Air Pollution Exposure Is Associated With Cognitive Performance: Results From the Einstein Aging Study

Prior research has established that those exposed to higher levels of fine particulate matter (PM1, PM2.5) air pollution have higher levels of accumulated amyloid-beta (Aβ) and tau in frontal cortex at autopsy, higher error rates on cognitive function assessments, and lower scores on memory and both verbal and non-verbal intelligence assessments. We explored the relationship between regional air quality monitoring measures (EPA AirData) and baseline cognitive performance of 312 older adults, from the Einstein Aging Study (EAS, NIA P01AG003949). Participants completed neuropsychological assessments at baseline and each followup wave (i.e., delayed free recall and total recall; Trails A & B, Digit Symbol substitution task (DSST), MoCA). For each participant, based on their zipcode, we computed average PM2.5 exposure at various exposure windows (1-15, 30-60, 60-90, 90-120 days prior to baseline). Adjusting for age, education, and gender across all models, mean of daily particulate matter exposure at various exposure windows (30-60, 60-90 days) was significantly related to performance on the MoCA and Trails A & B, in expected directions (i.e., higher pollution, worse cognitive performance - more error, slower speed). Models with memory performance as the outcome indicated that only distant time horizons were related to memory performance (i.e., 60-90, 90-120 days prior). These findings suggest that particulate matter air pollution likely affects different cognitive domains at different timescales. This methodology cannot address contributions from indoor air quality and mobility - an exposure misclassification likely resulting in significant biases towards the null in the estimation of the effects of air pollution.

Impact of antibiotic (ATB) exposure prior to immune checkpoint inhibitor (ICI) treatment on overall survival (OS): A population-based study.

303 Background: ICIs are a common therapeutic option for many solid tumors. While prior studies have shown that ATB exposure may negatively impact ICI outcomes through gut microbiome changes, many were small studies with heterogeneity in ATB classes and exposure windows. Here, we performed a population level retrospective cohort study to evaluate the impact of ATB exposure prior to ICI on OS. Methods: We used administrative data to identify a cohort of cancer patients > 65 years of age receiving ICIs from June 2012 to October 2018 in Ontario, Canada and deterministically linked with databases to obtain socio-demographic and clinical co-variates and ATB prescription claims. Multivariable cox-proportional hazard models evaluated the impact of ATB exposure both within 1 year and 60 days prior to starting ICI on OS, adjusted for age, gender, body mass index, comorbidities, autoimmune history, hospitalization in the past year and treatment facility level at start of ICI therapy. Results: Among 2737 patients, median age 73; 43% received Nivolumab, 41% Pembrolizumab and 13% Ipilimumab; 53% were lung cancer, 34% melanoma. Median ATB treatment duration for patients receiving ATB within 1 year (59%) and 60 days (19%) prior to ICI were 14 days (SD = 32) and 9 days (SD = 13) respectively. Median OS estimate was 306 days. Any ATB exposure within 1 year prior to ICI was associated with worse OS (aHR = 1.12 95% CI [1.12-1.23] p = 0.03). A non significant dose effect was seen based on weeks of ATB exposure 1 year prior to ICI (aHR = 1.01 per week [1.00-1.02] p = 0.10). ATB class analysis identified fluoroquinolone exposure within 1 year (aHR = 1.26 [1.13-1.40] p < 0.001) and 60 days before ICI (aHR = 1.20 [0.99-1.45] p = 0.06) were associated with worse OS; with a dose effect based on total weeks of exposure over 1 year (aHR = 1.07 per week [1.03-1.11] p < 0.001) and 60 days (aHR = 1.12 per week [1.03-1.23] p = 0.01). Subgroup analysis showed similar results for patients receiving anti-PD1 ICIs, where patients exposed to fluoroquinolone both 1 year (aHR = 1.28 [1.15-1.44] p < 0.001) and 60 days (aHR = 1.19 [0.98-1.44] p = 0.08) before ICIs had poorer OS with dose effects observed based on weeks of fluoroquinolone exposure. Similarly, subgroup analyses based on disease site identified that lung cancer patients exposed to fluoroquinolones 1 year before starting ICIs (aHR = 1.22 [1.06-1.39] p = 0.005) and melanoma patients exposed to fluoroquinolones 60 days before starting ICIs (aHR = 1.66 [1.12-2.47] p = 0.01) had poorer OS. Conclusions: Exposure to ATBs and specifically fluoroquinolones prior to ICI therapy is associated with worse OS. Interventions aimed at altering the gut microbiome may be required to help improve outcomes for patients on ICIs with prior ATB exposure.

