Health Benefits of Strategies for Carbon Mitigation in US Transportation, 2017‒2050.

Objectives. To quantify health benefits and carbon emissions of 2 transportation scenarios that contrast optimum levels of physical activity from active travel and minimal air pollution from electric cars. Methods. We used data on burden of disease, travel, and vehicle emissions in the US population and a health impact model to assess health benefits and harms of physical activity from transportation-related walking and cycling, fine particulate pollution from car emissions, and road traffic injuries. We compared baseline travel with walking and cycling a median of 150 weekly minutes for physical activity, and with electric cars that minimized carbon pollution and fine particulates. Results. In 2050, the target year for carbon neutrality, the active travel scenario avoided 167 000 deaths and gained 2.5 million disability-adjusted life years, monetized at $1.6 trillion using the value of a statistical life. Carbon emissions were reduced by 24% from baseline. Electric cars avoided 1400 deaths and gained 16 400 disability-adjusted life years, monetized at $13 billion. Conclusions. To achieve carbon neutrality in transportation and maximize health benefits, active travel should have a prominent role along with electric vehicles in national blueprints. (Am J Public Health. 2022; 112(3):426-433. https://doi.org/10.2105/AJPH.2021.306600).

Enteric Viruses and Pepper Mild Mottle Virus Show Significant Correlation in Select Mid-Atlantic Agricultural Waters.

Enteric viruses (EVs) are the largest contributors to foodborne illnesses and outbreaks globally. Their ability to persist in the environment, coupled with the challenges experienced in environmental monitoring, creates a critical aperture through which agricultural crops may become contaminated. This study involved a 17-month investigation of select human EVs and viral indicators in nontraditional irrigation water sources (surface and reclaimed waters) in the Mid-Atlantic region of the United States. Real-time quantitative PCR was used for detection of Aichi virus, hepatitis A virus, and norovirus genotypes I and II (GI and GII, respectively). Pepper mild mottle virus (PMMoV), a common viral indicator of human fecal contamination, was also evaluated, along with atmospheric (air and water temperature, cloud cover, and precipitation 24 h, 7 days, and 14 days prior to sample collection) and physicochemical (dissolved oxygen, pH, salinity, and turbidity) data, to determine whether there were any associations between EVs and measured parameters. EVs were detected more frequently in reclaimed waters (32% [n = 22]) than in surface waters (4% [n = 49]), similar to PMMoV detection frequency in surface (33% [n = 42]) and reclaimed (67% [n = 21]) waters. Our data show a significant correlation between EV and PMMoV (R2 = 0.628, P < 0.05) detection levels in reclaimed water samples but not in surface water samples (R2 = 0.476, P = 0.78). Water salinity significantly affected the detection of both EVs and PMMoV (P < 0.05), as demonstrated by logistic regression analyses. These results provide relevant insights into the extent and degree of association between human (pathogenic) EVs and water quality data in Mid-Atlantic surface and reclaimed waters, as potential sources for agricultural irrigation. IMPORTANCE Microbiological analysis of agricultural waters is fundamental to ensure microbial food safety. The highly variable nature of nontraditional sources of irrigation water makes them particularly difficult to test for the presence of viruses. Multiple characteristics influence viral persistence in a water source, as well as affecting the recovery and detection methods that are employed. Testing for a suite of viruses in water samples is often too costly and labor-intensive, making identification of suitable indicators for viral pathogen contamination necessary. The results from this study address two critical data gaps, namely, EV prevalence in surface and reclaimed waters of the Mid-Atlantic region of the United States and subsequent evaluation of physicochemical and atmospheric parameters used to inform the potential for the use of indicators of viral contamination.

Role of extreme weather events and El Niño Southern Oscillation on incidence of Enteric Fever in Ahmedabad and Surat, Gujarat, India.

