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. Author manuscript; available in PMC: 2023 Jan 1.
Published in final edited form as: Ear Hear. 2020 Jun 13;43(Suppl 1):5S–14S. doi: 10.1097/AUD.0000000000001149

Foundational Approaches to Advancing Hearing Health Equity: A Primer in Social Epidemiology

Carrie L Nieman 1,2,3, Jonathan J Suen 2,3, Lorraine T Dean 4,5, Aruna Chandran 4
PMCID: PMC9219014  NIHMSID: NIHMS1742187  PMID: 35724250

Abstract

Hearing health is inextricably linked to factors beyond biology. Social, demographic, environmental, geographic, and historical influences affect hearing health, but these factors are often unmeasured within traditional biological, clinical, and epidemiological studies of hearing health. With increasing recognition of hearing health over the life course as a public health priority, there is also a growing understanding of existing hearing health inequities at the individual, community, national, and global levels. To make progress in addressing these inequities, public health disciplines, such as social epidemiology, can provide valuable frameworks. With a focus on integrating the biological and functional with social and structural factors influencing health, social epidemiology provides key concepts and approaches for filling existing research and practice gaps. In this review, we introduce the discipline of social epidemiology and its associated concepts to inspire greater cross-disciplinary collaboration for the ultimate goal of advancing hearing health equity.

INTRODUCTION

Over 1.5 billion individuals globally have some degree of hearing loss, which equates to approximately 20% of the global population, and the number is expected to increase to almost 2.5 billion by 2050 (Haile et al., 2021). The burden of hearing loss is unequal, with the majority of hearing loss occurring among older adults and in countries with the lowest quality national health systems (Haile et al., 2021; World Health Organization, 2021). At an individual-level, hearing loss is associated with lower socioeconomic position, including lower income, unemployment, and early retirement, but the exact mechanisms underlying these associations are unclear (Emmett & Francis, 2015; Shan et al., 2020; World Health Organization, 2021). Management of hearing loss across the life course, from testing to the use of hearing aids and cochlear implants, is also unequal with disparities observed across race/ethnicity, socioeconomic position, and rurality, among other factors (Bush et al., 2017; Chan et al., 2017; Mamo et al., 2016; Nieman et al., 2016; Shayman et al., 2019).

Recent national and international efforts outline the identification and management of hearing loss at a population-level as a public health priority (National Academies of Sciences Engineering and Medicine, 2016; Wilson et al., 2019; World Health Organization, 2021). Foundational to a public health approach is an understanding of the scale of the problem, its drivers, and ultimately, avenues for intervention. Epidemiology, as the study of the distribution and determinants of health-related states or events (Last, 2001), serves as that foundation, including providing the means to understand and address differences in prevalence, outcomes, and care. When differences in health outcomes and their determinants arise between populations based on attributes related to social, demographic, environmental, geographic, or other factors, they are recognized as a “disparity” (Penman-Aguilar et al., 2016). When we acknowledge that such differences are avoidable, unfair, and systematic, we consider the difference an “inequity” (Penman-Aguilar et al., 2016). In order to advance our understanding of disparities and inequities within hearing health and how we can move to optimize hearing health for all, epidemiology, and specifically social epidemiology, provides that starting point. In this review, we will outline the fundamental principles of social epidemiology and its application within ear and hearing care research and practice.

Social Epidemiology and Social Determinants of Health

Recognition of the importance of one’s social context as critically influencing health is not new; Dr. John Snow, commonly referred to as the father of modern epidemiology, discussed the unexpectedly high incidence of cholera in notably “clean districts” in London as a critical point in discounting the popular theory of offensive effluvia causing the disease (Fine et al., 2013). Shortly thereafter, Dr. WEB DuBois showed the relationship between the social environment and mortality for African-Americans, advancing the integration of social sciences in medicine to understand and uncover health disparities and inequities (Jones-Eversley & Dean, 2018). However, it was not until 1950 that the field of “social epidemiology” was first named by Dr. Alfred Yankauer, who observed that racial segregation impacted fetal and infant mortalities across communities differently (Yankauer, 1950). Later, it would be defined by Dr. Leo G. Reeder to the American Sociological Association as the “study of social factors in the aetiology of disease” and further differentiated from traditional epidemiology by Dr. Nancy Krieger as “distinguished by its insistence on explicitly investigating social determinants of population distributions of health, disease, and wellbeing, rather than treating such determinants as mere background to biomedical phenomena” (Krieger, 2001b) (p. 693). Based on the premise that the distribution of risk factors and diseases in a community are associated with (if not caused by) the distribution of vulnerability or disadvantage in society, social epidemiologists seek to identify and quantify the sociocultural factors that affect health and wellbeing in a population (Petteway et al., 2019).

