Page 211 - Modern Spatiotemporal Geostatistics
P. 211

192      Modern  Spatiotemporal  Geostatistics —  Chapter 9

        the  PDX  is  consistently  negative).  Moreover,  the  PDX  magnitude  is  indica-
        tive  of  the  strength  of  this  association as a  function  of  time.  As  is shown
        in  Figure  9.10,  the  temperature exposure-death-rate association is of  varying
        magnitude  during  winter  season 5.
             Finally,  in  Figure  9.11  the  spatial  temperature  exposure-death-rate  asso-
        ciation  at  the  population  level  is  represented in  terms  of  the  parameter PDX
        map  (in  %).  In  this  case,  however, EDX  and  ED  denote temporal  averages
        calculated  for  each  one of  the  North  Carolina  counties  using all  e^\DX  an d
        e k\  D values obtained during the season.  The spatial distribution of  flux  is ev-
        erywhere  negative.  Notice that during winter  season  5, while the temperature
        exposure-death-rate association is relatively  weaker  at  the  eastern  part  of  the
        state  and along the  coastline (a fact which is probably due to  the  moderating
        effect  of the  ocean  on the  cold  temperature distribution),  it  is stronger  at  the
        western  side of the  state  (i.e.,  along the  mountain  ranges of the  Great Smoky
        Mountains  National  Park).




















        Figure  9.11.  Spatial map of (3ox  (in  %)  for  winter  season 5.

            PEP  provides  a  useful  framework  of  spatiotemporal  analysis  of  the
        exposure-mortality  association, and  its  extension to  more than  one environ-
        mental  variable is straightforward.  If,  e.g., a  possible  confounder Z  has been
        identified,  we can  use the  PEP parameter

        (in  %),  where EDXZ  is the  mortality  prediction  error  based  on death-rate  D,
        exposure X,  and confounder Z  data, and EDZ  is the mortality  prediction error
        based  on death-rate  D  and confounder Z  data.  The  $DXZ  parameter  incor-
        porates  Z  in  the  vector  BME  analysis  of  Postulate  9.1.  Then,  by comparing
        PDXZ  vs. PDX,  one could  assess  the  importance of  the  confounder Z  in  the
        exposure-effect  association under investigation.  The  approach  is illustrated  in
        Example  9.15.  The  same approach  may also serve to  evaluate competing  asso-
        ciation  theories by means of  prediction  accuracy at  a set of crucial observation
        points.
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