Smoking is the leading cause of @entity2 . Non-smoking factors have been associated with the disease. Existing Swiss survey data only capture the country partially and temporal coverage does not allow for a time lag between exposure to @entity286 and @entity2 outbreak. Knowledge about the distribution of @entity286 -use is essential to estimate its contribution to disease burden. @entity2357 models were applied to estimate spatial smoking patterns. Data were provided from the Swiss Health Survey (14521 @entity1 ). Regression models with spatial random effects (SREs) were employed to obtain smoking proxies based on mortality rates and SREs adjusted for environmental exposures. Population attributable fractions were estimated to assess the burden of @entity286 -use on @entity2 mortality. Correlation between observed smoking prevalence with smoking proxies was moderate and stronger in females. In the absence of sufficient survey data, smooth unadjusted mortality rates can be used to assess smoking patterns in Switzerland.

Using XXXX mortality to indirectly approximate smoking patterns in space.

@entity1 :: ('9606', 'Species') :: ['participants'] @entity286 :: ('4097', 'Species') :: ['tobacco'] @entity2 :: ('MESH:D008175', 'Disease') :: ['lung cancer'] @entity2357 :: ('MESH:C537770', 'Disease') :: ['Bayesian regression']
@entity2 :: (MESH:D008175,Disease) :: ['lung cancer']
