4 results found
Woodcock T, Novov V, Skirrow H, et al., 2022, Health and socio-demographic characteristics associated with uptake of seasonal influenza vaccination amongst pregnant women: retrospective cohort study, British Journal of General Practice, ISSN: 0960-1643
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.
Nakubulwa M, Junghans C, Novov V, et al., 2022, Factors associated with accessing long-term adult social care in people aged 75 and over: a retrospective cohort study., Age and Ageing, Vol: 51, Pages: 1-9, ISSN: 0002-0729
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.
Soong JTY, Poots AJ, Scott S, et al., 2015, Quantifying the prevalence of frailty in English hospitals, BMJ Open, Vol: 5, ISSN: 2044-6055
Objectives Population ageing has been associated with an increase in comorbid chronic disease, functional dependence, disability and associated higher health care costs. Frailty Syndromes have been proposed as a way to define this group within older persons. We explore whether frailty syndromes are a reliable methodology to quantify clinically significant frailty within hospital settings, and measure trends and geospatial variation using English secondary care data set Hospital Episode Statistics (HES).Setting National English Secondary Care Administrative Data HES.Participants All 50 540 141 patient spells for patients over 65 years admitted to acute provider hospitals in England (January 2005—March 2013) within HES.Primary and secondary outcome measures We explore the prevalence of Frailty Syndromes as coded by International Statistical Classification of Diseases, Injuries and Causes of Death (ICD-10) over time, and their geographic distribution across England. We examine national trends for admission spells, inpatient mortality and 30-day readmission.Results A rising trend of admission spells was noted from January 2005 to March 2013(daily average admissions for month rising from over 2000 to over 4000). The overall prevalence of coded frailty is increasing (64 559 spells in January 2005 to 150 085 spells by Jan 2013). The majority of patients had a single frailty syndrome coded (10.2% vs total burden of 13.9%). Cognitive impairment and falls (including significant fracture) are the most common frailty syndromes coded within HES. Geographic variation in frailty burden was in keeping with known distribution of prevalence of the English elderly population and location of National Health Service (NHS) acute provider sites. Overtime, in-hospital mortality has decreased (>65 years) whereas readmission rates have increased (esp.>85 years).Conclusions This study provides a novel methodology to reliably quantify clinically significant frailty. Applications in
Lovett DA, Poots AJ, Clements JTC, et al., 2014, Using geographical information systems and cartograms as a health service quality improvement tool, Spatial and Spatio-temporal Epidemiology, Vol: 10, Pages: 67-74, ISSN: 1877-5845
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 t
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.