Distributed lag non‐linear models

Gasparrini, Antonio; Armstrong, Ben; Kenward, Michael G.

doi:10.1002/sim.3940

Cited by 1,556 publications

(1,142 citation statements)

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“…It shows the relationship between temperature and mortality at each temperature point and lag. It also calculates the cumulative effect of the existence of delayed contributions (Gasparrini et al 2010). Graphs, summaries, and statistical inference can be obtained from DLNM estimates and standard errors (Armstrong 2006).…”

Section: Resultsmentioning

confidence: 99%

“…Given these increasing periods of extreme temperatures, understanding the effects of prolonged periods of hot and cold temperatures is of particular importance to human health (Anderson and Bell 2009;Gasparrini and Armstrong 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Several previous studies on associations of temperature with mortality have considered delayed effects (Kysely 2004;Hajat et al 2005;Bell et al 2008;Hertel et al 2009), including lagged effects of temperature on single days and the effects of moving average temperature on subsequent days. However, these approaches are susceptible to overestimating the effects of the current day's exposure by ignoring the effects of exposure on previous days (Gasparrini et al 2010), or underestimating the effects of exposure on mortality if they persist longer than the observed lag period (Schwartz 2000;Roberts and Martin 2007). Recently, a distributed lag non-linear model (DLNM) has been developed to simultaneously estimate the non-linear and delayed effects of temperature on mortality or morbidity (Armstrong 2006;Gasparrini et al 2010).…”

Section: Introductionmentioning

confidence: 99%

“…However, these approaches are susceptible to overestimating the effects of the current day's exposure by ignoring the effects of exposure on previous days (Gasparrini et al 2010), or underestimating the effects of exposure on mortality if they persist longer than the observed lag period (Schwartz 2000;Roberts and Martin 2007). Recently, a distributed lag non-linear model (DLNM) has been developed to simultaneously estimate the non-linear and delayed effects of temperature on mortality or morbidity (Armstrong 2006;Gasparrini et al 2010).…”

Section: Introductionmentioning

confidence: 99%

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Effects of temperature on mortality in Hong Kong: a time series analysis

Chan

2014

Int J Biometeorol

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Although interest in assessing the impacts of hot temperature and mortality in Hong Kong has increased, less evidence on the effect of cold temperature on mortality is available. We examined both the effects of heat and cold temperatures on daily mortality in Hong Kong for the last decade (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011). A quasi-Poisson model combined with a distributed lag non-linear model was used to assess the non-linear and delayed effects of temperatures on cause-specific and age-specific mortality.Non-linear effects of temperature on mortality were identified. The relative risk of non-accidental mortality associated with cold temperature (11.1°C, 1 st percentile of temperature) relative to 19.4°C(25 th percentile of temperature) was 1.17 (95% confidence interval (CI): 1.04, 1.29) for lags 0-13. The relative risk of non-accidental mortality associated with high temperature (31.5°C, 99 th percentile of temperature) relative to 27.8°C (75 th percentile of temperature) was 1.09 (95% CI: 1.03, 1.17) for lags 0-3. In Hong Kong, extreme cold and hot temperatures increased the risk of mortality. The effect of cold lasted longer and greater than that of heat. People older than 75 years were the most vulnerable group to cold temperature, while people aged 65-74 were the most vulnerable group to hot temperature.Our findings may have implications for developing intervention strategies for extreme cold and hot temperatures.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effects of temperature on mortality in Hong Kong: a time series analysis

Chan

2014

Int J Biometeorol

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“…Second, we employed a quasi-Poisson regression using the distributed lag nonlinear model (DLNM) [26,27,31] to examine the association of high temperature with the daily number of ambulance dispatches. We used a cross-basis function of a natural cubic spline with 4 df [32] for temperature and 4 df for lag [25], with a maximum lagged effect up to 7 days based on previous studies [25,33].…”

Section: Methodsmentioning

confidence: 99%

Effects of high ambient temperature on ambulance dispatches in different age groups in Fukuoka, Japan

