Abstract
Aim
Perceived uncontrollable mortality risk (PUMR) refers to people's beliefs regarding their risk of death due to factors outside of their control. Previous theoretical models and empirical studies provide evidence that those with greater PUMR are less motivated to invest in preventative health behaviors, but little is known about how accurately people estimate PUMR compared to objective measures of risk exposure, an important consideration for interventions designed to address the link between PUMR and health behavior. Here, we explore how objective risk indices and personal characteristics relate to PUMR.
Subject and methods
We performed a series of pre-registered analyses on a US-representative longitudinal study (N = 915), connecting these results to external data from the Global Burden of Diseases, Injuries, and Risk Factors Study.
Results
We show that (Study 1) PUMR is associated with objective measures of risk exposure, and that (Study 2) perceptions of risk due to disease drive PUMR, and more educated individuals report less perceived risk. Additionally, we find that (Study 3) estimates of PUMR are relatively stable over a 4-month period (R = 0.7), indicating that behaviors influenced by PUMR are likely to persist over time. Finally, we show that (Study 4) those who believe they are at greater risk of dying due to factors outside of their control (i.e., greater PUMR) are less likely to engage in general health behaviors.
Conclusion
By assessing the determinants of PUMR, we can create data-driven policy solutions that lead individuals to more accurate mortality risk assessments and improved health behavior.
Similar content being viewed by others
Availability of data, material, and code
All statistical analyses were conducted in R (R Core Team 2021). The R script used for data analysis is available in a public GitHub repository (https://github.com/CalvinIsch/pumr-omr).
References
Al-Maskari F (2010) Lifestyle diseases: An economic burden on the health services. UN Chronicle. The Magazine of the United Nations
Anadu EC, Harding AK (2000) Risk perception and bottled water use. J-Am Water Works Assoc 92(11):82–92
Bates D, Mächler M, Bolker B, Walker S (2015) Fitting Linear Mixed-Effects Models Using lme4. J Stat Software 67(1):1–48. https://doi.org/10.48550/arXiv.1406.5823
Beck U (1992) Risk society. SAGE, London
Bivand R, Rundel C, Pebesma E, Stuetz R, Hufthammer KO, Bivand MR (2017) Package ‘rgeos’. The Comprehensive R Archive Network (CRAN)
Blais AR, Weber EU (2006) A domain-specific risk-taking (DOSPERT) scale for adult populations. Judgment and Decision making 1 (1)
Brown RD, Pepper GV (2023) The Uncontrollable Mortality Risk Hypothesis of Health Behaviour: a Position Paper. PREPRINT. doi:https://doi.org/10.31219/osf.io/py7dw
Brown RD, Coventry L, Pepper GV (2021) COVID-19: the relationship between perceptions of risk and behaviors during lockdown. J Public Health:1-11
Brown RD, Sillence E, Pepper GV (2022) Perceptions of control over different causes of death and the accuracy of risk estimations. Manuscript in preparation
Brown RD, Sillence E, Pepper GV (2023) Individual characteristics associated with perceptions of control over mortality risk and determinants of health effort. Preprint. https://doi.org/10.31219/osf.io/dpgvf
Brownrigg R, Minka TP, Deckmyn A (2018) maps: Draw Geographical Maps. R package version 3.3.0. Original S code by R.A. Becker, A.R. Wilks
Center for Disease Control and Prevention (2022) National Center for Chronic Disease Prevention and Health Promotion: About the Center. Retrieved from https://www.cdc.gov/chronicdisease/center/index.htm
Chamberlain S, Teucher A (2021) geojsonio: Convert Data from and to 'GeoJSON' or 'TopoJSON'. R package version 0.9.4
Cheng J, Karambelkar B, Xie Y et al (2019) Package ‘leaflet’. R package version 2.1.1
Cori L, Donzelli G, Gorini F et al (2020) Risk Perception of Air Pollution: A Systematic Review Focused on Particulate Matter Exposure. Int J Environ Res Public Health 17(17):6424
de França Doria M (2010) Factors influencing public perception of drinking water quality. Water policy 12(1):1–19
Global Burden of Disease Study 2019 (2019) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2020. Data available from http://ghdx.healthdata.org/gbd-results-tool. Accessed 15 April 2022
Henrich J (2020) The WEIRDest people in the world: How the West became psychologically peculiar and particularly prosperous. Penguin UK
Jones J, Aslan A, Trivedi R, Olivas M, Hoffmann M (2018) Water quality and the perception of risk: A study of Georgia, USA, beachgoers. Ocean Coastal Manag 158:116–119
Kassambara A (2020) Package ‘ggpubr’. R package version 0.1, 6
Kim Y, Park I, Kang S (2018) Age and gender differences in health risk perception. Central Eur J Public Health 26(1):54–59
Komsta L, Novomestky F (2015) Moments, cumulants, skewness, kurtosis and related tests. R package version 14
Leiter MP, Zanaletti W, Argentero P (2009) Occupational risk perception, safety training, and injury prevention: testing a model in the Italian printing industry. J Occupational Health Psychol 14(1):1–10
