Exposure-response Curves Research Articles

Fine Particle Exposure and Clinical Aggravation in Neurodegenerative Diseases in New York State.

Background:Adult-onset neurodegenerative diseases affect millions and negatively impact health care systems worldwide. Evidence suggests that air pollution may contribute to aggravation of neurodegeneration, but studies have been limited.Objective:We examined the potential association between long-term exposure to particulate matter in aerodynamic diameter [fine particulate matter ()] and disease aggravation in Alzheimer’s (AD) and Parkinson’s (PD) diseases and amyotrophic lateral sclerosis (ALS), using first hospitalization as a surrogate of clinical aggravation.Methods:We used data from the New York Department of Health Statewide Planning and Research Cooperative System (SPARCS 2000–2014) to construct annual county counts of first hospitalizations with a diagnosis of AD, PD, or ALS (total, urbanicity-, sex-, and age-stratified). We used annual concentrations estimated by a prediction model at a resolution, which we aggregated to population-weighted county averages to assign exposure to cases based on county of residence. We used outcome-specific mixed quasi-Poisson models with county-specific random intercepts to estimate rate ratios (RRs) for a 1-y exposure. We allowed for nonlinear exposure–outcome relationships using penalized splines and accounted for potential confounders.Results:We found a positive nonlinear association that plateaued above (, 95% CI: 1.04, 1.14 for a increase from 8.1 to ). We also found a linear positive association (, 95% CI: 1.01, 1.09 per increase), and suggestive evidence of an association with AD. We found effect modification by age for PD and ALS with a stronger positive association in patients of age but found insufficient evidence of effect modification by sex or urbanization level for any of the outcomes.Conclusion:Our findings suggest that annual increase in county-level concentrations may contribute to clinical aggravation of PD and ALS. Importantly, the average annual concentration in our study was , below the current American national standards, suggesting the standards may not adequately protect the aging population. https://doi.org/10.1289/EHP7425

Effects of short-term exposure to ambient airborne pollutants on COPD-related mortality among the elderly residents of Chengdu city in Southwest China

BackgroundChronic obstructive pulmonary disease (COPD) has become a severe global burden in terms of both health and the economy. Few studies, however, have thoroughly assessed the influence of air pollution on COPD-related mortality among elderly people in developing areas in the hinterland of southwestern China. This study is the first to examine the association between short-term exposure to ambient airborne pollutants and COPD-related mortality among elderly people in the central Sichuan Basin of southwestern China.MethodsData on COPD-related mortality among elderly people aged 60 and older were obtained from the Population Death Information Registration and Management System (PDIRMS). Data on airborne pollutants comprised of particulate matter < 2.5 μm in aerodynamic diameter (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) were derived from 23 municipal environmental monitoring sites. Data on weather conditions, including daily mean temperature and relative humidity, were obtained from the Chengdu Meteorological Bureau. All data were collected from January 1, 2015, to December 31, 2018. A quasi-Poisson general additive model (GAM) was utilized to assess the effects of short-term exposure to airborne pollutants on COPD-related mortality among elderly people.ResultsA total of 61,058 COPD-related deaths of people aged 60 and older were obtained. Controlling the influences of daily temperature and relative humidity, interquartile range (IQR) concentration increases of PM2.5 (43 μg/m3), SO2 (8 μg/m3), NO2 (18 μg/m3), CO (0.4 mg/m3), and O3 (78 μg/m3) were associated with 2.7% (95% CI 1.0–4.4%), 4.3% (95% CI 2.1–6.4%), 3.6% (95% CI 1.7–5.6%), 2.7% (95% CI 0.6–4.8%), and 7.4% (95% CI 3.6–11.3%) increases in COPD-related mortality in people aged 60 and older, respectively. The exposure-response curves between each pollutant and the log-relative risk of COPD-related mortality exhibited linear relationships. Statistically significant differences in the associations between pollutants and COPD-related mortality were not observed among sociodemographic factors including age, gender, and marital status. The effects of O3 remained steady after adjusting for PM2.5, SO2, NO2, and CO each time in the two-pollutant models.ConclusionsIncreased concentrations of ambient airborne pollutants composed of PM2.5, SO2, NO2, O3, and CO were significantly and positively associated with COPD-related mortality in the central Sichuan Basin, which is located in the hinterland of southwestern China. The adverse effects of O3 were stable, a finding that should receive more attention.

