The Burden of Comorbidity Is Associated with Antibiotic Resistance Among Institutionalized Elderly with Urinary Infection: A Retrospective Cohort Study in a Single Italian Nursing Home Between 2009 and 2014

Abstract

Urinary tract infections (UTIs), which are common among nursing home patients, are associated with adverse outcomes and increased healthcare costs. Antibiotic resistance is an emerging problem, associated with excess morbidity and mortality; it has been suggested that this condition might be more prevalent among subjects with comorbid conditions. The aim of this study was to assess the association, if any, of antibiotic resistance with the burden of comorbidity in elderly with UTIs. This retrospective study enrolled 299 patients with culture-positive UTI consecutively admitted to the nursing home of the “Fondazione San Raffaele Cittadella della Carità”, Taranto, Italy, which includes 80 beds under the direction of two geriatricians. The burden of comorbidity was quantified using the Charlson comorbidity score index. Diagnosis of UTI was ascertained by urine culture. Antibiotic resistance was defined according to the European Centre for Disease Prevention and Control expert proposal. Logistic regression was used to assess the adjusted association of the variables of interest with the presence of antibiotic resistance. Antibiotic resistance was detected in 162/299 (54%) patients. In logistic regression, the presence of antibiotic resistance was independently associated with higher Charlson score, after adjusting (odds ratio = 1.06; 95% confidence interval = 1.01–1.10). Antibiotic resistance is highly prevalent among nursing home residents; it is associated with the burden of comorbidity, but not with single diseases. This association and its potential implications should be assessed in dedicated studies.

Introduction

Urinary tract infections (UTIs) are among the most common bacterial infectious diseases afflicting humans, resulting in nearly $2.5 billion healthcare costs in the year 2000 in the United States alone.¹ Accordingly, UTIs are the most common infection in patients living in nursing home, and account for 20–60% of all antibiotic prescriptions²; in particular, antibiotics are among the most commonly prescribed classes of medications for nursing home patients.³ Paralleling with increasing use of antibiotics, there is also an increasing prevalence of antibiotic resistance³; in fact, it has been estimated that up to 60% of nursing home residents are colonized with multidrug-resistant (MDR) organisms; also the prevalence rates are exceeding those in hospitals.⁴ Almost 2 million cases of infections due to resistant bacteria have been reported in the United States every year, leading to $20 billion incremental direct healthcare costs.⁵ Estimates of European Medicines Agency and European Centre for Disease Prevention and Control reported a toll of 25,000 deaths per year as a direct consequence of multidrug resistance infections, for a total cost of €1.5 billion.⁵

Despite the large amount of available evidence about the characteristics of bacteria and antimicrobial resistance profiles, little is known about host characteristics, particularly regarding the burden of comorbidity; there is evidence about the association between antimicrobial resistance and specific diseases, such as heart failure, dementia, chronic obstructive pulmonary disease, chronic renal failure, prostatic hypertrophy, and cancer^6,7; also, some conditions, such as the presence of pressure ulcers or urinary catheter, predisposed to the colonization with MDR organisms.^8,9

Together with information on bacteria and antimicrobial susceptibility, the characteristics of patients might have relevant implications for prevention and treatment. Furthermore, this information might help to modify risk factors, thus hindering the development of antimicrobial resistance.

The aim of this study was to evaluate the association of clinical characteristics of nursing home residents with UTIs with the finding of antimicrobial resistance.

Materials and Methods

Participants

This study enrolled 299 inpatients with symptomatic UTIs and positive urine culture consecutively diagnosed in the nursing home of the “Fondazione San Raffaele Cittadella della Carità,” Taranto, Italy from January 2009 to March 2014. In fact, we had excluded four subjects with mycotic infections from the original sample of 303 patients. All participants had been admitted at least 3 months before the urine collection. During the study period a total of 1,393 elderly subjects were admitted to the nursing home. Data on 6-month mortality were collected by analyzing nursing home records for inpatients, through interview of caregivers and, then, consultation of death certificates for patients who had been discharged.

Data were recorded using dedicated software. For all participants, comprehensive geriatric assessment was performed to collect clinical, objective, and nutritional parameters, as well as data on functional and cognitive status.

The Institutional Review Board approved the protocol of this study, and all patients provided written informed consent.

