From Gram-Negative Strains to Mortality: Understanding Bacterial Resistance in Mainland China

From Gram-Negative Strains to Mortality: Understanding Bacterial Resistance in Mainland China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Gram-Negative Strains to Mortality: Understanding Bacterial Resistance in Mainland China Yi-Chang Zhao, Zhi-Hua Sun, Jia-Kai Li, Huai-yuan Liu, Ming-Xuan Xiao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5712281/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Carbapenem-resistant Gram-negative bacteria significantly threaten public health due to limited treatment options and high mortality rates. Understanding the factors influencing their detection and resistance rates is crucial for effective interventions. Objective: This study aimed to investigate the detection and carbapenem resistance rates of Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii in China and identify associations with climate, agriculture, economy, and diet. Method Data were sourced from CARSS, NBS, and CMDC, covering 1435 hospitals. Descriptive statistics and double fixed effect regression models analyzed associations, using SPSS, RStudio, StataMP, and Python. Results From 2014 to 2021, bacterial counts increased from 2,227,420 to 3,743,027, with Gram-negative bacteria constituting 70.3–71.5%. Escherichia coli (29.2–29.9%), Klebsiella pneumoniae (19.4–20.7%), Pseudomonas aeruginosa (11.8–12.7%), and Acinetobacter baumannii (9.1–10.8%) were the most prevalent. Environmental data indicated significant geographic distributions, with median humidity at 65%, median temperature at 15.75°C, and median annual rainfall at 1164.50 mm. Regional disparities in detection and resistance rates were observed, with Escherichia coli showing a median resistance rate of 1.40%, Pseudomonas aeruginosa 18.55%, Klebsiella pneumoniae 6.10%, and Acinetobacter baumannii 55.30%. Factors like hospital environment and food consumption significantly affected detection rates, while GDP per capita impacted resistance rates. Detection rates of Pseudomonas aeruginosa correlated significantly with increased mortality (coefficient 0.2007). Conclusion This study highlights the significant regional disparities and factors influencing the detection and resistance rates of carbapenem-resistant bacteria in China, emphasizing the need for targeted interventions considering local climatic, economic, and dietary conditions. Detection and resistance profiles did not significantly affect birth rates and population growth. Carbapenem resistance Gram-negative bacteria Epidemiology Public health Mainland China Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Highlights This time series study investigated the detection and carbapenem resistance rates of four pathogenic Gram-negative bacteria in China from 2014 to 2021. Significant regional and climate-related variations in bacterial resistance rates and detection rates were observed, with higher resistance rates in tropical and subtropical regions. Economic factors and specific consumption patterns were associated with bacterial resistance rates, highlighting the role of socio-economic factors in resistance patterns. Higher bacterial resistance rates were associated with increased mortality rates, underscoring the potential impact of antimicrobial resistance on patient outcomes. Introduction Carbapenem-resistant Gram-negative bacteria (CRGNB) are a group of bacterial strains that are resistant to carbapenems, a class of antibiotics commonly used to treat severe infections[ 1 – 3 ]. These bacteria can cause a range of infections, from urinary tract infections to bloodstream infections, and are a growing public health concern worldwide[ 4 – 6 ]. CRGNB have been identified in various regions of the world, including Asia, Europe, and the Americas, and are associated with high morbidity and mortality rates[ 7 , 8 ]. In China, the prevalence of CRGNB has been increasing rapidly in recent years[ 9 , 10 ]. Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii were identified as the most frequently isolated CRGNB worldwide. Scudeller et al. collected data from 47 countries and regions and revealed that these four bacteria accounted for more than 80% of all CRGNB isolates[ 11 ]. A subsequent study also emphasized that these four bacteria are the predominant causes of CRGNB infections[ 12 ]. In addition, the detection rate and carbapenem resistance rate of these four bacteria are influenced by various factors. These factors encompass geographic location, hospital settings, antibiotic usage, and patient demographics[ 1 , 2 , 12 , 13 ]. Recent research has provided some valuable insights into the influence of environmental factors on the prevalence of infection and human health[ 14 – 18 ]. Ma, L. et al. found potential pathogenic species, which were influenced by factors like rainfall and water source and lead to health risks[ 19 ]. Besides, Davis, M. F. et al. argued that industrial food animal production (IFAP), with its widespread antimicrobial use, constitutes an anthropogenic ecosystem. Focusing on U.S. broiler chicken production, they used an ecosystem perspective to explore the changes in microbiomes, including the resistome and resistance flow between them[ 20 ]. However, the correlation between detection and carbapenem resistance rates of these four specific bacteria and environmental factors, agricultural, economic levels and diet structure remains insufficiently explored in the realm of infectious diseases. Given the potential impact of these factors on the spread and emergence of drug-resistant bacteria, further research is imperative to elucidate this relationship. Therefore, our goal was to identify significant relationships, offering meaningful insights into the interactions and dependencies between various factors. Method Data collection In this time series study, data were sourced from three primary databases: the China Antimicrobial Resistance Surveillance System (CARSS)[ 21 ], the National Bureau of Statistics (NBS) database[ 22 ], and the China Meteorological Data Service Centre (CMDC) online resources[ 23 ]. CARSS, a national surveillance system, provided comprehensive data on detection and carbapenem resistance rates of Gram-negative and Gram-positive bacteria from almost 1435 member hospitals across China. We focused on four bacterial types: Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii, and extracted data on their detection and carbapenem resistance rates up to 2021. The NBS database provided demographic and economic indicators. Meteorological data, including temperature, humidity, and precipitation, were obtained from the CMDC through historical records. Hu line was used to investigate the effects of detection and carbapenem resistance of these four bacteria. Data collection involved independent extraction by two researchers, with discrepancies resolved through discussion. Our primary focus centered on examining the detection and Carbapenem-resistant rates of Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii. We assessed their associations with various factors encompassing climate, agriculture, economy, diet structure, and other relevant variables. Our aim was to discern any meaningful associations between these bacteria and the aforementioned indicators. Furthermore, we also explored the potential influence of detecting the four specific bacteria and their Carbapenem-resistant rates on mortality, birth rate, and overall population growth. Statistical analysis During data analysis, descriptive statistics were crucial. Numerical variables were assessed for central tendency and variability using mean ± standard deviation (SD) for normally distributed data and median with interquartile range (IQR) for non-normally distributed data. Categorical variables were presented as frequency and percentage distributions. To evaluate the significance of Carbapenem-resistant and detection rates of these four specific bacteria across climate types and Huanyong line regions, we used the Kruskal-Wallis test with a significance level of 0.05. To identify multicollinearity among variables, we used the Variance Inflation Factor (VIF), with values below 10 indicating no serious multicollinearity. For assessing regression reliability, Durbin-Watson values between 1.80 and 2.20 were preferred, indicating minimal autocorrelation. The F-test assessed the overall statistical significance of the model, with a p-value < 0.05 suggesting a meaningful relationship between dependent and independent variables. Double fixed effect regression model is also estimated using a panel dataset comprising the collected data over years. The model controls for province and year fixed effects, accounting for unobserved heterogeneity across different provinces and time-specific effects. Statistical analyses and graphic rendering were conducted using SPSS (version 26), RStudio 4.2.2, StataMP 17, and Python 3.10.9. This study involves the analysis of publicly available data, and therefore, does not require ethical approval. Result Summary of CARSS report (2014–2021) The National Bacterial Resistance Monitoring Report for the period 2014 to 2021 provided an extensive overview of bacterial resistance surveillance activities in China as detailed in Table 1 . Throughout this interval, the national network comprised between 1412 and 1435 monitoring entities, including a range of 1110 to 1375 hospitals after a meticulous data review. Within the network, 269 to 363 were classified as secondary hospitals and 841 to 1023 as tertiary hospitals. Adhering to a methodology that retained only the first strain of identical bacteria from the same patient and excluded duplicate strains, the study encompassed a total bacterial count that increased from 2,227,420 in 2014 to 3,743,027 by 2021. The proportion of Gram-positive bacteria ranged from 28.5–32.6%, whereas Gram-negative bacteria (GNB) constituted between 70.3% and 71.5%. Among the GNB, the five most prevalent isolates were: Escherichia coli (29.2–29.9%), Klebsiella pneumoniae (19.4–20.7%), Pseudomonas aeruginosa (11.8–12.7%), Acinetobacter baumannii (9.1–10.8%), and Enterobacter cloacae (3.9–4.5%). The primary source of these strains were sputum specimens, which showed an increase from 954,224 in 2014 to 1,432,523 in 2021, representing 38.3–42.8% of samples. This was followed by urine specimens, which increased from 394,356 to 788,911 over the period, accounting for 15.6–21.1% of the total, and blood specimens, which ranged from 208,467 to 325,016, representing 8.7–9.8%. Table 1 Summary of overall bacterial detection data from CARSS Year Member Unit (n) Included Hospitals (n) Secondary Hospitals (n,%) Included Tertiary Hospitals (n,%) Non-repeated Bacteria (n) Gram-positive Bacteria (n,%) Gram-negative Bacteria (n,%) Escherichia Coli (n,%) Klebsiella Pneumoniae (n,%) Pseudomonas Aeruginosa (n,%) Acinetobacter Baumannii (n,%) Sputum Specimens (n,%) Urine Specimens (n,%) Blood Specimens (n,%) 2014 1429 1110 269 (24.2) 841 (76) 2227420 634414 (28.5) 1593006 (71.5) 465136 (29.2) 308951 (19.4) 202817 (12.7) 171662 (10.8) 954224 (42.8) 394356 (17.7) 208467 (9.4) 2015 1427 1143 272 (23.8) 871 (76.2) 2400786 695066 (28.9) 1705720 (71.1) 510140 (29.9) 336829 (19.8) 219630 (12.9) 183178 (10.7) 993205 (41.4) 372161 (15.6) 224481 (9.4) 2016 1412 1273 322 (25.3) 951 (74.7) 2727605 794073 (29.1) 1933532 (70.9) 575494 (29.8) 381198 (19.7) 246242 (12.7) 208689 (9.1) 1111456 (40.8) 499362 (18.3) 268114 (9.8) 2017 1412 1307 336 (25.7) 971 (74.3) 2894517 859388 (29.7) 2035129 (70.3) 597909 (29.4) 411487 (20.2) 253083 (12.4) 207046 (10.2) 1201531 (41.5) 540051 (18.7) 274599 (9.5) 2018 1429 1353 349 (25.8) 1004 (74.2) 3234372 952023 (32.5) 2282349 (70.6) 660261 (28.9) 465322 (20.4) 283222 (12.4) 227091 (9.9) 1340920 (41.5) 608667 (18.8) 296052 (9.2) 2019 1429 1375 352 (25.6) 1023 (74.4) 3528471 1043535 (29.6) 2484936 (70.4) 707968 (28.5) 503230 (20.3) 299318 (12) 239890 (9.7) 1462853 (41.5) 673824 (19.1) 320002 (9.1) 2020 1435 1371 352 (25.7) 1019 (74.3) 3249123 939201 (28.9) 2309922 (71.1) 686049 (29.7) 482330 (20.9) 281260 (12.2) 219921 (9.5) 1245951 (38.3) 667681 (20.5) 295868 (9.1) 2021 1434 1373 363 (26.4) 1010 (73.6) 3743027 1083580 (28.9) 2659447 (71.1) 776145 (29.2) 550618 (20.7) 314288 (11.8) 241383 (9.1) 1432523 (38.3) 788911 (21.1) 325016 (8.7) Statistics Description of basic information The demographic and environmental baseline data, as presented in Table 2 , reveal significant geographic distributions based on the Hu Huanyong line, which employs population density and geographical features to delineate China's eastern and western borders. The dataset indicates that 16.1% of the data originated from the northwest region, while only 9.7% were from within the Hu Huanyong line, with the vast majority, 74.2%, emanating from the southeastern regions. Key environmental metrics recorded included a median humidity of 65%, a median temperature of 15.75°C, and median annual rainfall of 1164.50 mm. Regarding consumption patterns, the study quantified median annual consumption rates of major livestock products: 139.60 kiloton for pork, 14.95 kiloton for beef, and 8.20 kiloton for mutton. Median agricultural yields were reported as 40.84 kiloton for wheat, 303.58 kiloton for corn, 30.71 kiloton for soybean, and 65.00 kiloton for potatoes. Healthcare infrastructure and demographics were also quantified, revealing a median of 919 hospitals and 26,182.50 healthcare institutions, supported by 602 healthcare professionals. The median population within the analyzed regions was documented at 3845.50, exhibiting a birth rate of 10.72‰, a mortality rate of 6.24‰, and a net population growth rate of 4.42‰. In the context of antimicrobial resistance, the study observed median prevalence rates for Carbapenem resistance as follows: 1.40% in Escherichia coli, 18.55% in Pseudomonas aeruginosa, 6.10% in Klebsiella pneumoniae, and 55.30% in Acinetobacter baumannii. The respective median detection rates for these pathogens were 20.99%, 8.74%, 14.78%, and 7.16%. Temporal trends in these detection rates and Carbapenem resistance rates across the specified GNB are comprehensively illustrated in Fig. 1 and Fig. 2 . Table 2 Statistical description of basic information Parameter level Overall Climatic and geographical related indicators Tropical (%) No 240 (96.8) Yes 8 (3.2) Temperate (%) No 208 (83.9) Yes 40 (16.1) Subtropical (%) No 136 (54.8) Yes 112 (45.2) Plateau (%) No 232 (93.5) Yes 16 (6.5) Monsoon (%) No 176 (71.0) Yes 72 (29.0) Huhuanyong line (%) Northwest 40 (16.1) On 24 (9.7) Southeast 184 (74.2) Humidity(%) 0.65 [0.57, 0.76] Temperature(°C) 15.75 [10.05, 17.72] Rainfall(mm) 1164.50 [564.75, 1664.00] Medical institution data Hospital(n) 919.00 [565.00, 1327.00] Normal health hygiene institution (n) 26182.50 [17164.00, 35260.00] Professional health hygiene institution (n) 602.00 [265.00, 1064.00] Core member unit (n) 48.00 [38.00, 64.00] Basic member unit (n) 95.00 [32.00, 