Epidemiology of Clostridioides difficile in South Africa.

BACKGROUND: Clostridioides difficile (CD) is the most common healthcare-associated enteric infection. There is currently limited epidemiological evidence on CD incidence in South Africa. AIM: To estimate the burden of CD infection (CDI) in the South African public sector between 1 July 2016 and 30 June 2017.
METHODS: A retrospective cohort study utilizing secondary data was conducted to describe the epidemiology of CD in South Africa. We assessed the patient-level association between variables of interest, CD, and CD recurrence, by undertaking both univariate and multivariable analysis. Adjusted incidence rate ratios (aIRR) were calculated utilizing multivariable Poisson regression. The incidence of CD, CD recurrence and CD testing was estimated by Poisson regression for various levels of care and provinces.
RESULTS: A total of 14 023 samples were tested for CD during the study period. After applying exclusion criteria, we were left with a sample of 10 053 of which 1 860 (18.50%) tested CD positive. A positive and significant association between CDI and level of care is found, with patients treated in specialized tuberculosis (TB) hospitals having a five-fold increased adjusted incidence risk ratio (aIRR) for CDI (aIRR 4.96 CI95% 4.08-6.04,) compared to those managed in primary care. Patients receiving care at a secondary, tertiary, or central hospital had 35%, 66% and 41% increased adjusted incidence of CDI compared to those managed in primary care, respectively. National incidence of CDI is estimated at 53.89 cases per 100 000 hospitalizations (CI95% 51.58-56.29), the incidence of recurrence at 21.39 (CI95% 15.06-29.48) cases per 1 000 cases and a recurrence rate of 2.14% (CI95% 1.51-2.94).
CONCLUSION: Compared to European countries, we found a comparable incidence of CD. However, our estimates are lower than those for the United States. Compared to high-income countries, this study found a comparatively lower CD recurrence.

Introduction

Clostridioides difficile (CD) is a spore-forming Gram-positive obligate anaerobic bacillus, first identified as part of the commensal gut flora in healthy infants [1]. CD spores are resistant to various environmental factors, including most antimicrobial agents and disinfectants, and can survive on external surfaces for months [2]. Spores are transmitted via the fecal-oral route and are activated in the duodenum by bile salts and L-glycine, acting as a co-germinant CspC receptor on the spore [3]. Germinants induce enzyme-mediated degradation of the spore cortex, releasing the chromosome-containing core and allowing the proliferation of vegetative cells [4]. Once vegetative CD bacilli have proliferated, the mucolytic enzyme Cwp84 degrades mucosal protective barriers in the gut, allowing access to the intestinal epithelium and subsequent colonization of the large intestine [5]. CD expresses several pathogen-associated molecular patterns (PAMPS), including Toxin A (TcdA), Toxin B (TcdB), flagellin, surface layer protein A (SlpA) and peptidoglycan fragments which each stimulate the host innate immune response via pattern recognition receptors, leading to both a local and systemic inflammatory response.

Virulence of CD is primarily mediated by the production of toxins encoded in the pathogenicity locus (PaLoc) [6]. PaLoc codes for three protein toxins: toxin A (TcdA), toxin B (TcdB) and CD transferase toxin (CDT) [7]. Of these TcdA and TcdB are more pathogenic, causing receptor-mediated inactivation of Rho with subsequent disruption of the cellular cytoskeleton, breakdown of tight junctions and enterocyte apoptosis [7, 8]. Depending on the virulence of the strain and host immunity, symptoms vary from asymptomatic colonization to fulminant colitis which is associated with high levels of morbidity and mortality [9]. Patients typically present with three or more episodes of watery diarrhea within 24 hours, with or without systemic signs such as fever and dehydration [10]. If the disease progresses to hemorrhagic colitis patients develop bloody diarrhea and severe abdominal pain. Clinical and laboratory markers of severity include body temperature greater than 38.5°C; severe abdominal pain; an elevated lactate level; elevated serum creatinine level; and a white cell count of greater than 15 x 109/L [11].

Clostridioides difficile infection (CDI) is defined by the Infectious Diseases Society of America (IDSA) and Society for Healthcare Epidemiology of America (SHEA) as ‘the presence of symptoms (usually diarrhea) and either a stool test positive for CD toxins or detection of toxigenic CD, or colonoscopy or histopathologic findings revealing pseudomembranous colitis’ [12].

Notwithstanding the increasing incidence of community-acquired CDI, recent hospitalization and administration of antibiotics remain the main risk factors for acquiring CDI [13]. Additional risk factors include outpatient antibiotic exposure, co-morbidities such as renal failure, malignancies and immunosuppression and age over 65 years [14]. Despite large variability in the reported incidence of CDI, it is clear that the burden of CDI has grown substantially since the late 1990s, particularly in high-income countries [1]. CDI is associated with significant morbidity and mortality and places an economic burden on healthcare systems [15]. However, our current understanding of the epidemiology of CDI is primarily informed by studies from high-income countries, with very few from South Africa estimating the burden of CDI [16-18]. We, therefore, undertook this study to estimate the incidence of CDI and associated risk factors in the South African public sector.

