Modelling the spatial risk pattern of dementia in Denmark using residential location data: A registry-based national cohort.

Dementia is a major global public health concern that is increasingly leading to morbidity and mortality among older adults. While studies have focused on the risk factors and care provision, there is currently limited knowledge about the spatial risk pattern of the disease. In this study, we employ Bayesian spatial modelling with a stochastic partial differential equation (SPDE) approach to model the spatial risk using complete residential history data from the Danish population and health registers. The study cohort consisted of 1.6 million people aged 65 years and above from 2005 to 2018. The results of the spatial risk map indicate high-risk areas in Copenhagen, southern Jutland and Funen. Individual socioeconomic factors and population density reduce the intensity of high-risk patterns across Denmark. The findings of this study call for the critical examination of the contribution of place of residence in the susceptibility of the global ageing population to dementia.

Automated local lockdowns for SARS-CoV-2 epidemic control-assessment of effect by controlled interrupted time series analysis.

Objectives: During the COVID-19 pandemic, broad non-pharmaceutical interventions such as national lockdowns were effective but had significant drawbacks, prompting targeted approaches, such as Denmark's localized lockdowns, based on specific epidemiological criteria. This study evaluates the effect of Denmark's automated local lockdown strategy on epidemic control to inform future response. Methods: This was a register-based controlled interrupted time series analysis, examining SARS-CoV-2 infection rates in Danish parishes from March to September 2021. The matching of control parishes was based on location, time, and pre-lockdown infection trends, with the lockdown's start defined as the day after a parish exceeded the lockdown criteria. Follow-up included 3-week pre-lockdown and 2-week post-lockdown. Results: A total of 30 parishes were mandated to lockdown, approximately 3.5% of the population of Denmark. A total of 94 control parishes were used as 109 controls. The decrease in the incidence during the 2-week follow-up period after the initiation of the lockdown was 13% points higher in case parishes: in case parishes, the incidence was reduced by 78% compared with 65% in control parishes. Conclusions: Our findings demonstrate that local lockdowns did have a positive effect in mitigating the spread of the SARS-CoV-2 virus, making them valuable in the fight against the COVID-19 pandemic and an important alternative to national lockdowns.

Multi-cancer risk stratification based on national health data: a retrospective modelling and validation study.

BACKGROUND: Health care is experiencing a drive towards digitisation, and many countries are implementing national health data resources. Although a range of cancer risk models exists, the utility on a population level for risk stratification across cancer types has not been fully explored. We aimed to close this gap by evaluating pan-cancer risk models built on electronic health records across the Danish population with validation in the UK Biobank. METHODS: In this retrospective modelling and validation study, data for model development and internal validation were derived from the following Danish health registries: the Central Person Registry, the Danish National Patient Registry, the death registry, the cancer registry, and full-text medical records from secondary care records in the capital region. The development data included adults aged 16-86 years without previous malignant cancers in the time period from Jan 1, 1995, to Dec 31, 2014. The internal validation period was from Jan 1, 2015, to April 10, 2018, and the data included all adults without a previous indication of cancer aged 16-75 years on Dec 31, 2014. The external validation cohort from the UK Biobank included all adults without a previous indication of cancer aged 50-75 years. We used time-dependent Bayesian Cox hazard models built on the combined medical history of Danish individuals. A set of 1392 covariates from available clinical disease trajectories, text-mined basic health factors, and family histories were used to train predictive models of 20 major cancer types. The models were validated on cancer incidence between 2015 and 2018 across Denmark and on individuals in the UK Biobank. The primary outcomes were discrimination and calibration performance. FINDINGS: From the Danish registries, we included 6 732 553 individuals covering 60 million hospital visits, 90 million diagnoses, and a total of 193 million life-years between Jan 1, 1978, and April 10, 2018. Danish registry data covering the period from Jan 1, 2015, to April 10, 2018, were used to internally validate risk models, containing a total of 4 248 491 individuals who remained at risk of a primary malignant cancer diagnosis and 67 401 cancer cases recorded. For the external validation, we evaluated the same time period in the UK Biobank covering 377 004 individuals with 11 486 cancer cases. The predictive performance of the models on Danish data showed good discrimination (concordance index 0·81 [SD 0·08], ranging from 0·66 [95% CI 0·65-0·67] for cervix uteri cancer to 0·91 [0·90-0·92] for liver cancer). Performance was similar on the UK Biobank in a direct transfer when controlling for shifts in the age distribution (concordance index 0·66 [SD 0·08], ranging from 0·55 [95% CI 0·44-0·66] for cervix uteri cancer to 0·78 [0·77-0·79] for lung cancer). Cancer risks were associated, in addition to heritable components, with a broad range of preceding diagnoses and health factors. The best overall performance was seen for cancers of the digestive system (oesophageal, stomach, colorectal, liver, and pancreatic) but also thyroid, kidney, and uterine cancers. INTERPRETATION: Data available in national electronic health databases can be used to approximate cancer risk factors and enable risk predictions in most cancer types. Model predictions generalise between the Danish and UK health-care systems. With the emergence of multi-cancer early detection tests, electronic health record-based risk models could supplement screening efforts. FUNDING: Novo Nordisk Foundation and the Danish Innovation Foundation.

