Pregnant women are at increased risk from influenza, yet maternal influenza vaccination levels remain suboptimal. This study aimed to estimate associations between socio-demographic and health characteristics and seasonal influenza vaccination uptake among pregnant women and understand trends over time to inform interventions to improve vaccine coverage. A retrospective cohort study using linked electronic health records of women in North West London with at least one pregnancy overlapping with an influenza season between September 2010 and February 2020. We used a multivariable mixed-effects logistic regression model to identify associations between characteristics of interest and primary outcome of influenza vaccination. 451,954 pregnancies, among 260,744 women, were included. In 85,376 (18.9%) pregnancies women were vaccinated against seasonal influenza. Uptake increased from 8.4% in 2010/11 to 26.3% in 2018/19, dropping again to 21.1% in 2019/20. Uptake was lowest among women: aged 15-19 years (12%) or over 40 years (15%; OR 1.17, 95% CI 1.10 to 1.24); of Black ethnicity (14.1%; OR 0.55, 95% CI 0.53 to 0.57), or unknown ethnicity (9.9%; OR 0.42, 95% CI 0.39 to 0.46), lived in more deprived areas (OR least vs most deprived 1.16, 95% CI 1.11 to 1.21), or with no known risk factors for severe influenza. Seasonal influenza vaccine uptake in pregnant women increased in the past decade, prior to the COVID-19 pandemic, but remained suboptimal. We recommend approaches to reducing health inequalities should focus on women of Black ethnicity, younger and older women, and women living in areas of greater socio-economic deprivation.

Background An ageing population and limited resources have put strain on state provision of adult social care (ASC) in England. With social care needs predicted to double over the next 20 years, there is a need for new approaches to inform service planning and development, including through predictive models of demand. Objective Describe risk factors for long-term ASC in two inner London boroughs and develop a risk prediction model for long-term ASC. Methods Pseudonymised person-level data from an integrated care dataset were analysed. We used multivariable logistic regression to model associations of demographic factors, and baseline aspects of health status and health service use, with accessing long-term ASC over 12 months. Results The cohort comprised 13,394 residents, aged ≥75 years with no prior history of ASC at baseline. Of these, 1.7% became ASC clients over 12 months. Residents were more likely to access ASC if they were older or living in areas with high socioeconomic deprivation. Those with preexisting mental health or neurological conditions, or more intense prior health service use during the baseline period, were also more likely to access ASC. A prognostic model derived from risk factors had limited predictive power. Conclusions Our findings reinforce evidence on known risk factors for residents aged 75 or over, yet even with linked routinely collected health and social care data, it was not possible to make accurate predictions of long-term ASC use for individuals. We propose that a paradigm shift towards more relational, personalised approaches, is needed.