Impact of a social prescribing intervention in North East England on adults with type 2 diabetes: the SPRING_NE multimethod study.

Background: Link worker social prescribing enables health-care professionals to address patients' non-medical needs by linking patients into various services. Evidence for its effectiveness and how it is experienced by link workers and clients is lacking. Objectives: To evaluate the impact and costs of a link worker social prescribing intervention on health and health-care costs and utilisation and to observe link worker delivery and patient engagement. Data sources: Quality Outcomes Framework and Secondary Services Use data. Design: Multimethods comprising (1) quasi-experimental evaluation of effects of social prescribing on health and health-care use, (2) cost-effectiveness analysis, (3) ethnographic methods to explore intervention delivery and receipt, and (4) a supplementary interview study examining intervention impact during the first UK COVID-19 lockdown (April-July 2020). Study population and setting: Community-dwelling adults aged 40-74 years with type 2 diabetes and link workers in a socioeconomically deprived locality of North East England, UK. Intervention: Link worker social prescribing to improve health and well-being-related outcomes among people with long-term conditions. Participants: (1) Health outcomes study, approximately n = 8400 patients; EuroQol-5 Dimensions, five-level version (EQ-5D-5L), study, n = 694 (baseline) and n = 474 (follow-up); (2) ethnography, n = 20 link workers and n = 19 clients; and COVID-19 interviews, n = 14 staff and n = 44 clients. Main outcome measures: The main outcome measures were glycated haemoglobin level (HbA1c; primary outcome), body mass index, blood pressure, cholesterol level, smoking status, health-care costs and utilisation, and EQ-5D-5L score. Results: Intention-to-treat analysis of approximately 8400 patients in 13 intervention and 11 control general practices demonstrated a statistically significant, although not clinically significant, difference in HbA1c level (-1.11 mmol/mol) and a non-statistically significant 1.5-percentage-point reduction in the probability of having high blood pressure, but no statistically significant effects on other outcomes. Health-care cost estimates ranged from £18.22 (individuals with one extra comorbidity) to -£50.35 (individuals with no extra comorbidity). A statistically non-significant shift from unplanned (non-elective and accident and emergency admissions) to planned care (elective and outpatient care) was observed. Subgroup analysis showed more benefit for individuals living in more deprived areas, for the ethnically white and those with fewer comorbidities. The mean cost of the intervention itself was £1345 per participant; the incremental mean health gain was 0.004 quality-adjusted life-years (95% confidence interval -0.022 to 0.029 quality-adjusted life-years); and the incremental cost-effectiveness ratio was £327,250 per quality-adjusted life-year gained. Ethnographic data showed that successfully embedded, holistic social prescribing providing supported linking to navigate social determinants of health was challenging to deliver, but could offer opportunities for improving health and well-being. However, the intervention was heterogeneous and was shaped in unanticipated ways by the delivery context. Pressures to generate referrals and meet targets detracted from face-to-face contact and capacity to address setbacks among those with complex health and social problems. Limitations: The limitations of the study include (1) a reduced sample size because of non-participation of seven general practices; (2) incompleteness and unreliability of some of the Quality and Outcomes Framework data; (3) unavailability of accurate data on intervention intensity and patient comorbidity; (4) reliance on an exploratory analysis with significant sensitivity analysis; and (5) limited perspectives from voluntary, community and social enterprise. Conclusions: This social prescribing model resulted in a small improvement in glycaemic control. Outcome effects varied across different groups and the experience of social prescribing differed depending on client circumstances. Future work: To examine how the NHS Primary Care Network social prescribing is being operationalised; its impact on health outcomes, service use and costs; and its tailoring to different contexts. Trial registration: This trial is registered as ISRCTN13880272. Funding: This project was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme, Community Groups and Health Promotion (grant no. 16/122/33) and will be published in full in Public Health Research; Vol. 11, No. 2. See the NIHR Journals Library website for further project information.

Social prescribing happens when health-care staff refer patients to a link worker. Link workers support and help patients to access community services to improve their health and well-being. Social prescribing is popular within the NHS, but there is little evidence that it works. We looked at a social prescribing model being delivered in a disadvantaged area in north-east England.

Worker adjustment to unexpected occupational risk: Evidence from COVID-19.

We study the link between the revelation of a hitherto non-existent occupational risk - mortality due to the COVID-19 pandemic in 2020 - and subsequent worker behaviour. We link occupation-specific data on COVID-19 mortality to individual level data sets. We find that wages did not adjust, but workers started leaving high-risk occupations during 2020. These effects are stronger for workers not affected by lockdowns or working from home orders and for those considered to be clinically vulnerable to COVID-19 and are not driven by negative health shocks or employer-initiated separations. Occupation-level results suggest that employment began to rebound in 2021.

COVID-19 and income inequality in OECD countries.

OBJECTIVE: To determine the association between income inequality and COVID-19 cases and deaths per million in OECD countries. METHODS: Cross-sectional regression methods are used to model the relationship between income inequality, as measured by the Gini coefficient, and COVID-19 reported cases and deaths per-million. RESULTS: The results demonstrate a significant positive association between income inequality and COVID-19 cases and death per million in all estimated models. A 1% increase in the Gini coefficient is associated with an approximately 4% increase in cases per-million and an approximately 5% increase in deaths per-million. CONCLUSIONS: The results demonstrate that countries with high levels of income inequality have performed significantly worse when dealing with the COVID-19 outbreak in terms cases and deaths. Income inequality is a proxy for many elements of socioeconomic disadvantage that may contribute to the spread of, and deaths from, COVID-19. These include poor housing, smoking, obesity and pollution. POLICY IMPLICATIONS: The findings suggest the importance of closing the gap in income inequality and improving the health and incomes of the poorest and most vulnerable groups.