Magnitude and determinants of excess total, age-specific and sex-specific all-cause mortality in 24 countries worldwide during 2020 and 2021: results on the impact of the COVID-19 pandemic from the C-MOR project.

INTRODUCTION: To examine the impact of the COVID-19 pandemic on mortality, we estimated excess all-cause mortality in 24 countries for 2020 and 2021, overall and stratified by sex and age. METHODS: Total, age-specific and sex-specific weekly all-cause mortality was collected for 2015-2021 and excess mortality for 2020 and 2021 was calculated by comparing weekly 2020 and 2021 age-standardised mortality rates against expected mortality, estimated based on historical data (2015-2019), accounting for seasonality, and long-term and short-term trends. Age-specific weekly excess mortality was similarly calculated using crude mortality rates. The association of country and pandemic-related variables with excess mortality was investigated using simple and multilevel regression models. RESULTS: Excess cumulative mortality for both 2020 and 2021 was found in Austria, Brazil, Belgium, Cyprus, England and Wales, Estonia, France, Georgia, Greece, Israel, Italy, Kazakhstan, Mauritius, Northern Ireland, Norway, Peru, Poland, Slovenia, Spain, Sweden, Ukraine, and the USA. Australia and Denmark experienced excess mortality only in 2021. Mauritius demonstrated a statistically significant decrease in all-cause mortality during both years. Weekly incidence of COVID-19 was significantly positively associated with excess mortality for both years, but the positive association was attenuated in 2021 as percentage of the population fully vaccinated increased. Stringency index of control measures was positively and negatively associated with excess mortality in 2020 and 2021, respectively. CONCLUSION: This study provides evidence of substantial excess mortality in most countries investigated during the first 2 years of the pandemic and suggests that COVID-19 incidence, stringency of control measures and vaccination rates interacted in determining the magnitude of excess mortality.

Association of Tumor Necrosis Factor-α Inhibitors with Incident Dementia: Analysis Based on Population-Based Cohort Studies.

BACKGROUND AND OBJECTIVE: Preliminary evidence suggests a possible preventive effect of tumor necrosis factor-α inhibitors (TNFi) on incident dementia. The objective of the analysis was to investigate the association between TNFi and the risk of incident dementia in a population undergoing treatment for rheumatological disorders. METHODS: We followed patients aged ≥ 65 years with dementia and rheumatological conditions in two cohort studies, DANBIO (N = 21,538), a Danish clinical database, and AOK PLUS (N = 7112), a German health insurance database. We defined incident dementia using diagnostic codes and/or medication use and used Cox regression to compare the associations of TNFi with other rheumatological therapies on the risk of dementia. To ensure that the patients were receiving long-term medication, we included patients with rheumatic diseases and systemic therapies. RESULTS: We observed similar trends towards a lower risk of dementia associated with TNFi versus other anti-inflammatory agents in both cohorts (hazard ratios were 0.92 [95% confidence interval 0.76, 1.10] in DANBIO and 0.89 [95% confidence interval 0.63, 1.24] in AOK PLUS, respectively). CONCLUSIONS: Tumor necrosis factor-α inhibitors may decrease the risk of incident dementia although the association did not reach statistical significance in this analysis. Further research, ideally with randomization, is needed to gauge the potential of repurposing TNFi for dementia prevention and/or treatment.

