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In 1974, the United States established the Safe Drinking Water Act (SDWA) to protect consumers from potential exposure to drinking water contaminants associated with health risks. Each contaminant is assigned a health-based standard meant to reflect the maximum level at which an adverse human health outcome is unlikely; measurements beyond that level have greater potential to result in adverse health outcomes. Although there is extensive research on human health implications following water contaminant exposure, few studies have specifically examined associations between fetal health and municipal drinking water violations. Therefore, the objective of this study is to assess whether SDWA drinking water violations are associated with fetal health outcomes, including preterm birth (PTB), low birth weight (LBW), and term-low birth weight (tLBW), in the Commonwealth of Virginia. Singleton births (n = 665,984) occurring between 2007 and 2015 in Virginia were geocoded and assigned to a corresponding estimated water service area. Health-based (HB) and monitoring and reporting (MR) violations for 12 contaminants were acquired from the US EPA Safe Drinking Water Information System, with exposure defined at the approximate service area level to limit exposure misclassification. A logistic regression model for each birth outcome assessed potential relationships with SDWA violations. When examining the association between individual MR violations and birth outcomes, Nitrate-Nitrite (OR = 1.10; 95% CI = 1.02, 1.18, P = 0.01) was positively associated with PTB and the total coliform rule was negatively associated with tLBW (OR = 0.93; 95% CI = 0.87, 1.00, P = 0.04). These findings indicate that a lack of regular monitoring and reporting by water providers (resulting in monitoring and reporting violations) may be concealing health-based violations as these health concerns cannot be revealed without testing, suggesting a need for additional technical, managerial, and financial support to enable often-underfunded water systems to adhere to monitoring and reporting requirements meant to protect public health., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Julia Gohlke reports financial support was provided by National Institute of Environmental Health Sciences. Holly Young reports a relationship with United States Environmental Protection Agency that includes: employment. Cristina Marcillo reports a relationship with United States Environmental Protection Agency that includes: employment., (Published by Elsevier Inc.)

A considerable body of research exists outlining ecological impacts of surface coal mining, but less work has explicitly focused on human health, and few studies have examined potential links between health and surface coal mining at fine spatial scales. In particular, relationships between individual birth outcomes and exposure to air contaminants from coal mining activities has received little attention. Central Appalachia (portions of Virginia, West Virginia, Kentucky, and Tennessee, USA), our study area, has a history of resource extraction, and epidemiologic research notes that the region experiences a greater level of adverse health outcomes compared to the rest of the country that are not fully explained by socioeconomic and behavioral factors. The purpose of this study is to examine associations between surface mining and birth outcomes at four spatial scales: individual, Census tract, county, and across county-sized grid cells. Notably, this study is among the first to examine these associations at the individual scale, providing a more direct measure of exposure and outcome. Airsheds were constructed for surface mines using an atmospheric trajectory model. We then implemented linear (birthweight) and logistic (preterm birth [PTB]) regression models to examine associations between airsheds and birth outcomes, which were geocoded to home address for individual analyses and then aggregated for areal unit analyses, while controlling for a number of demographic variables. This study found that surface mining airsheds are significantly associated with PTB and decreased birthweight at all four spatial scales, suggesting that surface coal mining activities impact birth outcomes via airborne contaminants., Competing Interests: The authors declare no conflicts of interest relevant to this study., (© 2022 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.)

