Abstract
Background
Colonization with antibiotic-resistant bacteria (ARB) increases the risk for antibiotic-resistant infections. Our previous study documented a high level of colonization with ARBs in a suburban area of Chennai, a city in South India. Here, we aimed to identify potential risk factors for colonization with extended-spectrum cephalosporin-resistant Enterobacterales (ESCrE) and carbapenem-resistant Enterobacterales (CRE).
Methods
Adult participants were enrolled in community and hospital surveys from November 2020 through March 2022, and ARB isolation was done from stool specimens. Multivariable logistic regression was used to calculate adjusted odds ratios (aOR) with 95% confidence intervals (CI).
Results
In the community, the adjusted odds of ESCrE colonization were lower among those reporting consumption of animal products (1–2 days a week, aOR: 0.51; 95% CI 0.27–0.96; > 2 days a week, aOR: 0.48; 95% CI 0.25–0.94). Among hospitalized patients, the adjusted odds of CRE colonization was higher among those with prior antibiotic use (aOR: 1.50; 95% CI 0.97–2.32) and who underwent invasive procedures (aOR: 1.80; 95% CI 0.95–3.39). No significant associations were identified for other risk factors among community or hospital participants and colonization outcomes.
Conclusions
Drivers of ARB colonization may be difficult to ascertain in high-burden settings. However, healthcare-based strategies to reduce ARB transmission should continue to emphasize infection prevention and control as well as antibiotic stewardship. Additional studies are needed to understand community factors contributing to ARB colonization.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13756-026-01747-2.
Keywords: Antibiotic-resistant bacteria, Extended-spectrum cephalosporin-resistant Enterobacterales, Carbapenem-resistant Enterobacterales, Colonization, Risk
Introduction
Antibiotic-resistant bacteria (ARB) are a global public health concern [1]. Extended-spectrum cephalosporin-resistant Enterobacterales (ESCrE) and carbapenem-resistant Enterobacterales (CRE) are ARB that are classified by the World Health Organization as critical priority organisms because of their limited treatment options, impact on mortality and other health outcomes, associated economic costs, and risk of transmission [2–4]. Although ESCrE and CRE have typically been associated with healthcare settings, their prevalence in the community is also increasing [5–8]. This is particularly true for low- and middle-income countries (LMIC) [9, 10]. Prior publications have suggested the emergence of colonization with ARB, such as ESCrE and CRE, in the community may be influenced by factors such as antibiotic usage, healthcare contact, animal contact, contaminated food consumption, or poor sanitation [11–15], whereas in the hospital, invasive procedures, antibiotic use, and underlying health conditions have been associated with ARB colonization [16–19].
Understanding the risk factors for ARB colonization is important from both a population and healthcare systems perspective [20]. Colonization can lead to onward transmission of ARB to others and increase the risk of subsequent infections [21]. Identifying risk factors for colonization provides an opportunity to identify the origins and transmission pathways of organisms and develop targeted interventions to contain ARB spread.
The Antibiotic Resistance in Communities and Hospitals (ARCH) studies, conducted in six LMIC settings, involved cross-sectional surveys designed to assess the prevalence within the population and identify risk factors associated with ARB colonization [21]. In our previous work we evaluated the prevalence of colonization with ESCrE, CRE, and colistin-resistant Enterobacterales [7]. However, due to the low prevalence of colistin resistance in this population, it was not included in the current assessment. The present study aimed to determine the risk factors associated with ESCrE and CRE colonization in the community and hospitals within a suburban area of south India [7].
Methods
Study setting and participants
We conducted community and hospital surveys in a semi-urban region of Chennai, Tamil Nadu, India, from November 2020 through March 2022. We enrolled community participants aged 18 years or older without fever, diarrhea, or cough at the time of both the interview and stool collection, and who had resided in the household for at least four weeks prior to the study staff’s visit. We enrolled hospitalized adults admitted to the two study hospitals, aged 18 years or older, without diarrhea, gastrointestinal bleeding, or severe neutropenia (absolute neutrophil count < 500 cells/µL). Hospital participants were different from the community participants. We excluded patients in Coronavirus disease 2019 (COVID-19) wards due to restricted access and to ensure the safety of the study staff.
