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Journal of Clinical Medicine Research

Original Article

Volume 14, Number 11, November 2022, pages 474-486

Trends and Disparities in Outcomes of Clostridioides difficile Infection Hospitalizations in the United States: A Ten-Year Joinpoint Trend Analysis

Clostridioides difficile infection (CDI) is the most frequently reported pathogen in nosocomial infections and accounts for 12.1% of healthcare-associated infections in the United States [1]. The clinical severity of CDI is a spectrum ranging from a mild, self-limited diarrheal illness to fulminant, life-threatening colitis [2]. Data from active population- and laboratory-based surveillance show that approximately half a million cases of CDI occur each year in the United States, with almost 30,000 deaths reported in 2011 [3]. The total annual CDI-attributable health care cost in the United States is estimated at US$6.3 billion with nearly 2.4 million days of inpatient stay attributable to CDI [4].

The Centers for Disease Control and Prevention’s Emerging Infections Program demonstrated a decrease in the estimated national burden of CDI and associated hospitalizations from 2011 through 2017 [5]. However, the proportion of community-acquired CDI has been increasing over time. Cases of CDI have been increasingly reported outside of acute care facilities over the last few years with progressively more cases being diagnosed and treated without hospitalization [3, 6].

While the above-stated changes in the epidemiology of CDI have been extensively documented in the literature, there is a relative paucity of research on the impact of sociodemographic indices on CDI hospitalizations and outcomes. This study aimed to describe the epidemiological trends, sex, race, and economic disparities in clinical and mortality outcomes among hospitalizations with CDI over a decade.

This was a retrospective longitudinal trends study involving hospitalizations with CDI in the US from 2010 to 2019. We sourced data from the Nationwide Inpatient Sample (NIS) databases from 2010 through 2019. The NIS is developed by the Healthcare Cost and Utilization Project (HCUP), a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ). The NIS is a database of hospital inpatient stays derived from billing data submitted by hospitals to statewide data organizations across the US, covering more than 97% of the US population [7]. The provided dataset for a given calendar year approximates a 20% stratified sample of discharges from US hospitals, excluding rehabilitation and long-term acute care hospitals. This dataset is weighted to obtain US national estimates [8]. Databases before 2016 were coded using the International Classification of Diseases, Ninth Revision, Clinical Modification/Procedure Coding System (ICD-9-CM/PCS). Databases from 2016 were coded using the ICD-10-CM/PCS. The 2015 NIS has both ICD-9 and 10 codes, hence requiring a combination of both codes to obtain the studied cohort in keeping with the HCUP regulations [9]. In this study, we weighted the 9 months of ICD-9 data in 2015 for the entire year. In the NIS, diagnoses are divided into two separate categories: principal diagnosis and secondary diagnoses. A principal diagnosis was the ICD code attributed as the reason for hospitalization. Secondary diagnoses were any ICD code discharge diagnosis other than the principal diagnosis. We obtained total yearly adult hospitalizations from the NIS databases. This manuscript conforms with the STROBE statement for reporting observational studies.

We queried NIS databases from 2010 to 2019 for this study. The study involved two cohorts of hospitalizations: any hospitalization with CDI and hospitalizations with a principal admitting diagnosis of CDI. We searched the databases for hospitalizations using ICD codes (008.45 and A04.7x). We excluded hospitalizations involving patients less than 18 years. The NIS includes variables on patient demographics, including age, sex, race, median household income (MHOI) for patient’s zip code (income quartiles referred to patients as 1 - low income, 2 - middle income, 3 - upper middle income, 4 - high income), and primary payer. It also contains hospital-specific variables including bed size, teaching status, and location. We assessed the comorbidity burden using Sundararajan’s adaptation of the modified Deyo’s Charlson comorbidity index (CCI). This modification groups CCI into four groups in increasing risk for mortality. It has been adapted to population-based research. A score of > 3 has about a 25% 10-year mortality, while a score of 2 or 1 has a 10% and 4% 10-year mortality, respectively. This cutoff point was chosen as a means of assessment of the increased risk of mortality [10]. Total hospital charge (THC) was adjusted for inflation using the Medical Expenditure Panel Survey index for hospital care, with 2019 as the reference point [11].

