Faculty Bibliography

BACKGROUND: The burden of chronic disease is increasing, and research and quality improvement will be less effective if case finding strategies are suboptimal. OBJECTIVE: To describe an ontology-driven approach to case finding in chronic disease and how this approach can be used to create a data dictionary and make the codes used in case finding transparent. METHOD: A five-step process: (1) identifying a reference coding system or terminology; (2) using an ontology-driven approach to identify cases; (3) developing metadata that can be used to identify the extracted data; (4) mapping the extracted data to the reference terminology; and (5) creating the data dictionary. RESULTS: Hypertension is presented as an exemplar. A patient with hypertension can be represented by a range of codes including diagnostic, history and administrative. Metadata can link the coding system and data extraction queries to the correct data mapping and translation tool, which then maps it to the equivalent code in the reference terminology. The code extracted, the term, its domain and subdomain, and the name of the data extraction query can then be automatically grouped and published online as a readily searchable data dictionary. An exemplar online is: www.clininf.eu/qickd-data-dictionary.html CONCLUSION: Adopting an ontology-driven approach to case finding could improve the quality of disease registers and of research based on routine data. It would offer considerable advantages over using limited datasets to define cases. This approach should be considered by those involved in research and quality improvement projects which utilise routine data.

BACKGROUND: Delivering good quality primary care for patients with chronic conditions has the potential to reduce non-elective hospital admissions. Practice nurse staffing levels in England have been linked to attainment of general practice performance targets for some chronic conditions. The aim of this study was to examine whether practice nurse staffing level is similarly associated with non-elective hospital admissions in three clinical areas: asthma, Chronic Obstructive Pulmonary Disease (COPD) and diabetes. METHODS: This observational study used cross sectional analysis of routinely collected data. Hospital admissions data for the period 2005-2006 (for asthma, COPD and diabetes) were linked with a database of practice characteristics, nurse staffing data and data on population characteristics for the same period. Statistical modelling explored the relationship between non-elective hospital admission rates for the three conditions and the list size per full time equivalent (FTE) practice nurse. RESULTS: Higher practice nurse staffing levels were significantly associated with lower rates of admission for asthma (p < 0.001) and COPD (p < 0.001). A similar association was seen for patients with two or more admissions (p < 0.05 for asthma and p < 0.001 for COPD). For diabetes, higher practice nurse staffing level was significantly associated with higher admission rates (p < 0.05), but this association was not significant in case of patients with two or more admissions. Across all models, increasing deprivation was associated with higher admission rates for all conditions. CONCLUSIONS: The inconsistent relationship between nurse staffing and patient outcomes across the different conditions and the fact that for diabetes the relationship between staffing and outcomes was in a different direction from the association between staffing and care quality, highlights the need to avoid making a simple causal interpretation of these findings and reduces the possible confidence in such conclusions. There is a need for more research into the organisation and delivery of diabetes care services in general practice, preferably using patient level data; in order to better understand the impact of the different staffing configurations on patient outcomes.

AIM OF THE STUDY: To use the hospital standardised mortality ratio (HSMR), as a tool for Dutch hospitals to analyse their death rates by comparing their risk-adjusted mortality with the national average. METHOD: The method uses routine administrative databases that are available nationally in The Netherlands--the National Medical Registration dataset for the years 2005-2007. Diagnostic groups that led to 80% of hospital deaths were included in the analysis. The method adjusts for a number of case-mix factors per diagnostic group determined through a logistic regression modelling process. RESULTS: In The Netherlands, the case-mix factors are primary diagnosis, age, sex, urgency of admission, length of stay, comorbidity (Charlson Index), social deprivation, source of referral and month of admission. The Dutch HSMR model performs well at predicting a patient's risk of death as measured by a c statistic of the receiver operating characteristic curve of 0.91. The ratio of the HSMR of the Dutch hospital with the highest value in 2005-2007 is 2.3 times the HSMR of the hospital with the lowest value. DISCUSSION: Overall hospital HSMRs and mortality at individual diagnostic group level can be monitored using statistical process control charts to give an early warning of possible problems with quality of care. The use of routine data in a standardised and robust model can be of value as a starting point for improvement of Dutch hospital outcomes. HSMRs have been calculated for several other countries.