Sampling design
We selected community households through simple random sampling from a previously enumerated Demographic and Health Surveillance (DHS) cohort. From each selected household, we randomly selected one eligible adult for enrollment. In hospitals, we conducted daily surveillance and considered all patients newly admitted to eligible (i.e., non–COVID-19) wards who had not been approached on the previous day for enrollment. We have described the sampling design and sample size in detail elsewhere [7].
Data collection
The field staff conducted pilot tests of questionnaires in community and hospital settings to ensure that the questions and translations were easily understood and accurate. Field staff used the pilot-tested structured questionnaire in Tamil (the local language) to interview community participants after obtaining written informed consent. The questionnaire covered demographics, water sources, healthcare history in the past six months, antibiotic use in the past three months, as well as animal contact and food consumption based in the past week. We also extracted information from hospital inpatients medical records, including demographics, hospital and medical history, invasive procedures, medical devices used during hospitalization, duration of hospital stay, and antibiotic exposure within 14 days prior to the survey.
Laboratory procedures
Community and hospital participants self-collected stool specimens. For hospitalized participants who could not self-collect, we obtained specimens from sterile bedpans that were immediately transferred into sterile, leak-proof containers to minimize environmental contamination. We transported all samples at 4 °C and processed them on the same day. Using ESwabs (COPAN Diagnostics, Inc., CA), we inoculated stool samples onto CHROMagar ESBL and mSuperCARBA agar plates to screen for ESCrE and CRE. We identified isolates and performed antimicrobial susceptibility testing (AST) using the Vitek 2 Compact system (bioMérieux) with Gram-negative ID and AST N281 cards. As ceftazidime was the only third-generation cephalosporin included on the AST N281 card, all isolates that grew on CHROMagar ESBL and tested susceptible to ceftazidime underwent ceftriaxone disk diffusion to detect CTX-M–type ESBL producers. Colistin-resistant and selected colistin-intermediate isolates were retested by broth microdilution (BMD), as previously described [7].
Data analysis
We included data from participants who consented to an interview and stool collection in this analysis. We categorized participants as colonized with ESCrE (a phenotype suggestive of extended-spectrum beta-lactamase production) if any Enterobacterales isolated from their fecal samples were resistant to ceftazidime or ceftriaxone, and susceptible or intermediate to all tested carbapenems (doripenem, meropenem, and imipenem). We categorized participants as colonized with CRE if any Enterobacterales isolated from their fecal samples were resistant to one or more carbapenems [21]. We applied antibiotic susceptibility criteria from the 2022 Clinical Laboratory and Standards Institute (CLSI) [22].
We calculated frequencies and proportions for all the factors among the resistant and non-resistant groups. The relationship between potential risk factors and ARB colonization was conceptualized using directed acyclic graphs (DAGs) (Supplementary Figs. 1 & 2). We created DAGs using DAGitty v3.1 software, based on literature review, biological plausibility, and contextual understanding among the study team [23, 24]. Using univariate logistic regression, we calculated unadjusted odds ratios (OR) with 95% confidence intervals (CI) for all variables. We constructed DAGs for all exposures and outcomes. For each exposure, we identified potential confounders using DAGs. We then used multivariable logistic regression models to separately adjust for the identified potential confounders for each exposure and presented their adjusted odds ratio (aOR). Crude OR was reported for exposures without identified confounders. We performed all analyses using Stata version 17 (StataCorp LLC, College Station, TX, USA).
Results
We analysed data from 756 community participants and 556 hospital inpatients. Among the 756 community participants, 540 (71.4%, 95% CI 68.1–74.6) were colonized with ESCrE, and 114 (15.1%, 95% CI 12.7%–17.8%) were colonized with CRE. Out of 556 hospital inpatients, 455 (81.8%, 95% CI 78.4–84.8) were colonized with ESCrE, and 126 (22.7%, 95% CI 19.4–26.3) with CRE [7].
Factors associated with ESCrE and CRE colonization in the community
Participants consuming animal products 1–2 days a week (aOR: 0.51; 95% CI 0.27 –0.96) and > 2 days a week (aOR: 0.48; 95% CI 0.25–0.94) had lower adjusted odds of ESCrE colonization (Table 1). None of the odds ratios for other hypothesized risk factors were found to have a definitive association with ESCrE or CRE colonization in the community.
Table 1.
