Environmental factors influencing the development and spread of resistance in erythromycin-resistant streptococcus pneumoniae

doi:10.21203/rs.3.rs-4246551/v1

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Environmental factors influencing the development and spread of resistance in erythromycin-resistant streptococcus pneumoniae

https://doi.org/10.21203/rs.3.rs-4246551/v1

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published 07 Nov, 2024

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Background: Bacterial drug resistance is becoming increasingly serious,This study aims to investigate the relationship between the resistance rate of erythromycin-resistant Streptococcus pneumoniae and reasons for the epidemic under complex geographical and climatic factors in China.

Methods: Data spanning from 2014 to 2021, including drug resistance rates, isolate rates, meteorological variables, and demographic statistics, were collected from the China Antimicrobial Resistance Monitoring System, the China Statistical Yearbook and China Meteorological Website. Our analysis involved nonparametric tests and the construction of multifaceted regression models for rigorous multivariate analysis.

Results: Single-factor analysis revealed significant differences in the resistance rate and isolate rate of erythromycin-resistant Streptococcus pneumoniae across different regions characterized by Hu Huanyong lines or different climate types. Multivariate regression analysis indicated positive correlations between the drug resistance rate and temperature, Subtropical climate, GDP, Hu Huanyong line, and Tm; the isolate rate showed a positive correlation with regional GDP and a negative correlation with monsoon climate.

Conclusion: The prediction model developed in this study holds significant value for forecasting the resistance rate of erythromycin-resistant Streptococcus pneumoniae amidst China's diverse meteorological and climate conditions.

drug resistance rate

climate

geographical distribution

erythromycin-resistant Streptococcus pneumoniae

China Mainland

Streptococcus pneumoniae is a Gram-positive bacterium that is widely distributed in nature. It is a common pathogenic microorganism in humans, often causing severe respiratory infections, meningitis, and sepsis [1–3], particularly pneumonia. In many underdeveloped countries, pneumonia caused by SP is a bacterial disease that is the leading cause of death in children under 5 years of age and in adults over 50 years of age [4–6]. The infection routes are diverse, primarily through droplet transmission [7], direct contact, and transmission from carriers to susceptible groups. In some cases, carriers may not exhibit obvious symptoms but can still spread the bacteria. Due to the long-term misuse and overuse of antibiotics, the incidence of infections caused by bacteria resistant to antibiotics is increasing [8], which increases the difficulty of infection treatment.

Erythromycin is one of the most used macrolide antibiotics [9], which exerts its antibacterial effect by inhibiting bacterial protein synthesis. Over time, however, bacteria such as SP evolved resistance to erythromycin [10, 11]. It is affected by many factors such as antibiotic use patterns, medical practices, sanitary conditions, and genetic transmission. The erythromycin resistance rate shows apparent differences in different geographical regions and different periods of the world [12].

Deng et al [13] studied the prevalence of pneumococcal infections in Canada and discussed the impact of PCV vaccine on the incidence, serotype distribution, antibiotic resistance, and nasopharyngeal (NP) carriage of pneumococcal infections; Tao [14] et al studied the clinical characteristics and drug resistance of patients with invasive pneumococcal disease in Ningxia Hui Autonomous Region, China.

However, China has a large population base, a vast territory, a wide range of latitudes, and a complex and diverse climate, significantly increasing the difficulty of responding to erythromycin-resistant SP infection [15]. This study examines the correlation between the drug resistance rate of erythromycin-resistant SP and geographical factors, including meteorological conditions, economic conditions, birth rate, mortality rate, and dietary structure in various provinces of China.

Data Collection:

The drug resistance rate of erythromycin-resistant SP, the isolate rate is from the China Antimicrobial Resistance Surveillance System (CARSS); the provincial weather, socio-economic data, diet structure, population change, and other data are extracted from the "China Statistical Yearbook." and China Meteorological Website.

Data Analysis

SPSS software version 26.0 was used for statistical analysis of the research results, while GraphPad Prism version 9.0 and ArcMap version 10.8 were used for data mapping, respectively. The Shapiro-Wilk test was used to assess the normality of the data. The test results show that non-normally distributed numerical variables are statistically described by the median (Median) and the interquartile range (IQR), while normally distributed numerical variables are described by the mean (Mean) ± standard deviation (SD). For categorical variables, statistical description is done using frequency and percentage. The Mann-Whitney U or Kruskal-Wallis H test was selected for numerical statistical variables with a non-normal distribution. A regression model was established for multivariate analysis. The significance level of the test is α = 0.05, and when the two-sided P-value is less than 0.05, it indicates that the difference is statistically significant.

