Indoor Pollution and Lung Function Decline in Current and Former Smokers: SPIROMICS AIR.

Rationale: Indoor pollutants have been associated with chronic obstructive pulmonary disease morbidity, but it is unclear whether they contribute to disease progression. Objectives: We aimed to determine whether indoor particulate matter (PM) and nitrogen dioxide (NO2) are associated with lung function decline among current and former smokers. Methods: Of the 2,382 subjects with a history of smoking in SPIROMICS AIR, 1,208 participants had complete information to estimate indoor PM and NO2, using individual-based prediction models, in relation to measured spirometry at two or more clinic visits. We used a three-way interaction model between time, pollutant, and smoking status and assessed the indoor pollutant-associated difference in FEV1 decline separately using a generalized linear mixed model. Measurements and Main Results: Participants had an average rate of FEV1 decline of 60.3 ml/yr for those currently smoking compared with 35.2 ml/yr for those who quit. The association of indoor PM with FEV1 decline differed by smoking status. Among former smokers, every 10 µg/m3 increase in estimated indoor PM was associated with an additional 10 ml/yr decline in FEV1 (P = 0.044). Among current smokers, FEV1 decline did not differ by indoor PM. The results of indoor NO2 suggest trends similar to those for PM ⩽2.5 µm in aerodynamic diameter. Conclusions: Former smokers with chronic obstructive pulmonary disease who live in homes with high estimated PM have accelerated lung function loss, and those in homes with low PM have lung function loss similar to normal aging. In-home PM exposure may contribute to variability in lung function decline in people who quit smoking and may be a modifiable exposure.

Black carbon content in airway macrophages is associated with increased severe exacerbations and worse COPD morbidity in SPIROMICS.

BACKGROUND: Airway macrophages (AM), crucial for the immune response in chronic obstructive pulmonary disease (COPD), are exposed to environmental particulate matter (PM), which they retain in their cytoplasm as black carbon (BC). However, whether AM BC accurately reflects environmental PM2.5 exposure, and can serve as a biomarker of COPD outcomes, is unknown. METHODS: We analyzed induced sputum from participants at 7 of 12 sites SPIROMICS sites for AM BC content, which we related to exposures and to lung function and respiratory outcomes. Models were adjusted for batch (first vs. second), age, race (white vs. non-white), income (<$35,000, $35,000~$74,999, ≥$75,000, decline to answer), BMI, and use of long-acting beta-agonist/long-acting muscarinic antagonists, with sensitivity analysis performed with inclusion of urinary cotinine and lung function as covariates. RESULTS: Of 324 participants, 143 were current smokers and 201 had spirometric-confirmed COPD. Modeled indoor fine (< 2.5 µm in aerodynamic diameter) particulate matter (PM2.5) and urinary cotinine were associated with higher AM BC. Other assessed indoor and ambient pollutant exposures were not associated with higher AM BC. Higher AM BC was associated with worse lung function and odds of severe exacerbation, as well as worse functional status, respiratory symptoms and quality of life. CONCLUSION: Indoor PM2.5 and cigarette smoke exposure may lead to increased AM BC deposition. Black carbon content in AMs is associated with worse COPD morbidity in current and former smokers, which remained after sensitivity analysis adjusting for cigarette smoke burden. Airway macrophage BC, which may alter macrophage function, could serve as a predictor of experiencing worse respiratory symptoms and impaired lung function.

Modeling residential indoor concentrations of PM2.5 , NO2 , NOx , and secondhand smoke in the Subpopulations and Intermediate Outcome Measures in COPD (SPIROMICS) Air study.

