Poverty is bad for health. People living in poverty are more likely to struggle to afford nutritious food, lack access to health care, or be overworked or stressed. This may make them susceptible to chronic diseases, contribute to faster aging, and shorten their lifespans. In high-income countries, there is growing evidence to suggest that a person’s ‘rank’ in society also impacts their health. For example, individuals who have a lower position in the social hierarchy report worse health outcomes, regardless of their incomes. But it is unclear why living in an unequal society or having a lower social status contributes to poorer health.

One possibility is that inequalities in society are creating a stressful environment that leads to worse physical and mental outcomes. It is thought that this stress largely comes from how humans evolved to prioritize reaching a higher social status over having a long and healthy life. If this is the case, this would mean that the link between social status and health would also be present in non-industrialized communities where social hierarchies tend to be less pronounced.

To test this, Jaeggi, Blackwell et al. studied the Indigenous Tsimane population in Bolivia who live in small communities and forage and farm their own food. The income and relative wealth of 870 households from 40 Tsimane communities were compared against various outcomes, including symptoms associated with depression, stress hormone levels, blood pressure, self-rated health and several diseases.

Jaeggi, Blackwell et al. found poverty and inequality did not negatively impact all of the health outcomes measured as has been previously reported for industrialized societies. However, blood pressure was higher among people with lower incomes or those who lived in more unequal communities. But because the Tsimane people generally have low blood pressure, the differences were too small to have much effect on their health. People who lived in more unequal communities were also three times more likely to have respiratory infections, but the reason for this was unclear.

This shows that social determinants such as a person’s wealth or inequality can affect health, even in communities with less rigid social hierarchies. In industrial societies the effect may be worse in part because they are compounded by lifestyle factors, such as diets rich in fat and sugar, and physical inactivity which can also increase blood pressure. This information may help policy makers reduce health disparities by addressing some of the social determinants of health and the lifestyle factors that cause them.

It is relatively uncontroversial that people with greater access to resources – usually operationalized as income, wealth, or broader indicators of socio-economic position, rank, or status (used interchangeably here) – are likely to be in better health as resources can be converted into better nutritional status, access to health care, or insulation against health risks. Such benefits of absolute rank are also commonly found in nonhuman primates (Cowlishaw and Dunbar, 1991; Pusey et al., 1997; Snyder-Mackler et al., 2020; van Noordwijk and van Schaik, 1999). However, there is increasing evidence that relative access to resources, that is, one’s relative position in a socio-economic hierarchy, may also affect health. Across developed societies, there is causal evidence for a health gradient along socio-economic hierarchies, independent of absolute wealth or use of health-care services (Ecob and Davey Smith, 1999; Marmot et al., 1991; Oakes et al., 1973; Sorlie et al., 1995; Wolfson et al., 1993). In other words, these studies find that relative rank – how one compares to others – is a critical variable in determining health outcomes (Anderson et al., 2012; Luttmer, 2005; Snyder-Mackler et al., 2020; Wood et al., 2012).

The steepness of socio-economic hierarchies (i.e., income or wealth inequality) is also associated with both physical and mental health outcomes – including self-rated health, all-cause mortality, heart disease, respiratory disease, obesity, or homicide rates – independent of absolute wealth (Nowatzki, 2012; Pickett and Wilkinson, 2015; Wilkinson and Pickett, 2006). While these findings are hotly debated and tests of this inequality hypothesis have been critiqued on methodological grounds (Kondo et al., 2009; Lynch et al., 2004; Macinko et al., 2003; Subramanian and Kawachi, 2004; Wagstaff and van Doorslaer, 2000), a formal meta-analysis on studies controlling for individual wealth found significant associations between inequality and mortality or self-rated health in high-income countries (Kondo et al., 2009). Thus, relative position in a socio-economic hierarchy and the steepness of such hierarchies seem to matter for health.

The most cited mechanism for such hierarchy-health associations is that hierarchies cause psychosocial stress, which in turn leads to poorer health outcomes (Chen and Miller, 2013; Pickett and Wilkinson, 2015). Chronic stress leads to altered hypothalamic-pituitary-adrenal (HPA) axis function, including chronically elevated cortisol levels. Increased cortisol can cause neural atrophy, cardiovascular damage, obesity, or immunosuppression, all resulting in increased susceptibility to chronic and infectious disease (Aiello et al., 2018; García et al., 2017; Kunz-Ebrecht et al., 2004; Quon and McGrath, 2014; Sapolsky, 2004). In addition, submission in status competition and learned helplessness are associated with depression in humans and other primates (Hagen, 2011; Nesse, 2000; Stieglitz et al., 2014). Experimental studies in nonhuman primates show that dominance rank also affects gene expression and immune function (Snyder-Mackler et al., 2016; Tung et al., 2012). Related results in humans show that early life experiences and other forms of social stress are also associated with increases in inflammation and blunted immunological responses to cortisol (Aiello et al., 2018; Miller et al., 2014; Miller et al., 2011; Miller et al., 2009).

But why are hierarchies stressful or otherwise detrimental to health? An evolutionary-medicine perspective suggests that many detrimental health outcomes may result from adaptive tradeoffs, as fitness gains are prioritized over detrimental health outcomes, developmental constraints, as long-term negative health effects result from short-term accommodations to conditions during development, or from evolutionary mismatch, as our bodies struggle to deal with conditions atypical for our species (Eaton et al., 2002; Gluckman et al., 2016; Lea et al., 2017; Nesse and Williams, 1994; Wells et al., 2017). Given the consistent fitness benefits of high status (Stulp et al., 2016; Von Rueden and Jaeggi, 2016), and given that fitness is always relative, humans arguably have evolved motivations for status-striving that are independent of one’s absolute access to resources (Johnson et al., 2012; Shenk et al., 2016). Status-striving activates the stress response, and not just for low-rankers: depending on how rank is achieved and maintained, high- or low-ranking individuals may be more stressed (Abbott et al., 2003; Sapolsky, 2005). Crucial to who is stressed is the availability of social support, which can be as or even more important for health and fitness as rank per se (Sapolsky, 2005; Snyder-Mackler et al., 2020). Other factors primarily impact low-ranking individuals: in many primate (and some human) societies, subordinates are regularly subjected to aggression and intimidation by higher-ranking individuals (Silk, 2003), resulting in the lack of control and learned helplessness that often cause depression (Sapolsky, 2005; Sapolsky, 2004).

Greater inequality, that is, steeper hierarchies, entails more skewed payoff distributions and thus also favors more intense competition and risk-taking as behavioral strategies, especially among low-ranking individuals; this is argued to explain the persistent association between income inequality and homicide rates as most homicides result from escalated contests over status (Daly, 2016; Daly and Wilson, 1997). If skewed payoff distributions and oppression of low-rankers favor life-history strategies focused on short-term payoffs (i.e., ‘faster’ life-history strategies sensu Wells et al., 2017), this could also explain hierarchy-health associations via present-oriented decision-making at the expense of long-term health (Daly and Wilson, 1997; Griskevicius et al., 2011; Pepper and Nettle, 2014). These relationships are expected even when hierarchies are based on prestige, rather than dominance, since prestige-based hierarchies still correlate with social support, insulation against shocks, influence, sense of control, and access to mates (Gurven et al., 2000; Sugiyama and Sugiyama, 2003; von Rueden et al., 2014).

Thus, stress and negative health consequences due to socio-economic hierarchies can result from perpetual status-striving, unequal distribution of social support, lack of control and learned helplessness, intensified competition especially among low-ranking individuals, and from physiological accommodations to generally ‘faster’ life histories. In short, hierarchies may cause stress and affect health largely because individuals engage in competitive strategies that function to maximize fitness at the expense of health, while failure to succeed in such competition negatively affects mental health. In addition, if hierarchies constrain access to resources individuals may face developmental constraints, causing long-term tradeoffs that negatively impact health.

While such adjustments of physiology and behavior to the local competitive environment may in principle generalize to all human societies, the effects of hierarchy on health may be exacerbated in industrialized, high-income countries due to mismatch. Specifically, such societies could represent a mismatch with the ancestral environments in which our competitive strategies have evolved because (i) socio-economic hierarchies may be steeper and more rigid than was typical of our hunter-gatherer ancestors (Borgerhoff Mulder et al., 2009; Kaplan et al., 2009), and include features such as lack of kin support, limited upward mobility, structural violence, and systemic racism, all of which are well-known to negatively affect health (Gravlee, 2009; Sapolsky, 2004); and (ii) novel lifestyle factors such as obesogenic diets, lack of physical activity, and chronic inflammation turn previously relatively harmless responses, such as temporarily elevated blood pressure or depressed mood, into ‘mismatch’ diseases, such as hypertension, atherosclerosis, and major depression (Gurven et al., 2012; Kaplan et al., 2017; Miller and Raison, 2016; Stieglitz et al., 2015). In sum, mismatch diseases often arise when risk factors that used to elicit an adequate acute response become chronic problems, which could well be the case with modern socio-economic hierarchies, and interact with novel lifestyle factors that push our physiology into novel and unhealthy ranges.

