Comments
Abstract
Rationale: Reducing the global burden of sepsis, a recognized global health challenge, requires comprehensive data on the incidence and mortality on a global scale.
Objectives: To estimate the worldwide incidence and mortality of sepsis and identify knowledge gaps based on available evidence from observational studies.
Methods: We systematically searched 15 international citation databases for population-level estimates of sepsis incidence rates and fatality in adult populations using consensus criteria and published in the last 36 years.
Measurements and Main Results: The search yielded 1,553 reports from 1979 to 2015, of which 45 met our criteria. A total of 27 studies from seven high-income countries provided data for metaanalysis. For these countries, the population incidence rate was 288 (95% confidence interval [CI], 215–386; τ = 0.55) for hospital-treated sepsis cases and 148 (95% CI, 98–226; τ = 0.99) for hospital-treated severe sepsis cases per 100,000 person-years. Restricted to the last decade, the incidence rate was 437 (95% CI, 334–571; τ = 0.38) for sepsis and 270 (95% CI, 176–412; τ = 0.60) for severe sepsis cases per 100,000 person-years. Hospital mortality was 17% for sepsis and 26% for severe sepsis during this period. There were no population-level sepsis incidence estimates from lower-income countries, which limits the prediction of global cases and deaths. However, a tentative extrapolation from high-income country data suggests global estimates of 31.5 million sepsis and 19.4 million severe sepsis cases, with potentially 5.3 million deaths annually.
Conclusions: Population-level epidemiologic data for sepsis are scarce and nonexistent for low- and middle-income countries. Our analyses underline the urgent need to implement global strategies to measure sepsis morbidity and mortality, particularly in low- and middle-income countries.
Sepsis is a major public health concern, but comprehensive knowledge on sepsis incidence and mortality worldwide is missing.
This article provides a systematic overview of epidemiologic data on population-level incidence rates and hospital mortality around the world. It includes data from seven countries on four continents over the last 36 years. The data were used to generate estimates for hospital-treated sepsis cases in high-income countries. In low- and middle-income countries, however, important knowledge gaps are evident.
Sepsis is the life-threatening condition that arises when the body’s response to an infection injures its own tissues and organs (1, 2). It has been called “one of the oldest and most elusive syndromes in medicine” (3). Despite advances in care, existing epidemiologic studies suggest that sepsis remains a huge burden across all economic regions. In the United States, admissions for sepsis have overtaken those for myocardial infarction and stroke (4). Sepsis incidence rates are up to 535 cases per 100,000 person-years and rising (5). In-hospital mortality remains high at 25–30% (6). However, there is no gold standard of sepsis diagnosis and nonstandardized definitions hamper comparability of results from clinical and epidemiologic studies (2). Thus, current sepsis criteria are under revision to overcome the limitation that they do not differentiate between infection and sepsis (6). Sepsis is also not tracked in the Global Burden of Disease taxonomy. Rather, infections are reported separately, and only neonatal sepsis is reported explicitly (7).
Indeed, controversy remains regarding the true attributable burden of sepsis because of notable differences in both the methods and results of epidemiologic studies, especially those based on administrative data (8). Reducing the global burden of sepsis, a recognized global health challenge, requires comprehensive data on incidence and mortality on a global scale. Therefore, the principal aim of this systematic review was to summarize existing epidemiologic studies of sepsis throughout the world. In addition, we aimed to generate estimates of global incidence and case-fatality rates of sepsis using metaanalyses. To address the expected heterogeneity of the studies, we predefined subgroups for stratification according to the World Bank classification of high-, middle-, and low-income countries and settings (intensive care unit [ICU], emergency department [ED], or hospital).
Some of the results of this study have been previously reported in the form of an abstract (9).
The literature search and review process followed a protocol designed before data collection. To identify observational epidemiologic studies reporting on population-level incidence of sepsis on a global scale, we used three different approaches: (1) a search for published or unpublished literature in regional and international databases, (2) a scan of reference lists for potentially relevant studies, and (3) queries to national experts for regions where no data were found.
We assessed 15 regional and international databases for published or unpublished studies on the sepsis incidence from January 1979 through May 2015: PubMed, EMBASE, LILACS, African Index Medicus, African Healthline, African Journals Online, OpenGREY, MedCarib, Pascal Biomed, Index Medicus for the WHO Eastern Mediterranean Region, IndMed, Web of Science, Index Medicus for South East Asia Region, Western Pacific Region Index Medicus, and WHOLIS. No language or publication restrictions were applied.
We used the same comprehensive list of search terms for each database and applied it to the title of publications: “(sepsis OR septic*) AND (epidemiology*, incidence, burden OR prevalence)” (* = truncation). Although we focused on incidence rates, the search term “prevalence” was included because sepsis in the acute setting is a nonchronic disease that rarely occurs twice in the same individual if the observational time period is sufficiently small.
Studies were considered for inclusion if they reported on sepsis or severe sepsis incidence on a population level within a defined population and period of time. Sepsis or severe sepsis were defined according to the following: American College of Chest Physicians/Society of Critical Care Medicine consensus criteria (10, 11), PROWESS-SHOCK (Prospective Recombinant Human Activated Protein C Worldwide Evaluation in Severe Sepsis and Septic Shock) criteria (12), sepsis-relevant International Classification of Diseases (ICD)-9/ICD-10 codes, or codes representing both infection and organ dysfunction to identify severe sepsis cases. We excluded studies that were limited to subgroups of sepsis (e.g., positive blood cultures or specific gram stain), selected patient groups (e.g., patients with cancer or pediatric patients), or treatment units (e.g., surgical ICU). Studies that cited incidence without giving details about the method of data collection or inclusion and exclusion criteria were also excluded.
Abstracts were reviewed by one investigator (C.F.) and those that seemed likely to fulfill the inclusion criteria underwent full-text review and analysis independently by two investigators (C.F. and T.T.). Discrepancies were resolved by discussion. Non-English articles were assessed and extracted by native speakers with medical backgrounds. Extracted data included authors; year of publication; country or countries classified by low-income, middle-income, and high-income countries according to World Bank country classification of income groups (13); observation period; observed cases of sepsis or severe sepsis; cases of sepsis or severe sepsis per 100 admissions; cases of sepsis or severe sepsis per 100,000 person-years; and ICU-, hospital-, or 28-day mortality. In our analysis, sepsis is considered as an umbrella term compiling cases of severe sepsis and septic shock. Likewise, cases of severe sepsis include cases of septic shock. If there was a distinction between patients with sepsis and observed sepsis episodes, the number of observed episodes was included for analysis. Thus, we present estimates on sepsis cases (rather than on patients) per 100,000 population. Finally, we classified the studies regarding (1) setting (ICU, ED, or hospital), (2) design (prospective or retrospective), and (3) sepsis definition (clinical consensus criteria or ICD coding) to perform stratified metaanalyses. We contacted authors, where required, for additional data.
