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The elephant in the room: Rapid population growth

It’s Raining Babies? Flooding and Fertility Choices in Bangladesh

Global Warming and the Demographic Future

Assistant Professor Jennifer Roberts will be a presenter for the University of Washington's Nature and Health Spring Talk series

Neighborhood attributes have been shown to influence health, but advances in neighborhood research has been constrained by the lack of neighborhood data for many geographical areas and few neighborhood studies examine features of nonmetropolitan locations. We leveraged a massive source of Google Street View (GSV) images and computer vision to automatically characterize national neighborhood built environments. Using road network data and Google Street View API, from December 15, 2017-May 14, 2018 we retrieved over 16 million GSV images of street intersections across the United States. Computer vision was applied to label each image. We implemented regression models to estimate associations between built environments and county  health outcomes , controlling for county-level demographics, economics, and  population density . At the county level, greater presence of highways was related to lower chronic diseases and  premature mortality . Areas characterized by street view images as ‘rural’ (having limited infrastructure) had higher obesity,  diabetes , fair/poor self-rated health, premature mortality, physical distress, physical inactivity and teen birth rates but lower rates of excessive drinking. Analyses at the  census  tract level for 500 cities revealed similar adverse associations as was seen at the county level for neighborhood indicators of less urban development. Possible mechanisms include the greater abundance of services and facilities found in more developed areas with roads, enabling access to places and resources for promoting health. GSV images represents an underutilized resource for building national data on neighborhoods and examining the influence of built environments on community health outcomes across the United States.

Increasing number of studies have investigated the impact of maternal exposure to air pollution during pregnancy and adverse birth outcomes, particularly low birth weight (LBW, <2,500 g at birth) and preterm birth (PTB, <37 completed weeks of gestation). We performed a comprehensive review of the peer-reviewed literature and a meta-analysis to quantify the association between maternal exposure to particulate matter with aerodynamic diameter 2.5 and 10 μm (PM 2.5  and PM 10 ) during pregnancy and the risk of LBW and PTB. We identified 20 peer-reviewed articles providing quantitative estimate of exposure and outcome that met our selection criteria. There was significant heterogeneity between studies, particularly for findings related to PM 10  exposure (LBW,  I -squared 54%,  p  = 0.01; PTB,  I -squared = 73%,  p  < 0.01). Results from random-effect meta-analysis suggested a 9% increase in risk of LBW associated with a 10-μg/m 3  increase in PM 2.5  (combined odds ratios (OR), 1.09; 95% confidence interval (CI), 0.90–1.32), but our 95% CI included the null value. We estimated a 15% increase in risk of PTB for each 10-μg/m 3  increase in PM 2.5  (combined OR, 1.15; CI, 1.14–1.16). The magnitude of risk associated with PM 10  exposure was smaller (2% per 10-μg/m 3  increase) and similar in size for both LBW and PTB, neither reaching formal statistical significance. We observed no significant publication bias, with  p  > 0.05 based on both Begg's and Egger's bias tests. Our results suggest that maternal exposure to PM, particularly PM 2.5  may have adverse effect on birth outcomes. Additional mechanistic studies are needed to understand the underlying mechanisms for this association.

Plant phenology (e.g. timing of spring green-up, flowering) is among the most sensitive indicator of ecological response to ongoing climate variability and change. While previous studies have documented changes in the timing of spring green-up and flowering across different parts of the world, empirical evidence regarding how such ongoing ecological changes impact allergic disease burden at population level is lacking. Because earlier spring green-up may increase season length for tree pollen, we hypothesized that early onset of spring (negative anomaly in start of season (SOS)) will be associated with increased hay fever burden. To test this, we first calculated a median cardinal date for SOS for each county within the contiguous US for the years 2001-2013 using phenology data from the National Aeronautics and Space Administration's Moderate Resolution Imaging Spectroradiometer (MODIS). We categorized yearly deviations in SOS for each county from their respective long-term averages as: very early (>3 wks early), early (1-3 wks early), average (within 1 wk), late (1-3 wks late) and very late (> 3 wks late). We linked these data to 2002-2013 National Health Interview Survey data, and investigated the association between changes in SOS and hay fever prevalence using logistic regression. We observed that adults living in counties with a very early onset of SOS had a 14% higher odds of hay fever compared to the reference group, i.e. those living in counties where onset of spring was within the normal range (Odds Ratios (OR): 1.14. 95% Confidence Interval (CI): 1.03-1.27). Likewise, adults living in counties with very late onset of SOS had a 18% higher odds hay fever compared to the reference group (OR: 1.18, CI: 1.05-1.32). Our data provides the first-ever national scale assessment of the impact of changing plant phenology-linked to ongoing climate variability and change-on hay fever prevalence. Our findings are likely tied to changes in pollen dynamics, i.e early onset of spring increases the duration of exposure to tree pollen, while very late onset of spring increases the propensity of exposure because of simultaneous blooming.

