Rationale Exposure to ambient air pollutants has been associated with increased lung cancer incidence and mortality but, due to the high case fatality rate, little is known about the impacts of air pollution exposures on survival after diagnosis. This study aimed to determine whether ambient air pollutant exposures are associated with lung cancer patient survival. Methods Participants were 352,053 patients with newly diagnosed lung cancer during 1988–2009 in California, ascertained by the California Cancer Registry. Average residential ambient air pollutant concentrations were estimated for each participant’s follow-up period. Cox proportional hazards models were used to estimate hazard ratios (HRs) relating air pollutant exposures to all-cause mortality overall and stratified by stage (localized only, regional, and distant site) and histology (squamous cell carcinoma, adenocarcinoma, small cell carcinoma, large cell carcinoma, and others) at diagnosis, adjusting for potential individual and area-level confounders. Results Adjusting for histology and other potential confounders, the HR associated with 1 standard deviation increases in NO2, O3, PM10, PM2.5 for patients with localized stage at diagnosis were 1.30 (95% CI: 1.28–1.32), 1.04 (95% CI: 1.02–1.05), 1.26 (95% CI: 1.25–1.28), and 1.38 (95% CI: 1.35–1.41), respectively. Adjusted HR were smaller in later stages, and varied by histological type within stage (p < 0.01, except O3). The largest associations were for patients with early stage non-small cell cancers, particularly adenocarcinomas. Conclusions These epidemiological findings support the hypothesis that air pollution exposures after lung cancer diagnosis shorten survival. Future studies should evaluate the impacts of exposure reduction.

Particulate air pollution (PM) exposure has been associated with cancer incidence and mortality especially with lung cancer. The liver is another organ possibly affected by PM due to its role in detoxifying xenobiotics absorbed from PM. Various studies have investigated the mechanistic pathways between inhaled pollutants and liver damage, cancer incidence, and tumor progression. However, little is known about the effects of PM on liver cancer survival. 20,221 California Cancer Registry patients with hepatocellular carcinoma (HCC) diagnosed between 2000–2009 were used to examine the effect of exposure to ambient PM with diameter less than 2.5µm (PM2.5) on HCC survival. Cox proportional hazards models were used to estimate hazard ratios (HRs) relating PM2.5 to all-cause and liver cancer-specific mortality linearly and non-linearly— overall and stratified by stage at diagnosis (local, regional, and distant)—adjusting for potential individual and geospatial confounders. PM2.5 exposure after diagnosis was statistically significantly associated with HCC survival. After adjustment for potential confounders, the all-cause mortality HR associated with a 1 standard deviation (5.0 µg/m3) increase in PM2.5 was 1.18 (95% CI: 1.16 – 1.20); 1.31 (95% CI:1.26 – 1.35) for local stage, 1.19 (95% CI:1.14 – 1.23) for regional stage, and 1.05 (95% CI:1.01 – 1.10) for distant stage. These associations were nonlinear, with substantially larger HRs at higher exposures. The associations between liver cancer-specific mortality and PM2.5 were slightly attenuated compared to all-cause mortality, but with the same patterns. Exposure to elevated PM2.5 after the diagnosis of HCC may shorten survival, with larger effects at higher concentrations.

Background Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity. Objective We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points. Methods We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones ( HARuS ) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma ( BREATHE ) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing. Results In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities. Conclusions In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as exp...

Background: Bronchitic symptoms in children pose a significant clinical and public health burden. Exposures to criteria air pollutants affect bronchitic symptoms, especially in children with asthma. Less is known about near-roadway exposures. Methods: Bronchitic symptoms (bronchitis, chronic cough, or phlegm) in the past 12 months were assessed annually with 8 to 9 years of follow-up on 6757 children from the southern California Children’s Health Study. Residential exposure to freeway and non-freeway near-roadway air pollution was estimated using a line-source dispersion model. Mixed-effects logistic regression models were used to relate near-roadway air pollutant exposures to bronchitic symptoms among children with and without asthma. Results: Among children with asthma, a two standard deviation increase in non-freeway exposures (odds ratio [OR]: 1.44; 95% confidence interval [CI]: 1.17–1.78) and freeway exposures (OR: 1.31; 95% CI: 1.06–1.60) were significantly associated with increased risk of bronchitic symptoms. Among children without asthma, only non-freeway exposures had a significant association (OR: 1.14; 95% CI: 1.00–1.29). Associations were strongest among children living in communities with lower regional particulate matter. Conclusions: Near-roadway air pollution was associated with bronchitic symptoms, especially among children with asthma and those living in communities with lower regional particulate matter. Better characterization of traffic pollutants from non-freeway roads is needed since many children live in close proximity to this source.

Background Chronic respiratory symptoms involving bronchitis, cough and phlegm in children are underappreciated but pose a significant public health burden. Efforts for prevention and management could be supported by an understanding of the relative importance of determinants, including environmental exposures. Thus, we aim to develop a prediction model for bronchitic symptoms. Methods Schoolchildren from the population-based southern California Children’s Health Study were visited annually from 2003 to 2012. Bronchitic symptoms over the prior 12 months were assessed by questionnaire. A gradient boosting model was fit using groups of risk factors (including traffic/air pollution exposures) for all children and by asthma status. Training data consisted of one observation per participant in a random study year (for 50% of participants). Validation data consisted of: (1) a random (later) year in the same participants ( within -participant); (2) a random year in participants excluded from the training data ( across -participant). Results At baseline, 13.2% of children had asthma and 18.1% reported bronchitic symptoms. Models performed similarly within- and across-participant. Previous year symptoms/medication use provided much of the predictive ability (across-participant area under the receiver operating characteristic curve (AUC): 0.76 vs 0.78 for all risk factors, in all participants). Traffic/air pollution exposures added modestly to prediction as did body mass index percentile, age and parent stress. Conclusions Regardless of asthma status, previous symptoms were the most important predictors of current symptoms. Traffic/air pollution variables contribute modest predictive information, but impact large populations. Methods proposed here could be generalized to personalized exacerbation predictions in future longitudinal studies to support targeted prevention efforts. Electronic supplementary material The online version of this article (10.1186/s12874-019-0708-x) contains supplementary material, which is available to authorized users.