Traditionally, clinical disciplines like audiology and medicine, as well as public health disciplines including epidemiology, were grounded in what is known as the “biological paradigm” which states that all diseases are fundamentally biological phenomena that can be explained through individual-level behaviors working through biological mechanisms to cause disease (Susser & Susser, 1996). In this model, social factors on their own are not causes of disease. In contrast, the bio-psychosocial paradigm assumed in social epidemiology posits that diseases are the products of interactions between social, individual, and biologic factors (Honjo, 2004). Social factors in this context may refer to structural conditions, such as group norms and beliefs, racism, sexism, discrimination, poverty, and public policies, and/or interpersonal ones, such as social support and social engagements (Berkman et al., 2000). Thus, the population to which an individual belongs and how their group is positioned socially within a society fundamentally influences that individual’s risk of disease (Rose, 1992).

Social determinants of health (SDOH) are defined as the social, political, and economic conditions in which people live, work, and play (Commission on Social Determinants of Health, 2008). Collectively, SDOH contribute to both creating and sustaining health inequities, which are the systematic and unjust disparities in circumstances that result in unequal opportunities for achieving optimal health and wellbeing across populations (Embrett & Randall, 2014). In a comprehensive review of SDOH affecting otolaryngic diseases, including hearing loss, evidence clearly illustrates the influence of several sociodemographic factors, such as income, education, healthcare coverage, and race/ethnicity, but numerous gaps in knowledge remain in the literature in this field (Bergmark & Sedaghat, 2017). The incorporation of key concepts and approaches for research and practice translation from social epidemiology into existing efforts within ear and hearing care may help fill these gaps.

Core Concepts & Theories in Social Epidemiology

Theory provides a way in which to structure complex ideas and relationships, and it is essential to informing the development of both research questions, interventions, and associated methodology. With social epidemiology, as our understanding of how to measure, model and interpret the effects of social structural factors on health outcomes has matured, the guiding frameworks and concepts are evolving. There are several theories and models that are commonly used, including the Ecosocial Theory of Disease Distribution (Krieger, 2001a, 2001b), the Life Course Model Approach (Kuh et al., 2003), the society-behavior-biology nexus (Glass & McAtee, 2006), and biological embedding (Hertzman, 2012; Hertzman & Boyce, 2010); social epidemiologists would champion being explicit about which is applied. These theories and models share some common ground in the pathways to health and disease, and they each suggest that social factors influence individual outcomes. Social epidemiologic theories also highlight an important distinction between the causes of disease versus the causes of the disease’s distribution: while the causes of the disease may indeed be biological, which populations are more or less likely affected by that disease may be due to differential exposures and/or risk factors attributed to varied social factors. Examples may include localities with facilities that produce higher noise pollution closer to neighborhoods with lower socioeconomic profiles or healthcare providers communicating complex information using clinical jargon to populations with varying levels of health literacy due to resource disparities within educational systems.

Pathways for how social factors influence disease itself involve “embodiment” or how social experiences alter human biological and development processes. These pathways are influenced by “upstream” social-structural conditions at the macro-level (e.g., policies, laws, built environment, poverty, and discrimination) and the mezzo-level (e.g., social networks, social capital, and group norms) (Figure 1). Influences at these levels become embodied, defined as “how we literally incorporate, biologically, the material and social world in which we live” (Krieger, 2001a, 2012) such that social infrastructures and conditions alter human biological and development processes. Applied to hearing health, this would point to the need, for example, for studies that includes genetics but also consider social factors. Furthermore, ear and hearing care research needs to integrate the multiple pathways and domains involved in the production of hearing health and its distribution in a population as well as explicitly recognize the level(s) of intervention considered or not considered in a particular study (Example: Augustine, 2018).

Figure 1.

Figure 1.

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Embodiment of social structural factors affecting individual functional development and biological processes.