Diagnosis of UTI and antimicrobial resistance

All the specimens were collected under aseptic conditions, and sent to the microbiology laboratory within 1 hour. In patients with indwelling urinary catheter, the catheter was replaced before urine collection. Identification of uropathogens was performed using routine microbiological methods. Polymicrobial urinary tract infection (UTI) was diagnosed when at least two uropathogenes were simultaneously isolated. UTIs were defined according to the Infectious Diseases Society of America guidelines, and the National Healthcare Safety Network document specifically developed for nursing home.^10,11 In particular, catheter-associated UTI was diagnosed in the presence of compatible symptoms or signs, along with ≥10³ colony-forming units/ml, with no more than two species of micro-organisms. The following signs and symptoms were considered compatible with catheter-associated UTI: fever, suprapubic tenderness, costovertebral angle pain or tenderness, altered mental status, malaise or lethargy, rigors, flank pain, or acute hematuria.

Susceptibility testing for antibiotics was performed by the disc diffusion method (bioMerieux, Hazelwood, MO). Breakpoints used for susceptibility testing were those recommended by the European Committee on Antimicrobial Susceptibility Testing or those listed on the product label.

Antimicrobial resistance was defined by the presence of any resistance to antimicrobial agents, according to the European Centre for Disease Prevention and Control expert proposal. Likewise, the definition of MDR, extensively drug-resistant (XDR) and pandrug-resistant (PDR) bacteria, was stated using the same proposal.¹²

Comorbidity

Comorbidity was quantified using the Charlson comorbidity index score by adding scores assigned to specific discharge diagnoses.¹³ A score of one was attributed to myocardial infarction (codes 410–410.9, 412), congestive heart failure (codes 428–428.9), peripheral vascular disease (443.9, 441–441.9, 785.4, V43.4), cerebrovascular disease (430–438), dementia (290–290.9), chronic pulmonary disease (490–496, 500–505, 506.4), rheumatologic disease (710.0, 710.1, 710.4, 714.0–714.2, 714.8, 725), peptic ulcer disease (531–534.9, 531.4–531.7, 532.4–532.7, 533.4–533.7, 534.4–534.7), mild liver disease (571.2, 571.4–571.6), and diabetes (250–250.3, 250.7). The following conditions scored two: diabetes with chronic complications (codes 250.4–250.6), hemiplegia or paraplegia (344.1, 342–342.9), renal disease (582–582.9, 583–583.7, 585, 586, 588–588.9), any malignancy, including leukemia and lymphoma (140–172.9, 174–195.8, 200–208.9). Moderate-to-severe liver disease (codes 572.2–572.8, 456.0–456.2) scored three.

Covariates

Cognitive performance was assessed using the 30-item Mini Mental State Examination, which is a widely used brief neuropsychological test of cognitive functioning, largely adopted as an effective screening instrument for cognitive impairment in general populations.¹⁴ Functional ability on admission was tested using the Barthel Index (BI), a widely used measure of the activities of daily living, which reliability and validity are acknowledged.¹⁵

Participants' diagnoses and treatments were obtained by medical records, and confirmed by the study researchers, who had received specific training. All drugs assumed by participants were coded according to the Anatomical Therapeutic and Chemical codes.¹⁶ Diagnoses were coded according to the International Classification of Diseases, ninth edition, Clinical Modification codes.¹⁷

Blood samples were obtained after 12-hour overnight fasting.

Data were recorded at the time point of the urinary culture.

Statistical analyses

Data were recorded using dedicated software. Statistical analyses were performed using Statistical Package for the Social Sciences (SPSS for Mac version 20.0, 2011; SPSS, Inc., Chicago, IL); differences were considered significant at the p < 0.050 level. Data of continuous variables are presented as mean values ± standard deviation. Medians and inter-quartile ranges were provided for non-normally distributed variables. Analyses of variance for normally distributed variables according to the presence of antimicrobial resistance were performed by analysis of variance comparisons; otherwise, the nonparametric Mann–Whitney U-test was adopted. The two-tailed Fisher exact test was used for dichotomous variables.

The covariates to be entered into analyses were chosen as explanatory according to available reviews and meta-analyses from the electronic databases of PubMed (MEDLINE) and Cochrane Library, and based upon their efficiency and cheapness. All participants were included only once in the analyses.

