Exploring the causes of the prevalence of vancomycin-resistant Enterococcus faecalis

Abstract

Background

Bacterial drug resistance represents a significant global concern, with vancomycin-resistant Enterococcus faecalis posing a particularly grave threat to contemporary healthcare systems. This study aims to reveal the reasons for the prevalence of VRE in China.

Methods

This study collected data from the China Antimicrobial Resistance Detection System, China Statistical Yearbook, and China Meteorological Network. The collected data are meticulously organized and subjected to both single-factor and multi-factor analyses. An accurate multiple linear regression model was developed by utilizing this comprehensive dataset.

Results

Single-factor analysis revealed significant regional variations in the resistance rate of vancomycin-resistant Enterococcus faecalis (P = 0.003). Specifically, there were noteworthy disparities observed between regions experiencing temperate and monsoon climates (P = 0.029; P = 0.005). Furthermore, multi-factor regression analysis demonstrated a negative correlation between the drug resistance rate and both rainfall and rGDP, while a positive correlation was observed with nPI.

Conclusion

We successfully established a prediction model for the VRE and found that the resistance rate was low in areas with high rainfall and high per capita economic income, but high in areas with many specialized public health institutions. This is critical for public health strategies and helps policymakers and healthcare practitioners tailor antibiotic resistance approaches to local geography, meteorology, economic conditions.

Introduction

The increasing disease burden posed by antibiotic resistance is emerging as a significant challenge to global public health security [1,2,3]. The World Health Organization (WHO) estimates that in 2019, bacterial antimicrobial resistance (AMR) directly caused 1.27 million deaths and was linked to a total of 4.95 million deaths worldwide. Projections suggest that AMR could cause up to 10 million deaths annually by 2050, with an economic impact of $100.2 trillion on global GDP [4, 5]. Enterococci are a group of bacteria that live in the intestines of humans and animals and are usually harmless. However, certain strains of Enterococcus faecalis (E. faecalis) can cause infections [6,7,8]. E. faecalis is an opportunistic pathogen. Infections caused by pathogenic include urinary tract infections, purulent abdominal infections, sepsis, endocarditis, diarrhea, fever, and other symptoms, which can cause infection in humans through food [9, 10].

Vancomycin is one of the important antibiotics used to treat these infections. However, over time, E. faecalis developed resistance to vancomycin [11, 12]. This may be caused by factors such as long-term abuse of antibiotics, irrational use, cross-infection, or genetic mutations [13]. This resistance makes it more difficult to treat the infection. According to the National Health care Safety Network (NHSN), in 2006–2007, approximately 33% of all enterococci were resistant to vancomycin [14]. Additionally, a recent analysis of NHSN data from 2014 reports that if all Enterococcus species were analyzed within their genus group, they would be considered the second-most common group of pathogens across all Healthcare-Associated infections types, and the single-most common group of pathogens among central line-associated bloodstream infections [15]. Besides, vancomycin-resistant E. faecalis (VRE) bacteremia remains associated with an increased risk of in-hospital mortality and total hospital length of stay [16]. In the United States, data from the Centers for Disease Control and Prevention (CDC) for the year 2020 indicate that approximately 35,000 deaths occur annually due to infections caused by antibiotic-resistant bacteria or fungi, among which VRE are a common source of hospital-acquired resistant infections [1, 17,18,19], while in China, E. faecalis also exhibits an escalating trend towards antibiotic resistance. In certain regions, the prevalence of VRE ranged from 1.6 to 11% [20,21,22,23]. Hospitals and nursing facilities are important sites for the spread of vancomycin-resistant bacteria [24, 25]. Contact between patients, medical equipment, and medical personnel are all possible routes of transmission of vancomycin resistance, and studies have found that vancomycin-resistant enterococci strains are not only present in medical settings, but are also found in the environment [26,27,28]. Research conducted by Emaneini M et al. in Iran delineated a significant resistance across a diverse array of antibiotics within E. faecalis, notably identifying a substantial incidence of gentamicin resistance alongside the widespread presence of the aac(6)-Ie-aph(2)-Ia gene [29]. Concurrently, investigations by Xuan Huiyong et al. in China unveiled that E. faecalis strains originating from animals demonstrated pronounced resistance to critical antibiotics, signifying potential implications for human health [30]. Further elucidation of the mechanisms underlying resistance gene dissemination was provided by Jahan Musarrat et al., who expounded on the transmission pathways through the food chain, thereby underlining the hazard posed by food-derived Enterococcus faecalis as a reservoir for antibiotic resistance [31]. Additionally, Ali Liaqat et al. explicated the influence of environmental factors, such as eutrophication, on the resistance profiles of enterococci, illustrating the environmental dimension of antimicrobial resistance [32]. The work of Makarov Dmitry A et al. further explored the impact of antibiotic utilization practices on the resistance levels among enterococci from farm animals, shedding light on the interconnectedness of antibiotic use and resistance emergence [33]. The emergence of antibiotic resistance in E. faecalis strains presents a formidable challenge to global public health, encompassing a broad spectrum of contexts from clinical settings to agricultural domains and aquatic environments. This has raised concerns about the relationship between the environment and vancomycin resistance. China has a large population, a vast territory, and a diverse environment, which makes it very difficult to deal with E. faecalis infection.

