Abstract
The proliferation of Enterobacteriaceae with extended-spectrum beta-lactamase (ESBL), including Escherichia coli and Klebsiella pneumoniae, poses a significant threat to public health due to their resistance to β-lactam antibiotics. This study focuses on the risk factors of ESBL-producing Enterobacteriaceae in the wastewater of dairy farms in East Java, Indonesia, a major milk-producing region. Utilizing an observational cross-sectional design, data were collected from 342 wastewater samples across six regions with high dairy cattle populations in East Java. Risk factors were assessed through questionnaires addressing variables related to farmers and farm management. Univariate analysis by regency/city identified several significant risk factors for the occurrence of ESBL-producing Enterobacteriaceae in dairy farm wastewater in East Java, and it differed between regencies/cities. Multivariate analysis of risk factors that significantly correlated with ESBL-producing Enterobacteriaceae showed a distance of ≤ 10 m between septic tanks and wells that significantly increased the risk of ESBL-producing Enterobacteriaceae occurrence (OR 3.24, 95% CI: 1.07–9.80), as did not using detergent for barn cleaning (OR 2.67, 95% CI: 1.23–5.67). Conversely, the use of water storage tanks and a distance of ≤ 500 m from other dairy farms appeared to reduce the risk. This study provides critical insights for developing strategies to control and prevent antibiotic resistance in the dairy farming sector, such as improved wastewater management, stricter regulations on antibiotic usage, and enhanced farmer education programs. Implementing these strategies is crucial to mitigating the spread of ESBL-producing Enterobacteriaceae, thereby improving public and environmental health in East Java.
Keywords: binomial maps, dairy farms, Enterobacteriaceae, ESBL, public health, wastewater
1. Introduction
The production of extended-spectrum beta-lactamase (ESBL) by Enterobacteriaceae such as Escherichia coli and Klebsiella pneumoniae represents a significant global public health challenge. These ESBL-producing bacteria can produce enzymes that break down β-lactam antibiotics, including penicillin and cephalosporins, which are among the most commonly used antibiotics [1]. This resistance diminishes the effectiveness of treatments for bacterial infections and increases the risk of spreading difficult-to-treat diseases [2–4].
One major source of ESBL bacterial spread is agricultural environments, particularly the wastewater generated by dairy farms [5–7]. This wastewater can contain high concentrations of antibiotic-resistant pathogenic bacteria, which can then contaminate surrounding environments, including water sources used by local communities. In East Java, Indonesia, dairy farming is a prominent agricultural activity. The Central Statistics Agency (BPS) noted that East Java is the largest producer of raw milk in Indonesia, which in 2022 produced 108.85 million liters of raw milk. The population of dairy cattle in East Java in 2022 reached 314,385 head, with a high concentration in several regions, including Pasuruan Regency with 100,080 head, Malang Regency with 90,237 head, Tulungagung Regency with 25,836 head, Blitar Regency with 20,209 head, Batu City with 13,053 head, and Kediri Regency with 11,189 head [8]. The high population of dairy cattle needs to be accompanied by increased awareness regarding the use of antibiotics that can be caused by ESBL-producing Enterobacteriaceae [9, 10].
The prevalence of ESBL-producing Enterobacteriaceae in dairy farm wastewater is a concerning issue in East Java, Indonesia. Several studies have shed light on the prevalence and risk factors associated with ESBL-producing bacteria in dairy farming environments. Gonggrijp et al. [11] conducted a study on a dairy farm to investigate the presence of ESBL- and AmpC-producing E. coli in slurry samples, highlighting the importance of bacterial protein production in cattle. Soekoyo et al. [12] focused on the distribution and risk factors of Enterobacteriaceae producing ESBL in the gut bacterial flora of dairy cows and individuals living near farming areas in Indonesia, emphasizing the role of farming practices in bacterial spread.
Moreover, Saekhow and Sriphannam [13] explored the prevalence of ESBL-producing E. coli strains in dairy farm wastewater, indicating the potential contamination of farm environments with antibiotic-resistant bacteria. Weber et al. [14] identified feeding with waste milk as a significant risk factor for the prevalence of ESBL/AmpC-E. coli in preweaned dairy calves, emphasizing the importance of proper waste management in dairy farming practices. Additionally, Waade et al. [15] collected data on antimicrobial use, farm hygiene, and management to identify risks for the occurrence of ESBL-producing bacteria in newborn dairy calves, highlighting the need for comprehensive farm management strategies.
The occurrence of ESBL E. coli and K. pneumoniae in dairy farm wastewater in East Java, Indonesia, is a critical issue that requires a comprehensive understanding of the risk factors associated with both the farmers and the farming practices. Several studies provide valuable insights into the factors influencing the prevalence of multidrug-resistant bacteria in dairy farms, particularly in East Java, Indonesia. Widodo et al. [16] focused on investigating the potential hazards of multidrug-resistant E. coli collected from wastewater on dairy farms in East Java, shedding light on the antibiotic resistance aspect. These findings underscore the importance of addressing antimicrobial resistance in dairy farming practices to mitigate the spread of resistant bacteria in wastewater.
Gonzalez et al. [17] pointed out that direct contact with animal manure and animal slaughter products are potential risk factors for fecal carriage of ESBL-producing E. coli and K. pneumoniae in both humans and animals. Homeier-Bachmann et al. [18] suggested that improving hygiene management and questioning antibiotic use could help reduce ESBL/AmpC-E. This study aims to identify the risk factors contributing to the high prevalence of these bacteria. These risk factors encompass both aspects related to the farmers and the farming practices.
Farmer-related factors include educational level, income, personal use of antibiotics, sources of drinking water, and the presence of water storage tanks. Farmers' knowledge and health practices significantly impact the management of animal health and farm environment. Farming practice-related factors include the number of dairy cows, the use of antibiotics in livestock, sanitation practices, the use and maintenance of farming tools, and the management of manure and wastewater. Farms with high livestock densities and poor sanitation practices are more likely to have higher risks of spreading resistant bacteria. The results of this research are expected to provide a clear picture of the risk factors influencing the spread of ESBL bacteria in the dairy farm environment. Consequently, this study can offer a scientific basis for controlling and preventing the spread of antibiotic resistance in the agricultural sector and improve environmental and public health in East Java.
