Annals of Behavioral Medicine

, Volume 51, Issue 2, pp 292–306 | Cite as

Numeracy Predicts Risk of Pre-Hospital Decision Delay: a Retrospective Study of Acute Coronary Syndrome Survival

  • Dafina Petrova
  • Rocio Garcia-Retamero
  • Andrés Catena
  • Edward Cokely
  • Ana Heredia Carrasco
  • Antonio Arrebola Moreno
  • José Antonio Ramírez Hernández
Original Article



Many patients delay seeking medical attention during acute coronary syndromes (ACS), profoundly increasing their risk for death and major disability. Although research has identified several risk factors, efforts to improve patient decision making have generally been unsuccessful, prompting a call for more research into psychological factors.


The purpose of this study is to estimate the relationship between ACS decision delay and numeracy, a factor closely related to general decision making skill and risk literacy.


About 5 days after experiencing ACS, 102 survivors (mean age = 58, 32–74) completed a questionnaire including measures of numeracy, decision delay, and other relevant factors (e.g., anxiety, depression, symptom severity, knowledge, demographics).


Low patient numeracy was related to longer decision delay, OR = 0.64 [95 % confidence interval (CI) 0.44, 0.92], which was in turn related to higher odds of positive troponin on arrival at the hospital, OR = 1.37 [95 % CI 1.01, 2.01]. Independent of the influence of all other assessed factors, a patient with high (vs. low) numeracy was about four times more likely to seek medical attention within the critical first hour after symptom onset (i.e., ORhigh-low = 3.84 [1.127, 11.65]).


Numeracy may be one of the largest decision delay risk factors identified to date. Results accord with theories emphasizing potentially pivotal roles of patient deliberation, denial, and outcome understanding during decision making. Findings suggest that brief numeracy assessments may predict which patients are at greater risk for life-threatening decision delay and may also facilitate the design of risk communications that are appropriate for diverse patients who vary in risk literacy.


Numeracy Delay in seeking medical attention Acute coronary syndrome Decision making, risk literacy Health literacy Heart attack 


