ORCID Profile
0000-0003-2549-8565
Current Organisations
Technological University Dublin
,
University of Southampton
,
Australian National University
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Publisher: Wiley
Date: 15-10-2014
DOI: 10.1111/JPHD.12077
Abstract: The objectives of this study were to describe the reported dentate status and complete denture use of older people with intellectual disability (ID) and compare with those of older people in the general population in Ireland. The first wave of the Intellectual Disability Supplement to The Irish Longitudinal Study on Ageing (IDS-TILDA) study provides opportunity to measure edentulism and complete denture use in a nationally representative s le of older people with ID in Ireland. Data drawn from the first wave of IDS-TILDA were matched using propensity score matching with data from The Irish Longitudinal Study on Ageing (TILDA), a study among older adults in Ireland. All IDS-TILDA variables showing significant association (P < 0.05) with edentulism were entered into a regression model to identify predictors of edentulism. The proportion of the 478 IDS-TILDA participants with no teeth was higher (34.1 percent) than the proportion of participants with no teeth in the 478 matched TILDA participants (14.9 percent). Only age was predictive of edentulism among older adults with ID. Edentulism was prevalent earlier for those with ID. Notably, 61.3 percent of edentulous older people with ID were without dentures. Older people with ID are more likely to be edentulous than those without ID in Ireland and when they lose their teeth, they are unlikely to use dentures. This suggests a need for targeted measures to maintain the teeth of this group and, in the short term, the provision of replacement teeth in this population, where indicated.
Publisher: American Association for Cancer Research (AACR)
Date: 14-06-2023
DOI: 10.1158/1055-9965.23516099.V1
Abstract: Supplementary Figure 1 shows a flowchart of exclusions to obtain the study population, with the number of cases and controls eligible for the analysis.
Publisher: Springer Science and Business Media LLC
Date: 16-07-2021
Publisher: American Association for Cancer Research (AACR)
Date: 14-06-2023
DOI: 10.1158/1055-9965.23516096.V1
Abstract: Supplementary Figure 2 shows two forest plots depicting the association between (A) dietary folate intake and (B) supplemental folate intake and ovarian cancer for women with endometriosis, stratified by potential effect modifiers
Publisher: Elsevier BV
Date: 12-2023
Publisher: American Association for Cancer Research (AACR)
Date: 14-06-2023
DOI: 10.1158/1055-9965.23516093.V1
Abstract: Supplementary Figure 3 shows scatter plots with the genetic association with folate on the x-axis andthe genetic association with ovarian cancer on the y-axis, for (A) women with and (B) without endometriosis. The regression line for the inverse variance weighted Mendelian randomization method is shown.
Publisher: American Association for Cancer Research (AACR)
Date: 14-06-2023
DOI: 10.1158/1055-9965.23516099
Abstract: Supplementary Figure 1 shows a flowchart of exclusions to obtain the study population, with the number of cases and controls eligible for the analysis.
Publisher: Springer Science and Business Media LLC
Date: 25-05-2022
DOI: 10.1057/S41599-022-01205-5
Abstract: Pandemics such as COVID-19 and their induced lockdowns/travel restrictions have a significant impact on people’s lives, especially for lower-income groups who lack savings and rely heavily on mobility to fulfill their daily needs. Taking the COVID-19 pandemic as an ex le, this study analysed the risk of returning to poverty for low-income households in Hubei Province in China as a result of the COVID-19 lockdown. Employing a dataset including information on 78,931 government-identified poor households, three scenarios were analysed in an attempt to identify who is at high risk of returning to poverty, where they are located, and how the various risk factors influence their potential return to poverty. The results showed that the percentage of households at high risk of returning to poverty (falling below the poverty line) increased from 5.6% to 22% due to a 3-month lockdown. This vulnerable group tended to have a single source of income, shorter working hours, and more family members. Towns at high risk (more than 2% of households returning to poverty) doubled (from 27.3% to 46.9%) and were mainly located near railway stations an average decrease of 10–50 km in the distance to the nearest railway station increased the risk from 1.8% to 9%. These findings, which were supported by the representativeness of the s le and a variety of robustness tests, provide new information for policymakers tasked with protecting vulnerable groups at high risk of returning to poverty and alleviating the significant socio-economic consequences of future pandemics.
