2_test

PMC:7444865 / 7095-11407 JSON TXT 11 Projects

Survey of the Health of Urban Residents: a Community-Driven Assessment of Conditions Salient to the Health of Historically Excluded Populations in the USA Abstract Background Data from the Survey of the Health of Urban Residents (SHUR) identified connections between police brutality and medical mistrust, generating significant media, policy, and research attention. Amidst intersecting crises of COVID-19, racism, and police brutality, this report describes survey development and data collection procedures for the SHUR. Basic Procedures We conducted focus groups with Black men, Latinxs, and immigrants in Allentown, Pennsylvania. Findings were used to develop and refine measures of conditions salient to the health of urban residents across the country. Quota sampling was employed; oversampling people of color and persons whose usual source of care was not a doctor’s office. Main Findings Non-Hispanic Whites made up just under two thirds of the sample (63.65%, n = 2793). Black/African American respondents accounted for 14.2% of the sample (n = 623), while 11.62% (n = 510) were Latinx. Only 43.46% of respondents reported a doctor’s office as their usual source of care. Novel measures of population-specific stressors include a range of negative encounters with the police, frequency of these encounters, and respondents’ assessments of whether the encounters were necessary. SHUR assessed the likelihood of calling the police if there is a problem, worries about incarceration, and cause-specific stressors such as race-related impression management. Principal Conclusions SHUR (n = 4389) is a useful resource for researchers seeking to address the health implications of experiences not frequently measured by national health surveillance surveys. It includes respondents’ zip codes, presenting the opportunity to connect these data with zip code-level health system, social and economic characteristics that shape health beyond individual factors. One of several overarching goals of the Healthy People 2030 initiative is to create conditions that promote health and well-being for all [1]. These conditions include social, physical, and economic environments that enable people to stay healthy, and that are grounded in the fundamental principle of health equity. Healthy People 2030 also seeks to eliminate health disparities by addressing the structural drivers of inequities in health [1, 2]. To achieve these goals, we must first identify the social determinants of health that are salient to the experiences of people who are socio-economically or racially marginalized. Social determinants of health are the conditions in which people are born, live, age, and work that shape a range of health outcomes including the likelihood of becoming sick, health status, and access to care [3]. COVID-19 has exposed how inequities in social, economic, and environmental conditions—social determinants—shape inequities in health outcomes [4]. Between health inequities made bare by COVID-19 and outrage over anti-Black racism and police brutality that followed the murder of George Floyd, understanding how structural racism shapes a range of social and economic conditions that impact the health outcomes of Black, Indigenous, and Latinx communities in the USA is critical. Access to care matters for health outcomes [5–7]. However, given similar access, people who belong to racially marginalized groups and those who are experiencing poverty are less likely to initiate care [8, 9]. Public hospitals, community health centers or clinics, and safety-net settings are defined by their shared vision to provide care to persons who need it regardless of their ability to pay [10]. As a result, these facilities are mostly used by people who are socio-economically disadvantaged—majority of whom belong to racial and ethnic minority groups, as well as undocumented persons and immigrants who might experience cost, cultural, language, and other barriers to care [11, 12]. One very challenging issue in health disparities research is understanding why in urban areas with safety-net clinics, the prevalence of people with unmet need for health care is still high [13–15]. Mistrust in medical institutions is one cause of unmet need [16, 17]. A recent publication using data from the Survey of the Health of Urban Residents (SHUR) identified connections between experiences of police brutality and medical mistrust [18]. That publication continues to receive significant media, policy, and research attention, and researchers are interested in obtaining access to the data amidst intersecting crises of COVID-19, racism, and police brutality. In this brief report, we describe the process of developing the SHUR. The survey assesses experiences of police brutality, as well as a range of health, health care, social and economic characteristics, and experiences of people who live in urbanized areas in the USA based on the 2010 Census. These are areas with a population of at least 50,000 people. We hope that this report will facilitate dissemination and further analyses of the data to inform policies and programs needed for addressing health inequities. Methods Survey Development Conceptualization of the survey came from an ongoing partnership between academic researchers, a federally qualified health center (FQHC), and an equity-driven non-profit that serves as a hub for community leadership, empowerment, and transformation through social engagement. Our main project focused on exploring the experiences and dimensions of social exclusion and their effects on health outcomes. Academic partners analyzed the existing literature on social exclusion. The non-profit and FQHC partners organized three focus groups in Allentown, Pennsylvania: The first with Latinx populations, the second with Black men, and the third with immigrant populations. All partners trained community members who then facilitated the focus groups. For example, a Latino man was trained to facilitate the Latinx focus group. In these focus groups, we found that participants experienced specific salient stressors that shaped their health outcomes, conditions that were neither regularly captured in our population health surveillance surveys nor were in the broad literature on social determinants of health. Using the data from focus groups, academic partners began developing a brief but comprehensive survey that includes these experiences. We worked with our non-profit and FQHC partners in a process that involved multiple conversations with community members who have a broad range of expertise. They included religious leaders, teachers, students and interns, health care providers, previously incarcerated and justice-involved individuals, and people with multiple chronic conditions, including substance use disorders. University partners searched for any existing instruments consistent with the experiences of marginalized communities. Community members critiqued some of the existing instruments to ensure that word choices reflected their experiences and co-created new measures. Measures Novel measures of stressors such as a range of negative encounters with the police and assessments of whether those encounters were necessary were included to assess experiences of police brutality. We conceptualize police brutality not merely as the use of force by a police officer, but police action that dehumanizes the victim, even without conscious intent [19, 20]. Respondents were provided with the following examples of police actions: police cursed at respondent; police searched, frisked, or patted the respondent; police threatened to arrest the respondent; police handcuffed the respondent; police threatened the respondent with a ticket; police shoved or grabbed the respondent; police hit or kicked the respondent; police used pepper spray or another chemical on the respondent; police used an electroshock weapon such as a stun gun on the respondent, and police pointed a gun at the respondent. For each of these actions, respondents were asked whether it never happened to them, has happened about once or twice in their lives, happens a few times a year, about once a month, or happens about weekly. SHUR also assessed respondents’ evaluations of the necessity of the police actions they had experienced. They were asked: “Thinking of your most recent experience(s) with the police, would you say the action of the officer was necessary?” Our focus group participants contend that individual perceptions of the necessity of police actions are important indicators of the dehumanizing impact of police violence. We also assessed the likelihood of calling the police if there is a problem, worries about potential police brutality, arrest or incarceration, and cause-specific stressors such as race-related impression management, concerns about housing, food, and medical bills. We collected data on reasons for perceived discrimination such as race, language or accent, religion, immigration status, sexual orientation, and gender identity. We also assessed spaces and perpetrators of discrimination—whether discrimination was experienced at work, school, or perpetuated by a health care provider, police or security officer, or an individual in one’s neighborhood. Other novel measures included in the survey are relational aspects of health care delivery, such as respondents’ perceptions of respect during their clinical encounter, and specifically by receptionists, nurses, medical or nursing assistants, and physicians. The survey included three indicators of respondents’ sense of social exclusion, feeling like they are not trusted, often feeling left out, and not feeling like a member of a community. We also included existing measures of stressors such as discrimination using the Everyday Discrimination and the Heightened Racial Vigilance scales [21], Group-Based Medical Mistrust scale [22], and the Adverse Childhood Experiences (ACEs) module [23]. We included the following measures of health status: self-rated health, activity limitations (respondent limited in any way in any activities because of physical, mental, or emotional problems), self-rated mental health, and depression and anxiety using the two-item patient health questionnaire [24]. Indicators of access to care include usual source of care, health insurance, perceived unmet need for medical care, perceived unmet need for mental health care, past use of mental health services, and the probability of seeking mental health care. Sociodemographic data collected include race, gender identity, sexual orientation, age, marital status, level of education, work status, years in the USA if born outside of the USA, and zip code. The survey instrument was pre-tested among a small subset of community members in Allentown (n = 11). Revisions were made, and the survey was then piloted using a convenient online sample (n = 100) with respondents from 65 zip codes across the country, majority being from the East Coast. The final version of the survey, after piloting, is presented in Appendix 1. Approval from Lehigh University’s Institutional Review Board was obtained both for the initial social exclusion focus groups and for the survey. The focus groups and survey were funded internally by Lehigh University’s Community-engaged Health Research Fellowship and the Faculty Innovation Grant, respectively. Data Collection The SHUR employed quota sampling, a non-probability sampling approach where we looked for specific characteristics of respondents and then obtained a tailored sample that is representative of the population of interest. The target was 4000 respondents living in urban areas in the contiguous USA. We assigned quotas for usual source of care and race/ethnicity. Black, Indigenous, and people color, as well as those who are poor, are more likely to receive care at specific sites rather than from a specific primary care physician with whom they have established a relationship [25]. Having a regular source of care, and the kind of place that people go to for usual care matters for relational aspects of care such as perceived respect and mistrust. Given this literature, we assigned a quota for usual source of care. At least half of the sample (n = 2000) must report a clinic or community health center, an emergency department or urgent care facility as their usual source of care, or report that they did not have a usual source of care. The second quota was specific for race/ethnicity. Because we needed 4000 respondents, 1000 respondents (at least 25%) must be people of color and no more than 65% should be non-Hispanic White. This falls within the range of the US Census and Pew Center estimates of the racial demographics of urbanized areas and provides enough sample sizes to complete analysis by race/ethnicity. We contracted with Qualtrics because their panels are relatively more demographically representative than other online survey platforms for convenience sampling [26]. Qualtrics invited respondents by partnering with over 20 Web-based panel providers to access potential respondents based on the specified quotas. Respondents received some form of incentive from panel providers, but the specific value of the incentive was not disclosed to researchers. Qualtrics monitored the specified quotas using screening questions on race/ethnicity and usual source of care. For example, when enough non-Hispanic Whites had completed the survey, anyone who identified as non-Hispanic White who expressed interest in taking the survey was not redirected to the full survey. This process continued until the quotas were met. A total of 7495 persons passed the screeners and met the quota requirements. Qualtrics performed quality checks on the data and removed incomplete responses. They also assessed the time it took for respondents to complete the survey. The median time for survey completion was 10 min. Respondents who took less than a third of the median time to complete the survey were excluded from the final sample because of the possibility that they were not paying attention to the questions and might have been checking response boxes as quickly as possible. After these checks, we were left with 4389 completed responses. Survey Results We provide a brief description of the survey results by select characteristics in Table 1. As shown, non-Hispanic Whites make up just under two thirds of the sample (63.65%, n = 2793). Black/African American respondents constitute 14.2% of the sample (n = 623), while 11.62% (n = 510) are Hispanic/Latinx. SHUR respondents are disproportionately cisgender women (70.85%, n = 3109), and the majority are under the age of 65; only 8.61% (n = 378) are 65 years of age or older. While slightly more than half of the respondents worked full-time or part-time, three in ten were not