Quantitative literacy

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In education and literacy, Quantitative literacy (also called numeracy) is "the knowledge and skills required to apply arithmetic operations, either alone or sequentially, using numbers embedded in printed materials; for example, balancing a checkbook, figuring out a tip, completing an order form, or determining the amount of interest from a loan advertisement."[1]

Comprehension of numbers can be divided into:[2][3]

  • Verbatim comprehension: "the ability to correctly read numbers from graphs"
  • Gist comprehension: "the ability to identify the essential point of the information presented", or more specifically, correctly rank the magnitude of two or more options.

Quantitative literacy is important in politics[4][5] and health care[6][7].

Health care

Health care numeracy is problematic. Health care providers[8][9][10][11][12] and patients[13][14][15][16][17][16][18] both have problems with quantitative reasoning. Some of the difficulty is doe to interpreting relative versus absolute measures of efficacy.[19][20] The problem is confounded by scientific journals not well presenting quantitative results.[21]

Comparing benefits of two treatments

Various formats including the number needed to treat have been tested to improve comprehension of quantitative comparisons of treatment benefit by patients[20][22][23][24][25][18][26] and by health care professionals[27][28][26].

In practicing evidence-based medicine, framing bias is best avoided by using numeracy with absolute measures of efficacy.[29][30]

Pictographs (Pictograms)

Pictographs may been studied, but patients may prefer bar graphs.[31]

When designing pictograms:

  • "Risk recall was significantly higher with more anthropomorphic icons (restroom icons, head outlines, and photos) than with other icon types, and participants rated restroom icons as most preferred."[32]
  • Patients may have more trust when the characters are randomly highlighted vs highlighted in groups.[31]

Comparing accuracy of diagnostic methods

Various formats have been tested to improve comprehension of quantitative comparisons of diagnostic accuracy.[33][34] [27]

