Life history strategy, energetics & breast cancer: the role of breast density

Previous life history (LH) approaches to breast cancer have focused on hormone receptor subtypes. Oestrogen receptor positive (ER+) tumours are related to slow-LH traits, whereas ER- types may be typical of the fast LH strategy. The link between ER+ incidence with slow LH  is inconsistent with the theory that a trade-off between reproduction and cancer defence underlies higher incidence rates in species or groups who prioritise reproduction.

Breast density and life history strategy

A useful trait for understanding breast cancer aetiology from a life history perspective may be breast density, one of the strongest risk factors for breast cancer. Breast density increases with glandular and connective tissue volume – the biological structures involved in lactation – compared to fat. It seems intuitive to link a higher mass of this functional tissue in the breast to increased biological investment in lactation.

In the slow LH strategy, more biological investment is made in the growth and development of offspring, who are usually fewer in number. Just as higher investment in reproduction increases cancer risk in the fast LH strategy, this higher investment in each offspring would be expected to increase cancer risk in the biological structures involved.

The slow LH strategy involves heightened cancer defence, but this must be massively outweighed by the inevitable risks of investing in breast function. The likely mechanism of carcinogenesis of breast density is a larger number of cell divisions – a theory also applied to the risks of earlier reproduction.

From a LH perspective, breast density would be considered a slow trait, if it is indeed associated with higher somatic investment in the quality and/or quantity of breast milk. Several typically slow LH traits are predictive of denser breasts: higher socioeconomic status (SES), greater birth weight, longer birth length, taller adult height, lower BMI, higher bone density (which may reflect greater somatic investment), lower parity, and later age at first birth. However, higher educational attainment has been associated with both less and more dense breasts.

Breastfeeding duration in rich and poor countries

Breastfeeding duration might be expected to correlate with breast density, if both are measures of investment in offspring. Breastfeeding duration has been put forward as a slow LH trait, with longer durations at higher SES in high income countries (HICs). Consistent with this, breast cancer risk increases up the income scale.

However, if anything longer breastfeeding duration has been found to be protective against breast cancer. This seems to go against the findings in HICs, but in low and middle income countries (LMICs) the social gradient in breastfeeding is reversed. Low SES women breastfeed for longer, and in fact most women breastfeed for much longer than any group of women in rich countries. However, many factors affect breastfeeding duration, not least the availability and affordability of formula milk, and these may significantly skew any LH link with breastfeeding duration.

The protective effect of breastfeeding may come from this distinct pattern of extended lactation, which is closer to the pattern in our evolutionary history. Consistent with this is that the protective effect is often only found after a certain duration of breastfeeding, and then becomes stronger with increasing duration. If HR- cancers are fast LH typical, breastfeeding may appear protective as it is a slow trait, at least in HICs. Taking this effect out may leave little effect of breastfeeding on HR+ types, especially at shorter durations. Some studies have only found a protective effect of breastfeeding in pre-menopausal women, and indeed HR- types mainly affect women in their reproductive years.

Evidence on breast density in developing countries is scarce, but it might be predicted to be lower. If so, it could be a better candidate for explaining the lower cancer risk, with breastfeeding duration just a confound. There would be no positive relationship between density and breastfeeding, and indeed one study found breastfeeding for more than 8 months has an odds ratio for dense breasts of 0.72.

Some studies even find an elevated risk of developing breast cancer in women who breastfeed for an intermediate length of time, compared to women who don’t breastfeed at all after giving birth. Again, this won’t be because of a harmful effect of breastfeeding, but rather that those women who breastfeed for intermediate lengths of time tend to be high SES women in high income countries, who are at the highest risk of breast cancer for other reasons. Caution should be taken when comparing with women who don’t breastfeed however, as there are many reasons why women don’t or can’t breastfeed. They can’t therefore be taken as a homogenous group who might be characterised as fast LH.

It appears that breastfeeding follows fundamentally different patterns in developed and developing countries. To conflate the two in analyses of breastfeeding’s effect on breast cancer might therefore be inadvisable, especially given the widely varying rates of the disease between countries. A protective effect of breastfeeding in rich countries seems incompatible with the higher rates of the disease higher up the social gradient, in women who tend to breastfeed for longer.

Prolonged breastfeeding in rich countries

Prolonged breastfeeding isn’t confined to women in low-income countries though. Immigrants initially retain the breastfeeding pattern of their country of origin, only taking on the traits of their new country after a couple of generations. Even this is contingent on the level of cultural assimilation, and less assimilated groups may retain their traditional nursing practices. Women in rural areas may also be more likely to practice extended lactation.

There is the question of what drives different weaning timings – in low income countries, it may have to do with calorie and nutrient availability. These are generally lower, so infants may require a longer duration of breastfeeding, with what may be smaller amounts of less nutritious breast milk.

The presence of prolonged breastfeeding in high income countries means that it could influence the apparent effect of breastfeeding on breast cancer risk in these countries. The duration of breastfeeding itself may not be the causal factor, rather the women who breastfeed for prolonged durations would be hypothesised to have lower breast densities.

It is an open question to what extent this is mediated by the breastfeeding, and to what extent these women have less dense breasts already. A LH approach might suggest that the fast LH trait of low breast density is already present, lowering breast cancer risk. Breastfeeding duration here isn’t considered a slow LH trait, as there seem to be other considerations.

