Multimorbidity, not a health condition or complexity by another name

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this recor

Getting impatient about person-centred health care

This is the final version. Available on open access from Taylor & Francis via the DOI in this recordEditoria

Epidemiology and outcomes of previously undiagnosed diabetes in older women with breast cancer: an observational cohort study based on SEER-Medicare

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.Background In breast cancer, diabetes diagnosed prior to cancer (previously diagnosed) is associated with advanced cancer stage and increased mortality. However, in the general population, 40% of diabetes is undiagnosed until glucose testing, and evidence suggests one consequence of increased evaluation and management around breast cancer diagnosis is the increased detection of previously undiagnosed diabetes. Biological factors – for instance, higher insulin levels due to untreated disease - and others underlying the association between previously diagnosed diabetes and breast cancer could differ in those whose diabetes remains undiagnosed until cancer. Our objectives were to identify factors associated with previously undiagnosed diabetes in breast cancer, and to examine associations between previously undiagnosed diabetes and cancer stage, treatment patterns, and mortality. Methods Using Surveillance, Epidemiology, and End Results-Medicare, we identified women diagnosed with breast cancer and diabetes between 01/2001 and 12/2005. Diabetes was classified as previously diagnosed if it was identified within Medicare claims between 24 and 4 months before cancer diagnosis, and previously undiagnosed if it was identified from 3 months before to ≤ 3 months after cancer. Patients were followed until 12/2007 or death, whichever came first. Multivariate analyses were performed to examine risk factors for previously undiagnosed diabetes and associations between undiagnosed (compared to previously diagnosed) diabetes, cancer stage, treatment, and mortality. Results Of 2,418 patients, 634 (26%) had previously undiagnosed diabetes; the remainder had previously diagnosed diabetes. The mean age was 77.8 years, and 49.4% were diagnosed with in situ or stage I disease. Age > 80 years (40% of the cohort) and limited health system contact (primary care physician and/or preventive services) prior to cancer were associated with higher adjusted odds of previously undiagnosed diabetes. Previously undiagnosed diabetes was associated with higher adjusted odds of advanced stage (III/IV) cancer (Odds Ratio = 1.37: 95% Confidence Interval (CI) 1.05 – 1.80; P = 0.02), and a higher adjusted mortality rate due to causes other than cancer (Hazard Ratio = 1.29; 95% CI 1.02 – 1.63; P = 0.03). Conclusions In breast cancer, previously undiagnosed diabetes is associated with advanced stage cancer and increased mortality. Identifying biological factors would require further investigation

Measurement tools and process indicators of patient safety culture in primary care. A mixed methods study by the LINNEAUS collaboration on patient safety in primary care.

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordBACKGROUND: There is little guidance available to healthcare practitioners about what tools they might use to assess the patient safety culture. OBJECTIVE: To identify useful tools for assessing patient safety culture in primary care organizations in Europe; to identify those aspects of performance that should be assessed when investigating the relationship between safety culture and performance in primary care. METHODS: Two consensus-based studies were carried out, in which subject matter experts and primary healthcare professionals from several EU states rated (a) the applicability to their healthcare system of several existing safety culture assessment tools and (b) the appropriateness and usefulness of a range of potential indicators of a positive patient safety culture to primary care settings. The safety culture tools were field-tested in four countries to ascertain any challenges and issues arising when used in primary care. RESULTS: The two existing tools that received the most favourable ratings were the Manchester patient safety framework (MaPsAF primary care version) and the Agency for healthcare research and quality survey (medical office version). Several potential safety culture process indicators were identified. The one that emerged as offering the best combination of appropriateness and usefulness related to the collection of data on adverse patient events. CONCLUSION: Two tools, one quantitative and one qualitative, were identified as applicable and useful in assessing patient safety culture in primary care settings in Europe. Safety culture indicators in primary care should focus on the processes rather than the outcomes of care.The research leading to these results has received funding from the European Community’s Seventh Framework Programme FP7/2008 – 2012 under grant agreement no. 223424

Out-of-pocket expenditure by Australian seniors with chronic disease: the effect of specific diseases and morbidity clusters