Agricultural water withdrawals account for the largest proportion of global freshwater use. Increasing municipal water demands and droughts are straining agricultural water supplies. Therefore, alternative solutions to agricultural water crises are urgently needed, including the use of nontraditional water sources such as advanced treated wastewater or reclaimed water, brackish water, return flows, and effluent from produce processing facilities. However, it is critical to ensure that such usage does not compromise soil, crop, and public health. Here, we characterized five different nontraditional water types (n = 357 samples) for the presence of pharmaceuticals, herbicides, and disinfectants using ultra-high-pressure liquid chromatography tandem mass spectrometry based method (UPLC-MS/MS). We then evaluated whether the levels of these contaminants were influenced by season. The highest level of herbicides (atrazine) was detected in untreated pond water (median concentration 135.9 ng/L). Reclaimed water had the highest levels of antibiotics and stimulants including azithromycin (215 ng/L), sulfamethoxazole (232.1 ng/L), and caffeine (89.4 ng/L). Produce processing plant water also tended to have high levels of atrazine (102.7 ng/L) and ciprofloxacin (80.1 ng/L). In addition, we observed seasonal variability across water types, with the highest atrazine concentrations observed during summer months, while the highest median azithromycin concentrations were observed in reclaimed water during the winter season. Further studies are needed to evaluate if economically feasible on-farm water treatment technologies can effectively remove such contaminants from nontraditional irrigation water sources.

Greywater is increasingly treated and reused for agricultural irrigation in off-grid communities in the Middle East and other water scarce regions of the world. However, there is a dearth of data regarding levels of antibiotics and herbicides in off-grid greywater treatment systems. To address this knowledge gap, we evaluated levels of these contaminants in two types of greywater treatment systems on four farms in the West Bank, Palestinian Territories. Samples of household greywater (influent, n = 23), treated greywater effluent intended for agricultural irrigation (n = 23) and pumped groundwater held in irrigation water ponds (n = 12) were collected from October 2017 to June 2018. Samples were analyzed using high performance liquid chromatography tandem mass spectrometry (LC-MS/MS) for the following antibiotics and herbicides: alachlor, ampicillin, atrazine, azithromycin, ciprofloxacin, erythromycin, linezolid, oxacillin, oxolinic acid, penicillin G, pipemidic acid, sulfamethoxazole, triclocarban, tetracycline, triflualin, and vancomycin. All tested antibiotics and herbicides were detected in greywater influent samples at concentrations ranging from 1.3 to 1592.9 ng/L and 3.1–22.4 ng/L, respectively. When comparing influent to effluent concentrations, removal was observed for azithromycin, alachlor, linezolid, oxacillin, penicillin G, pipemidic acid, sulfamethoxazole, triclocarban, and vancomycin. Removal was not observed for atrazine, ciprofloxacin, erythromycin, oxolinic acid, tetracycline, and trifluralin. Pond water also contained the majority of tested contaminants, but at generally lower concentrations. To our knowledge, this is the first description of an extensive array of antibiotics and herbicides detected in household greywater from off-grid treatment systems.

  BACKGROUND: Stroke is a leading cause of morbidity and mortality in the United States. Associations between short-term exposures to particulate matter (PM) air pollution and stroke are inconsistent. Many prior studies have used administrative and hospitalization databases where misclassification of the type and timing of the stroke event may be problematic. METHODS: In this case-crossover study, we used a nationwide kriging model to examine short-term ambient exposure to PM10 and PM2.5 and risk of ischemic and hemorrhagic stroke among men enrolled in the Health Professionals Follow-up Study. Conditional logistic regression models were used to obtain estimates of odds ratios (OR) and 95% confidence intervals (CI) associated with an interquartile range (IQR) increase in PM2.5 or PM10. Lag periods up to 3 days prior to the stroke event were considered in addition to a 4-day average. Stratified models were used to examine effect modification by patient characteristics. RESULTS: Of the 727 strokes that occurred between 1999 and 2010, 539 were ischemic and 122 were hemorrhagic. We observed positive statistically significant associations between PM10 and ischemic stroke (ORlag0-3 = 1.26; 95% CI: 1.03-1.55 per IQR increase [14.46 μg/m3]), and associations were elevated for nonsmokers, aspirin nonusers, and those without a history of high cholesterol. However, we observed no evidence of a positive association between short-term exposure to PM and hemorrhagic stroke or between PM2.5 and ischemic stroke in this cohort. CONCLUSIONS: Our study provides evidence that ambient PM10 may be associated with higher risk of ischemic stroke and highlights that ischemic and hemorrhagic strokes are heterogeneous outcomes that should be treated as such in analyses related to air pollution.