Contamination by Antibiotic-Resistant Bacteria in Selected Environments in Thailand

Thamlikitkul, Visanu; Tiengrim, Surapee; Thamthaweechok, Narisara; Buranapakdee, Preeyanuch; Chiemchaisri, Wilai

doi:10.3390/ijerph16193753

Cited by 19 publications

(15 citation statements)

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“…We identified estimates ( Table 1 ) for the prevalence of gut colonisation with ESBL-producing E. coli in Thailand for healthy humans across an eight-year timeframe (2004–2012) [ [22] , [23] , [24] , 28 , 29 ], estimates of sample level positivity for resistant bacteria in rectal swabs from animals and fresh food from Thailand in 2012–2013 [ 24 ], and environmental estimates of the proportion of bacteria that are resistant in Thailand from canal water sources [ 24 ], stagnant water on food animal farms, and liquid from hospital wastewater treatment tanks [ 30 ]. These data were used to calculate lower and upper bounds (95% credible intervals) in order to calibrate our model to a national-level Thailand-specific setting.…”

Section: Methodsmentioning

confidence: 99%

One Health drivers of antibacterial resistance: Quantifying the relative impacts of human, animal and environmental use and transmission

et al. 2021

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Objectives Antibacterial resistance (ABR) is a major global health security threat, with a disproportionate burden on lower-and middle-income countries (LMICs). It is not understood how ‘One Health’, where human health is co-dependent on animal health and the environment, might impact the burden of ABR in LMICs. Thailand's 2017 “National Strategic Plan on Antimicrobial Resistance” (NSP-AMR) aims to reduce AMR morbidity by 50% through 20% reductions in human and 30% in animal antibacterial use (ABU). There is a need to understand the implications of such a plan within a One Health perspective. Methods A model of ABU, gut colonisation with extended-spectrum beta-lactamase (ESBL)-producing bacteria and transmission was calibrated using estimates of the prevalence of ESBL-producing bacteria in Thailand. This model was used to project the reduction in human ABR over 20 years (2020–2040) for each One Health driver, including individual transmission rates between humans, animals and the environment, and to estimate the long-term impact of the NSP-AMR intervention. Results The model predicts that human ABU was the most important factor in reducing the colonisation of humans with resistant bacteria (maximum 65.7–99.7% reduction). The NSP-AMR is projected to reduce human colonisation by 6.0–18.8%, with more ambitious targets (30% reductions in human ABU) increasing this to 8.5–24.9%. Conclusions Our model provides a simple framework to explain the mechanisms underpinning ABR, suggesting that future interventions targeting the simultaneous reduction of transmission and ABU would help to control ABR more effectively in Thailand.

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Section: Methodsmentioning

confidence: 99%

One Health drivers of antibacterial resistance: Quantifying the relative impacts of human, animal and environmental use and transmission

et al. 2021

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“…The high prevalence of ESBL-Ec in wastewater discharged from the urban food markets, with more than 95% of samples positive, and a high mean concentration of ESBL-Ec (5.59 log 10 CFU/100 ml) is notable. Of even greater concern is the high prevalence (30%) of CR-Ec in wastewater samples with a mean concentration of 3.48 log 10 CFU/100 ml which is higher than the concentrations of CR-Ec in community wastewater reported by other studies in Bangladesh and Thailand (Islam et al, 2017;Thamlikitkul et al, 2019). Previous studies reported that hospital waste is a major source of CR-Ec in the environment, especially where hospital waste is disposed directly to the environment without treatment (King et al, 2020;Lamba et al, 2017;Al Salah et al, 2020;Daoud et al, 2018;Park et al, 2020).…”

Section: Discussionmentioning

confidence: 92%

Spatiotemporal Distribution of Antimicrobial Resistant Organisms in Different Water Environments in Urban and Rural Settings of Bangladesh

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“…The copyright holder for this preprint this version posted June 11, 2020. ; https://doi.org/10.1101/2020.06.09.20126433 doi: medRxiv preprint We identified estimates (Table 1) for the prevalence of gut colonisation with ESBLproducing E. coli in Thailand for healthy humans across an eight-year timeframe (2004 -2012) [22][23][24]28,29], estimates of sample level positivity for resistant bacteria in rectal swabs from animals and fresh food from Thailand in 2012-2013 [24], and environmental estimates of the proportion of bacteria that are resistant in Thailand from canal water sources [24], stagnant water on food animal farms, and liquid from hospital wastewater treatment tanks [30]. These data were used to calculate lower and upper bounds (95% credible intervals) in order to calibrate our model to a nationallevel Thailand-specific setting.…”

