Maximum Log Likelihood Estimation using EM Algorithm and Partition Maximum Log Likelihood Estimation for Mixtures of Generalized Lambda Distributions

Two mixture distribution fitting methods based on maximizing the likelihood using generalized lambda distributions are presented. The fitting algorithms are demonstrated on various data and the strengths and weakness of the algorithms which can influence their use under different mixture modeling situations are discussed. The procedures described are available in GLDEX package in R

Multivariate Air Pollutant Exposure Prediction and Characterization

Background: Air pollution is associated with adverse health outcomes ranging from increased respiratory incidence to increased mortality; however, the health impacts from exposure to multiple pollutants remain unclear. Large gaps in knowledge remain for developing flexible models that address the decomposition of chemical mixtures in relation to health outcomes. In particular, application of complex fusion models, which combine observed and modeled data, to areas with sparse monitoring networks with multiple chemical components is under-developed. Objective: The overarching objective of this proposal is to improve health effects studies of air pollution by improving predictive capabilities of multipollutant exposure characterizations across space and time. Approach: This project focuses on the development of methods for improved estimation of pollutant concentrations when only sparse monitor networks are found. (Aim 1) Particularly, a multivariate air pollutant statistical model to predict spatiotemporally resolved concentration fields for multiple pollutants is developed and evaluated. (Aim 2) Following on from that work a Bayesian latent grouping model for chemical mixtures is proposed and tested on existing air quality data and health registry data. Simulated evaluation with known mixtures classes and their health effects is also proposed. (Aim 3) Finally we propose to apply the developed methods to the analysis of COPD exacerbations resulting in hospitalization in South Carolina. We will also develop an implementation of the software from this work to allow greater public access to the methodology derived. Impact: These methods utilize only widely available data resources, meaning that the improved predictive accuracy of sparsely monitored pollutant concentrations can benefit future studies by improving estimation of health effects and saving resources needed for supplemental monitoring campaigns. Further, the characterization of air pollution as mixtures provides a more realistic understanding of the health impact of ambient air quality. Finally, the case study demonstrates the feasibility and ease of application of our methods (by using the software we develop) for future researchers, providing an example to be replicated and/or emulated by others

Interfacial properties of reservoir fluids and carbon dioxide with impurities

Interfacial tension measurements of the binary systems (N2 + H2O), (Ar + H2O), and (H2 + H2O), and ternary systems (CO2 + N2 + H2O), (CO2 + Ar + H2O) and (CO2 + H2 + H2O), are reported at pressures of (0.5 to 50.0) MPa, and temperatures of (298.15 to 473.15) K. The design of a custom-built Interfacial Properties Rig was detailed. The pendant drop method was used. The expanded uncertainties at 95 % confidence are 0.05 K for temperature; 0.07 MPa for pressure; 0.019•γ for interfacial tension in the (N2 + H2O) system; 0.016•γ for interfacial tension in the (Ar + H2O) system; 0.017•γ for interfacial tension in the (H2 + H2O) system; 0.032•γ for interfacial tension in the (CO2 + N2 + H2O) system; 0.018•γ for interfacial tension in the (CO2 + Ar + H2O) system; and 0.017•γ for interfacial tension in the (CO2 + H2 + H2O) system. The interfacial tensions of all systems were found to decrease with increasing pressure. The use of SGT + SAFT-VR Mie to model interfacial tensions of the binary and ternary systems was reported, for systems involving CO2, N2 and Ar. The binary systems (N2 + H2O) and (Ar + H2O), and ternary systems (CO2 + N2 + H2O) and (CO2 + Ar + H2O), were modelled with average absolute relative deviations of 1.5 %, 1.8 %, 3.6 % and 7.9 % respectively. For the (CO2 + Ar + H2O) system, the agreement is satisfactory at the higher temperatures, but differs significantly at the lower temperatures. Contact angles of (CO2 + brine) and (CO2 + N2 + brine) systems on calcite surfaces have also been measured, at 333 K and 7 pressures, from (2 to 50) MPa, for a 1 mol•kg-1 NaHCO3 brine solution, using the static method on captive bubbles.Open Acces

