Characteristics associated with influenza vaccination uptake in pregnancy: a retrospective cohort study.

BACKGROUND: Pregnant women are at increased risk from influenza, yet maternal influenza vaccination levels remain suboptimal. AIM: To estimate associations between sociodemographic and health characteristics and seasonal influenza vaccination uptake among pregnant women, and to understand trends over time to inform interventions to improve vaccine coverage. DESIGN AND SETTING: Retrospective cohort study using linked electronic health records of women in North West London with a pregnancy overlapping an influenza season between September 2010 and February 2020. METHOD: A multivariable mixed-effects logistic regression model was used to identify associations between characteristics of interest and the primary outcome of influenza vaccination. RESULTS: In total, 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 26.4% in 2017/18, dropping again to 21.1% in 2019/20. Uptake was lowest among women aged 15-19 years (11.9%; reference category) or ≥40 years (15.2%; odds ratio [OR] 1.17, 95% confidence interval [CI] = 1.10 to 1.24); of Black (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); who lived in more deprived areas (OR least versus most deprived [reference category] 1.16, 95% CI = 1.11 to 1.21); or with no known risk factors for severe influenza. CONCLUSION: Seasonal influenza vaccine uptake in pregnant women increased in the decade before the COVID-19 pandemic, but remained suboptimal. Targeted approaches are recommended to reducing inequalities in access to vaccination and should focus on women of Black ethnicity, younger and older women, and women living in deprived areas.

Factors associated with accessing long-term adult social care in people aged 75 and over: a retrospective cohort study.

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.

The role of patients and carers in diffusing a health-care innovation: A case study of "My Medication Passport".

BACKGROUND: Patients are increasingly recognized as playing important roles in improving health services. Little is known about the mechanisms by which patients develop and diffuse local innovations in a complex health-care system. OBJECTIVE: To ascertain how diffusion of an innovation, My Medication Passport, occurred and roles played by patients in it. DESIGN: Case study: quantitative mapping of innovation's diffusion and analysis of the routes and occupations of those through whom the innovation spread; documentary analysis; reflective assessment of patient's roles. SETTING AND PARTICIPANTS: NHS Trusts, third sector organizations, patients and health-care professionals. INTERVENTIONS STUDIED: Co-produced action to raise awareness and influence use of the innovation; order database which enabled ease of access to the innovation. MAIN OUTCOME MEASURES: Geographical spread of innovation; occupations of individuals; types of organizations using the innovation. RESULTS: The innovation spread from initial development and use in Northwest London across the UK and beyond. Key roles played by patients were as follows: co-producer; advocate; relationship builder; relationship broker; planner; presenter; awareness raiser; trainer; networker. Patients identified and introduced potential audiences and users to MMP, using social, organizational, sectoral, lay and professional networks to do so. They organized a range of awareness-raising and communication activities, monitored feedback, evaluated the impact and responded to new interest. DISCUSSION AND CONCLUSIONS: The roles of patients in diffusing innovations are under-recognized. Collaborative working between patients, carers and health-care professionals in planning and progressing the use and supporting diffusion of the innovation was important. Principles described in this study are relevant to progressing other patient-led ideas for innovative changes relating to health service development.

Using geographical information systems and cartograms as a health service quality improvement tool.

INTRODUCTION: Disease prevalence can be spatially analysed to provide support for service implementation and health care planning, these analyses often display geographic variation. A key challenge is to communicate these results to decision makers, with variable levels of Geographic Information Systems (GIS) knowledge, in a way that represents the data and allows for comprehension. The present research describes the combination of established GIS methods and software tools to produce a novel technique of visualising disease admissions and to help prevent misinterpretation of data and less optimal decision making. The aim of this paper is to provide a tool that supports the ability of decision makers and service teams within health care settings to develop services more efficiently and better cater to the population; this tool has the advantage of information on the position of populations, the size of populations and the severity of disease. METHODS: A standard choropleth of the study region, London, is used to visualise total emergency admission values for Chronic Obstructive Pulmonary Disease and bronchiectasis using ESRI's ArcGIS software. Population estimates of the Lower Super Output Areas (LSOAs) are then used with the ScapeToad cartogram software tool, with the aim of visualising geography at uniform population density. An interpolation surface, in this case ArcGIS' spline tool, allows the creation of a smooth surface over the LSOA centroids for admission values on both standard and cartogram geographies. The final product of this research is the novel Cartogram Interpolation Surface (CartIS). RESULTS: The method provides a series of outputs culminating in the CartIS, applying an interpolation surface to a uniform population density. The cartogram effectively equalises the population density to remove visual bias from areas with a smaller population, while maintaining contiguous borders. CartIS decreases the number of extreme positive values not present in the underlying data as can be found in interpolation surfaces. DISCUSSION: This methodology provides a technique for combining simple GIS tools to create a novel output, CartIS, in a health service context with the key aim of improving visualisation communication techniques which highlight variation in small scale geographies across large regions. CartIS more faithfully represents the data than interpolation, and visually highlights areas of extreme value more than cartograms, when either is used in isolation.