Knowledge, perceptions, and practices of community health workers in zoonotic disease detection and reporting in Southwestern Uganda

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Southwestern Uganda is a known hotspot for recurrent zoonotic disease outbreaks, yet limited evidence exists on the capacity of CHWs in this region to detect and respond to these threats. We assessed the knowledge, perceptions, and practices of CHWs in zoonotic disease detection and reporting and factors associated with these outcomes in Mbarara, Kazo, Kiruhura, and Ibanda districts, Southwestern Uganda. Methods We conducted a cross-sectional study between November 2024 and March 2025. We included CHWs from both human and animal health, using systematic simple random sampling. We administered face-to-face interviews using standardized questionnaires. Responses were scored as 1 for positive and 0 for negative, with total scores categorized using Bloom’s cut-off points: 80% as good. Modified Poisson regression was used to identify factors associated with knowledge, perceptions, and practices of CHWs. Results A total of 423 participants were included in the study. The majority (95%; n = 402) had low knowledge levels on zoonoses, with none reporting awareness of Crimean Congo Haemorrhagic Fever (CCHF). Community health workers who received training on their roles in detection and reporting were significantly more knowledgeable about anthrax (adjusted prevalence ratio [aPR] = 1.2, 95% CI: 1.1–5.3), viral haemorrhagic fevers (aPR = 2.2, 95% CI: 1.1–5.5), and rabies (aPR = 1.8, 95% CI: 1.6–2.1). CHWs in the human health sector had lower odds of recording zoonotic cases (aPR = 0.16, 95%CI: 0.07–0.38) and submitting reports (aPR = 0.42, 95% CI: 0.21–0.90) than animal extension workers. Trained CHWs reported better practices (aPR = 4.4, 95% CI:1.5–13) compared to those who were not trained. Conclusion We found that CHWs had low knowledge on zoonoses, with no participants aware of CCHF. Training on roles in detection and reporting was associated with improved knowledge and practices. CHWs in the animal sector were more likely to record and report zoonotic cases than their human health counterparts. Our findings highlight the need for targeted, cross-sectoral training programs to strengthen CHW capacity for early detection and reporting on zoonotic diseases in high-risk areas. Community Health Workers One Health Training Zoonoses detection reporting Uganda Figures Figure 1 Figure 2 INTRODUCTION Zoonotic diseases (also called zoonoses) are diseases caused by viruses, bacteria, parasites, or fungi that can be transmitted between animals to humans, either directly (for example, through contact with bodily fluids or animal bites) or indirectly through vectors like mosquitoes, ticks, flukes, or through contaminated food or water ( 1 ). They pose significant public health risks, especially in regions with frequent human-animal interactions, and contribute to substantial economic losses, with Africa and Asia facing the highest disease burdens ( 1 ). The World Health Organisation (WHO) has continuously reported that zoonotic diseases account for approximately two-thirds of newly emerging diseases ( 2 ). Their impacts manifest in various ways, including animal disease and productivity losses, loss of income for livestock-dependent populations, and human morbidity and mortality. However, the spread can be prevented by recognizing the signs and symptoms of zoonotic diseases in both animals and humans and by seeking timely care and treatment. Given the cattle/livestock interactions in Uganda’s cattle corridor districts, the populations are particularly susceptible to key priority diseases, including anthrax, rabies, and viral Haemorrhagic fevers, such as Rift Valley fever (RVF), Crimean Congo Haemorrhagic fever (CCHF), and Ebola virus disease (EVD) ( 3 , 4 ). In the past five years, Uganda has experienced approximately 30 outbreaks of zoonotic diseases (EVD, CCHF, RVF, and Rabies) in about 20 districts, with the majority being in western Uganda ( 5 – 7 ). For the case of RVF, Uganda has registered 17 outbreaks between 2010 to 2024 ( 8 ). Notably, districts in southwestern Uganda have been grappling with cyclic zoonotic outbreaks over the past five years, indicating a persistent and escalating challenge that necessitates immediate attention and comprehensive intervention ( 6 , 9 ). The recurrent outbreaks in the region signify a critical gap in existing interventions, highlighting