Exposure Estimates Research Articles

Understanding the In Vitro-In Vivo Nexus: Advanced correlation models predict clinical performance of liposomal doxorubicin

In the century of precision medicine and predictive modeling, addressing quality-related issues in the medical supply chain is critical, with 62 % of the disruptions being attributable to quality challenges. This study centers on the development and safety of liposomal doxorubicin, where animal studies alone often do not adequately explain the complex interplay between critical quality attributes and in vivo performances. Anchored in our aim to elucidate this in vitro-in vivo nexus, we compared TLD-1, a novel liposomal doxorubicin delivery system, against the established formulations Doxil® and Lipodox®. Robust in vitro-in vivo correlations (IVIVCs) with excellent coefficients of determination (R2 > 0.98) were obtained in the presence of serum under dynamic high-shear conditions. They provided the foundation for an advanced characterization and benchmarking strategy. Despite the smaller vesicle size and reduced core crystallinity of TLD-1, its release behavior closely resembled that of Doxil®. Nevertheless, subtle differences between the dosage forms observed in the in vitro setting were reflected in the bioavailabilities observed in vivo. Data from a Phase-I clinical trial facilitated the development of patient-specific IVIVCs using the physiologically-based nanocarrier biopharmaceutics model, enabling a more accurate estimation of doxorubicin exposure. This advancement could impact clinical practice by allowing for more precise dose estimation and aiding in the assessment of the interchangeability of generic liposomal doxorubicin.

Joint Spatial Modeling Bridges the Gap Between Disparate Disease Surveillance and Population Monitoring Efforts Informing Conservation of At-risk Bat Species

AbstractWhite-Nose Syndrome (WNS) is a wildlife disease that has decimated hibernating bats since its introduction in North America in 2006. As the disease spreads westward, assessing the potentially differential impact of the disease on western bat species is an urgent conservation need. The statistical challenge is that the disease surveillance and species response monitoring data are not co-located, available at different spatial resolutions, non-Gaussian, and subject to observation error requiring a novel extension to spatially misaligned regression models for analysis. Previous work motivated by epidemiology applications has proposed two-step approaches that overcome the spatial misalignment while intentionally preventing the human health outcome from informing estimation of exposure. In our application, the impacted animals contribute to spreading the fungus that causes WNS, motivating development of a joint framework that exploits the known biological relationship. We introduce a Bayesian, joint spatial modeling framework that provides inferences about the impact of WNS on measures of relative bat activity and accounts for the uncertainty in estimation of WNS presence at non-surveyed locations. Our simulations demonstrate that the joint model produced more precise estimates of disease occurrence and unbiased estimates of the association between disease presence and the count response relative to competing two-step approaches. Our statistical framework provides a solution that leverages disparate monitoring activities and informs species conservation across large landscapes. Stan code and documentation are provided to facilitate access and adaptation for other wildlife disease applications.

Physical activity during pregnancy and the risk of gestational diabetes mellitus: a systematic review and dose–response meta-analysis

BackgroundPrevious research has indicated the inverse association between physical activity (PA) and gestational diabetes mellitus (GDM). However, the dose–response relationship currently remains undetermined. This study aims to explore the dose–response relationship between PA during the first and second trimesters of pregnancy and GDM risk.MethodsStudies on the relationship between PA during pregnancy and GDM risk published before April 25, 2023, were searched for in six databases. According to the inclusion and exclusion criteria, all literature was screened for eligibility. The Newcastle-Ottawa Scale (NOS) was used to assess risk of bias. Publication bias was examined using funnel plots, Begg’s and Egger’s tests, as well as trim-and-fill analysis. We harmonized exposure estimates of PA during pregnancy to the common unit of the metabolic equivalent of task (MET)-h/week. Restricted cubic splines were used to model the dose–response relationship. The criteria from the World Cancer Research Fund were used to assess the certainty of evidence across outcomes. All analyses were performed using Stata 15.1.ResultsThe results indicated that in contrast with the lowest level of PA, promoting the highest PA level lowers the risk of GDM by 36% (RR = 0.64, 95%CI: 0.53 ~ 0.78). We found a curvilinear dose–response association between PA during the first trimester and incident GDM (Pnonlinearity = 0.012). Compared to inactive pregnant women, for those who achieved the guidelines-suggested minimum level (10 MET-h/week) of PA during the first trimester, the GDM risk was decreased by 13% (RR = 0.87, 95%CI: 0.79 ~ 0.96). A linear relationship was found between PA during the second trimester and the GDM risk (Pnonlinearity = 0.276). The results with a restricted cubic spline model suggested that pregnant women who accumulate 10 MET-h/week have a 1% reduced risk of GDM compared to completely inactive individuals. Twice (20 MET-h/week) or a higher amount of PA (50 MET-h/week) contributed to further reductions in GDM risk.ConclusionThere is a dose–response relationship between higher levels of PA in both the first and second trimesters and reduced risk of GDM; the relationship is stronger in the first trimester. Increasing PA during pregnancy can prevent the development of GDM.PROSPERO registration numberCRD42023420564.

