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We found that daily variations of PM 10) are positively associated with daily variations of mortality.
We did not take account of the potential shift in mortality counts between year 2000 and 2001 caused by switch of ICD-9 to ICD-10, however this shift may be small and only affect the temporal variations of mortality data during a short period.
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Therefore, we modeled the aforementioned covariates in an attempt to explore the potential effect of the population composition of the countries on the variation of mortality rates.
Although the cohort had a better life expectancy compared to the general Dutch population, we were able to observe a large variation of mortality and clear excess mortality in risk groups 4 and 5. Cardiovascular diseases and cardiovascular risk factors, especially diabetes mellitus, strongly contributed to variation of mortality risk.
Our model differs in some respects from the Serfling-Poisson model used by Thompson and colleagues which adopted a pair of sinusoidal terms to adjust for seasonal variation of mortality [14].
Cartesian coordinates only allow modelling of a linear trend surface related to spatial variation of mortality.
We observed only small differences in the geographical variation of mortality.
Both classifications, synoptic and mesoscale, explain the daily variation of mortality to a statistically significant degree.
Mortality tended to be stable across hospital peer groups as between-group variation of mortality reduced over the study period.
The variation of mortality in our study was not comparable to the variation pattern of severe PPH, unlike findings of Fong et al. [ 15].
We assessed variation of SMR and CRBSI on time using weighted linear regression, and variation of mortality on time using generalized-estimating equations.
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