In summary, humans, much like other primates, are sensitive to their relative rank and the distribution of fitness outcomes, and adjust behavior and physiological responses accordingly, resulting in negative influences of hierarchy on health. Several open questions remain, however. First, the inequality hypothesis remains hotly debated since parsing inequality from other correlated variables is difficult and requires careful statistical methods. Second, it remains unclear to what extent the observed health consequences of relative status and inequality in high-income countries (i) represent tradeoffs of potentially adaptive responses to lower relative rank and/or to inequality, or (ii) are caused by evolutionary mismatch, that is, novel conditions that cause maladaptive outcomes. If health consequences stem from tradeoffs from adaptive responses, then hierarchy should be associated with health in any population, independent of absolute access to resources. However, if the impacts of status and inequality are caused by evolutionary mismatch, then we would not expect detrimental effects on health in all societies, though we might observe related physiological responses in a subclinical range.

Small-scale societies practicing traditional subsistence lifestyles (henceforth ‘subsistence societies’) are an important test case for the universality of hierarchy-health associations as they generally have more informal, egalitarian hierarchies with relatively high individual autonomy and mobility (Borgerhoff Mulder et al., 2009; Kaplan et al., 2009; Mattison et al., 2016), and suffer from infectious rather than chronic disease as major sources of morbidity and mortality (Eaton et al., 1988; Gurven et al., 2007; Gurven et al., 2016; Gurven and Kaplan, 2007; Kaplan et al., 2017; Pontzer et al., 2018). Further, individuals in many subsistence societies have immune systems that are well calibrated by frequent exposure to pathogens and microbiota, and predominantly experience acute responses to infections (Blackwell et al., 2016a; McDade, 2005), unlike the chronic low-grade inflammation that links stress to hypertension, cardiovascular disease, and depression in high-income countries (Gurven et al., 2008a). Lastly, competition for mates and resources in such societies is usually fairly local, meaning that the scale at which relative rank and inequality should be measured is more recognizable than in large-scale modern societies with mass media, where people are simultaneously part of many hierarchies. Thus, subsistence societies may help us discern whether associations between hierarchy and health are caused by tradeoffs expected in any society, by evolutionary mismatch in modern, industrialized populations, or a combination of both.

Few studies have examined associations between rank or inequality and health in subsistence societies. Among Dominican farmers, socio-economic indicators were unrelated to cortisol levels whereas local influence was associated with lower cortisol (Decker, 2000). Among egalitarian Garisakang horticulturalists in Papua New Guinea, higher income coming from greater market exposure was associated with higher cortisol, whereas other locally relevant measures of wealth and status were not (Konečná and Urlacher, 2017). While results are mixed, there is some converging evidence that suggests market integration generates psychosocial stress in subsistence societies, arguably due to the threat of cultural loss and discrimination often experienced through contact with majority groups.

Among Tsimane forager-horticulturalists in Bolivia, it has been reported that traditional forms of status generally support a status-health gradient, but studies on income or wealth show mixed results. In a sample of four communities, politically influential men had lower cortisol and a lower incidence of respiratory infection, though there were also many null results, and higher income was associated with higher cortisol (von Rueden et al., 2014). In one village, women’s political influence was associated with improved growth and health outcomes for their children (Alami et al., 2020). Across 13 Tsimane villages, relative wealth was associated with better self-reported health (Undurraga et al., 2010); however, average self-reported health was lower in wealthier villages. In a larger sample of villages, relative income associated with lower body mass index (BMI) among individuals with smaller support networks (Brabec et al., 2007).

In terms of the relationship between inequality and health within communities, studies among Tsimane have also shown mixed results. One study found no association between income inequality and body fat (Godoy et al., 2005), but income inequality was associated with more negative emotions (Godoy et al., 2006). Greater wealth inequality did not associate with self-reported health in one study (Undurraga et al., 2010) but did associate with better self-reported health and lower self-reported stress in another, controlling for individual and village wealth level (Undurraga et al., 2016). Overall, these results provide mixed evidence for associations between inequality and health.

Here, we test for links between hierarchy and health among the Tsimane, expanding upon previous studies in several ways. First, we simultaneously assess the effects of within-community-relative wealth, mean community wealth, and community-level wealth inequality. Second, while previous studies have mostly relied on just one or two indirect health outcomes such as BMI, we include 13 different dependent variables (Table 1) capturing various health outcomes, including infectious disease morbidity, psychological well-being, social conflicts and connections, and other stressors. Third, we explicitly test whether these psychological and social variables and other stressors (henceforth ‘psychosocial variables’) mediate links between wealth and health as predicted if the adverse health effects of hierarchy occur through psychosocial stress. Note though that some of these ‘psychosocial’ variables may also be associated with health through more direct mechanisms, for example, non-social problems (food insecurity, debt, etc.) may cause stress but also represent poorer access to resources, which could affect health through energetic constraints. Fourth, we greatly increase the sample size relative to previous studies with inequality measured in 40 communities and wealth in 871 households, representing approximately one quarter of the Tsimane population (see Table 1, Figure 1—figure supplement 1). Thus, our study represents the most comprehensive test of hierarchy-health associations in a subsistence society.

We specifically test the following predictions stemming from the hypotheses that relative socio-economic position as well as the steepness of socio-economic hierarchies affect health and well-being, and that these effects are mediated by psychosocial stress.

• P1: Higher relative wealth is associated with better psychosocial and health outcomes.

• P2a: Greater wealth inequality is associated with worse psychosocial and health outcomes, and P2b: this should hold especially for low-rankers.

• P3: Psychosocial variables mediate wealth and inequality-health links found under P1 and P2.

Table 1 gives an overview of all variables used to test these predictions.

Study population

The Tsimane are a population of >16,000 Indigenous Amerindians living in >90 communities at the edge of the Amazon basin in lowland Bolivia. Tsimane communities consist of dispersed household clusters tied together by networks of kinship, cooperative production and consumption (Hooper et al., 2015; Jaeggi et al., 2016), as well as usually a school and soccer field. Community meetings convene to discuss and resolve important matters, including conflicts within the community. As such, we treat the community as the salient scale of status competition (Alami et al., 2020; von Rueden et al., 2018; von Rueden et al., 2008; von Rueden et al., 2019; von Rueden et al., 2014) and calculated relative wealth and inequality at this level.

The Tsimane remained relatively isolated from the larger Bolivian economy until the 1970s and still widely practice traditional subsistence (swidden horticulture, hunting, and fishing), which contributes >90% of their calories (Gurven et al., 2017; Kraft et al., 2018). Cattle, introduced by missionaries and ranchers, are owned by a small minority of Tsimane. Over the past few decades, wage labor opportunities with loggers or ranchers and produce sales in the local market towns of San Borja and Yucumo have been increasing, as have formal schooling, Spanish fluency, and access to modern amenities such as electricity and health care. The population thus exhibits quantifiable gradients of modernization (see Figure 1).

Figure 1 with 1 supplement see all

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In terms of morbidity and mortality, the Tsimane are characterized by high infectious disease burden, with respiratory infections as the leading cause of death at all ages (Gurven et al., 2007). Additionally, parasites such as helminths and giardia are highly prevalent (Blackwell et al., 2013; see also Table 2). These conditions result in frequent, acute immune responses (Blackwell et al., 2016a) but still a low incidence of chronic conditions such as hypertension or atherosclerosis, due to high levels of physical activity and other protective factors (Gurven et al., 2012; Gurven et al., 2016; Gurven et al., 2009; Kaplan et al., 2017).

Table 2

Infectious and parasitic diseases(CCS 1)	Diseases of the respiratory system (CCS 8)	Diseases of the digestive system (CCS 9)
Pediculosis due to Pediculus humanus capitis (B85.0)	Acute nasopharyngitis (common cold) (J00)	Intestinal helminthiasis (B82.0)
Tinea unguium (B35.1)	Acute streptococcal tonsillitis; unspecified (J03.00)	Infectious gastroenteritis and colitis (A09)
Candidiasis of vulva and vagina (B37.3)	Streptococcal pharyngitis (J02.0)	Dyspepsia (K30)
Pediculosis; unspecified (B85.2)	Acute upper respiratory infection; unspecified (J06.9)	Gastro-esophageal reflux disease with esophagitis (K21.0)
Superficial mycosis; unspecified (B36.9)	Acute bronchitis due to Mycoplasma pneumonia (J20.0)	Giardiasis (lambliasis) (A07.1)
Necatoriasis (B76.1)	Bronchopneumonia; unspecified organism (J18.0)	Gastritis; unspecified, without bleeding (K29.70)

We tested whether within-community relative wealth, community wealth, and community-level wealth inequality were associated with a broad range of psychological, social, and health outcomes in a large sample of households and communities in a relatively egalitarian small-scale subsistence society. Overall, our results showed substantial heterogeneity in terms of the direction and magnitude of associations between wealth, wealth inequality, and health, which contrasts with the more consistent socio-economic health gradients in high-income countries. Nevertheless, some findings supported an association between wealth or inequality and health outcomes, though these associations were not mediated by psychosocial factors.