Kotani

Ueda

Seposo

et al. 2018

Global Health Action

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Background: The elderly population has been the primary target of intervention to prevent heat-related illnesses. According to the literature, the highest risks have been observed among the elderly in the temperature–mortality relationship. However, findings regarding the temperature–morbidity relationship are inconsistent. Objectives: This study aimed to examine the association of temperature with ambulance dispatches due to acute illnesses, stratified by age group. Specifically, we explored the optimum temperature, at which the relative health risks were found to be the lowest, and quantified the health risk associated with higher temperatures among different age groups. Methods: We used the data for ambulance dispatches in Fukuoka, Japan, during May and September from 2005 to 2012. The data were grouped according to age in 20-year increments. We explored the pattern of the association of ambulance dispatches with temperature using a smoothing spline curve to identify the optimum temperature for each age group. Then, we applied a distributed lag nonlinear model to estimate the risks of the 85th–95th percentile temperature relative to the overall optimum temperature, for each age group. Results: The relative risk of ambulance dispatches at the 85th and 95th percentile temperature for all ages was 1.08 [95% confidence interval (CI): 1.05, 1.12] and 1.12 (95% CI: 1.08, 1.16), respectively. In comparison, among age groups, the optimum temperature was observed as 25.0°C, 23.2°C, and 25.3°C for those aged 0–19, 60–79, and ≥80, respectively. The optimum temperature could not be determined for those aged 20–39 and 40–59. The relative risks of high temperature tended to be higher for those aged 20–39 and 40–59 than those for other age groups. Conclusions: We did not find any definite difference in the effect of high temperature on ambulance dispatches for different age groups. However, more measures should be taken for younger and middle-aged people to avoid heat-related illnesses.

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COVID-19 Transmission Dynamics Among Close Contacts of Index Patients With COVID-19

Martínez

Sun

et al. 2021

JAMA Intern Med

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IMPORTANCE Much remains unknown about the transmission dynamics of COVID-19. How the severity of the index case and timing of exposure is associated with disease in close contacts of index patients with COVID-19 and clinical presentation in those developing disease is not well elucidated.OBJECTIVES To investigate the association between the timing of exposure and development of disease among close contacts of index patients with COVID-19 and to evaluate whether the severity of the index case is associated with clinical presentation in close contacts who develop COVID-19. DESIGN, SETTING, AND PARTICIPANTSThis study used a large, population-based cohort of 730 individuals (index patients) who received a diagnosis of COVID-19 in Zhejiang Province, China, from January 8 to July 30, 2020, along with a contact tracing surveillance program. Field workers visited 8852 close contacts of the index patients and evaluated them for COVID-19 through August 2020. A timeline was constructed to characterize different exposure periods between index patients and their contacts. MAIN OUTCOMES AND MEASURESThe primary outcome was the attack rate of COVID-19, defined as the total number of new COVID-19 cases diagnosed among contacts of index patients divided by the total number of exposed contacts. A secondary outcome was asymptomatic clinical presentation among infected contacts. Relative risks were calculated to investigate risk factors for COVID-19 among contacts and asymptomatic clinical presentation among infected contacts. RESULTS Among 8852 close contacts (4679 male contacts [52.9%]; median age, 41 years [interquartile range, 28-54 years]) of 730 index patients (374 male patients [51.2%]; median age, 46 years [interquartile range, 36-56 years]), contacts were at highest risk of COVID-19 if they were exposed between 2 days before and 3 days after the index patient's symptom onset, peaking at day 0 (adjusted relative risk [ARR], 1.3; 95% CI, 1.2-1.5). Compared with being exposed to an asymptomatic index patient, the risk of COVID-19 among contacts was higher when they were exposed to index patients with mild (ARR, 4.0; 95% CI, 1.8-9.1) and moderate (ARR, 4.3; 95% CI, 1.9-9.7) cases of COVID-19. As index case severity increased, infected contacts were less likely to be asymptomatic (exposed to patient with mild COVID-19: ARR, 0.3; 95% CI, 0.1-0.9; exposed to patient with moderate COVID-19: ARR, 0.3; 95% CI, 0.1-0.8).CONCLUSIONS AND RELEVANCE This cohort study found that individuals with COVID-19 were most infectious a few days before and after symptom onset. Infected contacts of asymptomatic index patients were less likely to present with COVID-19 symptoms, suggesting that quantity of exposure may be associated with clinical presentation in close contacts.

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Distributed lag non‐linear models

Cited by 1,556 publications

References 37 publications

Effects of temperature on mortality in Hong Kong: a time series analysis

Effects of temperature on mortality in Hong Kong: a time series analysis

Effects of high ambient temperature on ambulance dispatches in different age groups in Fukuoka, Japan

COVID-19 Transmission Dynamics Among Close Contacts of Index Patients With COVID-19

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