Abstract
Extreme heat causes more deaths than tornadoes and floods combined in the United States. While vulnerable populations are at higher risk of heat-health impacts, anyone can be at risk from extreme heat without appropriate actions. Therefore, heat risk communication efforts, especially those on a wide scale, should engage not only the vulnerable subgroups but also the entire population with the goal of encouraging everyone to take appropriate protective actions during extreme heat events. As one step to achieve this goal, this study examined how to effectively depict people’s susceptibility in heat risk messages. Using a survey experiment (N = 1386), this study compared the effectiveness of four statements that varied how they depicted which types of people were susceptible to heat-health impacts. Relative to traditional messaging that lists specific vulnerable subgroups, a statement that “anyone can be at risk” and a statement without susceptibility information were respectively more effective in making messages personally relevant. Mentioning the “anyone can be at risk” statement and the “certain subgroups are at more risk” statement together reduced belief in the hazard happening compared to mentioning the latter statement individually. Implications for risk communication in broader domains are discussed.
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References
Anderson BG, Bell ML (2009) Weather-related mortality: how heat, cold, and heat waves affect mortality in the United States. Epidemiology 20:205–213. https://doi.org/10.1097/EDE.0b013e318190ee08
Bargh JA (1982) Attention and automaticity in the processing of self-relevant information. J Pers Soc Psychol 43:425–436. https://doi.org/10.1037/0022-3514.43.3.425
Bruine de Bruin W, Lefevre CE, Taylor AL et al (2016) Promoting protection against a threat that evokes positive affect: the case of heat waves in the United Kingdom. J Exp Psychol Appl 22:261–271. https://doi.org/10.1037/xap0000083
Centers for Disease Control and Prevention (n.d.) Heat & Health Tracker. https://ephtracking.cdc.gov/Applications/heatTracker/. Accessed 26 Jan 2023
Centers for Disease Control and Prevention (2020) Underlying Cause of Death 1999–2018 on CDC WONDER Online Database. https://wonder.cdc.gov/ucd-icd10.html. Accessed 13 May 2020
Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Erlbaum Associates: Hillsdale
Coppock A, Leeper TJ, Mullinix KJ (2018) Generalizability of heterogeneous treatment effect estimates across samples. Proc Natl Acad Sci 115:12441–12446. https://doi.org/10.1073/pnas.1808083115
Cox J, House D, Lindell M (2013) Visualizing uncertainty in predicted hurricane tracks. Int J Uncertain Quant 3:143–156. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2012003966
Demeritt D, Nobert S (2014) Models of best practice in flood risk communication and management. Environ Hazards 13:313–328. https://doi.org/10.1080/17477891.2014.924897
Doyle JK (2006) Judging cumulative risk. J Appl Soc Psychol 27:500–524. https://doi.org/10.1111/j.1559-1816.1997.tb00644.x
Doyle EEH, Johnston DM, McClure J, Paton D (2011) The communication of uncertain scientific advice during natural hazard events. N Z J Psychol 40:39–50
Gallagher KM, Updegraff JA (2011) Health message framing effects on attitudes, intentions, and behavior: a meta-analytic review. Ann Behav Med 43:101–116. https://doi.org/10.1007/s12160-011-9308-7
Gallagher KM, Updegraff JA, Rothman AJ, Sims L (2011) Perceived susceptibility to breast cancer moderates the effect of gain-and loss-framed messages on use of screening mammography. Health Psychol 30:145. https://doi.org/10.1037/a0022264
Harduar Morano L, Watkins S, Kintziger K (2016) A comprehensive evaluation of the burden of heat-related illness and death within the Florida population. Int J Environ Res Public Health 13:551. https://doi.org/10.3390/ijerph13060551
Health Canada (2011) Communicating the health risks of extreme heat events: toolkit for public health and emergency management officials. Ottawa, Ontario
Hess JJ, Saha S, Luber G (2014) Summertime acute heat illness in US emergency departments from 2006 through 2010: analysis of a nationally representative sample. Environ Health Perspect (online) 122:1209. https://doi.org/10.1289/ehp.1306796
