Energy poverty (EP) is a growing problem in the European Union (EU) that affects the population's health. EP is structurally determined by broader political and socio-economic conditions. Our aims ...were to analyze the configuration of these determinants in each EU-27 country through the creation of a structural energy poverty vulnerability (SEPV) index, to group countries according to their SEPV index scores, and to explore the association between SEPV and EP prevalence, and also with excess winter mortality (EWM). We created a SEPV index through seriated principal component analyses and then validated the index. We performed a hierarchical cluster analysis (HCA) to group countries according to their SEPV. A Poisson regression model was fitted to analyze the association between SEPV and EWM. The final index comprised 13 indicators and showed an unequal distribution of SEPV across the EU. The HCA identified countries with high structural vulnerability (southeastern Europe) and countries with low structural vulnerability (northwestern Europe). The most vulnerable countries showed a statistically significant higher EP prevalence and risk of EWM. The SEPV index summarizes the structural determinants of EP across the EU, allows to identify geographical patterns and to study how the structural determinants of EP affect health.
•There are important structural energy poverty vulnerability inequalities in the EU.•A geographical pattern of structural energy poverty vulnerability was observed.•The most vulnerable countries are located in eastern and southern Europe.•Excess winter mortality risk is higher in countries with greater vulnerability.•Acting on structural determinants of energy poverty can have an impact on health.
The relationship between cancer and coronavirus disease 2019 (COVID‐19) infection and severity remains poorly understood. We conducted a population‐based cohort study between 1 March and 6 May 2020 ...describing the associations between cancer and risk of COVID‐19 diagnosis, hospitalisation and COVID‐19‐related death. Data were obtained from the Information System for Research in Primary Care (SIDIAP) database, including primary care electronic health records from ~80% of the population in Catalonia, Spain. Cancer was defined as any primary invasive malignancy excluding non‐melanoma skin cancer. We estimated adjusted hazard ratios (aHRs) for the risk of COVID‐19 (outpatient) clinical diagnosis, hospitalisation (with or without a prior COVID‐19 diagnosis) and COVID‐19‐related death using Cox proportional hazard regressions. Models were estimated for the overall cancer population and by years since cancer diagnosis (<1 year, 1‐5 years and ≥5 years), sex, age and cancer type; and adjusted for age, sex, smoking status, deprivation and comorbidities. We included 4 618 377 adults, of which 260 667 (5.6%) had a history of cancer. A total of 98 951 individuals (5.5% with cancer) were diagnosed, and 6355 (16.4% with cancer) were directly hospitalised with COVID‐19. Of those diagnosed, 6851 were subsequently hospitalised (10.7% with cancer), and 3227 died without being hospitalised (18.5% with cancer). Among those hospitalised, 1963 (22.5% with cancer) died. Cancer was associated with an increased risk of COVID‐19 diagnosis (aHR: 1.08; 95% confidence interval 1.05‐1.11), direct COVID‐19 hospitalisation (1.33 1.24‐1.43) and death following hospitalisation (1.12 1.01‐1.25). These associations were stronger for patients recently diagnosed with cancer, aged <70 years, and with haematological cancers. These patients should be prioritised in COVID‐19 vaccination campaigns and continued non‐pharmaceutical interventions.
What's new?
Studies addressing associations between cancer and severity of coronavirus disease 2019 (COVID‐19) have focused primarily on hospitalized patients. Findings have been inconsistent, however, owing to varying cancer criteria, lack of representative samples, and other factors. Here, the natural history of COVID‐19 in cancer patients during the first wave of the pandemic in 2020 in Spain was investigated in a large, representative cohort with a heterogenous cancer population. Patients with cancer were at increased risk of severe COVID‐19. Risk was notably high among those over age 70 and those with recent cancer diagnosis, hematological cancer, or lung and bladder cancer.
