The effect of ambient temperature on diabetes mortality in China: A multi-city time series study

Yang, Jun; Yin, Peng; Zhou, Maigeng; Chen, Ou; Li, Mengmeng; Liu, Yunning; Gao, Jinghong; Chen, Bin; Liu, Jiangmei; Bai, Li; Liu, Qiyong

doi:10.1016/j.scitotenv.2015.11.014

Cited by 70 publications

(42 citation statements)

References 47 publications

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“…The YRD is located in eastern China and has a subtropical monsoon climate and four distinct seasons. The PRD is located in southern China and has a subtropical monsoon climate, a warm winter, and a hot summer (Huang et al 2012;Yang et al 2016). The daily mortality data of five county-level cities, eight counties, and twelve urban districts from 1 January 2007 to 31 December 2012 were obtained from the Chinese Center for Disease Control and Prevention.…”

Section: Data Collectionmentioning

confidence: 99%

“…4 The newly generated weather data, which include mean temperature and relative humidity (RH), have superior accuracy, are available at 0.25°9 0.25°grid divisions, and thus were used in our study. A county/district's daily weather data are derived from average values of all grids covered by the county or district rather than employ a single value from within or around the county or district, as some previous studies have done (Chen et al 2015;Yang et al 2016). The averaging method employed in this study is more reasonable, because the derived climate measurements better characterize the spatial variations within a county than those employing station measurements.…”

Section: Data Collectionmentioning

confidence: 99%

“…Therefore, it is very important to identify a reasonable lag time that captures accurately the magnitude of the cold/heat effect. Many other studies have chosen the same lag time, such as 0-21 days (Guo et al 2014;Ma, Wang et al 2015a;Yang et al 2016), to calculate extreme heat/cold-related mortality risk. This practice would appear to underestimate the magnitude of the heat effect (acute and last only for a few days) and may not adequately explore the impact of heat stress.…”

Section: Identifying Cold/heat Effect Using Alternative Approachesmentioning

confidence: 99%

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Different Mortality Effects of Extreme Temperature Stress in Three Large City Clusters of Northern and Southern China

Zhang

Wang

et al. 2017

Int J Disaster Risk Sci

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Extreme temperature events have affected Chinese city residents more frequently and intensively since the early 2000s, but few studies have identified the impacts of extreme temperature on mortality in different city clusters of China. This study first used a distributed lag, nonlinear model to estimate the county/district-specific effects of extreme temperature on nonaccidental and cardiovascular mortality. The authors then applied a multivariate meta-analysis to pool the estimated effects in order to derive regional temperature-mortality relationship in three large city clusters-the Beijing-Tianjin-Hebei (BTH) region, the Yangtze River Delta (YRD), and the Pearl River Delta (PRD), which represent northern and southern regions. With 0-3 days' lag, the strongest heat-related mortality effect was observed in the BTH region (with relative risk (RR) of 1.29; 95% confidence interval (CI): 1.13-1.47), followed by the YRD (RR = 1.25; 95% CI: 1.13-1.35) and the PRD (RR = 1.14; 95% CI: 1.01-1.28) areas. With 0-21 days' lag, the cold effect was pronounced in all city clusters, with the highest extreme cold-related mortality risk found in the PRD area (RR = 2.27; 95% CI: 1.63-3.16), followed by the YRD area (RR = 1.85; 95% CI: 1.56-2.20) and BTH region (RR = 1.33; 95% CI: 0.96-1.83). People in the southern regions tended to be more vulnerable to cold stress, but the northern population was more sensitive to heat stress. By examining the effects of extreme temperature in city clusters of different regions, our findings underline the role of adaptation towards heat and cold, which has important implications for public health policy making and practice.

