Quality of care and health outcomes

Transcription

1 Quality of care and health outcomes Michael Soljak, Elizabeth Cecil, Laura Gunn, Andrew Broddle, Scott Hamilton, Aumran Tahir, Azeem Majeed, Josip Car May 2013

2 Contents 1. Executive summary... 3 Background... 3 Investigation plan/methods... 3 Findings... 3 Discussion Introduction: aims within the context of ICP objectives... 4 Background... 4 Processes and outcomes Investigation plan/methods... 8 Data sources and issues... 8 Statistical analysis Findings Diabetes Elderly: general risk reduction Elderly: dementia Elderly: falls management Discussion Summary Emerging issues Using patient reported outcome measures Conclusion References Appendix: EQ-5D Questionnaire The North West London Integrated Care Pilot is a large-scale, innovative programme designed to improve the coordination of care for people over 75 years of age and adults living with diabetes. A team of researchers from Imperial College and the Nuffield Trust was engaged to carry out evaluation of the first year of the new integrated care programme. This is a report of Work Programme 3 only. This report forms one chapter of the wider evaluation report which consists of four separate work programmes. Work Programme 3 was undertaken by Imperial College London. All four work programmes and a summary of them can be downloaded from 2

3 1. Executive summary Background The background to this evaluation is in the Overall Evaluation Framework, dated July 2011, which sets out a number of questions about how the Integrated Care Pilot (ICP) has affected healthcare process and outcomes for patients. Unfortunately there is very little routinely recorded NHS outcome data, and what outcome data there is relates mainly to adverse outcomes: complications, readmissions, and emergency admissions or A&E attendances for chronic diseases. We used these and intermediate outcomes, such as test results, to evaluate the ICP care pathways. Investigation plan/methods For this part of the evaluation, the main data sources were anonymised patient-level datasets provided by the ICP Operations Team. Data extraction for this report was delayed until mid- June so that it would contain any recent changes in care processes and health outcomes. This meant there was less than a month to extract, process and analyse all the data shown. The primary care data was updated on 14 June 2012, which should be complete up to the end of May 2012, and SUS (Secondary Uses Service) inpatient data was updated on 27 June 2012, and we have assumed it is complete up until the end of March Although we received data on A&E attendances which contained the Hospital Episodes Statistics (HES) A&E minimum dataset, we have been unable to use the diagnostic data, which was essential to analysing cause-specific attendances. The short time period only slightly more than two weeks between final data extraction and agreement on indicator definitions, was insufficient to allow statistical analysis in this depth. Instead we decided it was best to present a fairly wide range of data with minimal additional analysis to provide baselines for more detailed analysis, and to allow the Operations Team to consider relative priorities. We intend to continue the analysis as far as resources allow, but a more complete analysis, which would be robust to peer review, would require more resources. Findings For diabetes, without detailed statistical analysis to confirm it, we found that testing for diabetes control appears to be rising over time. There is a clear annual pattern in testing frequency, with peaks in June, October and March. In terms of HbA1c control, using smoothed monthly data, we found possible evidence of a decreasing proportion of patients with good control in ICP practices, which reflects a national trend. According to monthly data, admissions with diabetes as a primary diagnosis up to March 2012 may be increasing in ICP practices, although we cannot confirm this from annual totals. However, across England, there has been a fall in diabetes emergency admissions over the last five years. The highest emergency admission rates are in the youngest age group, nearly all of whom have Type 1 diabetes and who are admitted with acute complications, i.e. diabetes as a primary diagnosis. In the elderly cohort, there may be large increases in dementia diagnoses after August 2011 and in January There was a huge increase in care plans for dementia patients over the period August 2011 to February 2012, but this appears to be tapering off. There is no evidence of a change in the rate of admissions for fractures/falls up to March

4 Discussion In summary, we undertook baseline analyses of a number care processes, intermediate outcomes or adverse outcomes. Apart from the creation of care plans, we found little evidence of changes in them, with the possible exception of dementia diagnoses. ICP practices perform worse than the rest of England average in several areas. It appears that to have an effect on adverse outcomes, the work of the ICP has only just begun. The Operations Team have only recently agreed a number of short- to medium-term processes and intermediate outcome measures which could be tracked during 2012/13 to monitor progress. We recommend that these are adopted by the Integrated Management Board (IMB), and that further analyses are carried out during 2012/13 with case-mix adjustment and more thorough statistical methods. We also recommend that the IMB reviews the full range of evidence-based implementation strategies (for example feedback to practices and electronic patient-specific reminders), and puts as many of them in place as possible, as quickly as possible, in order to drive changes in care processes. 2. Introduction: aims within the context of ICP objectives In addition to examining service utilisation, Work Package 3 (WP3) evaluation aimed to investigate clinical effectiveness, both in terms of outcomes and process measures, for the two care groups (elderly and with diabetes) covered by the ICP. The WP3 analysis was carried out by the Department of Primary Care & Public Health (PCPH) in the School of Public Health at Imperial College London. Background The background to this evaluation is in the Overall Evaluation Framework, dated July This document states that examples of measures would include HbA1c, blood pressure and cholesterol control in people with diabetes Other key areas for quantitative evaluation are patient experience and impact on NHS efficiency and costs. There are a number of validated instruments for measuring quality of life (GHQ, EuroQol EQ-5D, CASP-19), and patient experience and patient satisfaction (e.g. GPASS, Picker). Impact on NHS efficiency would include areas such as unplanned admissions for ambulatory-sensitive conditions, A&E attendances, inappropriate prescribing, appropriate use of skill-mix etc (Other) examples would be the evaluation of screening to aid the early detection of diabetes and cardiovascular disease; and programmes to improve health and prevent disease. The Overall Evaluation Framework suggests a number of other research questions: what impact has the ICP had on the care and health of other patients? what impact has the integrated care model had on patients in terms of equity? has the model facilitated a more appropriate tailoring of clinical time and resources according to clinical need? has the use of integrated care records and the use of data from electronic records improved quality and completeness of electronic patient records and quality of care? has the ICP improved the diagnosis and registration of new cases of disease and high disease risk? has the ICP improved the integration of care (reduction in number of episodes of care and increase in episodes of package of care)? has the integrated care model as implemented resulted in a better patient/carer experience of care? 4

