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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