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There is extensive evidence that health promotion routinely benefits those who are already most socioeconomically advantaged. While the government's healthy ageing policy recognizes that improving health outcomes will require a range of strategies involving different target groups, recommendations focus on the issues and needs of the comfortable majority. This paper examines the scope and relevance of health promotion for one disadvantaged minority with extensive health needs: homeless older men. In an ethnographic study of older men (⩾ 50 years of age) living alone in the inner city (Sydney), 32 men were identified as homeless and are the focus of this paper. Face to face semi-structured interviews were used to record the men's accounts of their everyday lives, including their health and use of services. The conditions in which these men were living were observed and recorded, and the researchers were aware of health and other services available in the geographic area. All informants were living on or below the poverty line. They reported a range of health conditions, for which many accessed available mainstream and specialist health services. Some obstacles to accessing services were noted. Information relevant to widely endorsed prescriptions for ‘healthy ageing’ also emerged. These included physical activity (especially walking), healthy eating, social activity and adopting healthy lifestyle habits. Findings highlight the extent to which these men lack the basic requirements for healthy ageing, notably adequate incomes and housing. At the same time, within the constraints of the lifestyle they lead, they are motivated to maintain their health and independence. While there are limits to what can be achieved for such people at a local level of service delivery, it is possible to identify feasible health promotion goals and service strategies. [ABSTRACT FROM AUTHOR]

Background: This review provides an in-depth investigation into the difficulties facing older Australians when accessing health care services. Methods: A literature search was conducted in December 2016 using Academic Premier to identify relevant publications. Key search terms were accessibility, health service, older people and Australia. Papers published between 1999 and 2016 were included. Statements of accessibility were extracted and then grouped using the five dimensions of accessibility by Penchansky and Thomas (1981): availability, accessibility, accommodation, affordability and acceptability. Results: Forty-one papers were included. Availability issues identified were inadequate health care services, particularly for culturally and linguistically diverse (CALD) populations and those residing in rural areas. Accessibility issues included difficulties accessing transport to health care services, which in turn restricted choice of appointment time. Issues of accommodation identified were long waiting times for appointments with both general practitioners and medical specialists. Affordability was a common problem, compounded by multi-morbidity requiring high health care use. Issues of acceptability centred on the role of the family, feelings of shame when receiving care from a non-family member, traditional practices and gender sensitivity. Conclusions: The contribution of factors to health service accessibility varies according to an older person's geographical local and their accessibility to transport, as well as their level of multi-morbidity and cultural background. Improving access to health services could be improved by matching services to the population that they serve. [ABSTRACT FROM AUTHOR]

Objective: To understand the barriers and enablers to participation in family‐assisted therapy for older people in Transition Care. Methods: A qualitative study, underpinned by interpretive description, was conducted at two public health services in Melbourne, Australia. Participants included patients in Transition Care, or their family members, who either participated in or chose not to participate in a family‐assisted therapy trial. Semi‐structured interviews were conducted, transcribed verbatim and analysed thematically. Results: Forty‐four participants were interviewed (17 patients and 27 family members). The unifying theme was to let families decide about participation in family‐assisted therapy. The unifying theme was illustrated by three subthemes. The first, what is possible for the family now, described practical considerations including geography, paid and unpaid work structure and commitments and the presence of fit and willing social networks. The second, what is important to the family now, recognised the role of family priorities in deciding. Physical rehabilitation and extra therapy were of high importance to some families. For others, emotional support or searching for a residential aged care bed were more important at the time. Finally, how the family functions described the complexity of relationships and family history that impacted the decision to participate. Conclusions: The decision to participate in family‐assisted therapy is complex and is best made by patients and their families. Clinicians offering family‐assisted therapy are encouraged to avoid assuming what will or will not work for families and instead, to let families decide. [ABSTRACT FROM AUTHOR]

Internationally, sophisticated risk stratification models have been developed using routinely collected data from large organisations that provide care in both community and hospital settings. This paper outlines a simple approach in the Australian context, where federal and state governments have separate data for community and hospital care, with limited opportunities for data linkage. On the NSW Central Coast, just north of Sydney, the Central Coast Local Health District, CCLHD, is embarking on a 10 year program to integrate primary health and social care for vulnerable older people in the community. A key action is to provide integrated care for people at greatest risk of some deterioration in their health state. Practice change. Aim: To assess the utility of routinely collected hospital data for risk stratification. In the first year of the program, the risk is defined as a person with a high risk of being hospitalised in the next 12 months. Target Population: The Central Coast area of New South Wales, Australia, has a population of about 330,000 people, with 19% aged 65 years and over. Each year, about 10,000 people of this age have at least one unplanned hospital admission to a CCLHD hospital. There are over 400 General Practitioners, GPs, on the Central Coast, working from about 100 practice locations - with some solo practices to large group practices, operating as separate small business entities. People do not register for a GP, though nearly all people by 75 years of age have a regular GP. General practices also vary by the number and age profile of the patients they see in a year. Key Findings: The analysis of 5 years of hospital data demonstrated patterns in service delivery for the older age group eg for each year's cohort of 10,000 people with at least one admission, about 2700 people (27%) have at least one hospital admission in the following year. Knowing each person's nominated GP allowed some exploration of patterns of service use by GP. Some GPs had few patients admitted each year, and some GPs had many. It is likely this variability reflects differences in practice profiles of age distribution. There was consistency in these patterns of service use by GP over time, and also by general practice. The analysis of comorbidities allowed categorisation of the age group into those with 2 or more comorbidities - about 3500 people from a one year cohort have 2 or more chronic diseases. For this group of 3500 people, about 1500 people, or 40%, have a hospital admission in the following year. This approach was used to identify a small number of general practices in a low socio-economic status area, with an initial total cohort of about 600 people for a proof of concept of integrated care. Discussion: A risk stratification model needs to be linked with an effective intervention to improve the health of a population. Regression to the mean, when assessing whether a trial worked - it is important to have an understanding of patterns of service delivery over time, if they exist. Analysing hospital data, by general practice, allows contemporaneous analyses of other practices with similar activity and socio-economic profiles for comparison. Risk stratification models and c-statistics - the c-statistic is a useful measure of goodness of fit available for some regression models. It is not the only measure, and others should be considered. This approach using hospital data does not have a goodness of fit measure per se. Positive Predictive Value - Of equal importance is how well the model works when it is applied to the population of interest. If a model selects 100 people considered at high risk of hospitalisation, and 25 people are actually admitted to hospital, this represents a positive predictive value, PPV, of 25%. The PPV is influenced by the underlying risk of hospitalisation in your target population. Our 40% prediction of hospitalisation can be compared with the PPV of other risk stratification models. Conclusion: Further work is underway to apply a regression model to hospital data to facilitate comparisons with other risk stratification models reported in the literature, and quantify the incremental gains from increasingly sophisticated approaches to patient selection. Further work will also explore the gains from linking hospital data with primary care data. Using hospital data compares well with other models, and is an easily transferable, low tech approach to patient selection. A series of criteria for patient selection is a familiar way of doing business in many clinical settings. [ABSTRACT FROM AUTHOR]