the inadequacy of current strategies to break the transmission cycle and effectively engage communities in preventative measures. There has been support for engaging community health workers (CHWs) in providing basic health services, health education, and disease surveillance to enhance zoonotic disease detection through community-based surveillance ( 10 ). The CHWs are always trained using the Ministry of Health system and structures. However, they do not receive annual or periodic refresher trainings, but different activities offer on-the-job mentorships and training to them ( 10 , 11 ). Beyond CHWs, healthcare workers in health facilities and community members often lack the necessary knowledge and resources to actively participate in zoonotic disease prevention efforts; for example, the local terms for RVF and CCHF are not widely known in the region ( 12 ). Furthermore, there is a lack of community ownership in combating zoonotic diseases, which has contributed to delayed detection, hindering timely responses to potential outbreaks ( 9 ). Previous studies have also indicated a delay in seeking care and treatment among communities and animal owners ( 13 , 14 ). These place the burden of zoonotic disease recognition and care-seeking on CHWs, who are in charge of both community health education and referral of sick community members and animals to facilities and animal health workers. Community Health Workers are an important intermediary between facilities and the community, as well as animal owners. Exploring their understanding and perspectives would inform zoonotic disease surveillance strengthening interventions in low-resourced settings like Uganda. We sought to assess the knowledge, perceptions, and practices of community health workers in zoonotic disease detection and reporting and associated factors in Mbarara, Kazo, Kiruhura, and Ibanda districts, Southwestern Uganda. METHODOLOGY Study design and site This was a cross-sectional study conducted between November 2024 and March 2025 in four districts in the southwestern region of Uganda, i.e., Kiruhura, Ibanda, Kazo, and Mbarara (Fig. 1 ). These districts lie in the cattle corridor areas of Uganda ( 3 ). They were purposively selected as the areas with high populations of pastoralist communities, typically keeping large numbers of domestic ruminants (cattle, sheep, and goats). In all the selected districts, agro-pastoralism is the main economic and income-generating activity. Previous studies in the region have reported a high prevalence of zoonotic diseases such as RVF, CCHF, EVD, Anthrax, and Rabies ( 5 , 15 – 17 ). Study population The study was conducted among community health workers, including village health teams (VHTs) and community animal health workers (CAHWs). The VHTs were already in the healthcare system and had varying years of experience. Some of them were trained and deployed by the Ministry of Health in 2015, but others were recruited in subsequent years. Those in the animal health sector had also received modest training (about 14 days) and were deployed in 1994 by the Ministry of Agriculture, Animal Industry and Fisheries (MAAIF) ( 18 ). Both VHTs and CAHWs live and operate in their areas of residence and often get involved in different campaigns, activities from time to time. This helps them to apply their knowledge, skills, but also earn an income since they offer voluntary services, but activities such as campaigns provide a platform for them to engage and benefit ( 10 , 11 ). The majority of them have formal education skills that enable them to read and write. Sample size Using Kish Leslie’s formula, a sample size of 423 CHWs was considered for the study after considering a 10% non-response rate and assuming a prevalence of 50%. The assumption of a 50% prevalence rate was chosen conservatively due to the lack of local data on zoonotic disease knowledge and practices among CHWs. Sampling procedure We randomly selected villages from each district, proportionate to the population size of the district. Within each selected village, a list of all CHWs was obtained, and systematic random sampling was used to select CHWs per village. The sampling interval was equal to the total number of CHWs in each village divided by the assigned sample size for that particular village. Interview appointments were then made with the selected CHWs. If a selected CHW was not available or had shifted from the village, the next CHW on the list was selected