140.00] Agriculture-related index Agrgross(100 million RMB) 2028.70 [960.65, 3349.30] Crop production(kiloton) 1397.43 [597.39, 3377.95] Wheat(kiloton) 40.84 [6.04, 407.40] Corn production (kiloton) 303.58 [49.89, 978.39] Soybean production (kiloton) 30.71 [11.66, 54.18] Potato production (kiloton) 65.00 [31.89, 126.24] Pork production (kiloton) 139.60 [47.44, 259.28] Beef production (kiloton) 14.95 [5.88, 36.25] Mutton production (kiloton) 8.20 [2.48, 17.15] Per-capita consumption of major foods, (kg) Grain 133.62 [121.57, 147.88] Edible oil 9.79 [8.55, 12.11] Vegetables 96.45 [88.30, 105.94] Meat 26.74 [22.07, 31.64] Poultry 7.00 [5.06, 11.18] Aquatic 9.90 [3.47, 15.71] Egg 9.28 [6.98, 12.13] Dairy 13.37 [9.97, 16.99] Dry and fresh fruit 49.30 [39.43, 61.50] Sugar 1.26 [1.06, 1.53] Population-related information Population (10^5) 3845.50 [2476.50, 6156.70] Birth rate (‰) 10.72 [7.89, 13.17] Mortality rate (‰) 6.24 [5.61, 7.02] Population growth rate (‰) 4.42 [1.38, 6.80] Male (%) 0.51 [0.51, 0.52] Economic factor GDP (billion) 22201.79 [13998.13, 36564.39] DGDP (billion) 1426.35 [295.63, 3124.99] GDPG (%) 0.08 [0.03, 0.11] Bacterial information Ecoli_resi (%) 1.40 [0.90, 2.10] Pseud_resi (%) 18.55 [14.20, 24.80] Klebsiella_resi (%) 6.10 [3.08, 11.95] Acinet_resi (%) 55.30 [48.55, 61.10] Ecoli_dete (%) 20.99 [19.57, 23.57] Pseud_dete (%) 8.74 [7.64, 10.24] Klebsiella_dete (%) 14.78 [13.31, 16.38] Acinet_dete (%) 7.16 [6.18, 8.12] Sputum (%) 40.94 [37.22, 46.50] Urine (%) 9.76 [6.18, 17.42] Blood (%) 9.08 [7.96, 10.58] Pus (%) 14.18 [7.84, 18.88] Other (%) 24.47 [21.16, 26.73] Intensity (DDDs) 43.37 [38.98, 47.52] Note : Acinet_dete: Detection rate of Acinetobacter baumannii; Acinet_resi: Carbapenem resistance rate of Acinetobacter baumannii; Agrgross: Agriculture Gross output value; DDD: Defined Daily Doses; GDP: Gross domestic product per capita; DGDP: The amount of GDP growth; Ecoli_dete: Detection rate of Escherichia coli; Ecoli_resi: Carbapenem resistance rate of Escherichia coli; GDPG: GDP growth rate; Hospital: Number of included hospitals; Klebsiella_dete: Detection rate of Klebsiella pneumoniae; Klebsiella_resi: Carbapenem resistance rate of Klebsiella pneumoniae; Pseud_dete: Detection rate of Pseudomonas aeruginosa; Pseud_resi: Carbapenem resistance rate of Pseudomonas aeruginosa; Rainfall: Annual rainfall; Temperature: Annual mean temperature. Carbapenem detection and resistance rate distributions To furnish a detailed representation, the distribution of detection and carbapenem resistance rates along the Hu line in China is visually depicted in Fig. 3 . Quantitative results are comprehensively detailed in eTable 1 in the supplementary files. In the Northwest region, the observed carbapenem resistance rates for the GNB under study were as follows: Escherichia coli at 0.75% (95% CI: 0.45, 1.00), Pseudomonas aeruginosa at 12.60% (95% CI: 10.67, 15.57), Klebsiella pneumoniae at 1.35% (95% CI: 0.90, 3.02), and Acinetobacter baumannii at 50.50% (95% CI: 34.65, 55.25). The corresponding detection rates for these pathogens were 23.46% (95% CI: 20.99, 26.25), 5.59% (95% CI: 4.98, 7.91), 13.62% (95% CI: 13.04, 15.04), and 5.69% (95% CI: 4.63, 7.28), respectively. All comparisons demonstrated statistically significant differences, underscored by p-values < 0.001, emphasizing the substantial regional disparities. Similarly, the Southeast region exhibited distinct variations in resistance and detection rates compared to other regions, as depicted in eFigure 1 in the supplementary files. Statistical validation of these regional variations was affirmed by p-values < 0.001 across all comparisons. Additionally, the study extended its analysis to explore the influence of five distinct climate types—Temperate Monsoon, Plateau Mountain, Subtropical Monsoon, Temperate Continental, and Tropical Monsoon—on carbapenem resistance and detection rates across 31 provinces, as illustrated in Fig. 4 . Notably, variations in resistance and detection rates for Pseudomonas, Klebsiella, and Acinetobacter baumannii across these climates are depicted in eFigure 2 in the supplementary files. Escherichia coli demonstrated the lowest resistance rate in the Plateau Mountain climate at 0.30% (95% CI: 0.20, 0.92), whereas Pseudomonas showed the highest resistance in the Monsoon climate at 20.80% (95% CI: 15.75, 25.92) and the lowest in the Plateau Mountain climate at 11.75% (95% CI: 10.52, 14.23). Variability in resistance rates for Klebsiella and Acinetobacter across different climate types was further detailed in eTable 1 in the supplementary files. For detection rates, Escherichia coli registered the highest in the Plateau Mountain climate at 21.53% (95% CI: 16.82, 23.75) and the lowest in the Temperate Continental climate at 6.61% (95% CI: 5.37, 8.41). GNB detection rate A comprehensive double fixed-effect regression analysis was also conducted to elucidate the factors influencing the detection rates of the four specific GNB. Detailed empirical results are available in the supplementary materials (eTable 2). For Escherichia coli, the analysis revealed that variables such as the hospital environment, crop and grain production, and the consumption of various food types (including poultry, aquatic products, eggs, and sugar) significantly elevated the detection rate. Conversely, increased consumption of vegetables and meat was associated with a reduced detection rate. Notably, factors such as temperature, rainfall, and GDP per capita did not exhibit a significant impact on the detection rates. The model accounted for approximately 32% of the variance in the detection rates of Escherichia coli. In the case of Pseudomonas aeruginosa, higher GDP per capita correlated with a lower detection rate, whereas increased consumption of aquatic products raised the detection rate. The model demonstrated substantial efficacy, explaining over 57% of the variance in detection rates for this pathogen. For Klebsiella pneumoniae, the only significant factor linked to detection rates was higher poultry consumption, which was associated with a reduction in detection rates. The model accounted for about 20% of the variation in detection rates for Klebsiella pneumoniae. Lastly, for Acinetobacter baumannii, factors such as increased rainfall negatively impacted detection rates, while higher hospital density, crop production, and the consumption of poultry and aquatic products had positive impacts on detection rates. The regression model explained approximately 33% of the variation in detection rates for Acinetobacter baumannii. Carbapenem resistance rate We also used double fixed effect regression analysis to explore what factors influence the carbapenem detection rate. The detailed coefficients and p-values for each variable are in Table 3 . Table 3 Double fixed effect regression results of carbapenem resistance rate Variables Ecoli_resi Coef. (T-value) Pseud_resi Coef. (T-value) Klebsiella_resi Coef. (T-value) Acinet_resi Coef. (T-value) Temperature(°C) 0.0017 0.0017 0.0051 0.0072 (1.4401) (0.2853) (0.5504) (0.5357) Rainfall(mm) 0.0012 -0.0044 0.0227 * -0.0178 (0.7691) (-0.5719) (1.8875) (-1.0079) GDPG (billion) 0.0255 ** -0.1338 ** -0.0015 -0.1447 (2.3948) (-2.5809) (-0.0184) (-1.2105) Hospital (n) 0.0087 -0.0321 -0.0189 -0.0940 (1.5901) (-1.2035) (-0.4524) (-1.5297) Crop production(kiloton) 0.0006 0.0209 0.0103 0.0252 (0.1645) (1.2573) (0.3944) (0.6580) Intensity (DDDs) 0.0423 ** -0.0124 ** 0.0531 *** 0.0423 ** (2.4511) (-2.0471) (3.0983) (2.4511) Per-capita consumption of major foods, (kg) Grain 0.0059 -0.0081 0.0495 0.0313 (0.8250) (-0.2305) (0.9004) (0.3882) Edible oil -0.0012 0.0278 -0.0478 0.0345 (-0.2454) (1.1516) (-1.2583) (0.6181) Vegetables -0.0119 ** 0.0039 0.0331 -0.0770 (-2.0988) (0.1391) (0.7602) (-1.2057) Meat -0.0125 ** 0.0084 -0.0322 0.1344 ** (-2.1953) (0.3013) (-0.7365) (2.0975) Poultry 0.0019 -0.0363 -0.0859 -0.0718 (0.2697) (-1.0755) (-1.6217) (-0.9235) Aquatic -0.0151 ** -0.0268 0.0474 -0.0809 (-2.3115) (-0.8425) (0.9480) (-1.1034) Egg -0.0009 0.0307 0.0556 -0.0311 (-0.1366) (0.9834) (1.1317) (-0.4318) Dairy 0.0117 ** 0.0030 0.0059 -0.0336 (2.5645) (0.1359) (0.1692) (-0.6543) Dried and fresh fruits 0.0130 ** -0.0062 -0.0235 -0.0154 (2.1035) (-0.2068) (-0.4973) (-0.2223) Sugar 0.0108 ** 0.0222 -0.0128 0.0109 (2.4001) (1.0132) (-0.3720) (0.2148) Constant -0.0636 0.2940 -0.2572 1.1068 * (-1.2099) (1.1482) (-0.6394) (1.8749) Prov / Year Yes Yes Yes Yes N 248 248 248 248 F 2.5554 14.0646 2.9087 3.2946 p 0.0003 < 0.001 < 0.001 < 0.001 R 2 0.2325 0.6251 0.2564 0.2809 Note: T-values, which are in parentheses, indicate the significance of each coefficient. Additionally, p-values denote the statistical significance of the coefficients, where ** indicates p < 0.01 and * indicates p < 0.05. Acinet_resi: Carbapenem resistance rate of Acinetobacter baumannii; DDD: Defined Daily Doses; Ecoli_resi: Carbapenem resistance rate of Escherichia coli; GDP: Gross domestic product per capita; GDPG: GDP growth rate; Hospital: Number of included hospitals; Klebsiella_resi: Carbapenem resistance rate of Klebsiella pneumoniae; Pseud_resi: Carbapenem resistance rate of Pseudomonas aeruginosa; Rainfall: Annual rainfall. For Escherichia coli, we found significant relationships between its resistance rate and various factors. Positive impacts came from GDPD, dairy, dried fruit, and sugar consumption, with coefficients ranging from 0.0108 to 0.0255. On the other hand, vegetable, meat, and aquatic consumption showed negative impacts. However, other factors like temperature, rainfall, hospital density, and others did not significantly affect the resistance rate. Our model explains about 23% of the variation in Escherichia coli resistance. Regarding Pseudomonas aeruginosa, a higher GDP per capita was linked to a lower resistance rate, with a coefficient of -0.1338. Other variables did not show a significant impact. The model was quite effective, explaining over 62% of the resistance rate variation. For Klebsiella, only higher rainfall levels showed a significant, though moderate, relationship with increased resistance, with a coefficient of 0.0227. Other factors weren't significant, and our model accounts for about 26% of the resistance rate variation. Lastly, for Acinetobacter baumannii, we found that higher meat consumption could increase resistance, with a coefficient of 0.1344. This model explains about 28% of the resistance rate variation. In this study, the factors affecting the carbapenem resistance rates was analyzed in the four specific GNB. Comprehensive coefficients and significance levels for each predictor are tabulated in Table 3 . For Escherichia coli, our findings revealed that certain variables positively influenced resistance rates, including gross domestic product per capita (GDPD), and consumption of dairy, dried fruits, and sugar, with coefficients ranging from 0.0108 to 0.0255. Conversely, the consumption of vegetables, meat, and aquatic products was associated with reduced resistance rates. Variables such as temperature, rainfall, and hospital density showed no significant effect. The model explained roughly 23% of the variability in resistance rates for Escherichia coli. In the case of Pseudomonas aeruginosa, a notable correlation was observed where a higher GDP per capita corresponded with a lower resistance rate, evidenced by a coefficient of -0.1338. This model was particularly robust, explaining over 62% of the variation in resistance rates. For Klebsiella pneumoniae, increased rainfall was the sole significant predictor, showing a modest increase in resistance rates with a coefficient of 0.0227. The model, however, accounted for only about 26% of the resistance rate variability. Finally, for Acinetobacter baumannii, an increase in meat consumption was significantly correlated with higher resistance rates, indicated by a coefficient of 0.1344. This model explained about 28% of the variation in the resistance rates for Acinetobacter baumannii. Mortality We further conducted a detailed bi-fixed effect regression analysis to explore the relationship between mortality rates and various demographic and environmental factors. The comprehensive results of this analysis are documented in Table 4 and eTable 3 of the supplementary files. Figures 5 and 6 provide scatter plots illustrating the correlations between detection and carbapenem resistance rates with mortality, birth rate, and population growth rate. Our findings indicate a significant association between the detection rates of specific pathogens and mortality rates. Specifically, an increased detection rate of Pseudomonas aeruginosa was linked to higher mortality, with a coefficient of 0.2007. Conversely, a higher detection rate of Klebsiella pneumoniae was associated with lower mortality, demonstrated by a coefficient of -0.0762. Additionally, temperature exhibited a negative correlation with mortality, with a coefficient of -0.0027. Positive correlations were observed between mortality and both crop production and sugar consumption, with coefficients of 0.0105 and 0.0064, respectively. Moreover, a higher intensity of antibiotic usage was associated with reduced mortality, indicated by a coefficient of -0.0164. These variables collectively explained approximately 70% of the variation in mortality rates. Regarding the relationship between carbapenem resistance rates and mortality, Escherichia coli resistance displayed a positive correlation with increased mortality, evidenced by a coefficient of 0.0989, while resistance in Acinetobacter baumannii had a negative impact, with a coefficient of -0.0116. The influence of temperature, crop production, and antibiotic usage on mortality was consistent across both the detection and resistance rate analyses. The robustness of our model is highlighted by an F-statistic of 15.1328 (p < 0.001), explaining about 68% of the variation in mortality rates. Table 4 Effect of carbapenem resistance rate on birth rate, mortality and population growth rate: double fixed effect regression result Variables Birth rate Coef. (T-value) Mortality rate Coef. (T-value) Popgrow rate Coef. (T-value) Ecoli_resi (%) 0.2429 0.0989 * 0.1286 (1.4381) (1.6690) (0.7653) Pseud_resi (%) 0.0263 -0.0031 0.0340 (0.7670) (-0.2610) (0.9975) Klebsiella_resi (%) 0.0062 -0.0083 0.0130 (0.2906) (-1.0970) (0.6109) Acinet_resi (%) -0.0068 -0.0116 ** 0.0051 (-0.4476) (-2.1725) (0.3356) Temperature(°C) 0.0009 -0.0029 *** 0.0043 (0.3452) (-2.9613) (1.5621) Rainfall(mm) 0.0044 0.0007 0.0040 (1.2180) (0.5854) (1.1044) GDPG (billion) -0.0047 -0.0025 -0.0011 (-0.1858) (-0.2866) (-0.0452) Hospital (n) -0.0084 0.0026 -0.0129 (-0.6643) (0.5791) (-1.0210) Crop production(kiloton) -0.0020 0.0089 *** -0.0116 (-0.2542) (3.2627) (-1.5047) Intensity (DDDs) 0.0008 -0.0106 -0.0224 (0.1077) (-0.2892) (-0.3910) Per-capita consumption of major foods, (kg) Grain 0.0192 0.0051 0.0117 (1.1700) (0.8879) (0.7178) Edible oil -0.0050 -0.0027 -0.0024 (-0.4386) (-0.6653) (-0.2087) Vegetables 0.0081 -0.0060 0.0143 (0.6203) (-1.2979) (1.0993) Meat 0.0044 0.0056 -0.0019 (0.3259) (1.1889) (-0.1434) Poultry -0.0130 -0.0014 -0.0133 (-0.8176) (-0.2584) (-0.8408) Aquatic 0.0197 -0.0047 0.0259 * (1.3044) (-0.8821) (1.7233) Egg -0.0177 0.0058 -0.0214 (-1.2086) (1.1223) (-1.4632) Dairy -0.0129 -0.0007 -0.0109 (-1.2208) (-0.1882) (-1.0316) Dried and fresh fruits 0.0108 0.0076 0.0029 (0.7582) (1.5202) (0.2048) Sugar -0.0015 0.0064 * -0.0067 (-0.1477) (1.7497) (-0.6495) Constant -0.0003 -0.0116 0.0244 (-0.0021) (-0.2714) (0.2020) Prov / Year Yes Yes Yes N 248 248 248 F 24.7172 15.1328 39.3385 p < 0.001 < 0.001 < 0.001 R 2 0.7784 0.6826 0.8483 Note: T-values, which are in parentheses, indicate the significance of each coefficient. Additionally, p-values denote the statistical significance of the coefficients, where ** indicates p < 0.01 and * indicates p < 0.05. Acinet_resi: Carbapenem resistance rate of Acinetobacter baumannii; DDD: Defined Daily Doses; Ecoli_resi: Carbapenem resistance rate of Escherichia coli; GDP: Gross domestic product per capita; GDPG: GDP growth rate; Hospital: Number of included hospitals; Klebsiella_resi: Carbapenem resistance rate of Klebsiella pneumoniae; Pseud_resi: Carbapenem resistance rate of Pseudomonas aeruginosa; Rainfall: Annual rainfall. It is noteworthy that our study detected no significant effects of the detection and carbapenem resistance profiles of the four bacterial groups on birth rate and population growth rate. A detailed exposition of these findings were shown in Table 4 and eTable 3 in the supplementary files. Discussion The national wide time series study in China, spanning from 2014 to 2021, has offered valuable insights into the monitoring status[ 21 – 23 ]. The study encompassed a substantial number of monitoring strains from diverse hospitals, showcasing its comprehensive nature. These findings provide valuable insights not only into the intricate interplay between geographical regions and climate types with respect to carbapenem resistance and GNB detection rates but also the relationships between various factors and the detection or resistance rates of these bacterial strains. The data underscores the importance of considering regional and climatic factors in understanding the dynamics of antibiotic resistance in GNB, thus offering critical information for healthcare strategies and policy formulation. Simultaneously, it shed light on potential avenues for further research and public health interventions. During the surveillance period, both Gram-positive and GNB were prevalent, with Staphylococcus aureus and Escherichia coli being the most frequently isolated strains, which is consistent with previous reports[ 24 – 26 ]. Although there is a slight variation in the percentages, it is important to note that GNB consistently account for a higher proportion of non-repeated bacteria, reaching nearly 70%. This finding is in line with other published studies, as GNB are known to exhibit greater resistance to antibiotics, presenting a significant public health concern[ 3 , 6 , 27 ]. Notably, sputum specimens were the predominant source of bacterial strains, followed by urine and blood specimens, which may have implications for targeted diagnostics and treatment strategies and also be consistent with previous research finding[ 28 – 30 ]. Meanwhile, according to Fig. 1 and Fig. 2 , which presented the detection and resistance trend and distribution of four kinds of bacteria in 31 provinces and cities, the fluctuations over the years are not very large. However, there is a relatively significant variation in the carbapenem resistance rate, especially for Klebsiella pneumoniae, where the resistance rate shows a gradual increase. The trends were also consistent with those previously reported[ 31 – 33 ]. In addition, the investigation also delved into the relationship between bacterial resistance rates and various environmental factors across different regions and climate types in China. We found significant variations in carbapenem resistance and detection rates across different regions and climate types. The Northwest, On, and Southeast regions showed statistically significant differences in resistance rates and detection rates among the four bacteria. In particular, the Northwest region displayed low resistance rates for Escherichia coli and Klebsiella but higher rates for Pseudomonas and Acinetobacter baumannii. Conversely, the Southeast region exhibited higher resistance rates for all four bacteria. Furthermore, carbapenem resistance and detection rates varied across five climate types, with different provinces showing distinct patterns. For example, Pseudomonas had the highest resistance in the Monsoon climate and the lowest in the Plateau mountain climate. Escherichia coli had the lowest resistance in the Plateau mountain climate, while the highest detection rate was observed in the same climate type. These findings suggest that geographical and climatic factors may play a role in bacterial resistance and detection rates. These findings align with previous studies that have reported regional disparities in antibiotic resistance prevalence[ 34 – 37 ]. Besides, our study also utilized double fixed effect regression analysis to explore the factors affecting carbapenem resistance and detection rates of the four GNB. The regression results revealed several significant associations, some of which were consistent with previous research, while others offered new insights. Moreover, we observed a positive association between GDPG (Coefficient = 0.0255) and Escherichia coli resistance, implying that higher economic development might lead to increased antibiotic resistance in certain regions. Additionally, our study found a negative association between GDPG (Coefficient=-0.1338) and Pseudomonas resistance. Some literatures reported the same situation[ 38 – 40 ] and Klein EY et al. found that reducing global consumption is critical for reducing the threat of antibiotic resistance[ 39 ]. It is also noteworthy that the factors influencing the detection rate and antibiotic resistance of Escherichia coli appear to be closely tied to grain consumption patterns. Specifically, we observe negative correlations between the detection rate and the consumption of vegetable bacterial foods, poultry, and aquatic foods, suggesting that higher intake of these foods is associated with a lower Ecoli_dete. Conversely, increased consumption of eggs and sugar positively correlates with a higher Ecoli_dete. Interestingly, meat consumption exhibits a positive correlation with Acinet_resi, while poultry consumption has opposite influence on the detection rates of Klebsiella (Coefficient=-0.04) and Acinetobacter baumannii (Coefficient = 0.062). The results above might indicate a potential link between dietary habits and antibiotic resistance. While this finding is novel and not extensively studied in existing literature, some previous research has explored the impact of crop production systems and agricultural practices on antibiotic resistance[ 41 – 43 ]. Overall, the impact of dietary factors on different bacterial populations varies. The effect of the same dietary factor on the resistance or detection of different bacteria is not consistent. Furthermore, we evaluated the impact of the four specific bacteria detection rate and Carbapenem resistance rate on mortality, birth and population grow rate, our study revealed mixed results (Table 3 and eTable 3 in Supplement files). The mortality rate increases with the increase of Pseud_resi, Crop production, and sugar consumption, while it decreases with the increase of Acinet_resi, Temperature, and intensity of antibiotic use. The analysis results between detection rate and mortality rate are consistent with the analysis results of resistance rate. However, with the increase of aquatic product consumption, the mortality rate decreases. Founou. et al. found that Acinetobacter baumannii is associated with a high mortality risk and increased economic costs with Enterococcus faecium, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter pathogens implicated as the main cause of increased mortality, similar to our study result[ 38 ]. Regarding the relationship between Acinetobacter baumannii and mortality rate, resistance rate, and detection rate, we have conducted a comprehensive literature search. Most of the literature focuses on clinical outcomes of critically ill patients[ 41 – 43 ], with limited studies on healthy populations and large cohort studies investigating the risk of bacterial infection and overall population-wide mortality rate. Notably, sugar consumption has a significant impact, as it is not only correlated with bacterial detection and resistance but also influences mortality rates, corroborating previous research findings[ 44 – 46 ].Hence, controlling sugar intake can not only reduce overall mortality rates but also partially lower the detection and antibiotic resistance rates of Escherichia coli. However, large-scale epidemiological investigations that correlate bacterial detection and antibiotic resistance with dietary factors are currently lacking. Thus, conducting future research on the relationship between dietary factors and bacterial prevalence is essential. Overall, the study examined the correlation between the detection rate and carbapenem resistance rate of the specific GNB, including Escherichia coli, Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae, with various environmental factors, such as temperature, humidity, rainfall, economic level, agricultural level, animal husbandry, and antibiotic use in the food chain. Environmental factors were found to influence the detection and carbapenem resistance rates of these bacteria. However, further research is needed to address limitations and expand our understanding of bacterial resistance dynamics in diverse settings. The study has some acknowledged limitations, including the use of retrospective data that might not capture all relevant factors influencing antibiotic resistance. Regional variations in surveillance and reporting systems might introduce bias in the dataset. Additionally, some potential factors, such as antibiotic usage in animal husbandry, were not included due to data availability constraints. Future research should consider longitudinal studies and more comprehensive datasets to better capture the dynamic nature of antibiotic resistance patterns. Investigating the impact of specific antimicrobial stewardship interventions and regional infection control measures could shed further light on strategies to mitigate antibiotic resistance at the local level. Overall, these findings underscore the need for tailored prevention and control strategies to address regional variations in bacterial resistance patterns, considering economic and environmental factors in combating antimicrobial resistance effectively. Nevertheless, our comprehensive nationwide time series study offers valuable insights into the prevalence and patterns of the four specific bacterial resistance in China. The study also highlights the importance of continuous surveillance and research to monitor the four specific bacterial resistance trends and the influence of various environmental and demographic factors on resistance rates. In conclusion, microbial communities are influenced by factors like diet, climate, and economics. Among these, detection rates of Escherichia coli and Acinetobacter baumannii are complex due to various factors interacting. On the other hand, microorganisms like Pseudomonas aeruginosa and Klebsiella pneumoniae seem to be more limited in their responses, possibly due to specific environmental conditions. Declarations Data The databases of CARSS, NBS, and CMDC used in this study are publicly available. Funding This research was funded by the Health Commission of Hunan Provincial [NO. 202113012480]. It was also supported by the International Research Center for Precision Medicine, Transformative Technology, and Software Services, Hunan, China. Author Contribution Conceptualization, M.Y., Y.Z.; methodology, B.K., M.Y. and Y.Z.; software, Y.Z. and Z.S.; validation, Y.M. and W.C.; formal analysis, Y.Z.; investigation, J.L., H.L. and M.X.; resources, M.Y.; data curation, Z.S. and M.X.; writing original draft preparation, Y.Z.; review and editing, M.Y. and Y.Z.; visualization, Y.Z.; supervision, M.Y. and W.C.; project administration, Y.Z. and M.Y.; funding acquisition, M.Y. All authors have read and agreed to the published version of the manuscript. Acknowledgement Our heartfelt appreciation goes to Shang-Xun Wu, Mou-Ze Liu, Rao Fu, Hui Gong, Rui Ma, and Lin-Na Guo for their invaluable assistance and guidance. Special thanks are also due to Bo Wang for his remarkable contribution in creating professional figures. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5712281","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":396175537,"identity":"763d9efe-07a7-4a52-96f3-63211628eebc","order_by":0,"name":"Yi-Chang Zhao","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yi-Chang","middleName":"","lastName":"Zhao","suffix":""},{"id":396175538,"identity":"be8f7f6e-7e8f-41c4-ada7-9cf12a7beb75","order_by":1,"name":"Zhi-Hua Sun","email":"","orcid":"","institution":"China Pharmaceutical 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University","correspondingAuthor":false,"prefix":"","firstName":"Ming-Xuan","middleName":"","lastName":"Xiao","suffix":""},{"id":396175544,"identity":"aacee088-5bc7-48e4-8b8a-0a8dd2c17f91","order_by":5,"name":"Bi-Kui Zhang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Bi-Kui","middleName":"","lastName":"Zhang","suffix":""},{"id":396175545,"identity":"5ce1a1d9-3cfa-4cac-b140-75bb14d91648","order_by":6,"name":"Wei Cao","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Cao","suffix":""},{"id":396175546,"identity":"94c73fc7-46eb-4aac-a012-7934ff13ad64","order_by":7,"name":"Miao Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYLCCBAYGOQiLjQQtxiRqAYLEBqK1GBw/e3TDgxqb9P7+MwYMH8oOM/DPbiCg5Uxe2o2EY2m5M27kGDDOOHeYQeLOAQJaDuSY3UhgO5y7QYLHgJm37TCDgUQCAS3n3wC1/DucbsB/xoD5L1FabgBtSWw7nGDAkGPAzEiMFskbQFsS+9IMZ9xIKzjYcy6dR+IGAS1853PMbv74ZiPP339444MfZdZy/DMIaFE4gMQBsXnwqwcC+QaCSkbBKBgFo2DEAwBJ8kZ1La2uRwAAAABJRU5ErkJggg==","orcid":"","institution":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Miao","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2024-12-25 17:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5712281/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5712281/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72841276,"identity":"3cf951e6-69bd-44a6-b148-6c7f213a1fba","added_by":"auto","created_at":"2025-01-02 18:19:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3535886,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution and changing trend of detection rate of the four types of bacteria across 31 provinces from 2014 to 2021.