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Clostridium difficile infection: An emerging
cause of death in the twenty-first century∗
Viytta N. Abdullatif
Andrew Noymer†
Department of Population Health and Preventive Medicine
University of California
Irvine, CA 92697-
Abstract Enterocolitis due to Clostridium difficile is major emerging cause of death in the US. Between 1999 and 2012, C. diff. deaths rose by a staggering almost-10-fold increase, to 7,739 from 793. This paper has three goals. First, we present a demographic description of C. diff. mortality in the US since 1999. Second, we test a hypothesis that the increase in C. diff. deaths is due to population aging. We find that the emergence of this cause of death follows a proportional hazard pattern, above age
- Thus, population aging is not the only factor responsible for the increase in C. diff. deaths. This, combined with a contributory cause of death analysis, points towards healthcare-based strategies to combat C. diff. Third, we demonstrate a simple weighted least squares tech- nique for estimating Gompertz models that gives parameter estimates that are closer to full maximum likelihood, compared to conventional approaches.
Clostridium difficile is a gram-positive spore-forming bacterium, and a clinically- significant enteric pathogen (Sunenshine and McDonald 2006). Clinical C. diff. infection is almost always associated with antibiotic use (Bartlett 2008 a , Leffler and Lamont 2015). The Centers for Disease Control and Prevention (CDC) has identified C. diff. as one of three microoganisms at the highest ∗Final author version. Published as Biodemography and Social Biology 62(2):198–207 (2016). http://www.tandfonline.com/doi/full/10.1080/19485565.2016.1172957 † To whom correspondence should be addressed: noymer@uci.edu
Table 1: Number of deaths: C. diff. infection (ICD10 A04.7), US, 1999–
Number of deaths year underlying contributory total 1999 793 752 1, 2000 1,101 919 2, 2001 1,332 988 2, 2002 2,195 1,331 3, 2003 2,776 3,601 6, 2004 4,062 3,992 8, 2005 5,332 3,204 8, 2006 6,225 3,582 9, 2007 6,372 3,507 9, 2008 7,476 4,103 11, 2009 7,251 4,089 11, 2010 7,298 4,198 11, 2011 8,085 4,616 12, 2012 7,739 4,850 12,
threat level, “urgent”, along with carbapenem-resistant Enterobacteriaceae (CRE) and drug-resistant Neisseria gonorrhoeae , noting: “Although C. difficile is not currently significantly resistant to antibiotics used to treat it, it was included in the threat assessment because of its unique relationship with resistance issues, antibiotic use, and its high morbidity and mortality.” (Na- tional Center for Emerging Zoonotic and Infectious Diseases 2013).
Table 1 shows the stark rise in deaths due to Clostridium difficile en- terocolitis (ICD-10 A04.7) over a 14-year period. The United States began using ICD-10 for death codes in 1999, before which it is difficult to track C. diff. mortality. The reason is that in the ICD-9 coding scheme, C. diff. deaths were coded as 008.45 (5-digit code), whereas the mortality detail files report only 4-digit ICD-9 codes. For example, in 1998 there were 600 deaths in the US with cause of death ICD-9 008.4, “intestinal infections due to other spec-
rates do not change. We know, a priori , that a nearly 10-fold increase in deaths in 14 years (cf. table 1) must have a causal component beyond pop- ulation aging, but nonetheless it is desirable to describe the age-mortality profile for C. diff. , and whether it has changed over time.
We present underlying-cause and any-mention mortality rates for C. diff. infection, by age (5-year groups) and sex, calculated from data on every death in the US. We use cause-specific Gompertz models to test a propor- tional hazard hypothesis, and we introduce therein a weighted least squares technique for estimating Gompertz survival curves.
Clostridium difficile mortality
We used data from the multiple cause of death files of the National Center for Health Statistics (National Center for Health Statistics 2014), a database of all mortality in the United States. We extracted data on all deaths contain- ing C. diff. infection (ICD10 A04.7) as an underlying or contributory cause, during the period 1999–2012. The start of the time span corresponds to the beginning of ICD-10 death coding in the United States, and the end of the span reflects the most recent available data. Using population data from the Human Mortality Database (2014), we calculated age-specific and age- adjusted death rates for C. diff. infection, separately by sex. The year- United States standard population was used for the calculation of the age- adjusted rates. We also calculated all-mention death rates, i.e., where the numerators were all deaths involving C. diff. infection as an underlying or contributory cause (Wing and Manton 1981).