Consistent with the prediction that higher relative position in a socio-economic hierarchy improves outcomes (P1), we found that household wealth relative to others in the community, capturing one’s rank within the local socio-economic hierarchy, was associated with lower blood pressure, and for juveniles, lower total morbidity and fewer respiratory infections. Relative household wealth was also generally associated with better health and psychosocial outcomes, but with more uncertainty in the posterior estimates, and for juveniles, relative household wealth was associated with increased gastrointestinal illness. Community mean wealth, capturing the absolute access to resources of households within that community, was also strongly associated with lower blood pressure for adults, but there was high uncertainty in estimates for other outcomes. Conversely, in support of P2a inequality was associated with higher blood pressure in adults and more respiratory disease in both adults and juveniles. It was also associated with lower BMI in juveniles, which in this energetically limited population likely represents a negative outcome. However, contra P2a inequality was also associated with lower levels of other infections (mostly fungal and yeast infections and lice) as well as fewer non-social problems, and there were several null results (Figures 2 and 3). Although most effect sizes were weak to moderate (most Cohen’s d < 0.2), these statistically weak results could still have significant biological and clinical impacts, as elaborated below.

The finding that higher inequality associated with greater likelihood of respiratory disease is perhaps the most significant in terms of well-being and biological fitness. Respiratory illness is the leading cause of mortality at all ages in this population (Gurven et al., 2007) and continues to be a major source of morbidity. The likelihood of being diagnosed with respiratory illness was predicted to differ greater than threefold, 8–28%, between the least and most unequal communities indicating substantial fitness costs to inequality. However, this effect of inequality appears to primarily affect wealthy individuals, bringing their prevalence up to the level of poorer individuals (Figure 6). In one Tsimane community (with relatively high average income compared to other communities), von Rueden et al., 2014 found lower risk of respiratory infection among influential men but no effect on respiratory infection (though trending in direction of higher risk) for men with higher income. With current data, we cannot determine the mechanism responsible for this association between inequality and respiratory disease. The association could reflect differences in immune function as suggested by other research on psychosocial influences on infectious disease (Aiello et al., 2018; Chen and Miller, 2013; McDade et al., 2016), despite a lack of evidence for psychosocial mediation here. The association between inequality and respiratory disease could also be spurious, despite our best efforts to control for relevant covariates, or it could reflect differences in exposure not captured by distance to town or community size (such as population density or frequency of contact with outsiders); in this context, it should be noted that effects of inequality on health are arguably only expected for outcomes for which there is a socio-economic gradient in the first place (Pickett and Wilkinson, 2015), which was not the case for respiratory disease here.

One of the strongest, most certain and most consistent associations of wealth (both household and community level) and inequality was with blood pressure, a major contributor to chronic disease in high-income countries. There was a clear socio-economic gradient in blood pressure within and between communities, and blood pressure was higher in more unequal communities. These effects were observed primarily in men. While most Tsimane are not hypertensive and do not have heart disease (Gurven et al., 2012; Kaplan et al., 2017), the predicted effects of wealth and inequality on blood pressure were substantial: systolic blood pressure was predicted to increase by 0.32 SD (i.e., 4.0 mmHg) and diastolic blood pressure by 0.40 SD (3.7 mmHg) in the most unequal compared to the most equal communities; conversely, wealth was protective such that the lowest blood pressures were predicted for people in the richest communities (7.4 mmHg systolic and 4.2 mmHg diastolic lower) and the richest households within communities (1.0 mmHg systolic and 3.1 mmHg diastolic lower). In high-income countries, such changes in blood pressure correspond to as much as a 10% change in the risk of major cardiovascular disease events (see Figure 2 in Ettehad et al., 2016). Among the Tsimane, it corresponds to as much as 40 years of age-related increases in blood pressure (Gurven et al., 2012). As novel, obesogenic foods enter the Tsimane diet (Kraft et al., 2018), market integration increases stress (Konečná and Urlacher, 2017; von Rueden et al., 2014), sanitation improves (Dinkel et al., 2020), and protective lifestyle factors like physical activity and helminth infections are changing (Gurven et al., 2013; Gurven et al., 2016), people in unequal communities, especially the poor (see Figure 6), may be at increasingly greater risk of chronic disease. Increases in blood pressure with modernization have also been reported in many other subsistence populations, and may partly stem from stress caused by integrating into a dominant culture (Dressler, 1999; Konečná and Urlacher, 2017). In this context, it is also worth noting that while the range of our village-level Gini values (0.15–0.43) was similar to that of income inequality among high-income countries (e.g., Denmark: 0.24; USA: 0.45), it was considerably lower than the range of wealth inequality in these countries (e.g., Japan: 0.55; USA: 0.81 [Nowatzki, 2012]). Thus, the reported associations between wealth/inequality and blood pressure may still be relatively harmless for the Tsimane, but lay the foundation for chronic disease under more mismatched conditions.

An alternative interpretation for some of these associations may be that causality is reversed, with poor health leading to less wealth or exacerbated inequality. On the face of it, this seems plausible for respiratory illness, which reduces work productivity. However, the fact that we see no direct association with wealth for adults, and only an association with inequality, seems to argue against such a mechanism. We did find an association between wealth and respiratory disease for juveniles – perhaps having sicker children puts some strain on wealth accumulation. For blood pressure, it is harder to imagine how reverse causality might occur since the blood pressure changes we observed are unlikely to affect wealth. Regardless, a limitation of our data is that we cannot determine the direction of causation given our cross-sectional design. Other confounds might also be possible, for example, if people preferentially assort by health or wealth by moving between villages.

Beyond respiratory disease and blood pressure, many associations were inconclusive. This heterogeneous picture may seem surprising given robust directional findings from studies in high-income countries, especially for SES health gradients. One possibility for this difference is that hierarchy-stress associations produce more consistent health effects in an epidemiological context characterized by chronic, rather than infectious, disease. As argued above, our finding that one of the most consistent wealth-health associations was with blood pressure would support this argument since hypertension is a risk factor for most chronic diseases and consistently associated with socio-economic position and inequality in high-income countries (Kim et al., 2008; Shahu et al., 2019), but unlikely to be harmful for most Tsimane (Gurven et al., 2012; Kaplan et al., 2017). However, there are also consistent associations between socio-economic position and infectious disease in high-income countries (Aiello et al., 2018; Snyder-Mackler et al., 2020), suggesting that epidemiological context alone does not account for inconsistent results.

Another possible source of heterogeneity is the scale at which relative wealth and inequality are measured. Literature reviews suggest that at an international scale as many as 83% of studies find associations, while in studies of areas the size of neighborhoods, only 45% find associations (Kondo et al., 2012; Pickett and Wilkinson, 2015; Wilkinson and Pickett, 2006). Pickett and Wilkinson, 2015 suggest that this heterogeneity reflects the scale at which inequality is perceived as most salient. Here, we assessed relative wealth and inequality at the scale of the residential community, a salient arena of daily cooperation and competition (Alami et al., 2020; Gurven et al., 2015; Gurven et al., 2008b; Jaeggi et al., 2016; von Rueden et al., 2008; von Rueden et al., 2014). This local level is also similar in scale to group-level hierarchies in other social species that show hierarchy-associated stress responses (Sapolsky, 2005; Snyder-Mackler et al., 2016; Tung et al., 2012). Furthermore, substituting community-relative wealth with wealth relative to the whole Tsimane population made little difference for results (Supplementary file 1a-1m), suggesting that the choice of scale within this relatively small-scale society did not matter. Modern technologies, such as television, may upset these comparisons and the functioning of hierarchy-related adaptations by making the global seem local; however, few Tsimane have regular access to television and other media. Nevertheless, it is possible that at least some Tsimane perceive inequality in reference to the local non-Tsimane population, or other regions of Bolivia, which was not captured by our study. Interacting with members of the dominant culture can be a source of stress (Dressler, 1999; Konečná and Urlacher, 2017; von Rueden et al., 2014), even if the Tsimane are arguably doing fairly well financially compared to other rural Bolivians (Godoy et al., 2007). Thus, we might not have been able to capture a relevant scale of comparison for some people, which could explain why associations at other scales were less consistent. However, this argument also applies to studies in high-income countries – where the relevant scales could be anything from neighborhoods to countries – and does not necessarily explain why results were inconsistent (as opposed to simply weaker) when measured at a less salient scale.

Finally, another explanation for heterogeneous associations is that our measure of household wealth may capture several distinct dimensions of socio-economic status, with partly orthogonal effects on health. On the one hand, greater wealth affords more respect and influence within communities, which is associated with lower cortisol and better health among the Tsimane (von Rueden et al., 2014) and elsewhere (Decker, 2000); this is likely the dimension captured by our subjective status data. On the other hand, household wealth is accumulated through participation in the market economy, which is associated with greater stress – higher cortisol, blood pressure – among the Tsimane (von Rueden et al., 2014) and elsewhere (Dressler, 1999; Konečná and Urlacher, 2017). The risks of different infectious diseases may also vary along these dimensions, with people who more frequently visit town and interact with outsiders possibly being more exposed to respiratory pathogens (Kaplan et al., 2020). Thus, household wealth may in part be inconsistently associated with health because of these opposing processes.

