Sepsis Mortality Rates:

A Study of Six Counties

Ashley Mang and Kelly Mallie

Copyright 2012

Introduction:

Sepsis is a potentially deadly syndrome caused by a bacterial or fungal infection that can begin anywhere in the body. In the United States, it is the tenth leading cause of death, one of only two infectious conditions listed in the 15 leading causes of death but early treatment of sepsis can prevent fatality (Melamed and Sorvillo 2009). Sepsis infections can be community acquired or nonsocomial and is life threatening especially for those with weak immune systems or chronic illness. Medical risks factors for acquiring sepsis include chronic diseases such as HIV infection, cancer, cirrhosis, alcohol dependency and ulcers. Populations most vulnerable to sepsis mortality are the very young, the elderly, men, and blacks (Melamed and Sorvillo 2009). Methods: We used the CDC Wonder database to investigate and further our understanding of sepsis mortality in the United States. We were able to collect data on septicaemia (ICD-10 codes) deaths for the years 2000 through 2007. The first step was to generate the county list that showed 64 large counties with populations over 1,000,000 and rank that list to show rates within those counties (Table 1). We restricted our sample to large cities by selecting “large central metro” as the urbanization option. We chose three initial counties to compare age-adjusted rates of sepsis death. Initially we discovered that Philadelphia blacks had very high rates of sepsis deaths. In choosing to compare Philadelphia to other sepsis mortality rates in U.S. cities, we limited our comparisons to consolidated city-counties so that city and county boundaries perfectly coincide with where the majority of the population lives. We selected a total of six consolidated city-counties: Baltimore, Maryland; San Francisco, California; Denver, Colorado; New Orleans, Louisiana; and Miami, Florida. In medical literature there is disagreement about what precisely constitutes coding a death as sepsis. It is possible to restrict coding of sepsis to ICD-10 A40-A41 (Table 2) or to use a more expanded set of codes that cover deaths attributed to a group of 113 broad infection categories, (Table 3) (Wang, Devereaux, Yealy, Safford and Howard 2010).

Table 1: Restricted Rank Ordered Counties

County

Deaths Population Crude Rate Per 100,000

Age Adjusted Rate Per 100,000

Baltimore city, MD 2090 5142860 40.6 39.9 Philadelphia County, PA 4456 11797366 37.8 34.9 Essex County, NJ 1851 6285203 29.5 30 Richmond city, VA 520 1585525 32.8 29.5 Alexandria city, VA 261 1083373 24.1 26.5

Hudson County, NJ 1099 4822481 22.8 24.8 Norfolk city, VA 391 1903602 20.5 24.3 Orleans Parish, LA 794 3318107 23.9 24.1 Fulton County, GA 1163 7204444 16.1 22 Clark County, NV 2342 12860399 18.2 21.8 Cook County, IL 8571 42602326 20.1 20.9 District of Columbia, DC 918 4641077 19.8 19.8 St. Louis city, MO 562 2809431 20 19.1 Suffolk County, MA 986 5681089 17.4 19 Jefferson County, KY 1113 5592364 19.9 18.4 Erie County, NY 1726 7460320 23.1 18.3 Jefferson County, AL 1025 5277653 19.4 17.8 Harris County, TX 3351 29238600 11.5 17.6 Virginia Beach city, VA 442 3468849 12.7 17.2 Wayne County, MI 2496 16267219 15.3 15.7 Allegheny County, PA 2195 9997409 22 15.5 Shelby County, TN 941 7221673 13 15.4 Marion County, IN 912 6936450 13.1 14.4 Duval County, FL 777 6523801 11.9 13.9 Jackson County, MO 742 5287687 14 13.7 Cuyahoga County, OH 1806 10772829 16.8 13.4 Hartford County, CT 1121 6938359 16.2 13.4 Franklin County, OH 951 8746664 10.9 13.3 Orange County, FL 825 7870519 10.5 13.1 Oklahoma County, OK 698 5431382 12.9 13 Dallas County, TX 1649 18408152 9 12.6 Hamilton County, OH 935 6783972 13.8 12.5 Bexar County, TX 1266 11852633 10.7 12.5 Mecklenburg County, NC 562 6176465 9.1 12.5 Bronx County, NY 1096 10860673 10.1 11.7 Tarrant County, TX 1038 12575819 8.3 11.5 Milwaukee County, WI 888 7581983 11.7 11.4 Monroe County, NY 679 5884617 11.5 10.5 Providence County, RI 621 5045195 12.3 10.4 Denver County, CO 417 4518038 9.2 9.8 Miami-Dade County, FL 1848 18618476 9.9 8.9 Davidson County, TN 362 4758724 7.6 8.2 Travis County, TX 339 7064332 4.8 7.9 Hillsborough County, FL 644 8706294 7.4 7.7 Maricopa County, AZ 1887 27718438 6.8 7.3

