Confirmation of family cancer history reported in a population-based survey.

BACKGROUND: Knowledge of family cancer history is essential for estimating an individual's cancer risk and making clinical recommendations regarding screening and referral to a specialty cancer genetics clinic. However, it is not clear if reported family cancer history is sufficiently accurate for this purpose. METHODS: In the population-based 2001 Connecticut Family Health Study, 1019 participants reported on 20 578 first-degree relatives (FDR) and second-degree relatives (SDR). Of those, 2605 relatives were sampled for confirmation of cancer reports on breast, colorectal, prostate, and lung cancer. Confirmation sources included state cancer registries, Medicare databases, the National Death Index, death certificates, and health-care facility records. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated for reports on lung, colorectal, breast, and prostate cancer and after stratification by sex, age, education, and degree of relatedness and used to estimate report accuracy. Pairwise t tests were used to evaluate differences between the two strata in each stratified analysis. All statistical tests were two-sided. RESULTS: Overall, sensitivity and positive predictive value were low to moderate and varied by cancer type: 60.2% and 40.0%, respectively, for lung cancer reports, 27.3% and 53.5% for colorectal cancer reports, 61.1% and 61.3% for breast cancer reports, and 32.0% and 53.4% for prostate cancer reports. Specificity and negative predictive value were more than 95% for all four cancer types. Cancer history reports on FDR were more accurate than reports on SDR, with reports on FDR having statistically significantly higher sensitivity for prostate cancer than reports on SDR (58.9% vs 21.5%, P = .002) and higher positive predictive value for lung (78.1% vs 31.7%, P < .001), colorectal (85.8% vs 43.5%, P = .004), and breast cancer (79.9% vs 53.6%, P = .02). CONCLUSIONS: General population reports on family history for the four major adult cancers were not highly accurate. Efforts to improve accuracy are needed in primary care and other health-care settings in which family history is collected to ensure appropriate risk assessment and clinical care recommendations.

Grid-enabled measures: using Science 2.0 to standardize measures and share data.

Scientists are taking advantage of the Internet and collaborative web technology to accelerate discovery in a massively connected, participative environment--a phenomenon referred to by some as Science 2.0. As a new way of doing science, this phenomenon has the potential to push science forward in a more efficient manner than was previously possible. The Grid-Enabled Measures (GEM) database has been conceptualized as an instantiation of Science 2.0 principles by the National Cancer Institute (NCI) with two overarching goals: (1) promote the use of standardized measures, which are tied to theoretically based constructs; and (2) facilitate the ability to share harmonized data resulting from the use of standardized measures. The first is accomplished by creating an online venue where a virtual community of researchers can collaborate together and come to consensus on measures by rating, commenting on, and viewing meta-data about the measures and associated constructs. The second is accomplished by connecting the constructs and measures to an ontological framework with data standards and common data elements such as the NCI Enterprise Vocabulary System (EVS) and the cancer Data Standards Repository (caDSR). This paper will describe the web 2.0 principles on which the GEM database is based, describe its functionality, and discuss some of the important issues involved with creating the GEM database such as the role of mutually agreed-on ontologies (i.e., knowledge categories and the relationships among these categories--for data sharing).

Coherence and completeness of population-based family cancer reports.

BACKGROUND: Although family history of cancer is widely ascertained in research and clinical care, little is known about assessment methods, accuracy, or other quality measures. Given its widespread use in cancer screening and surveillance, better information is needed about the clarity and accuracy of family history information reported in the general population. METHODS: This telephone survey in Connecticut examined coherence and completeness of reports from 1,019 respondents about 20,504 biological relatives. RESULTS: Of 2,657 cancer reports, 97.7% were judged consistent with malignancy (versus benign or indeterminate conditions); 79% were site specific, 10.1% had unspecified cancer sites, and 8.6% had "ill-defined" sites. Only 6.1% of relatives had unknown histories. Unknown histories and ambiguous sites were significantly higher for second-degree relatives. The adjusted percentage of first-degree relative reports with ambiguous sites increased with decreasing education and African-American race of survey respondents, and with deceased vital status of relatives. Ambiguous second-degree relative reports were also associated with deceased vital status and with male gender of respondents. CONCLUSIONS: These findings suggest that family history of cancer reports from the general population are generally complete and coherent. IMPACT: Strategies are needed to improve site specificity and thus maximize the utility of such information in primary care settings.

Population estimates of extended family structure and size.

BACKGROUND: Population-based estimates of biological family size can be useful for planning genetic studies, assessing how distributions of relatives affect disease associations with family history and estimating prevalence of potential family support. METHODS: Mean family size per person is estimated from a population-based telephone survey (n = 1,019). RESULTS: After multivariate adjustment for demographic variables, older and non-White respondents reported greater mean numbers of total, first- and second-degree relatives. Females reported more total and first-degree relatives, while less educated respondents reported more second-degree relatives. CONCLUSIONS: Demographic differences in family size have implications for genetic research. Therefore, periodic collection of family structure data in representative populations would be useful.