Long-Term Exposure to Air Pollution and Risk of Acute Lower Respiratory Infections in the Danish Nurse Cohort.

RATIONALE: Air pollution is a major risk factor for chronic cardiorespiratory diseases, affecting both the immune and respiratory systems' functionality, while the epidemiological evidence on respiratory infections remains sparse. OBJECTIVE: We aimed to assess the association of long-term exposure to ambient air pollution with risk of developing new and recurrent ALRIs that characterized by persistently severe symptoms necessitating hospital contact, and identify the potential susceptible populations by socio-economic status (SES), smoking, physical activity status, overweight, and co-morbidity with chronic lung disease. METHODS: We followed 23,912 female nurses from the Danish Nurse Cohort (> 44 years) from baseline (1993 or 1999) until 2018 for the incident and recurrent ALRIs defined by hospital contact (in-, outpatient, and emergency room) data from the National Patient Register. Residential annual mean concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) were modelled using Danish DEHM/UBM/AirGIS system. We used marginal Cox models with time-varying exposures to assess the association of 3-year running-mean air pollution with incident and recurrent ALRIs and examine effect modification by age, socio-economic status (SES), smoking, physical activity, body mass index, and comorbidity with asthma or chronic obstructive pulmonary disease (COPD). RESULTS: During a 21.3 years mean follow-up, 4,746 ALRIs were observed, of which 2,553 were incident. We observed strong positive associations of all three pollutants with incident ALRIs, with hazard ratios and 95% confidence intervals of 1.19 (1.08-1.31) per 2.5 µg/m3 for PM2.5, 1.17 (1.11-1.24) per 8.0 µg/m3 for NO2, and 1.09 (1.05-1.12) per 0.3 µg/m3 for BC, and slightly stronger associations with recurrent ALRIs. Associations were strongest in COPD patients and nurses with low physical activity. CONCLUSION: Long-term exposure to air pollution at low levels was associated with risk of new and recurrent ALRIs, with COPD patients and physically inactive subjects most vulnerable. Primary Source of Funding: This study was supported by the Novo Nordisk Foundation Challenge Programme (NNF17OC0027812).

Harnessing AI to unmask Copenhagen's invisible air pollutants: A study on three ultrafine particle metrics.

Ultrafine particles (UFPs) are airborne particles with a diameter of less than 100 nm. They are emitted from various sources, such as traffic, combustion, and industrial processes, and can have adverse effects on human health. Long-term mean ambient average particle size (APS) in the UFP range varies over space within cities, with locations near UFP sources having typically smaller APS. Spatial models for lung deposited surface area (LDSA) within urban areas are limited and currently there is no model for APS in any European city. We collected particle number concentration (PNC), LDSA, and APS data over one-year monitoring campaign from May 2021 to May 2022 across 27 locations and estimated annual mean in Copenhagen, Denmark, and obtained additionally annual mean PNC data from 6 state-owned continuous monitors. We developed 94 predictor variables, and machine learning models (random forest and bagged tree) were developed for PNC, LDSA, and APS. The annual mean PNC, LDSA, and APS were, respectively, 5523 pt/cm3, 12.0 µm2/cm3, and 46.1 nm. The final R2 values by random forest (RF) model were 0.93 for PNC, 0.88 for LDSA, and 0.85 for APS. The 10-fold, repeated 10-times cross-validation R2 values were 0.65, 0.67, and 0.60 for PNC, LDSA, and APS, respectively. The root mean square error for final RF models were 296 pt/cm3, 0.48 µm2/cm3, and 1.60 nm for PNC, LDSA, and APS, respectively. Traffic-related variables, such as length of major roads within buffers 100-150 m and distance to streets with various speed limits were amongst the highly-ranked predictors for our models. Overall, our ML models achieved high R2 values and low errors, providing insights into UFP exposure in a European city where average PNC is quite low. These hyperlocal predictions can be used to study health effects of UFPs in the Danish Capital.

