversity in religious affiliation in the UK medical workforce, using data from the General Medical Council (GMC) specialist register and Health Education England (HEE) trainee applications to medical specialties. We performed conservative Bonferroni corrections for multiple comparisons using Chi-squared tests, as well as normalised mutual-information scores. Robust associations that persisted on all sensitivity analyses are reported, investigating whether ethnicity or foreign primary medical qualification could explain the underlying association.FindingsThe only significant and robust association in both GMC and HEE datasets affecting the same religious group and specialty was disproportionately fewer Anaesthesia & Intensive Care physicians with a religious affiliation of “Muslim”, both as consultants (RR 0.57[0.47,0.7]) and trainee applicants (RR 0.27[0.19,0.38]. Associations were not explained by ethnicity or foreign training. We discuss the myriad of implications of the findings for multi-cultural societies.ConclusionsLack of physician workforce diversity has far-reaching consequences, especially for specialties such as Anaesthesia and Intensive Care, where ethically contentious decisions could have a big impact. Religious beliefs and practices, or lack thereof, may have unmeasured influences on clinical decisions and on whether patients identify with physicians, which in turn can affect health outcomes. Examining an influencing variable such as religion in healthcare decisions should be prioritised, especially considering findings from the clinician-patient concordance literature. It is important to further explore potential historical and socio-cultural barriers to entry of training medics into under-represented specialties, such as Anaesthesia and Intensive Care.

Latest monthly statistics on Learning Disabilities and Autism (LDA) patients from the Assuring Transformation (AT) collection and Mental Health Services Data Set (MHSDS). Data on inpatients with learning disabilities and/or autism are being collected both within the AT collection and MHSDS. There are differences in the inpatient figures between the AT and MHSDS data sets and work has been ongoing to better understand these. LDA data from MHSDS are experimental statistics, however, while impacts from the cyber incident are still present they will be considered to be management information. From October 2021, LDA MHSDS data has been collected under MHSDS version 5. From 1 July 2022, Integrated Care Boards were established within Integrated Care Systems data and replaced Sustainability and Transformation Plans (STPs). Clinical Commissioning Groups have been replaced by sub-Integrated Care Boards. Data for the AT collection is now submitted by sub-Integrated Care Boards. This has resulted in some renaming within tables and the inclusion of a new Table 5.1b with a patient breakdown by submitting organisation. Patients by originating organisation and commissioning type are still available in Table 5.1a. Data in the tables are now presented by the current organisational structures. Old organisational structures have been mapped to new structures in any time series.

The goal of the Chicago Women's Health Risk Study (CWHRS) was to develop a reliable and validated profile of risk factors directly related to lethal or life-threatening outcomes in intimate partner violence, for use in agencies and organizations working to help women in abusive relationships. Data were collected to draw comparisons between abused women in situations resulting in fatal outcomes and those without fatal outcomes, as well as a baseline comparison of abused women and non-abused women, taking into account the interaction of events, circumstances, and interventions occurring over the course of a year or two. The CWHRS used a quasi-experimental design to gather survey data on 705 women at the point of service for any kind of treatment (related to abuse or not) sought at one of four medical sites serving populations in areas with high rates of intimate partner homicide (Chicago Women's Health Center, Cook County Hospital, Erie Family Health Center, and Roseland Public Health Center). Over 2,600 women were randomly screened in these settings, following strict protocols for safety and privacy. One goal of the design was that the sample would not systematically exclude high-risk but understudied populations, such as expectant mothers, women without regular sources of health care, and abused women in situations where the abuse is unknown to helping agencies. To accomplish this, the study used sensitive contact and interview procedures, developed sensitive instruments, and worked closely with each sample site. The CWHRS attempted to interview all women who answered "yes -- within the past year" to any of the three screening questions, and about 30 percent of women who did not answer yes, provided that the women were over age 17 and had been in an intimate relationship in the past year. In total, 705 women were interviewed, 497 of whom reported that they had experienced physical violence or a