Self-identified race/ethnicity is largely used to identify, monitor, and examine racial/ethnic inequalities. A growing body of work underscores the need to consider multiple dimensions of racethe social construction of race as a function of appearance, societal interactions, institutional dynamics, stereotypes, and social norms. One such multidimensional measure is socially-assigned race, the perception of one's race by others, that may serve as the basis for differential or unfair treatment and subsequently lead to deleterious health outcomes. We conducted a scoping review to systematically appraise the socially-assigned race and health literature. A systematic search of the PubMed, Web of Science, 28 EBSCO databases and 24 Proquest databases up to September 2019 was conducted and supplemented by a manual search of reference lists and grey literature. Quantitative and qualitative studies that examined socially-assigned race and health or health-related outcomes were considered for inclusion. Eighteen articles were included in the narrative synthesis. Self-rated health and mental health were among the most frequent outcomes studied. The majority of studies were conducted in the United States, with fewer studies conducted in New Zealand, Canada, and Latin America. While most studies demonstrate a positive association between social assignment as a disadvantaged racial or ethnic group and poorer health, some studies did not document an association. We describe key conceptual and methodological considerations that should be prioritized in future studies examining socially-assigned race and health. Socially-assigned race can provide additional insight into observed differential health outcomes among racial/ethnic groups in racialized societies based upon their lived experiences. Studies incorporating socially-assigned race warrants further investigation and may be leveraged to examine nuanced patterns of racial health advantage and disadvantage.

Background Type 2 diabetes mellitus (T2DM) is highly prevalent in American Samoa. Community health worker (CHW) interventions may improve T2DM care and be cost-effective. Current cost-effectiveness analyses (CEA) of CHW interventions have either overlooked important cost considerations or not been based on randomized clinical trials (RCTs). The Diabetes Care in American Samoa (DCAS) intervention which occurred in 2009–2010 was a cluster-randomized, culturally tailored, home-visiting CHW intervention and improved HbA1c levels. Objective To analyze the cost-effectiveness of the DCAS intervention against standard care using a RCT in a low-resource setting. Methods We collected clinical, utilization, and cost data over 2 years and modeled quality-adjusted life years (QALYs) gained based on the RCT glycated hemoglobin (HbA1c) improvements. We calculated an incremental cost-effectiveness ratio (ICER) from the societal perspective over a 2-year time horizon and reported all costs in 2012 USD ($). Results Two hundred sixty-eight American Samoans diagnosed with T2DM were cluster randomized into the CHW ( n = 104) or standard care control ( n = 164) arms. The CHW arm had a mean reduction of 0.53% in HbA1c, an increase of $594 in cost, and an increase of 0.05 QALYs. The ICER for the CHW arm compared to the control arm was $1121 per percentage point HbA1c reduced and $13 191 per QALY gained. Conclusions Compared to a variety of willingness-to-pay thresholds from $39 000 to $154 353 per QALY gained, this ICER shows that the CHW intervention is highly cost-effective. Future studies of the cost-effectiveness of CHW T2DM interventions in similar settings should model lifetime costs and QALYs gained to better assess long-term cost-effectiveness. Trial registration ClinicalTrials.gov , ID NCT00850824. Registered 9 February 2009, https://clinicaltrials.gov/ct2/show/NCT00850824 . Electronic supplementary material The online version of this article (10.1186/s12960-019-0356-6) contains supplementary material, which is available to authorized users.

