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# Prevalence data on chicken diseases in low-resource settings

V. Muñoz-Gómez* & P.R. Torgerson#

Section of Epidemiology, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 270, Zurich, CH 8057, Switzerland

*Corresponding author: ORCID: https://orcid.org/0000-0001-5874-0317

#ORCID: https://orcid.org/0000-0003-4277-9983

## Summary

The presence of transmissible disease in livestock has a major impact on welfare and economics in animal and public health. A lack of data enables the spread of diseases due to misinformed decision-making on prevention and control. Low-resource settings face challenges in providing data, turning data availability into a development issue. For this study, a large dataset (n = 997) was collected on prevalence and seroprevalence estimates on viral (n = 224), bacterial (n = 83) and parasitic (n = 690) diseases in backyard chickens in low- and middle-income countries (LMICs). These estimates originate from 306 studies identified during the screening phase of a systematic literature review. An attempt was made to classify the studies according to the Food and Agriculture Organization of the United Nations' classification system for family poultry production systems. Of the studies, 98.7% (302/306) focused on a single poultry production system, while 1.3% (4/306) targeted two different production systems. Within the group of studies that covered one production system, 85.4% (258/302) were classified as 'small extensive scavenging or extensive scavenging,' 'small extensive scavenging' and/or 'extensive scavenging'. In addition, 52% (159/306) of the studies did not report information on chicken breed type. No data were found on any relevant disease for 56.9% (78/137) of LMICs, signifying a potential data gap. Of the estimates on viral and bacterial diseases, 71.0% (218/307) corresponded to diseases notifiable to the World Organisation for Animal Health, highlighting a tendency to measure disease occurrence for diseases relevant to trade. The latter might not necessarily be priority diseases for the producers, however. Furthermore, 72.3% (222/307) of the estimates originate from random samples and could be used to estimate prevalence in backyard chickens using imputation methods, thus bridging the data gap.

## Keywords

Backyard - Chicken - Data - Disease - LMICs - Low- and middle-income countries - Prevalence.

## Introduction

Livestock diseases curtail the level of output in animal production and, hence, the profits that society generates through livestock [1]. Poultry diseases were associated with an increment of 5% in global hunger in 2019 [2]. In low-income countries, the poultry production level in 2018 decreased by 22%, probably due to diseases [2].

The World Organisation for Animal Health (WOAH) provides a global framework of standards according to which Members must report specific animal diseases that occur in their territories [3]. These are the so-called 'notifiable' or 'listed' diseases. There are 14 listed diseases specifically for poultry, including avian influenza, Newcastle disease, avian infectious bronchitis, infectious bursal disease, avian

infectious laryngotracheitis, pullorum disease, fowl typhoid and avian chlamydiosis, among others. Notifiable diseases for poultry include only viral and bacterial diseases; however, parasitic diseases, as well as reproductive issues, lameness and animal welfare issues, are not included [4].

The procedure for inclusion and removal of diseases on the WOAH list is standardised [5]. For a disease to be included in the list, an assessment is conducted of a pathogenic agent against specific criteria. The criteria include a) evidence of international spread of the pathogenic agent, b) demonstration that at least one country has proved freedom from that disease, c) an accurate case definition and the existence of reliable means for diagnosis and detection, and d) evidence of natural transmission to humans and severe consequence of human infection, or significant impact of the disease on
domestic animal health, or significant impact on wildlife animal health.

The establishment of transboundary trade networks of animals and animal products due to globalisation has increased the risk of spread of animal diseases to new grounds [6]. One of the purposes of notifying diseases to WOAH is to limit the spread through national and international trade by implementing movement restrictions [7]. However, the lack of resources for data collection and laboratory diagnosis in some regions can hinder the capturing and sharing of real-time data and thus enable the spread of animal diseases across a country and beyond [7].

Backyard chickens are a first step to addressing poverty, malnutrition and food security in low-resource settings [8]. This production system provides households with meat and eggs, sources of high-quality protein, which is especially important for those more vulnerable, such as children and pregnant women [9]. Keeping backyard chickens enables farmers to sell and trade chicken products to generate some income and therefore mitigate economic vulnerability. In addition, backyard chickens are usually managed by women, fostering women's empowerment through access to some income [10].

The Global Burden of Animal Diseases (GBADs) programme aims to improve societal outcomes through the enhancement of animal health and animal welfare investments. The GBADs programme is committed to filling data gaps on productivity losses, expenditure and disease burden in livestock [11].