Kings County, NY 1387 19968931 6.9 7.1 Salt Lake County, UT 344 7576140 4.5 6.4 Pinellas County, FL 781 7375113 10.6 6.4 King County, WA 833 14281581 5.8 6.3 Multnomah County, OR 313 5417928 5.8 6 Ramsey County, MN 248 4033123 6.1 5.9 Hennepin County, MN 496 8986748 5.5 5.7 New York County, NY 741 12675632 5.8 5.7 Queens County, NY 1020 17997772 5.7 5.3 Alameda County, CA 466 11609443 4 4.4 Sacramento County, CA 346 10568759 3.3 3.6 San Diego County, CA 762 23274882 3.3 3.5 Richmond County, NY 116 3731310 3.1 3.2 Santa Clara County, CA 328 13521435 2.4 2.9 Riverside County, CA 365 14448637 2.5 2.6 San Francisco County, CA 179 6248833 2.9 2.5 Los Angeles County, CA 860 78019052 1.1 1.3 Orange County, CA 264 23490925 1.1 1.3 Source: Center for Disease Control and Prevention

Table 2: Restricted ICD-10 Codes A40 (Streptococcal septicaemia) A40.0 (Septicaemia due to streptococcus, group A) A40.1 (Septicaemia due to streptococcus, group B) A40.2 (Septicaemia due to streptococcus, group D) A40.3 (Septicaemia due to Streptococcus pneumoniae) A40.8 (Other streptococcal septicaemia) A40.9 (Streptococcal septicaemia, unspecified) A41 (Other septicaemia) A41.0 (Septicaemia due to Staphylococcus aureus) A41.1 (Septicaemia due to other specified staphylococcus) A41.2 (Septicaemia due to unspecified staphylococcus) A41.3 (Septicaemia due to Haemophilus influenzae) A41.4 (Septicaemia due to anaerobes) A41.5 (Septicaemia due to other Gram-negative organisms) A41.8 (Other specified septicaemia) A41.9 (Septicaemia, unspecified)

Table 3: Expanded ICD-10 Codes A01 (Typhoid and paratyphoid fevers) A02 (Other salmonella infections) A03 (Shigellosis)

A06 (Amoebiasis) A04 (Other bacterial intestinal infections) A07 (Other protozoal intestinal diseases) A08 (Viral and other specified intestinal infections) A09 (Diarrhoea and gastroenteritis of infectious origin) A16 (Respiratory tuberculosis, not confirmed bacteriologically or histologically) A17 (Tuberculosis of nervous system) A18 (Tuberculosis of other organs) A19 (Miliary tuberculosis) A37 (Whooping cough) A38 (Scarlet fever) A46 (Erysipelas) A39 (Meningococcal infection) A40 (Streptococcal septicaemia) A41 (Other septicaemia) A50 (Congenital syphilis) A51 (Early syphilis) A52 (Late syphilis) A53 (Other and unspecified syphilis) A80 (Acute poliomyelitis) A83 (Mosquito-borne viral encephalitis) A84 (Tick-borne viral encephalitis) A85.2 (Arthropod-borne viral encephalitis, unspecified) B05 (Measles) B15-B19 (Viral hepatitis) B20-B24 (Human immunodeficiency virus [HIV] disease) B50 (Plasmodium falciparum malaria) B51 (Plasmodium vivax malaria) B52 (Plasmodium malariae malaria) B53 (Other parasitologically confirmed malaria) B54 (Unspecified malaria) A00 (Cholera) A05 (Other bacterial foodborne intoxications) A20-A28 (Certain zoonotic bacterial diseases) A30 (Leprosy [Hansen's disease]) A31 (Infection due to other mycobacteria) A32 (Listeriosis) A33 (Tetanus neonatorum) A34 (Obstetrical tetanus) A35 (Other tetanus) A36 (Diphtheria) A42 (Actinomycosis) A43 (Nocardiosis) A44 (Bartonellosis)