2. Materials and Methods
2.1. Study Design
This research is a continuation of the research published in February and November 2023 [19, 20], which uses an observational study with cross-sectional data collected in June 2022. A cross-sectional study design was chosen as it allows for simultaneous measurement of risk factors and the presence of ESBL-producing Enterobacteriaceae at a single point in time. This approach is suitable for identifying potential risk factors associated with ESBL occurrence without requiring long-term follow-ups, making it practical and efficient for field-based epidemiological studies.
The sample size was determined using the disease prevalence formula by Martin et al. [21], as follows:
| (1) |
where prevalence (p): 16% (based on [22], a study on ESBL-producing E. coli in dairy farm wastewater), confidence level: 95%, and margin of error (α): 0.1.
Based on this calculation, the minimum required sample size per region was 54, leading to a total of 324 samples. However, to increase representativeness and statistical power, we collected 342 samples. While this sample size may not capture all potential risk factors, it is sufficient for initial epidemiological assessment [21]. Future studies should consider larger sample sizes to validate these findings.
2.2. Participation Criteria
A nonprobability sampling technique, specifically purposive sampling, was employed in this study. This method was chosen because the study aimed to target dairy farms with specific characteristics related to antibiotic use and farm management practices. The inclusion criteria were as follows:
1. Dairy farms located in the six districts/cities with the highest dairy cattle populations in East Java (Pasuruan District, Malang District, Tulungagung District, Blitar District, Batu City, and Kediri District).
2. Farms with a recorded history of antibiotic use, identified through surveys and consultation with veterinary officers and cooperative units (KUD), but only farmers that agreed to participate in this study were included. The dairy farmer signed an informed consent form before being interviewed using a questionnaire.
By using purposive sampling, we ensured that the selected farms were relevant for assessing the relationship between risk factors and ESBL-producing Enterobacteriaceae [22]. While this approach may limit generalizability, it is effective for exploratory epidemiological studies where targeted sampling is necessary.
2.3. Ethical Approval and Informed Consent
Ethical approval for this study was obtained from the Health Research Ethics Committee of Universitas Brawijaya, Indonesia, under approval number 196-KEP-UB-2023. All procedures involving human participants were conducted in accordance with applicable ethical standards. Prior to data collection, written informed consent was obtained from all participants. Each participant was fully informed about the purpose, procedures, and voluntary nature of the study and provided their consent by signing a consent form.
2.4. Location of the Farms
To illustrate the location of farms that participated in the study, a map was created using QGIS. The farms were mainly located in East Java, Indonesia (Figure 1).
Figure 1.
Location of farms that participated in the study.
2.5. ESBL-Producing Enterobacteriaceae
ESBL-producing Enterobacteriaceae were obtained from 2 types of bacteria, namely, K. pneumoniae and E. coli. K. pneumoniae produces ESBL, according to the previous study by Dameanti et al. [20], and for E. coli producing ESBL, it is based on a previous publication from February 2023 [19], followed by testing using polymerase chain reaction (PCR) [20].
2.6. Questionnaire and Data Collection
The questionnaire collected information regarding potential risk for the occurrence of Enterobacter ESBL within the farm. The farmers were interviewed individually by the researcher. The researcher reads the questions, and the farmers answer them one by one. The questions in the questionnaire were divided into two groups, which had 40 questions (Table 1).
Table 1.
Risk factor variable category.
| No | Variables for farmers | Variables for farms |
|---|---|---|
| 1 | Education level | Number of dairy cows |
| 2 | Income | Number of barns |
| 3 | History of antibiotic use | Nearest water source |
| 4 | Bathroom wastewater disposal | Distance to nearest water source |
| 5 | Drinking water source | Distance of the barn to the river |
| 6 | Water source for nonconsumption needs | Other animals in the barn |
| 7 | Distance from septic tank to well | Distance to the nearest dairy farm |
| 8 | Distance from nearest healthcare facility | Distance to the nearest nondairy farm |
| 9 | Milking by farmers from other farms | |
| 10 | Sharing milking equipment between livestock | |
| 11 | Handwashing before milking | |
| 12 | Hand washing with soap | |
| 13 | Barn cleaning | |
| 14 | Livestock cleaning | |
| 15 | Water usage for barn cleaning | |
| 16 | Detergent use for barn cleaning | |
| 17 | Surface cleaning | |
| 18 | Type of water used for livestock maintenance | |
| 19 | Use of water storage tanks | |
| 20 | Drinking water source for livestock | |
| 21 | Presence of rodents | |
| 22 | Quarantine of livestock before entering the barn | |
| 23 | Separation of sick livestock from healthy ones | |
| 24 | Manure piles in the milking area | |
| 25 | Spread of solid waste in the milking area | |
| 26 | Disposal of solid waste to other farms | |
| 27 | Wastewater disposal pathways | |
| 28 | Wastewater puddles around the barn | |
| 29 | Presence of wastewater treatment | |
| 30 | History of antibiotic use in livestock | |
| 31 | Type of feed | |
| 32 | Hand milking system |
Risk factors for the occurrence of ESBL-producing Enterobacteriaceae are divided into two groups of variables: farmers and farms. The farmer variable group consists of risk factors originating from the farmers themselves, either directly related to the individual farmers or from their residences. The second group of risk factors is farm management. This group includes risk variables from the livestock side, encompassing the animals themselves, management practices, and the equipment used.
2.7. Data Analysis
All statistical analyses were carried out using IBM SPSS Statistics Version 22.0. The occurrence of ESBL-producing Enterobacteriaceae was categorized by 2 groups (binomial). Logistic regression was applied to analyze the influence of the binomial parameters on the occurrence of ESBL-producing Enterobacteriaceae. We classified the occurrence of ESBL-producing Enterobacteriaceae and the risk factor variable as positive or negative. The risk factor analysis was conducted twice. The first analysis of risk factors was based on regencies/cities, and the second was based on provinces or the entire sample. The risk factors were analyzed using univariate and multivariate analyses. The univariate analysis was performed using Pearson's chi-square test, and the relationship between risk factors and the occurrence of ESBL-producing Enterobacteriaceae was analyzed using the odds ratio (OR) with a 95% confidence interval (CI). Variables with a p-value < 0.05 were selected as candidates for multivariate analysis. The multivariate analysis was conducted to determine the correlation between risk factors, where a p-value < 0.05 indicates statistical significance [23]. The multivariate analysis is just used in provinces or the entire sample test.
