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Professor Matthias Ketzel


Visiting Professor (Senior Scientist, Aarhus University)

My publications

Publications

Hvidtfeldt Ulla Arthur, Sørensen Mette, Geels Camilla, Ketzel Matthias, Khan Jibran, Tjønneland Anne, Overvad Kim, Brandt Jørgen, Raaschou-Nielsen Ole (2019) Long-term residential exposure to PM2.5, PM10, black carbon, NO2, and ozone and mortality in a Danish cohort, Environment International 123 pp. 265-272 Elsevier
Air pollutants such as NO2 and PM2.5 have consistently been linked to mortality, but only few previous studies
have addressed associations with long-term exposure to black carbon (BC) and ozone (O3).
We investigated the association between PM2.5, PM10, BC, NO2, and O3 and mortality in a Danish cohort of
49,564 individuals who were followed up from enrollment in 1993?1997 through 2015. Residential address
history from 1979 onwards was combined with air pollution exposure obtained by the state-of-the-art, validated,
THOR/AirGIS air pollution modelling system, and information on residential traffic noise exposure, lifestyle and
socio-demography.
We observed higher risks of all-cause as well as cardiovascular disease (CVD) mortality with higher long-term
exposure to PM2.5, PM10, BC, and NO2. For PM2.5 and CVD mortality, a hazard ratio (HR) of 1.29 (95% CI:
1.13?1.47) per 5 ¼g/m3 was observed, and correspondingly HRs of 1.16 (95% CI: 1.05?1.27) and 1.11 (95% CI:
1.04?1.17) were observed for BC (per 1 ¼g/m3) and NO2 (per 10 ¼g/m3), respectively. Adjustment for noise gave
slightly lower estimates for the air pollutants and CVD mortality. Inverse relationships were observed for O3.
None of the investigated air pollutants were related to risk of respiratory mortality. Stratified analyses suggested
that the elevated risks of CVD and all-cause mortality in relation to long-term PM, NO2 and BC exposure were
restricted to males.
This study supports a role of PM, BC, and NO2 in all-cause and CVD mortality independent of road traffic
noise exposure.
Chen Jie, de Hoogh Kees, Gulliver John, Hoffmann Barbara, Hertel Ole, Ketzel Matthias, Bauwelinck Mariska, van Donkelaar Aaron, Hvidtfeldt Ulla A., Katsouyanni Klea, Janssen Nicole A.H., Martin Randall V., Samoli Evangelia, Schwartz Per E., Stafoggia Massimo, Bellander Tom, Strak Maciek, Wolf Kathrin, Vienneau Danielle, Vermeulen Roel, Brunekreef Bert, Hoek Gerard (2019) A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide, Environment International 130 104934 Elsevier

Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary linear regression. However, different algorithms have rarely been compared in terms of their predictive ability.

This study compared 16 algorithms to predict annual average fine particle (PM2.5) and nitrogen dioxide (NO2) concentrations across Europe. The evaluated algorithms included linear stepwise regression, regularization techniques and machine learning methods. Air pollution models were developed based on the 2010 routine monitoring data from the AIRBASE dataset maintained by the European Environmental Agency (543 sites for PM2.5 and 2399 sites for NO2), using satellite observations, dispersion model estimates and land use variables as predictors. We compared the models by performing five-fold cross-validation (CV) and by external validation (EV) using annual average concentrations measured at 416 (PM2.5) and 1396 sites (NO2) from the ESCAPE study. We further assessed the correlations between predictions by each pair of algorithms at the ESCAPE sites.

