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BackgroundAlthough the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health. ObjectiveThe aim of this study was to assess the impact of the use of big data analytics on people’s health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2–related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people’s health. MethodsSix databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist. ResultsThe literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. “Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease” and “suicide mortality rate” were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as “critically low” for 25 reviews, as “low” for 7 reviews, and as “moderate” for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data. ConclusionsAlthough the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes. Trial RegistrationInternational Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048

Background Individuals with rare diseases (RD) have been historically understudied. Previous publications reported that existing primary health care (PHC) workforces and associated infrastructure had been shown to improve their access and health-related outcomes in low- and middle-income countries (LMICs). As current evidence about the impact of PHC on patients diagnosed with RD is yet highly dispersed, this scoping review aimed to collate available evidence of the impact of PHC on patients with RD and summarize published information from multiple stakeholders about the perceived usefulness and barriers to effective use of the PHC system. Methods We searched Embase, Health System Evidence, PubMed, LILACS/BVS, and The Cochrane Library, from inception to September 1, 2022, for publications providing clear expert- or experience-based insights or data from patients living with RD at the PHC level of care. We included publications highlighting barriers to integrated care of patients with RD, reported by multiple social actors involved in caring for patients with RD. Two investigators screened publications, extracted data, and clustered information among records deemed eligible for inclusion. Data synthesis was performed using narrative and thematic-based analysis. Major findings identified and coded through a semantic-driven analysis were processed in vosViewer software and reported using descriptive statistics. Findings Eighty publications were included in this review. Quali-quantitative analyses evidenced that the PHC level is essential for approaching patients with RD, mainly due to its longitudinal, multidisciplinary, and coordinated care delivery. In addition, several publications highlighted that the medical curriculum is inappropriate for preparing health care providers to deal with patients presenting unusual signs and symptoms and being diagnosed with RD. PHC teams are essential in orienting patients and families on emergency events. Technology-related concepts were reported in 19 publications, emphasizing their effectiveness on early diagnosis, optimal treatment definition, improvement of quality of life, and long-lasting follow-up. Conclusions We provided valuable information on the effectiveness of the PHC in fostering a creative, integrative, and supportive environment for patients living with RD. Our results can be helpful to several stakeholders in deciding what actions are still pending to achieve a solid and positive experience for patients with RD in the PHC. [ABSTRACT FROM AUTHOR]

Liver test abnormalities are frequently observed in patients with coronavirus disease 2019 (COVID‐19) and are associated with worse prognosis. However, information is limited about pathological changes in the liver in this infection, so the mechanism of liver injury is unclear. Here we describe liver histopathology and clinical correlates of 27 patients who died of COVID‐19 in Manaus, Brazil. There was a high prevalence of liver injury (elevated alanine aminotransferase and aspartate aminotransferase in 44% and 48% of patients, respectively) in these patients. Histological analysis showed sinusoidal congestion and ischemic necrosis in more than 85% of the cases, but these appeared to be secondary to systemic rather than intrahepatic thrombotic events, as only 14% and 22% of samples were positive for CD61 (marker of platelet activation) and C4d (activated complement factor), respectively. Furthermore, the extent of these vascular findings did not correlate with the extent of transaminase elevations. Steatosis was present in 63% of patients, and portal inflammation was present in 52%. In most cases, hepatocytes expressed angiotensin‐converting enzyme 2 (ACE2), which is responsible for binding and entry of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), even though this ectoenzyme was minimally expressed on hepatocytes in normal controls. However, SARS‐CoV‐2 staining was not observed. Most hepatocytes also expressed inositol 1,4,5‐triphosphate receptor 3 (ITPR3), a calcium channel that becomes expressed in acute liver injury. Conclusion: The hepatocellular injury that commonly occurs in patients with severe COVID‐19 is not due to the vascular events that contribute to pulmonary or cardiac damage. However, new expression of ACE2 and ITPR3 with concomitant inflammation and steatosis suggests that liver injury may result from inflammation, metabolic abnormalities, and perhaps direct viral injury. [ABSTRACT FROM AUTHOR]

Background:The novel Coronavirus Disease 2019 (COVID-19) outbreak, caused by the pathogenic severe acute respiratory syndrome-2 (SARS-CoV-2) virus, is exponentially spreading across the globe. Methods: The current systematic review was performed utilising the following electronic databases PubMed, MEDLINE and EMBASE. We searched for the keywords "COVID-19 AND "pregnancy" between January 1, 2020 until December 31, 2020. Results: Out of 4005 records which were identified, 36 original studies were included in this systematic review. Pooled prevalence of vertical transmission was 10%, 95% CI: 4-17%. Pooled prevalence of neonatal mortality was 7%, 95% CI: 0-21%. Conclusion: The contemporary evidence suggests that the incubation period of COVID-19 is 2-14 days, and this infection could be transmitted even from the infected asymptomatic individuals. It is found that the clinical presentation of pregnant women with COVID-19 infection is comparable with the infected non-pregnant females, and the frequent symptoms were fever, cough, myalgia, sore throat and malaise. Some cases have severe maternal morbidity and perinatal deaths secondary to COVID-19 infection. Under these circumstances, pregnant women should focus on maintaining personal hygiene, proper nutrition and extreme social distancing to reduce the risk of COVID-19. Therefore, systematic data reporting for evidence based clinical assessment, management and pregnancy outcomes is essential for preventing of COVID-19 infection among pregnant women. [ABSTRACT FROM AUTHOR]