Background and Objectives Perceived discrimination is a risk factor for poor mental health. However, most studies measure discrimination at one time point, which does not account for heterogeneity in the cumulative patterning of exposure to discrimination. To address this gap, we examine the association between discrimination trajectories and depressive symptoms among black middle-aged and older adults. Research Design and Methods Data were analyzed from a subsample of black Health and Retirement Study respondents (2006 – 2018, N = 2,926, 50+). General discrimination and racial discrimination trajectories were constructed based on the Everyday Discrimination Scale using repeated measures latent profile analyses. We examined the extent to which the association between discrimination trajectories are differentially associated with depressive symptoms (8-item Center for Epidemiologic Studies – Depression scale) using negative binomial regression models adjusted for potential confounders. Effect modification by age and gender was tested. Results Individuals in the persistently high (IRR: 1.70; 95% CI: 1.49, 1.95) and moderate general discrimination trajectories (IRR: 1.19; 95% CI: 1.06, 1.33), were more likely to have elevated depressive symptoms in comparison to those in the persistently low trajectory. This relationship was strongest among older adults aged 65+. Respondents in the persistently high racial discrimination trajectory (IRR: 1.50; 95% CI: 1.29, 1.73) had higher risk of elevated depressive symptoms in comparison to respondents in the persistently low trajectory. Sensitivity analyses indicated that there was an independent association between persistently high racial discrimination trajectory class and elevated depressive symptoms, after adjusting for racial discrimination measured at a single time point. Discussion and Implications Characterizing longitudinal patterns of perceived discrimination may facilitate the stratification of mental health risk and vulnerability among black middle-aged and older adults. Trajectories of racial discrimination may inform risk of worse depressive symptoms more accurately than a single assessment of discrimination.

Background In the 1930s, the Home Owners’ Loan Corporation categorized neighborhoods by investment grade along racially discriminatory lines, a process known as redlining. Although other authors have found associations between Home Owners’ Loan Corporation categories and current impacts on racial segregation, analysis of current health impacts rarely use these maps. Objective To study whether historical redlining in Baltimore is associated with health impacts today. Approach Fifty-four present-day planning board-defined community statistical areas are assigned historical Home Owners’ Loan Corporation categories by area predominance. Categories are red (“hazardous”), yellow (”definitely declining”) with blue/green (“still desirable”/”best”) as the reference category. Community statistical area life expectancy is regressed against Home Owners’ Loan Corporation category, controlling for median household income and proportion of African American residents. Conclusion Red categorization is associated with 4.01 year reduction (95% CI: 1.47, 6.55) and yellow categorization is associated with 5.36 year reduction (95% CI: 3.02, 7.69) in community statistical area life expectancy at baseline. When controlling for median household income and proportion of African American residents, red is associated with 5.23 year reduction (95% CI: 3.49, 6.98) and yellow with 4.93 year reduction (95% CI: 3.22, 6.23). Results add support that historical redlining is associated with health today.

BACKGROUND The lack of publicly available, culturally relevant data sets on African American and bilingual/Spanish-speaking Hispanic adults’ disease prevention and health promotion priorities presents a major challenge to researchers and developers who want to create and test personalized tools for the preventive health behaviors intervention space. Personalization depends on prediction and performance data. To develop such a ‘recommender system’ (RecSys) that predicts the most culturally and personally relevant preventative health information and serve it to African American and Hispanic users of a novel smartphone application while also avoiding the ‘cold start’ problem, we needed population appropriate seed data that aligned with the app’s purposes of setting health goals and finding associated articles and topics in healthfinder.gov, a federally supported database of health conditions and disease prevention information. OBJECTIVE To address the lack of culturally specific preventive personal health data and sidestep the type of algorithmic bias inherent in a RecSys not trained in the target population, we created a novel dataset on prevention-focused health goals by collecting a large amount of data quickly and at low cost from members of the target population. We seeded our RecSys with data collected anonymously from self-identified Hispanic and self-identified non-Hispanic African American adult respondents utilizing Amazon Mechanical Turk. METHODS We developed an online survey in which respondents completed a personal profile, health literacy assessment, family health history, and personal health history. Respondents then selected their top three health goals related to preventable health conditions, and for each goal reviewed and rated the top three healthfinder.gov information returns by importance, personal utility, whether the item should be added to their personal health library, and their satisfaction with the quality of the information returned. RESULTS We collected data from 985 self-identified Hispanic (49%) and self-identified non-Hispanic African American (51%) adult respondents utilizing Amazon Mechanical Turk over only 64 days at a cost of $6.74 per respondent. Respondents rated 92 unique articles. Both African American and Hispanic groups noted physical fitness (62.9%), healthy eating (43.2%), and nutrition and weight (24.0%) as their most frequent personal goals for health. Both African American and Hispanic groups noted mental health issues (34.6%), hypertension (31.0%), and vision or hearing impairments (24.4%) as their most frequently experienced health conditions, and hypertension (55.0%), diabetes (46.1%), and obesity (39.6%) as their most frequent family health conditions, although there are statistically significant differences when considering prevalences of goals, personal health, and family health conditions. Though both groups note experiencing mental health issues more frequently than any other condition, neither respondent group identified mental health as a high priority personal health goal. Respondents’ personal goals align with potentially preventive conditions they report in their family health history. CONCLUSIONS Researchers have options, such as Amazon Mechanical Turk, for quick, low-cost means to avoid the ‘cold start’ problem for algorithms and sidestep bias and low relevance for an intended population of app users. Seeding a RecSys with responses from people like the intended users allows the development of a digital health tool that can recommend information to users based on similar demography, health goals, and health history. This approach minimizes potential initial gaps in algorithm performance, allows quicker algorithm refinement in use, and may deliver a better user experience to individuals seeking preventative health information to improve health and achieve health goals. Additionally, this approach allowed investigating the correlation between personal health goals and known health history in a sample of African American and Hispanic participants. Health goals for African American and Hispanic adults are more likely to reflect self-reported somatic health conditions, and less likely to reflect psychological health conditions, even when experiencing mental health issues.

