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Real World Data and Real World Evidence in Environmental and Clinical Epidemiology

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Infectious Disease Epidemiology".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 9593

Special Issue Editor


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Guest Editor
1. Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona, Spain
2. CIBER of Epidemiology and Public Health (CIBERESP), Barcelona, Spain
Interests: real world data; real world evidence; environmental epidemiology; clinical epidemiology

Special Issue Information

Dear Colleagues,

Real world evidence (RWE) means evidence obtained from real world data (RWD), which are observational studies based on real data obtained from daily clinical practice. Specifically, RWD could be defined as studies that collect data relevant to human health that do not come from conventional randomized clinical trials. Randomized controlled clinical trials (RCTs) are the gold standard for determining the efficacy and safety of a therapy. In fact, and as is known, RCTs constitute the research design with greater internal validity. In this sense, they are prospective, have definite results a priori, are characterized by randomization and control of the groups, apply standardized health interventions to the selected patients, blind the entire design to obtain results without risk of bias, etc.. However, they have at least two limitations. They usually limit themselves to evaluating specific interventions one by one. Second, when selecting the patient sample according to strict inclusion and exclusion criteria, they have reduced external validity (or generalization), which limits the transfer of their results. It is these limitations that determine the utility of RWD and RWE. They document the real care that patients receive in the clinic and include a variety of cases (for example, patients suffering from several diseases at once) without limiting strict inclusion and exclusion criteria. They can generate long-term data on the effectiveness and safety of health interventions, in addition to providing useful information for economic health analyses. RWD and RWE provide the external validity that RCTs lack. In fact, the main advantage of RWD and RWE, in addition to a lower cost, larger sample size, and greater representativeness than other research designs, is their high external validity. Although most RWE and RWD studies have focused on the use and potential benefits or risks of a medical product, their potential in other areas and objectives is very broad. Precisely for this reason, in this Special Issue, we intend to expand them, and we hope to have RWE and RWD studies in epidemiology in general, and in particular in environmental and cancer epidemiology, as well as in clinical research, among others.

Dr. Maria A Barceló
Guest Editor

Manuscript Submission Information

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Keywords

  • real world data
  • real world evidence
  • environmental epidemiology
  • clinical epidemiology

Published Papers (3 papers)