5 has the implementation of the integrated care model led to any other unintended consequences to the broader system? The Evaluation Framework also discusses cost effectiveness analysis (CEA) within the ICP evaluation. There are several references to CEA here: Under Methods : the new information tool will give the evaluation team access to rich clinical, operational and financial data to understand inputs, processes and outcomes including the cost effectiveness of interventions. In the table in the same section: Financial/Cost effectiveness for system of care pathways/output. Under six month review: Early assessment on questions of cost effectiveness, clinical outcomes. Under full evaluation: longer report to be published on impact of ICP and extent to which the model, and components of the model, is attributable for any recorded improvements in cost effectiveness. However, a suitable patient-reported outcome measure (PROM) is essential for a CEA. Clinical endpoints such as diabetes control cannot be used for this purpose. Data on clinical endpoints may allow estimation of the years of life saved by an intervention, but they say nothing about the effects on health-related quality of life (HRQoL). The EQ-5D is one option for measuring HRQoL in the ICP. We also took into account the comments made on the May interim report by the Imperial College Healthcare Charity: the (report) did not show much by way of analysis of improvements in terms of which groups of patients, conditions, GP practices, etc are most responsible for the reduction thus greater granularity will be needed in the data analysis to understand which conditions, which patients and which practices bring about the greatest change and why and thus understand what achieves the greatest impact so that replication can be done with much smaller numbers of patients and less investment in other parts of the country the evaluation will need to show that it did change the health status of patients, not just keep them out of A&E. Because there is a long-standing trend towards improved management of chronic diseases in the NHS (Campbell and others, 2007; Campbell and others, 2009), simply showing that care improved after the introduction of an ICP would not be sufficient to demonstrate its effectiveness. We would need to know that the improvement was greater than that based on underlying trends, and that the improvement was also better than in non-icp settings. Ideally this requires data from before the introduction of the ICP, and comparing performance against a non-icp site or sites. There are a number of approaches which can be taken to pragmatic quantitative evaluation of interventions like the ICP (Brown and Lilford, 2008). These include: 1. before and after studies without concurrent controls, examining time trends 2. comparative studies with concurrent controls (which may be randomised trials or nonrandomised natural experiments) 3. stepped wedge designs in which the intervention is rolled out to all individuals or clusters sequentially over the study period (with pre-intervention groups acting as controls) 5

6 4. mixed methods, which combine one or more of these methods with a qualitative evaluation. For this evaluation, we had access to patient-level data, including linked primary care, secondary care and social care from ICP practices only. For non-icp practices we had only practice level data. This meant that ICP/non-ICP comparative data analysis could only be carried out at the practice level. ICP patient-level analysis could include time trends and comparisons between similar ICP and non-icp patients within ICP practices. We used, or plan to use, both approaches (1) and (3) above. ICP adverse outcome objectives decrease hospital usage including emergency admissions by 30% and nursing home admissions by 10% for diabetics and frail elderly avoid 1,753 admissions across pilot of 506,000 population avoid 3,700 attendances across pilot of 506,000 population. Processes and outcomes We also had to decide what to evaluate. Figure shows the link between healthcare process and outcomes for a patient safety intervention. Currently there is very little routinely recorded NHS outcome data. This is changing with the introduction of PROMs, but at present these only cover a few surgical procedures. What outcome data there is relates mainly to adverse outcomes: complications, readmissions, and emergency admissions, or A&E attendances for chronic diseases. The ICP Board may state that the evaluation will need to show that it did change the health status of patients, but if the NHS is not collecting the data on a wider basis, either routinely, or as part of the ICP process, then the evaluation cannot include it. If the ICP Board wishes to see outcome data captured, it will need to implement this as part of ICP development. Both patients and professionals would agree that avoidable emergency admissions /attendances are adverse outcomes. Reducing these is a stated objective of the ICP, which aims takes an optimistic view of feasibility based on a selected evidence review of trials with positive outcomes. Figure 1: Causal chain linking patient safety interventions to outcomes (Brown and Lilford, 2008) 6