Multivariable logistic regression analysis was adopted to estimate the association of antimicrobial resistance with age, sex, the Charlson index, and all those variables which differed significantly (p < 0.050) in univariate analyses.

Results

The main characteristics of participants, and the prevalence of bacteria divided according to the presence of antimicrobial resistance, are depicted in Table 1.

Table 1.

Characteristics of Two Hundred Ninety-Nine Participants According to the Presence of Antibiotic Resistance

	Patients with antibiotic resistance (N = 162) (%) or mean ± SD	Patients without antibiotic resistance (N = 137) (%) or mean ± SD	p
Demographics
Age (years)	80 ± 9	78 ± 10	0.010
Sex (female)	120 (74%)	107 (78%)	0.498
Admitted from home	24 (15%)	34 (25%)	0.039
Previous antibiotic treatment (within 1 month)	43 (26%)	59 (43%)	0.002
Previous antibiotic therapy (days)	8 (4–12)	8 (4–13)	0.179
Previous penicillins + b-lactamase inhibitors	12 (7%)	18 (13%)	0.123
Previous extended-spectrum cephalosporins	18 (11%)	23 (17%)	0.178
Previous fluoroquinolones	14 (9%)	17 (12%)	0.342
Urinary catheter	136 (84%)	105 (77%)	0.142
Polymicrobial infection	85 (52%)	33 (24%)	<0.0001
Comorbid condition
Charlson index	2 ± 1	1 ± 0	0.022
Dementia	107 (66%)	69 (50%)	0.007
Stroke	23 (14%)	13 (9%)	0.285
Heart failure	28 (17%)	31 (23%)	0.307
Hip fracture	59 (36%)	36 (26%)	0.063
Diabetes	8 (5%)	6 (4%)	0.999
Chronic pulmonary disease	9 (6%)	5 (4%)	0.585
Pressure ulcers	46 (28%)	26 (19%)	0.077
Medications
Loop diuretics	75 (46%)	59 (43%)	0.641
Corticosteroids	23 (14%)	26 (19%)	0.277
Beta-blockers	52 (32%)	34 (25%)	0.200
Insulin	34 (21%)	22 (16%)	0.300
Neuroleptics	36 (22%)	31 (23%)	0.999
Objective tests
Fever	13 (8%)	16 (12%)	0.433
Barthel Index	2 (0–11)	10 (0–19)	0.002
White blood cells ( × 10³ μ/L)	8.8 ± 3.8	8.5 ± 3.5	0.513
Hemoglobin (g/dl)	11.6 ± 1.5	11.1 ± 1.8	0.022
Serum albumin (g/dl)	3.0 ± 0.5	3.1 ± 0.5	0.631
Isolates bacterial species
Escherichia Coli	93 (57%)	64 (47%)	0.081
Proteus (vulgaris or mirabilis)	68 (42%)	29 (21%)	<0.0001
Enterococci	45 (28%)	25 (18%)	0.056
Klebsiella (pneumoniae, oxytoca, or ozenae)	13 (8%)	22 (16%)	0.046
Pseudomonas (aeruginosa or putida)	8 (5%)	15 (11%)	0.080
Pantoea agglomerans	8 (5%)	4 (3%)	0.556
Providencia stuartii	6 (4%)	2 (1%)	0.296
Citrobacter amalonaticus	2 (1%)	3 (2%)	0.664
Acinetobacter baumanii	1 (1%)	3 (2%)	0.336
Serratia liquefaciens	0	1(1%)	0.458
Morganella morganii	2 (1%)	1 (1%)	0.999
Achromobacter spp.	1 (1%)	1 (1%)	0.999
E. coli plus Proteus (vulgaris or mirabilis)	32 (20%)	10 (7%)	0.002
E. coli plus Enterococci	14 (9%)	7 (5%)	0.264
Enterococci plus Proteus (vulgaris or mirabilis)	14 (9%)	1 (1%)	0.002
Klebsiella (pneumoniae, oxytoca, or ozenae) plus E. coli	4 (2%)	8 (6%)	0.153

Data were recorded at the time point of the urinary culture. All the patients were admitted at least 3 months before the urine collection.

SD, standard deviation.

The most common bacterial association was Escherichia coli with Proteus (vulgaris or mirabilis), which was detected in 42 urine cultures, followed by E. coli with Enterococci in 21 cases; Enterococci with Proteus (vulgaris or mirabilis) in 15 urine cultures, and Klebsiella (pneumoniae, oxytoca, or ozenae) and E. coli in 12 urine cultures.

