Combining randomized and non-randomized data to predict heterogeneous effects of competing treatments.

Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different sources of types in different formats: aggregate data (AD) and individual participant data (IPD) from randomized and non-randomized evidence. In the first stage, a prognostic model is developed to predict the baseline risk of the outcome using a large cohort study. In the second stage, we recalibrated this prognostic model to improve our predictions for patients enrolled in randomized trials. In the third stage, we used the baseline risk as effect modifier in a network meta-regression model combining AD, IPD randomized clinical trial to estimate heterogeneous treatment effects. We illustrated the approach in the re-analysis of a network of studies comparing three drugs for relapsing-remitting multiple sclerosis. Several patient characteristics influence the baseline risk of relapse, which in turn modifies the effect of the drugs. The proposed model makes personalized predictions for health outcomes under several treatment options and encompasses all relevant randomized and non-randomized evidence.

An In-Depth Study of Vibration Sensors for Condition Monitoring.

Heavy machinery allows for the efficient, precise, and safe management of large-scale operations that are beyond the abilities of humans. Heavy machinery breakdowns or failures lead to unexpected downtime, increasing maintenance costs, project delays, and leading to a negative impact on personnel safety. Predictive maintenance is a maintenance strategy that predicts possible breakdowns of equipment using data analysis, pattern recognition, and machine learning. In this paper, vibration-based condition monitoring studies are reviewed with a focus on the devices and methods used for data collection. For measuring vibrations, different accelerometers and their technologies were investigated and evaluated within data collection contexts. The studies collected information from a wide range of sources in the heavy machinery. Throughout our review, we came across some studies using simulations or existing datasets. We concluded in this review that due to the complexity of the situation, we need to use more advanced accelerometers that can measure vibration.

The International Working Group on New Developments in Pharmacovigilance: Advancing Methods and Communication in Pharmacovigilance.

PURPOSE: In 2019, the International Working Group (IWG), focusing on New Developments in Pharmacovigilance, was established. This group is coordinated by the Drug Safety Research Unit in the United Kingdom, and the mission of the IWG is to progress pharmacovigilance methodologies and promote the safe and effective use of medicines and vaccines, thereby further protecting patients. Novel therapeutics are continuously being developed to alleviate medical conditions, but with advancing technologies, innovative pharmacovigilance methodologies need to be developed to effectively monitor the use and safety of these products. With reduced timelines proposed for premarketing clinical trials and increased application of real-world evidence supporting regulatory approvals, products may be used in real-world clinical practice in shorter timeframes than before. Therefore, the need for effective methods of monitoring medicines and collecting safety data in real-time is of paramount importance to public health. METHODS: The IWG aims to advance existing methodologies used in the detection, monitoring, and analysis of safety data in pharmacovigilance and to communicate best practice proposals to support decision making in health care. The IWG will identify areas requiring review of current processes or methodologic research and will communicate the output of the IWG through peer-reviewed publications, reports, and presentation of findings at relevant conferences and scientific meetings. FINDINGS: The IWG is currently reviewing two areas in pharmacovigilance; case-level causality assessment and the strengths and limitations of data sources. The IWG is advancing these areas by producing two scoping reviews which will be easily accessible to regulatory agencies, industry, academia, and interested persons or organizations. IMPLICATIONS: The scoping reviews comply with the IWGs mission to progress pharmacovigilance methodologies and promote the safe and effective use of medicines and vaccines. The present article shares details of the objectives of the IWG and provides an overview on the status of IWG activities.

Overview of global real-world data sources for pediatric pharmacoepidemiologic research.

PURPOSE: Given limited information available on real-world data (RWD) sources with pediatric populations, this study describes features of globally available RWD sources for pediatric pharmacoepidemiologic research. METHODS: An online questionnaire about pediatric RWD sources and their attributes and capabilities was completed by members and affiliates of the International Society for Pharmacoepidemiology and representatives of nominated databases. All responses were verified by database representatives and summarized. RESULTS: Of 93 RWD sources identified, 55 unique pediatric RWD sources were verified, including data from Europe (47%), United States (38%), multiregion (7%), Asia-Pacific (5%), and South America (2%). Most databases had nationwide coverage (82%), contained electronic health/medical records (47%) and/or administrative claims data (42%) and were linkable to other databases (65%). Most (71%) had limited outside access (e.g., by approval or through local collaborators); only 10 (18%) databases were publicly available. Six databases (11%) reported having >20 million pediatric observations. Most (91%) included children of all ages (birth until 18th birthday) and contained outpatient medication data (93%), while half (49%) contained inpatient medication data. Many databases captured vaccine information for children (71%), and one-third had regularly updated data on pediatric height (31%) and weight (33%). Other pediatric data attributes captured include diagnoses and comorbidities (89%), lab results (58%), vital signs (55%), devices (55%), imaging results (42%), narrative patient histories (35%), and genetic/biomarker data (22%). CONCLUSIONS: This study provides an overview with key details about diverse databases that allow researchers to identify fit-for-purpose RWD sources suitable for pediatric pharmacoepidemiologic research.