As part of GBADs, this study releases a large dataset of prevalence estimates on backyard chicken disease in low- and middle-income countries (LMICs) to support researchers in secondary analysis and thus to improve public benefit from increased data use. This study describes the geographic distribution of the data, types of diseases, sampling used and attempts to classify the studies selected according to the Food and Agriculture Organization of the United Nations (FAO) classification system of family poultry production systems [12].

# Materials and methods

## Data collection

Studies reporting prevalence and seroprevalence data were collected in the screening step of a systematic literature review, the goal of which was to identify and evaluate studies that provide data to estimate the impact of diseases and other causes of morbidity or mortality in backyard chickens in LMICs. Countries classified as LMICs by the World Bank were

targeted [13]. Only prevalence and seroprevalence data from chickens that had undergone diagnosis by an animal health professional and/or laboratory diagnosis were collected. The ten most spoken languages were covered in the review and the search was restricted to the years 1981–2021 [14]. Backyard farms (including free-roaming chicken farms) were understood according to the FAO definition (sectors 3 and 4) [15]. An essential inclusion criterium was that farms had to have a low biosecurity level and chickens in contact with other wild birds. Information on the search strategy, including search strings in the different languages and databases consulted, can be found in Muñoz-Gómez et al. [14]. In total, 344 studies gathering prevalence and seroprevalence data were identified, of which 35 studies were excluded because the countries were not classified by the World Bank as LMICs, and a further 3 studies were excluded because they were redundant (double-published). Ultimately, 306 studies were selected.

## Data extraction and data management

Data extracted from selected studies included pathogen name, sample type (random versus non-random), breed type, breed name, prevalence, seroprevalence, sample size, testing level, continent, country, production system according to the FAO classification system for family poultry, publication year, first author surname and uniform resource locator, commonly known as URL. All data were extracted in an Excel file. The dataset contains 1,012 prevalence and seroprevalence estimates. However, data on comorbidities (n = 15) were excluded for this analysis, for simplicity.

The authors attempted to classify the studies according to the FAO classification system for family poultry [12] (small extensive scavenging, extensive scavenging, semi-intensified, small-scale intensified). In addition, information was extracted on the breed type (local breed, commercial breed, mixed breed, local and commercial breeds, local and mixed breeds, local and commercial and mixed breeds). Local breed refers to indigenous or native breeds from the region covered in the study. Commercial breed refers to specialised broiler/layer production breeds, also named 'exotic breeds'. Mixed breed refers to a breed that descends from different breeds.

A random sample was defined as a sample randomly selected from all possible chickens in the entire flock. Random samples were considered representative samples from the target population. A non-random sample was understood as a sample that is normally used for confirmatory diagnosis in which individuals are not randomly selected. The full data set is available for perusal<fnref n="1" />.

<footnote n="1">1. The complete dataset on prevalence and seroprevalence of diseases from backyard chickens in LMICs can be accessed through the following link: https://gbads-repro-pods.s3.ca-central-1.amazonaws.com/gbads-20231220-0001/DataPod/2023-Supplementary+material-dataset_RV_2d.xlsx. A companion document of the dataset is also available at: https://gbads-repro-pods.s3.ca-central-1.amazonaws.com/gbads-20231220-0001/DataPod/2023-Supplementary+material-readme_RV_2d.docx.</footnote>
# Descriptive analysis

A descriptive analysis was conducted using the stats package in R [16]. Maps were reproduced using the ggplot2 package in R [17]. All analyses and visualisations were performed using RStudio 4.2.4 [18].

# Results

Prevalence and seroprevalence data on backyard chickens were found from 43.1% (59/137) of LMICs. A total of 997 prevalence and seroprevalence estimates from backyard chicken diseases in LMICs were extracted from 306 studies. Of those 997 estimates, 83 corresponded to bacterial diseases, 224 to viral diseases and 690 to parasitic diseases (see Fig. 1).

Table I shows the classification of prevalence and seroprevalence estimates of viral and bacterial diseases according to whether the diseases are notifiable to WOAH. The data show that 71.0% of the prevalence and seroprevalence estimates of viral and bacterial diseases corresponded to notifiable diseases and 29.0% to non-notifiable diseases.

Table II displays information on the classification of prevalence and seroprevalence estimates of viral and bacterial diseases according to the sampling type. Results show that 72.3% of the prevalence and seroprevalence estimates were randomly sampled. Of the

total prevalence and seroprevalence estimates that corresponded to notifiable diseases, 76.1% were randomly selected, and in the case of non-notifiable diseases, 62.9%.

