β-Lactamase and Macrolide Resistance Gene Carriage in Escherichia coli Isolates Among Children Discharged From Inpatient Care in Western Kenya: A Cross-sectional Study

Polycarp Mogeni; Olusegun O Soge; Kirkby D Tickell; Stephanie N Tornberg; Rushlenne Pascual; Erika Wakatake; Mame M Diakhate; Doreen Rwigi; Kevin Kariuki; Samuel Kariuki; Benson O Singa; Ferric C Fang; Judd L Walson; Patricia B Pavlinac

doi:10.1093/ofid/ofae307

. 2024 Jun 3;11(6):ofae307. doi: 10.1093/ofid/ofae307

β-Lactamase and Macrolide Resistance Gene Carriage in Escherichia coli Isolates Among Children Discharged From Inpatient Care in Western Kenya: A Cross-sectional Study

Polycarp Mogeni ^1,^2,^✉, Olusegun O Soge ^3,^4,⁵, Kirkby D Tickell ^6,⁷, Stephanie N Tornberg ^8,⁹, Rushlenne Pascual ¹⁰, Erika Wakatake ¹¹, Mame M Diakhate ¹², Doreen Rwigi ¹³, Kevin Kariuki ¹⁴, Samuel Kariuki ¹⁵, Benson O Singa ^16,¹⁷, Ferric C Fang ^18,^19,²⁰, Judd L Walson ^21,^22,^23,²⁴, Patricia B Pavlinac ^25,^26,²

¹ Center for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya

² Department of Global Health, University of Washington, Seattle, Washington, USA

³ Department of Global Health, University of Washington, Seattle, Washington, USA

⁴ Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington, USA

⁵ Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA

⁶ Department of Global Health, University of Washington, Seattle, Washington, USA

⁷ The Childhood Acute Illness and Nutrition Network, Nairobi, Kenya

⁸ Department of Global Health, University of Washington, Seattle, Washington, USA

⁹ Department of Epidemiology, University of Washington, Seattle, Washington, USA

¹⁰ Department of Global Health, University of Washington, Seattle, Washington, USA

¹¹ Department of Global Health, University of Washington, Seattle, Washington, USA

¹² Department of Global Health, University of Washington, Seattle, Washington, USA

¹³ Center for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya

¹⁴ Center for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya

¹⁵ Center for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya

¹⁶ Center for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya

¹⁷ Department of Global Health, University of Washington, Seattle, Washington, USA

¹⁸ Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington, USA

¹⁹ Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA

²⁰ Department of Microbiology, University of Washington, Seattle, Washington, USA

²¹ Department of Global Health, University of Washington, Seattle, Washington, USA

²² Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington, USA

²³ The Childhood Acute Illness and Nutrition Network, Nairobi, Kenya

²⁴ Department of Pediatrics, University of Washington, Seattle, Washington, USA

²⁵ Department of Global Health, University of Washington, Seattle, Washington, USA

²⁶ Department of Epidemiology, University of Washington, Seattle, Washington, USA

^✉

Correspondence: Polycarp Mogeni, PhD, Center for Microbiology Research, Kenya Medical Research Institute, Off Raila Odinga Way, Nairobi 54840, Kenya (pkambona11@gmail.com).

Potential conflicts of interest. All authors: No reported conflicts.

PMCID: PMC11210497 PMID: 38938894

Abstract

Background

Antimicrobial resistance (AMR) is a global threat to infectious disease control, particularly among recently hospitalized children. We sought to determine the prevalence and mitigating factors of resistance in enteric Escherichia coli among children discharged from health facilities in western Kenya.

Methods

Between June 2016 and November 2019, children aged 1 to 59 months were enrolled at the point of discharge from the hospital. E coli was isolated by microbiological culture from rectal swabs at baseline. β-Lactamases and macrolide resistance–conferring genes were detected by polymerase chain reaction. A modified Poisson regression model was used to assess the predictors mph(A) and CTX-M–type extended-spectrum β-lactamase (ESBL).

Results

Of the 238 children whose E coli isolates were tested, 91 (38.2%) and 109 (45.8%) had detectable CTX-M–type ESBL and mph(A) genes, respectively. Antibiotic treatment during hospitalization (adjusted prevalence ratio [aPR], 2.47; 95% CI, 1.12–5.43; P = .025), length of hospitalization (aPR, 1.42; 95% CI, 1.00–2.01; P = .052), and the practice of open defecation (aPR, 2.47; 95% CI, 1.40–4.36; P = .002) were independent predictors for CTX-M–type ESBL and mph(A) genes. Pneumococcal vaccination was associated with a 43% lower likelihood of CTX-M–type ESBL (aPR, 0.57; 95% CI, .38–.85; P = .005), while measles vaccination was associated with a 32% lower likelihood of mph(A) genes (aPR, 0.68; 95% CI, .49–.93; P = .017) in E coli isolates.