In light of this, the main purpose of this study is to identify the key factors influencing the distribution and resistance patterns of VRE within China, taking into account the combined effects of environmental factors, levels of economic development, and public health facilities on the formation of VRE resistance. Through in-depth analysis of these key factors, this study aims to fill the current research gaps, providing a scientific basis for the development of targeted surveillance and control strategies, further developing effective prevention and control strategies to reduce the disease burden caused by E. faecalis, and ensuring public health safety.

Method

Data collection

The drug resistance rate of VRE was ascertained through meticulous analysis of data sourced from the authoritative China Antimicrobial Resistance Surveillance System (CARSS). Concurrently, data on meteorological factors, economics, medical, and other influencing factors are collected from the China Statistical Yearbook and the China Meteorological Network.

Data classification

We systematically classified the 31 provinces and cities within China based on their geographical alignment with the Hu Huanyong Line, stratifying them into three distinct categories: those predominantly situated to the northwest of the Hu Line (NW), those intersected by the Hu Line itself (ON), and those primarily located to the southeast of the Hu Line (SE). Moreover, we employed a comprehensive climate typology encompassing six distinct categories: tropical, temperate, subtropical, plateau, continental, and monsoon, to further delineate the climatic diversity across our dataset.

Data analysis

The research findings underwent rigorous statistical analysis using SPSS software version 26.0. To assess data normality, the Shapiro–Wilk test was meticulously employed. We selected either the Mann–Whitney U test or the Kruskal–Wallis H test as appropriate. Additionally, a multivariate regression model was thoughtfully constructed to unravel intricate relationships within our dataset. We set the significance level for our tests at α = 0.05, with a two-sided P-value less than 0.05 indicating statistical significance.

Results

Distribution of drug resistance rates in different regions

Eight-year cumulative average drug resistance rate were ranked in the top three, featuring Beijing, Jilin, and Gansu/Guizhou. Notably, Tibet's average resistance rate remarkably stood at 0 (Fig. 1).

Fig.1

Average distribution of drug resistance rates in various provinces (cities)

Geographic variations in E. faecalis resistance and detection rates

Utilizing the robust Kruskal–Wallis H test, our study unveiled significant variations in the resistance rates of VRE across different regions delineated by the Hu Huanyong Line. Specifically, the northwest region of the Hu Line, encompassing 36 samples, exhibited a median resistance rate of 0.20[0.00,0.48]. In contrast, along the Hu Line itself (with 24 samples), this rate increased to 0.60[0.23,0.80]. Meanwhile, the southeast region of the Hu Line (184 samples) showed a median resistance rate of 0.40[0.20,0.70]. These significant variations (P = 0.003, Table 1) highlighted the profound impact of geographical location on the resistance dynamics of E. faecalis. However, there was no statistical difference in detection rates. The drug resistance rate had the highest value in the northwest of the Hu Huanyong Line, and the detection rate appeared in the southeast (Fig. 2).

Table 1 Differences in drug resistance detection rates under different Hu Huanyong line distributions

Fig.2

Histogram of the detection rate of drug resistance detection rate under different Hu Huanyong line distributions

Climatic differences in resistance and detection rates of E. faecalis

The relationship between resistance and detection rates of VRE under different climate categories was tested using Mann–Whitney U. The results showed that the resistance rate differed between temperate and monsoon climate types (P = 0.029; P = 0.005). Detection rates varied in temperate climates (P = 0.032) (Table 2).

Table 2 Differences in drug resistance detection rates under different climate types

Multivariable regression analysis of meteorological, economic, medical, and other factors

Our analysis illuminated the significance of several variables in relation to VRE. It was found that the drug resistance rate is related to temperature, Tn,Rainfall, nPI, GDP,rGDP, temperate and monsoon (−0.179 to 0.202, p < 0.05). Similarly, the detection rate was correlated with Rainfall,H, nHI, nPI, temperate (−0.627 to 0.259, p < 0.05) (Table 3, Fig. 3).