Lifetime exposure to air pollution and academic achievement: A nationwide cohort study in Denmark.

Recent research suggests a link between air pollution and cognitive development in children, and studies on air pollution and academic achievement are emerging. We conducted a nationwide cohort study in Denmark to explore the associations between lifetime exposure to air pollution and academic performance in 9th grade. The study encompassed 785,312 children born in Denmark between 1989 and 2005, all of whom completed 9th-grade exit examinations. Using linear mixed models with a random intercept for each school, we assessed the relationship between 16 years of exposure to PM2.5, PM10, and gaseous pollutants and Grade Point Averages (GPA) in exit examinations, covering subjects such as Danish literature, Danish writing, English, mathematics, and natural sciences. The study revealed that a 5 µg/m3 increase in PM2.5 and PM10 was associated with a decrease of 0.99 (95 % Confidence Intervals: -1.05, -0.92) and 0.46 (-0.50, -0.41) in GPA, respectively. Notably, these negative associations were more pronounced in mathematics and natural sciences compared to language-related subjects. Additionally, girls and children with non-Danish mothers were found to be particularly susceptible to the adverse effects of air pollution exposure. These results underscore the potential long-term consequences of air pollution on academic achievement, emphasizing the significance of interventions that foster healthier environments for children's cognitive development.

Air pollution and human health: a phenome-wide association study.

OBJECTIVES: To explore the associations of long-term exposure to air pollution with onset of all human health conditions. DESIGN: Prospective phenome-wide association study. SETTING: Denmark. PARTICIPANTS: All Danish residents aged ≥30 years on 1 January 2000 were included (N=3 323 612). After exclusion of individuals with missing geocoded residential addresses, 3 111 988 participants were available for the statistical analyses. MAIN OUTCOME MEASURE: First registered diagnosis of every health condition according to the International Classification of Diseases, 10th revision, from 2000 to 2017. RESULTS: Long-term exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) were both positively associated with the onset of more than 700 health conditions (ie, >80% of the registered health conditions) after correction for multiple testing, while the remaining associations were inverse or insignificant. As regards the most common health conditions, PM2.5 and NO2 were strongest positively associated with chronic obstructive pulmonary disease (PM2.5: HR 1.06 (95% CI 1.05 to 1.07) per 1 IQR increase in exposure level; NO2: 1.14 (95% CI 1.12 to 1.15)), type 2 diabetes (PM2.5: 1.06 (95% CI 1.05 to 1.06); NO2: 1.12 (95% CI 1.10 to 1.13)) and ischaemic heart disease (PM2.5: 1.05 (95% CI 1.04 to 1.05); NO2: 1.11 (95% CI 1.09 to 1.12)). Furthermore, PM2.5 and NO2 were both positively associated with so far unexplored, but highly prevalent outcomes relevant to public health, including senile cataract, hearing loss and urinary tract infection. CONCLUSIONS: The findings of this study suggest that air pollution has a more extensive impact on human health than previously known. However, as this study is the first of its kind to investigate the associations of long-term exposure to air pollution with onset of all human health conditions, further research is needed to replicate the study findings.

Cardiovascular disease and the risk of incident falls and mortality among adults aged ≥ 65 years presenting to the emergency department: a cohort study from national registry data in Denmark.