Background: Satellite observations following flooding coupled with electronic health data collected through syndromic surveillance systems (SyS) may be useful in efficiently characterizing and responding to health risks associated with flooding., Results: There was a 10% (95% Confidence Interval (CI): 1%-19%) increase in asthma related ED visits and 22% (95% CI: 5%-41%) increase in insect bite related ED visits in the flooded ZCTAs compared to non-flooded ZCTAs during the flood period. One month following the floods, diarrhea related ED visits were increased by 15% (95% CI: 4%-27%) for flooded ZCTAs and children and adolescents from flooded ZCTAs had elevated risk for dehydration related ED visits. During the protracted period (2-3 months after the flood period), the risk for asthma, insect bite, and diarrhea related ED visits were elevated among the flooded ZCTAs. Effect modification by reported age, ethnicity and race was observed., Conclusion: Combining satellite observations with SyS data can be helpful in characterizing the location and timing of environmentally mediated adverse health outcomes, which may be useful for refining disaster resilience measures to mitigate health outcomes following flooding., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Flooding following heavy rains precipitated by hurricanes has been shown to impact the health of people. Earth observations can be used to identify inundation extents for subsequent analysis of health risks associated with flooding at a fine spatio-temporal scale., Objective: To evaluate emergency department (ED) visits before, during, and following flooding caused by Hurricane Harvey in 2017 in Texas., Methods: A controlled before and after design was employed using 2016-2018 ED visits from flooded and non-flooded census tracts. ED visits between landfall of the hurricane and receding of flood waters were considered within the flood period and post-flood periods extending up to 4 months were also evaluated. Modified Poisson regression models were used to estimate adjusted rate ratios for total and cause specific ED visits., Results: Flooding was associated with increased ED visits for carbon monoxide poisoning, insect bite, dehydration, hypothermia, intestinal infectious diseases, and pregnancy complications. During the month following the flood period, the risk for pregnancy complications and insect bite was still elevated in the flooded tracts., Significance: Earth observations coupled with ED visits increase our understanding of the short-term health risks during and following flooding, which can be used to inform preparedness measures to mitigate adverse health outcomes and identify localities with increased health risks during and following flooding events., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Maternal residency in Central Appalachia counties with coal production has been previously associated with increased rates of low birth weight (LBW). To refine the relationship between surface mining and birth outcomes, this study employs finer spatiotemporal estimates of exposure., Methods: We developed characterizations of annual surface mining boundaries in Central Appalachia between 1986 and 2015 using Landsat data. Maternal address on birth records was geocoded and assigned amount of surface mining within a 5 km radius of residence (street-level). Births were also assigned the amount of surface mining within residential ZIP code tabulation area (ZCTA). Associations between exposure to active mining during gestation year and birth weight, LBW, preterm birth (PTB), and term low birth weight (tLBW) were determined, adjusting for outcome rates before active mining and available covariates., Results: The percent of land actively mined within a 5 km buffer of residence (or ZCTA) was negatively associated with birth weight (5 km: β = -14.07 g; 95% confidence interval [CI] = -19.35, -8.79, P = 1.79 × 10 -7 ; ZCTA: β = -9.93 g; 95% CI = -12.54, -7.33, P = 7.94 × 10 -14 ). We also found positive associations between PTB and active mining within 5 km (odds ratio [OR] = 1.06; 95% CI = 1.03, 1.09, P = 1.43 × 10 -4 ) and within ZCTA (OR = 1.04; 95% CI = 1.03, 1.06, P = 9.21 × 10 -8 ). Positive relationships were also found between amount of active mining within 5 km or ZIP code of residence and LBW and tLBW outcomes., Conclusions: Maternal residency near active surface mining during gestation may increase risk of PTB and LBW., Competing Interests: The authors declare that they have no conflicts of interest with regard to the content of this report. Supported by a grant from the National Institute of Environmental Health Sciences (R21ES028396).Sponsorships or competing interests that may be relevant to content are disclosed at the end of the article., (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The Environmental Epidemiology. All rights reserved.)

Malaria is a leading cause of mortality and morbidity globally. Land Use and Land Cover (LULC) change have been found to affect the transmission of malaria in other regions, but no study has examined such relationships in Nepal. Therefore, this study has three aims: first, to analyze the spatial and temporal trend of Malaria Incidence Rate (MIR) between 1999 and 2015, second to assess LULC change between 2000 and 2010, and finally to understand the relationship between LULC and malaria in Nepal. The land cover types examined are forest, water bodies, agriculture, grassland, shrubland, barren areas, built-up areas, and rice paddies. The temporal trend of MIR and the relationship between MIR and LULC were evaluated using Poisson and negative binomial regression. Forest, water bodies, and built-up area increased in Nepal by 0.8%, 8.2%, and 28.4% respectively, while other LULC variables decreased between 2000 and 2010. MIR decreased significantly in 21 districts; however, four districts, namely Pyuthan, Kaski, Rupandehi, and Siraha, had a significantly increasing MIR trend between 1999 and 2015. MIR was positively related to water bodies and rice paddies during 2001, 2002, and 2003 but negatively related to grassland during 2010. However, there was no relationship between LULC and MIR during 2000, 2011, 2012 and 2013. This information will be helpful for public health officials to increase control efforts in those districts and in areas near water bodies and rice paddies to aid in their effort to eliminate malaria from Nepal.

