AbstractIllegal or poorly regulated wildlife trade may enhance parasite spread worldwide, leading to pathogen outbreaks and the emergence of diseases affecting native wildlife, domestic animals and humans. The order Psittaciformes has the largest proportion of endangered species among all birds worldwide and is one of the most trafficked taxa in the pet trade. However, despite the large number of parrot species commercialized worldwide, the influence of illegally traded wild birds on the introduction of exotic pathogens is still poorly investigated. Here we molecularly examined the prevalence and genetic diversity of haemosporidian parasites in illegally traded white‐winged parakeets (Brotogeris versicolurus), one of the most trafficked parrots in South America. We found that 18.5% of parakeets harboured Plasmodium relictum GRW04, a highly invasive malaria parasite provoking population decline and even extinctions in native avifauna when established outside its natural range. We also showed that malaria infected birds have lower body condition than uninfected parakeets, revealing the negative effects of malaria on their avian hosts. These outcomes highlight the risk of malaria spill over and disease outbreak in illegally traded wildlife. Our results also reveal epidemiological key concepts in disease transmission, such as the role of poorly studied parrot species as natural reservoir hosts of haemosporidians. These findings stress the importance of enforcing health control regulations and trade policies to fight wildlife trafficking effectively.

Gastroesophageal reflux disease (GERD) is a clinical condition in which gastric reflux causes symptoms or damage to the esophageal mucosa. It is managed with proton pump inhibitors, however, up to 45% of patients with suspected GERD are refractory to treatment. It is necessary to establish a true GERD diagnosis by means of a digestive endoscopy, which does not show lesions in approximately 70% of patients. In this scenario, it is necessary to perform an esophageal pH-impedance measurement, a procedure that allows to determine whether exposure to gastric acid is pathological. Of this group, patients with pathological acid exposure are diagnosed as true non-erosive reflux disease (NERD). If, in addition to not presenting esophageal lesions, they have a physiological exposure to gastric acid, they suffer from esophageal hypersensitivity or functional heartburn, which are functional disorders. These require a different approach from that of GERD or NERD, as the symptoms are not due to pathological exposure to gastric acid. The aim was to calculate the frequency of esophageal hypersensitivity and functional heartburn in patients with suspected NERD. This was a cross-sectional study. Data was collected by reviewing pH-impedance and manometry reports, 166 patients were selected. The frequency for functional disorders was 86.15%, being 46.9% for functional heartburn and 39.2% for esophageal hypersensitivity. The frequency of functional disorders was higher than that reported in previous studies. In conclusion, age, psychological conditions, dietary, cultural, ethnic or lifestyle factors inherent to our environment might play important roles in the development of functional disorders.

The Pinaya district hosts porphyry Cu–Au mineralization in the Pinaya deposit and other mineralized centers. Cu–Au mineralization is contained in quartz stockworks with chalcopyrite and bornite associated with potassic alteration in early to intermineral daci-andesitic dikes and irregular intrusions emplaced within terrigenous facies of the Puno Group. Prograde, garnet-skarn formed in adjacent, carbonate-bearing host rocks, likely simultaneously with the potassic alteration of the productive intrusions. Both potassic and skarn associations were overprinted by progressive hydrolytic phases of sericite-clay and dickite-dominated weak and fracture-controlled advanced argillic alteration. The first was predominantly pyritic, but the second introduced localized chalcocite, digenite, and covellite in addition to pyrite in a high to very high-sulfidation environment. A later, porphyry-centered event of intermediate-sulfidation mineralization formed proximal quartz-adularia containing nominal amounts of precious metals and distal polymetallic veins. Supergene alteration and mineralization produced an irregular profile with mixed oxide-sulfides and supergene chalcocite.The age of the porphyry-related magmatism is bracketed by two U–Pb (zircon) dates of ∼28 Ma on early and intermineral porphyry phases. This age and the associated trace-element signature of the host intrusions (Sr, V, Sc) not only indicate that Pinaya was emplaced late in the evolution of the larger Andahuaylas-Yauri porphyry-skarn metallogenic belt, but also confirms that it can be confidently extended for ∼60 km to the south. It is proposed here that porphyry Cu–Au mineralization at Pinaya was emplaced at the transition from the flat-slab subduction that characterized the evolution of the Andahuaylas-Yauri batholith and related metallogeny to normal-slab subduction at 28 Ma.

Objective: This study explores the role of resilience in facilitating the integration of Colombian refugees in New Zealand. Due to the lack of research on this specific population in New Zealand, this study was conducted to address the gap in academic literature. Methodology: This study used ethnography and oral history as its methodological approaches, conducting 12 oral history interviews with the participants. Additionally, four participants presented written personal diaries, resulting in a total of 13 Colombian refugees participating in the study. Findings: The findings showed that resilience has been the key factor enabling Colombian refugees to achieve successful integration in New Zealand. Conclusion: This study demonstrates how resilience has been instrumental in empowering Colombian refugees to navigate and overcome the challenges of integration in the country, including lack of English proficiency, mental health problems, discrimination, and unemployment.