The mortality burden attributable to wood heater smoke particulate matter (PM2.5) in Australia

Air pollution is the leading environmental risk factor for mortality worldwide. In Australia, residential wood heating is the single largest source of pollution in many regions of the country. Estimates around the world and in some limited locations across Australia have shown that the health burden attributable to wood heating PM2.5 is considerable, and that there is great potential to reduce this burden. Here, we aimed to calculate the mortality burden attributable to wood heating emissions (WHE)-related PM2.5 throughout Australia and estimate the potential health benefits of reducing WHE-related air pollution, by replacing wood heaters with cleaner heating technologies. In summary, we used a four-stage process to (1) compile a nationwide WHE inventory, (2) generate annual exposure estimates of WHE-PM2.5, (3) estimate the annual mortality burden attributable to wood heater use across Australia for the year 2015, and (4) assess the potential health benefits of replacing existing wood heaters with cleaner heating technologies. We estimated that population weighted WHE-PM2.5 exposure across Australia for 2015 ranged between 0.62 μg/m3 and 1.35 μg/m3, with differing exposures across State/Territories. We estimated a considerable mortality burden attributable to WHE-PM2.5 ranging between 558 (95 % CI, 364–738) and 1555 (95 % CI, 1180–1740) deaths annually, depending on the scenario assessed. We calculated that replacing 50 % of the current wood heater stock, with zero or lower emission technologies could produce relevant health benefits, of between $AUD 1.61 and $AUD 1.93 billion per year (303–364 attributable deaths). These findings provide a preliminary and likely conservative assessment of the health burden of wood heater smoke across Australia, and an estimation of the potential benefits from replacing the current wood heater stock with cleaner technologies. The results presented here underscore the magnitude of the health burden attributable to wood heating in Australia.

Alleviating anxiety while breastfeeding: evaluating buspirone transfer into human milk.

Buspirone, an anxiolytic with minimal risk of dependence or respiratory depression, lacks extensive published data on its transfer into human milk during lactation. The objective of this study was to 1) quantify the transfer of buspirone and its active metabolite 1-pyrimidinylpiperazine (1-PP) into human milk, allowing for an estimation of maternal drug exposure to the breastfed infant, and 2) report observations of the infants exposed to buspirone via breastmilk. Milk samples and health histories were collected from nine lactating mothers who donated milk samples to the InfantRisk Human Milk Biorepository while taking buspirone. The drug concentration-time profile of buspirone and 1-PP was determined using liquid chromatography-mass spectrometry. Buspirone was below the detection level of 1.5ng/mL in all milk samples with dosages ranging from 7.5 to 30mg twice daily. However, low levels of active metabolite 1-PP were observed at 7.5mg twice daily up to 30mg twice daily. The relative infant dose (RID) calculated ranged from 0.21 to 2.17%, which is below the standard 10% threshold for infant safety. There were no reports of adverse effects in the exposed infants. The levels of buspirone observed in all participants' milk samples were exceedingly low. The subsequently low relative infant dose (RID) in the range of 0.21% to 2.17% is below the 10% threshold for infant safety, suggesting that the transfer of maternal buspirone and its active metabolite (1-PP) into human milk is clinically insignificant and poses minimal risk to a breastfed infant.

