The natural history of coronavirus disease 2019 (COVID-19) has yet to be fully described. Here, we use patient-level data from the Information System for Research in Primary Care (SIDIAP) to ...summarise COVID-19 outcomes in Catalonia, Spain. We included 5,586,521 individuals from the general population. Of these, 102,002 had an outpatient diagnosis of COVID-19, 16,901 were hospitalised with COVID-19, and 5273 died after either being diagnosed or hospitalised with COVID-19 between 1st March and 6th May 2020. Older age, being male, and having comorbidities were all generally associated with worse outcomes. These findings demonstrate the continued need to protect those at high risk of poor outcomes, particularly older people, from COVID-19 and provide appropriate care for those who develop symptomatic disease. While risks of hospitalisation and death were lower for younger populations, there is a need to limit their role in community transmission.
A high body mass index (BMI) has been associated with increased risk of several cancers; however, whether BMI is related to a larger number of cancers than currently recognized is unclear. Moreover, ...whether waist circumference (WC) is more strongly associated with specific cancers than BMI is not well established. We aimed to investigate the associations between BMI and 26 cancers accounting for non-linearity and residual confounding by smoking status as well as to compare cancer risk estimates between BMI and WC.
Prospective cohort study with population-based electronic health records from Catalonia, Spain. We included 3,658,417 adults aged ≥ 18 years and free of cancer at baseline between 2006 and 2017. Our main outcome measures were cause-specific hazard ratios (HRs) with 99% confidence intervals (CIs) for incident cancer at 26 anatomical sites.
After a median follow-up time of 8.3 years, 202,837 participants were diagnosed with cancer. A higher BMI was positively associated with risk of nine cancers (corpus uteri, kidney, gallbladder, thyroid, colorectal, breast post-menopausal, multiple myeloma, leukemia, non-Hodgkin lymphoma) and was positively associated with three additional cancers among never smokers (head and neck, brain and central nervous system, Hodgkin lymphoma). The respective HRs (per 5 kg/m
increment) ranged from 1.04 (99%CI 1.01 to 1.08) for non-Hodgkin lymphoma to 1.49 (1.45 to 1.53) for corpus uteri cancer. While BMI was negatively associated to five cancer types in the linear analyses of the overall population, accounting for non-linearity revealed that BMI was associated to prostate cancer in a U-shaped manner and to head and neck, esophagus, larynx, and trachea, bronchus and lung cancers in an L-shaped fashion, suggesting that low BMIs are an approximation of heavy smoking. Of the 291,305 participants with a WC measurement, 27,837 were diagnosed with cancer. The 99%CIs of the BMI and WC point estimates (per 1 standard deviation increment) overlapped for all cancers.
In this large Southern European study, a higher BMI was associated with increased risk of twelve cancers, including four hematological and head and neck (only among never smokers) cancers. Furthermore, BMI and WC showed comparable estimates of cancer risk associated with adiposity.
Metabolic syndrome (MS) is the simultaneous occurrence of a cluster of predefined cardiovascular risk factors. Although individual MS components are associated with increased risk of cancer, it is ...still unclear whether the association between MS and cancer differs from the association between individual MS components and cancer. The aim of this matched case-control study was to estimate the association of 13 types of cancer with (1) MS and (2) the diagnosis of 0, 1 or 2 individual MS components.
Cases included 183,248 patients ≥40 years from the SIDIAP database with incident cancer diagnosed between January 2008-December 2017. Each case was matched to four controls by inclusion date, sex and age. Adjusted conditional logistic regression models were used to evaluate the association between MS and cancer risk, comparing the effect of global MS versus having one or two individual components of MS.