Several psychosocial variables were directly associated with health. Conflicts and depression were associated with lower BMI and blood pressure, perhaps indicating the effects of stress or lack of access to resources (depression is associated with low productivity among the Tsimane; Stieglitz et al., 2014). Depression and non-social problems were associated with worse self-rated health, again possibly via stress or direct effects of resource availability. However, associations between wealth or inequality and health outcomes were not mediated when including psychosocial variables in models (P3), and there was almost no evidence for indirect effects proceeding through these pathways. An obvious limitation is that our sample sizes for the mediation analysis were smaller than for other analyses (Supplementary file 1q-1s), though most were still large enough to capture any meaningful effect. It is also possible that our measures of psychosocial stress were inadequate, for example, a single urinary cortisol measure likely captures overall differences in cortisol excretion (Yehuda et al., 2003), but does not capture changes in diurnal cortisol patterns that are typically associated with chronic stress (García et al., 2017; Miller et al., 2007). But the lack of mediation found here may also point to more nuanced mechanisms such as changes in physical activity related to different subsistence strategies or other lifestyle factors not accounted for here. For subsistence societies experiencing socio-economic change, whether relative status increases, decreases, or has no effect on stress and health may depend on the status measure and its association with social support. A study of four Tsimane communities found that influential men with greater social support had lower cortisol (von Rueden et al., 2014), but higher cash income associated with higher cortisol (von Rueden et al., 2014; see also Konečná and Urlacher, 2017). In another study of the Tsimane, higher incomes predicted lower BMIs, unless individuals had relatively more social support (Brabec et al., 2007). It therefore remains unclear what mechanisms were responsible for the wealth-health associations found here, though hierarchy is known to affect immune function, and thereby infectious disease morbidity independently of stress and associated HPA activity (Aiello et al., 2018; Miller et al., 2011; Snyder-Mackler et al., 2020; Snyder-Mackler et al., 2016).

In sum, we present the most comprehensive test of hierarchy-health associations in a subsistence society to date. In support of an evolutionary argument that conceptualizes hierarchy-health effects as stemming from evolved reaction norms adjusting people’s behavior and physiology to the rank and local competitive regime they find themselves in (Daly and Wilson, 1997; Griskevicius et al., 2011; Pepper and Nettle, 2014), we found that wealth and inequality were associated with several health outcomes, though other associations were negligible or in the opposite direction to that predicted. In support of the argument that most hierarchy-health effects in high-income countries are caused by evolutionary mismatch (Sapolsky, 2004), we found that inequality was associated with blood pressure but in a range unlikely to affect health; however, this association could lead to hypertension, cardiovascular and metabolic disease as inequality further increases due to increased market integration and/or as novel foods and lifestyle factors enter the population (Gurven et al., 2012; Gurven et al., 2016; Kaplan et al., 2017; Kraft et al., 2018). Our study thus contributes to an evolutionary approach to public health that considers tradeoffs and mismatch as important links between socio-ecology, lifestyle, and health (Eaton et al., 2002; Wells et al., 2017).

All data and R code are available at https://doi.org/10.5281/zenodo.4567498 with any updates at https://github.com/adblackwell/wealthinequality (copy archived at https://archive.softwareheritage.org/swh:1:rev:da16ac6b20732fe1939478450d81ac32fdcce202).

1. 1. Jaeggi AV
   2. Blackwell AD
   3. von Rueden C
   4. Trumble BC
   5. Stieglitz J
   6. Garcia AR
   7. Kraft TS
   8. Beheim BA
   9. Hooper PL
   10. Kaplan H
   11. Gurven M

   (2021) Zenodo

   Data and code for "Do wealth and inequality associate with health in a small-scale subsistence society?".

   https://doi.org/10.5281/zenodo.4567498

1. 1. Abbott DH
   2. Keverne EB
   3. Bercovitch FB
   4. a SC
   5. Mendoza SP
   6. Saltzman W
   7. Snowdon CT
   8. Ziegler TE
   9. Banjevic M
   10. Garland T
   11. Sapolsky RM

   (2003) Are subordinates always stressed? A comparative analysis of rank differences in cortisol levels among primates

   Hormones and Behavior 43:67–82.

   https://doi.org/10.1016/S0018-506X(02)00037-5

   • Google Scholar
2. 1. Aiello AE
   2. Simanek AM
   3. Stebbins RC
   4. Dowd JB

   (2018)

   The Routledge International Handbook of Psychosocial Epidemiology

   Psychosocial influences on infectious disease, The Routledge International Handbook of Psychosocial Epidemiology, Routledge.

   • Google Scholar
3. 1. Alami S
   2. Von RC
   3. Seabright E
   4. Kraft TS
   5. Blackwell AD
   6. Stieglitz J
   7. Kaplan HS
   8. Gurven MD
   9. von Rueden CR
   10. Blackwell AD
   11. Seabright E
   12. Kraft TS
   13. Kaplan HS
   14. Gurven MD

   (2020) Mother’s social status is associated with child health in a horticulturalist population

   Proceedings of the Royal Society B 287:20192783.

   https://doi.org/10.1098/rspb.2019.2783

   • Google Scholar
4. 1. Amir D
   2. Valeggia C
   3. Srinivasan M
   4. Sugiyama LS
   5. Dunham Y

   (2019) Measuring subjective social status in children of diverse societies

   PLOS ONE 14:e0226550.

   https://doi.org/10.1371/journal.pone.0226550

   • PubMed
   • Google Scholar
5. 1. Anderson C
   2. Kraus MW
   3. Galinsky AD
   4. Keltner D

   (2012) The Local-Ladder Effect: Social Status and Subjective Well-Being

   Psychological Science 23:764–771.

   https://doi.org/10.1177/0956797611434537

   • PubMed
   • Google Scholar
6. 1. Baron RM
   2. Kenny DA

   (1986) The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations

   Journal of Personality and Social Psychology 51:1173–1182.

   https://doi.org/10.1037//0022-3514.51.6.1173

   • PubMed
   • Google Scholar
7. 1. Blackwell AD
   2. Martin M
   3. Kaplan H
   4. Gurven M

   (2013) Antagonism between two intestinal parasites in humans: The importance of co-infection for infection risk and recovery dynamics

   Proc R Soc B 280:20131671.

   https://doi.org/10.1098/rspb.2013.1671

   • Google Scholar
8. 1. Blackwell AD
   2. Trumble BC
   3. Suarez M
   4. Stieglitz J
   5. Beheim B
   6. Snodgrass JJ
   7. Kaplan H
   8. Gurven M

   (2016a) Immune function in Amazonian horticulturalists

   Annals of Human Biology 43:382–396.

   https://doi.org/10.1080/03014460.2016.1189963

   • Google Scholar
9. 1. Blackwell AD
   2. Urlacher SS
   3. Beheim BA
   4. von Rueden C
   5. Jaeggi A
   6. Stieglitz J
   7. Trumble BC
   8. Gurven MD
   9. Kaplan HS

   (2016b) Growth references for Tsimane forager-horticulturalists of the Bolivian Amazon

   American Journal of Physical Anthropology 162:441–461.

   https://doi.org/10.1002/ajpa.23128

   • PubMed
   • Google Scholar
10. Software

    1. Blackwell AD

    (2021) localgrowth, version 81ce799

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:20cec38a324e1e17b05a7622b3df22c20a09d230;origin=https://github.com/adblackwell/localgrowth;visit=swh:1:snp:0bd7ad9673aa22c3936a9fbe0bdc7c7f3329500d;anchor=swh:1:rev:81ce799bdc6da90d48e5ad8afd6ad0f3b19494d2
11. 1. Borgerhoff Mulder M
    2. Bowles S
    3. Hertz T
    4. Bell A
    5. Beise J
    6. Clark G
    7. Fazzio I
    8. Gurven M
    9. Hill K
    10. Hooper PL
    11. Irons W
    12. Kaplan H
    13. Leonetti D
    14. Low B
    15. Marlowe F
    16. Mcelreath R
    17. Naidu S
    18. Nolin D
    19. Piraino P
    20. Quinlan R
    21. Schniter E
    22. Sear R
    23. Shenk M
    24. Smith EA
    25. von Rueden C
    26. Wiessner P

    (2009) Intergenerational wealth transmission and the dynamics of inequality in small-scale societies

    Science 326:682–688.

    https://doi.org/10.1126/science.1178336

    • PubMed
    • Google Scholar
12. 1. Brabec M
    2. Godoy RA
    3. Reyes-García V
    4. Leonard WR

    (2007) BMI, income, and social capital in a native Amazonian society: Interaction between relative and community variables

    American Journal of Human Biology 19:459–474.

    https://doi.org/10.1002/ajhb.20606

    • PubMed
    • Google Scholar
13. 1. Bürkner PC

    (2017) brms : An R Package for Bayesian Multilevel Models Using Stan

    Journal of Statistical Software 80:v080.i01.

    https://doi.org/10.18637/jss.v080.i01

    • Google Scholar
14. 1. Chen E
    2. Miller GE

    (2013) Socioeconomic Status and Health: Mediating and Moderating Factors

    Annual Review of Clinical Psychology 9:723–749.

    https://doi.org/10.1146/annurev-clinpsy-050212-185634

    • PubMed
    • Google Scholar
15. 1. Cowlishaw G
    2. Dunbar R

    (1991) Dominance rank and mating success in male primates

    Animal Behaviour 41:1045–1056.

    https://doi.org/10.1016/S0003-3472(05)80642-6

    • Google Scholar
16. 1. Daly M
    2. Wilson M

    (1997)

    Life expectancy, economic inequality, homicide, and reproductive timing in Chicago neighborhoods

    British Medical Journal 314:1271–2174.