violent threat at the hands of an intimate partner in the past year (the abused, or AW, group). The remaining 208 women formed the comparison group (the non-abused, or NAW, group). Data from the initial interview sections comprise Parts 1-8. For some women, the AW versus NAW interview status was not the same as their screening status. When a woman told the interviewer that she had experienced violence or a violent threat in the past year, she and the interviewer completed a daily calendar history, including details of important events and each violent incident that had occurred the previous year. The study attempted to conduct one or two follow-up interviews over the following year with the 497 women categorized as AW. The follow-up rate was 66 percent. Data from this part of the clinic/hospital sample are found in Parts 9-12. In addition to the clinic/hospital sample, the CWHRS collected data on each of the 87 intimate partner homicides occurring in Chicago over a two-year period that involved at least one woman age 18 or older. Using the same interview schedule as for the clinic/hospital sample, CWHRS interviewers conducted personal interviews with one to three "proxy respondents" per case, people who were knowledgeable and credible sources of information about the couple and their relationship, and information was compiled from official or public records, such as court records, witness statements, and newspaper accounts (Parts 13-15). In homicides in which a woman was the homicide offender, attempts were made to contact and interview her. This "lethal" sample, all such homicides that took place in 1995 or 1996, was developed from two sources, HOMICIDES IN CHICAGO, 1965-1995 (ICPSR 6399) and the Cook County Medical Examiner's Office. Part 1 includes demographic variables describing each respondent, such as age, race and ethnicity, level of education, employment status, screening status (AW or NAW), birthplace, and marital status. Variables in Part 2 include details about the woman's household, such as whether she was homeless, the number of people living in the household and details about each person, the number of her children or other children in the household, details of any of her children not living in her household, and any changes in the household structure over the past year. Variables in Part 3 deal with the woman's physical and mental health, including pregnancy, and with her social support network and material resources. Variables in Part 4 provide information on the number and type of firearms in the household, whether the woman had experienced power, control, stalking, or harassment at the hands of an intimate partner in the past year, whether she had experienced specific types of violence or violent threats at the hands of an intimate partner in the past year, and whether she had experienced symptoms of Post-Traumatic Stress Disorder related to the incidents in the past month. Variables in Part 5 specify the partner or partners who were responsible for the incidents in the past year, record the type and length of the woman's relationship with each of these partners, and provide detailed information on the one partner she chose to talk about (called "Name"). Variables in Part 6 probe the woman's help-seeking and interventions in the past year. Variables in Part 7 include questions comprising the Campbell Danger Assessment (Campbell, 1993). Part 8 assembles variables pertaining to the chosen abusive partner (Name). Part 9, an event-level file, includes the type and the date of each event the woman discussed in a 12-month retrospective calendar history. Part 10, an incident-level file, includes variables describing each violent incident or threat of violence. There is a unique identifier linking each woman to her set of events or incidents. Part 11 is a person-level file in which the incidents in Part 10 have been aggregated into totals for each woman. Variables in Part 11 include, for example, the total number of incidents during the year, the number of days before the interview that the most recent incident had occurred, and the severity of the most severe incident in the past year. Part 12 is a person-level file that summarizes incident information from the follow-up interviews, including the number of abuse incidents from the initial interview to the last follow-up, the number of days between the initial interview and the last follow-up, and the maximum severity of any follow-up incident. Parts 1-12 contain a unique identifier variable that allows users to link each respondent across files. Parts 13-15 contain data from official records sources and information supplied by proxies for victims of intimate partner homicides in 1995 and 1996 in Chicago. Part 13 contains information about the homicide incidents from the "lethal sample," along with outcomes of the court cases (if any) from the Administrative Office of the Illinois Courts. Variables for Part 13 include the number of victims killed in the incident, the month and year of the incident, the gender, race, and age of both the victim and