The impact of a team-based intervention on the lifestyle risk factor management practices of community nurses: outcomes of the community nursing SNAP trial

BackgroundLifestyle risk factors like smoking, nutrition, alcohol consumption, and physical inactivity (SNAP) are the main behavioural risk factors for chronic disease. Primary health care is an appropriate setting to address these risk factors in individuals. Generalist community health nurses (GCHNs) are uniquely placed to provide lifestyle interventions as they see clients in their homes over a period of time. The aim of the paper is to examine the impact of a service-level intervention on the risk factor management practices of GCHNs.MethodsThe trial used a quasi-experimental design involving four generalist community nursing services in NSW, Australia. The services were randomly allocated to either an intervention group or control group. Nurses in the intervention group were provided with training and support in the provision of brief lifestyle assessments and interventions. The control group provided usual care. A sample of 129 GCHNs completed surveys at baseline, 6 and 12 months to examine changes in their practices and levels of confidence related to the management of SNAP risk factors. Six semi-structured interviews and four focus groups were conducted among the intervention group to explore the feasibility of incorporating the intervention into everyday practice.ResultsNurses in the intervention group became more confident in assessment and intervention over the three time points compared to their control group peers. Nurses in the intervention group reported assessing physical activity, weight and nutrition more frequently, as well as providing more brief interventions for physical activity, weight management and smoking cessation. There was little change in referral rates except for an improvement in weight management related referrals. Nurses’ perception of the importance of ‘client and system-related’ barriers to risk factor management diminished over time.ConclusionsThis study shows that the intervention was associated with positive changes in self-reported lifestyle risk factor management practices of GCHNs. Barriers to referral remained. The service model needs to be adapted to sustain these changes and enhance referral

Retrospective analysis of cancer survival across South-Western Victoria in Australia

OBJECTIVE: This paper aims to describe cancer survival and examine association between survival and socio-demographic characteristics across Barwon South-Western region (BSWR) in Victoria, Australia. DESIGN: This study is based on the retrospective cohort database of patients accessing oncology services across BSWR. SETTING: Six rural and three urban hospital settings across the BSWR. PARTICIPANTS: The participants were patients who were diagnosed with cancer in 2009. MAIN OUTCOME MEASURES: Overall survival (OS) of participants was the main outcome measure. RESULTS: Total of 1778 eligible patients had four-year OS for all cancers combined of 59.7% (95% CI, 57.4-62.0). Improved OS was observed for patients in the upper socio-economic tertile (64.2%; 95% CI, 60.9-67.5) compared to the middle (59.3%; 95% CI, 55.5-63.1) and lowest tertiles (49.6%; 95% CI, 44.2-54.9) (P < 0.01). On multivariate analyses, higher socio-economic status remained a significant predictor of OS adjusting for gender, remoteness and age (HR [hazard ratio] 0.81; 95% CI 0.74-0.89; P < 0.01). Remoteness was significantly associated with improved OS after adjusting for age, gender and socio-economic status (HR 0.86; 95% CI, 0.77-0.97; P = 0.01). Older age ≥70 years compared to <70 years conferred inferior OS (HR 3.08; 95% CI, 2.64-3.59; P < 0.01). CONCLUSIONS: Our study confirmed improved survival outcomes for patients of higher socio-economic status and younger age. Future research to explain the unexpected survival benefit in patients who lived in more remote areas should examine factors including the correlation between geographical residence and eventual treatment facility as well as compare the BSWR care model to other regions\u27 approaches