Applying an equity lens to social prescribing.

BACKGROUND: Social prescribing is often described as an intervention that can help reduce health inequalities yet there is little evidence exploring this. This study aimed to assess the feasibility of accessing and analysing social prescribing (SP) service user data to demonstrate the impact of SP on health inequalities. METHODS: The sample size consisted of records for 276 individuals in Site 1 and 1644 in Site 2. Descriptive analyses were performed to assess the characteristics of people accessing SP, the consistency of data collected and the missingness across both sites. RESULTS: Both sites collected basic demographic data (age gender, ethnicity and deprivation). However, data collection was inconsistent; issues included poor recording of ethnicity in Site 2, and for both sites, referral source data and health and well-being outcome measures were missing. There was limited data on the wider determinants of health. These data gaps mean that impacts on health inequalities could not be fully explored. CONCLUSIONS: It is essential that SP data collection includes information on user demographics and the wider determinants of health in line with PROGRESS Plus factors. Considering equity around who is accessing SP, how they access it and the outcomes is essential to evidencing how SP affects health inequalities and ensuring equitable service delivery.

Evaluating the impact on physical inactivity of Together an Active Future, a partnership approach to physical activity promotion. A difference-in-differences study.

BACKGROUND: Low physical activity is one of the leading causes of ill health in the UK and an important determinant of health inequalities. Little is known about the effectiveness of community-wide interventions to increase physical activity and whether effects differ by demographic groups, including area deprivation and ethnicity. SETTING: 6 relatively disadvantaged local authority areas in Lancashire, UK, between 2016 and 2021. METHODS: We conducted a doubly robust difference-in-differences study using a large nationally representative repeated cross-sectional survey to investigate the impact of Together an Active Future (TAAF), an intervention aiming to reduce physical inactivity through a programme of creative engagement, partnership building, training and communication. The primary outcome was physical inactivity (the percentage of the population engaging in less than 30 min physical activity of at least moderate intensity per week). RESULTS: While inactivity increased during the pandemic, it increased to a lesser extent in the intervention population. TAAF was associated with 2.63 percentage point lower level of physical inactivity (95% CI 0.80 to 4.45) in the intervention group relative to the control group. Subgroup analysis found no evidence of differences in effect between groups defined by deprivation, ethnicity, disability, gender or age. CONCLUSIONS: The study suggests that a programme of creative engagement, partnership building, training and communication can help reduce physical inactivity, potentially mitigating some of the effect of pandemic restrictions. Further monitoring is required to understand the impact of this intervention outside of the pandemic context.

Continuing inequalities in COVID-19 mortality in England and Wales, and the changing importance of regional, over local, deprivation.

BACKGROUND: Observational studies have highlighted that where individuals live is far more important for risk of dying with COVID-19, than for dying of other causes. Deprivation is commonly proposed as explaining such differences. During the period of localised restrictions in late 2020, areas with higher restrictions tended to be more deprived. We explore how this impacted the relationship between deprivation and mortality and see whether local or regional deprivation matters more for inequalities in COVID-19 mortality. METHODS: We use publicly available population data on deaths due to COVID-19 and all-cause mortality between March 2020 and April 2021 to investigate the scale of spatial inequalities. We use a multiscale approach to simultaneously consider three spatial scales through which processes driving inequalities may act. We go on to explore whether deprivation explains such inequalities. RESULTS: Adjusting for population age structure and number of care homes, we find highest regional inequality in October 2020, with a COVID-19 mortality rate ratio of 5.86 (95% CI 3.31 to 19.00) for the median between-region comparison. We find spatial context is most important, and spatial inequalities higher, during periods of low mortality. Almost all unexplained spatial inequality in October 2020 is removed by adjusting for deprivation. During October 2020, one standard deviation increase in regional deprivation was associated with 20% higher local mortality (95% CI, 1.10 to 1.30). CONCLUSIONS: Spatial inequalities are greatest in periods of lowest overall mortality, implying that as mortality declines it does not do so equally. During the prolonged period of low restrictions and low mortality in summer 2020, spatial inequalities strongly increased. Contrary to previous months, we show that the strong spatial patterning during autumn 2020 is almost entirely explained by deprivation. As overall mortality declines, policymakers must be proactive in detecting areas where this is not happening, or risk worsening already strong health inequalities.

Evaluating the impacts of tiered restrictions introduced in England, during October and December 2020 on COVID-19 cases: a synthetic control study.