    • Google Scholar
17. Book

    1. Daly M

    (2016) Killing the Competition: Economic Inequality and Homicide

    New York: Routledge.

    https://doi.org/10.4324/9780203787748

    • Google Scholar
18. 1. Decker SA

    (2000) Salivary cortisol and social status among Dominican men

    Hormones and Behavior 38:29–38.

    https://doi.org/10.1006/HBEH.2000.1597

    • PubMed
    • Google Scholar
19. 1. Dinkel KA
    2. Costa ME
    3. Kraft TS
    4. Stieglitz J
    5. Cummings DK
    6. Gurven M
    7. Kaplan H
    8. Trumble BC

    (2020) Relationship of sanitation, water boiling, and mosquito nets to health biomarkers in a rural subsistence population

    American Journal of Human Biology 32:1–13.

    https://doi.org/10.1002/ajhb.23356

    • PubMed
    • Google Scholar
20. 1. Dressler WW

    (1999)

    Modernization, stress, and blood pressure: New directions in research

    Human Biology 71:583–605.

    • PubMed
    • Google Scholar
21. 1. Eaton SB
    2. Konner M
    3. Shostak M

    (1988) Stone agers in the fast lane: chronic degenerative diseases in evolutionary perspective

    The American Journal of Medicine 84:739–749.

    https://doi.org/10.1016/0002-9343(88)90113-1

    • PubMed
    • Google Scholar
22. 1. Eaton ES
    2. Strassman BI
    3. Nesse RM
    4. Neel J
    5. Ewald PW
    6. Williams GC
    7. Weder AB
    8. Eaton Stanley B
    9. Lindeberg S
    10. Konner MJ
    11. Mysterud I
    12. Cordain L

    (2002) Evolutionary health promotion

    Prev Med (Baltim) 34:109–118.

    https://doi.org/10.1006/pmed.2001.0876

    • Google Scholar
23. 1. Ecob R
    2. Davey Smith G

    (1999) Income and health: what is the nature of the relationship?

    Social Science & Medicine 48:693–705.

    https://doi.org/10.1016/S0277-9536(98)00385-2

    • Google Scholar
24. 1. Ettehad D
    2. Emdin CA
    3. Kiran A
    4. Anderson SG
    5. Callender T
    6. Emberson J
    7. Chalmers J
    8. Rodgers A
    9. Rahimi K

    (2016) Blood pressure lowering for prevention of cardiovascular disease and death: A systematic review and meta-analysis

    Lancet 387:957–967.

    https://doi.org/10.1016/S0140-6736(15)01225-8

    • PubMed
    • Google Scholar
25. 1. García AR
    2. Gurven M
    3. Blackwell AD

    (2017) A matter of perception: Perceived socio-economic status and cortisol on the island of Utila, Honduras

    American Journal of Human Biology 29:23031.

    https://doi.org/10.1002/ajhb.23031

    • PubMed
    • Google Scholar
26. Book

    1. Gelman A
    2. Carlin JB
    3. Stern HS
    4. Dunson DB
    5. Vehtari A
    6. Rubin DB

    (2013) Bayesian Data Analysis (3rd editio. ed)

    New York: Chapman and Hall.

    https://doi.org/10.1201/b16018

    • Google Scholar
27. 1. Gelman A
    2. Goodrich B
    3. Gabry J
    4. Vehtari A

    (2019) R-squared for Bayesian regression models

    The American Statistician 73:307–309.

    https://doi.org/10.1080/00031305.2018.1549100

    • Google Scholar
28. Book

    1. Gluckman PD
    2. Beedle AS
    3. Buklijas T
    4. Low F
    5. Hanson M

    (2016) Principles of Evolutionary Medicine

    Oxford: Oxford Unniversity Press.

    https://doi.org/10.1093/acprof:oso/9780199663927.001.0001

    • Google Scholar
29. 1. Godoy R
    2. Byron E
    3. Reyes-García V
    4. Vadez V
    5. Leonard WR
    6. Apaza L
    7. Huanca T
    8. Pérez E
    9. Wilkie D

    (2005) Income inequality and adult nutritional status: Anthropometric evidence from a pre-industrial society in the Bolivian Amazon

    Social Science & Medicine 61:907–919.

    https://doi.org/10.1016/j.socscimed.2005.01.007

    • PubMed
    • Google Scholar
30. 1. Godoy RA
    2. Reyes-García V
    3. McDade T
    4. Huanca T
    5. Leonard WR
    6. Tanner S
    7. Vadez V

    (2006) Does village inequality in modern income harm the psyche? Anger, fear, sadness, and alcohol consumption in a pre-industrial society

    Social Science & Medicine 63:359–372.

    https://doi.org/10.1016/j.socscimed.2006.01.021

    • Google Scholar
31. 1. Godoy R
    2. Reyes-Garcia V
    3. Huanca T
    4. Leonard WR
    5. McDade T
    6. Tanner S
    7. Seyfried C

    (2007) On the measure of income and the economic unimportance of social capital: Evidence from a native Amazonian society of farmers and foragers

    Journal of Anthropological Research 63:239–260.

    https://doi.org/10.3998/jar.0521004.0063.207

    • Google Scholar
32. 1. Gravlee CC

    (2009) How race becomes biology: Embodiment of social inequality

    American Journal of Physical Anthropology 139:47–57.

    https://doi.org/10.1002/ajpa.20983

    • PubMed
    • Google Scholar
33. 1. Griskevicius V
    2. Tybur JM
    3. Delton AW
    4. Robertson TE

    (2011) The influence of mortality and socioeconomic status on risk and delayed rewards: A life history theory approach

    Journal of Personality and Social Psychology 100:1015–1026.

    https://doi.org/10.1037/a0022403

    • PubMed
    • Google Scholar
34. 1. Gurven M
    2. Allen-Arave W
    3. Hill K
    4. Hurtado M

    (2000) It’s a Wonderful Life”: signaling generosity among the Ache of Paraguay

    Evolution and Human Behavior 21:263–282.

    https://doi.org/10.1016/s1090-5138(00)00032-5

    • PubMed
    • Google Scholar
35. 1. Gurven M
    2. Kaplan HS

    (2007) Longevity among hunter-gatherers: a cross-cultural examination

    Population and Development Review 33:321–365.

    https://doi.org/10.1111/j.1728-4457.2007.00171.x

    • Google Scholar
36. 1. Gurven M
    2. Kaplan H
    3. Zelada Supa A

    (2007) Mortality experience of Tsimane Amerindians of Bolivia: Regional variation and temporal trends

    American Journal of Human Biology 19:376–398.

    https://doi.org/10.1002/ajhb

    • Google Scholar
37. 1. Gurven M
    2. Kaplan H
    3. Winking J
    4. Finch C
    5. Crimmins EM

    (2008a) Aging and inflammation in two epidemiological worlds

    The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences 63:196–199.

    https://doi.org/10.1093/gerona/63.2.196

    • Google Scholar
38. 1. Gurven M
    2. Zanolini A
    3. Schniter E

    (2008b) Culture sometimes matters: Intra-cultural variation in pro-social behavior among Tsimane Amerindians

    Journal of Economic Behavior & Organization 67:587–607.

    https://doi.org/10.1016/j.jebo.2007.09.005

    • Google Scholar
39. 1. Gurven MD
    2. Kaplan H
    3. Winking J
    4. Eid Rodriguez D
    5. Vasunilashorn S
    6. Kim JK
    7. Finch C
    8. Crimmins E

    (2009) Inflammation and infection do not promote arterial aging and cardiovascular disease risk factors among lean horticulturalists

    PLOS ONE 4:e6590.

    https://doi.org/10.1371/journal.pone.0006590

    • PubMed
    • Google Scholar
40. 1. Gurven M
    2. Blackwell AD
    3. Rodríguez DE
    4. Stieglitz J
    5. Kaplan H

    (2012) Does blood pressure inevitably rise with age? Longitudinal evidence among forager-horticulturalists

    Hypertension 60:25–33.

    https://doi.org/10.1161/HYPERTENSIONAHA.111.189100

    • PubMed
    • Google Scholar
41. 1. Gurven M
    2. Jaeggi AV
    3. Kaplan H
    4. Cummings D

    (2013) Physical activity and modernization among bolivian amerindians

    PLOS ONE 8:e55679.

    https://doi.org/10.1371/journal.pone.0055679

    • PubMed
    • Google Scholar
42. 1. Gurven M
    2. Jaeggi A
    3. Von Rueden CR
    4. Hooper PL
    5. Kaplan H

    (2015) Does market integration buffer risk, erode traditional sharing practices, and increase inequality? A test among Bolivian forager-farmers

    Human Ecology 43:515–530.

    https://doi.org/10.1007/s10745-015-9764-y

    • PubMed
    • Google Scholar
43. 1. Gurven MD
    2. Trumble BC
    3. Stieglitz J
    4. Blackwell AD
    5. Michalik DE
    6. Finch CE
    7. Kaplan HS

    (2016) Cardiovascular disease and type 2 diabetes in evolutionary perspective: A critical role for helminths?