IPCC (2012) Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Cambridge University Press, Cambridge, UK, and New York, NY, USA
Jaeger RG, Halliday TR (1998) On confirmatory versus exploratory research. Herpetologica 54:S64–S66
Jensen JD, King AJ, Carcioppolo N, Davis L (2012) Why are tailored messages more effective? A multiple mediation analysis of a breast cancer screening intervention. J Commun 62:851–868. https://doi.org/10.1111/j.1460-2466.2012.01668.x
Kalkstein AJ, Sheridan SC (2007) The social impacts of the heat–health watch/warning system in Phoenix, Arizona: assessing the perceived risk and response of the public. Int J Biometeorol 52:43–55. https://doi.org/10.1007/s00484-006-0073-4
Keller C, Siegrist M, Gutscherc H (2006) The role of the affect and availability heuristics in risk communication. Risk Anal 26:631–639. https://doi.org/10.1111/j.1539-6924.2006.00773.x
Ko LK, Campbell MK, Lewis MA et al (2011) Information processes mediate the effect of a health communication intervention on fruit and vegetable consumption. J Health Commun 16:282–299. https://doi.org/10.1080/10810730.2010.532294
Lakens D (2013) Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol 4:863. https://doi.org/10.3389/fpsyg.2013.00863
Lebel L, Lebel P, Lebel B et al (2018) The effects of tactical message inserts on risk communication with fish farmers in Northern Thailand. Reg Environ Change 18:2471–2481. https://doi.org/10.1007/s10113-018-1367-x
Li Y, Hughes AL, Howe PD (2021) Toward win-win message strategies: the effects of persuasive message content on retweet counts during natural hazard events. Weather Clim Soc 13:487–502. https://doi.org/10.1175/WCAS-D-20-0039.1
Li Y, Hughes AL, Howe PD (2018) Communicating crisis with persuasion: examining official twitter messages on heat hazards. In: Boersma K, Tomaszewski B (eds) Proceedings of the 15th international conference on information systems for crisis response and management. Rochester, NY, USA, pp 469–479
Lippmann SJ, Fuhrmann CM, Waller AE, Richardson DB (2013) Ambient temperature and emergency department visits for heat-related illness in North Carolina, 2007–2008. Environ Res 124:35–42. https://doi.org/10.1016/j.envres.2013.03.009
Litschge CM, Vaughn MG, McCrea C (2010) The empirical status of treatments for children and youth with conduct problems: an overview of meta-analytic studies. Res Soc Work Pract 20:21–35. https://doi.org/10.1177/1049731508331247
Mayrhuber EA-S, Dückers MLA, Wallner P et al (2018) Vulnerability to heatwaves and implications for public health interventions–a scoping review. Environ Res 166:42–54. https://doi.org/10.1016/j.envres.2018.05.021
Mehiriz K, Gosselin P, Tardif I, Lemieux M-A (2018) The effect of an automated phone warning and health advisory system on adaptation to high heat episodes and health services use in vulnerable groups—evidence from a randomized controlled study. Int J Environ Res Public Health 15:1581. https://doi.org/10.3390/ijerph15081581
Mileti DS, Sorensen JH (1990) Communication of emergency public warnings: a social science perspective and state-of-the-art assessment (No. ORNL-6609)
Mora C, Dousset B, Caldwell IR et al (2017b) Global risk of deadly heat. Nat Clim Chang 7:501–506. https://doi.org/10.1038/nclimate3322
Mora C, Counsell CWW, Bielecki CR, Louis LV (2017) Twenty-seven ways a heat wave can kill you: deadly heat in the era of climate change. Circulat Cardiovasc Qual Outcomes 10:e004233. https://doi.org/10.1161/CIRCOUTCOMES.117.004233
Morss RE, Cuite CL, Demuth JL et al (2018) Is storm surge scary? The influence of hazard, impact, and fear-based messages and individual differences on responses to hurricane risks in the USA. Int J Disas Risk Reduct 30:44–58. https://doi.org/10.1016/j.ijdrr.2018.01.023
National Weather Service (2020) NWS Experimental HeatRisk. https://www.wrh.noaa.gov/wrh/heatrisk/. Accessed 3 Mar 2020
Nitschke M, Krackowizer A, Hansen AL et al (2017) Heat health messages: a randomized controlled trial of a preventative messages tool in the older population of South Australia. Int J Environ Res Public Health 14:992. https://doi.org/10.3390/ijerph14090992
Petty RE, Cacioppo JT, Goldman R (1981) Personal involvement as a determinant of argument-based persuasion. J Pers Soc Psychol 41:847–855. https://doi.org/10.1037/0022-3514.41.5.847
