Excess Mortality Calculations to Assess the Impact of the COVID-19 Pandemic: Concepts and Methodological Issues.

We discuss some intriguing methodological aspects of excess mortality analyses, which have been widely used to describe the impact of the COVID-19 pandemic. We describe the main ways of presenting excess mortality: as a mortality rate (incidence rate) or as a percentage increase (relative risk or rate ratio). We discuss what should be regarded as the null value of excess mortality (i.e., when countries or regions can be judged as having fared equally well) and when age and sex standardization, adjustment for other determinants of the spread of a pandemic, or both is necessary. We discuss the level of detail by time and place and person that may be necessary. We note that an excess mortality comparison is essentially a difference-in-differences analysis. We conclude that, although one cannot rule out using excess mortality analyses for causal effect estimates, such analyses will remain most fruitful for generating hypotheses about both the efficiency of measures to curtail the pandemic and factors that cannot be influenced. Nevertheless, a judicious use of arguments and counterarguments can then lead to identifying best practices for various situations. (Am J Public Health. 2024;114(6):593-598. https://doi.org/10.2105/AJPH.2024.307572).

Correction: Migraine and risk of premature myocardial infarction and stroke among men and women: A Danish population-based cohort study.

[This corrects the article DOI: 10.1371/journal.pmed.1004238.].

Report on the Joint Workshop on the Relations between Health Inequalities, Ageing and Multimorbidity, Iceland, May 3-4, 2023.

This paper is a summary of key presentations from a workshop in Iceland on May 3-4, 2023 arranged by Aarhus University and with participation of the below-mentioned scientists. Below you will find the key messages from the presentations made by: Professor Jan Vandenbroucke, Department of Clinical Epidemiology, Aarhus University, Emeritus Professor, Leiden University; Honorary Professor, London School of Hygiene & Tropical Medicine, UKProfessor, Chair Henrik Toft Sørensen, Department of Clinical Epidemiology, Aarhus University and Aarhus University Hospital, DenmarkProfessor David H. Rehkopf, Director, the Stanford Center for Population Health Sciences, Stanford University, CA., USProfessor Jaimie Gradus, Department of Epidemiology, School of Public Health, Boston University, Boston, Massachusetts, USProfessor Johan Mackenbach, Emeritus Professor, Department of Public Health, Erasmus University Rotterdam, HollandProfessor, Chair M Maria Glymour, Department of Epidemiology, Boston University School of Public Health, Boston University, Boston, Massachusetts, USProfessor, Dean Sandro Galea, School of Public Health, Boston University, Boston, Massachusetts, USProfessor Victor W. Henderson, Departments of Epidemiology & Population Health and of Neurology & Neurological Sciences, Stanford University, Stanford, CA, US; Department of Clinical Epidemiology, Aarhus University, Aarhus, DK.

Risk factors for SARS-CoV-2 infection: a test-negative case-control study with additional population controls in Norway.

OBJECTIVES: This study aims to assess risk factors for SARS-CoV-2 infection by combined design; first comparing positive cases to negative controls as determined by PCR testing and then comparing these two groups to an additional prepandemic population control group. DESIGN AND SETTING: Test-negative design (TND), multicentre case-control study with additional population controls in South-Eastern Norway. PARTICIPANTS: Adults who underwent SARS-CoV-2 PCR testing between February and December 2020. PCR-positive cases, PCR-negative controls and additional age-matched population controls. PRIMARY OUTCOME MEASURES: The associations between various risk factors based on self- reported questionnaire and SARS-CoV-2 infection comparing PCR-positive cases and PCR-negative controls. Using subgroup analysis, the risk factors for both PCR-positive and PCR-negative participants were compared with a population control group. RESULTS: In total, 400 PCR-positive cases, 719 PCR-negative controls and 14 509 population controls were included. Male sex was associated with the risk of SARS-CoV-2 infection only in the TND study (OR 1.9, 95% CI 1.4 to 2.6), but not when PCR-positive cases were compared with population controls (OR 1.2, 95% CI 0.9. to 1.5). Some factors were positively (asthma, wood heating) or negatively (hypertension) associated with SARS-CoV-2 infection when PCR-positive cases were compared with population controls, but lacked convincing association in the TND study. Smoking was negatively associated with the risk of SARS-CoV-2 infection in both analyses (OR 0.5, 95% CI 0.3 to 0.8 and OR 0.6, 95% CI 0.4 to 0.8). CONCLUSIONS: Male sex was a possible risk factor for SARS-CoV-2 infection only in the TND study, whereas smoking was negatively associated with SARS-CoV-2 infection in both the TND study and when using population controls. Several factors were associated with SARS-CoV-2 infection when PCR-positive cases were compared with population controls, but not in the TND study, highlighting the strength of combining case-control study designs during the pandemic.