using the same systematic approach until the target sample size was reached. Data collection procedures and tools Interview data were collected using a researcher-administered questionnaire. Before data collection, the questionnaire was pre-tested in Isingiro District, located in southwestern Uganda, which had also experienced recurrent zoonotic outbreaks. The pre-test aimed to assess the clarity, relevance, flow of questions, and appropriateness of translations, and the findings of the pre-test were used to adjust the tool. The final version of the questionnaire incorporated these refinements before deployment in the selected study districts. The questionnaire collected data on the following areas: CHWs’ demographic characteristics such as sex, work experience (years), age, occupation, residence, household income, households served, and educational attainment. Knowledge of CHWs on zoonoses was assessed by their knowledge of diseases affecting animals and humans, the presenting signs and symptoms of anthrax, CCHF, RVF, EVD, and rabies, and ways these could be prevented. Perceptions of CHWs were assessed with two questions that asked them if they felt health workers recognized their efforts when they reported suspected zoonotic diseases and if the community members were supportive as they carried out their work. Practices of CHWs in the detection, reporting, and referral of zoonotic diseases were assessed with questions that inquired if they kept records of zoonotic cases in humans and animals and if they submitted reports to higher levels periodically. Other variables under study included receiving training on detection, training of CHWs on their roles and responsibilities, and receiving refresher training. Interviews were conducted by six research assistants who had a training in public health. Before field data collection, research assistants were trained on the study protocol and ethical conduct of research. Written informed consent was obtained from the study participants before the interviews. To ensure cultural and linguistic accuracy, the questionnaire was translated into the local language (Runyankore) and then back-translated into English by bilingual experts Data management and analysis All electronic data files of the survey were downloaded in Microsoft Excel and exported to Stata version 17 (STATA Corp, College Station, Texas, USA for management and analysis. The data were cleaned by checking for inconsistencies, incompleteness, and outliers, edited to resolve identified inconsistencies, and coded. We generated descriptive statistics to compute the frequencies, percentages, and summary statistics of individual characteristics, including sex, educational level, and level of income (which was converted from the reported local currency to USD at a rate of 1USD = UGX 3,760). Knowledge, perceptions, and practices of CHWs on anthrax, rabies, EVD, CCHF, and RVF were also assessed. Positive responses were scored with 1, while negative responses were scored with a 0 value. All scores were added to generate total scores. Bloom’s cut-off points were used to measure the knowledge, practices, and perception levels. Scores that were 80% were considered to be good. At the bivariate level, modified Poisson regression was used to provide the unadjusted prevalence ratios. In multivariate analysis, we only included variables with a p-value of < 0.2 and known confounders such as age and sex, regardless of the p-value, and additional variables were added stepwise to build the model. Three of the diseases, i.e., CCHF, RVF, and EVD, were grouped as viral haemorrhagic fevers at this level. Multicollinearity among independent variables was assessed, and collinear variables were eliminated. We used the Bayesian Information Criterion (BIC) to determine the goodness of fit of the models and considered the one with the lowest BIC score for our final model. RESULTS Socio-demographic characteristics of the study participants A total of 423 community health workers were recruited. The majority of the participants were female (59%, 251), and more than half were VHTs (62%, 263). Most participants resided in rural areas (79%, 333), reported a monthly household income of USD 53.2 or less (63%) and served more than fifty households (69%, 267) (Table 1 ). Table 1 Sociodemographic characteristics of the study participants Variable Frequency (n = 423) Percentage (%) Gender Female 251 59 Male 171 41 Education level No formal education 2 1 Primary 106 25 