in the labor force, and about one in ten were in the labor force but were unemployed and looking for work at the time of the survey. Table 1 Selected characteristics of SHUR respondents Percent Number x̅ (range) Race and ethnicity Non-Hispanic White 63.65 2793 Non-Hispanic Black 14.20 623 Hispanic/Latinx 11.62 510 American Indian and Alaskan Native 1.39 61 Asian 3.81 167 Other/multiple 5.33 234 Gender identity Cisgender man 24.52 1076 Cisgender woman 70.84 3109 Gender fluid 3.08 135 Transgender man 0.84 37 Transgender woman 0.73 32 Age category 18–24 19.25 845 25–34 27.59 1211 35–44 20.92 918 45–54 13.69 601 55–64 9.93 436 65 and older 8.61 378 Work status Not in the labor force 32.71 1390 Unemployed, looking for work 12.31 523 Working for pay, part time < 30 h/week 15.65 665 Working for pay, full time > =30 h/week 39.34 1672 Usual source of care Community clinic/health center 26.59 1167 Doctor’s office 42.36 1859 Emergency room 11.37 499 Outpatient department such as urgent care 11.07 486 No usual source of care 7.97 350 Some other place 0.64 28 Reports unmet need for medical care 37.72 1639 Often feels left out Strongly disagree 10.64 453 Disagree 18.06 769 Agree nor disagree 29.56 1259 Agree 15.45 658 Strongly agree 26.30 1120 Has experienced racial discrimination 14.42 633 Consciously acts in ways to make sure you do not live up to racial stereotypes Never 34.04 1464 Sometimes 42.46 1826 Always 23.51 1011 Worries about housing Never 31.67 1362 Sometimes 41.90 1802 Always 26.44 1137 Worries about paying medical bills Never 41.32 1777 Sometimes 39.55 1701 Always 7.95 823 Worries someone they know will become a victim of police brutality Never 53.99 2322 Sometimes 33.04 1421 Always 12.97 558 Has had a negative encounter with the police 56.86 2495 Mean medical mistrust score 4380 29.19 (12–60) Mean overall respect rating 4318 7.03(2–10) In terms of access to care and health services, most of the respondents had a usual source of care, but they were pretty spread out in terms of the specific places they regularly went to for care. For example, four in ten of the respondents received care from the doctor’s office, two in ten at a community clinic, and one in ten at the emergency room. More than a third of the respondents reported unmet need for medical care (37.72%, n = 1639). Response options on the 12-item group-based medical mistrust index ranged from strongly disagree (1) to strongly agree (5). Scores on the medical mistrust scale ranged from 12 to 60, with higher scores indicating greater mistrust of health institutions. The mean mistrust score for the sample was 29.19. Respondents rated if they felt, in general, that they were treated with a great deal of respect and dignity the last time they received healthcare. Ratings could range from 1 (no respect at all) to 10 (utmost respect). The range for our sample was 2 to 10, with a mean of 7.03, and a median of 9. Feeling left out is one indicator of social exclusion. About four in ten respondents agree or strongly agree that they often felt left out. Many respondents also reported experiencing salient sources of stress. For example, 14.42% of the sample (n = 633) felt hassled, inferior, or discriminated against because of race, accounting for more than half of the respondents who reported any kind of discrimination. Almost a quarter of the respondents engaged in race-related impression management—always careful to act in a way that did not consciously live up to the stereotypes of their racial and ethnic groups; 26.4% (n = 1137) were always worried about being able to pay rent/mortgage/housing costs while 19.14% (n = 823) always worried that they would not be able to pay their medical bills if they got sick or had an accident. Even though four in ten always or sometimes worried that someone they know would become a victim of police brutality, 56.86% (n = 2495) reported having experienced at least one of the ten listed negative interactions with the police. Public Health Implications The SHUR is a great resource for researchers and policymakers interested in understanding and addressing factors relevant to the health of marginalized populations. Research published using SHUR data can contribute significantly to ongoing conversations around the connections between police brutality and health, especially access to care and medical mistrust [18]. Nevertheless, there are caveats. First, SHUR does not employ probability sampling. Therefore, estimates from the survey might be sensitive to systematic errors because respondents might differ from non-respondents in significant ways. Second, we did not assess respondents’ perceptions of the necessity of each negative police encounter. Instead, we asked respondents to think about their most recent experiences with the police and to state their perceptions about whether the action(s) of the police were necessary. While we wanted to capture more recent exposures to police brutality, we think that perceptions about the necessity of negative police encounters might be different for different police actions. For example, an individual might perceive the police patting them down before an arrest as necessary and a previous encounter where the police kicked them as unnecessary. These actions have implications especially for assessing mental health correlates of police brutality such as anxiety and depression. Despite these limitations, SHUR can support health disparities research in several ways. First, the survey is informed by the experiences of racialized populations—specifically Black men, Latinxs, and immigrants—and assesses salient conditions including sources and spaces of discrimination, social exclusion, experiences of police brutality and stressful anticipations of these experiences, housing-related stress, as well as stress-related to arrests and incarceration. These data can help us identify connections between specific social determinants and a range of indicators of access to care and health status that are included in the data. These connections are important for formulating and implementing targeted policies to address health inequities. Second, SHUR measures relational aspects of care such as mistrust and perceptions of respect that we know are important indicators of the delivery of patient-centered care [27, 28]. When patients feel respected, they might then feel supported and empowered to share their own needs, perspectives, and preferences, and therefore engage in shared-decision making [29]. This might also equalize the inherent power differentials between clinicians and patients, regardless of race and socio-economic status. The data have the potential of helping researchers understand factors that shape relational aspects of care to improve engagement and reduce unmet need. Third, SHUR includes respondents’ zip codes. This presents researchers with the rare opportunity to link the data to zip code-level health system characteristics including the availability of physicians, housing characteristics, foreclosure rates, food insecurity, incarceration rates, voting and other indicators of political participation, as well as population-level indicators of structural racism such as Black to White ratios in rates of unemployment, poverty, health insurance, and college graduation. These larger structural factors, including structural racism, shape health beyond individual behaviors and attributes [30, 31]. Therefore, examining their interaction with individual factors in multi-level analyses is critical. In addition, researchers using these data can explore how variation in characteristics of urban areas, including population density, might be associated with variation in a range of experiences and health outcomes. The approach employed in SHUR—co-creating measures of salient stressors with communities for which our work bears relevance is important for understanding the mechanisms through which social conditions affect health, the contextual specificity of these mechanisms, and what kinds of interventions might help eliminate health disparities caused by structural inequalities. Measures in the current survey are critical for providing evidence needed to inform policies that would improve health among urbanized populations. We encourage others to use these data. Community-driven approaches to creating measures related to navigating COVID-19 that are salient to the experiences of populations marginalized by structural inequalities are important next steps. Appendix 1 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The authors thank focus group participants, interpreters, and entire staff of Promise Neighborhoods of the Lehigh Valley for their work in organizing the focus groups. Availability of Data and Material The data can be accessed by request to the corresponding author. Authors’ Contributions SA conceptualized the research project and lead in the development of the survey and the manuscript. CP and MM assisted in the review of instruments, survey data analysis, and writing. HB and AB were part of the conceptualization of study and assisted with focus group organization. JH, AS, and KB collected focus group data and assisted in identifying salient stressors and indicators of exclusion. KM assisted in the writing. All authors contributed significantly to this work. All authors take responsibility for the content of the manuscript. Funding The focus groups and survey were funded by Lehigh University’s Community-Engaged Health Research Fellowship and the Faculty Innovation Grant, respectively. Compliance with Ethical Standards Conflict of Interest The authors declare that they have no conflict of interests. Ethics Approval Both the focus groups and survey received approval from Lehigh University’s Institutional Review Board. Consent to Participate All focus group participants and survey respondents freely consented to participate. Consent for Publication N/A Code Availability Stata codes can be accessed by request to the corresponding author.

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article-title	Survey of the Health of Urban Residents: a Community-Driven Assessment of Conditions Salient to the Health of Historically Excluded Populations in the USA
abstract	Background Data from the Survey of the Health of Urban Residents (SHUR) identified connections between police brutality and medical mistrust, generating significant media, policy, and research attention. Amidst intersecting crises of COVID-19, racism, and police brutality, this report describes survey development and data collection procedures for the SHUR. Basic Procedures We conducted focus groups with Black men, Latinxs, and immigrants in Allentown, Pennsylvania. Findings were used to develop and refine measures of conditions salient to the health of urban residents across the country. Quota sampling was employed; oversampling people of color and persons whose usual source of care was not a doctor’s office. Main Findings Non-Hispanic Whites made up just under two thirds of the sample (63.65%, n = 2793). Black/African American respondents accounted for 14.2% of the sample (n = 623), while 11.62% (n = 510) were Latinx. Only 43.46% of respondents reported a doctor’s office as their usual source of care. Novel measures of population-specific stressors include a range of negative encounters with the police, frequency of these encounters, and respondents’ assessments of whether the encounters were necessary. SHUR assessed the likelihood of calling the police if there is a problem, worries about incarceration, and cause-specific stressors such as race-related impression management. Principal Conclusions SHUR (n = 4389) is a useful resource for researchers seeking to address the health implications of experiences not frequently measured by national health surveillance surveys. It includes respondents’ zip codes, presenting the opportunity to connect these data with zip code-level health system, social and economic characteristics that shape health beyond individual factors.
sec	Background Data from the Survey of the Health of Urban Residents (SHUR) identified connections between police brutality and medical mistrust, generating significant media, policy, and research attention. Amidst intersecting crises of COVID-19, racism, and police brutality, this report describes survey development and data collection procedures for the SHUR.
title	Background
p	Data from the Survey of the Health of Urban Residents (SHUR) identified connections between police brutality and medical mistrust, generating significant media, policy, and research attention. Amidst intersecting crises of COVID-19, racism, and police brutality, this report describes survey development and data collection procedures for the SHUR.
sec	Basic Procedures We conducted focus groups with Black men, Latinxs, and immigrants in Allentown, Pennsylvania. Findings were used to develop and refine measures of conditions salient to the health of urban residents across the country. Quota sampling was employed; oversampling people of color and persons whose usual source of care was not a doctor’s office.
title	Basic Procedures
p	We conducted focus groups with Black men, Latinxs, and immigrants in Allentown, Pennsylvania. Findings were used to develop and refine measures of conditions salient to the health of urban residents across the country. Quota sampling was employed; oversampling people of color and persons whose usual source of care was not a doctor’s office.
sec	Main Findings Non-Hispanic Whites made up just under two thirds of the sample (63.65%, n = 2793). Black/African American respondents accounted for 14.2% of the sample (n = 623), while 11.62% (n = 510) were Latinx. Only 43.46% of respondents reported a doctor’s office as their usual source of care. Novel measures of population-specific stressors include a range of negative encounters with the police, frequency of these encounters, and respondents’ assessments of whether the encounters were necessary. SHUR assessed the likelihood of calling the police if there is a problem, worries about incarceration, and cause-specific stressors such as race-related impression management.
title	Main Findings
p	Non-Hispanic Whites made up just under two thirds of the sample (63.65%, n = 2793). Black/African American respondents accounted for 14.2% of the sample (n = 623), while 11.62% (n = 510) were Latinx. Only 43.46% of respondents reported a doctor’s office as their usual source of care. Novel measures of population-specific stressors include a range of negative encounters with the police, frequency of these encounters, and respondents’ assessments of whether the encounters were necessary. SHUR assessed the likelihood of calling the police if there is a problem, worries about incarceration, and cause-specific stressors such as race-related impression management.