Phillips BD, Morrow BH (2007) Social science research needs: focus on vulnerable populations, forecasting, and warnings. Nat Hazard Rev 8:61–68. https://doi.org/10.1061/ASCE1527-698820078:361
Portnoy DB, Scott-Sheldon LAJ, Johnson BT, Carey MP (2008) Computer-delivered interventions for health promotion and behavioral risk reduction: a meta-analysis of 75 randomized controlled trials, 1988–2007. Prev Med 47:3–16. https://doi.org/10.1016/j.ypmed.2008.02.014
Potter SH, Kreft PV, Milojev P et al (2018) The influence of impact-based severe weather warnings on risk perceptions and intended protective actions. Int J Disas Risk Reduct 30:34–43. https://doi.org/10.1016/j.ijdrr.2018.03.031
Reynolds B, Seeger MW (2005) Crisis and emergency risk communication as an integrative model. J Health Commun 10:43–55. https://doi.org/10.1080/10810730590904571
Sampson NR, Gronlund CJ, Buxton MA et al (2013) Staying cool in a changing climate: reaching vulnerable populations during heat events. Glob Environ Chang 23:475–484. https://doi.org/10.1016/j.gloenvcha.2012.12.011
Saunders TJ, Taylor AH, Atkinson QD (2016) No evidence that a range of artificial monitoring cues influence online donations to charity in an MTurk sample. R Soc Open Sci 3:150710. https://doi.org/10.1098/rsos.150710
Sheridan SC (2007) A survey of public perception and response to heat warnings across four North American cities: an evaluation of municipal effectiveness. Int J Biometeorol 52:3–15. https://doi.org/10.1007/s00484-006-0052-9
Smoyer-Tomic KE, Kuhn R, Hudson A (2003) Heat wave hazards: an overview of heat wave impacts in Canada. Nat Hazards 28:465–486. https://doi.org/10.1023/A:1022946528157
So J, Kuang K, Cho H (2016) Reexamining fear appeal models from cognitive appraisal theory and functional emotion theory perspectives. Commun Monogr 83:120–144. https://doi.org/10.1080/03637751.2015.1044257
Sutton J, Vos SC, Wood MM, Turner M (2018) Designing effective tsunami messages: examining the role of short messages and fear in warning response. Weather Clim Soc 10:75–87. https://doi.org/10.1175/WCAS-D-17-0032.1
Takahashi N, Nakao R, Ueda K et al (2015) Community trial on heat related-illness prevention behaviors and knowledge for the elderly. Int J Environ Res Public Health 12:3188–3214. https://doi.org/10.3390/ijerph120303188
Talley L, Temple S (2015) How leaders influence followers through the use of nonverbal communication. Leadersh Org Dev J 36:69–80. https://doi.org/10.1108/LODJ-07-2013-0107
Tannenbaum MB, Hepler J, Zimmerman RS et al (2015) Appealing to fear: a meta-analysis of fear appeal effectiveness and theories. Psychol Bull 141:1178–1204. https://doi.org/10.1037/a0039729
Thomas KA, Clifford S (2017) Validity and mechanical turk: an assessment of exclusion methods and interactive experiments. Comput Hum Behav 77:184–197. https://doi.org/10.1016/j.chb.2017.08.038
U.S. Census Bureau (2019) American Community Survey 1-Year Data (2011-2018). https://www.census.gov/data/developers/data-sets/acs-1year.html. Accessed 1 Nov 2019
U.S. EPA (2006) Excessive Heat Events Guidebook. Washington, DC
Witte K (1992) Putting the fear back into fear appeals: the extended parallel process model. Commun Monogr 59:329–349. https://doi.org/10.1080/03637759209376276
Witte K (1993) Message and conceptual confounds in fear appeals: the role of threat, fear, and efficacy. South Commun J 58:147–155. https://doi.org/10.1080/10417949309372896
Wolf J, Adger WN, Lorenzoni I et al (2010) Social capital, individual responses to heat waves and climate change adaptation: an empirical study of two UK cities. Glob Environ Chang 20:44–52. https://doi.org/10.1016/j.gloenvcha.2009.09.004
Acknowledgments
This study was supported in part by the National Science Foundation, award SES-1459903 “Collaborative Research: Multi-Scale Modeling of Public Perceptions of Heat Wave Risk.”
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This work was supported by National Science Foundation (US) (SES-1459903).
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Li, Y., Howe, P.D. Universal or targeted approaches? an experiment about heat risk messaging. Nat Hazards 117, 381–398 (2023). https://doi.org/10.1007/s11069-023-05864-8
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DOI: https://doi.org/10.1007/s11069-023-05864-8