Resistance was found in 162/299 (54%) participants. The prevalence of resistance to antimicrobial categories by bacteria, adopting the European Centre for Disease Prevention and Control expert proposal, is depicted in Table 2. According to the same expert proposal, in our population there were 149 (50%) cases of MDR, four (1%) of XDR, and one (0.3%) case of PDR bacteria. Proteus (either mirabilis or vulgaris) was the single species most frequently associated with antibiotic resistance (Table 1), while Klebsiella showed the lowest prevalence (Table 1). Among participants with polymicrobial infection, the presence of E. coli with Proteus (vulgaris or mirabilis) and Enterococci with Proteus was associated with increased prevalence of antimicrobial resistance (32 cases, 20% vs. 10 cases, 7%; p = 0.002 and 14 cases, 9% vs. 1 case, 1%, p = 0.002, respectively, Table 1); also, the presence of E. coli with Proteus (vulgaris or mirabilis) was associated with higher Charlson comorbidity score index (2 ± 1 vs. 1 ± 0; p = 0.026).

Table 2.

Presence and Prevalence of Resistance to Antimirobial Categories by Bacteria

	Enterococcus spp. (n = 70)	Enterobacteriaceae (n = 252)	Pseudomonas aeruginosa (n = 12)	Acinetobacter spp. (n = 4)
Antimicrobial categories
Aminoglycosides	19 (27%)	58 (23%)	7 (58%)	1 (25%)
Antipseudomonal penicillins + b-lactamase inhibitors		10 (4%)
Carbapenems	2 (3%)	10 (4%)	4 (33%)	1 (25%)
Non-extended spectrum cephalosporins; first and second generation cephalosporins		57 (23%)
Antipseudomonal cephalosporins			4 (33%)	1 (25%)
Extended-spectrum cephalosporins; third and fourth generation cephalosporins		107 (42%)
Cephamycins		30 (12%)
Fluoroquinolones	25 (36%)	62 (25%)	4 (33%)	2 (50%)
Folate pathway inhibitors		58 (23%)		2 (50%)
Penicillins	18 (26%)	56 (22%)
Penicillins + b-lactamase inhibitors		50 (20%)	4 (33%)
Phosphonic acids		47 (19%)	4 (33%)
Polymyxins		1 (1%)	1 (8%)

European Centre for Disease Prevention and Control Expert Proposal.

Eventually, a significant difference was found regarding 6-month mortality rates between participants with and without evidence of antimicrobial resistance (70 patients [43%] vs. 28 patients [20%], p < 0.0001); also, a higher Charlson score was associated with reduced survival (2 ± 1 vs. 1 ± 0; p = 0.027).

Main characteristics of participants according to the presence of antibiotic resistance

Patients with antibiotic resistance were older (80 ± 9 vs. 78 ± 10, p = 0.010), and had more frequently a diagnosis of dementia (107, 66% vs. 69, 50%; p = 0.007); in addition, they showed a higher burden of comorbidity, as expressed by the Charlson comorbidity score index (2 ± 1 vs. 1 ± 0), and BI (2, 0–11 vs. 10, 0–19; p = 0.002). Also, they were more likely to be transferred from other departments rather than admitted from home. Participants with antimicrobial resistance, as compared with other subjects, had more frequently been treated with antibiotics before the urine culture (43, 26%, vs. 59, 43%; p = 0.002), and were found to have more frequently polymicrobial UTIs (85, 52%, vs. 33, 24%; p < 0.0001). No significant difference was found in the duration of previous antibiotic therapy, nor in the antimicrobial class used. Also, there was a negative correlation between the duration of previous antimicrobial therapy and the Charlson score, which was not statistically significant (r_s −0.20; p = 0.050). In addition, subjects who had received previous antimicrobial treatment within 6 months, (i.e., with a positive previous colonization status) had a higher prevalence of UTIs sustained by MDR bacteria (61, 60% vs. 88, 44%; p = 0.014); eventually, previous antibiotic treatment (within 6 months) was associated with higher Charlson score 2 ± 1 vs. 1 ± 0; p = 0.003).

