Bayesian methods in reporting and managing Australian clinical indicators.

Sustained clinical improvement is unlikely without appropriate measuring and reporting techniques. Clinical indicators are tools to help assess whether a standard of care is being met. They are used to evaluate the potential to improve the care provided by healthcare organisations (HCOs). The analysis and reporting of these indicators for the Australian Council on Healthcare Standards have used a methodology which estimates, for each of the 338 clinical indicators, the gains in the system that would result from shifting the mean proportion to the 20(th) centile. The results are used to provide a relative measure to help prioritise quality improvement activity within clinical areas, rather than simply focus on "poorer performing" HCOs. The method draws attention to clinical areas exhibiting larger between-HCO variation and affecting larger numbers of patients. HCOs report data in six-month periods, resulting in estimated clinical indicator proportions which may be affected by small samples and sampling variation. Failing to address such issues would result in HCOs exhibiting extremely small and large estimated proportions and inflated estimates of the potential gains in the system. This paper describes the 20(th) centile method of calculating potential gains for the healthcare system by using Bayesian hierarchical models and shrinkage estimators to correct for the effects of sampling variation, and provides an example case in Emergency Medicine as well as example expert commentary from colleges based upon the reports. The application of these Bayesian methods enables all collated data to be used, irrespective of an HCO's size, and facilitates more realistic estimates of potential system gains.

Clinical indicators in surgery: a critical review of the Australian experience.

BACKGROUND: A set of clinical measures (indicators), developed by an Australian Council on Healthcare Standards (ACHS) and Royal Australasian College of Surgeons (RACS) working party, was introduced into the accreditation programme in 1997. Although early qualitative and quantitative reporting by health-care organizations (HCOs) reflected their value in stimulating change, the number of HCOs reporting data on this set of clinical indicators (CIs) has declined, despite an increase in the number of HCOs reporting data on the CIs programme overall. Possible reasons for this decline were sought. METHODS: A retrospective review of prospectively collected surgical CI data was performed, a national survey of stakeholders in the ACHS programme was conducted and a comparison was made with published international data. RESULTS: From a maximum of 247 HCOs reporting data in 2002, the number fell to 168 by 2011. While favourable trends were evident with some CIs, for example, a decline in the rate of negative histology in childhood appendicectomy and in the rate of in-hospital infection in total hip joint replacement, there was minimal change with many of the CIs, suggesting limited responsiveness as measures of care. In the national survey, stakeholder's response was positive overall, but there was a requirement for regular review of CIs. Although some colleges viewed the CIs as simplistic and not reliable, comparisons with similar measures available in the international literature were favourable. CONCLUSIONS: Possible reasons for the declining number of HCOs reporting surgical CI data are a lack of a recent revision of the CIs and a lack of engagement of clinicians from the RACS. Revision of the surgical CI set is required.

Assessing the impact of socio-economic status on cancer survival in New South Wales, Australia 1996-2001.

OBJECTIVE: To assess the impact of socio-economic status (SES) on cancer survival in the state of New South Wales (NSW), Australia. METHODS: Patients diagnosed with one of 13 major cancers during 1992-2000 in NSW were followed-up to the end of 2001. The effect of SES on survival was estimated for each individual cancer and all 13 cancers combined using multivariable modeling. The numbers of lives that could be extended if all people had the same level of excess risk of death due to cancer as patients in the highest SES areas were also estimated. RESULTS: There were highly statistically significant variations in survival across SES groups for four cancers: stomach, liver, lung, and breast and all 13 cancers combined. Variation remained highly significant after adjusting for disease stage. Patients in lower SES areas had 10-20% higher excess risk than those in the highest SES areas. In total, there were 3,346 lives potentially extendable beyond 5 years; the highest number was for lung cancer (756). CONCLUSION: The significantly worse survival in lower SES areas from cancers of the stomach, liver, lung, and breast may be due to poorer access to high-quality cancer care. Estimates of the number of lives potentially extendable by improving cancer survival in lower SES areas suggest that priority should be given to improving lung cancer care in lower SES areas in NSW, Australia.

Misclassification of colorectal cancer stage and area variation in survival.