Combined Impact of Migraine and Pregnancy-Induced Hypertension on Long-term Risk of Premature Myocardial Infarction and Stroke.

BACKGROUND AND OBJECTIVES: Migraine and pregnancy-induced hypertension (PIH) are known to increase cardiovascular risk on their own. However, evidence is limited on the combined impact of migraine and PIH on risk of cardiovascular disease. The aim of this study was to examine the combined impact of migraine and PIH on risk of premature (age 60 years and younger) major adverse cardiovascular and cerebrovascular events (MACCE), a composite end point consisting of myocardial infarction, stroke, or death due to one of these diseases. METHODS: We conducted a population-based cohort study in Denmark (1996-2018) among women who had delivered at least one child. This population was stratified into 4 cohorts: women with neither migraine nor PIH, women with migraine, women with PIH, and women with both migraine and PIH. As a measure of absolute risk, we computed the 20-year cumulative incidence of premature MACCE, treating death by other causes than myocardial infarction and stroke as a competing risk. We used Cox regression to compute 20-year adjusted hazard ratios (HRs) of premature MACCE. Women with neither migraine nor PIH served as the comparison cohort. RESULTS: The 20-year absolute risk of premature MACCE was 1.3% (95% CI 1.2%; 1.3%) for women without migraine and without PIH (n = 1,288,541), 2.2% (95% CI 2.0%; 2.4%) for women with migraine (n = 54,827), 2.8% (95% CI 2.6%; 3.1%) for women with PIH (n = 49,008), and 3.1% (95% CI 2.1%; 4.4%) for women with both migraine and PIH (n = 3,140). The adjusted HR of premature MACCE was 1.66 (95% confidence interval [CI] 1.50-1.84) for women with migraine, 2.76 (95% CI 2.52-3.03) for women with PIH, and 2.41 (95% CI 1.61-3.61) for women with both migraine and PIH. DISCUSSION: Migraine and PIH separately increased the risk of premature MACCE. The risk of premature MACCE among women who had both migraine and PIH was similar to that among women with PIH only.

Migraine and risk of premature myocardial infarction and stroke among men and women: A Danish population-based cohort study.

