Short-term association between air temperature and mortality in seven cities in Norway: A time series analysis.

BACKGROUND: The association between ambient air temperature and mortality has not been assessed in Norway. This study aimed to quantify for seven Norwegian cities (Oslo, Bergen, Stavanger, Drammen, Fredrikstad, Trondheim and Tromsø) the non-accidental, cardiovascular and respiratory diseases mortality burden due to non-optimal ambient temperatures. METHODS: We used a historical daily dataset (1996-2018) to perform city-specific analyses with a distributed lag non-linear model with 14 days of lag, and pooled results in a multivariate meta-regression. We calculated attributable deaths for heat and cold, defined as days with temperatures above and below the city-specific optimum temperature. We further divided temperatures into moderate and extreme using cut-offs at the 1st and 99th percentiles. RESULTS: We observed that 5.3% (95% confidence interval (CI) 2.0-8.3) of the non-accidental related deaths, 11.8% (95% CI 6.4-16.4) of the cardiovascular and 5.9% (95% CI -4.0 to 14.3) of the respiratory were attributable to non-optimal temperatures. Notable variations were found between cities and subgroups stratified by sex and age. The mortality burden related to cold dominated in all three health outcomes (5.1%, 2.0-8.1, 11.4%, 6.0-15.4, and 5.1%, -5.5 to 13.8 respectively). Heat had a more pronounced effect on the burden of respiratory deaths (0.9%, 0.2-1.0). Extreme cold accounted for 0.2% of non-accidental deaths and 0.3% of cardiovascular and respiratory deaths, while extreme heat contributed to 0.2% of non-accidental and to 0.3% of respiratory deaths. CONCLUSIONS: Most of the burden could be attributed to the contribution of moderate cold. This evidence has significant implications for enhancing public-health policies to better address health consequences in the Norwegian setting.

A real-time regional model for COVID-19: Probabilistic situational awareness and forecasting.

The COVID-19 pandemic is challenging nations with devastating health and economic consequences. The spread of the disease has revealed major geographical heterogeneity because of regionally varying individual behaviour and mobility patterns, unequal meteorological conditions, diverse viral variants, and locally implemented non-pharmaceutical interventions and vaccination roll-out. To support national and regional authorities in surveilling and controlling the pandemic in real-time as it unfolds, we here develop a new regional mathematical and statistical model. The model, which has been in use in Norway during the first two years of the pandemic, is informed by real-time mobility estimates from mobile phone data and laboratory-confirmed case and hospitalisation incidence. To estimate regional and time-varying transmissibility, case detection probabilities, and missed imported cases, we developed a novel sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, enabling policy-relevant situational awareness at a local scale. By comparing the reproduction numbers before and after lockdowns, we identify spatially heterogeneous patterns in their effect on the transmissibility, with a stronger effect in the most populated regions compared to the national reduction estimated to be 85% (95% CI 78%-89%). Our approach is the first regional changepoint stochastic metapopulation model capable of real time spatially refined surveillance and forecasting during emergencies.