Methods

We conducted a retrospective cohort study. Secondary data sources for the period 1 July 2016 to 30 June 2017 were utilized to identify factors associated with CDI and CDI recurrence and to describe the epidemiology in the South African public sector. Data were accessed on 10 July 2020. Findings are reported in line with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement.

Ethics

The protocol for this study was reviewed by the Human Research Ethics Committee (Medical) at the University of the Witwatersrand, Johannesburg and clearance was obtained (Ref: M200114) for this specific study before commencing the study. This was a retrospective study utilizing secondary data sources. Permission was sought and obtained from the NHLS before accessing the data. Before data analysis, all unique identifiers were delinked to ensure anonymity. This study included data on minors over the age of one year.

Study setting

South Africa is an upper-middle-income country with a 2016 mid-year estimated population of 55.91 million [19]. The health sector has three tiers–a national level, provincial level (9 provinces) and district level (52 districts). Healthcare facilities are divided into community health centers providing primary and community-based care, district hospitals, regional hospitals, tertiary and central hospitals as well as specialized service hospitals (e.g. tuberculosis and psychiatric) [20]. Approximately 84% of South Africans rely on the public healthcare sector to access hospital care with laboratory diagnostic services provided by the National Health Laboratory Services (NHLS) [21, 22].

The NHLS has a national database of all laboratory results of all patients treated in the South African public sector. Routine healthcare utilization and private health insurance coverage rates are collected and annually reported by the District Health Barometer [23].

Data management and analysis

Data from all patients who had a stool sample submitted to the NHLS between 1 July 2016 and 31 June 2017 for CD testing were considered for inclusion in the study, therefore our sample can be considered representative of the South African public sector. The NHLS only tests unformed (Bristol stool class 5–7) specimens for CD. Testing is conducted utilizing enzyme immunoassay, polymerase chain reaction (PCR), GeneXpert or other PCR-based assays and standard diagnostic protocols. Data on healthcare facility level utilization rates for the period 1 July 2016 to 31 June 2017 were obtained from the District Health Barometer database. To estimate crude national incidence, mid-year population estimates from Statistics South Africa for 2016 were utilized and adjusted for medical insurance coverage.

Data from the NHLS was merged with that from the District Health Barometer to calculate incidence rates. No facility-level public sector utilization data stratified by age and sex, and no utilization data for the private sector were available. To estimate the incidence of CDI in the public sector we excluded tests from the private sector and those from patients under the age of 1 year. There were no missing data in the final data set utilized in the analysis.

For analysis, data were imported into Stata version 16 [24]. Patient ages were recoded into a categorical variable (1–30; 31–40; 41–60; 61 or older). Level of care was defined as primary [district, community health centre or non-infectious disease specialist facility (psychiatric and rehabilitation hospitals)]; secondary (regional hospital, typically providing general medical or surgical specialist-level care); tertiary (providing sub-specialty care), central hospital care (providing quaternary level care) and specialized tuberculosis (TB) hospitals. Finally, a binary variable was coded to compare high dependency care unit (ICU/HC) patients with those receiving care in a general ward (non-ICU/HC).

Categorical variables are reported in terms of frequencies, proportions and percentages, and continuous data in terms of measures of spread and central tendency. Univariate analysis was conducted to assess the relationship between CDI or CDI recurrence and variables of interest using χ2 or Fishers’ Exact test. The level of significance is defined at a two-tailed α of 0.05 for both the univariate and multivariable analyses. In the multivariable analysis, a Poisson regression model was developed to estimate crude and adjusted rate ratios and 95% confidence intervals for factors associated with CDI. Pearson goodness-of-fit testing was done to assess model performance.

The seasonality of CDI over the study period of interest was assessed by constructing an epidemiological curve and the proportion of CDI cases diagnosed for each month (July 2016 –June 2017). All incidence estimates and associated confidence intervals were calculated in Stata version 16 by Poisson regression and a period of one year. National crude incidence was estimated utilizing adjusted 2016 mid-year population estimates and expressed as cases per million population. Provincial and level of care incidence was calculated with healthcare facility utilization data and expressed as cases per 100 000 admissions. The incidence of recurrence is estimated in terms of the CD-positive population and expressed recurrence incidence as cases per 1000 CDI cases.

Finally, a geographic information system (GIS) analysis was undertaken to map the incidence and positivity rates across provinces. GIS analysis was conducted in GeoDa (https://geodacenter.github.io/) and the provincial shapefile was obtained from GDAM (https://gadm.org/index.html). Calculated incidence and positivity rate point estimates were manually entered into GeoDa before dividing these estimates into four quartiles to represent the data graphically.

