j o u r n a l o f s u r g i c a l r e s e a r c h 1 9 3 ( 2 0 1 5 ) 7 8 8 e7 9 4
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.JournalofSurgicalResearch.com
Sepsis after major cancer surgery Jesse D. Sammon, DO,a,* Dane E. Klett, MD,a Akshay Sood, MD,a Kola Olugbade Jr., MD,b Marianne Schmid, MD,b Simon P. Kim, MD,c Mani Menon, MD,a and Quoc-Dien Trinh, MDb a
Vattikuti Urology Institute Center for Outcomes Research Analytics and Evaluation, Henry Ford Health System, Detroit, Michigan b Center for Surgery and Public Health and Division of Urologic Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts c Department of Urology, Yale University, New Haven, Connecticut
article info
abstract
Article history:
Background: Cancer patients undergoing procedures are at increased risk of sepsis. We sought
Received 11 June 2014
to evaluate the incidence of postoperative sepsis following major cancer surgeries (MCS), and
Received in revised form
to describe patient and/or hospital characteristics associated with heightened risk.
14 July 2014
Methods: Patients undergoing 1 of 8 MCS (colectomy, cystectomy, esophagectomy, gas-
Accepted 18 July 2014
trectomy, hysterectomy, lung resection, pancreatectomy, and prostatectomy) within the
Available online 24 July 2014
Nationwide Inpatient Sample from 1999e2009 were identified (N ¼ 2,502,710). Logistic regression models fitted with generalized estimating equations were used to estimate
Keywords:
primary predictors (procedure, age, gender, race, insurance, Charlson Comorbidity Index,
Cancer surgery
hospital volume, and hospital bed size) effect on sepsis and sepsis-associated mortality.
Infection
Trends were evaluated with linear regression.
Sepsis
Results: The incidence of MCS-related sepsis increased 2.0% per year (P < 0.001), whereas
Mortality
mortality remained stable. Odds of sepsis were highest among esophagectomy patients
Nationwide Inpatient Sample
(odds ratio [OR]: 3.13, 2.76e3.55) and those with non-private insurance (OR: 1.33, 1.19e1.48 to OR: 1.89, 1.71e2.09). Odds of sepsis-related mortality were highest among lung resection patients (OR: 2.30, 2.00e2.64) and those experiencing perioperative liver failure (OR: 5.68, 4.30e7.52). Increasing hospital volume was associated with lower odds of sepsis and sepsis-related mortality (OR: 0.89, 0.84e0.95 to OR: 0.58, 0.53e0.62 and OR: 0.88, 0.77e0.99 to OR: 0.78, 0.67e0.93). Conclusions: Between 1999 and 2009, the incidence of MCS-related sepsis increased; however, sepsis-related mortality remained stable. Significant disparities exist in patient and hospital characteristics associated with MCS-related sepsis. Hospital volume is an important modifiable risk factor associated with improved sepsis-related outcomes following MCS. ª 2015 Elsevier Inc. All rights reserved.
1.
Introduction
Sepsis is a costly, life-threatening, and multifactorial condition commonly occurring in elderly, immunocompromised,
critically ill, and/or postoperative patients [1]. An estimated 2% of all hospitalizations nationwide are associated with sepsis [1]. In the United States, the direct health-care cost burden of sepsis may be as high as $50,000 per patient per
* Corresponding author. Vattikuti Urology Institute Center for Outcomes Research Analytics and Evaluation, Henry Ford Health System, 2799 W. Grand Boulevard, Detroit, MI 48202. Tel.: þ1 207 692 7167; fax: þ1 313 916 4352. E-mail address: [email protected] (J.D. Sammon). 0022-4804/$ e see front matter ª 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jss.2014.07.046
j o u r n a l o f s u r g i c a l r e s e a r c h 1 9 3 ( 2 0 1 5 ) 7 8 8 e7 9 4
discharge and approximately $17 billion annually [2]. The cost burden of sepsis derives from lengthier hospital stays, increased utilization of ventilators, and concurrent management of other comorbidities [3]. Despite a national focus on the prevention and treatment of healthcare-associated infections, mortality remains nearly 20% [1]. As such, sepsis is a significant cause of inpatient death, with surgical patients accounting for approximately one third of all sepsis cases [4]. Patients with an underlying malignant process are often hospitalized for sepsis, and one in six patients with severe sepsis have been shown to have a concurrent neoplastic process increasing their risk of death [3]. Patients with malignancy are at increased risk for developing sepsis given their likelihood of more frequent hospital stays, invasive procedures, and treatment with immunomodulating chemotherapies [1]. Furthermore, patients with malignancy are believed to have baseline immunosuppression due to inflammatory cytokine release, upregulation of immunosuppressive cells (T regulatory cells and myeloid-derived suppressor cells), immunosuppressive cell signaling receptors and/or ligands (programmed cell-death 1), and/or unresponsive and/or decreased T cells [5]. Similarly, opportunistic infections, disorganized neoplastic inflammatory responses, and organ dysfunction secondary to localized invasion place oncologic patients at an increased risk of death [6]. Based on the complex interplay between patient risk factors and the inherent risk of major