7 It is also important to measure changes in care processes and intermediate outcomes, since we would expect to see a prior change in them (their effect size is large) to explain any changes in adverse outcomes. Otherwise changes in adverse outcomes could be due to some measured or unmeasured confounders, e.g. changes in hospital bed availability. Moreover, given the short period for which the ICP has recruited a large number of patients, process changes could have occurred, but these might not yet be translated into admission/attendance rates. For many areas of healthcare, process changes and intermediate outcome standards already exist, e.g. the GP Quality and Outcomes Framework (QOF) and National Institute for Health and Care Excellence (NICE) guidelines. Examples for diabetes include foot and eye checks (care processes) and HbA1c, blood pressure, cholesterol and overall CVD risk control (intermediate outcomes). Another source of care process indicators is an evidence review undertaken prior to the ICP establishment. This included the NICE guidance in many instances, and lists for each intervention an impact metric, for example per cent reduction in number of falls. The review fed into the subsequent pathways designed by Clinical Working Groups. We reviewed relevant guidance, the ICP assumptions/design and the detailed pathways to produce an initial list of indicators for each care pathway. The Operations Team has already developed a regular reporting system which includes: patient consent and care planning by multidisciplinary group (MDG) patient consent and care planning by MDG by cohort care planning rate by MDG diabetes care planning rate by MDG elderly care planning detail risk stratification scores of patients with care plans risk stratification scores for patients with care plans profile by MDG emergency admissions tracker a monthly chart showing performance versus historic, target and baseline emergency admissions by cohort: actual vs actual admissions by MDG compared to historic, baseline and target quality metrics snapshot report diabetes staff satisfaction feedback on case conferences admissions detail length of stay (LOS), average LOS over year, bed days (both elderly and diabetic cohorts). We therefore aimed to ensure consistency with ICP pathway and report definitions, for example we used the QOF Business Rules for diabetes, as did the Operations team, to define the diabetes cohort. In using clinical and administrative data for evaluation, a fundamental fact is that the devil is in the detail in terms of defining the data to be used. With the exception of the diabetes quality metrics snapshot report above, neither the Operations Team nor the ICP Clinical Working Groups had so far clearly defined more specific care processes or intermediate outcome measures which could be used as short- to medium-term indicators that changes in care processes might reduce adverse outcomes. The actual indicators and definitions which we could use were not agreed with the Operations Team until the week beginning 25 June Moreover, the actual definitions can be complex. For example, we considered analysing uptake of pneumococcal vaccine to prevent pneumonia in elderly patients and those with chronic disease, but for diabetes the policy applies only to diabetic patients requiring insulin or oral hypoglycaemic drugs, i.e. Type I diabetes requiring insulin or Type 2 diabetes requiring insulin or oral hypoglycaemic drugs. It does not include diabetes that is diet 7

8 controlled, which entails an analysis of prescribing data. In April 2005, the policy for older people was fully implemented and all those aged 65 and over were recommended to have the vaccine. The Read codes extracted include both codes for the administrative procedure and codes for the vaccine. Only 23-valent pneumococcal polysaccharide vaccine is approved for over 65s. Primary care data are much more detailed in terms of coding than SUS data from local trusts. We wished to use queries which had been previously validated as producing valid and reliable primary care data, and ideally where there was comparative data from a national sample. For some indicators we therefore used the 2012/13 QOF Business Rules (NHS Employers, 2012) to obtain codes for QOF diseases or care processes which are also QOF indicators, but which we wished to track in the current year. For others where national audits had been supported by Primary Care Information Services (Primary Care Information Services, 2012; PRIMIS+, a national primary care data service based at Nottingham University), we obtained query specifications from the PRIMIS+ website. PRIMIS+ also has audit data for these queries from national samples of practices, but refused to provide this to us as this is not permitted by the commissioner of the service, the NHS Information Centre. We may be able to obtain this data from local practices who participated. 3. Investigation plan/methods Data sources and issues We already have a wide range of practice level data for all practices in England. As a first step, we provided in the May report baseline practice statistics for as many outcomes and covariates as possible, including comparisons with London/national data. The practices included in the pilot show a similar level of social and material deprivation, measured using the Index of Multiple Deprivation (IMD), as those across London. One practice in the pilot had no data available. The May report also included spatial analyses conducted using a Geographic Information Systems (GIS). Patient data were mapped at the Lower Super Output Area (LSOA) and practice locations to explore the spatial distribution of patients enrolled in the ICP compared to controls. Maps were created which display the geographic distribution of outcomes, both at practice level and aggregated from individual or practice level data as median values to LSOA level across the ICP area pre- and post-intervention. The mapping assists in identifying geographic areas and practices where there is higher uptake of the ICP and allows for monitoring of outcomes over time and space to detect where outcomes are affected by spatial factors. For this part of the evaluation, the main data source was patient level datasets provided by the ICP Operations Team. The dates of the extracts used are as follows: the linking table was updated on 14 June 2012 primary care data was updated on 14 June 2012, which should be complete up to the end of May However, we have assumed it is complete up to the end of April 2012 SUS inpatient data was updated on 27 June 2012, and we have assumed it is complete up until the end of March However, this data was not the full reconciled position for March, so this could change, e.g. coding updates could be added. 8

