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Increasing Age Has Limited Impact on Risk of Clostridium difficile Infection in an Elderly Population

Increasing Age Has Limited Impact on Risk of Clostridium difficile Infection in an Elderly... Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 Open Forum Infectious Diseases MAJOR ARTICLE FIDSA Increasing Age Has Limited Impact on Risk of Clostridium difficile Infection in an Elderly Population 1,2 1 3 1 Margaret A. Olsen, Dustin Stwalley, Clarisse Demont, and Erik R. Dubberke 1 2 3 Departments of Medicine and Surgery, Washington University School of Medicine, St. Louis, Missouri; Sanofi-Pasteur, Lyon, France Background. Numerous studies have found increased risk of Clostridium difficile infection (CDI) with increasing age. We hypothesized that increased CDI risk in an elderly population is due to poorer overall health status with older age. Methods. A total of 174 903 persons aged 66 years and older coded for CDI in 2011 were identified using Medicare claims data. e co Th mparison population consisted of 1 453 867 uninfected persons. Potential risk factors for CDI were identified in the prior     12 months and organized into categories, including infections, acute noninfectious conditions, chronic comorbidities, frailty indica- tors, and health care utilization. Multivariable logistic regression models with CDI as the dependent variable were used to determine the categories with the biggest impact on model performance. Results. Increasing age was associated with progressively increasing risk of CDI in univariate analysis, with 5-fold increased risk of CDI in 94–95-year-old persons compared with those aged 66–67 years. Independent risk factors for CDI with the highest effect sizes included septicemia (odds ratio [OR], 4.1), emergency hospitalization(s) (OR, 3.9), short-term skilled nursing facility stay(s) (OR, 2.7), diverticulitis (OR, 2.2), and pneumonia (OR, 2.1). Exclusion of age from the full model had no impact on model perfor- mance. Exclusion of acute noninfectious conditions followed by frailty indicators resulted in lower c-statistics and poor model fit. Further exclusion of health care utilization variables resulted in a large drop in the c-statistic. Conclusions. Age did not improve CDI risk prediction aer co ft ntrolling for a wide variety of infections, other acute conditions, frailty indicators, and prior health care utilization. Keywords. age; Clostridium difficile ; epidemiology; Medicare; risk factor. Clostridium dicffi ile is the most common pathogen causing incidence of CDI rose dramatically with age, from 47/100 000 health care–acquired infections and the leading cause of death in younger adults aged 18–44  years to 148.5 in persons aged associated with gastroenteritis in the United States [1, 2]. The 45–64  years, and up to 628/100 000 in persons aged 65  years incidence of C. difficile infection (CDI) during an acute care hos- and older [4]. In the EIP study, there was a more than 13-fold pital stay increased about 2.7-fold between 2000 and 2012, based increase in CDI incidence in the elderly compared with younger on the Healthcare Cost and Utilization Project Nationwide adults (18–44  years). Despite this, few studies have sought to Inpatient Sample [3]. CDI was associated with more than 29 000 elucidate the underlying biological reason(s) for the increased deaths in 2011, with an attributable mortality ranging from 5.7% incidence of CDI among elderly persons. in endemic settings to 16.7% in severe outbreaks since 2000 [4]. One feature that deserves closer analysis is the role of over- Age is considered one of the primary risk factors for CDI all health status, including frailty, and risk of CDI. Frailty, the in general [5–8] and for severe CDI [9–12]. In the most recent expression of biologic aging, increases susceptibility to a vari- report from the US Emerging Infections Program (EIP), 57% ety of adverse events, including falls, fractures, infections, and of the estimated CDI cases in 2011 were in the elderly [4]. The ultimately death [13–16]. Frailty also results in increased health care exposure, including emergency department (ED) encoun- ters, hospitalization, and institutionalization [17–19], resulting in increased opportunity for exposure to antibiotics, the most important risk factor for CDI [20]. Received 14 June 2018; editorial decision 25 June 2018; accepted 11 July 2018. Correspondence: M.  A. Olsen, PhD, MPH, Division of Infectious Diseases, Washington Although the association between overall health status and University School of Medicine, Campus Box 8051, 4523 Clayton Ave., St. Louis, MO 63110 increased risk of CDI has not been examined explicitly, a review (molsen@wustl.edu). of the literature reveals hints that the relationship between age Open Forum Infectious Diseases © The Author(s) 2018. Published by Oxford University Press on behalf of Infectious Diseases and CDI may be more complicated than previously thought. Society of America. This is an Open Access article distributed under the terms of the Creative Severity of illness has long been known to be associated with Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/ by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any CDI [5, 21, 22]. Rao et al. found that poor functional status was medium, provided the original work is not altered or transformed in any way, and that the work an independent risk factor for severe CDI [23]. More recently, is properly cited. For commercial re-use, please contact journals.permissions@oup.com DOI: 10.1093/ofid/ofy160 Ticinesi et  al. found that multimorbidity was associated with Risk of Clostridium difficile With Age • OFID • 1 Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 increased risk of CDI [24]. Frailty per se has not been taken population, with the only restriction being that the index date into account in prior studies in terms of progressive accumu- occurred before the death date for uninfected persons who died lation of deficits and their impact on CDI risk. Limitations of in 2011. many prior analyses include relatively small sample sizes, which Conditions Potentially Associated With CDI in the Prior Year restrict the ability to control for many underlying conditions, Conditions potentially associated CDI were identified in the use of summary measures (eg, Charlson index) not designed to year before the index date and grouped into 6 categories to determine CDI risk, or relatively geographically confined popu- explore their contribution in a model to predict CDI risk. The lations (eg, single hospitals) that may impact the distribution of categories included age in 2-year increments, comorbidities, underlying conditions within the population studied. A better acute infections, acute noninfectious conditions, health care understanding of the impact of overall health status on CDI risk utilization, and frailty indicators. Comorbidities were defined is necessary to understand how best to implement CDI preven- according to the Elixhauser classification, with modification of tion efforts. the algorithm for complete claims data according to Klabunde We used Medicare claims data to determine whether age et  al. [28, 29]. Diagnosis codes on laboratory claims were not remains an important predictor of CDI in an elderly population used to identify comorbidities or acute infectious or nonin- aer t ft aking into account overall health status, including recent fectious conditions, as they may indicate suspected, but not acute and chronic illnesses, health care utilization, and indica- confirmed, conditions. Acute infections were identified using tors of frailty. ICD-9-CM diagnosis codes and categorized into infection groups, as described previously [25]. Noninfectious acute con- METHODS ditions were also identified using ICD-9-CM diagnosis codes, We used 2010–2012 Medicare claims data from the Centers including myocardial infarction, gastrointestinal hemorrhage, for Medicare and Medicaid Services Chronic Conditions Data fractures, and others (Appendix). Only a single outpatient claim Warehouse (CCW) for all analyses. All patients aged 66  years coded for an acute infectious or noninfectious condition was and older with the International Classification of Diseases, 9th required, as acute conditions may not be coded repeatedly over Revision, Clinical Modification (ICD–9–CM) diagnosis code a prolonged period of time. All dates coded for acute infections for CDI (008.45) in 2011 in the Inpatient, Outpatient, or Carrier were used to determine the timing of infection compared with claims files were identified as CDI case patients (100% data). the CDI onset or index date for uninfected persons. The uninfected comparison group consisted of individuals in Health care utilization in the year before CDI included sur- the 2011 CCW 5% random sample, excluding those coded for gical procedures, defined by Uniform Billing (UB–04) revenue CDI. Individuals were excluded from both groups if they were codes for operating room expenses in inpatient and outpatient enrolled at any time during 2010–2011 in a health maintenance files, hospitalization, ED encounters, skilled nursing facil- organization, lacked complete Part A  and Part B coverage, or ity stays, and long-term facility (ie, nursing home) residence. if they were coded for CDI in the last quarter of 2010 (to iden- Hospitalizations were categorized as emergency hospitaliza- tify incident CDI in 2011). Also excluded were 135 329 indi- tions if they originated in the ED (defined by UB-04 revenue viduals with no health claims in 2010 and 2011, as there was codes 0450–0459) or nonemergency hospitalizations. Treat- no evidence for use of health care benefits. The Washington and-release ED visits were defined by revenue codes 0450–0459 University Human Research Protection Office gave approval to from outpatient facilities. Skilled nursing facility stays were conduct this research with a waiver of informed consent. identified using the Skilled Nursing Facility file. Residence in a long-term care facility was identified using method 2 in Date of Onset and