surgery, we examine postoperative sepsis in patients undergoing one of eight major cancer surgeries (MCS) including: colectomy, cystectomy, esophagectomy, gastrectomy, hysterectomy, lung resection, pancreatectomy, and prostatectomy. Large, high-volume academic centers have been associated with a salutary effect on postoperative complications and surgical outcomes across a wide range of surgical interventions [2,7e9], and we hypothesized the same would hold true for rates of sepsis and sepsisrelated mortality following MCS. We explore the relationship between postoperative sepsis and patient demographics, hospital characteristics, and surgeries performed in an effort to identify modifiable risk factors, as well as potential processes or structural changes that may aid in the reduction of postoperative sepsis following MCS.
2.
Materials and methods
2.1.
Data source
Relying on the Nationwide Inpatient Sample (NIS), hospital discharges in the United States between January first, 1999 and December 30th, 2009 were abstracted. The NIS is a set of longitudinal hospital inpatient databases included in the Healthcare Cost and Utilization Project family, created by the Agency for Healthcare Research and Quality through a federal-state partnership [10]. The database includes discharge abstracts from 8 million hospital stays and is the sole hospital database in the United States with discharge information on all patients regardless of payer, including persons covered by Medicare, Medicaid, private insurance, and the uninsured. Each discharge includes up to 15 inpatient diagnostic and 15 procedural codes. All procedures and
789
diagnoses are coded using the International Classification of Disease, ninth Revision, Clinical Modification.
2.2.
Study population
A total of eight major surgical oncological procedures were selected for evaluation of sepsis such as : colectomy, cystectomy, esophagectomy, gastrectomy, hysterectomy, lung resection, pancreatectomy, and prostatectomy. These operations include a group of commonly performed complex procedures, which are either associated with the most common cancers or carry a significant risk of morbidity and mortality, as previously examined [11]. Relying on specific International Classification of Disease, ninth Revision, Clinical Modification procedure codes, each surgical procedure was assessed independently and analyses were restricted to cancer diagnoses only, as previously described [12].
2.3.
Patient and hospital characteristics
For all patients, the following variables were available: age, race (White, Black, Hispanic, Asian and/or Pacific Islander, Native American, or other unspecified), insurance status, and Charlson comorbidity index (CCI). Baseline CCI was calculated according to Charlson et al. [13], as adapted by Deyo et al [14]. Insurance categories are combined in general groups, namely private insurance, Medicare, Medicaid, and other (self-pay). Hospital characteristics include hospital volume and number of beds, categorized as small, medium, and large, specific to the hospital’s region and teaching status [15].
2.4.
End points
The primary end points of interest were sepsis, which was identified via billing codes, and perioperative sepsis-related mortality, which was ascertained through discharge record.
2.5.
Statistical analysis
All demographic characteristics were weighted according to the discharge level estimates provided by the Healthcare Cost and Utilization Project [10]. First, descriptive statistics were generated on frequencies and proportions of categorical variables (gender, race, insurance status, CCI, annual hospital volume, hospital location, hospital region, hospital bed size, and hospital teaching status) and stratified according to sepsis occurrence. Medians and interquartile ranges were reported for continuously coded variables (age). Chi-square and KruskaleWallis tests were used to compare the statistical significance of differences within categorical and continuous variables, respectively. Second, temporal trends in rates were quantified by estimated annual percentage change using the linear regression methodology. Third, multivariable logistic regression models, fitted with generalized estimating equations, were used to assess independent predictors of sepsis following an MCS. Covariates composed of age, gender, race, CCI, insurance status, number of hospital beds, and hospital volume, while controlling for the effects for hospital clustering. Finally, in separate logistic regression models (fitted with generalized
790
j o u r n a l o f s u r g i c a l r e s e a r c h 1 9 3 ( 2 0 1 5 ) 7 8 8 e7 9 4
estimating equations), we assessed the relationship between sepsis and perioperative mortality during hospitalization within the entire cohort, and within each surgery. All statistical analyses were performed using the R statistical package system (R Foundation for Statistical Computing, Vienna, Austria), with a two-sided significance level set at P < 0.05. An institutional review board waiver was obtained before conducting this study, in accordance with institutional regulation when dealing with de-identified administrative data.