Factors associated with ESCrE and CRE colonization in the community, Chennai, India, 2020–2022
| Characteristics | ESCrE | CRE | ||||||
|---|---|---|---|---|---|---|---|---|
| Resistant (N = 540) n (%) | Non-resistant (N = 216) n (%) | Unadjusted odds ratio (95% CI) | Adjusted odds ratio (95% CI) | Resistant (N = 114) n (%) | Non-resistant (N = 642) n (%) | Unadjusted odds ratio (95% CI) | Adjusted odds ratio (95% CI) | |
| Individual characteristics | ||||||||
| Male | 229 (42.4) | 97 (44.9) | 0.90 (0.66–1.24) | 53 (46.5) | 273 (42.5) | 1.17 (0.79–1.75) | ||
| Age | ||||||||
| 18–24 | 29 (5.4) | 15 (6.9) | 0.86 (0.40–1.85) | 4 (3.5) | 40 (6.2) | 0.88 (0.25–3.03) | ||
| 25–49 | 306 (56.7) | 123 (56.9) | 1.10 (0.67–1.81) | 72 (63.2) | 357 (55.6) | 1.77 (0.85–3.69) | ||
| 50–64 | 144 (26.7) | 51 (23.6) | 1.25 (0.72–2.18) | 29 (25.4) | 166 (25.9) | 1.53 (0.69–3.39) | ||
| Participant growing or tending crops | 80 (14.8) | 30 (13.9) | 1.08 (0.69–1.70) | 18 (15.8) | 92 (14.3) | 1.12 (0.65–1.94) | ||
| Participant growing or tending livestock or poultry at home / farm | 8 (1.5) | 2 (0.9) | 1.61 (0.34–7.64) | |||||
| Participant preparing fish, chicken, or other meat for eating | 355 (65.7) | 156 (72.2) | 0.74 (0.52–1.04) | 0.76 (0.54–1.08)a | 78 (68.4) | 433 (67.4) | 1.05 (0.68–1.60) | 1.12 (0.72–1.73)a |
| Participant selling fish, chicken, or other meat products | 19 (3.5) | 6 (2.8) | 1.28 (0.50–3.24) | 1.28 (0.50–3.24)b | 1 (0.9) | 24 (3.7) | 0.23 (0.03–1.70) | 0.23 (0.03–1.74)b |
| Participant handling, treating or disposing waste other than from own household | 144 (26.7) | 66 (30.6) | 0.83 (0.58–1.17) | 36 (31.6) | 174 (27.1) | 1.24 (0.81–1.91) | ||
| Participant main source of drinking water | ||||||||
| Improved source | 185 (34.3) | 81 (37.5) | 0.87 (0.63–1.21) | 44 (38.6) | 222 (34.6) | 1.19 (0.79–1.79) | ||
| Participant main source of water for purpose other than drinking | ||||||||
| Improved source | 510 (94.4) | 198 (91.7) | 1.55 (0.84–2.84) | 108 (94.7) | 600 (93.5) | 1.26 (0.52–3.04) | ||
| Participant rearing chicken within household premises | 7 (1.3) | 2 (0.9) | 1.41 (0.29–6.82) | 3 (2.6) | 6 (0.9) | 2.86 (0.71–11.62) | ||
| Consumption of animal products | ||||||||
| 1–2 days | 322 (59.6) | 134 (62) | 0.51 (0.27–0.96) | 0.51 (0.27–0.96)c | 81 (71.1) | 375 (58.4) | 1.24 (0.62–2.45) | 1.24 (0.63–2.46)c |
| More than 2 days | 157 (29.1) | 69 (31.9) | 0.48 (0.25–0.94) | 0.48 (0.25–0.94)c | 22 (19.3) | 204 (31.8) | 0.62 (0.28–1.34) | 0.62 (0.29–1.36)c |
| Consumption of dairy products | ||||||||
| 1–2 days | 82 (15.2) | 34 (15.7) | 1.21 (0.21–6.90) | 1.33 (0.23–7.69)d | ||||
| More than 2 days | 454 (84.1) | 180 (83.3) | 1.26 (0.23–6.95) | 1.49 (0.27–8.38)d | ||||
| Consumption of Veg/fruit/herb | ||||||||
| 1–2 days | 50 (9.3) | 28 (13) | 1.79 (0.34–9.45) | |||||
| More than 2 days | 487 (90.2) | 185 (85.6) | 2.63 (0.53–13.16) | |||||
| Household characteristics | ||||||||