Attribution of CDI Goodwin et al., based on the work of Intrator et al. [30, 31]. The date of onset of CDI was defined as the first date correspond- Indicators suggestive of frailty were identified in the year ing to a coded diagnosis of CDI, unless additional information before the onset date, including dementia, decubitus ulcer, uri- was available to define an earlier date of onset, as previously nary incontinence, senility/frailty, failure to thrive, sleep dis- described [25, 26]. The location of onset and attribution for each turbances, and difficulty walking. In contrast to the criteria for CDI episode was determined using an algorithm based on the standard comorbidities, only a single inpatient or outpatient recommended CDI surveillance definitions [25–27]. claim coded for the frailty indicators was required, as they do For persons without CDI in 2011, an analogous date of onset not generally require diagnostic testing to establish the diagno- (termed “index date”) was created to anchor the prior time sis. The only exception was for Parkinson’s disease, which was period to identify comorbidities. Aer det ft ermining the onset identified using the same criteria as the comorbidities. date for all persons with CDI in 2011, the distribution func- tion of these dates was determined. This distribution was used Analysis to randomly select index dates in the comparison uninfected The association of age with risk of CDI in univariate analysis population to mirror the distribution of onset dates in the CDI was determined by chi–square and Mann-Whitney U tests. 2 • OFID • Olsen et al Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 Multivariable logistic regression was used to characterize the septicemia (OR, 4.1), emergency hospitalization(s) (OR, 3.9), independent association of age with risk of CDI, controlling for short-term skilled nursing facility stay(s) (OR, 2.7), diverticu- all comorbidities, acute and chronic noninfectious conditions, litis (OR, 2.2), and pneumonia (OR, 2.1). Factors associated health care utilization in the prior year, and acute infections. with moderately increased risk of CDI in the year prior (odds The variance inflation factor (VIF) was used to identify impor- ratios of 1.5–2.0) included 1 ED visit, gastrointestinal hemor- tant collinearity in the full model. No variable had a VIF greater rhage, decubitus ulcer, elective hospitalization(s), lymphoma, than 2.4 in the full model, suggesting no important collinear- long-term care facility residence, inpatient surgery, and surgical ity. To determine the impact of inclusion of the 6 categories of site infection. Additional factors associated with approximately potential risk factors on CDI prediction, the individual catego- 40% increased risk of CDI included white race, diverticulosis, ries were excluded sequentially, and the impact on model per- liver disease, skin and soft tissue infection, and oral infection. formance was determined by assessing the discrimination of the Aer ad ft justment for the large variety of infections at any model using the area under the receiver operating curve (c-sta- time in the year before CDI, other chronic and acute conditions, tistic), change in the Bayesian Information Criterion (BIC), frailty indicators, and health care utilization in the year before and the deviance statistic comparing nested models. BIC is a CDI, the risk of CDI associated with increasing age decreased measure used to select the “best” model from a nested set, based dramatically (Table  1, Figure  1). Although the odds of CDI on the log likelihood of the models. BIC includes a penalty for remained significantly elevated compared with the youngest increased number of terms in the model and takes into account persons (aged 66–67  years), the odds ratios fluctuated slightly the sample size in calculating the penalty. Because of this, BIC from 1.052 to a high of 1.174 in the 86–87-year-old group. The is more conservative and will select smaller models than the risk of CDI with increasing age remained only slightly elevated commonly used Akaike Information Criterion [32]. The devi- when acute infections were restricted to those coded more than ance statistic was used to compare the goodness of fit of nested 30 days before CDI (Table 1). models rather than the standard Hosmer-Lemeshow test, To test whether inclusion of age, comorbidities, and other because with large sample sizes, small deviations can result in conditions ae ff cted model performance, discrimination, fit, and rejection of the null hypothesis that the model fits the data [33]. BIC were assessed in the full model (including all variables in SAS Enterprise Guide, version 7.1 (SAS, Cary, NC), was used Table 1), compared with models with individual variable groups for all data management and analysis. removed. As shown in Table  2, removal of age from the full model had no impact on the c-statistic and resulted in a slight RESULTS decrease in the BIC, and the deviance statistic remained nonsig- nificant, indicating that the model performance improved with A total of 174 903 persons aged 66  years and older with com- removal of age. Removal of comorbidities, acute noninfectious plete fee-for-service Medicare coverage were identified with conditions, and frailty indicators had little impact on the c-sta- at least 1 episode of CDI in 2011 in the Medicare claims files. tistic but resulted in small increases in the BIC and significant For all persons, the first episode of CDI in 2011 was selected deviance statistics, indicating that these models did not fit the for further analyses. The first CDI episode was categorized as data as well as the full model. The biggest decrease in the c-sta- hospital-onset in 49 755 persons (28.4%), other health care tistic occurred aer r ft emoval of infections (from 0.918 to 0.911) facility–onset in 43 433 (24.8%), community-onset communi- and health care utilization (0.918 to 0.897), along with the larg- ty-associated in 46 738 (26.7%), community-onset health care est increases in the BIC, indicating that these models performed facility–associated in 21 952 (12.6%), and indeterminate asso- more poorly than the full model. The impact on the BIC and ciation in 13 025 persons (7.4%). The comparison population c-statistic of removal of only the septicemia variable was about consisted of 1 318 538 uninfected persons in the 5% random     half that of removal of the entire infection category from the full sample data. model (Table  2), consistent with the very elevated risk of CDI e a Th ssociation of age, sex, and acute and chronic medical associated with septicemia. and frailty conditions with CDI in univariate and multivariable analysis is shown in Table  1, and the odds ratios for increas- DISCUSSION ing age are displayed in Figure 1. Age was categorized in 2-year increments to show the relationship between risk of CDI and We found that exclusion of age in a multivariable model to increasing age. In univariate analysis, the risk of CDI increased predict risk of CDI had no demonstrable impact on model linearly with increasing age until approximately age 88  years, performance after controlling for acute infections, health care at which point the risk leveled o. Th ff e odds of CDI dropped utilization, acute noninfectious conditions, and indicators of slightly in the oldest age group (96 years and older), possibly in frailty in the year before CDI. These results suggest that overall part due to lower rates of testing for C. difficile in the very old. health status, including infections, health care utilization, acute In multivariable analysis, the risk factors in the year prior conditions in the past year, and frailty indicators are the most that were associated with >2-fold increased risk of CDI were important determinants of CDI risk in an elderly population. In Risk of Clostridium difficile With Age • OFID • 3 Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 Table 1. Risk Factors for CDI in Univariate and Multivariate Analysis, Including Comorbid Conditions, Acute Infections, Acute Noninfectious Conditions, Health Care Utilization, and Frailty Indicators Present in the Year Before CDI Risk Factor OR 95% CI aOR 95% CI Demographics Age (66–67 ref), y 68–69 1.098 1.066–1.132 0.998 0.961–1.036 70–71 1.235 1.198–1.273 1.013 0.976–1.053 72–73 1.434 1.391–1.477 1.052 1.013–1.092 74–75 1.539 1.493–1.585 1.024 0.986–1.063 76–77 1.801 1.748–1.855 1.059 1.020–1.100 78–79 2.063 2.003–2.124 1.103 1.063–1.145 80–81 2.305 2.239–2.372 1.097 1.057–1.138 82–83 2.634 2.559–2.710 1.102 1.062–1.144 84–85 3.016 2.931–3.104 1.131 1.089–1.174 86–87 3.386 3.289–3.486 1.174 1.130–1.220 88–89 3.606 3.499–3.716 1.166 1.121–1.214 90–91 3.683 3.567–3.803 1.122 1.076–1.171 92–93 3.947 3.809–4.089 1.148 1.095–1.203 94–95 4.061 3.897–4.232 1.131 1.071–1.194 ≥96 3.917 3.759–4.081 1.129 1.070–1.192 White race 1.089 1.073–1.106 1.373 1.344–1.403 Female 1.114 1.102–1.125 1.053 1.038–1.069 Comorbidities Congestive heart failure 5.921 5.853–5.991 0.900 0.883–0.917 Vascular disease 3.374 3.326–3.423 1.000 0.979–1.021 Pulmonary circulatory disorder 6.166 6.027–6.309 1.121 1.086–1.157 Peripheral vascular disease 3.862 3.816–3.908 1.095 1.076–1.113 Paralysis 6.412 6.259–6.570 0.950 0.919–0.983 Neurologic disease 5.523 5.437–5.610 0.974 0.952–0.996 Parkinson’s disease 3.162 3.076–3.251 1.023 0.985–1.063 Chronic pulmonary disease 3.493 3.453–3.533 0.987 0.970–1.005 Hypothyroidism 2.096 2.071–2.122 1.03 1.013–1.047 Chronic renal failure 5.462 5.397–5.529 1.271 1.248–1.294 Liver disease 3.737 3.602–3.878 1.428 1.357–1.502 Peptic ulcer disease 4.216 3.665–4.851 0.927 0.766–1.123 Lymphoma 3.150 3.042–3.262 1.636 1.559–1.716 Metastatic cancer 4.195 4.074–4.318 1.228 1.177–1.281 Solid tumor 1.963 1.935–1.992 1.115 1.091–1.141 Rheumatoid arthritis/collagen vascular disease 2.298 2.250–2.347 1.266 1.230–1.303 Coagulation disorder 5.880 5.768–5.993 1.143 1.113–1.174 Obesity 3.771 3.696–3.846 1.100 1.070–1.132 Blood loss anemia 6.105 5.915–6.302 0.763 0.732–0.797 Deficiency anemias 6.732 6.660–6.804 1.271 1.251–1.291 Psychoses 4.521 4.431–4.613 0.888 0.863–0.914 