Multivariable analyses

According to logistic regression modeling, the Charlson comorbidity score index was associated with antimicrobial resistance in the unadjusted model (odds ratio [OR] = 1.04; 95% confidence interval [CI] = 1.01–1.07), after adjusting for age and sex (OR = 1.04; 95% CI = 1.01–1.07), in the full multivariable model (OR = 1.06; 95% CI = 1.01–1.10), adjusting for those variables, which showed significant differences in univariate analyses, considering the single bacteria (Table 3), and in the same full multivariable model (OR = 1.06; 95% CI = 1.01–1.10), adjusting for those variables that showed significant differences in univariate analyses, considering the couples of germs (Table 3).

Table 3.

Association (Odds Ratios, and 95% Confidence Intervals) of Antibiotic Resistance with the Variables of Interest, Including the Charlson Comorbidity Score Index, in the Multivariable Regression Model

	All participants (N = 299)
	OR	95% CI
Age (years)	1.08	1.03–1.23
Sex (female)	0.35	0.15–0.83
Transferral from home	0.57	0.24–1.35
Previous antibiotic treatment (within 1 month)	0.53	0.27–1.06
Polymicrobial infection	1.63	0.80–3.32
Dementia	1.99	0.86–4.63
Barthel Index	0.90	0.97–1.02
Hemoglobin (g/dl)	1.18	0.97–1.45
Proteus (vulgaris or mirabilis)	1.76	0.82–3.78
Klebsiella (pneumoniae or oxytoca or ozenae)	0.69	0.25–1.93
Charlson comorbidity index score^a	1.06	1.01–1.10

All the covariates were entered simultaneously into the regression model.

The same odds for Charlson comorbidity index score were obtained after replaced Escherichia coli with Proteus (vulgaris or mirabilis) and Enterococci with Proteus (vulgaris or mirabilis) instead of Proteus as single germ to avoid confounding.

OR, odds ratio; CI, confidence interval.

Discussion

Results of this study indicate that among elderly nursing home residents the burden of comorbidity is independently associated with antimicrobial resistance. This finding is of potential interest, because all available information on the profile of antibiotic resistance has so far focused only on bacterial characteristics as the main correlates of antibiotic resistance. Also, in line with previous evidences, the presence of antibiotic resistance was associated with reduced survival.^18–20

Only a few studies analyzed the demographic characteristics of patients, reporting race as an additional risk factor for resistant isolates in pediatric patients.²¹ Also, there is evidence about the association of antimicrobial resistance with specific conditions, such as heart failure, dementia, chronic obstructive pulmonary disease, chronic renal failure, and cancer.^6,7 Yet, there are only few reports regarding comorbidity and antimicrobial resistance, moreover related to specific antibiotics.²²

Several factors may explain our results. First, older patients with comorbidity are at increased risk of infection, because aging itself disrupts the acquired immunity, resulting in T-cell dysfunction and blunted cytokine-mediated inflammatory response.²³ Such an impaired cellular function is accentuated in the setting of diabetes, cancer, and autoimmune disorders. In addition, comorbidities result in bladder and bowel incontinence and functional decline, all of which disrupt the host's innate defense mechanisms.²⁴

Second, patients with higher comorbidity often need nursing care more frequently and intensively. In fact, it has been reported that the presence of functional disability and nursing resource use are predictive of antimicrobial resistance in nursing home settings²⁵; interestingly, prolonged and intense contact with healthcare professionals, rather than the type, was associated with the development of multidrug resistance.²⁴ An indirect evidence of the robustness of such hypothesis is the significant lower prevalence of antimicrobial resistance in patients who were transferred from home. Third, patients with higher burden of comorbidity often presented with malnutrition, which in turn is associated with the presence of antimicrobial resistance, and impaired immunity.^26,27

Fourth, urinary catheter is most frequently placed just in compromised, often bedridden patients; this might allow the development of antimicrobial resistance by several mechanisms. In fact, urinary catheter placement not only eases the access of potential pathogens to the bladder, but also provides a ready surface on which biofilms can form.²⁴ Microscopic observations show that catheter biofilm-associated bacteria form polymicrobial microcolonies that are embedded within an amorphous, protective extracellular matrix, which makes microorganisms resistant to antibiotics and cause spread of antibiotic-resistant bacteria.^28,29 Indeed, in our population the prevalence of indwelling urinary catheter was high among subjects with antibiotic-resistant germs, but the difference did not reach statistical significance.

