Geospatial analysis of short-term exposure to air pollution and risk of cardiovascular diseases and mortality-A systematic review

The cardiovascular risk associated with short-term ambient air pollution exposure is well-documented. However, recent advancements in geospatial techniques have provided new insights into this risk. This systematic review focuses on short-term exposure studies that applied advanced geospatial pollution modelling to estimate cardiovascular disease (CVD) risk and accounted for additional unconventional neighbourhood-level confounders to analyse their modifier effect on the risk. Four databases were investigated to select publications between 2018 and 2023 that met the inclusion criteria of studying the effect of particulate matter (PM2.5 and PM10), SO2, NOx, CO, and O3 on CVD mortality or morbidity, utilizing pollution modelling techniques, and considering spatial and temporal confounders. Out of 3277 publications, 285 were identified for full-text review, of which 34 satisfied the inclusion criteria for qualitative analysis, and 12 of them were chosen for additional quantitative analysis. Quality assessment revealed that 28 out of 34 included articles scored 4 or above, indicating high quality. In 30 studies, advanced pollution modelling techniques were used, while in 4 only simpler methods were applied. The most pertinent confounders identified were socio-demographic variables (e.g., socio-economic status, population percentage by race or ethnicity) and neighbourhood-level built environment variables (e.g., urban/rural area, percentage of green space, proximity to healthcare), which exhibited varying modifier effects depending on the context. In the quantitative analysis, only PM 2.5 showed a significant positive association to all-cause CVD-related hospitalisation. Other pollutants did not show any significant effect, likely due to the high inter-study heterogeneity and a limited number of cases. The application of advanced geospatial measurement and modelling of air pollution exposure, as well as its risk, is increasing. This review underscores the importance of accounting for unconventional neighbourhood-level confounders to enhance the understanding of the CVD risk associated with short-term pollution exposure.

Implementation of a GEOAI model to assess the impact of agricultural land on the spatial distribution of PM2.5 concentration

Air pollution is considered one of the major environmental risks to health worldwide. Researchers are making significant efforts to study it, thanks to state-of-art technologies in data collection and processing, and to mitigate its effect. In this context, while a lot is known about the role of urbanization, industries, and transport, the impact of agricultural activities on the spatial distribution of pollution is less studied, despite knowledge about emissions suggest it is not a secondary factor. Therefore, the aim of this study was to assess this impact, and to compare it with that of traditional polluting sources, harvesting the capabilities of GEOAI (Geomatics and Earth Observation Artificial Intelligence). The analysis targeted the highly polluted territory of Lombardy, Italy, considering fine particulate matter (PM2.5). PM2.5 data were obtained from the Copernicus-Atmosphere-Monitoring-Service and processed to infer time-invariant spatial parameters (frequency, intensity and exposure) of concentration across the whole period. An ensemble architecture was implemented, with three blocks: correlation-based features selection, Multiscale-Geographically-Weighted-Regression for spatial enhancement, and a final random forest classifier. Finally, the SHapley Additive exPlanation algorithm was applied to compute the relevance of the different land-use classes on the model. The impact of land-use classes was found significantly higher compared to other published models, showing that the insignificant correlations found in the literature are probably due to an unfit experimental setup. The impact of agricultural activities on the spatial distribution of PM2.5 concentration was comparable to the other considered sources, even when focusing only on the most densely inhabited urban areas. In particular, the agriculture's contribution resulted in pollution spikes rather than in a baseline increase. These results allow to state that public policymakers should consider also agricultural activities for evidence-based decision-making about pollution mitigation.

Detection of patients with COVID-19 by the emergency medical services in Lombardy through an operator-based interview and machine learning models

BACKGROUND

The regional emergency medical service (EMS) in Lombardy (Italy) developed clinical algorithms based on operator-based interviews to detect patients with COVID-19 and refer them to the most appropriate hospitals. Machine learning (ML)-based models using additional clinical and geospatial epidemiological data may improve the identification of infected patients and guide EMS in detecting COVID-19 cases before confirmation with SARS-CoV-2 reverse transcriptase PCR (rtPCR).

METHODS

This was an observational, retrospective cohort study using data from October 2020 to July 2021 (training set) and October 2021 to December 2021 (validation set) from patients who underwent a SARS-CoV-2 rtPCR test within 7 days of an EMS call. The performance of an operator-based interview using close contact history and signs/symptoms of COVID-19 was assessed in the training set for its ability to determine which patients had an rtPCR in the 7 days before or after the call. The interview accuracy was compared with four supervised ML models to predict positivity for SARS-CoV-2 within 7 days using readily available prehospital data retrieved from both training and validation sets.