Depression 4.918 4.848–4.988 1.142 1.119–1.165 Cardiac 3.846 3.805–3.888 1.000 0.984–1.016 Prior fluid/electrolyte disorders 7.504 7.411–7.598 0.838 0.823–0.851 Prior weight loss/malnutrition 7.799 7.658–7.943 0.952 0.927–0.978 Diabetes 1.973 1.952–1.994 0.940 0.926–0.955 Hypertension 3.771 3.722–3.821 0.987 0.970–1.005 Infections Septicemia 34.006 33.245–34.783 4.104 3.994–4.217 Pneumonia 13.380 13.178–13.585 2.054 2.012–2.096 Urinary tract infection/prostatitis 5.029 4.969–5.089 1.245 1.224–1.267 Skin and soft tissue infection 4.092 4.025–4.161 1.368 1.336–1.402 Surgical site infection 12.880 12.342–13.441 1.479 1.395–1.567 Bone infection/osteomyelitis 10.246 9.823–10.686 1.266 1.193–1.344 Organ infection/meningitis 6.134 5.666–6.641 1.193 1.067–1.334 Sexually transmitted disease/pelvic infection 1.442 1.355–1.533 0.982 0.902–1.068 4 • OFID • Olsen et al Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 Table 1. Continued Risk Factor OR 95% CI aOR 95% CI Abdominal abscess/peritonitis 3.879 3.812–3.948 1.106 1.076–1.137 Diverticulitis 5.095 4.987–5.205 2.214 2.147–2.283 Upper respiratory infection 1.297 1.275–1.319 1.010 0.987–1.034 Tonsillitis/ocular infection/mastoiditis 1.554 1.471–1.641 0.772 0.716–0.832 Otitis media 0.978 0.927–1.033 0.940 0.877–1.007 Oral infection 2.172 1.989–2.371 1.430 1.269–1.611 Viral infection 2.778 2.689–2.871 1.196 1.142–1.252 Health care utilization Inpatient surgery 8.162 8.068–8.257 1.479 1.453–1.506 Outpatient surgery 1.668 1.645–1.692 1.099 1.078–1.121 Nonelective hospitalization(s) 20.497 20.248–20.748 3.907 3.836–3.979 Elective hospitalization(s) 4.873 4.814–4.933 1.689 1.658–1.721 1 treat-and-release ED encounter 2.547 2.519–2.575 1.841 1.809–1.872 2 or more treat-and-release ED encounters 5.558 5.498–5.619 1.259 1.236–1.283 Nursing home residence 6.762 6.668–6.858 1.604 1.568–1.640 Short-term skilled nursing facility stay 15.724 15.543–15.907 2.679 2.634–2.725 Acute noninfectious conditions Acute myocardial infarction 6.852 6.724–6.982 1.269 1.237–1.302 COPD exacerbation 5.286 5.203–5.369 1.198 1.169–1.227 Gastrointestinal bleed 6.276 6.194–6.359 1.826 1.792–1.861 Diverticulosis 3.022 2.984–3.061 1.445 1.416–1.474 Subdural hematoma 5.570 5.265–5.893 1.031 0.960–1.107 Cerebrovascular accident 3.707 3.668–3.747 1.114 1.097–1.132 Closed fracture, lower extremity 4.608 4.529–4.689 0.938 0.914–0.962 Open fracture, lower extremity 6.322 6.019–6.641 0.869 0.815–0.926 Closed fracture, other 3.464 3.407–3.522 0.986 0.963–1.009 Open fracture, other 4.074 3.808–4.358 0.871 0.797–0.952 Frailty indicators Decubitus ulcer 13.244 13.013–13.478 1.727 1.686–1.770 Dementia 4.910 4.851–4.970 1.206 1.183–1.230 Dehydration, past 30 d 7.332 7.235–7.429 1.058 1.038–1.078 Deep venous thrombosis 6.147 6.050–6.246 1.319 1.289–1.350 Pulmonary embolism 5.277 5.141–5.416 1.069 1.031–1.109 Urinary incontinence 2.727 2.686–2.770 1.227 1.201–1.253 Senility/frailty 10.281 10.121–10.444 1.362 1.333–1.391 Failure to thrive 8.144 7.936–8.357 0.958 0.925–0.992 Sleep disturbance 2.166 2.134–2.198 1.136 1.113–1.160 Difficulty walking 6.407 6.338–6.476 1.087 1.069–1.105 C-statistic of the full model = 0.918. Abbreviations: aOR, adjusted odds ratio; CDI, Clostridium difficile infection; CI, confidence interval; COPD, chronic obstructive pulmonary disease; ED, emergency department; OR, odds ratio. the multivariable model, the lack of a dose–response in risk of (2-fold increased odds). Other serious infections, including sur- CDI with increasing age is likely due to the large number of risk gical site, skin and soft tissue, and oral infections, were associ- factors we were able to control for in the very large Medicare ated with moderately increased odds of CDI, consistent with population, including acute conditions common in the elderly, need for antibiotic therapy of these infections. Interestingly, and indicators of frailty. This lack of an age dose–response is even past viral infections were associated with a small increased consistent with the hypothesis that, after adequately accounting risk of CDI, reflecting possible inappropriate use of antibiotics for overall health status, age per se is no longer an important in these patients. predictor of CDI. This is particularly relevant as individuals Not surprisingly, health care utilization in the past year “age” at different times in their lives, and thus a younger person was independently associated with increased risk of CDI and with serious medical conditions may have much higher risk of improved the t o fi f the model. Emergency hospitalization was CDI than a healthy older person. associated with almost 4-fold increased risk of CDI, followed Acute infections were associated with increased risk of CDI, by 2.7-fold increased risk associated with skilled nursing facility particularly septicemia (4-fold increased odds) and pneumonia stay(s). Our finding of increased risk of CDI with hospitalization Risk of Clostridium difficile With Age • OFID • 5 Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 Zarowitz et al. similarly reported that up to 67% of CDI in nurs- ing home residents was attributable to a recent hospitalization, 4.0 using the Minimum Data Set survey of skilled nursing residents 3.6 [38]. Using the 2009 Medicare 5% random sample, we previously 3.2 found the incidence of CDI among nursing home residents to be 10 093/100 000 person-years if they had a prior emergency hospi- 2.8     talization, and only 1505/100 000 person-years if the person did 2.4 not have any hospitalizations in the previous year [25]. 2.0 In addition to acute infections and health care encounters, several acute noninfectious conditions were also associated with 1.6 increased risk of CDI. These conditions, including diverticu- 1.2 losis, gastrointestinal hemorrhage, and myocardial infarction, 0.8 were likely a primary contributor to the patients’ underlying 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96+ severity of illness. In contrast, indicators of frailty, including Age, y decubitus ulcers and urinary incontinence, were likely mark- ers for advanced underlying illness. Exclusion of acute nonin- Figure 1. Unadjusted and adjusted odds ratios for Clostridium difficile infection fectious conditions reduced the c-statistic and resulted in an by age (years) in the elderly Medicare population. Green circles: unadjusted odds imperfectly fitting model according to the deviance statistic. In ratios; blue circles: adjusted odds ratios. the comparison of models with individual categories removed, the biggest impact on model fit was associated with removal of the health care utilization category, followed by infections. The is consistent with recent reports by McDonald and colleagues of big impact of removal of infections, including septicemia, on increased risk of CDI with hospitalization in the past 30 days [34] model performance was not surprising, as many of the indi- and with the number of hospitalizations in the past 90 days [35]. vidual infections were associated with high risk of CDI, likely Stays in a skilled nursing facility occur following discharge from a hospitalization and require documentation of the need for con- due to antibiotic treatment of the preceding infections with tinuation of nursing care to be reimbursed by Medicare. Thus resulting colonic dysbiosis. The bigger impact of removal of the the increased CDI risk associated with skilled nursing facility health care utilization category was also not surprising, as seri- stay(s) is consistent with overall health status as the primary ous infections would result in hospitalization, and more than driver of CDI risk. In contrast, residence in a long-term care half of hospitalized patients are treated with at least 1 dose of facility was associated with only 1.5-fold increased risk of CDI, antibiotics, even in the absence of documented infection [39]. aer ad ft justment for other variables in the model. This suggests Limitations of this study include identification of CDI by that while nursing home residence is a risk factor for CDI, it is ICD-9-CM diagnosis codes, which are not perfectly accurate not as important as acute infectious and noninfectious events [40], and the lack of data on antibiotic utilization for all patients, and health care utilization associated with those acute events, particularly during hospital stays. e Th use of Medicare claims particularly emergency hospitalization. This finding is con- data restricted analyses to the elderly; whether younger persons sistent with recent surveillance studies in which more than with poorer overall health status are at the same risk as much half of the incident cases of CDI with onset in a nursing home older persons remains to be determined in a future study. In occurred within 30  days following hospital discharge [36, 37]. addition, repeating this study using Medicare data from a more Table 2. Comparison of Performance of Full and Reduced Models, Excluding Categories of CDI Predictors Model No. of Variables BIC Change in BIC C-Statistic Deviance P Full 87 638 187.4 0.918 1.0000 Age 72 638 167.8 –19.6 0.918 1.0000 Comorbidities 60 642 135.4 3948.0 0.917 .0001 Acute noninfectious conditions 77 645 064.7 4968.9 0.916 .0001 Frailty indicators 77 643 156.3 6877.3 0.917 .0001 Septicemia 86 651 908.3 13 720.9 0.915 .0001 Infections 79 663 514.9 25 327.5 0.911 .0001 Health care utilization 72 702 080.9 63 893.5 0.897 .0001 Abbreviations: BIC, Bayesian Information Criterion; CDI, Clostridium difficile infection. As with other goodness of fit tests, the null hypothesis for the deviance statistic is that the model fits the data. Therefore, P < .05 indicates poor model fit. Includes septicemia. 6 • OFID • Olsen et al Odds Ratio Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 a Quebec medical intensive care unit. Infect Control Hosp Epidemiol 2007; recent year with higher utilization of nucleic acid amplification 28:1305–7. testing would be beneficial, to determine whether the relation- 8. Loo VG, Bourgault AM, Poirier L, et al. Host and pathogen factors for Clostridium difficile infection and colonization. N Engl J Med 2011; 365:1693–703. ship with age remains minimal in the setting of increased sensi- 9. Loo VG, Poirier L, Miller MA, et al. A predominantly clonal multi-institutional tivity to identify not just CDI but C. difficile colonization. outbreak of Clostridium difficile-associated diarrhea with high morbidity and Strengths of our study include the very large sample size, gen- mortality. N Engl J Med 2005; 353:2442–9. 