\u003c/p\u003e\n\u003cp\u003e(A) Detection rate distribution of Escherichia coli and climate differences in 31 provinces;\u003c/p\u003e\n\u003cp\u003e(B) Detection rate distribution of Pseudomonas aeruginosa and climate differences in 31 provinces;\u003c/p\u003e\n\u003cp\u003e(C) Detection rate distribution of Klebsiella pneumoniae and climate differences in 31 provinces;\u003c/p\u003e\n\u003cp\u003e(D) Detection rate distribution of Acinetobacter baumannii and climate differences in 31 provinces;\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5712281/v1/eda1bff061e58ca7143c77b2.png"},{"id":72841256,"identity":"20e5151f-ecfb-4238-9329-accc81d3150e","added_by":"auto","created_at":"2025-01-02 18:19:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3664154,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution and changing trend of resistance rate of the four types of bacteria across 31 provinces from 2014 to 2021.\u003c/p\u003e\n\u003cp\u003e(A) Carbapenem resistance distribution of Escherichia coli and climate differences in 31 provinces; (B) Carbapenem resistance distribution of Pseudomonas aeruginosa and climate differences in 31 provinces;\u003c/p\u003e\n\u003cp\u003e(C) Carbapenem resistance distribution of Klebsiella pneumoniae and climate differences in 31 provinces;\u003c/p\u003e\n\u003cp\u003e(D) Carbapenem resistance distribution of Acinetobacter baumannii and climate differences in 31 provinces;\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5712281/v1/01d454bd53771e237c6d2dc9.png"},{"id":72841250,"identity":"e0ea04a9-3798-4e9e-878f-0a2c6d0cb249","added_by":"auto","created_at":"2025-01-02 18:19:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1653217,"visible":true,"origin":"","legend":"\u003cp\u003eDetection and Carbapenem resistance rate of four specific gram-negative bacteria across 31 provinces with diverse climate types in Mainland China.\u003c/p\u003e\n\u003cp\u003e(A) Detection rate and Carbapenem resistance distribution of Escherichia coli (ECO) and climate differences in 31 provinces;\u003c/p\u003e\n\u003cp\u003e(B) Detection rate and Carbapenem resistance distribution of Pseudomonas aeruginosa(PAE) and climate differences in 31 province;\u003c/p\u003e\n\u003cp\u003e(C) Detection rate and Carbapenem resistance distribution of Klebsiella pneumoniae(KPN) and climate differences in 31 province;\u003c/p\u003e\n\u003cp\u003e(D) Detection rate and Carbapenem resistance distribution of Acinetobacter baumannii(ABA) and climate differences in 31 province;\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5712281/v1/0012e434c44941cf725c894a.png"},{"id":72841255,"identity":"1fe9965f-8a5d-411e-ba65-668d94e53a22","added_by":"auto","created_at":"2025-01-02 18:19:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3335644,"visible":true,"origin":"","legend":"\u003cp\u003eDetection and carbapenem resistance rate of four specific gram-negative bacteria across 31 provinces with varying distribution along the Hu Huanyong lines in Mainland China.\u003c/p\u003e\n\u003cp\u003e(A) Detection rate and Carbapenem resistance distribution of Escherichia coli (ECO) and Hu line distribution in 31 provinces;\u003c/p\u003e\n\u003cp\u003e(B) Detection rate and Carbapenem resistance distribution of Pseudomonas aeruginosa(PAE) and Hu line distribution in 31 province;\u003c/p\u003e\n\u003cp\u003e(C) Detection rate and Carbapenem resistance distribution of Klebsiella pneumoniae(KPN) and Hu line distribution in 31 province;\u003c/p\u003e\n\u003cp\u003e(D) Detection rate and Carbapenem resistance distribution of Acinetobacter baumannii(ABA) and Hu line distribution in 31 province;\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5712281/v1/64ffbcac85c4a4080e64f0cf.png"},{"id":72841263,"identity":"7c342c19-bb26-4028-bf4e-d2d6200e5d4c","added_by":"auto","created_at":"2025-01-02 18:19:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1899496,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of detection rate versus birth rate, mortality, and population growth rate\u003c/p\u003e\n\u003cp\u003e(A)Scatter plot of Escherichia coli detection rate versus birth rate, mortality, and population growth rate;\u003c/p\u003e\n\u003cp\u003e(B)Scatter plot of Pseudomonas aeruginosa detection rate versus birth rate, mortality, and population growth rate;\u003c/p\u003e\n\u003cp\u003e(C)Scatter plot of Klebsiella pneumoniae detection rate versus birth rate, mortality, and population growth rate;\u003c/p\u003e\n\u003cp\u003e(D)Scatter plot of Acinetobacter baumannii detection rate versus birth rate, mortality, and population growth rate;\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5712281/v1/3a437fa87c394cbbe4636729.png"},{"id":72841252,"identity":"c9787f8b-2f9a-4843-b073-d0a74916295d","added_by":"auto","created_at":"2025-01-02 18:19:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1954788,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of resistance rate versus birth rate, mortality, and population growth rate\u003c/p\u003e\n\u003cp\u003e(A)Scatter plot of Escherichia coli resistance rate versus birth rate, mortality, and population growth rate;\u003c/p\u003e\n\u003cp\u003e(B)Scatter plot of Pseudomonas aeruginosa resistance rate versus birth rate, mortality, and population growth rate;\u003c/p\u003e\n\u003cp\u003e(C)Scatter plot of Klebsiella pneumoniae resistance rate versus birth rate, mortality, and population growth rate;\u003c/p\u003e\n\u003cp\u003e(D)Scatter plot of Acinetobacter baumannii resistance rate versus birth rate, mortality, and population growth rate.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5712281/v1/90ac6b44248da3a3f0219dd4.png"},{"id":72843111,"identity":"8a39c0df-a46c-4ccb-b799-c942380e67ba","added_by":"auto","created_at":"2025-01-02 18:52:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16341811,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5712281/v1/410368d6-0626-42e7-897e-10f9d84d1c2a.pdf"},{"id":72841293,"identity":"db8ffe1d-fb2a-4b04-9f98-3851ba81386c","added_by":"auto","created_at":"2025-01-02 18:19:54","extension":"doc","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":58368,"visible":true,"origin":"","legend":"","description":"","filename":"eTable1.doc","url":"https://assets-eu.researchsquare.com/files/rs-5712281/v1/89049f631f5adf58d79afdde.doc"},{"id":72841279,"identity":"743cb8e1-056a-430b-95ab-a35b905ccd4a","added_by":"auto","created_at":"2025-01-02 18:19:53","extension":"doc","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":63488,"visible":true,"origin":"","legend":"","description":"","filename":"eTable2.doc","url":"https://assets-eu.researchsquare.com/files/rs-5712281/v1/bd88d6b829ae2a0d9ec69c74.doc"},{"id":72841262,"identity":"7f7e77be-2bb7-445d-9ad1-a4f78a07b60c","added_by":"auto","created_at":"2025-01-02 18:19:52","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":21174,"visible":true,"origin":"","legend":"","description":"","filename":"eTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5712281/v1/c1c29134d81dbf0fba86d6f7.docx"},{"id":72841295,"identity":"72fe51fb-65a8-4404-8379-8e1684a69195","added_by":"auto","created_at":"2025-01-02 18:19:54","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":28026694,"visible":true,"origin":"","legend":"","description":"","filename":"eFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-5712281/v1/158c083b99d4ab4fc6755605.tif"},{"id":72841304,"identity":"c3aeb7b9-5d73-4c64-8e3b-851c63ee58d4","added_by":"auto","created_at":"2025-01-02 18:19:54","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":41123678,"visible":true,"origin":"","legend":"","description":"","filename":"eFigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-5712281/v1/ab20fa903c1d6a39aa4c2f2f.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Gram-Negative Strains to Mortality: Understanding Bacterial Resistance in Mainland China","fulltext":[{"header":"Highlights","content":"\u003cp\u003eThis time series study investigated the detection and carbapenem resistance rates of four pathogenic Gram-negative bacteria in China from 2014 to 2021.\u003c/p\u003e\u003cp\u003eSignificant regional and climate-related variations in bacterial resistance rates and detection rates were observed, with higher resistance rates in tropical and subtropical regions.\u003c/p\u003e\u003cp\u003eEconomic factors and specific consumption patterns were associated with bacterial resistance rates, highlighting the role of socio-economic factors in resistance patterns.\u003c/p\u003e\u003cp\u003eHigher bacterial resistance rates were associated with increased mortality rates, underscoring the potential impact of antimicrobial resistance on patient outcomes.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eCarbapenem-resistant Gram-negative bacteria (CRGNB) are a group of bacterial strains that are resistant to carbapenems, a class of antibiotics commonly used to treat severe infections[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These bacteria can cause a range of infections, from urinary tract infections to bloodstream infections, and are a growing public health concern worldwide[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. CRGNB have been identified in various regions of the world, including Asia, Europe, and the Americas, and are associated with high morbidity and mortality rates[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In China, the prevalence of CRGNB has been increasing rapidly in recent years[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEscherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii were identified as the most frequently isolated CRGNB worldwide. Scudeller et al. collected data from 47 countries and regions and revealed that these four bacteria accounted for more than 80% of all CRGNB isolates[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A subsequent study also emphasized that these four bacteria are the predominant causes of CRGNB infections[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition, the detection rate and carbapenem resistance rate of these four bacteria are influenced by various factors. These factors encompass geographic location, hospital settings, antibiotic usage, and patient demographics[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Recent research has provided some valuable insights into the influence of environmental factors on the prevalence of infection and human health[\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Ma, L. et al. found potential pathogenic species, which were influenced by factors like rainfall and water source and lead to health risks[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Besides, Davis, M. F. et al. argued that industrial food animal production (IFAP), with its widespread antimicrobial use, constitutes an anthropogenic ecosystem. Focusing on U.S. broiler chicken production, they used an ecosystem perspective to explore the changes in microbiomes, including the resistome and resistance flow between them[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, the correlation between detection and carbapenem resistance rates of these four specific bacteria and environmental factors, agricultural, economic levels and diet structure remains insufficiently explored in the realm of infectious diseases. Given the potential impact of these factors on the spread and emergence of drug-resistant bacteria, further research is imperative to elucidate this relationship. Therefore, our goal was to identify significant relationships, offering meaningful insights into the interactions and dependencies between various factors.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eIn this time series study, data were sourced from three primary databases: the China Antimicrobial Resistance Surveillance System (CARSS)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the National Bureau of Statistics (NBS) database[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and the China Meteorological Data Service Centre (CMDC) online resources[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. CARSS, a national surveillance system, provided comprehensive data on detection and carbapenem resistance rates of Gram-negative and Gram-positive bacteria from almost 1435 member hospitals across China. We focused on four bacterial types: Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii, and extracted data on their detection and carbapenem resistance rates up to 2021. The NBS database provided demographic and economic indicators. Meteorological data, including temperature, humidity, and precipitation, were obtained from the CMDC through historical records. Hu line was used to investigate the effects of detection and carbapenem resistance of these four bacteria. Data collection involved independent extraction by two researchers, with discrepancies resolved through discussion. Our primary focus centered on examining the detection and Carbapenem-resistant rates of Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii. We assessed their associations with various factors encompassing climate, agriculture, economy, diet structure, and other relevant variables. Our aim was to discern any meaningful associations between these bacteria and the aforementioned indicators. Furthermore, we also explored the potential influence of detecting the four specific bacteria and their Carbapenem-resistant rates on mortality, birth rate, and overall population growth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDuring data analysis, descriptive statistics were crucial. Numerical variables were assessed for central tendency and variability using mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for normally distributed data and median with interquartile range (IQR) for non-normally distributed data. Categorical variables were presented as frequency and percentage distributions. To evaluate the significance of Carbapenem-resistant and detection rates of these four specific bacteria across climate types and Huanyong line regions, we used the Kruskal-Wallis test with a significance level of 0.05. To identify multicollinearity among variables, we used the Variance Inflation Factor (VIF), with values below 10 indicating no serious multicollinearity. For assessing regression reliability, Durbin-Watson values between 1.80 and 2.20 were preferred, indicating minimal autocorrelation. The F-test assessed the overall statistical significance of the model, with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 suggesting a meaningful relationship between dependent and independent variables. Double fixed effect regression model is also estimated using a panel dataset comprising the collected data over years. The model controls for province and year fixed effects, accounting for unobserved heterogeneity across different provinces and time-specific effects. Statistical analyses and graphic rendering were conducted using SPSS (version 26), RStudio 4.2.2, StataMP 17, and Python 3.10.9. This study involves the analysis of publicly available data, and therefore, does not require ethical approval.