Using Global Positioning Systems (GPS) and temperature data to generate time-activity classifications for estimating personal exposure in air monitoring studies: an automated method

Background: Personal exposure studies of air pollution generally use self-reported diaries to capture individuals’ time-activity data. Enhancements in the accuracy, size, memory and battery life of personal Global Positioning Systems (GPS) units have allowed for higher resolution tracking of study participants’ locations. Improved time activity classifications combined with personal continuous air pollution sampling can improve assessments of location-related air pollution exposures for health studies. Methods: Data was collected using a GPS and personal temperature from 54 children with asthma living in Montreal, Canada, who participated in a 10-day personal air pollution exposure study. A method was developed that incorporated personal temperature data and then matched a participant’s position against available spatial data (i.e., road networks) to generate time-activity categories. The diary-based and GPS-generated time-activity categories were compared and combined with continuous personal PM2.5 data to assess the impact of exposure misclassification when using diary based methods. Results: There was good agreement between the automated method and the diary method; however, the automated method (means: outdoors = 5.1%, indoors other =9.8%) estimated less time spent in some locations compared to the diary method (outdoors = 6.7%, indoors other = 14.4%). Agreement statistics (AC1 = 0.778) suggest ‘good’ agreement between methods over all location categories. However, location categories (Outdoors and Transit) where less time is spent show greater disagreement: e.g., mean time “Indoors Other” using the time-activity diary was 14.4% compared to 9.8% using the automated method. While mean daily time “In Transit” was relatively consistent between the methods, the mean daily exposure to PM2.5 while “In Transit” was 15.9 μg/m3 using the automated method compared to 6.8 μg/m3 using the daily diary. Conclusions: Mean times spent in different locations as categorized by a GPS-based method were comparable to those from a time-activity diary, but there were differences in estimates of exposure to PM2.5 from the two methods. An automated GPS-based time-activity method will reduce participant burden, potentially providing more accurate and unbiased assessments of location. Combined with continuous air measurements, the higher resolution GPS data could present a different and more accurate picture of personal exposures to air pollution

Local support for conservation is associated with perceptions of good governance, social impacts, and ecological effectiveness

Local support is important for the longevity of conservation initiatives. The literature suggests that perceptions of ecological effectiveness, social impacts, and good gov- ernance will influence levels of local support for conservation. This paper examines these relationships using data from a survey of small-scale fishermen in 11 marine pro- tected areas from six countries in the Mediterranean Sea. The survey queried small- scale fishermen regarding perceptions and support for conservation. We constructed composite scores for three categories of perceptions—ecological effectiveness, social impacts, and good governance—and tested the relationship with levels of support using ordinal regression models. While all three factors were positively correlated with support for conservation, perceptions of good governance and social impacts were stronger predictors of increasing support. These findings suggest that employ- ing good governance processes and managing social impacts may be more important than ecological effectiveness for maintaining local support for conservation

Household income and contraceptive methods among female youth: a cross-sectional study using the Canadian Community Health Survey (2009-2010 and 2013-2014).

BACKGROUND: Low socioeconomic status is one of many barriers that may limit access to family planning services. We aimed to examine the relation between household income and contraceptive methods among female youth in Canada. METHODS: Our study population included sexually active females aged 15-24 who were trying to avoid pregnancy. We used cross-sectional data from the 2009-2010 and 2013-2014 cycles of the Canadian Community Health Survey to compare household income and other sociodemographic covariates for those using oral contraceptives, injectable contraceptives, condoms or a dual method (condoms plus oral or injectable contraceptives). RESULTS: Of female youth at risk for unintended pregnancy, 59.2% reported using oral contraceptives, 29.0% used dual methods, 16.8% used condoms only, 2.5% used injectable contraceptives and 13.6% did not use contraception. In multiple regression models, lower annual household income (< $80 000) was associated with decreased use of oral contraceptives (relative risk [RR] 0.85, 95% confidence interval [CI] 0.80-0.91) and dual methods (RR 0.81, 95% CI 0.71-0.91), increased use of condoms (RR 1.36, 95% CI 1.11-1.67) and injectable contraceptives (RR 1.69, 95% CI 0.98-2.92), and a greater risk of contraceptive nonuse (RR 1.19, 95% CI 0.94-1.50). INTERPRETATION: We found that lower household income was associated with decreased use of oral contraceptives and increased reliance on injectable contraceptives and condoms only. Young, low-income females may face barriers to accessing the full range of contraceptive methods available in Canada. Easier access to affordable contraception may decrease the number of female youth at risk for unintended pregnancy due to financial barriers

The Influence of Living Near Roadways on Spirometry and Exhaled Nitric Oxide in Elementary Schoolchildren