We investigated the relationship between the resistance rate of erythromycin-resistant Streptococcus pneumoniae (SP) in 31 provinces of China from 2014 to 2021, along with the isolate rate and geographical distribution across the country. A map was utilized to visually depict the distribution of both the drug resistance rate and the isolate rate of erythromycin-resistant SP in each province, where darker colors indicate higher values (Fig. 1). Supplementary Table A provides statistical descriptions of meteorological data, economic indicators, medical facilities, dietary structures, and population changes for each province and city.

Differences in the drug resistance rate and isolate rate of erythromycin-resistant SP under different Hu Huanyong line distributions

We observed significant differences in the drug resistance rate of Streptococcus pneumoniae (SP) across different distributions relative to the Hu Huanyong line (P < 0.001). Here were the specific findings:Northwest side of the Hu Huanyong line: Sample size = 36, Median (Quartile) = 89.50 [86.72, 91.80]; On the Hu Huanyong line: Sample size = 24, Median (Quartile) = 96.15 [94.30, 96.80]; Southeast side of the Hu Huanyong line: Sample size = 184, Median (Quartile) = 95.65 [93.50, 96.70].These results indicated a statistically significant variation in the drug resistance rate of SP across different regions relative to the Hu Huanyong line, with the lowest median resistance rate observed on the northwest side and the highest on the southeast side. (Table 1)

Table 1

Table of isolate rate of drug resistance under different Hu line distribution
Level	N	resi	N	dete
Hu Line
Northwest	36	89.50[86.72,91.80]	15	3.65[3.00,5.03]
On	24	96.15[94.30,96.80]	6	4.25[3.95,4.89]
Southeast	184	95.65[93.50,96.70]	45	2.90[2.25,3.52]
P		<0.001**		0.008**
* p < 0.05 ** p < 0.01 Resi (N = 244) Dete (N = 66); Resi: Resistance rate of erythromycin-resistant SP Dete: Isolate rate of erythromycin-resistant SP

Isolate rate: On the northwest side of the Hu Huanyong line, the sample size was N = 15, with a median (quartile) of 3.65 [3.00, 5.03]. On the Hu Huanyong line, the sample size was N = 6, with a median (quartile) of 4.25 [3.95, 4.89]. On the southeast side of the Hu Huanyong line, the sample size was N = 45, with a median (quartile) of 2.90 [2.25, 3.52]. The isolate rate of SP showed significant differences under different distributions of the Hu Huanyong line distributions. (P = 0.008) (Table 2) There are significant differences in the rates of drug resistance and detection (Fig. 2).

Differences in drug resistance and isolate rates of erythromycin-resistant SP in different climate types.

The drug resistance rate exhibited significant differences among different climate types (P < 0.001).

The isolate rate exhibited a significant difference between plateau mountain climates and non-plateau mountain climates (P = 0.003). Additionally, there was a significant difference in the isolate rate between monsoon climates and non-monsoon climates (P = 0.002). (Table 2)

Table 2

Table of the isolate rate of drug resistance under different climate types
Level		Resi	N	Dete
Tropical
No	232	95.25[92.50,96.70]	60	3.15[2.48,4.22]
Yes	16	92.30[90.23,94.70]	6	4.47[2.61,7.47]
P		0.010*		0.255
Temperate
No	132	94.70[92.22,96.20]	42	3.08[2.31,4.41]
Yes	112	95.85[91.50,97.00]	24	3.21[2.82,3.69]
P		0.038*		0.889
Subtropic
No	116	94.00[90.10,96.50]	27	3.28[2.70,3.92]
Yes	128	95.70[93.80,96.70]	39	3.06[2.25,4.30]
P		0.002**		0.648
Plateau
No	208	95.65[93.22,96.70]	51	2.91[2.26,3.72]
Yes	36	90.60[87.87,92.95]	15	4.13[3.43,5.03]
P		<0.001**		0.003**
Monsoon
No	20	89.20[84.82,91.05]	9	4.35[3.79,5.61]
Yes	224	95.50[92.93,96.70]	57	3.00[2.30,3.96]
P		<0.001**		0.002**
Mainland
No	196	95.10[92.12,96.70]	54	3.26[2.55,4.35]
Yes	48	95.35[92.65,96.67]	12	2.96[2.35,3.45]
P		0.836		0.178
* p < 0.05 ** p < 0.01 Resi (N = 244); Dete (N = 66)

There are significant differences in the rates of drug resistance and detection among different climates (Fig. 3).

