Evaluating the performance of Bayesian geostatistical prediction with physical barriers in the Chesapeake Bay

The Chesapeake Bay is one of the most widely studied bodies of water in the United States and around the world. Routine monitoring of water quality indicators (e.g., salinity) relies on fixed sampling stations throughout the Bay. Utilizing this rich monitoring data, various methods produce surface predictions of water quality indicators to further characterize the health of the Bay as well as to support wildlife and human health research studies. Bayesian approaches for geostatistical modelling are becoming increasingly popular and can be preferred over frequentist approaches because full and exact inference can be computed, along with more accurate characterization of uncertainty. Traditional geostatistical prediction methods assume a Euclidean distance between two points when characterizing spatial dependence as a function of distance. However, Euclidean approaches may not be appropriate in estuarine environments when water-land boundaries are crossed during the modelling process. In this study, we compare stationary and barrier INLA geostatistical models with a classic kriging geostatistical model to predict salinity in the Chesapeake Bay during 4 months in 2019. Cross-validation is conducted for each approach to evaluate model performance based on prediction accuracy and precision. The results provide evidence that the two Bayesian-based models outperformed ordinary kriging, especially when examining prediction accuracy (most notably in the tributaries). We also suggest that the non-Euclidean model accounts for the appropriate water-based distances between sampling locations and is likely better at characterizing the uncertainty. However, more complex bodies of water may better showcase the capabilities and efficacy of the physical barrier INLA model.

Developing a geostatistical simulation method to inform the quantity and placement of new monitors for a follow-up air sampling campaign

Sampling campaign design is a crucial aspect of air pollution exposure studies. Selection of both monitor numbers and locations is important for maximizing measured information, while minimizing bias and costs. We developed a two-stage geostatistical-based method using pilot NO₂ samples from Lanzhou, China with the goal of improving sample design decision-making, including monitor numbers and spatial pattern. In the first step, we evaluate how additional monitors change prediction precision through minimized kriging variance. This was assessed in a Monte Carlo fashion by adding up to 50 new monitors to our existing sites with assigned concentrations based on conditionally simulated NO₂ surfaces. After identifying a number of additional sample sites, a second step evaluates their potential placement using a similar Monte Carlo scheme. Evaluations are based on prediction precision and accuracy. Costs are also considered in the analysis. It was determined that adding 28-locations to the existing Lanzhou NO₂ sampling campaign captured 73.5% of the total kriged variance improvement and resulted in predictions that were on average within 10.9 μg/m³ of measured values, while using 56% of the potential budget. Additional monitor sites improved kriging variance in a nonlinear fashion. This method development allows for informed sampling design by quantifying prediction improvement (accuracy and precision) against the costs of monitor deployment.

School environmental conditions and links to academic performance and absenteeism in urban, mid-Atlantic public schools

School facility conditions, environment, and perceptions of safety and learning have been investigated for their impact on child development. However, it is important to consider how the environment separately influences academic performance and attendance after controlling for school and community factors. Using results from the Maryland School Assessment, we considered outcomes of school-level proficiency in reading and math plus attendance and chronic absences, defined as missing 20 or more days, for grades 3-5 and 6-8 at 158 urban schools. Characteristics of the environment included school facility conditions, density of nearby roads, and an index industrial air pollution. Perceptions of school safety, learning, and institutional environment were acquired from a School Climate Survey. Also considered were neighborhood factors at the community statistical area, including demographics, crime, and poverty based on school location. Poisson regression adjusted for over-dispersion was used to model academic achievement and multiple linear models were used for attendance. Each 10-unit change in facility condition index, denoting worse quality buildings, was associated with a decrease in reading (1.0% (95% CI: 0.1-1.9%) and math scores (0.21% (95% CI: 0.20-0.40), while chronic absences increased by 0.75% (95% CI: 0.30-1.39). Each log increase the EPA's Risk Screening Environmental Indicator (RSEI) value for industrial hazards, resulted in a marginally significant trend of increasing absenteeism (p < 0.06), but no association was observed with academic achievement. All results were robust to school-level measures of racial composition, free and reduced meals eligibility, and community poverty and crime. These findings provide empirical evidence for the importance of the community and school environment, including building conditions and neighborhood toxic substance risk, on academic achievement and attendance.

The association between extreme precipitation and waterborne disease outbreaks in the United States, 1948-1994

OBJECTIVES

Rainfall and runoff have been implicated in site-specific waterborne disease outbreaks. Because upward trends in heavy precipitation in the United States are projected to increase with climate change, this study sought to quantify the relationship between precipitation and disease outbreaks.

METHODS

The US Environmental Protection Agency waterborne disease database, totaling 548 reported outbreaks from 1948 through 1994, and precipitation data of the National Climatic Data Center were used to analyze the relationship between precipitation and waterborne diseases. Analyses were at the watershed level, stratified by groundwater and surface water contamination and controlled for effects due to season and hydrologic region. A Monte Carlo version of the Fisher exact test was used to test for statistical significance.

RESULTS

Fifty-one percent of waterborne disease outbreaks were preceded by precipitation events above the 90th percentile (P = .002), and 68% by events above the 80th percentile (P = .001). Outbreaks due to surface water contamination showed the strongest association with extreme precipitation during the month of the outbreak; a 2-month lag applied to groundwater contamination events.

CONCLUSIONS

The statistically significant association found between rainfall and disease in the United States is important for water managers, public health officials, and risk assessors of future climate change.

Fine particulate air pollution and mortality in 20 U.S. cities, 1987-1994

BACKGROUND

Air pollution in cities has been linked to increased rates of mortality and morbidity in developed and developing countries. Although these findings have helped lead to a tightening of air-quality standards, their validity with respect to public health has been questioned.

METHODS

We assessed the effects of five major outdoor-air pollutants on daily mortality rates in 20 of the largest cities and metropolitan areas in the United States from 1987 to 1994. The pollutants were particulate matter that is less than 10 microm in aerodynamic diameter (PM10), ozone, carbon monoxide, sulfur dioxide, and nitrogen dioxide. We used a two-stage analytic approach that pooled data from multiple locations.

RESULTS

After taking into account potential confounding by other pollutants, we found consistent evidence that the level of PM10 is associated with the rate of death from all causes and from cardiovascular and respiratory illnesses. The estimated increase in the relative rate of death from all causes was 0.51 percent (95 percent posterior interval, 0.07 to 0.93 percent) for each increase in the PM10 level of 10 microg per cubic meter. The estimated increase in the relative rate of death from cardiovascular and respiratory causes was 0.68 percent (95 percent posterior interval, 0.20 to 1.16 percent) for each increase in the PM10 level of 10 microg per cubic meter. There was weaker evidence that increases in ozone levels increased the relative rates of death during the summer, when ozone levels are highest, but not during the winter. Levels of the other pollutants were not significantly related to the mortality rate.

CONCLUSIONS

There is consistent evidence that the levels of fine particulate matter in the air are associated with the risk of death from all causes and from cardiovascular and respiratory illnesses. These findings strengthen the rationale for controlling the levels of respirable particles in outdoor air.

The Effects of Institutional and Community Experiences on Nursing Students' Intentions Toward Work With the Elderly

The intentions of nursing students toward working with older adults are similar to those of nurses in general. Several authors have suggested that educational interventions are the key to reversing the reluctance of nursing students to work with elderly persons. In this longitudinal study, the intentions of 39 junior baccalaureate nursing students were examined at three points: prior to any treatment, after clinical work with aged persons in an institutional setting, and after clinical work with aged persons in a community setting. The analysis of variance model run on this data revealed no significant differences in students' intentions as a consequence of their clinical experiences.