T helper (Th) 17 cells are one of the most important T cell subsets in a number of autoimmune and chronic inflammatory diseases. During infections, Th17 cells appear to play an important role in the clearance of extracellular pathogens. Th17 cells, on the other hand, are engaged in inflammation and have been linked to the pathophysiology of a number of autoimmune illnesses and human inflammatory disorders. A diverse group of RNA molecules known as lncRNAs serve critical functions in gene expression regulation. They may interact with a wide range of molecules, including DNA, RNA, and proteins, and have a complex structure. LncRNAs, which have restricted or no protein-coding activity, are implicated in a number of illnesses due to their regulatory impact on a variety of biological processes such as cell proliferation, apoptosis, and differentiation. Several lncRNAs have been associated with Th7 cell development in the context of immune cell differentiation. In this article, we cover new studies on the involvement of lncRNAs in Th17 cell differentiation in a variety of disorders, including auto-immune diseases, malignancies, asthma, heart disease, and infections.

This article analyzes the levels of citizen satisfaction with LEGADO's quality management model service during the first year of vaccination against SARS-CoV-2 in public spaces administered by LEGADO, and its relationship with the user's emotional responses. To this end, a survey study has been developed from July 2021 until March 2022 at 4 moments to citizens (n = 1,697) who attended 3 vaccination locations administered by LEGADO (VIDENA, Complejo VMT and Polideportivo VES). The results show a high level of satisfaction with LEGADO's quality model service, which is associated with a positive emotional balance. Specifically, the elements that have the greatest effect on positive emotions are the cleanliness and facilities' organization and the agility of service. These results are discussed emphasizing the importance of the role of public institutions in developing inclusive quality public services for all citizens. This strategy of public quality model service according to citizens' necessities should result in confidence towards public institutions and socially responsible behavior among citizens through the reduction of social gaps. The research establishes the urgency to promote this model in order to bring legitimacy and confidence to public institutions in Perú.

Few studies have examined the burden of postacute sequelae of coronavirus disease 2019 (COVID-19) (PASC) in low- and middle-income countries. We sought to characterize PASC with self-reported questionnaires and clinical examinations of end-organ function in Lima, Peru. From January to July 2021, we recruited participants at least 8 weeks after COVID-19 diagnosis from a case registry in Lima, Peru. We evaluated participants for PASC with questionnaires, neuropsychiatric evaluations, chest X-ray, spirometry, electrocardiogram, and echocardiogram. We used multivariable models to identify risk factors for PASC. We assessed 989 participants for PASC at a median 4.7 months after diagnosis. Clinically significant respiratory symptoms were reported by 68.3% of participants, particularly those who had been severely ill during acute COVID-19, and were associated with cardiac findings of ventricular hypertrophy or dilation on echocardiogram. Neuropsychiatric questionnaires were consistent with depression in 20.7% and cognitive impairment in 8.0%. Female sex and older age were associated with increased risk of respiratory (adjusted odds ratio [aOR], 2.36 [95% confidence interval {CI}, 1.69-3.31] and aOR, 1.01 [95% CI, 1.00-1.03], respectively) and neuropsychiatric sequelae (aOR, 2.99 [95% CI, 2.16-4.18] and aOR, 1.02 [95% CI, 1.01-1.03], respectively). COVID-19 survivors in Lima, Peru, experienced frequent postacute respiratory symptoms and depression, particularly among older and female participants. Clinical examinations highlighted the need for cardiopulmonary rehabilitation among persons with severe COVID-19; psychosocial support may be required among all COVID-19 survivors.

e14033 Background: IDH-mutant tumors are a distinct disease entity accounting for one third of gliomas with a more favourable prognosis despite still harboring high morbidity and mortality rates. Most IDH mutations (muts) in gliomas group at R132/R172 hotspots in IDH1/IDH2, respectively. Our aim was to study IDH non-canonical muts and IDH co-mutations (co-muts) since the evidence is still limited. Methods: Prospective study of patients (pts) with gliomas at 4 third-level hospitals in Spain (Hospital Clinico Universitario San Carlos, Hospital Universitario La Paz, Hospital Universitario de Canarias, Hospital Universitario Nuestra Señora de la Candelaria). High throughput next generation sequencing (NGS) using a customized gene panel ((Illumina, Inc) including IDH1 and IDH2 was used in FFPE and/or fresh tumor samples, and run in an Illumina MiSeq instrument (Illumina, Inc). Only pathogenic mutations were considered as valid. Clonal IDH (cIDH) muts were defined as those with a MAF ≥ 1% and subclonal IDH (scIDH) muts as those with a MAF < 1%. Results: Between February 2017 and February 2021, 49 glioma pts enrolled and sequenced for IDH1/IDH2 tumor muts. Median age 51 (Min-max: 20-78). WHO 5th Ed (n = 42): IDH-MUT astrocytoma (n = 14), IDH-MUT oligodendroglioma (n = 7), IDH-WT glioblastoma (n = 21). 37/49 pts (75.5%) presented cIDH and/or scIDH muts, of which 21 pts (43%) harbored cIDH muts in IDH1 (R132H (n = 17), R132C (n = 1), R132G (n = 1), R100Q (n = 1), G161R (n = 1)) and in IDH2 (R140W (n = 1), R140Q (n = 1)). In 17/21 pts (81%) cIDH-mut MAF ≥ 10%. Of 21 cIDH-mutant pts, there were IDH co-muts in 11 pts (52%). Of these 11 pts, IDH co-muts were scIDH in 91%: #2 (IDH1 R100Q/G161R), #3 (IDH1 R132H/R119Q/G161R), #4 (IDH1 R132H/R132C; IDH2 R172G/R140Q), #11 (IDH1 R132H/R132C), #21 (IDH1 R132H/G161R; IDH2 R140Q), #23 (IDH1 R132H/R132C), #31 (IDH1 R132H/G161R; IDH2 R140Q/P162L), #32 (IDH1 R132H/R100Q), #33 (IDH1 R132H/R100Afs*29/I130V), #37 (IDH1 R132H; IDH2 R159H), #40 (IDH1 R132H; IDH2 R172G). Median overall survival (OS) longer in cIDH-mut vs IDH-wt (159 vs 16 months, P = 0.000). No difference in OS in: cIDH vs cIDH + scIDH (NR vs 67 m, P = 0.437), in scIDH vs cIDH (79 vs 159 m, P = 0.111), and scIDH vs IDH-wt (79 vs 12 m, P = 0.229). Conclusions: Subclonal (defined as MAF < 1%) canonical and non-canonical IDH-mutations and co-mutations occur frequently in gliomas. This may have implications for the design of future clinical trials and in the development of IDH inhibitors including the anticipation of potential resistance mechanisms.