9 Data extraction for this report was delayed until mid-june, so that it would contain any recent changes in care processes and health outcomes. This meant there was less than a month to extract, process and analyse all the data shown. Because of the large data volumes involved, it could not be processed immediately in Stata or other statistical/analytical software. It was processed first using the Microsoft SQL Server database package, then converted into Stata datasets. These data came from three main sources: general practice computer systems; SUS inpatient, outpatient, A&E attendance and community information datasets from local trusts; and social care data from local authorities (LAs). These sources had been linked via a database table containing patient demographics and NHS Numbers, before the data were anonymised by removing all confidential data items (including name, address, postcode and date of birth). The NHS Number was replaced by the Operations Team with another unique identifier, and the postcode with Lower Layer Super Output Area (LLSOA), the next highest level. We supplemented these data where necessary by joining with other datasets, e.g. an IMD score was joined to each patient record using the LLSOA variable. Although we received data on A&E attendances which contained the HES A&E minimum dataset, we have been unable to use the diagnostic data, which was essential to analysing cause-specific attendances. The A&E diagnosis is a six character code made up of, diagnosis condition (n2), sub-analysis (n1), anatomical area (n2) and anatomical side (an1). Although the diagnostic data is by no means complete nationally, 72% of attendances include diagnostic codes we had planned to use it in the analysis. However, it appears that the ICP provider trusts A&E systems do not conform to the national codes, so mapping of system-specific to national codes is problematic. The Operations Team has been in discussion with providers but, at the time of writing, there is no resolution. We have retained the data definitions in the report so that it is clear what we would have analysed had the data been available. The problem is soluble and (provided the local data is of reasonable quality) it should be a valuable data source because the volume of A&E attendances is much greater than that of inpatient admissions. It may therefore be large enough to indicate significant changes in utilisation sooner than inpatient admissions. The Operations Team also joined a flag to each patient record indicating that the patient was on the diabetes care pathway, or one of the elderly care pathways (unfortunately the flag does not specify which of the elderly pathways the patient was on). Another ICP intervention is the ICP information tool, which acts as the clinical front end of the data integration. As well as allowing members of general practice teams to view the linked data for an individual patient, it also allows them to record various data items, including consent to participate in the ICP and completion of an ICP care plan for each patient. Data could be obtained from the tool for further analyses. Statistical analysis In the evaluation plan for statistical analysis, we planned to compare (mainly) percentage differences in annual measurement of the outcome measures using χ2 tests. We stated that linear regressions for pre-icp data for each patient would also be generated with a time indicator, and the slope and intercept will be used to predict the future value. This value represents the expected value of the outcome if the ICP had not been established. We noted that an additional challenge in the statistical analyses would be to accommodate the hierarchical nature of the data, which are years of measurement nested within patients nested within practices. Ignoring this multi-level clustering would result in faulty estimation of 9

10 standard errors. We therefore planned to use random effects multi-level models in some of the analyses to adjust for case-mix at the patient and practice levels. The short time period only slightly more than two weeks between final data extraction and agreement on indicator definitions was insufficient to allow statistical analysis in this depth. Instead we decided it was best to present a fairly wide range of data with minimal additional analysis to provide baselines for more detailed analysis, and to allow the Operations Team to consider relative priorities. We intend to continue the analysis as far as resources allow, but a more complete analysis which would be robust to external academic peer review would require more resources. As this evaluation is being carried out in London, the most socio-economically and ethnically diverse part of the UK, it is important that the ICP evaluation takes into account the characteristics of the populations the ICP serves for example, by adjusting for population age structure and deprivation. Conversely, it is also important is to see how well the ICP addresses the well-recognised socio-economic and ethnic disparities in access to health services and in health outcomes. As a first step, we have stratified some key indicators by ethnic group and IMD quintile to highlight any apparent distributional effects before carrying out complete statistical modelling. We were charged with evaluating the effect of two main exposures to interventions, i.e. being in an ICP practice, or being flagged and on a care pathway. We have defined these exposures as follows: being registered with an ICP practice from the date the project started, which we have taken to be a fixed date of 1 April 2011 (exposure 1) being identified as having either diabetes or being high risk elderly, receiving a care plan and being on a care pathway, which we have taken to be from the date of the care plan (exposure 2). In other words, if the specific event date, however defined, is after 1 April 2011, but prior to the care plan date, whenever that is, patients have been exposed to exposure 1; if it is after the care plan date patients have had exposure 2. A first step in the analysis was to define the ICP cohort. Selection criteria were as follows: include practices currently enrolled in the ICP as of 22 June 2012 practices which withdrew before this date are not included include patients with registration status of registered in the data provided remove patients with a deregistration date before 1 April 2011 as they will not have been exposed to either intervention remove patients with a death date before 1 April 2011 as they will not have been exposed to either intervention assign an elderly flag defined as age 75 or older on 31 December 2011 use the diabetes flag provided by the Operations Team which was based on the QOF Business Rules exclude patients that are neither elderly or diabetic remove duplicate patients with a diabetes flag remove duplicates where duplicate is due to other flags and update flag information to missing. As the analysis involves a range of data types and sources, the specific methodology used is discussed before the findings for each pathway and in the corresponding results. 10

11 4. Findings Diabetes Figure 2 shows the latest version of the diabetes care package for off-target, complex Type 2 patients; the most rigorous package. Figure 2: Diabetes care package: Off-target, Type II, as of 27 June 2012 The diabetes dataset we used contained 2,800 (7.67%) patients with Type 1 diabetes and 33,659 (92.25%) of patients with Type 2 diabetes. 11

12 Care process: diabetes related blood tests per patient per year An evaluation problem with diabetes is that changes in metabolic risk factors (intermediate outcomes) can only be observed in the short term if patients are re-tested at more frequent intervals than annual QOF reviews. From an evaluation perspective, it would be highly desirable that patients were all re-tested at roughly the same time; say three times annually. This is clinically justifiable if patients are at higher than average risk of adverse outcomes, and is now incorporated into the care packages. The care pathway (see Figure 2) specifies that patients should have reviews at least three times annually, in addition to the standard diabetic tests and care planning meeting, and that additional diabetic tests should be carried out according to clinical discretion. Over-frequent testing could be interpreted as poor practice. However, in a high-risk patient, it is necessary in order to decide if risk factors are improving, and it is also needed for the evaluation. An increase in testing could therefore be seen as the earliest evidence of a change in care processes. The data source for this process measure is primary care data. For most of the ICP practices, GP-ordered tests are carried out by Central London Community Health (CLCH), and practices are still required to enter the results themselves into systems, so there is possibly a degree of under-recording. Figure 3 shows the proportion of the diabetes cohort with at least one HbA1c test initially by year (i.e. overall proportions with HbA1c testing). This increased steadily until 2009/10, and has increased subsequently. As a next step we plan to use multi-level linear regression, adjusting for age, sex, deprivation and ethnicity, and to include time to look for time trends in testing. Figure 3: Proportion with HbA1c testing by year Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar Apr However, the picture looks different over shorter periods. Figure 4 shows the monthly HbA1c testing pattern. This shows a saw-tooth pattern, with annual peaks in June, October and February March. This suggests that it would be better to monitor short-term changes in testing as a result of the ICP superimposed on the previous years data. There is no evidence of any increased testing activity in