10. Henrich TJ, Krakower D, Bitton A, Yokoe DS. Clinical risk factors for severe eralizability to the fee-for-service US elderly population, and Clostridium difficile-associated disease. Emerg Infect Dis 2009; 15:415–22. identification of CDI across many institutions and providers. 11. Khanna S, Aronson SL, Kammer PP, et al. Gastric acid suppression and outcomes in Clostridium difficile infection: a population-based study. Mayo Clin Proc 2012; e v Th ery large sample size allowed us to control for a very large 87:636–42. number of potential risk factors in the multivariable model and 12. Abou Chakra CN, McGeer A, Labbé AC, et al. Factors associated with complica- tions of Clostridium difficile infection in a multicenter prospective cohort. Clin separate out the independent effect of increasing age on risk Infect Dis 2015; 61:1781–8. of CDI. 13. Walter LC, Brand RJ, Counsell SR, et al. Development and validation of a prog- nostic index for 1-year mortality in older adults after hospitalization. JAMA 2001; Advancing age is frequently cited as one of the primary risk 285:2987–94. factors for CDI. This study demonstrates that overall health 14. Ensrud KE, Ewing SK, Taylor BC, et al. Comparison of 2 frailty indexes for pre- status, including recent acute infections and even acute non- diction of falls, disability, fractures, and death in older women. Arch Intern Med 2008; 168:382–9. infectious conditions, is more important than age with respect 15. Clegg A, Young J, Iliffe S, et al. Frailty in elderly people. Lancet 2013; 381:752–62. to CDI risk. Clearly, an 80-year-old with hypertension who 16. Kennedy CC, Ioannidis G, Rockwood K, et  al. A frailty index predicts 10-year fracture risk in adults age 25  years and older: results from the Canadian has never been hospitalized will be at lower risk for CDI than Multicentre Osteoporosis Study (CaMos). Osteoporos Int 2014; 25:2825–32. a 66-year-old person emergently hospitalized for management 17. Rockwood K, Howlett SE, MacKnight C, et  al. Prevalence, attributes, and out- comes of fitness and frailty in community-dwelling older adults: report from of congestive heart failure. Markers of poorer overall health the Canadian Study of Health and Aging. J Gerontol A  Biol Sci Med Sci 2004; status include frailty indicators, recent acute infections, and 59:1310–7. 18. Rockwood K, Mitnitski A, Song X, et  al. Long-term risks of death and institu- emergency hospitalizations. These conditions are not difficult tionalization of elderly people in relation to deficit accumulation at age 70. J Am to identify and can be used to target CDI prevention activities Geriatr Soc 2006; 54:975–9. to patients most likely to benefit. 19. Chamberlain AM, Finney Rutten LJ, Manemann SM, et al. Frailty trajectories in an elderly population-based cohort. J Am Geriatr Soc 2016; 64:285–92. 20. Stevens V, Dumyati G, Fine LS, et al. Cumulative antibiotic exposures over time Acknowledgments and the risk of Clostridium difficile infection. Clin Infect Dis 2011; 53:42–8. Disclosures. M. A. O.: grants and personal fees from Sanofi Pasteur, 21. Kyne L, Sougioultzis S, McFarland LV, Kelly CP. Underlying disease severity as during the conduct of the study; personal fees from Pfizer, outside the sub- a major risk factor for nosocomial Clostridium difficile diarrhea. Infect Control mitted work. D. S.: none. C. D.: employee of Sano. E. R fi . D.: grants from Hosp Epidemiol 2002; 23:653–9. 22. Pham VP, Luce AM, Ruppelt SC, et al. Age-stratified treatment response rates in Sano, fi during the conduct of the study; personal fees from Sano, fi grants hospitalized patients with Clostridium difficile infection treated with metronida- and personal fees from Pfizer, personal fees from Synthetic Biologics, per - zole. Antimicrob Agents Chemother 2015; 59:6113–6. sonal fees from Valneva, personal fees from Abbott, personal fees from 23. Rao K, Micic D, Chenoweth E, et  al. Poor functional status as a risk factor for Biofire, grants and personal fees from Rebiotix, grants and personal fees severe Clostridium difficile infection in hospitalized older adults. J Am Geriatr Soc from Merck, outside the submitted work. 2013; 61:1738–42. Financial support. This work was supported by Sanofi Pasteur. The 24. Ticinesi A, Nouvenne A, Folesani G, et al. Multimorbidity in elderly hospitalised sponsor participated in study design, interpretation of data, and final review patients and risk of Clostridium difficile infection: a retrospective study with the of the manuscript. Access to data and additional services were provided Cumulative Illness Rating Scale (CIRS). BMJ Open 2015; 5:e009316. by the Washington University Center for Administrative Data Research, 25. Dubberke ER, Olsen MA, Stwalley D, et al. Identification of Medicare recipients at highest risk for Clostridium difficile Infection in the US by population attributable supported in part by grant UL1 TR000448 from the National Center for risk analysis. PLoS One 2016; 11:e0146822. Advancing Translational Sciences of the National Institutes of Health and 26. Olsen MA, Young-Xu Y, Stwalley D, et  al. The burden of Clostridium difficile grant R24 HS19455 through the Agency for Healthcare Research and infection: estimates of the incidence of CDI from U.S. administrative databases. Quality. BMC Infect Dis 2016; 16:177. 27. Cohen SH, Gerding DN, Johnson S, et al; Society for Healthcare Epidemiology of References America; Infectious Diseases Society of America. Clinical practice guidelines for 1. Magill SS, Edwards JR, Bamberg W, et  al; Emerging Infections Program Clostridium difficile infection in adults: 2010 update by the Society for Healthcare Healthcare-Associated Infections and Antimicrobial Use Prevalence Survey Epidemiology of America (SHEA) and the Infectious Diseases Society of America Team. Multistate point-prevalence survey of health care-associated infections. N (IDSA). Infect Control Hosp Epidemiol 2010; 31:431–55. Engl J Med 2014; 370:1198–208. 28. Agency for Healthcare Research and Quality. HCUP Comorbidity Software. 2. Hall AJ, Curns AT, McDonald LC, et al. The roles of Clostridium difficile and nor- Healthcare Cost and Utilization Project (HCUP). 2014. http://www.hcup-us. ovirus among gastroenteritis-associated deaths in the United States, 1999-2007. ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp. Accessed 26 September Clin Infect Dis 2012; 55:216–23. 3. Agency for Healthcare Research and Quality. HCUPnet, Healthcare Cost and 29. Klabunde CN, Potosky AL, Legler JM, Warren JL. Development of a comorbidity Utilization Project. Agency for Healthcare Research and Quality; 2015. http:// index using physician claims data. J Clin Epidemiol 2000; 53:1258–67. hcupnet.ahrq.gov. Accessed 15 May 2015. 30. Goodwin JS, Li S, Zhou J, et al. Comparison of methods to identify long term care 4. Lessa FC, Mu Y, Bamberg WM, et al. Burden of Clostridium difficile infection in nursing home residence with administrative data. BMC Health Serv Res 2017; the United States. N Engl J Med 2015; 372:825–34. 17:376. 5. McFarland LV, Surawicz CM, Stamm WE. Risk factors for Clostridium difficile 31. Intrator O, Berg K. Benefits of home health care after inpatient rehabilitation for carriage and C. difficile-associated diarrhea in a cohort of hospitalized patients. J hip fracture: health service use by Medicare beneficiaries, 1987-1992. Arch Phys Infect Dis 1990; 162:678–84. Med Rehabil 1998; 79:1195–9. 6. Bignardi GE. Risk factors for Clostridium difficile infection. J Hosp Infect 1998; 32. Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, 40:1–15. Validation, and Updating. New York: Springer-Verlag; 2009. 7. Beaulieu M, Williamson D, Pichette G, Lachaine J. Risk of Clostridium diffi- 33. Paul P, Pennell ML, Lemeshow S. Standardizing the power of the Hosmer- cile-associated disease among patients receiving proton-pump inhibitors in Lemeshow goodness of fit test in large data sets. Stat Med 2013; 32:67–80. Risk of Clostridium difficile With Age • OFID • 7 Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 34. Tabak YP, Johannes RS, Sun X, et  al. Predicting the risk for hospital-onset 38. Zarowitz BJ, Allen C, O’Shea T, Strauss ME. Risk factors, clinical characteristics, Clostridium difficile infection (HO-CDI) at the time of inpatient admission: and treatment differences between residents with and without nursing home- and HO-CDI risk score. Infect Control Hosp Epidemiol 2015; 36:695–701. non-nursing home-acquired Clostridium difficile infection. J Manag Care Spec 35. Baggs J, Yousey-Hindes K, Ashley ED, et al. Identification of population at risk for Pharm 2015; 21:585–95. future Clostridium difficile infection following hospital discharge to be targeted 39. Baggs J, Fridkin SK, Pollack LA, et  al. Estimating national trends in inpatient for vaccine trials. Vaccine 2015; 33:6241–9. antibiotic use among US hospitals from 2006 to 2012. JAMA Intern Med 2016; 36. Mylotte JM, Russell S, Sackett B, et al. Surveillance for Clostridium difficile infec- 176:1639–48. tion in nursing homes. J Am Geriatr Soc 2013; 61:122–5. 40. Dubberke ER, Butler AM, Yokoe DS, et al; Prevention Epicenters Program of the 37. Hunter JC, Mu Y, Dumyati GK, et al. Burden of nursing home-onset Clostridium Centers for Disease Control and Prevention. Multicenter study of surveillance for difficile infection in the United States: estimates of incidence and patient out- hospital-onset Clostridium difficile infection by the use of ICD-9-CM diagnosis comes. Open Forum Infect Dis 2016; 3:ofv196. codes. Infect Control Hosp Epidemiol 2010; 31:262–8. 8 • OFID • Olsen et al http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Open Forum Infectious Diseases Oxford University Press