Results

Fig 1 summarizes the data management process utilized in assessing factors associated with CDI. A total of 14 023 CD tests were conducted by the NHLS nationally between 1 July 2016 and 30 July 2017. Of these, a total of 2 583 were removed due to duplication, namely a repeat test performed for the same patient within the 14 days, the period that distinguishes therapeutic failure from recurrence. A further 1,388 patients were removed because of age < 1 (n = 1,081) missing data, sex (n = 77); unknown age (n = 4) and tests conducted for private sector patients (n = 226). This resulted in a final sample of 10,053 of which 1,860 (18.50%) and 8,193 (81.5%) were CD positive and negative respectively.

Fig 1

Summary of data management.

Fig 2 illustrates the epidemiological curve for CDI cases between July 2016 and June 2017. CDI cases varied during the year with the greatest number of cases diagnosed during December (n = 195) and the lowest during May and July (n = 155). From the epidemiological curve, there were slightly more cases during the summer months compared to the winter months.

Fig 2

Seasonal variation in number of CDI cases diagnosed in the South African public healthcare sector 2016/2017.

Epidemiological curve for CDI cases between July 2016 and June 2017. CDI cases varied during the year with the greatest number of cases diagnosed during the month of December (n = 195) and the lowest during May and July (n = 155).

This analysis found a crude national CDI incidence of 0.101 (CI95% 0.097–0.105) per 1000 000 population and a crude national CD testing incidence of 0.544 (CI95% 0.533–0.554) per 1000 000 population for the South African public sector. Nationally, the estimate of CDI incidence per 100 000 public sector admissions was 53.89 (CI95% 51.58–56.29). Fig 3 summarizes results from the GIS analysis of CD testing, CDI incidence and positivity rates by quintile for various provinces. Incidence varied widely across provinces with Gauteng’s incidence of CDI estimated at 158.45 (CI95% 149.69–167.59) per 100 000 admissions followed by the Western Cape at 107.52 (CI95% 98.96–116.62). In contrast, the Northern Cape recorded no CDI cases and for Limpopo province, we estimated an incidence of 0.81 (CI95% 0.26–1.90) cases per 100 000 admissions (Table 1). There was less variation in the positivity rate across provinces. The national average positivity rate was estimated at 18.57% (CI95% 17.85–19.31). The highest positivity rate was recorded in the North West (28.57%) followed by the Eastern Cape (27.84%). The lowest positivity rates were in the Free State (6.65%) and KwaZulu Natal (6.98%).

Fig 3

Provincial clostridioides difficile testing, CDI incidence and positivity rates by quintile: Incidence varied widely across provinces with Gauteng’s incidence of CDI estimated at 158.45 per 100 000 admissions followed by the Western Cape at 107.52.

In contrast the Northern Cape recorded no CDI cases and for Limpopo province, we estimated an incidence of 0.81 cases per 100 000 admissions. There was less variation in the positivity rate across provinces. The national average positivity rate was estimated at 18.57%.

Table 1

Incidence of CDI, CD testing and positivity rate by province per 100 000 admissions.

	Total Admission	Total number of CDI Cases	CDI Incidence per 100 000 admissions	Testing Incidence per 100 000 admissions
			(CI95%)	(CI95%)
National	3 781 351	2 038	37.81 (33–56.29)	290.29 (284.89–295.78)
Eastern Cape	445 438	135	30.31 (25.41–35.87)	108.88 (99.41–119.02)
Free State	207 511	22	10.60 (6.64–16.05)	159.51 (142.79–177.65)
Gauteng	770 572	1 221	158.45 (149.69–167.59)	801.09 (781.23–821.33)
KwaZulu-Natal	703 882	25	3.55 (2.30–5.24)	50.86 (45.72–56.41)
Limpopo	615 364	5	0.81 (0.26–1.90)	3.90 (2.50–5.80)
North West	175 116	46	26.27 (19.23–35.04)	91.94 (78.29–107.29)
Northern Cape	75 091	0	0 (-)	94.55 (73.85–119.26)
Western Cape	541 305	582	107.52 (98.96–116.62)	620.91 (600.09–642.26)
Mpumalanga	247 072	2	0.81 (0.10–2.92)	5.26 (2.80–9.00)

A higher level of care was associated with a higher incidence of CDI. Central hospitals had a pooled CDI incidence of 230.92 (CI95% 216.43–246.14) per 100 000 admissions, more than double that of tertiary facilities with an estimated incidence of 100.06 (CI95% 91.35–109.37) per 100 000 admissions. A further reduction in the incidence of CDI was found in secondary (20.97 CI95% 18.30–23.92 cases per 100 000 admissions) and primary care (11.85 CI95% 10.23–101.63) facilities.