Multivariate regression analysis

Temperature, maxTemp12h, Rainfall, Pressure, H, nHI, GDP, iGDP, rGDP, Distributions by Hu Line, Temperature, Subtropical, and Monsoon were significantly positively correlated with the resistance rate of erythromycin-resistant SP. The Spearman correlation coefficients were as follows: 0.178 (P = 0.005), 0.379 (P < 0.001), 0.175 (P = 0.006), 0.337 (P < 0.001), 0.478 (P < 0.001), 0.376 (P < 0.001), 0.585 (P < 0.001), 0.448 (P < 0.001), 0.172 (P = 0.007), 0.356 (P < 0.001), 0.133 (P = 0.038), 0.195 (P = 0.002), and 0.366 (P < 0.001) (Supplementary Table B, Fig. 4).

There was a significant negative correlation between the Tropical and Plateau and the resistance rate of erythromycin-resistant SP. The Spearman correlation coefficients were − 0.164 (P = 0.010) and − 0.378 (P < 0.001).

There was a significant positive correlation between rGDP, Plateau, and the isolate rate of erythromycin-resistant SP. The Spearman correlation coefficients were 0.311 (P = 0.011) and 0.365 (P = 0.003). Temperature, Rainfall, Pressure, by Hu Huanyong Line, and Monsoon showed a significant negative correlation with the isolate rate of erythromycin-resistant SP. The Spearman correlation coefficients were − 0.262 (P = 0.034), -0.406 (P = 0.001), -0.284 (P = 0.021), -0.341 (P = 0.005), and − 0.386 (P = 0.001) (Supplementary Table B, Fig. 4).

The corresponding stepwise linear regression model was constructed by incorporating the influencing factors of the correlation rate of erythromycin-resistant SP drug resistance (Table 3). The final model of drug resistance rates revealed that Temperate, Subtropical Hu Huanyong line, GDP, and maxTemp12h were the factors influencing the drug resistance rate of SP.

The formula for the drug resistance rate model is as follows:

$$=0.272Temperate\times A+0.183Subtropical \times B+0.297Hu Line+0.250GDP+0.128 maxTemp12h +26.018$$

Table 3

Drug resistance rate stepwise linear regression analysis table
Variable	Unstandardized Coefficients		Standardized Coefficients	t	P value	VIF
Variable	Β	Std. Error	Standardized Coefficients	t	P value	VIF
Temperate	2.514	0.724	0.272	3.474	0.001	2.239
Subtropical	1.685	0.785	0.183	2.147	0.033	2.645
Hu Line	1.870	0.386	0.297	4.845	<0.001	1.369
GDP	$4.915\times$10^− 5	<0.001	0.250	4.041	<0.001	1.394
maxTemp12h	0.209	0.101	0.128	2.083	0.038	1.367
Constant
	F	25.276
	R²	0.347		Adjusted R²	0.333
p < 0.05 ** p < 0.01

The final isolate rate model showed that Monsoon and rGDP were the determinants of the S. pneumoniae isolate rate (Supplementary Table C).

The formula for the drug isolate rate model was as follows:

$$=-0.349Monsoon\times C+0.339rGDP+4.101$$

The studies have demonstrated that there are significant variations in the resistance rate and isolate rate of erythromycin-resistant SP under different Hu Huanyong lines [16]. Considering the uniqueness of the Hu Huanyong Line, 96% of China's population is concentrated on the southeast side, while only 4% of the population is distributed on the northwest side. Sociologically, the southeastern side of the line is more densely populated than the northwestern side. This leads to a higher frequency of social gatherings and events, which in turn increases the potential for the virus to spread. Antibiotic consumption is the main driver of antibiotic resistance [17], The population in the Southeast was higher than that in the Northwest, and as a result, the consumption of antibiotics was higher.

There were significant differences in the resistance rate and isolate rate of erythromycin-resistant SP across different climate types. The climate type affects the temperature and humidity of the environment. A warm and humid climate may provide favorable conditions for the growth and reproduction of bacteria. It may promote the spread of bacteria and may also affect the metabolism and survival strategies of bacteria and may provide favorable conditions for the growth and reproduction of bacteria. For example, the ambient temperature is positively correlated with the drug resistance rate of Klebsiella pneumoniae [18].