Increasing Age Has Limited Impact on Risk of Clostridium difficile Infection in an Elderly Population

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Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 Open Forum Infectious Diseases MAJOR ARTICLE FIDSA Increasing Age Has Limited Impact on Risk of Clostridium difficile Infection in an Elderly Population 1,2 1 3 1 Margaret A. Olsen, Dustin Stwalley, Clarisse Demont, and Erik R. Dubberke 1 2 3 Departments of Medicine and Surgery, Washington University School of Medicine, St. Louis, Missouri; Sanofi-Pasteur, Lyon, France Background. Numerous studies have found increased risk of Clostridium difficile infection (CDI) with increasing age. We hypothesized that increased CDI risk in an elderly population is due to poorer overall health status with older age. Methods. A total of 174 903 persons aged 66 years and older coded for CDI in 2011 were identified using Medicare claims data. e co Th mparison population consisted of 1 453 867 uninfected persons. Potential risk factors for CDI were identified in the prior     12 months and organized into categories, including infections, acute noninfectious conditions, chronic comorbidities, frailty indica- tors, and health care utilization. Multivariable logistic regression models with CDI as the dependent variable were used to determine the categories with the biggest impact on model performance. Results. Increasing age was associated with progressively increasing risk of CDI in univariate analysis, with 5-fold increased risk of CDI in 94–95-year-old persons compared with those aged 66–67 years. Independent risk factors for CDI with the highest effect sizes included septicemia (odds ratio [OR], 4.1), emergency hospitalization(s) (OR, 3.9), short-term skilled nursing facility stay(s) (OR, 2.7), diverticulitis (OR, 2.2), and pneumonia (OR, 2.1). Exclusion of age from the full model had no impact on model perfor- mance. Exclusion of acute noninfectious conditions followed by frailty indicators resulted in lower c-statistics and poor model fit. Further exclusion of health care utilization variables resulted in a large drop in the c-statistic. Conclusions. Age did not improve CDI risk prediction aer co ft ntrolling for a wide variety of infections, other acute conditions, frailty indicators, and prior health care utilization. Keywords. age; Clostridium difficile ; epidemiology; Medicare; risk factor. Clostridium dicffi ile is the most common pathogen causing incidence of CDI rose dramatically with age, from 47/100 000 health care–acquired infections and the leading cause of death in younger adults aged 18–44  years to 148.5 in persons aged associated with gastroenteritis in the United States [1, 2]. The 45–64  years, and up to 628/100 000 in persons aged 65  years incidence of C. difficile infection (CDI) during an acute care hos- and older [4]. In the EIP study, there was a more than 13-fold pital stay increased about 2.7-fold between 2000 and 2012, based increase in CDI incidence in the elderly compared with younger on the Healthcare Cost and Utilization Project Nationwide adults (18–44  years). Despite this, few studies have sought to Inpatient Sample [3]. CDI was associated with more than 29 000 elucidate the underlying biological reason(s) for the increased deaths in 2011, with an attributable mortality ranging from 5.7% incidence of CDI among elderly persons. in endemic settings to 16.7% in severe outbreaks since 2000 [4]. One feature that deserves closer analysis is the role of over- Age is considered one of the primary risk factors for CDI all health status, including frailty, and risk of CDI. Frailty, the in general [5–8] and for severe CDI [9–12]. In the most recent expression of biologic aging, increases susceptibility to a vari- report from the US Emerging Infections Program (EIP), 57% ety of adverse events, including falls, fractures, infections, and of the estimated CDI cases in 2011 were in the elderly [4]. The ultimately death [13–16]. Frailty also results in increased health care exposure, including emergency department (ED) encoun- ters, hospitalization, and institutionalization [17–19], resulting in increased opportunity for exposure to antibiotics, the most important risk factor for CDI [20]. Received 14 June 2018; editorial decision 25 June 2018; accepted 11 July 2018. Correspondence: M.  A. Olsen, PhD, MPH, Division of Infectious Diseases, Washington Although the association between overall health status and University School of Medicine, Campus Box 8051, 4523 Clayton Ave., St. Louis, MO 63110 increased risk of CDI has not been examined explicitly, a review (molsen@wustl.edu). of the literature reveals hints that the relationship between age Open Forum Infectious Diseases © The Author(s) 2018. Published by Oxford University Press on behalf of Infectious Diseases and CDI may be more complicated than previously thought. Society of America. This is an Open Access article distributed under the terms of the Creative Severity of illness has long been known to be associated with Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/ by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any CDI [5, 21, 22]. Rao et al. found that poor functional status was medium, provided the original work is not altered or transformed in any way, and that the work an independent risk factor for severe CDI [23]. More recently, is properly cited. For commercial re-use, please contact journals.permissions@oup.com DOI: 10.1093/ofid/ofy160 Ticinesi et  al. found that multimorbidity was associated with Risk of Clostridium difficile With Age • OFID • 1 Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 increased risk of CDI [24]. Frailty per se has not been taken population, with the only restriction being that the index date into account in prior studies in terms of progressive accumu- occurred before the death date for uninfected persons who died lation of deficits and their impact on CDI risk. Limitations of in 2011. many prior analyses include relatively small sample sizes, which Conditions Potentially Associated With CDI in the Prior Year restrict the ability to control for many underlying conditions, Conditions potentially associated CDI were identified in the use of summary measures (eg, Charlson index) not designed to year before the index date and grouped into 6 categories to determine CDI risk, or relatively geographically confined popu- explore their contribution in a model to predict CDI risk. The lations (eg, single hospitals) that may impact the distribution of categories included age in 2-year increments, comorbidities, underlying conditions within the population studied. A better acute infections, acute noninfectious conditions, health care understanding of the impact of overall health status on CDI risk utilization, and frailty indicators. Comorbidities were defined is necessary to understand how best to implement CDI preven- according to the Elixhauser classification, with modification of tion efforts. the algorithm for complete claims data according to Klabunde We used Medicare claims data to determine whether age et  al. [28, 29]. Diagnosis codes on laboratory claims were not remains an important predictor of CDI in an elderly population used to identify comorbidities or acute infectious or nonin- aer t ft aking into account overall health status, including recent fectious conditions, as they may indicate suspected, but not acute and chronic illnesses, health care utilization, and indica- confirmed, conditions. Acute infections were identified using tors of frailty. ICD-9-CM diagnosis codes and categorized into infection groups, as described previously [25]. Noninfectious acute con- METHODS ditions were also identified using ICD-9-CM diagnosis codes, We used 2010–2012 Medicare claims data from the Centers including myocardial infarction, gastrointestinal hemorrhage, for Medicare and Medicaid Services Chronic Conditions Data fractures, and others (Appendix). Only a single outpatient claim Warehouse (CCW) for all analyses. All patients aged 66  years coded for an acute infectious or noninfectious condition was and older with the International Classification of Diseases, 9th required, as acute conditions may not be coded repeatedly over Revision, Clinical Modification (ICD–9–CM) diagnosis code a prolonged period of time. All dates coded for acute infections for CDI (008.45) in 2011 in the Inpatient, Outpatient, or Carrier were used to determine the timing of infection compared with claims files were identified as CDI case patients (100% data). the CDI onset or index date for uninfected persons. The uninfected comparison group consisted of individuals in Health care utilization in the year before CDI included sur- the 2011 CCW 5% random sample, excluding those coded for gical procedures, defined by Uniform Billing (UB–04) revenue CDI. Individuals were excluded from both groups if they were codes for operating room expenses in inpatient and outpatient enrolled at any time during 2010–2011 in a health maintenance files, hospitalization, ED encounters, skilled nursing facil- organization, lacked complete Part A  and Part B coverage, or ity stays, and long-term facility (ie, nursing home) residence. if they were coded for CDI in the last quarter of 2010 (to iden- Hospitalizations were categorized as emergency hospitaliza- tify incident CDI in 2011). Also excluded were 135 329 indi- tions if they originated in the ED (defined by UB-04 revenue viduals with no health claims in 2010 and 2011, as there was codes 0450–0459) or nonemergency hospitalizations. Treat- no evidence for use of health care benefits. The Washington and-release ED visits were defined by revenue codes 0450–0459 University Human Research Protection Office gave approval to from outpatient facilities. Skilled nursing facility stays were conduct this research with a waiver of informed consent. identified using the Skilled Nursing Facility file. Residence in a long-term care facility was identified using method 2 in Date of Onset and Attribution of CDI Goodwin et al., based on the work of Intrator et al. [30, 31]. The date of onset of CDI was defined as the first date correspond- Indicators suggestive of frailty were identified in the year ing to a coded diagnosis of CDI, unless additional information before the onset date, including dementia, decubitus ulcer, uri- was available to define an earlier date of onset, as previously nary incontinence, senility/frailty, failure to thrive, sleep dis- described [25, 26]. The location of onset and attribution for each turbances, and difficulty walking. In contrast to the criteria for CDI episode was determined using an algorithm based on the standard comorbidities, only a single inpatient or outpatient recommended CDI surveillance definitions [25–27]. claim coded for the frailty indicators was required, as they do For persons without CDI in 2011, an analogous date of onset not generally require diagnostic testing to establish the diagno- (termed “index date”) was created to anchor the prior time sis. The only exception was for Parkinson’s disease, which was period to identify comorbidities. Aer det ft ermining the onset identified using the same criteria as the comorbidities. date for all persons with CDI in 2011, the distribution func- tion of these dates was determined. This distribution was used Analysis to randomly select index dates in the comparison uninfected The association of age with risk of CDI in univariate analysis population to mirror the distribution of onset dates in the CDI was determined by chi–square and Mann-Whitney U tests. 