BACKGROUND: Falls cause 58% of injury-related Emergency Department (ED) attendances. Previous research has highlighted the potential role of cardiovascular risk factors for falls. This study investigated the impact of cardiovascular disease (CVD) risk on three-year incident falls, with presentation to the ED, and mortality. METHODS: A matched cohort study design was employed using national registry data from 82,292 adults (33% male) aged ≥ 65 years living in Denmark who attended the ED in 2013. We compared age and gender matched ED attendees presenting with a fall versus another reason. The cohort was followed for three-year incident falls, with presentation to the ED, and mortality. The impact of falls-related CVDs was also examined. RESULTS: Three-year incident falls was twofold higher among age and gender matched ED attendees aged ≥ 65 years presenting with a fall versus another reason at baseline. A presentation of falls with hip fracture had the highest percentage of incident falls in the 65-74 age group (22%) and the highest percentage mortality in all age groups (27-62%). CVD was not a significant factor in presenting with a fall at the ED, nor did it contribute significantly to the prediction of three-year incident falls. CVD was strongly associated with mortality risk among the ED fall group (RR = 1.81, 95% CI: 1.67-1.97) and showed interactions with both age and fall history. CONCLUSION: In this large study of adults aged ≥ 65 years attending the ED utilising data from national administrative registers in Denmark, we confirm that older adults attending the ED with a fall, including those with hip fracture, were at greatest risk for future falls. While CVD did not predict incident falls, it increased the risk of mortality in the three-year follow up with advancing age. This may be informative for the provision of care pathways for older adults attending the ED due to a fall.

Using sequences of life-events to predict human lives.

Here we represent human lives in a way that shares structural similarity to language, and we exploit this similarity to adapt natural language processing techniques to examine the evolution and predictability of human lives based on detailed event sequences. We do this by drawing on a comprehensive registry dataset, which is available for Denmark across several years, and that includes information about life-events related to health, education, occupation, income, address and working hours, recorded with day-to-day resolution. We create embeddings of life-events in a single vector space, showing that this embedding space is robust and highly structured. Our models allow us to predict diverse outcomes ranging from early mortality to personality nuances, outperforming state-of-the-art models by a wide margin. Using methods for interpreting deep learning models, we probe the algorithm to understand the factors that enable our predictions. Our framework allows researchers to discover potential mechanisms that impact life outcomes as well as the associated possibilities for personalized interventions.

Development of geographic inequality in dental caries and its association with socioeconomic factors over an 18-year period in Denmark.

BACKGROUND: Few studies have examined the development of geographic and socioeconomic inequalities in caries over time or have simultaneously assessed individual-level socioeconomic position (SEP) and neighborhood-level factors as a multi-layered phenomenon influencing caries inequalities. This study examined (i) the trends in geographic inequalities in caries among adolescents in Denmark and (ii) how the association between SEP and caries has progressed over time, when accounting for individual and neighborhood-level confounding factors. METHODS: This nationwide repeated cross-sectional study included 15-year-olds in Denmark from 1995, 2003, and 2013 (n = 149,808). The outcome was caries experience (measured by the decayed, missing, and filled tooth surfaces [DMFS] index). The exposure of interest was SEP, indicated by the previous year's parental education, occupational social class, and (equivalized) disposable household income. Covariates included individual-level factors (immigration status, country of origin, number of children and persons in the family, and household type) and neighborhood (residence municipality)-level factors (Gini index; proportion of unemployed, low-educated, and unmarried/non-cohabiting individuals; proportion of single-parent households and households with overcrowding). Data sources included the Danish national dental and administrative social registers and Statistics Denmark's statistics database (StatBank). Data were analyzed using spatial and spatiotemporal modelling utilizing zero-inflated negative binomial regressions and integrated nested Laplace approximations for Bayesian parametric inference. Observed caries experience geo-maps of the Danish municipalities for 1995, 2003, and 2013 were created. RESULTS: Between 1995 and 2013, caries prevalence in the 15-year-olds declined sharply (1995, 71%; 2013, 45%). Caries experience declined in nearly all socioeconomic subgroups and municipalities. However, geographic inequalities persisted with higher caries levels largely concentrated in the relatively deprived areas of Denmark. Increasing relative socioeconomic inequalities in caries over time were observed with significant graded associations between SEP and caries despite adjustment for the various individual and neighborhood-level covariates and the effect of assessment year (e.g., 15-year-olds with parents having basic education had 1.91-fold [95% CI: 1.86-1.95] higher caries experience than those having parents with high education). CONCLUSIONS: Reducing these enduring inequalities will likely require additional resources and targeted supportive and preventive measures for adolescents from lower SEP backgrounds and those residing in municipalities with higher caries prevalence.

Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals.

BACKGROUND: The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using health care usage for predicting mortality remains unexplored. The aim of this study was to investigate if temporal patterns of healthcare expenditures, can improve the predictive performance for mortality, in spousal bereaved older adults, next to other widely used sociodemographic variables. METHODS: This is a population-based cohort study of 48,944 Danish citizens 65 years of age and older suffering bereavement within 2013-2016. Individuals were followed from date of spousal loss until death from all causes or 31st of December 2016, whichever came first. Healthcare expenditures were available on weekly basis for each person during the follow-up and used as predictors for mortality risk in Extreme Gradient Boosting models. The extent to which medical spending trajectories improved mortality predictions compared to models with sociodemographics, was assessed with respect to discrimination (AUC), overall prediction error (Brier score), calibration, and clinical benefit (decision curve analysis). RESULTS: The AUC of age and sex for mortality the year after spousal loss was 70.8% [95% CI 68.8, 72.8]. The addition of sociodemographic variables led to an increase of AUC ranging from 0.9% to 3.1% but did not significantly reduce the overall prediction error. The AUC of the model combining the variables above plus medical spending usage was 80.8% [79.3, 82.4] also exhibiting smaller Brier score and better calibration. Overall, patterns of healthcare expenditures improved mortality predictions the most, also exhibiting the highest clinical benefit among the rest of the models. CONCLUSION: Temporal patterns of medical spending have the potential to significantly improve our assessment on who is at high risk of dying after suffering spousal loss. The proposed methodology can assist in a more efficient risk profiling and prognosis of bereaved individuals.

Predicting mortality risk after a fall in older adults using health care spending patterns: a population-based cohort study.

OBJECTIVE: To develop a prognostic model of 1-year mortality for individuals aged 65+ presenting at the emergency department (ED) with a fall based on health care spending patterns to guide clinical decision-making. DESIGN: Population-based cohort study (n = 35,997) included with a fall in 2013 and followed 1 year. METHODS: Health care spending indicators (dynamical indicators of resilience, DIORs) 2 years before admission were evaluated as potential predictors, along with age, sex and other clinical and sociodemographic covariates. Multivariable logistic regression models were developed and internally validated (10-fold cross-validation). Performance was assessed via discrimination (area under the receiver operating characteristic curve, AUC), Brier scores, calibration and decision curve analysis. RESULTS: The AUC of age and sex for mortality was 72.5% [95% confidence interval 71.8 to 73.2]. The best model included age, sex, number of medications and health care spending DIORs. It exhibited high discrimination (AUC: 81.1 [80.5 to 81.6]), good calibration and potential clinical benefit for various threshold probabilities. Overall, health care spending patterns improved predictive accuracy the most while also exhibiting superior performance and clinical benefit. CONCLUSIONS: Patterns of health care spending have the potential to significantly improve assessments on who is at high risk of dying following admission to the ED with a fall. The proposed methodology can assist in predicting the prognosis of fallers, emphasising the added predictive value of longitudinal health-related information next to clinical and sociodemographic predictors.

Early-life air pollution and green space exposures as determinants of stunting among children under age five in Sub-Saharan Africa.

