Bruce Guthrie

Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study

BACKGROUND Long-term disorders are the main challenge facing health-care systems worldwide, but health systems are largely configured for individual diseases rather than multimorbidity. We examined the distribution of multimorbidity, and of comorbidity of physical and mental health disorders, in relation to age and socioeconomic deprivation. METHODS In a cross-sectional study we extracted data on 40 morbidities from a database of 1,751,841 people registered with 314 medical practices in Scotland as of March, 2007. We analysed the data according to the number of morbidities, disorder type (physical or mental), sex, age, and socioeconomic status. We defined multimorbidity as the presence of two or more disorders. FINDINGS 42·2% (95% CI 42·1-42·3) of all patients had one or more morbidities, and 23·2% (23·08-23·21) were multimorbid. Although the prevalence of multimorbidity increased substantially with age and was present in most people aged 65 years and older, the absolute number of people with multimorbidity was higher in those younger than 65 years (210,500 vs 194,996). Onset of multimorbidity occurred 10-15 years earlier in people living in the most deprived areas compared with the most affluent, with socioeconomic deprivation particularly associated with multimorbidity that included mental health disorders (prevalence of both physical and mental health disorder 11·0%, 95% CI 10·9-11·2% in most deprived area vs 5·9%, 5·8%-6·0% in least deprived). The presence of a mental health disorder increased as the number of physical morbidities increased (adjusted odds ratio 6·74, 95% CI 6·59-6·90 for five or more disorders vs 1·95, 1·93-1·98 for one disorder), and was much greater in more deprived than in less deprived people (2·28, 2·21-2·32 vs 1·08, 1·05-1·11). INTERPRETATION Our findings challenge the single-disease framework by which most health care, medical research, and medical education is configured. A complementary strategy is needed, supporting generalist clinicians to provide personalised, comprehensive continuity of care, especially in socioeconomically deprived areas. FUNDING Scottish Government Chief Scientist Office.

Designing and evaluating complex interventions to improve health care

Determining the effectiveness of complex interventions can be difficult and time consuming. Neil C Campbell and colleagues explain the importance of ground work in getting usable results

Adapting clinical guidelines to take account of multimorbidity

Care of patients with multimorbidity could be improved if new technology is used to bring together guidelines on individual conditions and tailor advice to each patient’s circumstances, say Bruce Guthrie and colleagues

Guidelines for people not for diseases: the challenges of applying UK clinical guidelines to people with multimorbidity

BACKGROUND currently one of the major challenges facing clinical guidelines is multimorbidity. Current guidelines are not designed to consider the cumulative impact of treatment recommendations on people with several conditions, nor to allow comparison of relative benefits or risks. This is despite the fact that multimorbidity is a common phenomenon. OBJECTIVE to examine the extent to which National Institute of Health and Clinical Excellence (NICE) guidelines address patient comorbidity, patient centred care and patient compliance to treatment recommendations. METHODS five NICE clinical guidelines were selected for review (type-2 diabetes mellitus, secondary prevention for people with myocardial infarction, osteoarthritis, chronic obstructive pulmonary disease and depression) as these conditions are common causes of comorbidity and the guidelines had all been produced since 2007. Two authors extracted information from each full guideline and noted the extent to which the guidelines accounted for patient comorbidity, patient centred care and patient compliance. The cumulative recommended treatment, follow-up and self-care regime for two hypothetical patients were then created to illustrate the potential cumulative impact of applying single disease recommendations to people with multimorbidity. RESULTS comorbidity and patient adherence were inconsistently accounted for in the guidelines, ranging from extensive discussion to none at all. Patient centred care was discussed in generic terms across the guidelines with limited disease-specific recommendations for clinicians. Explicitly following guideline recommendations for our two hypothetical patients would lead to a considerable treatment burden, even when recommendations were followed for mild to moderate conditions. In addition, the follow-up and self-care regime was complex potentially presenting problems for patient compliance. CONCLUSION clinical guidelines have played an important role in improving healthcare for people with long-term conditions. However, in people with multimorbidity current guideline recommendations rapidly cumulate to drive polypharmacy, without providing guidance on how best to prioritise recommendations for individuals in whom treatment burden will sometimes be overwhelming.