Background The lack of publicly available and culturally relevant data sets on African American and bilingual/Spanish-speaking Hispanic adults’ disease prevention and health promotion priorities presents a major challenge for researchers and developers who want to create and test personalized tools built on and aligned with those priorities. Personalization depends on prediction and performance data. A recommender system (RecSys) could predict the most culturally and personally relevant preventative health information and serve it to African American and Hispanic users via a novel smartphone app. However, early in a user’s experience, a RecSys can face the “cold start problem” of serving untailored and irrelevant content before it learns user preferences. For underserved African American and Hispanic populations, who are consistently being served health content targeted toward the White majority, the cold start problem can become an example of algorithmic bias. To avoid this, a RecSys needs population-appropriate seed data aligned with the app’s purposes. Crowdsourcing provides a means to generate population-appropriate seed data. Objective Our objective was to identify and test a method to address the lack of culturally specific preventative personal health data and sidestep the type of algorithmic bias inherent in a RecSys not trained in the population of focus. We did this by collecting a large amount of data quickly and at low cost from members of the population of focus, thereby generating a novel data set based on prevention-focused, population-relevant health goals. We seeded our RecSys with data collected anonymously from self-identified Hispanic and self-identified non-Hispanic African American/Black adult respondents, using Amazon Mechanical Turk (MTurk). Methods MTurk provided the crowdsourcing platform for a web-based survey in which respondents completed a personal profile and a health information–seeking assessment, and provided data on family health history and personal health history. Respondents then selected their top 3 health goals related to preventable health conditions, and for each goal, reviewed and rated the top 3 information returns by importance, personal utility, whether the item should be added to their personal health library, and their satisfaction with the quality of the information returned. This paper reports the article ratings because our intent was to assess the benefits of crowdsourcing to seed a RecSys. The analysis of the data from health goals will be reported in future papers. Results The MTurk crowdsourcing approach generated 985 valid responses from 485 (49%) self-identified Hispanic and 500 (51%) self-identified non-Hispanic African American adults over the course of only 64 days at a cost of US $6.74 per respondent. Respondents rated 92 unique articles to inform the RecSys. Conclusions Researchers have options such as MTurk as a quick, low-cost means to avoid the cold start problem for algorithms and to sidestep bias and low relevance for an intended population of app users. Seeding a RecSys with responses from people like the intended users allows for the development of a digital health tool that can recommend information to users based on similar demography, health goals, and health history. This approach minimizes the potential, initial gaps in algorithm performance; allows for quicker algorithm refinement in use; and may deliver a better user experience to individuals seeking preventative health information to improve health and achieve health goals.