Application of the noise annoyance equivalents model for aircraft, rail and road traffic noise to self-reported sleep disturbance

Sleep disturbances caused by noise exposure are the most relevant outcome measured in disability adjusted life years (DALYs). In analogy to the combined effects of noise from multiple sources on annoyance, these are also of interest for sleep disorders. The study “Overall Noise Assessment” Innsbruck examined data from 1031 personal interviews. Aircraft, rail and road noise were correlated. To choose participants combined exposures were clustered into small, medium and severely affected living situations. The effect of demographic features, noise sensitivity, access to a silent façade and other factors on self-reported sleep disturbances was investigated applying bivariate analyses. Exposure-response curves and their 95% confidence intervals with cut-off values of 72% for “highly disturbed sleep” were generated and the results were discussed in comparison to recently published curves. Using source-specific exposure-response relationships, an overall model for evaluating sleep disorders was developed based on the “annoyance equivalents model”. The “total sleep disturbance response” shows an accumulative outcome for each source measured. A protective effect of road traffic noise as background noise for air and railroad sources could not be proven. Both the “Total Sleep Disruption Assessment Model” and the “Dominant Source Model” have a Spearman-Rho of 0.3 and are therefore suitable for use in noise assessment.

Impacts of heat stress conditions on mortality from respiratory and cardiovascular diseases in Brazil

This study assesses the risk of exposure to heat stress conditions and their potential impact on mortality from cardiovascular and respiratory diseases in Brazilian capital cities for warming levels of 1.5 °C, 2.0 °C and 4.0 °C in the RCP8.5 scenario. The risk of exposure and the impact of heat stress conditions on mortality were measured by the Wet Bulb Globe Temperature (WBGT) index. The impact on health was estimated by applying exposure-response curves between WBGT and health outcomes in the projections. The potential impact on mortality was measured by attributable fraction of mortality due to heat stress. The results showed an increase in heat stress conditions for Brazil, especially in the Northern and Center-Western regions. The estimated curves showed an association between the WBGT and mortality by cardiovascular and respiratory diseases in Brazil, with an upward impact trend, according to the levels of warming and heterogeneous results among the capitals.

Particulate matter pollution and risk of outpatient visits for psychological diseases in Nanjing, China

Exposure to air pollution has been associated with increased risk for a range of adverse mental health conditions. Less is known about whether air pollution is also associated with increases in the utilization of mental health services, especially outpatient mental health service utilization. This study aimed to examine the association between the number of daily outpatient visits at the psychological disease departments of two major hospitals (PSYC) and daily average concentrations of PM2.5 and PM10 in a heavily polluted city in China, Nanjing, from 2013/7/1 to 2019/2/28, using generalized additive models with a quasi-Poisson regression. Results showed that each 10 μg/m3 increase in PM2.5 concentration on lag0 day was associated with a 0.40% increase (95% CI: 0.07-0.72) in PSYC visits, and each 10 μg/m3 increase in PM10 concentration on the same day a 0.31% increase (95% CI: 0.09-0.54) in PSYC visits. Exposure-response curves suggested linear relationships between PM concentration and daily PSYC outpatient visits, without evidence of a threshold. Associations remained positive, but were non-significant, with adjustment for co-pollutants, SO2, NO2 and CO. Significantly larger effects were found for older and male participants, vs. their counterparts. These findings add to the growing literature linking air pollution to mental health service utilization, demonstrating the critical need for both air pollution mitigation measures and increased capacity of the mental health system in China.

A hierarchical model for estimating the exposure-response curve by combining multiple studies of acute lower respiratory infections in children and household fine particulate matter air pollution.