Central hospitals were also much more likely to test for CD (1 298.46 tests per 100 000 admissions) than tertiary (539.30 tests per 100 000 admissions), secondary 133.45 tests per 100 000 admissions) and primary care facilities (10.87 tests per 100 000 admissions) (Table 2). Therefore, there is less variability in the positivity rate (12.29%– 19.82%) across levels of care than the incidence of CDI. As a group, specialized TB hospitals are a clear outlier with a CDI incidence of 683.98 (CI95%; 589.46–7789.35) cases per 100 000 admissions, a higher testing incidence of 1 115.58 (CI95%; 993.88–1, 248.07) per 100 000 admissions and positivity rates three times greater than the national average (61.31%; CI95% 55.59–66.81).

Table 2

Incidence of CDI, CD testing and positivity rate by level of care per 100 000 admissions.

	Total Admissions	Total number of CDI Cases	CDI Incidence per 100 000 admissions	Testing Incidence per 100 000 admissions
			(CI95%)	(CI95%)
Central	408 793	944	230.92 (216.43–246.14)	1, 298.46 (1, 263.76–1, 333.88)
Tertiary	484 732	485	100.06 (91.35–109.37)	504.82 (485.01–525.22)
Secondary	1 058 310	222	20.97 (18.30–23.92)	126.14 (119.47–133.10)
Primary	1 775 613	200	10.87 (9.39–12.52)	88.48 (84.15–92.96)
Specialized TB	2 734	187	683.98 (589.46–789.35)	1, 115.58 (993.88–1, 248.07)
Total	3 730 182	2038

Of those with CDI, non-ICU/HC, central hospital, female, and patients in the age group 41–60 years had a slightly higher representation (Table 3). The proportion of patients with CDI was significantly higher among those receiving care in a specialized TB hospital (62.01%) compared to tertiary, central, secondary, and primary care facilities respectively at 17.28%, 20.46%, 16.74% and 12.42%.

Table 3

Univariate analysis of factors associated with CDI.

	Total (10053)	CDI (1860)	Non-CDI (8193)	p-value
	N	n (%)	n (%)
Age (years)
1–30	2, 660	477 (25.65)	2, 183 (26.64)
31–40	2, 427	494 (26.56)	1, 933 (23.59)
41–60	3, 161	578 (31.07)	2, 583 (31.53)
61 or older	1, 805	311 (16.72)	1, 494 (18.24)	0.043
Gender
Female	5, 693	1, 039 (55.86)	4, 654 (56.80)
Male	4, 360	821 (44.14)	3, 539 (43.20)	0.458
Level of Care
Primary	1, 691	210 (11.30)	1, 481 (18.08)
Specialized TB	311	193 (10.37)	118 (1.44)
Secondary	1, 183	198 (10.65)	985 (12.02)
Tertiary	2, 278	466 (25.05)	1, 812 (22.12)
Central	4, 590	793 (42.63)	3, 797 (46.34)	<0.001
ICU/HC
Non-ICU/HC	8, 957	1, 675 (90.05)	7, 282 (88.88)
ICU/HC	1, 096	185 (9.95)	911 (11.12)	0.143

Results from the univariate and multivariable analysis are summarized in Table 4. No statistically significant differences were found between age groups, males, and females (IRR 1.03 CI95% 0.94–1.13, p = 0.503) or non-ICU/HC and ICU/HC patients (IRR 0.90 CI95% 0.78–1.05, p = 0.186). However, compared to primary care, patients receiving secondary level care were 34% (IRR 1.34 CI95% 1.11–1.64, p = 0.002) more likely to test positive. Similarly, patients receiving care at a tertiary or central hospital were 65% (IRR 1.65 CI95% 1.40–1.94, p < 0.001) and 39% (IRR 1.39 CI95% 1.19–1.62, p < 0.001) more likely to have CDI. Receiving care in a specialized TB hospital was associated with a five-fold increase incidence of CDI (IRR 5.00 CI95% 4.11–6.08, p < 0.001).

Table 4

Unadjusted and adjusted incidence rate ratios for factors associated with CDI.

	Unadjusted IRR (CI95%)	p-value	Adjusted IRR (CI95%)	p-value
Age			N = 10053
1–30	Reference		Reference
31–40	1.14 (1.00–1.29)	0.048	1.09 (0.96–1.24)	0.167
41–60	1.02 (0.90–1.15)	0.753	1.02 (0.90–1.15)	0.808
61 or older	0.96 (0.83–1.11)	0.584	1.02 (0.89–1.18)	0.741
Gender
Female	Reference		Reference
Male	1.03 (0.94–1.13)	0.503	1.00 (0.91–1.09)	0.974
Level of Care
Primary	Reference		Reference
Specialized TB	5.00 (4.11–6.08)	<0.001	4.96 (4.08–6.04)	<0.001
Secondary	1.34 (1.11–1.64)	0.002	1.35 (1.11–1.64)	0.003
Tertiary	1.65 (1.40–1.94)	<0.001	1.66 (1.41–1.95)	<0.001
Central	1.39 (1.19–1.62)	<0.001	1.41 (1.21–1.65)	<0.001
ICU/HC
Non-ICU/HC	Reference		Reference
ICU/HC	0.90 (0.78–1.05)	0.186	0.94 (0.80–1.10)	0.426