3. 3.1.
Results Baseline descriptives
A weighted estimate of 2,502,710 patients who underwent one of the eight examined procedures was obtained. Baseline sociodemographic characteristics are described in Table 1.
3.2.
Incidence of sepsis
Over the study period, 1.9% of patients experienced sepsis following MCS (Table 1). Of the study population, 37.1% underwent colectomy, 3.2% underwent cystectomy, 0.7% underwent esophagectomy, 3.3% underwent gastrectomy, 9.8% underwent hysterectomy, 14.6% underwent lung resection, 2.3% underwent pancreatectomy, and 29.0% underwent prostatectomy. Of the patients that experienced sepsis, 57.3% underwent colectomy, 6.5% underwent cystectomy, 3.1% underwent esophagectomy, 10.5% underwent gastrectomy, 1.8% underwent hysterectomy, 12.6% underwent lung resection, 7.0% underwent pancreatectomy, and 1.3% underwent prostatectomy (P < 0.001). The overall rate of sepsis increased from 1.25% in 1999 to 2.81% in 2009 with an estimated annual percentage change of 14.06% (P < 0.001, Fig. 1).
3.3.
Predictors of sepsis in MCS
Multivariable logistic regression analyses for predictors of sepsis following MCS are reported in Table 2. Females (odds ratio [OR]: 0.72, 0.69e0.75), and those treated at higher volume hospitals (OR: 0.89, 0.84e0.95 to OR: 0.58, 0.53e0.62) were less likely to experience sepsis postoperatively. In contrast, older age (OR: 1.02, 1.02e1.02), Black and Hispanic races (OR: 1.35, 1.25e1.45; OR: 1.16, 1.05e1.27; respectively), comorbidities 3 (OR: 1.16, 1.09e1.23), non-privately insured individuals (OR: 1.33 1.19e1.48 to OR: 1.89 1.71e2.09), and those treated in urban areas (OR: 1.63, 1.51e1.76) had increased odds of sepsis. Hospital teaching status had no effect on postoperative sepsis rates. Procedure-specific variability of sepsis odds were recorded with esophagectomy patients experiencing the highest odds of sepsis (OR: 3.13, 2.76e3.55).
3.4.
Table 1 e Weighted descriptive characteristics of 2,502,710 patients undergoing a MCS, NIS, 1999e2009. Variables Number of patients, % Age, years Median IQR Gender Male Female Procedure Colectomy Cystectomy Esophagectomy Gastrectomy Hysterectomy Lung resection Pancreatectomy Prostatectomy Race Caucasian Black Hispanic Other Unknown CCI 0 1 2 3 Insurance status Private Medicaid Medicare Uninsured Hospital location Rural Urban Hospital region Northeast Midwest South West Hospital teaching status Non-teaching Teaching Hospital bed size Small Medium Large Hospital volume Median IQR
Overall
Without sepsis
With sepsis
P*
100.0
98.1
1.9
d
66.0 58e74
66.0 58e74
73.0 64e80
60.3 39.7
60.4 39.6
58.4 41.6
37.1 3.2 0.7 3.3 9.8 14.6 2.3 29.0
36.7 3.1 0.7 3.1 10.0 14.7 2.2 29.6
57.3 6.5 3.1 10.5 1.8 12.6 7.0 1.3
60.9 7.1 3.9 3.7 24.4
60.9 7.1 3.9 3.7 24.4
60.0 9.2 5.3 4.7 20.9
62.4 24.9 5.1 7.6
62.6 24.9 5.1 7.4
54.1 23.9 5.1 16.9
42.1 3.2 50.5 4.2
42.6 3.2 50.1 4.2
21.4 5.2 69.5 3.9
10.7 89.3
10.7 89.3
9.5 90.5
21.0 24.3 35.2 18.8
21 24.4 35.1 19.5
21.1 20.9 39.1 18.8
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.JournalofSurgicalResearch.com
Sepsis after major cancer surgery Jesse D. Sammon, DO,a,* Dane E. Klett, MD,a Akshay Sood, MD,a Kola Olugbade Jr., MD,b Marianne Schmid, MD,b Simon P. Kim, MD,c Mani Menon, MD,a and Quoc-Dien Trinh, MDb a
Vattikuti Urology Institute Center for Outcomes Research Analytics and Evaluation, Henry Ford Health System, Detroit, Michigan b Center for Surgery and Public Health and Division of Urologic Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts c Department of Urology, Yale University, New Haven, Connecticut
article info
abstract
Article history:
Background: Cancer patients undergoing procedures are at increased risk of sepsis. We sought
Received 11 June 2014
to evaluate the incidence of postoperative sepsis following major cancer surgeries (MCS), and
Received in revised form
to describe patient and/or hospital characteristics associated with heightened risk.