Residential exposure to microbial emissions from livestock farms: Implementation and evaluation of land use regression and random forest spatial models

Adverse health effects have been linked with exposure to livestock farms, likely due to airborne microbial agents. Accurate exposure assessment is crucial in epidemiological studies, however limited studies have modelled bioaerosols. This study used measured concentrations in air of livestock commensals (Escherichia coli (E. coli) and Staphylococcus species (spp.)), and antimicrobial resistance genes (tetW and mecA) at 61 residential sites in a livestock-dense region in the Netherlands. For each microbial agent, land use regression (LUR) and random forest (RF) models were developed using Geographic Information System (GIS)-derived livestock-related characteristics as predictors. The mean and standard deviation of annual average concentrations (gene copies/m3) of E. coli, Staphylococcus spp., tetW and mecA were as follows: 38.9 (±1.98), 2574 (±3.29), 20991 (±2.11), and 15.9 (±2.58). Validated through 10-fold cross-validation (CV), the models moderately explained spatial variation of all microbial agents. The best performing model per agent explained respectively 38.4%, 20.9%, 33.3% and 27.4% of the spatial variation of E. coli, Staphylococcus spp., tetW and mecA. RF models had somewhat better performance than LUR models. Livestock predictors related to poultry and pig farms dominated all models. To conclude, the models developed enable enhanced estimates of airborne livestock-related microbial exposure in future epidemiological studies. Consequently, this will provide valuable insights into the public health implications of exposure to specific microbial agents.

Exposure to Paracetamol in Early Pregnancy and the Risk of Developing Cerebral Palsy: A Case-Control Study Using Serum Samples

To investigate whether maternal paracetamol use in early pregnancy is associated with cerebral palsy (CP) in offspring. We conducted a registry and biobank-based case-control study with mother-child pairs. We identified CP cases (n=322) born between 1995 and 2014 from a nationwide CP-registry. Randomly selected controls (n=343) and extra preterm controls (n=258) were obtained from a birth registry. For each mother, a single serum sample from early pregnancy (gestation weeks 10-14) was retrieved from a biobank and analyzed for serum concentrations of paracetamol, categorized into unexposed (<1ng/ml), mildly exposed (1-100ng/ml), and highly exposed (>100ng/ml), and in quartiles. Analyses were performed using logistic regression and adjusted for potential confounders. Separate analyses were conducted including only those children born preterm and only those born term. Of the 923 participants, 36.8% were unexposed, 53.2% mildly exposed, and 10% highly exposed to paracetamol. Overall, prenatal exposure to paracetamol was not associated with CP. Sensitivity and subgroup analyses showed no clear associations between paracetamol and CP across strata of term/preterm birth as well as subtypes of CP. The present study does not support an association between intrauterine exposure to paracetamol in early pregnancy and the risk of CP. However, it is important to stress that the exposure estimate is based on a single serum sample.

The impact of sampling scale: A comparison of methods for estimating external contaminant exposure in free-ranging wildlife

The impacts of contaminants on wildlife are dose dependent, and thus being able to track or predict exposure following contamination events is important for monitoring ecosystem health. However, the ability to track exposure in free-ranging wildlife is often severely limited. Consequently, researchers have predominantly relied on simple methods for estimating contaminant exposures in wildlife with little regard for spatial contaminant heterogeneity or an animal's use of diverse habitats. We evaluated the influence sampling scale (i.e., how finely contaminant distribution and organism's spatial use of the landscape is mapped) has on (1) realism and (2) conservativeness of exposure estimates. To do this, we monitored the actual exposure of wild boar (Sus scrofa) in Fukushima, Japan to radioactive contamination using GPS-coupled contaminant monitors placed on individual animals. We compared empirical exposures to estimates generated by combining varying amounts of information about an individual boar's location and/or movement, with the distribution of contamination on the landscape. We found that the most realistic exposure estimates were produced when finer-scale contaminant distribution surveys (e.g., airborne surveys) were combined with more accurate estimates of an individual's space use (e.g., home ranges or core areas). Importantly, estimates of exposure based on single point surveys at a trap site (a simple method commonly used in the literature), did not correlate with actual exposure rates, suggesting dose-effects studies using this method may result in spurious conclusions. These results suggest that researchers seeking realistic estimates of exposure, such as in dose-effect studies, should ensure they have adequately accounted for fine-scale contaminant distribution patterns and areas of higher use by study organisms. However, conservative estimates of exposure (i.e., intentionally over-predicting exposure as is done in initial tiers of ecological risk analyses) were not as scale sensitive and could be achieved with a single known location and coarse contaminant distribution maps.

A generic approach to estimate airborne concentrations of substances released by indoor spray processes using a deterministic 2-box model.