After adjusting for age, sex and ICU/HC, the level of care remained statistically significant in its association with CDI with a 35% (aIRR 1.35 CI95% 1.11–1.64, p = 0.003), 66% (aIRR 1.66 CI95% 1.41–1.95, p < 0.001) and 41% (aIRR 1.43 CI95% 1.21–1.65, p < 0.001) increased adjusted incidence compared to primary care for secondary, tertiary, and central hospital care respectively. The strong association with receiving care in a specialized TB hospital also remained statistically significant after adjusting for sex, age, and ICU/HC (aIRR 4.96 CI95% 4.08–6.04, p < 0.001).

After applying the exclusion criteria, 39 cases of CDI recurrence were identified between 1 July 2016 and 30 June 2017. The mean time to recurrence was 43.30 days (CI95% 28.97–57.65) and the probability of a subsequent episode of recurrence was 12.12% (CI95% 3.4–28.2). We estimated the incidence of recurrence to be 21.39 (CI95% 15.06–29.48) cases per 1, 000 cases of CDI. This translates to a recurrence rate of 2.14% (CI95% 1.51–2.94). On univariate analysis, none of the variables under investigation (age, gender, level of care or ICU/HC) were found to be significantly associated with recurrence (Table 5). However, there was a higher incidence of recurrence at specialized TB hospitals (3.11%) and Central hospitals (2.52%) compared to other levels of care (range 1.42% - 1.50%)

Table 5

Univariate analysis of factors associated with CDI recurrence.

	N	Recurrence	No recurrence	p-value
	N = 1860	N = 39 (%)	N = 1821 (%)
Age (years)
1–30	477	10 (25.64)	467 (25.65)
31–40	494	13 (33.33)	481 (26.41)
41–60	578	12 (30.77)	566 (31.08)
61 or older	311	4 (10.26)	307 (16.86)	0.662
Gender
Female	1, 039	21 (53.85)	1, 018 (55.90)
Male	821	18 (46.15)	803 (44.10)	0.871
Level of Care
Primary	210	3 (7.69)	203 (11.15)
Specialized TB	193	6 (15.38)	185 (10.16)
Secondary	198	3 (7.69)	185 (10.16)
Tertiary	466	7 (17.95)	430 (23.61)
Central	793	20 (51.28)	687 (37.73)	0.560
ICU/HC
Non-ICU/HC	1, 675	35 (89.74)	1, 640 (90.10)
ICU/HC	185	4 (10.26)	181 (9.90)	0.792

Discussion

This is the first study to determine the national incidence of CDI in the South African public sector. CDI incidence is estimated at 53.89 CDI cases per 100 000 hospitalizations, lower than estimates for the US (115.1), but higher than those for England (19.3) [25]. In Europe, the reported incidence of CDI at the healthcare facility level varies between 42 to 1 318 per 100 000 admissions [26], placing estimates for South Africa at the lower end of the European spectrum.

Previous studies to estimate the incidence of CDI in South Africa are limited. Compared to the central hospitals’ incidence in our study of 230.92 per 100 000 admissions, a single centre prospective study in a Cape Town central hospital estimated the incidence of hospital-acquired CDI at 87 per 100 000 admissions in 2013 [17]. A second study from two tuberculosis hospitals in Cape Town found the incidence to be 7007.0 per 100 000 admissions from 2014 to 2015 [18]. This is much higher than the pooled estimate for specialized TB hospitals nationally (683.98 per 100 000 admissions). A possible reason for these differences is that this study’s estimates included all public care facilities in South Africa, some of which had no cases of CDI. A facility-level analysis of our data also revealed significant variability in CDI incidence among central hospitals (range 5.35 to 529.36 cases per 100 000 admissions) and specialized TB hospitals (range 0 to 10 661.76 cases per 100 000 admissions). A second possible explanation is that we were unable to differentiate between community and hospital-acquired cases, therefore our estimates of CDI may include cases of community-acquired CDI which may overestimate the incidence. However, a study in 2019 found the prevalence of CD colonization among those living in residential care facilities in Cape Town to be 1.6% [27]. In contrast, an earlier study from a central hospital in Cape Town found that of those patients diagnosed with CDI (N = 59), 32% (n = 19) had community-acquired CDI [17].