BACKGROUND: Childhood malnutrition is a major public health issue in Sub-Saharan Africa (SSA) and 61.4 million children under the age of five years in the region are stunted. Although insight from existing studies suggests plausible pathways between ambient air pollution exposure and stunting, there are limited studies on the effect of different ambient air pollutants on stunting among children. OBJECTIVE: Explore the effect of early-life environmental exposures on stunting among children under the age of five years. METHODS: In this study, we used pooled health and population data from 33 countries in SSA between 2006 and 2019 and environmental data from the Atmospheric Composition Analysis Group and NASA's GIOVANNI platform. We estimated the association between early-life environmental exposures and stunting in three exposure periods - in-utero (during pregnancy), post-utero (after pregnancy to current age) and cumulative (from pregnancy to current age), using Bayesian hierarchical modelling. We also visualise the likelihood of stunting among children based on their region of residence using Bayesian hierarchical modelling. RESULTS: The findings show that 33.6% of sampled children were stunted. In-utero PM2.5 was associated with a higher likelihood of stunting (OR = 1.038, CrI = 1.002-1.075). Early-life exposures to nitrogen dioxide and sulphate were robustly associated with stunting among children. The findings also show spatial variation in a high and low likelihood of stunting based on a region of residence. IMPACT STATEMENT: This study explores the effect of early-life environmental exposures on child growth or stunting among sub-Saharan African children. The study focuses on three exposure windows - pregnancy, after birth and cumulative exposure during pregnancy and after birth. The study also employs spatial analysis to assess the spatial burden of stunted growth in relation to environmental exposures and socioeconomic factors. The findings suggest major air pollutants are associated with stunted growth among children in sub-Saharan Africa.

Long-term exposure to air pollution and risk of SARS-CoV-2 infection and COVID-19 hospitalisation or death: Danish nationwide cohort study.

BACKGROUND: Early ecological studies have suggested links between air pollution and risk of coronavirus disease 2019 (COVID-19), but evidence from individual-level cohort studies is still sparse. We examined whether long-term exposure to air pollution is associated with risk of COVID-19 and who is most susceptible. METHODS: We followed 3 721 810 Danish residents aged ≥30 years on 1 March 2020 in the National COVID-19 Surveillance System until the date of first positive test (incidence), COVID-19 hospitalisation or death until 26 April 2021. We estimated residential annual mean particulate matter with diameter ≤2.5 µm (PM2.5), nitrogen dioxide (NO2), black carbon (BC) and ozone (O3) in 2019 by the Danish DEHM/UBM model, and used Cox proportional hazards regression models to estimate the associations of air pollutants with COVID-19 outcomes, adjusting for age, sex, individual- and area-level socioeconomic status, and population density. RESULTS: 138 742 individuals were infected, 11 270 were hospitalised and 2557 died from COVID-19 during 14 months. We detected associations of PM2.5 (per 0.53 µg·m-3) and NO2 (per 3.59 µg·m-3) with COVID-19 incidence (hazard ratio (HR) 1.10 (95% CI 1.05-1.14) and HR 1.18 (95% CI 1.14-1.23), respectively), hospitalisations (HR 1.09 (95% CI 1.01-1.17) and HR 1.19 (95% CI 1.12-1.27), respectively) and death (HR 1.23 (95% CI 1.04-1.44) and HR 1.18 (95% CI 1.03-1.34), respectively), which were strongest in the lowest socioeconomic groups and among patients with chronic respiratory, cardiometabolic and neurodegenerative diseases. We found positive associations with BC and negative associations with O3. CONCLUSION: Long-term exposure to air pollution may contribute to increased risk of contracting severe acute respiratory syndrome coronavirus 2 infection as well as developing severe COVID-19 disease requiring hospitalisation or resulting in death.

Danish Pathology Life Course (PATHOLIFE) cohort: a register based cohort extending upon a national tissue biobank.

PURPOSE: The Danish Pathology Life Course (PATHOLIFE) cohort was established to facilitate epidemiological research relating histological and cytological features extracted from patient tissue specimens to the rich life course histories, including both prior and future register data, of the entire Danish population. Research results may increase quality of diagnosis, prognosis and stratification of patient subtypes, possibly identifying novel routes of treatment. PARTICIPANTS: All Danish residents from 1 January 1986 to 31 December 2019, totalling 8 593 421 individuals. FINDINGS TO DATE: We provide an overview of the subpopulation of Danish residents who have had a tissue specimen investigated within the Danish healthcare system, including both the primary sector and hospitals. We demonstrate heterogeneity in sociodemographic and prognostic factors between the general Danish population and the above mentioned subpopulation, and also between the general Danish population and subpopulations of patients with tissue specimens from selected anatomical sites. Results demonstrate the potential of the PATHOLIFE cohort for integrating many different factors into identification and selection of the most valuable tissue blocks for studies of specific diseases and their progression. Broadly, we find that living with a partner, having higher education and income associates with having a biopsy overall. However, this association varies across different tissue and patient types, which also display differences in time-to-death and causes of death. FUTURE PLANS: The PATHOLIFE cohort may be used to study specified patient groups and link health related events from several national health registries, and to sample patient groups, for which stored tissue specimens are available for further research investigations. The PATHOLIFE cohort thereby provides a unique opportunity to prospectively follow people that were characterised and sampled in the past.