Energetics and age at menarche

The mixed, and perhaps counter-intuitive evidence on factors like breastfeeding duration and age at menarche from a fast/slow life history perspective, may be better understood when energy availability constraints are considered. Age at menarche tends to be later in populations where low energy availability from food limits growth rates. This prevents any tendency for a fast LH strategy to engender early menarche.

Prolonged breastfeeding, which also tends to be found in LMICs, may be analogous. Low energy availability limits the quantity and quality of breast milk, as well as nutrition in utero, and so it may be necessary to breastfeed for longer to ensure the viability of offspring. Alternative infant feeding methods may be less widely available too.

Older age at menarche and longer breastfeeding duration are both thought of as slow LH traits, certainly in WEIRD populations, but they are found in LMICs due to energy constraints. Indeed menarcheal age can be much older, and breastfeeding duration much longer, than even slow LH women in HICs. The fast traits of a younger age at menarche and shorter breastfeeding are not possible or adaptive in an energy-poor context. Overall, the “protective” effect of the energy constraints masks the risks of the slow traits. Women who reach menarche later and breastfeed for longer tend to have a lower risk of breast cancer, not because these traits are protective themselves, but because they tend to go along with other traits which are protective.

The secular trend in age at menarche

However, some studies have found later age at menarche to be protective against breast cancer in HICs, where energy constraints wouldn’t be expected to exist to the same extent as in LMICs. The quality as well as quantity of nutrition may be important, and increasing nutrition is likely to lower menarcheal age in a dose-response manner. This may underlie the secular trend of decreasing age at menarche in HICs and some middle income countries in previous decades.

A 1970s study linked a higher proportion of girls reaching menarche over the age of 16 in Poland and Tokyo to their lower incidence of breast cancer, compared to women in Athens and Boston. In China, SES is negatively related to age at menarche. The studies which the risk of early age at menarche is based on have been published over the last several decades, on women of all ages. They therefore study women who were born over the course of more than a century. Young women affected by food shortages around WWII, or widespread famine in China, faced significant energetic constraint.

In many HICs the secular decline has continued, suggesting that there continues to be gains to be made from improving nutrition. Young women today may be less constrained by energy availability, but this is a recent phenomenon, and they are only just starting to appear in studies. Life history speed would have been very much secondary to energetics for the majority of women in studies before now.

A cohort effect may be detectable in the two studies which distinguish between pre- and postmenopausal women, both in Canada. In both older cohorts, there is a significant protective effect of older age at menarche, whereas in the younger cohorts, there is no effect or even a risk of older menarche. This is consistent with the significant but declining importance of energetics on menarcheal age in HICs. By contrast, for parity, the effect is if anything the other way around – the premenopausal women show the biggest protective effect of having more children, with postmenopausal women showing less of an effect.

The decreasing incidence of infectious disease may be a factor too, freeing up energy spent on immune response to be invested in growth instead. Illness and injury are likely to show a social gradient, with lower SES girls having to invest more energy on immune response and healing. Girls are also less active than in previous generations, meaning less energy is expended on exercise.

The alternative, LH explanation would be that LHs are accelerating in HICs – it is not clear why this would be the case, and it goes against other evidence. As the secular trend continues, inevitably there will be inequalities within countries in energy balance, nutrition and age at menarche, and this may be what the studies on menarche and breast cancer risk are picking up on. Again, energetics and LH speed have opposing effects on a social gradient in age at menarche.

Other factors play a role in nutrition. Energy balance is important – physical exercise uses up energy that could otherwise go into development, as evidenced by delayed menarche in young female athletes. The very idea of everyone having enough food to eat in rich countries is given the lie to by evidence of widespread food poverty and soaring food bank use. There is a potential link between animal protein, an expensive food source, and increased breast cancer risk. Both may be related to earlier menarche.

Age at menarche is strongly inversely correlated with life expectancy across countries. Even within HICs there is unlikely to be a positive correlation, diminishing the idea of early menarche as an exclusively fast LH trait, or rather making the case that LH speed is not the only determinant. An international study of breast cancer risk found that late menarche was only protective in women who weren’t overweight or obese. This could be interpreted as supporting the theory that only late menarche caused by energy constraint is protective against breast cancer.

The subset of women who reach menarche later and breastfeed for longer because they are following the slow LH strategy may actually have a higher risk. This may not be apparent as they are closer to the average for these traits – they only mature later and breastfeed for longer compared to women who aren’t energetically constrained either, but are following the fast strategy. For instance, in HICs high SES women tend to breastfeed for longer, and may reach menarche later, than low SES women. However, these high SES women are at higher risk of breast cancer, which suggests that late menarche and longer breastfeeding aren’t in themselves protective. Again, these traits aren’t themselves risky, but they tend to go along with other traits that are, in slow-strategy women.

Breast density – investment in lactation

Breast density may be one such trait. The important distinction is that the slow trait – high density – is also the one which requires the more energy availability. Because of this, the risk profiles according to energy availability and LH speed go the same way, rather than being countervailing forces. Energetics accounts for more variance in age at menarche and breastfeeding duration, especially at older ages and longer durations. Because of this, energetics comes out as the statistically significant predictor of these outcomes.

As a result, breast density is an extremely strong risk factor for breast cancer, with dense breasts having 4-6 times the risk of carcinogenesis. Density would be expected to be higher on average both in HICs compared to LICs, and at high SES compared to low SES within countries. Other traits that are both adaptive in energy-rich environments and slow LH-typical are late age at first birth, and low parity. These are also correlated with breast density and breast cancer risk.

