Funnel plots and risk-adjusted count data adverse events. A limitation of indirect standardisation

Impact of Disease Prevalence Adjustment on Hospitalization Rates for Chronic Ambulatory Care-Sensitive Conditions in Germany

OBJECTIVES

To explore effects of disease prevalence adjustment on ambulatory care-sensitive hospitalization (ACSH) rates used for quality comparisons.

DATA SOURCES/STUDY SETTING

County-level hospital administrative data on adults discharged from German hospitals in 2011 and prevalence estimates based on administrative ambulatory diagnosis data were used.

STUDY DESIGN

A retrospective cross-sectional study using in- and outpatient secondary data was performed.

DATA COLLECTION

Hospitalization data for hypertension, diabetes, heart failure, chronic obstructive pulmonary disease, and asthma were obtained from the German Diagnosis Related Groups (DRG) database. Prevalence estimates were obtained from the German Central Research Institute of Ambulatory Health Care.

PRINCIPAL FINDINGS

Crude hospitalization rates varied substantially across counties (coefficients of variation [CV] 28-37 percent across conditions); this variation was reduced by prevalence adjustment (CV 21-28 percent). Prevalence explained 40-50 percent of the observed variation (r = 0.65-0.70) in ACSH rates for all conditions except asthma (r = 0.07). Between 30 percent and 38 percent of areas moved into or outside condition-specific control limits with prevalence adjustment.

CONCLUSIONS

Unadjusted ACSH rates should be used with caution for high-stakes public reporting as differences in prevalence may have a marked impact. Prevalence adjustment should be considered in models analyzing ACSH.

Variation in the pharmaceutical costs of New Zealand general practices: a national database linkage study

BACKGROUND

Variation in prescription costs between general practices and within practices over time is poorly understood.

METHODS

From New Zealand's national health data collections, we extracted dispensed medicines data for 1045 general practices in 2011 and 917 practices continuously existing 2008-11. Using indirect standardization to account for patient demographics and morbidity, a standardized prescribing cost ratio (SPR: the ratio of actual : expected prescription costs) was calculated for each practice in each year. Case studies of three outlier clinics explored reasons for their status.

RESULTS

SPRs ranged from 0.53 to 2.28 (median = 0.98). Of 469 practices with higher than expected costs (SPR > 1.0) in 2011, 204 (43.5%) had a single medicine or therapeutic drug class accounting for >15% of total costs. Case studies contrasted practices with overall pharmaceutical expenditure influenced strongly by a few patients needing high-cost medicines, more patients using medicines in one high-cost therapeutic drug class (antiretrovirals), and high medicine use across all therapeutic drug classes.

CONCLUSIONS

Routine data collections can measure inter-practice variation in prescription costs, adjusted for differences in the demography and morbidity profile of each practice's patients. Small groups of patients using high-cost medicines influence general practices' expenditure on pharmaceuticals.

Surgical site infection: the principles and practice of surveillance: Part 2: analysing and interpreting data

In Part 1 of this two-part series on surveillance of surgical site infection (SSI) the principles of surveillance methodology and the role of surveillance in reducing the risk of infection were discussed. This second part focuses on the analysis and interpretation SSI surveillance data, the challenges this presents and of some of the solutions. The risk of SSI is conventionally expressed as the percentage of operations that develop SSI. However, this metric is strongly dependant on the length of post-operative stay, since infections take several days to become apparent and are difficult to identify after discharge. Comparisons based on more severe infections detected in inpatients or those readmitted with SSI are more likely to provide reliable data for inter-hospital comparisons. The precision of the estimated rates and adjustment for intrinsic risk factors are important considerations, although ultimately mechanisms for discriminating significantly higher rates merely indicate a problem requiring further investigation rather than definitive evidence of poor practice.

Use of surgical-site infection rates to rank hospital performance across several types of surgery

BACKGROUND

Comparing and ranking hospitals based on health outcomes is becoming increasingly popular, although case-mix differences between hospitals and random variation are known to distort interpretation. The aim of this study was to explore whether surgical-site infection (SSI) rates are suitable for comparing hospitals, taking into account case-mix differences and random variation.

METHODS

Data from the national surveillance network in the Netherlands, on the eight most frequently registered types of surgery for the year 2009, were used to calculate SSI rates. The variation in SSI rate between hospitals was estimated with multivariable fixed- and random-effects logistic regression models to account for random variation and case mix. 'Rankability' (as the reliability of ranking) of the SSI rates was calculated by relating within-hospital variation to between-hospital variation.

RESULTS

Thirty-four hospitals reported on 13 629 patients, with overall SSI rates per surgical procedure varying between 0 and 15·1 per cent. Statistically significant differences in SSI rate between hospitals were found for colonic resection, caesarean section and for all operations combined. Rankability was 80 per cent for colonic resection but 0 per cent for caesarean section. Rankability was 8 per cent in all operations combined, as the differences in SSI rates were explained mainly by case mix.

CONCLUSION

When comparing SSI rates in all operations, differences between hospitals were explained by case mix. For individual types of surgery, case mix varied less between hospitals, and differences were explained largely by random variation. Although SSI rates may be used for monitoring quality improvement within hospitals, they should not be used for ranking hospitals.