Sprays are used both in workplace and consumer settings. Although spraying has advantages, such as uniform distribution of substances on surfaces in a highly efficient manner, it is often associated with a high inhalation burden. For an adequate risk assessment, this exposure has to be reliably quantified. Exposure models of varying complexity are available, which are applicable to spray applications. However, a need for improvement has been identified. In this contribution, a simple 2-box approach is suggested for the assessment of the time-weighted averaged exposure concentration (TWA) using a minimum of input data. At the moment, the model is restricted to binary spray liquids composed of a non-volatile fraction and volatile solvents. The model output can be refined by introducing correction factors based on the classification and categorization of two key parameters, the droplet size class and the vapor pressure class of the solvent, or by using a data set of experimentally determined airborne release fractions related to the used spray equipment. A comparison of model results with measured data collected at real workplaces showed that this simple model based on readily available input parameters is very useful for screening purposes. The generic 2-box spray model without refinement overestimates the measurements of the considered scenarios in approximately 50% of the cases by more than a factor of 100. The generic 2-box model performs better for room spraying than for surface spraying, as the airborne fraction in the latter case is clearly overestimated. This conservatism of the prediction was significantly reduced when correction factors or experimentally determined airborne release fractions were used in addition to the generic input parameters. The resulting predictions still overestimate the exposure (ratio tool estimate to measured TWA > 10) or they are accurate (ratio 0.5-10). If the available information on boundary conditions (application type, equipment) does not justify the usage of airborne release fraction, room spraying should be used resulting in the highest exposure estimate. The model scope may be extended to (semi)volatile substances. However, acceptance may be compromised by the limited availability of measured data for this group of substances and thus may have limited potency to evaluate the model prediction.

Estimation of microbial inhalation exposure and prediction of microbial concentrations in rail transportation facilities during the COVID-19 pandemic

Estimation of microbial inhalation exposure and prediction of microbial concentrations in rail transportation facilities during the COVID-19 pandemic

Considerations for deriving a safe intake of propylene glycol

The use of propylene glycol (PG) in food and other applications is widespread, and some estimates of dietary exposure to PG approach or exceed the Acceptable Daily Intake (ADI) of 25 mg/kg bw-day. The current ADI for PG applies a cumulative uncertainty factor of 100, which includes factors of 10 for both interspecies and intraspecies differences. Available toxicology studies and human data, however, indicate a plausible mode of action (MoA) that would support a chemical-specific adjustment factor (CSAF) of 1 for interspecies toxicodynamic differences, reducing the total uncertainty factor from 100 to 40. The MoA involves an increase in serum PG concentrations after metabolic saturation, leading to serum hyperosmolarity, which can ultimately cause hemolytic changes and red blood cell damage. Therefore, the species similarities in toxicodynamic response for this critical effect could support increasing the ADI from 25 to 62.5 mg/kg bw-day, applicable to both children and adults.

Current and future rainfall-driven flood risk from hurricanes in Puerto Rico under 1.5 and 2 °C climate change

Abstract. Flooding associated with Hurricane Maria in 2017 had devastating consequences for lives and livelihoods in Puerto Rico. Yet, an understanding of current and future flood risk on small islands like Puerto Rico is limited. Thus, efforts to build resilience to flooding associated with hurricanes remain constrained. Here, we take an event set of hurricane rainfall estimates from a synthetic hurricane rainfall simulator as the input to an event-based rainfall-driven flood inundation model using the hydrodynamic code LISFLOOD-FP. Validation of our model against high-water-mark data for Hurricane Maria demonstrates the suitability of this model for estimating flood hazard in Puerto Rico. We produce event-based flood hazard and population exposure estimates for the present day and the future under the 1.5 and 2 ∘C Paris Agreement goals. Population exposure to flooding from hurricane rainfall in Puerto Rico for the present-day climate is approximately 8 %–10 % of the current population for a 5-year return period, with an increase in population exposure to flooding by 2 %–15 % and 1 %–20 % under 1.5 and 2 ∘C futures (5-year return period). This research demonstrates the significance of the 1.5 ∘C Paris Agreement goal for Small Island Developing States, providing the first event-based estimates of flooding from hurricane rainfall under climate change for a small island.

Assessment of NORMs (238U, 232Th, 40K) and radiation hazard indices in Beldih apatite mine region of Purulia district, West Bengal, India.