One exception is that breast density predicts both HR+ and HR- types of breast cancer, with HR- cancer possibly being fast LH typical, and breast density here being proposed as slow LH. Once again, energy availability may be the explanation – as it increases, both investing in breast density and following a fast LH become more possible and adaptive. They appear to be divergent paths, but they are both disproportionately present in a small group, mostly in HICs. There is greater variance in cancer risk between women with high and low energy availability, and this may be what the correlation between breast density and HR- types is picking up on.

It might also be predicted that breast milk quantity is another trait which fits into the cluster: high energy availability, slow LH, dense breasts and higher breast cancer risk. The composition of breast milk may also be different. All of these factors are linked to the function of the breast, and it may be fruitful to look at breast milk and associated physiological factors around the time of lactation – components of breast milk have previously been put forward as potential biomarkers of cancer risk. For example, most mammograms are carried out long after this stage, and information about disease risk may have been lost by then. The theory is that increased potential (if not actual) breast function, of which breast density is an indicator, carries with it an inherently higher risk of tumour development.


Age at menarche and breastfeeding duration follow different social gradients in low and high income countries because energetic constraints hold sway in LMICs, with LH strategy having more influence in HICs, with the effects being countervailing. With traits like breast density, parity and age at first birth, energetics and LH lead to similar social gradients, giving stronger risk or protective factors which are more universally applicable. Breast density may be a proximate mechanism for the slow LH and high energy availability strategy of investing more biological resources in each offspring. The increased cell division involved may be why breast density is such a strong risk factor for breast cancer.


The link between tonsil and appendix removal and higher fertility is due to life history strategy

The fast life history strategy is associated with both lower levels of somatic maintenance, which results in worse health, and earlier and more frequent childbearing. As such, it is the underlying factor in the link between appendix and tonsil removal, and more frequent pregnancies and higher birth rate. Inflammation, as a signal and consequence of worse overall health, would also be expected to speed up reproduction in itself, as part of the adaptive response to shorter expected (healthy) lifespan.

There is no evidence in the recent study for a causal effect of appendectomy or tonsillectomy on subsequent fertility. The control group would need to be women with similar rates of inflammation but who didn’t have the procedures, whereas in the recent study it was just women matched for age and practice. It is therefore not possible to conclude, as one study author did, that “More importantly, looking at both the appendix and tonsils together, this study confirms beyond doubt that removal of inflamed organs or organs likely to suffer from repeated inflammation, in women, improves their chances of pregnancy.”

Technically, it doesn’t necessarily mean that appendectomy has no negative effect on fertility, as the cohort who have had the procedure may have otherwise had even higher levels of fertility than the control group.

The Dundee team involved in the research take a behavioural  view of the evidence, with “women enjoying more “liberal sexual activity”, being both more likely to get pregnant and have pelvic inflammatory disease.” More “liberal sexual activity” is another trait of the fast life history strategy, so can be seen both as a proximate cause, and as another component of the coherent suite of biological and behavioural traits which serve to accelerate our life cycle, in order to calibrate it with our life expectancy given our environmental conditions.


By Breck MacGregor

Excess mortality in Glasgow and Scotland – a statistical artefact?

Faster life histories (LHs) underlie the excess mortality in Glasgow and Scotland. They are particularly consistent with premature mortality from risky behaviours, and may also contribute to overall mortality from chronic diseases.

Far from being pathological however, faster LHs evolved as an adaptive response to dangerous and unpredictable circumstances, where long-term survival is uncertain. LH theory simply describes the calibration of someone’s life course to the length of time they can expect to stay alive.

Risk-taking behaviour pays off more in unpredictable environments, where the benefits of long-term investment may not be realised. As a result, we have a tendency to “discount the future” and prefer immediate rewards in such situations. The relevance to drug and alcohol abuse is clear – they are used as a short-term escape from ongoing problems.

Future discounting is also reflected in ways of attaining status – education only leads to higher status in the long term, whereas risk-taking behaviours can lead to immediate social status (especially among males). Excess deaths from road traffic accidents are highest in young males, as driving fast is seen as a way of attaining status. Male competition drives the violence that leads to other excess deaths. Fast LHs, which encourage competition, are triggered by factors like low status and resource scarcity. This is because natural selection acts on reproduction as well as survival – we need to do both for our genes to survive. Low status males have nothing to lose, with dire prospects of reproduction and survival.

Lower investment in long-term health contributes to the excess overall mortality from chronic diseases. This is not just the result of conscious decision-making. At the heart of LHs is a trade-off between growth and reproduction. Curtailed growth means faster maturation and so reproduction, but comes at the cost of lower biological investment in the immune system, tissue repair and maintenance. A lifetime of lower investment in health inevitably leads to the earlier onset of chronic diseases, and higher mortality rates because of them. Unfortunately this is mostly irrelevant in evolutionary terms, as after we stop reproducing, it makes little odds to natural selection whether we die at 65 or 85. (An interesting exception may be due to grandmaternal care, which is a theory why the human menopause evolved, and why women live longer).

Faster LHs explain why poverty and inequality cause worse health and social outcomes. But why are LHs in Glasgow and Scotland even faster than would be expected after accounting for these risk factors? The Glasgow Centre for Population Health recently published a new synthesis of evidence on the excess mortality. The hypotheses in it can be compared to established triggers of faster LHs, to see if they are feasible threats to long-term survival and reproduction. Any potential trigger may not actually threaten survival in modern society, but as a species we evolved largely in small bands of hunter-gatherers, and in this context responses to threats can be understood as more adaptive.

