Oil extraction and indigenous livelihoods in the Northern Ecuadorian Amazon

Since oil was discovered in 1967 under present-day Lago Agrio in the indigenous Northern Ecuadorian Amazon, oil extraction has induced many changes. This research uses a large sample of household and community survey data through descriptive and multilevel regression analyses across ethnic groups and over time, constituting a novel, large-scale, comparative approach for analyzing the regional relationship between extractive industries and livelihoods. Five livelihood outcomes were analyzed, in relation to two key oil extraction predictors. Results showed that when oil companies were present at the community level, indigenous households had more off-farm employment and earned more annually from it, hunting yields increased, assets increased, marginally more land was cleared, and fishing yields decreased. In the short-term, oil companies may indeed have somewhat positive effects on indigenous communities. However, in the long-term, indigenous livelihoods may be vulnerable due to finite oil resources, contamination, cultural erosion, and regional market integration.Master of Art

Oil Extraction and Indigenous Livelihoods in the Northern Ecuadorian Amazon

Globally, the extraction of minerals and fossil fuels is increasingly penetrating into isolated regions inhabited by indigenous peoples, potentially undermining their livelihoods and well-being. To provide new insight to this issue, we draw on a unique longitudinal dataset collected in the Ecuadorian Amazon over an 11-year period from 484 indigenous households with varying degrees of exposure to oil extraction. Fixed and random effects regression models of the consequences of oil activities for livelihood outcomes reveal mixed and multidimensional effects. These results challenge common assumptions about these processes and are only partly consistent with hypotheses drawn from the Dutch disease literature

Declining use of wild resources by indigenous peoples of the Ecuadorian Amazon

Wild product harvesting by forest-dwelling peoples, including hunting, fishing, forest product collection and timber harvesting, is believed to be a major threat to the biodiversity of tropical forests worldwide. Despite this threat, few studies have attempted to quantify these activities across time or across large spatial scales. We use a unique longitudinal household survey (n = 480) to describe changes in these activities over time in 32 indigenous communities from five ethnicities in the northern Ecuadorian Amazon. To provide insight into the drivers of these changes, we also estimate multilevel statistical models of these activities as a function of household and community characteristics. These analyses reveal that participation in hunting, fishing, and forest product collection is high but declining across time and across ethnicities, with no evidence for a parallel decline in resource quality. However, participation in timber harvesting did not significantly decline and there is evidence of a decline in resource quality. Multilevel statistical models additionally reveal that household and community characteristics such as ethnicity, demographic characteristics, wealth, livelihood diversification, access to forest, participation in conservation programs and exposure to external markets are significant predictors of wild product harvesting. These characteristics have changed over time but cannot account for declining participation in resource harvesting. This finding suggests that participation is declining due to changes in the regional-scale social and economic context, including urbanization and the expansion of government infrastructure and services. The lesson for conservationists is that macro-scale social and economic conditions can drive reductions in wild product harvesting even in the absence of successful conservation interventions

Sociodemographic Patterns of Exposure to Civil Aircraft Noise in the United States

13-C-AJFF-BU-016This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Please cite this article as: Matthew C. Simon, Jaime E. Hart, Jonathan I. Levy, Trang VoPham, Andrew Malwitz, Daniel Nguyen, Matthew Bozigar, L. Adrienne Cupples, Peter James, Francine Laden, and Junenette L. Peters. 2022. Sociodemographic Patterns of Exposure to Civil Aircraft Noise in the United States Environmental Health Perspectives. 130:2 CID: 027009 https://doi.org/10.1289/EHP9307Background: Communities with lower socioeconomic status and higher prevalence of racial/ethnic minority populations are often more exposed to environmental pollutants. Although studies have shown associations between aircraft noise and property values and various health outcomes, little is known about how aircraft noise exposures are sociodemographically patterned. Objective: Our aim was to describe characteristics of populations exposed to aviation noise by race/ethnicity, education, and income in the United States

Using a Spatio-Temporal Framework to Understand Disparities in Pediatric Asthma Emergency Department Visits: A Case Study in South Carolina