MS was associated with an increased risk of the following cancers: colorectal (OR: 1.28, 95%CI: 1.23-1.32), liver (OR: 1.93, 95%CI: 1.74-2.14), pancreas (OR: 1.79, 95%CI: 1.63-1.98), post-menopausal breast (OR: 1.10, 95%CI: 1.06-1.15), pre-menopausal endometrial (OR: 2.14, 95%CI: 1.74-2.65), post-menopausal endometrial (OR: 2.46, 95%CI: 2.20-2.74), bladder (OR: 1.41, 95%CI: 1.34-1.48), kidney (OR: 1.84, 95%CI: 1.69-2.00), non-Hodgkin lymphoma (OR: 1.23, 95%CI: 1.10-1.38), leukaemia (OR: 1.42, 95%CI: 1.31-1.54), lung (OR: 1.11, 95%CI: 1.05-1.16) and thyroid (OR: 1.71, 95%CI: 1.50-1.95). Except for prostate, pre-menopause breast cancer and Hodgkin and non-Hodgkin lymphoma, MS is associated with a higher risk of cancer than 1 or 2 individual MS components. Estimates were significantly higher in men than in women for colorectal and lung cancer, and in smokers than in non-smokers for lung cancer.
MS is associated with a higher risk of developing 11 types of common cancer, with a positive correlation between number of MS components and risk of cancer.
Single body mass index (BMI) measurements have been associated with increased risk of 13 cancers. Whether life course adiposity-related exposures are more relevant cancer risk factors than baseline ...BMI (ie, at start of follow-up for disease outcome) remains unclear. We conducted a cohort study from 2009 until 2018 with population-based electronic health records in Catalonia, Spain. We included 2,645,885 individuals aged ≥40 years and free of cancer in 2009. After 9 years of follow-up, 225,396 participants were diagnosed with cancer. This study shows that longer duration, greater degree, and younger age of onset of overweight and obesity during early adulthood are positively associated with risk of 18 cancers, including leukemia, non-Hodgkin lymphoma, and among never-smokers, head and neck, and bladder cancers which are not yet considered as obesity-related cancers in the literature. Our findings support public health strategies for cancer prevention focussing on preventing and reducing early overweight and obesity.
A comprehensive understanding of the association between body mass index (BMI) and coronavirus disease 2019 (COVID-19) is still lacking.
To investigate associations between BMI and risk of COVID-19 ...diagnosis, hospitalization with COVID-19, and death after a COVID-19 diagnosis or hospitalization (subsequent death), accounting for potential effect modification by age and sex.
Population-based cohort study.
Primary care records covering >80% of the Catalan population, linked to regionwide testing, hospital, and mortality records from March to May 2020.
Adults (≥18 years) with at least 1 measurement of weight and height.
Hazard ratios (HR) for each outcome.
We included 2 524 926 participants. After 67 days of follow-up, 57 443 individuals were diagnosed with COVID-19, 10 862 were hospitalized with COVID-19, and 2467 had a subsequent death. BMI was positively associated with being diagnosed and hospitalized with COVID-19. Compared to a BMI of 22 kg/m2, the HR (95% CI) of a BMI of 31 kg/m2 was 1.22 (1.19-1.24) for diagnosis and 1.88 (1.75-2.03) and 2.01 (1.86-2.18) for hospitalization without and with a prior outpatient diagnosis, respectively. The association between BMI and subsequent death was J-shaped, with a modestly higher risk of death among individuals with BMIs ≤ 19 kg/m2 and a more pronounced increasing risk for BMIs ≥ 40 kg/m2. The increase in risk for COVID-19 outcomes was particularly pronounced among younger patients.
There is a monotonic association between BMI and COVID-19 diagnosis and hospitalization risks but a J-shaped relationship with mortality. More research is needed to unravel the mechanisms underlying these relationships.