Metaanalysis focused primarily on population-level incidence rates of hospital-treated sepsis or severe sepsis per 100,000 person-years and used all other extracted information for their description. As a secondary objective, we also report metaanalysis estimates for hospital case fatality rates in the included studies if such data were available. Missing population data were requested from the authors or searched in national census databases. We recalculated the population-level incidence rates to reduce additional heterogeneity caused by different calculations and standardizations reported in the original publications; nevertheless, the original calculations are also provided. We used information on the relationship between observed hospital sepsis cases and annual total of hospitalizations within a nation’s population (as provided by the authors) and finally related these numbers to the national population given by census inquiries. ICU data were excluded from metaanalysis because of substantial variations in national ICU capacities that would be expected to be highly correlated with the number of ICU-treated sepsis cases.
There was substantial heterogeneity between the estimates of the underlying studies as expressed by the estimated τ, the estimated square root of the between-study variance, and I2, a rescaled version of this variance that ranges between 0% and 100%, with larger values indicating more substantial heterogeneity (I2 = 100% for all analyses and τ between 0.13 and 0.99 for the incidence rates or τ between 0.21 and 0.58 for the case fatality rates). We present random effects estimates based on the method of Knapp and Hartung (14), which generates wider confidence intervals (CIs) compared with standard methods (15). Balancing between taking care of heterogeneity by stratification and the availability of a considerable number of studies to be metaanalyzed to derive a relatively robust estimator, we finally stratified all studies into two subgroups (hospital-treated sepsis and hospital-treated severe sepsis). Because some studies applied more restricted or wider criteria to derive their estimates for hospital-treated severe sepsis, we decided to run two additional sensitivity analyses (minimum and maximum) using these criteria. Studies applying multiple strategies for severe sepsis case identification to one dataset were included in sensitivity analyses according to the strategy used.
From these studies, all data rows were included in the combined metaanalysis, to balance the influence from wider and more restrictive definitions. The same analyses were also run separately for the most recent studies (first year of enrollment ≥2003; see online supplement). All analyses were done using metaprop of the R 3.0.2 package meta, which provides exact 95% CIs for the incidence rate estimates of the individual studies. For all incidence rate calculations we had to apply an approximation to circumvent computational difficulties (the number of input observations was first divided by 10 and the estimate was afterward up-scaled by the factor 10 and rounded to the next integer).
Finally, we extrapolated our estimates to the global scale based on the estimated size of the world population of about 7.2 billion people (16).
Our search yielded 1,553 abstracts including 24 publications, which were identified by hand-searching. Two independent investigators read in full and analyzed 129 publications (Figure 1). Overall, 45 studies from 18 high-income countries in North America, Europe, Asia, and Australia met the eligibility criteria, of which 27 contributed to metaanalyses (Figure 2). Interrater agreement (Kappa) for study inclusion was 0.85 (95% CI, 0.74–0.93). Thirty (67%) of these studies were retrieved by searching MEDLNE, three (7%) by EMBASE, and one (2%) by Web of Science. Another 11 (24%) were identified by hand-searching. Two were non-English language publications. Queries to experts in China, Russia, and the Middle East yielded no further studies. Two studies provided data from more than one country. Additional data for metaanalysis were obtained from 16 of the approached 33 study authors.
Figure 1. Flowchart of study selection for the systematic review and metaanalyses on global sepsis incidence and mortality. *CF is Carolin Fleischmann and TT is Thomas Tsaganos, both authors of this article. ICU = intensive care unit.
[More] [Minimize]
Figure 2. World map of included studies on sepsis and severe sepsis that present population-level incidence rates on hospital-treated sepsis and severe sepsis (United States, Germany, Australia, Taiwan, Norway, Spain, and Sweden). Note that only studies on hospital-treated sepsis and severe sepsis were included in the metaanalysis.
[More] [Minimize]The included 45 studies varied in terms of (1) study setting (ICU, ED, or hospital), (2) design (prospective: 1-day or period prevalence study, retrospective), and (3) sepsis criteria. None of the studies based their observations on a population as a whole. Therefore, all results need to be interpreted as incidence rates of treated sepsis cases. In the next step, we assessed the results of all studies providing population-level estimates of sepsis incidence rates in the categories of interest to generate metaanalytic estimates on the global burden of hospital-treated sepsis cases. Only studies with complete data on nominators and denominators were used for metaanalysis (Tables 1 and 2). Because only two studies (17, 18) reported on population-level incidence rates for patients with sepsis in EDs, we did not calculate any estimators for this category, but provide data in Table 3.
| Study Duration (d) | Population | Patients Observed | Age Range | Total Number of Sepsis Cases | Incidence (per 100,000 Person-Years) | Mean Age | Hospital Mortality (%) | Remarks | |
|---|---|---|---|---|---|---|---|---|---|
| Prospective studies | |||||||||
| Australia (Davis and colleagues) (20) | |||||||||
| 2007–2008 | 365 | 102,854 (ar) | 15,963 | ≥15 yr | 1,191 | 1,180 | 46.7 | 5 | Period prevalence study in the major hospital for Tropical Northern Territory, Australia (27% indigenous population) |
| Spain (Esteban and colleagues) (19) | |||||||||
| 2003 | 122 | 573,149 | 15,852 | >18 yr | 702 (2,106*) | 367 | 69 | 12.8 | Period prevalence study, three hospitals in Madrid, Spain, 4-mo period |
| Retrospective studies | |||||||||
| Norway (Flaatten) (37) | |||||||||
| 1999 | 365 | 4,461,913 | 700,107 | No neonates | 6,665 | 149 | 57.9 | 13.5 | Norwegian Patient Registry, all patients admitted in 1999, ICD-10 |