These pathways also implicate “downstream” factors, such as medical care availability and quality, as well as interpersonal and personal behaviors. Social factors are believed to influence behaviors, health-promoting or not, through (1) reducing or producing stress for which behaviors are a coping strategy; (2) enforcing patterns of social control; (3) shaping norms through people and environmental cues; and (4) providing or withholding environmental opportunities to engage in a behavior. Applied to hearing health, a social epidemiologic approach would prompt considerations of factors such as the affordability of rehabilitative technologies (Jilla et al., 2020), cultural stigma related to ageism (Wallhagen, 2010), mistrust of healthcare systems (Suen et al., 2019), and internal motivation to seek help for hearing difficulties (Ingram et al., 2016).

Importantly, the way in which social factors influence health might differ based on when in life these social factors are experienced or encountered. A life course perspective has been increasingly applied to hearing health, including the 2021 World Report on Hearing (Davis et al., 2016; Russ et al., 2018; World Health Organization, 2021). This model conceptualizes the complex factors that contribute to health and wellbeing across disciplines and attempts to capture the cumulative effects of multiple, interacting processes (Kuh et al., 2003). Specifically, a life course approach seeks to examine the long-term influence of “physical or social exposures during gestation, childhood, adolescence, young adulthood and later adult life” (p. 778) on the risk of health and disease (Kuh et al., 2003). Two models within this approach include the critical period model, which emphasizes the timing of exposure to risk factors that may lead to negative health outcomes, and the accumulation of risk model, where the focus is on the pattern of exposure to multiple risk factors over time (Cable, 2014). In addition to understanding the influence of factors along the life course, a life course approach also seeks to understand factors that influence health trajectories.

When applied to hearing health, incorporation of a life course perspective may include collectively considering relevant factors as early as maternal health during gestation (which is also impacted by the mother’s environment) (Shonkoff et al., 2009; Wallack & Thornburg, 1970). The early identification of hearing loss among newborns and timely enrollment in communication habilitation programs are also significant considerations, along with educational experiences during young adulthood. From there, occupational noise exposure in middle adulthood and the availability of social support during older adulthood for accessing hearing care needs represent other factors to subsequently consider within a life course approach. A focus on the life course suggests that researchers capture the diverse factors that contribute to hearing health at an individual and population-level and prioritize longitudinal, rather than cross-sectional, studies when possible (Russ et al., 2018).

Key Indicators, Measurement & Analysis Considerations in Social Epidemiology

An individual’s sociodemographic characteristics, as well as the social context in which they live, are a critical component to one’s ability to achieve optimal health and wellbeing. The constructs below, including race, socioeconomic position, and place, are common constructs within social epidemiology that are particularly relevant to understanding hearing health. We review each of these constructs, associated concepts, and include examples of these constructs within ear and hearing-related research. A simplified framework on how these social constructs influence hearing health highlights the pathways involved and opportunities for intervention (Figure 2).

Figure 2.

Figure 2.

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A Simplified Framework of the Influences & Opportunities for Intervention to Support Hearing Health

Adapted from: Braveman, P. A., Egerter, S. A., & Mockenhaupt, R. E. (2011). Broadening the focus: the need to address the social determinants of health. American journal of preventive medicine, 40(1), S4-S18.

Race, Racism & Segregation

Over the years, epidemiologic and clinical research have used self-reported race as a poorly defined proxy for genotype, socioeconomic status, racial discrimination, and other related constructs (Burchard et al., 2003). Within hearing-related research, studies have suggested that Black or African-American individuals have as much as approximately 50% lower prevalence of hearing loss than White individuals (Helzner et al., 2005; Lin et al., 2011). Other works elucidated the importance of melanocytes in protecting inner ear development and function (Steel & Barkway, 1989). While these differences in the prevalence of hearing loss can be interpreted as race-based biological differences, additional studies have refuted this interpretation and instead found that differences in the prevalence of hearing loss are not based on racial categories but instead on levels of skin pigmentation or Fitzpatrick skin type (Lin et al., 2012). This finding highlights that hearing research on race should be careful not to conflate race with biological factors.