    Evol Med Public Heal 2016:338–357.

    https://doi.org/10.1093/emph/eow028

    • PubMed
    • Google Scholar
44. 1. Gurven M
    2. Kaplan H
    3. Stieglitz J
    4. Trumble B
    5. Blackwell AD
    6. Beheim B
    7. Hooper P

    (2017) The Tsimane Health and Life History Project: Integrating anthropology and biomedicine

    Evolutionary Anthropology 26:54–73.

    https://doi.org/10.1002/evan.21515

    • PubMed
    • Google Scholar
45. 1. Gurven M
    2. Kraft TS
    3. Alami S
    4. Adrian JC
    5. Linares EC
    6. Cummings D
    7. Rodriguez DE
    8. Hooper PL
    9. Jaeggi AV
    10. Gutierrez RQ
    11. Suarez IM
    12. Seabright E
    13. Kaplan H
    14. Stieglitz J
    15. Trumble B

    (2020) Rapidly declining body temperature in a tropical human population

    Science Advances 6:eabc6599.

    https://doi.org/10.1126/sciadv.abc6599

    • PubMed
    • Google Scholar
46. 1. Hagen EH

    (2011) Evolutionary theories of depression: A critical review

    Canadian Journal of Psychiatry. Revue Canadienne de Psychiatrie 56:716–726.

    https://doi.org/10.1177/070674371105601203

    • PubMed
    • Google Scholar
47. 1. Hooper PL
    2. Gurven M
    3. Winking J
    4. Kaplan HS

    (2015)

    Inclusive fitness and differential productivity across the life course determine intergenerational transfers in a small-scale human society

    Proc R Soc B - Biol Sci 282:20142808.

    • Google Scholar
48. 1. Jaeggi A
    2. Hooper PL
    3. Beheim BA
    4. Kaplan HS
    5. Gurven MD

    (2016) Reciprocal exchange patterned by market forces helps explain cooperation in a small-scale society

    Current Biology 26:2180–2187.

    https://doi.org/10.1016/j.cub.2016.06.019

    • PubMed
    • Google Scholar
49. Software

    1. Jaeggi AV
    2. Blackwell AD
    3. von Rueden C
    4. Trumble BC
    5. Stieglitz J
    6. Garcia AR
    7. Kraft T
    8. Beheim BA
    9. Hooper PL
    10. Kaplan H
    11. Gurven MD

    (2021) wealthinequality, version da16ac6

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:363447d08e95b3aa00b9a2a624e17729c0202e5e;origin=https://github.com/adblackwell/wealthinequality;visit=swh:1:snp:d690509ede234bc49c3501922627641de5630f85;anchor=swh:1:rev:da16ac6b20732fe1939478450d81ac32fdcce202
50. 1. Johnson SL
    2. Leedom LJ
    3. Muhtadie L

    (2012) The dominance behavioral system and psychopathology: Evidence from self-report, observational, and biological studies

    Psychological Bulletin 138:692–743.

    https://doi.org/10.1037/a0027503

    • PubMed
    • Google Scholar
51. 1. Kaplan HS
    2. Hooper P
    3. Gurven M

    (2009) The evolutionary and ecological roots of human social organization

    Philos Trans R Soc B - Biol Sci 364:3289–3299.

    https://doi.org/10.1098/rstb.2009.0115

    • PubMed
    • Google Scholar
52. 1. Kaplan H
    2. Thompson RC
    3. Trumble BC
    4. Wann LS
    5. Allam AH
    6. Beheim B
    7. Frohlich B
    8. Sutherland ML
    9. Sutherland JD
    10. Stieglitz J
    11. Rodriguez DE
    12. Michalik DE
    13. Rowan CJ
    14. Lombardi GP
    15. Bedi R
    16. Garcia AR
    17. Min JK
    18. Narula J
    19. Finch CE
    20. Gurven M
    21. Thomas GS

    (2017) Coronary atherosclerosis in indigenous South American Tsimane: a cross-sectional cohort study

    Lancet 15:1–9.

    https://doi.org/10.1016/S0140-6736(17)30752-3

    • Google Scholar
53. 1. Kaplan HS
    2. Trumble BC
    3. Stieglitz J
    4. Mendez Mamany R
    5. Gutierrez Cayuba M
    6. Maito Moye L
    7. Alami S
    8. Kraft T
    9. Quispe Gutierrez R
    10. Copajira Adrian J
    11. Thompson RC
    12. Thomas GS
    13. Michalik DE
    14. Eid Rodriguez D
    15. Gurven MD

    (2020) Voluntary collective isolation as a best response to COVID-19 for indigenous populations? A case study and protocol from the Bolivian Amazon

    Lancet 6736:1–8.

    https://doi.org/10.1016/S0140-6736(20)31104-1

    • Google Scholar
54. 1. Kim D
    2. Kawachi I
    3. Vander HS
    4. Ezzati M

    (2008) Is inequality at the heart of it? Cross-country associations of income inequality with cardiovascular diseases and risk factors

    Social Science & Medicine 66:1719–1732.

    https://doi.org/10.1016/j.socscimed.2007.12.030

    • Google Scholar
55. 1. Kondo N
    2. Sembajwe G
    3. Kawachi I
    4. van Dam RM
    5. Subramanian S
    6. Yamagata Z

    (2009) Income inequality, mortality, and self rated health: meta-analysis of multilevel studies

    BMJ 339:b4471.

    https://doi.org/10.1136/bmj.b4471

    • PubMed
    • Google Scholar
56. 1. Kondo N
    2. van Dam RM
    3. Sembajwe G
    4. Subramanian S
    5. Kawachi I
    6. Yamagata Z

    (2012) Income inequality and health: The role of population size, inequality threshold, period effects and lag effects

    Journal of Epidemiology and Community Health 66:e11.

    https://doi.org/10.1136/jech-2011-200321

    • PubMed
    • Google Scholar
57. 1. Konečná M
    2. Urlacher SS

    (2017) Male social status and its predictors among Garisakang forager-horticulturalists of lowland Papua New Guinea

    Evolution and Human Behavior 38:789–797.

    https://doi.org/10.1016/J.EVOLHUMBEHAV.2017.05.005

    • Google Scholar
58. 1. Kraft TS
    2. Stieglitz J
    3. Trumble BC
    4. Martin M
    5. Kaplan H
    6. Gurven M

    (2018) Nutrition transition in 2 lowland Bolivian subsistence populations

    The American Journal of Clinical Nutrition 108:1–13.

    https://doi.org/10.1093/ajcn/nqy250

    • PubMed
    • Google Scholar
59. 1. Kreft IGG
    2. de Leeuw J
    3. Aiken LS

    (1995) The Effect of Different Forms of Centering in Hierarchical Linear Models

    Multivariate Behavioral Research 30:1–21.

    https://doi.org/10.1207/s15327906mbr3001_1

    • PubMed
    • Google Scholar
60. 1. Kunz-Ebrecht SR
    2. Kirschbaum C
    3. Marmot M
    4. Steptoe A

    (2004) Differences in cortisol awakening response on work days and weekends in women and men from the Whitehall II cohort

    Psychoneuroendocrinology 29:516–528.

    https://doi.org/10.1016/S0306-4530(03)00072-6

    • PubMed
    • Google Scholar
61. 1. Lea AJ
    2. Tung J
    3. Archie EA
    4. Alberts SC

    (2017) Developmental plasticity

    Evol Med Public Heal 2017:162–175.

    https://doi.org/10.1093/emph/eox019

    • Google Scholar
62. Software

    1. Lüdecke D

    (2021) Sjstats: Statistical functions for regression models

    Zenodo.

    https://doi.org/10.5281/zenodo.1284473
63. 1. Luttmer EFP

    (2005) Neighbors as negatives: Relative earnings and well-being

    The Quarterly Journal of Economics 120:963–1002.

    https://doi.org/10.1162/003355305774268255

    • Google Scholar
64. 1. Lynch J
    2. Smith GD
    3. Harper S
    4. Hillemeier M
    5. Ross N
    6. a KG
    7. Wolfson M

    (2004)

    Is income inequality a determinant of population health? Part 1. A systematic review

    The Milbank Quarterly 82:5–99.

    • Google Scholar
65. 1. Macinko JA
    2. Shi L
    3. Starfield B
    4. Wulu JT

    (2003) Income inequality and health: A critical review of the literature

    Medical Care Research and Review 60:407–452.

    https://doi.org/10.1177/1077558703257169

    • PubMed
    • Google Scholar
66. 1. MacKinnon DP
    2. Fairchild AJ
    3. Fritz MS

    (2007) Mediation analysis

    Annual Review of Psychology 58:593–614.

    https://doi.org/10.1146/annurev.psych.58.110405.085542

    • PubMed
    • Google Scholar
67. 1. Marmot MG
    2. Stansfeld S
    3. Patel C
    4. North F
    5. Head J
    6. White I
    7. Brunner E
    8. Feeney A
    9. Smith GD

    (1991) Health inequalities among British civil servants: the Whitehall II study

    Lancet 337:1387–1393.

    https://doi.org/10.1016/0140-6736(91)93068-K

    • PubMed
    • Google Scholar
68. 1. Mattison SM
    2. Smith EA
    3. Shenk MK
    4. Cochrane EE

    (2016) The evolution of inequality

    Evol Anthropol Issues, News, Rev 25:184–199.

    https://doi.org/10.1002/evan.21491

    • Google Scholar
69. 1. McDade TW

    (2005) THE ECOLOGIES OF HUMAN IMMUNE FUNCTION

    Annual Review of Anthropology 34:495–521.

    https://doi.org/10.1146/annurev.anthro.34.081804.120348

    • Google Scholar
70. 1. McDade TW
    2. Georgiev A
    3. Kuzawa CW

    (2016) Trade-offs between acquired and innate immune defenses in humans

    Evol Med Public Heal 2016:1–16.

    https://doi.org/10.1093/emph/eov033

    • PubMed
    • Google Scholar
71. Book

    1. McElreath R

    (2020)

    Statistical Rethinking: A Bayesian Course with Examples in R and Stan (2nd editio. ed)

    Boca Raton, FL: CRC Press.