Coccidioidomycosis is an understudied infectious disease acquired by inhaling fungal spores of Coccidioides species. While historically connected to the southwestern United States, the endemic region for this disease is not well defined. This study's objective was to estimate the impact of climate, soil, elevation and land cover on the Coccidioides species' ecological niche. This research used maximum entropy ecological niche modeling based on disease case data from 2015 to 2016. Results found mean temperature of the driest quarter, and barren, shrub, and cultivated land covers influential in characterizing the niche. In addition to hotspots in central California and Arizona, the Columbia Plateau ecoregion of Washington and Oregon showed more favorable conditions for fungus presence than surrounding areas. The identification of influential spatial drivers will assist in future modeling efforts, and the potential distribution map generated may aid public health officials in watching for potential hotspots, assessing vulnerability, and refining endemicity., Competing Interests: Declaration of Competing Interest The authors declare no competing financial interests., (Published by Elsevier Ltd.)

Lyme disease is the most significant vector-borne disease in the United States, and its southward advance over several decades has been quantified. Previous research has examined the potential role of climate change on the disease's expansion, but no studies have considered the role of future land cover upon its distribution. This research examines Lyme disease risk in the south-eastern U.S. based on projected land cover developed under four Intergovernmental Panel on Climate Change Scenarios: A1B, A2, B1, and B2. Land cover types and edge indices significantly associated with Lyme disease in Virginia were incorporated into a spatial Poisson regression model to quantify potential land cover suitability for Lyme disease in the south-eastern U.S. under each scenario. Our results indicate an intensification of potential land cover suitability for Lyme disease under the A scenarios and a decrease of potential land cover suitability under the B scenarios. The decrease under the B scenarios is a critical result, indicating that Lyme disease risk can be decreased by making different land cover choices. Additionally, health officials can focus efforts in projected high incidence areas.

Valley fever (coccidioidomycosis) is a disease caused by inhalation of spores from the soil-dwelling Coccidioides fungal species. The disease is endemic to semiarid areas in the western USA and parts of Central and South America. The region of interest for this study, Kern County, California, accounts for approximately 14% of the reported valley fever cases in the USA each year. It is hypothesized that the weather conditions that foster the growth and dispersal of the fungus influence the number of cases in the endemic area. This study uses regression-based analysis to model and assess the seasonal relationships between valley fever incidence and climatic variables including concurrent and lagged precipitation, temperature, Palmer Drought Severity Index, wind speed, and PM 10 using data from 2000 to 2015. We find statistically significant links between disease incidence and climate conditions in Kern County, California. The best performing seasonal model explains up to 76% of the variability in fall valley fever incidence based on concurrent and antecedent climate conditions. Findings are consistent with previous studies, suggesting that antecedent precipitation is an important predictor of disease. The significant relationships found support the "grow and blow" hypothesis for climate-related coccidioidomycosis incidence risk that was originally developed for Arizona.

Public health initiatives, including large-scale vaccination and disease eradication programmes, regularly pit the rights of the individual against broader benefits to society. At times, the public resists such initiatives, with the World Health Organisation's Smallpox Eradication Programme (SEP) in India being a case in point. Here, we critically investigate resistance to smallpox vaccines in India and argue that while the SEP successfully eradicated a global killer; individuals were stripped of human rights through coercion, forcible vaccination and quarantine. In many cases, resistance to vaccination was linked to deep-rooted social, cultural and religious beliefs. Critical points made in this paper are applicable to contemporary discussions on required vaccinations, quarantine during the outbreak of diseases and the current campaign to eradicate polio.

The goal of this work was to provide a community-academic partnership with actionable information for physical activity (PA) for a rural health-disparate region. Identified PA outlets were audited and combined with survey data for 813 residents in the region. Less than a third of sampled residents (28%) met PA recommendations, with low perceptions of safety for PA. PA resource outlets in rural areas had higher numbers of incivilities. On the basis of our findings, 4 actionable strategies are recommended: shared-use agreements, reducing incivilities, addressing issues related to safety from traffic, and increasing the number of PA resources in rural areas.

Lyme disease is the United States' most significant vector-borne illness. Virginia, on the southern edge of the disease's currently expanding range, has experienced an increase in Lyme disease both spatially and temporally, with steadily increasing rates over the past decade and disease spread from the northern to the southwestern part of the state. This study used a Geographic Information System and a spatial Poisson regression model to examine correlations between demographic and land cover variables, and human Lyme disease from 2006 to 2010 in Virginia. Analysis indicated that herbaceous land cover is positively correlated with Lyme disease incidence rates. Areas with greater interspersion between herbaceous and forested land were also positively correlated with incidence rates. In addition, income and age were positively correlated with incidence rates. Levels of development, interspersion of herbaceous and developed land, and population density were negatively correlated with incidence rates. Abundance of forest fragments less than 2 hectares in area was not significantly correlated. Our results support some findings of previous studies on ecological variables and Lyme disease in endemic areas, but other results have not been found in previous studies, highlighting the potential contribution of new variables as Lyme disease continues to emerge southward.