## Table I

Classification of prevalence and seroprevalence estimates of viral and bacterial diseases according to whether diseases are notifiable or non-notifiable to the World Organisation for Animal Health

<table><thead><tr><th>Disease</th><th>Notifiable disease (n, %)</th><th>Non-notifiable disease (n, %)</th><th>Total</th></tr></thead><tbody><tr><td>Viral</td><td>172 (76.8%)</td><td>52 (23.2%)</td><td>224 (100.0%)</td></tr><tr><td>Bacterial</td><td>46 (55.4%)</td><td>37 (44.6%)</td><td>83 (100.0%)</td></tr><tr><td>Total</td><td>218 (71.0%)</td><td>89 (29.0%)</td><td>307 (100.0%)</td></tr></tbody></table>

## Table II

Classification of prevalence and seroprevalence estimates of viral and bacterial diseases according to sampling type

<table><thead><tr><th>Disease</th><th>Non-random sampling (n, %)</th><th>Random sampling (n, %)</th><th>Total</th></tr></thead><tbody><tr><td>Notifiable</td><td>52 (23.9%)</td><td>166 (76.1%)</td><td>218 (100.0%)</td></tr><tr><td>Non-notifiable</td><td>33 (37.1%)</td><td>56 (62.9%)</td><td>89 (100.0%)</td></tr><tr><td>Total</td><td>85 (27.7%)</td><td>222 (72.3%)</td><td>307 (100.0%)</td></tr></tbody></table>

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A: Geographic location of all prevalence data (n = 997)

B: Geographic location of bacterial disease prevalence data (n = 83)

C: Geographic location of viral disease prevalence data (n = 224)

D: Geographic location of parasitic disease prevalence data (n = 690)

<caption>**Figure 1**</caption>

World map showing the geographic locations where prevalence and seroprevalence data were collected
Out of the 306 studies contained in the dataset, only 3 studies referred to the FAO classification system for family poultry production systems to define the chicken production system under study. None of the 306 studies provided information on each of the criteria used by FAO to define family poultry production systems [12]. Only some of those criteria ('production/farming type', 'other livestock raised', 'flock size', 'poultry breeds', 'source of new chicks', 'feed source', 'drinking water', 'poultry housing', 'access to veterinary services and veterinary pharmaceuticals' and 'access to urban markets') were used to classify the production systems described in the studies. These criteria were the ones regularly covered by most of the studies. To optimise the attempt to classify studies, additional categories of production systems were created to cover two close production systems within the FAO classification (e.g. 'small extensive scavenging or extensive scavenging') as it was difficult to discriminate between them due to a lack of information in the selected studies. The information extracted from each study is available in the dataset.

Of the total number of studies, 98.7% (302/306) focused on a single production system, while 1.3% (4/306) targeted two different production systems. Within the group of studies that focused on a single production system, 65.6% (198/302) were classified as 'small extensive scavenging or extensive scavenging', 11.6% (35/302) as 'small extensive scavenging', 8.3% (25/302) as 'extensive scavenging', 8.9% (27/302) as 'extensive scavenging or semi-intensified', 4.3% (13/302) as 'semi-intensified' and 0.3% (1/302) as 'small-scale intensified'; a further 1.0% (3/302) of the studies were not classified due to lack of information on the production system under study. Within the group of studies that targeted two different production systems, two studies covered a 'small extensive scavenging' production system and an 'extensive scavenging' production system, another study focused on a 'extensive scavenging' production system and a 'small-extensive scavenging or extensive scavenging' production system, and another focused on two production systems that were classified as a 'small extensive scavenging' production system and a 'small-extensive scavenging or extensive scavenging' production system.

Regarding breed type, 52.0% (159/306) of the studies did not report information on the type of breed, 35.3% (108/306) were based on local breeds, 4.6% (14/306) on local and commercial breeds, 3.6% (11/306) on commercial breeds only, 2.9% (9/306) on local and mixed breeds and 1.3% (4/306) on mixed breeds, while 0.3% (1/306) covered three different types of breeds (local, mixed and commercial).

## Discussion

Around 60-90% of the poultry population in LMICs is raised in backyard systems [19]. Prevalence and seroprevalence

data on backyard chicken diseases were found in only 43.1% (59/137) of LMICs. This suggests that there is a potential data gap in the remaining 56.9% of LMICs. The WOAH database and national surveillance reports were not included in the search strategy. Additionally, the search was restricted to 1981-2021. Therefore, it is possible that these data exist in sources that were not considered in the search strategy or were published in the last two years and that were not captured in the screening phase. However, although this could be the case for some countries, it is plausible that for many LMICs, no data on disease prevalence in backyard chickens are regularly generated, and thus there is a knowledge gap regarding the health status of the predominant production system in LMICs.