BACKGROUND: Migraine carries risk of myocardial infarction (MI) and stroke. The risk of premature MI (i.e., among young adults) and stroke differs between men and women; previous studies indicate that migraine is mainly associated with an increased risk of stroke among young women. The aim of this study was to examine impact of migraine on the risk of premature (age ≤60 years) MI and ischemic/hemorrhagic stroke among men and women. METHODS AND FINDINGS: Using Danish medical registries, we conducted a nationwide population-based cohort study (1996 to 2018). Redeemed prescriptions for migraine-specific medication were used to identify women with migraine (n = 179,680) and men with migraine (n = 40,757). These individuals were matched on sex, index year, and birth year 1:5 with a random sample of the general population who did not use migraine-specific medication. All individuals were required to be between 18 and 60 years old. Median age was 41.5 years for women and 40.3 years for men. The main outcome measures to assess impact of migraine were absolute risk differences (RDs) and hazard ratios (HRs) with 95% confidence intervals (CIs) of premature MI, ischemic, and hemorrhagic stroke, comparing individuals with migraine to migraine-free individuals of the same sex. HRs were adjusted for age, index year, and comorbidities. The RD of premature MI for those with migraine versus no migraine was 0.3% (95% CI [0.2%, 0.4%]; p < 0.001) for women and 0.3% (95% CI [-0.1%, 0.6%]; p = 0.061) for men. The adjusted HR was 1.22 (95% CI [1.14, 1.31]; p < 0.001) for women and 1.07 (95% CI [0.97, 1.17]; p = 0.164) for men. The RD of premature ischemic stroke for migraine versus no migraine was 0.3% (95% CI [0.2%, 0.4%]; p < 0.001) for women and 0.5% (95% CI [0.1%, 0.8%]; p < 0.001) for men. The adjusted HR was 1.21 (95% CI [1.13, 1.30]; p < 0.001) for women and 1.23 (95% CI [1.10, 1.38]; p < 0.001) for men. The RD of premature hemorrhagic stroke for migraine versus no migraine was 0.1% (95% CI [0.0%, 0.2%]; p = 0.011) for women and -0.1% (95% CI [-0.3%, 0.0%]; p = 0.176) for men. The adjusted HR was 1.13 (95% CI [1.02, 1.24]; p = 0.014) for women and 0.85 (95% CI [0.69, 1.05]; p = 0.131) for men. The main limitation of this study was the risk of misclassification of migraine, which could lead to underestimation of the impact of migraine on each outcome. CONCLUSIONS: In this study, we observed that migraine was associated with similarly increased risk of premature ischemic stroke among men and women. For premature MI and hemorrhagic stroke, there may be an increased risk associated with migraine only among women.

Thromboembolic events, bleeding, and mortality in patients with cerebral venous thrombosis: a nationwide cohort study.

Cerebral venous thrombosis (CVT) predominantly affects young to middle-aged women. Scarce data exist regarding the long-term prognosis. We examined the clinical course of patients with CVT overall and according to their age and sex. Using Danish registries, we identified all patients with a first-time primary inpatient diagnosis of CVT from 1996-2018 (N = 653; median age, 41 years; 67% women) and individuals from the general population matched for age, sex, and calendar year (N = 65 300). Patients with CVT were at an increased risk of venous thromboembolism (VTE) at other sites, ischemic stroke, major bleeding, and mortality. For both sexes, the increased risks of VTE at other sites were most prominent among younger patients (18-54 years), whereas the increased risks of ischemic stroke, major bleeding, and mortality were most prominent among older patients (≥55 years). Among young women, the 10-year risks of VTE at other sites for patients with CVT compared with members of the matched cohort were 2.2% vs 0.4% (risk difference, 1.8%; 95% confidence interval [CI], 0.0-3.6). Among older women, compared with members of the matched cohort, the 10-year risks were 12.8% vs 3.1% (risk difference, 9.7%; 95% CI, 1.6-17.9) for ischemic stroke, 11.1% vs 4.6% (risk difference, 6.5%; 95% CI, -1.0 to 14.1) for major bleeding, and 43.1% vs 26.7% (risk difference, 16.4%; 95% CI, 3.7-29.1) for all-cause mortality. The risk of myocardial infarction was not elevated. Clinicians should be aware of the importance of age and sex heterogeneity in the prognosis of CVT.

SARS-CoV-2 antibody persistence after five and twelve months: A cohort study from South-Eastern Norway.

OBJECTIVES: To assess total antibody levels against Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS CoV-2) spike protein up to 12 months after Coronavirus Disease (COVID-19) infection in non-vaccinated individuals and the possible predictors of antibody persistence. METHODS: This is the first part of a prospective multi-centre cohort study. PARTICIPANTS: The study included SARS-CoV-2 real-time polymerase chain reaction (RT-PCR) positive and negative participants in South-Eastern Norway from February to December 2020. Possible predictors of SARS-CoV-2 total antibody persistence was assessed. The SARS-CoV-2 total antibody levels against spike protein were measured three to five months after PCR in 391 PCR-positive and 703 PCR-negative participants; 212 PCR-positive participants were included in follow-up measurements at 10 to 12 months. The participants completed a questionnaire including information about symptoms, comorbidities, allergies, body mass index (BMI), and hospitalisation. PRIMARY OUTCOME: The SARS-CoV-2 total antibody levels against spike protein three to five and 10 to 12 months after PCR positive tests. RESULTS: SARS-CoV-2 total antibodies against spike protein were present in 366 (94%) non-vaccinated PCR-positive participants after three to five months, compared with nine (1%) PCR-negative participants. After 10 to 12 months, antibodies were present in 204 (96%) non-vaccinated PCR-positive participants. Of the PCR-positive participants, 369 (94%) were not hospitalised. The mean age of the PCR-positive participants was 48 years (SD 15, range 20-85) and 50% of them were male. BMI ≥ 25 kg/m2 was positively associated with decreased antibody levels (OR 2.34, 95% CI 1.06 to 5.42). Participants with higher age and self-reported initial fever with chills or sweating were less likely to have decreased antibody levels (age: OR 0.97, 95% CI 0.94 to 0.99; fever: OR 0.33, 95% CI 0.13 to 0.75). CONCLUSION: Our results indicate that the level of SARS-CoV-2 total antibodies against spike protein persists for the vast majority of non-vaccinated PCR-positive persons at least 10 to 12 months after mild COVID-19.

