Methods for interpolating missing data in aerobiological databases

Pollen and viruses contribute to spatio-temporal variation in asthma-related emergency department visits

BACKGROUND

Asthma exacerbations are an important cause of emergency department visits but much remains unknown about the role of environmental triggers including viruses and allergenic pollen. A better understanding of spatio-temporal variation in exposure and risk posed by viruses and pollen types could help prioritize public health interventions.

OBJECTIVE

Here we quantify the effects of regionally important Cupressaceae pollen, tree pollen, other pollen types, rhinovirus, seasonal coronavirus, respiratory syncytial virus, and influenza on asthma-related emergency department visits for people living near eight pollen monitoring stations in Texas.

METHODS

We used age stratified Poisson regression analyses to quantify the effects of allergenic pollen and viruses on asthma-related emergency department visits.

RESULTS

Young children (<5 years of age) had high asthma-related emergency department rates (24.1 visits/1,000,000 person-days), which were mainly attributed to viruses (51.2%). School-aged children also had high rates (20.7 visits/1,000,000 person-days), which were attributed to viruses (57.0%), Cupressaceae pollen (0.7%), and tree pollen (2.8%). Adults had lower rates (8.1 visits/1,000,000 person-days) which were attributed to viruses (25.4%), Cupressaceae pollen (0.8%), and tree pollen (2.3%). This risk was spread unevenly across space and time; for example, during peak Cuppressaceae season, this pollen accounted for 8.2% of adult emergency department visits near Austin where these plants are abundant, but 0.4% in cities like Houston where they are not; results for other age groups were similar.

CONCLUSIONS

Although viruses are a major contributor to asthma-related emergency department visits, airborne pollen can explain a meaningful portion of visits during peak pollen season and this risk varies over both time and space because of differences in plant composition.

Nutritional intake and growth until two years of age in moderate and late preterms

BACKGROUND AND AIM

Moderate and late preterm infants (MLPTI) (gestational age 32 0/7-36 6/7 weeks), are at risk for suboptimal growth. This study evaluated adherence to nutritional recommendations until 6 months corrected age (CA), growth until 2 years CA, and associations between nutritional intake and growth until 2 years CA.

METHODS

We prospectively collected nutritional intakes from 100 MLPTI during the first week of life and at 6 weeks, 3 months, and 6 months CA. Anthropometry was assessed at birth, discharge, term age, and at 6 weeks, 3 months, 6 months, 1 year, and 2 years CA.

RESULTS

On day 7, <40% reached nutritional recommendations. Thereafter, >80% reached protein recommendations until 6 months of life, but <60% reached energy recommendations. Weight z-scores increased from -0.44 at term-age to 0.59 at 3 months CA, but declined to -0.53 at 2 years CA on the TNO curves. No significant associations were found between nutritional intake and growth until 2 years CA.

CONCLUSION

No associations were demonstrated between nutritional intakes and growth until 2 years CA, despite not reaching recommended intakes. Despite high efforts to optimize growth, MLPTI find their own growth curve in the first 2 years of life.

IMPACT

This research is pioneering in identifying how nutrition influences growth in moderate and late preterm infants (MLPTI) up to 2 years corrected age (CA). MLPTI often do not meet the recommended protein and energy intake in their first week of life, suggesting that current guidelines might be too high. No association was demonstrated between nutritional intake and growth of MLPTI in the first 2 years of life. Initially, MLPTI show an increase in weight z-scores from term age up to 3 months CA but experience a decline in weight z-scores at 2 years CA, according to TNO growth charts.

Effects of climate change on Platanus flowering in Western Mediterranean cities: Current trends and future projections

Ornamental trees can reduce some of the negative impacts of urbanization on citizens but some species, such as Platanus spp., produce pollen with high allergenic potential. This can exacerbate the symptomatology in allergic patients, being a public health problem. Therefore, it would be relevant to determine the environmental conditions regulating the flowering onset of the Platanus species. The aims of this study were to use aerobiological records for modelling the thermal requirements of Platanus flowering and to make future projections based on the effects that climate change could have on it under several possible future scenarios. This study was conducted in Badajoz and Malaga, two Western Mediterranean cities with different climate conditions. In the first step, several main pollen season definitions were applied to the aerobiological data and their onset dates were compared with in situ phenological observations. The main pollen season definition that best fitted the Platanus flowering onset was based on the 4th derivative of a logistic function. This definition was used as a proxy to model the thermal requirements of the Platanus flowering onset by applying the PhenoFlex statistical framework. The errors obtained by this model during the external validation were 3.2 days on average, so it was fed with future temperature estimations to determine possible future trends. According to the different models, the flowering onset of Platanus in Badajoz will show heterogeneous responses in the short and medium term due to different balances in the chilling-forcing compensation, while it will clearly delay in Malaga due to a significant delay in the chilling requirement fulfilment. This may increase the chances of cross-reactivity episodes with other pollen types in the future, increasing its impact on public health.