Global antibiotic consumption has increased by 65%, primarily due to increased usage in low- and middle-income countries [19–22]. This rise can be attributed to various factors such as affordability, supply chain issues, and the presence of substandard and falsified medicines [23]. Multivariate analysis revealed a positive correlation between the resistance rate of erythromycin-resistant SP and GDP, as well as a positive correlation between the isolate rate and rGDP.

Antimicrobial resistance is the primary cause of death globally, with the greatest impact in areas with limited resources [24].

There were limitations in data collection, and some important factors that may affect drug resistance and isolate rates were not included. Further research could incorporate other, more potentially influential factors, such as the frequency of antibiotic use, infection control practices, and sanitation. Our study was based on a retrospective analysis of existing databases, and confounding factors may not have been fully considered. Future studies could design additional experiments or prospective studies to validate our findings.

The prediction model designed in this article is of great value for predicting the resistance rate of erythromycin-resistant SP based on my country's complex meteorological conditions and climate distribution and revealing its intrinsic relationship with population mortality and dietary factors. These findings provide important insights into addressing the global challenge of bacterial resistance and deserve serious consideration by the scientific community and policymakers.

Resi: Resistance rate of erythromycin-resistant SP Dete: Isolate rate of erythromycin-resistant SP ;Temperature /T: Atmospheric temperature 2 meters above the ground; minTemp12h / Tn:The lowest temperature in the past period; maxTemp12h / Tm: The highest temperature in the past period; H: Number of hospitals in each province; nHl: Number of primary health care institutions in each province ; nPI: Number of specialised public health institutions in each province; GDP: Gross national product; iGDP: Gross domestic product growth quantity; rGDP:GDP growth rate; Humidity: Relative humidity 2 meters above the ground; Rainfall: average annual rainfall; Pressure weather station horizontal air pressure.

Contributions

Conceptualization, Bi-Kui Zhang, Miao Yan, Yi-Chang Zhao; methodology, Yi-Chang Zhao and Zhi-Hua Sun; software, Jia-Kai Li and Huai-yuan Liu; validation, Bi-Kui Zhang, Miao Yan, Yi-Chang Zhao and Zhi-Hua Sun,; formal analysis, Yi-Chang Zhao and Zhi-Hua Sun; investigation, Yi-Chang Zhao, Wei Cao and Zhi-Hua Sun; resources, Zhi-Hua Sun; data curation, Zhi-Hua Sun; writing—original draft preparation, Zhi-Hua Sun; writing—review and editing, Miao Yan, Yi-Chang Zhao, and Bi-Kui Zhang; visualization, Zhi-Hua Sun; supervision, Miao Yan, Bi-Kui Zhang and Feng Yu; project admin-istration, Feng Yu and Miao Yan; funding acquisition, Miao Yan. All authors have read and agreed to the published version of the manuscript.

Ethical approval

No required

Funding

This research was funded by the Hunan Medical Association [NO. HMA202001002] and the Health Commission of Hunan Provincial [NO. 202113012480]. It was supported by the International Research Center for Precision Medicine, Transformative Technology, and Software Services, Hunan, China. It was also supported by the Hunan Province Degree and Graduate Education Reform Research Project [2023JGYB041].

Declaration of interest

All authors declare no competing interests.

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/).

