Solid fuel burning cookstoves are a major source of household air pollution (HAP) and a significant environmental health risk in Sri Lanka. We report results of the first field study in Sri Lanka to include direct measurements of both real-time indoor concentrations and personal exposures of fine particulate matter (PM ) in households using the two most common stove types in Sri Lanka. A purposive sample of 53 households was selected in the rural community of Kopiwatta in central Sri Lanka, roughly balanced for stove type (traditional or improved 'Anagi') and ventilation (chimney present or absent). At each household, 48-h continuous real-time measurements of indoor kitchen PM and personal (primary cook) PM concentrations were measured using the RTI MicroPEM personal exposure monitor. Questionnaires were used to collect data related to household demographics, characteristics, and self-reported health symptoms. All primary cooks were female and of an average age of 47 years, with 66% having completed primary education. Median income was slightly over half the national median monthly income. Use of Anagi stoves was positively associated with a higher education level of the primary cook (P = 0.026), although not associated with household income (P = 0.18). The MicroPEM monitors were well-received by participants, and this study's valid data capture rate exceeded 97%. Participant wearing compliance during waking hours was on average 87.2% on Day 1 and 83.3% on Day 2. Periods of non-compliance occurred solely during non-cooking times. The measured median 48-h average indoor PM concentration for households with Anagi stoves was 64 μg/m if a chimney was present and 181 μg/m if not. For households using traditional stoves, these values were 70 μg/m if a chimney was present and 371 μg/m if not. Overall, measured indoor PM concentrations ranged from a minimum of 33 μg/m to a maximum of 940 μg/m , while personal exposure concentrations ranged from 34 to 522 μg/m . Linear mixed effects modeling of the dependence of indoor concentrations on stove type and presence or absence of chimney showed a significant chimney effect (65% reduction; P < 0.001) and an almost significant stove effect (24% reduction; P = 0.054). Primary cooks in households without chimneys were exposed to substantially higher levels of HAP than those in households with chimneys, while exposures in households with traditional stoves were moderately higher than those with improved Anagi stoves. As expected, simultaneously measuring both indoor concentrations and personal exposure levels indicate significant exposure misclassification bias will likely result from the use of a stationary monitor as a proxy for personal exposure. While personal exposure monitoring is more complex and expensive than deploying simple stationary devices, the value an active personal PM monitor like the MicroPEM adds to an exposure study should be considered in future study designs.
BackgroundTumor testing for mutations in the epidermal growth factor receptor (EGFR) gene is indicated for all newly diagnosed, metastatic lung cancer patients, who may be candidates for first-line treatment with an EGFR tyrosine kinase inhibitor. Few studies have analyzed population-level testing.MethodsWe identified clinical, demographic, and regional predictors of EGFR & KRAS testing among Medicare beneficiaries with a new diagnosis of lung cancer in 2011–2013 claims. The outcome variable was whether the patient underwent molecular, EGFR and KRAS testing. Independent variables included: patient demographics, Medicaid status, clinical characteristics, and region where the patient lived. We performed multivariate logistic regression to identify factors that predicted testing.ResultsFrom 2011 to 2013, there was a 19.7% increase in the rate of EGFR testing. Patient zip code had the greatest impact on odds to undergo testing; for example, patients who lived in the Boston, Massachusetts hospital referral region were the most likely to be tested (odds ratio (OR) of 4.94, with a 95% confidence interval (CI) of 1.67–14.62). Patient demographics also impacted odds to be tested. Asian/Pacific Islanders were most likely to be tested (OR 1.63, CI 1.53–1.79). Minorities and Medicaid patients were less likely to be tested. Medicaid recipients had an OR of 0.74 (CI 0.72–0.77). Hispanics and Blacks were also less likely to be tested (OR 0.97, CI 0.78–0.99 and 0.95, CI 0.92–0.99), respectively. Clinical procedures were also correlated with testing. Patients who underwent transcatheter biopsies were 2.54 times more likely to be tested (CI 2.49–2.60) than those who did not undergo this type of biopsy.ConclusionsDespite an overall increase in EGFR testing, there is widespread underutilization of guideline-recommended testing. We observed racial, income, and regional disparities in testing. Precision medicine has increased the complexity of cancer diagnosis and treatment. Targeted interventions and clinical decision support tools are needed to ensure that all patients are benefitting from advances in precision medicine. Without such interventions, precision medicine may exacerbate racial disparities in cancer care and health outcomes.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-4190-3) contains supplementary material, which is available to authorized users.