Background: Navigating the rapidly growing body of scientific literature on the SARS-CoV-2 pandemic is challenging, and ongoing critical appraisal of this output is essential. We aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.Methods: Nine databases (Medline, EMBASE, Cochrane Library, CINAHL, Web of Sciences, PDQ-Evidence, WHO's Global Research, LILACS, and Epistemonikos) were searched from December 1, 2019, to March 24, 2020. Systematic reviews analyzing primary studies of COVID-19 were included. Two authors independently undertook screening, selection, extraction (data on clinical symptoms, prevalence, pharmacological and non-pharmacological interventions, diagnostic test assessment, laboratory, and radiological findings), and quality assessment (AMSTAR 2). A meta-analysis was performed of the prevalence of clinical outcomes.Results: Eighteen systematic reviews were included; one was empty (did not identify any relevant study). Using AMSTAR 2, confidence in the results of all 18 reviews was rated as "critically low". Identified symptoms of COVID-19 were (range values of point estimates): fever (82-95%), cough with or without sputum (58-72%), dyspnea (26-59%), myalgia or muscle fatigue (29-51%), sore throat (10-13%), headache (8-12%) and gastrointestinal complaints (5-9%). Severe symptoms were more common in men. Elevated C-reactive protein and lactate dehydrogenase, and slightly elevated aspartate and alanine aminotransferase, were commonly described. Thrombocytopenia and elevated levels of procalcitonin and cardiac troponin I were associated with severe disease. A frequent finding on chest imaging was uni- or bilateral multilobar ground-glass opacity. A single review investigated the impact of medication (chloroquine) but found no verifiable clinical data. All-cause mortality ranged from 0.3 to 13.9%.Conclusions: In this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all reviews was "critically low". Thus, systematic reviews that were published early on in the pandemic were of questionable usefulness. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards. [ABSTRACT FROM AUTHOR]

New evidence on the COVID-19 pandemic is being published daily. Ongoing high-quality assessment of this literature is therefore needed to enable clinical practice to be evidence-based. This review builds on a previous scoping review and aimed to identify associations between disease severity and various clinical, laboratory and radiological characteristics. We searched MEDLINE, CENTRAL, EMBASE, Scopus and LILACS for studies published between January 1, 2019 and March 22, 2020. Clinical studies including ≥10 patients with confirmed COVID-19 of any study design were eligible. Two investigators independently extracted data and assessed risk of bias. A quality effects model was used for the meta-analyses. Subgroup analysis and meta-regression identified sources of heterogeneity. For hospitalized patients, studies were ordered by overall disease severity of each population and this order was used as the modifier variable in meta-regression. Overall, 86 studies (n = 91,621) contributed data to the meta-analyses. Severe disease was strongly associated with fever, cough, dyspnea, pneumonia, any computed tomography findings, any ground glass opacity, lymphocytopenia, elevated C-reactive protein, elevated alanine aminotransferase, elevated aspartate aminotransferase, older age and male sex. These variables typically increased in prevalence by 30–73% from mild/early disease through to moderate/severe disease. Among hospitalized patients, 30–78% of heterogeneity was explained by severity of disease. Elevated white blood cell count was strongly associated with more severe disease among moderate/severe hospitalized patients. Elevated lymphocytes, low platelets, interleukin-6, erythrocyte sedimentation rate and D-dimers showed potential associations, while fatigue, gastrointestinal symptoms, consolidation and septal thickening showed non-linear association patterns. Headache and sore throat were associated with the presence of disease, but not with more severe disease. In COVID-19, more severe disease is strongly associated with several clinical, laboratory and radiological characteristics. Symptoms and other variables in early/mild disease appear non-specific and highly heterogeneous. Clinical Trial Registration: PROSPERO CRD42020170623. [ABSTRACT FROM AUTHOR]

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Background: Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health.Objective: The aim of this study was to assess the impact of the use of big data analytics on people's health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2-related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people's health.Methods: Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist.Results: The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. "Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease" and "suicide mortality rate" were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as "critically low" for 25 reviews, as "low" for 7 reviews, and as "moderate" for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data.Conclusions: Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes.Trial Registration: International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048. [ABSTRACT FROM AUTHOR]

Background: To date, there is no comprehensive systematic review and meta-analysis to assess the suitability of COVID-19 vaccines for mass immunization. The current systematic review and meta-analysis was conducted to evaluate the safety and immunogenicity of novel COVID-19 vaccine candidates under clinical trial evaluation and present a contemporary update on the development and implementation of a potential vaccines., Methods: For this study PubMed, MEDLINE, and Embase electronic databases were used to search for eligible studies on the interface between novel coronavirus and vaccine design until December 31, 2020., Results: We have included fourteen non-randomized and randomized controlled phase I-III trials. Implementation of a universal vaccination program with proven safety and efficacy through robust clinical evaluation is the long-term goal for preventing COVID-19. The immunization program must be cost-effective for mass production and accessibility. Despite pioneering techniques for the fast-track development of the vaccine in the current global emergency, mass production and availability of an effective COVID-19 vaccine could take some more time., Conclusion: Our findings suggest a revisiting of the reported solicited and unsolicited systemic adverse events for COVID-19 candidate vaccines. Hence, it is alarming to judiciously expose thousands of participants to COVID-19 candidate vaccines at Phase-3 trials that have adverse events and insufficient evidence on safety and effectiveness that necessitates further justification., Competing Interests: Competing interests There is no conflict of interest for any author of this manuscript., (© 2021 CEA& INEA.)