The association of travel burden with prenatal care utilization, what happens after provider-selection.

BACKGROUND: Birthing people in the United States face numerous challenges when accessing adequate prenatal care (PNC), with transportation being a significant obstacle. Nevertheless, previous studies that relied solely on the distance to the nearest provider cannot differentiate the effects of travel burden on provider selection and care utilization. These may exaggerate the degree of inequality in access and fail to capture perceived travel burden. This study investigated whether travel distances to the initially visited provider, to the predominant PNC provider, and perceived travel burden (measured by the travel disadvantage index (TDI)) are associated with PNC utilization. METHODS: A retrospective cohort of people with live births were identified from South Carolina Medicaid claims files in 2015-2018. Travel distances were calculated using Google Maps. The estimated TDI was derived from local pilot survey data. PNC utilization was measured by PNC initiation and frequency. Repeated measure logistic regression test was utilized for categorical variables and one-way repeated measures ANOVA for continuous variables. Unadjusted and adjusted ordinal logistic regressions with repeated measure were utilized to examine the association of travel burdens with PNC usage. RESULTS: For 25,801 pregnancies among those continuously enrolled in Medicaid, birthing people traveled an average of 24.9 and 24.2 miles to their initial and predominant provider, respectively, with an average TDI of -11.4 (SD, 8.5). Of these pregnancies, 60% initiated PNC in the first trimester, with an average of 8 total visits. Compared to the specialties of initial providers, predominant providers were more likely to be OBGYN-related specialists (81.6% vs. 87.9%, p < .001) and midwives (3.5% vs. 4.3%, p < .001). Multiple regression analysis revealed that every doubling of travel distance was associated with less likelihood to initiate timely PNC (OR: 0.95, p < .001) and a lower visit frequency (OR: 0.85, p < .001), and every doubling of TDI was associated with less likelihood to initiate timely PNC (OR: 0.94, p = .04). CONCLUSIONS: Findings suggest that the association between travel burden and PNC utilization was statistically significant but of limited practical significance.

Xylitol-Containing Chewing Gum Reduces Cariogenic and Periodontopathic Bacteria in Dental Plaque-Microbiome Investigation.

Background: Dental caries and periodontal disease remain the most prevalent oral health problems in the world. Chewing xylitol gum may help reduce the risk of caries and periodontitis for dental health benefits. However, little evidence has shown healthy food estimation by sequencing 16S rDNA in oral microbial communities. This study investigated the clinical effect of xylitol chewing gum on dental plaque accumulation and microbiota composition using the PacBio full-length sequencing platform in 24 young adults (N = 24). The participants were randomly assigned to xylitol chewing gum and control (no chewing gum) groups. Participants in the chewing gum group chewed ten pieces of gum (a total of 6.2 g xylitol/day). Dental plaque from all teeth was collected for weighing, measuring the pH value, and analysis of microbial communities at the beginning (baseline, M0) and end of the 2-week (effect, M1) study period. Results: The results suggested a 20% reduction in dental plaque accumulation (p < 0.05) among participants chewing xylitol gum for 2 weeks, and the relative abundance of Firmicutes (a type of pathogenic bacteria associated with caries) decreased by 10.26% (p < 0.05) and that of Bacteroidetes and Actinobacteria (two types of pathogenic bacteria associated with periodontitis) decreased by 6.32% (p < 0.001) and 1.66% (p < 0.05), respectively. Moreover, the relative abundance of Fusobacteria was increased by 9.24% (p < 0.001), which has been proven to have a higher proportion in dental plaque of healthy adults. However, the dental plaque pH value stayed in a healthy range for the two groups. Conclusion: In conclusion, chewing xylitol gum would benefit cariogenic and periodontal bacterial reduction in the oral cavity, which could help to prevent the diseases related to these bacteria.

A surrogate approach for estimating vehicle-related emissions under heterogenous traffic conditions.