sec	Principal Conclusions SHUR (n = 4389) is a useful resource for researchers seeking to address the health implications of experiences not frequently measured by national health surveillance surveys. It includes respondents’ zip codes, presenting the opportunity to connect these data with zip code-level health system, social and economic characteristics that shape health beyond individual factors.
title	Principal Conclusions
p	SHUR (n = 4389) is a useful resource for researchers seeking to address the health implications of experiences not frequently measured by national health surveillance surveys. It includes respondents’ zip codes, presenting the opportunity to connect these data with zip code-level health system, social and economic characteristics that shape health beyond individual factors.
body	One of several overarching goals of the Healthy People 2030 initiative is to create conditions that promote health and well-being for all [1]. These conditions include social, physical, and economic environments that enable people to stay healthy, and that are grounded in the fundamental principle of health equity. Healthy People 2030 also seeks to eliminate health disparities by addressing the structural drivers of inequities in health [1, 2]. To achieve these goals, we must first identify the social determinants of health that are salient to the experiences of people who are socio-economically or racially marginalized. Social determinants of health are the conditions in which people are born, live, age, and work that shape a range of health outcomes including the likelihood of becoming sick, health status, and access to care [3]. COVID-19 has exposed how inequities in social, economic, and environmental conditions—social determinants—shape inequities in health outcomes [4]. Between health inequities made bare by COVID-19 and outrage over anti-Black racism and police brutality that followed the murder of George Floyd, understanding how structural racism shapes a range of social and economic conditions that impact the health outcomes of Black, Indigenous, and Latinx communities in the USA is critical. Access to care matters for health outcomes [5–7]. However, given similar access, people who belong to racially marginalized groups and those who are experiencing poverty are less likely to initiate care [8, 9]. Public hospitals, community health centers or clinics, and safety-net settings are defined by their shared vision to provide care to persons who need it regardless of their ability to pay [10]. As a result, these facilities are mostly used by people who are socio-economically disadvantaged—majority of whom belong to racial and ethnic minority groups, as well as undocumented persons and immigrants who might experience cost, cultural, language, and other barriers to care [11, 12]. One very challenging issue in health disparities research is understanding why in urban areas with safety-net clinics, the prevalence of people with unmet need for health care is still high [13–15]. Mistrust in medical institutions is one cause of unmet need [16, 17]. A recent publication using data from the Survey of the Health of Urban Residents (SHUR) identified connections between experiences of police brutality and medical mistrust [18]. That publication continues to receive significant media, policy, and research attention, and researchers are interested in obtaining access to the data amidst intersecting crises of COVID-19, racism, and police brutality. In this brief report, we describe the process of developing the SHUR. The survey assesses experiences of police brutality, as well as a range of health, health care, social and economic characteristics, and experiences of people who live in urbanized areas in the USA based on the 2010 Census. These are areas with a population of at least 50,000 people. We hope that this report will facilitate dissemination and further analyses of the data to inform policies and programs needed for addressing health inequities. Methods Survey Development Conceptualization of the survey came from an ongoing partnership between academic researchers, a federally qualified health center (FQHC), and an equity-driven non-profit that serves as a hub for community leadership, empowerment, and transformation through social engagement. Our main project focused on exploring the experiences and dimensions of social exclusion and their effects on health outcomes. Academic partners analyzed the existing literature on social exclusion. The non-profit and FQHC partners organized three focus groups in Allentown, Pennsylvania: The first with Latinx populations, the second with Black men, and the third with immigrant populations. All partners trained community members who then facilitated the focus groups. For example, a Latino man was trained to facilitate the Latinx focus group. In these focus groups, we found that participants experienced specific salient stressors that shaped their health outcomes, conditions that were neither regularly captured in our population health surveillance surveys nor were in the broad literature on social determinants of health. Using the data from focus groups, academic partners began developing a brief but comprehensive survey that includes these experiences. We worked with our non-profit and FQHC partners in a process that involved multiple conversations with community members who have a broad range of expertise. They included religious leaders, teachers, students and interns, health care providers, previously incarcerated and justice-involved individuals, and people with multiple chronic conditions, including substance use disorders. University partners searched for any existing instruments consistent with the experiences of marginalized communities. Community members critiqued some of the existing instruments to ensure that word choices reflected their experiences and co-created new measures. Measures Novel measures of stressors such as a range of negative encounters with the police and assessments of whether those encounters were necessary were included to assess experiences of police brutality. We conceptualize police brutality not merely as the use of force by a police officer, but police action that dehumanizes the victim, even without conscious intent [19, 20]. Respondents were provided with the following examples of police actions: police cursed at respondent; police searched, frisked, or patted the respondent; police threatened to arrest the respondent; police handcuffed the respondent; police threatened the respondent with a ticket; police shoved or grabbed the respondent; police hit or kicked the respondent; police used pepper spray or another chemical on the respondent; police used an electroshock weapon such as a stun gun on the respondent, and police pointed a gun at the respondent. For each of these actions, respondents were asked whether it never happened to them, has happened about once or twice in their lives, happens a few times a year, about once a month, or happens about weekly. SHUR also assessed respondents’ evaluations of the necessity of the police actions they had experienced. They were asked: “Thinking of your most recent experience(s) with the police, would you say the action of the officer was necessary?” Our focus group participants contend that individual perceptions of the necessity of police actions are important indicators of the dehumanizing impact of police violence. We also assessed the likelihood of calling the police if there is a problem, worries about potential police brutality, arrest or incarceration, and cause-specific stressors such as race-related impression management, concerns about housing, food, and medical bills. We collected data on reasons for perceived discrimination such as race, language or accent, religion, immigration status, sexual orientation, and gender identity. We also assessed spaces and perpetrators of discrimination—whether discrimination was experienced at work, school, or perpetuated by a health care provider, police or security officer, or an individual in one’s neighborhood. Other novel measures included in the survey are relational aspects of health care delivery, such as respondents’ perceptions of respect during their clinical encounter, and specifically by receptionists, nurses, medical or nursing assistants, and physicians. The survey included three indicators of respondents’ sense of social exclusion, feeling like they are not trusted, often feeling left out, and not feeling like a member of a community. We also included existing measures of stressors such as discrimination using the Everyday Discrimination and the Heightened Racial Vigilance scales [21], Group-Based Medical Mistrust scale [22], and the Adverse Childhood Experiences (ACEs) module [23]. We included the following measures of health status: self-rated health, activity limitations (respondent limited in any way in any activities because of physical, mental, or emotional problems), self-rated mental health, and depression and anxiety using the two-item patient health questionnaire [24]. Indicators of access to care include usual source of care, health insurance, perceived unmet need for medical care, perceived unmet need for mental health care, past use of mental health services, and the probability of seeking mental health care. Sociodemographic data collected include race, gender identity, sexual orientation, age, marital status, level of education, work status, years in the USA if born outside of the USA, and zip code. The survey instrument was pre-tested among a small subset of community members in Allentown (n = 11). Revisions were made, and the survey was then piloted using a convenient online sample (n = 100) with respondents from 65 zip codes across the country, majority being from the East Coast. The final version of the survey, after piloting, is presented in Appendix 1. Approval from Lehigh University’s Institutional Review Board was obtained both for the initial social exclusion focus groups and for the survey. The focus groups and survey were funded internally by Lehigh University’s Community-engaged Health Research Fellowship and the Faculty Innovation Grant, respectively. Data Collection The SHUR employed quota sampling, a non-probability sampling approach where we looked for specific characteristics of respondents and then obtained a tailored sample that is representative of the population of interest. The target was 4000 respondents living in urban areas in the contiguous USA. We assigned quotas for usual source of care and race/ethnicity. Black, Indigenous, and people color, as well as those who are poor, are more likely to receive care at specific sites rather than from a specific primary care physician with whom they have established a relationship [25]. Having a regular source of care, and the kind of place that people go to for usual care matters for relational aspects of care such as perceived respect and mistrust. Given this literature, we assigned a quota for usual source of care. At least half of the sample (n = 2000) must report a clinic or community health center, an emergency department or urgent care facility as their usual source of care, or report that they did not have a usual source of care. The second quota was specific for race/ethnicity. Because we needed 4000 respondents, 1000 respondents (at least 25%) must be people of color and no more than 65% should be non-Hispanic White. This falls within the range of the US Census and Pew Center estimates of the racial demographics of urbanized areas and provides enough sample sizes to complete analysis by race/ethnicity. We contracted with Qualtrics because their panels are relatively more demographically representative than other online survey platforms for convenience sampling [26]. Qualtrics invited respondents by partnering with over 20 Web-based panel providers to access potential respondents based on the specified quotas. Respondents received some form of incentive from panel providers, but the specific value of the incentive was not disclosed to researchers. Qualtrics monitored the specified quotas using screening questions on race/ethnicity and usual source of care. For example, when enough non-Hispanic Whites had completed the survey, anyone who identified as non-Hispanic White who expressed interest in taking the survey was not redirected to the full survey. This process continued until the quotas were met. A total of 7495 persons passed the screeners and met the quota requirements. Qualtrics performed quality checks on the data and removed incomplete responses. They also assessed the time it took for respondents to