Process evaluations for cluster-randomised trials of complex interventions: a proposed framework for design and reporting

BackgroundProcess evaluations are recommended to open the ‘black box’ of complex interventions evaluated in trials, but there is limited guidance to help researchers design process evaluations. Much current literature on process evaluations of complex interventions focuses on qualitative methods, with less attention paid to quantitative methods. This discrepancy led us to develop our own framework for designing process evaluations of cluster-randomised controlled trials.MethodsWe reviewed recent theoretical and methodological literature and selected published process evaluations; these publications identified a need for structure to help design process evaluations. We drew upon this literature to develop a framework through iterative exchanges, and tested this against published evaluations.ResultsThe developed framework presents a range of candidate approaches to understanding trial delivery, intervention implementation and the responses of targeted participants. We believe this framework will be useful to others designing process evaluations of complex intervention trials. We also propose key information that process evaluations could report to facilitate their identification and enhance their usefulness.ConclusionThere is no single best way to design and carry out a process evaluation. Researchers will be faced with choices about what questions to focus on and which methods to use. The most appropriate design depends on the purpose of the process evaluation; the framework aims to help researchers make explicit their choices of research questions and methods.Trial registrationClinicaltrials.gov NCT01425502

Continuity of care matters

The current focus on increasing access makes it more difficult for patients to see the same doctor. But Bruce Guthrie and colleagues argue that relationships between doctors and patients are central to good care

High risk prescribing in primary care patients particularly vulnerable to adverse drug events: cross sectional population database analysis in Scottish general practice.

Objective To examine the prevalence and patterns of high risk prescribing, defined as potentially inappropriate prescribing of drugs to primary care patients particularly vulnerable to adverse drug events. Design Cross sectional population database analysis. Setting General practices in Scotland. Participants 315 Scottish general practices with 1.76 million registered patients, 139 404 (7.9%) of whom were defined as particularly vulnerable to adverse drug events because of age, comorbidity, or co-prescription. Main outcome measures How reliably each of 15 indicators—four each for non-steroidal anti-inflammatory drugs, co-prescription with warfarin, and prescribing in heart failure, two for dose instructions for methotrexate, and one for antipsychotic prescribing in dementia—and a composite of all 15 could distinguish practices in terms of their rates of high risk prescribing; and characteristics of patients and practices associated with high risk prescribing in a multilevel model. Results 19 308 of 139 404 (13.9%, 95% confidence interval 13.7% to 14.0%) patients had received at least one high risk prescription in the past year. This composite indicator was a reasonably reliable measure of practice rates of high risk prescribing (reliability >0.7 for 95.6% of practices, >0.8 for 88.2%). The patient characteristic most strongly associated with high risk prescribing was the number of drugs prescribed (>11 long term prescribed drugs v 0; odds ratio 7.90, 95% confidence interval 7.19 to 8.68). After adjustment for patient characteristics, rates of high risk prescribing varied by fourfold between practices, which was not explained by structural characteristics of the practices. Conclusions Almost 14% of patients defined as particularly vulnerable to adverse drug events were prescribed one or more high risk drugs. The composite indicator of high risk prescribing used could identify practices as having above average or below average high risk prescribing rates with reasonable confidence. After adjustment, only the number of drugs prescribed long term to patients was strongly associated with high risk prescribing, and considerable unexplained variation existed between practices. High risk prescribing will often be appropriate, but the large variation between practices suggests opportunities for improvement.

Managing patients with multimorbidity in primary care

#### The bottom line Multimorbidity, commonly defined as the presence of two or more chronic medical conditions in an individual,1 is associated with decreased quality of life, functional decline, and increased healthcare utilisation, including emergency admissions, particularly with higher numbers of coexisting conditions.2 3 4 5 6 The management of multimorbidity with drugs is often complex, resulting in polypharmacy with its attendant risks.7 8 9 Patients with multimorbidity have a high treatment burden in terms of understanding and self managing the conditions, attending multiple appointments, and managing complex drug regimens.10 Qualitative research highlights the “endless struggle” patients experience in trying to manage their conditions well.11 Psychological distress is common: in an Australian survey of 7620 patients in primary care, 23% of those with one chronic condition reported depression compared with 40% of those with five or more conditions.12 #### Sources and selection criteria We based this article on the authors’ experience and information from published literature. We carried out searches of PubMed and the Cochrane library using the search terms “co-morbidity” or “comorbidity” or “multimorbid” or “multimorbidity” or “multi-morbidity”. No MeSH term exists for multimorbidity. The searches were supplemented by a review of authors’ personal archives as well as relevant articles from the International Research …