We previously investigated the impact of health area of residence on colon and rectal cancer survival by estimating area-specific relative excess risk of death (RER), stratified by stage at diagnosis. The aims of this study were to quantify errors in colorectal cancer stage obtained from an Australian population-based cancer registry and assess the potential impact of errors in stage on these estimates. For a subset of cases, we compared the cancer registry stage with that from a survey of treating surgeons. We then randomly reallocated all cases to a simulated "corrected" stage according to the estimated misclassification probabilities and repeated the analysis of area variation stratified by simulated stage 1,000 times. We found 70% agreement between the Registry and Survey stage. This reallocation of the Registry cases by stage resulted in substantial variation in area-specific RERs across the simulated samples. Area variation in survival for localized colon and localized rectal cancer, which were previously statistically significant when classified using Registry stage, appeared no longer to be so. Misclassification of cancer registry stage can have an important impact on estimates of spatial variation in stage-specific colon and rectal cancer survival. If population-based cancer registry data are to be effectively used in evaluating and improving cancer care, the quality of the stage data may need to be improved.

Trends in survival and excess risk of death after diagnosis of cancer in 1980-1996 in New South Wales, Australia.

Survival from almost all cancers has improved during the last 30 years. There is debate over the reasons for the improvement. We examined trends in survival for 28 cancers from 1980 to 1996 in New South Wales (NSW), Australia, with adjustment for disease spread at diagnosis. NSW Central Cancer Registry data were used to estimate 5-year relative survival and relative excess risk of death for patients diagnosed in 1980-84, 1985-88, 1989-92 and 1993-96. Statistical significance of variation in excess deaths between periods of diagnosis was assessed using Poisson regression, with adjustment for age, sex, duration of follow-up, histology and spread of disease at diagnosis. There were statistically significant falls in excess deaths for 20 of the cancers with a 25% fall for all cancers combined. Cancers of the prostate, liver, thyroid, breast, gallbladder, body of uterus, rectum, cervix and ovary had falls of >30%. The falls varied by spread of disease; the largest being in localised and regionally spread tumours. Overall survival, when unadjusted for spread of cancer, generally fell in parallel with that in the specific categories of spread, which implies that stage migration did not contribute importantly to survival trends. While acknowledging the limitations of incomplete data on stage of cancer at diagnosis, we conclude that falls in excess deaths in NSW from 1980 to 1996 are unlikely, for many cancers, to be attributed to earlier diagnosis or stage migration; thus advances in cancer treatment have almost certainly contributed to them.

A population-based study from New South Wales, Australia 1996-2001: area variation in survival from colorectal cancer.

In this study, we have investigated the impact of area of residence on survival from colon and rectal cancer. Relative survival and relative excess risk of death from cancer were calculated for each of 17 health areas in New South Wales, Australia. There were statistically significant differences in survival across areas for both cancers after adjusting for demographic factors. The variation remained for colon cancer but was reduced for rectal cancer after adjustment for spread of disease at diagnosis. This persistent variation in colon cancer survival suggests that variation in treatment contributes to it, and there is separate evidence for such variation. Of the 7186 patients whose deaths within five years were attributable to colorectal cancer, 784 could have had their survival increased to more than five years if the excess risk of death in all areas was reduced to the 20th centile of its distribution. Estimates such as this can assist in prioritizing improvements in cancer services.

Estimating regional variation in cancer survival: a tool for improving cancer care.

OBJECTIVE: To improve estimation of regional variation in cancer survival and identify cancers to which priority might be given to increase survival. METHODS: Survival measures were calculated for 25 major cancer types diagnosed in each of 17 health service regions in New South Wales, Australia, from 1991 to 1998. Region-specific risks of excess death due to cancer were estimated adjusting for age, sex, and extent of disease at, and years since, diagnosis. Empirical Bayes (EB) methods were used to shrink the estimates. The additional numbers of patients who would survive beyond five years were estimated by shifting the State average risk to the 20th centile. RESULTS: Statistically significant regional variation in the shrunken estimates of risk of excess death was found for nine of the 25 cancer types. The lives of 2903 people (6.4%) out of the 45,047 whose deaths within 5 years were attributable to cancer could be extended with the highest number being for lung cancer (791). CONCLUSIONS: The EB approach gives more precise estimates of region-specific risk of excess death and is preferable to standard methods for identifying cancer sites where gains in survival might be made. The estimated number of lives that could be extended can assist health authorities in prioritising investigation of and attention to causes of regional variation in survival.