| Household members growing or tending livestock or poultry at home/farm | 10 (1.9) | 4 (1.9) | 1.00 (0.31–3.22) | 4 (3.5) | 10 (1.6) | 2.30 (0.71–7.46) | ||
| Household members visiting hospital for medical care as in-patient or out-patient for the past six months | 26 (4.8) | 5 (2.3) | 2.13 (0.81–5.63) | 3 (2.6) | 28 (4.4) | 0.59 (0.18–1.98) | ||
| Household members visiting clinic for medical care for the past six months | 12 (2.2) | 5 (2.3) | 0.96 (0.33–2.76) | 4 (3.5) | 13 (2) | 1.76 (0.56–5.49) | ||
| Household members visiting hospital for reasons other than medical care for the past six months | 11 (2) | 1 (0.5) | 4.47 (0.57–34.84) | 2 (1.8) | 10 (1.6) | 1.13 (0.24–5.22) | ||
| Household members visiting clinic for reasons other than medical care for the past six months | 6 (1.1) | 1 (0.5) | 2.42 (0.29–20.18) | 2 (1.8) | 5 (0.8) | 2.28 (0.44–11.87) | ||
| Routine treatment of drinking water in the household | 150 (27.8) | 63 (29.2) | 0.93 (0.66–1.32) | 29 (25.4) | 184 (28.7) | 0.85 (0.54–1.34) | ||
| Storage of drinking water at home | ||||||||
| Narrow mouth with cover or tap | 404 (74.8) | 163 (75.5) | 0.98 (0.68–1.43) | 90 (78.9) | 477 (74.3) | 1.47 (0.88–2.47) | ||
| Others | 10 (1.9) | 3 (1.4) | 1.32 (0.35–5.01) | 4 (3.5) | 9 (1.4) | 3.47 (0.98–12.30) | ||
| Purchase of animal product from market or roadside vendors for household food source | 538 (99.6) | 215 (99.5) | 1.25 (0.11–13.87) | 1.14 (0.10–12.70)e | ||||
| Purchase of dairy product from market or roadside vendors for household food source | 531 (98.3) | 213 (98.6) | 0.83 (0.22–3.10) | 0.77 (0.19–3.04)f | 113 (99.1) | 631 (98.3) | 1.97 (0.25–15.41) | |
ESCrE, Extended-Spectrum Cephalosporin-Resistant Enterobacterales; CRE, Carbapenem-Resistant Enterobacterales
*Improved source: Piped water into house; Piped water into courtyard/flat; Tube well/bore well; Protected dug well; RO own; Public tap; Protected Spring and Rain water collection
Adjusted for the following confounders:
aConsumption of animal products
bHousehold members growing or tending livestock or poultry at home/farm
cHousehold members growing or tending livestock or poultry at home/farm, Participant rearing chicken within household premises
dConsumption of animal products, Participant selling fish, chicken, or other meat products, Purchase of animal product from market or roadside vendors for household food source
eConsumption of animal products, dairy products, Participant selling fish, chicken, or other meat products, Household members growing or tending livestock or poultry at home/farm
fConsumption of dairy products
Factors associated with ESCrE and CRE colonization in the hospitals
Antibiotic consumption in the prior 14 days (aOR: 1.50; 95% CI 0.97–2.32) and patients who underwent invasive procedures (aOR: 1.80; 95% CI 0.95–3.39) had a higher adjusted odd of CRE colonization (Table 2). None of the other factors were found to have a definitive association with ESCrE or CRE colonization in hospitalized patients after adjusting for known confounders.
Table 2.