Adult Maltreatment Risk Factors: Adding Community-Level Factors to an Individual-Level Field.

Adult maltreatment is a pervasive problem in the United States and has serious individual and societal consequences. Adult protective services (APS) agencies are the social services programs responsible for serving older adults and adults with disabilities who may be experiencing adult maltreatment. The adult maltreatment literature differentiates elder maltreatment from the maltreatment of adults with disabilities, yet APS agencies serve both groups. Understanding the etiology of adult maltreatment as well as the associated risk and protective factors is crucial for APS workers, clinical practitioners, researchers, and policymakers. To advance the evidence in this area, we undertook a scoping review to examine recent evidence on risk and protective factors associated with adult maltreatment. Searches of nine electronic databases were conducted in 2020 to identify studies published in peer-reviewed journals since 2010. A total of 29 studies were included in the final review. The findings identified several categories of risk factors associated with the individual: demographic traits, socioeconomic characteristics, physical and mental health, interpersonal issues, and historical events. Several studies identified caregiver and alleged perpetrator risk factors. However, the current body of research lacks community and contextual risk and protective factors. Therefore, we present several potential data sources that may be leveraged to examine the links between social-contextual characteristics and adult maltreatment. These data may be combined with APS data to advance the field's understanding of risk and protective factors through advanced analytic techniques.

Evaluation of multiple data sources for predicting increased need for HIV prevention among cisgender women: understanding missed opportunities for Pre-exposure Prophylaxis (PrEP).

BACKGROUND: Ciswomen constitute a disproportionately low percentage of pre-exposure prophylaxis for HIV prevention (PrEP) users compared to men. Despite PrEP's effectiveness, women are 5.25 times less likely to take PrEP than men. Identifying women who have increased reasons for HIV prevention and educating and offering PrEP to these women is crucial to reducing HIV transmission and overall health equity. However, the best method of identifying women at highest risk of acquiring HIV remains unknown. This study aimed to identify common HIV risk factors and data sources for identifying these common factors (e.g., electronic medical record data, open source neighborhood data), as well as potential intervention points and missed opportunities for PrEP linkage. METHODS: We conducted an evaluation of multiple data sources: semi-structured qualitative interviews, electronic medical record (EMR) chart abstraction, and open source data abstraction. We accessed EMRs for enrolled participants and all participants signed a standard release of medical information (ROI) form for all institutions at which they had received medical care for the five-year period preceding their HIV diagnosis. Data were abstracted using a standardized procedure. Both structured and unstructured fields (i.e., narrative text of free notes) within the EMR were examined and included for analysis. Finally, open data sources (e.g., STI cases, HIV prevalence) were examined by community area of Chicago. Open data sources were used to examine several factors contributing to the overall Economic Hardship Index (EHI) score. We used these calculated scores to assess the economic hardship within participants' neighborhoods. RESULTS: A total of 18 cisgender women with HIV participated in our study. Participants were mostly Black/African American (55.6%) and young (median age of 34). Our analysis identified two main themes influencing HIV risk among participants: contextual factors and relationship factors. Further, potential pre-diagnosis intervention points and missed opportunities were identified during reproductive health/prenatal visits, behavioral/mental health visits, and routine STI testing. Our evaluation of multiple data sources included investigating the presence or absence of information in the EMR (STI history, HIV testing, substance use, etc.) as well as whether pertinent information could be gathered from open access sources. CONCLUSION: Ciswomen recently diagnosed with HIV identified many shared experiences, including syndemic conditions like mental illness and substance abuse, sex with men who have sex with men, and frequent moving in areas with high HIV incidence prior to their diagnosis. It is imperative that providers ask patients about social history, information about partners, and other key variables, in addition to the standardized questions. Findings can be used to better recognize ciswomen most vulnerable to HIV and offer PrEP to them, reducing HIV transmission.