INTRODUCTION: Enteric Fever (EF) affects over 14.5 million people every year globally, with India accounting for the largest share of this burden. The water-borne nature of the disease makes it prone to be influenced as much by unsanitary living conditions as by climatic factors. The detection and quantification of the climatic effect can lead to improved public health measures which would in turn reduce this burden. METHODOLOGY: We obtained a list of monthly Widal positive EF cases from 1995 to 2017 from Ahmedabad and Surat Municipalities. We obtained population data, daily weather data, and Oceanic Niño Index values from appropriate sources. We quantified the association between extreme weather events, phases of El Niño Southern Oscillations (ENSO) and incidence of EF. RESULTS: Both cities showed a seasonal pattern of EF, with cases peaking in early monsoon. Risk of EF was affected equally in both cities by the monsoon season -- Ahmedabad (35%) and Surat (34%). Extreme precipitation was associated with 5% increase in EF in Ahmedabad but not in Surat. Similarly, phases of ENSO had opposite effects on EF across the two cities. In Ahmedabad, strong El Niño months were associated with 64% increase in EF risk while strong La Niña months with a 41% reduction in risk. In Surat, strong El Niño was associated with 25% reduction in risk while moderate La Niña with 21% increase in risk. CONCLUSIONS: Our results show that the risk of EF incidence in Gujarat is highly variable, even between the two cities only 260 kms apart. In addition to improvements in water supply and sewage systems, preventive public health measures should incorporate variability in risk across season and phases of ENSO. Further studies are needed to characterize nationwide heterogeneity in climate-mediated risk, and to identify most vulnerable populations that can benefit through early warning systems.

Climate change, extreme events, and increased risk of salmonellosis: foodborne diseases active surveillance network (FoodNet), 2004-2014.

BACKGROUND: Infections with nontyphoidal Salmonella cause an estimated 19,336 hospitalizations each year in the United States. Sources of infection can vary by state and include animal and plant-based foods, as well as environmental reservoirs. Several studies have recognized the importance of increased ambient temperature and precipitation in the spread and persistence of Salmonella in soil and food. However, the impact of extreme weather events on Salmonella infection rates among the most prevalent serovars, has not been fully evaluated across distinct U.S. regions. METHODS: To address this knowledge gap, we obtained Salmonella case data for S. Enteriditis, S. Typhimurium, S. Newport, and S. Javiana (2004-2014; n = 32,951) from the Foodborne Diseases Active Surveillance Network (FoodNet), and weather data from the National Climatic Data Center (1960-2014). Extreme heat and precipitation events for the study period (2004-2014) were identified using location and calendar day specific 95th percentile thresholds derived using a 30-year baseline (1960-1989). Negative binomial generalized estimating equations were used to evaluate the association between exposure to extreme events and salmonellosis rates. RESULTS: We observed that extreme heat exposure was associated with increased rates of infection with S. Newport in Maryland (Incidence Rate Ratio (IRR): 1.07, 95% Confidence Interval (CI): 1.01, 1.14), and Tennessee (IRR: 1.06, 95% CI: 1.04, 1.09), both FoodNet sites with high densities of animal feeding operations (e.g., broiler chickens and cattle). Extreme precipitation events were also associated with increased rates of S. Javiana infections, by 22% in Connecticut (IRR: 1.22, 95% CI: 1.10, 1.35) and by 5% in Georgia (IRR: 1.05, 95% CI: 1.01, 1.08), respectively. In addition, there was an 11% (IRR: 1.11, 95% CI: 1.04-1.18) increased rate of S. Newport infections in Maryland associated with extreme precipitation events. CONCLUSIONS: Overall, our study suggests a stronger association between extreme precipitation events, compared to extreme heat, and salmonellosis across multiple U.S. regions. In addition, the rates of infection with Salmonella serovars that persist in environmental or plant-based reservoirs, such as S. Javiana and S. Newport, appear to be of particular significance regarding increased heat and rainfall events.

Food insecurity and compound environmental shocks in Nepal: Implications for a changing climate.