There is very limited data on recurrence rates of CDI in South Africa. From this analysis, recurrence is estimated at 2.14% which is lower than that reported in one South African prospective study where three (5.08%) of 59 patients had a recurrence [17]. A large British single-centre prospective study of 2 043 CDI patients with a median follow-up of 11 months found a recurrence rate of 22%, in a quarter of whom the recurrence was with a different CD strain [28]. In addition to advanced age, co-morbidities, healthcare and antibiotic exposure; initial infection with epidemic BI/NAP1/027 CD strain is a risk factor for relapse (as opposed to re-infection) [29]. The prevalence of BI/NAP1/027 was estimated to be 22% between 2013 and 2016 in the US, and 41.3% in the UK between 2007 and 2008 [30, 31]. There is very sparse data on the epidemiology of BI/NAP1/027 in South Africa. One study found the prevalence to be 3.4% (3/59) [17]. The possible reasons for our apparent low recurrence rates are multiple. Firstly, we only had data for one year which may have excluded possible cases of recurrence at the beginning and the end of the study period. Secondly, some may have been lost to follow-up or died before being diagnosed with a recurrence. Thirdly, South Africa’s demographic and population disease profile, antibiotic prescription practices and a lower prevalence of BI/NAP1/027 may have resulted in a true lower incidence of recurrence. Finally, CDI management protocols in South Africa differ from those in countries with higher recurrence rates. Recommended first-line treatment for CDI in South Africa in 2016/17 was oral metronidazole [32] and this was the most common first-line treatment prescribed [33]. Current IDSA recommendations for first-line treatment is either oral vancomycin or fidaxomicin and only where vancomycin or fidaxomicin is not available should metronidazole be considered as a treatment for mild CDI [12]. For recurrences vancomycin or fidaxomicin is administered for longer tapered periods or as pulsed regimens are recommended, and fecal microbiota transplantation can also be considered [12].

An assessment of CDI seasonality suggested a slight increase in cases during the summer months, however, any conclusions regarding this are limited by the fact that we only reviewed data over one year. We are not aware of any other studies from South Africa which have attempted to assess the seasonality of CDI. In contrast to our findings, a systematic review of twenty studies, of which two were conducted in the southern hemisphere, found consistent evidence of seasonality in the incidence of CDI with cases peaking in spring and reducing during summer [34].

Assessment of factors associated with CDI is limited by a lack of potential explanatory variables in this study: age, sex, level of care and high dependency care unit versus ward care. Notwithstanding these limitations, a strong positive association was found between CDI and treatment in a specialized TB hospital, a finding which is support by a previous facility-level analysis in the Western Cape [18]. This association, as well as the association found between CDI and tertiary or quaternary care, can be explained by the fact that patients in these facilities typically suffer from multiple comorbidities and tend to have longer hospital stays and healthcare exposures than primary care patients. Particularly in the case of specialized TB hospitals, patients are more likely to be on prolonged courses of antimicrobial therapy resulting in a disruption of the gut microbiome and subsequent CD proliferation.

Conclusion

This is the first study in South Africa to estimate the national burden of CDI. Compared to European countries, this analysis found a comparable incidence of CDI. However, estimates are lower than those for the United States. A significantly lower CDI recurrence was found in the South African public sector, compared to high-income countries. Further work is necessary to obtain a clearer picture of the burden of CDI in South Africa. Firstly, the analysis should be extended to include other years and if possible, the private sector. This will allow for the assessment of CDI trends, allow for a more robust analysis of CDI seasonality and a comparison between the public and private healthcare sectors. Secondly, the analysis should be extended to estimate incidence on a district level. Thirdly, the prevalence and trends in the incidence of BI/NAP1/027 should be determined. Of the 14 023 CD tests conducted by the NHLS between 1 July 2016 and 30 June 2017 8, 442 (60.20% utilized GeneXpert technology which routinely reported the presence or absence of BI/NAP1/027 strain. Finally, an economic analysis utilizing existing epidemiological and cost data should be undertaken to estimate the economic burden of CDI on the public healthcare system.

STROBE statement—checklist of items that should be included in reports of observational studies.

(DOCX)

Click here for additional data file.

23 Mar 2021

PONE-D-21-03916

Epidemiology of Clostridioides difficile in South Africa

PLOS ONE

Dear Dr. de Jager,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by May 07 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Orvalho Augusto, MD, MPH

Academic Editor

PLOS ONE

Additional Editor Comments:

Epidemiology of Clostridioides difficille in South Africa

PONE-D-21-03916

This work is about the incidence of clostridioides difficile infection (CDI) in South Africa (SA). Current knowledge, mostly derived from high income countries, suggests that clostridioides difficile (CD) is a major cause of healthcare associated diarrhea and it is increasingly present in the community. This manuscript contributes to raise clinical awareness of its existence in SA and, I dare say, to the all African continent.

To estimate the incidence, as numerator, the authors took the [newly?] number of cases diagnosed through the National Health Laboratory Service (NHLS) between 1 July 2016 and 30 June 2017. According to the authors the NHLS has a national database of all processed laboratory results in the South Africa public sector [the public health sector which covers 84% of South Africa population]. For the denominator they took the overall mid-year population of South Africa in 2016. Therefore, the CDI incidence is underestimated for both underestimated numerator and large denominator, but I agree with them that it is useful estimate.

