Search bioRxivSearch

bioRxiv · 10.1101/703298

Towards a bottom-up understanding of antimicrobial use and resistance on the farm: A knowledge, attitudes, and practices survey across livestock systems in five African countries

Abstract

The nutritional and economic potentials of livestock systems are compromised by the emergence and spread of antimicrobial resistance. A major driver of resistance is the misuse and abuse of antimicrobial drugs. The likelihood of misuse may be elevated in low- and middle-income countries where limited professional veterinary services and laissez faire access to drugs are assumed to promote non-prudent practices (e.g., self-administration of drugs). The extent of these practices, as well as the knowledge and attitudes motivating them, are largely unknown within most agricultural communities in low- and middle-income countries. The main objective of this study was to document dimensions of knowledge, attitudes and practices related to antimicrobial use and antimicrobial resistance in livestock systems and identify the livelihood factors associated with these dimensions. A mixed-methods ethnographic approach was used to survey households keeping layers in Ghana (N=110) and Kenya (N=76), pastoralists keeping cattle, sheep, and goats in Tanzania (N=195), and broiler farmers in Zambia (N=198), and Zimbabwe (N=298). Across countries, we find that it is individuals who live or work at the farm who draw upon their knowledge and experiences to make decisions regarding antimicrobial use and related practices. Input from animal health professionals is rare and antimicrobials are sourced at local, privately owned agrovet drug shops. We also find that knowledge, attitudes, and particularly practices significantly varied across countries, with poultry farmers holding more knowledge, desirable attitudes, and prudent practices compared to pastoralists households. Multivariate models showed that variation is related to several factors, including education, disease dynamics on the farm, and sources of animal health information. Study results emphasize that interventions to limit antimicrobial resistance must be founded upon a bottom-up understanding of antimicrobial use at the farm-level given limited input from animal health professionals and under-resourced regulatory capacities within most low- and middle-income countries. Establishing this bottom-up understanding across cultures and production systems will inform the development and implementation of the behavioral change interventions to combat AMR globally.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Caudell, M., Dorado-Garcia, A., Eckford, S., Byarugaba, D., Afakye, K., Chansa-Kabali, T., Kabali, E., Kiambi, S., Kimani, T., Mainda, G., Mangesho, P., Chimpangu, F., Dube, K., Kikimoto, B. B., Koka, E., Mugara, T., Swiswa, S.. 2019-07-15. Towards a bottom-up understanding of antimicrobial use and resistance on the farm: A knowledge, attitudes, and practices survey across livestock systems in five African countries. https://doi.org/10.1101/703298

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education

Bayesian hierarchical models for disease mapping applied to contagious pathologies

Disease mapping aims to determine the underlying disease risk from scattered epidemiological data and to represent it on a smoothed colored map. This methodology is based on Bayesian inference and is classically dedicated to non-infectious diseases whose incidence is low and whose cases distribution is spatially (and eventually temporally) structured. Over the last decades, disease mapping has received many major improvements to extend its scope of application: integrating the temporal dimension, dealing with missing data, taking into account various a prioris (environmental and population covariates, assumptions concerning the repartition and the evolution of the risk), dealing with overdispersion, etc. We aim to adapt this approach to rare infectious diseases. In the context of a contagious disease, the outcome of a primary case can in addition generate secondary occurrences of the pathology in a close spatial and temporal neighborhood; this can result in local overdispersion and in higher spatial and temporal dependencies due to direct and/or indirect transmission. We have proposed and tested 60 Bayesian hierarchical models on 400 simulated datasets and bovine tuberculosis real data. This analysis shows the relevance of the CAR (Conditional AutoRegressive) processes to deal with the structure of the risk. We can also conclude that the negative binomial models outperform the Poisson models with a Gaussian noise to handle overdispersion. In addition our study provided relevant maps which are congruent with the real risk (simulated data) and with the knowledge concerning bovine tuberculosis (real data).\n\nAuthor summaryDisease mapping is dedicated to non-infectious diseases whose incidence is low and whose distribution is spatially (and eventually temporally) structured. In this paper, we aim to adapt this approach to rare infectious pathologies. In the context of a contagious disease, the outcome of a primary case can in addition generate secondary occurrences of the pathology in a close spatial and temporal neighborhood, resulting in local overdispersion and in high spatial and temporal dependencies. We thus explored different adapted spatial, temporal and spatiotemporal links and highlight the most adapted to likely risk structures for infectious diseases. We also conclude that the negative binomial models outperform the Poisson models with a Gaussian noise to handle overdispersion. Our study also provided relevant maps which are congruent with the real risk (in case of simulated data) and with the knowledge concerning bovine tuberculosis (when applying to real data). Thus disease mapping appears as a promising way to investigate rare infectious diseases.

scientific communication and education

Review of layperson screening tools and model for a holistic mental health screener in lower and middle income countries

BackgroundThe needs of people diagnosed with Mental Neurological and Substance-Use (MNS) conditions are complex including interactions physical, social, medical and environmental factors. Treatment requires a multidisciplinary approach including health and social services at different levels of care. However, due to inadequate assessment, services and scarcity of human resource for mental health, treatment of persons diagnosed with MNS conditions in many LMICs is mainly facility-based pharmacotherapy with minimal non-pharmacology treatments and social support services. In low resource settings, gaps in human resource capacity may be met using layperson health workers. A layperson health working is one without formal mental health training and may be equivalent to community health worker (CHW) or less cadre in primary health care system.\n\nObjectivesThis study reviewed layperson mental health screening tools for use in supporting mental health in developing countries, including the content and psychometric properties of the tools. Based on this review this study proposes recommendations for the design and effective use of layperson mental health screening tools based on the Five Pillars of global mental health.\n\nMethodsA systematic review was used to identify and examine the use of mental health screening tools among laypersons supporting community-based mental health programs. PubMed, Scopus, CINAHL and PsychInfo databases were reviewed using a comprehensive list of keywords and MESH terms that included mental health, screening tools, lay-person, lower and middle income countries. Articles were included if they describe mental health screening tools used by laypersons for screening, delivery or monitoring of MNS conditions in community-based program in LMICs. Diagnostic tools were not included in this study. Trained research interviewers or research assistants were not considered as lay health workers for this study.\n\nResultsThere were eleven studies retained after 633 were screened. Twelve tools were identified covering specific disorders (E.g. alcohol and substance use, subcortical dementia associated with HIV/AIDS, PTSD) or common mental disorders (mainly depression and anxiety). These tools have been tested in LMICs including South Africa, Zimbabwe, Haiti, Malaysia, Pakistan, India, Ethiopia and Brazil. The included studies show that simple screening tools can enhance the value of laypersons and better support their roles in providing community-based mental health support. However, most of the layperson MH screening tools used in LMICs do not provide comprehensive information that can inform integrated comprehensive treatment planning and understanding of the broader mental health needs of the community.\n\nConclusionDeveloping a layperson screening tools is vital for integrated community-based mental health intervention. This study proposed a holistic framework which considers the relationship between individuals physical, mental and spiritual aspect of mental health, interpersonal as well as broader contextual determinants (community, policy and different level of the health system) that can be consulted for developing or selecting a layperson mental health screening instrument. More research are needed to evaluate the practical application of this framework.

scientific communication and education