A48 (Other bacterial diseases, not elsewhere classified) A49 (Bacterial infection of unspecified site) A54 (Gonococcal infection) A55 (Chlamydial lymphogranuloma (venereum)) A56 (Other sexually transmitted chlamydial diseases) A57 (Chancroid) A58 (Granuloma inguinale) A59 (Trichomoniasis) A60 (Anogenital herpesviral [herpes simplex] infection) A63 (Other predominantly sexually transmitted diseases, not elsewhere classified) A64 (Unspecified sexually transmitted disease) A65-A69 (Other spirochaetal diseases) A70-A74 (Other diseases caused by chlamydiae) A75-A79 (Rickettsioses) A81 (Slow virus infections of central nervous system) A82 (Rabies) A85.0 (Enteroviral encephalitis) A85.1 (Adenoviral encephalitis) A85.8 (Other specified viral encephalitis) A86 (Unspecified viral encephalitis) A87 (Viral meningitis) A88 (Other viral infections of central nervous system, not elsewhere classified) A89 (Unspecified viral infection of central nervous system) A90-A99 (Arthropod-borne viral fevers and viral haemorrhagic fevers) B00 (Herpesviral [herpes simplex] infections) B01 (Varicella [chickenpox]) B02 (Zoster [herpes zoster]) B03 (Smallpox) B04 (Monkeypox) B06 (Rubella [German measles]) B07 (Viral warts) B08 (Other viral infections characterized by skin and mucous membrane lesions, not elsewhere classified) B09 (Unspecified viral infection characterized by skin and mucous membrane lesions) B25-B34 (Other viral diseases) B35-B49 (Mycoses) B55 (Leishmaniasis) B56 (African trypanosomiasis) B57 (Chagas' disease) B58 (Toxoplasmosis) B59 (Pneumocystosis) B60 (Other protozoal diseases, not elsewhere classified) B64 (Unspecified protozoal disease) B65-B83 (Helminthiases)

B85-B89 (Pediculosis, acariasis and other infestations) B90-B94 (Sequelae of infectious and parasitic diseases) B99-B99 (Other infectious diseases) G00 (Bacterial meningitis, not elsewhere classified) G03 (Meningitis due to other and unspecified causes) I33 (Acute and subacute endocarditis) I30 (Acute pericarditis) I31 (Other diseases of pericardium) I40 (Acute myocarditis) J10 (Influenza due to identified influenza virus) J11 (Influenza, virus not identified) J12 (Viral pneumonia, not elsewhere classified) J13 (Pneumonia due to Streptococcus pneumoniae) J14 (Pneumonia due to Haemophilus influenzae) J15 (Bacterial pneumonia, not elsewhere classified) J16 (Pneumonia due to other infectious organisms, not elsewhere classified) J18 (Pneumonia, organism unspecified) J20-J22 (Other acute lower respiratory infections) J69 (Pneumonitis due to solids and liquids) K35-K38 (Diseases of appendix) K80 (Cholelithiasis) K81 (Cholecystitis) K82 (Other diseases of gallbladder) N10 (Acute tubulo-interstitial nephritis) N11 (Chronic tubulo-interstitial nephritis) N12 (Tubulo-interstitial nephritis, not specified as acute or chronic) N13.6 (Pyonephrosis) N15.1 (Renal and perinephric abscess) N70-N76 (Inflammatory diseases of female pelvic organs) Source: Wang et al., 2010 Sepsis mortality rates were standardized by age and determined for each county in the sample. We reported rates by gender and race/ethnicity. Due to a preponderance of zero-values for Asian and Hispanics in many counties, we present rates for white and black only. To understand the disparities within categories by county, we calculated the disparity as the percent difference between male/female and black/white. In addition, we calculated the ratio of high risk group to low risk group.