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.BACKGROUND: Out of pocket expenditure (OOPE) on healthcare is related to the burden of illness and the number of chronic conditions a patient experiences, but the relationship of these costs to particular conditions and groups of conditions is less studied. This study examines the effect on OOPE of various morbidity groupings, and explores the factors associated with a 'heavy financial burden of OOPE' defined by an expenditure of over 10% of equivalised household income on healthcare. METHODS: Data were collected from 4,574 senior Australians using a stratified sampling procedure by age, rurality and state of residence. Natural clusters of chronic conditions were identified using cluster analysis and clinically relevant clusters based on expert opinion. We undertook logistic regression to model the probability of incurring OOPE, and a heavy financial burden; linear regression to explore the significant factors of OOPE; and two-part models to estimate the marginal effect of factors on OOPE. RESULTS: The mean OOPE in the previous three months was AU$353; and 14% of respondents experienced a heavy financial burden. Medication and medical service expenses were the major costs. Those who experienced cancer, high blood pressure, diabetes or depression were likely to report higher OOPE. Patients with cancer or diabetes were more likely than others to face a heavy burden of OOPE relative to income. Total number of conditions and some specific conditions predict OOPE but neither the clusters nor pairs of conditions were good predictors of OOPE. CONCLUSIONS: Total number of conditions and some specific conditions predict both OOPE and heavy financial burden but particular comorbid groupings are not useful in predicting OOPE. Low-income patients pay a higher proportion of income than the well-off as OOPE for healthcare. Interventions targeting those who are likely to face severe financial burdens due to their health could address some of these differences.National Health and Medical Research Council, Australi

How to identify when a performance indicator has run its course

The official published version can be found at the link below.Increasing numbers of countries are using indicators to evaluate the quality of clinical care, with some linking payment to achievement. For performance frameworks to remain effective the indicators need to be regularly reviewed. The frameworks cannot cover all clinical areas, and achievement on chosen indicators will eventually reach a ceiling beyond which further improvement is not feasible. However, there has been little work on how to select indictors for replacement. The Department of Health decided in 2008 that it would regularly replace indicators in the national primary care pay for performance scheme, the Quality and Outcomes Framework, making a rigorous approach to removal a priority. We draw on our previous work on pay for performance and our current work advising the National Institute for Health and Clinical Excellence (NICE) on the Quality and Outcomes Framework to suggest what should be considered when planning to remove indicators from a clinical performance framework

Time spent on health-related activities by senior Australians with chronic diseases: what is the role of multimorbidity and comorbidity?

This is the final version of the article. Available from the publisher via the DOI in this record.OBJECTIVE: To examine the effect of various morbidity clusters of chronic diseases on health-related time use and to explore factors associated with heavy time burden (more than 30 hours/month) of health-related activities. METHODS: Using a national survey, data were collected from 2,540 senior Australians. Natural clusters were identified using cluster analysis and clinical clusters using clinical expert opinion. We undertook a set of linear regressions to model people's time use, and logistic regressions to model heavy time burden. RESULTS: Time use increases with the number of chronic diseases. Six of the 12 diseases are significantly associated with higher time use, with the highest effect for diabetes followed by depression; 18% reported a heavy time burden, with diabetes again being the most significant disease. Clusters and dominant comorbid groupings do not contribute to predicting time use or time burden. CONCLUSIONS: Total number of diseases and specific diseases are useful determinants of time use and heavy time burden. Dominant groupings and disease clusters do not predict time use. IMPLICATIONS: In considering time demands on patients and the need for care co-ordination, care providers need to be aware of how many and what specific diseases the patient faces.The Serious and Continuing Illnesses Policy and Practice Study (SCIPPS) is an NHMRC-funded program conducted at The Australian National University and the University of Sydney and administered by the Menzies Centre for Health Policy

Identifying patient and practice characteristics associated with patient-reported experiences of safety problems and harm: a cross-sectional study using a multilevel modelling approach.

