User login
ATLANTA – Patient sharing among hospital facilities contributed substantially to the overall Clostridium difficile infection rate, an analysis of interhospital contamination effects showed.
In fact, 7.6% of all Clostridium difficile infection (CDI) cases at the nearly 400 California hospitals included in the study were directly attributable to the patient-sharing network, Daniel Sewell, PhD, reported at the International Conference on Emerging Infectious Diseases.
“The methods that we employed allowed us to estimate the expected increase in CDI cases due to transfers as a function of the CDI rate at the hospital from which those patients were brought. These transfer patients were responsible for about 3.06 times the number of CDI cases as a normal patient,” said Dr. Sewell, a biostatistician at the University of Iowa, Iowa City.
The findings, which underscored the importance of regional (rather than local) efforts to minimize the spread of health care–associated infections, are based on an analysis of 27,200,873 hospital admissions and 532,320 same-day patient transfers identified from the Healthcare Cost and Utilization Project California State Inpatient Database for 2005-2011.
Transfer networks based on the monthly average number of patients discharged from one hospital and admitted to another on the same day were constructed, and the monthly average number of CDI cases per hospital were considered, along with hospital-level characteristics such as patient length of stay, age, and number of diagnoses. Network autocorrelation models that help eliminate bias were then used to assess the contamination effects between hospitals, he explained.
This led to development of an equation that can be used to determine the expected number of CDI cases in a hospital as a function of the number of transfers coming in and the contamination level of the source hospitals. The ability to calculate the expected number of CDI cases in this fashion is an important factor for the success of regional versus local intervention efforts, which are increasingly thought to be important for reducing health care–associated infections.
“If we want to design a coordinated or regional approach, we’ve got to have a much better understanding of the role that patient transfers have in these diseases,” Dr. Sewell said.
As most hospitals included in the study had a low CDI rate and a low transfer rate, the CDIs attributable to transfers represent a minority of cases, but they are a substantial minority, he said, noting that the main concern is with the “perfect storm” of high CDI rate plus high transfer rate.
The methodological approach used in this study to estimate CDI rates can be used for any health care–associated infection of interest, he added.
Dr. Sewell reported that he had no disclosures.
ATLANTA – Patient sharing among hospital facilities contributed substantially to the overall Clostridium difficile infection rate, an analysis of interhospital contamination effects showed.
In fact, 7.6% of all Clostridium difficile infection (CDI) cases at the nearly 400 California hospitals included in the study were directly attributable to the patient-sharing network, Daniel Sewell, PhD, reported at the International Conference on Emerging Infectious Diseases.
“The methods that we employed allowed us to estimate the expected increase in CDI cases due to transfers as a function of the CDI rate at the hospital from which those patients were brought. These transfer patients were responsible for about 3.06 times the number of CDI cases as a normal patient,” said Dr. Sewell, a biostatistician at the University of Iowa, Iowa City.
The findings, which underscored the importance of regional (rather than local) efforts to minimize the spread of health care–associated infections, are based on an analysis of 27,200,873 hospital admissions and 532,320 same-day patient transfers identified from the Healthcare Cost and Utilization Project California State Inpatient Database for 2005-2011.
Transfer networks based on the monthly average number of patients discharged from one hospital and admitted to another on the same day were constructed, and the monthly average number of CDI cases per hospital were considered, along with hospital-level characteristics such as patient length of stay, age, and number of diagnoses. Network autocorrelation models that help eliminate bias were then used to assess the contamination effects between hospitals, he explained.
This led to development of an equation that can be used to determine the expected number of CDI cases in a hospital as a function of the number of transfers coming in and the contamination level of the source hospitals. The ability to calculate the expected number of CDI cases in this fashion is an important factor for the success of regional versus local intervention efforts, which are increasingly thought to be important for reducing health care–associated infections.
“If we want to design a coordinated or regional approach, we’ve got to have a much better understanding of the role that patient transfers have in these diseases,” Dr. Sewell said.
As most hospitals included in the study had a low CDI rate and a low transfer rate, the CDIs attributable to transfers represent a minority of cases, but they are a substantial minority, he said, noting that the main concern is with the “perfect storm” of high CDI rate plus high transfer rate.
The methodological approach used in this study to estimate CDI rates can be used for any health care–associated infection of interest, he added.
Dr. Sewell reported that he had no disclosures.
ATLANTA – Patient sharing among hospital facilities contributed substantially to the overall Clostridium difficile infection rate, an analysis of interhospital contamination effects showed.
In fact, 7.6% of all Clostridium difficile infection (CDI) cases at the nearly 400 California hospitals included in the study were directly attributable to the patient-sharing network, Daniel Sewell, PhD, reported at the International Conference on Emerging Infectious Diseases.
“The methods that we employed allowed us to estimate the expected increase in CDI cases due to transfers as a function of the CDI rate at the hospital from which those patients were brought. These transfer patients were responsible for about 3.06 times the number of CDI cases as a normal patient,” said Dr. Sewell, a biostatistician at the University of Iowa, Iowa City.
The findings, which underscored the importance of regional (rather than local) efforts to minimize the spread of health care–associated infections, are based on an analysis of 27,200,873 hospital admissions and 532,320 same-day patient transfers identified from the Healthcare Cost and Utilization Project California State Inpatient Database for 2005-2011.
Transfer networks based on the monthly average number of patients discharged from one hospital and admitted to another on the same day were constructed, and the monthly average number of CDI cases per hospital were considered, along with hospital-level characteristics such as patient length of stay, age, and number of diagnoses. Network autocorrelation models that help eliminate bias were then used to assess the contamination effects between hospitals, he explained.
This led to development of an equation that can be used to determine the expected number of CDI cases in a hospital as a function of the number of transfers coming in and the contamination level of the source hospitals. The ability to calculate the expected number of CDI cases in this fashion is an important factor for the success of regional versus local intervention efforts, which are increasingly thought to be important for reducing health care–associated infections.
“If we want to design a coordinated or regional approach, we’ve got to have a much better understanding of the role that patient transfers have in these diseases,” Dr. Sewell said.
As most hospitals included in the study had a low CDI rate and a low transfer rate, the CDIs attributable to transfers represent a minority of cases, but they are a substantial minority, he said, noting that the main concern is with the “perfect storm” of high CDI rate plus high transfer rate.
The methodological approach used in this study to estimate CDI rates can be used for any health care–associated infection of interest, he added.
Dr. Sewell reported that he had no disclosures.
REPORTING FROM ICEID 2018
Key clinical point: Patient sharing among hospitals contributes substantially to Clostridium difficile infection (CDI) rates.
Major finding: Patient transfers account for 7.6% of the overall CDI burden.
Study details: A statistical analysis to estimate interhospital CDI transmissions.
Disclosures: Dr. Sewell reported that he had no disclosures.