complete the survey. The median time for survey completion was 10 min. Respondents who took less than a third of the median time to complete the survey were excluded from the final sample because of the possibility that they were not paying attention to the questions and might have been checking response boxes as quickly as possible. After these checks, we were left with 4389 completed responses. Survey Results We provide a brief description of the survey results by select characteristics in Table 1. As shown, non-Hispanic Whites make up just under two thirds of the sample (63.65%, n = 2793). Black/African American respondents constitute 14.2% of the sample (n = 623), while 11.62% (n = 510) are Hispanic/Latinx. SHUR respondents are disproportionately cisgender women (70.85%, n = 3109), and the majority are under the age of 65; only 8.61% (n = 378) are 65 years of age or older. While slightly more than half of the respondents worked full-time or part-time, three in ten were not in the labor force, and about one in ten were in the labor force but were unemployed and looking for work at the time of the survey. Table 1 Selected characteristics of SHUR respondents Percent Number x̅ (range) Race and ethnicity Non-Hispanic White 63.65 2793 Non-Hispanic Black 14.20 623 Hispanic/Latinx 11.62 510 American Indian and Alaskan Native 1.39 61 Asian 3.81 167 Other/multiple 5.33 234 Gender identity Cisgender man 24.52 1076 Cisgender woman 70.84 3109 Gender fluid 3.08 135 Transgender man 0.84 37 Transgender woman 0.73 32 Age category 18–24 19.25 845 25–34 27.59 1211 35–44 20.92 918 45–54 13.69 601 55–64 9.93 436 65 and older 8.61 378 Work status Not in the labor force 32.71 1390 Unemployed, looking for work 12.31 523 Working for pay, part time < 30 h/week 15.65 665 Working for pay, full time > =30 h/week 39.34 1672 Usual source of care Community clinic/health center 26.59 1167 Doctor’s office 42.36 1859 Emergency room 11.37 499 Outpatient department such as urgent care 11.07 486 No usual source of care 7.97 350 Some other place 0.64 28 Reports unmet need for medical care 37.72 1639 Often feels left out Strongly disagree 10.64 453 Disagree 18.06 769 Agree nor disagree 29.56 1259 Agree 15.45 658 Strongly agree 26.30 1120 Has experienced racial discrimination 14.42 633 Consciously acts in ways to make sure you do not live up to racial stereotypes Never 34.04 1464 Sometimes 42.46 1826 Always 23.51 1011 Worries about housing Never 31.67 1362 Sometimes 41.90 1802 Always 26.44 1137 Worries about paying medical bills Never 41.32 1777 Sometimes 39.55 1701 Always 7.95 823 Worries someone they know will become a victim of police brutality Never 53.99 2322 Sometimes 33.04 1421 Always 12.97 558 Has had a negative encounter with the police 56.86 2495 Mean medical mistrust score 4380 29.19 (12–60) Mean overall respect rating 4318 7.03(2–10) In terms of access to care and health services, most of the respondents had a usual source of care, but they were pretty spread out in terms of the specific places they regularly went to for care. For example, four in ten of the respondents received care from the doctor’s office, two in ten at a community clinic, and one in ten at the emergency room. More than a third of the respondents reported unmet need for medical care (37.72%, n = 1639). Response options on the 12-item group-based medical mistrust index ranged from strongly disagree (1) to strongly agree (5). Scores on the medical mistrust scale ranged from 12 to 60, with higher scores indicating greater mistrust of health institutions. The mean mistrust score for the sample was 29.19. Respondents rated if they felt, in general, that they were treated with a great deal of respect and dignity the last time they received healthcare. Ratings could range from 1 (no respect at all) to 10 (utmost respect). The range for our sample was 2 to 10, with a mean of 7.03, and a median of 9. Feeling left out is one indicator of social exclusion. About four in ten respondents agree or strongly agree that they often felt left out. Many respondents also reported experiencing salient sources of stress. For example, 14.42% of the sample (n = 633) felt hassled, inferior, or discriminated against because of race, accounting for more than half of the respondents who reported any kind of discrimination. Almost a quarter of the respondents engaged in race-related impression management—always careful to act in a way that did not consciously live up to the stereotypes of their racial and ethnic groups; 26.4% (n = 1137) were always worried about being able to pay rent/mortgage/housing costs while 19.14% (n = 823) always worried that they would not be able to pay their medical bills if they got sick or had an accident. Even though four in ten always or sometimes worried that someone they know would become a victim of police brutality, 56.86% (n = 2495) reported having experienced at least one of the ten listed negative interactions with the police. Public Health Implications The SHUR is a great resource for researchers and policymakers interested in understanding and addressing factors relevant to the health of marginalized populations. Research published using SHUR data can contribute significantly to ongoing conversations around the connections between police brutality and health, especially access to care and medical mistrust [18]. Nevertheless, there are caveats. First, SHUR does not employ probability sampling. Therefore, estimates from the survey might be sensitive to systematic errors because respondents might differ from non-respondents in significant ways. Second, we did not assess respondents’ perceptions of the necessity of each negative police encounter. Instead, we asked respondents to think about their most recent experiences with the police and to state their perceptions about whether the action(s) of the police were necessary. While we wanted to capture more recent exposures to police brutality, we think that perceptions about the necessity of negative police encounters might be different for different police actions. For example, an individual might perceive the police patting them down before an arrest as necessary and a previous encounter where the police kicked them as unnecessary. These actions have implications especially for assessing mental health correlates of police brutality such as anxiety and depression. Despite these limitations, SHUR can support health disparities research in several ways. First, the survey is informed by the experiences of racialized populations—specifically Black men, Latinxs, and immigrants—and assesses salient conditions including sources and spaces of discrimination, social exclusion, experiences of police brutality and stressful anticipations of these experiences, housing-related stress, as well as stress-related to arrests and incarceration. These data can help us identify connections between specific social determinants and a range of indicators of access to care and health status that are included in the data. These connections are important for formulating and implementing targeted policies to address health inequities. Second, SHUR measures relational aspects of care such as mistrust and perceptions of respect that we know are important indicators of the delivery of patient-centered care [27, 28]. When patients feel respected, they might then feel supported and empowered to share their own needs, perspectives, and preferences, and therefore engage in shared-decision making [29]. This might also equalize the inherent power differentials between clinicians and patients, regardless of race and socio-economic status. The data have the potential of helping researchers understand factors that shape relational aspects of care to improve engagement and reduce unmet need. Third, SHUR includes respondents’ zip codes. This presents researchers with the rare opportunity to link the data to zip code-level health system characteristics including the availability of physicians, housing characteristics, foreclosure rates, food insecurity, incarceration rates, voting and other indicators of political participation, as well as population-level indicators of structural racism such as Black to White ratios in rates of unemployment, poverty, health insurance, and college graduation. These larger structural factors, including structural racism, shape health beyond individual behaviors and attributes [30, 31]. Therefore, examining their interaction with individual factors in multi-level analyses is critical. In addition, researchers using these data can explore how variation in characteristics of urban areas, including population density, might be associated with variation in a range of experiences and health outcomes. The approach employed in SHUR—co-creating measures of salient stressors with communities for which our work bears relevance is important for understanding the mechanisms through which social conditions affect health, the contextual specificity of these mechanisms, and what kinds of interventions might help eliminate health disparities caused by structural inequalities. Measures in the current survey are critical for providing evidence needed to inform policies that would improve health among urbanized populations. We encourage others to use these data. Community-driven approaches to creating measures related to navigating COVID-19 that are salient to the experiences of populations marginalized by structural inequalities are important next steps.
p	One of several overarching goals of the Healthy People 2030 initiative is to create conditions that promote health and well-being for all [1]. These conditions include social, physical, and economic environments that enable people to stay healthy, and that are grounded in the fundamental principle of health equity. Healthy People 2030 also seeks to eliminate health disparities by addressing the structural drivers of inequities in health [1, 2]. To achieve these goals, we must first identify the social determinants of health that are salient to the experiences of people who are socio-economically or racially marginalized. Social determinants of health are the conditions in which people are born, live, age, and work that shape a range of health outcomes including the likelihood of becoming sick, health status, and access to care [3]. COVID-19 has exposed how inequities in social, economic, and environmental conditions—social determinants—shape inequities in health outcomes [4]. Between health inequities made bare by COVID-19 and outrage over anti-Black racism and police brutality that followed the murder of George Floyd, understanding how structural racism shapes a range of social and economic conditions that impact the health outcomes of Black, Indigenous, and Latinx communities in the USA is critical.
p	Access to care matters for health outcomes [5–7]. However, given similar access, people who belong to racially marginalized groups and those who are experiencing poverty are less likely to initiate care [8, 9]. Public hospitals, community health centers or clinics, and safety-net settings are defined by their shared vision to provide care to persons who need it regardless of their ability to pay [10]. As a result, these facilities are mostly used by people who are socio-economically disadvantaged—majority of whom belong to racial and ethnic minority groups, as well as undocumented persons and immigrants who might experience cost, cultural, language, and other barriers to care [11, 12]. One very challenging issue in health disparities research is understanding why in urban areas with safety-net clinics, the prevalence of people with unmet need for health care is still high [13–15]. Mistrust in medical institutions is one cause of unmet need [16, 17].
p	A recent publication using data from the Survey of the Health of Urban Residents (SHUR) identified connections between experiences of police brutality and medical mistrust [18]. That publication continues to receive significant media, policy, and research attention, and researchers are interested in obtaining access to the data amidst intersecting crises of COVID-19, racism, and police brutality. In this brief report, we describe the process of developing the SHUR. The survey assesses experiences of police brutality, as well as a range of health, health care, social and economic characteristics, and experiences of people who live in urbanized areas in the USA based on the 2010 Census. These are areas with a population of at least 50,000 people. We hope that this report will facilitate dissemination and further analyses of the data to inform policies and programs needed for addressing health inequities.