Sex differences in health care expenditures and mortality after spousal bereavement: A register-based Danish cohort study.

BACKGROUND: Spousal bereavement is a life event that affects older people differently. We investigated the impact of spousal bereavement on medical expenditures and mortality in the general population, emphasizing on age and sex. METHODS: Data are from a population-based, retrospective cohort study following 924,958 Danish citizens over the age of 65 years, within 2011-2016. Changes in health care expenditures in those who suffer bereavement were compared with time matched changes among those who did not. Mortality hazards were analysed with time to event analysis. RESULTS: A total of 77,722 (~8.4%) individuals experienced bereavement, 65.8% being females. Among males, bereavement was associated with increase of expenditures the year after, that was 42 Euros per week (95% CI, 36 to 48) larger than the non-bereaved group. The corresponding increase for females was 35 Euros per week (95% CI, 30 to 40). The increase of mortality hazards was highest in the first year after bereavement, higher in males than females, in young old and almost absent in the oldest old. Compared with the reference, mortality the year after spousal loss was 70% higher (HR 1.70 [95% CI 1.40 to 2.08]) for males aged 65-69 years and remained elevated for a period of six years. Mortality for females aged 65-69 years was 27% higher in the first year (HR 1.27, [1.07 to 1.52]), normalizing thereafter. CONCLUSION: Bereavement affects older people differently with younger males being most frail with limited recovery potential.

Long-term exposure to elemental components of fine particulate matter and all-natural and cause-specific mortality in a Danish nationwide administrative cohort study.

BACKGROUND: Fine particulate matter (PM2.5) is a well-recognized risk factor for premature death. However, evidence on which PM2.5 components are most relevant is unclear. METHODS: We evaluated the associations between mortality and long-term exposure to eight PM2.5 elemental components [copper (Cu), iron (Fe), zinc (Zn), sulfur (S), nickel (Ni), vanadium (V), silicon (Si), and potassium (K)]. Studied outcomes included death from diabetes, chronic kidney disease (CKD), dementia, and psychiatric disorders as well as all-natural causes, cardiovascular disease (CVD), respiratory diseases (RD), and lung cancer. We followed all residents in Denmark (aged ≥30 years) from January 1, 2000 to December 31, 2017. We used European-wide land-use regression models at a 100 × 100 m scale to estimate the residential annual mean levels of exposure to PM2.5 components. The models were developed with supervised linear regression (SLR) and random forest (RF). The associations were evaluated by Cox proportional hazard models adjusting for individual- and area-level socioeconomic factors and total PM2.5 mass. RESULTS: Of 3,081,244 individuals, we observed 803,373 death from natural causes during follow-up. We found significant positive associations between all-natural mortality with Si and K from both exposure modeling approaches (hazard ratios; 95% confidence intervals per interquartile range increase): SLR-Si (1.04; 1.03-1.05), RF-Si (1.01; 1.00-1.02), SLR-K (1.03; 1.02-1.04), and RF-K (1.06; 1.05-1.07). Strong associations of K and Si were detected with most causes of mortality except CKD and K, and diabetes and Si (the strongest associations for psychiatric disorders mortality). In addition, Fe was relevant for mortality from RD, lung cancer, CKD, and psychiatric disorders; Zn with mortality from CKD, RD, and lung cancer, and; Ni and V with lung cancer mortality. CONCLUSIONS: We present novel results of the relevance of different PM2.5 components for different causes of death, with K and Si seeming to be most consistently associated with mortality in Denmark.