Epidemiologic Reviews最新文献

Race is a social construct, commonly used in epidemiologic research to adjust for confounding. However, adjustment of race may mask racial disparities, thereby perpetuating structural racism. We conducted a systematic review of articles published in Epidemiology and American Journal of Epidemiology between 2020 and 2021 to (1) understand how race, ethnicity, and similar social constructs were operationalized, used, and reported; and (2) characterize good and poor practices of utilization and reporting of race data on the basis of the extent to which they reveal or mask systemic racism. Original research articles were considered for full review and data extraction if race data were used in the study analysis. We extracted how race was categorized, used-as a descriptor, confounder, or for effect measure modification (EMM)-and reported if the authors discussed racial disparities and systemic bias-related mechanisms responsible for perpetuating the disparities. Of the 561 articles, 299 had race data available and 192 (34.2%) used race data in analyses. Among the 160 US-based studies, 81 different racial categorizations were used. Race was most often used as a confounder (52%), followed by effect measure modifier (33%), and descriptive variable (12%). Fewer than 1 in 4 articles (22.9%) exhibited good practices (EMM along with discussing disparities and mechanisms), 63.5% of the articles exhibited poor practices (confounding only or not discussing mechanisms), and 13.5% were considered neither poor nor good practices. We discuss implications and provide 13 recommendations for operationalization, utilization, and reporting of race in epidemiologic and public health research.

The conventional use of racial categories in health research naturalizes "race" in problematic ways that ignore how racial categories function in service of a White-dominated racial hierarchy. In many respects, racial labels are based on geographic designations. For instance, "Asians" are from Asia. Yet, this is not always a tenable proposition. For example, Afghanistan resides in South Asia, and shares a border with China and Pakistan. Yet, people from Afghanistan are not considered Asian, but Middle Eastern, by the US Census. Furthermore, people on the west side of the Island of New Guinea are considered Asian, whereas those on the eastern side are considered Pacific Islander. In this article, we discuss the complexity of the racial labels related to people originating from Oceania and Asia, and, more specifically, those groups commonly referred to as Pacific Islander, Middle Eastern, and Asian. We begin with considerations of the aggregation fallacy. Just as the ecological fallacy refers to erroneous inferences about individuals from group data, the aggregation fallacy refers to erroneous inferences about subgroups (eg, Hmong) from group data (ie, all Asian Americans), and how these inferences can contribute to stereotypes such as the "model minority." We also examine how group averages can be influenced merely by the composition of the subgroups, and how these, in turn, can be influenced by social policies. We provide a historical overview of some of the issues facing Pacific Islander, Middle Eastern, and Asian communities, and conclude with directions for future research.

To mitigate the structural and institutional biases that contribute to inequities in health, we need a diverse cadre of individuals to feel included and advance within our field in order to bring a multicultural set of perspectives to the studies we conduct, the science we generate, the health and academic systems we design, and the medical and scientific knowledge we impart. There has been increasing focus on diversity, inclusion, and equity in recent years; however, often these terms are presented without adequate precision and, therefore, the inability to effectively operationalize inclusion and achieve diversity within organizations. This narrative review details several key studies, with the primary objective of presenting a roadmap to guide defining, measuring, and operationalizing inclusion within work and learning environments.

Critical analysis of the determinants of current and changing racialized health inequities, including the central role of racism, is an urgent priority for epidemiology, for both original research studies and epidemiologic review articles. Motivating our systematic overview review of Epidemiologic Reviews articles is the critical role of epidemiologic reviews in shaping discourse, research priorities, and policy relevant to the social patterning of population health. Our approach was first to document the number of articles published in Epidemiologic Reviews (1979-2021; n = 685) that either: (1) focused the review on racism and health, racial discrimination and health, or racialized health inequities (n = 27; 4%); (2) mentioned racialized groups but did not focus on racism or racialized health inequities (n = 399; 59%); or (3) included no mention of racialized groups or racialized health inequities (n = 250; 37%). We then conducted a critical content analysis of the 27 review articles that focused on racialized health inequities and assessed key characteristics, including (1) concepts, terms, and metrics used regarding racism and racialized groups (notably only 26% addressed the use or nonuse of measures explicitly linked to racism; 15% provided explicit definitions of racialized groups); (2) theories of disease distribution guiding (explicitly or implicitly) the review's approach; (3) interpretation of findings; and (4) recommendations offered. Guided by our results, we offer recommendations for best practices for epidemiologic review articles for addressing how epidemiologic research does or does not address ubiquitous racialized health inequities.