Year
Age-adjusted death rate (per 100,000)
any occurrenceunderlying cause
males females
Figure 1: Age-adjusted death rate, C. diff. infection, 1999–2012. Figure 1 shows the age-adjusted death rate for C. diff. infection, 1999–2012. This graph shows three important features of C. diff. mortal- ity in the US. First, C. diff. age-adjusted death rates have risen about 7-fold since 1999, from less than 0.5 per 100,000 to just under 2.5 per 100,000. As table 1 shows, this corresponds to a 10-fold increase in the absolute number of deaths in which C. diff. is the underlying cause, and an 8-fold increase in all deaths involving C. diff. , in just a fourteen-year time span. The in- crease over time has been uneven, with a plateau from 2008–12. Second, there is no meaningful sex difference in the age-adjusted death rates for the underlying cause of death. Third, the all-mention age-adjusted death rates follow the underlying age-adjusted death rates over time in a roughly par- allel fashion, but since 2003, a sex difference has emerged. Specifically, in
Age
C. diff death rate (per 100,000)^20061999 – –^20122005
males females
Figure 2: Age-mortality profile, C. diff. infection (underlying cause), 1999–2012.
male and female hazards often behave differently (Kohler and Kohler 2000), although in this case there is no qualitative distinction between the shapes or levels of the graph by sex, above age 40 (cf. figure 2). Five-year age groups, 40 ≤ x ≤ 99 (i.e., 40–44, 45–49,... ), were used to smooth age heaping. In each age group, the mean age of C. diff. mortality in the interval was used as the x value in the regressions. For example, for females age 65–69 in the more recent time period, the mean age of death due to C. diff. was 67.153, so this value was used as x instead of the midpoint of the interval, 67.5. This “centering” of the x values is used to polish the estimates, although it has small effects. Such centering, combined with parametric estimation of mortality, can help avoid some of the methodological pitfalls outlined by Gelman and Auerbach (2016).
Table 2: Proportional hazard analysis of C. diff. mortality, US, 1999–2005 vs. 2006–
WLS FEMALES MALES (1) (2) (3) (1) (2) (3) Early Late Early Late (1999–2005) (2006–12) ∆ (1999–2005) (2006–12) ∆ age term ( β ) 0.124*** 0.126*** 0.129*** 0.132*** (30.7) (26.8) (38.2) (43.2) intercept ( α ) −19.85*** −18.61*** −20.16*** −19.08*** (−59.8) (−48.0) (−75.6) (−78.0) period term ( γ ) 1.240 1. (1.80) (2.34) age×period ( δ ) 0.00157 0. (0.19) (0.56) N 12 12 24 12 12 24 R^2 0.9895 0.9863 0.9890 0.9932 0.9947 0. residual d.f. 10 10 20 10 10 20 F 944.9 718.6 599.1 1459 1870 1392 RMSE 0.139 0.161 0.157 0.121 0.110 0. t statistics in parentheses *** p<0.0001, ** p<0.001, * p<0.
Table 2 gives the weighted least squares (WLS) regression results. The weights are the numbers of C. diff. deaths in each age×sex×period cell; this is numerator-weighted rate regression, in other words. The use of death counts as weights has two appealing properties. First, it effectively down- weights data at the extremes of the age range, where deaths are fewer. This has the desirable consequence of reducing the importance of subjec- tive choices of age range (for instance, whether to use data beginning at age 40 or at age 45). Second, it moves the parameter estimates closer to those that would be achieved by maximum likelihood (ML) estimation — which can regarded as theoretically desirable (Brillinger 1986) — but with- out the computational expense of ML. To the best of our knowledge, this approach (using WLS expressly to approximate ML estimates) has not been
the level but not about the shape (slope) of the Gompertzian pattern above age 40.
To complete our empirical analysis, table 3 summarizes the under- lying causes of death when C. diff. infection was a contributory cause on the death certificate; the importance of multiple-cause analysis is noted by Dés- esquelles et al. (2014). Table 3 shows very clearly that C. diff. is involved in a wide variety of causes of death, especially those that occur in clinical set- tings. There is no single “typical” underlying cause of death when C. diff. is a contributory cause. The top 18 detailed (i.e., 4-digit) causes are listed in ta- ble 3, but these encompass only about half the deaths involving C. diff. , with the other half spread among 1,295 unique ICD codes. The clinical literature on C. diff. -associated morbidity clearly notes the association with healthcare (e.g. Sunenshine and McDonald 2006). Table 3 is fully consistent with this. For example, diseases of the circulatory system (ICD-10 Ixx.x) account for over 40% of deaths in which C. diff. is a contributory cause. While some mortality in this group may not be preceded by a hospital stay (for example, sudden death due to stroke or heart attack), admission to the emergency department and/or intensive care unit is common with these diseases (see ICD-10 “I” causes in table 3). Also, nearly 30% of deaths in which C. diff. is a contributory cause involve neoplasms (cancer, ICD-10 Cxx.x). Cancer is clearly a disease that involves interaction with healthcare prior to mortality. Moreover, immunosuppression is a risk factor for cancer (Vial and Descotes
- as well as infection. In short, table 3 shows that when C. diff. infec-
Table 3: Underlying cause of death when C. diff. infection (ICD10 A04.7) is a con-
elsewhere classified (^)
- tributory cause of death, US, 1999–
- Atherosclerotic heart disease I25.1 7. Underlying Cause of Death ICD10 code Percent
- Chronic obstructive pulmonary disease J44.9 5.
- Septicaemia A41.9 5.
- Acute myocardial infarction I21.9 3.