RESULTS

The training set includes 264 976 patients, median age 74 (IQR 55-84). Test characteristics for the detection of COVID-19-positive patients of the operator-based interview were: sensitivity 85.5%, specificity 58.7%, positive predictive value (PPV) 37.5% and negative predictive value (NPV) 93.3%. Contact history, fever and cough showed the highest association with SARS-CoV-2 infection. In the validation set (103 336 patients, median age 73 (IQR 50-84)), the best-performing ML model had an AUC of 0.85 (95% CI 0.84 to 0.86), sensitivity 91.4% (95 CI% 0.91 to 0.92), specificity 44.2% (95% CI 0.44 to 0.45) and accuracy 85% (95% CI 0.84 to 0.85). PPV and NPV were 13.3% (95% CI 0.13 to 0.14) and 98.2% (95% CI 0.98 to 0.98), respectively. Contact history, fever, call geographical distribution and cough were the most important variables in determining the outcome.

CONCLUSION

ML-based models might help EMS identify patients with SARS-CoV-2 infection, and in guiding EMS allocation of hospital resources based on prespecified criteria.

Heatwave Definition and Impact on Cardiovascular Health: A Systematic Review

Objectives: We aimed to analyze recent literature on heat effects on cardiovascular morbidity and mortality, focusing on the adopted heat definitions and their eventual impact on the results of the analysis. Methods: The search was performed on PubMed, ScienceDirect, and Scopus databases: 54 articles, published between January 2018 and September 2022, were selected as relevant. Results: In total, 21 different combinations of criteria were found for defining heat, 12 of which were based on air temperature, while the others combined it with other meteorological factors. By a simulation study, we showed how such complex indices could result in different values at reference conditions depending on temperature. Heat thresholds, mostly set using percentile or absolute values of the index, were applied to compare the risk of a cardiovascular health event in heat days with the respective risk in non-heat days. The larger threshold's deviation from the mean annual temperature, as well as higher temperature thresholds within the same study location, led to stronger negative effects. Conclusion: To better analyze trends in the characteristics of heatwaves, and their impact on cardiovascular health, an international harmonization effort to define a common standard is recommendable.

Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning

The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities. The model, implemented for the region of Lombardy, Italy, showed robust performance, with an overall 80% accuracy in identifying the active spread of the disease. The further post-processing of the output was implemented to classify the territory into five risk classes, resulting in effectively anticipating the demand for interventions by EMS. This model shows state-of-art potentiality for future applications in the early detection of the burden of the impact of COVID-19, or other similar epidemics, on the healthcare system.

Development and validation of an automated tool to scan scientific literature for the use of specific technologies in the field of cardiology

Background

Review of scientific literature is a time consuming but fundamental step in any kind of scientific research. A consistent manual filtering of papers is always necessary in order to evaluate their relevance with respect to the topic of interest, as the sorting provided by most common research engines is rarely efficient in terms of matching with the desired contents.

Purpose

The aim of this study was to develop, and validate versus manual analysis, an automated tool for performing an efficient search through medical scientific literature, according to keywords relevant to the application of specific technologies in the field of cardiology.

Methods

Using this multiplatform tool implemented in Python, PyQt5 library, the user is required to insert a list of keywords, from which all the possible search strings were built by connecting them with logical operators. The algorithm automatically queries the on-line database PubMed (NCBI) and downloads all the resulting abstracts, with titles and keywords. Results related to the field of cardiology are identified counting the occurrences of “marker” words collected in a dedicated dictionary, developed on the base of the Unified Medical Language System (U.S. NLM). Then, a search-specific dictionary is automatically developed according to the statistical distribution of words in the texts of abstracts, titles and keywords and weighting them according to their relative frequency (ratio between occurrences and number of considered papers). Finally, for each paper the occurrences of these “marker” words are counted and a matching-probability score is assigned, providing a sorting of the results according to expected matching with the topic of interest, together with a threshold-based binary classification.

In order to validate the algorithm, three different technologies with potential applications in cardiology were considered: smartphone applications (App), machine learning (ML) and virtual reality (VR). The related dictionaries were developed with the dedicated function embedded in the tool, while, for the validation of the results, a dataset of 461 manually-classified abstracts was considered, and algorithm thresholds were iteratively adjusted on the base of validation results.

Results

The algorithm applied to the validation dataset showed an overall accuracy (acc) of 88.5% (sensitivity (se) 85.78%, specificity (sp) 91.27%) in the identification of cardiology papers, while the results for the three inspected technologies were:

App: acc 90.89% (se 92.16%, sp 90.53%)

ML: acc 82.65% (se 94.06%, sp 79.44%)

VR: acc 91.54% (se 96%, sp 90.3%)

The algorithm can process 5000 abstracts in around 2 hours.

Conclusions

Results of the validation revealed that the proposed approach is highly valuable in speeding-up any search of medical literature focused on a specific technology or application, enabling a quick overview regarding its diffusion and maturity in a specific scientific domain.

Algorithm schema

Funding Acknowledgement

Type of funding source: None