There is limited literature on the measures and metrics used to examine racism in the health inequities literature. Health inequities research is continuously evolving, with the number of publications increasing over time. However, there is limited knowledge on the best measures and methods to examine the impact of different levels of racism (institutionalized, personally mediated, and internalized) on health inequities. Advanced statistical methods have the potential to be used in new ways to examine the relationship between racism and health inequities. In this review, we conduct a descriptive examination of the measurement of racism in the health inequities epidemiologic literature. We examine the study design, methods used for analysis, types of measures used (e.g., composite, absolute, relative), number of measures used, phase of research (detect, understand, solutions), viewpoint (oppressor, oppressed), and components of structural racism measures (historical context, geographical context, multifaceted nature). We discuss methods (e.g., Peters-Belson, latent class analysis, difference in differences) that have demonstrated potential for future work. The articles reviewed were limited to the detect (25%) and understand (75%) phases, with no studies in the solutions phase. Although the majority (56%) of studies had cross-sectional designs, many authors pointed to the need for longitudinal and multilevel data for further exploration. We examined study design features as mutually exclusive elements. However, racism is a multifaceted system and the measurement of racism in many studies does not fit into a single category. As the literature grows, the significance of methodological and measurement triangulation to assess racism should be investigated.

Improving race and ethnicity (hereafter, race/ethnicity) data quality is imperative to ensure underserved populations are represented in data sets used to identify health disparities and inform health care policy. We performed a scoping review of methods that retrospectively improve race/ethnicity classification in secondary data sets. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searches were conducted in the MEDLINE, Embase, and Web of Science Core Collection databases in July 2022. A total of 2 441 abstracts were dually screened, 453 full-text articles were reviewed, and 120 articles were included. Study characteristics were extracted and described in a narrative analysis. Six main method types for improving race/ethnicity data were identified: expert review (n = 9; 8%), name lists (n = 27, 23%), name algorithms (n = 55, 46%), machine learning (n = 14, 12%), data linkage (n = 9, 8%), and other (n = 6, 5%). The main racial/ethnic groups targeted for classification were Asian (n = 56, 47%) and White (n = 51, 43%). Some form of validation evaluation was included in 86 articles (72%). We discuss the strengths and limitations of different method types and potential harms of identified methods. Innovative methods are needed to better identify racial/ethnic subgroups and further validation studies. Accurately collecting and reporting disaggregated data by race/ethnicity are critical to address the systematic missingness of relevant demographic data that can erroneously guide policymaking and hinder the effectiveness of health care practices and intervention.

Although the literature on the differences between Black people and White people in terms of differences in major depressive disorder and related self-reported symptoms is robust, less robust is the literature on how these outcomes are patterned within the US Black population and why differences exist. Given increased ethnic diversity of Black Americans due to increases in immigration, continued aggregation may mask differences between Black ethnic-immigrant groups and Black Americans with more distant ancestral ties to Africa (African Americans). The purpose of this narrative review was to comprehensively synthesize the literature on depression and related symptoms within the US Black population across immigration- and ethnicity-related domains and provide a summary of mechanisms proposed to explain variation. Findings revealed substantial variation in the presence of these outcomes within the US Black population by nativity, region of birth, age at immigration, and Caribbean ethnic origin. Racial context and racial socialization were identified as important, promising mechanisms for better understanding variations by region of birth and among those born or socialized in the United States, respectively. Findings warrant data collection efforts and measurement innovation to better account for within-racial differences in outcomes under study. A greater appreciation of the growing ethnic-immigrant diversity within the US Black population may improve understanding of how racism differentially functions as a cause of depression and related symptoms within this group.