The first factor proposed is historical overcrowding. This was a problem in Glasgow and Scotland from around 1939 until after 2000. The extent was significant – in 1971, overcrowding was more than twice as widespread in Glasgow than in Liverpool or Manchester, across every deprivation decile. In the most deprived decile, more than 60% of households were overcrowded in Glasgow, compared to less than 30% in the English cities.

A time lag would be expected between exposure and a rise in mortality, due to life course effects – early exposure can take a lifetime to manifest itself. The gradual increase of the portion of mortality unexplained by deprivation fits with a mini “epidemic” of people affected by overcrowding growing old and appearing on the mortality statistics. In 1981, most of the higher mortality could still be explained by deprivation, but this declined over the next 20 years according to the previous synthesis of evidence by GCPH.

However, the emergence of unexplained mortality since 1981 may also partly be an artefact of the marked decline of overcrowding during this period. Overcrowding is one of four components of the Carstairs Index, along with car ownership, male unemployment and social class. The index is used to account for area deprivation. In 1981, overcrowding was twice as common in Glasgow as in Liverpool and Manchester, but it is unclear why this should immediately have led to higher mortality (indeed it is likely that all components of the index would show a significant lag effect).

There is no reason to expect mortality to follow immediately as overcrowding declines, as its effects can take a lifetime to show up in mortality statistics. The inverse is that higher levels of mortality from 1981 onwards can be seen as the lagged effect of pre-1981 overcrowding. It just happened to be around 1981 when overcrowding started to decline, creating the illusion of a new phenomenon emerging. The reason for the emerging excess isn’t (just) that mortality decreased more slowly in Glasgow over the past few decades; the decline in overcrowding meant that deprivation accounted for less and less of the mortality.

The natural response is to look for any conditions which worsened around that time, which could have caused the increasing excess mortality. If though it is just an artefact of lagged deprivation, the opposite in fact applies: the rapid improvement in overcrowding created the statistical excess. A more ecologically valid way of accounting for deprivation may be to introduce a lag period in the analysis itself. This could be done by adjusting mortality statistics with the deprivation measure from a certain number of years previous.

The decline in the percentage of mortality in Glasgow which the Carstairs Index can explain between 1981 and 2001 correlates almost perfectly with the decline in overcrowding during the same period. There is no equivalent unexplained excess of poor health, as there is no (or a much shorter) time lag from exposure to outcome. Conversely, people don’t die young solely because of what happens to them in the last year or even decade of their life.

The authors don’t seem to see the excess as the result of the decrease in the deprivation profile: However, the causal pathways are complex, and the relationship [of overcrowding] with excess mortality less clear, given that the excess has increased over a period in which Scotland has become relatively less deprived compared with the rest of Britain.” The two measures are partially confounded however, as the measure of deprivation is used to establish the level of excess mortality. In addition, two of the four components of deprivation (overcrowding and car ownership) have seen Scotland close the gap to England and Wales between 1981-2011.

People born in 1991 would be amongst the first to experience lower levels of overcrowding from birth. In 2011, they would have been 20 – barely old enough to register on premature mortality. As their generation ages though, there would be an expected plateau or decrease in premature mortality, if early years exposure to overcrowding and deprivation more generally is driving the excess mortality. This process of improvement may be slowed by intergenerational effects, including epigenetic effects, which perpetuate the influence of a stressor even after it has been removed. Rats born to depressed or anxious mothers showed increased DNA methylation at the glucocorticoid receptor gene, and increased stress reactivity. Overcrowding is a risk factor for poor mental health, suggesting a potential mechanism for overcrowding to harm health both directly and through intergenerational epigenetic effects.

The lagged effect of overcrowding might predict that premature mortality would show an excess before all age mortality did. If this model is correct, exposure to overcrowding would move through the population as it ages, and premature mortality from overcrowding would be expected to decline before a similar reduction in overall mortality.

Another effect would be that it is the same population exposed to overcrowding that shows up in both premature mortality and all age mortality. This could explain the excess being bigger for premature mortality (around 30% compared to 15%), as exposure shifts mortality towards younger ages. This has a disproportionate effect on premature mortality simply because many fewer people die before the age of 65.

The same process could also contribute to the strong socioeconomic gradient that is shown for premature mortality. Within the exposed cohort, people of low SES face a double disadvantage, and so are much more likely to die before 65. Over-65s in the exposed cohort are therefore disproportionately of higher SES, which cancels out the social gradient in mortality. In overall mortality, this translates into the flat pattern of excess mortality across deprivation deciles. There is still an excess as exposed, high-SES people die younger than non-exposed people of equivalent SES in the comparator areas.

A life course approach may also shed light on low SES groups suffering more early deaths. Early life SES is a strong predictor of SES in early adulthood, and young people experiencing low status both during childhood and currently are at much higher risk. As people age, the correlation between childhood and current SES weakens (although social mobility has declined since the 1980s). People who have moved up or down the social ladder over their life cloud the picture, and flatten the social gradient in all-age mortality. This is consistent with the “health constraint” hypothesis, which posits that socially mobile individuals have health characteristics of both SES groups they move between, minimising health differences between groups.