3. Result and Discussion
The data of occurrence of ESBL-producing Enterobacteriaceae are presented in Table 2 and Figure 2. In this study, the occurrence of ESBL-producing Enterobacteriaceae was 12.34%, while the occurrence of non-ESBL Enterobacteriaceae was 87.76%. The highest occurrence of ESBL-producing Enterobacteriaceae was in Batu City, reaching 3.50%, and the lowest occurrence was in Malang Regency, at 1.05%.
Table 2.
Occurrence of ESBL-producing Enterobacteriaceae in dairy farm wastewater.
| Cities/regencies | ESBL | Non-ESBL | Total | |||
|---|---|---|---|---|---|---|
| n | % | n | % | n | % | |
| Kediri | 5 | 1.75 | 45 | 15.73 | 50 | 17.48 |
| Blitar | 6 | 2.10 | 33 | 11.54 | 39 | 13.64 |
| Malang | 3 | 1.05 | 58 | 20.28 | 61 | 21.33 |
| Batu City | 10 | 3.50 | 37 | 12.94 | 47 | 16.43 |
| Pasuruan | 7 | 2.45 | 47 | 16.43 | 54 | 18.88 |
| Tulungagung | 4 | 1.40 | 31 | 10.84 | 35 | 12.24 |
|
| ||||||
| East Java | 35 | 12.24 | 251 | 87.76 | 286 | 100 |
Figure 2.
Percentage of ESBL-producing Enterobacteriaceae in dairy farm wastewater in East Java.
The result of the risk factor ESBL-producing Enterobacteriaceae is shown in Table 3. Univariate analysis by regency/city identified several significant risk factors for the occurrence of ESBL-producing Enterobacteriaceae in dairy farm wastewater in East Java, and it differed between regencies/cities (Table 4). The critical factors in Kediri Regency were handwashing before milking and using soap. The significant factor in Malang Regency was just the farmer's history of antibiotic use. Blitar Regency's significant factors included the presence of dispersed solid waste in the milking area and wastewater disposal pathways. Pasuruan Regency has the type of water used for livestock maintenance, and the drinking water source for cows was significant. Tulungagung Regency had multiple significant factors: the barn's distance from dairy farms, the type of bathroom wastewater disposal system, the drinking water source for the farmer, the water source for nonconsumption activities, and the proximity to the nearest healthcare facility. Batu City identified the frequency of barn cleaning, washing the cows/udder, the disposal of solid waste to other farms, and the use of clean equipment in livestock handling as critical factors.
Table 3.
Risk factor data of occurrence of ESBL-producing Enterobacteriaceae in dairy farm wastewater.
| Risk factors | Kediri | Blitar | Malang | Batu City | Pasuruan | Tulungagung | Total | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ESBL (n = 5) | No-ESBL (n = 45) | ESBL (n = 6) | No-ESBL (n = 33) | ESBL (n = 3) | No-ESBL (n = 58) | ESBL (n = 10) | No-ESBL (n = 37) | ESBL (n = 7) | No-ESBL (n = 47) | ESBL (n = 4) | No-ESBL (n = 31) | ESBL (n = 35) | No-ESBL (n = 251) | |
| Farmers | ||||||||||||||
| Education level | ||||||||||||||
| Nonmandatory education level | 0 | 13 | 6 | 19 | 2 | 48 | 5 | 19 | 6 | 28 | 2 | 18 | 21 | 145 |
| Mandatory education level | 5 | 32 | 0 | 14 | 1 | 10 | 5 | 18 | 1 | 19 | 2 | 13 | 14 | 106 |
| Income | ||||||||||||||
| ≤ Minimum wage | 5 | 36 | 1 | 19 | 2 | 43 | 5 | 21 | 2 | 24 | 3 | 18 | 18 | 161 |
| > Minimum wage | 0 | 9 | 5 | 14 | 1 | 15 | 5 | 16 | 5 | 23 | 1 | 13 | 17 | 90 |
| History of antibiotic use | ||||||||||||||
| Yes | 0 | 0 | 0 | 4 | 1 | 2 | 1 | 5 | 3 | 18 | 3 | 17 | 8 | 46 |
| No | 5 | 45 | 6 | 29 | 2 | 56 | 9 | 32 | 4 | 29 | 1 | 14 | 27 | 205 |
| Bathroom wastewater disposal | ||||||||||||||
| Open | 0 | 0 | 6 | 29 | 1 | 38 | 7 | 27 | 6 | 46 | 1 | 24 | 21 | 164 |