AbstractObjectiveTo investigate the use of repurposed and adjuvant drugs in patients admitted to hospital with covid-19 across three continents.DesignMultinational network cohort ...study.SettingHospital electronic health records from the United States, Spain, and China, and nationwide claims data from South Korea.Participants303 264 patients admitted to hospital with covid-19 from January 2020 to December 2020.Main outcome measuresPrescriptions or dispensations of any drug on or 30 days after the date of hospital admission for covid-19.ResultsOf the 303 264 patients included, 290 131 were from the US, 7599 from South Korea, 5230 from Spain, and 304 from China. 3455 drugs were identified. Common repurposed drugs were hydroxychloroquine (used in from <5 (<2%) patients in China to 2165 (85.1%) in Spain), azithromycin (from 15 (4.9%) in China to 1473 (57.9%) in Spain), combined lopinavir and ritonavir (from 156 (<2%) in the VA-OMOP US to 2,652 (34.9%) in South Korea and 1285 (50.5%) in Spain), and umifenovir (0% in the US, South Korea, and Spain and 238 (78.3%) in China). Use of adjunctive drugs varied greatly, with the five most used treatments being enoxaparin, fluoroquinolones, ceftriaxone, vitamin D, and corticosteroids. Hydroxychloroquine use increased rapidly from March to April 2020 but declined steeply in May to June and remained low for the rest of the year. The use of dexamethasone and corticosteroids increased steadily during 2020.ConclusionsMultiple drugs were used in the first few months of the covid-19 pandemic, with substantial geographical and temporal variation. Hydroxychloroquine, azithromycin, lopinavir-ritonavir, and umifenovir (in China only) were the most prescribed repurposed drugs. Antithrombotics, antibiotics, H2 receptor antagonists, and corticosteroids were often used as adjunctive treatments. Research is needed on the comparative risk and benefit of these treatments in the management of covid-19.
The relationship between cancer and coronavirus disease 2019 (COVID‐19) infection and severity remains poorly understood. We conducted a population‐based cohort study between 1 March and 6 May 2020 ...describing the associations between cancer and risk of COVID‐19 diagnosis, hospitalisation and COVID‐19‐related death. Data were obtained from the Information System for Research in Primary Care (SIDIAP) database, including primary care electronic health records from ~80% of the population in Catalonia, Spain. Cancer was defined as any primary invasive malignancy excluding non‐melanoma skin cancer. We estimated adjusted hazard ratios (aHRs) for the risk of COVID‐19 (outpatient) clinical diagnosis, hospitalisation (with or without a prior COVID‐19 diagnosis) and COVID‐19‐related death using Cox proportional hazard regressions. Models were estimated for the overall cancer population and by years since cancer diagnosis (<1 year, 1‐5 years and ≥5 years), sex, age and cancer type; and adjusted for age, sex, smoking status, deprivation and comorbidities. We included 4 618 377 adults, of which 260 667 (5.6%) had a history of cancer. A total of 98 951 individuals (5.5% with cancer) were diagnosed, and 6355 (16.4% with cancer) were directly hospitalised with COVID‐19. Of those diagnosed, 6851 were subsequently hospitalised (10.7% with cancer), and 3227 died without being hospitalised (18.5% with cancer). Among those hospitalised, 1963 (22.5% with cancer) died. Cancer was associated with an increased risk of COVID‐19 diagnosis (aHR: 1.08; 95% confidence interval 1.05‐1.11), direct COVID‐19 hospitalisation (1.33 1.24‐1.43) and death following hospitalisation (1.12 1.01‐1.25). These associations were stronger for patients recently diagnosed with cancer, aged <70 years, and with haematological cancers. These patients should be prioritised in COVID‐19 vaccination campaigns and continued non‐pharmaceutical interventions.
The association between air pollution and green spaces with breast cancer risk stratified by menopausal status has not been frequently investigated despite its importance given the different impact ...of risk factors on breast cancer risk depending on menopausal status.
To study the association between air pollution, green spaces and pre and postmenopausal breast cancer risk.
We conducted a population-based cohort study using electronic primary care records in Catalonia. We included women aged 17–85 years free of cancer at study entry between 2009 and 2017. Our exposures were particulate matter <2.5 μm (PM2.5) & <10 μm (PM10), nitrogen dioxide (NO2), normalized difference vegetation index (NDVI), and percentage of green spaces estimated at the census tract level. Breast cancer was identified with ICD-10 code C50. We estimated cause-specific hazard ratios (HR) for the relationship between each individual exposure and pre and postmenopausal breast cancer risk, using linear and non-linear models.
Of the 1,054,180 pre and 744,658 postmenopausal women followed for a median of 10 years, 6,126 and 17,858 developed breast cancer, respectively. Among premenopausal women, only very high levels of PM10 (≥46 μg/m3) were associated with increased cancer risk (compared to lower levels) in non-linear models. Among postmenopausal women, an interquartile range increase in PM2.5 (HR:1.03; 95%CI:1.01–1.04), PM10 (1.03; 1.01–1.05), and NO2 (1.05; 1.02–1.08) were associated with higher cancer risk. NDVI was negatively associated with decreased cancer risk only among postmenopausal women who did not change residence during follow-up (0.84; 0.71–0.99) or who were followed for at least three years (0.82; 0.69–0.98).