For PM2.5, the models performed similarly across algorithms with a mean CV R² of 0.59 and a mean EV R² of 0.53. Generalized boosted machine, random forest and bagging performed best (CV R²~0.63; EV R² 0.58?0.61), while backward stepwise linear regression, support vector regression and artificial neural network performed less well (CV R² 0.48?0.57; EV R² 0.39?0.46). Most of the PM2.5 model predictions at ESCAPE sites were highly correlated (R²/>/0.85, with the exception of predictions from the artificial neural network). For NO2, the models performed even more similarly across different algorithms, with CV R²s ranging from 0.57 to 0.62, and EV R²s ranging from 0.49 to 0.51. The predicted concentrations from all algorithms at ESCAPE sites were highly correlated (R²/>/0.9). For both pollutants, biases were low for all models except the artificial neural network. Dispersion model estimates and satellite observations were two of the most important predictors for PM2.5 models whilst dispersion model estimates and traffic variables were most important for NO2 models in all algorithms that allow assessment of the importance of variables.

Different statistical algorithms performed similarly when modelling spatial variation in annual average air pollution concentrations using a large number of training sites.

Poulsen Aslak Harbo, Raaschou-Nielsen Ole, Peña Alfredo, Hahmann Andrea N., Nordsborg Rikke Baastrup, Ketzel Matthias, Brandt Jørgen, Sørensen Mette (2019) Impact of Long-Term Exposure to Wind Turbine Noise on Redemption of Sleep Medication and Antidepressants: A Nationwide Cohort Study, Environmental Health Perspectives 127 (3) 037005 pp. 037005-1 - 037005-9 National Institute of Environmental Health Sciences (NIEHS)

Background:
Noise from wind turbines (WTs) is associated with annoyance and, potentially, sleep disturbances.

Objectives:
Our objective was to investigate whether long-term WT noise (WTN) exposure is associated with the redemption of prescriptions for sleep medication and antidepressants.

Methods:
For all Danish dwellings within a radius of 20-WT heights and for 25% of randomly selected dwellings within a radius of 20-to 40-WT heights, we estimated nighttime outdoor and low-frequency (LF) indoor WTN, using information on WT type and simulated hourly wind. During follow-up from 1996 to 2013, 68,696 adults redeemed sleep medication and 82,373 redeemed antidepressants, from eligible populations of 583,968 and 584,891, respectively. We used Poisson regression with adjustment for individual and area-level covariates.

Results:
Five-year mean outdoor nighttime WTN of e42 dB was associated with a hazard ratio (HR) = 1.14 [95% confidence interval (CI]: 0.98, 1.33) for sleep medication and HR = 1.17 (95% CI: 1.01, 1.35) for antidepressants (compared with exposure to WTN of Â24 dB). We found no overall association with indoor nighttime LF WTN. In age-stratified analyses, the association with outdoor nighttime WTN was strongest among persons e65y of age, with HRs (95% CIs) for the highest exposure group (e42 dB) of 1.68 (1.27, 2.21) for sleep medication and 1.23 (0.90, 1.69) for antidepressants. For indoor nighttime LF WTN, the HRs (95% CIs) among persons e65y of age exposed to e15 dB were 1.37 (0.81, 2.31) for sleep medication and 1.34 (0.80, 2.22) for antidepressants.

Conclusions:
We observed high levels of outdoor WTN to be associated with redemption of sleep medication and antidepressants among the elderly, suggesting that WTN may potentially be associated with sleep and mental health.

Poulsen Aslak Harbo, Raaschou-Nielsen Ole, Peña Alfredo, Hahmann Andrea N., Nordsborg Rikke Baastrup, Ketzel Matthias, Brandt Jørgen, Sørensen Mette (2019) Long-Term Exposure to Wind Turbine Noise and Risk for Myocardial Infarction and Stroke: A Nationwide Cohort Study, Environmental Health Perspectives 127 (3) 037004 pp. 037004-1 National Institute of Environmental Health Sciences (NIEHS)

Background:
Noise from wind turbines (WTs) is reported as more annoying than traffic noise at similar levels, raising concerns as to whether WT noise (WTN) increases risk for cardiovascular disease, as observed for traffic noise.

Objectives:
We aimed to investigate whether long-term exposure to WTN increases risk of myocardial infarction (MI) and stroke.