| Spain (Andreu Ballester and colleagues) (21) | |||||||||
| 1995–2004 | 3,650 | 41,677,000 (nc) | 23,351,859 | All ages | 33,767 | 45–114 | 55.9–62.4 | 42.5 | Discharge diagnoses in all 26 public hospitals in the Valencian Community, Spain, 10-yr period, ICD-9 |
| United States (Seymour and colleagues) (23) | |||||||||
| 2006 | 365 | 6,434,047 | 876,963 | ≥20 yr | 37,524 | 580 | 58–81 | 18–25 | Hospital discharge data from New Jersey from the Healthcare Cost and Utilization Project 2006 State Inpatient Database, ICD-9 |
| United States (Buechner and Williams) (24) | |||||||||
| 1990 | 365 | 1,003,464 (ar) | 141,027 (ar) | All ages | 1,998 | 187.7 | — | 25.5 | Discharge diagnoses from all acute hospitals in Rhode Island, ICD-9 |
| 2002 | 365 | 1,066,034 (ar) | 133,494 (ar) | All ages | 3,430 | 287.7 | — | 23.4 | |
| United States (Martin and colleagues) (25) | |||||||||
| 1979 | 365 | 224,567,000 (nc) | — | All ages | 164,072 | 82.7 | 57.4 (1979–1984) | 27.8 | Data from the National Hospital Discharge Survey, 1979–2000, ICD-9 |
| 2000 | 365 | 281,425,000 (nc) | — | All ages | 659,935 | 240.4 | 60.8 (1995–2000) | 17.9 | |
| United States (Hall and colleagues) (26) | |||||||||
| 2000 | 365 | 281,425,000 (nc) | — | All ages | 621,000 | 221 | — | — | Data from the National Hospital Discharge Survey, 2000 and 2008, ICD-9 |
| 2008 | 365 | 304,375,000 (nc) | — | All ages | 1,141,000 | 377 | — | 17 | |
| United States (CDC) (27) | |||||||||
| 1979 | 365 | 224,567,000 (nc) | — | All ages | 164,000 | 73.6 | — | 31 | Data from the National Hospital Discharge Survey, 1979 and 1987, ICD-9 |
| 1987 | 365 | 242,289,000 (nc) | — | All ages | 425,000 | 175.9 | — | 25.3 | |
| Australia (Sundararajan and colleagues) (39) | |||||||||
| 1999 | 365 | 4,500,000 | 3,122,515 (during 4 yr) | All ages | 33,741 (during 4 yr) | 166 | — | 18.4 (during 4 yr) | Hospital discharge database study based on the Victorian Admitted Episodes Dataset, Victoria, ICD-10 |
| 2003 | 365 | All ages | 194 | — | |||||
| Germany (Heublein and colleagues) (38) | |||||||||
| 2011 | 365 | 81,843,700 (nc) | — | All ages | 175,051 | 213* | 67.5 | 28.6 | Hospital discharge data for Germany 2011, ICD-10 coding |
| United States (Dombrovskiy and colleagues) (22) | |||||||||
| 1993 | 365 | 257,783,000 (ar) | — | All ages | 656,932 | 255* | — | — | Hospital discharge database study based on the National Inpatient Sample, ICD-9 |
| 2003 | 365 | 290,447,644 (ar) | — | All ages | 893,762 | 308* | — | — | |
| United States (Lagu and colleagues) (29) | |||||||||
| 2003 | 365 | 217,068,000 (ar) | 31,634,852 | ≥18 yr | 799,155 | 368* | — | — | Data from the Nationwide Inpatient Sample 2003–2007, ICD-9 |
| 2007 | 365 | 227,240,000 (ar) | 32,716,306 | ≥18 yr | 1,115,112 | 491* | — | — | |
| United States (Danai and colleagues) (36) | |||||||||
| 1979–2003 | 9,125 | 6,384,773,427 (nc) | — | All ages | 12,505,082 | 41.7–48.6 | 60.1–60.9 | 20 | Data from the National Hospital Discharge Survey, 1979–2003, ICD-9 |
| United States (Elixhauser and colleagues) (34) | |||||||||
| 2009 | 365 | 306,771,529 (ar) | — | All ages | 1,665,400 | 540 | 60.3 | 16.3 | Data from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample, 2009, ICD-9 |
| United States (Sutton and Friedman) (33) | |||||||||
| 2005 | 365 | 64,741,527 (ar) | — | ≥18 yr | 267,000 | 492 | — | — | Data from the Healthcare Cost and Utilization Project, State Inpatient Databases, 2005–2010, ICD-9 |
| 2010 | 365 | 68,599,514 (ar) | — | ≥18 yr | 354,000 | 651 | — | — | |
| United States (Walkey and colleagues) (5) | |||||||||
| 2003 | 365 | 290,107,933 (ar) | 227,000,000 | ≥18 yr | 789,410 (ar) | 359 | — | 24 | Data from the Nationwide Inpatient Sample, 2003–2009, ICD-9 |
| 2009 | 365 | 306,771,529 (ar) | ≥18 yr | 1,306,730 (ar) | 535 | — | 19 | ||
| Study Duration (d) | Population | Patients Observed | Age Range | Total Number of Severe Sepsis Cases | Incidence (per 100,000 Person-Years) | Mean Age | Hospital Mortality (%) | Remarks | |
|---|---|---|---|---|---|---|---|---|---|
| Prospective studies | |||||||||
| Australia (Davis and colleagues) (20) | |||||||||
| 2007–2008 | 365 | 102,854 (ar) | 15,963 | ≥15 yr | 194 (ar) | 188* | — | 17.1 | Period prevalence study in the major hospital for Tropical Northern Territory, Australia (27% indigenous population) |
| Spain (Esteban and colleagues) (19) | |||||||||
| 2003 | 122 | 573,149 | 15,852 | >18 yr | 199 (597*) | 104 | — | 28 | Period prevalence study, three hospitals in Madrid, 4-mo period |
| Retrospective studies | |||||||||
| Norway (Flaatten) (37) | |||||||||
| 1999 | 365 | 4,461,913 | 700,107 | No neonatals | 3,683 | 83* | 57.9 (severe sepsis) | 27 (severe sepsis) | Norwegian Patient Registry, all patients admitted in 1999, ICD-10 |
| Taiwan (Shen and colleagues) (42) | |||||||||
| 1997 | 365 | 191,510 (ar) | 191,510 (ar) | All ages | 300 (ar) | 153 | — | 30.8 | National Health Insurance Research Database, 99% of inpatient and outpatient data of the population, ICD-9 |
| 2006 | 365 | 210,127 (ar) | 210,127 (ar) | All ages | 662 (ar) | 359 | — | ||
| Spain (Inigo and colleagues) (41) | |||||||||
| 2001 | 365 | 5,423,384 | 537,223 | All ages | 6,968 | 141 | 62.5 | 33 | Minimum Basic Hospital Data Set from the Region of Madrid in 2001, ICD-9 |
| United States (Angus and colleagues) (40) | |||||||||
| 1995 | 365 | 63,497,167 | 6,621,559 | All ages | 192,980 | 300 | 63.8 | 28.6 | Hospital discharge databases from all nonfederal hospitals (n = 847) in seven U.S. states, ICD-9 coding |
| United States (Kumar and colleagues) (31) | |||||||||
| 2000 | 365 | 209,130,000 (nc) | 30,330,303 | ≥18 yr | 300,270 | 143 | — | 39.6 | Data from the Nationwide Inpatient Sample, ICD-9 |
| 2007 | 365 | 227,240,000 (nc) | 32,845,588 | ≥18 yr | 781,725 | 343 | — | 27.3 | |
| United States (Dombrovskiy and colleagues) (22) | |||||||||
| 1993 | 365 | 257,783,000 (ar) | — | All ages | 168,239 | 64.7 | — | 45.0 | Hospital discharge database study based on the National Inpatient Sample 1993–2003, ICD-9 |
| 2003 | 365 | 290,447,644 (ar) | — | All ages | 391,544 | 134.6 | — | 37.7 | |
| United States (Lagu and colleagues) (29) | |||||||||
| 2003 | 365 | 217,068,000 (ar) | 31,634,852 | ≥18 yr | 415,280 | 200 | — | 37 | Data from the Nationwide Inpatient Sample 2003–2007, ICD-9 |
| 2007 | 365 | 227,240,000 (ar) | 32,716,306 | ≥18 yr | 711,736 | 300 | — | 29 | |
| Australia (Sundararajan and colleagues) (39) | |||||||||
