The Uncontrollable Mortality Risk Hypothesis: Theoretical foundations and implications for public health

Abstract

The ‘Uncontrollable Mortality Risk Hypothesis’ employs a behavioural ecological model of human health behaviours to explain the presence of social gradients in health. It states that those who are more likely to die due to factors beyond their control should be less motivated to invest in preventative health behaviours. We outline the theoretical assumptions of the hypothesis and stress the importance of incorporating evolutionary perspectives into public health. We explain how measuring perceived uncontrollable mortality risk can contribute towards understanding socioeconomic disparities in preventative health behaviours. We emphasize the importance of addressing structural inequalities in risk exposure, and argue that public health interventions should consider the relationship between overall levels of mortality risk and health behaviours across domains. We suggest that measuring perceptions of uncontrollable mortality risk can capture the unanticipated health benefits of structural risk interventions, as well as help to assess the appropriateness of different intervention approaches.

Full text

Evolution, Medicine, and Public Health [2024] pp.86–96
https://doi.org/10.1093/emph/eoae009
Advance access date 9 May 2024

The Uncontrollable
Mortality Risk Hypothesis
Theoretical foundations and implica-
tions for public health
Richard D. Brown*, and Gillian V. Pepper
Psychology Department, Northumbria University, Newcastle, UK
*Corresponding author. Department of Psychology, Northumbria University, Newcastle, NE1 8SG, UK.
Tel: +44 (0)191 243 7169; E-mail: richard6.brown@northumbria.ac.uk
Received 20 November 2023; revised version accepted 26 April 2024.
ABSTRACT
The ‘Uncontrollable Mortality Risk Hypothesis’ employs a behavioural ecological model of human health
behaviours to explain the presence of social gradients in health. It states that those who are more likely
to die due to factors beyond their control should be less motivated to invest in preventative health
behaviours. We outline the theoretical assumptions of the hypothesis and stress the importance of incor-
porating evolutionary perspectives into public health. We explain how measuring perceived uncontrolla-
ble mortality risk can contribute towards understanding socioeconomic disparities in preventative health
behaviours. We emphasize the importance of addressing structural inequalities in risk exposure, and
argue that public health interventions should consider the relationship between overall levels of mortality
risk and health behaviours across domains. We suggest that measuring perceptions of uncontrollable
mortality risk can capture the unanticipated health benets of structural risk interventions, as well as help
to assess the appropriateness of dierent intervention approaches.
Lay Summary The Uncontrollable Mortality Risk Hypothesis argues that those more likely to die due to
uncontrollable factors should be less motivated to look after their health. This emphasizes the impor-
tance of improving the safety of people’s living environments and highlights the positive impact that this
can have on health behaviours.
: public health; risk perceptions; health behaviours; socioeconomic inequality; uncontrollable
mortality risk; perceived control
THE PUBLIC HEALTH ‘PUZZLE’ OF
PERSISTENT HEALTH INEQUALITIES
Despite improved access to healthcare and the
widespread use of health informational cam-
paigns in economically developed countries,
socioeconomic status remains associated with
long-term health outcomes [1–3]. In a system-
atic review of 283 public health studies, Niessen
et al. [4] found signicant support for a negative
association between socioeconomic status and
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COMMENTARY
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The Uncontrollable Mortality Risk Hypothesis Brown and Pepper | 
the prevalence of non-communicable diseases. Individuals
with lower socioeconomic status typically experience poorer
health, higher rates of chronic diseases, and reduced life expec-
tancy compared to those with higher socioeconomic status [5].
Notwithstanding the role that disparities in the availability of
resources and information can play in establishing social gra-
dients in health, such gradients still exist in areas where they
should theoretically be mitigated by freely available healthcare
and broadly disseminated health campaigns [6]. For example, the
high levels of expenditure devoted to freely available healthcare
in Scandinavian countries do not translate into systematically
smaller inequalities in health outcomes [7]. This public health
‘puzzle’ highlights the inability of existing theory to fully account
for the presence of socioeconomic gradients in health outcomes.
Addressing these socioeconomic gradients is crucial for achiev-
ing fairer public health outcomes.
