Air pollution is high on the global agenda and is widely recognised as a threat to both public health and economic progress. The World Health Organization (WHO) estimates that 4.2 million deaths annually can be attributed to outdoor air pollution. Recently, there have been major advances in methods that allow the quantification of air pollution-related indicators to track progress towards the Sustainable Development Goals and that expand the evidence base of the impacts of air pollution on health. Despite efforts to reduce air pollution in many countries there are regions, notably Central and Southern Asia and Sub-Saharan Africa, in which populations continue to be exposed to increasing levels of air pollution. The majority of the world’s population continue to be exposed to levels of air pollution substantially above WHO Air Quality Guidelines and, as such, air pollution constitutes a major, and in many areas, increasing threat to public health.

This paper aims to understand the relationship between area level deprivation and monthly COVID-19 cases in England in response to government policy throughout 2020. The response variable is monthly reported COVID-19 cases at the Middle Super Output Area (MSOA) level by Public Health England, with Index of Multiple Deprivation (IMD), ethnicity (percentage of the population across 5 ethnicity categories) and the percentage of the population older than 70 years old and time as predictors. A GEE population-averaged panel-data model was employed to model trends in monthly COVID-19 cases with the population of each MSOA included as the exposure variable. Area level deprivation is significantly associated with COVID-19 cases from March 2020; however, this relationship is reversed in December 2020. Follow up analysis found that this reversal was maintained when controlling for the novel COVID-19 variant outbreak in the South East of England. This analysis indicates that changes in the role of deprivation and monthly reported COVID-19 over time cases may be linked to two government policies: (1) the premature easing of national restrictions in July 2020 when cases were still high in the most deprived areas in England and (2) the introduction of a regional tiered system in October predominantly in the North of England. The analysis adds to the evidence showing that deprivation is a key driver of COVID-19 outcomes and highlights the unintended negative impact of government policy.

Global assessments of air quality and health require comprehensive estimates of the exposures to air pollution that are experienced by populations in every country. However, there are many countries in which measurements from ground-based monitoring are sparse or non-existent, with quality-control and representativeness providing additional challenges. While ground-based monitoring provides a far from complete picture of global air quality, there are other sources of information that provide comprehensive coverage across the globe. The World Health Organization developed the Data Integration Model for Air Quality (DIMAQ) to combine information from ground measurements with that from other sources, such as atmospheric chemical transport models and estimates from remote sensing satellites in order to produce the information that is required for health burden assessment and the calculation of air pollution-related Sustainable Development Goals indicators. Here, we show an example of the use of DIMAQ with the Copernicus Atmosphere Monitoring Service Re-Analysis (CAMSRA) of atmospheric composition, which represents the best practices in meteorology and climate monitoring that were developed under the World Meteorological Organization’s Global Atmosphere Watch programme. Estimates of PM_2.5 from CAMSRA are integrated within the DIMAQ framework in order to produce high-resolution estimates of air pollution exposure that can be aggregated in a coherent fashion to produce country-level assessments of exposures.

Objective (1) To compare clinical characteristics of patients with psoriatic arthritis (PsA) with PsA mutilans (PAM) and without PAM, and (2) to determine the rate of PAM radiographic progression. Methods A retrospective cohort study was conducted of all patients with PsA attending a teaching hospital. The most recent hand and feet radiographs were screened for PAM. Serial radiographs (earliest to most recent) were quantitatively scored for osteolysis, erosion, joint space narrowing, and osteoproliferation. Results Out of the 610 cases, 36 PsA cases had PAM (5.9%). PAM cases were younger at diagnosis of PsA than non-PAM cases (p = 0.04), had more prevalent psoriatic nail dystrophy (OR 5.43, p < 0.001), and worse health assessment questionnaire score (1.25 vs 0.63, p < 0.04). Radiographic axial disease (OR 2.31, adjusted p = 0.03) and especially radiographic sacroiliitis (OR 2.99, adjusted p = 0.01) were more prevalent in PAM. PAM were more likely than non-PAM cases to have used a disease-modifying antirheumatic drug (DMARD; OR 16.36, p < 0.001). Out of 33 cases, 29 PAM cases had initiated a synthetic DMARD and 4/13 had initiated anti-tumor necrosis factor (anti-TNF) prior to first demonstration of PAM. A median 5 radiographs were scored for each PAM case (interquartile range 3–7). PAM progressed from monoarticular (60%) to polyarticular (80%) involvement. Osteolysis was initially rapid and progressive in the hands and feet, tapering later during disease course. Nail dystrophy predicted more severe osteolysis (p = 0.03). Conclusion Compared with non-PAM cases, PAM cases have earlier age at PsA diagnosis, poorer function, more prevalent nail dystrophy, and more radiographic axial disease/sacroiliitis. The rate of osteolysis is higher in earlier disease, and more severe in those with nail dystrophy. DMARD and anti-TNF therapy appear not to prevent PAM occurrence.

OBJECTIVES: this paper is based upon work from COST Action ICSHNet. To develop and apply recently proposed methods for assessing the health impact of pollution from contaminated sites and apply them to the case of landfills using available large European datasets. METHODS: standard methods for health impact assessment and burden of disease were applied using the available evidence on the health effects of living near a landfill. Geo-referenced data on landfills from the European Pollutant and Transfer Register (E-PRTR) were combined with population density data (European Environment Agency dataset) and disease frequency data from European health for all database (HfA); uncertainty was assessed via simulation methods. Countries covered by the European Environment Agency's E-PRTR registry on contaminated sites were considered (European Union Member States plus four additional European countries) for the period 2007-2014. Four outcomes, for which suggestive evidence is available, were included: low birth weight; congenital anomalies; respiratory disease; annoyance from odour. Firstly, they were analysed separately, in terms of excess number of cases, and then combined into disability-adjusted life years (DALYs). RESULTS: 1,544 landfill sites were considered. 29.3 million people (6% of the total population) live within 4 km from one or more of these sites. The number of yearly attributable cases associated with low birth weight, congenital anomalies, respiratory diseases, and annoyance from odour were estimated, respectively, at 1,239, 70, 33,039, and 1,582,624. Associated DALYs were 10,192, 958, 2,688, and 47,505, respectively; 61,325 in total. CONCLUSIONS: estimates indicate a sizable health impact, largest for annoyance from odour, given the high frequency of the outcome and in spite of its lesser severity compared to the other ones. Application of the methodology is relatively straightforward, once the main assumption of causality is made. The present work offers a first approximation of the impact on health of waste landfills in Europe and can be further applied to other contaminated sites.

Advances in data science and artificial intelligence (AI) offer unprecedented opportunities to provide actionable insights, drive innovative solutions, and create long-term strategies for sustainable development in response to the triple existential crises facing humanity: climate change, pollution, and biodiversity loss. The rapid development of AI models has been the subject of extensive debate and is high on the political agenda, but at present the vast potential for AI to contribute positively to informed decision making, improved environmental risk management, and the development of technological solutions to sustainability challenges remains underdeveloped. In this paper, we consider four inter-dependent areas in which data science and AI can make a substantial contribution to developing sustainable future interactions with the environment: (i) quantification and tracking progress towards the United Nations Sustainable Development Goals; (ii) embedding AI technologies to reduce emissions at source; (iii) developing systems to increase our resilience to natural hazards; (iv) Net Zero and the built environment. We also consider the wider challenges associated with the widespread use of AI, including data access and discoverability, trust and regulation, inference and decision making, and the sustainable use of AI.