| 1999–2003 | 1,460 | 4,500,000 | 3,122,515 (during 4 yr) | All ages | 13,297 (during 4 yr) | 65–76 | — | 31.1 | Hospital discharge database study based on the Victorian Admitted Episodes Dataset, Victoria, Australia, ICD-10 |
| Germany (Heublein and colleagues) (38) | |||||||||
| 2011 | 365 | 81,843,700 (nc) | — | All ages | 87,901 | 107 | 69.4 (severe sepsis) | 42.8 (severe sepsis) | Hospital discharge data for Germany 2011, ICD-10 |
| United States (Gaieski and colleagues) (30) | |||||||||
| 2004–2009 | 2,190 | 1,355,961,219 (nc) | 196,096,962 | >18 yr | 12,267,065 | 905 | — | — | Nationwide Inpatient Sample, 2004–2009, ICD-9 |
| 2004–2009 | 2,190 | 1,355,961,219 (nc) | 196,096,962 | >18 yr | 13,980,089 | 1,031 | — | — | |
| 2004–2009 | 2,190 | 1,355,961,219 (nc) | 196,096,962 | >18 yr | 4,067,836 | 300 | — | 14.7 | |
| 2004–2009 | 2,190 | 1,355,961,219 (nc) | 196,096,962 | >18 yr | 5,001,750 | 369 | — | 29.9 | |
| Sweden (Wilhelms and colleagues) (28) | |||||||||
| 1987–2005 | 6,935 | 166,737,000 (nc) | — | All ages | 44,744 | 10–35 | — | 22.1 | Swedish hospital discharge database, 1987–2005, ICD-9 + ICD-10 |
| 1987–2005 | 6,935 | 166,737,000 (nc) | — | All ages | 32,649 | 26–43 | — | 22.4 | |
| 1987–2005 | 6,935 | 166,737,000 (nc) | — | All ages | 15,045 | 3–13 | — | 29.2 | |
| United States (Barnato and colleagues) (43) | |||||||||
| 2001 | 365 | 71,102,655 | 8,940,278 | All ages | 282,292 | 397 | — | 24.6 | Hospital discharge datasets from seven U.S. states in 2001, ICD-9 |
| United States (Lagu and colleagues) (32) | |||||||||
| 2007 | 365 | 227,240,000 (ar) | 32,716,306 | ≥18 yr | 2,513,425 | 1,061 | — | 14.1 | Data from the Nationwide Inpatient Sample 2007, ICD-9 |
| 2007 | 365 | 227,240,000 (ar) | 32,716,306 | ≥18 yr | 1,801,689 | 761 | — | 8.2 | |
| United States (Martin and colleagues) (25) | |||||||||
| 1979 | 365 | 224,567,000 (nc) | — | All ages | 31,338* | 14* | — | — | National Hospital Discharge Survey, 1979–2000, ICD-9 |
| 2000 | 365 | 281,425,000 (nc) | — | All ages | 256,033* | 91* | — | — | |
| United States (Danai and colleagues) (36) | |||||||||
| 1979–2003 | 9,125 | 6,384,773,427 (nc) | — | All ages | 3,831,394 | 13–15 | — | 37 | Data from the National Hospital Discharge Survey, 1979–2003, ICD-9 |
| Spain (Andreu Ballester and colleagues) (21) | |||||||||
| 1995–2004 | 3,650 | 41,677,000 (nc) | 23,351,859 | All ages | 17,834 | 43* | — | Discharge diagnoses in all 26 public hospitals in the Valencian Community, Spain, 10-yr period, ICD-9 | |
| Spain (Bouza and colleagues) (35) | |||||||||
| 2006 | 365 | 44,708,937 (ar) | 22,070,672 (during 6 yr) | All ages | 28,579 | 64 | 62.7 | 45.4 | National Hospital Discharge Registry, 2006–2011, ICD-9 |
| 2011 | 365 | 47,190,493 (ar) | All ages | 49,782 | 106 | 67.6 | 40.2 | ||
| Spain (Yebenes and colleagues) (44) | |||||||||
| 2008 | 365 | 7,364,000 (nc) | 4,761,726 (during 5 yr) | All ages | 12,809 | 174* | 69 | 23.7 | Administrative Registry of Minimum Basic dataset of Acute-care Hospitals, 2008–2012, ICD-9 |
| 2012 | 365 | 7,571,000 (nc) | All ages | 20,228 | 267* | 73 | 19.7 | ||
| Study Duration (d) | Population | Patients Observed | Age Range | Total Number of Sepsis Cases | Incidence (per 100,000 Person-Years) | Mean Age | Hospital Mortality (%) | Remarks | |
|---|---|---|---|---|---|---|---|---|---|
| Sepsis | |||||||||
| Prospective studies | |||||||||
| Denmark (Henriksen and colleagues) (18) | |||||||||
| 2010–2011 | 365 | 235,598 | 8,358 | ≥15 yr | 1,713 | 731 | 72 (median) | — | |
| Retrospective studies | |||||||||
| United States (Strehlow and colleagues) (17) | |||||||||
| 1992–2001 | 3,650 | — | 712,000,000 | Adults | 2,800,000 | 140 | — | — | |
| Severe sepsis | |||||||||
| Prospective studies | |||||||||
| Denmark (Henriksen and colleagues) (18) | |||||||||
| 2010–2011 | 365 | 235,598 | 8,358 | ≥15 yr | 1,071 | 265 | — | — | |
Data from 2 prospective and 25 retrospective hospital-based studies from seven countries on four continents were selected for further analysis (Figure 3). Both prospective studies were period prevalence studies and applied consensus criteria (American College of Chest Physicians/Society of Critical Care Medicine, PROWESS-SHOCK) to identify cases of sepsis or severe sepsis (19, 20). Retrospective studies (n = 25) mainly analyzed hospital discharge databases for the number of hospitalizations because of sepsis or severe sepsis based on various ICD code combinations. They generally used three different abstraction approaches to mirror the clinical criteria of sepsis or severe sepsis. Seventeen studies screened databases for all hospitalizations with ICD-9-CM codes for septicemia, sepsis, or severe sepsis (direct coding strategy) (5, 17, 21–36).
In this context, septicemia was defined as “a systemic disease associated with the presence of pathological microorganisms or toxins in the blood, which can include bacteria, viruses, fungi or other organisms” (ICD-9-CM Official Coding Guidelines). Codes for sepsis and severe sepsis were introduced in 2003 in a revised edition of the ICD-9-CM (26) and demand a code for a systemic infection and a code for sepsis or severe sepsis; thus they incorporate the consensus criteria that require a documented or suspected infection instead of a proven septicemia. Another four studies used different ICD-10-CM combinations including codes for septicemia, sepsis, and severe sepsis (28, 37–39). In this 10th edition, coding for sepsis and severe sepsis was also possible with negative blood cultures, as postulated in the consensus criteria (direct coding strategy). Eight studies selected all hospitalizations with ICD-codes for bacterial, viral, or fungal infection combined with a diagnosis of acute organ dysfunction to identify severe sepsis cases (indirect coding strategy) (28, 30, 32, 40–44).
A total of 17 studies were included (Table 1). These were mainly based on comprehensive hospital discharge registers with ICD-coded diagnoses of each patient (direct coding strategy). Worldwide, population incidence for sepsis cases in hospitals ranged from 73.6 per 100,000 inhabitants in 1979 in the United States (27) to 1,180 per 100,000 inhabitants in 2007–2008 in a mainly indigenous population in Australia’s Northern Territory (20), with an aggregate global estimator of 288 (95% CI, 215–386) sepsis cases per 100,000 person-years (τ = 0.55) (Figure 4A). For the last 13 years (2003–2015), this estimator was even higher (437 [95% CI, 334–571] sepsis cases per 100,000 person-years [τ = 0.38]) (see Figure E1 in the online supplement).