Food insecurity is a key global health challenge that is likely to be exacerbated by climate change. Though climate change is associated with an increased frequency of extreme weather events, little is known about how multiple environmental shocks in close succession interact to impact household health and well-being. In this paper, we assess how earthquake exposure followed by monsoon rainfall anomalies affect food insecurity in Nepal. We link food security data from the 2016 Nepal Demographic and Health Survey to data on shaking intensity during the 2015 Gorkha earthquake and rainfall anomalies during the 2015 monsoon season. We then exploit spatial variation in exposure to the earthquake and monsoon rainfall anomalies to isolate their independent and compound effects. We find that earthquake exposure alone was not associated with an increased likelihood of food insecurity, likely due in part to effective food aid distribution. However, the effects of rainfall anomalies differed by severity of earthquake exposure. Among households minimally impacted by the earthquake, low rainfall was associated with increased food insecurity, likely due to lower agricultural productivity in drought conditions. Among households that experienced at least moderate shaking, greater rainfall was positively associated with food insecurity, particularly in steep, mountainous areas. In these locations, rainfall events disproportionately increased landslides, which damaged roads, disrupted distribution of food aid, and destroyed agricultural land and assets. Additional research on the social impacts of compound environmental shocks is needed to inform adaptation strategies that work to improve well-being in the face of climate change.

Longitudinal Assessment of the Dynamics of Escherichia coli, Total Coliforms, Enterococcus spp., and Aeromonas spp. in Alternative Irrigation Water Sources: a CONSERVE Study.

As climate change continues to stress freshwater resources, we have a pressing need to identify alternative (nontraditional) sources of microbially safe water for irrigation of fresh produce. This study is part of the center CONSERVE, which aims to facilitate the adoption of adequate agricultural water sources. A 26-month longitudinal study was conducted at 11 sites to assess the prevalence of bacteria indicating water quality, fecal contamination, and crop contamination risk (Escherichia coli, total coliforms [TC], Enterococcus, and Aeromonas). Sites included nontidal freshwater rivers/creeks (NF), a tidal brackish river (TB), irrigation ponds (PW), and reclaimed water sites (RW). Water samples were filtered for bacterial quantification. E. coli, TC, enterococci (∼86%, 98%, and 90% positive, respectively; n = 333), and Aeromonas (∼98% positive; n = 133) were widespread in water samples tested. Highest E. coli counts were in rivers, TC counts in TB, and enterococci in rivers and ponds (P < 0.001 in all cases) compared to other water types. Aeromonas counts were consistent across sites. Seasonal dynamics were detected in NF and PW samples only. E. coli counts were higher in the vegetable crop-growing (May-October) than nongrowing (November-April) season in all water types (P < 0.05). Only one RW and both PW sites met the U.S. Food Safety Modernization Act water standards. However, implementation of recommended mitigation measures of allowing time for microbial die-off between irrigation and harvest would bring all other sites into compliance within 2 days. This study provides comprehensive microbial data on alternative irrigation water and serves as an important resource for food safety planning and policy setting.IMPORTANCE Increasing demands for fresh fruit and vegetables, a variable climate affecting agricultural water availability, and microbial food safety goals are pressing the need to identify new, safe, alternative sources of irrigation water. Our study generated microbial data collected over a 2-year period from potential sources of irrigation (rivers, ponds, and reclaimed water sites). Pond water was found to comply with Food Safety Modernization Act (FSMA) microbial standards for irrigation of fruit and vegetables. Bacterial counts in reclaimed water, a resource that is not universally allowed on fresh produce in the United States, generally met microbial standards or needed minimal mitigation. We detected the most seasonality and the highest microbial loads in river water, which emerged as the water type that would require the most mitigation to be compliant with established FSMA standards. This data set represents one of the most comprehensive, longitudinal analyses of alternative irrigation water sources in the United States.

Groundwater level changes with a focus on agricultural areas in the Mid-Atlantic region of the United States, 2002-2016.