Many of these dierential health outcomes are aected by
disparities in preventative health behaviours. For example, those
who occupy lower socioeconomic positions report lower rates of
exercise, use of medical services, and adherence to treatment.
They also have poorer diets and higher rates of smoking [8–13].
In a further review of the relationships between socioeconomic
status, health behaviours and mortality rates, it was found that
smoking, alcohol consumption, physical activity and diet are all
signicant contributors to socioeconomic gradients in health
[14]. An answer to the public health puzzle of persisting socio-
economic gradients in health outcomes will therefore require an
explanation of socioeconomic gradients in health behaviours.
Pampel et al. [3] proposed nine categories of explanation for
socioeconomic gradients in health behaviours: deprivation and
stress, fewer benets of health behaviours at lower socioeco-
nomic positions, latent traits such as attraction to risk, class
distinctions, lack of knowledge, sense of agency, aids to health
behaviours, community opportunities and social support and
inuence. Pepper and Nettle [15] subsequently categorized these
into three distinct groups, inspired by Tinbergen’s four levels of
explanation [16]. First, there are constraint-based explanations
for not performing preventative health behaviours, which Pepper
and Nettle class as non-adaptive (Glossary Term 1). In these types
of explanations, a lack of preventative health behaviour results
from constraints such as having insucient means to pursue
healthy behaviours or a lack of knowledge regarding health risks.
The second category is that of proximate explanations (Glossary
Term 2), which specify the mechanisms associated with lower
rates of investment in preventative health behaviours. Traits
such as attraction to risk, shorter time horizons or a reduced
sense of agency may constitute psychological mechanisms that
lead to a disinvestment in preventative health behaviours [17].
However, if these explanations are evoked to explain inequalities
in preventative health behaviours, they are incomplete, because
they do not explain why there are socioeconomic gradients in the
presence of the traits themselves. Third, there are ultimate expla-
nations (Glossary Term 3) for socioeconomic gradients in health
behaviours that suggest that those in lower socioeconomic
positions receive fewer benets from investing in preventative
health behaviours than their more auent counterparts. This
nal category of explanation suggests that less healthy behaviour
is a ‘contextually appropriate’ response to environmental cues
of risk, which may be delivered via the proximate mechanisms
listed above [18]. Importantly, the application of proximate and
ultimate labels to explanations for health behaviour emphasizes
that explanations in these categories are not mutually exclu-
sive—though they are often treated as such [3].
We argue that, given the pervasiveness of socioeconomic
gradients in preventative health behaviour, researchers must
go beyond oering constraint-based or proximate accounts. We
must provide ultimate explanations to identify eective strategies
for improving health behaviour. To address social disparities in
preventative health behaviours, we need accounts of why the psy-
chological mechanisms that produce these behaviours may have
evolved. Instead of basing interventions solely on ‘constraint-
based’ explanations of health inequalities (e.g. assuming a lack
of knowledge and providing educational material) or providing
interventions to mitigate the presence of specic proximate
mechanisms (e.g. mindfulness interventions to reduce delay dis-
counting), an evolutionary health perspective may look to iden-
tify and address the ultimate causes of health inequalities. Such
an account is provided by Nettle [19] in a behavioural ecological
model (discussed below) for explaining the presence of social
gradients in preventative health behaviours and provides the
basis for the Uncontrollable Mortality Risk Hypothesis.
THE UNCONTROLLABLE MORTALITY RISK
HYPOTHESIS
Uncontrollable mortality risk reects that portion of mortality
risk that cannot be mitigated by an individual allocating eort
to preventative health behaviour. Though people experience
varying levels of mortality risk, the degree to which these risks
are individually controllable is of central importance because it
determines the extent to which it may be possible for behavioural
eorts to help avoid potentially fatal consequences. Nettle [19]
stated that the optimal (Glossary Term 4) individual investment
in health behaviour should be less for people of lower socioeco-
nomic status because they are typically exposed to higher levels
of uncontrollable mortality risk. The rate of uncontrollable mor-
tality risk within an environment is expected to determine the
optimal amount of energy that is worth investing in health. A
broad range of evidence supports the assumption that people
of lower socioeconomic status are exposed to greater levels of
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Evolution, Medicine, and Public Health | Brown and Pepper
health-damaging environmental risk. This can include exposure
to air pollutants, inadequate housing, poor water quality, noise
exposure, hazardous waste and violence and injury [20–28].