13 Figure 4: Monthly HbA1c testing pattern Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar Apr Care process: impact on inequalities This is a generic indicator which could be applied over most data sources, as shown below, and would investigate whether the ICP has resulted in reductions in any pre-existing inequalities in care processes or outcomes by ethnicity or deprivation. We have shown its application to HbA1c testing and control below. We found that 66% of all patients in the whole ICP cohort had a valid ethnicity code, and have used this in the analysis. This proportion may be higher in patients with diabetes (not yet analysed separately). IMD quintiles were defined using IMD scores based on LLSOA, as described previously. Figure 5 shows the proportion of ICP patients with HbA1c testing by ethnicity and financial year. There is no obvious evidence of ethnic inequalities in testing. However, apart from an upward blip in 2009/10, patients with missing ethnicity data appear to have less frequent testing. One explanation is that ethnicity recording is a marker for poorer quality of care, as is a lower rate of HbA1c testing. 13

14 Figure 5: Proportion with HbA1c testing by ethnicity and year (1 April 31 March) Figure 6 shows the percentage with HbA1c testing by deprivation and year. Apart from 2009/10, when all groups appear to have had very similar testing rates, patients from deprived areas have higher testing rates. This may be a reflection of greater disease severity. This explanation can be explored further by adjusting for HbA1cs and medications in a regression model. There is certainly no evidence of socioeconomic inequalities. Figure 6: Percentage with HbA1c testing by deprivation and year 14

15 Care process: misclassification of diabetes The data source for this process indicator is primary care data. We aim to use the Classification of Diabetes Self Audit Toolkit developed by NHS Diabetes. This is available at The algorithms were too detailed for us to include the analysis in the July 2012 report. Care process: Increase in registered prevalence of diabetes The data source for this process indicator is primary care data. The indicator can use counts of patients with a diagnosis of diabetes by month. It is possible that an ICP focus on improving care in current patients may discourage finding of new cases. Intermediate outcome: HbA1c control The data source for this indicator is primary care data. We defined good HbA1c control according to 2012/13 QOF diabetes indicator 26 (NICE, 2010, menu NM14) is the percentage of patients with diabetes in whom the last IFCC-HbA1c is 59mmol/mol (+<7.5%) or less in the preceding 15 months, but using monthly data to look for recent changes. Nationally, this percentage has been decreasing in recent years in 2009/10 the mean practice score for the percentage of patients in whom the last HbA1c is 53mmol/mol (7.0%) or less was only 53.82%, having fallen by almost six percentage points over five years (Information Centre for Health and Social Care, 2010). Figure 7: Percentage of tested patients with good ( 59 mmol/l) HbA1c control by financial year Monthly HbA1c data is highly irregular for reasons we have not ascertained. Figure 8 shows monthly HbA1c data, as a smoothed three month rolling average. There is still a rather irregular pattern. Without statistical analysis, it appears that, in line with national trends, the proportion of patients meeting the standard is decreasing. The latest national data available is for 2010/11, which was unchanged from 2009/10, when there were two similar indicators for HbA1c: diabetes 23, the percentage of patients with diabetes in whom the last HbA1c is 7 15

16 or less; and diabetes 24, the percentage of patients with diabetes in whom the last HbA1c is 8 or less. National achievement for these was 54.2% and 78.0% respectively; a large gap. So it is very difficult to compare ICP data with previous national data. We will have no good national comparator until October, unless we revert to DM23 or DM24. Figure 8: Percentage with good ( 59 mmol/l) HbA1c control by month, three month rolling average Among those exposed to six months of the ICP (1 April September 2011) who have not yet had a care plan, the difference in proportions (before and after 1 April 2011), of those tested, with their latest HbA1c values under good control (i.e. <=59 mmol) is significant (p<0.0001). In particular, 60.56% (n=4,979 out of 8,221) had their latest HbA1c blood test under control within six months of the ICP (and who have not yet had a care plan). This was significantly lower than the 63.94% (n=8,700 out of 13,606) whose latest HbA1c value was under good control prior to the start of the ICP. There is a significant difference (p=0.0484) in the average latest HbA1c value before care plan creation compared to the average latest HbA1c value after being on a care plan for at least three months among the 1,388 individuals who have recorded values under these conditions (average difference in means is 0.76). In particular, the mean HbA1c is lower prior to being on a care plan for at least three months. Figure 9 shows the percentage of tested patients with good ( 59 mmol/l) HbA1c control by financial year and ethnicity. Control appears worst in those with South Asian ethnicity. This intermediate outcome differs from the testing findings, which are entirely a care process. There is previous published evidence of similar findings in NWL in 2000/03, with South Asians less likely to be on insulin (Soljak and others, 2007). Subsequent studies elsewhere have shown improved care in South Asians since then, but this may not be the case in the ICP practice (Fischbacher and others, 2009). Again, multivariate analysis including treatment factors will answer this question. 16

