[expand title=”Cancer risk and cancer outcomes among sexual minority women and men” tag=”h2″]
Data on cancer outcomes among lesbian, gay and bisexual women and men are limited as sexual orientation is not reliably collected in population databases or research cohorts. In our previous work we used the National Cancer Patient Experience Survey and found that the distribution of site-specific cancer diagnosis did not vary substantially by sexual orientation, with the exception of some HPV- and HIV-associated cancers.
To replicate this work using UK Biobank data to describe and compare patterns of cancer risk and cancer outcomes among sexual minority women and men, using UK Biobank linked to cancer registry data.
The initial step in this research will be to describe the responses to the sexual behaviour history questions among women and men in UK Biobank, specifically the responses to the questions about history of sex with people of the same and opposite sex, and numbers of partners, which were asked at the Biobank Baseline Assessment interview, and compare these responses with other UK population data such as the National Survey of Sexual Attitudes and Lifestyles
Once we have developed an appropriate approach to using these data, in further analyses, we will explore variation in cancer risk factors and cancer outcomes using appropriate univariable and multivariable statistical models.
PI: Dr Katie Saunders
Funder: University of Cambridge returning carers scheme
[expand title=”Clustering of multi-morbidity in stroke survivors” tag=”h2″]
Multimorbidity accounts for 25% of the adult UK population, and over half of hospital admissions and GP appointments. Multimorbidity is particularly prevalent in stroke/transient ischaemic attack (TIA) survivors (~94%). Despite the high prevalence of multimorbidity in stroke/TIA patients, no studies have investigated how multimorbidity is structured within the stroke/TIA survivor population.
Using the Clinical Practice Research Datalink (CPRD) GOLD database, which provides clinical primary care data from UK general practices, this research aims:
- To identify common clusters of multimorbidity within the stroke/TIA survivor population.
- To describe how health outcomes differ among different stroke-multimorbidity clusters.
- To describe how determinants of health differ among different stroke/TIA multimorbidity clusters.
In a cross-sectional approach, we focus on adults surviving a stroke/ TIA. Utilizing a 3-year follow up period we estimate death, primary care consultation and hospital admission rates. We link the CPRD GOLD data with area socioeconomic deprivation data and Hospital Episodes Statistics. Multimorbidity is defined as having two or more of 36 long-term conditions. A model-based person-centred cluster analysis, the latent class analysis (LCA) calculating the class membership probabilities among patients is used. The number of clusters is defined through statistical criteria and clinical input. We describe the clusters in terms of comorbidities and identify key characteristics and health outcomes associated with them.
[expand title=”Evaluating Google Trends as a primary care research tool” tag=”h2″]
Google searches represent a snapshot of the world’s interest in different topics. Data are collected and presented by Google Trends, a publicly available tool which collects relative search volume data for different search terms and different topics.
This multimethod study aimed to explore Google Trends as a research tool, with a focus on its potential in primary care research, and whether the tool can be used to give useful indications about aspects of primary care, such as health problems that present to General Practitioners (GPs) and the specific groups of people who might experience these.
This was a multimethod study that involved using Google Trends to explore relative search volumes for different search terms, incorporating patient involvement into the analysis and interpretation of data. Further investigations also included an attempt to manipulate real-time Google Trends data, as well as a comparison between Google Trends and Twitter data. Data were also collected from the GP Patient Survey (GPPS) and a rapid review into the existing literature around GT. All components of this study were evaluated together in order to make final conclusions about Google Trends as a primary care research tool.
[expand title=”Improving Primary Care After Stroke (IPCAS)” tag=”h2″]
The IPCAS trial (Improving Primary Care After Stroke) is randomised controlled trial (RCT) to evaluate a new model of care for stroke survivors living in the community. The project is funded by a two million pound NIHR PGfAR (Programme Grant for Applied Research) , and the chief investigator is Prof. Jonathan Mant, Professor of Primary Care Research. The study started in 2015 and is due to conclude in 2020. This uses a two-arm cluster RCT with 46 general practices as the unit of randomisation, containing 920 people on the stroke registers of GP practices in the East of England and the East Midlands. The primary endpoint for the trial will be two sub-scales (emotion and handicap) of the Stroke Impact Scale (SIS v3.0) as co-primary outcomes at 12 months (adjusted for baseline). The Cambridge Research Methods Hub (CRMH) are supporting the analysis of the trial.