We present hierarchical models for estimating long-term exposure concentrations and estimating a common exposure-response curve. The exposure concentration model combines temporally sparse, clustered longitudinal observations to estimate household-specific long-term average concentrations. The exposure-response model provides a flexible, semiparametric estimate of the exposure-response relationship while accommodating heterogeneous clustered data from multiple studies. We apply these models to three studies of fine particulate matter (PM2.5) and ALRIs in children in Nepal: a case-control study in Bhaktapur, a stepped-wedge trial in Sarlahi, and a parallel trial in Sarlahi. For each study, we estimate household-level long-term PM2.5 concentrations. We apply the exposure-response model separately to each study and jointly to the pooled data. The estimated long-term PM2.5 concentrations were lower for households using electric and gas fuel sources compared with households using biomass fuel. The exposure-response curve shows an estimated ALRI odds ratio of 3.39 (95% credible interval = 1.89, 6.10) comparing PM2.5 concentrations of 50 and 150 μg/m3 and a flattening of the curve for higher concentrations. These flexible models can accommodate additional studies and be applied to other exposures and outcomes. The studies from Nepal provides evidence of a nonlinear exposure-response curve that flattens at higher concentrations.

How protective is China’s National Ambient Air Quality Standards on short-term PM2.5? Findings from blood pressure measurements of 1 million adults

Although short-term exposure to fine particulate matter (PM2.5) air pollution has been shown to induce elevated blood pressure (BP), limited evidence is available regarding the association between ambient PM2.5 and BP levels in nationwide China and how the association may change. This study sought to explore acute BP changes with exposure to PM2.5 at levels below China’s current National Ambient Air Quality Standards (NAAQS). Based on a spatiotemporal study of over 1 million adults, we linked BP measurements to daily estimates of PM2.5 from multiple sources (i.e. in situ observations, satellite measurements of aerosol and numeric simulations of air quality model) after adjusting for several individual-level covariates and further conduced the below-criteria models by restricting the analyses within subsets of individuals with short-term PM2.5 exposure below 75 μg m−3 (i.e. NAAQS of 24 h PM2.5 in China). We further explored variations in BP-PM2.5 associations by pollution level and for different demographic groups. With full adjustments, a 10 μg m−3 increase in PM2.5 was statistically significantly associated with a 0.049 mmHg (95% confidence interval, CI: 0.041, 0.057) increase in systolic BP, a 0.022 mmHg (95% CI: 0.017, 0.027) increase in diastolic BP and a 0.77% (95% CI: 0.62%, 0.92%) increased risk of hypertension (HPN). For both BP and HPN, the exposure-response curves were linear, with no threshold effects, at the low-concentration ends and sublinear at the high-concentration ends. Below the current NAAQS, the associations in population level remained statistically significant and were even stronger. A 10 μg m−3 increase in below-NAAQS PM2.5 was associated with a 1.95% (95% CI: 1.44%, 2.47%) increased risk of HPN. Specific subpopulations were more susceptible to PM2.5 exposure. These findings can help support decisions by policymakers to revise related environmental regulations to protect public health.

Association of aerosols, trace gases and black carbon with mortality in an urban pollution hotspot over central Indo-Gangetic Plain

The short term effect of multiple air pollutants e.g. aerosols (black carbon, BC; PM2.5 and PM10) and trace gases (NO2, SO2, and O3) on all-cause mortality was systematically investigated in a typical urban pollution hotspot over central Indo-Gangetic Plain (IGP). To our knowledge, this would be the first report of mortality estimates for exposure to BC aerosols and multiple trace gases over South Asia. Daily all-cause mortality and ambient air quality were analyzed from 2009 to 2016 following a semiparametric quasi-Poisson regression model adjusting mean temperature (Tmean), relative humidity (RH), and long term time trend (Time) as potential confounders. Single pollutant model clearly established the significant impact of BC aerosols (against 10-unit increase in pollutant; 4.95%, 95% CI: 2.16–7.74), NO2 (2.38%, 95% CI: 0.88–3.87%) and PM2.5 exposure (1.06%, 95% CI: 0.45–1.66%) on mortality. The inclusion of co-pollutants in the multi-pollutant model increased the individual mortality risks for BC aerosols (7.3%). Mortality estimates were further stratified considering different effect modifiers viz. sex, age, place of death, and season. Almost in all the cases statistically insignificant differences in effect modification were noted for all the pollutants except PM10. We also explored a distributed lag nonlinear model to estimate the lag effect and all the pollutants showed significant lag up to 3 days while BC showed lag effect up to 5 days. The exposure-response curves for individual air pollutants were mostly linear, while a considerable increase in mortality was noted for an exposure >15 μg m−3 for BC aerosols and >60 μg m−3 for PM2.5. The effect estimates of air pollutants during haze and no-haze days were also defined. During haze days, mortality rose to 6.11% and 3.06% for each 10-unit increase in BC and NO2 exposure, respectively. Significant effect of BC aerosol exposure on human mortality was established which reaffirms its inclusion as a potential health regulator for epidemiological studies.