sec	Methods Survey Development Conceptualization of the survey came from an ongoing partnership between academic researchers, a federally qualified health center (FQHC), and an equity-driven non-profit that serves as a hub for community leadership, empowerment, and transformation through social engagement. Our main project focused on exploring the experiences and dimensions of social exclusion and their effects on health outcomes. Academic partners analyzed the existing literature on social exclusion. The non-profit and FQHC partners organized three focus groups in Allentown, Pennsylvania: The first with Latinx populations, the second with Black men, and the third with immigrant populations. All partners trained community members who then facilitated the focus groups. For example, a Latino man was trained to facilitate the Latinx focus group. In these focus groups, we found that participants experienced specific salient stressors that shaped their health outcomes, conditions that were neither regularly captured in our population health surveillance surveys nor were in the broad literature on social determinants of health. Using the data from focus groups, academic partners began developing a brief but comprehensive survey that includes these experiences. We worked with our non-profit and FQHC partners in a process that involved multiple conversations with community members who have a broad range of expertise. They included religious leaders, teachers, students and interns, health care providers, previously incarcerated and justice-involved individuals, and people with multiple chronic conditions, including substance use disorders. University partners searched for any existing instruments consistent with the experiences of marginalized communities. Community members critiqued some of the existing instruments to ensure that word choices reflected their experiences and co-created new measures. Measures Novel measures of stressors such as a range of negative encounters with the police and assessments of whether those encounters were necessary were included to assess experiences of police brutality. We conceptualize police brutality not merely as the use of force by a police officer, but police action that dehumanizes the victim, even without conscious intent [19, 20]. Respondents were provided with the following examples of police actions: police cursed at respondent; police searched, frisked, or patted the respondent; police threatened to arrest the respondent; police handcuffed the respondent; police threatened the respondent with a ticket; police shoved or grabbed the respondent; police hit or kicked the respondent; police used pepper spray or another chemical on the respondent; police used an electroshock weapon such as a stun gun on the respondent, and police pointed a gun at the respondent. For each of these actions, respondents were asked whether it never happened to them, has happened about once or twice in their lives, happens a few times a year, about once a month, or happens about weekly. SHUR also assessed respondents’ evaluations of the necessity of the police actions they had experienced. They were asked: “Thinking of your most recent experience(s) with the police, would you say the action of the officer was necessary?” Our focus group participants contend that individual perceptions of the necessity of police actions are important indicators of the dehumanizing impact of police violence. We also assessed the likelihood of calling the police if there is a problem, worries about potential police brutality, arrest or incarceration, and cause-specific stressors such as race-related impression management, concerns about housing, food, and medical bills. We collected data on reasons for perceived discrimination such as race, language or accent, religion, immigration status, sexual orientation, and gender identity. We also assessed spaces and perpetrators of discrimination—whether discrimination was experienced at work, school, or perpetuated by a health care provider, police or security officer, or an individual in one’s neighborhood. Other novel measures included in the survey are relational aspects of health care delivery, such as respondents’ perceptions of respect during their clinical encounter, and specifically by receptionists, nurses, medical or nursing assistants, and physicians. The survey included three indicators of respondents’ sense of social exclusion, feeling like they are not trusted, often feeling left out, and not feeling like a member of a community. We also included existing measures of stressors such as discrimination using the Everyday Discrimination and the Heightened Racial Vigilance scales [21], Group-Based Medical Mistrust scale [22], and the Adverse Childhood Experiences (ACEs) module [23]. We included the following measures of health status: self-rated health, activity limitations (respondent limited in any way in any activities because of physical, mental, or emotional problems), self-rated mental health, and depression and anxiety using the two-item patient health questionnaire [24]. Indicators of access to care include usual source of care, health insurance, perceived unmet need for medical care, perceived unmet need for mental health care, past use of mental health services, and the probability of seeking mental health care. Sociodemographic data collected include race, gender identity, sexual orientation, age, marital status, level of education, work status, years in the USA if born outside of the USA, and zip code. The survey instrument was pre-tested among a small subset of community members in Allentown (n = 11). Revisions were made, and the survey was then piloted using a convenient online sample (n = 100) with respondents from 65 zip codes across the country, majority being from the East Coast. The final version of the survey, after piloting, is presented in Appendix 1. Approval from Lehigh University’s Institutional Review Board was obtained both for the initial social exclusion focus groups and for the survey. The focus groups and survey were funded internally by Lehigh University’s Community-engaged Health Research Fellowship and the Faculty Innovation Grant, respectively. Data Collection The SHUR employed quota sampling, a non-probability sampling approach where we looked for specific characteristics of respondents and then obtained a tailored sample that is representative of the population of interest. The target was 4000 respondents living in urban areas in the contiguous USA. We assigned quotas for usual source of care and race/ethnicity. Black, Indigenous, and people color, as well as those who are poor, are more likely to receive care at specific sites rather than from a specific primary care physician with whom they have established a relationship [25]. Having a regular source of care, and the kind of place that people go to for usual care matters for relational aspects of care such as perceived respect and mistrust. Given this literature, we assigned a quota for usual source of care. At least half of the sample (n = 2000) must report a clinic or community health center, an emergency department or urgent care facility as their usual source of care, or report that they did not have a usual source of care. The second quota was specific for race/ethnicity. Because we needed 4000 respondents, 1000 respondents (at least 25%) must be people of color and no more than 65% should be non-Hispanic White. This falls within the range of the US Census and Pew Center estimates of the racial demographics of urbanized areas and provides enough sample sizes to complete analysis by race/ethnicity. We contracted with Qualtrics because their panels are relatively more demographically representative than other online survey platforms for convenience sampling [26]. Qualtrics invited respondents by partnering with over 20 Web-based panel providers to access potential respondents based on the specified quotas. Respondents received some form of incentive from panel providers, but the specific value of the incentive was not disclosed to researchers. Qualtrics monitored the specified quotas using screening questions on race/ethnicity and usual source of care. For example, when enough non-Hispanic Whites had completed the survey, anyone who identified as non-Hispanic White who expressed interest in taking the survey was not redirected to the full survey. This process continued until the quotas were met. A total of 7495 persons passed the screeners and met the quota requirements. Qualtrics performed quality checks on the data and removed incomplete responses. They also assessed the time it took for respondents to complete the survey. The median time for survey completion was 10 min. Respondents who took less than a third of the median time to complete the survey were excluded from the final sample because of the possibility that they were not paying attention to the questions and might have been checking response boxes as quickly as possible. After these checks, we were left with 4389 completed responses.
title	Methods
sec	Survey Development Conceptualization of the survey came from an ongoing partnership between academic researchers, a federally qualified health center (FQHC), and an equity-driven non-profit that serves as a hub for community leadership, empowerment, and transformation through social engagement. Our main project focused on exploring the experiences and dimensions of social exclusion and their effects on health outcomes. Academic partners analyzed the existing literature on social exclusion. The non-profit and FQHC partners organized three focus groups in Allentown, Pennsylvania: The first with Latinx populations, the second with Black men, and the third with immigrant populations. All partners trained community members who then facilitated the focus groups. For example, a Latino man was trained to facilitate the Latinx focus group. In these focus groups, we found that participants experienced specific salient stressors that shaped their health outcomes, conditions that were neither regularly captured in our population health surveillance surveys nor were in the broad literature on social determinants of health. Using the data from focus groups, academic partners began developing a brief but comprehensive survey that includes these experiences. We worked with our non-profit and FQHC partners in a process that involved multiple conversations with community members who have a broad range of expertise. They included religious leaders, teachers, students and interns, health care providers, previously incarcerated and justice-involved individuals, and people with multiple chronic conditions, including substance use disorders. University partners searched for any existing instruments consistent with the experiences of marginalized communities. Community members critiqued some of the existing instruments to ensure that word choices reflected their experiences and co-created new measures.
title	Survey Development
p	Conceptualization of the survey came from an ongoing partnership between academic researchers, a federally qualified health center (FQHC), and an equity-driven non-profit that serves as a hub for community leadership, empowerment, and transformation through social engagement. Our main project focused on exploring the experiences and dimensions of social exclusion and their effects on health outcomes. Academic partners analyzed the existing literature on social exclusion. The non-profit and FQHC partners organized three focus groups in Allentown, Pennsylvania: The first with Latinx populations, the second with Black men, and the third with immigrant populations. All partners trained community members who then facilitated the focus groups. For example, a Latino man was trained to facilitate the Latinx focus group. In these focus groups, we found that participants experienced specific salient stressors that shaped their health outcomes, conditions that were neither regularly captured in our population health surveillance surveys nor were in the broad literature on social determinants of health.
p	Using the data from focus groups, academic partners began developing a brief but comprehensive survey that includes these experiences. We worked with our non-profit and FQHC partners in a process that involved multiple conversations with community members who have a broad range of expertise. They included religious leaders, teachers, students and interns, health care providers, previously incarcerated and justice-involved individuals, and people with multiple chronic conditions, including substance use disorders. University partners searched for any existing instruments consistent with the experiences of marginalized communities. Community members critiqued some of the existing instruments to ensure that word choices reflected their experiences and co-created new measures.
sec	Measures Novel measures of stressors such as a range of negative encounters with the police and assessments of whether those encounters were necessary were included to assess experiences of police brutality. We conceptualize police brutality not merely as the use of force by a police officer, but police action that dehumanizes the victim, even without conscious intent [19, 20]. Respondents were provided with the following examples of police actions: police cursed at respondent; police searched, frisked, or patted the respondent; police threatened to arrest the respondent; police handcuffed the respondent; police threatened the respondent with a ticket; police shoved or grabbed the respondent; police hit or kicked the respondent; police used pepper spray or another chemical on the respondent; police used an electroshock weapon such as a stun gun on the respondent, and police pointed a gun at the respondent. For each of these actions, respondents were asked whether it never happened to them, has happened about once or twice in their lives, happens a few times a year, about once a month, or happens about weekly. SHUR also assessed respondents’ evaluations of the necessity of the police actions they had experienced. They were asked: “Thinking of your most recent experience(s) with the police, would you say the action of the officer was necessary?” Our focus group participants contend that individual perceptions of the necessity of police actions are important indicators of the dehumanizing impact of police violence. We also assessed the likelihood of calling the police if there is a problem, worries about potential police brutality, arrest or incarceration, and cause-specific stressors such as race-related impression management, concerns about housing, food, and medical bills. We collected data on reasons for perceived discrimination such as race, language or accent, religion, immigration status, sexual orientation, and gender identity. We also assessed spaces and perpetrators of discrimination—whether discrimination was experienced at work, school, or perpetuated by a health care provider, police or security officer, or an individual in one’s neighborhood. Other novel measures included in the survey are relational aspects of health care delivery, such as respondents’ perceptions of respect during their clinical encounter, and specifically by receptionists, nurses, medical or nursing assistants, and physicians. The survey included three indicators of respondents’ sense of social exclusion, feeling like they are not trusted, often feeling left out, and not feeling like a member of a community. We also included existing measures of stressors such as discrimination using the Everyday Discrimination and the Heightened Racial Vigilance scales [21], Group-Based Medical Mistrust scale [22], and the Adverse Childhood Experiences (ACEs) module [23]. We included the following measures of health status: self-rated health, activity limitations (respondent limited in any way in any activities because of physical, mental, or emotional problems), self-rated mental health, and depression and anxiety using the two-item patient health questionnaire [24]. Indicators of access to care include usual source of care, health insurance, perceived unmet need for medical care, perceived unmet need for mental health care, past use of mental health services, and the probability of seeking mental health care. Sociodemographic data collected include race, gender identity, sexual orientation, age, marital status, level of education, work status, years in the USA if born outside of the USA, and zip code. The survey instrument was pre-tested among a small subset of community members in Allentown (n = 11). Revisions were made, and the survey was then piloted using a convenient online sample (n = 100) with respondents from 65 zip codes across the country, majority being from the East Coast. The final version of the survey, after piloting, is presented in Appendix 1. Approval from Lehigh University’s Institutional Review Board was obtained both for the initial social exclusion focus groups and for the survey. The focus groups and survey were funded internally by Lehigh University’s Community-engaged Health Research Fellowship and the Faculty Innovation Grant, respectively.
title	Measures
p	Novel measures of stressors such as a range of negative encounters with the police and assessments of whether those encounters were necessary were included to assess experiences of police brutality. We conceptualize police brutality not merely as the use of force by a police officer, but police action that dehumanizes the victim, even without conscious intent [19, 20]. Respondents were provided with the following examples of police actions: police cursed at respondent; police searched, frisked, or patted the respondent; police threatened to arrest the respondent; police handcuffed the respondent; police threatened the respondent with a ticket; police shoved or grabbed the respondent; police hit or kicked the respondent; police used pepper spray or another chemical on the respondent; police used an electroshock weapon such as a stun gun on the respondent, and police pointed a gun at the respondent. For each of these actions, respondents were asked whether it never happened to them, has happened about once or twice in their lives, happens a few times a year, about once a month, or happens about weekly. SHUR also assessed respondents’ evaluations of the necessity of the police actions they had experienced. They were asked: “Thinking of your most recent experience(s) with the police, would you say the action of the officer was necessary?” Our focus group participants contend that individual perceptions of the necessity of police actions are important indicators of the dehumanizing impact of police violence.
p	We also assessed the likelihood of calling the police if there is a problem, worries about potential police brutality, arrest or incarceration, and cause-specific stressors such as race-related impression management, concerns about housing, food, and medical bills. We collected data on reasons for perceived discrimination such as race, language or accent, religion, immigration status, sexual orientation, and gender identity. We also assessed spaces and perpetrators of discrimination—whether discrimination was experienced at work, school, or perpetuated by a health care provider, police or security officer, or an individual in one’s neighborhood. Other novel measures included in the survey are relational aspects of health care delivery, such as respondents’ perceptions of respect during their clinical encounter, and specifically by receptionists, nurses, medical or nursing assistants, and physicians.
p	The survey included three indicators of respondents’ sense of social exclusion, feeling like they are not trusted, often feeling left out, and not feeling like a member of a community. We also included existing measures of stressors such as discrimination using the Everyday Discrimination and the Heightened Racial Vigilance scales [21], Group-Based Medical Mistrust scale [22], and the Adverse Childhood Experiences (ACEs) module [23].