Asians are likely to experience a high burden of chronic conditions, including, but not limited to, diabetes, cardiovascular disease, and cancer, due to differences in biologic, genetic, and environmental factors across Asian ethnic groups. A diagnosis of any chronic condition can contribute to increased mental health burdens, including depression, psychological distress, and posttraumatic stress disorder (PTSD). However, few studies have examined these comorbid conditions across distinct Asian ethnic groups-an important limitation given the differences in social, cultural, and behavioral drivers of mental health burdens within and across Asian ethnicities. To understand the disparities in mental health burdens among Asians living with a chronic health condition, we conducted a systematic literature review of relevant, peer-reviewed publication databases to identify studies reporting on mental health burdens (e.g., depression, anxiety, distress, PTSD) in distinct Asian ethnic groups in North America. Thirteen studies met the inclusion criteria for this review and collectively demonstrated a high burden of depression, psychological distress, and PTSD among Asians living with chronic conditions. Moreover, there were distinct disparities in mental health burdens across chronic conditions and across Asian ethnic groups. Despite the detrimental impact of poor mental health on chronic disease-specific outcomes, such as death and poor quality of life, few data exist that characterize mental health outcomes among Asian ethnicities living in North America with chronic conditions. Future work should prioritize estimating the national prevalence of mental health outcomes among adults with chronic conditions, by Asian ethnicities, to inform culturally tailored interventions to address this public health burden.

Indigenous people are often misracialized as other racial or ethnic identities in population health research. This misclassification leads to underestimation of Indigenous-specific mortality and health metrics, and subsequently, inadequate resource allocation. In recognition of this problem, investigators around the world have devised analytic methods to address racial misclassification of Indigenous people. We carried out a scoping review based on searches in PubMed, Web of Science, and the Native Health Database for empirical studies published after 2000 that include Indigenous-specific estimates of health or mortality and that take analytic steps to rectify racial misclassification of Indigenous people. We then considered the weaknesses and strengths of implemented analytic approaches, with a focus on methods used in the US context. To do this, we extracted information from 97 articles and compared the analytic approaches used. The most common approach to address Indigenous misclassification is to use data linkage; other methods include geographic restriction to areas where misclassification is less common, exclusion of some subgroups, imputation, aggregation, and electronic health record abstraction. We identified 4 primary limitations of these approaches: (1) combining data sources that use inconsistent processes and/or sources of race and ethnicity information; (2) conflating race, ethnicity, and nationality; (3) applying insufficient algorithms to bridge, impute, or link race and ethnicity information; and (4) assuming the hyperlocality of Indigenous people. Although there is no perfect solution to the issue of Indigenous misclassification in population-based studies, a review of this literature provided information on promising practices to consider.

Although observational studies have identified modifiable risk factors for Alzheimer disease and related dementias (ADRD), randomized controlled trials (RCTs) of risk factor modification for ADRD prevention have been inconsistent or inconclusive. This finding suggests a need to improve translation between observational studies and RCTs. However, many common features of observational studies reduce their relevance to designing related RCTs. Observational studies routinely differ from RCTs with respect to eligibility criteria, study population, length of follow-up, treatment conditions, outcomes, and effect estimates. Using the motivating example of blood pressure reduction for ADRD prevention, we illustrate the need for a tighter connection between observational studies and RCTs, discuss barriers to using typically reported observational evidence in developing RCTs, and highlight methods that may be used to make observational research more relevant to clinical trial design. We conclude that the questions asked and answered by observational research can be made more relevant to clinical trial design and that better use of observational data may increase the likelihood of successful, or at least definitive, trials. Although we focus on improving translation of observational studies on risk factors for ADRD to RCTs in ADRD prevention, the overarching themes are broadly applicable to many areas of biomedical research.