Increased outdoor concentrations of fine particulate matter (PM2.5 ) and oxides of nitrogen (NO2 , NOx ) are associated with respiratory and cardiovascular morbidity in adults and children. However, people spend most of their time indoors and this is particularly true for individuals with chronic obstructive pulmonary disease (COPD). Both outdoor and indoor air pollution may accelerate lung function loss in individuals with COPD, but it is not feasible to measure indoor pollutant concentrations in all participants in large cohort studies. We aimed to understand indoor exposures in a cohort of adults (SPIROMICS Air, the SubPopulations and Intermediate Outcome Measures in COPD Study of Air pollution). We developed models for the entire cohort based on monitoring in a subset of homes, to predict mean 2-week-measured concentrations of PM2.5 , NO2 , NOx , and nicotine, using home and behavioral questionnaire responses available in the full cohort. Models incorporating socioeconomic, meteorological, behavioral, and residential information together explained about 60% of the variation in indoor concentration of each pollutant. Cross-validated R2 for best indoor prediction models ranged from 0.43 (NOx ) to 0.51 (NO2 ). Models based on questionnaire responses and estimated outdoor concentrations successfully explained most variation in indoor PM2.5 , NO2 , NOx , and nicotine concentrations.

Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study.

We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO2; NO2-B43F), and oxidizing gases (OX-B431)-which refers to ozone (O3) and NO2. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R2 measures (CO: RMSE = 18 ppb, R2 = 0.97; NO: RMSE = 2 ppb, R2 = 0.97). Performance measures for NO2 and O3 were somewhat lower (NO2: RMSE = 3 ppb, R2 = 0.79; O3: RMSE = 4 ppb, R2 = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.

Spatial identification of potential health hazards: a systematic areal search approach.

BACKGROUND AND AIMS: Large metropolitan areas often exhibit multiple morbidity hotspots. However, the identification of specific health hazards, associated with the observed morbidity patterns, is not always straightforward. In this study, we suggest an empirical approach to the identification of specific health hazards, which have the highest probability of association with the observed morbidity patterns. METHODS: The morbidity effect of a particular health hazard is expected to weaken with distance. To account for this effect, we estimate distance decay gradients for alternative locations and then rank these locations based on the strength of association between the observed morbidity and wind-direction weighted proximities to these locations. To validate this approach, we use both theoretical examples and a case study of the Greater Haifa Metropolitan Area (GHMA) in Israel, which is characterized by multiple health hazards. RESULTS: In our theoretical examples, the proposed approach helped to identify correctly the predefined locations of health hazards, while in the real-world case study, the main health hazard was identified as a spot in the industrial zone, which hosts several petrochemical facilities. CONCLUSION: The proposed approach does not require extensive input information and can be used as a preliminary risk assessment tool in a wide range of environmental settings, helping to identify potential environmental risk factors behind the observed population morbidity patterns.

Application of the double kernel density approach to the analysis of cancer incidence in a major metropolitan area.

RATIONALE: Although cancer is a main cause of human morbidity worldwide, relatively small numbers of new cancer cases are recorded annually in single urban areas. This makes the association between cancer morbidity and environmental risk factors, such as ambient air pollution, difficult to detect using traditional methods of analysis based on age standardized rates and zonal estimates. STUDY GOAL: The present study investigates the association between air pollution and cancer morbidity in the Greater Haifa Metropolitan Area in Israel by comparing two analytical techniques: the traditional zonal approach and more recently developed Double Kernel Density (DKD) tools. While the first approach uses age adjusted Standardized Incidence Ratios (SIRs) for small census areas, the second approach estimates the areal density of cancer cases, normalized by the areal density of background population in which cancer events occurred. Both analyses control for several potential confounders, including air pollution, proximities to main industrial facilities and socio-demographic attributes. RESULTS: Air pollution variables and distances to industrial facilities emerged as statistically significant predictors of lung and NHL cancer morbidity in the DKD-based models (p<0.05) but not in the models based on SIRs estimates (p>0.2). CONCLUSION: DKD models appear to be a more sensitive tool for assessing potential environmental risks than traditional SIR-based models, because DKD estimates do not depend on a priory geographic delineations of statistical zones and produce a smooth and continuous disease 'risk surface' covering the entire study area. We suggest using the DKD method in similar studies of the effect of ambient air pollution on chronic morbidity, especially in cases in which the number of statistical areas available for aggregation and comparison is small and recorded morbidity events are relatively rare.

Work Ability Index in Israeli hospital nurses: applicability of the adapted questionnaire.

