Geocoding cryptosporidiosis cases in Ireland (2008-2017)-development of a reliable, reproducible, multiphase geocoding methodology

An ecological study of the spatiotemporal dynamics and drivers of domestically acquired campylobacteriosis in Ireland, 2011-2018

In 2021, Campylobacteriosis was the main gastrointestinal disease in the European Union since 2007 according to the European Centre for Disease Prevention and Control. In the Republic of Ireland, the incidence of the disease is particularly high with approximately 3,000 cases per annum, raising significant concerns for national health authorities with an expected increase in the number of cases in the light of climate change. The current study sought to assess the spatio-temporal patterns of campylobacteriosis in the Republic of Ireland using 20,391 cases from January 2011 to December 2018. An ensemble of spatial statistics techniques including seasonal decomposition, spatial clustering and space-time scanning, were used to elucidate the main individual and spatio-temporal characteristics of the disease in the country. Findings revealed that cases from the paediatric age group (i.e., under 5 years old) were more likely to occur in rural areas (aOR: 1.1.27, CI 95% 1.14-1.41) while cases from the intermediate age group (i.e., >5 & <65 years old) were associated with urban living (aOR: 1.30, CI 95% 1.21-1.4). The disease exhibited a peak during Irish summer, with a stronger seasonal signal reported in counties located on the Western part of the country. Infection hotspots were more likely to occur in urban areas, and more particularly on the Southern part of the island and around the main metropolitan areas. Overall, research findings pointed out the influence of local and spatio-temporally specific socio-demographic and environmental risk factors (i.e., cooking habits, local weather, dietary types) therefore highlighting the need for initiating spatio-temporally targeted health management and surveillance strategies.

Modelling COVID-19 severity in the Republic of Ireland using patient co-morbidities, socioeconomic profile and geographic location, February to November 2020

Understanding patient progression from symptomatic COVID-19 infection to a severe outcome represents an important tool for improved diagnoses, surveillance, and triage. A series of models have been developed and validated to elucidate hospitalization, admission to an intensive care unit (ICU) and mortality in patients from the Republic of Ireland. This retrospective cohort study of patients with laboratory-confirmed symptomatic COVID-19 infection included data extracted from national COVID-19 surveillance forms (i.e., age, gender, underlying health conditions, occupation) and geographically-referenced potential predictors (i.e., urban/rural classification, socio-economic profile). Generalised linear models and recursive partitioning and regression trees were used to elucidate COVID-19 progression. The incidence of symptomatic infection over the study-period was 0.96% (n = 47,265), of whom 3781 (8%) required hospitalisation, 615 (1.3%) were admitted to ICU and 1326 (2.8%) died. Models demonstrated an increasingly efficacious fit for predicting hospitalization [AUC 0.816 (95% CI 0.809, 0.822)], admission to ICU [AUC 0.885 (95% CI 0.88 0.89)] and death [AUC of 0.955 (95% CI 0.951 0.959)]. Severe obesity (BMI ≥ 40) was identified as a risk factor across all prognostic models; severely obese patients were substantially more likely to receive ICU treatment [OR 19.630] or die [OR 10.802]. Rural living was associated with an increased risk of hospitalization (OR 1.200 (95% CI 1.143-1.261)]. Urban living was associated with ICU admission [OR 1.533 (95% CI 1.606-1.682)]. Models provide approaches for predicting COVID-19 prognoses, allowing for evidence-based decision-making pertaining to targeted non-pharmaceutical interventions, risk-based vaccination priorities and improved patient triage.

Breakpoint modelling of temporal associations between non-pharmaceutical interventions and symptomatic COVID-19 incidence in the Republic of Ireland

Background

To constrain propagation and mitigate the burden of COVID-19, most countries initiated and continue to implement several non-pharmaceutical interventions (NPIs), including national and regional lockdowns. In the Republic of Ireland, the first national lockdown was decreed on 23^rd of March 2020, followed by a succession of restriction increases and decreases (phases) over the following year. To date, the effects of these interventions remain unclear, and particularly within differing population subsets. The current study sought to assess the impact of individual NPI phases on COVID-19 transmission patterns within delineated population subgroups in the Republic of Ireland.

Methods and findings

Confirmed, anonymised COVID-19 cases occurring between the 29^th of February 2020 and 30^th November 2020 (n = 72,654) were obtained. Segmented modelling via breakpoint regression with multiple turning points was employed to identify structural breaks across sub-populations, including primary/secondary infections, age deciles, urban/commuter/rural areas, patients with underlying health conditions, and socio-demographic profiles. These were subsequently compared with initiation dates of eight overarching NPI phases.

Five distinct breakpoints were identified. The first breakpoint, associated with a decrease in the daily COVID-19 incidence, was reported within 14 days of the first set of restrictions in mid-March 2020 for most population sub-groups. Results suggest that moderately strict NPIs were more effective than the strictest Phase 5 (National Lockdown). Divergences were observed across population sub-groups; lagged response times were observed among populations >80 years, residents of rural/ commuter regions, and cases associated with a below-median deprivation score.

Conclusions

Study findings suggest that many NPIs have been successful in decreasing COVID-19 incidence rates, however the strictest Phase 5 NPI was not. Moreover, NPIs were not equally successful across all sub-populations, with differing response times noted. Future strategies and interventions may need to be increasingly bespoke, based on sub-population profiles and required responses.