Factors associated with ESCrE and CRE colonization in the hospital, Chennai, India, 2020–2022
| Characteristics | ESCrE | CRE | ||||||
|---|---|---|---|---|---|---|---|---|
| Resistant (N = 455) n(%) | Non-resistant (N = 101) n(%) | Unadjusted odds ratio (95% CI) | Adjusted odds ratio (95% CI) | Resistant (N = 126) n(%) | Non-resistant (N = 430) n(%) | Unadjusted odds ratio (95% CI) | Adjusted odds ratio (95% CI) | |
| Type of hospital ward | ||||||||
| ICU | 41 (9) | 11 (10.9) | 0.94 (0.45–1.93) | 0.95 (0.45–2.02)a | 15 (11.9) | 37 (8.6) | 1.40 (0.72–2.70) | 1.59 (0.79–3.19)a |
| Obstetrics/ Gyn | 125 (27.5) | 19 (18.8) | 1.65 (0.94–2.90) | 1.63 (0.89–2.97)a | 31 (24.6) | 113 (26.3) | 0.95 (0.58–1.53) | 0.83 (0.50–1.40)a |
| Surgical/ Ortho | 58 (12.7) | 13 (12.9) | 1.12 (0.58–2.18) | 1.11 (0.55–2.21)a | 15 (11.9) | 56 (13) | 0.92 (0.49–1.74) | 0.83 (0.43–1.60)a |
| Age category (years) | ||||||||
| 18–24 | 51 (11.2) | 6 (5.9) | 1.86 (0.68–5.09) | 13 (10.3) | 44 (10.2) | 1.09 (0.49–2.42) | ||
| 25–49 | 206 (45.3) | 52 (51.5) | 0.87 (0.47–1.62) | 63 (50) | 195 (45.3) | 1.19 (0.67–2.13) | ||
| 50–64 | 125 (27.5) | 27 (26.7) | 1.01 (0.51–2.01) | 31 (24.6) | 121 (28.1) | 0.94 (0.50–1.79) | ||
| Male | 199 (43.7) | 47 (46.5) | 0.89 (0.58–1.38) | 59 (46.8) | 187 (43.5) | 1.14 (0.77–1.70) | ||
| Any underlying medical conditions# | 153 (33.6) | 39 (38.6) | 0.81 (0.52–1.26) | 41 (32.5) | 151 (35.1) | 0.89 (0.58–1.36) | ||
| Invasive Procedure* | 41 (9) | 10 (9.9) | 0.90 (0.44–1.87) | 0.83 (0.39–1.75)b | 17 (13.5) | 34 (7.9) | 1.82 (0.98–3.38) | 1.80 (0.95–3.39)b |
| Urinary Catheter | 23 (5.1) | 6 (5.9) | 0.84 (0.33–2.13) | 8 (6.3) | 21 (4.9) | 1.32 (0.57–3.06) | ||
| Antibiotic consumption 14 days before the survey | 203 (44.6) | 38 (37.6) | 1.34 (0.86–2.08) | 1.39 (0.85–2.27)c | 66 (52.4) | 175 (40.7) | 1.60 (1.08–2.39) | 1.50 (0.97–2.32)c |
| Third generation cephalosporin | 122 (26.8) | 19 (18.8) | 1.58 (0.92–2.71) | 1.54 (0.90–2.66)c | 31 (24.6) | 110 (25.6) | 0.95 (0.60–1.50) | 0.98 (0.61–1.56)d |
| Duration of stay ≥ 2 days at the time of sample collection | 399 (87.7) | 83 (82.2) | 1.55 (0.86–2.76) | 1.45 (0.79–2.67)e | 107 (84.9) | 375 (87.2) | 0.83 (0.47–1.45) | 0.85 (0.47–1.53)e |
*Appendicectomy (1), Arthroscopy (1), Emergency DNC (1), Hemorrhoidectomy (1), Hysterectomy (11), LSCS (31), ORIF surgery (1), Sphincterotomy (1), TURP(1), Umbilical plasty (1), Vesicolithotomy (1)
#Cardiovascular disease, Diabetes mellitus, Hypertension, Respiratory diseases, anemia, liver disease, malignancy, chronic kidney disease, pulmonary edema, sepsis, epilepsy, hyperthyroidism, and hernia
Abbreviations: ESCrE, Extended-Spectrum Cephalosporin-Resistant Enterobacterales; CRE, Carbapenem-Resistant Enterobacterales; ICU, Intensive care unit
Adjusted for the following confounders:
aAny underlying medical conditions
bAny underlying medical conditions
cAny underlying medical conditions, Invasive Procedure, Urinary catheter
dAny underlying medical conditions, Invasive Procedure, Urinary catheter
eAny underlying medical conditions, type of hospital wards
A sensitivity analysis including only participants hospitalized for at least two days showed that prior antibiotic consumption (aOR: 2.33; 95% CI 1.14–4.78) and exposure to third generation cephalosporins (aOR: 2.37; 95% CI 1.08–5.17) were associated with ESCrE colonization (Supplementary Table 1).