Climate change impacts all water sources, including high quality groundwater that supplies agricultural irrigation in many regions of the United States. This study assessed groundwater level changes in the U.S. Mid-Atlantic region with a focus on cultivated areas. Trends of groundwater level were estimated using linear regression, and examined for shallow, medium, and deep depths across physiographic regions of Mid-Atlantic. A hotspot analysis was conducted to identify spatial clusters of wells with rising or declining groundwater levels. In addition, differences in the percentage of cultivated area with declining groundwater between cultivated land categories was examined at the county level. From 2002-2016, the Mid-Atlantic region had an overall decline in groundwater level (0.06 m/yr, 95% CI: 0.03, 0.09) although groundwater changes varied by physiographic regions. The Coastal Plain physiographic region was dominated by declining groundwater wells (48%) and had the most significant groundwater level declines (0.23 m/yr, 95% CI: 0.19, 0.26). Significant groundwater level rises were detected in Southern Virginia adjacent to the Chesapeake Bay (0.92 m/yr on average), which could be due to the cessation of groundwater withdrawal from one of the region's largest groundwater users. In the Mid-Atlantic region, shallow groundwater was found to have slight rising trends (0.08 m, p < 0.05) while deeper groundwater showed distinctive declining trends (1.36 m, p < 0.05) between 2002 and 2016. There were significantly more cultivated areas with declining groundwater levels (88% vs. 35%, p < 0.05) in counties with high percentages of cropland (> 50%) compared to areas covered by less cropland. As climate and human pressures increase, it will be critical to identify and evaluate alternative water sources, such as reclaimed water, to sustain agricultural production and protect groundwater resources.

Association between community socioeconomic factors, animal feeding operations, and campylobacteriosis incidence rates: Foodborne Diseases Active Surveillance Network (FoodNet), 2004-2010.

BACKGROUND: Campylobacter is a leading cause of foodborne illness in the United States. Campylobacter infections have been associated with individual risk factors, such as the consumption of poultry and raw milk. Recently, a Maryland-based study identified community socioeconomic and environmental factors that are also associated with campylobacteriosis rates. However, no previous studies have evaluated the association between community risk factors and campylobacteriosis rates across multiple U.S. states. METHODS: We obtained Campylobacter case data (2004-2010; n = 40,768) from the Foodborne Diseases Active Surveillance Network (FoodNet) and socioeconomic and environmental data from the 2010 Census of Population and Housing, the 2011 American Community Survey, and the 2007 U.S. Census of Agriculture. We linked data by zip code and derived incidence rate ratios using negative binomial regression models. RESULTS: Community socioeconomic and environmental factors were associated with both lower and higher campylobacteriosis rates. Zip codes with higher percentages of African Americans had lower rates of campylobacteriosis (incidence rate ratio [IRR]) = 0.972; 95 % confidence interval (CI) = 0.970,0.974). In Georgia, Maryland, and Tennessee, three leading broiler chicken producing states, zip codes with broiler operations had incidence rates that were 22 % (IRR = 1.22; 95 % CI = 1.03,1.43), 16 % (IRR = 1.16; 95 % CI = 0.99,1.37), and 35 % (IRR = 1.35; 95 % CI = 1.18,1.53) higher, respectively, than those of zip codes without broiler operations. In Minnesota and New York FoodNet counties, two top dairy producing areas, zip codes with dairy operations had significantly higher campylobacteriosis incidence rates (IRR = 1.37; 95 % CI = 1.22, 1.55; IRR = 1.19; 95 % CI = 1.04,1.36). CONCLUSIONS: Community socioeconomic and environmental factors are important to consider when evaluating the relationship between possible risk factors and Campylobacter infection.

Presence of animal feeding operations and community socioeconomic factors impact salmonellosis incidence rates: An ecological analysis using data from the Foodborne Diseases Active Surveillance Network (FoodNet), 2004-2010.