The emergence of infectious diseases over the past several decades has highlighted the need to better understand epidemics and prepare for the spread of diseases into new areas. As these diseases expand their geographic range, cases are recorded at different geographic locations over time, making the analysis and prediction of this expansion complicated. In this study, we analyze spatial patterns of the disease using a statistical smoothing analysis based on areal (census tract level) count data of Lyme disease cases in Virginia from 1998 to 2011. We also use space and space-time scan statistics to reveal the presence of clusters in the spatial and spatiotemporal distribution of Lyme disease. Our results confirm and quantify the continued emergence of Lyme disease to the south and west in states along the eastern coast of the United States. The results also highlight areas where education and surveillance needs are highest., (© The American Society of Tropical Medicine and Hygiene.)

Tropical storms, such as cyclones, hurricanes and typhoons, present major threats to coastal communities. Around two million people worldwide have died and millions have been injured over the past two centuries as a result of tropical storms. Bangladesh is especially vulnerable to tropical cyclones, with around 718 000 deaths from them in the past 50 years. However, cyclone-related mortality in Bangladesh has declined by more than 100-fold over the past 40 years, from 500 000 deaths in 1970 to 4234 in 2007. The main factors responsible for these reduced fatalities and injuries are improved defensive measures, including early warning systems, cyclone shelters, evacuation plans, coastal embankments, reforestation schemes and increased awareness and communication. Although warning systems have been improved, evacuation before a cyclone remains a challenge, with major problems caused by illiteracy, lack of awareness and poor communication. Despite the potential risks of climate change and tropical storms, little empirical knowledge exists on how to develop effective strategies to reduce or mitigate the effects of cyclones. This paper summarizes the most recent data and outlines the strategy adopted in Bangladesh. It offers guidance on how similar strategies can be adopted by other countries vulnerable to tropical storms. Further research is needed to enable countries to limit the risks that cyclones present to public health.

Background: Malaria is endemic in the Rajasthali region of the Chittagong Hill Tracts in Bangladesh and the Rajasthali region is the most endemic area of Bangladesh. Quantifying the role of environmental and socio-economic factors in the local spatial patterns of malaria endemicity can contribute to successful malaria control and elimination. This study aimed to investigate the role of environmental factors on malaria risk in Rajasthali and to quantify the geographical clustering in malaria risk unaccounted by these factors., Method: A total of 4,200 (78.9%; N = 5,322) households were targeted in Rajasthali in July, 2009, and 1,400 individuals were screened using a rapid diagnostic test (Falci-vax). These data were linked to environmental and socio-economic data in a geographical information system. To describe the association between environmental factors and malaria risk, a generalized linear mixed model approach was utilized. The study investigated the role of environmental factors on malaria risk by calculating their population-attributable fractions (PAF), and used residual semivariograms to quantify the geographical clustering in malaria risk unaccounted by these factors., Results: Overall malaria prevalence was 11.7%. Out of 5,322 households, 44.12% households were living in areas with malaria prevalence of ≥ 10%. The results from statistical analysis showed that age, ethnicity, proximity to forest, household density, and elevation were significantly and positively correlated with the malaria risk and PAF estimation. The highest PAF of malaria prevalence was 47.7% for third tertile (n = 467) of forest cover, 17.6% for second tertile (n = 467) of forest cover and 19.9% for household density >1,000., Conclusion: Targeting of malaria health interventions at small spatial scales in Bangladesh should consider the social and socio-economic risk factors identified as well as alternative methods for improving equity of access to interventions across whole communities.

