Seasonality of mortality under a changing climate: a time-series analysis of mortality in Japan between 1972 and 2015

BACKGROUND@#Ambient temperature may contribute to seasonality of mortality; in particular, a warming climate is likely to influence the seasonality of mortality. However, few studies have investigated seasonality of mortality under a warming climate.@*METHODS@#Daily mean temperature, daily counts for all-cause, circulatory, and respiratory mortality, and annual data on prefecture-specific characteristics were collected for 47 prefectures in Japan between 1972 and 2015. A quasi-Poisson regression model was used to assess the seasonal variation of mortality with a focus on its amplitude, which was quantified as the ratio of mortality estimates between the peak and trough days (peak-to-trough ratio (PTR)). We quantified the contribution of temperature to seasonality by comparing PTR before and after temperature adjustment. Associations between annual mean temperature and annual estimates of the temperature-unadjusted PTR were examined using multilevel multivariate meta-regression models controlling for prefecture-specific characteristics.@*RESULTS@#The temperature-unadjusted PTRs for all-cause, circulatory, and respiratory mortality were 1.28 (95% confidence interval (CI): 1.27-1.30), 1.53 (95% CI: 1.50-1.55), and 1.46 (95% CI: 1.44-1.48), respectively; adjusting for temperature reduced these PTRs to 1.08 (95% CI: 1.08-1.10), 1.10 (95% CI: 1.08-1.11), and 1.35 (95% CI: 1.32-1.39), respectively. During the period of rising temperature (1.3 °C on average), decreases in the temperature-unadjusted PTRs were observed for all mortality causes except circulatory mortality. For each 1 °C increase in annual mean temperature, the temperature-unadjusted PTR for all-cause, circulatory, and respiratory mortality decreased by 0.98% (95% CI: 0.54-1.42), 1.39% (95% CI: 0.82-1.97), and 0.13% (95% CI: - 1.24 to 1.48), respectively.@*CONCLUSION@#Seasonality of mortality is driven partly by temperature, and its amplitude may be decreasing under a warming climate.

Excess mortality during the COVID-19 outbreak in Italy: a two-stage interrupted time series analysis

BackgroundItaly was the first country outside China to experience the impact of the COVID-19 pandemic, which resulted in a significant health burden. This study presents an analysis of the excess mortality across the 107 Italian provinces, stratified by sex, age group, and period of the outbreak. MethodsThe analysis was performed using a two-stage interrupted time series design using daily mortality data for the period January 2015 - May 2020. In the first stage, we performed province-level quasi-Poisson regression models, with smooth functions to define a baseline risk while accounting for trends and weather conditions and to flexibly estimate the variation in excess risk during the outbreak. Estimates were pooled in the second stage using a mixed-effects multivariate meta-analysis. ResultsIn the period 15 February - 15 May 2020, we estimated an excess of 47,490 (95% empirical confidence intervals: 43,984 to 50,362) deaths in Italy, corresponding to an increase of 29.5% (95%eCI: 26.8 to 31.9%) from the expected mortality. The analysis indicates a strong geographical pattern, with the majority of excess deaths occurring in northern regions, where few provinces experienced up to 800% increase during the peak in late March. There were differences by sex, age, and area both in the overall impact and in its temporal distribution. ConclusionsThis study offers a detailed picture of excess mortality during the first months of the COVID-19 pandemic in Italy. The strong geographical and temporal patterns can be related to implementation of lockdown policies and multiple direct and indirect pathways in mortality risk. Key MessagesO_LIThis study evaluated mortality trends in Italy during the COVID-19 pandemic, reporting an excess of 47,490 (95% empirical confidence intervals: 43,984 to 50,362) deaths in the period 15 February - 15 May 2020, corresponding to an increase of 29.5% (95%eCI: 26.8 to 31.9%) from the expected mortality. C_LIO_LIThere is a strong geographical pattern, with 71.0% of the estimated excess deaths occurring in just three northern regions (Lombardy, Veneto, and Emilia-Romagna), and few provinces showing increases in mortality up to 800% during the peak of the pandemic. C_LIO_LIThe impact was slightly higher is men compared to women, with 24,655 and 23,125 excess deaths respectively, and varied by age, with higher mortality in the group 70-79 years old and evidence of a lower but measurable risk even in people less than 60. C_LIO_LIThe analysis by week suggests differential trends, with more delayed impacts in women and elderly, and the risk limited to the early period in Central and Southern Italy, likely related to the implementation of lockdown policies and contributions from direct and indirect risk pathways. C_LI

The Association of Social Distancing, Population Density, and Temperature with the SARS-CoV-2 Instantaneous Reproduction Number in Counties Across the United States

ImportanceThe Covid-19 pandemic has been marked by considerable heterogeneity in outbreaks across the United States. Local factors that may be associated with variation in SARS-CoV-2 transmission have not been well studied. ObjectiveTo examine the association of county-level factors with variation in the SARS-CoV-2 reproduction number over time. DesignObservational study Setting211 counties in 46 states and the District of Columbia between February 25, 2020 and April 23, 2020. ParticipantsResidents within the counties (55% of the US population) ExposuresSocial distancing as measured by percent change in visits to non-essential businesses, population density, lagged daily wet bulb temperatures. Main Outcomes and MeasuresThe instantaneous reproduction number (Rt) which is the estimated number of cases generated by one case at a given time during the pandemic. ResultsMedian case incidence was 1185 cases and fatality rate was 43.7 deaths per 100,000 people for the top decile of 21 counties, nearly ten times the incidence and fatality rate in the lowest density quartile. Average Rt in the first two weeks was 5.7 (SD 2.5) in the top decile, compared to 3.1 (SD 1.2) in the lowest quartile. In multivariable analysis, a 50% decrease in visits to non-essential businesses was associated with a 57% decrease in Rt (95% confidence interval, 56% to 58%). Cumulative temperature effects over 4 to 10 days prior to case incidence were nonlinear; relative Rt decreased as temperatures warmed above 32{degrees}F to 53{degrees}F, which was the point of minimum Rt, then increased between 53{degrees}F and 66{degrees}F, at which point Rt began to decrease. At 55{degrees}F, and with a 70% reduction in visits to non-essential business, 96% of counties were estimated to fall below a threshold Rt of 1.0, including 86% of counties among the top density decile and 98% of counties in the lowest density quartile. Conclusions and RelevanceSocial distancing, lower population density, and temperate weather change were associated with a decreased SARS-Co-V-2 Rt in counties across the United States. These relationships can inform selective public policy planning in communities during the SARS-CoV-2 pandemic. Key PointsO_ST_ABSQuestionC_ST_ABSHow is the instantaneous reproduction number (Rt) of SARS-CoV-2 influenced by local area effects of social distancing, wet bulb temperature, and population density in counties across the United States? FindingsSocial distancing, temperate weather, and lower population density were associated with a decrease in Rt. Of these county-specific factors, social distancing appeared to be the most significant in reducing SARS-CoV-2 transmission. MeaningRt varies significantly across counties. The relationship between Rt and county-specific factors can inform policies to reduce SARS-CoV-2 transmission in selective and heterogeneous communities.