    • Google Scholar
72. 1. Miller GE
    2. Chen E
    3. Zhou ES

    (2007) If it goes up, must it come down? Chronic stress and the hypothalamic-pituitary-adrenocortical axis in humans

    Psychological Bulletin 133:25–45.

    https://doi.org/10.1037/0033-2909.133.1.25

    • PubMed
    • Google Scholar
73. 1. Miller GE
    2. Chen E
    3. Fok AK
    4. Walker H
    5. Lim A
    6. Nicholls EF
    7. Cole S
    8. Kobor MS

    (2009) Low early-life social class leaves a biological residue manifested by decreased glucocorticoid and increased proinflammatory signaling

    PNAS 106:14716–14721.

    https://doi.org/10.1073/pnas.0902971106

    • PubMed
    • Google Scholar
74. 1. Miller GE
    2. Chen E
    3. Parker KJ

    (2011) Psychological stress in childhood and susceptibility to the chronic diseases of aging: moving toward a model of behavioral and biological mechanisms

    Psychological Bulletin 137:959–997.

    https://doi.org/10.1037/a0024768

    • PubMed
    • Google Scholar
75. 1. Miller GE
    2. Murphy MLM
    3. Cashman R
    4. Ma R
    5. Ma J
    6. Arevalo JMG
    7. Kobor MS
    8. Cole SW

    (2014) Greater inflammatory activity and blunted glucocorticoid signaling in monocytes of chronically stressed caregivers

    Brain, Behavior, and Immunity 41:191–199.

    https://doi.org/10.1016/j.bbi.2014.05.016

    • PubMed
    • Google Scholar
76. 1. Miller AH
    2. Raison CL

    (2016) The role of inflammation in depression: From evolutionary imperative to modern treatment target

    Nature Reviews. Immunology 16:22–34.

    https://doi.org/10.1038/nri.2015.5

    • PubMed
    • Google Scholar
77. Book

    1. Nesse RM
    2. Williams GC

    (1994)

    Why We Get Sick: The New Science of Darwinian Medicine

    New York: Vintage.

    • Google Scholar
78. 1. Nesse RM

    (2000) Is depression an adaptation

    Archives of General Psychiatry 57:14–20.

    https://doi.org/10.1001/archpsyc.57.1.14

    • PubMed
    • Google Scholar
79. 1. Norton MI

    (2013) All ranks are local: Why humans are both (painfully) aware and (surprisingly) unaware of their lot in life

    Psychological Inquiry 24:124–125.

    https://doi.org/10.1080/1047840X.2013.794689

    • Google Scholar
80. 1. Nowatzki NR

    (2012) Wealth inequality and health: A political economy perspective

    Int J Heal Serv 42:403–424.

    https://doi.org/10.2190/HS.42.3.c

    • Google Scholar
81. 1. Oakes TW
    2. Syme SL
    3. Feldman R
    4. Friedman GD
    5. Siegelaub AB
    6. Collen MF

    (1973) Social factors in newly discovered elevated blood pressure

    Journal of Health and Social Behavior 14:198–204.

    https://doi.org/10.2307/2137111

    • PubMed
    • Google Scholar
82. Book

    1. Pepper G
    2. Nettle D

    (2014) Socioeconomic disparities in health behaviour: An evolutionary perspective

    In: Gibson MA, Lawson DW, editors. Applied Evolutionary Anthropology. New York: Springer. pp. 225–243.

    https://doi.org/10.1007/978-1-4939-0280-4

    • Google Scholar
83. 1. Pickett KE
    2. Wilkinson RG

    (2015) Income inequality and health: A causal review

    Social Science & Medicine 128:316–326.

    https://doi.org/10.1016/j.socscimed.2014.12.031

    • PubMed
    • Google Scholar
84. 1. Pontzer H
    2. Wood BM
    3. Raichlen DA

    (2018) Hunter-gatherers as models in public health

    Obesity Reviews 19:24–35.

    https://doi.org/10.1111/obr.12785

    • Google Scholar
85. 1. Pusey AE
    2. Williams J
    3. Goodall J

    (1997) The influence of dominance rank on the reproductive success of female chimpanzees

    Science 277:828–831.

    https://doi.org/10.1126/science.277.5327.828

    • PubMed
    • Google Scholar
86. 1. Quon EC
    2. McGrath JJ

    (2014) Subjective socioeconomic status and adolescent health: A meta-analysis

    Heal Psychol 33:433–447.

    https://doi.org/10.1037/a0033716

    • PubMed
    • Google Scholar
87. Book

    1. Sapolsky RM

    (2004)

    Why Zebras Don’t Get Ulcers (3rd ed)

    New York: Holt.

    • Google Scholar
88. 1. Sapolsky RM

    (2005) The influence of social hierarchy on primate health

    Science 308:648–652.

    https://doi.org/10.1126/science.1106477

    • PubMed
    • Google Scholar
89. 1. Schultz AF

    (2019) Status determinants, social incongruity and economic transition: Gender, relative material wealth and heterogeneity in the cultural lifestyle of forager-horticulturalists

    PLOS ONE 14:e0220432.

    https://doi.org/10.1371/journal.pone.0220432

    • PubMed
    • Google Scholar
90. 1. Shahu A
    2. Herrin J
    3. Dhruva SS
    4. Desai NR
    5. Davis BR
    6. Krumholz HM
    7. Spatz ES

    (2019) Disparities in Socioeconomic Context and Association With Blood Pressure Control and Cardiovascular Outcomes in ALLHAT

    Journal of the American Heart Association 8:e012277.

    https://doi.org/10.1161/JAHA.119.012277

    • PubMed
    • Google Scholar
91. 1. Shenk MK
    2. Kaplan HS
    3. Hooper PL

    (2016) Status competition, inequality, and fertility: Implications for the demographic transition

    Phil Trans R Soc B 371:20150150.

    https://doi.org/10.1098/rstb.2015.0150

    • Google Scholar
92. 1. Silk JB

    (2003) Practice random acts of aggression and senseless acts of intimidation: The logic of status contests in social groups

    Evol Anthropol Issues, News, Rev 11:221–225.

    https://doi.org/10.1002/evan.10038

    • Google Scholar
93. 1. Snyder-Mackler N
    2. Sanz J
    3. Kohn JN
    4. Brinkworth JF
    5. Morrow S
    6. Shaver AO
    7. Grenier JC
    8. Pique-Regi R
    9. Johnson ZP
    10. Wilson ME
    11. Barreiro LB
    12. Tung J

    (2016) Social status alters immune regulation and response to infection in macaques

    Science 354:1041–1045.

    https://doi.org/10.1126/science.aah3580

    • PubMed
    • Google Scholar
94. 1. Snyder-Mackler N
    2. Burger JR
    3. Gaydosh L
    4. Belsky DW
    5. Noppert GA
    6. Campos FA
    7. Bartolomucci A
    8. Yang YC
    9. Aiello AE
    10. Rand AO
    11. Harris KM
    12. Shively CA
    13. Alberts SC
    14. Tung J

    (2020) Social determinants of health and survival in humans and other animals

    Science 368:eaax9553.

    https://doi.org/10.1126/science.aax9553

    • Google Scholar
95. 1. Sorlie PD
    2. Backlund E
    3. Keller JB

    (1995) US mortality by economic, demographic, and social characteristics: the National Longitudinal Mortality Study

    American Journal of Public Health 85:949–956.

    https://doi.org/10.2105/AJPH.85.7.949

    • PubMed
    • Google Scholar
96. Book

    1. Stieglitz J
    2. Jaeggi A
    3. Blackwell AD
    4. Trumble BC
    5. Gurven M
    6. Kaplan HS

    (2014) Work to live and live to work: Productivity, transfers, and psychological well-being in adulthood and old age

    In: Weinstein M, Lane MA, editors. Sociality, Hierarchy, Health: Comparative Biodemography. National Academies Press. pp. 195–220.

    https://doi.org/10.17226/18822

    • Google Scholar
97. 1. Stieglitz J
    2. Trumble BC
    3. Thompson ME
    4. Blackwell AD
    5. Kaplan H
    6. Gurven M

    (2015) Depression as sickness behavior? A test of the host defense hypothesis in a high pathogen population

    Brain, Behavior, and Immunity 49:130–139.

    https://doi.org/10.1016/J.BBI.2015.05.008

    • PubMed
    • Google Scholar
98. 1. Stulp G
    2. Sear R
    3. Schaffnit SB
    4. Mills MC
    5. Barrett L

    (2016) The Reproductive Ecology of Industrial Societies, Part II: The Association between Wealth and Fertility

    Human Nature 27:445–470.

    https://doi.org/10.1007/s12110-016-9272-9

    • PubMed
    • Google Scholar
99. 1. Subramanian S
    2. Kawachi I

    (2004) Income inequality and health: What have we learned so far?