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSummary of CARSS report (2014\u0026ndash;2021)\u003c/h2\u003e \u003cp\u003eThe National Bacterial Resistance Monitoring Report for the period 2014 to 2021 provided an extensive overview of bacterial resistance surveillance activities in China as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Throughout this interval, the national network comprised between 1412 and 1435 monitoring entities, including a range of 1110 to 1375 hospitals after a meticulous data review. Within the network, 269 to 363 were classified as secondary hospitals and 841 to 1023 as tertiary hospitals. Adhering to a methodology that retained only the first strain of identical bacteria from the same patient and excluded duplicate strains, the study encompassed a total bacterial count that increased from 2,227,420 in 2014 to 3,743,027 by 2021. The proportion of Gram-positive bacteria ranged from 28.5\u0026ndash;32.6%, whereas Gram-negative bacteria (GNB) constituted between 70.3% and 71.5%. Among the GNB, the five most prevalent isolates were: Escherichia coli (29.2\u0026ndash;29.9%), Klebsiella pneumoniae (19.4\u0026ndash;20.7%), Pseudomonas aeruginosa (11.8\u0026ndash;12.7%), Acinetobacter baumannii (9.1\u0026ndash;10.8%), and Enterobacter cloacae (3.9\u0026ndash;4.5%). The primary source of these strains were sputum specimens, which showed an increase from 954,224 in 2014 to 1,432,523 in 2021, representing 38.3\u0026ndash;42.8% of samples. This was followed by urine specimens, which increased from 394,356 to 788,911 over the period, accounting for 15.6\u0026ndash;21.1% of the total, and blood specimens, which ranged from 208,467 to 325,016, representing 8.7\u0026ndash;9.8%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of overall bacterial detection data from CARSS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMember \u003c/p\u003e \u003cp\u003eUnit\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncluded\u003c/p\u003e \u003cp\u003eHospitals\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003cp\u003eHospitals\u003c/p\u003e \u003cp\u003e(n,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncluded\u003c/p\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003cp\u003eHospitals\u003c/p\u003e \u003cp\u003e(n,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon-repeated\u003c/p\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGram-positive\u003c/p\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003cp\u003e(n,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGram-negative\u003c/p\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003cp\u003e(n,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEscherichia\u003c/p\u003e \u003cp\u003eColi\u003c/p\u003e \u003cp\u003e(n,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eKlebsiella\u003c/p\u003e \u003cp\u003ePneumoniae\u003c/p\u003e \u003cp\u003e(n,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePseudomonas\u003c/p\u003e \u003cp\u003eAeruginosa\u003c/p\u003e \u003cp\u003e(n,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAcinetobacter\u003c/p\u003e \u003cp\u003eBaumannii\u003c/p\u003e \u003cp\u003e(n,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSputum\u003c/p\u003e \u003cp\u003eSpecimens\u003c/p\u003e \u003cp\u003e(n,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eUrine\u003c/p\u003e \u003cp\u003eSpecimens\u003c/p\u003e \u003cp\u003e(n,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eBlood\u003c/p\u003e \u003cp\u003eSpecimens\u003c/p\u003e \u003cp\u003e(n,%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e269 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e841 (76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2227420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e634414 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1593006 (71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e465136 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e308951 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e202817 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e171662 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e954224 (42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e394356 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e208467 (9.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e272 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e871 (76.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2400786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e695066 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1705720 (71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e510140 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e336829 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e219630 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e183178 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e993205 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e372161 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e224481 (9.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e322 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e951 (74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2727605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e794073 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1933532 (70.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e575494 (29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e381198 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e246242 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e208689 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1111456 (40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e499362 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e268114 (9.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e336 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e971 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2894517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e859388 (29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2035129 (70.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e597909 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e411487 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e253083 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e207046 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1201531 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e540051 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e274599 (9.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e349 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1004 (74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3234372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e952023 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2282349 (70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e660261 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e465322 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e283222 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e227091 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1340920 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e608667 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e296052 (9.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e352 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1023 (74.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3528471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1043535 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2484936 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e707968 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e503230 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e299318 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e239890 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1462853 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e673824 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e320002 (9.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e352 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1019 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3249123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e939201 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2309922 (71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e686049 (29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e482330 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e281260 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e219921 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1245951 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e667681 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e295868 (9.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e363 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1010 (73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3743027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1083580 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2659447 (71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e776145 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e550618 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e314288 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e241383 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1432523 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e788911 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e325016 (8.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistics Description of basic information\u003c/h3\u003e\n\u003cp\u003eThe demographic and environmental baseline data, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, reveal significant geographic distributions based on the Hu Huanyong line, which employs population density and geographical features to delineate China's eastern and western borders. The dataset indicates that 16.1% of the data originated from the northwest region, while only 9.7% were from within the Hu Huanyong line, with the vast majority, 74.2%, emanating from the southeastern regions. Key environmental metrics recorded included a median humidity of 65%, a median temperature of 15.75\u0026deg;C, and median annual rainfall of 1164.50 mm. Regarding consumption patterns, the study quantified median annual consumption rates of major livestock products: 139.60 kiloton for pork, 14.95 kiloton for beef, and 8.20 kiloton for mutton. Median agricultural yields were reported as 40.84 kiloton for wheat, 303.58 kiloton for corn, 30.71 kiloton for soybean, and 65.00 kiloton for potatoes. Healthcare infrastructure and demographics were also quantified, revealing a median of 919 hospitals and 26,182.50 healthcare institutions, supported by 602 healthcare professionals. The median population within the analyzed regions was documented at 3845.50, exhibiting a birth rate of 10.72\u0026permil;, a mortality rate of 6.24\u0026permil;, and a net population growth rate of 4.42\u0026permil;. In the context of antimicrobial resistance, the study observed median prevalence rates for Carbapenem resistance as follows: 1.40% in Escherichia coli, 18.55% in Pseudomonas aeruginosa, 6.10% in Klebsiella pneumoniae, and 55.30% in Acinetobacter baumannii. The respective median detection rates for these pathogens were 20.99%, 8.74%, 14.78%, and 7.16%. Temporal trends in these detection rates and Carbapenem resistance rates across the specified GNB are comprehensively illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical description of basic information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClimatic and geographical related indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTropical (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e240 (96.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e208 (83.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (16.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtropical (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (54.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (45.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlateau (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e232 (93.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (6.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonsoon (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176 (71.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (29.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuhuanyong line (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNorthwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (16.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (9.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSoutheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184 (74.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHumidity(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65 [0.57, 0.76]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature(\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.75 [10.05, 17.72]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1164.50 [564.75, 1664.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical institution data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e919.00 [565.00, 1327.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal health hygiene institution (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26182.50 [17164.00, 35260.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eProfessional health hygiene institution (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e602.00 [265.00, 1064.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCore member unit (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.00 [38.00, 64.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic member unit (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.00 [32.00, 140.