offender, who initiated the violence, the severity of any other violence immediately preceding the death, if leaving the relationship triggered the final incident, whether either partner was invading the other's home at the time of the incident, whether jealousy or infidelity was an issue in the final incident, whether there was drug or alcohol use noted by witnesses, the predominant motive of the homicide, location of the homicide, relationship of victim to offender, type of weapon used, whether the offender committed suicide after the homicide, whether any criminal charges were filed, and the type of disposition and length of sentence for that charge. Parts 14 and 15 contain data collected using the proxy interview questionnaire (or the interview of the woman offender, if applicable). The questionnaire used for Part 14 was identical to the one used in the clinic sample, except for some extra questions about the homicide incident. The data include only those 76 cases for which at least one interview was conducted. Most variables in Part 14 pertain to the victim or the offender, regardless of gender (unless otherwise labeled). For ease of analysis, Part 15 includes the same 76 cases as Part 14, but the variables are organized from the woman's point of view, regardless of whether she was the victim or offender in the homicide (for the same-sex cases, Part 15 is from the woman victim's point of view). Parts 14 and 15 can be linked by ID number. However, Part 14 includes five sets of variables that were asked only from the woman's perspective in the original questionnaire: household composition, Post-Traumatic Stress Disorder (PTSD), social support network, personal income (as opposed to household income), and help-seeking and intervention. To avoid redundancy, these variables appear only in Part 14. Other variables in Part 14 cover information about the person(s) interviewed, the victim's and offender's age, sex, race/ethnicity, birthplace, employment status at time of death, and level of education, a scale of the victim's and offender's severity of physical abuse in the year prior to the death, the length of the relationship between victim and offender, the number of children belonging to each partner, whether either partner tried to leave and/or asked the other to stay away, the reasons why each partner tried to leave, the longest amount of time each partner stayed away, whether either or both partners returned to the relationship before the death, any known physical or emotional problems sustained by victim or offender, including the four-item Medical Outcomes Study (MOS) scale of depression, drug and alcohol use of the victim and offender, number and type of guns in the household of the victim and offender, Scales of Power and Control (Johnson, 1996) or Stalking and Harassment (Sheridan, 1992) by either intimate partner in the year prior to the death, a modified version of the Conflict Tactics Scale (CTS)

IntroductionLyme disease (LD) incidence in the United States is highly regional, with most cases occurring in 16 high-incidence jurisdictions. LD incidence and severity of disease have been found to vary by race. This study describes racial differences in knowledge, attitudes toward vaccination, and risk practices related to LD.MethodsFour web-based surveys were conducted with adults and caregivers of children in high-incidence jurisdictions and 10 states neighboring them. Respondents were recruited via an established online panel to represent the general population. Self-reported race was pooled into 3 categories: ‘White’, ‘Black or African American’, and ‘Other’ for analysis. Analyses were conducted separately for each jurisdiction (high-incidence vs. neighboring) and respondent type (adult vs. caregiver).ResultsThe final sample across all surveys included 2,249 respondents who identified as White, 493 respondents who identified as Black or African American, and 674 respondents of other races. White respondents were older, had higher incomes, and were likelier to live in small cities and rural areas. Though attitudes toward vaccination in general were similar between racial categories, when differences were present, Black respondents were more likely to have concerns about vaccines than White respondents. In all surveys, White respondents engaged in more outdoor activities than Black respondents and performed these activities more often. However, both White adults and caregivers in high-incidence jurisdictions were significantly less likely to have occupations with primarily outdoor work than corresponding respondents in other racial groups. Black respondents also had lower knowledge about LD than White respondents across all surveys. This difference was significant after adjusting for state incidence level and urbanicity.ConclusionThere are some racial differences in knowledge, attitudes, and practices around LD, with White respondents reported having higher knowledge of LD, less concerns about vaccines, and higher frequency of risk practices. These differences might contribute to racial disparities in LD outcomes.