The Application of Time Series Analysis to Injury Epidemiology Data

Introduction: Injury fatality rates in the United States (US) decreased throughout the majority of the 20th century, mostly due to declining rates of occupational and motor vehicle injuries. However, near the beginning of the 21st century, fatal injury rates in the US began to increase. This is principally due to the nation’s opioid epidemic, which has been characterized by different epidemic “waves”, each driven by overdoses associated with specific substances. Given the temporally dynamic nature of US injury trends, this study aimed to explore the application of time series analysis to injury data in the US. First, rates of non-fatal occupational injuries treated in US emergency departments were assessed to determine if non-fatal occupational injury rates mirror the historic decline of fatal occupational injuries in the 20th and 21st centuries. Next, we explored the temporal shift from prescription to illicit opioid overdose deaths in West Virginia (WV) to elucidate the transition between the opioid epidemic’s first and second waves in the state with the highest fatality rates in the nation. Finally, we compared the forecasting performance of three time series models when applied to national US opioid overdose data to explore what time series approaches best predict future rates of overdose. Methods: Study one assessed temporal trends in non-fatal occupational injuries treated in US emergency departments (EDs) using data the National Electronic Injury Surveillance System – Occupational Supplement (NEISS-Work) dataset. Descriptive statistics were used to assess annual injury rate estimates and monthly seasonality. Autoregressive integrated moving average (ARIMA) modeling was used to quantify trends in ED-treated occupational injury rate estimates while controlling for serial data correlation. Analyses were conducted both overall and stratified by injury event type. Study two used data from the Drug Enforcement Agency’s (DEA) Automation of Reports and Consolidated Orders System (ARCOS) database (accessed via The Washington Post) to determine when shipments of oxycodone and hydrocodone tablets to WV began decreasing; tablet shipments were measured both as dosage units and morphine milligram equivalents (MMEs). To identify the exact point when tablet shipments began decreasing, we used locally estimated scatterplot smoothing (LOESS). The point when total tablet shipments began decreasing was used as an intervention point in an interrupted time series analysis (ITSA) of prescription and illicit opioid overdose death rates calculated using data from the WV Forensic Drug Database (FDD), which collects drug death data from the WV Office of the Chief Medical Examiner. Prescription opioid deaths were defined as those involving oxycodone or hydrocodone, while illicit opioid overdoses were defined as those involving heroin or synthetic opioids other than methadone. The ITSA impact of the LOESS-identified points was compared via Akaike Information Criteria (AIC) to that of the 2010 release of an abuse deterrent formulation (ADF) of OxyContin, which is widely cited as a driving factor initiating the transition between the opioid epidemic’s first and second waves. Study three examined the forecasting performance of ARIMA; Error, Trend, and Seasonality (ETS); and Facebook Prophet models when applied to national US opioid overdose death data, both overall and stratified by the type of opioid involved in overdoses. Overdose death counts were extracted from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) database. Overdose death rates were calculated using monthly all-cause ii mortality as a denominator. Forecasts were validated used time series cross validation (TSCV), while forecast bias and predictive coverage probability were measured using mean average percent error (MAPE) and Winkler Scores, respectively. Results: Study one found that US ED-treated non-fatal occupational injury rate estimates were highest in 2012 and lowest in 2019. Apart from falls, slips, and trips, all injuries occurred at the highest rate in a summer month. ARIMA modeling found that there was a significant decrease in monthly rate estimates for 2012-2019. Study two found that the point at which opioid tablet shipments (measured via dosage units) to WV began decreasing had a greater impact on changing rates of prescription and illicit opioid overdose rates than the 2010 ADF OxyContin release. Study three found that ETS models accurately forecasted monthly rates US opioid- involved overdoses while maintaining a high degree of precision relative to ARIMA or Facebook Prophet, particularly during the opioid epidemic’s fentanyl-dominated third wave. Discussion: The findings presented here indicate that although occupational injury rates have likely continued their decades-long decline in the US, the nation’s opioid epidemic has contributed significantly to recent US injury rate increases and is temporally dynamic. Future research should explore trends in other injury data by expanding the methodology used here to other epidemiological contexts

Extremely cold and hot temperatures increase the risk of ischaemic heart disease mortality: epidemiological evidence from China.

OBJECTIVE: To examine the effects of extremely cold and hot temperatures on ischaemic heart disease (IHD) mortality in five cities (Beijing, Tianjin, Shanghai, Wuhan and Guangzhou) in China; and to examine the time relationships between cold and hot temperatures and IHD mortality for each city. DESIGN: A negative binomial regression model combined with a distributed lag non-linear model was used to examine city-specific temperature effects on IHD mortality up to 20 lag days. A meta-analysis was used to pool the cold effects and hot effects across the five cities. PATIENTS: 16 559 IHD deaths were monitored by a sentinel surveillance system in five cities during 2004-2008. RESULTS: The relationships between temperature and IHD mortality were non-linear in all five cities. The minimum-mortality temperatures in northern cities were lower than in southern cities. In Beijing, Tianjin and Guangzhou, the effects of extremely cold temperatures were delayed, while Shanghai and Wuhan had immediate cold effects. The effects of extremely hot temperatures appeared immediately in all the cities except Wuhan. Meta-analysis showed that IHD mortality increased 48% at the 1st percentile of temperature (extremely cold temperature) compared with the 10th percentile, while IHD mortality increased 18% at the 99th percentile of temperature (extremely hot temperature) compared with the 90th percentile. CONCLUSIONS: Results indicate that both extremely cold and hot temperatures increase IHD mortality in China. Each city has its characteristics of heat effects on IHD mortality. The policy for response to climate change should consider local climate-IHD mortality relationships