| Closed | 5 | 45 | 0 | 4 | 2 | 20 | 3 | 10 | 1 | 1 | 3 | 7 | 14 | 87 |
| Drinking water source | ||||||||||||||
| Non-PDAM | 5 | 45 | 0 | 4 | 2 | 20 | 3 | 10 | 1 | 1 | 3 | 7 | 14 | 87 |
| PDAM | 0 | 0 | 6 | 29 | 1 | 38 | 7 | 27 | 6 | 46 | 1 | 24 | 21 | 164 |
| Water source for nonconsumption needs | ||||||||||||||
| Non-PDAM | 0 | 0 | 6 | 30 | 1 | 31 | 7 | 27 | 6 | 45 | 1 | 24 | 21 | 157 |
| PDAM | 5 | 45 | 0 | 3 | 2 | 27 | 3 | 10 | 1 | 2 | 3 | 7 | 14 | 94 |
| Distance from septic tank to well | ||||||||||||||
| ≤ 10 m | 5 | 45 | 5 | 18 | 3 | 23 | 7 | 31 | 7 | 31 | 4 | 30 | 31 | 178 |
| > 10 m | 0 | 0 | 1 | 15 | 0 | 35 | 3 | 6 | 0 | 16 | 0 | 1 | 4 | 73 |
| Distance from nearest healthcare facility | ||||||||||||||
| ≤ 500 m | 0 | 0 | 0 | 0 | 1 | 15 | 1 | 5 | 1 | 5 | 1 | 0 | 4 | 25 |
| > 500 m | 5 | 45 | 6 | 33 | 2 | 43 | 9 | 32 | 6 | 42 | 3 | 31 | 31 | 226 |
|
| ||||||||||||||
| Farms | ||||||||||||||
| Number of dairy cows | ||||||||||||||
| < 5 cows | 3 | 24 | 0 | 7 | 1 | 13 | 4 | 15 | 2 | 17 | 2 | 9 | 12 | 85 |
| ≥ 5 cows | 2 | 21 | 6 | 26 | 2 | 45 | 6 | 22 | 5 | 30 | 2 | 22 | 23 | 166 |
| Number of barns | ||||||||||||||
| 1 barn | 4 | 40 | 6 | 21 | 1 | 51 | 9 | 34 | 7 | 46 | 3 | 24 | 30 | 216 |
| > 1 barn | 1 | 5 | 0 | 12 | 2 | 7 | 1 | 3 | 0 | 1 | 1 | 7 | 5 | 35 |
| Nearest water source | ||||||||||||||
| Stagnant water | 4 | 42 | 6 | 31 | 3 | 48 | 9 | 36 | 7 | 47 | 4 | 30 | 33 | 234 |
| Flowing water | 1 | 3 | 0 | 2 | 0 | 10 | 1 | 1 | 0 | 0 | 0 | 1 | 2 | 17 |
| Distance to nearest water source | ||||||||||||||
| ≤ 100 m | 1 | 6 | 1 | 2 | 1 | 32 | 2 | 2 | 1 | 9 | 0 | 0 | 6 | 51 |
| > 100 m | 4 | 39 | 5 | 31 | 2 | 26 | 8 | 35 | 6 | 38 | 4 | 31 | 29 | 200 |
| Distance of the barn to the river | ||||||||||||||
| ≤ 500 m | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 4 | 4 | 20 | 0 | 5 | 6 | 33 |
| > 500 m | 5 | 45 | 6 | 33 | 3 | 54 | 8 | 33 | 3 | 27 | 4 | 26 | 29 | 218 |
| Other animals in the barn | ||||||||||||||
| Yes | 5 | 43 | 6 | 32 | 3 | 52 | 9 | 31 | 7 | 47 | 4 | 31 | 34 | 236 |
| No | 0 | 2 | 0 | 1 | 0 | 6 | 1 | 6 | 0 | 0 | 0 | 0 | 1 | 15 |
| Distance to the nearest dairy farm | ||||||||||||||
| ≤ 500 m | 2 | 24 | 2 | 21 | 2 | 47 | 8 | 33 | 4 | 40 | 2 | 28 | 20 | 193 |
| > 500 m | 3 | 21 | 4 | 12 | 1 | 11 | 2 | 4 | 3 | 7 | 2 | 3 | 15 | 58 |
| Distance to the nearest nondairy farm | ||||||||||||||
| ≤ 500 m | 0 | 6 | 2 | 13 | 1 | 39 | 10 | 36 | 7 | 42 | 3 | 26 | 23 | 162 |
| > 500 m | 5 | 39 | 4 | 20 | 2 | 19 | 0 | 1 | 0 | 5 | 1 | 5 | 12 | 89 |
| Milking by farmers from other farms | ||||||||||||||
| Yes | 1 | 3 | 0 | 0 | 0 | 4 | 0 | 2 | 1 | 9 | 1 | 2 | 3 | 20 |
| No | 4 | 42 | 6 | 33 | 3 | 54 | 10 | 35 | 6 | 38 | 3 | 29 | 32 | 231 |
| Sharing milking equipment between livestock | ||||||||||||||
| Yes | 1 | 17 | 1 | 17 | 0 | 11 | 1 | 7 | 2 | 14 | 4 | 31 | 9 | 97 |
| No | 4 | 28 | 5 | 16 | 3 | 47 | 9 | 30 | 5 | 33 | 0 | 0 | 26 | 154 |
| Hand washing before milking | ||||||||||||||
| No | 2 | 43 | 6 | 33 | 3 | 45 | 9 | 33 | 7 | 47 | 4 | 31 | 31 | 232 |
| Yes | 3 | 2 | 0 | 0 | 0 | 14 | 1 | 4 | 0 | 0 | 0 | 0 | 4 | 20 |
| Handwashing with soap | ||||||||||||||
| No | 3 | 9 | 0 | 0 | 0 | 23 | 1 | 4 | 1 | 2 | 0 | 0 | 5 | 38 |
| Yes | 2 | 36 | 6 | 33 | 3 | 35 | 9 | 33 | 6 | 45 | 4 | 31 | 30 | 213 |
| Barn cleaning | ||||||||||||||
| Rarely | 0 | 1 | 0 | 0 | 0 | 10 | 3 | 0 | 0 | 0 | 0 | 0 | 3 | 11 |
| Always | 5 | 44 | 6 | 33 | 3 | 48 | 7 | 37 | 7 | 47 | 4 | 31 | 32 | 240 |
| Livestock cleaning | ||||||||||||||
| Rarely | 0 | 1 | 0 | 2 | 0 | 4 | 3 | 0 | 1 | 7 | 0 | 0 | 4 | 14 |
| Always | 5 | 44 | 6 | 31 | 3 | 54 | 7 | 37 | 6 | 40 | 4 | 31 | 31 | 237 |