The consequences of data dispersion in genomics: a comparative analysis of data sources for precision medicine.

BACKGROUND: Genomics-based clinical diagnosis has emerged as a novel medical approach to improve diagnosis and treatment. However, advances in sequencing techniques have increased the generation of genomics data dramatically. This has led to several data management problems, one of which is data dispersion (i.e., genomics data is scattered across hundreds of data repositories). In this context, geneticists try to remediate the above-mentioned problem by limiting the scope of their work to a single data source they know and trust. This work has studied the consequences of focusing on a single data source rather than considering the many different existing genomics data sources. METHODS: The analysis is based on the data associated with two groups of disorders (i.e., oncology and cardiology) accessible from six well-known genomic data sources (i.e., ClinVar, Ensembl, GWAS Catalog, LOVD, CIViC, and CardioDB). Two dimensions have been considered in this analysis, namely, completeness and concordance. Completeness has been evaluated at two levels. First, by analyzing the information provided by each data source with regard to a conceptual schema data model (i.e., the schema level). Second, by analyzing the DNA variations provided by each data source as related to any of the disorders selected (i.e., the data level). Concordance has been evaluated by comparing the consensus among the data sources regarding the clinical relevance of each variation and disorder. RESULTS: The data sources with the highest completeness at the schema level are ClinVar, Ensembl, and CIViC. ClinVar has the highest completeness at the data level data source for the oncology and cardiology disorders. However, there are clinically relevant variations that are exclusive to other data sources, and they must be considered in order to provide the best clinical diagnosis. Although the information available in the data sources is predominantly concordant, discordance among the analyzed data exist. This can lead to inaccurate diagnoses. CONCLUSION: Precision medicine analyses using a single genomics data source leads to incomplete results. Also, there are concordance problems that threaten the correctness of the genomics-based diagnosis results.

Digital Twins in Healthcare: Methodological Challenges and Opportunities.

One of the most promising advancements in healthcare is the application of digital twin technology, offering valuable applications in monitoring, diagnosis, and development of treatment strategies tailored to individual patients. Furthermore, digital twins could also be helpful in finding novel treatment targets and predicting the effects of drugs and other chemical substances in development. In this review article, we consider digital twins as virtual counterparts of real human patients. The primary aim of this narrative review is to give an in-depth look into the various data sources and methodologies that contribute to the construction of digital twins across several healthcare domains. Each data source, including blood glucose levels, heart MRI and CT scans, cardiac electrophysiology, written reports, and multi-omics data, comes with different challenges regarding standardization, integration, and interpretation. We showcase how various datasets and methods are used to overcome these obstacles and generate a digital twin. While digital twin technology has seen significant progress, there are still hurdles in the way to achieving a fully comprehensive patient digital twin. Developments in non-invasive and high-throughput data collection, as well as advancements in modeling and computational power will be crucial to improve digital twin systems. We discuss a few critical developments in light of the current state of digital twin technology. Despite challenges, digital twin research holds great promise for personalized patient care and has the potential to shape the future of healthcare innovation.

Economic evaluations of non-communicable diseases conducted in Sub-Saharan Africa: a critical review of data sources.

BACKGROUND: Policymakers in sub-Saharan Africa (SSA) face challenging decisions regarding the allocation of health resources. Economic evaluations can help decision makers to determine which health interventions should be funded and or included in their benefits package. A major problem is whether the evaluations incorporated data from sources that are reliable and relevant to the country of interest. We aimed to review the quality of the data sources used in all published economic evaluations for cardiovascular disease and diabetes in SSA. METHODS: We systematically searched selected databases for all published economic evaluations for CVD and diabetes in SSA. We modified a hierarchy of data sources and used a reference case to measure the adherence to reporting and methodological characteristics, and descriptively analysed author statements. RESULTS: From 7,297 articles retrieved from the search, we selected 35 for study inclusion. Most were modelled evaluations and almost all focused on pharmacological interventions. The studies adhered to the reporting standards but were less adherent to the methodological standards. The quality of data sources varied. The quality level of evidence in the data domains of resource use and costs were generally considered of high quality, with studies often sourcing information from reliable databases within the same jurisdiction. The authors of most studies referred to data sources in the discussion section of the publications highlighting the challenges of obtaining good quality and locally relevant data. CONCLUSIONS: The data sources in some domains are considered high quality but there remains a need to make substantial improvements in the methodological adherence and overall quality of data sources to provide evidence that is sufficiently robust to support decision making in SSA within the context of UHC and health benefits plans. Many SSA governments will need to strengthen and build their capacity to conduct economic evaluations of interventions and health technology assessment for improved priority setting. This capacity building includes enhancing local infrastructures for routine data production and management. If many of the policy makers are using economic evaluations to guide resource allocation, it is imperative that the evidence used is of the feasibly highest quality.