Tipping Points - Do the Prognostic Values of Multimorbidity and Functional Status Vary with Age?

Aging of the population is a pressing challenge for healthcare systems and knowledge of a patient's prognosis is a key to shaping effective interventions. As the prevalence of multimorbidity strongly increases with age, the prognostic value of multiple disease diagnoses for survival among older people may diminish, whereas other measures of health, such as functional status (defined as a measure of an individual's ability to perform activities of daily living), may become more important. In this commentary, the impact of age on the prognostic value of multimorbidity is discussed, with the aim of identifying relevant alternative risk indicators for different age groups. The key question is to determine at what age the prognostic value of multimorbidity for meaningful clinical outcomes decreases and is overridden by the prognostic value of functional status. This tipping point likely depends on age, calendar time, and birth cohort. The public health and clinical implications of these tipping points are important. Among younger and middle-aged persons, interventions could be directed towards prevention and treatment of specific diseases, while among older persons efforts should focus more on improving functional levels that include physical, emotional, and social dimensions.

[Covid-19: excess deaths should be the outcome measure]. / Covid-19: oversterfte is de maat.

During the covid-19 pandemic there were large differences in excess deaths between high income countries, as shown in a study recently published in BMJ. The number of excess deaths gives a better estimate of the effects of covid-19 on mortality than the number of reported deaths from covid-19. This comment explains why and shows the consequences for the interpretation of Dutch mortality data. Differences in excess deaths are an important starting point for the evaluation of public health measures against coronavirus spreading.

Arguments about face masks and Covid-19 reflect broader methodologic debates within medical science.

There has perhaps been no issue as contentious in Covid-19 as face masks. The most contentious scientific debate has been between those who argue that "there is no scientific evidence", by which they mean that there are no randomized controlled trials (RCTs), versus those who argue that when the evidence is considered together, "the science supports that face coverings save lives". It used to be a 'given' that to decide whether a particular factor, either exogenous or endogenous, can cause a particular disease, and in what order of magnitude, one should consider all reasonably cogent evidence. This approach is being increasingly challenged, both scientifically and politically. The scientific challenge has come from methodologic views that focus on the randomized controlled trial (RCT) as the scientific gold standard, with priority being given, either to evidence from RCTs or to observational studies which closely mimic RCTs. The political challenge has come from various interests calling for the exclusion of epidemiological evidence from consideration by regulatory and advisory committees.

Does death from Covid-19 arise from a multi-step process?

The Covid-19 death rate increases exponentially with age, and the main risk factors are having underlying conditions such as hypertension, diabetes, cardiovascular disease, severe chronic respiratory disease and cancer. These characteristics are consistent with the multi-step model of disease. We applied this model to Covid-19 case fatality rates (CFRs) from China, South Korea, Italy, Spain and Japan. In all countries we found that a plot of log(CFR) against log(age) was approximately linear with a slope of about 5. We also conducted similar analyses for selected other respiratory diseases. SARS showed a similar log-log age-pattern to that of Covid-19, albeit with a lower slope, whereas seasonal and pandemic influenza showed quite different age-patterns. Thus, death from Covid-19 and SARS appears to follow a distinct age-pattern, consistent with a multi-step model of disease that in the case of Covid-19 is probably defined by comorbidities and age producing immune-related susceptibility.