| Water usage for barn cleaning | ||||||||||||||
| Non-PDAM | 4 | 35 | 5 | 31 | 2 | 25 | 10 | 32 | 6 | 44 | 4 | 28 | 31 | 195 |
| PDAM | 1 | 10 | 1 | 2 | 1 | 33 | 0 | 5 | 1 | 3 | 0 | 3 | 4 | 56 |
| Detergent use for barn cleaning | ||||||||||||||
| No | 1 | 38 | 4 | 16 | 3 | 34 | 2 | 19 | 3 | 22 | 1 | 17 | 14 | 146 |
| Yes | 4 | 7 | 2 | 17 | 0 | 24 | 8 | 18 | 4 | 25 | 3 | 14 | 21 | 105 |
| Surface cleaning | ||||||||||||||
| Partial | 2 | 21 | 0 | 11 | 1 | 20 | 0 | 4 | 0 | 0 | 0 | 0 | 3 | 56 |
| Complete | 3 | 24 | 6 | 22 | 2 | 38 | 10 | 33 | 7 | 47 | 4 | 31 | 32 | 195 |
| Type of water used for livestock maintenance | ||||||||||||||
| Non-PDAM | 4 | 36 | 6 | 31 | 1 | 28 | 8 | 30 | 6 | 47 | 4 | 29 | 29 | 201 |
| PDAM | 1 | 9 | 0 | 2 | 2 | 30 | 2 | 7 | 1 | 0 | 0 | 2 | 6 | 50 |
| Use of water storage tanks | ||||||||||||||
| Yes | 1 | 21 | 2 | 16 | 0 | 32 | 3 | 14 | 3 | 28 | 2 | 17 | 11 | 128 |
| No | 4 | 24 | 4 | 17 | 3 | 26 | 7 | 23 | 4 | 19 | 2 | 14 | 24 | 123 |
| Drinking water source for livestock | ||||||||||||||
| Non-PDAM | 4 | 36 | 6 | 31 | 1 | 23 | 8 | 31 | 6 | 47 | 4 | 29 | 29 | 197 |
| PDAM | 1 | 9 | 0 | 2 | 2 | 35 | 2 | 6 | 1 | 0 | 0 | 2 | 6 | 54 |
| Presence of rodents | ||||||||||||||
| Yes | 3 | 34 | 5 | 26 | 2 | 48 | 6 | 31 | 7 | 34 | 4 | 22 | 27 | 195 |
| No | 2 | 11 | 1 | 7 | 1 | 10 | 4 | 6 | 0 | 13 | 0 | 9 | 8 | 56 |
| Quarantine of livestock before entering the barn | ||||||||||||||
| No | 5 | 40 | 6 | 33 | 2 | 48 | 8 | 35 | 7 | 47 | 4 | 30 | 32 | 233 |
| Yes | 0 | 5 | 0 | 0 | 1 | 10 | 2 | 2 | 0 | 0 | 0 | 1 | 3 | 18 |
| Separation of sick livestock from healthy ones | ||||||||||||||
| No | 5 | 36 | 1 | 8 | 0 | 32 | 6 | 23 | 7 | 40 | 1 | 7 | 20 | 146 |
| Yes | 0 | 9 | 5 | 25 | 3 | 26 | 4 | 14 | 0 | 7 | 3 | 24 | 15 | 105 |
| Manure piles in the milking area | ||||||||||||||
| Yes | 1 | 10 | 5 | 25 | 1 | 37 | 8 | 29 | 2 | 16 | 4 | 26 | 21 | 143 |
| No | 4 | 35 | 1 | 8 | 2 | 21 | 2 | 8 | 5 | 31 | 0 | 5 | 14 | 108 |
| Spread of solid waste in the milking area | ||||||||||||||
| Yes | 1 | 10 | 4 | 31 | 0 | 19 | 8 | 29 | 4 | 34 | 2 | 22 | 19 | 145 |
| No | 4 | 35 | 2 | 2 | 3 | 39 | 2 | 8 | 3 | 13 | 2 | 9 | 16 | 106 |
| Disposal of solid waste to other farms | ||||||||||||||
| Yes | 0 | 1 | 0 | 0 | 0 | 8 | 3 | 2 | 0 | 1 | 0 | 0 | 3 | 12 |
| No | 5 | 44 | 6 | 33 | 3 | 50 | 7 | 35 | 7 | 46 | 4 | 31 | 32 | 239 |
| Wastewater disposal pathways | ||||||||||||||
| No | 0 | 5 | 1 | 2 | 1 | 8 | 0 | 0 | 0 | 3 | 0 | 0 | 2 | 18 |
| Yes | 5 | 40 | 5 | 31 | 2 | 50 | 10 | 37 | 7 | 44 | 4 | 31 | 33 | 233 |
| Wastewater puddles around the barn | ||||||||||||||
| Yes | 1 | 13 | 6 | 21 | 0 | 21 | 8 | 24 | 2 | 18 | 2 | 27 | 19 | 124 |
| No | 4 | 32 | 0 | 12 | 3 | 37 | 2 | 13 | 5 | 29 | 2 | 4 | 16 | 127 |
| Presence of wastewater treatment | ||||||||||||||
| Yes | 5 | 26 | 6 | 31 | 2 | 43 | 7 | 20 | 5 | 32 | 2 | 12 | 27 | 164 |
| No | 0 | 19 | 0 | 2 | 1 | 15 | 3 | 17 | 2 | 15 | 2 | 19 | 8 | 87 |
| History of antibiotic use in livestock | ||||||||||||||
| Yes | 5 | 38 | 6 | 26 | 3 | 58 | 10 | 34 | 5 | 43 | 3 | 23 | 31 | 222 |
| No | 0 | 7 | 0 | 7 | 0 | 0 | 0 | 3 | 2 | 4 | 1 | 8 | 3 | 29 |
| Type of feed | ||||||||||||||
| Full forage | 1 | 7 | 0 | 0 | 0 | 5 | 0 | 3 | 1 | 11 | 0 | 7 | 2 | 33 |
| Additional feed | 4 | 38 | 6 | 33 | 3 | 53 | 10 | 34 | 6 | 36 | 4 | 24 | 33 | 218 |
| Hand milking system | ||||||||||||||
| Hand milking | 5 | 39 | 6 | 25 | 3 | 53 | 7 | 31 | 5 | 39 | 3 | 27 | 29 | 214 |
| Machine milking | 0 | 6 | 0 | 8 | 0 | 5 | 3 | 6 | 2 | 8 | 1 | 4 | 6 | 37 |
Table 4.
