Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach

Grekousis, George; Feng, Zhixin; Marakakis, Ioannis; Lü, Yi; Wang, Ruoyu

doi:10.1016/j.healthplace.2022.102744

Cited by 81 publications

(64 citation statements)

References 66 publications

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“…Validation was estimated internally during the algorithm’s execution using the out-of-bag (OOB) method, which measures the prediction error of random forests using Bootstrap aggregation [ 21 ]. The assessment of the yielding of the training dataset showed an overall error rate of 30.4% and 12.3%, with an accuracy of 69.6% and 87.7% for the >80% and <50% compliance model, respectively.…”

Section: Resultsmentioning

confidence: 99%

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Longitudinal Study of Therapeutic Adherence in a Cystic Fibrosis Unit: Identifying Potential Factors Associated with Medication Possession Ratio

et al. 2022

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Cystic fibrosis (CF) is a genetic and multisystemic disease that requires a high therapeutic demand for its control. The aim of this study was to assess therapeutic adherence (TA) to different treatments to study possible clinical consequences and clinical factors influencing adherence. This is an ambispective observational study of 57 patients aged over 18 years with a diagnosis of CF. The assessment of TA was calculated using the Medication Possession Ratio (MPR) index. These data were related to exacerbations and the rate of decline in FEV1 percentage. Compliance was good for all CFTR modulators, azithromycin, aztreonam, and tobramycin in solution for inhalation. The patients with the best compliance were older; they had exacerbations and the greatest deterioration in lung function during this period. The three variables with the highest importance for the compliance of the generated Random Forest (RF) models were age, FEV1%, and use of Ivacaftor/Tezacaftor. This is one of the few studies to assess adherence to CFTR modulators and symptomatic treatment longitudinally. CF patient therapy is expensive, and the assessment of variables with the highest importance for a high MPR, helped by new Machine learning tools, can contribute to defining new efficient TA strategies with higher benefits.

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Section: Resultsmentioning

confidence: 99%

“…We mainly use the MDG index to assess the importance of the variables against the response variable, as the MDG-based rankings provide more robust results than those provided by MDA. Thus, higher MDG values imply that they are predictors with higher significance in the model [ 20 , 21 ].…”

Section: Resultsmentioning

confidence: 99%

Longitudinal Study of Therapeutic Adherence in a Cystic Fibrosis Unit: Identifying Potential Factors Associated with Medication Possession Ratio

et al. 2022

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“…Modelling analysis of COVID-19 outbreaks is a common process to predict confirmed COVID-19 cases and deaths using Artificial Intelligence (AI) and strongly assists national health agencies in developing response plans and mitigation measures [13][14][15]. Machine learning (ML) and especially ensemble (supervised) learning algorithms are dominant in the field of regression and time-series prediction tasks, achieving high performance regarding dataset complexity [16][17][18][19][20]. ML algorithms accurately predict COVID-19 cases and deaths, but now the problem is shifted in identifying the risk factors that cause the spread in order to establish countermeasures to prevent the spread of the pandemic in urban environments.…”

Section: Introductionmentioning

confidence: 99%

Novel Insights in Spatial Epidemiology Utilizing Explainable AI (XAI) and Remote Sensing

et al. 2022

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The COVID-19 pandemic has affected many aspects of human life around the world, due to its tremendous outcomes on public health and socio-economic activities. Policy makers have tried to develop efficient responses based on technologies and advanced pandemic control methodologies, to limit the wide spreading of the virus in urban areas. However, techniques such as social isolation and lockdown are short-term solutions that minimize the spread of the pandemic in cities and do not invert long-term issues that derive from climate change, air pollution and urban planning challenges that enhance the spreading ability. Thus, it seems crucial to understand what kind of factors assist or prevent the wide spreading of the virus. Although AI frameworks have a very efficient predictive ability as data-driven procedures, they often struggle to identify strong correlations among multidimensional data and provide robust explanations. In this paper, we propose the fusion of a heterogeneous, spatio-temporal dataset that combine data from eight European cities spanning from 1 January 2020 to 31 December 2021 and describe atmospheric, socio-economic, health, mobility and environmental factors all related to potential links with COVID-19. Remote sensing data are the key solution to monitor the availability on public green spaces between cities in the study period. So, we evaluate the benefits of NIR and RED bands of satellite images to calculate the NDVI and locate the percentage in vegetation cover on each city for each week of our 2-year study. This novel dataset is evaluated by a tree-based machine learning algorithm that utilizes ensemble learning and is trained to make robust predictions on daily cases and deaths. Comparisons with other machine learning techniques justify its robustness on the regression metrics RMSE and MAE. Furthermore, the explainable frameworks SHAP and LIME are utilized to locate potential positive or negative influence of the factors on global and local level, with respect to our model’s predictive ability. A variation of SHAP, namely treeSHAP, is utilized for our tree-based algorithm to make fast and accurate explanations.

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“…A growing number of studies show that the uneven spatial distribution of COVID‐19 deaths, which is evident worldwide, is related to demographic, socioeconomic, health and environmental disparities across geographical regions (Andersen et al, 2021 ; Feinhandler et al, 2020 ; Grekousis et al, 2021 , 2022 ; Maiti et al, 2021 ; Mollalo et al, 2020 ). Although these studies have attempted to identify the structural drivers (i.e., environmental, socioeconomic, demographic) and used different spatial regression models (geographically weighted regression [GWR], multiscale geographically weighted regression [MGWR], spatial lag model [SLM] and spatial error model [SEM]) to explain spatially the variability of COVID‐19 deaths, they all have two major limitations.…”

Section: Introductionmentioning

confidence: 99%

Exploring the socioeconomic drivers of COVID‐19 mortality across various spatial regimes

Grekousis

Lü

Wang

2022

Geographical Journal

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Identifying the socioeconomic drivers of COVID‐19 deaths is essential for designing effective policies and health interventions. However, how the significance and impact of these factors varies across different spatial regimes has been scantly explored. In this ecological cross‐sectional study, we apply the spatial lag by regimes regression model to examine how the socioeconomic and health determinants of COVID‐19 death rate vary across (a) metropolitan vs. non‐metropolitan, (b) shelter‐in‐place vs. no‐shelter‐in‐place order, and (c) Democratic vs. Republican US counties. A total of 20 variables were studied across 3108 counties in the contiguous US for the first year of the pandemic (6 February 2020 to 5 February 2021). The results show that the COVID‐19 death rate not only depends on a complex interplay of the population demographic, socioeconomic and health‐related characteristics, but also on the spatial regime that the residents live, work and play. Household median income, household size, percentage of African Americans, percentage aged 40–59 and heart disease mortality are significant to metropolitan but not to non‐metropolitan counties. We identified lack of insurance access as a significant driver across all regimes except for Democratic. We also showed that the political orientation of the governor might have impacted COVID‐19 death rates due to the public response (i.e., shelter‐in‐place vs. no‐shelter‐in‐place order). The proposed analysis allows for understanding the socioeconomic context in which public health policies can be applied, and importantly, it presents how COVID‐19 death related factors vary across different spatial regimes.

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Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach

Cited by 81 publications

References 66 publications

Longitudinal Study of Therapeutic Adherence in a Cystic Fibrosis Unit: Identifying Potential Factors Associated with Medication Possession Ratio

Longitudinal Study of Therapeutic Adherence in a Cystic Fibrosis Unit: Identifying Potential Factors Associated with Medication Possession Ratio

Novel Insights in Spatial Epidemiology Utilizing Explainable AI (XAI) and Remote Sensing

Exploring the socioeconomic drivers of COVID‐19 mortality across various spatial regimes

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