The other side of the lagged deprivation argument is that it might predict there would be a time period when overcrowding had begun to rise higher in Glasgow, but before it had time to have an impact on mortality. Mortality would then actually be lower than expected given contemporary deprivation as measured by overcrowding.

Early exposure is the core tenet of a cohort effect like this. People who grow up in Scotland and move away still show “Scottish” levels of excess mortality. Conversely, people who grow up elsewhere in the UK and move to Scotland retain lower levels of adult mortality. Both lines of evidence support the theory that early life exposure of some kind is driving the excess mortality in Scotland. Early exposure is also consistent with a “critical period”, during which LHs are calibrated.

The causes of death seem to reflect a cohort process too. The earliest causes, like drug and alcohol abuse and suicide, show a much bigger excess. The lower excess for later-life causes, like cancer and heart disease, may reflect the smaller cohort surviving long enough to contract and die from these diseases.

In terms of the mechanism by which overcrowding harms later health, established disease mechanisms have already been identified. Risk factors of overcrowding like early infections, damp, mould and disturbed sleep, all documented by a report by Shelter into poor housing, could all lead to worse later-life cardiovascular health.

High exposure to infectious diseases is however known to be a vital LH variable. Populations around the world with exceptionally high pathogen loads have independently come to the solution of radically curtailing growth, and become what are commonly known as pygmies. They die much younger on average than other populations, so have to prioritise early maturation and reproduction, at the expense of adult height.

Clearly, Glaswegians aren’t exposed to dangerous levels of tropical diseases. But socioeconomic status (SES) is a strong predictor of both adult height, and of exposure to overcrowding. Height itself correlates with health status. Again, faster LHs can be seen as the overarching factor, reducing height and worsening long-term health.

The effect of infections on LHs may be twofold: firstly, as an indicator of higher general pathogen load in the environment, which predicts more frequent future illnesses. Secondly, early infections can compromise ongoing health status, increasing susceptibility to future insults. Both are important predictors of expected lifespan, and lead to the perhaps counterintuitive outcome that in conditions where health is at risk, the adaptive solution is to sacrifice investment in long-term health.

Another theory put forward in the new synthesis of evidence is that housing policy, especially the building of New Towns and peripheral council estates, increased Glasgow’s vulnerability to poverty and deprivation. The socially selective process of populating the New Towns must have broken up established communities. Slums were cleared, and housing estates and high-rise flats were built on a much larger, and arguably inhuman scale in Glasgow than in Liverpool or Manchester. This suggests the scattering of traditional communities must have been greater. The resulting loss of community described by concepts like social capital and connectedness is thought to be an important social determinant of health.

From a LH perspective, social support is crucial in a social species to long-term survival and reproduction. Any perceived lack of affiliative relationships is likely to affect LHs by speeding them up, as throughout evolutionary history, threats to personal health and safety like victimisation or famine were better resisted with support from kith and kin. Normally, levels of support may be captured by measures of SES, but the fracturing of communities may have decimated support networks, without increasing deprivation per se.

Perhaps significantly, the resettlement was carried out along class lines, with the New Towns “skimming the cream” off Glasgow. The resulting social stratification may have undermined feelings of social solidarity, encouraging a more individualistic, hierarchical outlook, leading to increased status anxiety.

The democratic deficit argument, that Scots and Glaswegians in particular felt a lack of control over their lives from the 1980s onwards, chimes with the psychosocial risk of feeling out of control of one’s life, as the authors point out. LHs may be the mediating factor here, accelerating people’s lives in response to the inherent unpredictability of circumstances at the time.

The huge increase in premature mortality from around 1980 would appear to need a cause that emerged at the same time. Thatcherism is the obvious candidate, but any explanation needs to show why Glasgow and Scotland were particularly disadvantaged. The response of local government is cited as exacerbating UK economic policy in Glasgow, whereas it was mitigated in the other cities. This is linked to the lack of social capital, as lower levels of politicisation meant that there was less opposition to commercial development ahead of social investment in Glasgow.

A final specific risk factor identified was negative physical environment, in particular the amount of vacant and derelict land. This has already been explicitly linked with LHs, with the density of dilapidated structures found to be independently correlated with premature births and low birth weight in the area, two fast LH traits. These are bad health outcomes for infants, but they are also associated with worse lifelong health outcomes, and so increased mortality. The rationale is that dereliction signifies the unpredictability of future outcomes, in turn encouraging riskier reproduction.

Faster LHs are clearly implicated in the biological, cognitive and behavioural strategies which result in the excess mortality in Glasgow and Scotland as a whole. Several of the new hypotheses put forward to account for the excess are feasible triggers of faster strategies, independent of SES. However, just as the overwhelming majority of worse health and social outcomes and inequalities are caused by already understood socioeconomic factors, LHs are also overwhelmingly determined by SES. As the authors note, the definition of deprivation may need to be updated. Factors may need to be added that haven’t been considered part of the concept before, but which predictably lead to faster LHs, and so earlier deaths.

By Breck MacGregor

New voters look a lot like Yes voters

The Scottish independence referendum has produced an amazing engagement with democracy. Since 2011, the majority of eligible voters who weren’t on the electoral register have now registered. They make up over 7% of the electorate. So who are they?

Looking at the groups who are traditionally least likely to be registered gives some clues. According to reports by the Electoral Commission cited here, people renting accommodation from private landlords are much more likely to be unregistered than owner-occupiers. Relatedly, 16-24 year-olds, students and people who have moved recently are less likely to be registered.

