title: Does healthcare infrastructure have an impact on delay in diagnosis and survival?
abstract: BACKGROUND
  The objectives of this study were to evaluate whether healthcare infrastructure impacts delay in diagnosis, and to determine whether healthcare infrastructure and delay in diagnosis impacts survival in gastric cancer.
  
  
  METHODS
  Administrative data from 2175 gastric cancer patients was analyzed using two Cox proportional hazard models with (i) delay in diagnosis and (ii) survival as dependent variables. Density of general practitioners, density of gastroenterologists, characteristics of specialty treatment centers, demographic information, and comorbidities were included in the models. Differentiation was made between urban and rural areas.
  
  
  RESULTS
  The likelihood of being diagnosed increased with an increase in general practitioners (p<0.0001) and gastroenterologists (p<0.0001) in rural areas. In urban areas a higher density of general practitioners reduced delay in diagnosis (p=0.0262), while a higher density of gastroenterologists did not (p=0.2480). The number of gastric cancer cases performed in hospital had a positive impact on survival (p<0.0001), while outpatient infrastructure did not.
  
  
  CONCLUSIONS
  Delay in diagnosis can be reduced by higher availability of general practitioners and gastroenterologists in rural areas. Given the already very high density of physicians in urban areas there is no effect of additional gastroenterologists. As learning effects can be observed with increased hospital volumes, minimum volumes for treatment of gastric cancer may be defined.
authors.affiliation: Hamburg Center for Health Economics, University of Hamburg, Hamburg, Germany. rudolf.blankart@wiso.uni-hamburg.de
authors.forename: Carl Rudolf
authors.initials: CR
authors.lastname: Blankart
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D057210:Delayed Diagnosis / Q000706:statistics & numerical data*; D003695:Delivery of Health Care / Q000592:standards / Q000706:statistics & numerical data*; D005260:Female; D005762:Gastroenterology / Q000706:statistics & numerical data; D058005:General Practitioners / Q000600:supply & distribution; D005858:Germany / Q000453:epidemiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D016016:Proportional Hazards Models; D019035:Rural Health Services / Q000706:statistics & numerical data / Q000600:supply & distribution; D012737:Sex Factors; D013274:Stomach Neoplasms / Q000175:diagnosis* / Q000401:mortality; D016019:Survival Analysis; D019037:Urban Health Services / Q000706:statistics & numerical data / Q000600:supply & distribution; D000078329:Workforce