Publications in Public Health by NOMIS researchers

Addressing climate change with behavioral science: A global intervention tournament in 63 countries

NOMIS Researcher(s)

February 7, 2024

Details

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.

Research field(s)
Psychology & Cognitive Sciences, Public Health

DOI
10.1126/sciadv.adj5778

New Caregiver Diagnoses of Severe Depression and Child Asthma Controller Medication Adherence

NOMIS Researcher(s)

Published in International Journal of Environmental Research and Public Health

May 29, 2023

Details

Background and Objectives: Children with asthma who have depressed caregivers are known to be less adherent to medication regimes. However, it is less clear how adherence responds to a caregiver’s new diagnosis of severe depression or whether there is a similar relationship with other serious caregiver diagnoses. The hypothesis is that adherence worsens both with new diagnoses of depression and possibly with new diagnoses of other serious conditions. Methods: This study follows a cohort of 341,444 continuously insured children with asthma before and after a caregiver’s new diagnosis of severe depression or another serious health condition. The effect of a new depression diagnosis on a child’s medication adherence is compared to the effect of new diagnoses of other common caregiver chronic conditions including diabetes, cancer, congestive heart failure, coronary artery disease, and chronic obstructive pulmonary disease. Results: Results show that children’s medication adherence declines following a caregiver’s new diagnosis of severe depression, but that it also declines following a caregiver’s new diagnosis of diabetes. There is no association with new diagnoses of the other caregiver chronic conditions examined. Conclusions: Children whose caregivers have a new diagnosis of depression or diabetes may be at increased risk of deterioration in their medication adherence. These caregivers may benefit from additional support and follow-up. The relationship between caregivers’ health and children’s medication adherence is complex and deserves further study.

Research field(s)
Economic & Social Sciences, Pediatrics, Public Health

DOI
10.3390/ijerph20115986

Social alignment matters: Following pandemic guidelines is associated with better wellbeing

NOMIS Researcher(s)

Published in BMC Public Health

December 1, 2022

Details

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

DOI
10.1186/s12889-022-13130-y

Trajectories in mental health and socio-spatial conditions in a time of economic recovery and austerity: A longitudinal study in England 2011–17

NOMIS Researcher(s)

Published in Social Science and Medicine

February 1, 2021

Details

This paper examines trends in mental health among adults in England during the period of economic recovery and austerity following the 2008 ‘great recession’. We report analysis of data on 17,212 individuals living in England, from the longitudinal Understanding Society Survey (USS). We examined how individual’s self-reported mental health over time (2011–2017), related to their changing socio-geographical status. Self-reported mental health is reported in the USS using version 2 of the SF12 Mental Component Summary. Trends in this score (across 5 observations per subject) were categorised into Mental Health Trajectory Groups (MHTGs) using Group Based Trajectory Modelling. We used maximum-likelihood multinomial logit models to estimate for individuals the relative likelihood of belonging to different Mental Health Trajectory categories as compared with a ‘base’ category, for whom mental health was good and stable throughout the period. We focus on likelihood of belonging to a group showing ‘declining’ mental health. Predictor variables included individuals’ attributes and area conditions in their places of residence (including Office of National Statistics indicators of local employment deprivation and data on average income loss within districts due to welfare benefit reforms, published by the Centre of Regional Economic and Social Research at Sheffield Hallam University, UK). Our results emphasise the multiple socio-geographical ‘determinants’ likely to be operating on individual mental health. Declining mental health was associated both with conditions at the start of the study period and with social and socio-geographical mobility by the end of the study period. Risks of declining mental health were significantly greater for more deprived individuals and also (controlling for individual attributes) among those living in English neighbourhoods that were already economically disadvantaged at the beginning of the ‘great recession’ and located in districts where average incomes were most severely impacted by the effects of governmental austerity programmes on welfare benefits.

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

DOI
10.1016/j.socscimed.2020.113654

Using machine learning tools to investigate factors associated with trends in ‘no-shows’ in outpatient appointments

NOMIS Researcher(s)

Published in Health and Place

January 1, 2021

Details

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

DOI
10.1016/j.healthplace.2020.102496

Recession, local employment trends and change in self-reported health of individuals: A longitudinal study in England and Wales during the ‘great recession’

NOMIS Researcher(s)

Published in Health and Place

September 1, 2019

Details

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

DOI
10.1016/j.healthplace.2019.102174

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