This profile looks promising for the Yes campaign. In canvassing returns, home owners are much less likely than private renters to support independence. And poll results consistently show that Yes voters predominate in younger age groups. Lower socioeconomic status (SES) also predicts Yes support, and it can be inferred from the other characteristics of unregistered voters that they are more likely to have low SES. So most newly registered voters fit the mould of typical Yes voters.

The big unknown is whether the polls are taking this new chunk of the electorate into account, let alone people who were already registered but have never or rarely voted before. Criticisms include phone surveys only calling landlines – this would seem to exclude renters, young people and low SES groups who tend to rely more on mobile phones. YouGov’s online panel is a self-selecting group who are presumably already interested in politics, the opposite of people who have never voted.

The polls do of course weight their results to boost the numbers of under-represented groups in their sample. But can this really be accurate when none of a certain social group is present in the sample to begin with?

Support for independence is higher in almost every social group with less power, influence and opportunity – people who are younger, less well off, less well educated, renting. Traditionally they haven’t had a voice, and so have been largely ignored by mainstream politics and media. But the beauty of a referendum is that one vote is worth the same as every other. And the nature of inequality is that advantage is accumulated by a small minority. If turnout is high enough on the 18th, the disadvantaged majority will vote for independence.

By Breck MacGregor

Why older voters are not the key battleground in the independence referendum

The only age group in which a majority say they will vote No is 60+. It’s a heavy majority at that – 59% No to 36% Yes with 5% undecided in the latest YouGov poll – which explains why it tips the balance to give No an overall lead. This generational divide has led some to say the Yes campaign should focus on older voters as the key battleground in the referendum. But there are numerous reasons to believe that this would be the wrong strategy.

Pragmatically, older voters are more likely to use postal votes, and so a disproportionate number of the 60+ vote will already have been cast. 1 in 6 votes are postal in the referendum, a sizeable chunk of the electorate. There are fewer undecided voters too, with the last three You Gov polls having 4-8% of this age group as undecideds, compared to 9-13% of 25-39 year olds.

The polls indicate that the Yes gains are coming both from undecided voters and soft No voters, but there may be less prospect of older soft No voters changing their minds. As Mike Smithson pointed out on Political Betting: “We also know from other polling that the older you are the less likely it is that you will change your mind. That relates to all elections and not just the referendum.”

Is it just an age thing that makes older voters more likely to vote no – a desire to hold on to the status quo? The size of the fall-off in support for independence argues against this. It seems unlikely that so many current 40-59 year-olds will change their minds as they pass 60.

Growing up in the second world war and the ‘golden age’ that followed may have ingrained a sense of Britishness, and more importantly a sense of success and achievement with it. The building of the welfare state including the NHS alongside low unemployment and economic growth defined the early part of today’s pensioners’ lives. Many will have been established in jobs for life by the time the economy faltered.

By contrast, the Thatcher era dominated the early lives of many under 60s, which will inevitably colour their perception of Britishness. Alex Salmond and Nicola Sturgeon recognised this when mentioning Maggie within the first minute of their respective TV debate contributions.

For the reasons above, the 60+ No vote may be more immune to persuasion. It would certainly seem risky to focus on this age group at the expense of others.

By Breck MacGregor Tagged

‘Troubled families’

The term ‘troubled families’ covers a multitude of sins. It refers to both the social problems the families are victim to, and the trouble that the families themselves cause in their communities. The balance of emphasis is presumably down to your own political viewpoint.

The current government has announced that there are more families which fall under their definition of ‘troubled’ than previously thought. The proposed solution is to carry on with the programme of intensive interventions that aim to turn troubled families around. This style of intervention works – families in the programme are twice as likely to stop anti-social behaviour. But there is no attempt to tackle the underlying causes, and prevent families from falling into the category in the first place.

When you look at the government’s criteria, this is surprising. Most of them stem from structural issues, over which families have little control – being on a low income, unable to afford basics, poor housing, and parents with no qualifications. The intervention must then focus on the ‘trouble’ the families cause, rather than that they find themselves in. It’s the usual suspects: antisocial behaviour, domestic violence, and poor health behaviours.

But when they throw things like obesity and chronic disease at an early age into the mix, you blur the line between personal responsibility and the influence of outside factors. This may be just the point; to blame poverty on the poor. Of course both societal and individual factors are important determinants of social problems. But rather than being alternatives, they represent different levels of explanation.

‘Troubled families’ are people who have ended up on a very fast life history strategy. The social problems they experience map perfectly onto the traits identified in the life history theory literature. For instance, the government report aims to get children back into school, reduce youth crime and antisocial behaviour, and ‘put adults on a path back to work’. Domestic violence, and drug and alcohol abuse is also associated with troubled families. Compare these issues with those quoted in a paper on life history theory:

“For example, people who exhibit criminal and delinquent behaviours also tend to abuse legal or illegal substances, experience familial problems, such as familial distress, father absence, unemployment or underemployment, drop out of school, and exhibit social distress, teen pregnancy, and psychopathology.” – Figueredo et al. (2006)

This is in no way surprising – the link between the fast strategy and deprivation is iron-clad – and ‘troubled families’ is just a rebranding of the poorest in society.

The problems which people seem to bring upon themselves make a tragic sense when viewed through the lens of life history theory. Parenting functions to prepare children for adulthood. If all you have experienced in life is harsh conditions and poor relationships, it makes sense (evolutionarily) to prepare your children for the same. This is how we evolved to deal with adversity. It’s just that in modern societies, only some people experience adversity, leaving the rest uncomprehending of the consequences.