The absence of disease data on backyard chickens is not only an animal health issue but also a public health one. Backyard chickens can spread zoonotic diseases such as salmonellosis, avian influenza virus and campylobacteriosis to humans, and these diseases can heavily impact the health of vulnerable people [20]. Without data, there is no context to assess this risk or to reduce or prevent it. The lack of data on backyard chicken diseases can also create misconceptions on the current animal/public health system, leading to misinformed decision-making and incorrect conclusions.

The global presence of mobile phones has made it possible to undertake innovative initiatives in the field of animal health in LMICs. These include, for example, smartphone-based applications in Ethiopia that provide veterinarians with a differential diagnostic list based on selected signs and steps for diagnosis and treatment [21] and an e-veterinary diagnostic system in Tanzania in which diagnostic results are provided in a timely manner via SMS text message to livestock field officers [22]. Other initiatives have also targeted livestock owners or just community neighbours. This is the case, for example, of a phone-based surveillance system in Kenya in which farmers and community members directly report disease events to veterinarians [23]. Although the use of mobile phones can facilitate the report of animal health events, this information should be cautiously interpreted and validated by animal health experts. Building capacity on training in animal disease recognition by phone users is indispensable to prevent the saturation of Veterinary Services [24]. Furthermore, the need to conduct field evaluation with diagnostic support is necessary and inherent for the evaluation of phone-based surveillance systems [25,26] and, therefore, so is the need to strengthen its performance and cost-effectiveness. Field evaluation, for example, makes it possible to determine the diagnosis of diseases that share similar signs or symptoms, such as fowl cholera, Newcastle disease and aspergillosis [27], and therefore to discriminate when immediate response from Veterinary Services is required.
Although these initiatives are still at a premature stage, there is an increasing interest in using phones to report livestock diseases. Taking a long-term view, these approaches could offer significant benefits in terms of collecting data on backyard chicken diseases using cheap and fast technologies in low-resource settings and could thus fill the data gap. However, the factors that could hamper this progress should also be acknowledged. These include, for example, the limited internet connectivity in some areas [21], the consequences of having mobile phones for income and expenditure in the household [28], the possession of feature phones versus smartphones [22] and reporting incentives [23]. Therefore, although the digital transformation can contribute to meaningful progress in animal health, it highlights inequality and serves as a reminder that data availability is a development issue.

In this study, 71.0% of the prevalence and seroprevalence estimates of viral and bacterial diseases corresponded to WOAH notifiable diseases. This shows that, according to the data, there is a tendency to measure (or publish) disease occurrence of notifiable diseases in backyard chickens in LMICs. There is a lack of knowledge on whether notifiable diseases are the most economically pressing issues in this livestock production system in LMICs. One of the reasons for this finding, however, could be that Members are required to report occurrence of these diseases to WOAH, and as a consequence, more data are published [3]. In addition, the fact that more data are published on notifiable diseases is an indicator that international standards could be considered a national priority. However, the greater availability of data on notifiable diseases does not necessarily suggest that such diseases are the priority diseases in this production system in LMICs.

In the absence of information on livestock disease impacts, WOAH has commissioned surveys to Delegates on different continents with the aim of establishing a disease priority list for livestock species. Several factors can play a role in deciding which are the most important diseases, such as the impacts on food security, impacts on the export sector, impact on public opinion, and impact on wildlife and the environment [29]. The reason for establishing a disease priority list focusing on individual countries is to ensure that scarce resources target the most pressing issues to obtain the greatest benefits in animal and human health [30]. As far as the authors know, this type of survey has not yet been conducted in chickens.

The results also show that the most used sampling type for both notifiable and non-notifiable diseases was random sampling. This means that 72.3% of the prevalence and seroprevalence estimates of viral and bacterial diseases can be used to draw inferences about the target population. Therefore they can be used, for example, to estimate disease burden when the estimates are pooled together.

Random sampling is used, for example, as part of sentinel surveillance [31] and monitoring control programmes [32]. Results also showed that 27.7% of the estimates were not randomly selected, with 61.2% (52/85) of those involving notifiable diseases. Non-probability-based samples can produce useful information, for example to detect disease and to identify changes in temporal and geographical patterns of disease. However, they should not be applied to estimate disease prevalence in a population as the samples are biased [33].

Prevalence is a measure of disease occurrence and is useful in identifying disease problems, establishing priorities for research, defining control strategies that operate in the long term and evaluating diagnostic tests [34]. It is particularly valid if disease indicators are used that are present for a considerable period, such as antibodies.

As part of GBADs' endeavour, this study collected a large dataset on prevalence and seroprevalence estimates on viral, bacterial and parasitic diseases on backyard chickens based in LMICs. This dataset is intended to be used for new research and to bring value to the economy and society – as a whole and particularly in poor-resource settings.

