Measuring the psychosocial consequences of screening

Abstract

The last three decades have seen a dramatic rise in the implementation of screening programmes for cancer in industrialised countries. However, in contrast to screening for infectious diseases, most cancer screening programmes only have the potential to reduce mortality; they cannot lower the incidence of cancer in a population. In fact, most cancer screening programmes have been shown to increase the incidence of the disease as a consequence of over-diagnosis. A further dilemma of cancer screening programmes is that they do not distinguish between healthy people and those with disease. Rather, they identify a continuum of disease severity. Consequently, many healthy people who have abnormal screening tests are wrongly diagnosed. Indeed, studies have demonstrated that for each screening-prevented death from cancer, at least 200 false-positive results are given. Therefore, screening has the potential to be harmful as well as beneficial. The psychosocial consequences of false-positive screening results cannot be determined by diagnostic tests or by other technical means. Instead, patient reported outcome measures must be employed. To measure the outcomes of screening accurately and comprehensively patient reported outcome measures have to capture; the nature and extent of the psychosocial consequences and how these change over time. The outcome measures used must have high content validity and their psychometric properties should be determined prior to their use in the specific population. In particular it is important to establish unidimensionality, additivity and item ordering through the application of Item Response Theory.

Development of the Knee Numeric‐Entity Evaluation Score (KNEES – ACL): A condition‐specific questionnaire

“…Generic health status instruments include items that are not applicable to specific health conditions (Doward et al., ; Brodersen et al., ). Problems of non‐applicability are avoided and content validity maximized by deriving item content from relevant sources, thus ensuring that the instrument specifically addresses the condition of interest (Sireci, ; Swaine‐Verdier et al., ; Brodersen et al., 2007a). Regardless of whether items originate from clinicians or from patient interviews, feedback from the targeted patient group is mandatory in order to confirm content validity (Sireci, ; Doward & McKenna, ; Swaine‐Verdier et al., ; Brodersen et al., 2007a).…”

“…Problems of non‐applicability are avoided and content validity maximized by deriving item content from relevant sources, thus ensuring that the instrument specifically addresses the condition of interest (Sireci, ; Swaine‐Verdier et al., ; Brodersen et al., 2007a). Regardless of whether items originate from clinicians or from patient interviews, feedback from the targeted patient group is mandatory in order to confirm content validity (Sireci, ; Doward & McKenna, ; Swaine‐Verdier et al., ; Brodersen et al., 2007a). Once content validity is established, the psychometric properties of the constructs must be confirmed for the targeted patient group(s) (Comins et al., ; Brodersen et al., ).…”

Systematic review of the psychological consequences of false-positive screening mammograms

“…The difference in findings between those of OPCERG and others may be explained in a variety of ways by differences in their design, methods and populations or they may be an artefact of The disagreement about the psychological impact of false-positive screening results may be explained by the type of outcome measures used, whether disease-specific or generic. Brodersen et al, [116][117][118][119] from the University of Copenhagen, have written a number of papers on the subject of measuring psychological distress in women who have received false-positive mammograms. In particular, they conducted a literature review to find out how suitable the outcome measures used in studies of false-positive mammograms were for detecting psychological distress.…”

Dimensionality of the Knee Numeric‐Entity Evaluation Score (KNEES‐ACL): A condition‐specific questionnaire

“…Patient‐reported outcome measures are often validated within the framework of classical test theory (CTT), using methods such as exploratory factor analysis or Cronbach's alpha (Turk et al., ). However, CTT is fundamentally flawed for PROM validation (Brodersen et al., 2007a, b; Comins et al., ; Krogsgaard et al., ). One inherent problem is that CTT assumes interval level parametric data structure, while response options to PROM items are categorical entities (Wright & Linacre, ; Linacre, ; Linacre, ).…”