  1. 1.
    World Health Organization. The top 10 causes of death. Fact sheet N°310. 2014.Google Scholar
  2. 2.
    Heron M. Deaths: Leading causes for 2013. Natl Vital Stat Rep. 2016;65(2):1–95.Google Scholar
  3. 3.
    Turpie AG. Burden of disease: Medical and economic impact of acute coronary syndromes. Am J Manag Care. 2006;12(16):S430.PubMedGoogle Scholar
  4. 4.
    Amsterdam EA, Wenger NK, Brindis RG, et al. 2014 AHA/ACC guideline for the management of patients with non-ST-elevation acute coronary syndromes: A report of the American College of Cardiology/American Heart Association task force on practice guidelines. J Am Coll Cardiol. 2014;64(24):e139-e228.CrossRefPubMedGoogle Scholar
  5. 5.
    Newby LK, Rutsch WR, Califf RM, et al. Time from symptom onset to treatment and outcomes after thrombolytic therapy. J Am Coll Cardiol. 1996;27(7):1646–1655.CrossRefPubMedGoogle Scholar
  6. 6.
    Berger PB, Ellis SG, Holmes DR Jr, et al. Relationship between delay in performing direct coronary angioplasty and early clinical outcome in patients with acute myocardial infarction: Results from the global use of strategies to open occluded arteries in acute coronary syndromes (GUSTO-IIb) trial. Circulation. 1999;100(1):14–20.CrossRefPubMedGoogle Scholar
  7. 7.
    Goldberg RJ, Gurwitz J, Yarzebski J, et al. Patient delay and receipt of thrombolytic therapy among patients with acute myocardial infarction from a community-wide perspective. Am J Cardiol. 1992;70(4):421–425.CrossRefPubMedGoogle Scholar
  8. 8.
    Goldberg RJ, Mooradd M, Gurwitz JH, et al. Impact of time to treatment with tissue plasminogen activator on morbidity and mortality following acute myocardial infarction (the second National Registry of myocardial infarction). Am J Cardiol. 1998;82(3):259–264.CrossRefPubMedGoogle Scholar
  9. 9.
    Perkins-Porras L, Whitehead DL, Strike PC, Steptoe A. Pre-hospital delay in patients with acute coronary syndrome: Factors associated with patient decision time and home-to-hospital delay. Eur J Cardiovasc Nurs. 2009;8(1):26–33.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Moser DK, Kimble LP, Alberts MJ, et al. Reducing delay in seeking treatment by patients with acute coronary syndrome and stroke: A scientific statement from the American Heart Association Council on cardiovascular nursing and stroke council. J Cardiovasc Nurs. 2007;22(4):326–343.CrossRefPubMedGoogle Scholar
  11. 11.
    DeVon HA, Hogan N, Ochs AL, Shapiro M. Time to treatment for acute coronary syndromes: The cost of indecision. J Cardiovasc Nurs. 2010;25(2):106–114.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Wechkunanukul K, Grantham H, Clark RA. Global review of delay time in seeking medical care for chest pain: An integrative literature review. Australian Critical Care. 2016.Google Scholar
  13. 13.
    Mackay MH, Ratner PA, Nguyen M, Percy M, Galdas P, Grunau G. Inconsistent measurement of acute coronary syndrome patients’ pre-hospital delay in research: A review of the literature. Eur J Cardiovasc Nurs. 2014;13(6):483–493.CrossRefPubMedGoogle Scholar
  14. 14.
    Smolderen KG, Spertus JA, Nallamothu BK, et al. Health care insurance, financial concerns in accessing care, and delays to hospital presentation in acute myocardial infarction. JAMA. 2010;303(14):1392–1400.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Nguyen HL, Saczynski JS, Gore JM, Goldberg RJ. Age and sex differences in duration of prehospital delay in patients with acute myocardial infarction: A systematic review. Circ Cardiovasc Qual Outcomes. 2010;3(1):82–92.CrossRefPubMedGoogle Scholar
  16. 16.
    Mooney M, McKee G, Fealy G, O’Brien F, O’Donnell S, Moser D. A review of interventions aimed at reducing pre-hospital delay time in acute coronary syndrome: What has worked and why? Eur J Cardiovasc Nurs. 2012;11(4):445–453.CrossRefPubMedGoogle Scholar