Fourth, patients with greater comorbidity often receive several antibiotic treatments over time, and this could promote the alteration of normal bacterial flora and the development of infection by multidrug-resistant pathogens.²⁴ In fact, elderly and disabled nursing home residents are at increased risk for colonization with resistant organisms, and colonization may persist for months to years.²³

In line with previous findings, our data confirm that antimicrobial resistance was more common in the presence of polymicrobial UTIs^30,31; the genetic exchange of antimicrobial resistance determinants among Enterococci and Staphylococci has been extensively documented.³² The resistance genes are typically found on conjugative plasmids or transposons. One requirement for the conjugative transfer of mobile genetic elements is cell-to-cell contact between donor and recipient. To facilitate this contact, Enterococci have highly evolved conjugative systems that are responsible for the dissemination of antimicrobial resistance and virulence factors.³²

As already reported, our data confirm that Enterobacteriaceae are the most prevalent isolated pathogens. Proteus bacilli are more commonly associated with UTIs in those patients with structural or functional abnormalities³³; accordingly, Proteus (mirabilis or vulgaris) was the only pathogen associated with the presence of antimicrobial resistance. Indeed, a significant decrease in susceptibility to third-generation cephalosporins and ciprofloxacin has been reported in P. mirabilis over the past decade, which might be due to clonal spread, plasmid-mediated horizontal gene transfer, and to the increasing consumption of antibiotics.³⁴

Furthermore, the association of E. coli and Proteus (mirabilis or vulgaris) was the most prevalent. This couple of bacteria was significantly associated with the severity of comorbidities in our study, and with antimicrobial resistance. The issue of polymicrobial infections is gaining increasing attention by researchers, both because of its prevalence (it has been estimated that 33% of urine cultures from elderly patients are polymicrobial),³⁵ and because E. coli in polymicrobial UTI samples has been found to be more invasive than that isolated from monomicrobial culture samples.²⁷ Of notice, in vivo horizontal extended-spectrum beta-lactamase gene transfer between E. coli and Proteus has been demonstrated in a patient with recurrent UTIs,³⁶ and integration into the E. coli chromosome of a beta-lactamase gene from Proteus identified in a patient hospitalized in a long-term care facility.³¹ Thus, polymicrobial infections allow different Enterobacteriaceae to share extended spectrum beta-lactamase genes, and might induce the selection of more invasive species of E.coli. In fact, due to the large use of antibiotics with ensuing polymicrobial infections long-term care facilities are considered “the ideal setting for evolution” of antibiotic-resistant Enterobacteriaceae.³¹

Genes of E. coli and Proteus encoding for adhesions, motility, invasion and biofilm formation, avoidance of host immune response, and damage to the host and acquisition of nutrients have been identified; this set of genes confers the ability to colonize and persist in the urinary tract.³²

Eventually, previous antibiotic therapy within 6 months was associated with a higher burden of comorbidities. Although previous antibiotic treatment did not reach statistical significance in the adjusted model, it was tendentially protective in our model, with no differences regarding the length of therapy, nor the type of antimicrobial used. Also, although not significant, the duration of antibiotic therapy was inversely correlated with the Charlson score. It could be hypothesized that the most compromised patients had been treated most but worse, probably due their multimorbidity.

This study had some limitations. First, according to the single center design, our results need to be considered preliminary. Second, they refer to a nursing home population with heavy multimorbidity and disability, and might therefore not apply to populations with a better health status. However, the average health status profile of nursing home residents is progressively worsening, which makes our population representative of the vast majority of these patients. Finally, our results apply to symptomatic UTIs, and might not extend to asymptomatic infections.

This study also has some strengths. First, the patient management and data collection conformed to standardized procedures, which guarantee for the internal consistency of data. Second, the broad array of potential risk factors for antimicrobial resistance systematically collected makes the present findings reliable. Finally, our study relies upon and appraises a largely used comorbidity index originally developed to predict survival.

Conclusion

This study indicates an association of the burden of comorbidity with antimicrobial resistance. If our data will be confirmed by large dedicated studies, healthcare policies aiming to reduce antimicrobial resistance should address the characteristics of the host, and not only the epidemiological information about bacteria, and the good microbiology practices.

Footnotes

Disclosure Statement

No competing financial interests exist.

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