Origins of heath inequalities: the case for Allostatic Load

Delpierre, Cyrille; Barbosa-Solis, Cristina; Torrisani, Jérôme; Darnaudéry, Muriel; Bartley, Mel; Blane, David; Kelly-Irving, Michelle; Getz, Linn; Tómasdóttir, Margrét Ólafía; Roberston, Tony; Gustafsson, Per E.

doi:10.14301/llcs.v7i1.325

Cited by 23 publications

(28 citation statements)

References 82 publications

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“…However, while the simplest form, the risk index, showed lower discriminatory power than the more complex functional forms, the results indicate that while there are some gains from choosing a functional form that is more complex than the simple index, the total gain is modest. Therefore, we argue that in certain circumstances in which survey-based research focuses on generalized concepts like allostatic load (Delpierre et al 2016a;Juster, McEwen, and Lupien 2010;McEwen 1998;Seeman et al 2001), our results validate such a parsimonious approach, at least for the set of CVD-related biomarkers under study here. A similar approach might seem appropriate if health inequalities with respect to objective indicators are the subject of investigation (Dowd and Zajacova 2010;Rosero-Bixby and Dow 2009), as an alternative to looking at risk differences for specific health issues, such as hbA1c for diabetes (Bennett, Guo, and Dharmage 2007;Rohlfing et al 2000).…”

Section: Discussionsupporting

confidence: 62%

“…Generally, there are two opposing approaches: On the one hand, a focus on very specific conditions like diabetes could warrant the use of one specific biomarker like hbA1c or C-reactive protein (CRP); for example, using hbA1c as a screening tool for undiagnosed diabetes (Bennett, Guo, and Dharmage 2007;Rohlfing et al 2000), or investigating the relationship between CRP and depressive symptoms (Hamer and Chida 2009). On the other hand, research can focus more on the overall wear and tear of the body as represented by the concepts of allostatic load (Delpierre et al 2016a;Juster, McEwen, and Lupien 2010;McEwen 1998;Seeman et al 2001) and biological age (Levine and Crimmins 2014), or on more specific CVD indices, including the well-known Framingham Heart Score (Lloyd-Jones et al 2004). In many applications it is difficult to find an explicitly theoretically driven framework for the implementation of biomarkers in survey research.…”

Section: Introductionmentioning

confidence: 99%

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The association between CVD-related biomarkers and mortality in the Health and Retirement Survey

Kröger¹,

Hoffmann²

2018

DemRes

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BACKGROUNDIt has become increasingly common in multiple purpose general population surveys to integrate different kinds of biomarker in the data collection process. OBJECTIVEIn this article we test the predictive power of five different functional forms of CVDrelated biomarkers for all-cause and CVD mortality in the Health and Retirement Study (HRS). METHODSWe use five different functional forms of biomarker: A risk factor index, risk factors separately, continuous biomarkers, risk groups comprising every possible combination of risk factors, and a cluster analytic approach to identify risk profiles in the sample. We use data from the Health and Retirement Study (HRS) with information on four collected biomarkers (glycated hemoglobin (hbA1c), high-density lipoprotein (HDL), total cholesterol, and C-reactive protein (CRP)) with an eight-year mortality follow-up period. RESULTSThe results show that the additive index has comparatively high predictive power, relative to its simplicity. Risk profiles were identified in the data, with substantial differences in mortality risk between the profiles. The more complex functional forms improve prediction only moderately compared to the simple index, although we can identify groups with an elevated mortality risk that are not identified in more parsimonious approaches.1 Deutsches Institut für Wirtschaftsforschung (DIW), Berlin, Germany. Email: hkroeger@diw.de. 2 Max-Planck-Institut für Demografische Forschung, Rostock, Germany. Email: hoffmann@demogr.mpg.de. Kröger & Hoffmann:The association between CVD-related biomarkers and mortality 1934http://www.demographic-research.org CONCLUSIONSDepending on the specific research question, both a very simple modeling of biomarker information and more detailed examinations of specific complex risk profiles can be appropriate. CONTRIBUTIONThe study provides initial guidelines for the measurement of commonly used biomarkers, which can be a reference for other studies that use biomarkers as health indicators or for mortality prediction.