UNLABELLED: BACKGROUND/STUDY CONTEXT: The Work Ability Index (WAI) questionnaire is widely used for evaluation of the work ability of workers. This is the first application of the validated Hebrew version of this questionnaire to Israeli nurses in order to evaluate factors affecting their work ability. METHODS: A cross-sectional study was conducted among 515 nurses from two general hospitals in Israel (87.3% female). RESULTS: A significant negative correlation was found between the WAI score and age, years in current job, and number of reported diagnoses. The most frequently reported illnesses (as diagnosed by a physician) were musculoskeletal disorders, endocrine/metabolic diseases, and cardiovascular diseases. Agreement between illnesses as diagnosed by a physician and as self-reported by nurses was low, especially with respect to mental/emotional stress, (kappa statistics = 16.4%, p < .001). CONCLUSIONS: Mean WAI score found in Israeli hospital nurses is relatively high as compared with that of European nurses. It gradually decreases with age. The WAI questionnaire enables the early identification of those nurses with compromised work ability and who are in need of assistance in order to prevent early retirement. As retirement age is currently advancing, keeping elderly workers in the workforce is of prime importance.

Calibration of low-cost particulate matter sensors: Model development for a multi-city epidemiological study.

Low-cost air monitoring sensors are an appealing tool for assessing pollutants in environmental studies. Portable low-cost sensors hold promise to expand temporal and spatial coverage of air quality information. However, researchers have reported challenges in these sensors' operational quality. We evaluated the performance characteristics of two widely used sensors, the Plantower PMS A003 and Shinyei PPD42NS, for measuring fine particulate matter compared to reference methods, and developed regional calibration models for the Los Angeles, Chicago, New York, Baltimore, Minneapolis-St. Paul, Winston-Salem and Seattle metropolitan areas. Duplicate Plantower PMS A003 sensors demonstrated a high level of precision (averaged Pearson's r = 0.99), and compared with regulatory instruments, showed good accuracy (cross-validated R2 = 0.96, RMSE = 1.15 µg/m3 for daily averaged PM2.5 estimates in the Seattle region). Shinyei PPD42NS sensor results had lower precision (Pearson's r = 0.84) and accuracy (cross-validated R2 = 0.40, RMSE = 4.49 µg/m3). Region-specific Plantower PMS A003 models, calibrated with regulatory instruments and adjusted for temperature and relative humidity, demonstrated acceptable performance metrics for daily average measurements in the other six regions (R2 = 0.74-0.95, RMSE = 2.46-0.84 µg/m3). Applying the Seattle model to the other regions resulted in decreased performance (R2 = 0.67-0.84, RMSE = 3.41-1.67 µg/m3), likely due to differences in meteorological conditions and particle sources. We describean approach to metropolitan region-specific calibration models for low-cost sensors that can be used with cautionfor exposure measurement in epidemiological studies.

Residential proximity to petroleum storage tanks and associated cancer risks: Double Kernel Density approach vs. zonal estimates.

BACKGROUND AND AIMS: The relationship between exposure to petroleum products and cancer is well-established in occupational studies carried out among employees of transportation and oil-producing industries. However, question remains whether living near petroleum storage facilities may represent a cancer risk. In the present study, we examined cancer incidence rates associated with residential proximity to the Kiryat Haim industrial zone in Northern Israel, using different analytical techniques and adjusting for several potential confounders, such as road proximity, population density, smoking rates and socio-demographic attributes. METHODS: Both traditional zonal approaches and more recently developed Double Kernel Density (DKD) tools were used to estimate relative risks of lung and NHL cancers attributed to residential proximity to the petroleum storage site. RESULTS: Zonal approaches based on comparing ASRs across small census areas (SCAs) did not detect any significant association between residential proximity to the industrial zone and the two types of cancers under study (P>0.2). In contrast, the DKD approach revealed that the relative density of both lung and NHL cancers declined in line with distances from the industrial zone, especially among the elderly (Lung: t>-12.0; P<0.01; NHL: t>-9.0; P<0.01), adjusted for proximity to main roads, population density, smoking rate, average income, and several other potential confounders. CONCLUSIONS: Living near petroleum storage sites may represent significant cancer risk which cannot always be detected by traditional zonal approaches commonly used in epidemiological studies, especially if the number of census areas available for the analysis is small.