The radiation arising from naturally occurring radioactive materials (NORMs) is the foremost contributor to the collective dose received by the global population. The present study aims to measure the natural background radiation level and the associated gamma radiation dose in air in the Beldih apatite mine region of Purulia district, India. This study is primarily focused on the determination of 238U, 232Th and 40K activities in the sub-surface soil of the study area. The measurements were carried out using a High Purity Germanium (HPGe) detector-based gamma-ray spectrometer with a relative efficiency of 80%. To achieve uniformity in exposure estimations, radium equivalent activity has been calculated. Additionally, the internal hazard index, external hazard index, radioactivity level index and gamma dose rates have been evaluated to estimate the radiation hazard levels in the study area. The comparison of obtained concentrations and hazard indices with global data (UNSCEAR. (2008). Sources, effects and risks of ionizing radiation. United nations scientific committee on the effects of atomic radiation (report to the general assembly, with Annexes).) suggests that this region lies in a relatively high background radiation zone.

Integrated Flood Impact and Vulnerability Assessment Using a Multi-Sensor Earth Observation Mission with the Perspective of an Operational Service in Lombardy, Italy

The frequency and the accumulation of medium–small flood events can cause severe impacts. In a climate change context, real-time monitoring and a fast risk assessment are needed to support the post-disaster phases. The present work presents a novel methodology that leverages the potential of earth observation data to produce a proof-of-concept for flood vulnerability assessment, serving as the basis for a Map Operational Service for the Lombardy region. The proof-of-concept is related to both flood hazard estimation and vulnerability assessment, considering the evaluation of the potentialities of the synthetic aperture radar data when used to feed a downstream service. Using the city of Pavia (Italy), which was affected by a flood event in November 2019, as a case study, we present an integrated flood impact approach that includes a combination of social and physical parameters. The results contribute to a processing chain designed as a pre-operational service where each data analytic retrieves thematic products to support the exposure and damage estimates based on earth observation-derived hazard products for emergency and recovery responses. Three different satellites covered more than 40 h of the flood’s evolution, supporting the great potential of the multi-sensor approach. Therefore, different sensor configurations in terms of spectral bands (X and C bands) and resolutions (from 10 to 1 m) provide a near real-time view of the event. Comparing the results obtained through the three hazard scenarios, a final social and physical Integrated Impact Index is obtained. The added value information leads to the determination of hotspots with which to prioritize effective interventions during emergency and recovery phases, crucial for capturing inherent conditions that allow communities to absorb impacts and cope with a damaging flood event.

Dietary exposure levels to polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans and non-ortho-polychlorinated biphenyls in US meat, poultry and siluriform fish from 2018 to 2019

Daily dietary exposure estimates from beef, pork, chicken, turkey and siluriform fish were calculated using toxic equivalency (TEQ) data from the U.S. Department of Agriculture’s survey of dioxins and dioxin-like compounds (DLCs) in the domestic meat supply and consumption data. Exposure estimates for the whole population and age groups were based on mean consumption of a commodity and mean or 90th percentile TEQ dioxin levels from the survey. Ratios of the exposure estimates to the U.S. EPA oral reference dose (RfD) of 0.7 pg TEQ/kg bw/day were calculated to determine if domestic meat might contribute materially to consumer exposure. In general, normal consumption of lean beef, pork, chicken and turkey will not cause exposures exceeding the RfD. Non-lean meats will have higher dioxin levels as dioxins accumulate in fat, therefore consumption of non-lean meat might cause higher exposure than compared to lean meat. However, on a mean basis, none of the exposure estimates for non-lean beef, pork, chicken or turkey exceeded the RfD for any of the age groups. For some age groups, especially toddlers, there are commodities such as non-lean beef in the 90th percentile of dioxin TEQs and siluriform fish that might exceed the RfD on occasion.

Estimation of Radiation Exposure for Various Flights from Athens International Airport

In recent decades, the exposure of aviation crews and passengers to cosmic radiation has been progressively increasing due to the growing need of flights. Therefore, there is a need for radiation assessment software tools to act as a complement to other radiation protection techniques and countermeasures. In this work, the exposure to cosmic radiation is estimated for flights from Athens International Airport to various international destinations, by performing Monte Carlo simulations with the validated tool DYASTIMA/DYASTIMA-R. The results of the estimated total ambient dose equivalent, as well as the ambient dose equivalent rate for different flights, applying a typical flying level and constant atmospheric conditions, are presented for the first time. This study is carried out for different phases of solar activity for the time period from 1996 to 2019, which includes the two recent Solar Cycles 23 and 24.