BACKGROUND: In many UK general practices, nurses have been used to deliver results against the indicators of the Quality and Outcomes Framework (QOF), a 'pay for performance' scheme. AIM: To determine the association between the level of nurse staffing in general practice and the quality of clinical care as measured by the QOF. DESIGN OF THE STUDY: Cross-sectional analysis of routine data. SETTING: English general practice in 2005/2006. METHOD: QOF data from 7456 general practices were linked with a database of practice characteristics, nurse staffing data, and census-derived data on population characteristics and measures of population density. Multi-level modelling explored the relationship between QOF performance and the number of patients per full-time equivalent nurse. The outcome measures were achievement of quality of care for eight clinical domains as rated by the QOF, and reported achievement of 10 clinical outcome indicators derived from it. RESULTS: A high level of nurse staffing (fewer patients per full-time equivalent practice-employed nurse) was significantly associated with better performance in 4/8 clinical domains of the QOF (chronic obstructive pulmonary disease, coronary heart disease, diabetes, and hypertension, P = 0.004 to P<0.001) and in 4/10 clinical outcome indicators (diabetes: glycosylated haemoglobin [HbA(1C)] < or =7.4%, HbA(1C) < or =10% and total cholesterol < or =193 mg/dl; and stroke: total cholesterol < or =5 mmol/L, P = 0.0057 to P<0.001). CONCLUSION: Practices that employ more nurses perform better in a number of clinical domains measured by the QOF. This improved performance includes better intermediate clinical outcomes, suggesting real patient benefit may be associated with using nurses to deliver care to meet QOF targets.

OBJECTIVE: To determine whether observable changes in waiting times occurred for certain key elective procedures between 1997 and 2007 in the English National Health Service and to analyse the distribution of those changes between socioeconomic groups as an indicator of equity. DESIGN: Retrospective study of population-wide, patient level data using ordinary least squares regression to investigate the statistical relation between waiting times and patients' socioeconomic status. SETTING: English NHS from 1997 to 2007. PARTICIPANTS: 427,277 patients who had elective knee replacement, 406,253 who had elective hip replacement, and 2,568,318 who had elective cataract repair. MAIN OUTCOME MEASURES: Days waited from referral for surgery to surgery itself; socioeconomic status based on Carstairs index of deprivation. RESULTS: Mean and median waiting times rose initially and then fell steadily over time. By 2007 variation in waiting times across the population tended to be lower. In 1997 waiting times and deprivation tended to be positively related. By 2007 the relation between deprivation and waiting time was less pronounced, and, in some cases, patients from the most deprived fifth were waiting less time than patients from the most advantaged fifth. CONCLUSIONS: Between 1997 and 2007 waiting times for patients having elective hip replacement, knee replacement, and cataract repair in England went down and the variation in waiting times for those procedures across socioeconomic groups was reduced. Many people feared that the government's NHS reforms would lead to inequity, but inequity with respect to waiting times did not increase; if anything, it decreased. Although proving that the later stages of those reforms, which included patient choice, provider competition, and expanded capacity, was a catalyst for improvements in equity is impossible, the data show that these reforms, at a minimum, did not harm equity.

Genetically engineered lymphocytes hold promise for the treatment of genetic disease, viral infections and cancer. However, current methods for genetic transduction of peripheral blood lymphocytes rely on viral vectors, which are hindered by production and safety-related problems. In this study, we demonstrated an efficient novel nonviral platform for gene transfer to lymphocytes. The Sleeping Beauty transposon-mediated approach allowed for long-term stable expression of transgenes at approximately 50% efficiency. Utilizing transposon constructs expressing tumor antigen-specific T-cell receptor genes targeting p53 and MART-1, we demonstrated sustained expression and functional reactivity of transposon-engineered lymphocytes on encountering target antigen presented on tumor cells. We found that transposon- and retroviral-modified lymphocytes had comparable transgene expression and phenotypic function. These results demonstrate the promise of nonviral ex vivo genetic modification of autologous lymphocytes for the treatment of cancer and immunologic disease.