Secondary and above 315 74 Category of community health workers Animal extension worker 160 38 Village health teams 263 62 Household income ≤ 53.2 267 63 > 53.2 156 37 Residence Urban 90 21 Rural 333 79 Households served ≤ 50 131 31 > 50 292 69 Own mobile phone Yes 421 99 No 2 1 Years in service Less than a year 12 3 More than a year 411 97 Mean Age (Standard deviation) = 44.7 (4.5) Knowledge of community health workers on zoonoses Our findings noted that most CHWs were knowledgeable about EVD (42%, 178) and anthrax (41%, 173), followed by Rift Valley fever (7%, 30) and rabies (6%, 25). Importantly, none of the respondents was knowledgeable about Crimean Congo haemorrhagic fever. Our study highlighted that the majority of the participants had low knowledge levels (95%, 402) about zoonotic diseases (Fig. 2 a,b). Perceptions and practices of the community health workers on zoonoses, Mbarara, Kazo, Kiruhura, and Ibanda districts, Uganda Most community health workers reported positive perceptions (78%, 331) and practices (60%, 256) related to zoonotic disease response. Most participants (91%, 386) felt recognized by health workers, and 85% (359) felt supported by their communities. In terms of practice, 94% (394) provided referrals to health or animal clinics, while 91% (386) gave feedback to the community and maintained records of the zoonotic cases in humans and animals (Table 2 ). Table 2 Perceptions and practices of the community health workers on zoonoses, Mbarara, Kazo, Kiruhura, and Ibanda districts, Uganda, November 2024–March 2025 Perception Frequency (n = 423) Percentage (%) Felt recognized by health workers 386 91 Felt had community support 359 85 Overall positive perception 331 78 Practise Records of zoonotic cases in the community 384 91 Regularly submits reports 281 66 Provides referrals to the facility or animal clinic 396 94 Gives feedback to the community 386 91 Overall good practices 256 60 Factors associated with knowledge, perceptions, and practices of community health workers on zoonoses In terms of knowledge, CHWs who had received training on their roles were significantly more knowledgeable about zoonoses i.e. anthrax (adjusted prevalence ratio [aPR] = 1.2, 95% CI: 1.1–5.3), viral haemorrhagic fevers (VHFs) (aPR = 2.2, 95% CI: 1.1–5.5), and rabies (aPR = 1.8, 95% CI: 1.6–2.1) compared to those who had not received such training. Additionally, CHWs with a household income of less than or equal to USD 53.2 were significantly more likely to have more knowledge about VHFs (aPR = 1.8, 95% CI: 1.1–3.1). Training specifically on the detection of zoonoses was also associated with increased knowledge of VHFs (aPR = 2.3, 95% CI: 1.2–4.3). Regarding perceptions, CHWs who had been trained on their roles were significantly more likely to feel recognized by health workers (aPR = 9.7, 95% CI: 2.8–33). Furthermore, lower household income was associated with greater perceived support from the community (aPR = 4.2, 95% CI: 2.1–8.6). CHWs residing in rural areas had higher odds of reporting having community support compared to their urban counterparts (aPR = 3.7, 95% CI: 1.2–12.0). In terms of practices, CHWs had significantly lower odds of recording zoonotic cases (aPR = 0.16, 95% CI: 0.07–0.38) and submitting reports (aPR = 0.42, 95% CI: 0.21–0.90) compared to the AEWs. Community health workers who had received training on roles had better practices (aPR = 4.4, 95% CI: 1.5–13) compared to those who hadn’t received training on roles and responsibilities (Table 3 ). Table 3 Factors associated with knowledge, perceptions, and practices of community health workers on zoonoses, Mbarara, Kazo. Kiruhura and Ibanda districts, Uganda, November 2024 to March 2025 Variables Knowledge (aPR,95%CI) Perception (aPR,95%CI) Practices (aPR,95%CI) Anthrax VHFs Rabies Recognized by health workers Community support Records cases Submits reports Category of CHWs AEW 1 1 1 1 1 1 1 VHT 1.4 (0.69–2.9) 1.4 (0.68–2.9) 0.74 (0.13–4.3) 1.4 (0.68–4.9) 3.7 (0.2–12) 0.16 (0.07–0.38) 0.42 (0.21–0.9) Residence Urban 1 1 1 1 1 1 1 Rural 0.79 (0.33–1.9) 2.8 (1.1–7.3) 0.34 (0.02–1.2) 2.5 (0.19–3.2) 3.7 (1.2–12) 4.4 (0.76-25) 1.2 (0.01–3.8) Gender Female 1 1 1 1 1 1 1 Male 1.3 (0.19-10) 0.94 (0.49–1.8) 0.45 (0.01–8.9) 0.12 (0.02–1.1) 0.76 (0.29–1.9) 0.24 (0.05–1.1) 1.2 (0.58–2.3) Trained on roles No 1 1 1 1 1 1 1 Yes 1.2 (1.1–5.3) 2.2 (1.1–5.5) 1.8 (1.6–2.1) 9.7 (2.8–33) 0.53 (0.14–2.1) 4.4 (1.5–13) 0.69 (0.21–2.1) Household income (USD) > 53.2 1 1 1 1 1 1 1 ≤ 53.2 