Section: Methodsmentioning

confidence: 99%

One Health drivers of antibacterial resistance: quantifying the relative impacts of human, animal and environmental use and transmission

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Meeyai

Alhusein

et al. 2020

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Introduction Antimicrobial resistance (AMR), particularly antibacterial resistance (ABR) is a major global health security threat projected to cause over ten million human deaths annually by 2050. There is a disproportionate burden of ABR within lower- and middle-income countries (LMICs), but it is not well understood how 'One Health' drivers, where human health is co-dependent on the health of animals and environmental factors, might also impact the burden of ABR in different countries. Thailand's "National Strategic Plan on Antimicrobial Resistance in Thailand" (NSP-AMR) aims to reduce AMR morbidity by 50% through a reduction of 20% in human antibacterial use and a 30% reduction in animal use starting in 2017. There is a need to understand the implications of such a plan within a One Health perspective that mechanistically links humans, animals and the environment. Methods A mathematical model of antibacterial use, gut colonisation with extended-spectrum beta-lactamase (ESBL)-producing bacteria and faecal/oral transmission between populations of humans, animals and the environment was calibrated using estimates of the prevalence of ESBL-producing bacteria in Thailand, taken from published studies. This model was used to project the reduction in human ABR (% reduction in colonisation with resistant bacteria) over 20 years (2020-2040) for each potential One Health driver, including each individual transmission rate between humans, animals and the environment, exploring the sensitivity of each parameter calibrated to Thai-specific data. The model of antibacterial use and ABR transmission was used to estimate the long-term impact of the NSP-AMR intervention and quantify the relative impacts of each driver on human ABR. Results Our model predicts that human use of antibacterials is the most important factor in reducing the colonisation of humans with resistant bacteria (accounting for maximum 72.3 - 99.8% reduction in colonisation over 20 years). The current NSP-AMR is projected to reduce the human burden of ABR by 7.0 - 21.0%. If a more ambitious target of 30% reduction in antibacterial use in humans were set, a greater (9.9 - 27.1%) reduction in colonisation among humans is projected. We project that completely limiting antibacterial use within animals could have a lower impact (maximum 0.8 - 19.0% reductions in the colonisation of humans with resistant bacteria over 20 years), similar to completely stopping animal-to-human transmission (0.5 - 17.2%). Entirely removing environmental contamination of antibacterials was projected to reduce the percentage colonisation of humans with resistant bacteria by 0.1 - 6.2%, which was similar to stopping environment-human transmission (0.1 - 6.1%). Discussion Our current understanding of the interconnectedness of ABR in a One Health setting is limited and precludes the ability to generate projected outcomes from existing ABR action plans (due to a lack of fit-for-purpose data). Using a theoretical approach, we explored this using the Thai AMR action plan, using the best available parameters to model the estimated impact of reducing antibacterial use and transmission of resistance between populations. Under the assumptions of our model, human use of antibacterials was identified as the main driver of human ABR, with slightly more ambitious reductions in usage (30% versus 20%) predicted to achieve higher impacts within the NSP-AMR programme. Considerable long-term impact may be also achieved through increasing the rate of loss of resistance and limiting One Health transmission events, particularly human-to-human transmission. Our model provides a simple framework to explain the mechanisms underpinning ABR, but further empirical evidence is needed to fully explain the drivers of ABR in LMIC settings. Future interventions targeting the simultaneous reduction of transmission and antibacterial usage would help to control ABR more effectively in Thailand.

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Contamination by Antibiotic-Resistant Bacteria in Selected Environments in Thailand

Cited by 19 publications

References 24 publications

One Health drivers of antibacterial resistance: Quantifying the relative impacts of human, animal and environmental use and transmission

One Health drivers of antibacterial resistance: Quantifying the relative impacts of human, animal and environmental use and transmission

Spatiotemporal Distribution of Antimicrobial Resistant Organisms in Different Water Environments in Urban and Rural Settings of Bangladesh

One Health drivers of antibacterial resistance: quantifying the relative impacts of human, animal and environmental use and transmission

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