Sepsis Mortality by County: Baltimore The age-adjusted rates for sepsis mortality in Baltimore are 39.9 per 100,000 deaths. Broken down, we see that males have higher rates than women, at 47.1 and 34.9 respectively. This shows a difference of about 12 deaths per 100,000. Accordingly, males have a 34.9% greater chance of death by sepsis compared to females. In addition to this gender disparity, we observe a racial disparity. Blacks in Baltimore have death rates of 46.4 while whites have rates of 31.8, a difference of 14.6 deaths per 100,000. Blacks, thus, have a 45.9% greater chance of death by sepsis compared to whites. Philadelphia The age-adjusted rates for sepsis mortality in Philadelphia are 34.9 per 100,000 deaths. Broken down, we see that males have higher rates than women, at 42.1 and 30.4 respectively. This shows a difference of 11.7 deaths per 100,000. Accordingly, males have a 38.5% greater chance of death by sepsis compared to females. In addition to this gender disparity, we observe a racial disparity. Blacks in Philadelphia have death rates of 45.7 while whites have rates of 28.8, a difference of 16.9 deaths per 100,000. Blacks, thus, have a 58.7% greater chance of death by sepsis compared to whites. San Francisco The age-adjusted rates for sepsis mortality in San Francisco are 2.5 per 100,000 deaths. Broken down, we see that males have higher rates than women, at 3 and 2 respectively. This shows a difference of 1 death per 100,000. Accordingly, males have a 50% greater chance of death by sepsis compared to females. In addition to this gender disparity, we observe a racial disparity. Blacks in San Francisco have death rates of 5.5 while whites have rates of 2.5, a difference of 3 deaths per 100,000. Blacks, thus, have a 120% greater chance of death by sepsis compared to whites. Denver The age-adjusted rates for sepsis mortality in Denver are 9.8 per 100,000 deaths. Broken down, we see that males have higher rates than women, at 10.6 and 9.3 respectively. This shows a difference of 1.3 deaths per 100,000. Accordingly, males have a 13.9% greater chance of death by sepsis compared to females. In addition to this gender disparity, we observe a racial disparity. Blacks in Denver have death rates of 13.2 while whites have rates of 9.5, a difference of 3.7 deaths per 100,000. Blacks, thus, have a 38.9% greater chance of death by sepsis compared to whites.

Miami The age-adjusted rates for sepsis mortality in Miami are 8.9 per 100,000 deaths. Broken down, we see that males have higher rates than women, at 10.4 and 7.9 respectively. This shows a difference of 2.5 deaths per 100,000. Accordingly, males have a 31.7% greater chance of death by sepsis compared to females. In addition to this gender disparity, we observe a racial disparity. Blacks in Miami have death rates of 14.6 while whites have rates of 8, a difference of 6.6 deaths per 100,000. Blacks, thus, have an 81.3% greater chance of death by sepsis compared to whites. New Orleans The age-adjusted rates for sepsis mortality in New Orleans are 24.1 per 100,000 deaths. Broken down, we see that males have higher rates than women, at 29.1 and 20.9 respectively. This shows a difference of 8.2 deaths per 100,000. Accordingly, males have a 39.2% greater chance of death by sepsis compared to females. In addition to this gender disparity, we observe a racial disparity. Blacks in New Orleans have death rates of 31.5 while whites have rates of 16.6, a difference of 14.9 deaths per 100,000. Blacks, thus, have an 89.8% greater chance of death by sepsis compared to whites. Discussion: According to the 2010 U.S. Census Bureau, San Francisco, Denver and Miami all have significantly lower percentages of black residents than white. In Philadelphia, New Orleans and Baltimore, the black population exceeds the white population. These demographics are reflected when observing the racial disparities in sepsis mortality rates. Further research is required but these racial disparities may be attributed to the higher proportion of black residents in counties with higher rates. If this is true, it would coincide with the “black belt”. Geographic distribution of sepsis mortality rates differs between blacks and whites, suggesting that racial differences may explain the variations in sepsis mortality. Observed regional differences remain unclear but may imply differences in patients, the environment, or patterns of care (Wang, et al. 2010). Of the six sample counties examined, Baltimore, Philadelphia and New Orleans see the highest rates of sepsis mortality deaths. Denver, Miami and San Francisco show much lower sepsis mortality rates. Investigation into rates broken down by gender and race mirror this gradation. Gender Overall, each of these six counties shows that males experience higher rates of death by sepsis than females. The chances of male deaths compared to female deaths range from a maximum of 50% in San Francisco to a minimum of 13.9% in Denver, an average among all counties of 34.7%. The range of ratios of male to female deaths is a low of 1.13 in Denver and a high of 1.5 in San Francisco, an average of 1.34.

