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Probability perceptions and preventive health care

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Abstract

We study the effect of perceptions in comparison with more objective measures of risk on individuals’ decisions to decline or accept risk reducing interventions such as flu shots, mammograms, and aspirin for the prevention of heart disease. In particular, we elicit individuals’ subjective probabilities of risk, with and without the interventions, and compare these perceptions to individually predicted risk based on epidemiological models. Respondents, especially women, appear to be aware of some of the qualitative relationships between risk factors and probabilities. However, on average they have very poor perceptions of the absolute probability levels as reported in the epidemiological literature. Perceptions of the level of risk are less accurate if a respondent is female and has poor numeracy skills. We find that perceived probabilities significantly affect the subsequent take-up rate of flu shots, mammograms, and aspirin, even after controlling for individually predicted risk using epidemiological models.

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Notes

  1. See Grossman (1972), Cropper (1977), Hey and Patel (1983), Dardanoni and Wagstaff (1987 and 1990), Selden (1993), Chang (1996), and Byrne and Thompson (2001) for early formalizations.

  2. Viscusi and Evans (2006) distinguish between prior and posterior stated probabilities, and behavioral probabilities, which are the probabilities that actually drive (stated) decisions. Identification of these concepts requires various exclusion and functional form assumptions. For example, individual characteristics such as education are assumed to affect preferences, but not risk perceptions.

  3. In some versions of the Health and Retirement Study respondents were asked to select an integer number between 0 and 10 to express the likelihood of an event. Viscusi and Hakes (2003) discuss a number of problems with this risk scale. For example, the scale implies a lack of precision and requires a monotonic transformation to translate the responses into probabilities. These transformations are likely to be respondent specific, which complicates the comparison of responses between respondents.

  4. Basic insurance is subsidized for low income households and insurance companies must accept all applicants.

  5. Pap smears are also provided by RIVM. Other common preventive care measures, such as colonoscopies and Prostate-Specific Antigen (PSA) tests are not currently provided on a systematic basis either through the basic insurance packages or through the RIVM.

  6. Additional information about the preventive care interventions is discussed in the appendix.

  7. It is recommended that people begin daily low dose aspirin after speaking with their doctor, but given the prominence of this intervention in the media it is possible that some people would choose to begin such therapy on their own.

  8. The survey also contained information about Pap smears and testing for sexually transmitted diseases. Because epidemiological risks cannot be easily estimated for cervical cancer and sexually transmitted diseases, we do not include these in our main analysis. Results on the impact of risk perceptions on take up are, however, included in the online appendix.

  9. Those who do not have access to the Internet are provided with a simple, easy to use computer (a SimPC) and internet access.

  10. The text of the subjective probability questions is included in the online appendix. The full survey in both English and the original Dutch is available from lissdata.nl. The survey is labeled “33 Disease Prevention”.

  11. Respondents saw this description: “Now, we will ask some questions about future uncertain events. Try to think of all possible outcomes, and think about how probable they are. Try to estimate the PERCENT CHANCE that this will happen. Your answer must be a number between 0 and 100. You can also use numbers after the decimal point.

    0% chance = “absolutely impossible”

    Less than 1% chance = “a very small chance”

    2 to 15% chance = “a small chance”

    15 to 40% chance = “some chance”

    40 to 60% chance = “a pretty even chance”

    60 to 80% chance = “a large chance”

    80 to 95% chance = “a very large chance”

    95 to 99% chance = “nearly certain”

    100% chance = “absolutely certain”.

    You can also see “percent chance” as the number of times out of 100.”

  12. Screen shots of the visual scales are included in the appendix.

  13. These scales are discussed in more detail in a companion paper: Bruine de Bruin and Carman (2011).

  14. Additional information is available in the online appendix.

  15. Further details regarding the calculation of individual risks are available in the online appendix.

  16. Similar results are found looking at a ten-year time horizon for breast cancer or heart disease or looking at the risk of death. This information is available in the online appendix.

  17. Histograms of the distribution of responses are available in the online appendix.

  18. Detailed information is available in the online appendix.

  19. Our estimates are based on the risk of dying from influenza, with and without a flu shot, the risk of getting breast cancer, the risk of dying of breast cancer, with and without mammograms over 10 and 20 years, the risk of developing heart disease, and the risk of dying of heart disease, with and without aspirin over 10 and 20 years.

  20. The results are more similar if we exclude those between 60 and 65, who were not eligible for free flu shots in 2007 but were eligible for free flu shots in 2008.

  21. Similar results for Pap smears and STD testing are available in the online appendix.

  22. These results are available in the online appendix.

  23. Everyone over the age of 65 should receive an invitation for a flu shot. However, some (12%) report not being invited. This may be due to clerical error, poor memory, or simply a failure to open their mail. For those under the age of 65: some receive invitations from their physician because they are at high risk, and others receive invitations from their employer or other sources.

  24. These changes are discussed in the appendix.

  25. Because influenza risk is determined by age only and because the flu shot invitations are also mostly determined by age groups, we include age and age squared rather than 5 year age groups. Thus the epidemiological risk captures the non-linearities in take-up with respect to age.

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Acknowledgments

We thank the editor and two anonymous referees for constructive comments. We also thank Joe Aldy, Orley Ashenfelter, Floriaan van Bemmelen, Wändi Bruine de Bruin, Wendy Carman, Eline van der Heijden, Arie Kapteyn, Emmett Keeler, Charles Manski, Miquelle Marchand, Angela Neijmeijer-Leloux, Johan Polder, Jan Potters, David Ribar, Annette Scherpenzeel, Arthur van Soest, George Van Houtven, and Corrie Vis, as well as seminar and conference participants for helpful comments. This paper draws on data of the LISS panel of CentERdata. Much of this research was completed while Katherine Carman was an Assistant Professor at Tilburg University. CentER and Netspar provided financial support for the data collection. Supporting materials, including a translation in English of the survey in Dutch, are available from the authors.

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Authors and Affiliations

  1. RAND Corporation, Santa Monica, CA, 90407, USA

    Katherine Grace Carman

  2. Department of Economics, Tilburg University, Tilburg, The Netherlands

    Peter Kooreman

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  1. Katherine Grace Carman
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Correspondence to Katherine Grace Carman.

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Carman, K.G., Kooreman, P. Probability perceptions and preventive health care. J Risk Uncertain 49, 43–71 (2014). https://doi.org/10.1007/s11166-014-9196-x

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