Race is a social factor that is socially and politically designated and has shifted over time. For example, in 1980 the US Census categorized race in four categories, whereas in 2000, there were five categories with the option to select more than one (Ulmer et al., 2009). Race and ethnicity are at times used interchangeably, sometimes considered separate and distinct social constructs, and at other times viewed as mutually exclusive terms (Flanagin et al., 2021; Oakes & Kaufman, 2006). The extent to which race and genetic ancestry groups overlap varies widely, though nevertheless are distinct constructs within social epidemiologic frameworks. Race should not be assumed nor implied as a biological construct, nor the differences or associations being examined by race discussed in a way that implies a biological or genetic underpinning. Furthermore, given differences in social-structural conditions attributable to racial designations in society (e.g., discrimination, legacies of segregation, racism, built environments), controlling for race in statistical models effectively ignores such social-structural factors’ contributions in shaping health disparities (Bailey et al., 2017, 2021; Krieger, 2000). Controlling for statistical effects from race in analyses therefore risks providing an incomplete picture of a phenomenon under study, and how racial or ethnic categories are conceptualized within theoretical frameworks deserves critical reflections (Krieger, 2000). Future hearing-related research should therefore include improved methods for capturing relevant social-structural factors for critical interpretation of findings. Pertinent research should also explore more valid ways for measuring levels of melanocytes, rather than relying on self-identified race as a proxy for it via assessing skin pigmentation.

Preferred nomenclature related to race is nuanced and evolving (Flanagin et al., 2021). The use of self-reported race and ethnicity is preferred over proxy measures, such as investigator-observed or indicators from electronic health records; regardless, the source of the categorization should be identified in the methods section (Flanagin et al., 2021). General terms, like minorities (versus racial or ethnic minority groups), should be avoided. In addition, the use of racial and ethnic terms as nouns (e.g., Blacks, Whites) is inappropriate, and instead person-first language (i.e., Black individuals) should be prioritized (Flanagin et al., 2021).

Race should be differentiated from the effects of racism and segregation. Racism can be further divided into multiple levels of racism, including personally-mediated or interpersonal racism, institutionalized or systemic racism, internalized racism (Jones, 2000; Oakes & Kaufman, 2006), or structural racism as the totality of how all of the other forms of racism interact (Bailey et al., 2017). Measures of racism exist and should be tailored to the level of exposure (e.g., structural, individual, or internalized) (Krieger, 2020). At an individual level, explicit self-report measures are most commonly used and can include a focus on “everyday discrimination” and/or “major life events” (Krieger, 2020; Oakes & Kaufman, 2006). Implicit measures of racism include measures such as the Implicit Association Test (IAT) (Krieger, 2020). At an area-level, measures of racism generally include residential racial segregation as well as other measures of racial or economic segregation (Krieger, 2020).

To the authors’ knowledge, little to no research exists within hearing health on the relationship between racism, its multiple levels, and hearing health outcomes. Evidence about the effects of segregation on hearing health are similarly lacking. However, research on the effects of racism among healthcare providers as well as the role of segregation as a driver of racial and ethnic disparities are well-documented more broadly (Ben et al., 2017; Paradies et al., 2013; Williams, 2001; Williams et al., 2019). A systematic review and meta-analysis based on over 290 studies from 1983 to 2013 found that racism was associated with worse mental, physical, and general health and the effects were not moderated by age, sex, birthplace or education level but differed by race and ethnicity (Paradies et al., 2015). In another systematic review and meta-analysis, racism was associated with more negative experiences with health services (Ben et al., 2017). Ear and hearing care practitioners and researchers must engage in these areas of inquiry in order to make progress toward hearing health equity.

Socioeconomic Position

Socioeconomic Position (SEP) is a broad term that indicates an individual’s access to desired resources, including material items, education/employment opportunities, money/wealth, power/social status, personal and societal stability, access to service resources, and time/contentment (Oakes & Rossi, 2003). This construct is preferable to the more commonly used “socioeconomic status (SES)” because SES focuses solely on the social status or class associated with those resources (Krieger et al., 1997), while SEP includes both resource- and prestige-based measures as linked to both childhood and adult social class position. Experts call for further evaluation of how individual, familial, and neighborhood-level social class influence health outcomes (Krieger et al., 1997). Given the strong relationship between SEP indicators and health-related outcomes, it is imperative to consider SEP differences when examining differences in health outcomes among subgroups. When examining subgroup differences, whether SEP is considered the core driver, an upstream influencing factor, or a confounder in the relationship between an exposure and outcome of interest depends on the research question, the proposed conceptual framework, and the researcher’s interests. Fundamental to answering any of these questions is the need to identify an appropriate measure to represent SEP.