14 July 2014
Methods: Patients undergoing 1 of 8 MCS (colectomy, cystectomy, esophagectomy, gas-
Accepted 18 July 2014
trectomy, hysterectomy, lung resection, pancreatectomy, and prostatectomy) within the
Available online 24 July 2014
Nationwide Inpatient Sample from 1999e2009 were identified (N ¼ 2,502,710). Logistic regression models fitted with generalized estimating equations were used to estimate
Keywords:
primary predictors (procedure, age, gender, race, insurance, Charlson Comorbidity Index,
Cancer surgery
hospital volume, and hospital bed size) effect on sepsis and sepsis-associated mortality.
Infection
Trends were evaluated with linear regression.
Sepsis
Results: The incidence of MCS-related sepsis increased 2.0% per year (P < 0.001), whereas
Mortality
mortality remained stable. Odds of sepsis were highest among esophagectomy patients
Nationwide Inpatient Sample
(odds ratio [OR]: 3.13, 2.76e3.55) and those with non-private insurance (OR: 1.33, 1.19e1.48 to OR: 1.89, 1.71e2.09). Odds of sepsis-related mortality were highest among lung resection patients (OR: 2.30, 2.00e2.64) and those experiencing perioperative liver failure (OR: 5.68, 4.30e7.52). Increasing hospital volume was associated with lower odds of sepsis and sepsis-related mortality (OR: 0.89, 0.84e0.95 to OR: 0.58, 0.53e0.62 and OR: 0.88, 0.77e0.99 to OR: 0.78, 0.67e0.93). Conclusions: Between 1999 and 2009, the incidence of MCS-related sepsis increased; however, sepsis-related mortality remained stable. Significant disparities exist in patient and hospital characteristics associated with MCS-related sepsis. Hospital volume is an important modifiable risk factor associated with improved sepsis-related outcomes following MCS. ª 2015 Elsevier Inc. All rights reserved.
1.
Introduction
Sepsis is a costly, life-threatening, and multifactorial condition commonly occurring in elderly, immunocompromised,
critically ill, and/or postoperative patients [1]. An estimated 2% of all hospitalizations nationwide are associated with sepsis [1]. In the United States, the direct health-care cost burden of sepsis may be as high as $50,000 per patient per
* Corresponding author. Vattikuti Urology Institute Center for Outcomes Research Analytics and Evaluation, Henry Ford Health System, 2799 W. Grand Boulevard, Detroit, MI 48202. Tel.: þ1 207 692 7167; fax: þ1 313 916 4352. E-mail address: [email protected] (J.D. Sammon). 0022-4804/$ e see front matter ª 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jss.2014.07.046
j o u r n a l o f s u r g i c a l r e s e a r c h 1 9 3 ( 2 0 1 5 ) 7 8 8 e7 9 4
discharge and approximately $17 billion annually [2]. The cost burden of sepsis derives from lengthier hospital stays, increased utilization of ventilators, and concurrent management of other comorbidities [3]. Despite a national focus on the prevention and treatment of healthcare-associated infections, mortality remains nearly 20% [1]. As such, sepsis is a significant cause of inpatient death, with surgical patients accounting for approximately one third of all sepsis cases [4]. Patients with an underlying malignant process are often hospitalized for sepsis, and one in six patients with severe sepsis have been shown to have a concurrent neoplastic process increasing their risk of death [3]. Patients with malignancy are at increased risk for developing sepsis given their likelihood of more frequent hospital stays, invasive procedures, and treatment with immunomodulating chemotherapies [1]. Furthermore, patients with malignancy are believed to have baseline immunosuppression due to inflammatory cytokine release, upregulation of immunosuppressive cells (T regulatory cells and myeloid-derived suppressor cells), immunosuppressive cell signaling receptors and/or ligands (programmed cell-death 1), and/or unresponsive and/or decreased T cells [5]. Similarly, opportunistic infections, disorganized neoplastic inflammatory responses, and organ dysfunction secondary to localized invasion place oncologic patients at an increased risk of death [6]. Based on the complex interplay between patient risk factors and the inherent risk of major surgery, we examine postoperative sepsis in patients undergoing one of eight major cancer surgeries (MCS) including: colectomy, cystectomy, esophagectomy, gastrectomy, hysterectomy, lung resection, pancreatectomy, and prostatectomy. Large, high-volume academic centers have been associated with a salutary effect on postoperative complications and surgical outcomes across a wide range of surgical interventions [2,7e9], and we hypothesized the same would hold true for rates of sepsis and sepsisrelated mortality following MCS. We explore the relationship between postoperative sepsis and patient demographics, hospital characteristics, and surgeries performed in an effort to identify modifiable risk factors, as well as potential processes or structural changes that may aid in the reduction of postoperative sepsis following MCS.