2 • OFID • Olsen et al Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 Multivariable logistic regression was used to characterize the septicemia (OR, 4.1), emergency hospitalization(s) (OR, 3.9), independent association of age with risk of CDI, controlling for short-term skilled nursing facility stay(s) (OR, 2.7), diverticu- all comorbidities, acute and chronic noninfectious conditions, litis (OR, 2.2), and pneumonia (OR, 2.1). Factors associated health care utilization in the prior year, and acute infections. with moderately increased risk of CDI in the year prior (odds The variance inflation factor (VIF) was used to identify impor- ratios of 1.5–2.0) included 1 ED visit, gastrointestinal hemor- tant collinearity in the full model. No variable had a VIF greater rhage, decubitus ulcer, elective hospitalization(s), lymphoma, than 2.4 in the full model, suggesting no important collinear- long-term care facility residence, inpatient surgery, and surgical ity. To determine the impact of inclusion of the 6 categories of site infection. Additional factors associated with approximately potential risk factors on CDI prediction, the individual catego- 40% increased risk of CDI included white race, diverticulosis, ries were excluded sequentially, and the impact on model per- liver disease, skin and soft tissue infection, and oral infection. formance was determined by assessing the discrimination of the Aer ad ft justment for the large variety of infections at any model using the area under the receiver operating curve (c-sta- time in the year before CDI, other chronic and acute conditions, tistic), change in the Bayesian Information Criterion (BIC), frailty indicators, and health care utilization in the year before and the deviance statistic comparing nested models. BIC is a CDI, the risk of CDI associated with increasing age decreased measure used to select the “best” model from a nested set, based dramatically (Table  1, Figure  1). Although the odds of CDI on the log likelihood of the models. BIC includes a penalty for remained significantly elevated compared with the youngest increased number of terms in the model and takes into account persons (aged 66–67  years), the odds ratios fluctuated slightly the sample size in calculating the penalty. Because of this, BIC from 1.052 to a high of 1.174 in the 86–87-year-old group. The is more conservative and will select smaller models than the risk of CDI with increasing age remained only slightly elevated commonly used Akaike Information Criterion [32]. The devi- when acute infections were restricted to those coded more than ance statistic was used to compare the goodness of fit of nested 30 days before CDI (Table 1). models rather than the standard Hosmer-Lemeshow test, To test whether inclusion of age, comorbidities, and other because with large sample sizes, small deviations can result in conditions ae ff cted model performance, discrimination, fit, and rejection of the null hypothesis that the model fits the data [33]. BIC were assessed in the full model (including all variables in SAS Enterprise Guide, version 7.1 (SAS, Cary, NC), was used Table 1), compared with models with individual variable groups for all data management and analysis. removed. As shown in Table  2, removal of age from the full model had no impact on the c-statistic and resulted in a slight RESULTS decrease in the BIC, and the deviance statistic remained nonsig- nificant, indicating that the model performance improved with A total of 174 903 persons aged 66  years and older with com- removal of age. Removal of comorbidities, acute noninfectious plete fee-for-service Medicare coverage were identified with conditions, and frailty indicators had little impact on the c-sta- at least 1 episode of CDI in 2011 in the Medicare claims files. tistic but resulted in small increases in the BIC and significant For all persons, the first episode of CDI in 2011 was selected deviance statistics, indicating that these models did not fit the for further analyses. The first CDI episode was categorized as data as well as the full model. The biggest decrease in the c-sta- hospital-onset in 49 755 persons (28.4%), other health care tistic occurred aer r ft emoval of infections (from 0.918 to 0.911) facility–onset in 43 433 (24.8%), community-onset communi- and health care utilization (0.918 to 0.897), along with the larg- ty-associated in 46 738 (26.7%), community-onset health care est increases in the BIC, indicating that these models performed facility–associated in 21 952 (12.6%), and indeterminate asso- more poorly than the full model. The impact on the BIC and ciation in 13 025 persons (7.4%). The comparison population c-statistic of removal of only the septicemia variable was about consisted of 1 318 538 uninfected persons in the 5% random     half that of removal of the entire infection category from the full sample data. model (Table  2), consistent with the very elevated risk of CDI e a Th ssociation of age, sex, and acute and chronic medical associated with septicemia. and frailty conditions with CDI in univariate and multivariable analysis is shown in Table  1, and the odds ratios for increas- DISCUSSION ing age are displayed in Figure 1. Age was categorized in 2-year increments to show the relationship between risk of CDI and We found that exclusion of age in a multivariable model to increasing age. In univariate analysis, the risk of CDI increased predict risk of CDI had no demonstrable impact on model linearly with increasing age until approximately age 88  years, performance after controlling for acute infections, health care at which point the risk leveled o. Th ff e odds of CDI dropped utilization, acute noninfectious conditions, and indicators of slightly in the oldest age group (96 years and older), possibly in frailty in the year before CDI. These results suggest that overall part due to lower rates of testing for C. difficile in the very old. health status, including infections, health care utilization, acute In multivariable analysis, the risk factors in the year prior conditions in the past year, and frailty indicators are the most that were associated with >2-fold increased risk of CDI were important determinants of CDI risk in an elderly population. In Risk of Clostridium difficile With Age • OFID • 3 Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 Table 1. Risk Factors for CDI in Univariate and Multivariate Analysis, Including Comorbid Conditions, Acute Infections, Acute Noninfectious Conditions, Health Care Utilization, and Frailty Indicators Present in the Year Before CDI Risk Factor OR 95% CI aOR 95% CI Demographics Age (66–67 ref), y 68–69 1.098 1.066–1.132 0.998 0.961–1.036 70–71 1.235 1.198–1.273 1.013 0.976–1.053 72–73 1.434 1.391–1.477 1.052 1.013–1.092 74–75 1.539 1.493–1.585 1.024 0.986–1.063 76–77 1.801 1.748–1.855 1.059 1.020–1.100 78–79 2.063 2.003–2.124 1.103 1.063–1.145 80–81 2.305 2.239–2.372 1.097 1.057–1.138 82–83 2.634 2.559–2.710 1.102 1.062–1.144 84–85 3.016 2.931–3.104 1.131 1.089–1.174 86–87 3.386 3.289–3.486 1.174 1.130–1.220 88–89 3.606 3.499–3.716 1.166 1.121–1.214 90–91 3.683 3.567–3.803 1.122 1.076–1.171 92–93 3.947 3.809–4.089 1.148 1.095–1.203 94–95 4.061 3.897–4.232 1.131 1.071–1.194 ≥96 3.917 3.759–4.081 1.129 1.070–1.192 White race 1.089 1.073–1.106 1.373 1.344–1.403 Female 1.114 1.102–1.125 1.053 1.038–1.069 Comorbidities Congestive heart failure 5.921 5.853–5.991 0.900 0.883–0.917 Vascular disease 3.374 3.326–3.423 1.000 0.979–1.021 Pulmonary circulatory disorder 6.166 6.027–6.309 1.121 1.086–1.157 Peripheral vascular disease 3.862 3.816–3.908 1.095 1.076–1.113 Paralysis 6.412 6.259–6.570 0.950 0.919–0.983 Neurologic disease 5.523 5.437–5.610 0.974 0.952–0.996 Parkinson’s disease 3.162 3.076–3.251 1.023 0.985–1.063 Chronic pulmonary disease 3.493 3.453–3.533 0.987 0.970–1.005 Hypothyroidism 2.096 2.071–2.122 1.03 1.013–1.047 Chronic renal failure 5.462 5.397–5.529 1.271 1.248–1.294 Liver disease 3.737 3.602–3.878 1.428 1.357–1.502 Peptic ulcer disease 4.216 3.665–4.851 0.927 0.766–1.123 Lymphoma 3.150 3.042–3.262 1.636 1.559–1.716 Metastatic cancer 4.195 4.074–4.318 1.228 1.177–1.281 Solid tumor 1.963 1.935–1.992 1.115 1.091–1.141 Rheumatoid arthritis/collagen vascular disease 2.298 2.250–2.347 1.266 1.230–1.303 Coagulation disorder 5.880 5.768–5.993 1.143 1.113–1.174 Obesity 3.771 3.696–3.846 1.100 1.070–1.132 Blood loss anemia 6.105 5.915–6.302 0.763 0.732–0.797 Deficiency anemias 6.732 6.660–6.804 1.271 1.251–1.291 Psychoses 4.521 4.431–4.613 0.888 0.863–0.914 Depression 4.918 4.848–4.988 1.142 1.119–1.165 Cardiac 3.846 3.805–3.888 1.000 0.984–1.016 Prior