1. Epidemiology and Global Health
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Health: Going beyond lifestyle factors

  1. Milagros Ruiz  Is a corresponding author
  1. Research Department of Epidemiology and Public Health, University College London, United Kingdom
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Cite this article as: eLife 2021;10:e70548 doi: 10.7554/eLife.70548

Abstract

Wealth and inequality impact blood pressure in a population with the lowest risk of heart disease in the world.

Main text

The idea of a ‘social ladder’ may be metaphorical, but actual and perceived societal ranking have real consequences for the health of an individual (Adler et al., 2000). How social structure influences health and disease is overwhelmingly studied in high-income countries, where coronary heart disease (CHD for short) is the leading cause of death (Institute for Health Metrics and Evaluation, 2019).

In these societies, the relationship between an individual’s social position and their CHD risk is astonishingly consistent, with disadvantaged populations being more likely to suffer from the disease and to die from it (Schultz et al., 2018). Despite the clarity of this evidence, the public health workforce has not yet reached a unified consensus on why these inequalities occur, and what can be done to reduce them (Marmot, 2004).

Lifestyle factors such as diet, sedentarism or smoking, and their ensuing effects like hypertension, only partially account for the excess burden of CHD in disadvantaged groups (Schultz et al., 2018). Yet primary prevention efforts appear to focus on these health behaviours over other factors linked to social inequality. In fact, targeting lifestyle alone is likely to exacerbate inequalities in post-industrial societies (Marmot, 2004).

Investigating inequalities in populations that have not adopted Western diets and activity levels – a challenging undertaking given the proliferation of this lifestyle worldwide – could be a way to confront the underlying assumption that behavioural differences are responsible for the observed inequality in CHD (Kopp, 2019). Now, in eLife, Adrian Jaeggi (University of Zurich and Emory University), Aaron Blackwell (Washington State University) and co-workers based in the United States, France and Germany report the most comprehensive study on social structure and health in a pre-industrial society in the Bolivian Amazon known as the Tsimane (Jaeggi et al., 2021). 

This population relies on subsistence farming supplemented by hunter-gatherer practices, resulting in an extremely physically active life and a diet that is rich in fibres and micronutrients. In turn, they have remarkably modest rates of obesity and hypertension, and the lowest prevalence of biological markers for poor artery health ever recorded around the world (Pontzer et al., 2018). Thus, any putative relationship between social position and heart health is unlikely to be the result of differences in health behaviour.

Overall, Jaeggi et al. discovered consistent links between wealth-related circumstances and blood pressure in the Tsimane: the poorer the individual, the higher their blood pressure. In people over the age of 15, the pressure on artery walls during and between heartbeats was lower in those with higher household wealth, that is, those with more common household assets: this can include traditional goods made from local organic materials, industrially produced items acquired through trade or purchase, and livestock. The researchers also investigated the association between wealth inequality and overall health in several geographically separated communities – defined as clusters of households connected through kin networks that produce or consume food together. They found that communities with greater inequality between rich and poor members had higher blood pressure.

Most Tsimane have normal blood pressure. This means that associations between wealth and individual blood pressure within communities, or between wealth inequality and overall blood pressure across communities both capture variations below a clinically significant level (Jaeggi et al., 2021; Pontzer et al., 2018). However, these findings are not inconsequential: in post-industrial societies, small reductions in blood pressure in the overall population have proved effective in lowering CHD incidence (Cook et al., 1995).

If no members of the Tsimane population live an unhealthy lifestyle, and if they all have little to no access to healthcare, then what drives higher blood pressure in poorer adults and in more unequal communities? Psychosocial mechanisms and pathways to poor health may provide an answer, drawing on how feelings which result from inequality, domination, or subordination may directly alter biological processes (Bartley, 2017). Social hierarchies, maintained by societal arrangements of power, lead to disadvantaged populations being disproportionally exposed to psychosocial stressors such as lack of community support, low control and autonomy, and an imbalance between effort and reward. In turn, psychosocial stress can have a severe impact on the body, triggering a sustained fight or flight response and altering the hormone system that controls biological reactions to stress (Bartley, 2017; Jaeggi et al., 2021).

Jaeggi et al. therefore tested how psychosocial factors related to unequal wealth and wealth distribution may have influenced feelings and interactions among the Tsimane (e.g., depression, social conflicts), or altered their body chemistry (e.g., the level of the stress hormone cortisol in urine). The analyses highlighted a weak connection between these factors and increased levels of blood pressure in individuals who possess less wealth or are from more unequal communities. However, this link may only be weakly supported by the analyses because the markers used could have insufficiently measured psychosocial stress. It may therefore be worth also examining whether a pathway can be identified when looking at C-reactive protein, an inflammatory biomarker for blood pressure which is relatively elevated in the Tsimane population (Pontzer et al., 2018). Yet, detecting these small effects in such a healthy society requires a large sample size, and psychosocial markers were only collected in a subset of participants with blood pressure data: thus, it is more likely that the analyses were underpowered.