Development and implementation of the Ebola Exposure Window Calculator: A tool for Ebola virus disease outbreak field investigations

During an Ebola virus disease (EVD) outbreak, calculating the exposure window of a confirmed case can assist field investigators in identifying the source of infection and establishing chains of transmission. However, field investigators often have difficulty calculating this window. We developed a bilingual (English/French), smartphone-based field application to assist field investigators in determining the exposure window of an EVD case. The calculator only requires the reported date of symptoms onset and the type of symptoms present at onset or the date of death. Prior to the release of this application, there was no similar electronic capability to enable consistent calculation of EVD exposure windows for field investigators. The Democratic Republic of the Congo Ministry of Health endorsed the application and incorporated it into trainings for field staff. Available for Apple and Android devices, the calculator continues to be downloaded even as the eastern DRC outbreak resolved. We rapidly developed and implemented a smartphone application to estimate the exposure window for EVD cases in an outbreak setting

Maternal PM10 Exposure Increases Risk for Spina Bifida: A Population-Based Case-Control Study

Limited studies have focused on the impact of ambient air pollution on spina bifida. A population-based case-control study was conducted in Liaoning Province, China to assess the associations between maternal PM10 exposures in various exposure windows and spina bifida risk. Data on spina bifida cases born between 2010 and 2015 were available from the Maternal and Child Health Certificate Registry of Liaoning Province. Controls were a random sample of healthy livebirths without any birth defects delivered in the selected five cities during 2010–2015. Ambient air monitoring data for PM10 were obtained from 75 monitoring stations in Liaoning Province. The multivariable logistic regression models were established to calculate adjusted odds ratios (OR) and 95% confidence intervals (CI). We further performed sensitivity analyses by using three propensity score methods. A total of 749 spina bifida cases and 7,950 controls were included. After adjusting for potential confounders, spina bifida was associated with a 10 μg/m3 increment in PM10 during the first trimester of pregnancy (adjusted OR = 1.06, 95% CI: 1.00–1.12) and the 3 months before pregnancy (adjusted OR = 1.12, 95% CI: 1.06–1.19). The adjusted ORs in the final model for the highest vs. the lowest quartile were 1.51 (95% CI: 1.04–2.19) for PM10 during the first trimester of pregnancy and 2.01 (95% CI: 1.43–2.81) for PM10 during the 3 months before pregnancy. Positive associations were found between PM10 exposures during the single month exposure windows and spina bifida. Sensitivity analyses based on two propensity score methods largely reported similar positive associations. Our findings support the evidence that maternal PM10 exposure increases the risk of spina bifida in offspring. Further, validation with a prospective design and a more accurate exposure assessment is warranted.

The Association between Preterm Birth and Ambient Air Pollution Exposure in Shiyan, China, 2015–2017