Figure 3. Framework for the classification of all epidemiologic studies on hospital-treated sepsis and severe sepsis from 27 articles that were included for metaanalysis (note that studies could be counted more than once if they targeted both sepsis and severe sepsis). ICD = International Classification of Diseases.
[More] [Minimize]
Figure 4. Population-level incidence rates per 100,000 person-years with 95% confidence intervals (CI) and a summarizing random effects estimator. (A) Hospital-treated sepsis cases. (B) Hospital-treated severe sepsis cases. Note that we applied approximations (see Methods, Statistical Analysis) to derive the estimates in A and B. *Studies that use multiple severe sepsis identification strategies in administrative data. They were considered in sensitivity analysis according to the identification strategy and included in the combined metaanalysis to balance wider and more restricted severe sepsis definitions. CDC = Centers for Disease Control and Prevention.
[More] [Minimize]Twenty studies were included (Table 2). Again, the results show a marked heterogeneity; the lowest incidences were found in Northern Europe, ranging from 3 to 49 hospital-treated severe sepsis cases per 100,000 person-years in Sweden (28) and Norway (37). In contrast, a hospital incidence of up to 1,061 severe sepsis cases per 100,000 inhabitants in the United States was observed (32) by mirroring clinical sepsis criteria in ICD codings. Jointly analyzing all included studies, we estimate a population incidence of severe sepsis of 148 (95% CI, 98–226; τ = 0.99) cases per 100,000 person-years (Figure 4B). The estimate was larger for more recent investigations (270 [95% CI, 176–412] cases per 100,000 person-years [τ = 0.60] for 2003–2015) (see Figure E2). In sensitivity analyses including studies that applied a restricted severe sepsis definition, these numbers changed to 94 (95% CI, 56–158; τ = 0.87) and more recently 183 (95% CI, 112–297; τ = 0.31) cases per 100,000 person-years (see Figure E3). In studies using wider severe sepsis definitions, we found an aggregate global estimate of 317 (95% CI, 158–634; τ = 0.27) and more recently 560 (95% CI, 277–1129; τ = 0.13) hospital-treated severe sepsis cases per 100,000 person-years (see Figure E4).
Case fatality rates of hospital-treated cases from 14 studies on sepsis and 18 studies on severe sepsis were analyzed. For sepsis, these rates ranged from 5% (20) to 42.5% (21) between 1979 and 2015 resulting in a metaanalytic estimate of 21% (95% CI, 17–25%; τ = 0.21). For the years 2003–2015 the estimator was 17% (95% CI, 11–26%; τ = 0.24) (see Figure E5). For severe sepsis, the estimated case fatality rates for severe sepsis was higher: 28% (95% CI, 24–32%; τ = 0.61) from 1979 until 2015. Focusing on the last 13 years (2003–2015), metaanalysis resulted in a lower estimated case fatality rate of 26% (95% CI, 20–33%; τ = 0.62) (see Figure E6) for severe sepsis. For severe sepsis from 1979–2015, depending on the definition, case fatality rates were higher (33% [95% CI, 28–38%]; τ=0.49) for a more restricted definition and 22% (95% CI, 16–30%; τ = 0.58) for a wider definition (see Figures E7 and E8). For 2003–2015, we found similar estimated case fatality rates of 33% (95% CI, 25–42%; τ = 0.52) for a more restricted definition and 18% (95% CI, 9–32%; τ = 0.44) for a wider definition of severe sepsis (see Figures E7 and E8).
High-income countries only represent 13% of the world’s population. Because we aimed to generate estimates on the global burden of sepsis, we need to acknowledge that 87% of the world population has understudied sepsis epidemiology. If we assume that the incidence rates for hospital-treated sepsis and severe sepsis estimated here similarly apply to low- and middle-income countries, a total annual number of 20.7 million sepsis and 10.7 million severe sepsis cases could be expected based on a global population of 7.2 billion people. Focusing only on data from the last decade, 31.5 million sepsis and 19.4 million severe sepsis cases would be expected to be treated in hospitals around the globe each year. Finally, if the case fatality rates for sepsis and severe sepsis in the hospital setting from the last decade are applied to the estimated global incidence, sepsis may cause or contribute to up to 5.3 million deaths worldwide per annum.
A main finding of this systematic review on a global level is that studies on population-level incidence and case-fatality rates for sepsis and severe sepsis are scarce, and none exist for low- and middle-income countries. The available data from high-income countries are mainly derived from large retrospective database studies for hospitalizations because of sepsis identified by different ICD-coding strategies. Because for most prospective cohort studies population denominators were not available or predictable, we had to exclude these studies from analyses; however, they reveal that ICU admissions rates for infections and sepsis are comparable throughout the world, even though causative organisms differ remarkably between continents (45, 46). Furthermore, data on population-level incidences of ICU-treated sepsis cases were excluded from metaanalysis because it was expected to be highly dependent on national ICU capacities. In the included studies from high-income countries, we observed large heterogeneity between single-study estimates, which can be related to varying sepsis definitions and other methodologic differences among the studies, but also to a differing prevalence of the underlying infections. Results suggest high population-level incidence rates for sepsis and severe sepsis treated in hospitals in high-income countries compared with other diseases, such as myocardial infarction (47).
The intended evaluation of the global burden of sepsis turned out to be limited because of missing reliable population-based data from low- and middle-income countries. If the incidence and case fatality rates we estimated in this review would also apply to low- and middle-income countries, tentative extrapolations suggest a global number of more than 31 million sepsis cases and 5 million deaths from sepsis globally merely in the hospital setting. However, the true incidence and burden of sepsis in these countries remains uncertain because of a lack of information on sepsis epidemiology and may even be higher because infectious diseases are considerably more prevalent in these areas of the world and cause a substantially higher proportion of deaths than in high-income countries (48). In 2010, lower respiratory tract infections and malaria ranked second and sixth among the leading causes of disability-adjusted life-years (7), and accounted for 2,652,600 (49) and 854,568 (50) deaths in 2013, respectively. Malaria (51) and viral infections, such as dengue fever (52), are also a major source of systemic infections in low- and middle-income countries, with most deaths attributable to sepsis (53–56). Furthermore, HIV infection, which is also most prevalent in low- and middle-income countries, is associated with a high risk of coinfection and sepsis (52, 57, 58): Mayanja and colleagues (59) reported 3,240 cases of septicemia per 100,000 person-years in a population of 45.7% HIV-positive individuals (i.e., up to 10-fold higher incidence of septicemia than that reported for high-income countries; this study included only a subgroup of patients with sepsis with positive blood cultures and was therefore excluded from metaanalysis).