SEP is most often considered at the level of the individual or that individual’s household. This allows for the most detailed examinations of the pathways resulting in inequities in health between groups of individuals and identify subgroups that are most vulnerable and, therefore, in need for targeted intervention. In contrast, SEP can also be measured at an area-level, which is a composite measure representing the SEP status of those individuals living within a certain geographical area. Area-level indicators are sometimes used either as a proxy for individual-level characteristics when individual-level data are not available, or as a separate measure indicative of an individual’s surroundings. However, this approach is limited and is generally not recommended, as it is subject to ecological fallacy – drawing conclusions about individuals from group-level data. If a researcher wishes to assess area-level indicators of SEP as a measure of social context, it is important to employ appropriate modeling techniques, such as hierarchical or multi-level modeling, to account appropriately for the differential analysis of individual-level versus neighborhood-level measures.

Identifying which indicator(s) to incorporate for defining SEP is also challenging. While education, income, and occupation are often used (Liberatos et al., 1988; Preston & Taubman, 1994), typically because they are most often measured in research studies, it is now understood that numerous social indicators each exert influence on health outcomes. This has led to an interest in the derivation of composite indicators of SEP, the use of which remains the subject of debate among experts. Composite indicators are often more readily employed when SEP is considered a confounding factor to be controlled for in the analysis, rather than the primary exposure, given the composite scores adjust for multiple aspects of SEP at the same time (Oakes & Kaufman, 2006).

When considering specific populations, such as children or older adults, where typical measures of education or income may not adequately apply, additional SEP indicators may need to be considered. For children, measures may include parent’s education, occupation, and household income in addition to measures related to housing, such as housing conditions, as well as the child’s gender, schooling, or the family’s use of health care (Oakes & Kaufman, 2006; Sankar et al., 2019). For older adults, factors like accumulated wealth and financial assets may be more informative than income, as a loss of income in retirement may be balanced by accumulated assets (Oakes & Kaufman, 2006). For occupation, relevant details may include the most recent or longest held occupation of the older adult as well as the availability of secondary insurance, such as Medicare Advantage, versus the use of public insurance available to low-income older adults, such as dual-Medicaid and Medicare plans (Oakes & Kaufman, 2006). In particular, composite indicators may be well-suited for capturing SEP among older adults (Grundy & Holt, 2001).

Regardless of the population of interest, the variables employed in a model should directly stem from a theoretical framework guiding an investigation (Grundy & Holt, 2001). A theory-driven approach to the selection of SEP-related indicators must also be informed by the level of analysis (e.g., individual, family/household, community, etc.) (Bartley et al., 1999). Generally, the relationship between individual SEP and health relates to three primary mechanisms: 1) Materialist, where higher income is believed to equate to better housing, healthier food options, and better access to care and general resources; and measures of income are preferred, 2) Behavioral, where lifestyle factors vary by access to information; and educational level may be the preferred measures, and 3) Psychosocial factors like empowerment and social status, which may be most closely reflected by occupation (Grundy & Holt, 2001). However, these measures are likely highly correlated which should be considered in analyses, and they are frequently employed in studying health disparities and inequities.

A range of theories and approaches exist within social epidemiology on the management of SEP within analyses and particular care should be taken when analyses also include race (Oakes & Andrade, 2014; Oakes & Rossi, 2003). Generally, multivariate methods are employed in an attempt to address the potential confounding of SEP and race (LaVeist, 2005; Shavers, 2007). Adequate sample size must be present within each comparator group to in order to make valid comparisons across both varying levels of SEP and race for a particular outcome (LaVeist, 2005). Additionally, one may also consider stratifying by a particular variable, such as race, rather than using race as a covariate, given that the influence of specific aspects of SEP may vary between groups (Shavers, 2007).

