Does healthcare infrastructure have an impact on delay in diagnosis and survival?

• Published in: Health policy (Amsterdam) Vol. 105; no. 2; pp. 128 - 137
• Main Author: Blankart, Carl Rudolf
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 01.05.2012
• Subjects: Age Factors; Aged; Aged, 80 and over; Cancer; Cox proportional hazard model; Delayed Diagnosis - statistics & numerical data; Delivery of Health Care - standards; Delivery of Health Care - statistics & numerical data; Demographics; Density; Diagnosis; Doctors; Female; Gastric cancer; Gastroenterology - manpower; Gastroenterology - statistics & numerical data; General practice; General Practitioners - supply & distribution; Germany - epidemiology; Health administration; Health care; Health policy; Health services; Hospital volumes; Hospitals; Humans; Infrastructure; Internal Medicine; Male; Medical sociology; Middle Aged; Models, Theoretical; Patients; Physicians; Proportional Hazards Models; Rare disease; Rural Health Services - statistics & numerical data; Rural Health Services - supply & distribution; Rural–urban differences; Sex Factors; Stomach cancer; Stomach Neoplasms - diagnosis; Stomach Neoplasms - mortality; Survival Analysis; Urban areas; Urban Health Services - statistics & numerical data; Urban Health Services - supply & distribution; Germany; Cox proportional hazard model; Rural–urban differences; Hospital volumes; Stomach cancer; Gastric cancer; Rare disease
• ISSN: 0168-8510; 1872-6054
• DOI: 10.1016/j.healthpol.2012.01.006

Abstract Introduction 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. Conclusion 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.