Conclusions

Among children discharged from the hospital, history of vaccination, shorter hospital stay, lack of in-hospital antibiotic exposure, and improved sanitation were associated with a lower likelihood of AMR genes. To mitigate the continued spread of AMR, AMR control programs should consider strategies beyond antimicrobial stewardship, including improvements in sanitation, increased vaccine coverage, and the development of novel vaccines.

Keywords: β-lactamase, CTX-M, hospital, mph(A), routine vaccination

Approximately 5 million deaths worldwide were associated with antimicrobial resistance (AMR) in 2019, 22% of which occurred in sub-Saharan Africa (SSA) [1]. Bacteria can acquire AMR through chromosomal mutations or horizontal exchange of genetic material [2]. The development and persistence of AMR are strongly associated with the inappropriate use of antimicrobial drugs [3, 4]. Infections caused by bacteria with AMR have been associated with prior inpatient admission, length of hospital stay, antimicrobial drug use, and hygiene [5–7]. Hospitalized children who acquire antibiotic-resistant infections present considerable treatment challenges [8], and this may partly explain the high postdischarge mortality reported in SSA, which ranges from 3% to 5% and is similar to inpatient mortality rates [1, 9, 10]. AMR acquired during hospitalization may predispose patients to treatment failure during the postdischarge period, thus elevating the likelihood of adverse outcomes [11]. In addition, prolonged antibiotic use due to AMR may alter the gut microbiota, which is associated with immune function, or it may increase the likelihood of resistance gene sharing from commensal bacteria to pathogenic bacteria [12].

Escherichia coli is a gram-negative bacterium that is a normal component of the human gastrointestinal microbiota with disease-causing potential [13]. E coli, including antibiotic-resistant E coli, can be spread from person to person in crowded environments or in settings with poor access to clean water and sanitation [14]. As a permanent resident of the gastrointestinal tract, AMR E coli may act as a reservoir for AMR genetic elements that can be transferred to other bacteria that reside in or pass through the intestinal tract [15]. Understanding the risk factors for exposure/acquisition of resistant bacteria can inform interventions to interrupt transmission. Vaccination, for example, has been shown to decrease not only the burden of infectious diseases but also AMR in controlled study settings [16–19]. However, the impact of routine vaccination on AMR bacteria in field studies is poorly documented, especially among children in SSA.

We previously described the prevalence of phenotypically determined resistance to commonly used antibiotics in E coli isolates and assessed the correlates of phenotypically determined extended-spectrum β-lactamase (ESBL)–producing E coli and Klebsiella isolates in a sample of children discharged from the hospital in western Kenya [5, 7]. In our previously reported cross-sectional survey, phenotypic AMR was high and associated with prior hospitalization, antibiotic use, sanitation, and hygiene variables [5]. However, phenotypic resistance testing does not provide information about potential mechanisms of AMR. Understanding the dynamics of resistant strains and resistance determinants is vital for the establishment of appropriate control measures. Here, we report the prevalence of genetic markers of macrolide and β-lactam resistance—2 important classes of antibiotics for common bacterial infections—among children living in SSA. We also report an exploratory analysis of the potentially modifiable correlates (including vaccination history) of CTX-M–type ESBL and mph(A): the most common genetic determinants of ESBL [20] and macrolide [21] resistance, respectively, in SSA. In addition, CTX-M–type ESBLs are the most prevalent and globally disseminated ESBLs in Enterobacteriaceae and confer cross-resistance to noncephalosporin classes of antimicrobials [22, 23].

METHODS

Patient Consent Statement

Ethical approval was provided by the institutional review boards of the Kenya Medical Research Institute (SERU 3086), the Kenyan Pharmacy and Poisons Board (PPB/ECCT/15/10/04), and the University of Washington (49120). The parent trial was registered at ClinicalTrials.gov (NCT02414399). Before recruitment, written informed consent was sought from caregivers of the participating children in their preferred language. If a caregiver could not read and write, a thumbprint was obtained with a witness countersigning following informed consent.

Study Design and Sample Collection

We conducted a cross-sectional study using E coli isolates derived from samples collected at baseline from children who were enrolled to participate in a randomized controlled trial. A detailed description of the trial’s study design, inclusion criteria, sample collection, and preparations for laboratory processing has been reported [24, 25]. Briefly, between June 2016 and November 2019, children aged 1 to 59 months who were discharged from the hospital in the Kisii and Homa Bay counties of western Kenya were enrolled in a clinical trial testing the efficacy of azithromycin for prevention of morbidity and mortality in the 6 months following hospitalization. Kisii Teaching and Referral Hospital, a level 6 hospital, serves an urban population of about 1.2 million people and is a major referral hospital in western Kenya. Homa Bay County Teaching and Referral Hospital, a level 5 hospital, serves a predominantly rural population of around 1.1 million people. Homa Bay County has one of the highest under-5 childhood mortality rates and HIV prevalence in the country [26].