fluid/electrolyte disorders 7.504 7.411–7.598 0.838 0.823–0.851 Prior weight loss/malnutrition 7.799 7.658–7.943 0.952 0.927–0.978 Diabetes 1.973 1.952–1.994 0.940 0.926–0.955 Hypertension 3.771 3.722–3.821 0.987 0.970–1.005 Infections Septicemia 34.006 33.245–34.783 4.104 3.994–4.217 Pneumonia 13.380 13.178–13.585 2.054 2.012–2.096 Urinary tract infection/prostatitis 5.029 4.969–5.089 1.245 1.224–1.267 Skin and soft tissue infection 4.092 4.025–4.161 1.368 1.336–1.402 Surgical site infection 12.880 12.342–13.441 1.479 1.395–1.567 Bone infection/osteomyelitis 10.246 9.823–10.686 1.266 1.193–1.344 Organ infection/meningitis 6.134 5.666–6.641 1.193 1.067–1.334 Sexually transmitted disease/pelvic infection 1.442 1.355–1.533 0.982 0.902–1.068 4 • OFID • Olsen et al Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 Table 1. Continued Risk Factor OR 95% CI aOR 95% CI Abdominal abscess/peritonitis 3.879 3.812–3.948 1.106 1.076–1.137 Diverticulitis 5.095 4.987–5.205 2.214 2.147–2.283 Upper respiratory infection 1.297 1.275–1.319 1.010 0.987–1.034 Tonsillitis/ocular infection/mastoiditis 1.554 1.471–1.641 0.772 0.716–0.832 Otitis media 0.978 0.927–1.033 0.940 0.877–1.007 Oral infection 2.172 1.989–2.371 1.430 1.269–1.611 Viral infection 2.778 2.689–2.871 1.196 1.142–1.252 Health care utilization Inpatient surgery 8.162 8.068–8.257 1.479 1.453–1.506 Outpatient surgery 1.668 1.645–1.692 1.099 1.078–1.121 Nonelective hospitalization(s) 20.497 20.248–20.748 3.907 3.836–3.979 Elective hospitalization(s) 4.873 4.814–4.933 1.689 1.658–1.721 1 treat-and-release ED encounter 2.547 2.519–2.575 1.841 1.809–1.872 2 or more treat-and-release ED encounters 5.558 5.498–5.619 1.259 1.236–1.283 Nursing home residence 6.762 6.668–6.858 1.604 1.568–1.640 Short-term skilled nursing facility stay 15.724 15.543–15.907 2.679 2.634–2.725 Acute noninfectious conditions Acute myocardial infarction 6.852 6.724–6.982 1.269 1.237–1.302 COPD exacerbation 5.286 5.203–5.369 1.198 1.169–1.227 Gastrointestinal bleed 6.276 6.194–6.359 1.826 1.792–1.861 Diverticulosis 3.022 2.984–3.061 1.445 1.416–1.474 Subdural hematoma 5.570 5.265–5.893 1.031 0.960–1.107 Cerebrovascular accident 3.707 3.668–3.747 1.114 1.097–1.132 Closed fracture, lower extremity 4.608 4.529–4.689 0.938 0.914–0.962 Open fracture, lower extremity 6.322 6.019–6.641 0.869 0.815–0.926 Closed fracture, other 3.464 3.407–3.522 0.986 0.963–1.009 Open fracture, other 4.074 3.808–4.358 0.871 0.797–0.952 Frailty indicators Decubitus ulcer 13.244 13.013–13.478 1.727 1.686–1.770 Dementia 4.910 4.851–4.970 1.206 1.183–1.230 Dehydration, past 30 d 7.332 7.235–7.429 1.058 1.038–1.078 Deep venous thrombosis 6.147 6.050–6.246 1.319 1.289–1.350 Pulmonary embolism 5.277 5.141–5.416 1.069 1.031–1.109 Urinary incontinence 2.727 2.686–2.770 1.227 1.201–1.253 Senility/frailty 10.281 10.121–10.444 1.362 1.333–1.391 Failure to thrive 8.144 7.936–8.357 0.958 0.925–0.992 Sleep disturbance 2.166 2.134–2.198 1.136 1.113–1.160 Difficulty walking 6.407 6.338–6.476 1.087 1.069–1.105 C-statistic of the full model = 0.918. Abbreviations: aOR, adjusted odds ratio; CDI, Clostridium difficile infection; CI, confidence interval; COPD, chronic obstructive pulmonary disease; ED, emergency department; OR, odds ratio. the multivariable model, the lack of a dose–response in risk of (2-fold increased odds). Other serious infections, including sur- CDI with increasing age is likely due to the large number of risk gical site, skin and soft tissue, and oral infections, were associ- factors we were able to control for in the very large Medicare ated with moderately increased odds of CDI, consistent with population, including acute conditions common in the elderly, need for antibiotic therapy of these infections. Interestingly, and indicators of frailty. This lack of an age dose–response is even past viral infections were associated with a small increased consistent with the hypothesis that, after adequately accounting risk of CDI, reflecting possible inappropriate use of antibiotics for overall health status, age per se is no longer an important in these patients. predictor of CDI. This is particularly relevant as individuals Not surprisingly, health care utilization in the past year “age” at different times in their lives, and thus a younger person was independently associated with increased risk of CDI and with serious medical conditions may have much higher risk of improved the t o fi f the model. Emergency hospitalization was CDI than a healthy older person. associated with almost 4-fold increased risk of CDI, followed Acute infections were associated with increased risk of CDI, by 2.7-fold increased risk associated with skilled nursing facility particularly septicemia (4-fold increased odds) and pneumonia stay(s). Our finding of increased risk of CDI with hospitalization Risk of Clostridium difficile With Age • OFID • 5 Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 Zarowitz et al. similarly reported that up to 67% of CDI in nurs- ing home residents was attributable to a recent hospitalization, 4.0 using the Minimum Data Set survey of skilled nursing residents 3.6 [38]. Using the 2009 Medicare 5% random sample, we previously 3.2 found the incidence of CDI among nursing home residents to be 10 093/100 000 person-years if they had a prior emergency hospi- 2.8     talization, and only 1505/100 000 person-years if the person did 2.4 not have any hospitalizations in the previous year [25]. 2.0 In addition to acute infections and health care encounters, several acute noninfectious conditions were also associated with 1.6 increased risk of CDI. These conditions, including diverticu- 1.2 losis, gastrointestinal hemorrhage, and myocardial infarction, 0.8 were likely a primary contributor to the patients’ underlying 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96+ severity of illness. In contrast, indicators of frailty, including Age, y decubitus ulcers and urinary incontinence, were likely mark- ers for advanced underlying illness. Exclusion of acute nonin- Figure 1. Unadjusted and adjusted odds ratios for Clostridium difficile infection fectious conditions reduced the c-statistic and resulted in an by age (years) in the elderly Medicare population. Green circles: unadjusted odds imperfectly fitting model according to the deviance statistic. In ratios; blue circles: adjusted odds ratios. the comparison of models with individual categories removed, the biggest impact on model fit was associated with removal of the health care utilization category, followed by infections. The is consistent with recent reports by McDonald and colleagues of big impact of removal of infections, including septicemia, on increased risk of CDI with hospitalization in the past 30 days [34] model performance was not surprising, as many of the indi- and with the number of hospitalizations in the past 90 days [35]. vidual infections were associated with high risk of CDI, likely Stays in a skilled nursing facility occur following discharge from a hospitalization and require documentation of the need for con- due to antibiotic treatment of the preceding infections with tinuation of nursing care to be reimbursed by Medicare. Thus resulting colonic dysbiosis. The bigger impact of removal of the the increased CDI risk associated with skilled nursing facility health care utilization category was also not surprising, as seri- stay(s) is consistent with overall health status as the primary ous infections would result in hospitalization, and more than driver of CDI risk. In contrast, residence in a long-term care half of hospitalized patients are treated with at least 1 dose of facility was associated with only 1.5-fold increased risk of CDI, antibiotics, even in the absence of documented infection [39]. aer ad ft justment for other variables in the model. This suggests Limitations of this study include identification of CDI by that while nursing home residence is a risk factor for CDI, it is ICD-9-CM diagnosis codes, which are not perfectly accurate not as important as acute infectious and noninfectious events [40], and the lack of data on antibiotic utilization for all patients, and health care utilization associated with those acute events, particularly during hospital stays. e Th use of Medicare claims particularly emergency hospitalization. This finding is con- data restricted analyses to the elderly; whether younger persons sistent with recent surveillance studies in which more than with poorer overall health status are at the same risk as much half of the incident cases of CDI with onset in a nursing home older persons remains to be determined in a future study. In occurred within 30  days following hospital discharge [36, 37]. addition, repeating this study using Medicare data from a more Table 2. Comparison of Performance of Full and Reduced Models, Excluding Categories of CDI Predictors Model No. of Variables BIC Change in BIC C-Statistic Deviance P Full 87 638 187.4 0.918 1.0000 Age 72 638 167.8 –19.6 0.918 1.0000 Comorbidities 60 642 135.4 3948.0 0.917 .0001 Acute noninfectious conditions 77 645 064.7 4968.9 0.916 .0001 Frailty indicators 77 643 156.3 6877.3 0.917 .0001 Septicemia 86 651 908.3 13 720.9 0.915 .0001 Infections 79 663 514.9 25 327.5 0.911 .0001 Health care utilization 72 702 080.9 63 893.5 0.897 .0001 Abbreviations: BIC, Bayesian Information Criterion; CDI, Clostridium difficile infection. As with other goodness of fit tests, the null hypothesis for the deviance statistic is that the model fits the data. Therefore, P < .05 indicates poor model fit. Includes septicemia. 6 • OFID • Olsen et al Odds Ratio Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 a Quebec medical intensive care unit. Infect Control Hosp Epidemiol 2007; recent year with higher utilization of nucleic acid amplification 28:1305–7. testing would be beneficial, to determine whether the relation- 8. Loo VG, Bourgault AM, Poirier L, et al. Host and pathogen factors for Clostridium difficile infection and colonization. N Engl J Med 2011; 365:1693–703. ship with age remains minimal in the setting of increased sensi- 9. Loo VG, Poirier L, Miller MA, et al. A predominantly clonal multi-institutional tivity to identify not just CDI but C. difficile colonization. outbreak of Clostridium difficile-associated diarrhea with high morbidity and Strengths of our study include the very large sample size, gen- mortality. N Engl J Med 2005; 353:2442–9. 