17 Figure 9: Percentage of tested patients with good ( 59 mmol/l) HbA1c control, by ethnicity Table 1 shows national QOF achievement for diabetes blood pressure and cholesterol indicators over the period Table 1: National QOF achievement for diabetes blood pressure and cholesterol indicators, / / / / / / /11 Blood pressure 70.3% 74.9% 78.7% 79.3% 79.9% 80.6% 81.2% Cholesterol 71.8% 79.0% 83.1% 83.2% 82.6% 83.0% 82.9% Care process: diabetes related blood pressure tests per patient per year Figure 10 shows percentage with blood pressure testing by year. It is not very informative but appears to show an increase. 17

18 Figure 10: Percentage with blood pressure testing by year Intermediate outcome: blood pressure control The data sources for this analysis were local primary care data and national QOF data. We used the 2012/13 QOF diabetes indicator DM31: the percentage of patients with diabetes in whom the last blood pressure is 140/80 or less (NICE, 2010, menu ID: NM02), but examined monthly trends. It suffers from the same problems as other QOF indicators of already high achievement levels, but as an intermediate outcome achievement is less high than for process indicators. Figure 11: Percentage with good ( 140/80) blood pressure control by year 18

19 Figure 11 shows the percentage with good ( 140/80) blood pressure control by year. There may be a positive gradient change in 2011/12, but this needs to be confirmed. It may show continuing improvement, but nationally good blood pressure control has increased from 70.3% in 2004/05 to 81.2% in 2010/11. However, we have not at this stage removed data which was exception reported by ICP practices, whereas this has been removed from the national data, and could bias the results up slightly. This can be checked by validating against national QOF data for ICP practices and/or incorporating exception reporting Read codes in our analysis. ICP practices therefore lag well behind national results. Figure 12 shows the percentage with good ( 140/80) blood pressure control by month as a three month rolling average. There is an unexplained spike in June There appears to have been improvement, but nationally, the prevalence of good control is over 80%. ICP practices therefore lag well behind national results. Among those exposed to six months of the ICP (1 April September 2011) who have not yet had a care plan, the difference in proportions (before and after 1 April 2011) of those tested with their latest blood pressure under good control (i.e. <=140/80) is non-significant (p=0.1249). In particular, 58.74% (n=3,907 out of 6,651) had their latest blood pressure under control within six months of the ICP (and who have not yet had a care plan), which is not significantly different from 57.63% (n=8,399 out of 14,574) who had their blood pressure under good control prior to ICP. Figure 12: Percentage with good ( 140/80) blood pressure control by month (three month rolling average) Figure 13 shows the percentage with good ( 140/80) blood pressure control by ethnicity. Black and Mixed patients may have poorer control, a finding which has been documented previously (Chowdhury and others, 2006; Lanting and others, 2005; Verma and others, 2010). 19

20 Figure 13: Percentage with good ( 140/80) blood pressure control by ethnicity Intermediate outcome: Cholesterol control The proportion with cholesterol testing by year is similar to that for HbA1c; having improved then plateaued at about 80%. The data sources for this analysis were local primary care data and national QOF data. We used the 2012/13 QOF diabetes indicator DM17: the percentage of patients with diabetes whose last measured total cholesterol within the preceding 15 months is 5mmol/l or less. As for blood pressure, it suffers from the same problems as other QOF indicators of already high achievement levels, but as an intermediate outcome achievement is less high than for process indicators. Figure 14 shows the percentage of tested patients with good ( 5 mmol/l) cholesterol control by year in ICP patients compared to national QOF data. Unlike HbA1c, the QOF cholesterol indicator has been unchanged for a long period so national comparisons are possible. In ICP practices there has been a catch-up with the national achievement levels over , but this appears to have plateaued in the last two years. However, as for blood pressure, we have not at this stage removed data which was exception reported by ICP practices, whereas this has been removed from the national data, and could bias the results up slightly. This can be checked by validating against national QOF data for ICP practices and/or incorporating exception reporting Read codes in our analysis. 20

21 Figure 14: Percentage of tested patients with good ( 5 mmol/l) cholesterol control by year Among those exposed to six months of the ICP (1 April September 2011) who have not yet had a care plan, the difference in proportions (before and after 1 April 2011) of those tested, with their latest cholesterol reading having good control (i.e. <=5 mmol/l) is non-significant (p=0.1406). In particular, prior to the ICP, about 78.06% (n=11,027) of those tested (14,127) had their latest cholesterol reading under good control (<= 5 mmol/l). This did not change significantly, with about 78.90% (n=5,936) of patients who were tested within six months of the ICP (and who have not yet had a care plan) (n=7,523) having their latest cholesterol reading under good control. Figure 15 shows the percentage with good ( 5 mmol/l) cholesterol control by month, as a three month rolling average. The recent improvement seems to follow the pattern of previous years. 21

22 Figure 15: Percentage with good ( 5 mmol/l) cholesterol control by month, three month rolling average Figure 16 shows the percentage of tested patients with good ( 5 mmol/l) cholesterol control by year and ethnicity. There is some evidence of a recent decline in black patients. Figure 16: Percentage of tested patients with good ( 5 mmol/l) cholesterol control by ethnicity 22

