Consistency and precision of cancer reporting in a multiwave national panel survey

Abstract Background Many epidemiological studies rely on self-reported information, the accuracy of which is critical for unbiased estimates of population health. Previously, accuracy has been analyzed by comparing self-reports to other sources, such as cancer registries. Cancer is believed to be a well-reported condition. This paper uses novel panel data to test the consistency of cancer reports for respondents with repeated self-reports. Methods Data come from 978 adults who reported having been diagnosed with cancer in at least one of four waves of the Panel Study of Income Dynamics, 1999-2005. Consistency of cancer occurrence reports and precision of timing of onset were studied as a function of individual and cancer-related characteristics using logistic and ordered logistic models. Results Almost 30% of respondents gave inconsistent cancer reports, meaning they said they never had cancer after having said they did have cancer in a previous interview; 50% reported the year of diagnosis with a discrepancy of two or more years. More recent cancers were reported with a higher consistency and timing precision; cervical cancer was reported more inaccurately than other cancer types. Demographic and socio-economic factors were only weak predictors of reporting quality. Conclusions Results suggest that retrospective reports of cancer contain significant measurement error. The errors, however, are fairly random across different social groups, meaning that the results based on the data are not systematically biased by socio-economic factors. Even for health events as salient as cancer, researchers should exercise caution about the presumed accuracy of self-reports, especially if the timing of diagnosis is an important covariate.http://deepblue.lib.umich.edu/bitstream/2027.42/112656/1/12963_2010_Article_108.pd

Physiological threat responses predict number processing

Being able to adequately process numbers is a key competency in everyday life. Yet, self-reported negative affective responses towards numbers are known to deteriorate numerical performance. Here, we investigated how physiological threat responses predict numerical performance. Physiological responses reflect whether individuals evaluate a task as exceeding or matching their resources and in turn experience either threat or challenge, which influences subsequent performance. We hypothesized that, the more individuals respond to a numerical task with physiological threat, the worse they would perform. Results of an experiment with cardiovascular indicators of threat/challenge corroborated this expectation. The findings thereby contribute to our understanding of the physiological mechanism underlying the influence of negative affective responses towards numbers on numerical performance.Social decision makin