    Epidemiologic Reviews 26:78–91.

    https://doi.org/10.1093/epirev/mxh003

    • PubMed
    • Google Scholar
100. 1. Sugiyama LS
     2. Sugiyama MS

     (2003) Social roles, prestige, and health risk: Social niche specialization as a risk-buffering strategy

     Human Nature 14:165–190.

     https://doi.org/10.1007/s12110-003-1002-4

     • PubMed
     • Google Scholar
101. 1. Trumble BC
     2. Stieglitz J
     3. Jaeggi A
     4. Beheim BA
     5. Schwartz M
     6. Seabright E
     7. Cummings D
     8. Kaplan HS
     9. Gurven M

     (2018) Parental hormones are associated with crop loss and family sickness following catastrophic flooding in lowland Bolivia

     Physiology & Behavior 193:101–107.

     https://doi.org/10.1016/j.physbeh.2018.02.028

     • PubMed
     • Google Scholar
102. 1. Tung J
     2. Barreiro LB
     3. Johnson ZP
     4. Hansen KD
     5. Michopoulos V
     6. Toufexis D
     7. Michelini K
     8. Wilson ME
     9. Gilad Y

     (2012) Social environment is associated with gene regulatory variation in the rhesus macaque immune system

     PNAS 109:6490–6495.

     https://doi.org/10.1073/pnas.1202734109

     • PubMed
     • Google Scholar
103. 1. Undurraga EA
     2. Nyberg C
     3. Eisenberg DTA
     4. Magvanjav O
     5. Reyes-García V
     6. Huanca T
     7. Leonard WR
     8. McDade TW
     9. Tanner S
     10. Vadez V
     11. Godoy R

     (2010) Individual wealth rank, community wealth inequality, and self-reported adult poor health: A test of hypotheses with panel data (2002-2006) from native amazonians, bolivia

     Medical Anthropology Quarterly 24:522–548.

     https://doi.org/10.1111/j.1548-1387.2010.01121.x

     • PubMed
     • Google Scholar
104. 1. Undurraga EA
     2. Nica V
     3. Zhang R
     4. Mensah IC
     5. Godoy RA

     (2016) Individual health and the visibility of village economic inequality: Longitudinal evidence from native Amazonians in Bolivia

     Economics and Human Biology 23:18–26.

     https://doi.org/10.1016/j.ehb.2016.06.004

     • PubMed
     • Google Scholar
105. 1. van Noordwijk MA
     2. van Schaik CP

     (1999) The effects of dominance rank and group size on female lifetime reproductive success in wild long-tailed macaques, Macaca fascicularis

     Primates; Journal of Primatology 40:105–130.

     https://doi.org/10.1007/BF02557705

     • PubMed
     • Google Scholar
106. 1. von Rueden C
     2. Gurven M
     3. Kaplan H

     (2008) The multiple dimensions of male social status in an Amazonian society

     Evolution and Human Behavior 29:402–415.

     https://doi.org/10.1016/j.evolhumbehav.2008.05.001

     • PubMed
     • Google Scholar
107. 1. von Rueden CR
     2. Trumble BC
     3. Emery Thompson M
     4. Stieglitz J
     5. Hooper PL
     6. Blackwell AD
     7. Kaplan HS
     8. Gurven M

     (2014) Political influence associates with cortisol and health among egalitarian forager-farmers

     Evolution, Medicine, and Public Health 2014:122–133.

     https://doi.org/10.1093/emph/eou021

     • PubMed
     • Google Scholar
108. 1. Von Rueden CR
     2. Jaeggi A

     (2016) Men’s status and reproductive success in 33 nonindustrial societies: Effects of subsistence, marriage system, and reproductive strategy

     PNAS 113:10824–10829.

     https://doi.org/10.1073/pnas.1606800113

     • PubMed
     • Google Scholar
109. 1. von Rueden C
     2. Alami S
     3. Kaplan H
     4. Gurven M

     (2018) Sex differences in political leadership in an egalitarian society

     Evolution and Human Behavior 39:402–411.

     https://doi.org/10.1016/J.EVOLHUMBEHAV.2018.03.005

     • PubMed
     • Google Scholar
110. 1. von Rueden CR
     2. Redhead D
     3. O’Gorman R
     4. Kaplan H
     5. Gurven M

     (2019) The dynamics of men’s cooperation and social status in a small-scale society

     Proc R Soc B Biol Sci 286:20191367.

     https://doi.org/10.1098/rspb.2019.1367

     • Google Scholar
111. 1. Wagstaff A
     2. van Doorslaer E

     (2000) Income inequality and health: What does the literature tell us?

     Annual Review of Public Health 21:543–567.

     https://doi.org/10.1111/j.1467-8276.2007.01012.x

     • PubMed
     • Google Scholar
112. 1. Wells J
     2. Sear R
     3. Stearns S
     4. Nesse R
     5. Johnstone R

     (2017) Evolutionary public health: introducing the concept

     Lancet 390:500–509.

     https://doi.org/10.1016/S0140-6736(17)30572-X

     • PubMed
     • Google Scholar
113. 1. Wilkinson RG
     2. Pickett KE

     (2006) Income inequality and population health: A review and explanation of the evidence

     Social Science & Medicine 62:1768–1784.

     https://doi.org/10.1016/j.socscimed.2005.08.036

     • PubMed
     • Google Scholar
114. 1. Wolfson M
     2. Rowe G
     3. Gentleman JF
     4. Tomiak M

     (1993) Career earnings and death: A longitudinal analysis of older Canadian men

     Journal of Gerontology 48:S167–S179.

     https://doi.org/10.1093/geronj/48.4.S167

     • Google Scholar
115. 1. Wood AM
     2. Boyce CJ
     3. Moore SC
     4. Brown GDA

     (2012) An evolutionary based social rank explanation of why low income predicts mental distress: A 17 year cohort study of 30,000 people

     Journal of Affective Disorders 136:882–888.

     https://doi.org/10.1016/j.jad.2011.09.014

     • PubMed
     • Google Scholar
116. 1. Woolard K
     2. Garcia AR
     3. Stieglitz J
     4. Kaplan HS
     5. Gurven M
     6. Trumble BC

     (2019)

     Do perceptions of social status and material wealth associate with biomarkers of health in a relatively egalitarian population?

     American Journal of Human Biology 31:2019.

     • Google Scholar
117. 1. Yehuda R
     2. Halligan SL
     3. Yang RK
     4. Guo LS
     5. Makotkine I
     6. Singh B
     7. Pickholtz D

     (2003) Relationship between 24-hour urinary-free cortisol excretion and salivary cortisol levels sampled from awakening to bedtime in healthy subjects

     Life Sciences 73:349–358.

     https://doi.org/10.1016/S0024-3205(03)00286-8

     • PubMed
     • Google Scholar

Author details

1. Adrian V Jaeggi

   1. Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland
   2. Department of Anthropology, Emory University, Atlanta, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Aaron D Blackwell

   For correspondence

   adrian.jaeggi@iem.uzh.ch

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1695-0388

2. Aaron D Blackwell

   Department of Anthropology, Washington State University, Pulman, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Adrian V Jaeggi

   For correspondence

   aaron.blackwell@wsu.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5871-9865

3. Christopher von Rueden

   Jepson School of Leadership Studies, University of Richmond, Richmond, United States

   Contribution

   Data curation, Funding acquisition, Project administration, Writing – original draft

   Competing interests

   No competing interests declared

4. Benjamin C Trumble

   1. School of Human Evolution and Social Change, Arizona State University, Tempe, United States
   2. Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, United States

   Contribution

   Data curation, Funding acquisition, Project administration, Writing – original draft

   Competing interests

   No competing interests declared

5. Jonathan Stieglitz

   Institute for Advanced Study in Toulouse, Toulouse, France

   Contribution

   Data curation, Funding acquisition, Project administration, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5985-9643

6. Angela R Garcia

   1. Department of Anthropology, Emory University, Atlanta, United States
   2. School of Human Evolution and Social Change, Arizona State University, Tempe, United States
   3. Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, United States

   Contribution

   Data curation, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6685-5533

7. Thomas S Kraft

   Department of Anthropology, University of California, Santa Barbara, Santa Barbara, United States

   Contribution

   Data curation, Writing – original draft

   Competing interests

   No competing interests declared

8. Bret A Beheim

   Department of Human Behavior, Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

9. Paul L Hooper

   1. Economic Science Institute, Chapman University, Irvine, United States
   2. Department of Anthropology, University of New Mexico, Albuquerque, United States

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

10. Hillard Kaplan

    Economic Science Institute, Chapman University, Irvine, United States

    Contribution

    Data curation, Funding acquisition, Project administration, Writing – original draft

    Competing interests

    No competing interests declared

11. Michael Gurven

    Department of Anthropology, University of California, Santa Barbara, Santa Barbara, United States

    Contribution

    Conceptualization, Data curation, Funding acquisition, Project administration, Resources, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5661-527X

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