Table 3 Correlation table between meteorological, economic, hygienic factors and drug resistance detection rate

Fig. 3

Correlation combination chart. A Correlation between the number of hospitals in each province (H) and drug resistance rate; B correlation between the number of public health professional institutions in each province (nPI) and drug resistance rate; C correlation between the number of primary health care institutions (nHI) and drug resistance rates in each province; D correlation between gross national product (GDP) and drug resistance rate; E correlation between gross domestic product growth quantity (iGDP) and drug resistance rate; F correlation between GDP growth rate (rGDP) and drug resistance rate; G correlation between relative humidity and drug resistance rate; H correlation between average air pressure (P) and drug resistance rate; I correlation between average rainfall and drug resistance rate; J correlation between average temperature and drug resistance rate; K correlation between maximum temperature (Tx) and drug resistance rate; L correlation between minimum temperature(Tn) and drug resistance rate

Through rigorous stepwise linear regression models, we constructed comprehensive prediction models for drug resistance rates and detection, respectively (Table 4, Table 5).

Table 4 Resistance rate regression linear model

Table 5 Detection rate regression linear model

The formulas were as follows:

${\text{Resistance~rate}}: = - 0.237Rainfall + 0.201nPI - 0.137rGDP + 0.529$

${\text{Detection~rate}}: = - 0.582nPI - 0.200Rainfall + 3.357$

Here, Rainfall: average annual rainfall; nPI: number of specialized public health institutions in each province; rGDP: GDP growth rate. The final models for resistance and detection rates of E. faecalis present intriguing insights into how environmental and socioeconomic factors influence health outcomes. In the resistance rate model, increased rainfall decreases resistance rates by 0.237 units for each unit increase in rainfall. Additionally, a rise in rGDP correlates with a 0.137 unit decrease in resistance rates per unit, indicating that economic growth likely supports better healthcare infrastructure, which in turn helps in controlling resistant strains. Conversely, the nPI (coefficient = 0.201) directly correlates with increasing resistance rates. In terms of detection rates, an increase in nPI significantly reduces detection capabilities by 0.582 units per unit increase. Moreover, each unit increase in rainfall leads to a 0.200 unit decrease in detection rates, which suggests that conditions during higher rainfall complicate the logistics or operations necessary for detecting VRE infections.

Discussion

Our results indicate that the resistance rate of E. faecalis is affected to a certain extent by economic, medical, climatic, factors. VRE is a highly resistant bacterium, and its ability to resist antibiotics makes it a threat to the medical industry and public health [34].

Economic level has a significant impact on the resistance rate of VRE. In low-income countries, health facilities and health care resources may be limited, which may result in inadequate infection control measures and the spread of VRE more easily. Additionally, low-income populations may be more susceptible to infection because they may not have access to adequate health care, making the spread of VRE in these communities easier [35,36,37].

The rate of resistance to VRE in medical practice is huge. Medical facilities such as hospitals are the focus of transmission of VRE infections, especially patients who use broad-spectrum antibiotics. Overuse of antibiotics, improper use, and cleanliness of medical facilities can increase VRE resistance rates [38, 39]. In addition, improper implementation of hand hygiene and infection control measures can also aggravate the spread of VRE [40].

Climate change can affect the spread of VRE. Climatic factors such as temperature and humidity can affect the survival and spread of VRE in the environment [41]. High temperatures and humidity may help VRE survive in the environment longer, increasing the chance of spread. In addition, climate change may trigger extreme weather events, leading to outbreaks in densely populated areas and increasing the risk of VRE transmission.

Demographic factors such as population density, mobility, and health status also have an impact on VRE resistance rates. Areas with high population density are more likely to spread infected pathogens, including VRE. Population mobility (such as international travel) can spread VRE to different regions and increase the spread of drug-resistant strains. People in poor health are more susceptible to infection, aggravating the spread of VRE.

When considering these factors comprehensively, we can see that they are interconnected. For example, medical facilities in low-income areas may be poorer, which makes VRE more likely to spread. Climate change may cause natural disasters and further exacerbate the spread of infections. Therefore, solving the problem of VRE resistance requires comprehensive efforts from many aspects, including improving medical facilities, strengthening antibiotic management, improving medical standards, strengthening infection control measures, and responding to climate change. This will help reduce the spread of VRE and safeguard public health.

The study has limitations.

Due to the retrospective analysis of data, some information is missing. We will design more prospective experiments to verify this in the future. At the same time, the scope of research is expanded beyond China.

Conclusion

We successfully established a prediction model for the VRE and found that the resistance rate was low in areas with high rainfall and high per capita economic income, but high in areas with many specialized public health institutions. This is critical for public health strategies and helps policymakers and healthcare practitioners tailor antibiotic resistance approaches to local geography, meteorology, and economic conditions.

Data sharing statement

The datasets analyzed during the current study are publicly available at the China Antimicrobial Resistance Surveillance System (CARSS) (http:// www.carss.cn/), and the National Bureau of Statistics (http://www.stats.gov.cn/).