Inference with non-probability samples and survey data integration: a science mapping study.

In recent years, survey data integration and inference based on non-probability samples have gained considerable attention. Because large probability-based samples can be cost-prohibitive in many instances, combining a probabilistic survey with auxiliary data is appealing to enhance inferences while reducing the survey costs. Also, as new data sources emerge, such as big data, inference and statistical data integration will face new challenges. This study aims to describe and understand the evolution of this research field over the years with an original approach based on text mining and bibliometric analysis. In order to retrieve the publications of interest (books, journal articles, proceedings, etc.), the Scopus database is considered. A collection of 1023 documents is analyzed. Through the use of such methodologies, it is possible to characterize the literature and identify contemporary research trends as well as potential directions for future investigation. We propose a research agenda along with a discussion of the research gaps which need to be addressed.

Study on the Use of Artificially Generated Objects in the Process of Training MLP Neural Networks Based on Dispersed Data.

This study concerns dispersed data stored in independent local tables with different sets of attributes. The paper proposes a new method for training a single neural network-a multilayer perceptron based on dispersed data. The idea is to train local models that have identical structures based on local tables; however, due to different sets of conditional attributes present in local tables, it is necessary to generate some artificial objects to train local models. The paper presents a study on the use of varying parameter values in the proposed method of creating artificial objects to train local models. The paper presents an exhaustive comparison in terms of the number of artificial objects generated based on a single original object, the degree of data dispersion, data balancing, and different network structures-the number of neurons in the hidden layer. It was found that for data sets with a large number of objects, a smaller number of artificial objects is optimal. For smaller data sets, a greater number of artificial objects (three or four) produces better results. For large data sets, data balancing and the degree of dispersion have no significant impact on quality of classification. Rather, a greater number of neurons in the hidden layer produces better results (ranging from three to five times the number of neurons in the input layer).

Secure Biomedical Document Protection Framework to Ensure Privacy Through Blockchain.

In the recent health care era, biomedical documents play a crucial role, and they contain much evidence-based documentation associated with many stakeholders data. Protecting those confidential research documents is more difficult and effective, and a significant process in the medical-based research domain. Those bio-documentation related to health care and other relevant community-valued data are suggested by medical professionals and processed. Many traditional security mechanisms such as akteonline and Health Insurance Portability and Accountability Act (HIPAA) are used to protect the biomedical documents as they consider the problem of non-repudiation and data integrity related to the retrieval and storage of documents. Thus, there is a need for a comprehensive framework that improves protection in terms of cost and response time related to biomedical documents. In this research work, blockchain-based biomedical document protection framework (BBDPF) is proposed, which includes blockchain-based biomedical data protection (BBDP) and blockchain-based biomedical data retrieval (BBDR) algorithms. BBDP and BBDR algorithms provide consistency on the data to prevent data modification and interception of confidential data with proper data validation. Both the algorithms have strong cryptographic mechanisms to withstand post-quantum security risks, ensuring the integrity of biomedical document retrieval and non-deny of data retrieval transactions. In the performance analysis, Ethereum blockchain infrastructure is deployed BBDPF and smart contracts using Solidity language. In the performance analysis, request time and searching time are determined based on the number of request to ensure data integrity, non-repudiation, and smart contracts for the proposed hybrid model as it gets increased gradually. A modified prototype is built with a web-based interface to prove the concept and evaluate the proposed framework. The experimental results revealed that the proposed framework renders data integrity, non-repudiation, and support for smart contracts with Query Notary Service, MedRec, MedShare, and Medlock.

Estimation of the completeness of road traffic mortality data in Zambia using a three source capture recapture method.