Univariate analysis of risk factor of occurrence of ESBL-producing Enterobacteriaceae in dairy farm wastewater.
| Risk factors | Cities/regencies | Total | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Kediri | Blitar | Malang | Batu City | Pasuruan | Tulungagung | ||||
| ρ | ρ | ρ | ρ | ρ | ρ | Crude OR | 95% CI | ρ | |
| Farmers | |||||||||
| Education level | 0.16 | 0.19 | 0.50 | 0.94 | 0.18 | 0.76 | 0.91 | 0.44–1.88 | 0.80 |
| Income | 0.27 | 0.18 | 0.44 | 0.70 | 0.27 | 0.52 | 1.69 | 0.83–3.44 | 0.15 |
| History of antibiotic used | — | 0.40 | 0.01∗ | 0.77 | 0.82 | 0.44 | 0.78 | 0.32–1.77 | 0.52 |
| Bathroom wastewater disposal | — | 0.40 | 0.06 | 0.85 | 0.11 | 0.03∗ | 1.26 | 0.61–2.59 | 0.54 |
| Drinking water source | — | 0.40 | 0.06 | 0.85 | 0.11 | 0.03∗ | 1.26 | 0.61–2.59 | 0.54 |
| Water source for nonconsumption needs | — | 0.40 | 0.13 | 0.85 | 0.28 | 0.03∗ | 0.90 | 0.44–1.85 | 0.77 |
| Distance from septic tank to well | — | 0.11 | 0.95 | 0.33 | 0.07 | 0.72 | 0.32 | 0.11–0.92 | 0.03∗ |
| Distance from nearest healthcare facility | — | — | 0.44 | 0.77 | 0.78 | 0.01∗ | 0.86 | 0.28–2.63 | 0.79 |
| Farms | |||||||||
| Number of dairy cows | 0.78 | 0.78 | 0.43 | 0.98 | 0.69 | 0.40 | 0.98 | 0.47–2.07 | 0.96 |
| Number of barns | 0.56 | 0.30 | 0.15 | 0.85 | 0.70 | 0.91 | 1.03 | 0.37–2.83 | 0.96 |
| Nearest water source | 0.30 | 0.64 | 0.50 | 0.31 | — | 0.72 | 1.20 | 0.27–5.43 | 0.81 |
| Distance to nearest water source | 0.68 | 0.47 | 0.91 | 0.14 | 0.76 | — | 1.23 | 0.49–3.13 | 0.66 |
| Distance of the barn to the river | — | — | 0.70 | 0.44 | 0.50 | 0.39 | 0.73 | 0.28–1.90 | 0.52 |
| Other animals in the barn | 0.63 | 0.64 | 0.64 | — | — | — | 0.46 | 0.06–2.62 | 0.45 |
| Distance to the nearest dairy farm | 0.57 | 0.07 | 0.48 | 0.44 | 0.08 | 0.03∗ | 2.5 | 1.20–5.18 | 0.01∗ |
| Distance to the nearest nondairy farm | 0.38 | 0.55 | 0.64 | 0.60 | 0.37 | 0.66 | 0.95 | 0.45–1.99 | 0.89 |
| Milking by farmers from other farms | 0.30 | — | 0.70 | 0.45 | 0.76 | 0.21 | 0.92 | 0.26–3.28 | 0.90 |
| Sharing milking equipment between livestock | 0.43 | 0.06 | 0.50 | 0.51 | 0.95 | — | 1.82 | 0.82–4.05 | 0.14 |
| Handwashing before milking | 0.00∗ | — | 0.43 | 0.94 | — | — | 0.67 | 0.22–2.09 | 0.49 |
| Handwashing with soap | 0.04∗ | — | 0.26 | 0.94 | 0.28 | — | 0.93 | 0.34–2.56 | 0.90 |
| Barn cleaning | 0.74 | — | 0.52 | 0.00∗ | — | — | 0.50 | 0.13–1.85 | 0.28 |
| Livestock cleaning | 0.74 | 0.50 | 0.70 | 0.00∗ | 0.97 | — | 0.46 | 0.14–1.48 | 0.18 |
| Water usage for barn cleaning | 0.91 | 0.50 | 0.22 | 0.22 | 0.46 | 0.52 | 2.23 | 0.75–6.57 | 0.14 |
| Detergent use for barn cleaning | 0.80 | 0.62 | 0.64 | 0.08 | 0.85 | 0.26 | 0.48 | 0.23–0.97 | 0.04∗ |
| Surface cleaning | 0.78 | 0.06 | 0.95 | 0.23 | — | — | 0.33 | 0.10–1.11 | 0.06 |
| Type of water used for livestock maintenance | 1.00 | 0.50 | 0.17 | 0.94 | 0.01∗ | 0.60 | 1.20 | 0.47–3.05 | 0.70 |
| Use of water storage tanks | 0.25 | 0.30 | 0.13 | 0.65 | 0.40 | 0.86 | 2.27 | 1.07–4.83 | 0.03∗ |
| Drinking water source for livestock | 1.00 | 0.50 | 0.25 | 0.78 | 0.01∗ | 0.60 | 1.33 | 0.52–3.36 | 0.55 |
| Presence of rodents | 0.45 | 0.56 | 0.50 | 0.10 | 0.11 | 0.21 | 1.03 | 0.44–2.40 | 0.94 |
| Quarantine of livestock before entering the barn | 0.43 | — | 0.23 | 0.14 | — | 0.72 | 0.82 | 0.23–2.96 | 0.77 |
| Separation of sick livestock from healthy ones | 0.27 | 0.54 | 0.13 | 0.90 | 0.27 | 0.92 | 0.96 | 0.47–0.196 | 0.91 |
| Manure piles in the milking area | 0.91 | 0.70 | 0.72 | 0.91 | 0.78 | 0.39 | 0.88 | 0.43–1.82 | 0.73 |
| Spread of solid waste in the milking area | 0.91 | 0.00∗ | 0.33 | 0.91 | 0.41 | 0.40 | 1.15 | 0.57–2.35 | 0.70 |
| Disposal of solid waste to other farms | 0.74 | — | 0.58 | 0.03∗ | 0.70 | — | 0.54 | 0.14–2.00 | 0.35 |
| Wastewater disposal pathways | 0.43 | 0.02∗ | 0.55 | — | 0.49 | — | 0.79 | 0.17–3.45 | 0.75 |
| Wastewater puddles around the barn | 0.43 | 0.30 | 0.30 | 0.36 | 0.62 | 0.06 | 0.82 | 0.40–1.67 | 0.59 |
| Presence of wastewater treatment | 0.07 | 0.50 | 0.44 | 0.37 | 0.86 | 0.66 | 1.79 | 0.78–4.12 | 0.17 |
| History of antibiotic use in livestock | 0.34 | 0.78 | 0.85 | 0.35 | 0.12 | 0.97 | 0.99 | 0.33–3.00 | 0.98 |
| Type of feed | 0.80 | — | 0.67 | 0.35 | 0.59 | 0.29 | 2.50 | 0.57–10.90 | 0.21 |
| Hand milking system | 0.38 | 0.17 | 0.64 | 0.33 | 0.46 | 0.52 | 1.20 | 0.47–3.08 | 0.71 |
Abbreviations: CI = confidence interval; crude OR = crude odds ratio.