Vol. 131, No. 5 Research LetterOpen AccessLevels of Arsenic, Cadmium, and Mercury in Urine of Indigenous People Living Close to Oil Extraction Areas in the Peruvian Amazon Cristina O’Callaghan-Gordo, Jaime Rosales, Pilar Lizárraga, Frederica Barclay, Tami Okamoto, Diana M. Papoulias, Ana Espinosa, Martí Orta-Martinez, Manolis Kogevinas, and John Astete Cristina O’Callaghan-Gordo Address correspondence to Cristina O’Callaghan-Gordo. Email: E-mail Address: [email protected] https://orcid.org/0000-0002-4229-2991 Faculty of Health Sciences, Universitat Oberta de Catalunya, Barcelona, Spain ISGlobal, Barcelona, Spain Universitat Pompeu Fabra, Barcelona, Spain CIBER Epidemiología y Salud Pública, Spain Search for more papers by this author , Jaime Rosales Centro Nacional de Salud Ocupacional y Protección del Ambiente para la Salud, Instituto Nacional de Salud, Lima, Peru Search for more papers by this author , Pilar Lizárraga Centro Nacional de Salud Ocupacional y Protección del Ambiente para la Salud, Instituto Nacional de Salud, Lima, Peru Search for more papers by this author , Frederica Barclay Centro de Políticas Públicas y Derechos Humanos–Perú Equidad, Lima, Peru Search for more papers by this author , Tami Okamoto Department of Geography, University of Cambridge, Cambridge, UK Search for more papers by this author , Diana M. Papoulias E-Tech International, El Sobrante, California, USA Search for more papers by this author , Ana Espinosa ISGlobal, Barcelona, Spain Universitat Pompeu Fabra, Barcelona, Spain CIBER Epidemiología y Salud Pública, Spain Hospital del Mar Medical Research Institute, Barcelona, Spain Search for more papers by this author , Martí Orta-Martinez Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Barcelona, Spain Search for more papers by this author , Manolis Kogevinas ISGlobal, Barcelona, Spain Universitat Pompeu Fabra, Barcelona, Spain CIBER Epidemiología y Salud Pública, Spain Hospital del Mar Medical Research Institute, Barcelona, Spain Search for more papers by this author , and John Astete Centro Nacional de Salud Ocupacional y Protección del Ambiente para la Salud, Instituto Nacional de Salud, Lima, Peru Search for more papers by this author Published:3 May 2023CID: 057701https://doi.org/10.1289/EHP11932AboutSectionsPDF ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InReddit IntroductionOil extraction can lead to long-term harm to the environment and human communities.1 In the 1970’s, oil extraction started in the northern Peruvian Amazon, in the Corrientes, Pastaza, and Tigre river basins, all major tributaries of the Marañón River, leading to high levels of environmental contamination in these four river basins. The oil concessions of this area, which are currently among of the most contaminated areas of the country [see reports on oil Blocks 8 and 192 (formerly 1AB)], overlap with the territories of the Achuar, Quechua, Kichwa, and Kukama Peoples. These Indigenous groups belong to the Jivaro, Quechua, and Tupi linguistic families, respectively. They live in the northern Amazon, on the border between Peru and Ecuador. According to the 2017 Peruvian National Census (indigenous communities module), it is estimated that approximately 7,944∼7,944 Achuar, 11,347 Quechua, 4,742 Kichwa, and 9,532 Kukama Peoples live in these four river basins.2 These groups were mostly nomadic-hunter gatherers until the 1960s when they settled in small communities. Nowadays, they continue to rely on subsistence agriculture and on hunting and fishing for their daily protein intake. Since the arrival of the oil companies to the area, the inhabitants of the area have shown concerns about the potential health effects of the environmental contamination caused in the area. High blood lead levels (greater than 5 micrograms per deciliter>5μg/dL in 49% of children and in 60% of adults) were reported among the population of these river basins,3 but there is no information on other metals. The primary aim of this study was to estimate concentrations of metals in urine of