ObjectiveWe aimed to estimate how longitudinal trends in cardiovascular disease, hypertension and type 2 diabetes mellitus incidence in Catalonia, Spain from 2009 to 2018 may differ by age, sex and ...socioeconomic deprivation.DesignCohort study using prospectively collected data.SettingElectronic health records from primary healthcare centres in Catalonia, Spain.Participants3 247 244 adults (≥40 years).Outcome measuresWe calculated the annual incidence (per 1000 persons-year) and incidence rate ratios (IRR) between three time periods of cardiovascular disease, hypertension and type 2 diabetes mellitus to measure trends and changes in incidence during the study period.ResultsIn 2016–2018 compared with 2009–2012, cardiovascular disease incidence increased in the 40–54 (eg, IRR=1.61, 95% CI: 1.52 to 1.69 in women) and 55–69 age groups. There was no change in cardiovascular disease incidence in women aged 70+ years, and a slight decrease in men aged 70+ years (0.93, 0.90 to 0.95). Hypertension incidence decreased in all age groups for both sexes. Type 2 diabetes mellitus incidence decreased in all age groups for both sexes (eg, 0.72, 0.70 to 0.73 in women aged 55–69 years), except for the 40–54 year age group (eg, 1.09, 1.06 to 1.13 in women). Higher incidence levels were found in the most deprived areas, especially in the 40–54 and 55–69 groups.ConclusionsOverall cardiovascular disease incidence has increased while hypertension and type 2 diabetes mellitus incidence have decreased in the last years in Catalonia, Spain, with differences in trends by age group and socioeconomic deprivation.
A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 ...patients living with obesity (PLWO) to those of patients living without obesity.
We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status.
We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8-40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0-33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity.
We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies.
Background
We investigated the association between body mass index (BMI) and obesity‐related cancer risk among individuals with/without incident hypertension (HTN), type 2 diabetes mellitus (T2DM), ...and cardiovascular disease (CVD) and the joint associations of overweight/obesity (BMI ≥25 kg/m2) and each cardiometabolic condition with obesity‐related cancer risk
Methods
We conducted a population‐based cohort (n = 1,774,904 individuals aged ≥40 years and free of cancer and cardiometabolic conditions at baseline) study between 2010 and 2018 with electronic health records from Spain. Our main outcome measures were hazard ratios (HRs) for incident obesity‐related cancers and relative excess risk due to interaction (RERI).
Results
A total of 38,082 individuals developed obesity‐related cancers after a median of 8 years of follow‐up. The positive association between BMI and obesity‐related cancer risk was similar among individuals free of cardiometabolic conditions (hazard ratio, HR per 5 kg/m2: 1.08, 95% confidence interval, CI: 1.06–1.10) and with incident HTN (1.05, 1.01–1.08). The association among those with incident T2DM was null (0.98, 0.93–1.03). There was a positive additive interaction between overweight/obesity and CVD (relative excess risk due to interaction RERI: 0.19 0.09, 0.30), meaning that the combined association was 0.19 more than the sum of the individual associations. In contrast, a RERI of −0.24 (−0.28, −0.20) was observed for the combined association between overweight/obesity and T2DM.
Conclusions
Public health strategies to reduce overweight can help prevent cancer cases among the general population and individuals with incident HTN/CVD. Further, weight‐loss interventions seem to lead to a greater cancer risk reduction among individuals with CVD.
Disease trajectories as indicated by red arrows were investigated for the associations between BMI and obesity‐related cancer risk. A BMI increment of 5 kg/m2 in multivariable‐adjusted models was associated with an 8% (HR: 1.08, 95% CI: 1.06‐1.10) higher relative risk of obesity‐related cancers among “healthy” individuals and a 5% higher relative risk among those with HTN (HR: 1.05, 95%CI: 1.01‐1.08). The HRs for the remaining transitions were as follows: CVD (1.08, 0.97‐1.21), HTN, T2DM, & CVD (1.05, 0.82‐1.33), HTN & CVD (1.03, 0.92‐1.15), T2DM & CVD (1.02, 0.84‐1.24), HTN & T2DM (1.00, 0.93‐1.07) and T2DM (0.98, 0.93‐1.03) (in descending order of effect size).
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.
PDF
