[Lag effect and influencing factors of temperature on other infectious diarrhea in Zhejiang province].
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Objective: To study the lag effect of temperature and the source of heterogeneity on other infectious diarrhea (OID) in Zhejiang province, so as to identify related vulnerable populations at risk. Methods: Data on OID and meteorology in Zhejiang province from 2014 to 2016 were collected. A two-stage model was conducted, including: 1) using the distributed lag non-linear model to estimate the city-specific lag effect of temperature on OID, 2) applying the multivariate Meta- analysis to pool the estimated city-specific effect, 3) using the multivariate Meta-regression to explore the sources of heterogeneity. Results: There were 301 593 cases of OID in Zhejiang province during the study period. At the provincial level, temperature that corresponding to the lowest risk of OID was 16.7 ℃, and the temperature corresponding to the highest risk was 6.2℃ (RR=2.298, 95%CI: 1.527- 3.459). 16.7 ℃ was recognized as the reference temperature. P(5) and P(95) of the average daily temperature represented low and high temperature respectively. When the temperature was cold, the risk was delayed by 2 days, with the highest risk found on the 5(th) day (RR=1.057, 95%CI: 1.030-1.084) before decreasing to the 23(rd) day. When the temperature got hot, the risk of OID occurred on the first day (RR=1.081, 95%CI: 1.045-1.118) and gradually decreasing to the 8(th) day. Differences on heterogeneous sources related to the risks of OID in different regions, presented on urban latitude and the rate of ageing in the population. Conclusions: Both high or low temperature could increase the risk of OID, with a lag effect noticed. Prevention program on OID should be focusing on populations living in the high latitude and the elderly population at the low temperature areas.目的： 研究气温对浙江省不同城市其他感染性腹泻的滞后效应，并探讨其异质性来源，找出脆弱人群。 方法： 收集2014－2016年浙江省其他感染性腹泻资料及同期气象资料。采用两阶段模型，首先在各个市利用分布滞后非线性模型评价气温对其他感染性腹泻的滞后效应，然后采用多变量Meta分析合并效应值，再通过Meta回归进一步探索其异质性来源。 结果： 研究期间浙江省共发生其他感染性腹泻301 593例。在全省水平上，其他感染性腹泻发病风险最低时对应的温度为16.7 ℃，以16.7 ℃作为参照温度，发病风险最高时对应的温度为6.2 ℃（RR=2.298，95%CI：1.527~3.459）。以日平均气温的P(5)、P(95)分别代表低温和高温，低温时其他感染性腹泻的发病风险滞后2 d显现，第5天时风险最高（RR=1.057，95%CI：1.030~1.084），然后持续降低至第23天。高温对应的发病风险当天就会出现（RR=1.081，95%CI：1.045~1.118），并逐渐减小至第8天。不同地区其他感染性腹泻发病风险差异的异质性来源有城市纬度及人口老龄化率。 结论： 高温或低温均会增加其他感染性腹泻的发病风险，且存在滞后效应。低温时应加强对高纬度地区人群及老年人群其他感染性腹泻的预防。.Keywords:
Distributed lag
Maximum temperature
Lag time
Objective: To study the lag effect of temperature and the source of heterogeneity on other infectious diarrhea (OID) in Zhejiang province, so as to identify related vulnerable populations at risk. Methods: Data on OID and meteorology in Zhejiang province from 2014 to 2016 were collected. A two-stage model was conducted, including: 1) using the distributed lag non-linear model to estimate the city-specific lag effect of temperature on OID, 2) applying the multivariate Meta- analysis to pool the estimated city-specific effect, 3) using the multivariate Meta-regression to explore the sources of heterogeneity. Results: There were 301 593 cases of OID in Zhejiang province during the study period. At the provincial level, temperature that corresponding to the lowest risk of OID was 16.7 ℃, and the temperature corresponding to the highest risk was 6.2℃ (RR=2.298, 95%CI: 1.527- 3.459). 