Following this study, the authors have set out to estimate disease prevalence for relevant diseases through imputation methods to bridge the data gap on backyard chicken diseases in low-resource settings [35]. To estimate the total economic burden attributable to diseases, this requires not only prevalence data, but also data on productivity losses due to diseases and health conditions (e.g. mortality rate, yield reduction in egg production, yield reduction in body weight) and data on expenditure on healthcare and preventive measures (e.g. expenditure in vaccination, expenditure in veterinary medicines). Identifying the major causes of economic burden can optimise the allocation of resources to preventive and control measures [36].

## Conclusions

This study provides access to a large global dataset on prevalence and seroprevalence data on bacterial, viral and parasite diseases in backyard chickens in LMICs. No data were found for 56.9% of the LMICs, signifying a potential data gap. Modern communication technologies such as mobile phones can be used to supplement data on backyard chicken diseases. The FAO classification of family poultry production systems was not generally used in the selected studies to define chicken production systems in LMICs. An attempt to classify the studies revealed that 85.4% of the studies were based on 'small extensive scavenging or extensive scavenging farms', 'small extensive scavenging' and/or 'extensive scavenging'. More than half of the studies (52.0%) did not report information on the chicken breed type. In total, 71.0% of the prevalence and seroprevalence
estimates of viral and bacterial diseases correspond to
diseases notifiable to WOAH, highlighting a tendency to
measure disease occurrence for these diseases. Overall,
72.3% of the prevalence and seroprevalence estimates
of viral and bacterial diseases were drawn from random
sampling and can be used to estimate disease occurrence
in low-resource settings using, for example, imputation
methods, thus bridging the data gap.

Acknowledgements

The authors thank the Global Burden of Animal Diseases (GBADs) programme for helpful discussions, as well as Katharina Stärk and Alexandra Shaw for their comments on this manuscript. Thanks are also given to the authors of the studies included in the systematic literature review for helping in the data collection process.

Les données sur la prévalence des maladies des poulets dans les contextes faiblement dotés en ressources

V. Muñoz-Gómez & P.R. Torgerson

Résumé

Les maladies transmissibles affectant les animaux d'élevage ont un impact majeur sur la santé animale et la santé publique, avec des effets sur le bien-être et sur l'économie. L'absence de données favorise la propagation des maladies puisque les mesures de prévention et de contrôle reposent sur des décisions mal informées. Les contextes faiblement dotés en ressources se heurtent à la difficulté de produire des données, ce qui fait de la disponibilité des données un enjeu de développement. Pour les besoins de la présente étude, un vaste jeu de données (n = 997) a été constitué, regroupant les taux de prévalence et de séroprévalence estimés d'un certain nombre de maladies virales (n = 224), bactériennes (n = 83) et parasitaires (n = 690) affectant les poulets de basse-cour dans les pays à revenu faible et intermédiaire. Ces estimations sont extraites des 306 études retenues lors de la phase de sélection initiale d'un examen systématique de la littérature. Une tentative de classement de ces études a été réalisée en se basant sur la classification des systèmes d'aviculture familiale élaborée par l'Organisation des Nations Unies pour l'alimentation et l'agriculture. Au total, 98,7 % (302/306) des études portaient sur un système unique de production de volailles, les 1,3 % restantes (4/306) portant sur deux systèmes de production différents. Dans le groupe des études couvrant un seul système de production, 85,4 % des élevages étudiés (258/302) relevaient des catégories « petits systèmes extensifs en liberté ou systèmes extensifs en liberté », « petits systèmes extensifs en liberté » et/ou « systèmes extensifs en liberté ». En outre, dans 52 % des études (159/306), la race des poulets n'était pas précisée. Aucune donnée n'a pu être trouvée concernant les maladies importantes des volailles dans 56,9 % (78/137) des pays à revenu faible ou intermédiaire, ce qui indique un déficit potentiel de données. S'agissant des maladies virales et bactériennes, 71,0 % des estimations (218/307) correspondaient à des maladies à déclaration obligatoire à l'Organisation mondiale de la santé animale, ce qui souligne la tendance à signaler la survenue des maladies ayant une incidence sur les échanges internationaux. Toutefois, ce ne sont pas nécessairement ces maladies qui sont prioritaires pour les éleveurs. D'autre part, 72,3 % (222/307) des estimations provenaient d'échantillons aléatoires et pourraient donc servir à estimer la prévalence chez les poulets de basse-cour en appliquant des méthodes d'imputation, ce qui permettrait de combler les écarts.

