Estimating personal exposures from a multi-hazard sensor network.

Occupational exposure assessment is almost exclusively accomplished with personal sampling. However, personal sampling can be burdensome and suffers from low sample sizes, resulting in inadequately characterized workplace exposures. Sensor networks offer the opportunity to measure occupational hazards with a high degree of spatiotemporal resolution. Here, we demonstrate an approach to estimate personal exposure to respirable particulate matter (PM), carbon monoxide (CO), ozone (O3), and noise using hazard data from a sensor network. We simulated stationary and mobile employees that work at the study site, a heavy-vehicle manufacturing facility. Network-derived exposure estimates compared favorably to measurements taken with a suite of personal direct-reading instruments (DRIs) deployed to mimic personal sampling but varied by hazard and type of employee. The root mean square error (RMSE) between network-derived exposure estimates and personal DRI measurements for mobile employees was 0.15 mg/m3, 1 ppm, 82 ppb, and 3 dBA for PM, CO, O3, and noise, respectively. Pearson correlation between network-derived exposure estimates and DRI measurements ranged from 0.39 (noise for mobile employees) to 0.75 (noise for stationary employees). Despite the error observed estimating personal exposure to occupational hazards it holds promise as an additional tool to be used with traditional personal sampling due to the ability to frequently and easily collect exposure information on many employees.

Mapping Occupational Hazards with a Multi-sensor Network in a Heavy-Vehicle Manufacturing Facility.

Due to their small size, low-power demands, and customizability, low-cost sensors can be deployed in collections that are spatially distributed in the environment, known as sensor networks. The literature contains examples of such networks in the ambient environment; this article describes the development and deployment of a 40-node multi-hazard network, constructed with low-cost sensors for particulate matter (SHARP GP2Y1010AU0F), carbon monoxide (Alphasense CO-B4), oxidizing gases (Alphasense OX-B421), and noise (developed in-house) in a heavy-vehicle manufacturing facility. Network nodes communicated wirelessly with a central database in order to record hazard measurements at 5-min intervals. Here, we report on the temporal and spatial measurements from the network, precision of network measurements, and accuracy of network measurements with respect to field reference instruments through 8 months of continuous deployment. During typical production periods, 1-h mean hazard levels ± standard deviation across all monitors for particulate matter (PM), carbon monoxide (CO), oxidizing gases (OX), and noise were 0.62 ± 0.2 mg m-3, 7 ± 2 ppm, 155 ± 58 ppb, and 82 ± 1 dBA, respectively. We observed clear diurnal and weekly temporal patterns for all hazards and daily, hazard-specific spatial patterns attributable to general manufacturing processes in the facility. Processes associated with the highest hazard levels were machining and welding (PM and noise), staging (CO), and manual and robotic welding (OX). Network sensors exhibited varying degrees of precision with 95% of measurements among three collocated nodes within 0.21 mg m-3 for PM, 0.4 ppm for CO, 9 ppb for OX, and 1 dBA for noise of each other. The median percent bias with reference to direct-reading instruments was 27%, 11%, 45%, and 1%, for PM, CO, OX, and noise, respectively. This study demonstrates the successful long-term deployment of a multi-hazard sensor network in an industrial manufacturing setting and illustrates the high temporal and spatial resolution of hazard data that sensor and monitor networks are capable of. We show that network-derived hazard measurements offer rich datasets to comprehensively assess occupational hazards. Our network sets the stage for the characterization of occupational exposures on the individual level with wireless sensor networks.

Sensor Selection to Improve Estimates of Particulate Matter Concentration from a Low-Cost Network.