Discussion
Consumption of animal products was negatively associated with ESCrE colonization in the community, while prior use of antibiotics and undergoing invasive procedures indicated a positive association with CRE colonization in hospital settings. Notably, none of the other potential risk factors were found to be significantly associated with ARB colonization in community and hospital settings.
The observed negative association between consumption of animal products and ESCrE colonization should be interpreted carefully. Dietary patterns can influence the composition and diversity of the gut microbiome, which plays an important role in colonization resistance against pathogenic and antimicrobial-resistant organisms [25]. Consumption of animal products may also act as a proxy for other socioeconomic or lifestyle factors, such as dietary diversity, nutritional status, hygiene practices, or healthcare access, which may influence exposure to ARB. Observational studies assessing dietary exposures are susceptible to residual confounding, and therefore the observed association may reflect complex interactions between host, environmental, and behavioural determinants rather than a direct protective effect. Further studies investigating these links may provide additional insights into underlying mechanisms that would explain the observed relationship.
Despite the observed protective association, evidence linking antimicrobial use in livestock to antimicrobial resistance remains important to consider. Antimicrobial use in livestock has experienced the greatest growth in Asia, with India as one of the top five consumers of antibiotics used in livestock. Increasing use of antimicrobials in livestock promotes the selection of resistant bacteria with the potential for transmission to humans through multiple pathways, including direct contact with animals or animal products, and environmental exposure [26]. Studies from other ARCH sites have reported animal contact as being linked with ESCrE colonization in the community [15, 27]. These findings underscore the need for continued investigations of factors across human, animal and environmental interfaces contributing to ARB. Recent studies have demonstrated shared resistance genes between humans and animals, reinforcing the need for coordinated cross-sectional interventions. Strengthening integrated surveillance systems and antimicrobial stewardship across sectors remains essential in India where antibiotic use continues to be widespread in human, animal, and environmental settings [28, 29].
CRE colonization among hospitalized patients in this study was observed to be linked to antibiotic exposure and invasive procedures, both of which have been previously identified as risk factors for ARB colonization [17, 18]. The likely mechanism behind this linkage is that both antibiotics and acute illness have been shown to disrupt the gut microbiome, leading to microbiome imbalance and increased susceptibility to colonization by resistant organisms [30–32]. Additionally, undergoing invasive procedures may provide a pathway for nosocomial transmission of ARB via medical devices, which can become contaminated and serve as reservoirs for resistant organisms.
In LMIC settings, where routine isolation and gut decolonization are often not feasible and access to diagnostics is limited, routine screening of individual patients to guide clinical management may have limited utility. Instead, periodic surveys can provide valuable insights into the burden of ARB colonization. Such surveillance can complement clinical culture data, support antimicrobial stewardship efforts, and inform local treatment guidelines in resource-limited settings [33, 34]
Colonization may occur through multiple pathways, diluting the identification of discrete risk factors owing to potentially ubiquitous exposure. Assessing colonization risk factors at the individual level can be problematic because colonization is not an independent event. It can be influenced by the colonization status of and pressure from household members, the community, or the hospital environment. Contact with other individuals can lead to ARB colonization creating non-linear infection dynamics that can weaken individual-level exposure-outcome relationships [35]. Future studies should seek to understand how colonization pressure within a given community may influence colonization outcomes compared with individual-level exposures. Alternatively, selective pressures may not be needed to drive ARB colonization as bacteria may acquire and harbor resistance genes without a fitness cost. This is supported by studies showing the in-vitro selective advantages of ESCrE over susceptible strains in the absence of antibiotic exposure, attributed to more diverse carbohydrate metabolism conferred by resistance genes [36]. Additionally, the lack of microbiome disruption found in ESCrE-colonized individuals may indicate an absence of selective pressures required for ARB colonization [37].
