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Table 4 Covariate estimates for the model with four groups (G2, GMA, GCum1t, Gcum1t-2)

From: A predictive model relating daily fluctuations in summer temperatures and mortality rates

Temperature indicator Coefficient estimate β (SE) Percentile P10% – P90% of the indicator Relative risk estimate of P90%/P10%a Pr > |Z|b
G2     
   Tmint -0.0219 (0.0027) 8.74 – 17.35 0.83 0.0145
   Tmaxt-1 -0.0124 (0.0018) 17.74 – 29.98 0.86 0.2561
   Tmint × Tmaxt-1 0.0013 (0.0001) 164.25 – 515.27 1.58 0.0000
GMA     
   Moving average -0.0158 (0.0009) 14.60 – 22.52 0.88 0.0000
GCum1 t     
   Tmaxt 0.0045 (0.0006) 17.75 – 29.98 1.06 0.0000
   Ctmaxt -0.007 (0.0026) 0.00 – 10.17 0.93 0.2024
   Tmaxt × Ctmaxt 0.0003 (0.0001) 0.00 – 298.61 1.09 0.0015
   GCum1 t-2     
   Tmaxt-2 -0.0002 (0.0006) 17.75 – 29.98 1.00 0.9319
   Ctmaxt-2 -0.0093 (0.0027) 0.00 – 10.17 0.91 0.0181
   Tmax-2 × Ctmaxt-2 0.0004 (0.0001) 0.00 – 298.61 1.13 0.0000
  1. a Multiplying factor for the increase in mortality when the indicator increases from the 10th percentile (P10%) to the 90th percentile (P90%),
  2. b Pr > |Z| is the significance probability