Risk factors for surgical site infections among patients undergoing major colon surgery in the United States 2007-2009

Background: Surgical site infections (SSIs) after abdominal surgeries account for approximately 26% of all reported SSIs. The Center for Disease Control and Prevention (CDC) defines 3 types of SSIs: superficial incisional, deep incisional, and organ/space. Preventing SSIs has become a national focus. This dissertation assesses several associations with the individual types of SSI in patients that have undergone colon surgery. ^ Methods: Data for this dissertation was obtained from the American College of Surgeons' National Surgical Quality Improvement Program (NSQIP); major colon surgeries were identified in the database that occurred between the time period of 2007 and 2009. NSQIP data includes more than 50 preoperative and 30 intraoperative factors; 40 collected postoperative occurrences are based on a follow-up period of 30 days from surgery. Initially, four individual logistic regressions were modeled to compare the associations between risk factors and each of the SSI groups: superficial, deep, organ/space and a composite of any single SSI. A second analysis used polytomous regression to assess simultaneously the associations between risk factors and the different types of SSIs, as well as, formally test the different effect estimates of 13 common risk factors for SSIs. The final analysis explored the association between venous thromboembolism (VTEs) and the different types of SSIs and risk factors. ^ Results: A total of 59,365 colon surgeries were included in the study. Overall, 13% of colon cases developed a single type of SSI; 8% of these were superficial SSIs, 1.4% was deep SSIs, and 3.8% were organ/space SSIs. The first article identifies the unique set of risk factors associated with each of the 4 SSI models. Distinct risk factors for superficial SSIs included factors, such as alcohol, chronic obstructive pulmonary disease, dyspnea and diabetes. Organ/space SSIs were uniquely associated with disseminated cancer, preoperative dialysis, preoperative radiation treatment, bleeding disorder and prior surgery. Risk factors that were significant in all models had different effect estimates. The second article assesses 13 common SSI risk factors simultaneously across the 3 different types of SSIs using polytomous regression. Then each risk factor was formally tested for the effect heterogeneity exhibited. If the test was significant the final model would allow for the effect estimations for that risk factor to vary across each type of SSI; if the test was not significant, the effect estimate would remain constant across the types of SSIs using the aggregate SSI value. The third article explored the relationship of venous thromboembolism (VTE) and the individual types of SSIs and risk factors. The overall incidence of VTEs after the 59,365 colon cases was 2.4%. All 3 types of SSIs and several risk factors were independently associated with the development of VTEs. ^ Conclusions: Risk factors associated with each type of SSI were different in patients that have undergone colon surgery. Each model had a unique cluster of risk factors. Several risk factors, including increased BMI, duration of surgery, wound class, and laparoscopic approach, were significant across all 4 models but no statistical inferences can be made about their different effect estimates. These results suggest that aggregating SSIs may misattribute and hide true associations with risk factors. Using polytomous regression to assess multiple risk factors with the multiple types of SSI, this study was able to identify several risk factors that had significant effect heterogeneity across the 3 types of SSI challenging the use of aggregate SSI outcomes. The third article recognizes the strong association between VTEs and the 3 types of SSIs. Clinicians understand the difference between superficial, deep and organ/space SSIs. Our results indicate that they should be considered individually in future studies.^

Estimates of hospital and physician costs of epilepsy in the United States for 1995 comparing the provider -based survey method and a patient -based medical charts and billing method

This investigation compares two different methodologies for calculating the national cost of epilepsy: provider-based survey method (PBSM) and the patient-based medical charts and billing method (PBMC&BM). The PBSM uses the National Hospital Discharge Survey (NHDS), the National Hospital Ambulatory Medical Care Survey (NHAMCS) and the National Ambulatory Medical Care Survey (NAMCS) as the sources of utilization. The PBMC&BM uses patient data, charts and billings, to determine utilization rates for specific components of hospital, physician and drug prescriptions. ^ The 1995 hospital and physician cost of epilepsy is estimated to be $722 million using the PBSM and $1,058 million using the PBMC&BM. The difference of $336 million results from $136 million difference in utilization and $200 million difference in unit cost. ^ Utilization. The utilization difference of $136 million is composed of an inpatient variation of $129 million, $100 million hospital and $29 million physician, and an ambulatory variation of $7 million. The $100 million hospital variance is attributed to inclusion of febrile seizures in the PBSM, $−79 million, and the exclusion of admissions attributed to epilepsy, $179 million. The former suggests that the diagnostic codes used in the NHDS may not properly match the current definition of epilepsy as used in the PBMC&BM. The latter suggests NHDS errors in the attribution of an admission to the principal diagnosis. ^ The $29 million variance in inpatient physician utilization is the result of different per-day-of-care physician visit rates, 1.3 for the PBMC&BM versus 1.0 for the PBSM. The absence of visit frequency measures in the NHDS affects the internal validity of the PBSM estimate and requires the investigator to make conservative assumptions. ^ The remaining ambulatory resource utilization variance is $7 million. Of this amount, $22 million is the result of an underestimate of ancillaries in the NHAMCS and NAMCS extrapolations using the patient visit weight. ^ Unit cost. The resource cost variation is $200 million, inpatient is $22 million and ambulatory is $178 million. The inpatient variation of $22 million is composed of $19 million in hospital per day rates, due to a higher cost per day in the PBMC&BM, and $3 million in physician visit rates, due to a higher cost per visit in the PBMC&BM. ^ The ambulatory cost variance is $178 million, composed of higher per-physician-visit costs of $97 million and higher per-ancillary costs of $81 million. Both are attributed to the PBMC&BM's precise identification of resource utilization that permits accurate valuation. ^ Conclusion. Both methods have specific limitations. The PBSM strengths are its sample designs that lead to nationally representative estimates and permit statistical point and confidence interval estimation for the nation for certain variables under investigation. However, the findings of this investigation suggest the internal validity of the estimates derived is questionable and important additional information required to precisely estimate the cost of an illness is absent. ^ The PBMC&BM is a superior method in identifying resources utilized in the physician encounter with the patient permitting more accurate valuation. However, the PBMC&BM does not have the statistical reliability of the PBSM; it relies on synthesized national prevalence estimates to extrapolate a national cost estimate. While precision is important, the ability to generalize to the nation may be limited due to the small number of patients that are followed. ^