In addition to the incidence estimation, the authors produce some measure of association using as exposures (the call risk factors) age, gender, level of care and ICU/HC, and outcomes the state of positive or negative to diagnosis of CDI. Although the negatives here are not good controls it is OK to this exercise.

Few issues:

In the next version please add line numbering. That helps to track and indicate the place of an issue.

I. Introduction is adequate and quite instructive.

II. Methods:

Why the study of risk factors ignore incidence rates? The authors proceed as if they built a case-control with OR estimation. I guess you can obtain population per age and gender. That could be used on a Poisson regression which gives IRR/RR. Just a clarification.

1. Somewhere in the first paragraph there is an indication that a STROBE form was filled. I did not find the attached form. Please add this in the supplements.

2. Data management and analysis

- For the mid-year population conceptually it represents the number of people living in a specific geographic area on 1st July (others would say 30th of June). That is too simplistic. It is more useful to think of it as the total of the fractions year that each individual in South Africa lived. Eg: some lived the full year so their fraction is 1; others just the first quarter so 0.25; and others up to 3rd quarter so 0.75 and so on. So it is a person-years contribution to one year. So, why the authors here decided to mid-year population of 2016 and the numerator covers 2 years? The mid-year population between 1st July 2016 and 30th June 2017 is something close to 1st January of 2017. Just a clarification please.

- State in the methods how the CID incidence confidence intervals were estimated.

- The statement starting as “The NHLS only test specimens for CD that conform to the shape of the container”. This is unclear and confusing. Please clarify.

- Figure 1 is important but it needs changes to 1) to separate clearly exclusions and 2) distinguish sample count from patient counts. Otherwise we will be confused on the calculations as one of the reviewers rightly asks. A sort of suggestion is attached as picture [pardon me for lack of technological sophistication, see the photo of a drawing attached].

- This paragraph related to figure 1 has a lot of information that belongs to results section. Please review.

- Be careful when interpreting odds-ratios as risk-ratios as in, for example, “receiving care in a specialized TB hospital resulted in a more than 11-fold increase in the risk of CDI”. This is incorrect. An example: the proportions of positivity among males and females are 19.12% and 18.28%, respectively. So the is [19.12/(100 – 19.12)]/[18.28/(100 – 18.28)] = 1.06 which is not a ratio of proportion 19.12/18.28 = 1.05. The difference between these two gets worse for higher OR.

III. Results

1. Table 1.

* The percentages cause confusion. I propose these changes

- Please add a row of totals

- For the 3 columns ‘n(%)’, ‘CDI’, and ‘non-CID’ make them the percentages be in columns

- Add one column named ‘CD positivity rate’ so the current percentage calculated in CDI would move to a column dedicated for row percentage.

- Why we need p-values here? Those p-values inform less than the ones on table 2.

* One of the reviewers complains about the numbers. Please re-verify the calculations.

- Eg: The positivity frequency for females 974/5,329 = 18.28% that is OK. But for males should be 756/3,953 = 19.12%.

* Why province is not here?

2. Table 2 –

* Why province is not here?

3. Table 3 needs similar changes as table 1.

4. Table 4 – please what is the unity of measurement of the incidence.

Remove the positivity rate. That is fine on table one.

Three columns are missing. One for the population and the numerators. That will be important in a future meta-analysis.

Add a row for the total. This is important to understand the 0.101 described in the results.

5. Table 5 – the same comments as for table 4.

be careful with abbreviation of CD.

6. The first paragraph of results ends with something as “On univariate analysis only level of patient care was statistically significantly associated with CDI (p < 0.001) (table 1)” Please do not use p-values as evidence of associations. For that, first describe the association measure.

7. If the p-value is below the 0.001 please write “p-value < 0.001” not “p = 0.000” as in “81% (OR 1.81 CI95% 1.51 – 2.17, p = 0.000)”.

8. All figures are numbered figure 1. Please correct this.

9. I am not sure about the seasonality based just with one year of data. Please note this limitation in your discussion

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Thank you for providing the date(s) when patient medical information was initially recorded. Please also include the date(s) on which your research team accessed the databases/records to obtain the retrospective data used in your study.

3. To comply with PLOS ONE submission guidelines, in your Methods section, please provide additional information regarding your statistical analyses. For more information on PLOS ONE's expectations for statistical reporting, please see https://journals.plos.org/plosone/s/submission-guidelines.#loc-statistical-reporting.

4. In your Methods section, please provide additional information about the participant recruitment method and the demographic details of your participants. Please ensure you have provided sufficient details to replicate the analyses such as:

a) a description in the methods section of any inclusion/exclusion criteria that were applied to participant selection,

b) a statement as to whether your sample can be considered representative of a larger population.

5. Thank you for stating in your ethics statement "The protocol for this study was reviewed by the Human Research Ethics Committee

(Medical) at the University of the Witwatersrand, Johannesburg and clearance was obtained (Ref: M200114) prior to commencing the study. This was a retrospective study utilizing secondary data sources. Prior to data analysis all unique identifiers were delinked to ensure anonymity." In your ethics statement, please clarify:

- whether the ethics committee specifically approved this study.