During enrollment, a physical examination, clinical history, and caregiver interview were conducted. When available, vaccine cards were abstracted into case report forms; when not, a set of questions to ascertain vaccination status against each pathogen was asked and recorded. Fecal samples were collected from children prior to randomization, immediately placed in Cary-Blair media, and sent to the Center for Microbiology Laboratory at the Kenya Medical Research Institute for bacterial culture. The samples were plated on MacConkey agar and incubated aerobically at 37 °C for 24 hours. Distinct morphologies of lactose-fermenting colonies were picked and plated on Mueller-Hilton agar for overnight incubation for biochemical testing. E coli isolates were confirmed with the API 20E system (bioMérieux) and reaction to oxidase. The isolates were stored in 15% tryptic soy broth with glycerol prior to resistance testing.

In this nested study, we randomly selected a subset of baseline E coli isolates, previously tested for phenotypic resistance [5], for genotypic testing. The selection process utilized simple random sampling with stratification by site. The isolates were suspended in tryptic soy broth (Oxoid) with 15% glycerol and frozen at −80°C. From a random subset of 238 children, baseline E coli isolates were shipped on dry ice to the University of Washington Neisseria Reference Laboratory for molecular characterization of β-lactamase and macrolide resistance genes. Distinct morphologies of E coli, up to 3 in total, were stored at −80°C. Individual morphologies underwent phenotypic antimicrobial susceptibility testing, while DNA extraction took place from the combined vial. Phenotypic resistance was determined by the disc diffusion technique following steps described by the Clinical and Laboratory Standards Institute [27] and detailed in our previous work [5]. Isolates were categorized as ESBL positive if the difference in zone diameters of CTX and CAZ and their combinations with clavulanic acid was >5. DNA extraction was performed on overnight cultures of E coli isolates with Qiagen kits. Purified DNA samples were used as templates in polymerase chain reactions with previously published universal primer sets to detect β-lactamase genes (bla_CTX-M, bla_TEM, bla_SHV, bla_OXA, and bla_ampC) [28, 29] and gene-specific primers for detection of macrolide-conferring resistance genes: erm(B), erm(C), mef(A), and mph(A) [30, 31]. All polymerase chain reaction assays included negative controls and DNA of characterized positive control strains, including whole genome–sequenced antibiotic-resistant strains obtained from the Antibiotic Resistance Isolate Bank (E coli AR 0346, Klebsiella pneumoniae AR 0347, E coli AR 0348, K pneumoniae 0497; Centers for Disease Control and Prevention, Food and Drug Administration) and standard control strains harboring macrolide resistance genes (faculty.washington.edu/marilynr).

Statistical Analysis

Descriptive statistics were used to characterize variations in social, demographic, and vaccination characteristics between the study sites. The prevalence of each AMR gene, as well as genes grouped by 1 or more β-lactamase– or macrolide resistance–conferring genes, was estimated with 95% CIs calculated by the binomial exact method. CTX-M–type ESBLs are currently the most widely distributed and globally dominant ESBL genotypes [32], while mph(A) is the most common genotype for macrolide resistance [30]. Therefore, our outcome variables in correlate analysis, selected a priori, were the presence of CTX-M–type ESBL and mph(A) genes detected in E coli isolates.

We assessed the following potential correlates in regression analyses: (1) child characteristics (sex, age, study site, HIV exposure, nutritional status, breastfeeding); (2) complete age-appropriate vaccination variables (pneumococcal, rotavirus, DPT [diphtheria, pertussis, and tetanus], and bacille Calmette-Guérin vaccination); (3) hospitalization variables (length of hospital stay and antibiotic use during hospital stay); (4) social-economic variables (caregiver-reported income, caregiver education level); and (5) water, sanitation, and hygiene variables (household toilet type, water source and treatment, and household crowding).

Nutritional status was determined with anthropometric z scores constructed per the 2006 World Health Organization growth references for children aged <5 years [33]. We defined stunting as a height-for-age z score <−2 SD, underweight as a weight-for-age z score <−2 SD, and wasting as weight-for-height/length z score <−2 SD. Vaccination status (including date of vaccination) was ascertained from childhood vaccination cards if available at the hospital. If the cards were not available, the caregiver provided a report on the child's vaccination status, including the doses taken and the approximate date. Children were considered fully vaccinated if they received the recommended dosage within 2 weeks of the age specified in the Kenya Vaccine Schedule [34]. In addition, we derived an overall vaccine variable that defined children who had completed all the essential age-appropriate vaccines listed previously (hereinafter, complete age-appropriate vaccination). A household was considered to have access to improved water if the caregiver reported access to reliable piped water in the dwelling or community or if the household primarily used water from a borehole, a protected spring, a well with a pump, bottled water, or rainwater from storage tanks for household chores. Household crowding was defined as >2 individuals sharing a room.

We used a modified Poisson regression model to estimate the relative risk (prevalence ratio) of CTX-M–type ESBL or mph(A) detected in E coli between children with and without risk factors of interest. Univariable regression models were performed for all predictor variables. In the multivariate regression models, each risk factor, including each vaccine variable, was adjusted for a priori–defined confounders (age, sex, and recruiting site). All statistical analyses were performed in Stata 17 (StataCorp).

RESULTS

Of the 1400 children recruited into the parent study, 448 were randomly selected to participate in the AMR phenotypic study and 238 in the AMR genotypic study (Figure 1). Participant characteristics in the parent study and the phenotypic study have been reported elsewhere [5, 25].