Given the considerably higher prevalence of acute infections that may lead to sepsis in low- and middle-income countries where studies on the epidemiology of sepsis are missing, any estimates derived from high-income countries that add hospital-acquired to community-acquired cases may underestimate the true global cumulative incidence of sepsis. Furthermore, we estimated incidence rates based on hospitalized patients only. By searching death registers in the United States, Melamed and Sorvillo (60) showed that around 13% of sepsis-related deaths occur outside of the hospital environment in nursing homes and residences, even in the United States. Finally, the studies we included in our metaanalysis had substantial methodologic differences. Prospective and retrospective studies differed enormously in their approach to identify sepsis cases. In clinical practice, systemic inflammatory response syndrome criteria for sepsis definition seem insufficiently precise, with many nonsepsis conditions presenting with systemic inflammatory response syndrome and many patients with severe with sepsis shown to be negative for systemic inflammatory response syndrome (61). This diagnostic imprecision contributes to the heterogeneity observed in this metaanalysis and limits comparability of studies. Moreover, clinical consensus criteria and ICD-codes for sepsis changed over time and may be influenced by changes in clinical practice.
Generally, there is an ongoing controversy about the accuracy of ICD-identification of sepsis cases (8, 28, 30, 62) because of challenges with defining sepsis, severe sepsis, and organ dysfunction in administrative data. The different approaches to mirror clinical sepsis criteria using codes for infection and organ dysfunction for severe sepsis (e.g., Angus and colleagues, wider case definition [40]), codes for septicemia and sepsis (e.g., Martin and colleagues, more restricted case definition [25]), or variations of these coding schemes are susceptible to underestimation or overestimation (63). As confirmed by sensitivity analyses, these different approaches of ICD case identification add substantially to the heterogeneity observed in our metaanalyses. Chart-based clinical validation of sepsis cases identified through administrative databases revealed severalfold higher incidence rates (18). These observations are in accordance with several studies, which suggested that in-hospital administrative data, septicemia, sepsis, or severe sepsis themselves may not be coded correctly or missed (64, 65). However, it was argued that sepsis may be coded too frequently for billing reasons, based on the observation that over the same time period where the sepsis incidence increased, the incidence of pneumonia decreased and the incidence of intraabdominal infections and urinary tract infections remained unchanged (8).
Most recent representative data from the United States, however, suggest that both sepsis and infection hospitalizations increased in parallel when principal and secondary claims were used for case identification (5). Likewise, severe sepsis cases requiring mechanical ventilation increased at the same rate as overall sepsis cases. Furthermore, differences in hospital admission rates are likely to be related to hospital- and country-specific availability of hospital beds, admission policies, insurance systems, and other factors. Data extrapolation to a national level also assumes certainty regarding the correct estimation of the total number of hospitalized or treated patients. Regarding case fatality rates, the interpretation of administrative data is similarly challenging. Our data show decreased sepsis mortality rates in the last decade.
The impact of improved diagnostic measures, an increased awareness, and more accurate ICD coding for sepsis and severe sepsis, which may lead to a better recognition of milder sepsis cases, is difficult to determine (62, 66). However, the decline of mortality rates has been shown to be similarly evident in both retrospective register-based studies and multicenter randomized trials (67). Even though our estimated temporal trends are in line with a recent large Australian cohort (68), our summary estimates of incidence and case fatality rates may nevertheless be biased and CIs may be too narrow given the heterogeneity among studies. Indeed, some may argue that the studies should not be summarized by metaanalysis techniques at all. But given the systematic and transparent approach and the broad search strategy for a time frame of more than 30 years, we decided that reporting these summaries is more important for the reader than not providing them. Nevertheless, we also included detailed information for each study to enable alternative interpretations.
Aside from these limitations, our review captured a comprehensive number of observational epidemiologic studies from high-income countries, for which we observed and confirmed a high incidence rate for hospital-treated sepsis. The lack of data from low- and middle-income countries and the differences in methodology of studies from high-income countries highlight the need for additional studies and more consistent methodologic approaches. A revision of sepsis definitions by the international community is necessary to pave the way toward comparable sepsis criteria for clinical practice and research, and to allow a more consistent abstraction in ICD coding. Further research on sepsis coding using administrative data seems necessary to derive sensitive and specific sepsis case identifications. Most importantly, more population-based cohort studies are required to generate more accurate estimates on the global burden of sepsis. In conclusion, our review underlines the urgent need to implement global strategies to monitor sepsis morbidity and mortality as well as prevention and treatment regimens, especially in low- and middle-income countries.
The authors thank Rahul Nanchal, Bojana Beovic, Chin-Li Lu, Andrzej Kubler, Tara Lagu, Viktor Dombrovskiy, Juan Carlos Andreu-Ballester, Bertrand Guidet, Josh Davis, Christian Brun-Buisson, Samara Viner-Brown, Allan J. Walkey, Anne Elixhauser, Carmen Bouza, Janet Sutton, and David Harrison for providing additional data from their studies. They also thank Miguel Cabral, Katerina Proksova, Jirayu Phillip Chantanakomes, Hanna Schröder, Luise Theuß, Anna Kern, Tamás Farka, and Chihiro Ishihara for contributing language support and translations. They thank Miriam Kesselmeier for analytical support and useful comments for the manuscript.
| 1. | Czura CJ. Merinoff symposium 2010: sepsis. Speaking with one voice. Mol Med 2011;17:2–3. |
| 2. | Vincent JL, Opal SM, Marshall JC, Tracey KJ. Sepsis definitions: time for change. Lancet 2013;381:774–775. |