Marquart ME. Pathogenicity and virulence of Streptococcus pneumoniae: Cutting to the chase on proteases. Virulence. 2021;12:766–87. doi: 10.1080/21505594.2021.1889812.
van der Poll T, Opal SM. Pathogenesis, treatment, and prevention of pneumococcal pneumonia. Lancet. 2009;374:1543–56. doi: 10.1016/s0140-6736(09)61114-4.
Lynch JP, 3rd, Zhanel GG. Streptococcus pneumoniae: epidemiology and risk factors, evolution of antimicrobial resistance, and impact of vaccines. Curr Opin Pulm Med. 2010;16:217–25. doi: 10.1097/MCP.0b013e3283385653.
Blasi F, Mantero M, Santus P, et al. Understanding the burden of pneumococcal disease in adults. Clin Microbiol Infect. 2012;18 Suppl 5:7–14. doi: 10.1111/j.1469-0691.2012.03937.x.
Şimşek Veske N, Uslu Ö, Oruç Ö, et al. Does Pneumococcal Vaccination Have an Effect on Hospital Costs? Thorac Res Pract. 2023;24:165–69. doi: 10.5152/ThoracResPract.2023.22171.
Malene BM, Oyvind H, Tor M, et al. Cost-effectiveness of 20-valent pneumococcal conjugate vaccine compared with 23-valent pneumococcal polysaccharide vaccine among adults in a Norwegian setting. Cost Eff Resour Alloc. 2023;21:52. doi: 10.1186/s12962-023-00458-4.
Hartzell JD, Oster CN, Gaydos JC. How contagious are common respiratory tract infections? N Engl J Med. 2003;349:95. doi: 10.1056/NEJMc031032.
Bassetti S, Tschudin-Sutter S, Egli A, et al. Optimizing antibiotic therapies to reduce the risk of bacterial resistance. Eur J Intern Med. 2022;99:7–12. doi: 10.1016/j.ejim.2022.01.029.
Shen D, Gu X, Zheng Y, et al. The fate of erythromycin in soils and its effect on soil microbial community structure. Sci Total Environ. 2022;820:153373. doi: 10.1016/j.scitotenv.2022.153373.
Hussein SS, Shibl AM, Bahakem HM, et al. Antimicrobial resistance in Streptococcus pneumoniae: a growing universal concern. J Natl Med Assoc. 1989;81:937–41.
Jacobs MR, Koornhof HJ, Robins-Browne RM, et al. Emergence of multiply resistant pneumococci. N Engl J Med. 1978;299:735–40. doi: 10.1056/nejm197810052991402.
Lu Z, Tadi DA, Fu J, et al. Global status of Azithromycin and Erythromycin Resistance Rates in Neisseria gonorrhoeae: A Systematic Review and Meta-analysis. Yale J Biol Med. 2022;95:465–78.
Deng X, Church D, Vanderkooi OG, et al. Streptococcus pneumoniae infection: a Canadian perspective. Expert Rev Anti Infect Ther. 2013;11:781–91. doi: 10.1586/14787210.2013.814831.
Cai K, Wang Y, Guo Z, et al. Clinical characteristics and antimicrobial resistance of pneumococcal isolates of pediatric invasive pneumococcal disease in China. Infect Drug Resist. 2018;11:2461–69. doi: 10.2147/idr.S183916.
Bengtsson-Palme J, Kristiansson E, Larsson DGJ. Environmental factors influencing the development and spread of antibiotic resistance. FEMS Microbiol Rev. 2018;42. doi: 10.1093/femsre/fux053.
Wang NN, Zhu CY, Li W, et al. Air quality improvement assessment and exposure risk of Shandong Province in China during 2014 to 2020. Int J Environ Sci Technol (Tehran). 2022:1–10. doi: 10.1007/s13762-022-04651-5.
Malhotra-Kumar S, Lammens C, Coenen S, et al. Effect of azithromycin and clarithromycin therapy on pharyngeal carriage of macrolide-resistant streptococci in healthy volunteers: a randomised, double-blind, placebo-controlled study. Lancet. 2007;369:482–90. doi: 10.1016/s0140-6736(07)60235-9.
Zeng Y, Li W, Zhao M, et al. The association between ambient temperature and antimicrobial resistance of Klebsiella pneumoniae in China: a difference-in-differences analysis. Front Public Health. 2023;11:1158762. doi: 10.3389/fpubh.2023.1158762.
Klein EY, Van Boeckel TP, Martinez EM, et al. Global increase and geographic convergence in antibiotic consumption between 2000 and 2015. Proc Natl Acad Sci U S A. 2018;115:E3463-e70. doi: 10.1073/pnas.1717295115.
Laxminarayan R, Matsoso P, Pant S, et al. Access to effective antimicrobials: a worldwide challenge. Lancet. 2016;387:168–75. doi: 10.1016/s0140-6736(15)00474-2.
Laxminarayan R, Duse A, Wattal C, et al. Antibiotic resistance-the need for global solutions. Lancet Infect Dis. 2013;13:1057–98. doi: 10.1016/s1473-3099(13)70318-9.
Lim C, Takahashi E, Hongsuwan M, et al. Epidemiology and burden of multidrug-resistant bacterial infection in a developing country. Elife. 2016;5. doi: 10.7554/eLife.18082.
Lewis K. The Science of Antibiotic Discovery. Cell. 2020;181:29–45. doi: 10.1016/j.cell.2020.02.056.
Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022;399:629–55. doi: 10.1016/s0140-6736(21)02724-0.

No competing interests reported.

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You are reading this latest preprint version

Environmental factors influencing the development and spread of resistance in erythromycin-resistant streptococcus pneumoniae

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Abstract

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Introduction

Method

Data Collection:

Data Analysis

Results

Multivariate regression analysis

Discussion

Conclusion

Abbreviations

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References

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