References

  1. Irwin S. Kirsch, Ann Jungeblut, Lynn Jenkins, and Andrew Kolstad. (1993). Adult Literacy in America: a first look at the findings of the National Adult Literacy Survey, (NCES 93275). U.S. Department of Education.
  2. Nelson W, Reyna VF, Fagerlin A, Lipkus I, Peters E (2008). "Clinical implications of numeracy: theory and practice". Ann Behav Med. 35 (3): 261–74. doi:10.1007/s12160-008-9037-8. PMC 3694344. PMID 18677452.
  3. Hawley ST, Zikmund-Fisher B, Ubel P, Jancovic A, Lucas T, Fagerlin A (2008). "The impact of the format of graphical presentation on health-related knowledge and treatment choices". Patient Educ Couns. 73 (3): 448–55. doi:10.1016/j.pec.2008.07.023. PMID 18755566.
  4. Best, Joel (2001). Damned lies and statistics: untangling numbers from the media, politicians, and activists. Berkeley: University of California Press. ISBN 0-520-21978-3.
  5. Best, Joel (2004). More damned lies and statistics: how numbers confuse public issues. Berkeley: University of California Press. ISBN 0-520-23830-3.
  6. Mark Kutner, Elizabeth Greenberg, Ying Jin, Christine Paulsen. (2006) The Health Literacy of America’s Adults: Results From the 2003 National Assessment of Adult Literacy. U.S. Department of Education.
  7. Schwartz, Lisa A.; Steven Woloshin (2008). Know Your Chances: Understanding Health Statistics. Berkeley: University of California Press. ISBN 0-520-25222-5.
  8. Bergman DA, Pantell RH (1986). "The impact of reading a clinical study on treatment decisions of physicians and residents". J Med Educ. 61 (5): 380–6. PMID 3701813.
  9. Berwick DM, Fineberg HV, Weinstein MC (1981). "When doctors meet numbers". Am J Med. 71 (6): 991–8. PMID 7315859.
  10. Phelps MA, Levitt MA (2004). "Pretest probability estimates: a pitfall to the clinical utility of evidence-based medicine?". Acad Emerg Med. 11 (6): 692–4. PMID 15175211.
  11. Reid MC, Lane DA, Feinstein AR (1998). "Academic calculations versus clinical judgments: practicing physicians' use of quantitative measures of test accuracy". Am J Med. 104 (4): 374–80. PMID 9576412.
  12. Steurer J, Fischer JE, Bachmann LM, Koller M, ter Riet G (2002). "Communicating accuracy of tests to general practitioners: a controlled study". BMJ. 324 (7341): 824–6. PMC 100792. PMID 11934776.
  13. Epstein RM, Alper BS, Quill TE (2004). "Communicating evidence for participatory decision making". JAMA. 291 (19): 2359–66. doi:10.1001/jama.291.19.2359. PMID 15150208.
  14. Friedmann PD, Brett AS, Mayo-Smith MF (1996). "Differences in generalists' and cardiologists' perceptions of cardiovascular risk and the outcomes of preventive therapy in cardiovascular disease". Ann Intern Med. 124 (4): 414–21. PMID 8554250.
  15. Hamm RM, Smith SL (1998). "The accuracy of patients' judgments of disease probability and test sensitivity and specificity". J Fam Pract. 47 (1): 44–52. PMID 9673608.
  16. 16.0 16.1 Malenka DJ, Baron JA, Johansen S, Wahrenberger JW, Ross JM (1993). "The framing effect of relative and absolute risk". J Gen Intern Med. 8 (10): 543–8. PMID 8271086.
  17. Naylor CD, Chen E, Strauss B (1992). "Measured enthusiasm: does the method of reporting trial results alter perceptions of therapeutic effectiveness?". Ann Intern Med. 117 (11): 916–21. PMID 1443954.
  18. 18.0 18.1 Schwartz LM, Woloshin S, Black WC, Welch HG (1997). "The role of numeracy in understanding the benefit of screening mammography". Ann Intern Med. 127 (11): 966–72. PMID 9412301.
  19. Bucher HC, Weinbacher M, Gyr K (1994). "Influence of method of reporting study results on decision of physicians to prescribe drugs to lower cholesterol concentration". BMJ. 309 (6957): 761–4. PMC 2541000. PMID 7950558.
  20. 20.0 20.1 Sheridan SL, Pignone MP, Lewis CL (2003). "A randomized comparison of patients' understanding of number needed to treat and other common risk reduction formats". J Gen Intern Med. 18 (11): 884–92. PMC 1494938. PMID 14687273.
  21. Nuovo J, Melnikow J, Chang D (2002). "Reporting number needed to treat and absolute risk reduction in randomized controlled trials". JAMA. 287 (21): 2813–4. PMID 12038920. Unknown parameter |month= ignored (help)
  22. Schwartz LM, Woloshin S, Welch HG (2007). "The drug facts box: providing consumers with simple tabular data on drug benefit and harm". Med Decis Making. 27 (5): 655–62. doi:10.1177/0272989X07306786. PMID 17873258.
  23. Woloshin S, Schwartz LM, Welch HG (2007). "The effectiveness of a primer to help people understand risk: two randomized trials in distinct populations". Ann. Intern. Med. 146 (4): 256–65. PMID 17310049. Unknown parameter |month= ignored (help)
  24. Stovring H, Gyrd-Hansen D, Kristiansen IS, Nexoe J, Nielsen JB (2008). "Communicating effectiveness of intervention for chronic diseases: what single format can replace comprehensive information?". BMC Med Inform Decis Mak. 8: 25. doi:10.1186/1472-6947-8-25. PMC 2467410. PMID 18565218.
  25. Dolan JG, Iadarola S (2008). "Risk communication formats for low probability events: an exploratory study of patient preferences". BMC Med Inform Decis Mak. 8: 14. doi:10.1186/1472-6947-8-14. PMC 2330036. PMID 18402680.
  26. 26.0 26.1 Wen L, Badgett R, Cornell J (2005). "Number needed to treat: a descriptor for weighing therapeutic options". Am J Health Syst Pharm. 62 (19): 2031–6. doi:10.2146/ajhp040558. PMID 16174840. Unknown parameter |month= ignored (help)
  27. 27.0 27.1 Sheridan SL, Pignone M (2002). "Numeracy and the medical student's ability to interpret data". Eff Clin Pract. 5 (1): 35–40. PMID 11874195.
  28. Gigerenzer G, Edwards A (2003). "Simple tools for understanding risks: from innumeracy to insight". BMJ. 327 (7417): 741–4. doi:10.1136/bmj.327.7417.741. PMC 200816. PMID 14512488. Unknown parameter |month= ignored (help)
  29. Perneger TV, Agoritsas T (2011). "Doctors and Patients' Susceptibility to Framing Bias: A Randomized Trial". J Gen Intern Med. doi:10.1007/s11606-011-1810-x. PMID 21792695.
  30. Woloshin S, Schwartz LM (2011). "Communicating data about the benefits and harms of treatment: a randomized trial". Ann Intern Med. 155 (2): 87–96. doi:10.1059/0003-4819-155-2-201107190-00004. PMID 21768582.
  31. 31.0 31.1 Schapira MM, Nattinger AB, McAuliffe TL (2006). "The influence of graphic format on breast cancer risk communication". J Health Commun. 11 (6): 569–82. doi:10.1080/10810730600829916. PMID 16950729.
  32. Zikmund-Fisher BJ, Witteman HO, Dickson M, Fuhrel-Forbis A, Kahn VC, Exe NL; et al. (2014). "Blocks, ovals, or people? Icon type affects risk perceptions and recall of pictographs". Med Decis Making. 34 (4): 443–53. doi:10.1177/0272989X13511706. PMC 3991751. PMID 24246564.
  33. Puhan MA, Steurer J, Bachmann LM, ter Riet G (2005). "A randomized trial of ways to describe test accuracy: the effect on physicians' post-test probability estimates". Ann. Intern. Med. 143 (3): 184–9. PMID 16061916.
  34. Poses RM; et al. (1995). "You can lead a horse to water--improving physicians' knowledge of probabilities may not affect their decisions". Medical Decision Making. 15: 65–75. PMID 7898300.

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