Nontyphoidal Salmonella spp. are a leading cause of foodborne illness. Risk factors for salmonellosis include the consumption of contaminated chicken, eggs, pork and beef. Agricultural, environmental and socioeconomic factors also have been associated with rates of Salmonella infection. However, to our knowledge, these factors have not been modeled together at the community-level to improve our understanding of whether rates of salmonellosis are variable across communities defined by differing factors. To address this knowledge gap, we obtained data on culture-confirmed Salmonella Typhimurium, S. Enteritidis, S. Newport and S. Javiana cases (2004-2010; n=14,297) from the Foodborne Diseases Active Surveillance Network (FoodNet), and socioeconomic, environmental and agricultural data from the 2010 Census of Population and Housing, the 2011 American Community Survey, and the 2007 U.S. Census of Agriculture. We linked data by zip code and derived incidence rate ratios using negative binomial regressions. Multiple community-level factors were associated with salmonellosis rates; however, our findings varied by state. For example, in Georgia (Incidence Rate Ratio (IRR)=1.01; 95% Confidence Interval (CI)=1.005-1.015) Maryland (IRR=1.01; 95% CI=1.003-1.015) and Tennessee (IRR=1.01; 95% CI=1.002-1.012), zip codes characterized by greater rurality had higher rates of S. Newport infections. The presence of broiler chicken operations, dairy operations and cattle operations in a zip code also was associated with significantly higher rates of infection with at least one serotype in states that are leading producers of these animal products. For instance, in Georgia and Tennessee, rates of S. Enteritidis infection were 48% (IRR=1.48; 95% CI=1.12-1.95) and 46% (IRR=1.46; 95% CI=1.17-1.81) higher in zip codes with broiler chicken operations compared to those without these operations. In Maryland, New Mexico and Tennessee, higher poverty levels in zip codes were associated with higher rates of infection with one or more Salmonella serotypes. In Georgia and Tennessee, zip codes with higher percentages of the population composed of African Americans had significantly higher rates of infection with one or more Salmonella serotypes. In summary, our findings show that community-level agricultural, environmental and socioeconomic factors may be important with regard to rates of infection with Salmonella Typhimurium, Enteritidis, Newport and Javiana.

Extreme precipitation events and increased risk of campylobacteriosis in Maryland, U.S.A.

Consumption of contaminated poultry, raw milk and water are significant risk factors for Campylobacter infection. Previous studies also have investigated the association between weather (temperature and precipitation) and increased risk of campylobacteriosis, but limited information exists regarding the impacts of extreme heat and precipitation events on campylobacteriosis risk, and how such risk may differentially impact coastal communities. We obtained Campylobacter case data 2002-2012; n=4804) from the Maryland Foodborne Diseases Active Surveillance Network (FoodNet). We identified extreme heat and extreme precipitation events during this time (2002-2012) using location and calendar day specific thresholds (95th percentile for extreme heat and 90th percentile for extreme precipitation) that were computed based on a 30-year baseline (1960-1989). We linked these datasets using GIS and used negative binomial generalized estimating equations adjusted for demographic confounders to calculate the association between exposure to extreme events and risk of campylobacteriosis in Maryland. We observed that a one-day increase in exposure to extreme precipitation events was associated with a 3% increase in risk of campylobacteriosis in coastal areas of Maryland (Incidence Rate Ratio (IRR): 1.03, 95% confidence interval (CI): 1.01, 1.05), but such an association was not observed in noncoastal areas. Furthermore, the risk associated with extreme precipitation events was considerably higher during La Niña periods (IRR: 1.09, 95% CI: 1.05, 1.13), while there was no evidence of elevated risk during El Niño or ENSO Neutral periods. Exposure to extreme heat events was not associated with an increased risk of campylobacteriosis, except during La Niña periods (IRR: 1.04, 95% CI: 1.01, 1.08). Extreme precipitation events could result in flooding within coastal areas that may bring water contaminated with bacterial pathogens (originating from sources such as septic systems, municipal wastewater treatment plants and concentrated animal feeding operations) into close proximity with individuals, where frequency of contact may be higher.

Exposure to extreme heat and precipitation events associated with increased risk of hospitalization for asthma in Maryland, U.S.A.