We highlighted the biodemographic trends over time of CDI hospitalizations. Specifically, we obtained the CDI admission rate and the incidence of CDI among all hospitalizations. We calculated the crude CDI admission rate per 100,000 adult hospitalizations and the incidence of CDI per 100,000 adult hospitalizations during each calendar year. We analyzed trends in inpatient mortality rate, mean length of hospital stay (LOS), and mean THC of both cohorts. We also highlighted disparities in outcomes of CDI hospitalizations stratified by sex (male and female), race (Whites, Blacks, and Hispanics), and MHOI quartile (low-income quartile - LIQ and high-income quartile - HIQ).

Stata^® Version 16 software (StataCorp, TX, USA) and Joinpoint Regression Program, Version 4.9.1.0 were used for data analysis. We analyzed and reported the weighted sample following HCUP regulations for using the NIS database. Age was grouped as 18 - 44 years representing young adults, 45 - 64 years representing middle-aged adults, and 65 years and above representing elderly. The incidence of CDI among hospitalizations was calculated following the HCUP methodology for disease incidence and prevalence [12]. The crude admission rate was calculated by dividing total CDI hospitalizations as the principal diagnosis by the total adult hospitalization for each calendar year studied and expressed per 100,000 hospitalizations. The incidence of CDI per 100,000 adult hospitalizations was obtained by dividing the total hospitalizations with CDI by the total number of adult hospitalizations. This was also expressed as per 100,000 adult hospitalizations. We used multivariable regression analysis to calculate the incidence and admission rates adjusted for age categories, sex, and race using predictive margins. We subsequently used Joinpoint regression analysis to obtain the trends in rates over the study duration, using the adjusted rates and standard errors. Joinpoint regression analysis has been widely used and validated by the National Institutes of Health (NIH) National Cancer Institute to model non-linear trends in cancer rates over a given period [13]. This has been adopted in prior HCUP database research [14, 15]. We used multivariable regression trend analysis to obtain trends in mortality, LOS, and THC adjusted for age categories, sex, and race. All P values were two-sided, with 0.05 set as the threshold for statistical significance.

The NIS database lacks patient and hospital level identifiers. This study, therefore, did not require Cook County Health Institutional Review Board approval.

The NIS is a large publicly available all-payer inpatient care database in the United States, containing data on more than seven million hospital stays per year. Its large sample size is ideal for developing national and regional estimates and enables analyses of rare conditions, uncommon treatments, and special populations. Datasets are available through the HCUP central distributor on request.

Over 300 million hospitalizations were included in our study over the period from 2010 to 2019. Of these hospitalizations, 1.1% (more than 3.3 million) were complicated by CDI with the CDI incidence rate amongst hospitalizations down-trending from 1,001 per 100,000 hospitalizations in 2010 to 871 per 100,000 hospitalizations in 2019. When adjusted for age and sex, there was an average annual percentage change (AAPC) of -2% reduction in the incidence of CDI among hospitalizations over the study period. In 2010, CDI was the principal reason for hospitalization in 33.1% of these hospitalizations with CDI, with a reduction in that proportion to 29% in 2019. Other top reasons for hospitalizations included unspecified sepsis, pneumonia from unspecified organism, and acute kidney injury. Joinpoint regression analysis showed an increase in the adjusted CDI incidence rate from 2010 to 2015 (annual percent change (APC) = 2.88%). There subsequently was a decreased incidence from 2015 to 2019 (APC = -8.11%), as shown in Figure 1.

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Figure 1. Trend in the adjusted incidence of CDI among all hospitalizations from 2010 to 2019. *Statistically significant. APC: annual percentage change; CDI: Clostridioides difficile infection.

Table 1 shows the biodemographic distribution of hospitalizations with CDI. Older age groups were consistently at higher odds of hospitalization with CDI compared to young adults with elderly hospitalizations having higher odds than middle-aged hospitalizations. Middle-aged and elderly patients had adjusted odds ratios (aORs) of 2.54 and 3.86 respectively for hospitalization with CDI when compared to young adults (P < 0.001). Females had a 12% increase in adjusted odds of hospitalization with CDI compared to males over the study period (P < 0.001).