Overall, the selection of SEP measures in a study should be driven by the hypothesis of the mechanism that connects SEP to the health outcome of interest, as well as the ones that will most likely capture the underlying mechanism (Oakes & Kaufman, 2006). Furthermore, when possible, employ multiple theoretically-grounded measures of SEP and incorporate the theoretical foundation when interpreting the results (Oakes & Kaufman, 2006). In the interpretation of results, one should also consider life course effects, including cohort effects and contextual factors, particularly if only measuring individual-level factors (Oakes & Kaufman, 2006). As applied to ear and hearing, these measures may include a history of recreational noise exposure and/or access to health financing coverage for audiologic care. From a life course perspective, for example, noise exposure among pregnant individuals has been associated with lower birth weight (Dzhambov & Lercher, 2019). Lastly, when interpreting results, consider what other measured or unmeasured factors across structural and functional levels may affect the primary outcome of interests as well (Oakes & Kaufman, 2006). For example, self-reported regular hearing aid use (or non-use) may inherently reflect other associated factors, such as access to public transportation in the built environment to travel to clinics (structural) and/or physical disability impacting mobility for accessing care (functional). Ear and hearing-related research has often emphasized individual-level measures of SEP and available measures may be limited by available clinic-based data (Barnett et al., 2017; Inglebret et al., 2017). However, clearly identifying a guiding framework and level of analysis along with associated limitations can aid in advancing existing literature when more robust measures of social disadvantage may be lacking.

Place

As the value of the relationship between the physical environment and health has grown, incorporating geographic information systems (GIS) into public health has come to the forefront in recent years (Rushton, 2003). GIS is a framework for compiling, analyzing, and displaying place-based information referenced to a geographic location. Visual depiction of the locations of disease occurrences and outbreaks is now commonplace; these are often overlayed with important geographic-level covariates and other characteristics of the physical environment (Bithell, 2000).

Geospatial data and analysis can leverage information across person, time, and space to allow for the quantification of the relationships between geographic features and specific outcomes, as well as the spatial distribution and clustering of risk factors or diseases. For example, Joost and colleagues showed clustering of daytime sleepiness associated with levels of nighttime noise levels, allowing for better tailoring of public health interventions (Joost et al., 2018). In addition, spatial analysis allows the appropriate accounting for spatial autocorrelation in epidemiologic models; that is, the knowledge that disease rates are most similar in areas of closer proximity and that rates generally decrease as the distance increases from a core area (Fortin & Jacquez, 2000). The importance of including spatial autocorrelation in analytic models was highlighted by Verbeek, who showed the significance of income inequities in contributing to noise pollution in Ghent, Belgium was only apparent when models accounted for spatial autocorrelation (Verbeek, 2019).

Geospatial data are generally collected in vector format, allowing representation of points (a home or a factory), lines (a highway or a boundary), and polygons (a county or a park). Areas, if aligned with governmental or other boundaries, can be merged with other data and enable assessments of effects from local exposures as well as influence from nearby areas. For example, Sakieh and colleagues used distance-based buffer zones around noise pollution centers to show the mitigating effects of green space on noise pollution (Sakieh et al., 2017). In fact, studies have even employed GIS-based noise mapping methods to estimate the continuous sound levels in an indoor industrial setting (Majidi et al., 2019).

Geospatial data can also be applied for studying hearing care and hearing health (Lantos et al., 2018). In assessing the accessibility of care in Arizona, Coco and colleagues analyzed publicly available data from the US Census, State Department of Health Services, and the US Veterans Administration to derive person-to-provider ratios throughout the state’s counties (Coco et al., 2018). They analyzed the geographic distribution of the hearing aid dispensing workforce relevant to local populations ages 65 years and older with hearing loss. This revealed an average ratio of 6,049:1 (SD = 5,119) older adults with hearing loss to audiologists (in 9/15 counties) and 2,652:1 (SD = 1,473) with hearing instrument specialists (in 13/15 counties). This distribution translated to an average travel distance of 23.84 miles to either an audiologist or hearing instrument specialist across all 15 counties (range: 2.30 – 118.90 miles), with populations in six counties anticipating an average travel time greater than 20 miles to access hearing care. This highlights implications for population hearing health disparities across geography attributed to the differential distribution of available care and present an example of considering place within epidemiologic studies of hearing health.

Applying Social Epidemiology to Hearing Health

While this review provides only a cursory overview, social epidemiology as a discipline offers multiple perspectives and approaches that may serve to advance the fields of ear and hearing care. Concepts, such as structural disadvantage, the influence of racism and segregation, and hearing health trajectories, to name a few, are not necessarily novel to the research and clinical approaches employed in ear and hearing care. However, a systematic attempt to categorize the influences, relationships, and context within which hearing health exists can benefit our research and our practice. Furthermore, social epidemiology’s orientation toward equity and its inherent focus on mechanism and intervention provides a necessary and motivating foundation to advancing hearing health equity. As a starting point, there are significant opportunities to integrate some of the concepts and approaches from social epidemiology into hearing health practice (Table 1).