BACKGROUND: Living near major roadways has been associated with an increase in respiratory symptoms, but little is known about how this relates to airway inflammation. OBJECTIVE: We assessed the effects of living near local residential roadways based on objective indicators of ventilatory function and airway inflammation. METHODS: We estimated ambient air pollution, resolved to the level of the child's neighborhood, using a land-use regression model for children 9-11 years of age. We also summed the length of roadways found within a 200-m radius of each child's neighborhood. We had measurements of both air pollution exposure and spirometry for 2,328 children, and also had measurements of exhaled nitric oxide (eNO) for 1,613 of these children. RESULTS: Each kilometer of local roadway within a 200-m radius of the home was associated with a 6.8% increase in eNO (p = 0.045). Each kilometer of any type of roadway (local, major, highway) was also associated with an increase in eNO of 10.1% (p = 0.002). Each microgram per cubic meter increase in PM2.5 was associated with a 3.9% increase in eNO (p = 0.058) and 0.70% decrease in forced vital capacity (FVC) expressed as a percentage of predicted (p = 0.39). Associations between roadway density and both forced expired volume in 1 sec and FVC were negative but not statistically significant at p < 0.05. CONCLUSION: Traffic from local neighborhood roadways may cause airway inflammation as indicated by eNO. This may be a more sensitive indicator of adverse air pollution effects than traditional measures of ventilatory function

Telomerase inhibition abolishes the tumorigenicity of pediatric ependymoma tumor-initiating cells

Pediatric ependymomas are highly recurrent tumors resistant to conventional chemotherapy. Telomerase, a ribonucleoprotein critical in permitting limitless replication, has been found to be critically important for the maintenance of tumor-initiating cells (TICs). These TICs are chemoresistant, repopulate the tumor from which they are identified, and are drivers of recurrence in numerous cancers. In this study, telomerase enzymatic activity was directly measured and inhibited to assess the therapeutic potential of targeting telomerase. Telomerase repeat amplification protocol (TRAP) (n = 36) and C-circle assay/telomere FISH/ATRX staining (n = 76) were performed on primary ependymomas to determine the prevalence and prognostic potential of telomerase activity or alternative lengthening of telomeres (ALT) as telomere maintenance mechanisms, respectively. Imetelstat, a phase 2 telomerase inhibitor, was used to elucidate the effect of telomerase inhibition on proliferation and tumorigenicity in established cell lines (BXD-1425EPN, R254), a primary TIC line (E520) and xenograft models of pediatric ependymoma. Over 60 % of pediatric ependymomas were found to rely on telomerase activity to maintain telomeres, while no ependymomas showed evidence of ALT. Children with telomerase-active tumors had reduced 5-year progression-free survival (29 +/- A 11 vs 64 +/- A 18 %; p = 0.03) and overall survival (58 +/- A 12 vs 83 +/- A 15 %; p = 0.05) rates compared to those with tumors lacking telomerase activity. Imetelstat inhibited proliferation and self-renewal by shortening telomeres and inducing senescence in vitro. In vivo, Imetelstat significantly reduced subcutaneous xenograft growth by 40 % (p = 0.03) and completely abolished the tumorigenicity of pediatric ependymoma TICs in an orthotopic xenograft model. Telomerase inhibition represents a promising therapeutic approach for telomerase-active pediatric ependymomas found to characterize high-risk ependymomas.Canadian Institutes of Health Research [MOP 82727]info:eu-repo/semantics/publishedVersio

Gestational diabetes screening changes and impacts on diagnosis

Background: Gestational diabetes mellitus (GDM) affects between 2-40% of pregnancies worldwide, depending on diagnostic and screening methods. Changes in screening practices are not well understood because administrative sources lack data on whether or how individuals were screened. The objectives of this thesis were to: 1) validate a method to identify prenatal screening for GDM and other conditions in administrative health data; 2) describe changes in GDM screening; 3) evaluate the relative contributions of screening and population characteristics to changes in GDM risk; 4) characterize the impact of the COVID-19 pandemic on pregnancy weight gain and infant birthweight. Methods: Laboratory billing records from BC’s universal health insurance system for prenatal screening tests were compared with medical records by calculating validation properties. All pregnancies (birth >20wks or >500g) in British Columbia, Canada, 2005-2019, with linked perinatal health and administrative data, were used to examine time trends in GDM screening methods, trends within subgroups, and the effect of screening changes on prevalence. A second cohort from Washington State, 2016-2020, was analyzed using an interrupted time series design, to assess COVID-19 impacts on pregnancy weight gain and infant birthweight using z-scores. Results: GDM screening in laboratory billing records had a high sensitivity (97% [95% CI: 90, 99]) and specificity (>99% [95% CI: 86, <99]) compared with medical records. GDM diagnoses in BC more than doubled from 7.2% in 2005 to 14.7% in 2019 (n=550,783 pregnancies). Most of this increase was explained by changes in screening; adjustment for population factors had minimal impact. In Washington state, using an interrupted time series, pregnancy weight gain z-score increased by 0.08 (95% CI 0.03, 0.13) after the COVID-19 pandemic onset and infant birthweight z-scores were unchanged (-0.004, 95% CI (-0.04, 0.03)). Conclusion: Prenatal screening tests can be accurately ascertained using BC insurance billing data. Changes in GDM screening completion and in screening methods accounted for most of the increase in GDM diagnosis in BC since 2005. Covid-19 pandemic countermeasures were associated with an increase in pregnancy weight gain but not infant birthweight. Public health and future researchers should understand how screening changes can directly affect disease prevalence.Medicine, Faculty ofPopulation and Public Health (SPPH), School ofGraduat

