Mark Gold

The health effects of swimming in ocean water contaminated by storm drain runoff

In a case-control study we assessed whether exposure to high job strain during the first 20 weeks of pregnancy increases the risk of preeclampsia and gestational hypertension. Cases (128 with preeclampsia and 201 with gestational hypertension) and controls (N = 401) were primiparous women who had a paid occupation for at least 1 week during the first 20 weeks of their pregnancy and who delivered between 1984 and 1986 in 10 hospitals of Quebec, Canada. Based on their job title, we assigned women scores of psychological demand and decision latitude derived from the National Population Health Survey and classified these women as exposed to high (high demand, low latitude) versus low (low demand, high latitude) job strain. Women exposed to high job strain were more likely to develop preeclampsia [adjusted odds ratio (aOR) = 2.1; 95% confidence interval (CI) = 1.1-4.1] than women exposed to low job strain. The risk was quite similar for women exposed to a full-time, high strain job (> or =35 hours per week) (aOR = 2.0) than in a part-time, high strain job (aOR = 1.8). High job strain increased the risk of gestational hypertension slightly (aOR = 1.3; 95% CI = 0.8-2.2). These results indicate that women exposed to high job strain are at higher risk of developing preeclampsia and, to a lesser extent, gestational hypertension.

A sea change ahead for recreational water quality criteria

The United States Environmental Protection Agency is committed to developing new recreational water quality criteria for coastal waters by 2012 to provide increased protection to swimmers. We review the uncertainties and shortcomings of the current recreational water quality criteria, describe critical research needs for the development of new criteria, as well as recommend a path forward for new criteria development. We believe that among the most needed research needs are the completion of epidemiology studies in tropical waters and in waters adversely impacted by urban runoff and animal feces, as well as studies aimed to validate the use of models for indicator and pathogen concentration and health risk predictions.

Using rapid indicators for Enterococcus to assess the risk of illness after exposure to urban runoff contaminated marine water

BACKGROUND Traditional fecal indicator bacteria (FIB) measurement is too slow (>18 h) for timely swimmer warnings. OBJECTIVES Assess relationship of rapid indicator methods (qPCR) to illness at a marine beach impacted by urban runoff. METHODS We measured baseline and two-week health in 9525 individuals visiting Doheny Beach 2007-08. Illness rates were compared (swimmers vs. non-swimmers). FIB measured by traditional (Enterococcus spp. by EPA Method 1600 or Enterolert™, fecal coliforms, total coliforms) and three rapid qPCR assays for Enterococcus spp. (Taqman, Scorpion-1, Scorpion-2) were compared to health. Primary bacterial source was a creek flowing untreated into ocean; the creek did not reach the ocean when a sand berm formed. This provided a natural experiment for examining FIB-health relationships under varying conditions. RESULTS We observed significant increases in diarrhea (OR 1.90, 95% CI 1.29-2.80 for swallowing water) and other outcomes in swimmers compared to non-swimmers. Exposure (body immersion, head immersion, swallowed water) was associated with increasing risk of gastrointestinal illness (GI). Daily GI incidence patterns were different: swimmers (2-day peak) and non-swimmers (no peak). With berm-open, we observed associations between GI and traditional and rapid methods for Enterococcus; fewer associations occurred when berm status was not considered. CONCLUSIONS We found increased risk of GI at this urban runoff beach. When FIB source flowed freely (berm-open), several traditional and rapid indicators were related to illness. When FIB source was weak (berm-closed) fewer illness associations were seen. These different relationships under different conditions at a single beach demonstrate the difficulties using these indicators to predict health risk.

Swimmer illness associated with marine water exposure and water quality indicators: Impact of widely used assumptions

Background: Studies of health risks associated with recreational water exposure require investigators to make choices about water quality indicator averaging techniques, exposure definitions, follow-up periods, and model specifications; however, investigators seldom describe the impact of these choices on reported results. Our objectives are to report illness risk from swimming at a marine beach affected by nonpoint sources of urban runoff, measure associations between fecal indicator bacteria levels and subsequent illness among swimmers, and investigate the sensitivity of results to a range of exposure and outcome definitions. Methods: In 2009, we enrolled 5674 people in a prospective cohort at Malibu Beach, a coastal marine beach in California, and measured daily health symptoms 10–19 days later. Concurrent water quality samples were analyzed for indicator bacteria using culture and molecular methods. We compared illness risk between nonswimmers and swimmers, and among swimmers exposed to various levels of fecal indicator bacteria. Results: Diarrhea was more common among swimmers than nonswimmers (adjusted odds ratio = 1.88 [95% confidence interval = 1.09–3.24]) within 3 days of the beach visit. Water quality was generally good (fecal indicator bacteria levels exceeded water quality guidelines for only 7% of study samples). Fecal indicator bacteria levels were not consistently associated with swimmer illness. Sensitivity analyses demonstrated that overall inference was not substantially affected by the choice of exposure and outcome definitions. Conclusions: This study suggests that the 3 days following a beach visit may be the most relevant period for health outcome measurement in recreational water studies. Under the water quality conditions observed in this study, fecal indicator bacteria levels were not associated with swimmer illness.

Predicting water quality at Santa Monica Beach: evaluation of five different models for public notification of unsafe swimming conditions.