p	We included the following measures of health status: self-rated health, activity limitations (respondent limited in any way in any activities because of physical, mental, or emotional problems), self-rated mental health, and depression and anxiety using the two-item patient health questionnaire [24]. Indicators of access to care include usual source of care, health insurance, perceived unmet need for medical care, perceived unmet need for mental health care, past use of mental health services, and the probability of seeking mental health care. Sociodemographic data collected include race, gender identity, sexual orientation, age, marital status, level of education, work status, years in the USA if born outside of the USA, and zip code.
p	The survey instrument was pre-tested among a small subset of community members in Allentown (n = 11). Revisions were made, and the survey was then piloted using a convenient online sample (n = 100) with respondents from 65 zip codes across the country, majority being from the East Coast. The final version of the survey, after piloting, is presented in Appendix 1. Approval from Lehigh University’s Institutional Review Board was obtained both for the initial social exclusion focus groups and for the survey. The focus groups and survey were funded internally by Lehigh University’s Community-engaged Health Research Fellowship and the Faculty Innovation Grant, respectively.
sec	Data Collection The SHUR employed quota sampling, a non-probability sampling approach where we looked for specific characteristics of respondents and then obtained a tailored sample that is representative of the population of interest. The target was 4000 respondents living in urban areas in the contiguous USA. We assigned quotas for usual source of care and race/ethnicity. Black, Indigenous, and people color, as well as those who are poor, are more likely to receive care at specific sites rather than from a specific primary care physician with whom they have established a relationship [25]. Having a regular source of care, and the kind of place that people go to for usual care matters for relational aspects of care such as perceived respect and mistrust. Given this literature, we assigned a quota for usual source of care. At least half of the sample (n = 2000) must report a clinic or community health center, an emergency department or urgent care facility as their usual source of care, or report that they did not have a usual source of care. The second quota was specific for race/ethnicity. Because we needed 4000 respondents, 1000 respondents (at least 25%) must be people of color and no more than 65% should be non-Hispanic White. This falls within the range of the US Census and Pew Center estimates of the racial demographics of urbanized areas and provides enough sample sizes to complete analysis by race/ethnicity. We contracted with Qualtrics because their panels are relatively more demographically representative than other online survey platforms for convenience sampling [26]. Qualtrics invited respondents by partnering with over 20 Web-based panel providers to access potential respondents based on the specified quotas. Respondents received some form of incentive from panel providers, but the specific value of the incentive was not disclosed to researchers. Qualtrics monitored the specified quotas using screening questions on race/ethnicity and usual source of care. For example, when enough non-Hispanic Whites had completed the survey, anyone who identified as non-Hispanic White who expressed interest in taking the survey was not redirected to the full survey. This process continued until the quotas were met. A total of 7495 persons passed the screeners and met the quota requirements. Qualtrics performed quality checks on the data and removed incomplete responses. They also assessed the time it took for respondents to complete the survey. The median time for survey completion was 10 min. Respondents who took less than a third of the median time to complete the survey were excluded from the final sample because of the possibility that they were not paying attention to the questions and might have been checking response boxes as quickly as possible. After these checks, we were left with 4389 completed responses.
title	Data Collection
p	The SHUR employed quota sampling, a non-probability sampling approach where we looked for specific characteristics of respondents and then obtained a tailored sample that is representative of the population of interest. The target was 4000 respondents living in urban areas in the contiguous USA. We assigned quotas for usual source of care and race/ethnicity. Black, Indigenous, and people color, as well as those who are poor, are more likely to receive care at specific sites rather than from a specific primary care physician with whom they have established a relationship [25]. Having a regular source of care, and the kind of place that people go to for usual care matters for relational aspects of care such as perceived respect and mistrust. Given this literature, we assigned a quota for usual source of care. At least half of the sample (n = 2000) must report a clinic or community health center, an emergency department or urgent care facility as their usual source of care, or report that they did not have a usual source of care.
p	The second quota was specific for race/ethnicity. Because we needed 4000 respondents, 1000 respondents (at least 25%) must be people of color and no more than 65% should be non-Hispanic White. This falls within the range of the US Census and Pew Center estimates of the racial demographics of urbanized areas and provides enough sample sizes to complete analysis by race/ethnicity. We contracted with Qualtrics because their panels are relatively more demographically representative than other online survey platforms for convenience sampling [26].
p	Qualtrics invited respondents by partnering with over 20 Web-based panel providers to access potential respondents based on the specified quotas. Respondents received some form of incentive from panel providers, but the specific value of the incentive was not disclosed to researchers. Qualtrics monitored the specified quotas using screening questions on race/ethnicity and usual source of care. For example, when enough non-Hispanic Whites had completed the survey, anyone who identified as non-Hispanic White who expressed interest in taking the survey was not redirected to the full survey. This process continued until the quotas were met. A total of 7495 persons passed the screeners and met the quota requirements. Qualtrics performed quality checks on the data and removed incomplete responses. They also assessed the time it took for respondents to complete the survey. The median time for survey completion was 10 min. Respondents who took less than a third of the median time to complete the survey were excluded from the final sample because of the possibility that they were not paying attention to the questions and might have been checking response boxes as quickly as possible. After these checks, we were left with 4389 completed responses.
sec	Survey Results We provide a brief description of the survey results by select characteristics in Table 1. As shown, non-Hispanic Whites make up just under two thirds of the sample (63.65%, n = 2793). Black/African American respondents constitute 14.2% of the sample (n = 623), while 11.62% (n = 510) are Hispanic/Latinx. SHUR respondents are disproportionately cisgender women (70.85%, n = 3109), and the majority are under the age of 65; only 8.61% (n = 378) are 65 years of age or older. While slightly more than half of the respondents worked full-time or part-time, three in ten were not in the labor force, and about one in ten were in the labor force but were unemployed and looking for work at the time of the survey. Table 1 Selected characteristics of SHUR respondents Percent Number x̅ (range) Race and ethnicity Non-Hispanic White 63.65 2793 Non-Hispanic Black 14.20 623 Hispanic/Latinx 11.62 510 American Indian and Alaskan Native 1.39 61 Asian 3.81 167 Other/multiple 5.33 234 Gender identity Cisgender man 24.52 1076 Cisgender woman 70.84 3109 Gender fluid 3.08 135 Transgender man 0.84 37 Transgender woman 0.73 32 Age category 18–24 19.25 845 25–34 27.59 1211 35–44 20.92 918 45–54 13.69 601 55–64 9.93 436 65 and older 8.61 378 Work status Not in the labor force 32.71 1390 Unemployed, looking for work 12.31 523 Working for pay, part time < 30 h/week 15.65 665 Working for pay, full time > =30 h/week 39.34 1672 Usual source of care Community clinic/health center 26.59 1167 Doctor’s office 42.36 1859 Emergency room 11.37 499 Outpatient department such as urgent care 11.07 486 No usual source of care 7.97 350 Some other place 0.64 28 Reports unmet need for medical care 37.72 1639 Often feels left out Strongly disagree 10.64 453 Disagree 18.06 769 Agree nor disagree 29.56 1259 Agree 15.45 658 Strongly agree 26.30 1120 Has experienced racial discrimination 14.42 633 Consciously acts in ways to make sure you do not live up to racial stereotypes Never 34.04 1464 Sometimes 42.46 1826 Always 23.51 1011 Worries about housing Never 31.67 1362 Sometimes 41.90 1802 Always 26.44 1137 Worries about paying medical bills Never 41.32 1777 Sometimes 39.55 1701 Always 7.95 823 Worries someone they know will become a victim of police brutality Never 53.99 2322 Sometimes 33.04 1421 Always 12.97 558 Has had a negative encounter with the police 56.86 2495 Mean medical mistrust score 4380 29.19 (12–60) Mean overall respect rating 4318 7.03(2–10) In terms of access to care and health services, most of the respondents had a usual source of care, but they were pretty spread out in terms of the specific places they regularly went to for care. For example, four in ten of the respondents received care from the doctor’s office, two in ten at a community clinic, and one in ten at the emergency room. More than a third of the respondents reported unmet need for medical care (37.72%, n = 1639). Response options on the 12-item group-based medical mistrust index ranged from strongly disagree (1) to strongly agree (5). Scores on the medical mistrust scale ranged from 12 to 60, with higher scores indicating greater mistrust of health institutions. The mean mistrust score for the sample was 29.19. Respondents rated if they felt, in general, that they were treated with a great deal of respect and dignity the last time they received healthcare. Ratings could range from 1 (no respect at all) to 10 (utmost respect). The range for our sample was 2 to 10, with a mean of 7.03, and a median of 9. Feeling left out is one indicator of social exclusion. About four in ten respondents agree or strongly agree that they often felt left out. Many respondents also reported experiencing salient sources of stress. For example, 14.42% of the sample (n = 633) felt hassled, inferior, or discriminated against because of race, accounting for more than half of the respondents who reported any kind of discrimination. Almost a quarter of the respondents engaged in race-related impression management—always careful to act in a way that did not consciously live up to the stereotypes of their racial and ethnic groups; 26.4% (n = 1137) were always worried about being able to pay rent/mortgage/housing costs while 19.14% (n = 823) always worried that they would not be able to pay their medical bills if they got sick or had an accident. Even though four in ten always or sometimes worried that someone they know would become a victim of police brutality, 56.86% (n = 2495) reported having experienced at least one of the ten listed negative interactions with the police.