∗ρ < 0.05.
The univariate analysis based on city/regency reveals discrepancies compared to the analysis conducted for the entire sample or at the provincial level. In this result, variables from the farmers' group exhibit ρ values ranging from 0.03 to 0.08. Notably, among these variables, the significant factor influencing ESBL occurrence from the farmers' group is the distance between the septic tank and the well (ρ value < 0.03). Variables from the farms group showcase ρ values from 0.01 to 0.98, with significant variables registering ρ values of 0.01, 0.03, and 0.04. These variables include the distance to the nearest dairy farm, the utilization of water storage tanks, and the application of detergent for barn cleaning. Subsequently, these four variables underwent multivariate analysis to unveil correlations among them, with the resulting outcomes elucidated in Table 5.
Table 5.
Multivariate analysis of risk factors of occurrence of ESBL-producing Enterobacteriaceae in dairy farm wastewater.
| Variable | Adjusted OR | 95% CI | ρ |
|---|---|---|---|
| Farmer | |||
| Distance from septic tank to well ≤ 10 m | 3.24∗ | 1.07–9.80 | 0.04 |
| Farms | |||
| Distance to the nearest dairy farm ≤ 500 m | 0.50 | 0.23–1.08 | 0.07 |
| Detergent use for barn cleaning | 2.67∗ | 1.23–5.67 | 0.01 |
| Use of water storage tanks | 0.43∗ | 0.19–0.94 | 0.03 |
Abbreviations: adjusted OR = adjusted odds ratio; CI = confidence interval.
∗ρ < 0.05.
Table 5 showed the result of multivariate analysis. It indicates that a distance of ≤ 10 m between the septic tank and the well poses a threefold risk (OR 3.24, 95% CI: 1.07–9.80, p = 0.04). This finding underscores the role of groundwater contamination in bacterial spread, as poorly managed septic systems can introduce antibiotic-resistant bacteria into the water supply [24]. Additionally, farms that did not use detergent for barn cleaning had an increased risk (OR 2.67, 95% CI: 1.23–5.67), highlighting the importance of proper hygiene in reducing bacterial persistence [25]. Conversely, the presence of water storage tanks and proximity to other dairy farms (< 500 m) appeared to reduce ESBL risk, possibly due to improved water management and biosecurity practices.
4. Discussion
The occurrence of ESBL-producing Enterobacteriaceae in this study is lower than that found in studies by Maulana et al. [22] and Santman-Berends et al. [26], where the ESBL occurrence from dairy cow farm feces in Yogyakarta reached 25% and from organic dairy farm slurry in the Netherlands reached 13%. Overall, the ESBL occurrence in each area studied is still higher than in Heuvelink et al. [27], where individual analysis showed that fecal samples from dairy cows in the Netherlands had an ESBL prevalence of 0.8%. The high occurrence of ESBL should be a public health concern, as these bacteria can be zoonotic and transmitted to humans through various routes. Contaminated environments with ESBL-producing bacteria can potentially spread these bacteria to humans [5].
The risk factors of occurrence of ESBL-producing Enterobacteriaceae in dairy farm wastewater vary significantly across different regions in East Java, including Kediri, Malang, Blitar, Pasuruan, Tulungagung, and Batu. This variation is driven by a number of interrelated factors. First, variations in farm management practices, particularly with respect to the protocols for antibiotic use, hygiene levels, and waste systems, are an important factor. The occurrence of ESBL is lower in regions with stricter antibiotic regulations, better adherence to veterinary guidelines, and improved waste treatment facilities. Second, the implementation of effective farm management practices is determined by demographic and economic factors, like education and income levels of farmers. Higher farmer education and income correlate with better management practices and lower ESBL prevalence in these areas. Thus, the regional diversity of ESBL-producing Enterobacteriaceae occurrence in East Java's dairy farm wastewater is a complex problem that requires holistic and targeted intervention to tackle the various influencing factors involved.
On the first variable, the group of risk factors of ESBL-producing Enterobacteriaceae is the farmers. In this variable group, the distance between septic tanks and wells, which is ≤ 10 m, had statistical significance, with an ESBL occurrence rate of 88.57% (31 occurrences). Septic tanks within 10 m pose a threefold risk for ESBL-producing Enterobacteriaceae. Conventional septic tanks collect wastewater from toilets and allow feces to settle and seep slowly into the soil, causing microbiological contamination of groundwater. Groundwater contamination is indicated by the presence of E. coli from septic tank waste, where close proximity to groundwater prevents proper filtration, affecting surrounding water quality [28]. Studies show a significant relationship between septic tank distance and E. coli content in well water, which is at risk of pathogenic contamination [24, 29]. E. coli in spring water indicates the presence of pathogenic bacteria [30]. Contaminated environments are key factors in gene mutations. Reference [31] showed that the environment is crucial in the transmission and emergence of resistant bacteria. Environmental stress can also cause E. coli resistance gene development [32].
The second variable group of risk factors of ESBL-producing Enterobacteriaceae is from the farms. The risk factors that are statistically significant are the distance to the nearest dairy farm, the utilization of water storage tanks, and the application of detergent for barn cleaning. The distance to other dairy farms within 500 m has a statistically significant impact (p-value of 0.01). Close farm locations to others can lead to waste (feces or urine) entering other farms via water flow, especially during the rainy season. Dairy cows can spread antibiotic-resistant bacteria through feces [33]. These bacteria can migrate around the farm, contaminating even distant environments [34]. Additionally, pets from other barns can spread ESBL-producing Enterobacteriaceae between farms since pets are often free-ranging. Studies by Benavides et al. [35] found ESBL genes in chickens, cattle, dogs, and rodents. ESBL-producing E. coli prevalence was significantly high in dogs (24% [CI: 16%–35%]; 20 of 82).
