The effect on survival of early detection of breast cancer in South Australia

Early detection of breast cancer is an important public health policy. Programs of regular screening examinations have been widely established in an attempt to detect the disease when the primary tumour diameter is small. In South Australia, BreastScreen SA suggests that women between the ages of 50 and 70 years be screened every 24 months. Our aim in this paper is to make assessments of various screening procedures by using statistical models with parameters estimated exclusively from South Australian data. We establish a relationship between primary tumour diameter and ultimate survival time. We estimate an advantage of 2.9 (.7) years in median survival time for those women detected with the disease by BreastScreen SA, compared with an unscreened population. We construct a computer model from which we determine the consequences of using a 12 month screening interval, and also the effect of beginning screening at the age of 40 rather than the current conventional commencement age of 50 years

Is Length of Life Predictable?

A random sample of death records of adult males from the period 1967 to 1970 was chosen from the South Australian Registry of Births, Deaths, and Marriages. The natural parents of these individuals were identified by cross-referencing to birth certificates, and an extensive search was made of the death records for these parents. In this manner random families were selected for which, where possible, the cause of death and length of life of each family member were determined. Here, we analyze the association between sons and their parents in length of life and report the statistically useful correlations that were found. These correlations enable the calculation of a life table for a male conditional on his current age and the lifetimes of his parents. Comparisons are made with the uninformed population life table based solely on sex and year of birth

Are Causes of Death Predictable?

A random sample of death records of adult males from 1967 to 1970 was chosen from the South Australian Registry of Births, Deaths, and Marriages. The natural parents of these individuals were identified by cross-reference to birth certificates, and an extensive search was made of the death records for these parents. In this manner random families were selected for which, where possible, the cause of death and length of life of each family member were determined. From the information pertaining to the stated cause of death, each individual was assigned to one of five death categories

Validating the MERIS Terrestrial Chlorophyll Index (MTCI) with ground chlorophyll content data at MERIS spatial resolution

The Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI), a standard level 2 European Space Agency (ESA) product, provides information on the chlorophyll content of vegetation (amount of chlorophyll per unit area of ground). This is a combination of information on Leaf Area Index (LAI, area of leaves per unit area of ground) and the chlorophyll concentration of those leaves. The MTCI correlates strongly with chlorophyll content when using model, laboratory and field spectrometry data. However, MTCI calculated with MERIS data has only been correlated with surrogate chlorophyll content data. This is because of the logistical difficulties of determining the chlorophyll content of the area covered by a MERIS pixel (9 × 104 m2). This paper reports the first attempt to determine the relationship between MTCI and chlorophyll content using actual MERIS data and actual chlorophyll content data.During the summer of 2006 LAI and chlorophyll concentration data were collected for eight large (&gt; 25 ha) fields around Dorchester in southern England. The fields contained six crops (beans, linseed, wheat, grass, oats and maize) at different stages of maturity and with different canopy structures, LAIs and chlorophyll concentrations. A stratified sampling method was used in which each field contained sampling units in proportion to the spatial variability of the crop. Within each unit 25 random points were sampled. This approach captured the variability of the field and reduced the potential bias introduced by the planting pattern or later agricultural treatments (e.g. pesticides or herbicides). At each random point LAI was estimated using an LAI-2000 plant canopy analyser and chlorophyll concentration was estimated using a Minolta-SPAD chlorophyll meter. In addition, for each field a calibration set of 30 contiguous SPAD measurements and associated leaf samples were collected.The relationship between MTCI and chlorophyll content was positive. The coefficient of determination (R 2) was 0.62, root mean square error (RMSE) was 244 g per MERIS pixel and accuracy of estimation (in relation to the mean) was 65%. However, one field included a high proportion of seed heads, which artificially increased the measured LAI and thus chlorophyll content. Removal of this field from the dataset resulted in a stronger relationship between MTCI and chlorophyll content with an R 2 of 0.8, an RMSE of 192 g per MERIS pixel and accuracy of estimation (in relation to the mean) of 71%.<br/