title	Survey Results
p	We provide a brief description of the survey results by select characteristics in Table 1. As shown, non-Hispanic Whites make up just under two thirds of the sample (63.65%, n = 2793). Black/African American respondents constitute 14.2% of the sample (n = 623), while 11.62% (n = 510) are Hispanic/Latinx. SHUR respondents are disproportionately cisgender women (70.85%, n = 3109), and the majority are under the age of 65; only 8.61% (n = 378) are 65 years of age or older. While slightly more than half of the respondents worked full-time or part-time, three in ten were not in the labor force, and about one in ten were in the labor force but were unemployed and looking for work at the time of the survey. Table 1 Selected characteristics of SHUR respondents Percent Number x̅ (range) Race and ethnicity Non-Hispanic White 63.65 2793 Non-Hispanic Black 14.20 623 Hispanic/Latinx 11.62 510 American Indian and Alaskan Native 1.39 61 Asian 3.81 167 Other/multiple 5.33 234 Gender identity Cisgender man 24.52 1076 Cisgender woman 70.84 3109 Gender fluid 3.08 135 Transgender man 0.84 37 Transgender woman 0.73 32 Age category 18–24 19.25 845 25–34 27.59 1211 35–44 20.92 918 45–54 13.69 601 55–64 9.93 436 65 and older 8.61 378 Work status Not in the labor force 32.71 1390 Unemployed, looking for work 12.31 523 Working for pay, part time < 30 h/week 15.65 665 Working for pay, full time > =30 h/week 39.34 1672 Usual source of care Community clinic/health center 26.59 1167 Doctor’s office 42.36 1859 Emergency room 11.37 499 Outpatient department such as urgent care 11.07 486 No usual source of care 7.97 350 Some other place 0.64 28 Reports unmet need for medical care 37.72 1639 Often feels left out Strongly disagree 10.64 453 Disagree 18.06 769 Agree nor disagree 29.56 1259 Agree 15.45 658 Strongly agree 26.30 1120 Has experienced racial discrimination 14.42 633 Consciously acts in ways to make sure you do not live up to racial stereotypes Never 34.04 1464 Sometimes 42.46 1826 Always 23.51 1011 Worries about housing Never 31.67 1362 Sometimes 41.90 1802 Always 26.44 1137 Worries about paying medical bills Never 41.32 1777 Sometimes 39.55 1701 Always 7.95 823 Worries someone they know will become a victim of police brutality Never 53.99 2322 Sometimes 33.04 1421 Always 12.97 558 Has had a negative encounter with the police 56.86 2495 Mean medical mistrust score 4380 29.19 (12–60) Mean overall respect rating 4318 7.03(2–10)
table-wrap	Table 1 Selected characteristics of SHUR respondents Percent Number x̅ (range) Race and ethnicity Non-Hispanic White 63.65 2793 Non-Hispanic Black 14.20 623 Hispanic/Latinx 11.62 510 American Indian and Alaskan Native 1.39 61 Asian 3.81 167 Other/multiple 5.33 234 Gender identity Cisgender man 24.52 1076 Cisgender woman 70.84 3109 Gender fluid 3.08 135 Transgender man 0.84 37 Transgender woman 0.73 32 Age category 18–24 19.25 845 25–34 27.59 1211 35–44 20.92 918 45–54 13.69 601 55–64 9.93 436 65 and older 8.61 378 Work status Not in the labor force 32.71 1390 Unemployed, looking for work 12.31 523 Working for pay, part time < 30 h/week 15.65 665 Working for pay, full time > =30 h/week 39.34 1672 Usual source of care Community clinic/health center 26.59 1167 Doctor’s office 42.36 1859 Emergency room 11.37 499 Outpatient department such as urgent care 11.07 486 No usual source of care 7.97 350 Some other place 0.64 28 Reports unmet need for medical care 37.72 1639 Often feels left out Strongly disagree 10.64 453 Disagree 18.06 769 Agree nor disagree 29.56 1259 Agree 15.45 658 Strongly agree 26.30 1120 Has experienced racial discrimination 14.42 633 Consciously acts in ways to make sure you do not live up to racial stereotypes Never 34.04 1464 Sometimes 42.46 1826 Always 23.51 1011 Worries about housing Never 31.67 1362 Sometimes 41.90 1802 Always 26.44 1137 Worries about paying medical bills Never 41.32 1777 Sometimes 39.55 1701 Always 7.95 823 Worries someone they know will become a victim of police brutality Never 53.99 2322 Sometimes 33.04 1421 Always 12.97 558 Has had a negative encounter with the police 56.86 2495 Mean medical mistrust score 4380 29.19 (12–60) Mean overall respect rating 4318 7.03(2–10)
label	Table 1
caption	Selected characteristics of SHUR respondents
p	Selected characteristics of SHUR respondents
table	Percent Number x̅ (range) Race and ethnicity Non-Hispanic White 63.65 2793 Non-Hispanic Black 14.20 623 Hispanic/Latinx 11.62 510 American Indian and Alaskan Native 1.39 61 Asian 3.81 167 Other/multiple 5.33 234 Gender identity Cisgender man 24.52 1076 Cisgender woman 70.84 3109 Gender fluid 3.08 135 Transgender man 0.84 37 Transgender woman 0.73 32 Age category 18–24 19.25 845 25–34 27.59 1211 35–44 20.92 918 45–54 13.69 601 55–64 9.93 436 65 and older 8.61 378 Work status Not in the labor force 32.71 1390 Unemployed, looking for work 12.31 523 Working for pay, part time < 30 h/week 15.65 665 Working for pay, full time > =30 h/week 39.34 1672 Usual source of care Community clinic/health center 26.59 1167 Doctor’s office 42.36 1859 Emergency room 11.37 499 Outpatient department such as urgent care 11.07 486 No usual source of care 7.97 350 Some other place 0.64 28 Reports unmet need for medical care 37.72 1639 Often feels left out Strongly disagree 10.64 453 Disagree 18.06 769 Agree nor disagree 29.56 1259 Agree 15.45 658 Strongly agree 26.30 1120 Has experienced racial discrimination 14.42 633 Consciously acts in ways to make sure you do not live up to racial stereotypes Never 34.04 1464 Sometimes 42.46 1826 Always 23.51 1011 Worries about housing Never 31.67 1362 Sometimes 41.90 1802 Always 26.44 1137 Worries about paying medical bills Never 41.32 1777 Sometimes 39.55 1701 Always 7.95 823 Worries someone they know will become a victim of police brutality Never 53.99 2322 Sometimes 33.04 1421 Always 12.97 558 Has had a negative encounter with the police 56.86 2495 Mean medical mistrust score 4380 29.19 (12–60) Mean overall respect rating 4318 7.03(2–10)
tr	Percent Number x̅ (range)
th	Percent
th	Number
th	x̅ (range)
tr	Race and ethnicity
td	Race and ethnicity
tr	Non-Hispanic White 63.65 2793
td	Non-Hispanic White
td	63.65
td	2793
tr	Non-Hispanic Black 14.20 623
td	Non-Hispanic Black
td	14.20
td	623
tr	Hispanic/Latinx 11.62 510
td	Hispanic/Latinx
td	11.62
td	510
tr	American Indian and Alaskan Native 1.39 61
td	American Indian and Alaskan Native
td	1.39
td	61
tr	Asian 3.81 167
td	Asian
td	3.81
td	167
tr	Other/multiple 5.33 234
td	Other/multiple
td	5.33
td	234
tr	Gender identity
td	Gender identity
tr	Cisgender man 24.52 1076
td	Cisgender man
td	24.52
td	1076
tr	Cisgender woman 70.84 3109
td	Cisgender woman
td	70.84
td	3109
tr	Gender fluid 3.08 135
td	Gender fluid
td	3.08
td	135
tr	Transgender man 0.84 37
td	Transgender man
td	0.84
td	37
tr	Transgender woman 0.73 32
td	Transgender woman
td	0.73
td	32
tr	Age category
td	Age category
tr	18–24 19.25 845
td	18–24
td	19.25
td	845
tr	25–34 27.59 1211
td	25–34
td	27.59
td	1211
tr	35–44 20.92 918
td	35–44
td	20.92
td	918
tr	45–54 13.69 601
td	45–54
td	13.69
td	601
tr	55–64 9.93 436
td	55–64
td	9.93
td	436
tr	65 and older 8.61 378
td	65 and older
td	8.61
td	378
tr	Work status
td	Work status
tr	Not in the labor force 32.71 1390
td	Not in the labor force
td	32.71
td	1390
tr	Unemployed, looking for work 12.31 523
td	Unemployed, looking for work
td	12.31
td	523
tr	Working for pay, part time < 30 h/week 15.65 665
td	Working for pay, part time < 30 h/week
td	15.65
td	665
tr	Working for pay, full time > =30 h/week 39.34 1672
td	Working for pay, full time > =30 h/week
td	39.34
td	1672
tr	Usual source of care
td	Usual source of care
tr	Community clinic/health center 26.59 1167
td	Community clinic/health center
td	26.59
td	1167
tr	Doctor’s office 42.36 1859
td	Doctor’s office
td	42.36
td	1859
tr	Emergency room 11.37 499
td	Emergency room
td	11.37
td	499
tr	Outpatient department such as urgent care 11.07 486
td	Outpatient department such as urgent care
td	11.07
td	486
tr	No usual source of care 7.97 350
td	No usual source of care
td	7.97
td	350
tr	Some other place 0.64 28
td	Some other place
td	0.64
td	28
tr	Reports unmet need for medical care 37.72 1639
td	Reports unmet need for medical care
td	37.72
td	1639
tr	Often feels left out
td	Often feels left out
tr	Strongly disagree 10.64 453
td	Strongly disagree
td	10.64
td	453
tr	Disagree 18.06 769
td	Disagree
td	18.06
td	769
tr	Agree nor disagree 29.56 1259
td	Agree nor disagree
td	29.56
td	1259
tr	Agree 15.45 658
td	Agree
td	15.45
td	658
tr	Strongly agree 26.30 1120
td	Strongly agree
td	26.30
td	1120
tr	Has experienced racial discrimination 14.42 633
td	Has experienced racial discrimination
td	14.42
td	633
tr	Consciously acts in ways to make sure you do not live up to racial stereotypes
td	Consciously acts in ways to make sure you do not live up to racial stereotypes
tr	Never 34.04 1464
td	Never
td	34.04
td	1464
tr	Sometimes 42.46 1826
td	Sometimes
td	42.46
td	1826
tr	Always 23.51 1011
td	Always
td	23.51
td	1011
tr	Worries about housing
td	Worries about housing
tr	Never 31.67 1362
td	Never
td	31.67
td	1362
tr	Sometimes 41.90 1802
td	Sometimes
td	41.90
td	1802
tr	Always 26.44 1137
td	Always
td	26.44
td	1137
tr	Worries about paying medical bills
td	Worries about paying medical bills
tr	Never 41.32 1777
td	Never
td	41.32
td	1777
tr	Sometimes 39.55 1701
td	Sometimes
td	39.55
td	1701
tr	Always 7.95 823
td	Always
td	7.95
td	823
tr	Worries someone they know will become a victim of police brutality
td	Worries someone they know will become a victim of police brutality
tr	Never 53.99 2322
td	Never
td	53.99
td	2322
tr	Sometimes 33.04 1421
td	Sometimes
td	33.04
td	1421
tr	Always 12.97 558
td	Always
td	12.97
td	558
tr	Has had a negative encounter with the police 56.86 2495
td	Has had a negative encounter with the police
td	56.86
td	2495
tr	Mean medical mistrust score 4380 29.19 (12–60)
td	Mean medical mistrust score
td	4380
td	29.19 (12–60)
tr	Mean overall respect rating 4318 7.03(2–10)
td	Mean overall respect rating
td	4318
td	7.03(2–10)
p	In terms of access to care and health services, most of the respondents had a usual source of care, but they were pretty spread out in terms of the specific places they regularly went to for care. For example, four in ten of the respondents received care from the doctor’s office, two in ten at a community clinic, and one in ten at the emergency room. More than a third of the respondents reported unmet need for medical care (37.72%, n = 1639). Response options on the 12-item group-based medical mistrust index ranged from strongly disagree (1) to strongly agree (5). Scores on the medical mistrust scale ranged from 12 to 60, with higher scores indicating greater mistrust of health institutions. The mean mistrust score for the sample was 29.19. Respondents rated if they felt, in general, that they were treated with a great deal of respect and dignity the last time they received healthcare. Ratings could range from 1 (no respect at all) to 10 (utmost respect). The range for our sample was 2 to 10, with a mean of 7.03, and a median of 9.