One explanation for the gender disparities is that men are more hesitant to visit a doctor than women. Endorsement of dominant masculine behaviors harms men’s health and is indicated by reduced compliance with preventative health care (Springer and Mouzon 2011). Another explanation for disparities between genders is the finding that “male patients with sepsis were more likely to have chronic obstructive pulmonary disease, cancer, alcohol abuse, or HIV than were female patients with sepsis” (Moss 2005: 492). Given the limitations of our data, we can only speculate the factors contributing to gender disparities among sepsis mortalities. Race Overall, each of these six counties shows that blacks experience higher rates of death by sepsis than whites. The chances of black deaths compared to white deaths range from a maximum of 120% in San Francisco to a minimum of 38.9% in Denver, an average among all counties of 72.4%. The range of ratios of black to white deaths is a low of 1.39 in Denver and a high of 2.2 in San Francisco, an average of 1.73. One explanation for the racial disparities is that blacks may be more susceptible to chronic diseases that increase the risk for acquiring sepsis than whites. Blacks with sepsis were “more likely to have HIV, diabetes, chronic renal failure, burns, and to be obese than whites” (Dombrovskiy, Martin, Sunderram, and Paz 2007: 766). Other explanations for blacks’ higher incidences of sepsis mortalities include the confounding effect of economic disadvantage along with a lesser chance of having health insurance. These factors,therefore, decrease the likelihood that blacks would seek both preventative and emergency medical care (Reade and Angus 2009). Given the limitations of our data, we can only speculate the factors contributing to racial disparities among sepsis mortalities. Age Unfortunately, our samples did not allow for an accurate analysis of age. The CDC Wonder database provided inconsistent and sometimes missing information on age-based sepsis mortality data. According to research though, “young children and the elderly experienced the greatest burden of sepsis-related death” (Melamed and Sorvillo 2009: 4). “Improved care of the elderly and debilitated, chemotherapy for malignancies, organ transplantation, and cardiac surgery, plus the widespread use of internal devices such as chest drains, intravascular catheters, endotracheal tubes, and prosthetic material, all probably contribute to the increasing incidence of sever sepsis and septic shock...” (Friedman, Silva, Vincent 1998: 2079). Essentially, the elderly are prolonging their lives with medical care and eventually die from sepsis as a co-morbid factor along with their previous illness(es).

Restricted vs. Expanded: Using ICD-10 codes for all deaths attributed to infection rather than only looking at deaths coded as A-40 (streptococcal) and A-41 (septicemia), we found similar patterns in mortality rates and their disparities (Table 4). In all counties, male age adjusted mortality rates exceed those of females for both the expanded coding and the restricted coding. When expanding the codes and examining these gender patterns, the percent change of total deaths increases across the board for males and decreases for females. We found similar patterns when expanding the codes and examining race. In all counties, black age adjusted mortality rates exceed those of whites for both the expanded coding and the restricted coding. For four of the six counties, the percent change of total deaths for blacks increased while white percent change decreased, the exceptions being Denver and San Francisco. In these two counties we see the opposite, with white percent change increasing while black percent change decreases.

Table 4: Expanded Rank Ordered Counties

County

Deaths Population Crude Rate Per 100,000

Age Adjusted Rate Per 100,000

Baltimore city, MD 7427 5142860 144.4 143.7 Bronx County, NY 9430 10860673 86.8 97.5 Richmond city, VA 1636 1585525 103.2 95.2 Philadelphia County, PA 11671 11797366 98.9 92.5 District of Columbia, DC 4280 4641077 92.2 92.2 Essex County, NJ 5702 6285203 90.7 91.2 Orleans Parish, LA 2722 3318107 82 82.4 Fulton County, GA 4800 7204444 66.6 81.1 Norfolk city, VA 1319 1903602 69.3 81.1 St. Louis city, MO 2284 2809431 81.3 75.5 Shelby County, TN 4643 7221673 64.3 74 New York County, NY 9764 12675632 77 73.5 Suffolk County, MA 3893 5681089 68.5 73.3 Hudson County, NJ 3154 4822481 65.4 69.7 Duval County, FL 3905 6523801 59.9 67.3 Richmond County, NY 2415 3731310 64.7 66.6 Kings County, NY 12882 19968931 64.5 66 Jefferson County, AL 3746 5277653 71 64.9 Cook County, IL 26671 42602326 62.6 64.4