| 3. | Angus DC, van der Poll T. Severe sepsis and septic shock. N Engl J Med 2013;369:2063. |
| 4. | Seymour CW, Rea TD, Kahn JM, Walkey AJ, Yealy DM, Angus DC. Severe sepsis in pre-hospital emergency care: analysis of incidence, care, and outcome. Am J Respir Crit Care Med 2012;186:1264–1271. |
| 5. | Walkey AJ, Lagu T, Lindenauer PK. Trends in sepsis and infection sources in the United States. A population-based study. Ann Am Thorac Soc 2015;12:216–220. |
| 6. | Cohen J, Vincent JL, Adhikari NK, Machado FR, Angus DC, Calandra T, Jaton K, Giulieri S, Delaloye J, Opal S, et al. Sepsis: a roadmap for future research. Lancet Infect Dis 2015;15:581–614. |
| 7. | Murray CJ, Lopez AD. Measuring the global burden of disease. N Engl J Med 2013;369:448–457. |
| 8. | Rhee C, Gohil S, Klompas M. Regulatory mandates for sepsis care: -reasons for caution. N Engl J Med 2014;370:1673–1676. |
| 9. | Fleischmann C, Scherag A, Adhikari NK, Hartog CS, Tsaganos T, Schlattmann P, Angus D, Reinhart K. Global burden of sepsis: a systematic review [abstract]. Crit Care 2015;19(Suppl 1):P21. |
| 10. | Bone RC, Balk RA, Cerra FB, Dellinger RP, Fein AM, Knaus WA, Schein RM, Sibbald WJ; The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Chest 1992;101:1644–1655. |
| 11. | Levy MM, Fink MP, Marshall JC, Abraham E, Angus D, Cook D, Cohen J, Opal SM, Vincent JL, Ramsay G; SCCM/ESICM/ACCP/ATS/SIS. 2001 SCCM/ESICM/ACCP/ATS/SIS international sepsis definitions conference. Crit Care Med 2003;31:1250–1256. |
| 12. | Bernard GR, Vincent JL, Laterre PF, LaRosa SP, Dhainaut JF, Lopez-Rodriguez A, Steingrub JS, Garber GE, Helterbrand JD, Ely EW, et al.; Recombinant human protein C Worldwide Evaluation in Severe Sepsis (PROWESS) study group. Efficacy and safety of recombinant human activated protein C for severe sepsis. N Engl J Med 2001;344:699–709. |
| 13. | World Bank country and lending groups. 2013 [accessed 2013 Jul 24]. Available from: http://data.worldbank.org/about/country-and-lending-groups |
| 14. | Knapp G, Hartung J. Improved tests for a random effects meta-regression with a single covariate. Stat Med 2003;22:2693–2710. |
| 15. | Cornell JE, Mulrow CD, Localio R, Stack CB, Meibohm AR, Guallar E, Goodman SN. Random-effects meta-analysis of inconsistent effects: a time for change. Ann Intern Med 2014;160:267–270. |
| 16. | US and world population clock. 2014 [accessed 2014 Nov 8]. Available from: http://www.census.gov/popclock/ |
| 17. | Strehlow MC, Emond SD, Shapiro NI, Pelletier AJ, Camargo CA Jr. National study of emergency department visits for sepsis, 1992 to 2001. Ann Emerg Med 2006;48:326–331.e3. |
| 18. | Henriksen DP, Laursen CB, Jensen TG, Hallas J, Pedersen C, Lassen AT. Incidence rate of community-acquired sepsis among hospitalized acute medical patients: a population-based survey. Crit Care Med 2015;43:13–21. |
| 19. | Esteban A, Frutos-Vivar F, Ferguson ND, Peñuelas O, Lorente JA, Gordo F, Honrubia T, Algora A, Bustos A, García G, et al. Sepsis incidence and outcome: contrasting the intensive care unit with the hospital ward. Crit Care Med 2007;35:1284–1289. |
| 20. | Davis JS, Cheng AC, McMillan M, Humphrey AB, Stephens DP, Anstey NM. Sepsis in the tropical top end of Australia’s Northern Territory: disease burden and impact on indigenous Australians. Med J Aust 2011;194:519–524. |
| 21. | Andreu Ballester JC, Ballester F, Gonzalez Sanchez A, Almela Quilis A, Colomer Rubio E, Penarroja Otero C. Epidemiology of sepsis in the Valencian community (Spain), 1995–2004. Infect Control Hosp Epidemiol 2008;29:630–634. |
| 22. | Dombrovskiy VY, Martin AA, Sunderram J, Paz HL. Rapid increase in hospitalization and mortality rates for severe sepsis in the United States: a trend analysis from 1993 to 2003. Crit Care Med 2007;35:1244–1250. |
| 23. | Seymour CW, Iwashyna TJ, Cooke CR, Hough CL, Martin GS. Marital status and the epidemiology and outcomes of sepsis. Chest 2010;137:1289–1296. |
| 24. | Buechner JS, Williams KA. Epidemiology of hospitalizations for sepsis in Rhode Island, 1990-2002. Med Health R I 2004;87:286–287. |
| 25. | Martin GS, Mannino DM, Eaton S, Moss M. The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med 2003;348:1546–1554. |
| 26. | Hall MJ, Williams SN, DeFrances CJ, Golosinskiy A. Inpatient care for septicemia or sepsis: a challenge for patients and hospitals. NCHS Data Brief 2011;(62):1–8. |
| 27. | Centers for Disease Control (CDC). Increase in national hospital discharge survey rates for septicemia—United States, 1979–1987. MMWR Morb Mortal Wkly Rep 1990;39:31–34. |
| 28. | Wilhelms SB, Huss FR, Granath G, Sjöberg F. Assessment of incidence of severe sepsis in Sweden using different ways of abstracting International Classification of Diseases codes: difficulties with methods and interpretation of results. Crit Care Med 2010;38:1442–1449. |
| 29. | Lagu T, Rothberg MB, Shieh MS, Pekow PS, Steingrub JS, Lindenauer PK. Hospitalizations, costs, and outcomes of severe sepsis in the United States 2003 to 2007. Crit Care Med 2012;40:754–761. |
| 30. | Gaieski DF, Edwards JM, Kallan MJ, Carr BG. Benchmarking the incidence and mortality of severe sepsis in the United States. Crit Care Med 2013;41:1167–1174. |
| 31. | Kumar G, Kumar N, Taneja A, Kaleekal T, Tarima S, McGinley E, Jimenez E, Mohan A, Khan RA, Whittle J, et al.; Milwaukee Initiative in Critical Care Outcomes Research Group of Investigators. Nationwide trends of severe sepsis in the 21st century (2000-2007). Chest 2011;140:1223–1231. |
| 32. | Lagu T, Rothberg MB, Shieh MS, Pekow PS, Steingrub JS, Lindenauer PK. What is the best method for estimating the burden of severe sepsis in the United States? J Crit Care 2012;27:414.e9. |
| 33. | Sutton JP, Friedman B. Trends in septicemia hospitalizations and readmissions in selected HCUP states, 2005 and 2010. HCUP Statistical Brief #161. Rockville, MD: Agency for Healthcare Research and Quality; 2013. |
| 34. | Elixhauser A, Friedman B, Stranges E. Septicemia in U.S. hospitals, 2009. HCUP Statistical Brief #122. Rockville, MD: Agency for Healthcare Research and Quality; 2011. |
| 35. | Bouza C, López-Cuadrado T, Saz-Parkinson Z, Amate-Blanco JM. Epidemiology and recent trends of severe sepsis in Spain: a nationwide population-based analysis (2006-2011). BMC Infect Dis 2014;14:3863. |
| 36. | Danai PA, Sinha S, Moss M, Haber MJ, Martin GS. Seasonal variation in the epidemiology of sepsis. Crit Care Med 2007;35:410–415. |
| 37. | Flaatten H. Epidemiology of sepsis in Norway in 1999. Crit Care 2004;8:R180–R184. |
| 38. | Heublein S, Hartmann M, Hagel S, Hutagalung R, Brunkhorst F. Epidemiology of sepsis in German hospitals derived from administrative databases. Infection 2013;41:S71. |
