Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol

INTRODUCTION

Asthma has a considerable, but potentially, avoidable burden on many populations globally. Scotland has some of the poorest health outcomes from asthma. Although ambient pollution, weather changes and sociodemographic factors have been associated with asthma attacks, it remains unclear whether modelled environment data and geospatial information can improve population-based asthma predictive algorithms. We aim to create the afferent loop of a national learning health system for asthma in Scotland. We will investigate the associations between ambient pollution, meteorological, geospatial and sociodemographic factors and asthma attacks.

METHODS AND ANALYSIS

We will develop and implement a secured data governance and linkage framework to incorporate primary care health data, modelled environment data, geospatial population and sociodemographic data. Data from 75 recruited primary care practices (n=500 000 patients) in Scotland will be used. Modelled environment data on key air pollutants at a horizontal resolution of 5 km×5 km at hourly time steps will be generated using the EMEP4UK atmospheric chemistry transport modelling system for the datazones of the primary care practices' populations. Scottish population census and education databases will be incorporated into the linkage framework for analysis. We will then undertake a longitudinal retrospective observational analysis. Asthma outcomes include asthma hospitalisations and oral steroid prescriptions. Using a nested case-control study design, associations between all covariates will be measured using conditional logistic regression to account for the matched design and to identify suitable predictors and potential candidate algorithms for an asthma learning health system in Scotland.Findings from this study will contribute to the development of predictive algorithms for asthma outcomes and be used to form the basis for our learning health system prototype.

ETHICS AND DISSEMINATION

The study received National Health Service Research Ethics Committee approval (16/SS/0130) and also obtained permissions via the Public Benefit and Privacy Panel for Health and Social Care in Scotland to access, collate and use the following data sets: population and housing census for Scotland; Scottish education data via the Scottish Exchange of Data and primary care data from general practice Data Custodians. Analytic code will be made available in the open source GitHub website. The results of this study will be published in international peer reviewed journals.

[Environmental control of zooplankton community structure in Tangshan Bay, China]

In order to study the relationship between zooplankton community structure and environmental factors in Tangshan Bay, the zooplankton community structure and environmental factors were investigated in 2015 April (spring), July (summer), October (autumn) and December (winter). The temporal and spatial variation of zooplankton community structure and its main environmental driving factors were analyzed by means of multivariate analysis and correlation analysis. The results showed that the main environmental factors affecting the abundance of zooplankton in Tangshan Bay were DIN, SS, temperature and Chla. Multivariate analysis indicated that DO, temperature and Chl a were the principal factors driving spatial differentiation of zooplankton community structure in Tangshan Bay. In different waters of Tangshan Bay, the environmental factors affecting zooplankton community structure were different. The main influencing factors were physical variables for Laoting and Sandao sea areas, while chemical variables for Caofeidian sea area, respectively. The results revealed the zooplankton community structure was more influenced by chemical variables (DIN, SRP) in sea areas heavily affected by human activity, while it was more influenced by phy-sical variables (T, SS) in sea areas less affected by human activity.

O3 uptake and drought stress effects on carbon acquisition of ponderosa pine in natural stands

• The effect of O₃ exposure or uptake on carbon acquisition (net assimilation (A) or gross photosynthesis (P_g )), with and without drought stress, is reported here in 40-yr-old-ponderosa pine (Pinus ponderosa) trees. • Maximum daily gas exchange was measured monthly for 12 trees at four sites differing in pollutant exposure over two growing seasons with above- and below-average annual precipitation. Gas exchange measures were estimated between sampling periods using a generalized additive regression model. • Both A and P_g generally declined with cumulative O₃ exposure or uptake at all sites. As a response variable, P_g was slightly more sensitive than A to cumulative O₃ exposure. As a metric, O₃ uptake vs exposure permitted slightly better statistical resolution of seasonal response between sites. • The effect of late summer drought stress was statistically significant only at the moderate pollution site, and combined synergistically with O₃ exposure or uptake to reduce P_g . The general additive model allows the user to define a deleterious level of cumulative O₃ exposure or uptake, and to quantitatively assess biological response.