Figure 1. — Participant flowchart and reasons for exclusion at each stage. AMR, antimicrobial resistance; *E coli*, *Escherichia coli*.

Population Characteristics

The median age of children in the AMR genetic study was 19 months (IQR, 9–32), and 90 (37.8%) were female. The prevalence of exclusive breastfeeding was higher among children recruited at the Homa Bay site (61.7%) than the Kisii site (31.9%). Similarly, HIV seropositivity was higher among children recruited in the Homa Bay site (25.5%) vs the Kisii site (6.2%). Overall, the median hospital stay was 3 days (IQR, 2–5) in Kisii and 3 days (IQR, 2–5) in the Homa Bay site. Antibiotic use during hospitalization was 84.8%, with marked heterogeneity between sites (Table 1). In addition, geographic variability in antibiotic administration during hospitalization was evident in the prescription of gentamicin and penicillin. Furthermore, we observed marked variations in complete age-appropriate vaccination (51.4% vs 23.4%), which was largely driven by rotavirus vaccination (81.9% vs 69.1%) and measles vaccination (71.5% vs 45.7%) at the Kisii and Homa Bay sites, respectively (Supplementary Figure 1).

Table 1.

Characteristics of Children Enrolled in the Genetic Antimicrobial Resistance Study

	No. (%)^a
	Homa Bay (n = 94)	Kisii (n = 144)	Total (n = 238)
Child characteristics
Age, mo
0–5	8 (9)	19 (13)	27 (11)
6–11	14 (15)	31 (22)	45 (19)
12–23	31 (33)	42 (29)	73 (31)
24–59	41 (44)	52 (36)	93 (39)
Sex
Male	58 (62)	90 (63)	148 (62)
Female	36 (38)	54 (38)	90 (38)
Breastfeeding^b
Exclusively breastfed	58 (62)	46 (32)	104 (44)
Partially breastfed	35 (37)	82 (57)	117 (49)
Unknown	1 (1)	16 (11)	17 (7)
Child HIV status^c
HIV unexposed	69 (74)	130 (94)	199 (86)
HIV positive or exposed	24 (26)	9 (6)	33 (14)
Underweight (WAZ <−2)
WAZ ≥−2	86 (91)	124 (86)	210 (88)
WAZ <−2	8 (9)	20 (14)	28 (12)
Stunting (HAZ/LAZ <−2)
HAZ/LAZ ≥−2	70 (75)	103 (72)	173 (73)
HAZ/LAZ <−2	23 (25)	40 (28)	63 (27)
Acute malnutrition
None	83 (88)	128 (89)	211 (89)
MAM	8 (9)	7 (5)	15 (6)
SAM	3 (3)	9 (6)	12 (5)
Vaccination status
Pneumococcal vaccination^d
No	5 (5)	13 (9)	18 (8)
Yes	89 (95)	131 (91)	220 (92)
Rotavirus vaccination^e
No	29 (31)	26 (18)	55 (23)
Yes	65 (69)	118 (82)	183 (77)
DTP vaccination^f
No	7 (7)	9 (6)	16 (7)
Yes	87 (93)	135 (94)	222 (93)
Measles vaccination^g
No	51 (54)	41 (28)	92 (39)
Yes	43 (46)	103 (72)	146 (61)
Completed all essential vaccines^h
No	72 (77)	70 (49)	142 (60)
Yes	22 (23)	74 (51)	96 (40)
Hospitalization information
Length of hospital stay
≤3 d	50 (53)	75 (53)	125 (53)
>3 d	44 (47)	67 (47)	111 (47)
Antibiotic use during admission
No	25 (27)	11 (8)	36 (15)
Yes	69 (73)	133 (92)	202 (85)
Ceftriaxone use during admission
No	57 (61)	102 (71)	159 (67)
Yes	37 (39)	42 (29)	79 (33)
Gentamicin use during admission
No	65 (69)	47 (33)	112 (47)
Yes	29 (31)	97 (67)	126 (53)
Chloramphenicol use during admission
No	92 (98)	135 (94)	227 (95)
Yes	2 (2)	9 (6)	11 (5)
Penicillin use during admission
No	61 (65)	38 (26)	99 (42)
Yes	33 (35)	106 (74)	139 (58)
Household information
Caregiver-reported income, Kenyan shilling
≥5000	13 (14)	53 (37)	66 (28)
<5000	74 (79)	86 (60)	160 (67)
Unknown or refuse to answer	7 (7)	5 (3)	12 (5)
Crowding (>2 persons per room)
No (≤2)	32 (34)	89 (62)	121 (51)
Yes (>2)	62 (66)	55 (38)	117 (49)
Improved water source
No	23 (24)	18 (13)	41 (17)
Yes	71 (76)	126 (88)	197 (83)
Treated drinking water
No	26 (28)	89 (63)	115 (49)
Yes	67 (72)	53 (37)	120 (51)
Toilet
Private, for household only	30 (33)	80 (56)	110 (47)
Shared with ≥1 household	50 (54)	64 (44)	114 (48)
Open defecation	12 (13)	0 (0)	12 (5)

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Abbreviations: DPT, diphtheria, pertussis, and tetanus; HAZ, height for age; LAZ, length for age; MAM, moderate acute malnutrition; SAM, severe acute malnutrition; WAZ, weight for age.