Background: Dengue viruses (DENV) are endemic in over 100 countries worldwide, and annually 50 to 100 million people are infected by one of the four DENV serotypes, whereas over 2.5 billion people are at risk for infection. West African countries lack the surveillance to determine the true incidence of dengue; hence, this disease is likely significantly underestimated. In Mali, ?14 million people are potentially at risk of acquiring a dengue infection., Methods and Findings: A serosurvey for DENV was conducted on 95 human serum samples obtained from the Institute National de Recherche en Sante Publique in 2006. DENV-specific IgM and IgG enzyme-linked immunosorbent assays were performed on all samples, and a subset was tested using the plaque-reduction neutralization test against the DENV and yellow fever virus (YFV). Samples collected during the acute infection (0-5 days postonset of symptoms) were tested for dengue NS1 antigen and reverse-transcriptase polymerase chain reaction for Flaviviruses, Alphaviruses, and Bunyaviruses RNA. A total of 87 (93%) of samples were positive for anti-DENV IgG antibodies. Of a subset of 13 IgG positive samples, 2 samples neutralized monotypically against DENV-1 and -2, whereas 3 others neutralized broadly against YFV and multiple DENV. Although no polymerase chain reaction positives were found, DENV NS1 was detected in 1 of the 20 acute samples tested., Conclusions: Of the 93 human serum samples tested, the dengue prevalence based on dengue IgG enzyme-linked immunosorbent assay results was 93%. Three DENV specific positive samples and two YFV positives were identified by plaque-reduction neutralization test. Finally, one sample tested positive for dengue NS1, thus suggestive of an acute infection within 14 days of obtaining the sample from the patient. Based on these serological data from this study, YFV and DENV appear to be co-circulating in Mali.

To determine the risk for Chagas disease (American trypanosomiasis) in the United States, the characteristics that make the triatomine vector effective and the areas most at risk for transmission were delineated. In addition, the status of Chagas disease awareness among physicians in areas with a potential risk for the disease was determined. A geographical information system (GIS) was used to analyze three triatomine species within the United States known to harbor Trypanosoma cruzi and that exhibit qualities of domesticity. An analysis of the minimum temperature threshold for increased triatomine activity delineates the current population at increased risk, and by incorporating temperature predictions for 2030, the population at risk under a future climate scenario was also delineated. Considering both environmental and social factors, a vignette-based physician survey, based on the results of the GIS analysis, was used to gauge the level of awareness of Chagas disease within the delineated higher risk range. The current area at increased risk for Chagas disease includes much of the southern United States, and the higher risk range is expected to expand into the central United States based upon the 1 degrees C (1.8 degrees F) increase in temperature predicted by the Intergovernmental Panel on Climate Change (IPCC) by the year 2030. Survey results indicate a limited consideration of Chagas disease during differential diagnosis, illustrating that the low number of Chagas disease cases discovered in the United States may be attributable to a lack of disease awareness as opposed to a lack of disease threat. This study combines GIS and survey analyses to evaluate the role that temperature variability and disease awareness among physicians play in the potential emergence of Chagas disease in the United States. This approach indicates that there is a potential for Chagas disease to emerge in the United States.

Flash floods are one of the most dangerous weather-related natural disasters in the world. These events develop less than six hours after a rainfall event and create hazardous situations for people and extensive damage to property. It is critical for flash flood conditions to be warned of in a timely manner to minimize impacts. There is currently a knowledge gap between flood experts and the general public about the level of perceived risk that the latter has toward the powerful flood waters and how events should be warned of, which affects the communication capabilities and efficiency of the warning process. Prior research has addressed risk perception of natural disasters, but there is little emphasis on flash floods within flood-prone regions of the United States. This research utilizes an online survey of 300 respondents to determine the current state of flash flood awareness and preparation in southwest Virginia. Analysis of trends involved the use of chi-squared tests (chi2) and simple frequency and percentage calculations. Results reveal that a knowledge base of flash floods does exist, but is not advanced enough for proper awareness. Young adults have a lower understanding and are not as concerned about flood impacts. Increased exposure and perceived risk play a key role in shaping the way a person approaches flash floods. People do monitor flood events, but they are unaware of essential guidance and communication mechanisms. Finally, results suggest that the current method of warning about flash floods is not provided at an appropriate level of detail for effective communication.

Valley fever (coccidioidomycosis) is a disease endemic to arid regions within the Western Hemisphere, and is caused by a soil-dwelling fungus, Coccidioides immitis. Incidence data for Pima County, reported to the Arizona Department of Health Services as new cases of valley fever, were used to conduct exploratory analyses and develop monthly multivariate models of relationships between valley fever incidence and climate conditions and variability in Pima County, Arizona, USA. Bivariate and compositing analyses conducted during the exploratory portion of the study revealed that antecedent temperature and precipitation in different seasons are important predictors of incidence. These results were used in the selection of candidate variables for multivariate predictive modeling, which was designed to predict deviation from mean incidence on the basis of past, current, and forecast climate conditions. The models were specified using a backward stepwise procedure, and were most sensitive to key predictor variables in the winter season and variables that were time-lagged 1 year or more prior to the month being predicted. Model accuracy was generally moderate ( r(2) values for the monthly models, tested on independent data, ranged from 0.15 to 0.50), and months with high incidence can be predicted more accurately than months with low incidence.