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A disease risk model is a statistical method which assesses the probability that an individual will develop one or more diseases within a stated period of time. Such models take into account the presence or absence of specific epidemiological risk factors associated with the disease and thereby potentially identify individuals at higher risk. Such models are currently used clinically to identify people at higher risk, including identifying women who are at increased risk of developing breast cancer. Many genetic and non-genetic breast cancer risk models have been developed previously. We have evaluated existing non-genetic/non-clinical models for breast cancer that incorporate modifiable risk factors. This review focuses on risk models that can be used by women themselves in the community in the absence of clinical risk factors characterization. The inclusion of modifiable factors in these models means that they can be used to improve primary prevention and health education pertinent for breast cancer. Literature searches were conducted using PubMed, ScienceDirect and the Cochrane Database of Systematic Reviews. Fourteen studies were eligible for review with sample sizes ranging from 654 to 248,407 participants. All models reviewed had acceptable calibration measures, with expected/observed (E/O) ratios ranging from 0.79 to 1.17. However, discrimination measures were variable across studies with concordance statistics (C-statistics) ranging from 0.56 to 0.89. We conclude that breast cancer risk models that include modifiable risk factors have been well calibrated but have less ability to discriminate. The latter may be a consequence of the omission of some significant risk factors in the models or from applying models to studies with limited sample sizes. More importantly, external validation is missing for most of the models. Generalization across models is also problematic as some variables may not be considered applicable to some populations and each model performance is conditioned by particular population characteristics. In conclusion, it is clear that there is still a need to develop a more reliable model for estimating breast cancer risk which has a good calibration, ability to accurately discriminate high risk and with better generalizability across populations.

The Aim of this thesis: To evaluate the impact of a validated Assessment Risk Tool had on clinical pharmacists at Midland Regional Hospital, Tullamore, Ireland. Objectives of this research: (1) To evaluate the impact of the ART by performing a comparative analysis between the before- after study since implementation. (2) To assess the communication between prescriber and pharmacist. (3) To conduct a survey that evaluates the pharmacist’s preference on the implementation of the ART. Methodology: The before – after study was carried out focusing on specific measures from July 2017 to March 2018. A quantitative review was conducted with a selected number of drug charts to evaluate the full impact of the Assessment Risk Tool. For the before study 149 drug charts was assessed and for the after study 140. Prescribers actions were followed in response to pharmacists interventions. A total of 15 pharmacists, after implementation of the ART, were surveyed to evaluate the impact the tool had on daily work as clinical pharmacists. Data were analysed using Excel and SPSS software program. Descriptive statistics were generated in the form of numbers and percentage. To evaluate statistical significance of relation between variables chi-square (χ2) test was applied. Results: Comparative analysis between before- and after - study showed an increase in endorsement of drug charts for P1 (19 % vs 36.8%) and P2 (39.5% vs 54.0%) and a decrease in P3 drug charts (40.7% vs 9.2%). Evidence from the study shows a low response rate from prescribers to pharmacist’s interventions: 43% no response rate of drug charts with a recommendation from a pharmacist – 29% was P1 and 34.1% P2 patient drug charts. Surveyed pharmacists were overall satisfied (87.6%) with the ART and a positive preference when working with the ART. 66.7% have followed by agreeing to the ART has assisted in identifying patients who are more medically complicated and the tool has made them notice certain aspects of patient care management pre-implementation. Conclusion: Pharmacists response to survey mirrors the positive impact the tool has done overall. Assessment Risk Tool put in place was found to be useful in prioritising patients for pharmacists interventions such as medication reviews