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.

Modeling geographic vaccination strategies for COVID-19 in Norway

Vaccination was a key intervention in controlling the COVID-19 pandemic globally. In early 2021, Norway faced significant regional variations in COVID-19 incidence and prevalence, with large differences in population density, necessitating efficient vaccine allocation to reduce infections and severe outcomes. This study explored alternative vaccination strategies to minimize health outcomes (infections, hospitalizations, ICU admissions, deaths) by varying regions prioritized, extra doses prioritized, and implementation start time. Using two models (individual-based and meta-population), we simulated COVID-19 transmission during the primary vaccination period in Norway, covering the first 7 months of 2021. We investigated alternative strategies to allocate more vaccine doses to regions with a higher force of infection. We also examined the robustness of our results and highlighted potential structural differences between the two models. Our findings suggest that early vaccine prioritization could reduce COVID-19 related health outcomes by 8% to 20% compared to a baseline strategy without geographic prioritization. For minimizing infections, hospitalizations, or ICU admissions, the best strategy was to initially allocate all available vaccine doses to fewer high-risk municipalities, comprising approximately one-fourth of the population. For minimizing deaths, a moderate level of geographic prioritization, with approximately one-third of the population receiving doubled doses, gave the best outcomes by balancing the trade-off between vaccinating younger people in high-risk areas and older people in low-risk areas. The actual strategy implemented in Norway was a two-step moderate level aimed at maintaining the balance and ensuring ethical considerations and public trust. However, it did not offer significant advantages over the baseline strategy without geographic prioritization. Earlier implementation of geographic prioritization could have more effectively addressed the main wave of infections, substantially reducing the national burden of the pandemic.

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.

Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: a three-stage modelling study

BACKGROUND

Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures.

METHODS

In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature-mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature-mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division.

FINDINGS

Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967-5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58-11·07) of all deaths (8·52% [6·19-10·47] were cold-related and 0·91% [0·56-1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60-87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000-03 to 2016-19, the global cold-related excess death ratio changed by -0·51 percentage points (95% eCI -0·61 to -0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13-0·31), leading to a net reduction in the overall ratio. The largest decline in overall excess death ratio occurred in South-eastern Asia, whereas excess death ratio fluctuated in Southern Asia and Europe.

INTERPRETATION

Non-optimal temperatures are associated with a substantial mortality burden, which varies spatiotemporally. Our findings will benefit international, national, and local communities in developing preparedness and prevention strategies to reduce weather-related impacts immediately and under climate change scenarios.

FUNDING

Australian Research Council and the Australian National Health and Medical Research Council.

The warm summer of 2018 - increased mortality among older people?

BACKGROUND

Heatwaves in Europe and the USA have been shown to cause excess mortality among older persons. The summer of 2018 was unusually hot in south-eastern Norway. The purpose of this study was to investigate whether more older persons died that summer compared with the average for the previous ten summers.

MATERIAL AND METHOD

Temperature data from the Norwegian Meteorological Institute and mortality data for the summer of 2018 (June, July and August), divided into age groups and counties, were compared to the previous ten summers.

RESULTS

For Norway as a whole, there was no increase in mortality among persons more than 75 years and 85 years of age in summer 2018. None of the counties in south-eastern Norway stood out as having elevated mortality for persons more than 75 years of age, apart from Vest-Agder county. Three counties, among them Aust-Agder, had somewhat lower mortality than expected.

INTERPRETATION

We are unable to show any increase in mortality among older persons in summer 2018 compared with the average for the period 2008-17. Due to climate change and prognoses of more frequent heatwaves, mortality should nevertheless be monitored and public warning systems considered.

Assessing Alternative Population Size Proxies in a Wastewater Catchment Area Using Mobile Device Data

Modeling and prediction of a city's (Oslo, Norway) daily dynamic population using mobile device-based population activity data and three low cost markers is presented for the first time. Such data is useful for wastewater-based epidemiology (WBE), which is an approach used to estimate the population level use of licit and illicit drugs, new psychoactive substances, human exposure to a wide range of pollutants, such as pesticides or phthalates, as well as the release of endogenous substances such as oxidative stress and allergen biomarkers. Comparing WBE results between cities often requires normalization to population size, and inaccuracy in the measured population can introduce high levels of uncertainty. In this study mobile phone data from 8-weeks in 2016 was used to train three linear models based on drinking water production, electricity consumption and online measurements of ammonium in wastewater. The ammonium model showed the best correlation with R² = 0.88 while drinking water production and electricity consumption showed more discrepancies. The three models were then re-evaluated against 5-week of mobile phone data from 2017 showing mean absolute errors <10%. The ammonium-based estimated mean annual population for Oslo in 2017 was 645 000 inhabitants, 4% higher than the "de jure" population reported by the wastewater treatment plant. Due to changing conditions and seasonality, drinking water production underestimated the population by 27% and electricity consumption overestimated the population by 59%. Therefore, the results of this work showed that the ammonium mass loads can be used as an anthropogenic proxy to monitor and correct the fluctuations in population for a specific catchment area. Furthermore, this approach uses a simple, yet reliable indicator for population size that can be used also in other areas of research.

MRSA infections in Norway: A study of the temporal evolution, 2006-2015

Background

Norway has one of the lowest prevalences of methicillin-resistant Staphylococcus aureus (MRSA) infections in the world. This study exploits the extensive data on MRSA infections in the Norwegian surveillance system to investigate the important factors defining the MRSA epidemiology.

Methods

We performed a quasi-Poisson regression of the monthly notification rate (NR) of MRSA infections reported from January 2006 to December 2015, comparing the time trend among people with an immigrant vs. Norwegian background and domestic vs. imported infections, stratified by age groups.

Findings

A total of 5289 MRSA infections were reported during the study period, of which 2255 (42·6%) were acquired in Norway, 1370 (25·9%) abroad, and 1664 (31·5%) with an unknown place of acquisition. Overall, the monthly NR increased significantly from 2006 to 2015 (+0·8% each month). The monthly increase in immigrants (+1·3%) was steeper than that in people with a Norwegian background (+0·6%). There was a significant growth (+0·4%) in the rate of domestically acquired infections, however, the NR of infections acquired abroad increased faster (+0·8%). For both imported and domestic infections, the increase occurred in persons aged < 70 years.

Interpretation

Our analysis suggests that immigration and importation, especially among persons aged < 40 years, represent important factors for the increasing notification rate of MRSA infections in Norway.