Living in areas with high concentrations of PM2.5, PM10, and NO2 increases breast cancer risk in postmenopausal women while long-term exposure to green spaces may decrease this risk. Only very high concentrations of PM10 increase breast cancer risk in premenopausal women.
•High levels of PM and NO2 increase breast cancer risk in postmenopausal women.•Very high concentrations of PM10 increase breast cancer risk in premenopausal women.•Long-term exposure to green spaces may decrease breast cancer risk in older women.•We used survival models with restricted cubic splines to investigate associations.
OBJECTIVES
To characterize the demographics, comorbidities, symptoms, in-hospital treatments, and health outcomes among children and adolescents diagnosed or hospitalized with coronavirus disease ...2019 (COVID-19) and to compare them in secondary analyses with patients diagnosed with previous seasonal influenza in 2017–2018.
METHODS
International network cohort using real-world data from European primary care records (France, Germany, and Spain), South Korean claims and US claims, and hospital databases. We included children and adolescents diagnosed and/or hospitalized with COVID-19 at age <18 between January and June 2020. We described baseline demographics, comorbidities, symptoms, 30-day in-hospital treatments, and outcomes including hospitalization, pneumonia, acute respiratory distress syndrome, multisystem inflammatory syndrome in children, and death.
RESULTS
A total of 242 158 children and adolescents diagnosed and 9769 hospitalized with COVID-19 and 2 084 180 diagnosed with influenza were studied. Comorbidities including neurodevelopmental disorders, heart disease, and cancer were more common among those hospitalized with versus diagnosed with COVID-19. Dyspnea, bronchiolitis, anosmia, and gastrointestinal symptoms were more common in COVID-19 than influenza. In-hospital prevalent treatments for COVID-19 included repurposed medications (<10%) and adjunctive therapies: systemic corticosteroids (6.8%–7.6%), famotidine (9.0%–28.1%), and antithrombotics such as aspirin (2.0%–21.4%), heparin (2.2%–18.1%), and enoxaparin (2.8%–14.8%). Hospitalization was observed in 0.3% to 1.3% of the cohort diagnosed with COVID-19, with undetectable (n < 5 per database) 30-day fatality. Thirty-day outcomes including pneumonia and hypoxemia were more frequent in COVID-19 than influenza.
CONCLUSIONS
Despite negligible fatality, complications including hospitalization, hypoxemia, and pneumonia were more frequent in children and adolescents with COVID-19 than with influenza. Dyspnea, anosmia, and gastrointestinal symptoms could help differentiate diagnoses. A wide range of medications was used for the inpatient management of pediatric COVID-19.
Abstract
Objective
Patients with autoimmune diseases were advised to shield to avoid coronavirus disease 2019 (COVID-19), but information on their prognosis is lacking. We characterized 30-day ...outcomes and mortality after hospitalization with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza.
Methods
A multinational network cohort study was conducted using electronic health records data from Columbia University Irving Medical Center USA, Optum (USA), Department of Veterans Affairs (USA), Information System for Research in Primary Care-Hospitalization Linked Data (Spain) and claims data from IQVIA Open Claims (USA) and Health Insurance and Review Assessment (South Korea). All patients with prevalent autoimmune diseases, diagnosed and/or hospitalized between January and June 2020 with COVID-19, and similar patients hospitalized with influenza in 2017–18 were included. Outcomes were death and complications within 30 days of hospitalization.
Results
We studied 133 589 patients diagnosed and 48 418 hospitalized with COVID-19 with prevalent autoimmune diseases. Most patients were female, aged ≥50 years with previous comorbidities. The prevalence of hypertension (45.5–93.2%), chronic kidney disease (14.0–52.7%) and heart disease (29.0–83.8%) was higher in hospitalized vs diagnosed patients with COVID-19. Compared with 70 660 hospitalized with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2–4.3% vs 6.32–24.6%).
Conclusion
Compared with influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality.
Electronic health records are becoming an increasingly valuable resource for epidemiology but their data quality needs to be quantified. We aimed to validate twenty-five types of incident cancer ...cases in the Information System for Research in Primary Care (SIDIAP) in Catalonia with the population-based cancer registries of Girona and Tarragona as the gold-standard.
We calculated the sensitivity, positive predictive values (PPV), and the time-difference between the date of diagnosis entered into the SIDIAP and into the registries. We added hospital discharge cancer diagnoses to the SIDIAP to assess sensitivity changes.
