European Journal of Epidemiology最新文献

Increased incidence rates for childhood leukaemia have been reported in municipalities close to the nuclear power plant (NPP) Krümmel (Geesthacht, Germany). Methodological challenges arise when analysing this association at ecological level. They include the use of an appropriate reference population, unstable estimates of standardised incidence ratios (SIRs), and the potential role of prevailing winds. The aim of our study is to address these challenges. The German Childhood Cancer Registry provided data on leukaemia in children under 15 years (2004–2019). The German Federal Statistical Office provided the population data. The study region included all municipalities with ≥ 75% surface area within 50 kms from the Krümmel NPP. We calculated SIRs using national and regional reference rates. Smoothed incidence relative rates (IRRs) were calculated and mapped to check for potential patterns associated with prevailing winds. Overall 356 cases of childhood leukaemia were observed in the study region (321 municipalities) during 2004–2019. SIRs based on national reference rates show nearly no difference to those calculated using the regional rates as reference. Increased SIR and IRR were observed in Geesthacht (observed-cases = eight; SIR = 2.29; 95% confidence interval: 0.99–4.51. IRR = 1.80; 95% credibility interval: 0.88–2.79). The analysis of the IRR map does not show patterns associated with prevailing winds. Using a regional population as the reference, we found evidence that there may still be an increased risk for childhood leukaemia in Geesthacht. However, IRR estimates are uncertain and credibility intervals are compatible with the absence of elevated risk. The persistent evidence of risk of childhood leukaemia in Geesthacht warrants further epidemiological surveillance.

While pancreatic adenocarcinoma (PA) incidence is increasing, especially in France, the association between pesticides and PA remains unclear. The aims of this study were to assess the spatiotemporal distribution of the incidence of PA in France between 2011 and 2021 and to determine whether pesticide exposure was associated with higher risk of PA. We employed a disease-mapping and ecological regression approach with medicoadministrative data covering 99% of the French population. Exposure data were drawn from an open purchase database. A pesticide exposure intensity index (PEXI), defined as the logged and scaled median of the ratio quantity of substance over agricultural surface per spatial unit was used for total quantity (total PEXI) and 9 specific substances. The analyses were adjusted for tobacco-induced diseases, deprivation, community medicine accessibility, alcohol-related disease and morbid obesity. A Bayesian hierarchical spatiotemporal model was used to both model the incidence over time and space, and to estimate the risk of PA for pesticide use. We identified 134 102 incident cases of PA between 2011 and 2021. The relative risk of PA was heterogeneous across space with greater risk around Paris, central France and the Mediterranean coast. We observed an association, albeit small, between the total PEXI and PA incidence over the study period (RR: 1.0130; CI95% [1.0057;1.0204]). Sulphur for spraying, mancozeb, and glyphosate showedevidence of an association of the same magnitude. These findings show that new cases of PA occur heterogeneously in space, raising questions about our understanding of PA environmental risk factors. The association with pesticide exposure should be confirmed and underlying mechanisms understood using individual-level studies.

The International Nuclear Workers Study (INWORKS) contributes knowledge on the dose-response association between predominantly low dose, low dose rate occupational exposures to penetrating forms of ionizing radiation and cause-specific mortality. By extending follow-up of 309,932 radiation workers from France (1968–2014), the United Kingdom (1955–2012), and the United States (1944–2016) we increased support for analyses of temporal variation in radiation-cancer mortality associations. Here, we examine whether age at exposure, time since exposure, or attained age separately modify associations between radiation and mortality from all solid cancers, solid cancers excluding lung cancer, lung cancer, and lymphohematopoietic cancers. Multivariable Poisson regression was used to fit general relative rate models that describe modification of the linear excess relative rate per unit organ absorbed dose. Given indication of greater risk per unit dose for solid cancer mortality among workers hired in more recent calendar years, sensitivity analyses considering the impact of year of hire on results were performed. Findings were reasonably compatible with those from previous pooled and country-specific analyses within INWORKS showing temporal patterns of effect measure modification that varied among cancers, with evidence of persistent radiation-associated excess cancer risk decades after exposure, although statistically significant temporal modification of the radiation effect was not observed. Analyses stratified by hire period (< 1958, 1958+) showed temporal patterns that varied; however, these analyses did not suggest that this was due to differences in distribution of these effect measure modifiers by hire year.

Twins suffer a disproportionately higher burden of adverse perinatal outcomes than singletons. However, the degree to which preterm delivery shapes the relationship between abruption and perinatal mortality in twins is unknown. Through causal mediation decomposition, we examine how preterm delivery mediates the effect of abruption on perinatal mortality among twins using the US-matched multiple birth data (1995–2000). We estimated the hazard ratio (HR) from Cox models with gestational age as the timescale. We decomposed the total effect (TE) into counterfactual natural direct (NDE) and natural indirect (NIE) effects. 557,220 matched twin births, 1.3% (n = 7032) resulted in abruption with higher perinatal mortality rates than non-abruption births (143 versus 36 per 1000 births, respectively) and a 4.53-fold (95% confidence interval [CI]: 4.23, 4.82) increased hazard of perinatal mortality. HRs for NDE and NIE were 3.05 (95% CI: 2.84, 3.24) and 1.49 (95% CI: 1.49, 1.47, 1.50), respectively, and the proportion mediated (PM) was 41%. PM increased as the gestational age at delivery decreased. Associations persisted after correction for unmeasured confounders. The best strategies to improve perinatal delivery are delivery when abruption complicates twin pregnancies at term gestations and expectant management (avoiding early preterm delivery), if feasible, when abruption complicates twin pregnancies at preterm gestations.

