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The short-term mortality fluctuation data series, monitoring mortality shocks across time and space.

ABSTRACT: The COVID-19 pandemic has revealed substantial coverage and quality gaps in existing international and national statistical monitoring systems. It is striking that obtaining timely, accurate, and comparable across countries data in order to adequately respond to unexpected epidemiological threats is very challenging. The most robust and reliable approach to quantify the mortality burden due to short-term risk factors is based on estimating weekly excess deaths. This approach is more reliable than monitoring deaths with COVID-19 diagnosis or calculating incidence or fatality rates affected by numerous problems such as testing coverage and comparability of diagnostic approaches. In response to the emerging data challenges, a new data resource on weekly mortality has been established. The Short-term Mortality Fluctuations (STMF, available at www.mortality.org ) data series is the first international database providing open-access harmonized, uniform, and fully documented data on weekly all-cause mortality. The STMF online vizualisation tool provides an opportunity to perform a quick assessment of the excess weekly mortality in one or several countries by means of an interactive graphical interface.

SUBMITTER: Jdanov DA

PROVIDER: S-EPMC8421508 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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The short-term mortality fluctuation data series, monitoring mortality shocks across time and space.

Jdanov Dmitri A DA Galarza Ainhoa Alustiza AA Shkolnikov Vladimir M VM Jasilionis Domantas D Németh László L Leon David A DA Boe Carl C Barbieri Magali M

Scientific data 20210906 1

The COVID-19 pandemic has revealed substantial coverage and quality gaps in existing international and national statistical monitoring systems. It is striking that obtaining timely, accurate, and comparable across countries data in order to adequately respond to unexpected epidemiological threats is very challenging. The most robust and reliable approach to quantify the mortality burden due to short-term risk factors is based on estimating weekly excess deaths. This approach is more reliable tha ...[more]

PMID: 34489477

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Project description:BackgroundPrevious studies on associations between short-term exposure to fine particulate matter (PM2.5) and ischemic stroke (IS) mortality reported inconclusive results. Additionally, whether and how PM2.5 and green space interact to precipitate IS deaths remains unclear. We aimed to examine the impacts of short-term exposure to PM2.5 on IS mortality and the role of green space in the association.MethodsWe collected data on daily IS deaths, daily PM2.5 concentrations, and monthly normalized difference vegetation index (NDVI) in Zibo City from 2015 to 2019. Generalised additive models were adopted to investigate the short-term impacts of PM2.5 on IS mortality, and subgroup analyses were used to examine effect modification by population characteristics. Stratified analyses by green space levels and joint effect model were conducted to test the interactions of PM2.5 and green space on IS mortality.ResultsA total of 10 799 IS deaths were included in our study. Exposure to PM2.5 was associated with an increased risk of IS mortality, with odds ratios (ORs) of 1.0263 (95% confidence interval (CI) = 1.0017, 1.0516) for each interquartile range (IQR) increase in PM2.5 on lag0 and 1.0317 (95% CI = 1.0016, 1.0627) on lag01. The links between PM2.5 and IS mortality were not significantly different across genders, ages, or PM2.5 zones. Furthermore, our results showed that the effects of PM2.5 on IS mortality were higher in low levels of green space. Specifically, for each IQR increase in PM2.5, the ORs (95% CIs) of IS death in the low level and the high level of NDVI were 1.0287 (95% CI = 1.0019, 1.0563) and 0.9934 (95% CI = 0.9296, 1.0615), respectively. In addition, PM2.5 and NDVI exhibited significant interactive effects on IS mortality, with relative excess odds due to interaction (REOI) of greater than 0.ConclusionsOur findings showed that PM2.5 was significantly associated with increasing odds of IS mortality. Furthermore, there were synergetic impacts between PM2.5 and lack of greenness on IS mortality. Our results suggest that expanding green spaces, such as increasing park coverage and street greening, along with regulating industrial emissions to reduce PM2.5 levels, can help prevent premature deaths from IS.

Project description:BackgroundIt is crucial to comprehend the interplay between air pollution and meteorological conditions in relation to population health within the framework of "dual-carbon" targets. The purpose of this study was to investigate the impact of intricate environmental factors, encompassing both meteorological conditions and atmospheric pollutants, on respiratory disease (RD) mortality in Qingdao, a representative coastal city in China.MethodsThe RD mortality cases were collected from the Chronic Disease Surveillance Monitoring System in Qingdao during Jan 1st, 2014 and Dec 31st, 2020. The distributed-lag nonlinear model and generalized additivity model were used to assess the association between daily mean temperature (DMT), air pollutant exposure and RD mortality. To ascertain the robustness of the model and further investigate this relationship, a stratified analysis and sensitivity analysis were conducted to mitigate potential confounding factors.ResultsA total of 19,905 mortalities from RD were recorded. The minimum mortality temperature (MMT) was determined to be 23.5°C, and DMT and RD mortality showed an N-shaped relationship. At the MMT of 23.5°C, the cumulative relative risk (cumRR) for mortality within a lag period of 0-14 days from the highest temperature (31°C) was estimated at 2.114 (95% confidence interval [CI]: 1.475 ~ 3.028). The effect value of particulate matter (PM) also increased with a longer cumulative lag time. In the single pollutant model, the highest risk of RD mortality was observed on the lag1-day of per 10 μg/m3 increase in PM2.5 exposure, with an excess risk ratio (ER) of 0.847% (95% CI: 0.335% ~ 1.362%). The largest cumulative effect was found at a lag of 8 days, with an ER of 1.546% (95% CI: 0.483% ~ 2.621%). A similar trend was found for PM10. For O3 exposure, the highest risk was observed on the lag1-day of per 10 μg/m3 increase, with an ER of 1.073% (95% CI: 0.502% ~ 1.647%), and the largest cumulative effect occurred at a lag of 2 days with an ER of 1.113% (95%CI: 0.386% ~ 1.844%). Results from the dual-pollutants model demonstrated that the effect of PM on the risk of RD mortality remained significant and slightly increased in magnitude. Moreover, composite pollutants exhibited a higher risk effect, reaching its peak after one week; however, there was a decrease in single-day cumulative effects as more pollutant types were included. Subgroup analysis showed that females, elderly individuals, and those exposed during warm seasons demonstrated greater susceptibility to PM exposure.ConclusionThe present study revealed a significant association between short-term exposure to high temperature, PM2.5, PM10 and O3 and the risk of RD mortality in Qingdao, even in dual- and composite-pollutants models. Furthermore, our findings indicate that females, the elderly population, and warm seasons exhibit heightened sensitivity to PM exposure.

Dataset Information

The short-term mortality fluctuation data series, monitoring mortality shocks across time and space.

Publications

The short-term mortality fluctuation data series, monitoring mortality shocks across time and space.

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