The Tsimane face growing exposure to psychosocial stress as contact with ethnic majority groups increase, and their economy becomes more integrated. These developments urge researchers to explore individual-level and macro-level mechanisms for health inequality in the Tsimane, and remind us, once again, to look beyond lifestyle when tackling public health problems.

References

  1. Book
    1. Bartley M
    (2017)
    Health Inequality: An Introduction to Concepts, Theories and Methods
    Cambridge Polity Press.

Article and author information

Author details

  1. Milagros Ruiz

    Milagros Ruiz is in the Research Department of Epidemiology and Public Health, University College London, London, United Kingdom

    For correspondence
    m.a.ruiz@ucl.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7492-9873

Publication history

  1. Version of Record published: June 24, 2021 (version 1)

Copyright

© 2021, Ruiz

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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Further reading

    1. Epidemiology and Global Health
    David Bann et al.
    Research Article Updated

    Background:

    Risk factors or interventions may affect the variability as well as the mean of health outcomes. Understanding this can aid aetiological understanding and public health translation, in that interventions which shift the outcome mean and reduce variability are typically preferable to those which affect only the mean. However, most commonly used statistical tools do not test for differences in variability. Tools that do have few epidemiological applications to date, and fewer applications still have attempted to explain their resulting findings. We thus provide a tutorial for investigating this using GAMLSS (Generalised Additive Models for Location, Scale and Shape).

    Methods:

    The 1970 British birth cohort study was used, with body mass index (BMI; N = 6007) and mental wellbeing (Warwick-Edinburgh Mental Wellbeing Scale; N = 7104) measured in midlife (42–46 years) as outcomes. We used GAMLSS to investigate how multiple risk factors (sex, childhood social class, and midlife physical inactivity) related to differences in health outcome mean and variability.

    Results:

    Risk factors were related to sizable differences in outcome variability—for example males had marginally higher mean BMI yet 28% lower variability; lower social class and physical inactivity were each associated with higher mean and higher variability (6.1% and 13.5% higher variability, respectively). For mental wellbeing, gender was not associated with the mean while males had lower variability (–3.9%); lower social class and physical inactivity were each associated with lower mean yet higher variability (7.2% and 10.9% higher variability, respectively).

    Conclusions:

    The results highlight how GAMLSS can be used to investigate how risk factors or interventions may influence the variability in health outcomes. This underutilised approach to the analysis of continuously distributed outcomes may have broader utility in epidemiologic, medical, and psychological sciences. A tutorial and replication syntax is provided online to facilitate this (https://osf.io/5tvz6/).

    Funding:

    DB is supported by the Economic and Social Research Council (grant number ES/M001660/1), The Academy of Medical Sciences / Wellcome Trust (“Springboard Health of the Public in 2040” award: HOP001/1025); DB and LW are supported by the Medical Research Council (MR/V002147/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

    1. Epidemiology and Global Health
    2. Medicine
    ISARIC Clinical Characterisation Group et al.
    Research Article Updated

    Background:

    There is potentially considerable variation in the nature and duration of the care provided to hospitalised patients during an infectious disease epidemic or pandemic. Improvements in care and clinician confidence may shorten the time spent as an inpatient, or the need for admission to an intensive care unit (ICU) or high dependency unit (HDU). On the other hand, limited resources at times of high demand may lead to rationing. Nevertheless, these variables may be used as static proxies for disease severity, as outcome measures for trials, and to inform planning and logistics.

    Methods:

    We investigate these time trends in an extremely large international cohort of 142,540 patients hospitalised with COVID-19. Investigated are: time from symptom onset to hospital admission, probability of ICU/HDU admission, time from hospital admission to ICU/HDU admission, hospital case fatality ratio (hCFR) and total length of hospital stay.

    Results:

    Time from onset to admission showed a rapid decline during the first months of the pandemic followed by peaks during August/September and December 2020. ICU/HDU admission was more frequent from June to August. The hCFR was lowest from June to August. Raw numbers for overall hospital stay showed little variation, but there is clear decline in time to discharge for ICU/HDU survivors.

    Conclusions:

    Our results establish that variables of these kinds have limitations when used as outcome measures in a rapidly evolving situation.

    Funding:

    This work was supported by the UK Foreign, Commonwealth and Development Office and Wellcome [215091/Z/18/Z] and the Bill & Melinda Gates Foundation [OPP1209135]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.