BACKGROUND: Several studies have investigated the association between asthma exacerbations and exposures to ambient temperature and precipitation. However, limited data exists regarding how extreme events, projected to grow in frequency, intensity, and duration in the future in response to our changing climate, will impact the risk of hospitalization for asthma. The objective of our study was to quantify the association between frequency of extreme heat and precipitation events and increased risk of hospitalization for asthma in Maryland between 2000 and 2012. METHODS: We used a time-stratified case-crossover design to examine the association between exposure to extreme heat and precipitation events and risk of hospitalization for asthma (ICD-9 code 493, n = 115,923). RESULTS: Occurrence of extreme heat events in Maryland increased the risk of same day hospitalization for asthma (lag 0) by 3 % (Odds Ratio (OR): 1.03, 95 % Confidence Interval (CI): 1.00, 1.07), with a considerably higher risk observed for extreme heat events that occur during summer months (OR: 1.23, 95 % CI: 1.15, 1.33). Likewise, summertime extreme precipitation events increased the risk of hospitalization for asthma by 11 % in Maryland (OR: 1.11, 95 % CI: 1.06, 1.17). Across age groups, increase in risk for asthma hospitalization from exposure to extreme heat event during the summer months was most pronounced among youth and adults, while those related to extreme precipitation event was highest among ≤4 year olds. CONCLUSION: Exposure to extreme heat and extreme precipitation events, particularly during summertime, is associated with increased risk of hospitalization for asthma in Maryland. Our results suggest that projected increases in frequency of extreme heat and precipitation event will have significant impact on public health.

Assessment of sociodemographic and geographic disparities in cancer risk from air toxics in South Carolina.

Populations of color and low-income communities are often disproportionately burdened by exposures to various environmental contaminants, including air pollution. Some air pollutants have carcinogenic properties that are particularly problematic in South Carolina (SC), a state that consistently has high rates of cancer mortality for all sites. The purpose of this study was to assess cancer risk disparities in SC by linking risk estimates from the U.S. Environmental Protection Agency's 2005 National Air Toxics Assessment (NATA) with sociodemographic data from the 2000 US Census Bureau. Specifically, NATA risk data for varying risk categories were linked by tract ID and analyzed with sociodemographic variables from the 2000 census using R. The average change in cancer risk from all sources by sociodemographic variable was quantified using multiple linear regression models. Spatial methods were further employed using ArcGIS 10 to assess the distribution of all source risk and percent non-white at each census tract level. The relative risk (RR) estimates of the proportion of high cancer risk tracts (defined as the top 10% of cancer risk in SC) and their respective 95% confidence intervals (CIs) were calculated between the first and latter three quartiles defined by sociodemographic factors, while the variance in the percentage of high cancer risk between quartile groups was tested using Pearson's chi-square. The average total cancer risk for SC was 26.8 people/million (ppl/million). The risk from on-road sources was approximately 5.8 ppl/million, higher than the risk from major, area, and non-road sources (1.8, 2.6, and 1.3 ppl/million), respectively. Based on our findings, addressing on-road sources may decrease the disproportionate cancer risk burden among low-income populations and communities of color in SC.

Being overburdened and medically underserved: assessment of this double disparity for populations in the state of Maryland.

BACKGROUND: Environmental justice research has shown that many communities of color and low-income persons are differentially burdened by noxious land uses including Toxic Release Inventory (TRI) facilities. However, limited work has been performed to assess how these populations tend to be both overburdened and medically underserved. We explored this "double disparity" for the first time in Maryland. METHODS: We assessed spatial disparities in the distribution of TRI facilities in Maryland across varying levels of sociodemographic composition using 2010 US Census Health Professional Shortage Area (HPSA) data. Univariate and multivariate regression in addition to geographic information systems (GIS) were used to examine relationships between sociodemographic measures and location of TRI facilities. Buffer analysis was also used to assess spatial disparities. Four buffer categories included: 1) census tracts hosting one or more TRI facilities; 2) tracts located more than 0 and up to 0.5 km from the closest TRI facility; 3) tracts located more than 0.5 km and up to 1 km from a TRI facility; and 4) tracts located more than 1 km and up to 5 km from a TRI facility. RESULTS: We found that tracts with higher proportions of non-white residents and people living in poverty were more likely to be closer to TRI facilities. A significant increase in income was observed with an increase in distance between a census tract and the closest TRI facility. In general, percent non-white was higher in HPSA tracts that host at least one TRI facility than in non-HPSA tracts that host at least one TRI facility. Additionally, percent poverty, unemployment, less than high school education, and homes built pre-1950 were higher in HPSA tracts hosting TRI facilities than in non-HPSA tracts hosting TRI facilities. CONCLUSIONS: We found that people of color and low-income groups are differentially burdened by TRI facilities in Maryland. We also found that both low-income groups and persons without a high school education are both overburdened and medically underserved. The results of this study provide insight into how state agencies can better address the double disparity of disproportionate environmental hazards and limited access to health care resources facing vulnerable communities in Maryland.