1.3 (0.77–2.2) 1.8 (1.1–3.1) 1.1 (0.29–4.1) 1.3 (0.43–3.9) 4.2 (2.1–8.6) 0.91 (0.42–1.9) 0.49 (0.26–0.94) Households served =50 1.3 (0.79–2.2) 1.7 (0.97–2.8) 1.6 (0.49–5.4) 0.33 (0.09–1.1) 0.89 (0.41–1.9) 0.95 (0.46–2.1) 0.63 (0.35–1.2) Trained on detection No 1 1 1 1 1 1 1 Yes 1.9 (0.98–3.5) 2.3 (1.2–4.3) 2.8 (0.61-12) 0.98 (0.26–3.7) 1.8 (0.69–4.6) 0.46 (0.18–1.1) 0.28 (0.14–0.56) Refresher training Yes 1 1 1 1 1 1 1 No 0.76 (0.42–1.4) 0.72 (0.39–1.3) 0.89(0.32-24) 1.6 (0.39–6.8) 1.3 (0.51–3.5) 1.6 (0.68–4.1) 2.8 (0.4–5.5) VHF: Viral haemorrhagic fever; CHW: Community health worker; AEW: Animal extension worker; aPR: Adjusted prevalence ratios; CI: Confidence interval DISCUSSION Our findings revealed an overall lack of knowledge about zoonoses, despite relatively high levels of positive perceptions and practices among CHWs. Ebola virus disease and anthrax were the most recognized zoonotic diseases, whereas awareness of other zoonoses such as rabies, RVF, and CCHF was limited or non-existent. Training CHWs on their roles in reporting and detection of zoonoses was associated with good knowledge of zoonotic diseases among the CHWs. Furthermore, CHWs in the human health sector exhibited lower odds of recording zoonotic cases and submitting reports compared to animal extension workers. Our study found that most of the participants had low levels of zoonotic disease knowledge. The low knowledge levels found in this study are consistent with findings from other East African countries, such as Kenya, where community-level awareness of zoonoses was similarly limited ( 19 , 20 ). Given that CHWs act as the point of contact for health information and surveillance for the community, their limited awareness of zoonoses poses a risk for delayed detection, underreporting, and mismanagement of outbreaks, especially for diseases that may present with nonspecific febrile illnesses ( 21 ). Strengthening training programs for CHWs could enhance their knowledge of zoonotic diseases and improve early detection and reporting at the community level. Despite the observed gaps in zoonotic disease knowledge, most CHWs demonstrated positive perceptions of their roles and a sense of value and recognition, both from healthcare workers and the communities they served. These findings align with evidence from Tanzania, where CHWs who felt integrated into the formal health system demonstrated greater motivation and effectiveness ( 22 ). Our study further revealed that CHWs who received training on their roles were more likely to feel recognized. As noted in the review of CHW programs, key features that enable positive CHW program outcomes include community embeddedness and continuous education ( 23 ). Continuous capacity building of CHWs could sustain motivation and strengthen their contribution to zoonotic disease surveillance. Our results revealed that CHWs who had been trained on their roles and responsibilities exhibited better zoonotic disease surveillance practices, including reporting and giving feedback to the community, compared to their counterparts. This finding reinforces the importance of clarity in the roles and responsibilities of CHWs’ performance. Studies across low- and middle-income countries have consistently shown that role clarity through formal training enhances CHW performance and accountability ( 24 – 26 ). Without a clear understanding of their scope of work, CHWs may overlook critical tasks such as timely reporting of unusual animal deaths, fever cases in humans, or clusters of symptoms that are vital in preventing outbreaks. Our findings also indicated that rural-based CHWs were significantly more likely to report strong support from their communities compared to those from urban areas and higher household income. Unlike urban CHWs, rural CHWs are often deeply embedded within the local social networks. They are more likely to be seen not just as health workers, but also as trusted neighbours and informal leaders ( 27 ). Urban CHWs, on the other hand, due to higher population mobility, less cohesive communities, fragmented health-seeking behaviours, or competing job responsibilities, may not command the same level of trust or recognition ( 26 ). Additionally, lower-income CHWs reported strong community support compared to those who earned high incomes. Lower-income CHWs may rely heavily on communal reciprocity and social capital, which can manifest as higher perceived community support ( 27 , 28 ). A study in low- and middle-income