Mots-clés

Basse-cour - Donnée - Maladie - Pays à revenu faible et intermédiaire - Poulet - Prévalence - PRFI.
# Datos sobre la prevalencia de las enfermedades de los pollos en entornos de bajos recursos

V. Muñoz-Gómez & P.R. Torgerson

## Resumen

La presencia de enfermedades transmisibles en los animales de granja tiene importantes repercusiones en el bienestar y la economía tanto en el ámbito de la sanidad animal como en el de la salud pública. La falta de datos favorece la propagación de enfermedades debido a la toma de decisiones en materia de prevención y control basada en información mal fundada. En los entornos de bajos recursos existen dificultades para el suministro de información, lo que convierte la disponibilidad de datos en un problema de desarrollo. Para este estudio, se recopiló un amplio conjunto de datos (n = 997) sobre estimaciones de prevalencia y seroprevalencia de enfermedades víricas (n = 224), bacterianas (n = 83) y parasitarias (n = 690) en pollos de traspatio en países de ingresos medios y bajos (PIMB). Estas estimaciones provienen de 306 estudios encontrados durante la fase de selección de una revisión bibliográfica sistemática. Se intentó clasificar los estudios según la clasificación de sistemas de producción avícola familiar de la Organización de las Naciones Unidas para la Alimentación y la Agricultura. El 98,7 % de los estudios (302/306) se centraron en un único sistema de producción avícola, mientras que el 1,3 % (4/306) se centraron en dos sistemas de producción diferentes. Dentro del grupo de estudios que abarcaban un solo sistema de producción, el 85,4 % (258/302) se clasificaron como sistema «extensivo de escarbado pequeño o extensivo de escarbado», «extensivo de escarbado pequeño» o «extensivo de escarbado». Además, en el 52 % (159/306) de los estudios no se proporcionó información sobre el tipo de raza de los pollos. No se encontraron datos sobre ninguna enfermedad pertinente para el 56,9 % (78/137) de los PIMB, lo que indica una posible carencia de datos. De las estimaciones sobre enfermedades víricas y bacterianas, el 71,0 % (218/307) correspondían a enfermedades de declaración obligatoria a la Organización Mundial de Sanidad Animal, lo que evidencia una tendencia a medir la aparición de enfermedades pertinentes para el comercio. Sin embargo, estas podrían no ser necesariamente enfermedades prioritarias para los productores. Por otro lado, el 72,3 % (222/307) de las estimaciones proceden de muestras aleatorias y podrían utilizarse para estimar la prevalencia en los pollos de traspatio utilizando métodos de imputación, lo que permitiría subsanar la carencia de datos.

## Palabras clave

Datos – Enfermedad – Países de ingresos medios y bajos – PIMB – Pollo – Prevalencia – Traspatio.

## References

[1] Häsler B, Howe KS, Stärk KD. Conceptualising the technical relationship of animal disease surveillance to intervention and mitigation as a basis for economic analysis. BMC Health Serv. Res. 2011;11:225. https://doi.org/10.1186/1472-6963-11-225

[2] Bertoni G. Human, animal and planet health for complete sustainability. Animals. 2021;11:1301. https://doi.org/10.3390/ani11051301

[3] World Organisation for Animal Health (OIE). Notification of animal and human diseases – global legal basis. Paris (France): OIE; 2010. Available at: https://rr-africa.woah.org/en/news/notification-of-animal-and-human-diseases-global-legal-basis (accessed on 19 September 2023).

[4] World Organisation for Animal Health (WOAH). Terrestrial animal health code. 31st ed. Paris (France): WOAH; 2023. Chapter 1.3. Diseases, infections and infestations listed by WOAH; 4 p. Available at: https://www.woah.org/fileadmin/Home/eng/Health_standards/tahc/2023/chapitre_oie_listed_disease.pdf (accessed on 19 September 2023).

[5] World Organisation for Animal Health (WOAH). Standard Operating Procedure for listing decisions for pathogenic agents of terrestrial animals. Paris (France): WOAH; 2022. 5 p. Available at: https://www.woah.org/en/document/sop_forderlisting_pathogens_for_terrestrial_animals (accessed on 19 September 2023).

