Health: Going beyond lifestyle factors
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
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BookHealth Inequality: An Introduction to Concepts, Theories and MethodsCambridge Polity Press.
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Implications of small reductions in diastolic blood pressure for primary preventionArchives of Internal Medicine 155:701–709.https://doi.org/10.1001/archinte.1995.00430070053006
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How western diet and lifestyle drive the pandemic of obesity and civilization diseasesDiabetes, Metabolic Syndrome and Obesity: Targets and Therapy 12:2221–2236.https://doi.org/10.2147/DMSO.S216791
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Commentary: Risk factors or social causes?International Journal of Epidemiology 33:297–298.https://doi.org/10.1093/ije/dyh084
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Hunter-gatherers as models in public healthObesity Reviews 19:24–35.https://doi.org/10.1111/obr.12785
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© 2021, Ruiz
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Further reading
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- Epidemiology and Global Health
Background:
Biological aging exhibits heterogeneity across multi-organ systems. However, it remains unclear how is lifestyle associated with overall and organ-specific aging and which factors contribute most in Southwest China.
Methods:
This study involved 8396 participants who completed two surveys from the China Multi-Ethnic Cohort (CMEC) study. The healthy lifestyle index (HLI) was developed using five lifestyle factors: smoking, alcohol, diet, exercise, and sleep. The comprehensive and organ-specific biological ages (BAs) were calculated using the Klemera–Doubal method based on longitudinal clinical laboratory measurements, and validation were conducted to select BA reflecting related diseases. Fixed effects model was used to examine the associations between HLI or its components and the acceleration of validated BAs. We further evaluated the relative contribution of lifestyle components to comprehension and organ systems BAs using quantile G-computation.
Results:
About two-thirds of participants changed HLI scores between surveys. After validation, three organ-specific BAs (the cardiopulmonary, metabolic, and liver BAs) were identified as reflective of specific diseases and included in further analyses with the comprehensive BA. The health alterations in HLI showed a protective association with the acceleration of all BAs, with a mean shift of –0.19 (95% CI −0.34, –0.03) in the comprehensive BA acceleration. Diet and smoking were the major contributors to overall negative associations of five lifestyle factors, with the comprehensive BA and metabolic BA accounting for 24% and 55% respectively.
Conclusions:
Healthy lifestyle changes were inversely related to comprehensive and organ-specific biological aging in Southwest China, with diet and smoking contributing most to comprehensive and metabolic BA separately. Our findings highlight the potential of lifestyle interventions to decelerate aging and identify intervention targets to limit organ-specific aging in less-developed regions.
Funding:
This work was primarily supported by the National Natural Science Foundation of China (Grant No. 82273740) and Sichuan Science and Technology Program (Natural Science Foundation of Sichuan Province, Grant No. 2024NSFSC0552). The CMEC study was funded by the National Key Research and Development Program of China (Grant No. 2017YFC0907305, 2017YFC0907300). The sponsors had no role in the design, analysis, interpretation, or writing of this article.
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- Epidemiology and Global Health
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Background:
In many settings, a large fraction of the population has both been vaccinated against and infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, quantifying the protection provided by post-infection vaccination has become critical for policy. We aimed to estimate the protective effect against SARS-CoV-2 reinfection of an additional vaccine dose after an initial Omicron variant infection.
Methods:
We report a retrospective, population-based cohort study performed in Shanghai, China, using electronic databases with information on SARS-CoV-2 infections and vaccination history. We compared reinfection incidence by post-infection vaccination status in individuals initially infected during the April–May 2022 Omicron variant surge in Shanghai and who had been vaccinated before that period. Cox models were fit to estimate adjusted hazard ratios (aHRs).
Results:
275,896 individuals were diagnosed with real-time polymerase chain reaction-confirmed SARS-CoV-2 infection in April–May 2022; 199,312/275,896 were included in analyses on the effect of a post-infection vaccine dose. Post-infection vaccination provided protection against reinfection (aHR 0.82; 95% confidence interval 0.79–0.85). For patients who had received one, two, or three vaccine doses before their first infection, hazard ratios for the post-infection vaccination effect were 0.84 (0.76–0.93), 0.87 (0.83–0.90), and 0.96 (0.74–1.23), respectively. Post-infection vaccination within 30 and 90 days before the second Omicron wave provided different degrees of protection (in aHR): 0.51 (0.44–0.58) and 0.67 (0.61–0.74), respectively. Moreover, for all vaccine types, but to different extents, a post-infection dose given to individuals who were fully vaccinated before first infection was protective.
Conclusions:
In previously vaccinated and infected individuals, an additional vaccine dose provided protection against Omicron variant reinfection. These observations will inform future policy decisions on COVID-19 vaccination in China and other countries.
Funding:
This study was funded the Key Discipline Program of Pudong New Area Health System (PWZxk2022-25), the Development and Application of Intelligent Epidemic Surveillance and AI Analysis System (21002411400), the Shanghai Public Health System Construction (GWVI-11.2-XD08), the Shanghai Health Commission Key Disciplines (GWVI-11.1-02), the Shanghai Health Commission Clinical Research Program (20214Y0020), the Shanghai Natural Science Foundation (22ZR1414600), and the Shanghai Young Health Talents Program (2022YQ076).