^aColumn percentages.

^bWhether the child is currently breastfeeding (≤6 months old) or if the caregiver practiced breastfeeding when the child was ≤6 months old.

^cSix children had exposure with an unknown infection status and were excluded.

^dPneumococcal vaccination defined as vaccination completed for the 6-, 10-, and 14-week schedule or up to the age of the child allowing a 2-week margin.

^eRotavirus vaccine defined as vaccination completed for the 6- and 10-week schedule or up to the age of the child allowing a 2-week margin.

^fDPT vaccine defined as vaccination completed for the 6-, 10-, and 14-week schedule or up to the age of the child allowing a 2-week margin.

^gMeasles vaccine defined as vaccination completed for the 9- and 18-month schedule or up to the age of the child allowing a 2-week margin.

^hAll essential vaccination defined as having complete vaccination for pneumococcal, rotavirus, DPT, measles, and bacille Calmette-Guérin.

Distribution of Genetic and Phenotypic AMR Among Study Participants

Overall, 89.9% of children had at least 1 β-lactamase–conferring gene, and 47.1% had at least 1 macrolide resistance–conferring gene. The most common β-lactamase–conferring gene was bla_TEM (87.9%; 95% CI, 83.0%–91.7%), followed by bla_SHV (48.1%; 95% CI, 41.6%–54.7%), bla_CTX-M (38.1%; 95% CI, 31.9%–44.6%), bla_ampC (15.9%; 95% CI, 11.5%–21.2%), and bla_OXA (10.5%; 95% CI, 6.9%–15.1%). The most common macrolide-resistance conferring gene was mph(A) (45.6%; 95% CI, 39.2%–52.2%), followed by erm(B) (1.67%; 95% CI, .46%–4.23%), mef(A) (0.84%; 95% CI, .10%–2.99%), and erm(C) (0%; 95% CI, 0%–1.53%). Detailed site variations in prevalence of β-lactamase genes and macrolide resistance genes are presented in Supplementary Figure 2. The subsequent analyses examine the risk factors of bla_CTX-M as a proxy for ESBL-producing genes and mph(A) as a proxy for macrolide resistance. Among the 214 children with at least 1 ESBL-conferring gene, the presence of phenotypic ESBL was 47.7%, and among those with a macrolide-conferring gene (n = 112), the prevalence of azithromycin resistance was 63.4%. Sixty-one (25.6%) children had cocarriage of bla_CTX-M and mph(A) genes. Further details on phenotypic ESBL among children whose E coli isolates underwent genetic analyses are shown in Supplementary Figures 3 and 4.

Risk Factors for bla_CTX-M Carriage

In the univariable and multivariable analyses, children who had received the age-appropriate pneumococcal vaccine doses in full had >40% lower likelihood of bla_CTX-M detection in their E coli isolates when compared with children who did not (adjusted prevalence ratio [aPR], 0.57; 95% CI, .38–.85; P = .005). However, there were no significant associations between bla_CTX-M in E coli and full age-appropriate rotavirus vaccination (aPR, 0.82; 95% CI, .56–1.21; P = .319), DPT vaccination (aPR, 0.70; 95% CI, .42–1.12; P = .137), or measles vaccination (aPR, 1.06; 95% CI, .73–1.54; P = .746). Antibiotic use during hospital admission was associated with a higher likelihood of harboring bla_CTX-M–producing E coli (aPR, 2.47; 95% CI, 1.12–5.43; P = .025), and this appeared to be driven by children who received ceftriaxone during admission as compared with other antibiotics (aPR, 2.51; 95% CI, 1.79–3.52; P< .001). Administration of gentamicin (aPR, 0.62; 95% CI, .44–.87; P = .006) and penicillin (aPR, 0.63; 95% CI, .45–.88; P = .008) was associated with a lower likelihood of harboring bla_CTX-M–producing E coli when compared with other antibiotics. Relative to children who stayed in the hospital for ≤3 days, longer hospital stays (>3 days) were associated with an increased risk of carrying bla_CTX-M–producing E coli (aPR, 1.42; 95% CI, 1.00–2.01; P = .052). In addition, there was an increased risk of bla_CTX-M–producing E coli among children residing in households that practiced open defecation (aPR, 2.47; 95% CI, 1.40–4.36; P = .002). Site, age, sex, improved water sources, caregiver income, and anthropometric measures were not significantly associated with bla_CTX-M–producing E coli (Table 2).

Table 2.