TABLE 1.

Recommendations on Applying Social Epidemiology in Practice

Readers
   Identify the stated or implied theoretical framework(s)
   Identify the level of influence on which the manuscript focuses (e.g., individual, community, multi-level, etc.)
   Consider the hypothesis and hypothesized mechanisms, including connection with the theoretical framework
   Assess inclusion and reporting of demographics and social factors, such as race, ethnicity, measures of socioeconomic position
   Interpret the generalizability of the study findings in terms of the included participants and the community or populations represented
   Consider how social and contextual factors (e.g., segregation, racism) were measured or, if not included, their lack of inclusion identified as limitations
   Identify the implications and action-oriented steps learned or recommended by the study findings
Researchers
   Select a guiding theory and/or framework(s) that guides variable selection, hypothesis generation, including hypothesized mechanisms, and analyses
   Select level of outcomes on which to focus and/or multiple levels
   Identify appropriate and theory-driven measures to include in data collection and/or analyses
   Collect and report race, ethnicity, additional demographic and social constructs
   Employ person-first language throughout documentation and reporting
   Incorporate measures of socioeconomic position with the goal of including multiple aspects of socioeconomic position
   Accurately use the terms socioeconomic status versus position with a preference for position
   Ensure using race and ethnicity as a social construct and clearly identify why and how the construct is being employed
   Seek out cross-disciplinary collaborators early in the research process and involve throughout the process
Clinicians & Educators
   Consider available data in the electronic health record regarding demographic and social factors, including potential limitations
   Learn about the historical and social context of the institution and communities within which you provide care
   Learn about racism, its various forms, and existing literature related to health and public health
   Reflect upon institutional and structural barriers to care as well as those embedded within the educational system
   Monitor care outcomes by demographic and social factors to identify potential disparities and inform the need for targeted interventions
   Review practice and educational environment and processes for increasing access and fostering inclusion and belonging
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Recommendations for Readers

When interpreting literature, particularly literature with a focus on hearing health equity, readers should first consider the stated or implied theoretical framework(s) employed by the authors and the level of influence on which they are focused (e.g., individual, family, community, societal, multi-level, etc.). In close connection with the theoretical framework, the readers should consider whether the authors have clearly identified their hypothesis, including hypothesized mechanism(s) underlying an expected association or outcome. From the theoretical framework and the hypothesis, readers should be able to assess whether the authors go beyond defining the variables and provide a rationale for the selected variables and outcomes, particularly those related to SEP. Whenever possible, readers should be able to evaluate the race and ethnicity of participants, including how race and ethnicity were determined and how it aligns with a theoretical framework guiding research and/or practice. When interpreting the results, including the analytic cohort and findings, the reader should consider who was included in the study and, more importantly, who was not represented, particularly in terms of considering the finding’s external generalizability. The reader should also consider how frequently unmeasured social and contextual factors, such as residential segregation, interpersonal racism, etc., may contribute to the outcomes in addition to outcome measurements themselves. With an emphasis on intervention, the reader should consider the implications of the study results and necessary next steps proposed by the authors in advancing equity.

Recommendations for Researchers

While topics within ear and hearing can vary significantly, theories aid researchers in placing their area of inquiry related to hearing health within broader contexts. Using theory to define the scope of the research question, associated methodology, and its placement within a biopsychosocial perspective not only allows for recognizing the limits of our findings but also highlights a potential path to cross-disciplinary collaborations. Researchers focusing on patient, community, and/or population-level outcomes should consider how the driving theoretical frameworks capture the relevant, multi-level influences on hearing health and/or the multi-level approaches needed to intervene effectively. The selection of guiding theories, frameworks and hypotheses should dictate variable selection and this rationale for using them should be explicit whenever possible. When collecting data related to social constructs, employ both self-identification of race and ethnicity and multiple measures of SEP whenever possible. Multiple measures of SEP may more accurately capture SEP, including its various aspects and/or over the life course, as well as provide more meaningful insights into potential mechanisms (Oakes & Kaufman, 2006). Funders, such as the National Institutes of Health, and journals may provide guidelines in terms of when and how these various constructs should be reported. During initial planning, researchers should give thoughtful consideration to measurement, particularly regarding the balance between available data and participant burden and acceptability in terms of various questions, especially in relationship to SEP. Discussions regarding expansion of measures around social constructs and indicators may be an important opportunity to engage collaborators beyond ear and hearing who may have additional experience with diverse measures and/or populations. For research questions and analyses that focus on differences by race and ethnicity, careful attention should be made in delineating how race as a construct is being employed and reported as well as what unmeasured factors, such as interpersonal racism or segregation, may limit findings and their interpretation. For research questions that focus on disparities, researchers should design studies that inform understanding of the underlying mechanisms towards the ultimate goal of intervention development, as well as for evaluating interventions themselves. Cross-disciplinary innovations are critical for adopting a more holistic approach to understanding and advancing population hearing health, as well as promoting principles of diversity, equity, and inclusion within ear and hearing research. Expanding our conceptualizations of the individual as part of a diverse society built on legacies of socially held beliefs and analyzing the interactions of social and biological factors in our frameworks will help effectively hone our study questions to rightly target modifiable factors that ushers us closer towards hearing health equity.

