Loneliness and Patterns of Health Service Usage: Evidence From Healthy Ageing in Scotland

Loneliness is associated with poorer health status and health outcomes. Yet, little is known about how loneliness in ageing populations is associated with health service usage. Loneliness (UCLA-3) was measured in older people in Scotland (Healthy Ageing in Scotland, HAGIS, n = 1,057). We analysed socio-demographic, perceived health, and health behaviour characteristics using descriptive statistics and logistic regression. The survey data (HAGIS, 2016/17) were linked to retrospective administrative health data to investigate patterns of health service usage (from 2005), such as the number of hospital visits and mean length of stay, and their associated costs. Two-part models were used to highlight variation i) in those who had ever vs never been admitted to hospital, and ii) between those who had been admitted. Our results highlight the variation in hospital service usage in those experiencing loneliness and opens discussion on the implications for older people and hospital services.

Health service usage and re-referral rates: comparison of a dietitian-first clinic with a medical specialist-first model of care in a cohort of gastroenterology patients

BackgroundThe dietitian-first gastroenterology clinic (DFGC) is an expanded scope of practice initiative implemented in response to increased gastroenterology specialist demand. This study examined re-referral rates to gastroenterology and overall health service usage up to 24 months post management in DFGC compared with a traditional, gastroenterology specialist-first model.MethodsPatients discharged from DFGC in the first year were matched with those seen in the traditional model. Demographic, clinical and process-related service characteristics were compared, and logistic regression analysis was undertaken to model re-presentation and model of care (MoC) as the variable of interest considering covariates in univariate analyses. Analyses were performed at 12, 18 and 24 months post discharge.ResultsThe DFGC (122 patients) and traditional-model (62 patients) cohorts had similar baseline demographic characteristics. Wait-times (68.6 vs 272.9 days; p<0.001), treatment-times (89.4 vs 259.9 days; p<0.001) and usage of other services (1.4 vs 2.1 specialities; p=0.01) were lower in DFGC. Re-referral rates were low in both DFGC and traditional models at 12 months (0.82% vs 1.61%), 18 months (2.46% vs 6.45%) and 24 months (4.91% vs 8.06%), respectively, with no significant difference between the models at any time point.ConclusionMost patients do not re-present for similar conditions within 2 years when managed in the DFGC or traditional medical model. Patients managed in DFGC have lower overall health service usage compared with patients managed in the traditional model. These findings support the safety and effectiveness of a DFGC model as one strategy to manage specialist gastroenterology service demands.

The relationship between loneliness, social isolation and health service usage in an older population: an example of administrative data linkage using Healthy Ageing In Scotland (HAGIS) and NHS records

Objectives Social isolation and loneliness in older populations have been widely reported since 2000s, and are both associated with poorer health status, and physical and mental health conditions. Yet, little is known about how patterns of social isolation and loneliness in ageing populations are reflected in health service usage. Further, the range of definitions and the limitations of, often used, indices of social isolation and loneliness can make it difficult to understand how social isolation and loneliness is manifest within populations and across place. AimTo understand variation in health service usage in an older population in Scotland who self-report loneliness and social isolation. MethodsLatent class analysis (LCA) was used to determine profiles (population groups) of loneliness and social isolation in older people in Scotland (Healthy Ageing in Scotland, HAGIS, n = 1,057) using model-fit criteria. Loneliness was measured using the UCLA Loneliness Scale and social isolation used a measure of social networks and social contact. We then analysed the socio-demographic, and subjective health (physical and mental) of these profiles using logistic regression. The survey data (HAGIS, 2016/17) were linked to retrospective administrative health data to investigate patterns of health service usage (from 2005). ResultsOur results highlight the distinction and inter-relation between social isolation and loneliness and the variation in health service usage between these population groups, in particular, the number of hospital admissions and length of stay. ConclusionThis study adds further evidence of the association between social isolation, loneliness and poor health, and offers new insights into variation in health service usage. Such an approach also offers substantive potential for the adoption of a public health approach to benefit those most at risk of social isolation and loneliness, and poorer health outcomes.