  17. 17.
    Mooney M, McKee G, Fealy G, O’Brien F, O’Donnell S, Moser D. A randomized controlled trial to reduce prehospital delay time in patients with acute coronary syndrome (ACS). J Emerg Med. 2014;46(4):495–506.CrossRefPubMedGoogle Scholar
  18. 18.
    Albarqouni L, Smenes K, Meinertz T, et al. Patients’ knowledge about symptoms and adequate behaviour during acute myocardial infarction and its impact on delay time: Findings from the multicentre MEDEA Study. Patient Educ Couns. 2016.Google Scholar
  19. 19.
    Reyna VF, Nelson WL, Han PK, Dieckmann NF. How numeracy influences risk comprehension and medical decision making. Psychol Bull. 2009;135(6):943–973.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Peters E. Beyond comprehension: The role of numeracy in judgments and decisions. Curr Dir Psychol Sci. 2012;21(1):31–35.CrossRefGoogle Scholar
  21. 21.
    Peters E, Hibbard J, Slovic P, Dieckmann N. Numeracy skill and the communication, comprehension, and use of risk-benefit information. Health Aff. 2007;26(3):741–748.CrossRefGoogle Scholar
  22. 22.
    Garcia-Retamero R, Galesic M. Transparent communication of health risks: Overcoming cultural differences. New York: Springer; 2013.CrossRefGoogle Scholar
  23. 23.
    Garcia-Retamero R, Andrade A, Sharit J, Ruiz JG. Is patients’ numeracy related to physical and mental health? Med Decis Making. 2015;35(4):501–511.CrossRefPubMedGoogle Scholar
  24. 24.
    Apter AJ, Cheng J, Small D, et al. Asthma numeracy skill and health literacy. J Asthma. 2006;43(9):705–710.CrossRefPubMedGoogle Scholar
  25. 25.
    Ginde AA, Clark S, Goldstein JN, Camargo CA. Demographic disparities in numeracy among emergency department patients: Evidence from two multicenter studies. Patient Educ Couns. 2008;72(2):350–356.CrossRefPubMedGoogle Scholar
  26. 26.
    Cavanaugh K, Huizinga MM, Wallston KA, et al. Association of numeracy and diabetes control. Ann Intern Med. 2008;148(10):737–746.CrossRefPubMedGoogle Scholar
  27. 27.
    Marden S, Thomas P, Sheppard Z, Knott J, Lueddeke J, Kerr D. Poor numeracy skills are associated with glycaemic control in type 1 diabetes. Diabetic Med. 2012;29(5):662–669.CrossRefPubMedGoogle Scholar
  28. 28.
    Osborn CY, Cavanaugh K, Wallston KA, Rothman RL. Self-efficacy links health literacy and numeracy to glycemic control. J Health Commun. 2010;15:146–158.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Abdel-Kader K, Dew MA, Bhatnagar M, et al. Numeracy skills in CKD: Correlates and outcomes. Clin J Am Soc Nephrol. 2010;5(9):1566–1573.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    McNaughton CD, Collins SP, Kripalani S, et al. Low numeracy is associated with increased odds of 30-day emergency department or hospital recidivism for patients with acute heart failure. Circ Heart Fail. 2013;6(1):40–46.CrossRefPubMedGoogle Scholar
  31. 31.
    Estrada CA, Martin-Hryniewicz M, Peek BT, Collins C, Byrd JC. Literacy and numeracy skills and anticoagulation control. Am J Med Sci. 2004;328(2):88–93.CrossRefPubMedGoogle Scholar
  32. 32.
    Galesic M, Garcia-Retamero R. Communicating consequences of risky behaviors: Life expectancy versus risk of disease. Patient Educ Couns. 2011;82(1):30–35.CrossRefPubMedGoogle Scholar
  33. 33.
    Zikmund-Fisher BJ, Smith DM, Ubel PA, Fagerlin A. Validation of the subjective numeracy scale: Effects of low numeracy on comprehension of risk communications and utility elicitations. Med Decis Making. 2007;27(5):663–671.CrossRefPubMedGoogle Scholar
  34. 34.
    Cokely ET, Feltz A, Allan J, Ghazal S, Petrova D, Garcia-Retamero R. Decision making skill: From intelligence to numeracy and expertise. In: Ericsson A, ed. Cambridge Handbook on Expertise and Expert Performance. 2nd ed. Cambridge University Press; 2016.Google Scholar