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAgriculture-related index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgrgross(100\u0026nbsp;million RMB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2028.70 [960.65, 3349.30]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop production(kiloton)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1397.43 [597.39, 3377.95]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheat(kiloton)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.84 [6.04, 407.40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorn production (kiloton)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e303.58 [49.89, 978.39]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoybean production (kiloton)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.71 [11.66, 54.18]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotato production (kiloton)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.00 [31.89, 126.24]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePork production (kiloton)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139.60 [47.44, 259.28]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeef production (kiloton)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.95 [5.88, 36.25]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutton production (kiloton)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.20 [2.48, 17.15]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePer-capita consumption of major foods, (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133.62 [121.57, 147.88]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdible oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.79 [8.55, 12.11]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.45 [88.30, 105.94]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.74 [22.07, 31.64]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoultry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.00 [5.06, 11.18]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAquatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.90 [3.47, 15.71]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.28 [6.98, 12.13]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDairy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.37 [9.97, 16.99]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry and fresh fruit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.30 [39.43, 61.50]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26 [1.06, 1.53]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePopulation-related information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation (10^5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3845.50 [2476.50, 6156.70]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth rate (\u0026permil;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.72 [7.89, 13.17]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality rate (\u0026permil;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.24 [5.61, 7.02]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation growth rate (\u0026permil;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.42 [1.38, 6.80]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51 [0.51, 0.52]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP (billion)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22201.79 [13998.13, 36564.39]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDGDP (billion)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1426.35 [295.63, 3124.99]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPG (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08 [0.03, 0.11]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBacterial information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEcoli_resi (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40 [0.90, 2.10]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseud_resi (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.55 [14.20, 24.80]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKlebsiella_resi (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.10 [3.08, 11.95]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcinet_resi (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.30 [48.55, 61.10]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEcoli_dete (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.99 [19.57, 23.57]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseud_dete (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.74 [7.64, 10.24]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKlebsiella_dete (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.78 [13.31, 16.38]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcinet_dete (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.16 [6.18, 8.12]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSputum (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.94 [37.22, 46.50]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrine (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.76 [6.18, 17.42]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.08 [7.96, 10.58]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePus (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.18 [7.84, 18.88]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.47 [21.16, 26.73]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensity (DDDs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.37 [38.98, 47.52]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote\u003c/b\u003e: Acinet_dete: Detection rate of Acinetobacter baumannii; Acinet_resi: Carbapenem resistance rate of Acinetobacter baumannii; Agrgross: Agriculture Gross output value; DDD: Defined Daily Doses; GDP: Gross domestic product per capita; DGDP: The amount of GDP growth; Ecoli_dete: Detection rate of Escherichia coli; Ecoli_resi: Carbapenem resistance rate of Escherichia coli; GDPG: GDP growth rate; Hospital: Number of included hospitals; Klebsiella_dete: Detection rate of Klebsiella pneumoniae; Klebsiella_resi: Carbapenem resistance rate of Klebsiella pneumoniae; Pseud_dete: Detection rate of Pseudomonas aeruginosa; Pseud_resi: Carbapenem resistance rate of Pseudomonas aeruginosa; Rainfall: Annual rainfall; Temperature: Annual mean temperature.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCarbapenem detection and resistance rate distributions\u003c/h2\u003e \u003cp\u003eTo furnish a detailed representation, the distribution of detection and carbapenem resistance rates along the Hu line in China is visually depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Quantitative results are comprehensively detailed in eTable 1 in the supplementary files. In the Northwest region, the observed carbapenem resistance rates for the GNB under study were as follows: Escherichia coli at 0.75% (95% CI: 0.45, 1.00), Pseudomonas aeruginosa at 12.60% (95% CI: 10.67, 15.57), Klebsiella pneumoniae at 1.35% (95% CI: 0.90, 3.02), and Acinetobacter baumannii at 50.50% (95% CI: 34.65, 55.25). The corresponding detection rates for these pathogens were 23.46% (95% CI: 20.99, 26.25), 5.59% (95% CI: 4.98, 7.91), 13.62% (95% CI: 13.04, 15.04), and 5.69% (95% CI: 4.63, 7.28), respectively. All comparisons demonstrated statistically significant differences, underscored by p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001, emphasizing the substantial regional disparities. Similarly, the Southeast region exhibited distinct variations in resistance and detection rates compared to other regions, as depicted in eFigure 1 in the supplementary files. Statistical validation of these regional variations was affirmed by p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001 across all comparisons. Additionally, the study extended its analysis to explore the influence of five distinct climate types\u0026mdash;Temperate Monsoon, Plateau Mountain, Subtropical Monsoon, Temperate Continental, and Tropical Monsoon\u0026mdash;on carbapenem resistance and detection rates across 31 provinces, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Notably, variations in resistance and detection rates for Pseudomonas, Klebsiella, and Acinetobacter baumannii across these climates are depicted in eFigure 2 in the supplementary files. Escherichia coli demonstrated the lowest resistance rate in the Plateau Mountain climate at 0.30% (95% CI: 0.20, 0.92), whereas Pseudomonas showed the highest resistance in the Monsoon climate at 20.80% (95% CI: 15.75, 25.92) and the lowest in the Plateau Mountain climate at 11.75% (95% CI: 10.52, 14.23). Variability in resistance rates for Klebsiella and Acinetobacter across different climate types was further detailed in eTable 1 in the supplementary files. For detection rates, Escherichia coli registered the highest in the Plateau Mountain climate at 21.53% (95% CI: 16.82, 23.75) and the lowest in the Temperate Continental climate at 6.61% (95% CI: 5.37, 8.41).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGNB detection rate\u003c/h3\u003e\n\u003cp\u003eA comprehensive double fixed-effect regression analysis was also conducted to elucidate the factors influencing the detection rates of the four specific GNB. Detailed empirical results are available in the supplementary materials (eTable 2). For Escherichia coli, the analysis revealed that variables such as the hospital environment, crop and grain production, and the consumption of various food types (including poultry, aquatic products, eggs, and sugar) significantly elevated the detection rate. Conversely, increased consumption of vegetables and meat was associated with a reduced detection rate. Notably, factors such as temperature, rainfall, and GDP per capita did not exhibit a significant impact on the detection rates. The model accounted for approximately 32% of the variance in the detection rates of Escherichia coli. In the case of Pseudomonas aeruginosa, higher GDP per capita correlated with a lower detection rate, whereas increased consumption of aquatic products raised the detection rate. The model demonstrated substantial efficacy, explaining over 57% of the variance in detection rates for this pathogen. For Klebsiella pneumoniae, the only significant factor linked to detection rates was higher poultry consumption, which was associated with a reduction in detection rates. The model accounted for about 20% of the variation in detection rates for Klebsiella pneumoniae. Lastly, for Acinetobacter baumannii, factors such as increased rainfall negatively impacted detection rates, while higher hospital density, crop production, and the consumption of poultry and aquatic products had positive impacts on detection rates. The regression model explained approximately 33% of the variation in detection rates for Acinetobacter baumannii.\u003c/p\u003e\n\u003ch3\u003eCarbapenem resistance rate\u003c/h3\u003e\n\u003cp\u003eWe also used double fixed effect regression analysis to explore what factors influence the carbapenem detection rate. The detailed coefficients and p-values for each variable are in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDouble fixed effect regression results of carbapenem resistance rate\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEcoli_resi\u003c/p\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003cp\u003e(T-value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePseud_resi\u003c/p\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003cp\u003e(T-value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKlebsiella_resi\u003c/p\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003cp\u003e(T-value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAcinet_resi\u003c/p\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003cp\u003e(T-value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTemperature(\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.4401)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.2853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.5504)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.5357)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0227\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.7691)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.5719)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.8875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.0079)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPG (billion)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0255\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1338\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.3948)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.5809)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.0184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.2105)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.5901)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.2035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.4524)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.5297)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop production(kiloton)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.1645)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.2573)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.3944)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.6580)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensity (DDDs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0423\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0124\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0531\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0423\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.4511)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.0471)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.0983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.4511)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePer-capita consumption of major foods, (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.8250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.2305)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.9004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.3882)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdible oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.2454)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.1516)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.2583)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.6181)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0119\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-2.0988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1391)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.7602)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.2057)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0125\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1344\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-2.1953)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.3013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.7365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.0975)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoultry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.2697)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.0755)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.6217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.9235)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAquatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0151\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-2.3115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.8425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.9480)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.1034)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.1366)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.9834)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.1317)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.4318)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDairy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0117\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.5645)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1359)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1692)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.6543)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDried and fresh fruits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0130\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.1035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.2068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.4973)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.2223)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0108\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.4001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.0132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.3720)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.2148)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1068\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-1.2099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.1482)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.6394)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.8749)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProv / Year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: T-values, which are in parentheses, indicate the significance of each coefficient. Additionally, p-values denote the statistical significance of the coefficients, where ** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and * indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAcinet_resi: Carbapenem resistance rate of Acinetobacter baumannii; DDD: Defined Daily Doses; Ecoli_resi: Carbapenem resistance rate of Escherichia coli; GDP: Gross domestic product per capita; GDPG: GDP growth rate; Hospital: Number of included hospitals; Klebsiella_resi: Carbapenem resistance rate of Klebsiella pneumoniae; Pseud_resi: Carbapenem resistance rate of Pseudomonas aeruginosa; Rainfall: Annual rainfall.