Asthma is the leading chronic pediatric disease in the US and globally, making it a serious and costly public health issue. Emergency department (ED) visits for asthma also exhibit large disparities across many factors (e.g., race) that are not well understood. Furthermore, socioenvironmental determinants of health disparities have geographic components, and thus a clearer picture of the role that “place” has can better elucidate risk factors, synergistic effects, and improved interventions. We used the pediatric population (ages 5-19) of South Carolina (SC), a state with previously identified disparities in many health outcomes across numerous factors, as a case study. We employed a spatio-temporal Bayesian hierarchical modeling (BHM) framework to estimate associations between socioenvironmental factors, such as air pollutants and dimensions of socioeconomic status, and asthma ED visits. Methodologic innovations included an assignment algorithm for missing geographic identifiers of records, systematic variable selection, random effects for unmeasured confounding, geographically adaptive regression, spatio-temporal quantile regression, and a BHM case crossover model. Long-term (i.e., annual) and short-term (i.e., daily) models were used to detail the health effects of chronic and immediate socioenvironmental exposures, respectively. Key socioenvironmental factors associated with increased long-term asthma ED visit risk were demographics, distance to the nearest pharmacy, average household size, and complex interactions involving carbon monoxide (CO). These factors contributed to high risk clusters in southeastern SC, north of Columbia, the Lower Savannah River District, and particular urban areas. However, associations with ED visit risk varied spatially by census tract within the state, and additionally varied by percentile of risk. When considering environmental factors immediately preceding an ED visit, nitrogen oxides (NOx) and particulate matter sizes \u3c10µm (PM10) were implicated across seasons. In the Fall, the highest asthma burden season, asthma ED visits were associated with PM10 and elemental carbon (EC). The PM10 association likely captures an association with Fall allergens, such as respirable ragweed antigens. Our spatio-temporal methods helped identify and locate health disparities for asthma in children, in addition to quantifying associations with potential long- and short-term drivers. Numerous place-based socioenvironmental exposures we identified should be considered by researchers and policymakers attempting to reduce health disparities. Specific Aims: Asthma is the leading chronic pediatric disease in the US, making it a serious and costly public health issue. Children visit the emergency department (ED) due to asthma for a variety of reasons that include neighborhood factors and patterns in healthcare. Asthma ED visits also exhibit large disparities across many social determinants of health (including local environment). Past research mainly highlighted increased disparities for urban locations, though more recent studies have detailed rural burdens of pediatric asthma ED visits, especially in the rural South. Much can still be learned and little research has contrasted disparities across different contextual factors, especially rural/urban residence. Furthermore, determinants of health disparities have geographic components, and thus a clearer picture of the role that “place” has in the asthma burden could better elucidate risk factors and their synergistic effects, leading to improved interventions to address disparities. Problematically, most previous studies in this field did not consider local geography, a scenario that can result in reduced precision of estimates due to assigning variation in the outcome to error terms rather than an effect of location (i.e., spatial confounding). Improving knowledge of asthma’s determinants has been highlighted as a research priority, and separating the effects of a variety of socioenvironmental risk factors that drive disparities is an ongoing challenge in epidemiologic research. Integrating spatial, environmental, and social epidemiology can support this mission, as improved assessment of risk factors over space and time can improve public health strategies for addressing disparities in this complex disease. The overarching goal of this research was to improve the understanding of socioenvironmental risk factors driving disparities in pediatric asthma ED visits. The primary hypothesis for this proposal was that disparities in such risk factors would associate with asthma- related health disparities in children and that these associations would be modified by characteristics of place. To address this, we employed innovative statistical methodologies to analyze highly resolved spatio-temporal data comprised of health outcomes, demographic and neighborhood information, and environmental measures from South Carolina, a diverse population that exhibits health disparities across numerous factors. Furthermore, we assessed differences in results and interpretations from two strategies to address missing census tract identifiers: geographic imputation and complete case analyses. In addition, we had access to novel highly resolved air pollution exposure data as well as advanced climatic estimates for covariates. The following specific aims and supporting hypotheses were proposed to investigate our primary hypothesis. Aim 1: Specific Aim 1: Quantify the drivers of long-term asthma disparities. Hypothesis 1.1: social and environmental neighborhood-level factors would be associated with risk for asthma ED visits that drove disparities. We used a Bayesian space-time “convolution” model of neighborhood-level asthma ED counts, incorporating covariates and random effects, and contrasted results for datasets employing geographic imputation versus complete case analyses. Results improved understanding of exposures and neighborhood-level risks associated with long- term asthma disparities. Aim 2: Specific Aim 2: Identify the spatio-temporal health disparity patterns in pediatric asthma. Hypothesis 2.1: Excess risk for pediatric asthma ED visits would cluster after known neighborhood risk factors were controlled. We used mapping, Bayesian exceedance probability clustering, quantile regression, and geographically adaptive regression to contrast results from geographic imputation against complete case analyses, in addition to a space-time uncorrelated random effect. Aim 3: Specific Aim 3: Quantify the drivers of short-term asthma disparities. Hypothesis 3.1: Acute, temporarily adverse environmental factors would modify the seasonal effects on individual ED visits after controlling for confounding. Within a case-crossover design extended to account for spatial confounding, we used logistic regression to model individual-level pediatric asthma ED visits. Results were important for detailing the modifying effects of seasonal variability in short-term exposures to environmental factors for asthma ED visits. The immediate impacts of this work identified, disaggregated, and refined understanding of the neighborhood factors that interacted and contributed to pediatric asthma ED visit disparities. The long-term impacts included advancing epidemiologic analysis of disparities in pediatric asthma, a complex and multifactorial disease that has proven challenging to study with existing methods. The research informed future case-control and cohort research designs of the importance for both collecting and analyzing highly precise spatio-temporal data to better detail health disparities. Furthermore, this research generated tangible location-specific evidence to direct interventions to reduce disparities in ED visits and their heavy human and financial costs

