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Approximately 1.5 million individuals in Ontario are supplied by private water wells (private groundwater supplies). Unlike municipal supplies, private well water quality remains unregulated, with owners responsible for testing, treating, and maintaining their own water supplies. The COVID-19 global pandemic and associated non-pharmaceutical interventions (NPIs) have impacted many environmental (e.g., surface water and air quality) and human (e.g., healthcare, transportation) systems over the past 15-months (January 2020 to March 2021). To date, the impact of these interventions on private groundwater systems remains largely unknown. Accordingly, the current study aimed to investigate the impact of a province-wide COVID-19 lockdown (late-March 2020) on health behaviours (i.e., private domestic groundwater sampling) and groundwater quality (via Escherichia coli (E. coli) detection and concentration) in private well water in Ontario, using time-series analyses (seasonal decomposition, interrupted time-series) of a large-spatio-temporal dataset (January 2016 to March 2021; N = 743,200 samples). Findings indicate that lockdown concurred with an immediate (p = 0.015) and sustained (p < 0.001) decrease in sampling rates, equating to approximately 2200 fewer samples received per week post-interruption. Likewise, a slightly decreased E. coli detection rate was observed approximately one month after lockdowns began (p = 0.003), while the proportion of "highly contaminated" samples (i.e., E. coli > 10 CFU/100 mL) was shown to increase within one month (p = 0.02), followed by a sustained decrease for the remainder of the year (May 2020-December 2020). Analyses strongly suggest that COVID-19 interventions resulted in discernible impacts on both well user behaviours and hydrogeological mechanisms. Findings may be used as an evidence-base for assisting policy makers, public health practitioners and private well owners in developing recommendations and mitigation strategies to manage public health risks during extreme and/or unprecedented future events.

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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.

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Extreme Weather Events (EWEs) impose a substantial health and socio-economic burden on exposed populations. Projected impacts on public health, based on increasing EWE frequencies since the 1950s, alongside evidence of human-mediated climatic change represents a growing concern. To date, the impacts of EWEs on mental health remain ambiguous, largely due to the inherent complexities in linking extreme weather phenomena with psychological status. This exploratory investigation provides a new empirical and global perspective on the psychological toll of EWEs by exclusively focusing on psychological morbidity among individuals exposed to such events. Morbidity data collated from a range of existing psychological and well-being measures have been integrated to develop a single ("holistic") metric, namely, psychological impairment. Morbidity, and impairment, were subsequently pooled for key disorders-, specifically PTSD, anxiety and depression. A "composite" (any impairment) post-exposure pooled-prevalence rate of 23% was estimated, with values of 24% calculated for depression and ⁓17% for both PTSD and anxiety. Notably, calculated pooled odds ratios (pOR = 1.9) indicate a high likelihood of any negative psychological outcome (+90%) following EWE exposure. Pooled analyses of reported risk factors (p < 0.05) highlight the pronounced impacts of EWEs among individuals with higher levels of event exposure or experienced stressors (14.5%) and socio-demographic traits traditionally linked to vulnerable sub-populations, including female gender (10%), previous history (i.e., pre-event) of psychological impairment (5.5%), lower socio-economic status (5.5%), and a lower education level (5.2%). Inherent limitations associated with collating mental health data from populations exposed to EWEs, and key knowledge gaps in the field are highlighted. Study findings provide a robust evidence base for developing and implementing public health intervention strategies aimed at ameliorating the psychological impacts of extreme weather among exposed populations.

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BACKGROUND: Ireland frequently reports the highest annual Crude Incidence Rates (CIRs) of cryptosporidiosis in the EU, with national CIRs up to ten times the EU average. Accordingly, the current study sought to examine the spatiotemporal trends associated with this potentially severe protozoan infection. METHODS: Overall, 4509 cases of infection from January 2008 to December 2017 were geo-referenced to a Census Small Area (SA), with an ensemble of geo-statistical approaches including seasonal decomposition, Local Moran's I, and space-time scanning used to elucidate spatiotemporal patterns of infection. RESULTS: One or more confirmed cases were notified in 3413 of 18,641 Census SAs (18.3%), with highest case numbers occurring in the 0-5-year range (n = 2672, 59.3%). Sporadic cases were more likely male (OR 1.4) and rural (OR 2.4), with outbreak-related cases more likely female (OR 1.4) and urban (OR 1.5). Altogether, 55 space-time clusters (≥ 10 confirmed cases) of sporadic infection were detected, with three "high recurrence" regions identified; no large urban conurbations were present within recurrent clusters. CONCLUSIONS: Spatiotemporal analysis represents an important indicator of infection patterns, enabling targeted epidemiological intervention and surveillance. Presented results may also be used to further understand the sources, pathways, receptors, and thus mechanisms of cryptosporidiosis in Ireland.