SOCIAL ISOLATION AND LONELINESS: A LATENT CLASS APPROACH TO COSTING HEALTH SERVICE ENGAGEMENT IN AGING POPULATIONS

Social isolation and loneliness are associated with poorer health status and poorer health outcomes. Little is known the impact on health service usage, and its inherent cost, although it is considered to be higher. Latent class analysis (LCA) was used to determine profiles (population groups) of loneliness and social isolation in older people (aged 50+, n=1,057) using model-fit criteria. Loneliness was measured using the UCLA Loneliness Scale and social isolation used a measure of social networks and social contact. We then analysed the socio-demographic, perceived health, and health behaviour of these profiles using descriptive statistics and logistic regression. The survey data (HAGIS, 2016/17) were linked to retrospective administrative health data to investigate patterns of repeat prescription use (from 2009) and health service usage (from 2005) and their associated costs. Our results highlight the distinction and inter-relation between social isolation and loneliness (including associations with socio-demographic and health characteristics), and the variation in health service usage and costs between the population groups. LCA profiles may help focussed targeting of these groups for health interventions. Further, the data-driven approach of LCA may overcome some of the limitations of indices of social isolation and loneliness. As such, this will extend the existing methodological approaches to quantitative analyses of social isolation and loneliness and demonstrate the benefits of using linked administrative health data. Significantly, this study incorporates the social and financial cost of social isolation and loneliness on health and its implications for health services.

SMOKING BEHAVIOUR: PATTERNS AND COSTS OF HEALTH SERVICE USAGE USING HAGIS AND LINKED ADMINISTRATIVE HEALTH DATA

The associations between smoking and health are well documented. Using the Healthy Ageing In Scotland (HAGIS) survey linked to the administrative Scottish National Health Service (NHS) records this study analyses health service resource usage by older people according to self-reported smoking status. Individual level smoking status (current, ex-smoker, or never smoked), socio-demographic characteristics (age, gender, level of deprivation) and subjective health are sourced from people aged 50+ across Scotland using HAGIS. These responses are then linked to NHS Scottish Morbidity Records to analyse variation in health service usage as measured by the total number of days spent in hospital (daycases and inpatient stays), number of stays, and mean length of stay. Costs are then assigned by medical speciality. We use a two-part model to analyse the i) the probability of having been hospitalised at all, and ii) the quantum of resource usage and its associated cost for those who have been in hospital. Our study provides a conceptual and empirical framework for the associative relationship between smoking status and actual (rather than self-reported) health service usage and expenditure. This study demonstrates the insights to be gained from the linkage of individual survey responses to administrative health service data on resource usage and costs, and discusses the implications for health policy.

Challenges of Analysing Case-Control Datasets: A Health and Homelessness Case Study

BackgroundWe discuss the methodological challenges of analysing a casecontrol dataset. ObjectivesThe study seeks to explore the relationship between health activity and homelessness in Scotland. MethodsOur study involved 430,000 people with experience of homelessness, each matched with two controls of the same age and sex: one from each of the 20% least deprived and 20% most deprived areas. This gives 1.3 million people in total. We aimed to compare health service usage among the different groups to ascertain whether the health needs of the homeless group exceeded that of the general population, and in particular those of non-homeless deprived people.However, as the cases were defined individually, and the controls were defined only by proxy (via datazone of residence), observed differences between the groups could simply result from differences in the proportion of the group that were deprived, rather than specifically relating homelessness itself.To address this we compare the timing of health activity with the timing of the homelessness assessment to more directly isolate the relationship between homelessness and health. This temporal analysis also allows discussion of causation.This method raised further difficulties: effectively being a convolution of complicated functions. However as each group has the same age–sex structure the complexity applies equally. Thus direct comparisons can be made between the groups resulting in more-straightforward analysis. FindingsFindings of the study are presented in a separate talk (The Relationship Between Health and Homelessness in Scotland) at this conference. ConclusionsIdentifying the causal relationships involved in correlations can be difficult. However by comparing the case cohort to the control cohort, and more specifically comparing the time of activity of these relative to the identifying event of the casegroup (the homelessness assessment), it is possible to identify some causal relationships.

Modeling Offender Decision Making with Secondary Data

Many questions about offender decision making are answered by analyzing data from secondary sources, which include police records and other law enforcement sources, but also full population registries containing data on birth, death, family composition, schooling, employment, and social and health service usage. Although secondary data are not collected for research purposes, they have a number of advantages over regular survey data. This chapter discusses the use of secondary data, using as examples two types of offender decisions: whether or not to offend (explored in developmental and life course criminology) and where to offend (explored in geographic and environmental criminology). These are decisions that can fruitfully be studied from a rational choice perspective.