23 Outcome: rate of unscheduled hospital admissions (and readmissions) with diabetes as a principal or early secondary diagnosis We wished to determine if the introduction of ICP in April 2011 is associated with any change in diabetes emergency admissions. The data source for this indicator is SUS inpatient data. We selected emergency admissions (i.e. admimeth = '21', '22', '23', '24', '28; exclude where admisorc='50', '51', '52', '53; exclude where firstreg='0', '1' i.e. where the admission is not a first regular day/night admission; and selected where episode order = '1', '2'). It should be possible to compare this data with Nuffield Trust results, although our diagnostic criteria are broader. We intend to fit negative binomial regression models, adjusting for age, sex, deprivation and ethnicity, and to include time in models to look for time trends. Diabetes diagnoses were defined as E10* (Type 1 diabetes), E11*(Type 2 diabetes), E14* (unspecified diabetes mellitus) and E162 (hypoglycaemia) in either primary diagnosis or any diagnosis fields. Figure 17 shows the crude monthly rate of diabetes emergency admissions, but only if diabetes is the primary diagnosis. The denominator is the diabetic and diabetic/elderly population of the ICP practices. Numbers are small so variability is high. There are 20 diagnostic fields in the HES/SUS inpatient datasets. As a generalisation, the more closely related diabetes is as a cause of the admission, the closer it should be to a primary diagnosis. For example, if a patient is admitted with renal disease secondary to diabetes, we would expect diabetes to be the second or at least third diagnosis. Conversely, in a patient with comorbidities admitted with a problem unrelated to diabetes (for example, pneumonia secondary to chronic obstructive pulmonary disease), diabetes may appear as the third, fourth or later disease coded. Thus selecting diabetes in any diagnostic field risks selecting admissions unrelated to ICP care pathways. Figure 17: Numbers of ICP emergency admissions with diabetes as a primary diagnosis, by month 23

24 Figure 18 shows the same data with fitted values to display the trends, and with the monthly rates weighted by the number of days in a month, and April 2011 set as the start date. Table 2 shows annual rates of emergency diabetes admissions per 1,000 ICP diabetes population, with 95% confidence intervals (CIs), comparing rates with diabetes in the first or any diagnosis fields. CIs overlap for primary diagnosis admissions showing that these rates are not significantly different from year to year. However, they are different from diabetes in any field. This could mean that diabetes complications are increasing, but this needs to be examined further by analysing specific complications categories, which are expressed as decimal points. Figure 18: Monthly rate emergency diabetes (all diagnosis) admissions per 1,000 diabetes population standardised for days in month Mar 09 Jan 10 Nov 10 Sep 11 Jul 12 Monthly rate DM admission (per 1000 population) Fitted values Table 2: Yearly rates of emergency diabetes admissions per 1,000 ICP diabetes population (95%CI) 2009/ / /12 Primary diagnosis ( ) ( ) ( ) All diagnosis ( ) ( ) ( ) Across England, there has been a fall in diabetes emergency admissions (see Table 3), but we do not have national data for 2011/12 for comparison. The national data show the differences between using diagnosis as a primary diagnosis or searching in any field. The smallest discrepancy is for acute complications of diabetes, which are usually primary diagnoses. The best option for the ICP would be to use an algorithm which selects admissions which are largely diabetes-related. 24

25 Table 3: Diabetes prevalence and rates of emergency admissions for diabetes complications per 1, diabetes patients by year, England Year Diabetes prevalence All diabetes complications Without Chronic Acute Hypoglycaemia 1st Any 1st Any 1st Any 1st Any 1st Any 2004/ * / * / / / / Figure 19 shows admission rate ratios (ARRs, England in 2004 = 1.00) for diabetes emergency admissions as a primary diagnosis, comparing ICP practices with the rest of England. CIs are wide, but the rate in ICP practices for the years available has been rising, while in England it has been falling. Figure 19: Admission Rate Ratios for diabetes emergency admissions (primary diagnosis), comparing ICP practices with England Admission Rate Ratio England ICP practices England 95% CI ICP 95% CI We also wished to determine whether the introduction of ICP, taking April 2011 as the start date initially, was associated with a change in the number of times individual diabetes patients are admitted in a year. Table 4 shows the number of individuals who have had 1, >2 or >5 admissions in the year. There is some evidence that the proportion of patients who have 5+ admissions in a year has increased since the introduction of ICP: using a Pearson χ 2 test shows that annual rates are significantly different (p = 0.014). 25

26 Table 4: Number of ICP individuals who have had 1, >2 or >5 admissions in the year 2009/ / /12 1 admissions 833 (73.6) 1012 (72.3) 1125 (69.2) 2+ admissions 299 (26.4) 387 (27.7) 500 (30.8) 5+ admissions 28 (2.5) 33 (2.4) 51 (3.1) We were also asked to examine how admission rates differ between different age/deprivation and ethnicity categories. Table 5 shows age-specific rates of emergency admissions per 1,000 diabetic patients, again separating diabetes as a primary diagnosis or in any diagnosis field. It illustrates how high admission rates are in the youngest age group, nearly all of whom have Type 1 diabetes and who are admitted with acute complications, i.e. diabetes as a primary diagnosis. Nearly all these patients will be managed by Paediatric Diabetes Units, who will not be involved in the ICP. Failing to exclude them will mean diabetes admissions data may be swamped by acute admissions of young people. Also older patients are much more likely to be admitted with other conditions, probably unrelated to diabetes. We recommend a much tighter definition of the patients the ICP is targeting, both in terms of age and diagnosis. Table 5: Age-specific rates of emergency admissions per 1000 ICP diabetic patients, 2011/12 Age group Primary diagnosis Any diagnosis ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Figure 20 shows rates of emergency admissions per 1,000 diabetes population (diabetes in any diagnosis field) by deprivation level. As expected, rates are higher in patients from deprived or very deprived areas. 26