Sociodemographic Patterns of Exposure to Civil Aircraft Noise in the United States

Communities with lower socioeconomic status and higher prevalence of racial/ethnic minority populations are often more exposed to environmental pollutants. Although studies have shown associations between aircraft noise and property values and various health outcomes, little is known about how aircraft noise exposures are sociodemographically patterned.Our aim was to describe characteristics of populations exposed to aviation noise by race/ethnicity, education, and income in the United States.Aircraft noise contours characterized as day-night average sound level (DNL) were developed for 90 U.S. airports in 2010 for DNLin 1-dB(A) increments. We compared characteristics of exposed U.S. Census block groups at three thresholds (,, and), assigned on the basis of the block group land area beingwithin the threshold, vs. unexposed block groups near study airports. Comparisons were made across block group race/ethnicity, education, and income categories within the study areas (). We performed both multinomial and other various multivariable regression approaches, including models controlling for airport and models with random intercepts specifying within-airport effects and adjusting for airport-level means.Aggregated across multiple airports, block groups with a higher Hispanic population had higher odds of being exposed to aircraft noise. For example, the multinomial analysis showed that a 10-percentage point increase in a block group's Hispanic population was associated with an increased odds ratio of 39% (95% CI: 25%, 54%) of being exposed tocompared with block groups exposed to. Block groups with higher proportions of residents with only a high school education had higher odds of being exposed to aircraft noise. Results were robust across multiple regression approaches; however, there was substantial heterogeneity across airports.These results suggest that across U.S. airports, there is indication of sociodemographic disparities in noise exposures. https://doi.org/10.1289/EHP9307

Associations between Aircraft Noise Exposure and Self-Reported Sleep Duration and Quality in the United States-Based Prospective Nurses Health Study Cohort.

BACKGROUND: Sleep disruption is linked with chronic disease, and aircraft noise can disrupt sleep. However, there are few investigations of aircraft noise and sleep in large cohorts. OBJECTIVES: We examined associations between aircraft noise and self-reported sleep duration and quality in the Nurses Health Study, a large prospective cohort. METHODS: Aircraft nighttime equivalent sound levels (Lnight) and day-night average sound levels (DNL) were modeled around 90 U.S. airports from 1995 to 2015 in 5-y intervals using the Aviation Environmental Design Tool and linked to geocoded participant residential addresses. Lnight exposure was dichotomized at the lowest modeled level of 45 A-weighted decibels [dB(A)] and at multiple cut points for DNL. Multiple categories of both metrics were compared with <45 dB(A). Self-reported short sleep duration (<7 h/24-h day) was ascertained in 2000, 2002, 2008, 2012, and 2014, and poor sleep quality (frequent trouble falling/staying asleep) was ascertained in 2000. We analyzed repeated sleep duration measures using generalized estimating equations and sleep quality by conditional logistic regression. We adjusted for participant-level demographics, behaviors, comorbidities, and environmental exposures (greenness and light at night) and examined effect modification. RESULTS: In 35,226 female nurses averaging 66.1 years of age at baseline, prevalence of short sleep duration and poor sleep quality were 29.6% and 13.1%, respectively. In multivariable models, exposure to Lnight ≥45 dB(A) was associated with 23% [95% confidence interval (CI): 7%, 40%] greater odds of short sleep duration but was not associated with poor sleep quality (9% lower odds; 95% CI: -30%, 19%). Increasing categories of Lnight and DNL ≥45 dB(A) suggested an exposure-response relationship for short sleep duration. We observed higher magnitude associations among participants living in the West, near major cargo airports, and near water-adjacent airports and among those reporting no hearing loss. DISCUSSION: Aircraft noise was associated with short sleep duration in female nurses, modified by individual and airport characteristics. https://doi.org/10.1289/EHP10959

Associations between Aircraft Noise Exposure and Self-Reported Sleep Duration and Quality in the United States-Based Prospective Nurses' Health Study Cohort

