The Characteristics of Community-Driven Allotments and
                        Revitalisation of Allotments in Deprived Areas - Focusing on the Case of
                        Sheffield, UK -

BackgroundFrequent attendances of the same users in emergency departments (ED) can intensify workload pressures and are common among children, yet little is known about the characteristics of paediatric frequent users in EDs.AimTo describe the volume of frequent paediatric attendance in England and the demographics of frequent paediatric ED users in English hospitals.MethodWe analysed the Hospital Episode Statistics dataset for April 2014–March 2017. The study included 2 308 816 children under 16 years old who attended an ED at least once. Children who attended four times or more in 2015/2016 were classified as frequent users. The preceding and subsequent years were used to capture attendances bordering with the current year. We used a mixed effects logistic regression with a random intercept to predict the odds of being a frequent user in children from different sociodemographic groups.ResultsOne in 11 children (9.1%) who attended an ED attended four times or more in a year. Infants had a greater likelihood of being a frequent attender (OR 3.24, 95% CI 3.19 to 3.30 vs 5 to 9 years old). Children from more deprived areas had a greater likelihood of being a frequent attender (OR 1.57, 95% CI 1.54 to 1.59 vs least deprived). Boys had a slightly greater likelihood than girls (OR 1.05, 95% CI 1.04 to 1.06). Children of Asian and mixed ethnic groups were more likely to be frequent users than those from white ethnic groups, while children from black and 'other' had a lower likelihood (OR 1.03, 95% CI 1.01 to 1.05; OR 1.04, 95% CI 1.01 to 1.06; OR 0.88, 95% CI 0.86 to 0.90; OR 0.90, 95% CI 0.87 to 0.92, respectively).ConclusionOne in 11 children was a frequent attender. Interventions for reducing paediatric frequent attendance need to target infants and families living in deprived areas.

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Doing Democracy Differently: How Can Participatory Democracy Take Hold In Deprived Areas?

Representation ◽

10.1080/00344893.2019.1682647 ◽

2021 ◽

Vol 57 (2) ◽

pp. 175-191

Author(s):

Rod Dacombe

Keyword(s):

Participatory Democracy ◽

Deprived Areas

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A deep learning approach to identify unhealthy advertisements in street view images

Scientific Reports ◽

10.1038/s41598-021-84572-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Gregory Palmer ◽

Mark Green ◽

Emma Boyland ◽

Yales Stefano Rios Vasconcelos ◽

Rahul Savani ◽

...

Keyword(s):

Deep Learning ◽

Fast Food ◽

Social Inequalities ◽

Social Inequalities In Health ◽

Street Level ◽

Street View ◽

Food Advertisements ◽

Data Collection Effort ◽

Deprived Areas

AbstractWhile outdoor advertisements are common features within towns and cities, they may reinforce social inequalities in health. Vulnerable populations in deprived areas may have greater exposure to fast food, gambling and alcohol advertisements, which may encourage their consumption. Understanding who is exposed and evaluating potential policy restrictions requires a substantial manual data collection effort. To address this problem we develop a deep learning workflow to automatically extract and classify unhealthy advertisements from street-level images. We introduce the Liverpool $${360}^{\circ }$$ 360 ∘ Street View (LIV360SV) dataset for evaluating our workflow. The dataset contains 25,349, 360 degree, street-level images collected via cycling with a GoPro Fusion camera, recorded Jan 14th–18th 2020. 10,106 advertisements were identified and classified as food (1335), alcohol (217), gambling (149) and other (8405). We find evidence of social inequalities with a larger proportion of food advertisements located within deprived areas and those frequented by students. Our project presents a novel implementation for the incidental classification of street view images for identifying unhealthy advertisements, providing a means through which to identify areas that can benefit from tougher advertisement restriction policies for tackling social inequalities.

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Socioeconomic Inequalities and Ethnicity Are Associated with a Positive COVID-19 Test among Cancer Patients in the UK Biobank Cohort

Cancers ◽

10.3390/cancers13071514 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1514

Author(s):

Shing Fung Lee ◽

Maja Nikšić ◽

Bernard Rachet ◽

Maria-Jose Sanchez ◽

Miguel Angel Luque-Fernandez

Keyword(s):

Cancer Patients ◽

Socioeconomic Inequalities ◽

Uk Biobank ◽

Incident Cancer ◽

Increased Risk ◽

Exposure Variable ◽

Independent Risk Factors ◽

The Uk ◽

Deprived Areas

We explored the role of socioeconomic inequalities in COVID-19 incidence among cancer patients during the first wave of the pandemic. We conducted a case-control study within the UK Biobank cohort linked to the COVID-19 tests results available from 16 March 2020 until 23 August 2020. The main exposure variable was socioeconomic status, assessed using the Townsend Deprivation Index. Among 18,917 participants with an incident malignancy in the UK Biobank cohort, 89 tested positive for COVID-19. The overall COVID-19 incidence was 4.7 cases per 1000 incident cancer patients (95%CI 3.8–5.8). Compared with the least deprived cancer patients, those living in the most deprived areas had an almost three times higher risk of testing positive (RR 2.6, 95%CI 1.1–5.8). Other independent risk factors were ethnic minority background, obesity, unemployment, smoking, and being diagnosed with a haematological cancer for less than five years. A consistent pattern of socioeconomic inequalities in COVID-19 among incident cancer patients in the UK highlights the need to prioritise the cancer patients living in the most deprived areas in vaccination planning. This socio-demographic profiling of vulnerable cancer patients at increased risk of infection can inform prevention strategies and policy improvements for the coming pandemic waves.

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