We identified 27,046 incident cancer diagnoses in the SIDIAP from 2009-2015 among the 949,841 residents of Girona and Tarragona. The cancer types with the highest sensitivity were breast (89%, 95% CI: 88-90%), colorectal (81%, 95% CI: 80-82%), and prostate (81%, 95% CI: 80-83%). Trachea, bronchus and lung cancers had the highest PPV (76%, 95% CI: 74%-78%) followed by stomach (72%, 95% CI: 68-75%) and pancreas (71%, 95% CI: 67-75%). Most cancer diagnoses were reported with less than three months of difference between the SIDIAP and the registries. More cases were registered first in the registries than in the SIDIAP. By adding cancer diagnoses based on hospital discharge data, sensitivity increased for all cancers, especially for gallbladder and biliary tract for which the sensitivity increased by 21%.
The SIDIAP includes 76% of the cancer diagnoses in the cancer registries but includes a considerable number of cases that are not in the registries. The SIDIAP reports most of the cancer diagnoses within a three-month period difference from the date of diagnosis in the cancer registries. Our results support the use of the SIDIAP cancer diagnoses for epidemiological research when cancer is the outcome of interest. We recommend adding hospital discharge data to the SIDIAP to increase data quality, particularly for less frequent cancer types.
Purpose: The primary aim of this work was to convert the Information System for Research in Primary Care (SIDIAP) from Catalonia, Spain, to the Observational Medical Outcomes Partnership (OMOP) ...Common Data Model (CDM). Our second aim was to provide a descriptive analysis of COVID-19-related outcomes among the general population. Patients and Methods: We mapped patient-level data from SIDIAP to the OMOP CDM and we performed more than 3,400 data quality checks to assess its readiness for research. We established a general population cohort as of the 1 st March 2020 and identified outpatient COVID-19 diagnoses or tested positive for, hospitalised with, admitted to intensive care units (ICU) with, died with, or vaccinated against COVID-19 up to 30th June 2022. Results: After verifying the high quality of the transformed dataset, we included 5,870,274 individuals in the general population cohort. Of those, 604,472 had either an outpatient COVID-19 diagnosis or positive test result, 58,991 had a hospitalisation, 5,642 had an ICU admission, and 11,233 died with COVID-19. A total of 4,584,515 received a COVID-19 vaccine. People who were hospitalised or died were more commonly older, male, and with more comorbidities. Those admitted to ICU with COVID-19 were generally younger and more often male than those hospitalised and those who died. Conclusion: We successfully transformed SIDIAP to the OMOP CDM. From this dataset, a general population cohort of 5.9 million individuals was identified and their COVID-19-related outcomes over time were described. The transformed SIDIAP database is a valuable resource that can enable distributed network research in COVID-19 and beyond. Keywords: electronic health records, medical ontologies, secondary data use, common data model, OMOP
Purpose: The primary aim of this work was to convert the Information System for Research in Primary Care (SIDIAP) from Catalonia, Spain, to the Observational Medical Outcomes Partnership (OMOP) ...Common Data Model (CDM). Our second aim was to provide a descriptive analysis of COVID-19-related outcomes among the general population. Patients and Methods: We mapped patient-level data from SIDIAP to the OMOP CDM and we performed more than 3,400 data quality checks to assess its readiness for research. We established a general population cohort as of the 1 st March 2020 and identified outpatient COVID-19 diagnoses or tested positive for, hospitalised with, admitted to intensive care units (ICU) with, died with, or vaccinated against COVID-19 up to 30th June 2022. Results: After verifying the high quality of the transformed dataset, we included 5,870,274 individuals in the general population cohort. Of those, 604,472 had either an outpatient COVID-19 diagnosis or positive test result, 58,991 had a hospitalisation, 5,642 had an ICU admission, and 11,233 died with COVID-19. A total of 4,584,515 received a COVID-19 vaccine. People who were hospitalised or died were more commonly older, male, and with more comorbidities. Those admitted to ICU with COVID-19 were generally younger and more often male than those hospitalised and those who died. Conclusion: We successfully transformed SIDIAP to the OMOP CDM. From this dataset, a general population cohort of 5.9 million individuals was identified and their COVID-19-related outcomes over time were described. The transformed SIDIAP database is a valuable resource that can enable distributed network research in COVID-19 and beyond. Keywords: electronic health records, medical ontologies, secondary data use, common data model, OMOP
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