Deployment of low-cost sensors in the field is increasingly popular. However, each sensor requires on-site calibration to increase the accuracy of the measurements. We established a laboratory method, the Average Slope Method, to select sensors with similar response so that a single, on-site calibration for one sensor can be used for all other sensors. The laboratory method was performed with aerosolized salt. Based on linear regression, we calculated slopes for 100 particulate matter (PM) sensors, and 50% of the PM sensors fell within ±14% of the average slope. We then compared our Average Slope Method with an Individual Slope Method and concluded that our first method balanced convenience and precision for our application. Laboratory selection was tested in the field, where we deployed 40 PM sensors inside a heavy-manufacturing site at spatially optimal locations and performed a field calibration to calculate a slope for three PM sensors with a reference instrument at one location. The average slope was applied to all PM sensors for mass concentration calculations. The calculated percent differences in the field were similar to the laboratory results. Therefore, we established a method that reduces the time and cost associated with calibration of low-cost sensors in the field.

Surveillance for the safety and effectiveness of artemether-lumefantrine in patients with uncomplicated Plasmodium falciparum malaria in the USA: a descriptive analysis.

BACKGROUND: Data from clinical studies show that artemether-lumefantrine (AL) is effective and well tolerated in adults and children with uncomplicated Plasmodium falciparum malaria. However, data on effectiveness and safety of AL in patients in non-endemic settings are limited. METHODS: A 5-year surveillance plan included all AL-treated adult and paediatric patients with confirmed or suspected P. falciparum malaria in the USA, as reported to the National Malaria Surveillance System at the Centers for Disease Control and Prevention. Descriptive analyses included demographics, baseline characteristics, clinical effectiveness, and safety. From May 2010 to April 2015, demographics and baseline characteristics were collected for 203 patients and safety data for 108 patients. Treatment effectiveness data at day 7 were collected for 117 patients and at day 28 for 98 patients. RESULTS: The majority of patients were male (58.6 %), Black (62.6 %), non-Hispanic (92.6 %), and likely malaria non-immune (80.8 %). The median age was 32 (range 1-88) years and the median body mass index was 25.5 (range 13.8-42.4) kg/m(2). All patients with effectiveness data had confirmed (n = 116) or suspected (n = 1) malaria. The overall cure rate for patients treated with AL was 91.5 % (95 % CI 84.8-95.8 %) at day 7 and 96.9 % (95 % CI 91.3-99.4 %) at day 28. Adverse events were reported in four (3.7 %) patients, and there were no new or unexpected safety signals. CONCLUSION: AL was effective and well tolerated in the treatment of likely non-immune patients with P. falciparum malaria.

Chlamydia trachomatis infections among women attending prenatal clinics: United States, 2004-2009.

BACKGROUND: Chlamydia screening practices, positivity, and trends from 2004 to 2009 in publicly funded prenatal clinics have not been described. METHODS: A phone-based survey assessing chlamydia screening practices was conducted among a random sample of clinics providing prenatal services (prenatal, family planning, and integrated clinics: "prenatal clinics") that reported data to the Infertility Prevention Project (IPP) in 2008. Using existing IPP data, chlamydia positivity and trends were assessed among women aged 15 to 24 years seeking care in any prenatal clinic reporting ≥3 years of data to IPP from 2004 to 2009. Linear trends of the effect of year (a continuous variable) on positivity were evaluated using a correlated modeling approach with a random intercept where the unit of analysis was the individual clinic performing chlamydia tests (clinic-based analysis). Covariates included race, age, test technology, and geography. RESULTS: Of 210 sampled clinics, 166 (79%) completed the survey. Of these, 163 (98.2%) had documented chlamydia screening criteria. Most clinics screened all women during their first trimester and reported 100% screening coverage. From 2004 to 2009, 267,416 tests among women aged 15 to 24 years were reported to IPP from eligible prenatal clinics. Overall chlamydia positivity was 8.3%. Controlling for all covariates, positivity decreased from 2004 to 2009 (odds ratio: 0.93 per year, 95% confidence interval: 0.92, 0.95, 35% decrease overall). CONCLUSIONS: The substantial burden of chlamydia among young women tested in prenatal clinics reporting data to IPP suggests the continued need for routine screening. Decreasing trends from 2004 to 2009 in the IPP prenatal population correspond to findings of overall decreasing chlamydia prevalence in the United States.