Water-related risk factors play a crucial role in the occurrence of ESBL-producing Enterobacteriaceae in dairy farm wastewater in East Java. As it has been demonstrated earlier, the variable associated with water tank storage units is again statistically significant, and it is also a risk factor for the presence of ESBL in Enterobacteriaceae. Further supporting this, research by Gay et al. [36] noted a 0.38 times increase in the occurrence of ESBL E. coli due to the presence of water storage tanks in dairy settings. Looking to the future, it is suspected that this contamination is caused by sediment accumulation in the tanks. van der Wielen and Lut [37] and Liu et al. [38] showed that sediments from water distribution systems had organic nitrogen and high amounts of bacteria and invertebrates, which nourished and sheltered bacteria. Sediment traps prevent bacteria from being exposed to high storage temperatures, fecal material contamination, and degradation of water quality [38]. The survey clientele of Tsholo et al. [39] included raw and treated drinking water from facilities for the production of drinking water in the North West Province of South Africa and appeared to contain antibiotic resistance genes and antibiotics, especially in rare and treated water, which shows that antibiotic resistance is related to one of the environmental sources of drinking water.
A further important risk factor is the insufficiency of detergent use and improper cleaning of barn areas. Use of detergent, which was related to barn cleaning, and improper cleaning techniques were highly significant (p-values 0.04 and 0.06, respectively). Detergents are chemicals that can inhibit the growth of microorganisms, even pathogenic bacteria, on surfaces and are critical in the prevention of communicable diseases [25]. Cleaning with detergents is important in curtailing the spread of antibiotic-resistant bacteria [40]. However, when overused, detergents cause resistance of microorganisms. Hamdi and Sami [40] have found a direct relationship between the period of exposure and the detergent sensitivity of the bacterial strains tested. Out of 15 types of detergents tested, all of them inhibited some degree of growth of bacteria, but the bactericidal effect was found to be weak to strong in all the tested cases. These findings corroborated with the present study in supporting the fact that the use of compounds that contain detergent to wash all surfaces of the barn reduces the chances of occurrence of ESBL. Also [41], in their study, reported that floors of dairy barns, if cleaned correctly, are devoid of ESBL.
While many risk factors did not significantly influence the occurrence of ESBL, they still warrant attention. Education: About 60% of ESBL occurrences occurred in farmers who did not meet mandatory education requirements (until senior high school). Although education level affects knowledge of farm management and antibiotic use, statistical results were not significant, aligning with findings from Soekoyo et al. [12] and Suherman et al. [42]. Income: Approximately 51.43% of ESBL occurrences were among farmers with incomes below the East Java minimum wage (Rp. 1,891,567.12). Low incomes limit access to resources, impeding proper farm management and increasing the risk of antibiotic resistance [10]. Antibiotic use history: Farmers' antibiotic consumption accounted for 22.86% of occurrences, as direct contact without proper hygiene facilitates resistant bacteria transfer to livestock [43]. Bathroom drainage and water Source: Open drainage systems caused 60% of occurrences, as they allow antibiotics from human waste to contaminate the environment [44]. Non-PDAM water sources, used by 60% of farms, are more susceptible to contamination from resistant bacteria, with wild animals and polluted environments serving as additional reservoirs [45]. Accessibility and livestock factors: Farms located over 500 m from healthcare facilities had 80% of occurrences, highlighting limited healthcare access as a risk factor for unsupervised antibiotic use [46]. Farms with over five cows or only one barn also showed higher ESBL rates due to crowding, waste accumulation, and poor sanitation [47]. Hygiene practices: Shared equipment and poor hygiene, such as failure to wash hands before milking (88.57% of occurrences), contributed to bacterial spread [48]. Regular barn cleaning did not fully mitigate risks, likely due to contaminated water sources and substandard cleaning practices.
While several of these factors did not reach statistical significance, this may be attributed to sample size limitations. A larger sample size could improve statistical power, potentially revealing significant associations that were not detected in this study. Additionally, stratifying samples based on farm management practices and antibiotic usage patterns may offer deeper insights into the transmission dynamics of ESBL-producing Enterobacteriaceae. Future research should consider expanding sample sizes and incorporating longitudinal assessments to better evaluate the long-term impact of these risk factors on antimicrobial resistance trends in dairy farms.
In conclusion, this study highlights key risk factors contributing to the presence of ESBL-producing Enterobacteriaceae in dairy farm wastewater. However, some risk factors did not show statistical significance, which may be due to the sample size limitations. Future studies with larger sample sizes and a more detailed assessment of farm-level antibiotic use and sanitation practices are needed to validate these findings. Addressing these factors through improved farm management, stricter antibiotic regulations, and hygiene practices can help mitigate the spread of antibiotic resistance in dairy farms.
Acknowledgments
The authors would like to thank Universitas Airlangga; the management of KUD Medowo (Kediri Regency); KUD Jabung; KUD Princi and KUD Poncokusumo (Malang Regency); KUD Semen (Blitar Regency); KUD Batu and KUD Junrejo (Batu City); KUD KPSP Setia Kawan (Pasuruan Regency); KUD Sendang (Tulungangung Regency); and the Laboratory of Veterinary Microbiology and Immunology, Faculty of Veterinary Medicine, Universitas Brawijaya, for providing the facilities for the study.
Data Availability Statement
The datasets used and analyzed in this study are available from the corresponding author upon reasonable request.
Conflicts of Interest
The authors declare no conflicts of interest.
Author Contributions
Fidi Nur Aini Eka Puji Dameanti conceptualized the study, was involved in data collection, administered the project, and wrote the draft. Sheila Marty Yanestria performed data interpretation. Mustofa Helmi Effendi conceptualized the study, designed the study, and edited the final manuscript. Wiwiek Tyasningsih and Hani Plumeriastuti conceptualized and designed the study. Emmanuel Nnabuike Ugbo performed data interpretation and finalized the manuscript. Rahayu Sutrisno did data analysis and statistical analysis and wrote the draft. Muhammad Ali Akramsyah Safri performed laboratory procedures, collected samples, and collected data. All authors commented on and discussed the manuscript.
Funding
This study was partly supported by the International Research Consortium, Lembaga Penelitian dan Pengabdian Masyarakat, Universitas Airlangga, Surabaya, Indonesia Year 2024, with grant no.: 171/UN3.LPPM/PT.01.03/2024.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and analyzed in this study are available from the corresponding author upon reasonable request.