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Table 1. Biodemographic Data of Hospitalizations With CDI

The mortality rate in the cohort of hospitalizations with CDI was 6.9% over the study period and decreased from 8.4% in 2010 to 5.8% in 2019. The mean LOS decreased from 11.6 to 9.8 days and the mean THC increased from US$93,521 to US$108,357 (all P-trends < 0.001). When adjusted for age and sex, there was a -4% APC in mortality among hospitalizations with CDI over the study period. On sex subgroup analysis, females had a significantly lower mortality rate (Fig. 2). On racial subgroup analysis, mortality rates were lowest among Whites over the study period, most improved in Hispanics over the study period with the least improvement in Blacks (Fig. 3). Mean LOS trends were also similar to mortality trends across the three racial subgroups. Mortality rates, mean LOS, and mean THC followed similar trends over the study period in hospitalizations with mean household income (MHOI) quartile 1 when compared to those with MHOI quartile 4 (Table 2).

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Figure 2. Trend in the mortality rate for hospitalizations with CDI (sex subgroup analysis). CDI: Clostridioides difficile infection.

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Figure 3. Trend in the mortality rate for hospitalizations with CDI (racial subgroup analysis). CDI: Clostridioides difficile infection.

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Table 2. Outcomes of Hospitalizations With CDI

Middle-aged and elderly patients had aOR of 2.15 and 3.58 respectively for mortality when compared to young patients (P < 0.001). Females had a 22% decrease in adjusted odds of mortality compared to males over the study period (P < 0.001).

When adjusted for age and sex, there was an annual 0.19 decrease in LOS over the study period (P < 0.001), with this finding similar across the different racial subgroups.

There were over 1 million hospitalizations with CDI as the reason for inpatient stay, representing 30.6% of all hospitalizations with CDI over the study period. The AAPC for CDI admissions was -3.2% over the study period. Joinpoint regression analysis showed a non-statistically significant increase in adjusted CDI admission rate from 2010 to 2012 (APC = 4.15%), with a subsequent reduction in adjusted CDI admission rate from 2012 to 2016 (APC = -2.17%) which was also not statistically significant. From 2016 to 2019, there was a statistically significant trend towards reduction in adjusted CDI admission rate (APC = -9.87%) (Fig. 4).

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Figure 4. Trends in adjusted CDI admission rate from 2010 to 2019. *Statistically significant. APC: annual percentage change; CDI: Clostridioides difficile infection.

The mean age of CDI admissions decreased from 69.4 years in 2010 to 66.6 years in 2019 with the proportion of elderly patients similarly down-trending from 66.3% in 2010 to 60.3% in 2019 (both P-trends < 0.001). The proportion of CDI admissions with CCI scores ≥ 3 increased from 28.4% in 2010 to 39.3% in 2019 (P-trend < 0.001). The highest proportion of the primary payer across each year was Medicare, and most patients were managed at large hospitals (both P < 0.001) (Table 3).

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Table 3. Biodemographic Data of Hospitalizations With Principal Admitting Diagnosis of CDI

The mortality rate in patients admitted for CDI was 1.9% over the study period, down-trending from 3.2% in 2010 to 1.4% in 2019. When adjusted for age and sex, the AAPC for mortality was -10.2%. The mean LOS reduced from 6.6 days in 2010 to 5.3 days in 2019 and the mean THC increased from US$40,593 to US$42,934 (all P-trends < 0.001). On sex subgroup analysis, mortality rates showed a downtrend in males and females over the study period (Fig. 5). A similar trend was observed in mean LOS in males and females while mean THC increased in both groups over the study period (Table 4). On racial subgroup analysis, mortality rates showed a steady decline among Whites and Blacks over the study period (Fig. 6). Mean LOS trends were also similar across the three racial subgroups. Mortality rates, mean LOS, and mean THC followed similar trends over the study period in hospitalizations among LIQ and HIQ patients.