Methods:
We identified all Danish dwellings within a radius 20 times the height of the closest WT and 25% of the dwellings within 20?40 times the height of the closest WT. Using data on WT type and simulated hourly wind at each WT, we estimated hourly outdoor and low frequency (LF) indoor WTN for each dwelling and derived 1-y and 5-y running nighttime averages. We used hospital and mortality registries to identify all incident cases of MI (n=19,145) and stroke (n=18,064) among all adults age 25?85 y (n=717,453), who lived in one of these dwellings for eone year over the period 1982?2013. We used Poisson regression to estimate incidence rate ratios (IRRs) adjusted for individual- and area-level covariates.

Results:
IRRs for MI in association with 5-y nighttime outdoor WTN Ã42 (vs. Â24) dB(A) and indoor LF WTN Ã15 (vs. Â5) dB(A) were 1.21 [95% confidence interval (CI): 0.91, 1.62; 47 exposed cases] and 1.29 (95% CI: 0.73, 2.28; 12 exposed cases), respectively. IRRs for intermediate categories of outdoor WTN [24?30, 30?36, and 36?42 dB(A) vs. Â24 dB(A)] were slightly above the null and of similar size: 1.08 (95% CI: 1.04, 1.12), 1.07 (95% CI: 1.00, 1.12), and 1.06 (95% CI: 0.93, 1.22), respectively. For stroke, IRRs for the second and third outdoor exposure groups were similar to those for MI, but near or below the null for higher exposures.

Conclusions:
We did not find convincing evidence of associations between WTN and MI or stroke.

Cramer Johannah, Therming Jørgensen Jeanette, Sørensen Mette, Backalarz Claus, Laursen Jens Elgaard, Ketzel Matthias, Hertel Ole, Jensen Steen Solvang, Simonsen Mette Kildevæld, Bräuner Elvira Vaclavik, Andersen Zorana Jovanovic (2019) Road traffic noise and markers of adiposity in the Danish Nurse Cohort: A cross-sectional study, Environmental Research 172 pp. 502-510 Elsevier

Background

Studies have suggested that traffic noise is associated with markers of obesity. We investigated the association of exposure to road traffic noise with body mass index (BMI) and waist circumference in the Danish Nurse Cohort.

Methods

We used data on 15,501 female nurses (aged >44 years) from the nationwide Danish Nurse Cohort who, in 1999, reported information on self-measured height, weight, and waist circumference, together with information on socioeconomic status, lifestyle, work and health. Road traffic noise at the most exposed façade of the residence was estimated using Nord2000 as the annual mean of a weighted 24-h average (Lden). We used multiple linear regression models to examine associations of road traffic noise levels in 1999 (1-year mean) with BMI and waist circumference, adjusting for potential confounders, and evaluated effect modification by degree of urbanization, air pollution levels, night shift work, job strain, sedative use, sleep aid use, and family history of obesity.

Results

We did not observe associations between road traffic noise (per 10/dB increase in the 1-year mean Lden) and BMI (kg/m2) (²: 0.00; 95% confidence interval (CI): ?0.07, 0.07) or waist circumference (cm) (²: ?0.09; 95% CI: ?0.31, 0.31) in the fully adjusted model. We found significant effect modification of job strain and degree of urbanization on the associations between Lden and both BMI and waist circumference. Job strained nurses were associated with a 0.41 BMI-point increase, (95% CI: 0.06, 0.76) and a 1.00/cm increase in waist circumference (95% CI: 0.00, 2.00). Nurses living in urban areas had a statistically significant positive association of Lden with BMI (²: 0.26; 95% CI: 0.11, 0.42), whilst no association was found for nurses living in suburban and rural areas.

Conclusion

Our results suggest that road traffic noise exposure in nurses with particular susceptibilities, such as those with job strain, or living in urban areas, may lead to increased BMI, a marker of adiposity.