Nettle’s model assumes that there is a trade-o between allo-
cating resources to preventative health behaviours and investing
in other activities that might contribute towards one’s tness
(Glossary Term 5). Activities that could potentially compete with
an individual’s behavioural investment in health might include
accruing resources, increasing status or dominance, developing
social bonds, searching for a mate, or any other tness-enhancing
activity. The reduced potential payo from engaging in preventa-
tive health behaviours, in combination with the trade-os, makes
the optimal investment in health less for those exposed to uncon-
trollable risks, which are more common at lower socioeconomic
positions. This produces a secondary increase in mortality risk
due to reduced investment in preventative health behaviours.
Therefore, initial inequalities in health outcomes due to dieren-
tial exposure to uncontrollable risk are worsened by a reduction
in preventative health behaviour, producing a compound eect,
which further entrenches health poverty.
Calculating objective mortality risk at the level of the individ-
ual is challenging. Objective measures of mortality risk typically
operate at the aggregate level of a chosen population (e.g. area
level risk of trac fatality, or gender-based risk of violent crime)
and may not provide an accurate appraisal of personal risk.
Furthermore, perceptions of mortality risk often do not align with
objective levels of exposure to risk. For example, a primary bias
of risk perception has consistently been reported in which peo-
ple typically overestimate rare risks to their health and underes-
timate common risks [29, 30]. Slovic [31] found that people often
overestimate their risk of dying as a result of homicide or natural
disaster, but underestimate the likelihood of dying due to diabe-
tes, cancer or stroke. In addition, humans may not be accurately
attuned to particular mortality risks that are evolutionarily novel.
If a specic risk was absent or not commonly associated with
death, illness or injury in our ancestral environment, the accu-
racy of our perceptions of this risk may be limited [32]. This sug-
gests a possible mismatch between our perceptual responses
to modern risks and the ancestral environments in which these
psychological mechanisms evolved [33]. For example, we may
be less adept at accurately perceiving the degree of mortality
risk posed by industrial air pollution compared with interper-
sonal violence, as the former is a novel risk that was not directly
linked to survival threats in our evolutionary history [34]. The
Uncontrollable Mortality Risk Hypothesis assumes that psycho-
logical mechanisms should respond to the presence of environ-
mental cues of risk to determine the optimal level of investment
in preventative health, resulting in dierences in motivation
to invest in health eort. We suggest that these dierences in
motivation arise in response to dierences in perceived levels
of exposure to uncontrollable mortality risk. The oft-reported
misalignment between objective and perceived levels of risk
emphasizes the importance of capturing perceptions of mortal-
ity risk, and not just objective levels of exposure. Modelling this
relationship between perceptions of control over risk and health
behaviours provides the basis for the Uncontrollable Mortality
Risk Hypothesis: that those who perceive themselves as being
more likely to die due to factors beyond their control should be
less motivated to invest in preventative health behaviours (see
Fig. 1) [35]. It also provides the basis for a concept we call the
double dividend of safety: improving people’s safety will pro-
vide the additional secondary benet of improving their health
behaviour.
Theoretical origins of the Uncontrollable Mortality Risk
Hypothesis
The Uncontrollable Mortality Risk Hypothesis draws on termi-
nology and theoretical resources from both evolutionary biol-
ogy and life history theory. Classical life history theory is based
on optimization models that aim to explain variation in growth
and reproduction at a species level in terms of maximizing t-
ness (Glossary Term 5) within an environment [36, 37]. Life
history theory has been extended from the species level to the
individual level to predict that an organism should respond to
environmental cues and adapt their energy and resource alloca-
tion strategies accordingly [38–40]. Our specic focus on health
behaviour (rather than energetic allocation to growth, repro-
duction or somatic maintenance) at the individual level (rather
than the species level) dierentiates the Uncontrollable Mortality
Risk Hypothesis from the life history theory which inspired it.