A framework for evaluating clinical artificial intelligence systems without ground-truth annotations.

A clinical artificial intelligence (AI) system is often validated on data withheld during its development. This provides an estimate of its performance upon future deployment on data in the wild; those currently unseen but are expected to be encountered in a clinical setting. However, estimating performance on data in the wild is complicated by distribution shift between data in the wild and withheld data and the absence of ground-truth annotations. Here, we introduce SUDO, a framework for evaluating AI systems on data in the wild. Through experiments on AI systems developed for dermatology images, histopathology patches, and clinical notes, we show that SUDO can identify unreliable predictions, inform the selection of models, and allow for the previously out-of-reach assessment of algorithmic bias for data in the wild without ground-truth annotations. These capabilities can contribute to the deployment of trustworthy and ethical AI systems in medicine.

Travel patterns during pregnancy: comparison between Global Positioning System (GPS) tracking and questionnaire data.

BACKGROUND: Maternal exposures to traffic-related air pollution have been associated with adverse pregnancy outcomes. Exposures to traffic-related air pollutants are strongly influenced by time spent near traffic. However, little is known about women's travel activities during pregnancy and whether questionnaire-based data can provide reliable information on travel patterns during pregnancy. OBJECTIVES: Examine women's in-vehicle travel behavior during pregnancy and examine the difference in travel data collected by questionnaire and global positioning system (GPS) and their potential for exposure error. METHODS: We measured work-related travel patterns in 56 pregnant women using a questionnaire and one-week GPS tracking three times during pregnancy (<20 weeks, 20-30 weeks, and >30 weeks of gestation). We compared self-reported activities with GPS-derived trip distance and duration, and examined potentially influential factors that may contribute to differences. We also described in-vehicle travel behavior by pregnancy periods and influences of demographic and personal factors on daily travel times. Finally, we estimated personal exposure to particle-bound polycyclic aromatic hydrocarbon (PB-PAH) and examined the magnitude of exposure misclassification using self-reported vs. GPS travel data. RESULTS: Subjects overestimated both trip duration and trip distance compared to the GPS data. We observed moderately high correlations between self-reported and GPS-recorded travel distance (home to work trips: r = 0.88; work to home trips: r = 0.80). Better agreement was observed between the GPS and the self-reported travel time for home to work trips (r = 0.77) than work to home trips (r = 0.64). The subjects on average spent 69 and 93 minutes traveling in vehicles daily based on the GPS and self-reported data, respectively. Longer daily travel time was observed among participants in early pregnancy, and during certain pregnancy periods in women with higher education attainment, higher income, and no children. When comparing self-reported vs. GPS data, we found that estimated personal exposure to PB-PAH did not differ remarkably at the population level, but the difference was large at an individual level. CONCLUSION: Self-reported home-to-work data overestimated both trip duration and trip distance compared to GPS data. Significant differences in PAH exposure estimates were observed at individual level using self-reported vs. GPS data, which has important implications in air pollution epidemiological studies.

Spatial disparity in the distribution of superfund sites in South Carolina: an ecological study.

