Estimating the prevalence of breast cancer using a disease model: data problems and trends

BACKGROUND: Health policy and planning depend on quantitative data of disease epidemiology. However, empirical data are often incomplete or are of questionable validity. Disease models describing the relationship between incidence, prevalence and mortality are used to detect data problems or supplement missing data. Because time trends in the data affect their outcome, we compared the extent to which trends and known data problems affected model outcome for breast cancer. METHODS: We calculated breast cancer prevalence from Dutch incidence and mortality data (the Netherlands Cancer Registry and Statistics Netherlands) and compared this to regionally available prevalence data (Eindhoven Cancer Registry, IKZ). Subsequently, we recalculated the model adjusting for 1) limitations of the prevalence data, 2) a trend in incidence, 3) secondary primaries, and 4) excess mortality due to non-breast cancer deaths. RESULTS: There was a large discrepancy between calculated and IKZ prevalence, which could be explained for 60% by the limitations of the prevalence data plus the trend in incidence. Secondary primaries and excess mortality had relatively small effects only (explaining 17% and 6%, respectively), leaving a smaller part of the difference unexplained. CONCLUSION: IPM models can be useful both for checking data inconsistencies and for supplementing incomplete data, but their results should be interpreted with caution. Unknown data problems and trends may affect the outcome and in the absence of additional data, expert opinion is the only available judge

Quantitative trait loci affecting growth-related traits in wild barley (Hordeum spontaneum) grown under different levels of nutrient supply

The genetic basis of phenotypic plasticity of relative growth rate (RGR), its components and associated morphological traits was studied in relation to nutrient limitation. In all, 140 F3 lines from a cross, made between two Hordeum spontaneum (wild barley) accessions sampled in Israel, were subjected to growth analysis under two nutrient levels. Quantitative trait loci (QTLs) were detected for RGR and three of its components, leaf area ratio (LAR), specific leaf area and leaf mass fraction (LMF). Indications for close linkage (potential pleiotropy) were found, for example, for LAR and LMF. An interesting case is on chromosome 6, at which QTLs for RGR and seed mass were detected in the same region. These QTLs had opposite additive effects, supporting earlier results that plants growing from lighter seeds had a higher RGR. Only two QTLs were significant under both nutrient conditions, suggesting large QTL ´ environment interactions for most traits. For 21 out of 26 QTLs, however, the additive genetic effect was of identical sign in both nutrient environments, but reached the significance threshold in only one of them. Nevertheless, some QTLs detected in one of the two environments had virtually no effect in the other, and QTLs for plasticity were detected for RGR, LAR and LMF, as well as for some morphological traits. QTLs with opposite effects under high and low nutrients were not found. Thus, at the genetic level, there was no evidence for a trade-off between faster growth at high versus low nutrient levels [KEYWORDS: QTL; plasticity; nutrient limitation; relative growth rate; wild barley; QTL ´ environment interaction]