Evaluation of publicly available in vitro drug sensitivity models for ovarian and uterine cancer

ObjectivePublicly available data on drug sensitivity for cancer cell lines have been curated into a single, integrated database, PharmacoDB. The contributing datasets report modeled estimates of drug effect from high throughput assays. These databases have been informative for developing new broad insights, but the reliability of these data specifically for drugs used to treat ovarian and uterine cancers in related cell lines has not been reported. MethodsIn vitro viability assays were performed on A2780, OVCAR-3, TOV-21G, and RL95–2 cells with nine drugs to produce high resolution exposure-response curves. Lab generated data were compared to publicly available datasets by IC20, IC50, and IC80 values, and the area between the logarithmic logistic regression curves. ResultsFor exposure-response curve comparisons with clinically indicated drugs between lab generated and publicly available data, the majority had area-between-curves less than 20%, indicating similarity. However, 15 out of 40 of these dataset curves were incomplete as indicated by the lack of, or extrapolated, IC50 value. The common ovarian and uterine cancer drug, carboplatin, exemplified this incomplete status as all of the available dataset curves were incomplete and therefore non-informative. ConclusionsFor gynecologic malignancy cell line models, experimental drug sensitivity data is comparable to the available data in PharmacoDB when exposure-response curves are complete. Incomplete exposure-response curves due to incomplete concentration ranges tested and related extrapolation of IC values can mislead individual drug/cell line pair data for downstream applications.

Comparison of heat-illness associations estimated with different temperature metrics in the Australian Capital Territory, 2006-2016.

While the associations of heat with health outcomes is well researched, there is less consensus on the measures used to define heat exposure and the short-term and delayed impacts of different temperature metrics on health outcomes. We investigate the nonlinear and short-term relationship of three temperature metrics andreported incidence of three gastrointestinal illnesses: salmonellosis, campylobacteriosis and cryptosporidiosis in the Australian Capital Territory (ACT). We also examine the nonlinear association of these illnesses with extreme heat (5th, 75th, 90th percentile of all heat measures). Generalized linear models with Poisson regression accounting for overdispersion, seasonal and long-term trend, weekly number of outbreaks and rainfall were developed for mean and maximum weekly temperature and the heat stress index (EHIaccl). Bacterial illnesses (salmonellosis and campylobacteriosis) showed an overall positive association with extreme heat (75th and 90th percentile of all three heat measures) and an inverse association with low temperature (5th percentile).The shape of the exposure-response curve across a range of temperatures and the lagged effects varied for each disease.Modelling the short-term and delayed effects of heat using different metrics across a range ofillnesses can help identify the most appropriate measure to inform local public health intervention planning for heat-related emergencies.

A Hierarchical Model for Estimating Exposure-Response Curves from Multiple Studies of Household Air Pollution

Estimating flexible exposure-response curves is critical for quantifying the impacts of environmental exposures on health. In intervention trials targeting household air pollution, the limited range of concentrations and small sample sizes of individual studies are important factors that may be limiting their statistical power. Pooling data from multiple studies provides an opportunity to increase power for estimating the exposure-response relationship.We present hierarchical approaches to modeling exposure concentrations and combining data from multiple studies to estimate a common exposure-response curve. The models are designed to accommodate features relevant to many environmental epidemiology studies: exposure measurements and outcome ascertainments that are irregularly spaced across time, clustered by individual and groups of individuals, and impacted by seasonal trends. The exposure concentration model additionally incorporates shrinkage to reduce error from high-variance observations. The exposure-response curve model provides a flexible, semi-parametric estimate of the joint exposure-response relationship across multiple studies from different times and contexts.We demonstrate this modeling approach using data from three studies of cookstoves and acute lower respiratory infections in children in Nepal.

