Choice of ICD‐10 codes for the identification of acute coronary syndrome in the French hospitalization database

The objective of this study was to evaluate the performance of the ICD‐10 (International Classification of Diseases and Related Health Problems, 10^th Edition) coding in the French hospitalization database (PMSI) to identify acute coronary syndrome (ACS) occurrence. Eligible hospitalizations were those that occurred at the Bordeaux teaching hospitals between 1 January 2011 and 31 December 2011 and had one of the ICD‐10 codes related to ischaemic heart diseases (I20 to I25, excluding I23 and I25.2). Among these, 100 hospitalizations were randomly selected; for each case, the ACS diagnosis was confirmed/excluded after medical file examination by an independent events validation committee and the performance of codes, and combinations of codes, to identify ACS was evaluated by calculating the positive predictive value (PPV). Of the individual codes, I20.0, I21 and I24 had the highest PPV; 100.0% for I24 (95%CI [15.8–100.0]); 90.0% for I21 (95%CI [76.3–97.2]); and 66.7% for I20.0 (95%CI [38.4–88.2]). The combination of I20.0 or I24 codes was able to identify 12 of the 56 validated ACS cases with a PPV of 70.6% (95%CI [44.0–89.7]), the combination of I21 or I24 identified 38 cases with a PPV of 90.5% (95%CI [77.4–97.3]), the combination of I20.0 or I21 identified 46 cases with a PPV of 83.6% (95%CI [71.2–92.2]), and the combination of I20.0, I21 or I24 identified 48 cases with a PPV of 84.2% (95%CI [72.1–92.5]). The combination of I20.0, I21 or I24 codes had the best performance to identify occurrence of ACS in the French hospitalization database.     

Measuring the productivity of residential long-term care in England: methods for quality adjustment and regional comparison

Productivity trend information is valuable in developing policy and for understanding changes in the ‘value for money’ of the care system. In this paper, we consider approaches to measuring productivity of adult social care (ASC), and particularly care home services. Productivity growth in the public sector is traditionally measured by comparing change in total output to change in total inputs, but has not accounted for changes in service quality and need. In this study, we propose a method to estimate ‘quality adjusted’ output based on indicators of the Adult Social Care Outcomes Toolkit (ASCOT), using data collected in the annual adult social care survey (ASCS). When combined with expenditure and activity data for 2010 to 2012, we found that this approach was feasible to implement with current data and that it altered the productivity results compared with non-adjusted productivity metrics. Overall, quality-adjusted productivity grew in most regions between 2010 and 2011 and remained unchanged for most regions from 2011 to 2012.