Our study has several limitations. This study was conducted during the COVID-19 pandemic, when movement restrictions and physical distancing measures could have impacted colonization prevalence in either direction. In Botswana, for example, ARB colonization prevalence estimates showed a decline following a two-month lockdown [38], while in Guatemala, no substantial difference was observed before versus during the COVID-19 pandemic [39]. In contrast, other studies showed increased ARB during the COVID-19 pandemic [40, 41]. Additionally, the pandemic may have altered antibiotic access and use patterns [42] by the study population. Further, not all hospital wards were accessible for the study, as most of the wards were converted into COVID-19 wards, which were excluded from the study. This may have led to incomplete data (ward-specific). We have earlier reported a high refusal rate in our study [7]. This could have introduced selection bias, as households or inpatients who agreed to participate might have systematically differed from those who declined, potentially resulting in an incomplete and biased representation of relevant risk factors. Further, the sample size may have been insufficient to detect associations, as this study was initially powered to assess colonization prevalence rather than risk factors. Relatedly, the high prevalence of colonization in the community may have limited the ability to identify hospital-specific colonization patterns that could be associated with healthcare exposures. Finally, given the cross-sectional design of the study, it was not possible to clearly infer whether colonization identified among the hospitalized individuals was community- or hospital-acquired.
Notwithstanding the limitations, our findings indicate potential use of antibiotics and invasive devices in the hospital could drive ARB colonization. Further investigation with a more robustly powered study is warranted to understand the role of known risk factors for ARB colonization in our setting, alongside assessments of community-level colonization patterns, socio-economic and infrastructural factors. Additionally, further studies are needed for understanding how consumption of animal products relates to colonization patterns and how environmental contributions to community colonization. Whole genome sequencing could complement existing analyses to better describe transmission dynamics and genomic epidemiology of ARBs and describe the evolving molecular epidemiology of ARB colonization in India and similar settings globally.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
The authors thank the study participants, participating hospitals’ staff, and ARCH India study team members (in alphabetical order): B. Ancilla Sennoretta, K. Arul, P. R. Asish, M. Chella Kumar, Elizabeth Varghese, M. M. E. Gowtham, A. R. Heamchandsaravanan, K. Kalaiyarasi, C. Kanagasivam, N. N. Karthick, M. Kavitha, D. Lavanya Grace, P. Lavanya, R. Mercury, M. Murali Mohan, M. Purushothaman, R. Sabarinathan, J. Saranya, M. P. Sarath Kumar, N. Shameena, R. A. Sridharan, T. Subba Rao, K. Vasanthi, and G. Veeravel for their participation and contributions. The authors also thank Dr. Manoj Murhekar, Dr. Meghna Desai, Dr. Arasi Srivatsan, Dr. Rajshekar Kalgudi, and Dr. Diya Surie for their support and contributions to the study.
Abbreviations
- ARB
Antibiotic-resistant bacteria
- ESCrE
Extended-spectrum cephalosporin-resistant Enterobacterales
- CRE
Carbapenem-resistant Enterobacterales
- LMIC
Low- and middle-income countries
- ARCH
The Antibiotic Resistance in Communities and Hospitals
- CLSI
Clinical Laboratory and Standards Institute
- DAGs
Directed acyclic graphs
- aOR
Adjusted odds ratios
- CI
Confidence Intervals
- COVID-19
Coronavirus disease 2019
Author contributions
GKCP, TB, and RMS were involved in the conception and design of the study. GKCP, TB, GSN, SSS, VS, AV, VAS, and DV were involved in the study implementation. TB, GKCP, VS, AS, SSS, GSN, AB, SB, AV, VAS, and DV were involved in data analysis and interpretation. VS, SSS, GSN, TB, and GKCP drafted the manuscript and AS, AB, SB, VAS, DV, PM and RMS critically revised it. All authors reviewed and approved the final manuscript.
Funding
This work was supported by the U.S. Centers for Disease Control and Prevention, Cooperative Agreement for Global Health Security to ICMR-NIE; Grant number [NU2GGH001859-03-03].
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was reviewed and approved by the Indian Council of Medical Research – National Institute of Epidemiology (ICMR-NIE) Institutional Human Ethics Committee, Chennai, India. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the U.S. Centers for Disease Control and Prevention or the Indian Council of Medical Research.
Consent for publication
All authors have consented for publication.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
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Contributor Information
Tarun Bhatnagar, Email: tarunbhatnagar@nie.gov.in.
Girish Kumar Chethrapilly Purushothaman, Email: girishkumar@nie.gov.in.
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.