Alexandria city, VA 651 1083373 60.1 64.2 Clark County, NV 6526 12860399 50.7 61.6 Jefferson County, KY 3655 5592364 65.4 60.5 San Francisco County, CA 4433 6248833 70.9 59.8 Harris County, TX 12043 29238600 41.2 59.1 Hartford County, CT 4995 6938359 72 59 Wayne County, MI 9250 16267219 56.9 57.8 Mecklenburg County, NC 2781 6176465 45 57.4 Marion County, IN 3656 6936450 52.7 57 Orange County, FL 3733 7870519 47.4 56.9 Oklahoma County, OK 3057 5431382 56.3 56.8 Erie County, NY 5341 7460320 71.6 56.3 Jackson County, MO 3063 5287687 57.9 56.3 Davidson County, TN 2543 4758724 53.4 56 Franklin County, OH 3965 8746664 45.3 55.4 Dallas County, TX 7616 18408152 41.4 55.3 Virginia Beach city, VA 1409 3468849 40.6 54.6 Denver County, CO 2381 4518038 52.7 54.6 Allegheny County, PA 7697 9997409 77 53.9 Miami-Dade County, FL 10813 18618476 58.1 53.6 Sacramento County, CA 5092 10568759 48.2 52.6 Monroe County, NY 3423 5884617 58.2 51.1 Hamilton County, OH 3851 6783972 56.8 50.7 Milwaukee County, WI 3978 7581983 52.5 50.1 Providence County, RI 3002 5045195 59.5 49.8 Bexar County, TX 4922 11852633 41.5 47.9 Alameda County, CA 5058 11609443 43.6 47.5 Cuyahoga County, OH 6389 10772829 59.3 47.4 Tarrant County, TX 4303 12575819 34.2 46.1 Salt Lake County, UT 2426 7576140 32 45.1 Los Angeles County, CA 29637 78019052 38 44.7 Maricopa County, AZ 11490 27718438 41.5 44.4 Travis County, TX 1979 7064332 28 43.5 Multnomah County, OR 2320 5417928 42.8 43.1 Queens County, NY 8271 17997772 46 42.8 King County, WA 5775 14281581 40.4 42.5 San Diego County, CA 9054 23274882 38.9 41.5 Hillsborough County, FL 3494 8706294 40.1 41.4 Pinellas County, FL 4729 7375113 64.1 40.3

Santa Clara County, CA 4561 13521435 33.7 40.2 Riverside County, CA 4959 14448637 34.3 36.1 Orange County, CA 7237 23490925 30.8 35.4 Hennepin County, MN 3006 8986748 33.4 33.9 Ramsey County, MN 1473 4033123 36.5 33.6

Discussion: Divergent patterns in Denver and San Francisco from what we see on the East coast may be attributable in methods of regional reporting. At this point we do not understand what the source of those differences might be, but future studies will seek expert opinion about possible reasons that may be attributed to regional differences in methods of coding deaths. Perhaps The South and East Coasts tend to count sepsis deaths as the limited A40-A41 set of codes while the West uses the aforementioned broader set of codes. If this is true, a final explanation for geographic variation in sepsis mortality rates is that there are different coding schemes or classification schemes at work. The observed geographic variation seems statistical rather than actual. We are unable to explore this hypothesis at this time, and there is not a firm medical consensus about how to code sepsis deaths (Survivingsepsis.org).

References Dombroskiy, Viktor Y., Andrew A. Martin, Jagadeeshan Sunderram, Harold L. Paz. 2007,

“Occurrence and Outcomes of Sepsis: Influence of Race”. Critical Care Medicine. 35(3): 763-768.

Friedman, Gilberto, Eliezer Silva, and Jean-Louis Vincent. 1998. “Has the Mortality of Septic

Shock Changed With Time?”. Critical Care Medicine. 26(12): 2078-2086. Melamed, Alexander and Frank J. Sorvillo. 2009. “The Burden of Sepsis-Associated Mortality

in the United States From 1999 to 2005: An Analysis of Multiple-Cause-of-Death Data”. Critical Care. 13(1): 1-8.

Moss, Marc. 2005. “Epidemiology of Sepsis: Race, Sex, and Chronic Alcohol Abuse”. Critical

Infectious Diseases. 41: S490-97. Reade, Michael C. and Derek C. Angus. 2009. “Epidemiology of Sepsis and Non-Infectious

SIRS”. Sepsis and Non-Infectious Systemic Inflammation. 13-34. Springer, Kristen W. and Dawn M. Mouzon, 2011.“Macho Men’ and Preventive Care :

Implications for Older Men in Different Social Classes”. Journal of Health and Social Behavior. 52: 212-227.

Wang, Henry E., Randolph S. Devereaux, Donald M. Yealy, Monka M. Safford, and George

Howard. 2010. “National Variation in United States Sepsis Mortality: A Descriptive Study”. International Journal of Health Geographics. 9:1-9.

Mortality data were accessed via the Centers for Disease Control and Prevention, National

Center for Health Statistics (CDC WONDER Online Database 2012). http://wonder.cdc.gov/controller/datarequest/D67.