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor Escherichia coli, we found significant relationships between its resistance rate and various factors. Positive impacts came from GDPD, dairy, dried fruit, and sugar consumption, with coefficients ranging from 0.0108 to 0.0255. On the other hand, vegetable, meat, and aquatic consumption showed negative impacts. However, other factors like temperature, rainfall, hospital density, and others did not significantly affect the resistance rate. Our model explains about 23% of the variation in Escherichia coli resistance. Regarding Pseudomonas aeruginosa, a higher GDP per capita was linked to a lower resistance rate, with a coefficient of -0.1338. Other variables did not show a significant impact. The model was quite effective, explaining over 62% of the resistance rate variation. For Klebsiella, only higher rainfall levels showed a significant, though moderate, relationship with increased resistance, with a coefficient of 0.0227. Other factors weren't significant, and our model accounts for about 26% of the resistance rate variation. Lastly, for Acinetobacter baumannii, we found that higher meat consumption could increase resistance, with a coefficient of 0.1344. This model explains about 28% of the resistance rate variation.\u003c/p\u003e \u003cp\u003eIn this study, the factors affecting the carbapenem resistance rates was analyzed in the four specific GNB. Comprehensive coefficients and significance levels for each predictor are tabulated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. For Escherichia coli, our findings revealed that certain variables positively influenced resistance rates, including gross domestic product per capita (GDPD), and consumption of dairy, dried fruits, and sugar, with coefficients ranging from 0.0108 to 0.0255. Conversely, the consumption of vegetables, meat, and aquatic products was associated with reduced resistance rates. Variables such as temperature, rainfall, and hospital density showed no significant effect. The model explained roughly 23% of the variability in resistance rates for Escherichia coli.\u003c/p\u003e \u003cp\u003eIn the case of Pseudomonas aeruginosa, a notable correlation was observed where a higher GDP per capita corresponded with a lower resistance rate, evidenced by a coefficient of -0.1338. This model was particularly robust, explaining over 62% of the variation in resistance rates. For Klebsiella pneumoniae, increased rainfall was the sole significant predictor, showing a modest increase in resistance rates with a coefficient of 0.0227. The model, however, accounted for only about 26% of the resistance rate variability. Finally, for Acinetobacter baumannii, an increase in meat consumption was significantly correlated with higher resistance rates, indicated by a coefficient of 0.1344. This model explained about 28% of the variation in the resistance rates for Acinetobacter baumannii.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMortality\u003c/h2\u003e \u003cp\u003eWe further conducted a detailed bi-fixed effect regression analysis to explore the relationship between mortality rates and various demographic and environmental factors. The comprehensive results of this analysis are documented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and eTable 3 of the supplementary files. Figures\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e provide scatter plots illustrating the correlations between detection and carbapenem resistance rates with mortality, birth rate, and population growth rate. Our findings indicate a significant association between the detection rates of specific pathogens and mortality rates. Specifically, an increased detection rate of Pseudomonas aeruginosa was linked to higher mortality, with a coefficient of 0.2007. Conversely, a higher detection rate of Klebsiella pneumoniae was associated with lower mortality, demonstrated by a coefficient of -0.0762. Additionally, temperature exhibited a negative correlation with mortality, with a coefficient of -0.0027. Positive correlations were observed between mortality and both crop production and sugar consumption, with coefficients of 0.0105 and 0.0064, respectively. Moreover, a higher intensity of antibiotic usage was associated with reduced mortality, indicated by a coefficient of -0.0164. These variables collectively explained approximately 70% of the variation in mortality rates. Regarding the relationship between carbapenem resistance rates and mortality, Escherichia coli resistance displayed a positive correlation with increased mortality, evidenced by a coefficient of 0.0989, while resistance in Acinetobacter baumannii had a negative impact, with a coefficient of -0.0116. The influence of temperature, crop production, and antibiotic usage on mortality was consistent across both the detection and resistance rate analyses. The robustness of our model is highlighted by an F-statistic of 15.1328 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), explaining about 68% of the variation in mortality rates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of carbapenem resistance rate on birth rate, mortality and population growth rate: double fixed effect regression result\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBirth rate \u003c/p\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003cp\u003e(T-value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMortality rate\u003c/p\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003cp\u003e(T-value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopgrow rate\u003c/p\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003cp\u003e(T-value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEcoli_resi (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0989\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.4381)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.6690)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.7653)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseud_resi (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.7670)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.2610)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.9975)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKlebsiella_resi (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.2906)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.0970)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.6109)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcinet_resi (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0116\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.4476)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.1725)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.3356)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTemperature(\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0029\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.3452)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.9613)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.5621)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.2180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.5854)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.1044)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPG (billion)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.1858)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.2866)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.0452)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.6643)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.5791)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.0210)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop production(kiloton)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0089\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.2542)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.2627)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.5047)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensity (DDDs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.1077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.2892)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.3910)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePer-capita consumption of major foods, (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.1700)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.8879)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.7178)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdible oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.4386)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.6653)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.2087)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.6203)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.2979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.0993)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.3259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.1889)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.1434)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoultry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.8176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.2584)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.8408)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAquatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0259\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.3044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.8821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.7233)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-1.2086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.1223)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.4632)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDairy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-1.2208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.1882)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.0316)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDried and fresh fruits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.7582)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.5202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.2048)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0064\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.1477)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.7497)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.6495)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-0.0021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.2714)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProv / Year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.7172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.1328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.3385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: T-values, which are in parentheses, indicate the significance of each coefficient. Additionally, p-values denote the statistical significance of the coefficients, where ** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and * indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAcinet_resi: Carbapenem resistance rate of Acinetobacter baumannii; DDD: Defined Daily Doses; Ecoli_resi: Carbapenem resistance rate of Escherichia coli; GDP: Gross domestic product per capita; GDPG: GDP growth rate; Hospital: Number of included hospitals; Klebsiella_resi: Carbapenem resistance rate of Klebsiella pneumoniae; Pseud_resi: Carbapenem resistance rate of Pseudomonas aeruginosa; Rainfall: Annual rainfall.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt is noteworthy that our study detected no significant effects of the detection and carbapenem resistance profiles of the four bacterial groups on birth rate and population growth rate. A detailed exposition of these findings were shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and eTable 3 in the supplementary files.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe national wide time series study in China, spanning from 2014 to 2021, has offered valuable insights into the monitoring status[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The study encompassed a substantial number of monitoring strains from diverse hospitals, showcasing its comprehensive nature. These findings provide valuable insights not only into the intricate interplay between geographical regions and climate types with respect to carbapenem resistance and GNB detection rates but also the relationships between various factors and the detection or resistance rates of these bacterial strains. The data underscores the importance of considering regional and climatic factors in understanding the dynamics of antibiotic resistance in GNB, thus offering critical information for healthcare strategies and policy formulation. Simultaneously, it shed light on potential avenues for further research and public health interventions.