Ambient Carbon Monoxide and Daily Mortality: A Global Time-Series Study in 337 cities

Background Epidemiological evidence on the short-term association between ambient carbon monoxide (CO) and mortality is inconclusive and limited to single cities, regions, or countries. Generalization of results from previous studies is challenged by potential publication bias and different modeling approaches. We, therefore, assessed the associations between short-term exposure to CO and daily mortality in a multi-city multi-country setting.Methods We collected daily data on air pollution, meteorology, and total mortality from 337 cities in 18 countries from the Multi-Country Multi-City Collaborative Research Network. We first estimated city-specific associations using confounder-adjusted generalized additive models with a quasi-Poisson distribution; then, we pooled the estimates using a random-effects multilevel meta-analytical model. We also assessed the overall shape of the exposure-response curve and evaluated the existence of a threshold value.Results Overall, a 1 mg/m3 increase in the average CO concentration of the previous day was associated with a percent increase of 0.91% (95% confidence interval [95% CI]: 0.32%, 1.50%) in daily total mortality. The pooled exposure-response curve showed a continuously elevated mortality risk with increasing CO concentrations, suggesting no evidence for a threshold. Larger mortality risk estimates were found at daily CO levels below 1 mg/m3 and persisted at concentrations as low as below 0.2 mg/m3.Conclusions This international study is, to our knowledge, by far the largest epidemiological study on short-term CO-related mortality. We found significant associations between short-term exposure to ambient CO and daily mortality, even at levels well below the current global and national air quality guidelines.The study was conducted on behalf of the MCC Collaborative Research Network

Short-term exposure to indoor PM2.5 on depressive symptoms: Ko-CHENS Mom study

Background/Aim The results of previous studies evaluating the associations between short-term PM exposure and depressive symptoms were inconsistent. However, no studies have evaluated on the relationship between indoor particulate matters and depressive symptoms in housewives. Most housewives spend a lot of time at home and do housework, which can generate indoor particulate matter, such as cleaning and cooking. Therefore, we investigated the impact of short-term exposure to indoor PM on depressive symptoms among Korean housewives in Ko-CHENS Mom study. Methods We recruited a total of 306 housewives (from January 2018 to February 2020) in Seoul and Ulsan, Republic of Korea. The indoor PM concentrations of each participant were measured by sensors for consecutive 7-days before completing health questionnaire. In addition, indoor PM concentration, two days before completing health questionnaire, was measured by gravimetric analysis. The Korean version of Center for Epidemiologic Studies Depression Scale (KCES-D) was used to evaluate depressive symptom during the study. The CES-D cut-off score was 16 or higher. We analyzed the association between indoor PM and CES-D by logistic regression adjusting for BMI, income level, education level, regions, smoking status, job status, time of stay at home and meteorological data (daily mean temperature and relative humidity). Results A total of 138 housewives included in this study. The average (SD) indoor PM2.5 and PM10 concentrations was 27.6 (17.4) μg/m3 and 43.8 (23.9) μg/m3. 26 (18.8%) out of total housewives were a CES-D score of 16 or higher. The 10 μg/m3 increase in concentration of indoor PM2.5 (OR=1.42, 95% CI: 1.06, 1.88) measured by gravimetric analysis was statistically associated with in CES-D. Exposure-response curve was used to capture linear relationship between indoor PM and depressive symptom. Conclusion Our study has found evidence that short-term exposure to indoor PM2.5 levels is related to the depressive symptoms in housewives.

Hot under the collar: The association between daily temperature and temperature deviations with violent criminal behavior