Respirable crystalline silica and lung cancer in community-based studies: impact of job-exposure matrix specifications on exposure-response relationships.

The quantitative job-exposure matrix SYN-JEM consists of various dimensions: job-specific estimates, region-specific estimates, and prior expert ratings of jobs by the semi-quantitative DOM-JEM. We analyzed the effect of different JEM dimensions on the exposure-response relationships between occupational silica exposure and lung cancer risk to investigate how these variations influence estimates of exposure by a quantitative JEM and associated health endpoints. Using SYN-JEM, and alternative SYN-JEM specifications with varying dimensions included, cumulative silica exposure estimates were assigned to 16 901 lung cancer cases and 20 965 controls pooled from 14 international community-based case-control studies. Exposure-response relationships based on SYN-JEM and alternative SYN-JEM specifications were analyzed using regression analyses (by quartiles and log-transformed continuous silica exposure) and generalized additive models (GAM), adjusted for age, sex, study, cigarette pack-years, time since quitting smoking, and ever employment in occupations with established lung cancer risk. SYN-JEM and alternative specifications generated overall elevated and similar lung cancer odds ratios ranging from 1.13 (1st quartile) to 1.50 (4th quartile). In the categorical and log-linear analyses SYN-JEM with all dimensions included yielded the best model fit, and exclusion of job-specific estimates from SYN-JEM yielded the poorest model fit. Additionally, GAM showed the poorest model fit when excluding job-specific estimates. The established exposure-response relationship between occupational silica exposure and lung cancer was marginally influenced by varying the dimensions of SYN-JEM. Optimized modelling of exposure-response relationships will be obtained when incorporating all relevant dimensions, namely prior rating, job, time, and region. Quantitative job-specific estimates appeared to be the most prominent dimension for this general population JEM.

Quantifying flood risks during monsoon and post-monsoon seasons: An integrated framework for resource-constrained coastal regions

Global coastal regions are transforming into “flooding hot-spots” due to the multi-faceted confluence of several flood-drivers. Unfortunately, these regions are often poorly gauged, hindering comprehensive flood hazard quantification. Besides summer-monsoon floods, post-monsoon rainfalls induced by cyclonic-depressions have escalated the flood risks in the coastal regions. For the first time, the present study captures the seasonal flood hazards by disentangling monsoon (June, July, August, and September) and post-monsoon (October, November, and December) periods of rainfall. An integrated MIKE + 1D-2D coupled model was used to generate flood hazard risks for Jagatsinghpur district (Odisha, India), a resource-constrained multi-hazard catchment in the Lower Mahanadi River Basin. Due to limited data for this coastal region, we investigated the performance of CHIRPS v2.0, a global Satellite Precipitation Product (SPP) with high spatiotemporal characteristics, for capturing extreme rainfall events and deriving flood hazards at the inundation scale. We observed higher magnitude of flood hazards are prominent in the coastal villages, emerging from a complex interaction between rainfall and storm-tide events. Three widely used Archimedean copulas were implemented to capture the rainfall and storm-tide events' joint (bivariate) behavior and their implications on population exposure, which is valuable for mapping flood hazard risks. We observed that univariate analysis (Ua) significantly underestimates the flood hazard over bivariate analysis (Ba) during both periods. Interestingly, the results are more pronounced in the population exposure estimates as ∼47 % (monsoon) and ∼54 % (post-monsoon) of the population are exposed based on univariate analysis against ∼65 % (monsoon) and ∼60 % (post-monsoon) under bivariate analysis. Our study highlights the necessity of disentangling rainfall time-series for foreseeing under-shadowed flood risks, vital for designing flood combating structures, evacuation plans, land use planning, and flood governance. The results further enhance our understanding of monsoon and post-monsoon flood hazards and their associated risks under different return periods.