Risk Factors of CTX-M in Commensal Escherichia coli Isolated From Fecal Samples of the Participating Children

	No. (%)^a		Model 1: Univariable Analysis			Model 2: Multivariable Analysis
	CTX-M+ (n = 91)	CTX-M– (n = 147)	PR	95% CI	P Value	Adjusted PR^b	95% CI	P Value
Location of the facility						…	…	…
Kisii	58 (64)	86 (59)	1 [Ref]
Homa Bay	33 (36)	61 (41)	0.87	.62–1.22	.428	0.89	.63- 1.25	.504
Child characteristics
Age, mo						…	…	…
0–5	13 (14)	14 (10)	1 [Ref]
6–11	18 (20)	27 (18)	0.83	.49–1.41	.494	0.84	.49- 1.43	.516
12–23	28 (31)	45 (31)	0.80	.49–1.30	.362	0.81	.49- 1.33	.410
24–59	32 (35)	61 (41)	0.71	.44–1.16	.172	0.73	.45- 1.18	.202
Sex						…	…	…
Male	55 (60)	93 (63)	1 [Ref]
Female	36 (40)	54 (37)	1.08	.77–1.50	.661	1.08	.78- 1.50	.647
Breastfeeding^c
Exclusively breastfed	39 (43)	65 (44)	1 [Ref]			1 [Ref]
Partially breastfed	46 (51)	71 (48)	1.05	.75–1.47	.782	1.01	.72–1.42	.949
Unknown	6 (7)	11 (7)	0.94	.47–1.88	.864	0.84	.41–1.75	.646
Child HIV status^d
HIV unexposed	80 (89)	119 (84)	1 [Ref]			1 [Ref]
HIV positive or exposed	10 (11)	23 (16)	0.75	.44–1.30	.310	0.79	.45–1.38	.413
Underweight (WAZ <−2)
WAZ ≥−2	77 (85)	133 (90)	1 [Ref]			1 [Ref]
WAZ <−2	14 (15)	14 (10)	1.36	.90–2.06	.140	1.35	.89–2.05	.157
Stunting (HAZ/LAZ <−2)
HAZ/LAZ ≥−2	66 (73)	107 (73)	1 [Ref]			1 [Ref]
HAZ/LAZ <−2	24 (27)	39 (27)	1.00	.69–1.44	.994	1.02	.70–1.49	.904
Acute malnutrition
None	78 (86)	133 (90)	1 [Ref]			1 [Ref]
MAM	7 (8)	8 (5)	1.26	.71–2.23	.423	1.30	.73–2.33	.375
SAM	6 (7)	6 (4)	1.35	.75–2.45	.319	1.30	.70–2.40	.409
Vaccination status
Pneumococcal vaccination^e
No	12 (13)	6 (4)	1 [Ref]			1 [Ref]
Yes	79 (87)	141 (96)	0.54	.37–.78	.001	0.57	.38–.85	.005
Rotavirus vaccination^f
No	23 (25)	32 (22)	1 [Ref]			1 [Ref]
Yes	68 (75)	115 (78)	0.89	.62–1.28	.526	0.82	.56–1.21	.319
DPT vaccination^g
No	9 (10)	7 (5)	1 [Ref]			1 [Ref]
Yes	82 (90)	140 (95)	0.66	.41–1.05	.077	0.70	.43–1.12	.137
Measles vaccination^h
No	32 (35)	60 (41)	1 [Ref]
Yes	59 (65)	87 (59)	1.16	.82–1.64	.391	1.06	.73–1.54	.746
Completed all essential vaccinesⁱ
No	56 (62)	86 (59)	1 [Ref]			1 [Ref]
Yes	35 (38)	61 (41)	0.92	.66–1.29	.645	0.85	.60–1.20	.356
Hospitalization information
Length of hospital stay
≤3 d	39 (44)	86 (59)	1 [Ref]			1 [Ref]
>3 d	50 (56)	61 (41)	1.44	1.04–2.01	.030	1.42	1.00–2.01	.052
Any antibiotic use during admission
No	6 (7)	30 (20)	1 [Ref]			1 [Ref]
Yes	85 (93)	117 (80)	2.52	1.19–5.34	.015	2.47	1.12–5.43	.025
Ceftriaxone use during admission^j
No	34 (40)	89 (76)	1 [Ref]			1 [Ref]
Yes	51 (60)	28 (24)	2.34	1.68–3.25	<.001	2.51	1.79–3.52	<.001
Gentamicin use during admission^j
No	41 (48)	35 (30)	1 [Ref]			1 [Ref]
Yes	44 (52)	82 (70)	0.65	.47–.89	.007	0.62	.44–.87	.001
Chloramphenicol use during admission^j
No	83 (98)	108 (92)	1 [Ref]			1 [Ref]
Yes	2 (2)	9 (8)	0.42	.12–1.49	.178	0.44	.12–1.58	.207
Penicillin use during admission^j
No	35 (41)	28 (25)	1 [Ref]			1 [Ref]
Yes	50 (59)	89 (75)	0.65	.47–.89	.007	0.63	.45–.88	.008
Household information
Caregiver-reported income, Kenyan shilling
≥5000	30 (33)	36 (24)	1 [Ref]			1 [Ref]
<5000	58 (64)	102 (69)	0.80	.57–1.12	.186	0.78	.55–1.10	.156
Unknown or refuse to answer	3 (3)	9 (6)	0.55	.20–1.52	.249	0.53	.18–1.53	.242
Crowding (>2 persons per room)
No (≤2)	46 (51)	75 (51)	1 [Ref]			1 [Ref]
Yes (>2)	45 (49)	72 (49)	1.01	.73–1.40	.944	1.05	.75–1.48	.762
Improved water source
No	12 (13)	29 (20)	1 [Ref]			1 [Ref]
Yes	79 (87)	118 (80)	1.37	.83–2.27	.223	1.33	.79–2.24	.279
Treated drinking water
No	46 (51)	69 (48)	1 [Ref]			1 [Ref]
Yes	44 (49)	76 (52)	0.92	.66–1.27	.600	0.97	.68–1.37	.854
Toilet
Private, for household only	37 (41)	73 (50)	1 [Ref]			1 [Ref]
Shared with ≥1 household	46 (51)	68 (47)	1.20	.85–1.69	.302	1.22	.86–1.74	.267
Open defecation	8 (9)	4 (3)	1.98	1.23–3.20	.005	2.47	1.40–4.36	.002