BACKGROUND: According to the US Environmental Protection Agency (EPA), Superfund is a federal government program implemented to clean up uncontrolled hazardous waste sites. Twenty-six sites in South Carolina (SC) have been included on the National Priorities List (NPL), which has serious human health and environmental implications. The purpose of this study was to assess spatial disparities in the distribution of Superfund sites in SC. METHODS: The 2000 US census tract and block level data were used to generate population characteristics, which included race/ethnicity, socioeconomic status (SES), education, home ownership, and home built before 1950. Geographic Information Systems (GIS) were used to map Superfund facilities and develop choropleth maps based on the aforementioned sociodemographic variables. Spatial methods, including mean and median distance analysis, buffer analysis, and spatial approximation were employed to characterize burden disparities. Regression analysis was performed to assess the relationship between the number of Superfund facilities and population characteristics. RESULTS: Spatial coincidence results showed that of the 29.5% of Blacks living in SC, 55.9% live in Superfund host census tracts. Among all populations in SC living below poverty (14.2%), 57.2% were located in Superfund host census tracts. Buffer analyses results (0.5mi, 1.0mi, 5.0mi, 0.5km, 1.0km, and 5.0km) showed a higher percentage of Whites compared to Blacks hosting a Superfund facility. Conversely, a slightly higher percentage of Blacks hosted (30.2%) a Superfund facility than those not hosting (28.8%) while their White counterparts had more equivalent values (66.7% and 67.8%, respectively). Regression analyses in the reduced model (Adj. R2 = 0.038) only explained a small percentage of the variance. In addition, the mean distance for percent of Blacks in the 90th percentile for Superfund facilities was 0.48mi. CONCLUSION: Burden disparities exist in the distribution of Superfund facilities in SC at the block and census tract levels across varying levels of demographic composition for race/ethnicity and SES.

A systematic approach towards missing lab data in electronic health records: A case study in non-small cell lung cancer and multiple myeloma.

Real-world data derived from electronic health records often exhibit high levels of missingness in variables, such as laboratory results, presenting a challenge for statistical analyses. We developed a systematic workflow for gathering evidence of different missingness mechanisms and performing subsequent statistical analyses. We quantify evidence for missing completely at random (MCAR) or missing at random (MAR), mechanisms using Hotelling's multivariate t-test, and random forest classifiers, respectively. We further illustrate how to apply sensitivity analyses using the not at random fully conditional specification procedure to examine changes in parameter estimates under missing not at random (MNAR) mechanisms. In simulation studies, we validated these diagnostics and compared analytic bias under different mechanisms. To demonstrate the application of this workflow, we applied it to two exemplary case studies with an advanced non-small cell lung cancer and a multiple myeloma cohort derived from a real-world oncology database. Here, we found strong evidence against MCAR, and some evidence of MAR, implying that imputation approaches that attempt to predict missing values by fitting a model to observed data may be suitable for use. Sensitivity analyses did not suggest meaningful departures of our analytic results under potential MNAR mechanisms; these results were also in line with results reported in clinical trials.

Considerations for the Use of Machine Learning Extracted Real-World Data to Support Evidence Generation: A Research-Centric Evaluation Framework.

A vast amount of real-world data, such as pathology reports and clinical notes, are captured as unstructured text in electronic health records (EHRs). However, this information is both difficult and costly to extract through human abstraction, especially when scaling to large datasets is needed. Fortunately, Natural Language Processing (NLP) and Machine Learning (ML) techniques provide promising solutions for a variety of information extraction tasks such as identifying a group of patients who have a specific diagnosis, share common characteristics, or show progression of a disease. However, using these ML-extracted data for research still introduces unique challenges in assessing validity and generalizability to different cohorts of interest. In order to enable effective and accurate use of ML-extracted real-world data (RWD) to support research and real-world evidence generation, we propose a research-centric evaluation framework for model developers, ML-extracted data users and other RWD stakeholders. This framework covers the fundamentals of evaluating RWD produced using ML methods to maximize the use of EHR data for research purposes.

Automated time activity classification based on global positioning system (GPS) tracking data.

BACKGROUND: Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. METHODS: We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. RESULTS: Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data. CONCLUSIONS: Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns.

Global Population Exposed to Extreme Events in the 150 Most Populated Cities of the World: Implications for Public Health.

Climate change driven increases in the frequency of extreme heat events (EHE) and extreme precipitation events (EPE) are contributing to both infectious and non-infectious disease burden, particularly in urban city centers. While the share of urban populations continues to grow, a comprehensive assessment of populations impacted by these threats is lacking. Using data from weather stations, climate models, and urban population growth during 1980-2017, here, we show that the concurrent rise in the frequency of EHE, EPE, and urban populations has resulted in over 500% increases in individuals exposed to EHE and EPE in the 150 most populated cities of the world. Since most of the population increases over the next several decades are projected to take place in city centers within low- and middle-income countries, skillful early warnings and community specific response strategies are urgently needed to minimize public health impacts and associated costs to the global economy.