The contribution of primary care practitioners to interventions reducing loneliness and social isolation in older people—An integrative review

Background

Social connection is a fundamental human need. Its absence can lead to loneliness and social isolation, adversely impacting health and well-being. Given their regular contact and trusted relationships with older people, practitioners delivering community-based primary care are well-positioned to address this issue. However, their contribution to addressing loneliness and social isolation is unclear.

Aim

This integrative review explores the contribution of the primary care workforce to interventions aimed at reducing loneliness and social isolation in community-dwelling older people.

Method

Using an integrative review method, Scopus, Web of Science, CINAHL and PubMed were searched for original research published between 2000 and 2022. Fourteen papers reporting 13 primary studies were appraised for methodological quality and included in the review. Data were extracted into a summary table and analysed using thematic analysis.

Results

Included studies came from over six countries. Internationally, primary care services have diverse structures, funding and workforces influencing their response to loneliness and social isolation. All but one intervention was multi-component, with ten studies including a group-based activity and three providing primarily individual-level activities. Only six studies reported reductions in loneliness following the intervention. Three themes were identified: characteristics of interventions; implementation context, barriers and facilitators; and differing contributions of primary care practitioners in addressing loneliness and social isolation of older people.

Conclusion

There is increasing demand and scope for primary care practitioners to assist lonely and socially isolated older people. It is important to understand how to equip and incentivise these practitioners to routinely identify, assess and respond to lonely and socially isolated older people despite varying implementation contexts. There is a need for further research that explores how the primary care team can be better utilised to deliver effective interventions that reduce the health impacts of loneliness and social isolation.     

Health literacy among cancer survivors: Results from the 2016 behavioral risk factor surveillance system survey

Health literacy is a set of knowledge and skills that enables individuals to obtain, communicate, process and understand information, and services to make appropriate health decisions and to successfully navigate the health care system. Health literacy is important to quality of cancer survivorship care and patient self-management of their disease.We examined health literacy among cancer survivors, using data from the 2016 Behavioral Risk Factor Surveillance System. We compared health literacy across various demographic and socioeconomic groups and estimated the adjusted odds in favor of low health literacy for these characteristics.We found that about 16% of the cancer survivors had low health literacy. The prevalence was higher among Hispanic and Black individuals, and among those with poor health status, low income and educational attainment.A sizeable percentage of cancer survivors have low health literacy which is likely to complicate their ability to self-manage their disease and navigate the health care system for optimal care. In order to ensure the quality and appropriateness of cancer survivorship care, effective interventions are needed to address low health literacy in these populations.     

Personalized treatment selection using data from crossover designs with carry-over effects

In this work, we propose a semiparametric method for estimating the optimal treatment for a given patient based on individual covariate information for that patient when data from a crossover design are available. Here, we assume there are carry-over effects for patients switching from one treatment to another. For the K treatment (K ≥ 2) scenario, we show that nonparametric estimation of carry-over effects can have the undesirable property that comparison of treatment means can only be done using independent outcome measurements from different groups of patients rather than using available joint measurements for each patient. To overcome this barrier, we compare probabilities of outcome variable of each treatment dominating outcome variables for all other treatments conditional on patient-specific scores constructed from patient covariates. We suggest single-index models as appropriate models connecting outcome variables to covariates and our empirical investigations show that frequencies of correct treatment assignments are highly accurate. The proposed method is also rather robust against departures from a single-index model structure. We also conduct a real data analysis to show the applicability of the proposed procedure.