2.
Materials and methods
2.1.
Data source
Relying on the Nationwide Inpatient Sample (NIS), hospital discharges in the United States between January first, 1999 and December 30th, 2009 were abstracted. The NIS is a set of longitudinal hospital inpatient databases included in the Healthcare Cost and Utilization Project family, created by the Agency for Healthcare Research and Quality through a federal-state partnership [10]. The database includes discharge abstracts from 8 million hospital stays and is the sole hospital database in the United States with discharge information on all patients regardless of payer, including persons covered by Medicare, Medicaid, private insurance, and the uninsured. Each discharge includes up to 15 inpatient diagnostic and 15 procedural codes. All procedures and
789
diagnoses are coded using the International Classification of Disease, ninth Revision, Clinical Modification.
2.2.
Study population
A total of eight major surgical oncological procedures were selected for evaluation of sepsis such as : colectomy, cystectomy, esophagectomy, gastrectomy, hysterectomy, lung resection, pancreatectomy, and prostatectomy. These operations include a group of commonly performed complex procedures, which are either associated with the most common cancers or carry a significant risk of morbidity and mortality, as previously examined [11]. Relying on specific International Classification of Disease, ninth Revision, Clinical Modification procedure codes, each surgical procedure was assessed independently and analyses were restricted to cancer diagnoses only, as previously described [12].
2.3.
Patient and hospital characteristics
For all patients, the following variables were available: age, race (White, Black, Hispanic, Asian and/or Pacific Islander, Native American, or other unspecified), insurance status, and Charlson comorbidity index (CCI). Baseline CCI was calculated according to Charlson et al. [13], as adapted by Deyo et al [14]. Insurance categories are combined in general groups, namely private insurance, Medicare, Medicaid, and other (self-pay). Hospital characteristics include hospital volume and number of beds, categorized as small, medium, and large, specific to the hospital’s region and teaching status [15].
2.4.
End points
The primary end points of interest were sepsis, which was identified via billing codes, and perioperative sepsis-related mortality, which was ascertained through discharge record.
2.5.
Statistical analysis
All demographic characteristics were weighted according to the discharge level estimates provided by the Healthcare Cost and Utilization Project [10]. First, descriptive statistics were generated on frequencies and proportions of categorical variables (gender, race, insurance status, CCI, annual hospital volume, hospital location, hospital region, hospital bed size, and hospital teaching status) and stratified according to sepsis occurrence. Medians and interquartile ranges were reported for continuously coded variables (age). Chi-square and KruskaleWallis tests were used to compare the statistical significance of differences within categorical and continuous variables, respectively. Second, temporal trends in rates were quantified by estimated annual percentage change using the linear regression methodology. Third, multivariable logistic regression models, fitted with generalized estimating equations, were used to assess independent predictors of sepsis following an MCS. Covariates composed of age, gender, race, CCI, insurance status, number of hospital beds, and hospital volume, while controlling for the effects for hospital clustering. Finally, in separate logistic regression models (fitted with generalized
790
j o u r n a l o f s u r g i c a l r e s e a r c h 1 9 3 ( 2 0 1 5 ) 7 8 8 e7 9 4
estimating equations), we assessed the relationship between sepsis and perioperative mortality during hospitalization within the entire cohort, and within each surgery. All statistical analyses were performed using the R statistical package system (R Foundation for Statistical Computing, Vienna, Austria), with a two-sided significance level set at P < 0.05. An institutional review board waiver was obtained before conducting this study, in accordance with institutional regulation when dealing with de-identified administrative data.