BACKGROUND: Accurate and reliable data are essential for tracking progress and evaluating the effectiveness of road safety intervention measures. However, in many low- and medium-income countries, good quality data on road traffic crashes are often difficult to obtain. This situation has led to an underestimation of the severity of the problem and distortions in trends when the reporting changes over time. This study estimates the completeness of road traffic crash fatality data in Zambia. METHODS: Data from the police, hospitals, and the civil registration and vital statistics (CRVS) databases was collected for the period 1st January to 31st December 2020 and analyzed using a three-source capture-recapture technique. RESULTS: A total of 666 unique records on mortalities as a result of road traffic crashes were collected from the three data sources during the period under review. The capture-recapture technique estimated the completeness of police, hospital, and CRVS databases to be 19%, 11% and 14% respectively. The combination of the three data sets was found to increase completeness to 37%. Based on this completion rate, we estimate that the actual number of people who died as a result of road traffic crashes in Lusaka Province in the year 2020 was approximately 1,786 (95% CI [1,448-2,274]). This corresponds to an estimated mortality rate of around 53 deaths per 100,000 population. CONCLUSIONS: There is no single database contains complete data to provide a comprehensive picture of Lusaka province and by extension the country's road traffic injury burden. This study has shown how capture and recapture method can address this problem. It shows the need for the continuous review of the data collection processes and procedures in order to identify gaps and bottlenecks, improve efficiency, and increase the quality and completeness of road traffic data on injuries and fatalities. Based on the findings of this study, it is recommended that the city of Lusaka province and Zambia as a whole utilize more than one database for official reporting of road traffic fatalities to increase completeness.

Detection of Alzheimer's disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning.

The scientists are dedicated to studying the detection of Alzheimer's disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer's disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer's disease onset.

Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics.

Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Understanding National Nonprofit Data Environments.

We develop the concept of the nonprofit data environment as all data collected and reported in a country resulting from law implemented into practice. We map data environments across 20 countries and propose explanations for differences between the information nongovernmental organizations report (collected) and what is made publicly available (reported). Domestic factors including regime type, civil society autonomy, and regulatory quality increase the amount of information collected and released publicly. Exposure to international political forces, including aid flows and globalization, increases the gap, which runs counter to expectations of greater openness with global engagement. Our findings point to the need for a better understanding of patterns in non-profit organizations (NPOs) data environments; while all governments collect information, countries with similar legal codes have widely varying data environments. This matters for NPOs as their ability to learn and improve depends on access to quality data and coincides with a feared global political backlash.

Addressing data deficiencies in assistive technology by using statistical matching methodology: a case study from Malawi.

PURPOSE: To address the data gap on efforts to assess use of assistive technology among children with disability in sub-Saharan Africa. Contribute towards efforts examining access to assistive technologies in sub-Saharan Africa. MATERIALS AND METHODS: The paper uses data from the 2017 survey on Living conditions among persons with disabilities in Malawi and the 2015-16 Malawi Demographic and Health survey to address the objective of the study. The two datasets were statistically matched through random hot deck technique, by integrating the two datasets using randomly selected units from a subset of all available data donors. RESULTS: Results indicate that statistical matching technique produces a composite dataset with an uncertainty value of 2.2%. An accuracy assessment test of the technique also indicates that the marginal distribution of use of assistive technology in the composite dataset is similar to that of the donor dataset with an Overlap index value of close to 1 (Overlap = 0.997). CONCLUSIONS: The statistical matching procedure does enable generation of good data in data constrained contexts. In the current study, this approach enabled measurement of access to assistive products among children with disabilities, in situations where the variables of interest have not been jointly observed. Such a technique can be valuable in mining secondary data, the collection of which may have been funded from different sources and for different purposes. This is of significance for the efficient use of current and future data sets, allowing new questions to be asked and addressed by locally based researchers in poor settings. Implications for RehabilitationIn resource-poor settings, the technique of statistical matching can be used to examine factors that predict the use of assistive technology among persons with disabilities.The statistical matching technique is of significance for the efficient use of current and future datasets, allowing new questions to be asked and addressed by locally based researchers.

The evolution of pharmacovigilance ecosystems: Does Moore's law invite the use of Occam's razor?