Plasma proteomics could enhance risk prediction for multiple diseases beyond conventional risk factors or polygenic scores (PS). To assess utility of proteomics for risk prediction of ischemic heart disease (IHD) compared with conventional risk factors and PS in Chinese and European populations. A nested case-cohort study measured plasma levels of 2923 proteins using Olink Explore panel in ~ 4000 Chinese adults (1976 incident IHD cases and 2001 sub-cohort controls). We used conventional and machine learning (Boruta) methods to develop proteomics-based prediction models of IHD, with discrimination assessed using area under the curve (AUC), C-statistics and net reclassification index (NRI). These were compared with conventional risk factors and PS in Chinese and in 37,187 Europeans. Overall, 446 proteins were associated with IHD (false discovery rate < 0.05) in Chinese after adjustment for conventional cardiovascular disease risk factors. Proteomic risk models alone yielded higher C-statistics for IHD than conventional risk factors or PS (0.855 [95%CI 0.841–0.868] vs. 0.845 [0.829–0.860] vs 0.553 [0.528–0.578], respectively). Addition of 446 proteins to PS improved C-statistics to 0.857 (0.843–0.871) and NRI by 109.1%; and addition to conventional risk factors improved C-statistics to 0.868 (0.854–0.882) and NRI by 86.9%. Boruta analysis identified 30 proteins accounting for ~ 90% of improvement in NRI for IHD conferred by all 2923 proteins. Similar proteomic panels yielded comparable improvements in risk prediction of IHD in Europeans. Plasma proteomics improved risk prediction of IHD beyond conventional risk factors and PS and could enhance precision medicine approaches for primary prevention of IHD.

Healthcare workers may have different risk for severe outcomes compared with the general population during diverse crises. This paper introduces the concept of healthcare worker versus population hazard (HPH), the risk of an outcome of interest in active healthcare workers compared with the general population they serve. HPH can be expressed with relative risk (HPH(r)) and absolute risk difference (HPH(a)) metrics. Illustrative examples are drawn from infectious outbreaks, war, and the COVID-19 pandemic on death outcomes. HPH can be extreme for lethal outbreaks (HPH(r) = 30 to 143, HPH(a) = 8 to 91 per 1000 for Ebola deaths in 3 Western African countries in 2013-5), and modestly high in relative terms and very high in absolute terms for protracted, major armed conflicts (HPH(r) = 1.38 and HPH(a) = 10.2 for Syria during 2011–2024). Conversely, healthcare workers had 8-12-fold lower risk than the population they served for pandemic excess deaths (physicians in USA) or COVID-19 deaths (physicians in Ontario, healthcare workers in Finland), while healthcare workers in Indonesia did not have this advantage for COVID-19 deaths versus the general population. HPH is susceptible to data inaccuracies in numbers of at-risk populations and of outcomes of interest. Importantly, inferences about healthcare worker risk can be misleading, if deaths of retired healthcare workers contaminate the risk calculations– as in the case of misleading early perceptions of exaggerated COVID-19 risk for healthcare professionals. HPH can offer useful insights for risk assessment to healthcare professionals, the general public, and policy makers and may be useful to monitor for planning and interventions during crises.

The Jiangsu Biobank for the Prevention and Control of Diabetes (JBPCD) is a community-based prospective cohort study initiated in Jiangsu province, to examine the burden of complications associated with type 2 diabetes mellitus (T2DM). This study aims to better understand the incidence, risk factors, and long-term outcomes of T2DM complications to inform prevention and control strategies. From October 2013 to July 2014, 20,053 T2DM patients (7,862 males and 12,191 females) were recruited from Suzhou city and Huai’an city. Baseline data were collected through questionnaire survey, physical examination and biochemical testing, with blood samples stored in a biobank. The follow-up focused on the incidence and mortality related to T2DM complications, linked to national and local medical datasets. Between August 2019 and October 2020, the repeated assessments were completed for 13,973 participants, including questionnaire, physical examination and repeated blood collection. The study identified 1,479 new cancer cases and 3,324 cardiovascular disease cases, with an overall mortality rate of 25.66 per 1,000 person-years. JBPCD welcomes research collaborations and data access requests via email. Currently, there are no plans to provide cohort data for free public access, but specific proposals for further collaboration are welcome. For further information and collaboration, please email [jswuming@vip.sina.com] and [sc@njmu.edu.cn].

Non-response to surveys is a common problem; even more so during the COVID-19 pandemic with social distancing measures challenging data collection. As respondents often differ from non-respondents, this can introduce bias. The goal of the current study was to see if we can reduce bias and restore sample representativeness in a series of COVID-19 surveys embedded within five UK cohort studies by using the rich data available from previous waves of data collection. Three surveys were conducted during the pandemic across five UK cohorts: National Survey of Health and Development (NSHD, born 1946), 1958 National Child Development Study (NCDS), 1970 British Cohort Study (BCS70), Next Steps (born 1989-90) and Millennium Cohort Study (MCS, born 2000-02). Response rates in the COVID-19 surveys were lower compared to previous waves, especially in the younger cohorts. We identified bias due to systematic non-response in several variables, with more respondents in the most advantaged social class and among those with higher childhood cognitive ability. Making use of the rich data available pre-pandemic in these longitudinal studies, the application of non-response weights and multiple imputation was successful in reducing bias in parental social class and childhood cognitive ability, nearly eliminating it for the former. Surveys embedded within existing cohort studies offer a clear advantage over cross-sectional samples collected during the pandemic in terms of their ability to mitigate selection bias. This will enhance the quality and reliability of future research studying the medium and long-term effects of the pandemic.