Furthermore, the behavioural ecological model from Nettle [19]
underlying the Uncontrollable Mortality Risk Hypothesis, along
with subsequent research [18, 35, 41, 42], previously referred to
‘extrinsic mortality risk’, whereas ‘uncontrollable mortality risk’
has been used more recently to refer to mortality risk that cannot
be reduced by behaviour [43–46]. This is because the denition
of ‘extrinsic mortality risk’ employed by evolutionary models for
understanding senescence [47–51] diers from that of human
health behaviour literature relevant to the Uncontrollable
Mortality Risk Hypothesis [15, 18, 19, 41, 44, 46, 52–54]. The
former typically denes ‘extrinsic mortality risk’ as an age and
condition-independent component of environmental risk caused
by external hazards such as predation, parasitism and inclement
weather [19, 51]. The Uncontrollable Mortality Risk Hypothesis
broadens the focus from the age, or physiological state of the
organism, to the level of behavioural control that an individual
has in mitigating their own risk of death. It is for this reason that
we refer to the behavioural treatment of extrinsic mortality risk as
‘uncontrollable mortality risk’.
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The Uncontrollable Mortality Risk Hypothesis Brown and Pepper | 
Evidence for the Uncontrollable Mortality Risk Hypothesis
Using a novel measure of perceived uncontrollable mortality risk
in an online survey of US adults, Pepper and Nettle [41] empirically
tested the behavioural ecological predictions of the Uncontrollable
Mortality Risk Hypothesis. They found that the positive relation-
ship between subjective socioeconomic position and health eort
(an association that is commonly reported in public health litera-
ture [1–3]) was mediated by perceived uncontrollable mortality
risk. This nding is supported by our recent replication study [45],
which found that perceived uncontrollable mortality risk partially
mediated the positive relationship between subjective discretionary
income and health eort. Our recent mini meta-analysis found that
perceived uncontrollable mortality risk has been repeatedly shown
to predict lower self-reported health eort [44–46], providing addi-
tional support for the assumptions of the Uncontrollable Mortality
Risk Hypothesis. Levels of perceived uncontrollable mortality risk
were also shown to be higher when taking the threat of COVID-19
into account [42, 55]. During the rst UK lockdown in response to
the COVID-19 pandemic, perceived uncontrollable mortality risk
was associated with lower reported adherence to Government
advice on physical activity, diet and smoking [42]. This evidence for
the Uncontrollable Mortality Risk Hypothesis shows that percep-
tions of uncontrollable mortality risk are a consistent predictor of
health eort, though we acknowledge that many other factors also
inuence the relationship between environmental risk and health
behaviour.
Experimental evidence supporting the Uncontrollable
Mortality Risk Hypothesis is provided by Pepper and Nettle [35].
The authors conducted three experiments that primed percep-
tions of uncontrollable mortality risk by providing participants
with manipulated personalized life expectancy projections and
stating whether these were or were not caused by controllable
individual behaviours. They found that priming perceptions of
uncontrollable mortality risk inuenced a subsequent health
decision—that of choosing a healthy food reward versus an
unhealthy alternative. Whilst this is the only experimental
research directly investigating the eects of perceived uncon-
trollable mortality risk on health perceptions and behaviours,
research exploring the broader impact of structural changes to
risk exposure oers support for the Uncontrollable Mortality Risk
Hypothesis. For example, a neighbourhood renewal programme
in the North East of England that made general improvements to
living conditions (such as addressing damp and draughty hous-
ing) led to improved perceptions of safety and a decline in smok-
ing [56]. Similarly, a regeneration programme involving deprived
communities in Glasgow reported that improvements to inter-
nal housing conditions resulted in a lower likelihood of smoking
and a higher likelihood of eating more fruit and vegetables [57].
Insucient research has been conducted to determine which
specic categories of risk exposure, when addressed, would
most likely result in the greatest reductions in perceived uncon-
trollable mortality risk and subsequent improvements to health
behaviours. However, evidence from the World Risk Poll shows
Figure . Infographic representing the Uncontrollable Mortality Risk Hypothesis of health behaviour.
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Evolution, Medicine, and Public Health | Brown and Pepper
that trac accidents and crime remain the greatest perceived
sources of risk in daily life globally [58]. Similarly, a systematic
review of research on neighbourhood safety factors in the USA
found that levels of trac safety and crime were most relevant to
health behaviours, such as physical activity among older adults
[59]. These categories of risk exposure present potential targets
for structural risk interventions which could lead to a reduction
in perceived uncontrollable mortality risk, and improve health
behaviours.