3. 3.1.
Results Baseline descriptives
A weighted estimate of 2,502,710 patients who underwent one of the eight examined procedures was obtained. Baseline sociodemographic characteristics are described in Table 1.
3.2.
Incidence of sepsis
Over the study period, 1.9% of patients experienced sepsis following MCS (Table 1). Of the study population, 37.1% underwent colectomy, 3.2% underwent cystectomy, 0.7% underwent esophagectomy, 3.3% underwent gastrectomy, 9.8% underwent hysterectomy, 14.6% underwent lung resection, 2.3% underwent pancreatectomy, and 29.0% underwent prostatectomy. Of the patients that experienced sepsis, 57.3% underwent colectomy, 6.5% underwent cystectomy, 3.1% underwent esophagectomy, 10.5% underwent gastrectomy, 1.8% underwent hysterectomy, 12.6% underwent lung resection, 7.0% underwent pancreatectomy, and 1.3% underwent prostatectomy (P < 0.001). The overall rate of sepsis increased from 1.25% in 1999 to 2.81% in 2009 with an estimated annual percentage change of 14.06% (P < 0.001, Fig. 1).
3.3.
Predictors of sepsis in MCS
Multivariable logistic regression analyses for predictors of sepsis following MCS are reported in Table 2. Females (odds ratio [OR]: 0.72, 0.69e0.75), and those treated at higher volume hospitals (OR: 0.89, 0.84e0.95 to OR: 0.58, 0.53e0.62) were less likely to experience sepsis postoperatively. In contrast, older age (OR: 1.02, 1.02e1.02), Black and Hispanic races (OR: 1.35, 1.25e1.45; OR: 1.16, 1.05e1.27; respectively), comorbidities 3 (OR: 1.16, 1.09e1.23), non-privately insured individuals (OR: 1.33 1.19e1.48 to OR: 1.89 1.71e2.09), and those treated in urban areas (OR: 1.63, 1.51e1.76) had increased odds of sepsis. Hospital teaching status had no effect on postoperative sepsis rates. Procedure-specific variability of sepsis odds were recorded with esophagectomy patients experiencing the highest odds of sepsis (OR: 3.13, 2.76e3.55).
3.4.
Table 1 e Weighted descriptive characteristics of 2,502,710 patients undergoing a MCS, NIS, 1999e2009. Variables Number of patients, % Age, years Median IQR Gender Male Female Procedure Colectomy Cystectomy Esophagectomy Gastrectomy Hysterectomy Lung resection Pancreatectomy Prostatectomy Race Caucasian Black Hispanic Other Unknown CCI 0 1 2 3 Insurance status Private Medicaid Medicare Uninsured Hospital location Rural Urban Hospital region Northeast Midwest South West Hospital teaching status Non-teaching Teaching Hospital bed size Small Medium Large Hospital volume Median IQR
Overall
Without sepsis
With sepsis
P*
100.0
98.1
1.9
d
66.0 58e74
66.0 58e74
73.0 64e80
60.3 39.7
60.4 39.6
58.4 41.6
37.1 3.2 0.7 3.3 9.8 14.6 2.3 29.0
36.7 3.1 0.7 3.1 10.0 14.7 2.2 29.6
57.3 6.5 3.1 10.5 1.8 12.6 7.0 1.3
60.9 7.1 3.9 3.7 24.4
60.9 7.1 3.9 3.7 24.4
60.0 9.2 5.3 4.7 20.9
62.4 24.9 5.1 7.6
62.6 24.9 5.1 7.4
54.1 23.9 5.1 16.9
42.1 3.2 50.5 4.2
42.6 3.2 50.1 4.2
21.4 5.2 69.5 3.9
10.7 89.3
10.7 89.3
9.5 90.5
21.0 24.3 35.2 18.8
21 24.4 35.1 19.5
21.1 20.9 39.1 18.8