Recommendations for Clinicians and Clinical Educators

While the focus of social epidemiology is on public health and its implications for populations, there are ways that clinicians can incorporate elements of social epidemiology into practice. For example, the electronic health record is a growing source of data for healthcare systems and an understanding of what and how your practice or employer captures demographics, such as race and ethnicity, may cue some necessary considerations of potential SDOH for tailoring effective care recommendations accordingly. A greater appreciation for the context within which clinicians provide patient care, including historical factors like legacies of medical abuse and mistrust, may help strengthen an understanding of a practice’s or institution’s position and perceived value within a community. While currently little to no research has explored the role of racism within ear and hearing care, the concepts of racism, including interpersonal racism, are recognized as primary drivers of racial and ethnic disparities within healthcare more broadly (Bailey et al., 2021). Institutionalized barriers to achieving graduate education, including clinical trainings within hearing health professions, by traditionally underrepresented demographics must also be critically reviewed (Bhopal, 2017; Tienda, 2013). Such barriers stall progress in supporting diversity within the field, which subsequently adversely impacts public health. Recognition and understanding of racism and its multiple forms are important starting points for providers and educators in ensuring equitable delivery of inclusive higher education for clinical trainees (Museus et al., 2015). Furthermore, clinicians should be regularly monitoring care outcomes, including measures of patient satisfaction, based on race, ethnicity, SEP, or other key factors to ensure equitable distributions of care and identifying when tailored interventions are needed for targeting relevant upstream and/or downstream factors for supporting outcomes (Figure 3). Clinicians should review one’s practice environment regularly to ensure the setting and operational processes are welcoming and accessible to individuals from diverse backgrounds and responsive to the needs of a range of patients, including individuals with disabilities and other competing healthcare demands. Holistic care in these respects would benefit from engaging in interdisciplinary and collaborative approaches with other professionals.

Figure 3.

Figure 3.

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Proposed social epidemiologic framework of factors driving hearing health outcomes.

Adapted from: Berkman, L. F., Glass, T., Brissette, I., & Seeman, T. E. (2000). From social integration to health: Durkheim in the new millennium. Social Science and Medicine, 51, 843–857.

CONCLUSION

Current understanding of hearing health both nationally and globally point to significant inequities in hearing health and healthcare. In order to realize a future of hearing health for all, as charged by the 2021 World Report on Hearing, ear and hearing care disciplines must consider new and complementary approaches to meet the needs of the expected 2.5 billion individuals to be living with hearing loss by 2050 (Haile et al., 2021; World Health Organization, 2021). Systematic approaches that attempt to identify and address the primary drivers of inequities exist, such as those found within social epidemiology, which can help identify existing gaps in ear and hearing care and inform innovative practice and research. While no one approach or discipline will provide a comprehensive understanding or solution to hearing health inequities, we as researchers and practitioners are charged with looking beyond our traditional disciplines and collaborators to explore cross-disciplinary approaches and perspectives for innovations that will only strengthen our abilities to realize that future.

Conflicts of Interest and Sources of Funding:

This work was supported by the National Institute on Aging (NIA)/National Institutes of Health (NIH) (K23 AG059900 to CLN). C.L. Nieman serves as a volunteer board member for the nonprofits, Access HEARS and the Hearing Loss Association of America.

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