IMPLICATIONS FOR PUBLIC HEALTH
Highlighting the immediate benets of healthy behaviours
The implications of the Uncontrollable Mortality Risk Hypothesis
draw attention to the potential advantages of highlighting the
immediate benets of healthy behaviours. This is particularly
important in circumstances where individuals may be less moti-
vated to invest in preventive health measures due to an inac-
curately held belief that they are unlikely to live long enough to
enjoy the long-term benets. In such cases, emphasizing the
benets that they are likely to experience more immediately may
help to incentivise healthy behaviours. For example, instead of
highlighting that a healthy diet may improve health in old age,
health messages could emphasize that increased fruit and
vegetable intake gives you visibly more attractive skin within 6
weeks [60]. Or they might emphasize that increased fruit and
vegetable consumption has been linked to improved fertility in
women and better semen quality in men [61]. Research suggests
that it can be more eective to communicate the shorter-term
benets associated with quitting smoking (increasing stamina
and saving money) than the longer-term benets (healthier
teeth and lungs over time) when encouraging smoking cessa-
tion [62]. By highlighting the shorter-term benets of a healthy
lifestyle, health messages may incentivise increased investment
in positive health behaviours, even in those who perceive their
longevity to be largely beyond their personal control. However,
this raises ethical questions about whether it is right to tailor
health information, with the aim of changing health behaviours,
in situations where people’s health perceptions accurately reect
their degree of exposure to uncontrollable risks. As discussed
above, the Uncontrollable Mortality Risk Hypothesis explains
how lower investment in health behaviour can be a ‘contextually
appropriate’ response to environmental cues of risk [18]. It may
be considered unethical to tailor the provision of information to
emphasize shorter-term benets in order to encourage health
behaviours among people who are objectively less likely to enjoy
the longer-term benets. More generally, health interventions
that emphasize the consequences of lifestyle-related risk factors
can encourage the perception that people can and should alter
their behaviour, plausibly contributing to the view that people
are morally responsible for their health [63]. This potential moral-
ization of health promotion may contribute to misguided beliefs
about what healthcare interventions are appropriate and draw
attention away from more eective solutions, such as addressing
exposures to uncontrollable risk at a structural level.
Addressing structural disparities in risk exposure
Chater and Loewenstein [64] recently argued that researchers
and policymakers across the behavioural sciences have typically
sought to frame societal issues in individual (‘i-frame’), not sys-
temic (‘s-frame’) terms. Numerous health interventions have
aimed to address behaviours cheaply and eectively by inuenc-
ing individual choices. For example, a large body of research has
investigated the benets of calorie labelling for tackling obesity
[65–67], whilst others have argued that adopting a more systemic
approach would have a greater impact, such as increasing the tax
on sugar [68–70]. Given the complex challenges of researching
and enacting systemic change, there is likely to be a tendency
for researchers and policymakers alike to opt for an i-frame
approach rather than an s-frame approach. For example, simply
making people (including policymakers) aware of the option of
an i-frame approach to tackling climate change (behaviourally
nudging people towards green energy) has been found to reduce
support for more systemic change (carbon tax) [71]. A similar
distinction exists between high- versus low-agency health inter-
ventions [72]. For example, interventions that focus on delivering
advice and encouragement to adopt healthier lifestyles typically
require high levels of agency for individuals to engage with infor-
mation and change target behaviours. In contrast, interventions
such as food reformulation of high-fat-sugar-salt products, or
fortifying processed foods with essential nutrients, require recip-
ients to use little or no agency to benet. Health interventions
that require a low degree of agency for individuals to benet
are likely to be most eective and equitable. The Uncontrollable
Mortality Risk Hypothesis suggests that those in lower socioeco-
nomic positions, who are generally exposed to greater levels of
uncontrollable risk, are likely to be in greater need of eective
health interventions that facilitate positive health behaviours.