27 Figure 20: Rates of emergency admissions per 1,000 diabetes population (any diagnosis field) by deprivation level Figure 21 shows rates of emergency admissions per 1,000 diabetes population (any diagnosis field) by ethnicity. Note that 38% of diabetic patients are missing ethnicity data; a surprisingly large proportion. This may be because the source is SUS data, and more complete data will be available shortly. From a 2011 report on ethnicity coding, in the 2009/10 data year there were 219 trusts supplying HES data, of whose data 8.4% of FCEs have missing ethnic codes, consisting of 1.9% Not known and 6.5% Not stated. Figure 21: Rates of emergency admissions per 1,000 diabetes population (any diagnosis field) by ethnicity 27

28 Care process: speed of referral for recognised foot complications (number of days) The data sources for this indicator would be primary care plus SUS outpatient data, but as it is quite complex time did not permit this analysis for the July report. Foot complications should be recorded well in primary care as the examination is a QOF indicator. We did not have time to analyse this indicator. Outcome: rate of A&E attendances and re-attendances The data source for this indicator is SUS A&E data, which includes A&E attendance primary diagnosis (3 character); or if not present, diabetic A&E attendance primary diagnosis (2 character). However, as noted in the Methods section, we have been unable to use the diagnostic data, which was essential to analysing cause-specific attendances. It appears that the ICP provider trusts A&E systems do not conform to the national codes, so mapping of systemspecific to national codes is problematic. The Operations Team has been in discussion with providers, but at the time of writing there is no resolution. Elderly: general risk reduction The aim of these overall indicators is to detect improvements in overall management of patients in the elderly cohort. They could be implemented immediately after care planning if not carried out already, or as part of each pathway. Care process: Uptake of pneumococcal vaccine in elderly and diabetes cohorts The data source for this indicator is primary care data. A single pneumococcal immunisation is recommended by the Health Protection Agency (HPA) for diabetes (requiring insulin or oral hypoglycaemic drugs). This includes Type 1 diabetes requiring insulin or Type 2 diabetes requiring oral hypoglycaemic drugs. It does not include diabetes that is diet controlled. In April 2005, the policy for older people was fully implemented and all those aged 65 and over were recommended to have the pneumococcal polysaccharide vaccine. We plan to use the PRIMIS+ pneumococcal immunisation specification Coverage of over 65s in 2007/08 (latest available national data) was 69% nationally. Data are collated either by automated uploads via GP IT suppliers or via the PRIMIS+ CHART online service. We did not have time to analyse this indicator. Care process: registered prevalence of non-qof diseases affecting elderly, fragility fracture and falls The data source for this indicator is the PRIMIS+ Data Quality Query Library 2010 prevalence specification (Fragility Fracture Falls only). The numerator is patients with a coded record of fragility fracture, in our cases since This indicator measures identification of health needs. Osteoporosis is likely to become a QOF domain in 2013/14. We did not have time to analyse this indicator. Care process: increases in registered prevalence of QOF diseases common in older people: hypertension, atrial fibrillation, dementia, depression and chronic kidney disease The data source for this indicator is primary care data. We plan to use QOF indicator definitions and the PRIMIS+ GRASP atrial fibrillation Ruleset v2.0. There has been a previous national audit so data quality should be fair. We are attempting to access national data via ICP practices participating in the PRIMIS+ audit as PRIMIS+ data is not available to researchers. We did not have time to analyse this indicator. 28

29 Care process: reduction in number of episodes of care and increase in episodes of package of care The data sources for this indicator are primary care, SUS outpatients and inpatients, community social care. However, the definition of a package has not yet been established by the Operations Team. It could mean measuring proximity in time. It might be possible to use the National Tariff costs as a means of weighting. We also do not know what is the desired direction of change. Elderly: dementia Figure 22 shows the potential patient pathway for dementia developed by the NHS NWL Elderly Care Clinical Working Group in Figure 22: Potential patient pathway for dementia Figure 23 shows the updated pathway produced in June We have used both these to define process of care indicators for dementia, as well as current NICE/SCIE guidance (National Collaborating Centre for Mental Health, 2011). 29

30 Figure 23: Dementia pathway as of June 2012 Care process: prevalence of dementia On average, practice dementia registers in England include only half the expected number (estimated from age/sex specific prevalence in research studies) of patients with dementia (Personal Social Services Research Unit (PSSRU) at the London School of Economics and Institute of Psychiatry at King s College London, 2007; Department of Health, 2011). Increasing the registered prevalence of dementia is a London GP Outcome Standard (NHS London, 2012). 30

31 The national age-specific GP registered or observed prevalence of dementia in the 75+ age group is 6.48%. The prevalence with a whole population denominator, as used for the QOF, is obviously much lower. Figure 24 shows trends in practice-registered prevalence of dementia in ICP practices and England from national QOF data (Information Centre for Health and Social Care, 2009). However, because of ICP practices younger age structure, the difference is much smaller using the 75+ denominator, which give an age-specific registered prevalence in ICP practices of 6.72%. Figure 24: Trends in practice-registered prevalence of dementia in ICP practices and England, Quality and Outcomes Framework (whole population denominator) We hypothesised that an early care process which could be improved by the pathway, and raised awareness in ICP practices, might be a recent increase in the registered prevalence of dementia. QOF prevalence is assessed annually, and 2011/12 data (which uses a prevalence day snapshot of 31 March each year), will not be available until October There were 1,403 patients on ICP practices QOF dementia registers on 31 March The data source for these ICP dementia indicators is primary care data. There were 1,353 patients in the dementia diagnosis dataset. Because it started in April 2006 and a small number of dementia patients were diagnosed before that date, numbers differ from ICP QOF dementia registers. Table 6 shows the diagnoses of patients in the dementia dataset. There are three main diagnostic groups: Alzheimer's disease, senile/pre-senile organic psychosis, and vascular dementia. 31