The academic literature on life history theory recognises that many social problems fall within its purview. To quote Figueredo et al. (2006):

“The social and behavioural literature indicates that many behavioural traits commonly considered “social problems” in modern industrial society occur in such clusters…[Life History Theory] construes such clusters to be coordinated arrays of contingently adaptive life-history traits.”

The message is that what is considered problem behaviour is in fact our default response to deprivation. Unfortunately, this message hasn’t been communicated to policymakers who could use it as a powerful tool to prevent problems developing. Of course some existing policies are consistent with the implication from life history theory that reducing poverty will have manifold benefits. But their case could be considerably strengthened by a wider understanding of the behavioural syndrome which underlies such a range of social problems.

The strength of the link between the fast life history strategy and social problems, and the variety of separate research fields it has been identified in, suggests that no amount of remedial effort can fully negate it. What actually troubles deprived families, and society in general, is the fast life history strategy.

Why inequality undermines societies – an evolutionary perspective


This article originally appeared on Contributoria, at .

The level of economic inequality within countries has been linked to a range of worse health and social outcomes. Evidence from the evolutionary behavioural sciences of how we have evolved to react to our environment may shed light on why we fail to flourish under extreme inequality.

Five years ago The Spirit Level was published, bringing into the mainstream the evidence on the harms of economic inequality. The two academics behind the book, Richard Wilkinson and Kate Pickett, drew on research in the various social science disciplines involved in their day jobs. They went beyond their own field of social epidemiology to show that more unequal societies are not only less healthy, but have worse outcomes on myriad other measures. It’s worth listing these to emphasise the diversity of inequality’s ill effects: infant mortality, child wellbeing, educational attainment, teenage births, social mobility, obesity, mental health, drug abuse, trust, altruism and cooperation, violence and homicide, imprisonment, foreign aid, and environmental sustainability. In the years since the book was published, inequality has become a major political issue internationally.

The explanations which are put forward to explain the effects of inequality usually focus on our immediate responses. Biologically, our levels of stress hormones rise; behaviourally, we become more prone to compete and be aggressive with others; psychologically, we become anxious and obsessed with our social status. But that’s where most explanations stop asking why – short of asking ‘why do we respond in these ways to inequality?’ On one level, it’s sufficient to know the proximate causes and ignore the ultimate causes, but this misses an opportunity to learn something instructive about human nature.

“Why do we respond in these ways to inequality?”

To go beyond the visible and measurable factors, a more expansive timeframe is needed. As a species we evolved over millions of years – for physical traits, this is uncontested scientifically. There is more vigorous debate over the extent to which mental traits have been subject to natural selection. Evolutionary psychology is often thought to portray traits as genetically determined, innate and fixed. While this is an approach that some academics take, it is clearly inappropriate to apply this to the inequality evidence. However, the responses to inequality appear to be consistent across societies. This indicates that there are adaptive traits which respond to inequality systematically – we have evolved the ability to adjust to our current situation.

This principle of calibration to the environment is well established in the evolutionary field of life history (LH) theory. It characterises a strategy which we take throughout life as a trade-off between growth and reproduction. At any given time, we can invest (unconsciously!) biological resources in growth at the expense of reproduction, or vice-versa. Ideally, we would take time to grow for as long as possible to ensure long-term health, before bringing the next generation into the world. This wasn’t always possible in the ancestral environment we evolved in – famine, war, diseases and other components of ‘environmental harshness’ all reduced life expectancy. With such considerations, it would have been no good taking your time to develop, only to die before you could reproduce. Rather, the onus is on as quickly as possible getting to a state where you can pass on your genes.

What results from the trade-off is a scale of LH strategies from slow to fast. In an uncertain environment, early maturation and reproduction protect against the risk of dying before reproducing. Other traits typical of the fast LH strategy include having more children, as a hedge against high infant mortality, and investing less parental care in them, be it shorter breastfeeding duration or father absence. Babies are born smaller, which leads to lower adult height, but with an increased risk of obesity and certain chronic diseases later in life.

The obvious objection to applying LH theory to modern problems is that thanks to developments of civilisation like modern medicine, sanitation, and the rule of law, we no longer really need to worry about making it to adulthood. But we’ve simply not had enough time to evolve and adapt to our new environment. The result is a mismatch between the environment we are adapted to and the one we find ourselves in. This is why we still respond to cues of environmental harshness.

It appears that inequality is a cue of environmental harshness which accelerates LH strategies. This is understandable in the context of access to resources – food, shelter, social capital – which are essential to survival. But egalitarianism was the norm for our early human ancestors. They lived in bands of hunter-gatherers, where meat was shared equally in the group regardless of who killed it. We can infer this from the archaeological record, as well as anthropological reports of the few remaining tribes of hunter-gatherers. In these groups, counter-dominance strategies like ridicule and ostracism prevent any one individual from gaining too much power.

“Egalitarianism was the norm for our early ancestors”

Groups became unequal with the advent of agriculture and other developments. Again, this may be too recent for evolution to have taken effect. However, we may have adapted to inequality in ancestral groups of hierarchical primates, prior to the emergence of the Homo genus. Either way, inequality seems to elicit faster LH strategies, whether as a functional adaptation or not.