countries found that CHW effectiveness and community support were heavily dependent on contextual factors, including their embeddedness within community structures and their perceived social alignment ( 29 ). Programs should recognize and harness the strong community support and social capital among lower-income CHWs to sustain their effectiveness. Study limitations The cross-sectional nature of the study limits the ability to establish causal relationships. For example, while training was associated with higher knowledge, the design does not allow for the determination of whether training preceded or directly caused this outcome. However, the randomly selected participants across multiple districts enhanced the robustness of observed associations and provided a basis for future longitudinal studies. Our study was also conducted in purposively selected districts in southwestern Uganda, which may limit generalizability to other regions. However, focusing on high-risk areas for zoonotic outbreaks provides context-specific insights into CHW capacities in settings most vulnerable to zoonoses, where strengthening surveillance and response mechanisms is most critical. CONCLUSION We found that community health workers in southwestern Uganda had low levels of knowledge about zoonotic diseases. Training on roles in disease detection and reporting, recognition by health workers, community support, rural residence, and lower incomes of CHWs were significantly associated with better knowledge, perceptions, and practices in zoonotic disease detection and reporting. To enhance the effectiveness of CHWs, national and district-level health systems should invest in comprehensive and continuous training programs that clarify roles, improve disease recognition, and build technical capacity. Abbreviations AEWs – Animal Extension Workers aPR – Adjusted Prevalence Ratio CCHF – Crimean Congo Haemorrhagic Fevers CHWs – Community Health Workers CAHWs – Community Animal Health Workers CI – Confidence Intervals RVF – Rift Valley Fever VHFs – Viral Haemorrhagic Fevers VHTs – Village Health Teams Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Makerere University School of Public Health Research Ethics Committee (MaKSPH), Reference number SPH-2024-671. Additionally, approval was granted by the Uganda National Council of Science and Technology (UNCST), Reference number HS5420ES. Written informed consent was obtained from all participants prior to data collection. Participants were informed of their right to withdraw from the study at any time without any consequences. For participants with hypertension, appropriate referrals were made to nearby public health facilities for further care. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was funded by Korea Foundation for International Healthcare (KOFIH). The funding body had no role in the design of the study, data collection, analysis, or interpretation of data, or in writing the manuscript. Authors' contributions RM, CM, DA, AWW, AB, ARA, PDK, SA, and FM conceptualized and designed the study. CM and AM collected the data while RM analysed and interpreted the data. CM and RM drafted the manuscript, and all authors read and approved the final manuscript. 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Taking a Multisectoral One Health Approach : A Tripartite Guide to Addressing Zoonotic Diseases in Countries. Food & Agriculture Org.; 2019. 166 p. Mpembeni RNM, Bhatnagar A, LeFevre A, Chitama D, Urassa DP, Kilewo C, et al. Motivation and satisfaction among community health workers in Morogoro Region, Tanzania: nuanced needs and varied ambitions. Hum Resour Health. 2015 Jun 5;13(1):44. Scott K, Beckham SW, Gross M, Pariyo G, Rao KD, Cometto G, et al. What do we know about community-based health worker programs? A systematic review of existing reviews on community health workers. Hum Resour Health. 2018 Dec;16(1):1–17. Gilmore B, McAuliffe E. Effectiveness of community health workers delivering preventive interventions for maternal and child health in low- and middle-income countries: a systematic review. BMC Public Health [Internet]. 2013 Dec [cited 2025 May 1];13(1). Available from: http://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-13-847 Musoke D, Lubega GB, Twesigye B, Nakachwa B, Brown MO, Gibson L. Enhancing the capacity of community health workers in prevention and control of epidemics and pandemics in Wakiso district, Uganda: evaluation of a pilot project. BMC Prim Care. 2024 Jul 17;25(1):260. Perry HB, Zulliger R, Rogers MM. Community Health Workers in Low-, Middle-, and High-Income Countries: An Overview of Their History, Recent Evolution, and Current Effectiveness. Annu Rev Public Health. 