| 39. | Sundararajan V, Macisaac CM, Presneill JJ, Cade JF, Visvanathan K. Epidemiology of sepsis in Victoria, Australia. Crit Care Med 2005;33:71–80. |
| 40. | Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med 2001;29:1303–1310. |
| 41. | Inigo J, Sendra JM, Diaz R, Bouza C, Sarria-Santamera A. Epidemiology and costs of severe sepsis in Madrid: a hospital discharge study [in Spanish]. Med Intensiva 2006;30:197–203. |
| 42. | Shen HN, Lu CL, Yang HH. Epidemiologic trend of severe sepsis in Taiwan from 1997 through 2006. Chest 2010;138:298–304. |
| 43. | Barnato AE, Alexander SL, Linde-Zwirble WT, Angus DC. Racial variation in the incidence, care, and outcomes of severe sepsis: analysis of population, patient, and hospital characteristics. Am J Respir Crit Care Med 2008;177:279–284. |
| 44. | Yebenes JC, Ruiz JC, Ferrer R, Artigas A, Lorencio C, Rodriguez A, Nuvials X, Martin-Loeches I, Bordeje L, Bosch A, et al. Trends in incidence and hospital outcomes among patients with severe sepsis in Catalonia during the 2008–2012 period [abstract 0537]. Intensive Care Med 2014;40(Suppl 1):S152. |
| 45. | Vincent JL, Rello J, Marshall J, Silva E, Anzueto A, Martin CD, Moreno R, Lipman J, Gomersall C, Sakr Y, et al.; EPIC II Group of Investigators. International study of the prevalence and outcomes of infection in intensive care units. JAMA 2009;302:2323–2329. |
| 46. | Vincent JL, Marshall JC, Namendys-Silva SA, François B, Martin-Loeches I, Lipman J, Reinhart K, Antonelli M, Pickkers P, Njimi H, et al.; ICON investigators. Assessment of the worldwide burden of critical illness: the intensive care over nations (ICON) audit. Lancet Respir Med 2014;2:380–386. |
| 47. | Yeh RW, Sidney S, Chandra M, Sorel M, Selby JV, Go AS. Population trends in the incidence and outcomes of acute myocardial infarction. N Engl J Med 2010;362:2155–2165. |
| 48. | Adhikari NK, Fowler RA, Bhagwanjee S, Rubenfeld GD. Critical care and the global burden of critical illness in adults. Lancet 2010;376:1339–1346. |
| 49. | GBD 2013 Mortality and Causes of Death Collaborators. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the global burden of disease study 2013. Lancet 2015;385:117–171. |
| 50. | Murray CJ, Ortblad KF, Guinovart C, Lim SS, Wolock TM, Roberts DA, Dansereau EA, Graetz N, Barber RM, Brown JC, et al. Global, regional, and national incidence and mortality for HIV, tuberculosis, and malaria during 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014;384:1005–1070. |
| 51. | D’Acremont V, Kilowoko M, Kyungu E, Philipina S, Sangu W, Kahama-Maro J, Lengeler C, Cherpillod P, Kaiser L, Genton B. Beyond malaria: causes of fever in outpatient Tanzanian children. N Engl J Med 2014;370:809–817. |
| 52. | Prasad N, Murdoch DR, Reyburn H, Crump JA. Etiology of severe febrile illness in low- and middle-income countries: a systematic review. PLoS One 2015;10:e0127962. |
| 53. | Gordon MA, Walsh AL, Chaponda M, Soko D, Mbvwinji M, Molyneux ME, Gordon SB. Bacteraemia and mortality among adult medical admissions in Malawi: -predominance of non-typhi salmonellae and Streptococcus pneumoniae. J Infect 2001;42:44–49. |
| 54. | Ssali FN, Kamya MR, Wabwire-Mangen F, Kasasa S, Joloba M, Williams D, Mugerwa RD, Ellner JJ, Johnson JL. A prospective study of community-acquired bloodstream infections among febrile adults admitted to Mulago Hospital in Kampala, Uganda. J Acquir Immune Defic Syndr 1998;19:484–489. |
| 55. | Ukaga CN, Orji CN, Orogwu S, Nwoke BE, Anosike JC, Udujih OS, Onyeka PI, Awujo NC. Concomitant bacteria in the blood of malaria patients in Owerri, southeastern Nigeria. Tanzan Health Res Bull 2006;8:186–188. |
| 56. | Schofield L, Grau GE. Immunological processes in malaria pathogenesis. Nat Rev Immunol 2005;5:722–735. |
| 57. | Archibald LK, McDonald LC, Nwanyanwu O, Kazembe P, Dobbie H, Tokars J, Reller LB, Jarvis WR. A hospital-based prevalence survey of bloodstream infections in febrile patients in Malawi: implications for diagnosis and therapy. J Infect Dis 2000;181:1414–1420. |
| 58. | Vugia DJ, Kiehlbauch JA, Yeboue K, N’Gbichi JM, Lacina D, Maran M, Gondo M, Kouadio K, Kadio A, Lucas SB, et al. Pathogens and predictors of fatal septicemia associated with human immunodeficiency virus infection in Ivory Coast, West Africa. J Infect Dis 1993;168:564–570. |
| 59. | Mayanja BN, Todd J, Hughes P, Van der Paal L, Mugisha JO, Atuhumuza E, Tabuga P, Maher D, Grosskurth H. Septicaemia in a population-based HIV clinical cohort in rural Uganda, 1996-2007: incidence, aetiology, antimicrobial drug resistance and impact of antiretroviral therapy. Trop Med Int Health 2010;15:697–705. |
| 60. | Melamed A, Sorvillo FJ. The burden of sepsis-associated mortality in the United States from 1999 to 2005: an analysis of multiple-cause-of-death data. Crit Care 2009;13:R28. |
| 61. | Kaukonen KM, Bailey M, Pilcher D, Cooper DJ, Bellomo R. Systemic inflammatory response syndrome criteria in defining severe sepsis. N Engl J Med 2015;372:1629–1638. |
| 62. | Iwashyna TJ, Angus DC. Declining case fatality rates for severe sepsis: good data bring good news with ambiguous implications. JAMA 2014;311:1295–1297. |
| 63. | Liu V, Escobar GJ, Greene JD, Soule J, Whippy A, Angus DC, Iwashyna TJ. Hospital deaths in patients with sepsis from 2 independent cohorts. JAMA 2014;312:90–92. |
| 64. | Ollendorf DA, Fendrick AM, Massey K, Williams GR, Oster G. Is sepsis accurately coded on hospital bills? Value Health 2002;5:79–81. |
| 65. | Whittaker SA, Mikkelsen ME, Gaieski DF, Koshy S, Kean C, Fuchs BD. Severe sepsis cohorts derived from claims-based strategies appear to be biased toward a more severely ill patient population. Crit Care Med 2013;41:945–953. |
| 66. | Angus DC, Wax RS. Epidemiology of sepsis: an update. Crit Care Med 2001;29(Suppl. 7):S109–S116. |
| 67. | Stevenson EK, Rubenstein AR, Radin GT, Wiener RS, Walkey AJ. Two decades of mortality trends among patients with severe sepsis: a comparative meta-analysis. Crit Care Med 2014;42:625–631. |
| 68. | Kaukonen KM, Bailey M, Suzuki S, Pilcher D, Bellomo R. Mortality related to severe sepsis and septic shock among critically ill patients in Australia and New Zealand, 2000-2012. JAMA 2014;311:1308–1316. |
*These authors contributed equally to the article.
Author Contributions: C.F. and T.T. performed the literature review and data extraction. A.S. and P.S. performed the metaanalyses. C.F., K.R., and C.S.H. drafted the manuscript. N.K.J.A., D.C.A., K.R., C.S.H., C.F., and A.S. revised the manuscript for important intellectual content.
This article has an online supplement, which is accessible from this issue's table of contents at www.atsjournals.org
Originally Published in Press as DOI: 10.1164/rccm.201504-0781OC on September 28, 2015
Author disclosures are available with the text of this article at www.atsjournals.org.