This is the author accepted manuscript. The final version is available from BMJ Publishing Group via the DOI in this record.OBJECTIVE: To identify patient and family practice characteristics associated with patient-reported experiences of safety problems and harm. DESIGN: Cross-sectional study combining data from the individual postal administration of the validated Patient Reported Experiences and Outcomes of Safety in Primary Care (PREOS-PC) questionnaire to a random sample of patients in family practices (response rate=18.4%) and practice-level data for those practices obtained from NHS Digital. We built linear multilevel multivariate regression models to model the association between patient-level (clinical and sociodemographic) and practice-level (size and case-mix, human resources, indicators of quality and safety of care, and practice safety activation) characteristics, and outcome measures. SETTING: Practices distributed across five regions in the North, Centre and South of England. PARTICIPANTS: 1190 patients registered in 45 practices purposefully sampled (maximal variation in practice size and levels of deprivation). MAIN OUTCOME MEASURES: Self-reported safety problems, harm and overall perception of safety. RESULTS: Higher self-reported levels of safety problems were associated with younger age of patients (beta coefficient 0.15) and lower levels of practice safety activation (0.44). Higher self-reported levels of harm were associated with younger age (0.13) and worse self-reported health status (0.23). Lower self-reported healthcare safety was associated with lower levels of practice safety activation (0.40). The fully adjusted models explained 4.5% of the variance in experiences of safety problems, 8.6% of the variance in harm and 4.4% of the variance in perceptions of patient safety. CONCLUSIONS: Practices' safety activation levels and patients' age and health status are associated with patient-reported safety outcomes in English family practices. The development of interventions aimed at improving patient safety outcomes would benefit from focusing on the identified groups.This research is part-funded by the National Institute for Health Research School for Primary Care Research (NIHR SPCR). The views expressed are those of the authors and not necessarily those of the NIHR, the NHS or the Department of Health

Health data processes. A framework for analysing and discussing efficient use and reuse of health data with focus on Patient Reported Outcome (PRO) measures

This is is the final version. Available from Journal of Medical Internet Research via the DOI in this record.The collection and use of patient health data is central to any kind of activity in the healthcare system. This data may be produced during routine clinical processes or obtained directly from the patient using patient-reported outcome (PRO) measures. Although efficiency and other reasons justify data availability for a range of potential relevant uses, these data are nearly always collected for a single specific purpose. The healthcare data literature reflects this narrow scope, and there is limited literature on the joint use of health data for daily clinical use, clinical research, surveillance and administrative purposes. The aim of this paper is to provide a framework for a discussion of the efficient use of health data with specific focus on the role of PRO measures. PRO data may be used: i) at an individual patient level to inform patient care or shared-decision making and tailor care to individual needs or ii) at group level as a complement to health record data e.g. on mortality and readmission to inform service delivery and measure real-world effectiveness of treatment. PRO may be used either for their own sake, to provide valuable information from the patient perspective, or as proxy for clinical data that would be otherwise not feasible to collect. We introduce a framework to analyse any health care activity that involves health data. The framework consists of four data processes (patient identification, data collection, data aggregation and data use), further structured into two dichotomous dimensions in each data process (level: group vs patient; and timeframe: ad hoc vs systematic). This framework is used to analyse various health activities with respect to joint use of data considering the technical, legal, organisational and logistical challenges that characterize each data process. Finally, we propose a model for joint use of health data with data collected during follow-up as basis. Demands for health data will continue to increase which will further add to the need for the concerted use and reuse of PRO data for parallel purposes. Repeated and uncoordinated PRO data collection for the same patient for different purposes results in misuse of resources for the healthcare system as well as reduced response rates owing to questionnaire fatigue. PRO data can be routinely collected both at the hospital (in- as well as outpatients) and outside of hospital settings, in primary or social care settings, or in the patient’s home provided the health informatics infrastructure is in place. In the future, clinical settings are likely to be a prominent source of PRO data; however we are also likely to see increased remote collection of PRO data by patients in their own home (telePRO). Data collection for research and quality surveillance will have to adapt to this circumstance and adopt complementary data capture methods which take advantage of the utility of PRO data collected during daily clinical practice. The European Union’s regulation with respect to the protection of personal data, General Data Protection Regulation, imposes severe restrictions on use of health data for parallel purposes and steps should be taken to alleviate the consequences while still protecting personal data against misuse.National Institute for Health Research (NIHR

Adapting QOF to focus on wellbeing and health

This is the final version of the article. Available from BMJ Publishing Group via the DOI in this record