Variations in hospital costs after traffic injuries: The importance of sociodemographic aspects and comorbidities.

OBJECTIVE: The impact of sociodemographic aspects and comorbidities on the inpatient hospital care costs of traffic victims are not clear. The main goal of this study is to provide insights into the sociodemographic characteristics and clinical conditions (including comorbidities) of the victims that result in higher hospital costs. PARTICIPANTS: For the period 2009-2011, people admitted to a hospital as a result of a road traffic crash (N=64,304) were identified in the national Minimal Hospital Dataset, after which they were linked to their respective claims data from the sickness funds. METHODS: A generalized linear model was used to analyse hospital costs controlling for roadway user categories, demographics (gender, age, individual socioeconomic status (SES)), and clinical factors (the nature, location, and severity of injury, and comorbidities). RESULTS: The median hospital cost was € 2801 (IQR € 1510-€ 7175, 2015 Euros). There was no significant difference between gender. Low SES inpatients incurred 16% (95% CI: 14%-18%) higher hospital costs than inpatients of high SES. The presence of comorbidities was associated with an increased hospital cost, however with varying magnitude. For example traffic victims suffering from dementia incur significantly higher hospital costs than those who were not (49% higher, 95% CI: 44%-53%), whereas diabetes was associated with a smaller increase in costs compared to non-diabetics (13%, 95% CI: 10%-16%). CONCLUSION: Comorbidities and low SES are associated with higher hospital costs for traffic victims, notwithstanding their age, and the nature and the severity of their injury. The broad variability of hospital costs among trauma inpatients should be accounted for when reconsidering financing models. Furthermore, the strong predictive value of some comorbidities and SES on hospital costs should be considered when projections of future health care utilisation in traffic safety scenarios are prepared.

Effect of the shape of the exposure-response function on estimated hospital costs in a study on non-elective pneumonia hospitalizations related to particulate matter.

OBJECTIVE: We used log-linear and log-log exposure-response (E-R) functions to model the association between PM2.5 exposure and non-elective hospitalizations for pneumonia, and estimated the attributable hospital costs by using the effect estimates obtained from both functions. METHODS: We used hospital discharge data on 3519 non-elective pneumonia admissions from UZ Brussels between 2007 and 2012 and we combined a case-crossover design with distributed lag models. The annual averted pneumonia hospitalization costs for a reduction in PM2.5 exposure from the mean (21.4µg/m(3)) to the WHO guideline for annual mean PM2.5 (10µg/m(3)) were estimated and extrapolated for Belgium. RESULTS: Non-elective hospitalizations for pneumonia were significantly associated with PM2.5 exposure in both models. Using a log-linear E-R function, the estimated risk reduction for pneumonia hospitalization associated with a decrease in mean PM2.5 exposure to 10µg/m(3) was 4.9%. The corresponding estimate for the log-log model was 10.7%. These estimates translate to an annual pneumonia hospital cost saving in Belgium of €15.5 million and almost €34 million for the log-linear and log-log E-R function, respectively. DISCUSSION: Although further research is required to assess the shape of the association between PM2.5 exposure and pneumonia hospitalizations, we demonstrated that estimates for health effects and associated costs heavily depend on the assumed E-R function. These results are important for policy making, as supra-linear E-R associations imply that significant health benefits may still be obtained from additional pollution control measures in areas where PM levels have already been reduced.