However, the ability to exercise personal agency often relies on
time, physical, and mental resources, which are usually inu-
enced by socioeconomic factors [72]. Therefore, high-agency
health interventions are likely to exacerbate existing socioeco-
nomic gradients in health. Overemphasising the importance of
i-frame and high-agency approaches to improving preventative
health behaviours may inadvertently lead people to blame indi-
viduals for being unable to overcome the eects of uncontrolla-
ble features of their environment. Furthermore, xating on how
best to inuence health choices may direct academic and public
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The Uncontrollable Mortality Risk Hypothesis Brown and Pepper | 
health attention away from addressing structural disparities in
risk exposure.
Cross-domain benets to tackling structural disparities of
risk
What might perceived risk of natural disaster or interpersonal
violence have to do with motivation to eat healthily or quit
smoking? The Uncontrollable Mortality Risk Hypothesis sug-
gests that higher levels of perceived uncontrollable mortality
risk overall should be associated with a lower general investment
in healthy behaviour. Thus, the hypothesis extends beyond the
individual relationships between specic sources of risk and
the health behaviours most obviously related to those sources
of risk. When assessing the impact of structural change to
exposure to risk, interventions based on previous fear appeal
literature (such as Protection Motivation Theory [73–76] and
the Extended Parallel Process Model [77–79]), or those lacking
a broader consideration of causal pathways of behaviour, are
unlikely to go beyond assessing the success of an intervention
in terms of the direct links between specic risks and single,
directly related, health behaviours. For example, in an environ-
ment with a high number of trac fatalities, as well as high
rates of interpersonal violence and air pollution, there may be
less motivation to improve healthy eating behaviour, if such a
change is believed to have little impact on overall mortality risk.
However, if signicant eorts were made to diminish multiple
sources of uncontrollable risk, this may lead to improvements in
a range of health behaviours seemingly unrelated to the sources
of risk. For example, eating a healthier diet may not be an imme-
diately obvious result of improvements to road safety in one’s
environment. However, an improved diet may occur because of
increased health motivation resulting from a reduction in over-
all perceived uncontrollable mortality risk (see Fig. 2). We argue
that greater attention should be given to cross-domain tracking
of behavioural changes resulting from interventions. Clusters
of unhealthy lifestyle behaviours are common, yet most health
intervention research has addressed risk factors as categori-
cally separate entities [80]. It has previously been suggested that
more remains unknown than known about how to change multi-
ple health behaviours both at an individual and population level
[81, 82], leading to calls for greater attention to be given to the
science of multiple health behaviour change [80, 83]. Measuring
perceptions of uncontrollable mortality risk over time, alongside
multiple health behaviours, may help to capture the eects of
changes in exposure to dierent sources of risk (brought about
by both policy changes and naturally occurring shifts in environ-
mental risk). This may help to map out the pathways between
structural changes in exposure to risk and seemingly unrelated
health behaviours.
Figure . Infographic highlighting the benets of cross-domain tracking of behavioural health changes resulting from structural risk interventions.
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Evolution, Medicine, and Public Health | Brown and Pepper
Understanding the public’s ‘general sense’ of risk
Recent research into the drivers of perceived uncontrollable mor-
tality risk suggests that it broadly reects a ‘general sense’ of
one’s environment that is inuenced by perceived exposure to
risk, as well as the level of personally available resources that
someone has to avoid threats to their health and longevity [54].
Similarly, the 2023 Safety Perceptions Index (SPI) report found
a rise in what it calls ‘ambiguous risk’—people’s general sense
that risk exists in the world around them but cannot be precisely
dened [84]. The SPI uses data from the World Risk Poll, which
involves over 125,000 interviews conducted in 121 countries,
to provide a comprehensive assessment of perceptions of risk
across the world [84]. The 2023 SPI report suggested that this
rise in perceptions of ambiguous risk may reect a societal
response to the COVID-19 pandemic [84]. However, the exact
drivers of this rise in perceived ambiguous risk are unknown
and could be a consequence of a changing media landscape that
has an increased tendency to amplify misleading or inconclusive
information [85].
Further study of what informs the public’s general sense of risk
(by using measures such as perceived uncontrollable mortality
risk and the SPI’s construct of ambiguous risk) may identify use-
ful targets for interventions aimed at reducing risk exposure. For
example, objective measures of risk, using data from the Global
Burden of Diseases, Injuries, and Risk Factors Study (GBD) have
been found to predict levels of perceived uncontrollable mortal-
ity risk, although not as strongly as one might expect, suggesting
that more research is needed [44]. Identifying objective mea-
sures of risk exposure that impact the public’s general sense of
risk may indicate the structural changes to risk exposure that will
most eectively harness the double dividend of safety: the ini-
tial risk reduction, plus the subsequent secondary benets from
improved health behaviours.