  35. 35.
    Cokely ET, Galesic M, Schulz E, Ghazal S, Garcia-Retamero R. Measuring risk literacy: The Berlin numeracy test. Judg Decis Making. 2012;7:25–47.Google Scholar
  36. 36.
    Cokely ET, Kelley CM. Cognitive abilities and superior decision making under risk: A protocol analysis and process model evaluation. Judg Decis Making. 2009;4(1):20–33.Google Scholar
  37. 37.
    Reyna VF. A new intuitionism: Meaning, memory, and development in fuzzy-trace theory. Judg Decis Making. 2012;7(3):332–359.Google Scholar
  38. 38.
    Peters E, Västfjäll D, Slovic P, Mertz CK, Mazzocco K, Dickert S. Numeracy and decision making. Psychol Sci. 2006;17(5):407–413.CrossRefPubMedGoogle Scholar
  39. 39.
    Garcia-Retamero R, Cokely ET, Wicki B, Joeris A. Improving risk literacy in surgeons. Patient Educ Couns. 2016;99(7):1156–1161.CrossRefPubMedGoogle Scholar
  40. 40.
    Liberali JM, Reyna VF, Furlan S, Stein LM, Pardo ST. Individual differences in numeracy and cognitive reflection, with implications for biases and fallacies in probability judgment. J Behav Decis Making. 2012;25(4):361–381.CrossRefGoogle Scholar
  41. 41.
    Finucane ML, Gullion CM. Developing a tool for measuring the decision making competence of older adults. Psychol Aging. 2010;25(2):271.CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Ghazal S, Cokely ET, Garcia-Retamero R. Predicting biases in very highly educated samples: Numeracy and metacognition. Judg Decis Making. 2014;9(1):15–34.Google Scholar
  43. 43.
    Dieckmann NF, Peters E, Leon J, et al. The role of objective numeracy and fluid intelligence in sex-related protective behaviors. Curr HIV Res. 2015;13(5):337–346.CrossRefPubMedGoogle Scholar
  44. 44.
    Gigerenzer G, Gaissmaier W, Kurz-Milcke E, Schwartz LM, Woloshin S. Helping doctors and patients make sense of health statistics. Psychol Sci Public Interest. 2007;8(2):53–96.CrossRefPubMedGoogle Scholar
  45. 45.
    Gigerenzer G, Hoffrage U. How to improve Bayesian reasoning without instruction: Frequency formats. Psychol Rev. 1995;102(4):684–704.CrossRefGoogle Scholar
  46. 46.
    Garcia-Retamero R, Cokely ET, Hoffrage U. Visual aids improve diagnostic inferences and metacognitive judgment calibration. Front Psychol. 2015;6:932.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Nunes T, Bryant P, Evans D, Gottardis L, Terlektsi ME. Teaching mathematical reasoning: Probability and problem solving in primary school. Nuffield Foundation. 2015.Google Scholar
  48. 48.
    Garcia-Retamero R, Cokely ET. The influence of skills, message frame, and visual aids on prevention of sexually transmitted diseases. J Behav Decis Making. 2014;27(2):179–189.CrossRefGoogle Scholar
  49. 49.
    Garcia-Retamero R, Cokely ET. Communicating health risks with visual aids. Curr Dir Psychol Sci. 2013;22(5):392–399.CrossRefGoogle Scholar
  50. 50.
    Garcia-Retamero R, Petrova D, Arrebola-Moreno A, Catena A, Ramírez-Hernández JA. Type D personality is related to severity of acute coronary syndrome in patients with recurrent cardiovascular disease. Br J Health Psychol. 2016.Google Scholar
  51. 51.
    Burnett RE, Blumenthal JA, Mark DB, Leimberger JD, Califf RM. Distinguishing between early and late responders to symptoms of acute myocardial infarction. Am J Cardiol. 1995;75(15):1019–1022.CrossRefPubMedGoogle Scholar
  52. 52.
    Riegel B, McKinley S, Moser DK, Meischke H, Doering L, Dracup K. Psychometric evaluation of the acute coronary syndrome (ACS) response index. Res Nurs Health. 2007;30(6):584–594.CrossRefPubMedGoogle Scholar
  53. 53.
    Schwartz LM, Woloshin S, Black WC, Welch HG. The role of numeracy in understanding the benefit of screening mammography. Ann Int Med. 1997;127(11):966–972.CrossRefPubMedGoogle Scholar
  54. 54.
