What We Have Here is a Failure to Communicate! Improving Communication between Tertiary to Primary Care for Chronic Heart Failure Patients

Background:  The aims of this study were to determine the documentation of pharmacotherapy optimization goals in the discharge letters of patients with the principal diagnosis of chronic heart failure. Methods:  A retrospective practice audit of 212 patients discharged to the care of their local general practitioner from general medical units of a large tertiary hospital. Details of recommendations regarding ongoing pharmacological and non-pharmacological management were reviewed. The doses of medications on discharge were noted and whether they met current guidelines recommending titration of angiotensin-converting enzyme inhibitors and beta-blockers. Ongoing arrangements for specialist follow up were also reviewed. Results:  The mean age of patients whose letters were reviewed was 78.4 years (standard deviation ± 8.6); 50% were men. Patients had an overall median of six comorbidities and eight regular medications on discharge. Mean length of stay for each admission was 6 days. Discharge letters were posted a median of 4 days after discharge, with 25% not posted at 10 days. No discharge letter was sent in 9.4% (20) of the cases. Only six (2.8%) letters had any recommendations regarding future titration of angiotensin-converting enzyme inhibitors and 6.6% (14) for beta-blockers. Recommendations for future non-pharmacological management, for example, diuretic action plans, regular weight monitoring and exercise plans were not found in the letters in this audit. Conclusion:  Hospital discharge is an opportunity to communicate management plans for treatment optimization effectively, and while this opportunity is spurned, implementation gaps in the management of cardiac failure will probably remain.

Inequitable Provision of Optimal Services for Patients with Chronic Heart failure: A National Geo-Mapping Study

Objective: To compare the location and accessibility of current Australian chronic heart failure (CHF) management programs and general practice services with the probable distribution of the population with CHF. Design and setting: Data on the prevalence and distribution of the CHF population throughout Australia, and the locations of CHF management programs and general practice services from 1 January 2004 to 31 December 2005 were analysed using geographic information systems (GIS) technology. Outcome measures: Distance of populations with CHF to CHF management programs and general practice services. Results: The highest prevalence of CHF (20.3–79.8 per 1000 population) occurred in areas with high concentrations of people over 65 years of age and in areas with higher proportions of Indigenous people. Five thousand CHF patients (8%) discharged from hospital in 2004–2005 were managed in one of the 62 identified CHF management programs. There were no CHF management programs in the Northern Territory or Tasmania. Only four CHF management programs were located outside major cities, with a total case load of 80 patients (0.7%). The mean distance from any Australian population centre to the nearest CHF management program was 332 km (median, 163 km; range, 0.15–3246 km). In rural areas, where the burden of CHF management falls upon general practitioners, the mean distance to general practice services was 37 km (median, 20 km; range, 0–656 km). Conclusion: There is an inequity in the provision of CHF management programs to rural Australians.

Hot Summers and Heart Failure: Seasonal Variations in Morbidity and Mortality in Australian Heart Failure Patients (1994-2005)

Background There are minimal reports of seasonal variations in chronic heart failure (CHF)-related morbidity and mortality beyond the northern hemisphere. Aims and methods We examined potential seasonal variations with respect to morbidity and all-cause mortality over more than a decade in a cohort of 2961 patients with CHF from a tertiary referral hospital in South Australia subject to mild winters and hot summers. Results Seasonal variation across all event-types was observed. CHF-related morbidity peaked in winter (July) and was lowest in summer (February): 70 (95% CI: 65 to 76) vs. 33 (95% CI: 30 to 37) admissions/1000 at risk (p<0.005). All-cause admissions (113 (95% CI: 107 to 120) vs. 73 (95% CI 68 to 79) admissions/1000 at risk, p<0.001) and concurrent respiratory disease (21% vs. 12%,p<0.001) were consistently higher in winter. 2010 patients died, mortality was highest in August relative to February: 23 (95% CI: 20 to 27) vs. 12 (95% CI: 10 to 15) deaths per 1000 at risk, p<0.001. Those aged 75 years or older were most at risk of seasonal variations in morbidity and mortality. Conclusion Seasonal variations in CHF-related morbidity and mortality occur in the hot climate of South Australia, suggesting that relative (rather than absolute) changes in temperature drive this global phenomenon.

Rural and Urban Differentials in Primary Care Management of Chronic Heart Failure: New Data from the CASE Study

Objective: To determine whether primary care management of chronic heart failure (CHF) differed between rural and urban areas in Australia. Design: A cross-sectional survey stratified by Rural, Remote and Metropolitan Areas (RRMA) classification. The primary source of data was the Cardiac Awareness Survey and Evaluation (CASE) study. Setting: Secondary analysis of data obtained from 341 Australian general practitioners and 23 845 adults aged 60 years or more in 1998. Main outcome measures: CHF determined by criteria recommended by the World Health Organization, diagnostic practices, use of pharmacotherapy, and CHF-related hospital admissions in the 12 months before the study. Results: There was a significantly higher prevalence of CHF among general practice patients in large and small rural towns (16.1%) compared with capital city and metropolitan areas (12.4%) (P < 0.001). Echocardiography was used less often for diagnosis in rural towns compared with metropolitan areas (52.0% v 67.3%, P < 0.001). Rates of specialist referral were also significantly lower in rural towns than in metropolitan areas (59.1% v 69.6%, P < 0.001), as were prescribing rates of angiotensin-converting enzyme inhibitors (51.4% v 60.1%, P < 0.001). There was no geographical variation in prescribing rates of β-blockers (12.6% [rural] v 11.8% [metropolitan], P = 0.32). Overall, few survey participants received recommended “evidence-based practice” diagnosis and management for CHF (metropolitan, 4.6%; rural, 3.9%; and remote areas, 3.7%). Conclusions: This study found a higher prevalence of CHF, and significantly lower use of recommended diagnostic methods and pharmacological treatment among patients in rural areas.