Bathing beaches are monitored for fecal indicator bacteria (FIB) to protect swimmers from unsafe conditions. However, FIB assays take ∼24 h and water quality conditions can change dramatically in that time, so unsafe conditions cannot presently be identified in a timely manner. Statistical, data-driven predictive models use information on environmental conditions (i.e., rainfall, turbidity) to provide nowcasts of FIB concentrations. Their ability to predict real time FIB concentrations can make them more accurate at identifying unsafe conditions than the current method of using day or older FIB measurements. Predictive models are used in the Great Lakes, Hong Kong, and Scotland for beach management, but they are presently not used in California - the location of some of the worlds most popular beaches. California beaches are unique as point source pollution has generally been mitigated, the summer bathing season receives little to no rainfall, and in situ measurements of turbidity and salinity are not readily available. These characteristics may make modeling FIB difficult, as many current FIB models rely heavily on rainfall or salinity. The current study investigates the potential for FIB models to predict water quality at a quintessential California Beach: Santa Monica Beach. This study compares the performance of five predictive models, multiple linear regression model, binary logistic regression model, partial least square regression model, artificial neural network, and classification tree, to predict concentrations of summertime fecal coliform and enterococci concentrations. Past measurements of bacterial concentration, storm drain condition, and tide level are found to be critical factors in the predictive models. The models perform better than the current beach management method. The classification tree models perform the best; for example they correctly predict 42% of beach postings due to fecal coliform exceedances during model validation, as compared to 28% by the current method. Artificial neural network is the second best model which minimizes the number of incorrect beach postings. The binary logistic regression model also gives promising results, comparable to classification tree, by adjusting the posting decision thresholds to maximize correct beach postings. This study indicates that predictive models hold promise as a beach management tool at Santa Monica Beach. However, there are opportunities to further refine predictive models.

Sunny with a chance of gastroenteritis: predicting swimmer risk at California beaches.

Traditional beach management that uses concentrations of cultivatable fecal indicator bacteria (FIB) may lead to delayed notification of unsafe swimming conditions. Predictive, nowcast models of beach water quality may help reduce beach management errors and enhance protection of public health. This study compares performances of five different types of statistical, data-driven predictive models: multiple linear regression model, binary logistic regression model, partial least-squares regression model, artificial neural network, and classification tree, in predicting advisories due to FIB contamination at 25 beaches along the California coastline. Classification tree and the binary logistic regression model with threshold tuning are consistently the best performing model types for California beaches. Beaches with good performing models usually have a rainfall/flow related dominating factor affecting beach water quality, while beaches having a deteriorating water quality trend or low FIB exceedance rates are less likely to have a good performing model. This study identifies circumstances when predictive models are the most effective, and suggests that using predictive models for public notification of unsafe swimming conditions may improve public health protection at California beaches relative to current practices.

Systems Analysis and Optimization of Local Water Supplies in Los Angeles

AbstractLos Angeles, which relies on large infrastructure systems that import water over hundreds of miles, faces a future of reduced imports. Within Los Angeles and its hundreds of water agencies,...

The economic value of local water supplies in Los Angeles

Los Angeles imports water over long distances to supplement local supplies. Reduced reliability of the available imports is driving many local agencies to promote conservation and enhance local water sources. These include stormwater capture, water reuse and groundwater. But financial considerations are often a significant impediment to project development, especially when comparing new and existing sources. Here we demonstrate a comprehensive approach for evaluating the economic implications of shifting to local water reliance in Los Angeles County. We show that local water supplies are economically competitive. Results from integrated hydroeconomic modelling of urban water in Los Angeles identify cost-effective water supply portfolios and conservation targets. Considering costs across the ‘full-cycles’ of urban water supply that span agency boundaries yields better comparisons of planning alternatives. Throughout the region, many water retailers could successfully mitigate effects of imported water cuts while still supporting drought-tolerant landscapes, but some would suffer due to over-reliance on imports. Updating economic assessment methods would support needed innovations to achieve local reliance in Los Angeles, including infrastructure investments, institutional reforms, many more drought-tolerant landscapes and reallocated groundwater rights.A large-scale economic analysis of the economics of water supplies in the greater Los Angeles area, based on the ‘full-cycle’ costs of water sources such as imported water, groundwater, and reused and storm-water capture. The study showcases an updated model and framework for urban water studies that can be applied to other cities.

Implementation of an automated beach water quality nowcast system at ten California oceanic beaches

Fecal indicator bacteria like Escherichia coli and entercococci are monitored at beaches around the world to reduce incidence of recreational waterborne illness. Measurements are usually made weekly, but FIB concentrations can exhibit extreme variability, fluctuating at shorter periods. The result is that water quality has likely changed by the time data are provided to beachgoers. Here, we present an automated water quality prediction system (called the nowcast system) that is capable of providing daily predictions of water quality for numerous beaches. We created nowcast models for 10 California beaches using weather, oceanographic, and other environmental variables as input to tuned regression models to predict if FIB concentrations were above single sample water quality standards. Rainfall was used as a variable in nearly every model. The models were calibrated and validated using historical data. Subsequently, models were implemented during the 2017 swim season in collaboration with local beach managers. During the 2017 swim season, the median sensitivity of the nowcast models was 0.5 compared to 0 for the current method of using day-to-week old measurements to make beach posting decisions. Model specificity was also high (median of 0.87). During the implementation phase, nowcast models provided an average of 140 additional days per beach of updated water quality information to managers when water quality measurements were not made. The work presented herein emphasizes that a one-size-fits all approach to nowcast modeling, even when beaches are in close proximity, is infeasible. Flexibility in modeling approaches and adaptive responses to modeling and data challenges are required when implementing nowcast models for beach management.