A Novel Missing Data Imputation Approach for Time Series Air Quality Data Based on Logistic Regression

Missing values in air quality datasets bring trouble to exploration and decision making about the environment. Few imputation methods aim at time series air quality data so that they fail to handle the timeliness of the data. Moreover, most imputation methods prefer low-missing-rate datasets to relatively high-missing-rate datasets. This paper proposes a novel missing data imputation method, called FTLRI, for time series air quality data based on the traditional logistic regression and a presented “first Five & last Three” model, which can explain relationships between disparate attributes and extract data that are extremely relevant, both in terms of time and attributes, to the missing data, respectively. To investigate the performance of FTLRI, it is benchmarked with five classical baselines and a new dynamic imputation method using a neural network with average hourly concentration data of pollutants from three disparate stations in Lanzhou in 2019 under different missing rates. The results show that FTLRI has a significant advantage over the compared imputation approaches, both in the particular short-term and long-term time series air quality data. Furthermore, FTLRI has good performance on datasets with a relatively high missing rate, since it only selects the data extremely related to the missing values instead of relying on all the other data like other methods.

Environmental drivers of the seasonal exposure to airborne Alternaria spores in Spain

Alternaria conidia have high allergenic potential and they can trigger important respiratory diseases. Due to that and to their extensive detection period, airborne Alternaria spores are considered as a relevant airborne allergenic particle. Several studies have been developed in order to predict the human exposure to this aeroallergen and to prevent their negative effects on sensitive population. These studies revealed that some sampling locations usually have just one single Alternaria spore season while other locations generally have two seasons within the same year. However, the reasons of these two different seasonal patterns remain unclear. To understand them better, the present study was carried out in order to determine if there are any weather conditions that influence these different behaviours at different sampling locations. With this purpose, the airborne Alternaria spore concentrations of 18 sampling locations in a wide range of latitudinal, altitudinal and climate ranges of Spain were studied. The aerobiological samples were obtained by means of Hirst-Type volumetric pollen traps, and the seasonality of the airborne Alternaria spores were analysed. The optimal weather conditions for spore production were studied, and the main weather factor affecting Alternaria spore seasonality were analysed by means of random forests and regression trees. The results showed that the temperature was the most relevant variable for the Alternaria spore dispersion and it influenced both the spore integrals and their seasonality. The water availability was also a very significant variable. Warmer sampling locations generally have a longer period of Alternaria spore detection. However, the spore production declines during the summer when the temperatures are extremely warm, what splits the favourable period for Alternaria spore production and dispersion into two separate ones, detected as two Alternaria spore seasons within the same year.

Data Mining Methods to Detect Airborne Pollen of Spring Flowering Arboreal Taxa

Variations in the airborne pollen load are among the current and expected impacts on plant pollination driven by climate change. Due to the potential risk for pollen-allergy sufferers, this study aimed to analyze the trends of the three most abundant spring-tree pollen types, Pinus, Platanus and Quercus, and to evaluate the possible influence of meteorological conditions. An aerobiological study was performed during the 1993–2020 period in the Ourense city (NW Spain) by means of a Hirst-type volumetric sampler. Meteorological data were obtained from the ‘Ourense’ meteorological station of METEOGALICIA. We found statistically significant trends for the Total Pollen in all cases. The positive slope values indicated an increase in pollen grains over the pollen season along the studied years, ranging from an increase of 107 to 442 pollen grains. The resulting C5.0 Decision Trees and Rule-Based Models coincided with the Spearman’s correlations since both statistical analyses showed a strong and positive influence of temperature and sunlight on pollen release and dispersal, as well as a negative influence of rainfall due to washout processes. Specifically, we found that slight rainfall and moderate temperatures promote the presence of Pinus pollen in the atmosphere and a marked effect of the daily thermal amplitude on the presence of high Platanus pollen levels. The percentage of successful predictions of the C5.0 models ranged between 62.23–74.28%. The analysis of long-term datasets of pollen and meteorological information provides valuable models that can be used as an indicator of potential allergy risk in the short term by feeding the obtained models with weather prognostics.