Exploring health behaviour determinants of ageing Australians with chronic diseases

Background: Chronic disease presents overwhelming challenges to elderly patients, their families, health care providers and the health care system. The aim of this study was to explore a theoretical model for effective management of chronic diseases, especially type 2 diabetes mellitus and/or cardiovascular disease. The assumed theoretical model considered the connections between physical function, mental health, social support and health behaviours. The study effort was to improve the quality of life for people with chronic diseases, especially type 2 diabetes and/or cardiovascular disease and to reduce health costs. Methods: A cross-sectional post questionnaire survey was conducted in early 2009 from a randomised sample of Australians aged 50 to 80 years. A total of 732 subjects were eligible for analysis. Firstly, factors influencing respondents‘ quality of life were investigated through bivariate and multivariate regression analysis. Secondly, the Theory of Planned Behaviour (TPB) model for regular physical activity, healthy eating and medication adherence behaviours was tested for all relevant respondents using regression analysis. Thirdly, TPB variable differences between respondents who have diabetes and/or cardiovascular disease and those without these diseases were compared. Finally, the TPB model for three behaviours including regular physical activity, healthy eating and medication adherence were tested in respondents with diabetes and/or cardiovascular diseases using Structure Equation Modelling (SEM). Results: This was the first study combining the three behaviours using a TPB model, while testing the influence of extra variables on the TPB model in one study. The results of this study provided evidence that the ageing process was a cumulative effect of biological change, socio-economic environment and lifelong behaviours. Health behaviours, especially physical activity and healthy eating were important modifiable factors influencing respondents‘ quality of life. Since over 80% of the respondents had at least one chronic disease, it was important to consider supporting older people‘s chronic disease self-management skills such as healthy diet, regular physical activity and medication adherence to improve their quality of life. Direct measurement of the TPB model was helpful in understanding respondents‘ intention and behaviour toward physical activity, healthy eating and medication adherence. In respondents with diabetes and/or cardiovascular disease, the TPB model predicted different proportions of intention toward three different health behaviours with 39% intending to engage in physical activity, 49% intending to engage in healthy eating and 47% intending to comply with medication adherence. Perceived behavioural control, which was proven to be the same as self-efficacy in measurement in this study, played an important role in predicting intention towards the three health behaviours. Also social norms played a slightly more important role than attitude for physical activity and medication adherence, while attitude and social norms had similar effects on healthy eating in respondents with diabetes and/or cardiovascular disease. Both perceived behavioural control and intention directly predicted recent actual behaviours. Physical activity was more a volitional control behaviour than healthy eating and medication adherence. Step by step goal setting and motivation was more important for physical activity, while accessibility, resources and other social environmental factors were necessary for improving healthy eating and medication adherence. The extra variables of age, waist circumference, health related quality of life and depression indirectly influenced intention towards the three behaviours mainly mediated through attitude and perceived behavioural control. Depression was a serious health problem that reduced the three health behaviours‘ motivation, mediated through decreased self-efficacy and negative attitude. This research provided evidence that self-efficacy is similar to perceived behavioural control in the TPB model and intention is a proximal goal toward a particular behaviour. Combining four sources of information in the self-efficacy model with the TPB model would improve chronic disease patients‘ self management behaviour and reach an improved long-term treatment outcome. Conclusion: Health intervention programs that target chronic disease management should focus on patients‘ self-efficacy. A holistic approach which is patient-centred and involves a multidisciplinary collaboration strategy would be effective. Supporting the socio-economic environment and the mental/ emotional environment for older people needs to be considered within an integrated health care system.

Editoral : Correspondence regarding Anker SD, Koehler F, Abraham WT. Telemedicine and remote management of patients with heart failure. The Lancet 2011; 378: 731–39. Published 20 August 2011.

We read the excellent review of telemonitoring in chronic heart failure (CHF)1 with interest and commend the authors on the proposed classification of telemedical remote management systems according to the type of data transfer, decision ability and level of integration. However, several points require clarification in relation to our Cochrane review of telemonitoring and structured telephone support2. We included a study by Kielblock3. We corresponded directly with this study team specifically to find out whether or not this was a randomised study and were informed that it was a randomised trial, albeit by date of birth. We note in our review2 that this randomisation method carries a high risk of bias. Post-hoc metaanalyses without these data demonstrate no substantial change to the effect estimates for all cause mortality (original risk ratio (RR) 0·66 [95% CI 0·54, 0·81], p<0·0001; revised RR 0·72 [95% CI 0·57, 0·92], p=0·008), all-cause hospitalisation (original RR 0·91 [95% CI 0·84, 0·99] p=0·02; revised RR 0.92 [95% CI 0·84, 1·02], p=0·10 ) or CHF-related hospitalisation (original RR 0·79 [95% CI 0·67, 0·94] p=0·008; revised RR 0·75 [95% CI 0·60, 0·94] p=0·01). Secondly, we would classify the Tele-HF study4, 5 as structured telephone support, rather than telemonitoring. Again, inclusion of these data alters the point-estimate but not the overall result of the meta-analyses4. Finally, our review2 does not include invasive telemonitoring as the search strategy was not designed to capture these studies. Therefore direct comparison of our review findings with recent studies of these interventions is not recommended.