    Cokely ET, Ghazal S, Garcia-Retamero R. Measuring numeracy. In: Anderson BL, Schulkin J, eds. Numerical reasoning in judgments and decision making about health. Cambridge: Cambridge University Press; 2014:11–38.CrossRefGoogle Scholar
  55. 55.
    Fagerlin A, Zikmund-Fisher BJ, Ubel PA, Jankovic A, Derry HA, Smith DM. Measuring numeracy without a math test: Development of the subjective numeracy scale. Med Decis Making. 2007;27(5):672–680.CrossRefPubMedGoogle Scholar
  56. 56.
    Zigmond AS, Snaith RP. The Hospital Anxiety and Depression Scale. Acta Psychiatr Scand. 1983;67(6):361–370.CrossRefPubMedGoogle Scholar
  57. 57.
    Park H, Yoon C, Kang S, et al. Early-and late-term clinical outcome and their predictors in patients with ST-segment elevation myocardial infarction and non-ST-segment elevation myocardial infarction. Int J Cardiol. 2013;169(4):254–261.CrossRefPubMedGoogle Scholar
  58. 58.
    del Val Martin D, Fernández MS, Gómez JLZ. Biomarkers in acute coronary syndrome. IJC Metabolic & Endocrine. 2015;8:20–23.CrossRefGoogle Scholar
  59. 59.
    Hamm CW, Bassand JP, Agewall S, et al. ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: The task force for the management of acute coronary syndromes (ACS) in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J. 2011;32(23):2999–3054.CrossRefPubMedGoogle Scholar
  60. 60.
    Task Force for diagnosis and treatment of non-ST-segment elevation acute coronary syndromes of European Society of Cardiology, Bassand JP, Hamm CW, et al. Guidelines for the diagnosis and treatment of non-ST-segment elevation acute coronary syndromes. Eur Heart J. 2007;28(13):1598–1660.CrossRefGoogle Scholar
  61. 61.
    Task Force on the management of ST-segment elevation acute myocardial infarction of the European Society of Cardiology (ESC), Steg PG, James SK, et al. ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation. Eur Heart J. 2012;33(20):2569–2619.CrossRefGoogle Scholar
  62. 62.
    Peters E, Bjalkebring P. Multiple numeric competencies: When a number is not just a number. J Pers Soc Psychol. 2014;108(5):802–822.CrossRefPubMedGoogle Scholar
  63. 63.
    Montorsi P, Villa M, Dessanai MA. Temporal profile of protein release in myocardial infarction. Heart Metab. 2009;43:31–35.Google Scholar
  64. 64.
    Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40(3):879–891.CrossRefPubMedGoogle Scholar
  65. 65.
    French WJ. Trends in acute myocardial infarction management: Use of the National Registry of myocardial infarction in quality improvement. Am J Cardiol. 2000;85(5):5–9.CrossRefGoogle Scholar
  66. 66.
    Parker AM, Fischhoff B. Decision making competence: External validation through an individual differences approach. J Behav Decis Making. 2005;18(1):1–27.CrossRefGoogle Scholar
  67. 67.
    Bruine de Bruin W, Parker AM, Fischhoff B. Individual differences in adult decision making competence. J Pers Soc Psychol. 2007;92(5):938–956.Google Scholar
  68. 68.
    Okan Y, Garcia-Retamero R, Cokely ET, Maldonado A. Individual differences in graph literacy: Overcoming denominator neglect in risk comprehension. J Behav Decis Making. 2012;25(4):390–401.CrossRefGoogle Scholar
  69. 69.
    Garcia-Retamero R, Cokely ET. Effective communication of risks to young adults: Using message framing and visual aids to increase condom use and STD screening. J Exp Psychol Appl. 2011;17(3):270–287.CrossRefPubMedGoogle Scholar
  70. 70.
    Zikmund-Fisher BJ, Ubel PA, Smith DM, et al. Communicating side effect risks in a tamoxifen prophylaxis decision aid: The debiasing influence of pictographs. Patient Educ Couns. 2008;73(2):209–214.CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Petrova D, Garcia-Retamero R, Cokely ET. Understanding the harms and benefits of cancer screening: A model of factors that shape informed decision making. Med Decis Making. 2015;35(7):847–858.CrossRefPubMedGoogle Scholar