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OBJECTIVE: This prospective, longitudinal study investigated the contributive role of childbirth pain, perinatal distress and perinatal dissociation to the development of PTSD symptoms following childbirth. PATIENTS AND METHODS: One hundred and seventeen women participated at the study. The first day after delivery they completed a questionnaire to evaluate pain, the peritraumatic distress inventory (PDI) and the peritraumatic dissociative experience questionnaire (PDEQ). Six weeks after birth, they completed the impact of event scale-revised (IES-R) to measure posttraumatic stress symptoms and the Edinburgh Postnatal Depression Scale (EPDS) to assess maternal depression. RESULTS: A multiple regression analysis revealed that only both components of perinatal distress, life-threat perception and dysphoric emotions were significant predictors of posttraumatic stress symptoms. In another multiple regression analysis predicting dysphoric emotions, affective dimension of pain was the only significant predictor. DISCUSSION AND CONCLUSION: Perinatal distress was the best predictor of posttraumatic stress symptoms. Dysphoric emotions were associated with affective dimension of pain, suggesting that women distressed by the childbirth pain would have higher risk to develop posttraumatic stress symptoms.

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AIM: The aim of this study was to highlight a link between childbirth pain and mood disorders in the immediate postpartum. METHOD: We met 43 women at three days postpartum in a maternity unit in Toulouse (France) between January and April 2004. The mean age of the mothers was 30 years (S.D., 4.8 years; range, 18-39 years). Mothers were excluded if they did not speak French, if they had past psychiatric history, and if their baby was premature, ill, or stillborn. Pain was measured using a French version of the McGill pain questionnaire (Melzack, 1975) [Br J Psychiatry 171 (1997) 550-555]. This questionnaire called questionnaire douleur Saint-Antoine (QDSA) is composed of 58 words and 16 classes (Boureau et at., 1984) [Thérapie 39 (1984) 119-129]. Classes 1-9 provide data on the sensory qualities of pain while Classes 10-16 reflect affective characteristics. Blues symptoms was assessed with the French version of the maternity blues questionnaire of Kennerley and Gath (1989) [Br J Psychiatry 145 (1984) 620-625]. For each 28 items women have to decide how much change there is from their usual self, by ticking one choice out of five, from "much less than usual" to "much more than usual". We used the French version of the EPDS (Cox et al., 1987) [Br J Psychiatry 150 (1987) 782-786]. This scale was used to assess the intensity of depressive mood. RESULTS: The results revealed a significant positive correlation between the pain scores and the "maternity blues" questionnaire scores, and between pain scores and EPDS score at three days postpartum. This study shows a stronger association between intensity of postpartum blues and affective aspect of childbirth pain (r=0.48; p<0.05) than between blues and sensorial aspect of pain (r=0.40; p<0.05). The level of depressive mood was found to be associated with affective (r=0.32; p<0.05) but not with sensory qualities of childbirth pain (r=0.28; p<0.05). In a multiple regression analysis predicting intensity of postpartum blues, we entered sensorial and affective scores of QDSA, age, and postpartum blues scores. The subjects to predictors ratio was adequate for multiple regression analysis as it was around the traditional guideline of at least ten participants per predictor [Howell DC. Statistical methods for psychology. Fourth ed. Duxbury press; 1997]. This model accounted for 31% of the variance of intensity of blues (F3,39=5.9, p=0.002). Affective dimension of pain was the only significant predictor (p=0.36, p=0.047). In another multiple regression analysis predicting intensity of depressive mood, we entered the same predictors. This model accounted for 20% of the variance of blues intensity (F3,39=3.26, p=0.03). Age was the only significant predictor (beta=-0.31, p=0.04). These results confirm our hypothesis that intensity of the childbirth pain is associated with mood disorders in the immediate postpartum. Several explications can be advanced. First, maternity blues could be a reaction to stress caused by childbirth pain. Moreover, pain can be felt as a failure for women who prepared themselves to a painless labor. Indeed, the prepared childbirth training pretends to give women the ability to overcome pain through physical and mental training. Thus, their responsibility in coping with the labor is heavy and might make them feel guilty if they fail. In addition, since "the labor itself should be experienced as a positive moment" [Chertock L. Féminité et maternité: étude clinique et expérimentale sur l'accouchement sans douleur. Paris: Desclée de Brouwer; 1996], pain might be at the origin of a great disappointment [Acta Obstet Gynecol Scand 83 (2004) 57-61]. It should be noted that we used the QDSA as a measure of past pain and not as a measure of immediate pain, as Melzack recommended [Pain 1 (1975) 277-299]. CONCLUSION: According to the results of this study, our hypothesis assuming a link between the intensity of labor pain and mood disorders in early postpartum appears to be confirmed. The intensity of postpartum blues is the best predictor of postnatal depression. Hence, knowledge of the risk factors, such as pain, could help to improve the efficiency of detection, and let professionals focus on the psychological impact of labor and especially on post-traumatic stress disorders.

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