Our response to inequality goes beyond behaviour directly related to reproduction. Natural selection will have acted on aspects of our biology, psychology and behaviour to form a coherent suite of responses to the social environment. For instance, the rational reaction to living with social stratification is to compete for all you can get, as you’re not guaranteed a fair share. Appeasing those above you in the pecking order while exploiting those below you is the usual result –the opposite of the healthy disrespect for authority found in the egalitarian groups. Sycophantic worship of celebrities alongside demonization of people at the lower end of society may be the modern-day version of these social processes.

The cluster of traits associated with the fast LH strategy share the theme of short-termism. This even extends into the choices we make, in the form of a cognitive bias known as future discounting. When offered the choice of a smaller reward now or a larger reward at a certain point in the future, some are better than others at resisting the temptation of instant gratification. But when the long-term future is uncertain, it’s rational to discount it and take what you can get today. This is another trade-off, between taking a hit for a bigger payoff in the long run, or short-term benefits at the expense of long-term costs.

“When the future is uncertain, it’s rational to take what you can today”

LH strategy has a strong social gradient – lower socioeconomic status (SES) tends to mean a faster LH strategy. Returning to the health and social outcomes of The Spirit Level, a recurring theme is that most of them show a social gradient, with worse outcomes at lower SES. What Wilkinson and Pickett demonstrated was that outcomes that are associated with deprivation are also associated with inequality in itself: average outcomes are worse in more unequal societies. It may be that LH traits follow the same pattern, and that the well-established link with SES can be extended to inequality.

Indeed, the effects of inequality seem to reflect the consequences of the fast LH strategy. More babies being born underweight leads to higher infant mortality. Less parental investment in children lowers child wellbeing. An obsession with social status exacerbates depression and anxiety. A bias to discount the future shortens perceived time horizons, leading to lower educational attainment and lower impulse control in drug abuse. Sustainability and climate change are similarly neglected by a short-term perspective. And a general quickening of life stages shortens childhood, increases teenage births and curtails healthy lives.

Excess inequality can be added to deprivation as a scourge that stunts our growth and flourishing. This inevitably follows from our evolved tendency to react to our social environment. What this means for social policy is that outcomes which have been seen as pathological must be reappraised as contingent responses to our circumstances.

This doesn’t mean that they should be accepted of course. But interventions like education campaigns that appeal to reason can’t solve social problems, when social conditions affect our biology, psychology and behaviour through mechanisms which are outside of conscious control. To get to the root cause, society must be made more equal.



Flannery, K. (2012). The creation of inequality: how our prehistoric ancestors set the stage for monarchy, slavery, and empire. Harvard University Press.

Wilkinson, R. G., & Pickett, K. (2011). The spirit level. Penguin, London

Life History Theory

Brumbach, B. H., Figueredo, A. J., & Ellis, B. J. (2009). Effects of Harsh and Unpredictable Environments in Adolescence on Development of Life History Strategies: A Longitudinal Test of an Evolutionary Model. Human Nature (Hawthorne, N.Y.), 20(1), 25–51. doi:10.1007/s12110-009-9059-3


Ellis, B. J., Figueredo, A. J., Brumbach, B. H., & Schlomer, G. L. (2009). Fundamental Dimensions of Environmental Risk. Human Nature (Vol. 20, pp. 204–268). doi:10.1007/s12110-009-9063-7

Figueredo, a, Vasquez, G., Brumbach, B., Schneider, S., Sefcek, J., Tal, I., … Jacobs, W. (2006). Consilience and Life History Theory: From genes to brain to reproductive strategy. Developmental Review, 26(2), 243–275. doi:10.1016/j.dr.2006.02.002

Griskevicius, V., Ackerman, J. M., Cantú, S. M., Delton, A. W., Robertson, T. E., Simpson, J. a, … Tybur, J. M. (2013). When the economy falters, do people spend or save? Responses to resource scarcity depend on childhood environments. Psychological Science, 24(2), 197–205. doi:10.1177/0956797612451471

Kruger, D. J., Munsell, M. A., & French-turner, T. (2011). USING A LIFE HISTORY FRAMEWORK TO UNDERSTAND THE RELATIONSHIP BETWEEN NEIGHBORHOOD STRUCTURAL DETERIORATION AND ADVERSE BIRTH OUTCOMES, 5(4), 260–274. Nettle, D. (2010). Dying young and living fast: variation in life history across English neighborhoods. Behavioral Ecology, 21(2), 387–395. doi:10.1093/beheco/arp202

Future discounting

Daly, M., & Wilson, M. (2005). Carpe diem: Adaptation and devaluing the future. The Quarterly Review of Biology, 80(1), 55-60.

Griskevicius, V., Ackerman, J. M., Cantú, S. M., Delton, A. W., Robertson, T. E., Simpson, J. a, … Tybur, J. M. (2013). When the economy falters, do people spend or save? Responses to resource scarcity depend on childhood environments. Psychological Science, 24(2), 197–205. doi:10.1177/0956797612451471


Scott-samuel, A., Bambra, C., Collins, C., Hunter, D. J., Mccartney, G., & Smith, K. (2013). THE IMPACT OF THATCHERISM ON HEALTH AND WELL-BEING IN BRITAIN, 44(1), 53–71.

Whiten, A., & Erdal, D. (2012). The human socio-cognitive niche and its evolutionary origins. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 367(1599), 2119–29. doi:10.1098/rstb.2012.0114

Image Paul Anderson [CC-BY-SA-2.0 (, via Wikimedia Commons