  72. 72.
    Petrova D, Garcia-Retamero R, Catena A, van der Pligt J. To screen or not to screen: What factors influence complex screening decisions? J Exp Psychol Appl. 2016;22(2):247–260.CrossRefPubMedGoogle Scholar
  73. 73.
    Petrova D, van der Pligt J, Garcia-Retamero R. Feeling the numbers: On the interplay between risk, affect, and numeracy. J Behav Decis Making. 2014;27:191–199.CrossRefGoogle Scholar
  74. 74.
    Pachur T, Galesic M. Strategy selection in risky choice: The impact of numeracy, affect, and cross-cultural differences. J Behav Decis Making. 2013;26(3):260–271.CrossRefGoogle Scholar
  75. 75.
    Garcia-Retamero R, Okan Y, Cokely E. Using visual aids to improve communication of risks about health: A review. Sci World J. 2012;2012:562637.CrossRefGoogle Scholar
  76. 76.
    Peters E, Baker DP, Dieckmann NF, Leon J, Collins J. Explaining the effect of education on health: A field study in Ghana. Psychol Sci. 2010;21(10):1369–1376.CrossRefPubMedGoogle Scholar
  77. 77.
    O’Brien F, McKee G, Mooney M, O’Donnell S, Moser D. Improving knowledge, attitudes and beliefs about acute coronary syndrome through an individualized educational intervention: A randomized controlled trial. Patient Educ Couns. 2014;96(2):179–187.CrossRefPubMedGoogle Scholar
  78. 78.
    Elwyn G, O’ Connor A, Stacey D, et al. Developing a quality criteria framework for patient decision aids: Online international Delphi consensus process. BMJ 2006;333(7565):417.CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    Okan Y, Garcia-Retamero R, Cokely ET, Maldonado A. Improving risk understanding across ability levels: Encouraging active processing with dynamic icon arrays. J Exp Psychol Appl. 2015;21(2):178–194.CrossRefPubMedGoogle Scholar
  80. 80.
    Dunlosky J, Rawson KA, Marsh EJ, Nathan MJ, Willingham DT. Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychol Sci Public Interest. 2013;14(1):4–58.CrossRefPubMedGoogle Scholar
  81. 81.
    Waldrop-Valverde D, Jones DL, Gould F, Kumar M, Ownby RL. Neurocognition, health-related reading literacy, and numeracy in medication management for HIV infection. AIDS Patient Care STDS. 2010;24(8):477–484.CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low health literacy and health outcomes: An updated systematic review. Ann Intern Med. 2011;155(2):97–107.CrossRefPubMedGoogle Scholar
  83. 83.
    Schlyter M, André-Petersson L, Engström G, Tydén P, Östman M. The impact of personality factors on delay in seeking treatment of acute myocardial infarction. BMC Card Dis. 2011;11(1):1.CrossRefGoogle Scholar
  84. 84.
    Frasure-Smith N, Lespérance F, Gravel G, Masson A, Juneau M, Bourassa MG. Long-term survival differences among low-anxious, high-anxious and repressive copers enrolled in the Montreal heart attack readjustment trial. Psychosom Med. 2002;64(4):571–579.PubMedGoogle Scholar
  85. 85.
    Heart Research Institute UK. UK heart facts. http://www.hriuk org/about-heart-disease/facts-about-heart-disease. 2015.
  86. 86.
    American Heart Association. Use of mobile devices, social media, and crowdsourcing as digital strategies to improve emergency cardiovascular care. A scientific statement from the American Heart Association. Circulation. 2016;134:00–00.Google Scholar

Copyright information

© The Society of Behavioral Medicine 2016

Authors and Affiliations

  1. 1.Mind, Brain, and Behavior Research CenterUniversity of GranadaGranadaSpain
  2. 2.Max Planck Institute for Human DevelopmentBerlinGermany
  3. 3.National Institute for Risk and Resilience, and Department of PsychologyUniversity of OklahomaNormanUSA
  4. 4.Cardiology DepartmentUniversity Hospital Virgen de las NievesGranadaSpain

Personalised recommendations