Indigenous People residing in four major river basins in oil concessions areas in Peru. Associations were then explored between previously reported urinary metal concentrations and sociodemographic, environmental, occupational, and lifestyle factors.MethodsWe conducted a cross-sectional study and assessed urinary concentrations of total arsenic (U-As), cadmium (U-Cd) and total mercury (U-Hg) in the populations of the Corrientes, Pastaza, Tigre, and Marañón River basins (Figure 1) in collaboration with indigenous federations from the northern Peruvian Amazon (ACODECOSPAT, FECONACOR, OPIKAFPE, FEDIQUEP, PUINAMUDT) in May–June 2016. The study design was described in detail elsewhere.3 Briefly, we followed a two-stage stratified random strategy to select study participants. Thirty-nine communities were selected and between 14% and 15% of families were randomly selected in each community. Participation was offered to all members of the selected families, excluding infants under 6 months of age. The study protocol was reviewed and accepted by the Ethics and Research Committee of the National Institute of Health (NIH), Peru. Written informed consent was given from traditional leaders to conduct the study in each of the communities. Participants greater than or equal to 18≥18 years of age provided written informed consent, and participants between greater than or equal to 7≥7 and less than 18<18 years of age provided personal verbal consent and their parents provided informed written consent. For participants less than 7<7 years of age parents provided informed written consent.Figure 1. Map of the study area. Block 1AB/192 and Block 8 refer to the two oil concessions areas that overlap with the territories of the Achuar, Quechua, Kichwa, and Kukama Peoples in the Corrientes, Pastaza, and Tigre river basins of the northern Peruvian Amazon. Central processing facilities include production facilities where oil, gas, and produced water are collected from the oilfield and separated, as well as storage tanks, flare systems, utilities, and support buildings. The figure was elaborated using ArcGIS Pro (version 2.5.0; ESRI) and open-access spatial data on oil concessions and infrastructure, indigenous communities, and natural protected areas.Face-to-face questionnaires were administered to the heads of households to collect information on dwelling and to all family members to collect information on individual risk factors. Urine samples were collected, preserved, and analyzed by atomic absorption spectrophotometry following protocols validated by the Peruvian NIH.4 The limits of detection (LODs) were 2.5 micrograms per liter2.5μg/L for U-As and U-Hg and 0.5 microgram per liter0.5μg/L for U-Cd. We replaced metal values below the LOD by the LOD divided by 2. Thirty-two percent (266), 31% (259), and 50% (408) of measurements were below the LOD for U-As, U-Hg, and U-Cd, respectively.We used linear regression models of log-transformed variables, taking into account the multilevel study design.3 Results were back-transformed and presented as geometric mean ratios with 95% confidence intervals [GMR (95% CI)], stratified by age using a threshold of 12 years of age. Associations were tested using the Wald test, and variables associated in individual models (lowercase italic p less than 0.1p<0.1) were considered in multiple regression models. If multicollinearity was observed (variable inflation factors greater than 5>5), we dropped one of the correlated variables from the model. All analyses were made using Stata (version 14; StataCorp). The map in Figure 1 was elaborated using ArcGIS Pro (version 2.5.0; ESRI) and open-access spatial data on oil concessions and infrastructure, indigenous communities, and natural protected areas.Results and DiscussionCreatinine-corrected concentrations of metals were available for 824 participants, of which 230 were children (less than 12<12 years of age) and 594 were adults (greater than or equal to 12≥12 years of age). Characteristics are presented in Table 1. Average concentrations