Breast cancer treatment and survival differences in women in remote and socioeconomically disadvantaged areas, as demonstrated by linked data from New South Wales (NSW), Australia.

INTRODUCTION: Reducing variations in cancer treatment and survival is a key aim of the NSW Cancer Plan. Variations in breast cancer treatment and survival in NSW by remoteness and socioeconomic status of residence were investigated to determine benchmarks. Reducing variations in cancer treatment and survival is a key aim of the NSW Cancer Plan. Variations in breast cancer treatment and survival in NSW by remoteness and socioeconomic status of residence were investigated to determine benchmarks. METHODS: A retrospective cohort study used linked data for invasive breast cancers, diagnosed in May 2002 to December 2015 from the NSW Cancer Registry, with corresponding inpatient, and medical and pharmaceutical insurance data. Associations between treatment modalities, area socioeconomic status and residential remoteness were explored using logistic regression. Predictors of breast cancer survival were investigated using Kaplan-Meier product-limit estimates and multivariate competing risk regression. RESULTS: Results indicated a high 5-year disease-specific survival in NSW of 90%. Crude survival was equivalent by residential remoteness and marginally lower in lower socioeconomic areas. Competing risk regression showed equivalent outcomes by area socioeconomic status, except for the least disadvantaged quintile, which showed a higher survival. Higher sub-hazard ratios for death occurred for women with breast cancer aged 70 + years, and more advanced stage. Adjusted analyses indicated more advanced stage in lower socioeconomic areas, with less breast reconstruction and radiotherapy, and marginally less hormone therapy for women from these areas. Conversely, among these women who had breast conserving surgery, there was higher use of chemotherapy. Remoteness of residence was associated in adjusted analyses with less radiotherapy and less immediate breast reconstruction. In these short term data, remoteness of residence was not associated with lower survival. CONCLUSION: This study provides benchmarks for monitoring future variations in treatment and survival.

Using prediction to improve elective surgery scheduling.

BACKGROUND: An ageing population and higher rates of chronic disease increase the demand on health services. The Australian Institute of Health and Welfare reports a 3.6% per year increase in total elective surgery admissions over the past four years.1 The newly introduced National Elective Surgery Target (NEST) stresses the need for efficiency and necessitates the development of improved planning and scheduling systems in hospitals. AIMS: To provide an overview of the challenges of elective surgery scheduling and develop a prediction based methodology to drive optimal management of scheduling processes. METHOD: Our proposed two stage methodology initially employs historic utilisation data and current waiting list information to manage case mix distribution. A novel algorithm uses current and past perioperative information to accurately predict surgery duration. A NEST-compliance guided optimisation algorithm is then used to drive allocation of patients to the theatre schedule. RESULTS: It is expected that the resulting improvement in scheduling processes will lead to more efficient use of surgical suites, higher productivity, and lower labour costs, and ultimately improve patient outcomes. CONCLUSION: Accurate prediction of workload and surgery duration, retrospective and current waitlist as well as perioperative information, and NEST-compliance driven allocation of patients are employed by our proposed methodology in order to deliver further improvement to hospital operating facilities.