p	Feeling left out is one indicator of social exclusion. About four in ten respondents agree or strongly agree that they often felt left out. Many respondents also reported experiencing salient sources of stress. For example, 14.42% of the sample (n = 633) felt hassled, inferior, or discriminated against because of race, accounting for more than half of the respondents who reported any kind of discrimination. Almost a quarter of the respondents engaged in race-related impression management—always careful to act in a way that did not consciously live up to the stereotypes of their racial and ethnic groups; 26.4% (n = 1137) were always worried about being able to pay rent/mortgage/housing costs while 19.14% (n = 823) always worried that they would not be able to pay their medical bills if they got sick or had an accident. Even though four in ten always or sometimes worried that someone they know would become a victim of police brutality, 56.86% (n = 2495) reported having experienced at least one of the ten listed negative interactions with the police.
sec	Public Health Implications The SHUR is a great resource for researchers and policymakers interested in understanding and addressing factors relevant to the health of marginalized populations. Research published using SHUR data can contribute significantly to ongoing conversations around the connections between police brutality and health, especially access to care and medical mistrust [18]. Nevertheless, there are caveats. First, SHUR does not employ probability sampling. Therefore, estimates from the survey might be sensitive to systematic errors because respondents might differ from non-respondents in significant ways. Second, we did not assess respondents’ perceptions of the necessity of each negative police encounter. Instead, we asked respondents to think about their most recent experiences with the police and to state their perceptions about whether the action(s) of the police were necessary. While we wanted to capture more recent exposures to police brutality, we think that perceptions about the necessity of negative police encounters might be different for different police actions. For example, an individual might perceive the police patting them down before an arrest as necessary and a previous encounter where the police kicked them as unnecessary. These actions have implications especially for assessing mental health correlates of police brutality such as anxiety and depression. Despite these limitations, SHUR can support health disparities research in several ways. First, the survey is informed by the experiences of racialized populations—specifically Black men, Latinxs, and immigrants—and assesses salient conditions including sources and spaces of discrimination, social exclusion, experiences of police brutality and stressful anticipations of these experiences, housing-related stress, as well as stress-related to arrests and incarceration. These data can help us identify connections between specific social determinants and a range of indicators of access to care and health status that are included in the data. These connections are important for formulating and implementing targeted policies to address health inequities. Second, SHUR measures relational aspects of care such as mistrust and perceptions of respect that we know are important indicators of the delivery of patient-centered care [27, 28]. When patients feel respected, they might then feel supported and empowered to share their own needs, perspectives, and preferences, and therefore engage in shared-decision making [29]. This might also equalize the inherent power differentials between clinicians and patients, regardless of race and socio-economic status. The data have the potential of helping researchers understand factors that shape relational aspects of care to improve engagement and reduce unmet need. Third, SHUR includes respondents’ zip codes. This presents researchers with the rare opportunity to link the data to zip code-level health system characteristics including the availability of physicians, housing characteristics, foreclosure rates, food insecurity, incarceration rates, voting and other indicators of political participation, as well as population-level indicators of structural racism such as Black to White ratios in rates of unemployment, poverty, health insurance, and college graduation. These larger structural factors, including structural racism, shape health beyond individual behaviors and attributes [30, 31]. Therefore, examining their interaction with individual factors in multi-level analyses is critical. In addition, researchers using these data can explore how variation in characteristics of urban areas, including population density, might be associated with variation in a range of experiences and health outcomes. The approach employed in SHUR—co-creating measures of salient stressors with communities for which our work bears relevance is important for understanding the mechanisms through which social conditions affect health, the contextual specificity of these mechanisms, and what kinds of interventions might help eliminate health disparities caused by structural inequalities. Measures in the current survey are critical for providing evidence needed to inform policies that would improve health among urbanized populations. We encourage others to use these data. Community-driven approaches to creating measures related to navigating COVID-19 that are salient to the experiences of populations marginalized by structural inequalities are important next steps.
title	Public Health Implications
p	The SHUR is a great resource for researchers and policymakers interested in understanding and addressing factors relevant to the health of marginalized populations. Research published using SHUR data can contribute significantly to ongoing conversations around the connections between police brutality and health, especially access to care and medical mistrust [18]. Nevertheless, there are caveats. First, SHUR does not employ probability sampling. Therefore, estimates from the survey might be sensitive to systematic errors because respondents might differ from non-respondents in significant ways. Second, we did not assess respondents’ perceptions of the necessity of each negative police encounter. Instead, we asked respondents to think about their most recent experiences with the police and to state their perceptions about whether the action(s) of the police were necessary. While we wanted to capture more recent exposures to police brutality, we think that perceptions about the necessity of negative police encounters might be different for different police actions. For example, an individual might perceive the police patting them down before an arrest as necessary and a previous encounter where the police kicked them as unnecessary. These actions have implications especially for assessing mental health correlates of police brutality such as anxiety and depression.
p	Despite these limitations, SHUR can support health disparities research in several ways. First, the survey is informed by the experiences of racialized populations—specifically Black men, Latinxs, and immigrants—and assesses salient conditions including sources and spaces of discrimination, social exclusion, experiences of police brutality and stressful anticipations of these experiences, housing-related stress, as well as stress-related to arrests and incarceration. These data can help us identify connections between specific social determinants and a range of indicators of access to care and health status that are included in the data. These connections are important for formulating and implementing targeted policies to address health inequities.
p	Second, SHUR measures relational aspects of care such as mistrust and perceptions of respect that we know are important indicators of the delivery of patient-centered care [27, 28]. When patients feel respected, they might then feel supported and empowered to share their own needs, perspectives, and preferences, and therefore engage in shared-decision making [29]. This might also equalize the inherent power differentials between clinicians and patients, regardless of race and socio-economic status. The data have the potential of helping researchers understand factors that shape relational aspects of care to improve engagement and reduce unmet need.
p	Third, SHUR includes respondents’ zip codes. This presents researchers with the rare opportunity to link the data to zip code-level health system characteristics including the availability of physicians, housing characteristics, foreclosure rates, food insecurity, incarceration rates, voting and other indicators of political participation, as well as population-level indicators of structural racism such as Black to White ratios in rates of unemployment, poverty, health insurance, and college graduation. These larger structural factors, including structural racism, shape health beyond individual behaviors and attributes [30, 31]. Therefore, examining their interaction with individual factors in multi-level analyses is critical. In addition, researchers using these data can explore how variation in characteristics of urban areas, including population density, might be associated with variation in a range of experiences and health outcomes.
p	The approach employed in SHUR—co-creating measures of salient stressors with communities for which our work bears relevance is important for understanding the mechanisms through which social conditions affect health, the contextual specificity of these mechanisms, and what kinds of interventions might help eliminate health disparities caused by structural inequalities. Measures in the current survey are critical for providing evidence needed to inform policies that would improve health among urbanized populations. We encourage others to use these data. Community-driven approaches to creating measures related to navigating COVID-19 that are salient to the experiences of populations marginalized by structural inequalities are important next steps.
back	Appendix 1 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The authors thank focus group participants, interpreters, and entire staff of Promise Neighborhoods of the Lehigh Valley for their work in organizing the focus groups. Availability of Data and Material The data can be accessed by request to the corresponding author. Authors’ Contributions SA conceptualized the research project and lead in the development of the survey and the manuscript. CP and MM assisted in the review of instruments, survey data analysis, and writing. HB and AB were part of the conceptualization of study and assisted with focus group organization. JH, AS, and KB collected focus group data and assisted in identifying salient stressors and indicators of exclusion. KM assisted in the writing. All authors contributed significantly to this work. All authors take responsibility for the content of the manuscript. Funding The focus groups and survey were funded by Lehigh University’s Community-Engaged Health Research Fellowship and the Faculty Innovation Grant, respectively. Compliance with Ethical Standards Conflict of Interest The authors declare that they have no conflict of interests. Ethics Approval Both the focus groups and survey received approval from Lehigh University’s Institutional Review Board. Consent to Participate All focus group participants and survey respondents freely consented to participate. Consent for Publication N/A Code Availability Stata codes can be accessed by request to the corresponding author.
appendix	Appendix 1
sec	Appendix 1
title	Appendix 1
footnote	Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
p	Publisher’s Note
p	Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
ack	The authors thank focus group participants, interpreters, and entire staff of Promise Neighborhoods of the Lehigh Valley for their work in organizing the focus groups. Availability of Data and Material The data can be accessed by request to the corresponding author.
p	The authors thank focus group participants, interpreters, and entire staff of Promise Neighborhoods of the Lehigh Valley for their work in organizing the focus groups.
sec	Availability of Data and Material The data can be accessed by request to the corresponding author.
title	Availability of Data and Material
p	The data can be accessed by request to the corresponding author.
notes	Authors’ Contributions SA conceptualized the research project and lead in the development of the survey and the manuscript. CP and MM assisted in the review of instruments, survey data analysis, and writing. HB and AB were part of the conceptualization of study and assisted with focus group organization. JH, AS, and KB collected focus group data and assisted in identifying salient stressors and indicators of exclusion. KM assisted in the writing. All authors contributed significantly to this work. All authors take responsibility for the content of the manuscript.
title	Authors’ Contributions
p	SA conceptualized the research project and lead in the development of the survey and the manuscript. CP and MM assisted in the review of instruments, survey data analysis, and writing. HB and AB were part of the conceptualization of study and assisted with focus group organization. JH, AS, and KB collected focus group data and assisted in identifying salient stressors and indicators of exclusion. KM assisted in the writing. All authors contributed significantly to this work. All authors take responsibility for the content of the manuscript.
notes	Funding The focus groups and survey were funded by Lehigh University’s Community-Engaged Health Research Fellowship and the Faculty Innovation Grant, respectively.
title	Funding
p	The focus groups and survey were funded by Lehigh University’s Community-Engaged Health Research Fellowship and the Faculty Innovation Grant, respectively.
notes	Compliance with Ethical Standards Conflict of Interest The authors declare that they have no conflict of interests. Ethics Approval Both the focus groups and survey received approval from Lehigh University’s Institutional Review Board. Consent to Participate All focus group participants and survey respondents freely consented to participate. Consent for Publication N/A Code Availability Stata codes can be accessed by request to the corresponding author.
title	Compliance with Ethical Standards
notes	Conflict of Interest The authors declare that they have no conflict of interests.
title	Conflict of Interest
p	The authors declare that they have no conflict of interests.
notes	Ethics Approval Both the focus groups and survey received approval from Lehigh University’s Institutional Review Board.
title	Ethics Approval
p	Both the focus groups and survey received approval from Lehigh University’s Institutional Review Board.
notes	Consent to Participate All focus group participants and survey respondents freely consented to participate.
title	Consent to Participate
p	All focus group participants and survey respondents freely consented to participate.
notes	Consent for Publication N/A
title	Consent for Publication
p	N/A
notes	Code Availability Stata codes can be accessed by request to the corresponding author.
title	Code Availability
p	Stata codes can be accessed by request to the corresponding author.

Annnotations TAB TSV DIC JSON TextAE

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PMC:7444865 / 7095-11407 JSONTXT 11 Projects

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Annnotations TAB TSV DIC JSON TextAE

PMC:7444865 / 7095-11407 JSON TXT 11 Projects