Academic journal article Health Sociology Review

The Multidimensionality of Health: Associations between Allostatic Load and Self-Report Health Measures in a Community Epidemiologic Study

Academic journal article Health Sociology Review

The Multidimensionality of Health: Associations between Allostatic Load and Self-Report Health Measures in a Community Epidemiologic Study

Article excerpt

Background

Rather than collaborating to address issues of health equity, researchers often allow disciplinary boundaries to hinder progress toward explaining what being healthy means, why health is socially distributed, and how population health can be improved. Use of biomarkers in basic research (see Chae et al., 2014; Geronimus, Hicken, Keene, & Bound, 2006; Needham et al., 2013; Needham, Fernandez, Lin, Epel, & Blackburn, 2012; Seeman, Singer, Rowe, Horwitz, & McEwen, 1997; Seeman, Singer, Ryff, Love, & LevyStorms, 2002), a critical advance necessary for demonstrating how social inequality 'gets under the skin' and 'how our bodies tell stories' (Epel et al., 2004; Green & Darity, 2010; Krieger, 2005; Miller, Chen, & Parker, 2011; Taylor, Repetti, & Seeman, 1997), stimulated further debate about disciplinary distinctions inherent to the measurement of health status. One aspect of the debate regards the distinction between objective and subjective health indicators, and implicitly, the value of self-report health measures commonly used in community epidemiologic studies. Specifically, some public health researchers, epidemiologists, and physicians criticise health-related research that relies on respondents' self-reports and perceptions.

When estimating health status, biological markers such as leukocyte telomere length, body mass index (BMI), cortisol, C-reactive protein (CRP), and so on, appear to offer advantages over popular self-report measures (Karlamangla, Gruenewald, & Seeman, 2012). First, biological markers minimise present state bias. When respondents are feeling particularly healthy or unhealthy, asking them to assess their status induces bias. In contrast, assessing assays in blood or urine or saliva does not depend upon a respondent's present state. Second, biological markers eliminate bias related to health-care access. If researchers ask people to report on serious health problems diagnosed by a health-care provider, then those reports depend upon routine access to (and probably, high quality) health care. Third, biological markers often capture reactivity and nascent disease states rather than experienced symptomatology and/or decreased function (Karlamangla et al., 2012). Consequently, scholars may predict when a person is at risk for developing a disease before the person becomes conscious of their deteriorating health status. Fourth, biological markers capture health at the very moment in time they are measured, which facilitates the time ordering of self-reported and retrospective variables in cross-sectional data. Finally, biological markers often index the function of multiple, interdependent biochemical systems inside the body. For example, allostatic load (Dowd, Simanek, & Aiello, 2009; McEwen, 1998, 2002; McEwen & Gianaros, 2010, 2011; McEwen & Seeman, 1999; McEwen & Stellar, 1993; McEwen & Wingfield, 2003) a summary of biochemical dysregulation according to concatenation of deleterious scores across several biomarkers, is configured typically to capture cardiovascular, metabolic, hypothalamicpituitary-adrenal (HPA) axis, autonomic nervous system and sympathetic nervous system, and inflammatory system dysregulation. (For more information on allostasis and allostatic load, see Allostatic load and allostasis (2009).) Moreover, using a summary statistic such as allostatic load avoids the problem of strong correlations among certain biomarkers (see Coffman & Richmond-Bryant, 2015).

Biomarkers are not without limitations but most of those limitations are controllable including such issues as standardised procedures for specimen collection, methodological precision during estimation, and capturing subjects' prescription medication use. Therefore, given the benefits and limitations just outlined, one might argue that health status is estimated best by biological markers. However, we suggest and will argue that healthrelated perceptions and experiences are meaningful for contextualising health status, and that measures of illness (i. …

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