- whether consent was obtained

- whether consent was informed

- what type of consent you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed).

- if your study included minors, state whether you obtained consent from parents or guardians.

- if the need for consent was waived by the ethics committee, please include this information.

6. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

7. Please amend either the abstract on the online submission form (via Edit Submission) or the abstract in the manuscript so that they are identical.

8. We note that Figure 3 in your submission contain map images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

8.1.    You may seek permission from the original copyright holder of Figure 1 to publish the content specifically under the CC BY 4.0 license.

We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text:

“I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.”

Please upload the completed Content Permission Form or other proof of granted permissions as an "Other" file with your submission.

In the figure caption of the copyrighted figure, please include the following text: “Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].”

8.2.    If you are unable to obtain permission from the original copyright holder to publish these figures under the CC BY 4.0 license or if the copyright holder’s requirements are incompatible with the CC BY 4.0 license, please either i) remove the figure or ii) supply a replacement figure that complies with the CC BY 4.0 license. Please check copyright information on all replacement figures and update the figure caption with source information. If applicable, please specify in the figure caption text when a figure is similar but not identical to the original image and is therefore for illustrative purposes only.

The following resources for replacing copyrighted map figures may be helpful:

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/

Landsat: http://landsat.visibleearth.nasa.gov/

USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#

Natural Earth (public domain): http://www.naturalearthdata.com/

9. Please include a caption for figures 2 and 3.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is a very interesting and informative article. This is the first study to determine the incidence of CDI in the South African public sector. All of data are well presented. Considering this, I suggest to accept this article

Reviewer #2: The authors of this paper have addressed key health concerns unique to African continent and a growing problem globally. The paper is well written, organized and easy to read. The English language is from a first speaking point and the paper reads well overall.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Fernando Gil

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Submitted filename: CD Manuscript Review comments-PONE-D-21-03916.docx

Click here for additional data file.

23 Sep 2021

Dear Prof Augusto

Thank you for your positive response to our manuscript and, to the reviewers for their valuable suggestions to strengthen our paper. Thank for allowing us an extension on addressing the reviewer comments. We apologize for the delay in submitting a revised manuscript.

We have revised the manuscript based on the reviewers’ comments, including:

• Figure 1 has been extensively revised.

• The analysis of factors associated with Clostridioides difficile was redone. We agree that estimating incidence rate ratios rather than odds ratios utilizing logistic regression is more appropriate.

Attached you will find a detailed point-by-point response to each of the reviewers’ comments.

Best regards,

Pieter de Jager

2 August 2021

Fellow: Anaesthesiology, Mount Sinai Hospital, Toronto

MBChB(UFS), FCA(SA), MSc(LSE)

Email: ppdejager@gmail.com

Submitted filename: CDI_Response_V3_2.7.21.docx

Click here for additional data file.

27 Oct 2021

Epidemiology of Clostridioides difficile in South Africa

PONE-D-21-03916R1

Dear Dr. de Jager,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Orvalho Augusto, MD, MPH

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The issues raised on the previous round have been address properly.

As previously noted, this is an important contribution for the body of literature on this not that rare infectious condition in times of a “pandemic” of antimicrobial resistance. I applaud the authors for this exercise.

Few more:

1. Please correct multivariate to multivariable in the whole document. Multivariate is usually reserved for situations simultaneous assessment of multiple outcomes, whereas multivariable is for situations of one outcome and multiple predictors as is the case here.

2. Good that the authors do present the association analysis through Poisson regressions (a tricky to approximate the log-binomial regression which is so prone to fail to converge). However, let’s be careful with the interpretation of exponentiated coefficients here. Because you do not use the SA population (as an offset) in the model, then you are not modelling the incidence rate in the same spirit of the incidence definition (as in the current table 4). These models are about the proportion of positivity so the coefficients are multiplicative changes of such proportions. Can you make a note on those lines, please? (or may use some other designation eg: ratio of positivity for table 2).

3. The current table 3 is built as in table 1. But the table 1, is supplemented with the current table 2 presenting an unadjusted and adjusted analysis. I would suggest to make an unadjusted and adjusted as well for this.

4. Line 203: please put citation for Stata 16.

5. The presentation of the results is curious. Please, start with the measures of frequency (the incidence) and its geographic distribution. Then move to the analysis of factors.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The previous review comments have been adequately addressed. The paper addresses key concerns about infection prevention and control mechanisms in health-care environment especially Clostridium difficile infections as a potential emerging global public health problem that needs urgent attention.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

15 Nov 2021

PONE-D-21-03916R1

Epidemiology of Clostridioides difficile in South Africa

Dear Dr. de Jager:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Orvalho Augusto

Academic Editor

PLOS ONE