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Model 1 presents results from the univariable Poisson regression model with robust SE, and model 2 presents results of an adjusted multivariable Poisson regression model (adjusted for age, sex, and site) with robust SE. CTX-M+ and CTX-M– denote the presence and absence of the CTX-M gene in the isolated E coli from fecal samples.

Abbreviations: DPT, diphtheria, pertussis, and tetanus; HAZ, height for age; LAZ, length for age; MAM, moderate acute malnutrition; PR, prevalence ratio; Ref, reference; SAM, severe acute malnutrition; WAZ, weight for age.

^aColumn percentages.

^bAdjusted for sex, age, and site. Models including one of the three confounders were adjusted for the other two.

^cWhether the child is currently breastfeeding (≤6 months old) or if the mother practiced breastfeeding when the child was ≤6 months old.

^dSix children had exposure with an unknown infection status and were excluded.

^ePneumococcal vaccination defined as vaccination completed for the 6-, 10-, and 14-week schedule or up to the age of the child allowing a 2-week margin.

^fRotavirus vaccine defined as vaccination completed for the 6- and 10-week schedule or up to the age of the child allowing a 2-week margin.

^gDPT vaccine defined as vaccination completed for the 6-, 10-, and 14-week schedule or up to the age of the child allowing a 2-week margin.

^hMeasles vaccine defined as vaccination completed for the 9- and 18-month schedule or up to the age of the child allowing a 2-week margin.

ⁱAll essential vaccination defined as having complete vaccination for pneumococcal, rotavirus, DPT, measles, and bacille Calmette-Guérin.

^jAmong children who received at least 1 antibiotic during hospitalization (antibiotic of interest vs other antibiotics).

Risk Factors for mph(A) Carriage

Children who had completed all age-appropriate essential vaccination had a 32% decrease in risk of harboring mph(A)–positive E coli (aPR, 0.68; 95% CI, .49–.93; P = .017). In particular, full age-appropriate measles vaccination was associated with a 30% decrease in mph(A)–producing E coli (aPR, 0.70; 95% CI, .52–.95; P = .022). However, there were no associations between detection of mph(A)–producing E coli and pneumococcal, rotavirus, or DPT vaccination (Table 3).

Table 3.

Risk Factors of the mph(A) Gene in Commensal Escherichia coli Isolated From Fecal Samples of the Participating Children