OBJECTIVES: To analyse the impact on SARS-CoV-2 transmission of tier 3 restrictions introduced in October and December 2020 in England, compared with tier 2 restrictions. We further investigate whether these effects varied between small areas by deprivation. DESIGN: Synthetic control analysis. SETTING: We identified areas introducing tier 3 restrictions in October and December, constructed a synthetic control group of places under tier 2 restrictions and compared changes in weekly infections over a 4-week period. Using interaction analysis, we estimated whether this effect varied by deprivation and the prevalence of a new variant (B.1.1.7). INTERVENTIONS: In both October and December, no indoor between-household mixing was permitted in either tier 2 or 3. In October, no between-household mixing was permitted in private gardens and pubs and restaurants remained open only if they served a 'substantial meal' in tier 3, while in tier 2 meeting with up to six people in private gardens were allowed and all pubs and restaurants remained open. In December, in tier 3, pubs and restaurants were closed, while in tier 2, only those serving food remained open. The differences in restrictions between tier 2 and 3 on meeting outside remained the same as in October. MAIN OUTCOME MEASURE: Weekly reported cases adjusted for changing case detection rates for neighbourhoods in England. RESULTS: Introducing tier 3 restrictions in October and December was associated with a 14% (95% CI 10% to 19%) and 20% (95% CI 13% to 29%) reduction in infections, respectively, compared with the rates expected with tier 2 restrictions only. The effects were similar across levels of deprivation and by the prevalence of the new variant. CONCLUSIONS: Compared with tier 2 restrictions, additional restrictions in tier 3 areas in England had a moderate effect on transmission, which did not appear to increase socioeconomic inequalities in COVID-19 cases.

Interrogating structural inequalities in COVID-19 mortality in England and Wales.

BACKGROUND: Numerous observational studies have highlighted structural inequalities in COVID-19 mortality in the UK. Such studies often fail to consider the hierarchical, spatial nature of such inequalities in their analysis, leading to the potential for bias and an inability to reach conclusions about the most appropriate structural levels for policy intervention. METHODS: We use publicly available population data on COVID-19-related mortality and all-cause mortality between March and July 2020 in England and Wales to investigate the spatial scale of such inequalities. We propose a multiscale approach to simultaneously consider three spatial scales at which processes driving inequality may act and apportion inequality between these. RESULTS: Adjusting for population age structure and number of local care homes we find highest regional inequality in March and June/July. We find finer grained within region inequality increased steadily from March until July. The importance of spatial context increases over the study period. No analogous pattern is visible for non-COVID-19 mortality. Higher relative deprivation is associated with increased COVID-19 mortality at all stages of the pandemic but does not explain structural inequalities. CONCLUSIONS: Results support initial stochastic viral introduction in the South, with initially high inequality decreasing before the establishment of regional trends by June and July, prior to reported regionality of the 'second-wave'. We outline how this framework can help identify structural factors driving such processes, and offer suggestions for a long-term, locally targeted model of pandemic relief in tandem with regional support to buffer the social context of the area.

Ideation and implementation of an open science drug discovery business model - M4K Pharma.

M4K Pharma was incorporated to launch an open science drug discovery program that relies on regulatory exclusivity as its primary intellectual property and commercial asset, in lieu of patents.In many cases and in key markets, using regulatory exclusivity can provide equivalent commercial protection to patents, while also being compatible with open science. The model is proving attractive to government, foundation and individual funders, who collectively have different expectations for returns on investment compared with biotech, pharmaceutical companies, or venture capital investors.In the absence of these investor-driven requirements for returns, it should be possible to commercialize therapeutics at affordable prices.M4K is piloting this open science business model in a rare paediatric brain tumour, but there is no reason it should not be more widely applicable.

Does neighbourhood deprivation affect the genetic influence on body mass?

Most research into the role of gene-environment interactions in the etiology of obesity has taken environment to mean behaviours such as exercise and diet. While interesting, this is somewhat at odds with research into the social determinants of obesity, in which the focus has shifted away from individuals and behaviours to the types of wider obesogenic environments in which individuals live, which influence and produce these behaviours. This study combines these two strands of research by investigating how the genetic influence on body mass index (BMI), used as a proxy for obesity, changes across different neighbourhood environments measured by levels of deprivation. Genetics are incorporated using a classical twin design with data from Twins UK, a longitudinal study of UK twins running since 1992. A multilevel modelling approach is taken to decompose variation between individuals into genetic, shared environmental, and non-shared environmental components. Neighbourhood deprivation is found to be a statistically significant predictor of BMI after conditioning on individual characteristics, and a heritability of 0.75 is estimated for the entire sample. This heritability estimate is shown, however, to be higher in more deprived neighbourhoods and lower in less deprived ones, and this relationship is statistically significant. While this research cannot say anything directly about the mechanisms behind the relationship, it does highlight how the relative importance of genetic factors can vary across different social environments, and therefore the value of considering both genetic and social determinants of health simultaneously.