The performance of kriging methods in predicting maximum m-day (m = 1, 7, 14, or 30) rolling averages of atrazine concentrations in 42 site-years of Midwest Corn Belt watersheds under two systematic sampling designs (sampling every 7 or 14 d) was examined. Daily atrazine monitoring data obtained from the Atrazine Ecological Monitoring Program in the Corn Belt region (2009-2014) were used in the evaluation. Both ordinary and universal kriging methods were considered, with the covariate for universal kriging derived from the deterministic Pesticide Root Zone Model (PRZM). For the maximum 1-d rolling averages, prediction did not differ among methods. For rolling averages of longer duration (m > 1), predictions obtained by linear interpolation on a logarithmic scale were better (up to 15% lower for 7-d sampling and 22% lower for 14-d sampling in terms of the relative root mean squared prediction error) than those obtained by linear interpolation on the original linear scale and also less variable. For kriging methods, empirical semivariograms of daily atrazine time series suggest a negligible noise process, supported by replicate analysis of selected field samples; piecewise linear semivariogram models were found to perform best for predicting sampled data. We demonstrate that kriging prediction intervals offer close to nominal coverage for unsampled values.
A survey sampling approach is presented for estimating upper centiles of aggregate distributions of surface water pesticide measurements obtained from datasets with large sample sizes but variable sampling frequency. It is applied to three atrazine monitoring programs of Community Water Systems (CWS) that used surface water as their drinking water source: the nationwide Safe Drinking Water Act (SDWA) data, the Syngenta Voluntary Monitoring Program (VMP), and the Atrazine Monitoring Program (AMP).The VMP/AMP CWS were selected on the basis of atrazine monitoring history (CWS having at least one annual average concentration from SDWA ≥ 1.6 ppb atrazine since 1997 in the AMP). Estimates of the raw water 95th, 99th, and 99.9th centile atrazine concentrations for the VMP/AMP CWS are 4.82, 11.85, and 34.00 ppb, respectively. The corresponding estimates are lower for the finished drinking water samples, with estimates of 2.75, 7.94, and 22.66 ppb, respectively. Finished water centile estimates for the VMP/AMP CWS using only the SDWA data for these sites are consistent with the results. Estimates are provided for the April through July period and for CWS based on surface water source type (static, flowing, or mixed). Requisite sample sizes are determined using statistical tolerance limits, relative SE, and the Woodruff interval sample size criterion. These analyses provide 99.9% confidence that the existing data include the 99.9th centile atrazine concentration for CWS raw and finished water in the Midwest atrazine high-use areas and in the nationwide SDWA dataset. The general validity of this approach is established by a simulation that shows estimates to be close to target quantities for weights based on sampling probabilities or time intervals between samples. Recommendations are given for suitable effective sample sizes to reliably determine interval estimates.
This study examines the use of physiologically based pharmacokinetic (PBPK) models for inferring exposure when the number of biomarker observations per individual is limited, as commonly occurs in population exposure surveys. The trade-off between sampling multiple biomarkers at a specific time versus fewer biomarkers at multiple time points was investigated, using a simulation-based approach based on a revised and updated chlorpyrifos PBPK model originally published. Two routes of exposure, oral and dermal, were studied as were varying levels of analytic measurement error. It is found that adding an additional biomarker at a given time point adds substantial additional information to the analysis, although not as much as the addition of another sampling time. Furthermore, the precision of the estimates of exposed dose scaled approximately with the analytic precision of the biomarker measurement. For acute exposure scenarios such as those considered here, the results of this study suggest that the number of biomarkers can be balanced against the number of sampling times to obtain the most efficient estimator after consideration of cost, intrusiveness, and other relevant factors.
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