Finally, comparing perceptions of uncontrollable mortality risk
with objective measures of risk exposure may also help to deter-
mine which intervention approaches are most appropriate. For
example, in a situation where levels of perceived uncontrollable
mortality risk are high, and driven by fears of interpersonal vio-
lence, it would be potentially dangerous to pursue interventions
aimed at lowering the public’s sense of risk if their actual level
of risk is high. This is because lowering perceptions of risk may
lead to an increase in risky behaviours, in an already risky envi-
ronment. Similarly, it would be a waste of resources to design
structural interventions to tackle risk of violence in order to
address elevated levels of perceived risk, in situations where the
objective level of risk is low (see Fig. 3). As discussed, percep-
tions of uncontrollable mortality risk can inuence preventative
health behaviours. Assessing the accuracy of these perceptions
may help to determine the suitability of dierent intervention
approaches.
CONCLUSION
The Uncontrollable Mortality Risk Hypothesis has a range
of implications for future public health strategies. Structural
Figure . Infographic showing the benet of comparing perceptions of uncontrollable mortality risk with objective measures of risk when assessing the suitabil-
ity of interventions (using the example of the risk of interpersonal violence).
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The Uncontrollable Mortality Risk Hypothesis Brown and Pepper | 
changes to address environmental exposures to risk can lead to
the double dividend of safety: the initial risk reduction, plus the
subsequent secondary benets from improved health behaviours.
Public health strategies that consider the broader causal rela-
tionships between overall levels of uncontrollable mortality risk
and multiple health behaviours, rather than focussing on narrow
links between individual risks and obviously related behaviours,
may enable more eective health interventions. By measuring
perceptions of uncontrollable mortality risk, researchers can cap-
ture underappreciated health benets of risk interventions. This
may also help to determine the most eective and appropriate
intervention approaches for addressing exposures to uncontrol-
lable risk.
GLOSSARY OF TERMS
. Adaptive behaviour refers to a behaviour of a living organism
which, in the environment they inhabit, improves their chances
of survival and ultimately of leaving descendants, in comparison
with the chances of a similar organism that does not exhibit the
specied behaviour [86].
. Proximate explanations of behaviour are concerned with
the mechanisms that underpin a behaviour and how it works.
They refer to accounts that point to a cause or event which is
closest to, or immediately responsible for causing, an observed
behaviour. These contrast with ultimate explanations of behaviour
[16, 87].
. Ultimate explanations of behaviour are concerned with
why a behaviour exists. They look to identify the underlying
reason for how a behaviour has manifested through natural
selection. Ultimate explanations are concerned with the tness
consequences and identify why it is (or is not) selected for by
the process of natural selection. This contrasts with proximate
explanations of behaviour [16, 87].
. Optimal strategies in the context of behavioural ecology are
behavioural strategies that maximize tness, given the relevant
environmental circumstances [88].
. Fitness in the context of behavioural ecology typically refers
to a measure of the contribution of an individual to the genetic
composition of subsequent generations through its own o-
spring or the long-term growth rate of a lineage [89, 90].
ACKNOWLEDGEMENTS
We would like to thank Dr Elizabeth Sillence for kindly reviewing
our manuscript and for her thoughtful comments that helped to
enhance the paper. We would also like to acknowledge and thank
the two anonymous peer reviewers who provided comprehen-
sive and constructive feedback that helped to improve the nal
manuscript.
AUTHOR CONTRIBUTIONS
Richard Brown (Conceptualization [Equal], Investigation [Equal],
Project administration [Lead], Visualization [Equal], Writing—
original draft [Lead], Writing—review & editing [Equal]), and
Gillian V. Pepper (Conceptualization [Equal], Investigation
[Equal], Project administration [Supporting], Supervision [Lead],
Visualization [Equal], Writing—review & editing [Equal])
CONFLICTS OF INTEREST
None declared.
FUNDING
This research received no specic grant from any funding agency,
commercial, or not-for-prot sectors.
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