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Publications in Public Health by NOMIS researchers

NOMIS Researcher(s)

Published in

December 1, 2022

Background: In response to the Covid-19 pandemic, most countries implemented physical distancing measures. Many mental health experts warned that through increasing social isolation and anxiety, these measures could negatively affect psychosocial wellbeing. However, socially aligning with others by adhering to these measures may also be beneficial for wellbeing. Methods: We examined these two contrasting hypotheses using cross-national survey data (N = 6675) collected fortnightly from participants in 115 countries over 3 months at the beginning of the pandemic. Participants reported their wellbeing, perceptions of how vulnerable they were to Covid-19 (i.e., high risk of infection) and how much they, and others in their social circle and country, were adhering to the distancing measures. Results: Linear mixed-effects models showed that being a woman, having lower educational attainment, living alone and perceived high vulnerability to Covid-19 were risk factors for poorer wellbeing. Being young (18–25) was associated with lower wellbeing, but longitudinal analyses showed that young people’s wellbeing improved over 3 months. In contrast to widespread views that physical distancing measures negatively affect wellbeing, results showed that following the guidelines was positively associated with wellbeing even for people in high-risk groups. Conclusions: These findings provide an important counterpart to the idea that pandemic containment measures such as physical distancing negatively impacted wellbeing unequivocally. Despite the overall burden of the pandemic on psychosocial wellbeing, social alignment with others can still contribute to positive wellbeing. The pandemic has manifested our propensity to adapt to challenges, particularly highlighting how social alignment can forge resilience.

Research field(s)
Health Sciences, Public Health & Health Services, Public Health

NOMIS Researcher(s)

Published in

January 1, 2021

Missed appointments are estimated to cost the UK National Health Service (NHS) approximately £1 billion annually. Research that leads to a fuller understanding of the types of factors influencing spatial and temporal patterns of these so-called “Did-Not-Attends” (DNAs) is therefore timely. This research articulates the results of a study that uses machine learning approaches to investigate whether these factors are consistent across a range of medical specialities. A predictive model was used to determine the risk-increasing and risk-mitigating factors associated with missing appointments, which were then used to assign a risk score to patients on an appointment-by-appointment basis for each speciality. Results show that the best predictors of DNAs include the patient’s age, appointment history, and the deprivation rank of their area of residence. Findings have been analysed at both a geographical and medical speciality level, and the factors associated with DNAs have been shown to differ in terms of both importance and association. This research has demonstrated how machine learning techniques have real value in informing future intervention policies related to DNAs that can help reduce the burden on the NHS and improve patient care and well-being.

Research field(s)
Health Sciences, Public Health & Health Services, Public Health

NOMIS Researcher(s)

Published in

September 1, 2019

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Research field(s)
Health Sciences, Public Health & Health Services, Public Health