Lo AY (2014) Negative income effect on perception of long-term environmental risk. Ecol Econ 107:51–58
Lorenzo PD (2019) usmap: US Maps Including Alaska and Hawaii. R package version 0.5.0
Morgenroth T, Fine C, Ryan MK, Genat AE (2018) Sex, drugs, and reckless driving: Are measures biased toward identifying risk-taking in men? Social Psychol Person Sci 9(6):744–753
Murray CJ, Aravkin AY, Zheng P et al (2020) Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396(10258):1223–1249
Nettle D (2010) Why are there social gradients in preventative health behavior? A perspective from behavioral ecology. PLoS One 5(10):e13371
Pachur T, Hertwig R, Rieskamp J (2013) Intuitive judgments of social statistics: How exhaustive does sampling need to be? J Exp Social Psychol 49(6):1059–1077
Pebesma EJ (2018) Simple features for R: standardized support for spatial vector data. R J. 10(1):439
Pepper GV, Nettle D (2013) Death and the time of your life: experiences of close bereavement are associated with steeper financial future discounting and earlier reproduction. Evol Human Behavior 34(6):433–439
Pepper GV, Nettle D (2014a) Perceived extrinsic mortality risk and reported effort in looking after health. Human Nat 25(3):378–392
Pepper GV, Nettle D (2014b) Out of control mortality matters: the effect of perceived uncontrollable mortality risk on a health-related decision. PeerJ 2:e459
Pepper GV, Nettle D (2017) The behavioral constellation of deprivation: Causes and consequences. Behavioral and Brain Sciences 40:e314
Peterson RA, Peterson, MRA (2020) Package ‘bestNormalize’. Published online 27
R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
Rattay P, Michalski N, Domanska OM et al (2021) Differences in risk perception, knowledge and protective behavior regarding COVID-19 by education level among women and men in Germany. Results from the COVID-19 Snapshot Monitoring (COSMO) study. Plos one 16(5):e0251694
Rhodes N, Pivik K (2011) Age and gender differences in risky driving: The roles of positive affect and risk perception. Accident Anal Prev 43(3):923–931
Ritchie H, Mathieu E, Rodés-Guirao L et al (2020) Coronavirus pandemic (COVID-19). Our world in data. Retrieved from https://ourworldindata.org/coronavirus
Sun Y, Han Z (2018) Climate change risk perception in Taiwan: Correlation with individual and societal factors. Int J Environ Res Public Health 15(1):91
Szrek H, Chao LW, Ramlagan S, Peltzer K (2012) Predicting (un) healthy behavior: A comparison of risk-taking propensity measures. Judgment Decision Making 7(6):716–727
Uggla C, Mace R (2015) Effects of local extrinsic mortality rate, crime and sex ratio on preventable death in Northern Ireland. Evol Med Public Health 1:266–277
Wickham H (2011) ggplot2. Wiley Interdisciplin Rev: Comput Stat 3(2):180–185
Wickham H, Averick M, Bryan J et al (2019) Welcome to the Tidyverse. J Open Source Software 4(43):1686
Funding
This study was funded by the Interdisciplinary Cooperation Initiative, ASU President's Office, the Cooperation Science Network, the Institute for Mental Health Research, the University of New Mexico, the Indiana University College of Arts & Sciences, the Rutgers University Center for Human Evolutionary Studies, the Charles Koch Foundation, and the John Templeton Foundation. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of our funders.
Author information
Authors and Affiliations
Contributions
Conceptualization: Calvin Isch, Richard Brown, and Gillian Pepper. Methodology: Calvin Isch, Richard Brown, and Gillian Pepper. Formal analysis and investigation: Calvin Isch, Richard Brown, and Gillian Pepper. Writing—original draft preparation: Calvin Isch, Richard Brown, and Gillian Pepper. Writing—review and editing: Calvin Isch, Richard Brown, Peter Todd, Athena Aktipis, and Gillian Pepper. Funding acquisition: Peter Todd and Athena Aktipis. Resources: Peter Todd and Athena Aktipis. Supervision: Peter Todd, Athena Aktipis, and Gillian Pepper
Corresponding author
Ethics declarations
Ethics approval
This study was approved by the internal review board at Arizona State University (HRP-503a). Our predictions, measures, and analysis plan are pre-registered with the Open Science Framework (https://osf.io/wy63d/)
Consent to participate
All participants provided informed consent to participate by responding to the following statement: “By continuing and completing the associated survey you certify that you are at least 18 years old, have carefully read this consent form, consent to be contacted through Prolific as a follow up, and agree to participate in this research study. You also understand that you are free to withdraw from this study at any time. No one under the age of 18 is allowed to participate.”
Consent for publication
Not applicable
Conflicts of interest
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
ESM 1
(PDF 748 kb)
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Isch, C., Brown, R., Todd, P.M. et al. Objective risk exposure, perceived uncontrollable mortality risk, and health behaviors. J Public Health (Berl.) (2023). https://doi.org/10.1007/s10389-023-01994-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10389-023-01994-2