Viral CpG Deficiency Provides No Evidence That Dogs Were Intermediate Hosts for SARS-CoV-2.

Due to the scope and impact of the COVID-19 pandemic there exists a strong desire to understand where the SARS-CoV-2 virus came from and how it jumped species boundaries to humans. Molecular evolutionary analyses can trace viral origins by establishing relatedness and divergence times of viruses and identifying past selective pressures. However, we must uphold rigorous standards of inference and interpretation on this topic because of the ramifications of being wrong. Here, we dispute the conclusions of Xia (2020. Extreme genomic CpG deficiency in SARS-CoV-2 and evasion of host antiviral defense. Mol Biol Evol. doi:10.1093/molbev/masa095) that dogs are a likely intermediate host of a SARS-CoV-2 ancestor. We highlight major flaws in Xia's inference process and his analysis of CpG deficiencies, and conclude that there is no direct evidence for the role of dogs as intermediate hosts. Bats and pangolins currently have the greatest support as ancestral hosts of SARS-CoV-2, with the strong caveat that sampling of wildlife species for coronaviruses has been limited.

Evaluation of heat stress and cumulative incidence of acute kidney injury in sugarcane workers in Guatemala.

OBJECTIVE: Agricultural workers worldwide exposed to heat stress could be at the risk of kidney injury, which could lead to chronic kidney disease of an unknown origin (CKDu). Hydration has been promoted as a key measure to reduce kidney injury. In the presence of a hydration intervention, the incidence of acute kidney injury (AKI) was calculated in a sugarcane worker population in Guatemala and several risk factors were evaluated. METHODS: We measured kidney function at the beginning and end of the work shift at three time points in 517 sugarcane workers. We defined AKI as an increase in serum creatinine of 26.5 µmol/L or 50% or more from the pre-shift value. Associations between AKI and risk factors were examined, including interactions with hydration status. RESULTS: The prevalence of dehydration post-shift (> 1.020 specific gravity) was 11% in February, 9% in March, and 6% in April. Cumulative incidence of AKI was 53% in February, 54% in March, and 51% in April. AKI was associated with increasing post-shift specific gravity, a dehydration marker, (OR 1.24, 95% CI 1.02-1.52) and with lower electrolyte solution intake (OR 0.94, 95% CI 0.89-0.99). CONCLUSIONS: Dehydration and insufficient electrolyte consumption are risk factors for AKI. However even well-hydrated sugarcane workers routinely experience AKI. While hydration is important and protective, there is a need to understand other contributors to risk of AKI and identify prevention strategies with these workers.

Spatiotemporal Error in Rainfall Data: Consequences for Epidemiologic Analysis of Waterborne Diseases.

The relationship between rainfall, especially extreme rainfall, and increases in waterborne infectious diseases is widely reported in the literature. Most of this research, however, has not formally considered the impact of exposure measurement error contributed by the limited spatiotemporal fidelity of precipitation data. Here, we evaluate bias in effect estimates associated with exposure misclassification due to precipitation data fidelity, using extreme rainfall as an example. We accomplished this via a simulation study, followed by analysis of extreme rainfall and incident diarrheal disease in an epidemiologic study in Ecuador. We found that the limited fidelity typical of spatiotemporal rainfall data sets biases effect estimates towards the null. Use of spatial interpolations of rain-gauge data or satellite data biased estimated health effects due to extreme rainfall (occurrence) and wet conditions (accumulated totals) downwards by 35%-45%. Similar biases were evident in the Ecuadorian case study analysis, where spatial incompatibility between exposed populations and rain gauges resulted in the association between extreme rainfall and diarrheal disease incidence being approximately halved. These findings suggest that investigators should pay greater attention to limitations in using spatially heterogeneous environmental data sets to assign exposures in epidemiologic research.

The impact of heat and impaired kidney function on productivity of Guatemalan sugarcane workers.

BACKGROUND: Climate change has implications for human health and productivity. Models suggest that heat extremes affect worker health, reduce labor capacity, and commodity supply. Chronic health conditions are on the rise internationally. However there is a paucity of direct empirical evidence relating increasing temperatures to both agricultural worker health and productivity. METHODS AND FINDINGS: We evaluated the relationship between temperature exposure, kidney function, and two measures of productivity-tons of commodity produced and job attrition, of 4,095 Guatemalan sugarcane cutters over a 6-month harvest. We used distributed lag non-linear models to evaluate associations between wet bulb globe temperature (WBGT) and productivity of workers with normal or impaired kidney function. The cumulative effect of exposure to a max WBGT of 34°C was 1.16 tons (95% CI: -2.87, 0.54) less sugarcane cut over the next five days by workers with impaired kidney function, compared to exposure to 29°C. Impaired kidney function was associated with premature workforce attrition. Workers starting the harvest season with impaired kidney function were more than twice as likely to leave employment (HR: 2.92, 95% CI: 1.88, 4.32). CONCLUSIONS: Heat extremes may be associated with loss of agricultural worker productivity and employment, especially among those with impaired kidney function. Agricultural workers who develop health conditions, such as kidney disease, are particularly vulnerable in the face of climate change and increasing heat extremes. The resultant loss of employment and productivity has significant implications for global commodity supplies.

Estimating the Risk of Domestic Water Source Contamination Following Precipitation Events.

Climate change is expected to increase precipitation extremes, threatening water quality. In low resource settings, it is unclear which water sources are most vulnerable to contamination following rainfall events. We evaluated the relationship between rainfall and drinking water quality in southwest Guatemala where heavy rainfall is frequent and access to safe water is limited. We surveyed 59 shallow household wells, measured precipitation, and calculated simple hydrological variables. We compared Escherichia coli concentration at wells where recent rainfall had occurred versus had not occurred, and evaluated variability in the association between rainfall and E. coli concentration under different conditions using interaction models. Rainfall in the past 24 hours was associated with greater E. coli concentrations, with the strongest association between rainfall and fecal contamination at wells where pigs were nearby. Because of the small sample size, these findings should be considered preliminary, but provide a model to evaluate vulnerability to climate change.

Heavy rainfall events and diarrhea incidence: the role of social and environmental factors.

The impact of heavy rainfall events on waterborne diarrheal diseases is uncertain. We conducted weekly, active surveillance for diarrhea in 19 villages in Ecuador from February 2004 to April 2007 in order to evaluate whether biophysical and social factors modify vulnerability to heavy rainfall events. A heavy rainfall event was defined as 24-hour rainfall exceeding the 90th percentile value (56 mm) in a given 7-day period within the study period. Mixed-effects Poisson regression was used to test the hypothesis that rainfall in the prior 8 weeks, water and sanitation conditions, and social cohesion modified the relationship between heavy rainfall events and diarrhea incidence. Heavy rainfall events were associated with increased diarrhea incidence following dry periods (incidence rate ratio = 1.39, 95% confidence interval: 1.03, 1.87) and decreased diarrhea incidence following wet periods (incidence rate ratio = 0.74, 95% confidence interval: 0.59, 0.92). Drinking water treatment reduced the deleterious impacts of heavy rainfall events following dry periods. Sanitation, hygiene, and social cohesion did not modify the relationship between heavy rainfall events and diarrhea. Heavy rainfall events appear to affect diarrhea incidence through contamination of drinking water, and they present the greatest health risks following periods of low rainfall. Interventions designed to increase drinking water treatment may reduce climate vulnerability.