Vehicle emission analysis currently faces a trade-off between easy-to-use, low-accuracy macroscopic models, and computationally intensive, high-accuracy microscopic models. In this study, we develop a surrogate model that leverages microscopic traffic and emission simulations to predict link-level emission rates. The input variables are obtained by aggregating 1 Hz simulated vehicle trajectories into hourly traffic condition factors (e.g., link average/variation of speed, truck fleet percentage, road grade, etc.). The emission ground truth data are generated using the Motor Vehicle Emission Simulator (MOVES) opmode-based analysis module. We explore different parameter and machine learning model structures to establish the statistical relationship of the input variables and the link-level emission rates. We demonstrate the ability of our model to accurately estimate vehicle-related emissions by using the Columbia, South Carolina road network as an example. This model can serve as a high-level planning tool to assess the impacts of emissions from transportation projects.Implications: Vehicle emission analysis is facing trade-offs between easy-to-use macroscopic emission models with low accuracy and computationally intensive microscopic models with high accuracy. Existing studies attempted to cope with the trade-off by pre-selecting representative emission rates but are still subject to the risk of not considering differentiated traffic patterns by using single emission rate. To fill in the knowledge gap in the literature, we develop a surrogate approach that fully integrates driving trajectories of heterogenous traffic patterns into a link-level emissions estimation model considering road characteristics. The model can achieve high accuracy and utilize publicly available traffic data in vehicle emission prediction. We apply the proposed model in a middle size city road network and demonstrate its capability to capture and quantify the impacts of traffic patterns on link-level vehicle-related emissions. Additionally, the proposed model can serve as a sketch planning tool for researchers and transportation air quality practitioners to quickly assess bounds of emissions benefits due to traffic operational and transportation strategies.

On-Road NOx and Smoke Emissions of Diesel Light Commercial Vehicles-Combining Remote Sensing Measurements from across Europe.

Light commercial vehicles (LCVs) account for about 10-15% of road traffic in Europe. There have only been few investigations on their on-road emission performance. Here, on-road remote sensing vehicle emission measurements from 18 locations across four European countries are combined for a comprehensive analysis of NOx and smoke emission rates from diesel LCV in the past two decades. This allows differentiating the performance by emission standards, model years, curb weights, engine loads, manufacturers, vehicle age, and temperature, as well as by measurement devices. We find a general consistency between devices and countries. On-road NOx emission rates have been much higher than type approval limit values for all manufacturers, but some perform systematically better than others. Emission rates have gone down only with the introduction of Euro 6a-b emission standards since the year 2015. Smoke emission rates are considered a proxy for particulate emissions. Their emissions have decrease substantially from the year 2010 onward for all countries and size classes measured. This is consistent with the substantial tightening of the particulate matter emission limit value that typically forced the introduction of a diesel particulate filter. The average NOx emission rate increases with engine load and decreasing ambient temperatures, particularly for Euro 4 and 5 emission classes. This explains to a large extent the differences in the absolute level between the measurement sites together with differences in fleet composition. These dependencies have already been observed earlier with diesel passenger cars; they are considered part of an abnormal emission control strategy. Some limited increase of the NOx emission rate is observed for Euro 3 vehicles older than 10 years. The strong increase for the youngest Euro 6 LCVs might rather reflect technology advances with successively younger models than genuine deterioration. However, the durability of emission controls for Euro 6 vehicles should be better monitored closely. Smoke emission rates continuously increase with vehicle age, suggesting a deterioration of the after-treatment system with use.

When is Enough? Minimum Sample Sizes for On-Road Measurements of Car Emissions.

The power of remote vehicle emission sensing stems from the big sample size obtained and its related statistical representativeness for the measured emission rates. But how many records are needed for a representative measurement and when does the information gain per record become insignificant? We use Monte Carlo simulations to determine the relationship between the sample size and the accuracy of the sample mean and variance. We take the example of NO emissions from diesel cars measured by remote emission monitors between 2011 and 2018 at various locations in Europe. We find that no more than 200 remote sensing records are sufficient to approximate the mean emission rate for Euro 4, 5, and 6a/b diesel cars with 80% certainty within a ±1 g NO per kg fuel tolerance margin (∼±50 mg NO per km). Between 300 and 800 remote sensing records are needed to approximate also the variance of the mean NO emission rates for those diesel car technologies. This translates to only 2 and up to 9 measurement days to characterize the means and their variance for a car fleet typical in Europe.

NOx Emissions from Diesel Passenger Cars Worsen with Age.

Commonly, the NOx emissions rates of diesel vehicles have been assumed to remain stable over the vehicle's lifetime. However, there have been hardly any representative long-term emission measurements. Here we present real-driving emissions of diesel cars and light commercial vehicles sampled on-road over 15 years in Zurich/Switzerland. Results suggest deterioration of NOx unit emissions for Euro 2 and Euro 3 diesel technologies, while Euro 1 and Euro 4 technologies seem to be stable. We can exclude a significant influence of high-emitting vehicles. NOx emissions from all cars and light commercial vehicles in European emission inventories increase by 5-10% accounting for the observed deterioration, depending on the country and its share of diesel cars. We suggest monitoring the stability of emission controls particularly for high-mileage light commercial as well as heavy-duty vehicles.