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Research

14 pages, 2193 KiB  
Article
PM2.5 Pollution Strongly Predicted COVID-19 Incidence in Four High-Polluted Urbanized Italian Cities during the Pre-Lockdown and Lockdown Periods
by Ourania S. Kotsiou, Vaios S. Kotsios, Ioannis Lampropoulos, Thomas Zidros, Sotirios G. Zarogiannis and Konstantinos I. Gourgoulianis
Int. J. Environ. Res. Public Health 2021, 18(10), 5088; https://doi.org/10.3390/ijerph18105088 - 11 May 2021
Cited by 7 | Viewed by 2675
Abstract
Background: The coronavirus disease in 2019 (COVID-19) heavily hit Italy, one of Europe’s most polluted countries. The extent to which PM pollution contributed to COVID-19 diffusion is needing further clarification. We aimed to investigate the particular matter (PM) pollution and its correlation with [...] Read more.
Background: The coronavirus disease in 2019 (COVID-19) heavily hit Italy, one of Europe’s most polluted countries. The extent to which PM pollution contributed to COVID-19 diffusion is needing further clarification. We aimed to investigate the particular matter (PM) pollution and its correlation with COVID-19 incidence across four Italian cities: Milan, Rome, Naples, and Salerno, during the pre-lockdown and lockdown periods. Methods: We performed a comparative analysis followed by correlation and regression analyses of the daily average PM10, PM2.5 concentrations, and COVID-19 incidence across four cities from 1 January 2020 to 8 April 2020, adjusting for several factors, taking a two-week time lag into account. Results: Milan had significantly higher average daily PM10 and PM2.5 levels than Rome, Naples, and Salerno. Rome, Naples, and Salerno maintained safe PM10 levels. The daily PM2.5 levels exceeded the legislative standards in all cities during the entire period. PM2.5 pollution was related to COVID-19 incidence. The PM2.5 levels and sampling rate were strong predictors of COVID-19 incidence during the pre-lockdown period. The PM2.5 levels, population’s age, and density strongly predicted COVID-19 incidence during lockdown. Conclusions: Italy serves as a noteworthy paradigm illustrating that PM2.5 pollution impacts COVID-19 spread. Even in lockdown, PM2.5 levels negatively impacted COVID-19 incidence. Full article
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18 pages, 3542 KiB  
Article
Association between the New COVID-19 Cases and Air Pollution with Meteorological Elements in Nine Counties of New York State
by Carlos Díaz-Avalos, Pablo Juan, Somnath Chaudhuri, Marc Sáez and Laura Serra
Int. J. Environ. Res. Public Health 2020, 17(23), 9055; https://doi.org/10.3390/ijerph17239055 - 04 Dec 2020
Cited by 3 | Viewed by 2266
Abstract
The principal objective of this article is to assess the possible association between the number of COVID-19 infected cases and the concentrations of fine particulate matter (PM2.5) and ozone (O3), atmospheric pollutants related to people’s mobility in urban areas, [...] Read more.
The principal objective of this article is to assess the possible association between the number of COVID-19 infected cases and the concentrations of fine particulate matter (PM2.5) and ozone (O3), atmospheric pollutants related to people’s mobility in urban areas, taking also into account the effect of meteorological conditions. We fit a generalized linear mixed model which includes spatial and temporal terms in order to detect the effect of the meteorological elements and COVID-19 infected cases on the pollutant concentrations. We consider nine counties of the state of New York which registered the highest number of COVID-19 infected cases. We implemented a Bayesian method using integrated nested Laplace approximation (INLA) with a stochastic partial differential equation (SPDE). The results emphasize that all the components used in designing the model contribute to improving the predicted values and can be included in designing similar real-world data (RWD) models. We found only a weak association between PM2.5 and ozone concentrations with COVID-19 infected cases. Records of COVID-19 infected cases and other covariates data from March to May 2020 were collected from electronic health records (EHRs) and standard RWD sources. Full article
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18 pages, 2493 KiB  
Article
Associations between Household-Level Exposures and All-Cause Diarrhea and Pathogen-Specific Enteric Infections in Children Enrolled in Five Sentinel Surveillance Studies
by Josh M. Colston, Abu S. G. Faruque, M. Jahangir Hossain, Debasish Saha, Suman Kanungo, Inácio Mandomando, M. Imran Nisar, Anita K. M. Zaidi, Richard Omore, Robert F. Breiman, Samba O. Sow, Anna Roose, Myron M. Levine, Karen L. Kotloff, Tahmeed Ahmed, Pascal Bessong, Zulfiqar Bhutta, Estomih Mduma, Pablo Penatero Yori, Prakash Sunder Shrestha, Maribel P. Olortegui, Gagandeep Kang, Aldo A. M. Lima, Jean Humphrey, Andrew Prendergast, Francesca Schiaffino, Benjamin F. Zaitchik and Margaret N. Kosekadd Show full author list remove Hide full author list
Int. J. Environ. Res. Public Health 2020, 17(21), 8078; https://doi.org/10.3390/ijerph17218078 - 02 Nov 2020
Cited by 12 | Viewed by 3150
Abstract
Diarrheal disease remains a major cause of childhood mortality and morbidity causing poor health and economic outcomes. In low-resource settings, young children are exposed to numerous risk factors for enteric pathogen transmission within their dwellings, though the relative importance of different transmission pathways [...] Read more.
Diarrheal disease remains a major cause of childhood mortality and morbidity causing poor health and economic outcomes. In low-resource settings, young children are exposed to numerous risk factors for enteric pathogen transmission within their dwellings, though the relative importance of different transmission pathways varies by pathogen species. The objective of this analysis was to model associations between five household-level risk factors—water, sanitation, flooring, caregiver education, and crowding—and infection status for endemic enteric pathogens in children in five surveillance studies. Data were combined from 22 sites in which a total of 58,000 stool samples were tested for 16 specific enteropathogens using qPCR. Risk ratios for pathogen- and taxon-specific infection status were modeled using generalized linear models along with hazard ratios for all-cause diarrhea in proportional hazard models, with the five household-level variables as primary exposures adjusting for covariates. Improved drinking water sources conferred a 17% reduction in diarrhea risk; however, the direction of its association with particular pathogens was inconsistent. Improved sanitation was associated with a 9% reduction in diarrhea risk with protective effects across pathogen species and taxa of around 10–20% risk reduction. A 9% reduction in diarrhea risk was observed in subjects with covered floors, which were also associated with decreases in risk for zoonotic enteropathogens. Caregiver education and household crowding showed more modest, inconclusive results. Combining data from diverse sites, this analysis quantified associations between five household-level exposures on risk of specific enteric infections, effects which differed by pathogen species but were broadly consistent with hypothesized transmission mechanisms. Such estimates may be used within expanded water, sanitation, and hygiene (WASH) programs to target interventions to the particular pathogen profiles of individual communities and prioritize resources. Full article
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