of U-Hg were 4.1 micrograms per gram4.1μg/g for children and 4.4 micrograms per gram4.4μg/g for adults. Corresponding concentrations for U-As were 27.7 micrograms per gram27.7μg/g and 15 micrograms per gram15μg/g, and for U-Cd 0.8 microgram per gram0.8μg/g and 1.1 micrograms per gram1.1μg/g. Twenty-five percent (lowercase italic n equals 57n=57) of children and 28% (164) of adults had U-Hg levels above reference values (RVs) established by the Peruvian Ministry of Health (MINSA) (5 micrograms per gram5μg/g). For U-As, the corresponding percentages (reference value equals 20 micrograms per gramRV=20μg/g) above the RV were 48% (110) for children and 23% (135) for adults, and for U-Cd (reference value equals 2 micrograms per gramRV=2μg/g), 2% (6) and 13% (76), respectively.Table 1 Urine creatinine-corrected concentrations (micrograms per gramμg/g) of total arsenic (As), cadmium (Cd), and total mercury (Hg) and associations with sociodemographic, lifestyle, environmental, and occupational exposures by age group among Indigenous People living close to oil extraction areas in the Peruvian Amazon, May–June 2016 (230 participants less than 12<12 y old; 594 participants greater than or equal to 12≥12 y old).CategoryStudy populationAsCdHgless than 12<12 ygreater than or equal to 12≥12 yless than 12<12 ygreater than or equal to 12≥12 yless than 12<12 ygreater than or equal to 12≥12 yless than 12<12 ygreater than or equal to 12≥12 yN (%) or mean plus or minus SDmean±SDN (%) or mean plus or minus SDmean±SDGM (95% CI)GMR (95% CI)p-ValueGM (95% CI)GMR (95% CI)p-ValueGM (95% CI)GMR (95% CI)p-ValueGM (95% CI)GMR (95% CI)p-ValueGM (95% CI)GMR (95% CI)p-ValueGM (95% CI)GMR (95% CI)p-ValueAge (y)a7.3 plus or minus 2.77.3±2.736.3 plus or minus 16.336.3±16.3—0.93 (0.89, 0.98)0.008—0.99 (0.99, 1.00)0.008—0.99 (0.96, 1.03)0.608—1.02 (1.01, 1.02)less than 0.001<0.001—0.98 (0.94, 1.02)0.304—1.01 (1.00, 1.01)0.001Sexb Female119 (51.7)331 (55.7)15.1 (12.6, 0.98)Ref—10.9 (9.9, 12.0)Ref—0.6 (0.5, 0.7)Ref—0.9 (0.8, 1.0)Ref—3.2 (2.8, 3.7)Ref—3.1 (2.9, 3.4)Ref— Male111 (48.3)263 (44.3)20.8 (17.6, 24.7)1.35 (1.08, 1.70)0.01310.0 (9.1, 11.0) 0.99 (0.88, 1.11)0.8550.6 (0.5, 0.7)0.97 (0.81, 1.17)0.7690.8 (0.7, 0.8)0.85 (0.75, 0.95)0.0092.8 (2.4, 3.2)0.91 (0.74, 1.11)0.3502.9 (2.6, 3.2)0.86 (0.76, 0.99)0.037Ethnic origin Achuar91 (39.6)195 (32.8)17.7 (14.5, 21.7)Ref—9.3 (8.3, 10.3)Ref—0.8 (0.7, 0.9)Ref—0.8 (0.8, 0.9)Ref—2.5 (2.2, 2.8)Ref—2.2 (2.0, 2.4)Ref— Quechua and Kichwa79 (34.3)231 (38.9)20.1 (16.1, 25.2)1.19 (0.83, 1.72)—9.9 (8.8, 11.1)1.14 (0.95, 1.38)—0.6 (0.5, 0.6)0.77 (0.62, 0.96)—0.8 (0.7, 0.9)0.93 (0.80, 1.09)—3.6 (3.0, 4.4)1.48 (1.17, 1.89)—3.3 (3.0, 3.7)1.54 (1.30, 1.81)— Mestizo, Kukama, and other peoples60 (26.1)168 (28.3)14.6 (11.8, 18.1)0.88 (0.60, 1.30)0.25113.2 (11.6, 15.0)1.48 (1.22, 1.78)less than 0.001<0.0010.5 (0.4, 0.6)0.62 (0.50, 0.79)0.0010.9 (0.8, 1.0)0.91 (0.77, 1.07)bless than 0.001<0.0013.1 (2.6, 3.7)1.37 (1.07, 1.75)0.0063.9 (3.4, 4.5)1.76 (1.44, 2.16)less than 0.001<0.001River basin Marañón55 (23.9)145 (24.4)14.3 (11.4, 17.9)Ref—12.7 (11.0, 14.6)Ref—0.5 (0.4, 0.6)Ref—0.9 (0.8, 1.0)Ref—3.2 (2.6, 4.0)Ref—4.2 (3.6, 4.9)Ref— Pastaza68 (29.6)198 (33.3)24.2 (18.7, 31.4)1.88 (1.24, 2.85)—10.2 (9.0, 11.6)0.83 (0.68, 1.02)—0.5 (0.4, 0.6)1.19 (0.95, 1.51)—0.6 (0.6, 0.7)0.86 (0.74, 1.01)—3.5 (2.9, 4.2)1.03 (0.76, 1.39)—3.0 (2.7, 3.3)0.72 (0.58, 0.91)— Tigre15 (6.5)51 (8.6)13.0 (8.6, 19.7)0.87 (0.56, 1.33)—8.5 (6.6, 11.0)0.68 (0.50, 0.94)—0.7 (0.6, 0.9)1.55 (1.15, 2.09)—1.5 (1.2, 1.8)1.91 (1.52, 2.39)—4.0 (2.3, 7.0)1.18 (0.74, 1.88)—3.9 (3.1, 5.0)0.99 (0.66, 1.49)— Corrientes92 (40.0)200 (33.7)16.6 (13.8, 19.9)1.11 (0.79, 1.56)0.01210.0 (9.0, 11.0)0.78 (0.64, 0.94)0.0560.7 (0.6, 0.8)1.60 (1.26, 2.03)0.0030.9 (0.8, 1.0)1.22 (1.03, 1.45)bless than 0.001<0.0012.5 (2.1, 2.8)0.72 (0.55, 0.95)0.0182.2 (2.0, 2.4)0.54 (0.43, 0.68)less than 0.001<0.001 Total fish consumption (times 100 grams×100g)c1.3 plus or minus 1.31.3±1.31.6 plus or minus 1.51.6±1.5—1.00 (0.99, 1.02)0.426—1.00 (0.99, 1.01)0.9541.00 (0.98, 1.01)0.404—1.00 (1.00, 1.01)0.594—0.99 (0.98, 1.00)0.1081.01 (1.00, 1.01)0.038Fish offal consumption No82 (35.7)150 (25.3)21.1 (17.2, 26.0)Ref—11.9 (10.4, 13.6)Ref—0.6 (0.6, 0.7)Ref—0.8 (0.7, 0.9)Ref—2.8 (2.4, 3.3)Ref—3.0 (2.6, 3.4)Ref— Yes148 (64.3)444 (74.7)15.9 (13.7, 18.6)0.80 (0.60, 1.08)0.15010.1 (9.3, 10.9)0.85 (0.71, 1.01)0.0720.6 (0.5, 0.6)0.92 (0.76, 1.11)0.3810.9 (0.8, 0.9)1.03 (0.90, 1.18)0.6853.1 (2.7, 3.5)1.06 (0.86, 1.32)0.5783.0 (2.8, 3.3)0.94 (0.79, 1.13)0.524Alcohol consumption (only greater than or equal to 12≥12 y old) No—495 (83.3)———10.8 (10.1, 11.7)Ref————0.8 (0.8, 0.9)Ref————3.0 (2.8, 3.3)Ref— Yes—99 (16.7)———9.1 (7.8, 10.5)0.86 (0.70, 1.06)0.161———0.9 (0.8, 1.0)1.17 (0.99, 1.38)0.070———3.0 (2.5, 3.6)1.07 (0.86, 1.34)0.550Smoking (only greater than or equal to 12≥12 y old) No—531 (89.4)———10.7 (9.9, 11.5)1.00 (1.00, 1.00)————0.8 (0.8, 0.9)1.00 (1.00, 1.00)————3.0 (2.8, 3.2)1.00 (1.00, 1.00)— Yes—63 (10.6)———9.2 (7.6, 11.1)0.88 (0.73, 1.07)0.219———1.0 (0.8, 1.2)1.28 (0.98, 1.65)0.073———3.0 (2.3, 3.9)1.03 (0.78, 1.36)0.840Burning of household waste No128 (55.7)330 (55.6)15.9 (13.6, 18.7)Ref—10.8 (9.8, 11.8)Ref—0.6 (0.5, 0.7)Ref—0.9 (0.8, 0.9)Ref—2.9 (2.6, 3.3)Ref—3.0 (2.8, 3.3)Ref— Yes102 (44.3)264 (44.4)20.0 (16.5, 24.2)1.26 (0.92, 1.74)0.16210.2 (9.2, 11.3)1.00 (0.85, 1.18)0.9910.6 (0.5, 0.7)1.02 (0.85, 1.23)0.8260.8 (0.7, 0.9)0.87 (0.76, 1.00)b0.0543.1 (2.7, 3.6)1.07 (0.85, 1.35)0.5603.0 (2.7, 3.4)0.99 (0.83, 1.18)0.934Main source of water for consumption Public water source116 (50.4)265 (44.6)15.4 (12.9, 18.4)Ref—9.6 (8.7, 10.6)Ref—0.6 (0.5, 0.7)Ref—0.8 (0.7, 0.9)Ref—3.2 (2.8, 3.7)Ref—3.1 (2.8, 3.4)Ref— Well or spring water56 (24.3)170 (28.6)27.6 (21.8, 34.8)1.69 (1.16, 2.46)—11.1 (9.9, 12.6)1.13 (0.94, 1.37)—0.6 (0.5, 0.8)1.12 (0.89, 1.42)—0.8 (0.7, 0.9)1.04 (0.90, 1.20)—2.4 (2.0, 2.9)0.76 (0.59, 0.99)—2.4 (2.1, 2.6)0.78 (0.65, 0.94)— Rain22 (9.6)68 (11.4)15.4 (10.7, 22.3)0.99 (0.64, 1.53)—12.3 (9.8, 15.4)1.29 (0.98, 1.70)—0.6 (0.4, 0.7)1.00 (0.75, 1.33)—1.2 (1.0, 1.4)1.49 (1.21, 1.82)—3.0 (2.1, 4.4)0.97 (0.65, 1.45)—4.5 (3.6, 5.6)1.41 (1.07, 1.86)— Surface water (river, ravine, lagoon)36 (15.7)91 (15.3)14.6 (10.9, 19.7)0.81 (0.53, 1.24)0.01111.0 (9.2, 13.1)1.10 (0.90, 1.34)0.3130.7 (0.6, 0.8)1.19 (0.91, 1.55)0.5680.8 (0.7, 1.0)1.15 (0.93, 1.41)0.0063.2 (2.6, 4.1)0.98 (0.71, 1.35)0.2623.2 (2.7, 3.9)1.10 (0.84, 1.43)0.001Main bathing place Well38 (16.5)118 (19.9)25.9 (18.6, 36.1)Ref—10.4 (9.0, 12.0)Ref—0.6 (0.4, 0.7)Ref—0.8 (0.7, 0.9)Ref—2.8 (2.2, 3.5)Ref—2.6 (2.2, 2.9)Ref— Surface water (river, ravine, lagoon)156 (67.8)399 (67.2)16.5 (14.2, 19.1)0.68 (0.47, 0.98)—10.5 (9.7, 11.4)1.03 (0.85, 1.24)—0.6 (0.5, 0.7)1.04 (0.79, 1.35)—0.8 (0.8, 0.9)1.07 (0.91, 1.26)—3.2 (2.9, 3.6)1.09 (0.85, 1.41)—3.4 (3.1, 3.7)1.32 (1.10, 1.58)— Others36 (15.7)77 (13.0)15.7 (11.9, 20.6)0.82 (0.66, 1.02)0.07110.6 (8.8, 12.8)0.98 (0.77, 1.26)0.9060.6 (0.5, 0.8)0.82 (0.66, 1.02)0.7450.8 (0.7, 0.9)0.94 (0.77, 1.16)0.3262.3 (1.7, 3.1)0.82 (0.66, 1.02)0.1752.1 (1.7, 2.5)0.80 (0.60, 1.08)0.001Residence at less than 1<1 h walking None of these places75 (32.6)160 (26.9)16.8 (13.6, 20.7)Ref—10.1 (8.9, 11.6)Ref—0.6 (0.5, 0.7)Ref—0.8 (0.7, 0.9)Ref—3.1 (2.7, 3.6)Ref—3.3 (2.9, 3.8)Ref— Active oil infrastructures30 (13.0)117 (19.7)24.2 (16.4, 35.7)1.38 (0.84, 2.25)—11.7 (10.0, 13.6)1.16 (0.91, 1.48)—0.5 (0.4, 0.7)0.84 (0.63, 1.12)—0.8 (0.7, 0.9)1.10 (0.91, 1.33)—2.3 (1.8, 2.9)0.70 (0.51, 0.95)—2.8 (2.5, 3.3)0.96 (0.75, 1.22)— Oil spill, environmental remediation spot107 (46.5)288 (48.5)17.1 (14.3, 20.4)0.97 (0.66, 1.44)—10.5 (9.5, 11.6)1.05 (0.86, 1.30)—0.6 (0.5, 0.7)1.02 (0.80, 1.29)—0.9 (0.8, 1.0)1.25 (1.05, 1.49)—3.4 (2.9, 4.0)1.07 (0.84, 1.38)—3.0 (2.7, 3.3)0.96 (0.77, 1.19)— Old infrastructures (not in use)18 (7.8)29 (4.9)15.4 (9.7, 24.6)0.85 (0.50, 1.44)0.3808.7 (6.8, 11.2)0.89 (0.62, 1.29)0.4810.7 (0.5, 0.9)1.08 (0.77, 1.50)0.4230.8 (0.6, 1.0)1.02 (0.80, 1.31)0.0932.0 (1.4, 2.7)0.60 (0.43, 0.84)b0.0042.8 (2.1, 3.7)0.88 (0.58, 1.33)0.942Vegetable garden at less than 1<1 h