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Figure 5. Trend in mortality rate hospitalizations with a principal admitting diagnosis of CDI (sex subgroup analysis). CDI: Clostridioides difficile infection.

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Table 4. Outcomes of Hospitalizations With Principal Admitting Diagnosis of CDI

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Figure 6. Trend in mortality rate hospitalizations with a principal admitting diagnosis of CDI (racial subgroup analysis). CDI: Clostridioides difficile infection.

Middle-aged and elderly patients had aOR of 4.96 and 14.74 respectively for mortality when compared to young adults (P < 0.001). Females had a 21% decrease in adjusted odds of mortality compared to males over the study period (P < 0.001).

When adjusted for age and sex, there was a 0.14 annual decrease in the mean LOS in days over the study period (P < 0.001), with this finding similar across the different racial subgroups and MHOC quartiles. However, there was no significant change in the adjusted mean THC over the study period (P = 0.249).

We analyzed the trends in CDI hospitalizations during the decade under review and found that the incidence and admission rates of CDI initially trended up, likely representing a continuation of the increasing trend in CDI incidence in the first decade of the 21st century [16-18]. After 2015, there was a marked decline in incidence and admission rates among hospitalizations with CDI. Given the transition from ICD-9 to ICD-10 codes in 2015, we ensured that the change in administrative code for the disease was accounted for. The sensitivity and specificity of these administrative codes at identifying CDI has been established at 88% and 100% respectively [19].

When the cohort of admissions with a primary diagnosis of CDI was analyzed, we found that this downward trend in incidence predated 2015 and began in 2012. This is likely explained by increased caution with antimicrobial drug use following the widespread initiation of antibiotic stewardship programs. A report by the Blue Cross Blue Shield Association in 2017 showed that the fill rate of outpatient antibiotic prescriptions declined 9% among commercially insured Americans from 2010 to 2016 with broad-spectrum antibiotic fill rates dropping the most at 13% [20]. Several studies have shown that antimicrobial stewardship programs and formulary restrictions have resulted in significant reductions in the rates of occurrence of CDI [2]. An interrupted time-series analysis conducted by Valiquette et al [21] in a secondary/tertiary-care hospital in Quebec showed that there was no significant change in nosocomial CDI incidence after strengthening of infection control procedures (P = 0.63), but the implementation of an antimicrobial stewardship program was followed by a 60% reduction in CDI incidence (P = 0.007). Similar findings were observed in two other interrupted time-series studies conducted by Fowler et al [22] and Talpaert et al [23] among acute medical admissions which showed that CDI rates fell by 65% (P = 0.009) and 66% (P < 0.0001) respectively compared to controls when “narrow-spectrum” antibiotic policies were applied.

Most of the hospitalizations for CDI in our study were among elderly patients, although the mean age of both cohorts progressively reduced while the proportion of patients with CCI scores of 3 or more increased over the study period. When adjusted for race and sex, elderly patients were found to have increased odds of hospitalizations for CDI. An older age group and multiple medical comorbidities are established risk factors for CDI [24]. Other risk factors include immunocompromised states (as seen in patients with human immunodeficiency virus infection or patients with cancer who are receiving chemotherapy), broad-spectrum antibiotic therapy (especially clindamycin, cephalosporins, amoxicillin, and fluoroquinolones), gastric acid suppression therapy, intensive care unit admission, recent non-surgical gastrointestinal procedures, and tube feeding [4, 22, 24].

We noted improvements in adjusted mortality rates and LOS amongst hospitalizations with CDI and admissions with a principal admitting diagnosis of CDI over the study period. This is likely due to new treatment strategies which were implemented over the decade under review. Between 2010 and 2019, therapeutic options including oral vancomycin, fidaxomicin, fecal microbiota transplantation, and bezlotoxumab were approved for the treatment of index episodes or recurrences of CDI and have been incorporated into clinical guidelines [25]. Another possible explanation for the improvement in mortality and LOS is the increased use of Clostridium difficile toxin polymerase chain reaction (PCR) testing assays over the study period. The rapid stool PCR test was approved by the FDA in 2011 [26] after the test was shown to have higher sensitivity and comparable specificity to the previously prevalent enzyme immunoassay [27, 28].

