Page 59 - Contributed Paper Session (CPS) - Volume 3

CPS1944 Oyelola A.
Ethical approval was obtained from the THHS Human Research Ethics
Committee (HREC/16/QTHS/221) and the Queensland Public Health Act
(RD007802) for the data linkage project.

Statistical analysis
Estimation of the climate-pneumonia association
Weekly cases of pneumonia and climatic variables (rainfall, temperature)
were analysed using distributed lag non-linear model (DLNM) [13-16] to
investigate the association between pneumonia cases and rainfall or
temperature in THHS from 2006 to 2016.
The weekly counts of pneumonia cases was fitted via quasi-Poisson
generalized linear regression models adjusting for season, long-term trend,
weekly mean temperature (ºC) and total weekly rainfall (mm). We used
distributed lag non-linear models (DLNMs) [13-16] to model the potential
non-linear and delayed (lagged) effects of temperature and rainfall.

~( )

( ) =∝ + ∑ ( ) + ∑

=1 =1

Where represents the weekly observed pneumonia cases on week t with

mean , ∝ is the model intercept. The function, is used to specify the

functional relationship between variables and the nonlinear exposure-

response curve, defined by the parameter vectors . The variables include

other predictors with linear effects specified by the related coefficients .

Previous studies have suggested that the effect of a specific exposure
event is not limited to the period when it is observed, association may spread
over a few time periods [15, 17]. Therefore, we modelled the non-linear and
delayed effects of a rainfall and temperature through functions which define

the relationship along the two dimensions of predictor and lags. That is, the
exposure-lag-response was modelled by applying a bi-dimensional cross-
basis spline function describing simultaneously the dependency of the
relationship along the temperature range and its distributed lag effects.
The relaxed cross-basis parameterization for exposure-lag-response is given
by:

(, ) = ∫ ∙ ( − , ) ≈ ∑ ∙ ( − , ) =

,
0
0
Where the bi-dimensional function ∙ ( − , ) define the exposure–lag–
response function, and models simultaneously the exposure–response ()
curve along temperature/rainfall range and lag–response curve, () [14].

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