Sleep disruption is linked with chronic disease, and aircraft noise can disrupt sleep. However, there are few investigations of aircraft noise and sleep in large cohorts. We examined associations between aircraft noise and self-reported sleep duration and quality in the Nurses' Health Study, a large prospective cohort. Aircraft nighttime equivalent sound levels (Lnight) and day-night average sound levels (DNL) were modeled around 90 U.S. airports from 1995 to 2015 in 5-y intervals using the Aviation Environmental Design Tool and linked to geocoded participant residential addresses. Lnight exposure was dichotomized at the lowest modeled level of 45 A-weighted decibels [dB(A)] and at multiple cut points for DNL. Multiple categories of both metrics were compared with dB(A). Self-reported short sleep duration ( h/24-h day) was ascertained in 2000, 2002, 2008, 2012, and 2014, and poor sleep quality (frequent trouble falling/staying asleep) was ascertained in 2000. We analyzed repeated sleep duration measures using generalized estimating equations and sleep quality by conditional logistic regression. We adjusted for participant-level demographics, behaviors, comorbidities, and environmental exposures (greenness and light at night) and examined effect modification. In 35,226 female nurses averaging 66.1 years of age at baseline, prevalence of short sleep duration and poor sleep quality were 29.6% and 13.1%, respectively. In multivariable models, exposure to Lnight dB(A) was associated with 23% [95% confidence interval (CI): 7%, 40%] greater odds of short sleep duration but was not associated with poor sleep quality (9% lower odds; 95% CI: , 19%). Increasing categories of Lnight and DNL dB(A) suggested an exposure-response relationship for short sleep duration. We observed higher magnitude associations among participants living in the West, near major cargo airports, and near water-adjacent airports and among those reporting no hearing loss. Aircraft noise was associated with short sleep duration in female nurses, modified by individual and airport characteristics. https://doi.org/10.1289/EHP10959

Associations between long-term aircraft noise exposure, cardiovascular disease, and mortality in US cohorts of female nurses

Background:There is limited research examining aircraft noise and cardiovascular disease (CVD) risk. The objective of this study was to investigate associations of aircraft noise with CVD among two US cohorts, the Nurses’ Health Study (NHS) and Nurses’ Health Study II (NHSII).Methods:Between 1994 and 2014, we followed 57,306 NHS and 60,058 NHSII participants surrounding 90 airports. Aircraft noise was modeled above 44 A-weighted decibels (dB(A)) and linked to geocoded addresses. Based on exposure distributions, we dichotomized exposures at 50 dB(A) and tested sensitivity of this cut-point by analyzing aircraft noise as categories (<45, 45–49, 50–54, ≥55) and continuously. We fit cohort-specific Cox proportional hazards models to estimate relationships between time-varying day-night average sound level (DNL) and CVD incidence and CVD and all-cause mortality, adjusting for fixed and time-varying individual- and area-level covariates. Results were pooled using random effects meta-analysis.Results:Over 20 years of follow-up, there were 4529 CVD cases and 14,930 deaths. Approximately 7% (n = 317) of CVD cases were exposed to DNL ≥50 dB(A). In pooled analyses comparing ≥50 with <50 dB(A), the adjusted hazard ratio for CVD incidence was 1.00 (95% confidence interval: 0.89, 1.12). The corresponding adjusted hazard ratio for all-cause mortality was 1.02 (95% confidence interval: 0.96, 1.09). Patterns were similar for CVD mortality in NHS yet underpowered.Conclusions:Among participants in the NHS and NHSII prospective cohorts who generally experience low exposure to aircraft noise, we did not find adverse associations of aircraft noise with CVD incidence, CVD mortality, or all-cause mortality

Declining use of wild resources by indigenous peoples of the Ecuadorian Amazon

Wild product harvesting by forest-dwelling peoples, including hunting, fishing, forest product collection and timber harvesting, is believed to be a major threat to the biodiversity of tropical forests worldwide. Despite this threat, few studies have attempted to quantify these activities across time or across large spatial scales. We use a unique longitudinal household survey (n = 480) to describe changes in these activities over time in 32 indigenous communities from five ethnicities in the northern Ecuadorian Amazon. To provide insight into the drivers of these changes, we also estimate multilevel statistical models of these activities as a function of household and community characteristics. These analyses reveal that participation in hunting, fishing, and forest product collection is high but declining across time and across ethnicities, with no evidence for a parallel decline in resource quality. However, participation in timber harvesting did not significantly decline and there is evidence of a decline in resource quality. Multilevel statistical models additionally reveal that household and community characteristics such as ethnicity, demographic characteristics, wealth, livelihood diversification, access to forest, participation in conservation programs and exposure to external markets are significant predictors of wild product harvesting. These characteristics have changed over time but cannot account for declining participation in resource harvesting. This finding suggests that participation is declining due to changes in the regional-scale social and economic context, including urbanization and the expansion of government infrastructure and services. The lesson for conservationists is that macro-scale social and economic conditions can drive reductions in wild product harvesting even in the absence of successful conservation interventions