Elderly patients were found to have worse outcomes compared to young and middle-aged individuals. Older patients have previously been shown to be at higher risk of severe and fulminant CDI [29] which invariably have worse outcomes. This increased risk among the elderly is likely related to increased odds of comorbidities and underlying conditions that are also well-recognized risk factors for CDI [28]. Institutionalization, longer hospital stays, and infections that necessitate frequent treatment with antibiotics are more common in the elderly [2].

Females were at increased odds of CDI hospitalization but had better adjusted outcomes. Sex disparities in CDI incidence and prevalence vary across studies, but male sex has previously been associated with severe/complicated CDI [30, 31]. While the reasons behind this are unclear, it may be related to male patients being at higher odds of comorbid conditions that predispose them to CDI [32].

We also found that most hospitalizations were among White patients, but adjusted outcomes were significantly worse in ethnic minorities. Blacks and Hispanics showed significantly worse mortality outcomes, longer hospital stays, and higher total hospitalization costs compared to Whites. These are similar to findings of a retrospective analysis of the US National Hospital Discharge Surveys from 2001 to 2010 which showed that while CDI incidence was higher among White patients (7.7/1,000 discharges vs. 4.9/1,000 discharges in Blacks, P < 0.0001), the Black race was independently associated with mortality (odds ratio 1.12, 95% CI 1.09 - 1.15) and severe CDI (odds ratio 1.09, 95 % CI 1.07 - 1.11), despite being a negative predictor for hospital LOS (OR 0.93, 95 % CI 0.93 - 0.94) [33]. The reasons behind these findings are still uncertain and require further studies to elucidate. However, they may suggest socioeconomic and structural inequities disparately affecting minoritized communities including an inequitable distribution of quality healthcare, differential access to public and private insurance, and insufficient research evaluating and addressing these inequities [34].

This study has some important limitations. Firstly, the NIS uses “claims data”, and ICD-9 and ICD-10 codes were used to confirm diagnoses that may not completely match clinical/laboratory parameters. Hence our findings were dependent on appropriate data collection and correct representation of these administrative codes. Secondly, the NIS lacks laboratory or radiological data and does not appropriately grade clinical disease severity, hence we could not stratify outcomes specific to severe/fulminant CDI. Thirdly, the NIS reports data on hospitalizations rather than individual patients, hence patients with recurrent CDI infections may count as multiple admissions. Also, the sampling data from NIS do not include rehabilitation and long-term acute care hospitals which is a subset of the population that is at risk for CDI, hence that subset is not accounted for. Lastly, we were unable to determine treatment modalities and the effect of medication adherence on outcomes. Lastly, we were unable to determine treatment modalities and the effect of medication adherence on outcomes.

Outcomes of CDI hospitalizations improved over the studied decade, but CDI still places significant mortality and economic burden on the US health care system. Older age, male sex, and belonging to a minority racial group were associated with worse outcomes. Further studies are needed to elucidate the reasons for these findings. Continued efforts to address other established risk factors are needed for primary and secondary prevention of CDI.

Acknowledgments

None to declare.

Financial Disclosure

No external funding was received for this research.

Conflict of Interest

The authors have no current or potential conflict to declare.

Informed Consent

Not required as the study involves publicly available data from the Nationwide Inpatient Sample database.

Author Contributions

Pius Ehiremen Ojemolon drafted the manuscript, created the tables and figures, and participated in the review and final approval of the manuscript. Hafeez Shaka conceptualized the manuscript, carried out the data analysis, and participated in the review and final approval of the manuscript. Robert Kwei-Nsoro and Hisham Laswi participated in drafting the introduction and in the review and final approval of the manuscript. Ebehiwele Ebhohon, Abdultawab Shaka and Abdul-Rahman Abusalim participated in drafting the discussion and in the review and final approval of the manuscript. Benjamin Mba revised the manuscript for intellectual content and participated in the review and final approval of the manuscript.

Data Availability

Datasets are available through the HCUP central distributor on request.

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