Publisher: Springer Science and Business Media LLC
Date: 03-06-2022
DOI: 10.1038/S41467-022-30897-1
Abstract: Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches for mitigating the coronavirus disease 2019 (COVID-19) pandemic. However, the real-world impact of NPIs versus vaccination, or a combination of both, on COVID-19 remains uncertain. To address this, we built a Bayesian inference model to assess the changing effect of NPIs and vaccination on reducing COVID-19 transmission, based on a large-scale dataset including epidemiological parameters, virus variants, vaccines, and climate factors in Europe from August 2020 to October 2021. We found that (1) the combined effect of NPIs and vaccination resulted in a 53% (95% confidence interval: 42–62%) reduction in reproduction number by October 2021, whereas NPIs and vaccination reduced the transmission by 35% and 38%, respectively (2) compared with vaccination, the change of NPI effect was less sensitive to emerging variants (3) the relative effect of NPIs declined 12% from May 2021 due to a lower stringency and the introduction of vaccination strategies. Our results demonstrate that NPIs were complementary to vaccination in an effort to reduce COVID-19 transmission, and the relaxation of NPIs might depend on vaccination rates, control targets, and vaccine effectiveness concerning extant and emerging variants.
Publisher: Springer Science and Business Media LLC
Date: 13-06-2021
DOI: 10.1186/S12936-021-03804-0
Abstract: Considerable progress towards controlling malaria has been made in Papua New Guinea through the national malaria control programme’s free distribution of long-lasting insecticidal nets, improved diagnosis with rapid diagnostic tests and improved access to artemisinin combination therapy. Predictive prevalence maps can help to inform targeted interventions and monitor changes in malaria epidemiology over time as control efforts continue. This study aims to compare the predictive performance of prevalence maps generated using Bayesian decision network (BDN) models and multilevel logistic regression models (a type of generalized linear model, GLM) in terms of malaria spatial risk prediction accuracy. Multilevel logistic regression models and BDN models were developed using 2010/2011 malaria prevalence survey data collected from 77 randomly selected villages to determine associations of Plasmodium falciparum and Plasmodium vivax prevalence with precipitation, temperature, elevation, slope (terrain aspect), enhanced vegetation index and distance to the coast. Predictive performance of multilevel logistic regression and BDN models were compared by cross-validation methods. Prevalence of P. falciparum, based on results obtained from GLMs was significantly associated with precipitation during the 3 driest months of the year, June to August (β = 0.015 95% CI = 0.01–0.03), whereas P. vivax infection was associated with elevation (β = − 0.26 95% CI = − 0.38 to − 3.04), precipitation during the 3 driest months of the year (β = 0.01 95% CI = − 0.01–0.02) and slope (β = 0.12 95% CI = 0.05–0.19). Compared with GLM model performance, BDNs showed improved accuracy in prediction of the prevalence of P. falciparum (AUC = 0.49 versus 0.75, respectively) and P. vivax (AUC = 0.56 versus 0.74, respectively) on cross-validation. BDNs provide a more flexible modelling framework than GLMs and may have a better predictive performance when developing malaria prevalence maps due to the multiple interacting factors that drive malaria prevalence in different geographical areas. When developing malaria prevalence maps, BDNs may be particularly useful in predicting prevalence where spatial variation in climate and environmental drivers of malaria transmission exists, as is the case in Papua New Guinea.
Publisher: Springer Science and Business Media LLC
Date: 29-08-2023
DOI: 10.1038/S41467-023-40940-4
Abstract: Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Eimear Cleary.