10. Henrich TJ, Krakower D, Bitton A, Yokoe DS. Clinical risk factors for severe eralizability to the fee-for-service US elderly population, and Clostridium difficile-associated disease. Emerg Infect Dis 2009; 15:415–22. identification of CDI across many institutions and providers. 11. Khanna S, Aronson SL, Kammer PP, et al. Gastric acid suppression and outcomes in Clostridium difficile infection: a population-based study. Mayo Clin Proc 2012; e v Th ery large sample size allowed us to control for a very large 87:636–42. number of potential risk factors in the multivariable model and 12. Abou Chakra CN, McGeer A, Labbé AC, et al. Factors associated with complica- tions of Clostridium difficile infection in a multicenter prospective cohort. Clin separate out the independent effect of increasing age on risk Infect Dis 2015; 61:1781–8. of CDI. 13. Walter LC, Brand RJ, Counsell SR, et al. Development and validation of a prog- nostic index for 1-year mortality in older adults after hospitalization. JAMA 2001; Advancing age is frequently cited as one of the primary risk 285:2987–94. factors for CDI. This study demonstrates that overall health 14. Ensrud KE, Ewing SK, Taylor BC, et al. Comparison of 2 frailty indexes for pre- status, including recent acute infections and even acute non- diction of falls, disability, fractures, and death in older women. Arch Intern Med 2008; 168:382–9. infectious conditions, is more important than age with respect 15. Clegg A, Young J, Iliffe S, et al. Frailty in elderly people. Lancet 2013; 381:752–62. to CDI risk. Clearly, an 80-year-old with hypertension who 16. Kennedy CC, Ioannidis G, Rockwood K, et  al. A frailty index predicts 10-year fracture risk in adults age 25  years and older: results from the Canadian has never been hospitalized will be at lower risk for CDI than Multicentre Osteoporosis Study (CaMos). Osteoporos Int 2014; 25:2825–32. a 66-year-old person emergently hospitalized for management 17. Rockwood K, Howlett SE, MacKnight C, et  al. Prevalence, attributes, and out- comes of fitness and frailty in community-dwelling older adults: report from of congestive heart failure. Markers of poorer overall health the Canadian Study of Health and Aging. J Gerontol A  Biol Sci Med Sci 2004; status include frailty indicators, recent acute infections, and 59:1310–7. 18. Rockwood K, Mitnitski A, Song X, et  al. Long-term risks of death and institu- emergency hospitalizations. These conditions are not difficult tionalization of elderly people in relation to deficit accumulation at age 70. J Am to identify and can be used to target CDI prevention activities Geriatr Soc 2006; 54:975–9. to patients most likely to benefit. 19. Chamberlain AM, Finney Rutten LJ, Manemann SM, et al. Frailty trajectories in an elderly population-based cohort. J Am Geriatr Soc 2016; 64:285–92. 20. Stevens V, Dumyati G, Fine LS, et al. Cumulative antibiotic exposures over time Acknowledgments and the risk of Clostridium difficile infection. Clin Infect Dis 2011; 53:42–8. Disclosures. M. A. O.: grants and personal fees from Sanofi Pasteur, 21. Kyne L, Sougioultzis S, McFarland LV, Kelly CP. Underlying disease severity as during the conduct of the study; personal fees from Pfizer, outside the sub- a major risk factor for nosocomial Clostridium difficile diarrhea. Infect Control mitted work. D. S.: none. C. D.: employee of Sano. E. R fi . D.: grants from Hosp Epidemiol 2002; 23:653–9. 22. Pham VP, Luce AM, Ruppelt SC, et al. Age-stratified treatment response rates in Sano, fi during the conduct of the study; personal fees from Sano, fi grants hospitalized patients with Clostridium difficile infection treated with metronida- and personal fees from Pfizer, personal fees from Synthetic Biologics, per - zole. Antimicrob Agents Chemother 2015; 59:6113–6. sonal fees from Valneva, personal fees from Abbott, personal fees from 23. Rao K, Micic D, Chenoweth E, et  al. Poor functional status as a risk factor for Biofire, grants and personal fees from Rebiotix, grants and personal fees severe Clostridium difficile infection in hospitalized older adults. J Am Geriatr Soc from Merck, outside the submitted work. 2013; 61:1738–42. Financial support. This work was supported by Sanofi Pasteur. The 24. Ticinesi A, Nouvenne A, Folesani G, et al. Multimorbidity in elderly hospitalised sponsor participated in study design, interpretation of data, and final review patients and risk of Clostridium difficile infection: a retrospective study with the of the manuscript. Access to data and additional services were provided Cumulative Illness Rating Scale (CIRS). BMJ Open 2015; 5:e009316. by the Washington University Center for Administrative Data Research, 25. Dubberke ER, Olsen MA, Stwalley D, et al. Identification of Medicare recipients at highest risk for Clostridium difficile Infection in the US by population attributable supported in part by grant UL1 TR000448 from the National Center for risk analysis. PLoS One 2016; 11:e0146822. Advancing Translational Sciences of the National Institutes of Health and 26. Olsen MA, Young-Xu Y, Stwalley D, et  al. The burden of Clostridium difficile grant R24 HS19455 through the Agency for Healthcare Research and infection: estimates of the incidence of CDI from U.S. administrative databases. Quality. BMC Infect Dis 2016; 16:177. 27. Cohen SH, Gerding DN, Johnson S, et al; Society for Healthcare Epidemiology of References America; Infectious Diseases Society of America. Clinical practice guidelines for 1. Magill SS, Edwards JR, Bamberg W, et  al; Emerging Infections Program Clostridium difficile infection in adults: 2010 update by the Society for Healthcare Healthcare-Associated Infections and Antimicrobial Use Prevalence Survey Epidemiology of America (SHEA) and the Infectious Diseases Society of America Team. Multistate point-prevalence survey of health care-associated infections. N (IDSA). Infect Control Hosp Epidemiol 2010; 31:431–55. Engl J Med 2014; 370:1198–208. 28. Agency for Healthcare Research and Quality. HCUP Comorbidity Software. 2. Hall AJ, Curns AT, McDonald LC, et al. The roles of Clostridium difficile and nor- Healthcare Cost and Utilization Project (HCUP). 2014. http://www.hcup-us. ovirus among gastroenteritis-associated deaths in the United States, 1999-2007. ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp. Accessed 26 September Clin Infect Dis 2012; 55:216–23. 3. Agency for Healthcare Research and Quality. HCUPnet, Healthcare Cost and 29. Klabunde CN, Potosky AL, Legler JM, Warren JL. Development of a comorbidity Utilization Project. Agency for Healthcare Research and Quality; 2015. http:// index using physician claims data. J Clin Epidemiol 2000; 53:1258–67. hcupnet.ahrq.gov. Accessed 15 May 2015. 30. Goodwin JS, Li S, Zhou J, et al. Comparison of methods to identify long term care 4. Lessa FC, Mu Y, Bamberg WM, et al. Burden of Clostridium difficile infection in nursing home residence with administrative data. BMC Health Serv Res 2017; the United States. N Engl J Med 2015; 372:825–34. 17:376. 5. McFarland LV, Surawicz CM, Stamm WE. Risk factors for Clostridium difficile 31. Intrator O, Berg K. Benefits of home health care after inpatient rehabilitation for carriage and C. difficile-associated diarrhea in a cohort of hospitalized patients. J hip fracture: health service use by Medicare beneficiaries, 1987-1992. Arch Phys Infect Dis 1990; 162:678–84. Med Rehabil 1998; 79:1195–9. 6. Bignardi GE. Risk factors for Clostridium difficile infection. J Hosp Infect 1998; 32. Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, 40:1–15. Validation, and Updating. New York: Springer-Verlag; 2009. 7. Beaulieu M, Williamson D, Pichette G, Lachaine J. Risk of Clostridium diffi- 33. Paul P, Pennell ML, Lemeshow S. Standardizing the power of the Hosmer- cile-associated disease among patients receiving proton-pump inhibitors in Lemeshow goodness of fit test in large data sets. Stat Med 2013; 32:67–80. Risk of Clostridium difficile With Age • OFID • 7 Downloaded from https://academic.oup.com/ofid/article-abstract/5/7/ofy160/5055921 by Ed 'DeepDyve' Gillespie user on 16 October 2019 34. Tabak YP, Johannes RS, Sun X, et  al. Predicting the risk for hospital-onset 38. Zarowitz BJ, Allen C, O’Shea T, Strauss ME. Risk factors, clinical characteristics, Clostridium difficile infection (HO-CDI) at the time of inpatient admission: and treatment differences between residents with and without nursing home- and HO-CDI risk score. Infect Control Hosp Epidemiol 2015; 36:695–701. non-nursing home-acquired Clostridium difficile infection. J Manag Care Spec 35. Baggs J, Yousey-Hindes K, Ashley ED, et al. Identification of population at risk for Pharm 2015; 21:585–95. future Clostridium difficile infection following hospital discharge to be targeted 39. Baggs J, Fridkin SK, Pollack LA, et  al. Estimating national trends in inpatient for vaccine trials. Vaccine 2015; 33:6241–9. antibiotic use among US hospitals from 2006 to 2012. JAMA Intern Med 2016; 36. Mylotte JM, Russell S, Sackett B, et al. Surveillance for Clostridium difficile infec- 176:1639–48. tion in nursing homes. J Am Geriatr Soc 2013; 61:122–5. 40. Dubberke ER, Butler AM, Yokoe DS, et al; Prevention Epicenters Program of the 37. Hunter JC, Mu Y, Dumyati GK, et al. Burden of nursing home-onset Clostridium Centers for Disease Control and Prevention. Multicenter study of surveillance for difficile infection in the United States: estimates of incidence and patient out- hospital-onset Clostridium difficile infection by the use of ICD-9-CM diagnosis comes. Open Forum Infect Dis 2016; 3:ofv196. codes. Infect Control Hosp Epidemiol 2010; 31:262–8. 8 • OFID • Olsen et al

Journal

Open Forum Infectious Diseases – Oxford University Press

Published: Jul 1, 2018

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