Background: Criminal behavior is a significant public health threat, serving as both cause and consequence of violent actions. Violent assaults constitute over 1 million emergency room visits annually and represent the 8th leading cause of non-fatal injuries. However, the underlying environmental causes of violent behavior are still poorly understood. Our study investigates the role that daily temperature and temperature deviation plays on the risk of crime incidence. We further consider the ongoing debate as to whether temperature affects crime through a temperature-aggression relationship or by modifying routine activities.Methods: We used a hierarchical time series model to estimate the risk of criminal behavior associated with daily temperature and daily departure from normal temperatures in 436 U.S. counties. Departure from normal temperatures denote the deviation of daily temperature from the expected conditions. Daily crime incidence was acquired from the FBI’s National Incidence Based Reporting System and daily weather data from gridMET. We explored seasonality of crime risks and estimated linear and non-linear exposure-response relationships.Results: We found each 10°C increase in daily mean temperature increased the risk of violent crime by 11.92% (95% CI: 11.57, 12.27) and each 10°C increase in departure from normal temperatures increased violent crime risk by 10.37% (95% CI: 10.05, 10.69). The exposure response curve revealed increasing risk with absolute daily temperature until flattening off above 25°C. For departure from normal temperatures, we observed a strong linear relationship with crime, and significantly higher risk during fall and winter seasons. Similar overall effects, but with lower risk, were observed for non-violent property crime.Conclusions: Our results suggest that short-term changes in daily temperature may be associated with greater risk of criminal behavior. Based on the exposure-response curves and seasonal trends, our findings provide evidence of a routine activities pathway, rather than the temperature-aggression hypothesis.

Short-term exposure to ambient particulate matter and hospital outpatient visits for respiratory diseases among children: A five-city time-series study in China

Background: Evidence concerning the association between ambient particulate matter (PM) and outpatient visits for respiratory diseases is quite limited, especially in children at a multicity level. Methods: A time-series study was performed in five Chinese cities from 2013 to 2018 among children aged 0-14 years. The city-specific effects of PM were estimated for time lags of zero up to seven days using the overdispersed generalized additive models after adjusting for time trends, meteorological variables, day of the week and holidays. Meta-analyses using random effects models were applied to pool the effect estimates in the five cities. The exposure-response relationship curves were also evaluated. Results: Exposure to PM was dose-responsive related to an increased respiratory outpatient visits among children. The cumulative lag effects of PM showed higher estimates than that of single-day exposure, and the lag07 (moving averages for the present day and the previous 7 days) concentrations of fine particles (PM2.5) and inhalable particles (PM10) produced the largest estimates. Each 10 μg/m3 increment in PM2.5 and PM10 at the lag07 was associated with a 1.39% (95% CI: 0.36%, 2.43%) and 1.11% (95% CI: 0.38%, 1.84%) increase in outpatient visits for all respiratory diseases among children, respectively, and the associations remained statistically significant at levels below the current Chinese Ambient Air Quality Standards (CAAQS). The effect estimates for acute lower respiratory infections (ALRIs) tended to be higher. Children 7-14 years old and 4-6 years old are the most susceptible groups for acute upper respiratory infections (AURIs) and ALRIs, respectively, but no sex differences were observed. Moreover, the effects of PM were stronger in the transition season of Beijing and Xining, whereas stronger in the cold season of the other 3 cities. Conclusions: Ambient PM concentrations, even at levels below the CAAQS, was dose-responsive associated with increased respiratory outpatient visits among children.

Short term effects of air pollutants on hospital admissions for respiratory diseases among children: A multi-city time-series study in China

Evidence concerning short-term acute association between air pollutants and hospital admissions for respiratory diseases among children in a multi-city setting was quite limited. We conducted a time-series analysis to evaluate the association of six common air pollutants with hospital admissions for respiratory diseases among children aged 0-14 years in 4 cities (Guangzhou, Shanghai, Wuhan and Xining), China during 2013-2018. We used generalized additive models incorporating penalized smoothing splines and random-effect meta-analysis to calculate city-specific and pooled estimates, respectively. The exposure-response relationship curves were fitted using the cubic spline regression. Subgroup analyses by gender, age, season and disease subtype were also performed. A total of 183,036 respiratory diseases hospitalizations were recorded during the study period, and 94.1% of the cases were acute respiratory infections. Overall, we observed that increased levels of air pollutants except O3, were significantly associated with increased hospital admissions for respiratory disease. Each 10μg/m3 increase in PM2.5, SO2 and NO2 at lag 07, PM10 at lag 03 and per 1mg/m3 increase in CO at lag 01 corresponded to increments of 1.19%, 3.58%, 2.23%, 0.51% and 6.10% in total hospitalizations, respectively. Generally, exposure-response relationships of PM2.5 and SO2 in Guangzhou, SO2, NO2 and CO in Wuhan, as well as SO2 and NO2 in Xining with respiratory disease hospitalizations were also found. Moreover, the adverse effects of these pollutants apart from PM2.5 in certain cities remained significant even at exposure levels below the current Chinese Ambient Air Quality Standards (CAAQS) Grade II. Children aged 4-14 years appeared to be more vulnerable to the adverse effects of PM2.5, SO2 and NO2. Furthermore, with the exception of O3, the associations were stronger in cold season than in warm season. Short-term exposure to PM2.5, SO2, NO2 and CO were associated, in dose-responsive manners, with increased risks of hospitalizations for childhood respiratory diseases, and adverse effects of air pollutants except PM2.5 held even at exposure levels below the current CAAQS Grade II in certain cities.