Community-Acquired Escherichia coli Bacteremia after Age 50 and Subsequent Incidence of a Cancer Diagnosis: A Danish Population-Based Cohort Study.

BACKGROUND: Community-acquired bacteremia (CAB) with Escherichia coli may signal occult cancer. This might differ between phylogenetic groups. METHODS: We conducted a population-based cohort study in northern Denmark (1994-2013) to examine whether E. coli CAB after age 50 is associated with incident cancer. We followed patients from their bacteremia diagnosis date to identify subsequent gastrointestinal, hepatobiliary, and urinary tract cancer diagnoses. We calculated 1- and 5-year cumulative cancer incidence. We compared the observed incidence with that expected based on national cancer incidence rates, and computed standardized incidence ratios (SIR) at 0-<1 year and ≥1 year. In a subcohort, we assessed the prevalence of phylogenetic groups. RESULTS: Among 2,735 patients with E. coli CAB, 173 later were diagnosed with cancer. The 1-year cumulative incidence of a gastrointestinal or hepatobiliary tract cancer was 1.9%, and the 0-<1-year SIR was 5.44 [95% confidence interval (CI), 4.06-7.14]. For urinary tract cancer, the corresponding estimates were 1.0% and 3.41 (95% CI, 2.27-4.93). All individual cancers occurred more often than expected during the first year following E. coli CAB, but thereafter the relative risks declined toward unity. Still, the ≥1-year SIR for colorectal cancer remained 1.4-fold elevated, and the SIR for liver, pancreas, gallbladder, and biliary tract cancer was 2-fold elevated. The prevalence of phylogenetic groups was similar among patients with and without cancer. CONCLUSIONS: Gastrointestinal, hepatobiliary, and urinary tract cancer may debut with E. coli CAB. IMPACT: Owing to the high incidence of E. coli bacteremia, cancers missed at the time of bacteremia diagnosis represent a clinically significant problem.

A Test-Negative Design with Additional Population Controls Can Be Used to Rapidly Study Causes of the SARS-CoV-2 Epidemic.

Testing of symptomatic persons for infection with severe acute respiratory syndrome coronavirus-2 is occurring worldwide. We propose two types of case-control studies that can be carried out jointly in test settings for symptomatic persons. The first, the test-negative case-control design (TND) is the easiest to implement; it only requires collecting information about potential risk factors for Coronavirus Disease 2019 (COVID-19) from the tested symptomatic persons. The second, standard case-control studies with population controls, requires the collection of data on one or more population controls for each person who is tested in the test facilities, so that test-positives and test-negatives can each be compared with population controls. The TND will detect differences in risk factors between symptomatic persons who have COVID-19 (test-positives) and those who have other respiratory infections (test-negatives). However, risk factors with effect sizes of equal magnitude for both COVID-19 and other respiratory infections will not be identified by the TND. Therefore, we discuss how to add population controls to compare with the test-positives and the test-negatives, yielding two additional case-control studies. We describe two options for population control groups: one composed of accompanying persons to the test facilities, the other drawn from existing country-wide healthcare databases. We also describe other possibilities for population controls. Combining the TND with population controls yields a triangulation approach that distinguishes between exposures that are risk factors for both COVID-19 and other respiratory infections, and exposures that are risk factors for just COVID-19. This combined design can be applied to future epidemics, but also to study causes of nonepidemic disease.

Educational note: types of causes.

We explore the different types of causes that are commonly investigated by epidemiologists. We first distinguish between causes which are events (including actions) and causes which are states. Second, we distinguish between modifiable and non-modifiable states. This yields three types of causes: fixed states (non-modifiable), dynamic states (modifiable) and events (including actions). Different causes may have different characteristics: the methods available to study them, the types of possible biases, and therefore the types of evidence needed to infer causality, may differ according to the specific cause-effect relationship under study. Nevertheless, there are also substantial commonalities. This paper is intended to improve understanding of the different types of causes, and the different types of causality, that underpin epidemiological practice.