\u003c/p\u003e \u003cp\u003eDuring the surveillance period, both Gram-positive and GNB were prevalent, with Staphylococcus aureus and Escherichia coli being the most frequently isolated strains, which is consistent with previous reports[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Although there is a slight variation in the percentages, it is important to note that GNB consistently account for a higher proportion of non-repeated bacteria, reaching nearly 70%. This finding is in line with other published studies, as GNB are known to exhibit greater resistance to antibiotics, presenting a significant public health concern[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Notably, sputum specimens were the predominant source of bacterial strains, followed by urine and blood specimens, which may have implications for targeted diagnostics and treatment strategies and also be consistent with previous research finding[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMeanwhile, according to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which presented the detection and resistance trend and distribution of four kinds of bacteria in 31 provinces and cities, the fluctuations over the years are not very large. However, there is a relatively significant variation in the carbapenem resistance rate, especially for Klebsiella pneumoniae, where the resistance rate shows a gradual increase. The trends were also consistent with those previously reported[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, the investigation also delved into the relationship between bacterial resistance rates and various environmental factors across different regions and climate types in China. We found significant variations in carbapenem resistance and detection rates across different regions and climate types. The Northwest, On, and Southeast regions showed statistically significant differences in resistance rates and detection rates among the four bacteria. In particular, the Northwest region displayed low resistance rates for Escherichia coli and Klebsiella but higher rates for Pseudomonas and Acinetobacter baumannii. Conversely, the Southeast region exhibited higher resistance rates for all four bacteria. Furthermore, carbapenem resistance and detection rates varied across five climate types, with different provinces showing distinct patterns. For example, Pseudomonas had the highest resistance in the Monsoon climate and the lowest in the Plateau mountain climate. Escherichia coli had the lowest resistance in the Plateau mountain climate, while the highest detection rate was observed in the same climate type. These findings suggest that geographical and climatic factors may play a role in bacterial resistance and detection rates. These findings align with previous studies that have reported regional disparities in antibiotic resistance prevalence[\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBesides, our study also utilized double fixed effect regression analysis to explore the factors affecting carbapenem resistance and detection rates of the four GNB. The regression results revealed several significant associations, some of which were consistent with previous research, while others offered new insights. Moreover, we observed a positive association between GDPG (Coefficient\u0026thinsp;=\u0026thinsp;0.0255) and Escherichia coli resistance, implying that higher economic development might lead to increased antibiotic resistance in certain regions. Additionally, our study found a negative association between GDPG (Coefficient=-0.1338) and Pseudomonas resistance. Some literatures reported the same situation[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and Klein EY et al. found that reducing global consumption is critical for reducing the threat of antibiotic resistance[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is also noteworthy that the factors influencing the detection rate and antibiotic resistance of Escherichia coli appear to be closely tied to grain consumption patterns. Specifically, we observe negative correlations between the detection rate and the consumption of vegetable bacterial foods, poultry, and aquatic foods, suggesting that higher intake of these foods is associated with a lower Ecoli_dete. Conversely, increased consumption of eggs and sugar positively correlates with a higher Ecoli_dete. Interestingly, meat consumption exhibits a positive correlation with Acinet_resi, while poultry consumption has opposite influence on the detection rates of Klebsiella (Coefficient=-0.04) and Acinetobacter baumannii (Coefficient\u0026thinsp;=\u0026thinsp;0.062). The results above might indicate a potential link between dietary habits and antibiotic resistance. While this finding is novel and not extensively studied in existing literature, some previous research has explored the impact of crop production systems and agricultural practices on antibiotic resistance[\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Overall, the impact of dietary factors on different bacterial populations varies. The effect of the same dietary factor on the resistance or detection of different bacteria is not consistent.\u003c/p\u003e \u003cp\u003eFurthermore, we evaluated the impact of the four specific bacteria detection rate and Carbapenem\u003c/p\u003e \u003cp\u003eresistance rate on mortality, birth and population grow rate, our study revealed mixed results (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and eTable 3 in Supplement files). The mortality rate increases with the increase of Pseud_resi, Crop production, and sugar consumption, while it decreases with the increase of Acinet_resi, Temperature, and intensity of antibiotic use. The analysis results between detection rate and mortality rate are consistent with the analysis results of resistance rate. However, with the increase of aquatic product consumption, the mortality rate decreases. Founou. et al. found that Acinetobacter baumannii is associated with a high mortality risk and increased economic costs with Enterococcus faecium, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter pathogens implicated as the main cause of increased mortality, similar to our study result[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegarding the relationship between Acinetobacter baumannii and mortality rate, resistance rate, and detection rate, we have conducted a comprehensive literature search. Most of the literature focuses on clinical outcomes of critically ill patients[\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], with limited studies on healthy populations and large cohort studies investigating the risk of bacterial infection and overall population-wide mortality rate.\u003c/p\u003e \u003cp\u003eNotably, sugar consumption has a significant impact, as it is not only correlated with bacterial detection and resistance but also influences mortality rates, corroborating previous research findings[\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].Hence, controlling sugar intake can not only reduce overall mortality rates but also partially lower the detection and antibiotic resistance rates of Escherichia coli. However, large-scale epidemiological investigations that correlate bacterial detection and antibiotic resistance with dietary factors are currently lacking. Thus, conducting future research on the relationship between dietary factors and bacterial prevalence is essential.\u003c/p\u003e \u003cp\u003eOverall, the study examined the correlation between the detection rate and carbapenem resistance rate of the specific GNB, including Escherichia coli, Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae, with various environmental factors, such as temperature, humidity, rainfall, economic level, agricultural level, animal husbandry, and antibiotic use in the food chain. Environmental factors were found to influence the detection and carbapenem resistance rates of these bacteria. However, further research is needed to address limitations and expand our understanding of bacterial resistance dynamics in diverse settings. The study has some acknowledged limitations, including the use of retrospective data that might not capture all relevant factors influencing antibiotic resistance. Regional variations in surveillance and reporting systems might introduce bias in the dataset. Additionally, some potential factors, such as antibiotic usage in animal husbandry, were not included due to data availability constraints. Future research should consider longitudinal studies and more comprehensive datasets to better capture the dynamic nature of antibiotic resistance patterns. Investigating the impact of specific antimicrobial stewardship interventions and regional infection control measures could shed further light on strategies to mitigate antibiotic resistance at the local level. Overall, these findings underscore the need for tailored prevention and control strategies to address regional variations in bacterial resistance patterns, considering economic and environmental factors in combating antimicrobial resistance effectively. Nevertheless, our comprehensive nationwide time series study offers valuable insights into the prevalence and patterns of the four specific bacterial resistance in China. The study also highlights the importance of continuous surveillance and research to monitor the four specific bacterial resistance trends and the influence of various environmental and demographic factors on resistance rates.\u003c/p\u003e \u003cp\u003eIn conclusion, microbial communities are influenced by factors like diet, climate, and economics. Among these, detection rates of Escherichia coli and Acinetobacter baumannii are complex due to various factors interacting. On the other hand, microorganisms like Pseudomonas aeruginosa and Klebsiella pneumoniae seem to be more limited in their responses, possibly due to specific environmental conditions.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e "},{"header":"Declarations","content":"\u003ch2\u003eData\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe databases of CARSS, NBS, and CMDC used in this study are publicly available.\u0026nbsp;\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by the Health Commission of Hunan Provincial [NO. 202113012480]. It was also supported by the International Research Center for Precision Medicine, Transformative Technology, and Software Services, Hunan, China.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, M.Y., Y.Z.; methodology, B.K., M.Y. and Y.Z.; software, Y.Z. and Z.S.; validation, Y.M. and W.C.; formal analysis, Y.Z.; investigation, J.L., H.L. and M.X.; resources, M.Y.; data curation, Z.S. and M.X.; writing original draft preparation, Y.Z.; review and editing, M.Y. and Y.Z.; visualization, Y.Z.; supervision, M.Y. and W.C.; project administration, Y.Z. and M.Y.; funding acquisition, M.Y. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eOur heartfelt appreciation goes to Shang-Xun Wu, Mou-Ze Liu, Rao Fu, Hui Gong, Rui Ma, and Lin-Na Guo for their invaluable assistance and guidance. Special thanks are also due to Bo Wang for his remarkable contribution in creating professional figures. Lastly, we extend our thanks to all the members of the CARSS system for their collaboration and support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLogan LK, Weinstein RA. The Epidemiology of Carbapenem-Resistant Enterobacteriaceae: The Impact and Evolution of a Global Menace. J Infect Dis. 2017;215(suppl1):S28\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/infdis/jiw282\u003c/span\u003e\u003cspan address=\"10.1093/infdis/jiw282\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT\u0026auml;ngd\u0026eacute;n T, Giske CG. 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BMJ (Clinical research ed.) 381(e071609. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmj-2022-071609\u003c/span\u003e\u003cspan address=\"10.1136/bmj-2022-071609\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Carbapenem resistance, Gram-negative bacteria, Epidemiology, Public health, Mainland China","lastPublishedDoi":"10.21203/rs.3.rs-5712281/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5712281/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCarbapenem-resistant Gram-negative bacteria significantly threaten public health due to limited treatment options and high mortality rates. Understanding the factors influencing their detection and resistance rates is crucial for effective interventions. Objective: This study aimed to investigate the detection and carbapenem resistance rates of Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii in China and identify associations with climate, agriculture, economy, and diet.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eData were sourced from CARSS, NBS, and CMDC, covering 1435 hospitals. Descriptive statistics and double fixed effect regression models analyzed associations, using SPSS, RStudio, StataMP, and Python.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFrom 2014 to 2021, bacterial counts increased from 2,227,420 to 3,743,027, with Gram-negative bacteria constituting 70.3\u0026ndash;71.5%. Escherichia coli (29.2\u0026ndash;29.9%), Klebsiella pneumoniae (19.4\u0026ndash;20.7%), Pseudomonas aeruginosa (11.8\u0026ndash;12.7%), and Acinetobacter baumannii (9.1\u0026ndash;10.8%) were the most prevalent. Environmental data indicated significant geographic distributions, with median humidity at 65%, median temperature at 15.75\u0026deg;C, and median annual rainfall at 1164.50 mm. Regional disparities in detection and resistance rates were observed, with Escherichia coli showing a median resistance rate of 1.40%, Pseudomonas aeruginosa 18.55%, Klebsiella pneumoniae 6.10%, and Acinetobacter baumannii 55.30%. Factors like hospital environment and food consumption significantly affected detection rates, while GDP per capita impacted resistance rates. Detection rates of Pseudomonas aeruginosa correlated significantly with increased mortality (coefficient 0.2007).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study highlights the significant regional disparities and factors influencing the detection and resistance rates of carbapenem-resistant bacteria in China, emphasizing the need for targeted interventions considering local climatic, economic, and dietary conditions. Detection and resistance profiles did not significantly affect birth rates and population growth.\u003c/p\u003e","manuscriptTitle":"From Gram-Negative Strains to Mortality: Understanding Bacterial Resistance in Mainland China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-02 18:19:46","doi":"10.21203/rs.3.rs-5712281/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e475cd46-0923-4a17-ba00-8110724560ae","owner":[],"postedDate":"January 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-02T18:19:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-02 18:19:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5712281","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5712281","identity":"rs-5712281","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}