Mechanistic prediction of first-in-human dose for bispecific CD3/EpCAM T-cell engager antibody M701, using an integrated PK/PD modeling method

AimM701 is a bispecific CD3/EpCAM T-cell engager antibody to treat malignant ascites. This study aimed to predict in vivo exposure-cytotoxicity relationship and human pharmacokinetics (PK) characteristics of M701, as well as to design optimal starting dose and effective dose for M701 first-in-human (FIH) study. MethodMechanistic in vitro PK/PD model was firstly developed based on in vitro data of M701’s cytotoxicity and binding affinities with targeting receptors. The cell killing effect of M701 in vitro was driven by tri-molecular synapse, which formed by binding drug to both CD3 receptor on T cells and EpCAM receptor on tumor cells. Human exposure-response (E-R) curve in ascites was estimated using the same model structure with clinical systemic model parameters. Human PK was predicted by allometrically scaling monkey PK data, which was characterized using a two compartment model. Human PK model was integrated into in vivo synapse-based cell killing model to provide human PK/PD characteristics. Integrated human PK/PD model was applied in FIH dose design. Clinical starting dose and effective dose were suggested as the simulated drug concentration in human ascites that achieved the estimated in vivo minimally anticipated biological effect level (MABEL) and pharmacologically active level. Other approaches including PK-driven and receptor occupancy calculation were also employed in this study to verify the starting dose prediction. ResultsIn vitro M701 cytotoxicity curves under 24, 48, 72 h incubations were well captured by mechanistic synapse-based cell killing model. Human E-R curve in ascites was obtained based on in vitro model structure and clinical systematic parameters. We defined 10~20% and 80% of maximum cytotoxicity effect as in vivo MABEL and pharmacologically active level. Human E-R curve indicated in vivo EC10, EC20 and EC80 were 0.56, 1.26 and 31.6 ng/mL. For human PK model, clearance (CL, CLd), distribution volumes (Vc, Vp) and absorption rate were allometrically scaled using exponent of 0.9, 1 and -0.25. Predicted clearance and volume were 0.53- and 1.19-fold of observed data. Simulated average ascites M701 concentrations (calculated as Cave_ ascites = AUCτ/τ) were 0.81 and 32.5 ng/mL under dose of 5 and 200 μg within 2-hour i.p. infusion. By integrating human E-R curve and the simulated PK profile in ascites, we suggested 5 and 200 μg within 2-hour i.p. infusion as MABEL dose and pharmacologically active dose (PAD) for M701 FIH study. PK-driven approach predicted a starting dose of 5 μg, which was comparable to that predicted via PK/PD-driven approach. ConclusionsThis study predicted human ascites PK and E-R curve by integrating human PK model into in vivo synapse-based cell killing model. Optimal clinical MABEL dose and PAD of bispecific T cell engager antibody M701 were suggested based on current integrated PK/PD approach.

Association between short-term exposure to ambient air pollution and hospital admissions for transient ischemic attacks in Beijing, China

