The role of beliefs in the supply of health screenings

Last registered on June 24, 2024


Trial Information

General Information

The role of beliefs in the supply of health screenings
Initial registration date
June 17, 2024

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
June 24, 2024, 2:04 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.


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Primary Investigator

UMIT TIROL - Private University for Health Sciences and Health Technology

Other Primary Investigator(s)

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Universal screening programs, where the entire population is invited for diagnostic tests, should have a favorable benefit-harm ratio and undergo continuous evaluation (WHO, 2020).
Despite promoting early detection as life-saving, evidence regarding cancer screening is mixed. A meta-analysis found that only colorectal cancer screening significantly increased life expectancy, while mammography and PSA tests did not (Bretthauer et al., 2023). Significant advances in breast cancer treatment and the unclear benefit-harm ratio of screenings call for re-evaluation of this program (Bretthauer et al., 2023; Saquib et al., 2015; Voss et al., 2023).
Austria offers free mammograms every two years for women aged 45-74, following European guidelines (Loibl et al., 2023). Clinical guidelines for prostate cancer have changed, and the PSA test for early detection is no longer recommended (Parker et al., 2020).
This project will investigate general practitioners' expectations and recommendations regarding breast cancer mammography and prostate cancer PSA tests, exploring their awareness of the actual benefits and how this influences their adherence to guidelines using a survey experiment with randomized information provision.


Bretthauer, M., Wieszczy, P., Løberg, M., Kaminski, M. F., Werner, T. F., Helsingen, L. M., Mori, Y., Holme, Ø., Adami, H.-O., & Kalager, M. 2023. Estimated Lifetime Gained With Cancer Screening Tests: A Meta-Analysis of Randomized Clinical Trials. JAMA Internal Medicine, 183(11): 1196-1203.
Loibl, S., André, F., Bachelot, T., Barrios, C. H., Bergh, J., Burstein, H. J., Cardoso, L. M. J., Carey, L. A., Dawood, S., Del Mastro, L., Denkert, C., Fallenberg, E. M., Francis, P. A., Gamal-Eldin, H., Gelmon, K., Geyer, C. E., Gnant, M., Guarneri, V., Gupta, S., Kim, S. B., Krug, D., Martin, M., Meattini, I., Morrow, M., Janni, W., Paluch-Shimon, S., Partridge, A., Poortmans, P., Pusztai, L., Regan, M. M., Sparano, J., Spanic, T., Swain, S., Tjulandin, S., Toi, M., Trapani, D., Tutt, A., Xu, B., Curigliano, G., & Harbeck, N. 2023. Early breast cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up<sup>&#x2020;</sup>. Annals of Oncology.
Parker, C., Castro, E., Fizazi, K., Heidenreich, A., Ost, P., Procopio, G., Tombal, B., & Gillessen, S. 2020. Prostate cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up<sup>&#x2020;</sup>. Annals of Oncology, 31(9): 1119-1134.
Saquib, N., Saquib, J., & Ioannidis, J. P. A. 2015. Does screening for disease save lives in asymptomatic adults? Systematic review of meta-analyses and randomized trials. International journal of epidemiology, 44(1): 264-277.
Voss, T., Krag, M., Martiny, F., Heleno, B., Jørgensen, K. J., & Brandt Brodersen, J. 2023. Quantification of overdiagnosis in randomised trials of cancer screening: an overview and re-analysis of systematic reviews. Cancer Epidemiology, 84: 102352.
WHO. 2020. Screening programmes: a short guide. Increase effectiveness, maximize benefits and minimize harm (2020). Copenhagen: WHO Regional Office for Europe.
External Link(s)

Registration Citation

Angerer, Silvia. 2024. "The role of beliefs in the supply of health screenings." AEA RCT Registry. June 24.
Sponsors & Partners

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Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Recommendation for PSA-Test (self-reported)
Recommendation for Mammography (self-reported)
Primary Outcomes (explanation)
Variable coding for both primary outcomes separately:
0=No recommendation for mammography/PSA (independent from age)
1=Recommendation for mammography/PSA for 50-69 y. olds
2=Recommendation for mammography/PSA for 50-69 y. olds and younger or older
3=Recommendation for mammography/PSA for all age groups

Secondary Outcomes

Secondary Outcomes (end points)
Beliefs about PSA/Mammography for (i) positive benefit-harm ratio and (ii) reduction in all-cause mortality
Attitudes towards PSA and Mammography screening
Secondary Outcomes (explanation)
For both PSA and Mammography separately:
1= Benefits outweigh harms, 0 otherwise
1= Reduction in all-cause mortality, 0 otherwise
Attitudes towards (i) general screening, (ii) PSA and (iii) Mammography using an index of two items each and an index of all items in combination.

Experimental Design

Experimental Design
Survey experiment comprises random assignment of General Practitioners (GPs) to one of four experimental groups: (i) No information, (ii) Information about Guidelines regarding PSA and Mammography in Austria, (iii) Information about Guidelines and uncertainty regarding Benefit-Harm Ratio Mammography, (iv) Information about Guidelines, uncertainty regarding Benefit-Harm Ratio Mammography and evidence on all-cause-mortality.
Experimental Design Details
Not available
Randomization Method
Randomization done by the computer program used for the survey.
Randomization Unit
Individual randomization to one of four experimental groups.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
400 GPs
Sample size: planned number of observations
400 GPs
Sample size (or number of clusters) by treatment arms
100 GPs per experimental group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Minimum detectable effect size is a difference of 40% of a standard deviation between experimental conditions.

Institutional Review Boards (IRBs)

IRB Name
RCSEQ (Research Committee for Scientific Ethical Questions) Privatuniversität UMIT TIROL und fh gesundheit
IRB Approval Date
IRB Approval Number