16.7 ℃ was recognized as the reference temperature. P(5) and P(95) of the average daily temperature represented low and high temperature respectively. When the temperature was cold, the risk was delayed by 2 days, with the highest risk found on the 5(th) day (RR=1.057, 95%CI: 1.030-1.084) before decreasing to the 23(rd) day. When the temperature got hot, the risk of OID occurred on the first day (RR=1.081, 95%CI: 1.045-1.118) and gradually decreasing to the 8(th) day. Differences on heterogeneous sources related to the risks of OID in different regions, presented on urban latitude and the rate of ageing in the population. Conclusions: Both high or low temperature could increase the risk of OID, with a lag effect noticed. Prevention program on OID should be focusing on populations living in the high latitude and the elderly population at the low temperature areas.目的： 研究气温对浙江省不同城市其他感染性腹泻的滞后效应，并探讨其异质性来源，找出脆弱人群。 方法： 收集2014－2016年浙江省其他感染性腹泻资料及同期气象资料。采用两阶段模型，首先在各个市利用分布滞后非线性模型评价气温对其他感染性腹泻的滞后效应，然后采用多变量Meta分析合并效应值，再通过Meta回归进一步探索其异质性来源。 结果： 研究期间浙江省共发生其他感染性腹泻301 593例。在全省水平上，其他感染性腹泻发病风险最低时对应的温度为16.7 ℃，以16.7 ℃作为参照温度，发病风险最高时对应的温度为6.2 ℃（RR=2.298，95%CI：1.527~3.459）。以日平均气温的P(5)、P(95)分别代表低温和高温，低温时其他感染性腹泻的发病风险滞后2 d显现，第5天时风险最高（RR=1.057，95%CI：1.030~1.084），然后持续降低至第23天。高温对应的发病风险当天就会出现（RR=1.081，95%CI：1.045~1.118），并逐渐减小至第8天。不同地区其他感染性腹泻发病风险差异的异质性来源有城市纬度及人口老龄化率。 结论： 高温或低温均会增加其他感染性腹泻的发病风险，且存在滞后效应。低温时应加强对高纬度地区人群及老年人群其他感染性腹泻的预防。.
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A retrospective study to determine relationships between the incidence of dengue cases and climatological variables and to obtain a predictive equation was carried out for the relatively small Caribbean island of Barbados which is divided into 11 parishes. The study used the weekly dengue cases and precipitation data for the years (1995 - 2000) that occurred in the small area of a single parish. Other climatological data were obtained from the local meteorological offices. The study used primarily cross correlation analysis and found the strongest correlation with the vapour pressure at a lag of 6 weeks. A weaker correlation occurred at a lag of 7 weeks for the precipitation. The minimum temperature had its strongest correlation at a lag of 12 weeks and the maximum temperature a lag of 16 weeks. There was a negative correlation with the wind speed at a lag of 3 weeks. The predictive models showed a maximum explained variance of 35%.
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ABSTRACT Background: The disease burden of infectious diarrhea cannot be underestimated. Its seasonal patterns indicate that weather patterns may play an important role and have an important effect on it. The objective of this study was to clarify the relationship between temperature and infectious diarrhea, and diarrhea-like illness. Methods: Distributed lag non-linear model, which was based on the definition of a cross-basis, was used to examine the effect. Results: Viral diarrhea usually had high incidence in autumn-winter and spring with a peak at -6°C; Norovirus circulated throughout the year with an insignificant peak at 8°C, while related bacteria usually tested positive in summer and peaked at 22°C. The lag-response curve of the proportion of diarrhea-like cases in outpatient and emergency cases revealed that at -6°C, with the lag days increasing, the proportion increased. Similar phenomena were observed at the beginning of the curves of virus and bacterial positive rate, showing that the risk increased as the lag days increased, peaking on days 16 and 9, respectively. The shape of lag-response curve of norovirus positive rate was different from others, presenting m-type, with 2 peaks on day 3 and day 18. Conclusion: Weather patterns should be taken into account when developing surveillance programs and formulating relevant public health intervention strategies.