Shortening of the gestational duration has been found associated with ambient air pollution exposure. However, the critical exposure windows of ambient air pollution for gestational duration remain inconsistent, and the association between ambient air pollution and early term births (ETB, 37 to 38 weeks) has rarely been studied relative to preterm births (PTB, 28–37 weeks). A time-series study was conducted in Shiyan, a medium-sized city in China. Birth information was collected from the Shiyan Maternity and Child Health Hospital, and 13,111 pregnant women who gave birth between 2015 and 2017 were included. Data of the concentrations of air pollutants, including PM10, PM2.5, NO2, and SO2 and meteorological data, were collected in the corresponding gestational period. The Cox regression analysis was performed to estimate the relationship between ambient air pollution exposure and the risk of preterm birth after controlling the confounders, including maternal age, education, Gravidity, parity, fetal gender, and delivery mode. Very preterm birth (VPTB, 28–32 weeks) as a subtype of PTB was also incorporated in this study. The risk of VPTB and ETB was positively associated with maternal ambient air pollution exposure, and the correlation of gaseous pollutants was stronger than particulate matter. With respect to exposure windows, the critical trimester of air pollutants for different adverse pregnancy outcomes was different. The exposure windows of PM10, PM2.5, and SO2 for ETB were found in the third trimester, with HRs (hazard ratios) of 1.06 (95%CI: 1.04, 1.09), 1.07 (95%CI: 1.04, 1.11), and 1.28 (95%CI: 1.20, 1.35), respectively. However, for NO2, the second and third trimesters exhibited similar results, the HRs reaching 1.10 (95%CI: 1.03, 6.17) and 1.09 (95%CI: 1.03,1.15), respectively. This study extends and strengthen the evidence for a significant correlation between the ambient air pollution exposure during pregnancy and the risk of not only PTB but, also, ETB. Moreover, our findings suggest that the exposure windows during pregnancy vary with different air pollutants and pregnancy outcomes.

Chainchecker: An application to visualise and explore transmission chains for Ebola virus disease

2020 saw the continuation of the second largest outbreak of Ebola virus disease (EVD) in history. Determining epidemiological links between cases is a key part of outbreak control. However, due to the large quantity of data and subsequent data entry errors, inconsistencies in potential epidemiological links are difficult to identify. We present chainchecker, an online and offline shiny application which visualises, curates and verifies transmission chain data. The application includes the calculation of exposure windows for individual cases of EVD based on user defined incubation periods and user specified symptom profiles. It has an upload function for viral hemorrhagic fever data and utility for additional entries. This data may then be visualised as a transmission tree with inconsistent links highlighted. Finally, there is utility for cluster analysis and the ability to highlight nosocomial transmission. chainchecker is a R shiny application which has an offline version for use with VHF (viral hemorrhagic fever) databases or linelists. The software is available at https://shiny.dide.imperial.ac.uk/chainchecker which is a web-based application that links to the desktop application available for download and the github repository, https://github.com/imperialebola2018/chainchecker.

Cardiovascular and metabolic risk of antipsychotics in children and young adults: a multinational self-controlled case series study

Aims The risk of antipsychotic-associated cardiovascular and metabolic events may differ among countries, and limited real-world evidence has been available comparing the corresponding risks among children and young adults. We, therefore, evaluated the risks of cardiovascular and metabolic events in children and young adults receiving antipsychotics. Methods We conducted a multinational self-controlled case series (SCCS) study and included patients aged 6–30 years old who had both exposure to antipsychotics and study outcomes from four nationwide databases of Taiwan (2004–2012), Korea (2010–2016), Hong Kong (2001–2014) and the UK (1997–2016) that covers a total of approximately 100 million individuals. We investigated three antipsychotics exposure windows (i.e., 90 days pre-exposure, 1–30 days, 30–90 days and 90 + days of exposure). The outcomes were cardiovascular events (stroke, ischaemic heart disease and acute myocardial infarction), or metabolic events (hypertension, type 2 diabetes mellitus and dyslipidaemia). Results We included a total of 48 515 individuals in the SCCS analysis. We found an increased risk of metabolic events only in the risk window with more than 90-day exposure, with a pooled IRR of 1.29 (95% CI 1.20–1.38). The pooled IRR was 0.98 (0.90–1.06) for 1–30 days and 0.88 (0.76–1.02) for 31–90 days. We found no association in any exposure window for cardiovascular events. The pooled IRR was 1.86 (0.74–4.64) for 1–30 days, 1.35 (0.74–2.47) for 31–90 days and 1.29 (0.98–1.70) for 90 + days. Conclusions Long-term exposure to antipsychotics was associated with an increased risk of metabolic events but did not trigger cardiovascular events in children and young adults.