walking None of these places102 (44.3)261 (43.9)16.6 (13.7, 20.1)Ref—10.8 (9.8, 12.0)Ref—0.6 (0.5, 0.7)Ref—0.8 (0.7, 0.9)Ref—3.0 (2.6, 3.4)Ref—3.3 (3.0, 3.7)Ref— Active oil infrastructures31 (13.5)87 (14.6)19.8 (13.3, 29.6)1.05 (0.64, 1.74)—11.1 (9.4, 13.0)0.99 (0.79, 1.25)—0.6 (0.5, 0.8)1.04 (0.77, 1.41)—0.8 (0.7, 0.9)0.96 (0.79, 1.15)—2.1 (1.7, 2.6)0.69 (0.52, 0.93)—2.6 (2.2, 2.9)0.79 (0.64, 0.98)— Oil spill, environmental remediation spot93 (40.4)232 (39.1)18.2 (15.2, 21.8)1.09 (0.76, 1.55)—10.2 (9.1, 11.4)0.90 (0.75, 1.08)—0.6 (0.5, 0.7)1.02 (0.83, 1.26)—0.9 (0.8, 1.0)1.20 (1.03, 1.39)—3.5 (2.9, 4.2)1.14 (0.89, 1.46)—2.9 (2.6, 3.3)0.88 (0.72, 1.08)— Old infrastructures (not in use)4 (1.7)14 (2.4)14.4 (3.2, 64.5)0.98 (0.45, 2.12)0.9737.8 (5.8, 10.6)0.73 (0.55, 0.96)0.1740.6 (0.2, 2.0)1.08 (0.47, 2.48)0.9910.5 (0.4, 0.7)0.75 (0.54, 1.04)b0.0311.9 (0.8, 4.7)0.61 (0.41, 0.90)0.0052.6 (1.9, 3.6)0.85 (0.55, 1.32)0.246Use of crude oil to keep insects away from the house No138 (60.0)368 (62.0)16.3 (13.8, 19.4)Ref—9.9 (9.1, 10.8)Ref—0.6 (0.5, 0.7)Ref—0.8 (0.8, 0.9)Ref—3.3 (2.9, 3.7)Ref3.2 (2.9, 3.5)Ref— Yes92 (40.0)226 (38.0)19.7 (16.6, 23.4)1.14 (0.85, 1.53)0.39911.5 (10.3, 12.9)1.12 (0.95, 1.32)0.2020.6 (0.5, 0.7)1.00 (0.83, 1.22)0.9690.8 (0.7, 0.9)0.96 (0.84, 1.10)0.5692.6 (2.3, 3.1)0.83 (0.66, 1.05)0.1242.8 (2.5, 3.1)0.91 (0.75, 1.09)0.305Contact with crude oil in the last 6 months (only greater than or equal to 12≥12 y old) No—513 (86.4)———10.6 (9.9, 11.5)Ref————0.8 (0.8, 0.9)Ref————3.1 (2.8, 3.3)Ref— Yes—81 (13.6)———9.8 (8.3, 11.5)0.89 (0.72, 1.10)0.301———0.8 (0.7, 0.9)1.02 (0.85, 1.23)0.848———2.8 (2.3, 3.4)0.95 (0.75, 1.20)0.645Participation in environmental remediation activities in the last 6 months (only greater than or equal to 12≥12 y old)d No—449 (75.6)———10.7 (9.9, 11.5)Ref————0.8 (0.8, 0.9)Ref————3.1 (2.9, 3.4)Ref— Yes—145 (24.4)———10.0 (8.8, 11.4)0.95 (0.80, 1.12)0.532———0.9 (0.8, 1.0)1.11 (0.95, 1.29)0.185———2.7 (2.4, 3.1)0.92 (0.75, 1.12)0.388Euclidean distance to closest oil processing facility (times 10 kilometers×10km)2.5 plus or minus 3.92.5±3.92.2 plus or minus 3.52.2±3.5—0.96 (0.92, 1.00)0.067—1.00 (0.98, 1.03)0.747—0.97 (0.95, 0.99)0.006—0.99 (0.97, 1.01)0.185—1.05 (1.02, 1.08)0.002—1.06 (1.04, 1.09)less than 0.001<0.001Minimum upstream fluvial distance processing facility (times 10 kilometers×10km)e14.9 plus or minus 19.214.9±19.215.8 plus or minus 20.315.8±20.3—0.99 (0.98, 1.00)0.053—1.00 (1.00, 1.01)0.218—0.99 (0.99, 1.00)0.00—1.00 (0.99, 1.00)0.034—1.00 (1.00, 1.01)0.108—1.01 (1.00, 1.02)less than 0.001<0.001Note: Individual linear regression models adjusted for age and sex, unless otherwise noted. p-Values based on Wald test less than 0.05<0.05. Limit of detection was 2.5 micrograms per liter2.5μg/L for U-As and U-Hg and 0.5 microgram per liter0.5μg/L for U-Cd. —, Not applicable; CI, confidence interval; GM, geometric mean; GMR, geometric mean ratio; Ref, reference.aIndividual model adjusted for sex.bIndividual model adjusted for age.cRestricted to total fish consumption less than or equal to 7 kilograms per week≤7kg/wk (166 participants less than 12<12 y old; 433 participants greater than or equal to 12≥12 y old).dEnvironmental remediation activities include handling of solid waste, cleaning of environmental liabilities or contaminated sites, and reforestation of contaminated areas.eOnly among those living downstream from central production facilities (161 participants less than 12<12 y old; 410 participants greater than or equal to 12≥12 y old).U-Hg concentration (Table 1) increased with age among adults and were higher in the Kukama, mestizo (i.e., peoples that do not identify as belonging to an indigenous group themselves, often mixed-blood people) and other peoples, and among those living around the Marañón basin. Elevated U-Hg was also associated with increased fish consumption, which was higher in the Marañón (1,842 grams per week1,842g/wk) than in other river basins. Elevated mercury in Amazonian fish has been associated with oil contamination,5 and