AIMS: Moore's law predicts the doubling of complexity of integrated circuits every 2 years; Kryder's corollary assumes a doubling of data storage every 13 months. With the increasing volume of legislation, pharmacovigilance systems today are inherently complex, and the emphasis has shifted from reactive (responding to emerging risks) to planned, active, risk-proportionate approaches operating throughout the life cycle of medicines. METHODS: Exploration of the drivers for increasing complexity of pharmacovigilance systems, focusing on regulatory environment, data management and evaluation. RESULTS: Evaluation of postmarketing data plays an increasingly important role in pharmacovigilance. There is great interest on the part of all stakeholders in optimizing the use of these data. Innovative approaches, including pharmacogenetics and passive measures (sensors), will lead to increased complexity and volumes of data and inevitably to an increase in the volume of case reports. There is a multiplicity of regulations and guidelines on how to manage these data, with an inherent lack of harmonization. CONCLUSION: We summarize the current characterization of safety data types, sources and the classification of these data. Using this benchmark, we discuss the future requirements of an effective pharmacovigilance ecosystem, keeping the principle of parsimony in mind. In this complex, continuously and rapidly changing environment, there is a need for a return to simplicity and pragmatism. The application of Occam's razor could help to support the rapid provision of new, affordable medicines with a positive benefit to risk profile.

Availability of open data related to COVID-19 epidemic in Italy.

Background: Since the beginning of the COVID-19 outbreak in Italy, health authorities have released epidemiologic data about this disease. These data were the most important sources of information which were periodically updated and analyzed by researchers to predict the spread of the epidemic. However, comprehensive and timely data on the evolution of COVID-19 have not always been made available to researchers and physicians. Method: The aim of our work is to investigate quality, availability and format of epidemiologic data about COVID-19 in Italy in different territorial and temporal areas. We tried to access the online resources made available by each of the 19 Italian Regions and the two autonomous Provinces, and in more detail by the Local Health Authorities of one of them, the Emilia-Romagna Region. We analyzed the main sources and flows of data (namely new and cumulative cases of infection, total swabs, new and cumulative COVID-19 deaths, overall and divided by sex), describing their characteristics such as accessibility, format and completeness. We eventually reviewed the data published by the Italian Ministry of Health, the National Institute of Health (ISS) and the Civil Protection Department. The Tim Berners-Lee scale was used to evaluate the open data format. Results: The flow of COVID-19 epidemiologic data in Italy originated from the Local Health Authorities that transmitted the data - on a daily basis - to the regional authorities, which in turn transferred them to the national authorities. We found a rather high heterogeneity in both the content and the format of the released data, both at the local and the regional level. Few Regions were releasing data in open format. ISS was the only national source of data that provided the number of COVID-19 health outcomes divided by sex and age groups since Spring 2020. Conclusions: Despite multiple potential useful sources for COVID-19 epidemiology are present in Italy, very few open format data were available both at a macro geographical level (e.g. per Region) and at the provincial level. The access to open format epidemiologic data should be eased, to allow researchers to adequately assess future epidemics and therefore favor timely and effective public health interventions.

Structural racism, air pollution and the association with adverse birth outcomes in the United States: the value of examining intergenerational associations.

Structurally racist policies and practices of the past are likely to be a driving factor in current day differences in exposure to air pollution and may contribute to observed racial and ethnic disparities in adverse birth outcomes in the United States (U.S.). Non-Hispanic Black women in the U.S. experience poorer health outcomes during pregnancy and throughout the life course compared to non-Hispanic White women. This disparity holds even among non-Hispanic Black women with higher socioeconomic status. Reasons for this finding remain unclear, but long-term environmental exposure, either historical exposure or both historical and ongoing exposure, may contribute. Structural racism likely contributes to differences in social and environmental exposures by race in the U.S. context, and these differences can affect health and wellbeing across multiple generations. In this paper, we briefly review current knowledge and recommendations on the study of race and structural racism in environmental epidemiology, specifically focused on air pollution. We describe a conceptual framework and opportunities to use existing historical data from multiple sources to evaluate multi-generational influences of air pollution and structurally racist policies on birth and other relevant health outcomes. Increased analysis of this kind of data is critical for our understanding of structural racism's impact on multiple factors, including environmental exposures and adverse health outcomes, and identifying how past policies can have enduring legacies in shaping health and well-being in the present day. The intended purpose of this manuscript is to provide an overview of the widespread reach of structural racism, its potential association with health disparities and a comprehensive approach in environmental health research that may be required to study and address these problems in the U.S. The collaborative and methodological approaches we highlight have the potential to identify modifiable factors that can lead to effective interventions for health equity.