[6] Bianchini J, Simons X, Faes C, Nicolas G, Vilain A, Hendrickx G, Saegerman C. Assessing the use of animal health platforms: user's needs, preferences and constraints. Transbound. Emerg. Dis. 2022;69(2):501-15. https://doi.org/10.1111/tbed.14008

[7] Poissonnier C, Teissier M. The role of the OIE in information exchange and the control of animal diseases, including zoonoses. Rev. Sci. Tech. 2013;32(2):447-57. https://doi.org/10.20506/rst.32.2.2226

[8] Nchinda VP, Thieme O, Ankers P, Crespi V, Ariste S. Food security and economic importance of family poultry (chicken) husbandry program in Artibonite and South departments of Haiti. Livest. Res. Rural Dev. 2011;23(9):201. Available at: http://www.lrrd.org/lrrd23/9/nchi23201.htm (accessed on 19 September 2023).
[9] Alders R. Success story on the control of Newcastle disease in village chickens using thermotolerant vaccines. Bangkok (Thailand): Asia-Pacific Association of Agricultural Research Institution; 2003. Available at: https://www.apaari.org/web/wp-content/uploads/2009/05/ss_2003_01.pdf (accessed on 19 September 2023).

[10] Food and Agriculture Organization of the United Nations (FAO). Poultry in the 21st century: avian influenza and beyond. In: Proc. International Poultry Conference; 2007 Nov 5-7; Bangkok. FAO Animal Production and Health Proceedings No. 9. Rome (Italy): FAO; 2008. 793 p. Available at: https://www.fao.org/3/i0323e/i0323e00.htm (accessed on 19 September 2023).

[11] Rushton J, Huntington B, Gilbert W, Herrero M, Torgerson PR, Shaw APM, et al. Roll-out of the Global Burden of Animal Diseases programme. Lancet. 2021;397(10279):1045-6. https://doi.org/10.1016/S0140-6736(21)00189-6

[12] Food and Agriculture Organization of the United Nations (FAO), International Fund for Agricultural Development. Farmer field schools for family poultry producers: a practical manual for facilitators. Rome (Italy): FAO; 2022. 220 p. https://doi.org/10.4060/cc0254en

[13] World Bank country and lending groups. Washington, DC (United States of America): World Bank; 2021. Available at: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups (accessed on 19 September 2021).

[14] Muñoz-Gómez V, Shaw AP, Abdykerimov K, Abo-Shehada M, Bulbuli F, Charypkhan D, et al. Economic impact of chicken diseases and other causes of morbidity or mortality in backyard farms in low-income and middle-income countries: a systematic review and meta-analysis [pre-print, version 1]. Durham (United States of America): Research Square; 2023. https://doi.org/10.21203/rs.3.rs-3415543/v1

[15] Food and Agriculture Organization of the United Nations (FAO). Small-scale poultry production: technical guide. Rome (Italy): FAO; 2004. 120 p. Available at: https://www.fao.org/documents/card/en/c/0f882006-36b1-5ee5-9d51-2cec48e3de51 (accessed on 19 September 2023).

[16] R Core Team. R: a language and environment for statistical computing. Vienna (Austria): R Foundation for Statistical Computing; 2012. Available at: https://www.r-project.org (accessed on 19 September 2023).

[17] Wickham H. Ggplot2: elegant graphics for data analysis. 2nd ed. Cham (Switzerland): Springer International Publishing; 2016. 260 p. Available at: https://ggplot2.tidyverse.org (accessed on 19 September 2023).

[18] Posit Team. RStudio: integrated development environment for R. Boston (United States of America): Posit Software, PBC; 2023. Available at: https://posit.co (accessed on 19 September 2023).

[19] Conan A, Goutard FL, Sorn S, Vong S. Biosecurity measures for backyard poultry in developing countries: a systematic review. BMC Vet. Res. 2012;8:240. https://doi.org/10.1186/1746-6148-8-240

[20] Behravesh CB. Do backyard chickens pose any health risks to humans? Clinician's Brief. 2018;March:60-1. Available at: https://wsava.org/wp-content/uploads/2020/01/Clinician-s-brief-March-2018-TP-Backyard-Chickens.pdf (accessed on 19 September 2023).

[21] Beyene TJ, Asfaw F, Getachew Y, Tufa TB, Collins I, Beyi AF, et al. A smartphone-based application improves the accuracy, completeness, and timeliness of cattle disease reporting and surveillance in Ethiopia. Front. Vet. Sci. 2018;5:2. https://doi.org/10.3389/fvets.2018.00002

[22] Karimuribo ED, Batamuzi EK, Massawe LB, Silayo RS, Mgongo FOK, Kimbita E, et al. Potential use of mobile phones in improving animal health service delivery in underserved rural areas: experience from Kilosa and Gairo districts in Tanzania. BMC Vet. Res. 2016;12(1):219. https://doi.org/10.1186/s12917-016-0860-z

[23] Thumbi SM, Njenga MK, Otiang E, Otieno L, Munyua P, Eichler S, et al. Mobile phone-based surveillance for animal disease in rural communities: implications for detection of zoonoses spillover. Philos. Trans. R. Soc. B Biol. Sci. 2019;374(1782):20190020. https://doi.org/10.1098/rstb.2019.0020

[24] United States Agency for International Development (USAID), Colorado State University, CGIAR, University of Florida, Institute of Food and Agricultural Sciences. Feed the Future Innovation Lab for Livestock Systems. Washington, DC (United States of America): USAID; 2019. Available at: https://livestocklab.ifas.ufl.edu/media/livestocklabifasufledu/pdf-/innovations/Bowen_COState_NEPAL_Innovation-Summaries_April-2019.pdf (accessed on 19 September 2023).