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The aim of this paper was to study the trend of COVID-19 cases and fit appropriate multivariate time series models as research to complement the clinical and non-clinical measures against the menace. The cases of COVID-19, as reported by the National Centre for Disease Control (NCDC) on a daily and weekly basis, include Total Cases (TC), New Cases (NC), Active Cases (AC), Discharged Cases (DC) and Total Deaths (TD). The three waves of the COVID-19 pandemic are graphically represented in the various time plots, indicating the peaks as (June–August, 2020), (December–February, 2021), and (July–September, 2021). Multivariate Autoregressive Distributed Lag Models (MARDLM) and Multivariate Autoregressive Distributed Lag Moving Average (MARDL-MA) models have been found to be suitable for fitting different categories of the COVID-19 pandemic in Nigeria. The graphical representation and estimates have shown a gradual decline in the reported cases after the peak in September 2021. So far, the introduction of vaccines and non-pharmaceutical measures by relevant organisations are yielding plausible results, as evident in the recent decrease in New Cases, Active Cases and an increasing number of Discharged Cases, with fewer deaths. This paper advocates consistency in all clinical and non-clinical measures as a way towards the extinction of the dreaded COVID-19 pandemic in Nigeria and the world.
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Abstract Background: Studies indicated that air pollutions were associated with respiratory disease have with a lag exposure–response relationship, but not linear. However, few evidences in Zhengzhou, one of the most polluted cities for China. Method: Upper respiratory tract infection (URTI) outpatient visits in the hospital, meteorological parameters and air pollutions data were obtained from October 28, 2013 to May 1, 2018 and were used for evaluating the risk effects of the air pollutants with a distributed lag non-linear model (DLNM), including the stratified analysis of gender and age. Result: 475013 cases were included, with obvious seasonal fluctuations,higher in cool/cold and lower in warm. Every increase of 10μg/m 3 of PM 2.5 , PM 10 , SO 2 , NO 2 and CO showed similar impacts on URTI outpatient visits in different genders and age sub-groups,within 0 to15 days of lag. PM 10 , SO 2 and NO 2 had the strongest immediately risk at lag 0 [RR PM10 : 1.0011, 95%CI (1.0002-1.0020); RR SO2 : 1.0084, 95%CI (1.0039-1.0130); RR NO2 : 1.0149, 95%CI: (1.0111-1.0188), respectively], while PM 2.5 and CO got highest risk at lag 15 days [RR PM2.5 : 1.0014, 95%CI (1.0003-1.0025); RR CO : 1.0002, 95%CI: (1.0001-1.0003), respectively]. In addition, calculating overall accumulated effects of each 10μg/m3 increase in PM 10 , SO 2 , NO 2 , and CO was greater in females than in males, as well as greater in the adolescents (aged 0-18 years) and elderly (aged ≥ 60 years) than in adults (aged 19-59 years), except CO was greater in the adolescents and adults than in the elderly. No significant cumulative effects were found in PM 2.5 . O 3 levelwasno significant correlation withURTI outpatient visits throughout the lag period. Conclusions: Our results indicated that PM 10 , SO 2 , NO 2 and CO had strong immediate and lag cumulative effects in the females, adolescents, and elderly. PM 2.5 has lag effects but has no significant lag cumulative impact effects on gender and age.
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Ahmedabad implemented South Asia's first heat action plan (HAP) after a 2010 heatwave. This study evaluates the HAP's impact on all-cause mortality in 2014-2015 relative to a 2007-2010 baseline.We analyzed daily maximum temperature (Tmax)-mortality relationships before and after HAP. We estimated rate ratios (RRs) for daily mortality using distributed lag nonlinear models and mortality incidence rates (IRs) for HAP warning days, comparing pre- and post-HAP periods, and calculated incidence rate ratios (IRRs). We estimated the number of deaths avoided after HAP implementation using pre- and post-HAP IRs.The maximum pre-HAP RR was 2.34 (95%CI 1.98-2.76) at 47°C (lag 0), and the maximum post-HAP RR was 1.25 (1.02-1.53) estimated at 47°C (lag 0). Post-to-pre-HAP nonlagged mortality IRR for Tmax over 40°C was 0.95 (0.73-1.22) and 0.73 (0.29-1.81) for Tmax over 45°C. An estimated 1,190 (95%CI 162-2,218) average annualized deaths were avoided in the post-HAP period.Extreme heat and HAP warnings after implementation were associated with decreased summertime all-cause mortality rates, with largest declines at highest temperatures. Ahmedabad's plan can serve as a guide for other cities attempting to increase resilience to extreme heat.