- Lung cancer C34.9 3.
- Dementia (unspecified) F03 3.
- Urinary tract infection N39.0 2.
- Stroke I64 2.
- Alzheimer’s disease G30.9 2.
- Aspiration pneumonia J69.0 2.
- Congestive heart failure I50.0 2.
- Colon cancer C18.9 1.
- Diabetes mellitus (without complications) E14.9 1.
- Non-Hodgkin’s lymphoma C85.9 1.
- Angina pectoris I25.0 1.
- COPD with ALRI J44.0 1.
- Atrial fibrillation and flutter I48 1.
- Colitis/enteritis/enterocolitis K55.9 1.
- Other (1,295 unique ICD codes) 50.
- Certain infectious and parasitic diseases A 3. Of which:
- Certain infectious and parasitic diseases B 3.
- Neoplasms C 23.
- Diseases of the blood and blood-forming organs and etc. D 2.
- Endocrine, nutritional and metabolic diseases E 5.
- Mental and behavioural disorders F 1.
- Diseases of the nervous system G 4.
- Diseases of the eye and adnexa, and the ear and mastoid process H <0.
- Diseases of the circulatory system I 21.
- Diseases of the respiratory system J 7.
- Diseases of the digestive system K 10.
- Diseases of the skin and subcutaneous tissue L 0.
- Diseases of the musculoskeletal system and connective tissue M 2.
- Diseases of the genitourinary system N 7.
- Pregnancy, childbirth and the puerperium O <0.
- Certain conditions originating in the perinatal period P 0.
- Congenital malformations, deformations and chromosomal abnormalities Q 0.
- Symptoms, signs and abnormal clinical and laboratory findings, not R 0.
- External causes WX 1.31.
verse gut microbiome-related approaches (e.g., Rupnik 2015, Buffie et al. 2015).
One limitation of this study is that C. diff. mortality is probably the “tip of the iceberg” of infection, on which we do not have data. Moreover, the elderly, who are hospitalized disproportionately and for longer stays, may play a catalyzing role in the epidemiology of C. diff. in ways that are not captured by the mortality rates, which we have asserted are not rising in an age-specific way.
Especially because of the broad age range of increased mortality and the nonspecific nature of observed co-morbidities, public health agencies should closely monitor C. diff. mortality. The most important substantive les- son from this study is that the emergence of C. diff. as a cause of death in the United States is not due to population aging, but is healthcare-associated. We also demonstrate weighted least squares (WLS) regression as an approx- imation to full maximum likelihood for Gompertz parameter estimation, and we strongly recommend WLS over OLS whenever weights are available.
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Appendix
Here we present a regression tables of the same models as in table 2, except estimated using two alternate ways from the WLS presented therein. The first is maximum likelihood (ML). Specifically, we estimate the following Poisson regression models: log (deaths) = α + βx + log (exposure) log (deaths) = α + βx + γp + δ ( p × x ) + log (exposure) ,
as an alternative to the WLS models of logged mortality rates, as in the main body of the paper; these are presented in table A-1. In table A-2, we estimate Gompertz estimates by OLS in the typical way (see, e.g., Wachter 2014, p.69; Preston et al. 2001, p.193; Horiuchi and Coale 1982). These are the exact same models as in the main text of the paper, except OLS is used instead of the weighted approach.
For both males and females, the six coefficients in the ML estima- tion in table A-1 ( α , β for each time period, and the interactive coefficients γ , δ ) are closer to the WLS (table 2) than to the OLS coefficients of table A- 2, as was asserted. The ML estimation treats each death as an observation, whereas the WLS treats each age-cell as a single data point (see “ N cells”, “ N deaths” in table A-1). This necessarily results in much higher z -statistics in table A-1, compared to the t -statistics in table 2 or table A-2; the usual cautions apply, about null hypothesis significance testing with large sample sizes. The likelihood-based estimation of coefficients, using Poisson likeli- hood (see Brillinger 1986), where each death influences the estimates, is ap- proximated by using each death as part of a weighting. The WLS approach is
Table A-2: OLS version of table 2 OLS FEMALES MALES (1) (2) (3) (1) (2) (3) Early Late Early Late (1999–2005) (2006–12) ∆ (1999–2005) (2006–12) ∆ age term ( β ) 0.127*** 0.130*** 0.130*** 0.134*** (48.6) (44.7) (47.6) (52.0) intercept ( α ) −20.17*** −19.05*** −20.31*** −19.27*** (−107) (−91.1) (−104) (−105) period term ( γ ) 1.125** 1.039** (4.01) (3.87) age×period ( δ ) 0.00294 0. (0.75) (1.06) N 12 12 24 12 12 24 R^2 0.9958 0.9950 0.9958 0.9956 0.9963 0. residual d.f. 10 10 20 10 10 20 F 2364 1997 1572 2267 2701 1791 RMSE 0.155 0.172 0.164 0.161 0.152 0. t statistics in parentheses *** p<0.0001, ** p<0.001, * p<0.