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Section: Discussionsupporting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

The association between CVD-related biomarkers and mortality in the Health and Retirement Survey

Kröger¹,

Hoffmann²

2018

DemRes

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“…Important advances have been made in such areas as understanding the physiological mechanisms by which chronic stress associated with privation and insecurity impairs health, often with a long time lag between initial exposure and effect (Barboza Solís, Fantin, Kelly-Irving, & Delpierre, 2016;Barboza Solís et al, 2015;Gallo, Fortmann, & Mattei, 2014;McEwen, 2015;McEwen, 2012;Delpierre et al, 2016) and the multiple pathways by which reduced 'control over destiny', which may occur at the individual level but as a consequence of macro-scale economic and social processes, leads to negative health outcomes (Whitehead et al, 2016). In addition, the importance of environmental pollution as a contributor to the burden of noncommunicable disease, especially (although not only) in low-and middleincome countries, is belatedly being acknowledged (Vineis, Stringhini, & Porta, 2014;Fuller et al, 2018;Landrigan et al, 2018).…”

Section: Figure 1 a Tale Of A Sinking Stone?mentioning

confidence: 99%

The Commission on Social Determinants of Health: Ten years on, a tale of a sinking stone, or of promise yet unrealised?

Schrecker

2018

Critical Public Health

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Ten years after the August 2008 release of the report of the WHO Commission on Social Determinants of Health, it is important to reflect on the fate of its recommendations for reducing 'health inequity'. The article describes some key developments in the decade, notably in understanding the etiology of health inequalities, and then juxtaposes a hopeful comparison of an earlier (1987) UN Commission on Environment and Development with a sceptical view based on the expanding social science literature on the politics of economic inequality. The last ten years On 28 August 2008, the World Health Organization's Commission on Social Determinants of Health released its final report. The end product of three years' work by 19 commissioners led by Sir Michael Marmot and supported by nine transnational knowledge networks, the report was organised around the concept of health equity * and began with the assertion that the 'unequal distribution of health-damaging experiences' within and across national borders 'is in no sense a "natural" phenomenon but is the result of a toxic combination of poor social policies and programmes, unfair economic arrangements, and bad politics' (Commission on Social Determinants of Health, 2008, p. 1). Such language is not normally encountered in United Nations system documents and led The Economist, in a generally laudatory review, to comment that the Commission 'seems, at times, to be baying at the moon when it attacks global imbalances in the distribution of power and money' (The price of being well, 2008). The report was released as a financial crisis spread across the world from an epicentre in the United States, with effects that underscored the value and urgency of the Commission's focus on "upstream" influences on health * The Commission defined health equity with reference to 'systematic differences in health [that] are judged to be avoidable by reasonable action'. Whilst this definition invites debate about what might constitute reasonable action, there is widespread agreement on the distinction between health equity and the strictly descriptive terminology of health inequalities or health disparities.

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“…The prolonged activation of compensatory physiological mechanisms can lead to a physiological ‘wear-and-tear’, termed allostatic load (AL) [ 9 , 11 – 13 ]. The AL model of chronic stress focuses on glucocorticoid dysregulation as part of a ‘network of allostasis’ involving autonomic, endocrine, metabolic, and immune mediators [ 14 , 15 ]. A variety of studies using measures of AL have suggested its association with numerous health outcomes and higher all-cause of mortality risk [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Allostatic load and subsequent all-cause mortality: which biological markers drive the relationship? Findings from a UK birth cohort

et al. 2018

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The concept of allostatic load (AL) refers to the idea of a global physiological ‘wear and tear’ resulting from the adaptation to the environment through the stress response systems over the life span. The link between socioeconomic position (SEP) and mortality has now been established, and there is evidence that AL may capture the link between SEP and mortality. In order to quantitatively assess the role of AL on mortality, we use data from the 1958 British birth cohort including eleven year mortality in 8,113 adults. Specifically, we interrogate the hypothesis of a cumulative biological risk (allostatic load) reflecting 4 physiological systems potentially predicting future risk of death (N = 132). AL was defined using 14 biomarkers assayed in blood from a biosample collected at 44 years of age. Cox proportional hazard regression analysis revealed that higher allostatic load at 44 years old was a significant predictor of mortality 11 years later [HR = 3.56 (2.3 to 5.53)]. We found that this relationship was not solely related to early-life SEP, adverse childhood experiences and young adulthood health status, behaviours and SEP [HR = 2.57 (1.59 to 4.15)]. Regarding the ability of each physiological system and biomarkers to predict future death, our results suggest that the cumulative measure was advantageous compared to evaluating each physiological system sub-score and biomarker separately. Our findings add some evidence of a biological embodiment in response to stress which ultimately affects mortality.Electronic supplementary materialThe online version of this article (10.1007/s10654-018-0364-1) contains supplementary material, which is available to authorized users.

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Origins of heath inequalities: the case for Allostatic Load

Abstract: In an opening paper Delpierre et al. explore

Cited by 23 publications

References 82 publications

The association between CVD-related biomarkers and mortality in the Health and Retirement Survey

The association between CVD-related biomarkers and mortality in the Health and Retirement Survey

The Commission on Social Determinants of Health: Ten years on, a tale of a sinking stone, or of promise yet unrealised?

Allostatic load and subsequent all-cause mortality: which biological markers drive the relationship? Findings from a UK birth cohort

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