Geospatial analysis of Plasmodium falciparum serological indicators: school versus community sampling in a low-transmission malaria setting

BackgroundDue to low numbers of active infections and persons presenting to health facilities for malaria treatment, case-based surveillance is inefficient for understanding the remaining disease burden in low malaria transmission settings. Serological data through the detection of IgG antibodies from previous malaria parasite exposure can fill this gap by providing a nuanced picture of where sustained transmission remains. Study enrollment at sites of gathering provides a potential approach to spatially estimate malaria exposure and could preclude the need for more intensive community-based sampling.MethodsThis study compared spatial estimates of malaria exposure from cross-sectional school- and community-based sampling in Haiti. A total of 52,405 blood samples were collected from 2012 to 2017. Multiplex bead assays (MBAs) tested IgG against P. falciparum liver stage antigen-1 (LSA-1), apical membrane antigen 1 (AMA1), and merozoite surface protein 1 (MSP1). Predictive geospatial models of seropositivity adjusted for environmental covariates, and results were compared using correlations by coordinate points and communes across Haiti.ResultsConsistent directional associations were observed between seroprevalence and environmental covariates for elevation (negative), air temperature (negative), and travel time to urban centers (positive). Spearman’s rank correlation for predicted seroprevalence at coordinate points was lowest for LSA-1 (ρ = 0.10, 95% CI: 0.09–0.11), but improved for AMA1 (ρ = 0.36, 95% CI: 0.35–0.37) and MSP1 (ρ = 0.48, 95% CI: 0.47–0.49).ConclusionsIn settings approaching P. falciparum elimination, case-based prevalence data does not provide a resolution of ongoing malaria transmission in the population. Immunogenic antigen targets (e.g., AMA1, MSP1) that give higher population rates of seropositivity provide moderate correlation to gold standard community sampling designs and are a feasible approach to discern foci of residual P. falciparum transmission in an area.

Radon Exposure, Clonal Hematopoiesis, and Stroke Susceptibility in the Women's Health Initiative.

Studies suggest that clonal hematopoiesis of indeterminate potential (CHIP) may increase risk of hematologic malignancy and cardiovascular disease, including stroke. However, few studies have investigated plausible environmental risk factors for CHIP such as radon, despite the climate-related increases in and documented infrequency of testing for this common indoor air pollutant.The purpose of this study was to estimate the risk of CHIP related to radon, an established environmental mutagen. We linked geocoded addresses of 10,799 Women's Health Initiative Trans-Omics for Precision Medicine (WHI TOPMed) participants to US Environmental Protection Agency-predicted, county-level, indoor average screening radon concentrations, categorized as follows: Zone 1 (>4 pCi/L), Zone 2 (2-4 pCi/L), and Zone 3 (<2 pCi/L). We defined CHIP as the presence of one or more leukemogenic driver mutations with variant allele frequency >0.02. We identified prevalent and incident ischemic and hemorrhagic strokes; subtyped ischemic stroke using Trial of ORG 10172 in Acute Stroke Treatment (TOAST) criteria; and then estimated radon-related risk of CHIP as an odds ratio (OR) and 95% CI using multivariable-adjusted, design-weighted logistic regression stratified by age, race/ethnicity, smoking status, and stroke type/subtype. The percentages of participants with CHIP in Zones 1, 2, and 3 were 9.0%, 8.4%, and 7.7%, respectively (ptrend = 0.06). Among participants with ischemic stroke, Zones 2 and 1 were associated with higher estimated risks of CHIP relative to Zone 3: 1.39 (1.15-1.68) and 1.46 (1.15-1.87), but not among participants with hemorrhagic stroke: 0.98 (0.68-1.40) and 1.03 (0.70-1.52), or without stroke: 1.04 (0.74-1.46) and 0.95 (0.63-1.42), respectively (pinteraction = 0.03). Corresponding estimates were particularly high among TOAST-subtyped cardioembolism: 1.78 (1.30-2.47) and 1.88 (1.31-2.72), or other ischemic etiologies: 1.37 (1.06-1.78) and 1.50 (1.11-2.04), but not small vessel occlusion: 1.05 (0.74-1.49) and 1.00 (0.68-1.47), respectively (pinteraction = 0.10). Observed patterns of association among strata were insensitive to attrition weighting, ancestry adjustment, prevalent stroke exclusion, separate analysis of DNMT3A driver mutations, and substitution with 3 alternative estimates of radon exposure. The robust elevation of radon-related risk of CHIP among postmenopausal women who develop incident cardioembolic stroke is consistent with a potential role of somatic genomic mutation in this societally burdensome form of cerebrovascular disease, although the mechanism has yet to be confirmed.