there is evidence that consumption of freshwater fish is associated with U-Hg concentrations.6 However, previous studies from the same region, suggest that the main route of exposure to mercury in the area is through dermal uptake of mercury present in the water.7 This is consistent with our results given that concentrations of U-Hg were 1.09 and 1.32 times higher among children and adults bathing in river water compared with those bathing in wells, and 1.42 times higher among adults consuming rain water compared with those drinking from a public water source.U-As concentrations (Table 1) decreased slightly with age in adults, tended to be higher among children who drank well water and, similar to U-Hg, were highest among Kukama, mestizo and other peoples, and among those living around Marañón. Elevated arsenic of geologic origin has been reported in the aquifers of the western Amazon8 and at relatively high concentrations in crude oil9; however, the source of arsenic in the study area remains unknown.U-Cd concentrations (Table 1) increased with age in adults and were higher in females. The highest concentrations were observed for the Achuar People and among those living around the Corrientes and Tigre river basins. Historically, these two basins have had relatively greater oil extraction activity and higher volumes of produced water released than the Pastaza and Marañón basins (discussed by O’Callaghan-Gordo et al.3) Additional factors associated with elevated U-Cd included proximity of residences or vegetable gardens to oil spill sites and participation in oil spill remediation activities. Consumption of contaminated vegetables is a known route of exposure to cadmium, and high levels of cadmium in vegetables are associated with environmental conditions.10Multivariable analyses did not indicate substantially different patterns from the univariable analyses (supporting information). Concentrations without creatinine correction were available for another 211 study participants. Models including noncorrected concentration of metals (lowercase italic n equals 1,035n=1,035) yielded similar results (supporting information).A considerable proportion of the population of children and adults exceeded the recommended RV levels. Concentrations of the three metals were associated with the sources of water for consumption and U-Hg was also associated with the sources of water for bathing. Remarkably, mercury can be absorbed through dermal uptake.7 Participation in oil activities was associated with U-Cd, and the higher levels were observed in Corrientes and Tigre river basins, which are considered the two most polluted river basins.The strengths of this research are the large sample size, the random sampling of families from different river basins with varying characteristics, and the active participation of the indigenous organizations in this research. Without the cooperation of the indigenous organizations it would not have been possible to conduct such a study in a remote area of the Amazon.The observed pattern of high concentrations for all metals supports anthropogenic sources of contamination, including oil extraction activities. The identification of high concentrations of metals in a population living in a nonindustrial setting is concerning regarding health effects, such as childhood neurodevelopment and chronic diseases, through long-term exposure. Prevention of these exposures and provision of clean water and ensuring food security are a high priority for the indigenous populations living in these river basins.AcknowledgmentsThis study was funded by National Institute of Health of Peru. We acknowledge support from the Spanish Ministry of Science and Innovation through the Centro de Excelencia Severo Ochoa 2019–2023 Program (CEX2018-000806-S). Supporting information can be found at the website of the project: https://www.isglobal.org/-/levels-of-and-risk-factors-for-exposure-to-heavy-metals-and-hydrocarbons-in-the-inhabitants-of-the-communities-of-the-pastaza-tigre-corrientes-and-mar.