Sensitivity and representativeness of One-Health surveillance for diseases of zoonotic potential at health facilities relative to household visits in rural Guatemala

Most human and animal disease notification systems are unintegrated and passive, resulting in underreporting. Active surveillance can complement passive efforts, but because they are resource-intensive, their attributes must be evaluated. We assessed the sensitivity and representativeness of One-Health surveillance conducted at health facilities compared to health facilities plus monthly household visits in three rural communities of Guatemala. From September 2017 to November 2018, we screened humans for acute diarrheal, febrile and respiratory infectious syndromes and canines, swine, equines and bovines for syndromic events or deaths. We estimated the relative sensitivity as the incidence rate ratio of detecting an event in health facility surveillance compared to household surveillance from Poisson models. We used interaction terms between the surveillance method and sociodemographic factors or time trends to assess effect modification as a measure of relative representativeness. We used generalized additive models with smoothing splines to model incidence over time by surveillance method. We randomized 216 households to health facility surveillance and 198 to health facility surveillance plus monthly household visits. Health facility surveillance alone was less sensitive than when combined with household surveillance by 0.42 (95% CI: 0.34, 0.53), 0.56 (95% CI: 0.39, 0.79), 0.02 (95% CI: 0.00, 0.10), 0.28 (95% CI: 0.15, 0.50) and 0.22 (95% CI: 0.03, 0.92) times for human acute infections, human severe acute infections, and deaths in canines, swine and equines, respectively. Health facility surveillance alone underrepresented Spanish speakers (interaction p-value = 0.0003) and persons in higher economic assets (interaction p-values = 0.0008). The trend in incidence over time was different between the two study groups, with a larger decrease in the group with household surveillance (all interaction p-values <0.10). Surveillance at health facilities under ascertains syndromes in humans and animals which leads to underestimation of the burden of zoonotic disease. The magnitude of under ascertainment was differentially by sociodemographic factors, yielding an unrepresentative sample of health events. However, it is less time-intensive, thus might be sustained over time longer than household surveillance. The choice between methodologies should be evaluated against surveillance goals and available resources

A cross-sectional study of determinants of indoor environmental exposures in households with and without chronic exposure to biomass fuel smoke

BACKGROUND: Burning biomass fuels indoors for cooking is associated with high concentrations of particulate matter (PM) and carbon monoxide (CO). More efficient biomass-burning stoves and chimneys for ventilation have been proposed as solutions to reduce indoor pollution. We sought to quantify indoor PM and CO exposures in urban and rural households and determine factors associated with higher exposures. A secondary objective was to identify chronic vs. acute changes in cardiopulmonary biomarkers associated with exposure to biomass smoke. METHODS: We conducted a census survey followed by a cross-sectional study of indoor environmental exposures and cardiopulmonary biomarkers in the main household cook in Puno, Peru. We measured 24-hour indoor PM and CO concentrations in 86 households. We also measured PM(2.5) and PM(10) concentrations gravimetrically for 24 hours in urban households and during cook times in rural households, and generated a calibration equation using PM(2.5) measurements. RESULTS: In a census of 4903 households, 93% vs. 16% of rural vs. urban households used an open-fire stove; 22% of rural households had a homemade chimney; and <3% of rural households participated in a national program encouraging installation of a chimney. Median 24-hour indoor PM(2.5) and CO concentrations were 130 vs. 22 μg/m(3) and 5.8 vs. 0.4 ppm (all p<0.001) in rural vs. urban households. Having a chimney did not significantly reduce median concentrations in 24-hour indoor PM(2.5) (119 vs. 137 μg/m(3); p=0.40) or CO (4.6 vs. 7.2 ppm; p=0.23) among rural households with and without chimneys. Having a chimney did not significantly reduce median cook-time PM(2.5) (360 vs. 298 μg/m(3), p=0.45) or cook-time CO concentrations (15.2 vs. 9.4 ppm, p=0.23). Having a thatched roof (p=0.007) and hours spent cooking (p=0.02) were associated with higher 24-hour average PM concentrations. Rural participants had higher median exhaled CO (10 vs. 6 ppm; p=0.01) and exhaled carboxyhemoglobin (1.6% vs. 1.0%; p=0.04) than urban participants. CONCLUSIONS: Indoor air concentrations associated with biomass smoke were six-fold greater in rural vs. urban households. Having a homemade chimney did not reduce environmental exposures significantly. Measures of exhaled CO provide useful cardiopulmonary biomarkers for chronic exposure to biomass smoke

Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration.We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life.Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p < 0.001) when using a linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p < 0.001) and slopes (p < 0.001) of the individual growth trajectories. We also identified important serial correlation within the structure of the data (ρ = 0.66; 95 % CI 0.64 to 0.68; p < 0.001), which we modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19,598, respectively). While the regression parameters are more complex to interpret in the former, we argue that inference for any problem depends more on the estimated curve or differences in curves rather than the coefficients. Moreover, use of cubic regression splines provides biological meaningful growth velocity and acceleration curves despite increased complexity in coefficient interpretation.Through this stepwise approach, we provide a set of tools to model longitudinal childhood data for non-statisticians using linear mixed-effect models

Surface Doping Quantum Dots with Chemically Active Native Ligands: Controlling Valence without Ligand Exchange

One remaining challenge in the field of colloidal semiconductor nanocrystal quantum dots is learning to control the degree of functionalization or valence per nanocrystal. Current quantum dot surface modification strategies rely heavily on ligand exchange, which consists of replacing the nanocrystal\u27s native ligands with carboxylate- or amine-terminated thiols, usually added in excess. Removing the nanocrystal\u27s native ligands can cause etching and introduce surface defects, thus affecting the nanocrystal\u27s optical properties. More importantly, ligand exchange methods fail to control the extent of surface modification or number of functional groups introduced per nanocrystal. Here, we report a fundamentally new surface ligand modification or doping approach aimed at controlling the degree of functionalization or valence per nanocrystal while retaining the nanocrystal\u27s original colloidal and photostability. We show that surface-doped quantum dots capped with chemically active native ligands can be prepared directly from a mixture of ligands with similar chain lengths. Specifically, vinyl and azide-terminated carboxylic acid ligands survive the high temperatures needed for nanocrystal synthesis. The ratio between chemically active and inactive-terminated ligands is maintained on the nanocrystal surface, allowing to control the extent of surface modification by straightforward organic reactions. Using a combination of optical and structural characterization tools, including IR and 2D NMR, we show that carboxylates bind in a bidentate chelate fashion, forming a single monolayer of ligands that are perpendicular to the nanocrystal surface. Moreover, we show that mixtures of ligands with similar chain lengths homogeneously distribute themselves on the nanocrystal surface. We expect this new surface doping approach will be widely applicable to other nanocrystal compositions and morphologies, as well as to many specific applications in biology and materials science

Phytoestrogen Concentrations in Human Urine as Biomarkers for Dietary Phytoestrogen Intake in Mexican Women

There has been substantial interest in phytoestrogens, because of their potential effect in reducing cancer and heart disease risk. Measuring concentrations of phytoestrogens in urine is an alternative method for conducting epidemiological studies. Our objective was to evaluate the urinary excretion of phytoestrogens as biomarkers for dietary phytoestrogen intake in Mexican women. Participants were 100 healthy women from 25 to 80 years of age. A food frequency questionnaire (FFQ) and a 24 h recall were used to estimate habitual and recent intakes of isoflavones, lignans, flavonols, coumestrol, resveratrol, naringenin, and luteolin. Urinary concentrations were measured by liquid chromatography (HPLC) coupled to mass spectrometry (MS) using the electrospray ionization interface (ESI) and diode array detector (DAD) (HPLC-DAD-ESI-MS). Spearman correlation coefficients were used to evaluate associations between dietary intake and urine concentrations. The habitual consumption (FFQ) of total phytoestrogens was 37.56 mg/day. In urine, the higher compounds were naringenin (60.1 µg/L) and enterolactone (41.7 µg/L). Recent intakes (24 h recall) of isoflavones (r = 0.460, p &lt; 0.001), lignans (r = 0.550, p &lt; 0.0001), flavonoids (r = 0.240, p &lt; 0.05), and total phytoestrogens (r = 0.410, p &lt; 0.001) were correlated to their urinary levels. Total phytoestrogen intakes estimated by the FFQ showed higher correlations to urinary levels (r = 0.730, p &lt; 0.0001). Urinary phytoestrogens may be useful as biomarkers of phytoestrogen intake, and as a tool for evaluating the relationship of intake and disease risk in Mexican women