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Globally, studies have shown that diurnal changes in weather conditions and extreme weather events have a profound effect on mortality. Here, we assessed the effect of apparent temperature on all-cause mortality and the modifying effect of sex on the apparent temperature-mortality relationship using mortality and weather data archived over an eleven-year period. An overdispersed Poisson regression and distributed lag nonlinear models were used for this analysis. With these models, we analysed the relative risk of mortality at different temperature values over a 10-day lag period. By and large, we observed a nonlinear association between mean daily apparent temperature and all-cause mortality. An assessment of different temperature values over a 10-day lag period showed an increased risk of death at the lowest apparent temperature (18°C) from lag 2 to 4 with the highest relative risk of mortality (RR = 1.61, 95% CI: 1.2, 2.15, p
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The aim of the present study was to determine the relationship between temperature and air pollution, and preterm birth in Tehran, Iran.In this time series study, the daily data of preterm births, air pollution, and maximum, minimum and mean temperature from March 2015 to March 2018 were used. To evaluate the effect of air pollution and temperature with and without adjustment of their mutual effects on preterm birth in lags (days) 0-21, the Distributed Lag Non-linear Models (DLNM) was used. The relative risk (RR) was estimated for extreme, moderate and mild heat (99th, 95th, 75th percentile) and cold (1st, 5th, 25th percentile) compared with the median, and for each 10-unit increase in PM2.5, NO2, and O3, 5-unit increase in SO2, and 1-unit increase in CO.The highest RR was seen in extreme (26.9 °C) and moderate (24.8 °C) heat of minimum temperature on lag 0 (RR = 1.17; 1.05-1.31, Adjusted RR = 1.16; 1.04-1.29, RR = 1.15; 1.05-1.26, Adjusted RR = 1.14; 1.03-1.25, respectively). In regard of cold, the only significant effect was for maximum temperature on lags 7-9 (RR = 1.02; 1.00-1.04). Each 10-unit increase in PM2.5 in Lag 0 (RR = 1.008; 1.001-1.014) and lag 1 (RR = 1.004; 1.001-1.007) and in NO2 in lag 0 (RR = 1.006; 1.000-1.012) had significant effects.Maternal exposure to a minimum daily temperature of 26.9 and 24.8 °C compared to 13.2 °C increased the risk of preterm birth by 17 and 15% on the same day, respectively. This risk increased by 0.8 and 0.6%, on the same day for each 10-unit increase in PM2.5 and NO2, respectively.
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Abstract Objectives Meteorological factors and climatic variability have an immense influence on the transmission of infectious diseases and significantly impact human health. Present study quantifies the delayed effect of atmospheric temperature on the risk of hospitalization due to the Coronavirus disease 2019 (COVID-19) with adjusting the effects of other environmental factors in Mumbai, India. Methods The daily reported data of the number of hospitalized COVID-19 positive cases and the environmental factors at Mumbai, Maharashtra, India were collected and analyzed to quantify the main and the delayed effects. Exploratory data analysis and Distributed Linear and Non-linear lag Model (DLNM) with Generalized Additive Model (GAM) specification have applied to analyze the data. Results The study identified the Diurnal Temperature Range (DTR) delayed effect on the risk of hospitalization changed over the lag period of 0–14 days with increasing Relative Risk (RR) at the low DTR and decreasing RR at the higher DTR values. The extreme DTR suggests a high risk of hospitalization at earlier lags (i.e., 0–5 days). DTR’s cumulative effect was significant at higher 0–10 lag days (p-value <0.05). Exposure to the low and moderate DTR suggests a high risk of hospitalization with more than six days of lag. The RR for daily average humidity with 95% C.I was 0.996 (0.967, 1.027). The risk of hospitalization due to COVID-19 showed an increasing nature (p-value <0.05) with the increase in air pollution and average wind speed (WSAvg) at lag 0. Also, the risk of hospitalization changed through different lag periods of DTR. The analysis confirms the higher amount of delayed effect due to low DTR compared with moderate and high DTR. Conclusions The study suggests that both the climatic variations and air quality have significant impact on the transmission of the global pandemic COVID-19.
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Diurnal temperature variation
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