A simple filter model to guide the allocation of healthcare resources for improving the treatment of depression among cancer patients.

BACKGROUND: Depression is highly prevalent yet often poorly detected and treated among cancer patients. In light of the move towards evidence-based healthcare policy, we have developed a simple tool that can assist policy makers, organisations and researchers to logically think through the steps involved in improving patient outcomes, and to help guide decisions about where to allocate resources. METHODS: The model assumes that a series of filters operate to determine outcomes and cost-effectiveness associated with depression care for cancer patients, including: detection of depression, provider response to detection, patient acceptance of treatment, and effectiveness of treatment provided. To illustrate the utility of the model, hypothetical data for baseline and four scenarios in which filter outcomes were improved by 15% were entered into the model. RESULTS: The model provides outcomes including: number of people successfully treated, total costs per scenario, and the incremental cost-effectiveness ratio per scenario compared to baseline. The hypothetical data entered into the model illustrate the relative effectiveness (in terms of the number of additional incremental successes) and relative cost-effectiveness (in terms of cost per successful outcome and total cost) of making changes at each step or filter. CONCLUSIONS: The model provides a readily accessible tool to assist decision makers to think through the steps involved in improving depression outcomes for cancer patents. It provides transparent guidance about how to best allocate resources, and highlights areas where more reliable data are needed. The filter model presents an opportunity to improve on current practice by ensuring that a logical approach, which takes into account the available evidence, is applied to decision making.

Identification of higher hospital costs and more frequent admissions among mid-aged Australian women who self-report diabetes mellitus.

OBJECTIVE: To ascertain whether the hospital costs for mid-aged Australian women who self-reported diabetes mellitus (DM) and who had one or more hospital admission during an eight and a half year period were higher than the hospital costs for other similarly aged non-DM women. METHODS: The sample comprised 2,392 mid-aged women, resident in New South Wales (NSW) Australia and participating in the Australian Longitudinal Study on Women's Health (ALSWH), who had any NSW hospital admissions during the eight and a half year period 1 July 2000 to 31 December 2008. Analyses were conducted on linked data from ALSWH surveys and the NSW Admitted Patient Data Collection (APDC). Hospital costs were compared for the DM and non-DM cohorts of women. A generalized linear model measured the association between hospital costs and self-reported DM. RESULTS: Eight and a half year hospital costs were 41% higher for women who self-reported DM in the ALSWH surveys (p<0.0001). On average, women who self-reported DM had significantly (p<0.0001) more hospital admissions (5.3) than women with no reported DM (3.4). The average hospital stay per admission was not significantly different between the two groups of women. CONCLUSIONS: Self-reported DM status in mid-aged Australian women is a predictor of higher hospital costs. This simple measure can be a useful indicator for public policy makers planning early-stage interventions that target people in the population at risk of DM.

Workplace-based assessment for international medical graduates: at what cost?

OBJECTIVE: To estimate the cost of resources required to deliver a program to assess international medical graduates (IMGs) in Newcastle, Australia, known as the Workplace Based Assessment (WBA) Program. DESIGN AND SETTING: A costing study to identify and evaluate the resources required and the overheads of delivering the program for a cohort of 15 IMGs, based on costs in 2012. MAIN OUTCOME MEASURES: Labour-related costs. RESULTS: The total cost in 2012 for delivering the program to a typical cohort of 15 candidates was $243,384. This equated to an average of $16,226 per IMG. After allowing for the fees paid by IMGs, the WBA Program had a deficit of $153,384, or $10,226 per candidate, which represents the contribution made by the health system. CONCLUSION: The cost per candidate to the health system of this intensive WBA program for IMGs is small.

General practitioner referral patterns for women with gynaecological symptoms: a randomised incomplete block study design.

OBJECTIVE: To describe why, when and to whom general practitioners refer women with symptoms possibly attributable to cervical, endometrial or ovarian cancers, and to identify patient and GP factors that predict referral to either a gynaecologist or a gynaecological oncologist. DESIGN AND SETTING: A national survey of GPs between 1 April and 31 August 2009 using a randomised incomplete block design based on case vignettes, and using a self-completed postal or online questionnaire. PARTICIPANTS: A sample of GPs, stratified by location and randomly selected from a database of GPs maintained by the Australasian Medical Publishing Company. MAIN OUTCOME MEASURES: Proportion of vignettes that were deemed to reflect a high probability of cancer being referred; and the patient and clinician factors that were the strongest predictors of referral. RESULTS: Of the 3082 GPs who were selected for participation, 1402 responded, giving a response rate of 45.5%. Overall, for vignettes identified as describing women with a high probability of cancer, 75% were referred by metropolitan GPs and 73% by rural practitioners. Metropolitan GPs were significantly more likely to refer women in scenarios indicative of endometrial cancer than rural GPs. For all three cancers, GPs were significantly more likely to refer a patient to a gynaecologist (between 70.8% and 95.4%) than a gynaecological oncologist. Metropolitan GPs had significantly greater access to both private and public gynaecological oncologists than their rural counterparts. Referral rates were higher for ovarian and cervical cancer (83% and 80%, respectively) and lower for endometrial cancer (68%). For all three cancers, patient factors were stronger predictors of referral than the demographic factors of participating GPs. CONCLUSION: There appears to be significant variation in referral practices among GPs and this variation is greater for endometrial cancer, for which there are currently no evidence-based clinical practice guidelines in Australia. There is a need for further research into understanding the basis of these differences, including a review of the existing guidelines for ovarian and cervical cancer and the development of guidelines for endometrial cancer.