2022
DOI: 10.1016/s2542-5196(22)00138-3
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Small-area assessment of temperature-related mortality risks in England and Wales: a case time series analysis

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Cited by 76 publications
(64 citation statements)
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“…In JJA, mortality risk started increasing as the temperatures increase above around 14.5 °C in Scotland. This is comparable to the temperature thresholds corresponding to the lowest mortality risk (heat threshold thereafter) in Scotland found in another study [ 29 ] but lower than previous studies in other places [ 13 , 38 , 39 ]. This indicates that the heat-health impacts can also be observed at relatively low temperatures in places with a cool climate.…”
Section: Discussionsupporting
confidence: 84%
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“…In JJA, mortality risk started increasing as the temperatures increase above around 14.5 °C in Scotland. This is comparable to the temperature thresholds corresponding to the lowest mortality risk (heat threshold thereafter) in Scotland found in another study [ 29 ] but lower than previous studies in other places [ 13 , 38 , 39 ]. This indicates that the heat-health impacts can also be observed at relatively low temperatures in places with a cool climate.…”
Section: Discussionsupporting
confidence: 84%
“…Further research on the modification effects of deprivation, particularly with the interaction of age on the cold and heat effects is needed. Some previous studies found an increase in cold-and heat-related mortality risks among people in more deprived areas in England and Wales [39,70]. Higher excess winter mortality was also found in regions with higher deprivation in Scotland [51].…”
Section: Discussionmentioning
confidence: 77%
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“…In the present study, we conducted a retrospective assessment of the time-dependent non-linear and delayed associations between daily R t , retail and recreation mobility patterns, and mean ambient temperature, utilizing a cutting-edge time-series regression analytic approach of statistical modeling. Specifically, we formulated a standard time-series generalized additive model (GAM) with a gamma probability distribution family and logarithmic-link probability function, allowing for overdispersion in the observational epidemiologic data, combined with distributed lag nonlinear models (DLNMs) [ 64 ]. Briefly, the general algebraic definition of time-series statistical models is formulated as follows, with further extensions attempted: where Y t is the outcome time series; R t is the expected time series of the daily time-dependent effective reproductive number in prefecture i on day t ; α corresponds to the overall intercept; and f G (x τ,t,i ) denotes the cross-basis function with exposure and lag effects modelled by a natural cubic spline function and a linear function of mean ambient temperature and retail and recreation mobility in prefecture i on day t, respectively.…”
Section: Methodsmentioning
confidence: 99%