From measures to models : predicting exposure to air pollution among pregnant women

Introduction: Exposure assessment is a key challenge in environmental epidemiology. When modeling exposures for populations, one should consider (1) the applicability of the exposure model to the health effect of interest (i.e. chronic, acute), (2) the applicability of the model to the population of interest, (3) the extent to which modeled exposures account for individual factors and (4) the sources of variability within the model. Epidemiological studies of traffic-related air pollution and birth outcomes have used a variety of exposure models to estimate exposures for pregnant women. These models are rarely evaluated, let alone specifically for pregnant women. Methods: Measured and modeled personal exposures to air pollutants (nitric oxide: NO, nitrogen dioxide: N0₂ , filter absorbance and fine particles: PM₂․₅) were obtained for 62 pregnant women from 2005-2006 in Vancouver, Canada. Exposures were measured for 48-hours, 1-3 times over the pregnancy. Mobility was assessed using Global Positioning System monitoring and self-reported activity logs; individual factors (dwelling characteristics, socio-economic factors) were assessed using questionnaires. Results: Modeled home concentrations using a traffic-based land-use regression model were moderately predictive of personal samples for NO only (Pearson's r=0.49). Models for NO including home and work locations explained more between subject variance than using home only (4% home only, 2 0 % with home and work). Modeled exposures using ambient monitoring stations were predictive of personal samples for NO (Pearson's r=0.54), absorbance (r=0.29) and PM₂․₅ (r=0.12) mainly due to temporal correlations (within subject variance: NO = 37% , absorbance = 11%, PM, 5 = 9%). Home gas stove was an important determinant of personal exposure for all pollutants. There was a significant (1 hour/day/trimester) increase in time spent at home with increased trimester of pregnancy. Conclusions: In this evaluation, based upon repeated 48-hour exposure measurements, models currently used in air pollution studies were moderately reflective of personal exposures, depending on the specific pollutant and model. Land-use regression shows promise for capturing spatial variability, especially when including mobility (work or school locations) in exposures, whereas monitor-based models are better for capturing temporal variability. Future models should include mobility, where possible, and consider the implications of increasing time at home over pregnancy in assessing exposures for pregnant women.Medicine, Faculty ofPopulation and Public Health (SPPH), School ofGraduat

Development of temporally refined land-use regression models predicting daily household-level air pollution in a panel study of lung function among asthmatic children

Regulatory monitoring data and land-use regression (LUR) models have been widely used for estimating individual exposure to ambient air pollution in epidemiologic studies. However, LUR models lack fine-scale temporal resolution for predicting acute exposure and regulatory monitoring provides daily concentrations, but fails to capture spatial variability within urban areas. This study coupled LUR models with continuous regulatory monitoring to predict daily ambient nitrogen dioxide (NO2) and particulate matter (PM2.5) at 50 homes in Windsor, Ontario. We compared predicted versus measured daily outdoor concentrations for 5 days in winter and 5 days in summer at each home. We also examined the implications of using modeled versus measured daily pollutant concentrations to predict daily lung function among asthmatic children living in those homes. Mixed effect analysis suggested that temporally refined LUR models explained a greater proportion of the spatial and temporal variance in daily household-level outdoor NO2 measurements compared with daily concentrations based on regulatory monitoring. Temporally refined LUR models captured 40% (summer) and 10% (winter) more of the spatial variance compared with regulatory monitoring data. Ambient PM2.5 showed little spatial variation; therefore, daily PM2.5 models were similar to regulatory monitoring data in the proportion of variance explained. Furthermore, effect estimates for forced expiratory volume in 1 s (FEV1) and peak expiratory flow (PEF) based on modeled pollutant concentrations were consistent with effects based on household-level measurements for NO2 and PM2.5. These results suggest that LUR modeling can be combined with continuous regulatory monitoring data to predict daily household-level exposure to ambient air pollution. Temporally refined LUR models provided a modest improvement in estimating daily household-level NO2 compared with regulatory monitoring data alone, suggesting that this approach could potentially improve exposure estimation for spatially heterogeneous pollutants. These findings have important implications for epidemiologic studies — in particular, for research focused on short-term exposure and health effects