Numerous studies have examined the associations between air pollution and stroke. However, little is known about the associations between air pollution and transient ischemic attack (TIA). In this study, we aimed to conduct a time-series study to systematically examine the associations between hospital admissions for TIA and air pollutants. Admissions for TIA (ICD-10: G45) from 1 January 2014 to 31 December 2016 were identified based on the primary diagnosis from 134 hospitals in Beijing, China. Hourly measurements of air pollutants were obtained from the National Air Pollution Monitoring System. Generalized additive models with quasi-Poisson regression were used to determine the associations for each pollutant. Additionally, stratified analysis was implemented to examine whether age, gender, temperature, and season were the potential effect modifiers. Restricted cubic spline was applied to investigate the exposure-response curve. In total, 109,975 hospital admissions for TIA were included. The positive associations were detected between PM2.5, PM10, SO2, NO2, and CO and hospital admissions for TIA. The effects of PM2.5 and PM10 in men are stronger than in women. Additionally, the effects of PM2.5, PM10, SO2, and O3 are more pronounced on warm days than cool days. From exposure-response curves, we observe a nearly linear relationship for PM2.5, PM10, CO, and NO2. Further studies are needed to verify the association. This research contributes evidence on the association between air pollution and admissions for TIA in the low- and middle-income countries and may promote related public health policy development.

Contrasting patterns of temperature related mortality and hospitalization by cardiovascular and respiratory diseases in 52 Spanish cities

BackgroundClimate change is a severe public health challenge. Understanding to what extent fatal and non-fatal consequences of specific diseases are associated with temperature may help to improve the effectiveness of preventive public health efforts. This study examines the effects of temperature on deaths and hospital admissions by cardiovascular and respiratory diseases, empathizing the difference between mortality and morbidity. MethodsDaily counts for mortality and hospital admissions by cardiovascular and respiratory diseases were collected for the 52 provincial capital cities in Spain, between 1990 and 2014. The association with temperature in each city was investigated by means of distributed lag non-linear models using quasi-Poisson regression. City-specific exposure-response curves were pooled by multivariate random-effects meta-analysis to obtain countrywide risk estimates of mortality and hospitalizations due to heat and cold, and attributable fractions were computed. ResultsHeat and cold exposure were identified to be associated with increased risk of cardiovascular and respiratory mortality. Heat was not found to have an impact on hospital admissions. The estimated fraction of mortality attributable to cold was of greater magnitude in hospitalizations (17.5% for cardiovascular and 12.5% for respiratory diseases) compared to deaths (9% and 2.7%, respectively). ConclusionsThere were noteworthy differences between temperature-related mortality and hospital admissions regarding cardiovascular and respiratory diseases, hence reinforcing the convenience of cause-specific measures to prevent temperature-related deaths.

Japanese Encephalitis and Associated Environmental Risk Factors in Eastern Uttar Pradesh: A time series analysis from 2001 to 2016

India and other Southeast Asian countries are severely affected by Japanese encephalitis (JE), one of the deadliest vector-borne disease threat to human health. Several epidemiological observations suggest climate variables play a role in providing a favorable environment for mosquito development and virus transmission. In this study, generalized additive models were used to determine the association of JE admissions and mortality with climate variables in Gorakhpur district, India, from 2001–2016. The model predicted that every 1 unit increase in mean (Tmean;°C), and minimum (Tmin;°C) temperature, rainfall (RF; mm) and relative humidity (RH; %) would on average increase the JE admissions by 22.23 %, 17.83 %, 0.66 %, and 5.22 % respectively and JE mortality by 13.27 %, 11.77 %, 0.94 %, and 3.27 % respectively Conversely, every unit decrease in solar radiation (Srad; MJ/m2/day) and wind speed (WS; Kmph) caused an increase in JE admission by 17% and 11.42% and in JE mortality by 9.37% and 4.88% respectively suggesting a protective effect at higher levels. The seasonal analysis shows that temperature was significantly associated with JE in pre-monsoon and post-monsoon while RF, RH, Srad, and WS are associated with the monsoon. Effect modification due to age and gender showed an equal risk for both genders and increased risk for adults above 15 years of age, however, males and age groups under 15 years outnumbered females and adults. Sensitivity analysis results to explore lag effects in climate variables showed that climate variables show the strongest association at lag 1 to 1.5 months with significant lag effect up tp lag 0-60 days. The exposure-response curve for climate variables showed a more or less linear relationship, with an increase in JE admissions and mortality after a certain threshold and decrease were reported at extreme levels of exposure. The study concludes that climate variables could influence the JE vector development and multiplication and parasite maturation and transmission in the Gorakhpur region whose indirect impact was noted for JE admission and mortality. In response to the changing climate, public health interventions, public awareness, and early warning systems would play an unprecedented role to compensate for future risk.