	No. (%)^a		Model 1: Univariable Analysis			Model 2: Multivariable Analysis
	mph(A)+ (n = 109)	mph(A)– (n = 129)	PR	95% CI	P Value	Adjusted PR^b	95% CI	P Value
Location of the facility						…	…	…
Kisii	64 (59)	80 (62)	1 [Ref]
Homa Bay	45 (41)	49 (38)	1.08	.81–1.42	.602	1.09	.82- 1.44	.560
Child characteristics
Age, mo						…	…	…
0–5	14 (13)	13 (10)	1 [Ref]
6–11	21 (19)	24 (19)	0.90	.56–1.45	.667	0.92	.57- 1.48	.719
12–23	28 (26)	45 (35)	0.74	.46–1.18	.205	0.74	.46- 1.18	.207
24–59	46 (42)	47 (36)	0.95	.63–1.45	.825	0.94	.62- 1.43	.777
Sex						…	…	…
Male	61 (56)	87 (67)	1 [Ref]
Female	48 (44)	42 (33)	1.29	.98–1.70	.065	1.28	.98- 1.69	.075
Breastfeeding^c
Exclusively breastfed	53 (49)	51 (40)	1 [Ref]			1 [Ref]
Partially breastfed	49 (45)	68 (53)	0.82	.62–1.09	.178	0.82	.61–1.10	.177
Unknown	7 (6)	10 (8)	0.81	.44–1.47	.486	0.82	.44–1.54	.540
Child HIV status^d
HIV unexposed	95 (88)	104 (84)	1 [Ref]			1 [Ref]
HIV positive or exposed	13 (12)	20 (16)	0.83	.53–1.29	.401	0.80	.51–1.26	.343
Underweight (WAZ <−2)
WAZ ≥−2	99 (91)	111 (86)	1 [Ref]			1 [Ref]
WAZ <−2	10 (9)	18 (14)	0.76	.45–1.27	.294	0.82	.48–1.38	.451
Stunting (HAZ/LAZ <−2)
HAZ/LAZ ≥−2	79 (73)	94 (73)	1 [Ref]			1 [Ref]
HAZ/LAZ <−2	29 (27)	34 (27)	1.01	.74–1.38	.960	1.08	.79–1.48	.622
Acute malnutrition
None	96 (88)	115 (89)	1 [Ref]			1 [Ref]
MAM	8 (7)	7 (5)	1.17	.71–1.93	.531	1.16	.70–1.90	.569
SAM	5 (5)	7 (5)	0.92	.46–1.82	.802	0.89	.44–1.82	.753
Vaccination status
Pneumococcal vaccination^e
No	8 (7)	10 (8)	1 [Ref]			1 [Ref]
Yes	101 (93)	119 (92)	1.03	.60–1.77	.906	1.03	.59–1.78	.926
Rotavirus vaccination^f
No	25 (23)	30 (23)	1 [Ref]			1 [Ref]
Yes	84 (77)	99 (77)	1.01	.73–1.40	.954	1.06	.75–1.50	.725
DPT vaccination^g
No	6 (6)	10 (8)	1 [Ref]			1 [Ref]
Yes	103 (94)	119 (92)	1.24	.65–2.37	.521	1.30	.69–2.47	.421
Measles vaccination^h
No	50 (46)	42 (33)	1 [Ref]
Yes	59 (54)	87 (67)	0.74	.57–.98	.033	0.70	.52–.95	.022
Completed all essential vaccinesⁱ
No	74 (68)	68 (53)	1 [Ref]			1 [Ref]
Yes	35 (32)	61 (47)	0.70	.51–.95	.023	0.68	.49–.93	.017
Hospitalization information
Length of hospital stay
≤3 d	47 (44)	78 (60)	1 [Ref]			1 [Ref]
>3 d	60 (56)	51 (40)	1.44	1.08–1.91	.012	1.47	1.10–1.98	.009
Antibiotic use during admission
No	10 (9)	26 (20)	1 [Ref]			1 [Ref]
Yes	99 (91)	103 (80)	1.76	1.02–3.05	.042	1.83	1.06–3.18	.031
Ceftriaxone use during admission^j
No	59 (60)	64 (62)	1 [Ref]			1 [Ref]
Yes	40 (40)	39 (38)	1.06	.79–1.40	.711	1.00	.74–1.34	.981
Gentamicin use during admission^j
No	33 (33)	43(42)	1 [Ref]			1 [Ref]
Yes	66 (67)	60 (58)	1.21	.89–1.64	.231	1.33	.97–1.83	.073
Chloramphenicol use during admission^j
No	94 (95)	97 (94)	1 [Ref]			1 [Ref]
Yes	5 (5)	6 (6)	0.92	.48–1.80	.815	1.04	.53–2.01	.916
Penicillin use during admission^j
No	27 (27)	36 (35)	1 [Ref]			1 [Ref]
Yes	72 (73)	67 (65)	1.21	.87–1.68	.257	1.36	.97–1.89	.071
Household characteristics
Caregiver-reported monthly income, Kenyan shilling
≥5000	33 (30)	33 (26)	1 [Ref]			1 [Ref]
<5000	73 (67)	87 (67)	0.91	.68–1.23	.543	0.89	.65–1.20	.438
Unknown or refuse to answer	3 (3)	9 (7)	0.50	.18–1.37	.179	0.42	.15–1.22	.110
Crowding (>2 persons per room)
No (≤2)	53 (49)	68 (53)	1 [Ref]			1 [Ref]
Yes (>2)	56 (51)	61 (47)	1.09	.83–1.44	.531	1.05	.79–1.41	.717
Improved water source
No	19 (17)	22 (17)	1 [Ref]			1 [Ref]
Yes	90 (83)	107 (83)	0.99	.69–1.42	.939	1.04	.72–1.49	.851
Treated drinking water
No	49 (46)	66 (52)	1 [Ref]			1 [Ref]
Yes	58 (54)	62 (48)	1.13	0.86–1.50	.381	1.12	.83–1.51	.456
Toilet
Private, for household only	46 (43)	64 (50)	1 [Ref]			1 [Ref]
Shared with ≥1 households	53 (50)	61 (47)	1.11	0.83–1.50	.483	1.15	.85–1.56	.368
Open defecation	8 (7)	4 (3)	1.59	1.01–2.52	.046	1.58	.95–2.62	.076

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Model 1 presents results from the univariable Poisson regression model with robust SE, and model 2 presents results of an adjusted multivariable Poisson regression model (adjusted for age, sex, and site) with robust SE. mph(A)+ and mph(A)– denote the presence and absence of the mph(A) gene in the isolated E coli from fecal samples.