[25] Vrbova L, Stephen C, Kasman N, Boehnke R, Doyle-Waters M, Chablitt-Clark A, et al. Systematic review of surveillance systems for emerging zoonoses. Transbound. Emerg. Dis. 2010;57(3):154-61. https://doi.org/10.1111/j.1865-1682.2010.01100.x

[26] Mtema Z, Changalucha J, Cleaveland S, Elias M, Ferguson HM, Halliday JEB, et al. Mobile phones as surveillance tools: implementing and evaluating a large-scale intersectoral surveillance system for rabies in Tanzania. PLoS Med. 2016;13(4):e1002002. https://doi.org/10.1371/journal.pmed.1002002

[27] Abdisa T, Hasen R, Tagesu T, Regea G, Tadese G. Poultry coccidiosis and its prevention, control. J. Vet. Anim. Res. 2019;2(1):101. Available at: https://www.scholarena.com/article/Poultry-Coccidiosis-and-its-Prevention-Control.pdf (accessed on 19 September 2023).

[28] Parlasca MC. A vital technology: review of the literature on mobile phone use among pastoralists. J. Int. Dev. 2021;33(4):780-99. https://doi.org/10.1002/jid.3540

[29] Grace D, Songe M, Knight-Jones T. Impact of neglected diseases on animal productivity and public health in Africa: 21st Conference of the World Organisation for Animal Health Regional Commission for Africa, 16-20 February 2015, Rabat, Morocco. Paris (France): World Organisation for Animal Health; 2015. 50 p. Available at: https://hdl.handle.net/10568/56836 (accessed on 19 September 2023).

[30] Brookes VJ, Del Rio Vilas VJ, Ward MP. Disease prioritization: what is the state of the art? Epidemiol. Infect. 2015;143(14):2911-22. https://doi.org/10.1017/S0950268815000801

[31] Hoinville L. Animal health surveillance terminology final report from Pre-ICAHS workshop – July 13 (version 1.2). Fredriksberg (Denmark): International Conference on Animal Health; 2013. 27 p. Available at: https://www.fp7-risksur.eu/sites/default/files/partner_logos/icahs-workshop-2011_surveillance_tewrminology_report_V1.2.pdf (accessed on 19 September 2023).
[32] Federal Food Safety and Veterinary Office (FSVO). Supplement to the animal disease surveillance report: methods and specific information concerning the 2020 surveillance programmes. Bern (Switzerland): FSVO; 2021. 22 p. Available at: https://www.blv.admin.ch/dam/blv/en/dokumente/tiere/publikationen-und-forschung/statistik-und-berichte/supplement-zum-bericht-tierseuchen-2020.pdf.
download.pdf/Supplement%20to%20the%20Animal%20Disease%20Surveillance%20Report%202020.pdf (accessed on 19 September 2023).

[33] Berezowski J, De Balogh K, Dórea FC, Ruegg S, Broglia A, Zancanaro G, et al. Coordinated surveillance system under the One Health approach for cross-border pathogens that threaten the Union – options for sustainable surveillance strategies for priority pathogens. EFSA J. 2023;21(3):e07882. https://doi.org/10.2903/j.efsa.2023.7882

[34] Thrusfield M. Veterinary epidemiology. 3rd ed. Oxford (United Kingdom): Blackwell Science Ltd; 2005. 610 p. Available at: http://librodigital.sangregorio.edu.ec/librosusgp/28347.pdf (accessed on 19 September 2023).

[35] Muñoz-Gómez V, Furrer R, Yin J, Shaw APM, Rasmussen P, Torgerson PR. Prediction of coccidiosis prevalence in extensive backyard chickens in countries and regions of the Horn of Africa. Vet. Parasitol. 2024;327:110143. https://doi.org/10.1016/j.vetpar.2024.110143

[36] Huntington B, Bernardo TM, Bondad-Reantaso M, Bruce M, Devleesschauwer B, Gilbert W, et al. Global Burden of Animal Diseases: a novel approach to understanding and managing disease in livestock and aquaculture. Rev. Sci. Tech. 2021;40(2):567-84.
https://doi.org/10.20506/rst.40.2.3246

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