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Section: Data Collectionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Identifying Cold/heat Effect Using Alternative Approachesmentioning

confidence: 99%

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Different Mortality Effects of Extreme Temperature Stress in Three Large City Clusters of Northern and Southern China

Zhang

Wang

et al. 2017

Int J Disaster Risk Sci

Self Cite

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“…Current literature that conduct time series analysis diabetes employ classic time series methods [8][9][10][11]. These classic time series data are parametric and do not accurately depict the original due to violations of the associated assumptions.…”

Section: Introductionmentioning

confidence: 99%

“…Parametric time series methods are not able to accurately account for the complexity of non-stationary and non-normally distributed data [1,2,[12][13][14][15]. Much like most mortality data, diabetes mortality time series displayed a non-stationary signal indicating the necessary use of non-parametric methods such as the KZ Filter methodology [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis on Diabetes Mortality in the United States, 1999- 2015 by Kolmogorov-Zurbenko Filter

Arndorfer¹,

Žurbenko²

2017

J Biom Biostat

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Kolmogorov-Zurbenko filters can be utilized in the public health context analyzing mortality data. This paper aims to expand upon the robust methodology of the KZ filters and their many applications. As a low-pass filter the KZ filters are proven to be the optimal means of analysis for non-stationary data such as mortality data which usually contains various underlying signals: seasonality, long-term trend, and short-term fluctuations. As diabetes incidence and prevalence increases, the burden of health care cost increases, thus prompting the need to understand patterns underlying adverse events related to diabetes, such as mortality. Increasing incidence and prevalence of diabetes prompts the need for preventative measures and understanding what environmental factors are related to adverse events as a result of diabetes. Diabetes mortality across time analyzed with non-parametric models has not previously been studied, thus this extension to the KZ filters is utilized as a preliminary analysis to address the gap in knowledge of diabetes mortality in the United States. Non-parametric time series analysis methods identify an 8.5-year long-term trend as well as annual seasonality of diabetes mortality. Spectral and time analysis of diabetes mortality introduces the relationship between solar activity and diabetes mortality, which is quantified utilizing the cross-correlation between diabetes mortality and total solar irradiation. The strong correlation between solar activity and diabetes mortality confirms the environmental role related specifically to diabetes mortality.

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Hourly Heat Exposure and Acute Ischemic Stroke

Zhu,

Chen,

Yuan

et al. 2024

JAMA Netw Open

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ImportancePrevious studies have demonstrated the associations of daily high temperature with hospitalizations and mortality from ischemic stroke, but the hourly association of ambient heat and acute ischemic stroke (AIS) onset has been rarely examined.ObjectivesTo evaluate the association between hourly high ambient temperature and the onset of AIS.Design, Setting, and ParticipantsThis time-stratified case-crossover study was conducted using a nationwide registry that collects data from more than 200 stroke centers in China. Participants were adult patients with AIS who were hospitalized in the warm seasons between January 1, 2019, and December 31, 2021.ExposuresHourly temperature and single-hour temperature exposure lag up to 24 hours before the AIS onset (lag 0 hours to lag 24 hours).Main Outcomes and MeasuresThe main outcome was onset of AIS. Associations between hourly mean temperatures and AIS onset were analyzed using conditional logistic regression integrated with the distributed lag nonlinear model. Stratification analyses were applied to examine potential association modifiers. Several sensitivity analyses were conducted to examine the robustness of the results.ResultsA total of 82 455 patients with AIS (mean [SD] age, 65.8 [11.9] years; 52 267 males [63.4%]) were included in the final analysis. A monotonically increasing risk of AIS onset was associated with higher temperatures. The excess AIS risk occurred immediately at lag 0 hours and persisted for 10 hours. Compared with the reference temperature (12.1 °C), the cumulative odds ratio (OR) over lag 0 to 10 hours of AIS onset associated with extremely high temperature (33.3 °C) was 1.88 (95% CI, 1.65-2.13) nationwide. The exposure-response curve was steeper in the north than in the south (OR, 1.80 [95% CI, 1.53-2.11] vs 1.57 [95% CI, 1.31-1.87]). The ORs were greater for males and patients with a history of dyslipidemia or atrial fibrillation, but the differences were not significant.Conclusions and RelevanceResults of this study suggest that hourly heat exposure is associated with increased risk of AIS onset. This finding may benefit the formulation of public health strategies to reduce cerebrovascular risk associated with high ambient temperature under global warming.

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The effect of ambient temperature on diabetes mortality in China: A multi-city time series study

Cited by 70 publications

References 47 publications

Different Mortality Effects of Extreme Temperature Stress in Three Large City Clusters of Northern and Southern China

Different Mortality Effects of Extreme Temperature Stress in Three Large City Clusters of Northern and Southern China

Time Series Analysis on Diabetes Mortality in the United States, 1999- 2015 by Kolmogorov-Zurbenko Filter

Hourly Heat Exposure and Acute Ischemic Stroke

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