Effective feedback to improve primary care prescribing safety (EFIPPS) a pragmatic three-arm cluster randomised trial: Designing the intervention

BackgroundHigh-risk prescribing in primary care is common and causes considerable harm. Feedback interventions have small/moderate effects on clinical practice, but few trials explicitly compare different forms of feedback. There is growing recognition that intervention development should be theory-informed, and that comprehensive reporting of intervention design is required by potential users of trial findings. The paper describes intervention development for the Effective Feedback to Improve Primary Care Prescribing Safety (EFIPPS) study, a pragmatic three-arm cluster randomised trial in 262 Scottish general practices.MethodsThe NHS chose to implement a feedback intervention to utilise a new resource, new Prescribing Information System (newPIS). The development phase required selection of high-risk prescribing outcome measures and design of intervention components: (1) educational material (the usual care comparison), (2) feedback of practice rates of high-risk prescribing received by both intervention arms and (3) a theory-informed behaviour change component to be received by one intervention arm. Outcome measures, educational material and feedback design, were developed with a National Health Service Advisory Group. The behaviour change component was informed by the Theory of Planned Behaviour and the Health Action Process Approach. A focus group elicitation study and an email Delphi study with general practitioners (GPs) identified key attitudes and barriers of responding to the prescribing feedback. Behaviour change techniques were mapped to the psychological constructs, and the content was informed by the results of the elicitation and Delphi study.ResultsSix high-risk prescribing measures were selected in a consensus process based on importance and feasibility. Educational material and feedback design were based on current NHS Scotland practice and Advisory Group recommendations. The behaviour change component was resource constrained in development, mirroring what is feasible in an NHS context. Four behaviour change interventions were developed and embedded in five quarterly rounds of feedback targeting attitudes, subjective norms, perceived behavioural control and action planning (2×).ConclusionsThe paper describes a process which is feasible to use in the resource-constrained environment of NHS-led intervention development and documents the intervention to make its design and implementation explicit to potential users of the trial findings.Trial registrationClinicalTrials.gov: NCT01602705

The effect of physical multimorbidity, mental health conditions and socioeconomic deprivation on unplanned admissions to hospital: a retrospective cohort study

Background: Multimorbidity, the presence of more than 1 long-term disorder, is associated with increased use of health services, but unplanned admissions to hospital may often be undesirable. Furthermore, socioeconomic deprivation and mental health comorbidity may lead to additional unplanned admissions. We examined the association between unplanned admission to hospital and physical multimorbidity, mental health and socioeconomic deprivation. Methods: We conducted a retrospective cohort study using data from 180 815 patients aged 20 years and older who were registered with 40 general practices in Scotland. Details of 32 physical and 8 mental health morbidities were extracted from the patients’ electronic health records (as of Apr. 1, 2006) and linked to hospital admission data. We then recorded the occurrence of unplanned or potentially preventable unplanned acute (nonpsychiatric) admissions to hospital in the subsequent 12 months. We used logistic regression models, adjusting for age and sex, to determine associations between unplanned or potentially preventable unplanned admissions to hospital and physical multimorbidity, mental health and socioeconomic deprivation. Results: We identified 10 828 (6.0%) patients who had at least 1 unplanned admission to hospital and 2037 (1.1%) patients who had at least 1 potentially preventable unplanned admission to hospital. Both unplanned and potentially preventable unplanned admissions were independently associated with increasing physical multimorbidity (for ≥ 4 v. 0 conditions, odds ratio [OR] 5.87 [95% confidence interval (CI) 5.45–6.32] for unplanned admissions, OR 14.38 [95% CI 11.87–17.43] for potentially preventable unplanned admissions), mental health conditions (for ≥ 1 v. 0 conditions, OR 2.01 [95% CI 1.92–2.09] for unplanned admissions, OR 1.80 [95% CI 1.64–1.97] for potentially preventable unplanned admissions) and socioeconomic deprivation (for most v. least deprived quintile, OR 1.56 [95% CI 1.43–1.70] for unplanned admissions, OR 1.98 [95% CI 1.63–2.41] for potentially preventable unplanned admissions). Interpretation: Physical multimorbidity was strongly associated with unplanned admission to hospital, including admissions that were potentially preventable. The risk of admission to hospital was exacerbated by the coexistence of mental health conditions and socioeconomic deprivation.