2014 Mar 18;35(1):399–421. Glenton C, Colvin CJ, Carlsen B, Swartz A, Lewin S, Noyes J, et al. Barriers and facilitators to the implementation of lay health worker programmes to improve access to maternal and child health: a qualitative evidence synthesis. Cochrane Database Syst Rev [Internet]. 2013 [cited 2025 May 1];(10). Available from: https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD010414.pub2/abstract Gatua JG. Decentralization, social connections and primary health care: Evidence from Kenya. World Dev. 2024;178:106562. Kok MC, Broerse JEW, Theobald S, Ormel H, Dieleman M, Taegtmeyer M. Performance of community health workers: situating their intermediary position within complex adaptive health systems. Hum Resour Health [Internet]. 2017 Dec [cited 2025 May 1];15(1). Available from: https://human-resources-health.biomedcentral.com/articles/10.1186/s12960-017-0234-z Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7353666","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510080545,"identity":"bf108d30-b2e4-46db-95f1-89b53f21c980","order_by":0,"name":"Richard Migisha","email":"","orcid":"","institution":"Uganda Public Health Fellowship Program, Uganda National Institute of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Migisha","suffix":""},{"id":510080546,"identity":"e8b7395d-5842-4a75-9d9b-bd9b2c467b79","order_by":1,"name":"Charity Mutesi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDACCSBOgHESKhgY2Bh4iNEC0sMGYpwhVgsDTAtjG4hHQAv/7OZnHx7+OBxtcL/58IeH8w7n8Un3HmD8UsGQON8BhyV3jhnPSEg4nLvhGFuaROK2w8VsMucSmGXOMCRuPIBdi4FEgjEDRAuPGQNQS2KbRI4Bs2QbUEsDLi3pn6Fa+D9/SJxDlJYcuC0MEokNEC2MH4Fa5uPwvsSNnGKGhLT03JnH0swkEo6lJ7bJnDE4zHBGwngDrhCbkb6Z8YeNdW7f4cOPP/6osU6cP7vH8OGPChvZ+TgcBgXNyBYzMBwGOpLB4ABeLXWoWhh/AGl5/LaMglEwCkbByAEAjiZh0DfVDgsAAAAASUVORK5CYII=","orcid":"","institution":"Uganda Public Health Fellowship Program, Uganda National Institute of Public Health","correspondingAuthor":true,"prefix":"","firstName":"Charity","middleName":"","lastName":"Mutesi","suffix":""},{"id":510080547,"identity":"0f3818dc-9806-468b-ab3c-fdd2933cc2ac","order_by":2,"name":"Derrick Asiimwe","email":"","orcid":"","institution":"Department of Physiology, Mbarara University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Derrick","middleName":"","lastName":"Asiimwe","suffix":""},{"id":510080548,"identity":"04783911-cc22-4098-8f5e-2c62cee18b13","order_by":3,"name":"Aggrey Byaruhanga","email":"","orcid":"","institution":"Department of Integrated Epidemiology Surveillance \u0026 Public Health Emergencies, Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Aggrey","middleName":"","lastName":"Byaruhanga","suffix":""},{"id":510080549,"identity":"7248076e-1b4e-4470-abc8-b252cd3e6e1e","order_by":4,"name":"Abraham Muhwezi","email":"","orcid":"","institution":"Department of Community Health, Mbarara University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Abraham","middleName":"","lastName":"Muhwezi","suffix":""},{"id":510080550,"identity":"34fa34e2-b96e-41c1-8d05-29f8b0ec2d98","order_by":5,"name":"Prichard D. Kavuma","email":"","orcid":"","institution":"Korea Foundation for International Healthcare (KOFIH) Country Office","correspondingAuthor":false,"prefix":"","firstName":"Prichard","middleName":"D.","lastName":"Kavuma","suffix":""},{"id":510080551,"identity":"d8d9f2e2-f7ec-4c74-8395-74920f7e86b0","order_by":6,"name":"Sarah Achiro","email":"","orcid":"","institution":"Korea Foundation for International Healthcare (KOFIH) Country Office","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Achiro","suffix":""},{"id":510080552,"identity":"1077330d-0b49-4356-9075-b849e771c7a3","order_by":7,"name":"Fred Monje","email":"","orcid":"","institution":"Ministry of Agriculture, Animal Industry and Fisheries","correspondingAuthor":false,"prefix":"","firstName":"Fred","middleName":"","lastName":"Monje","suffix":""},{"id":510080554,"identity":"b81a65e0-dcbc-4490-8db9-0ab5437dda86","order_by":8,"name":"Alex R. Ario","email":"","orcid":"","institution":"Uganda Public Health Fellowship Program, Uganda National Institute of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"R.","lastName":"Ario","suffix":""},{"id":510080557,"identity":"b67abe6b-1974-4fea-8be7-32097988b92a","order_by":9,"name":"Abel W. Walekhwa","email":"","orcid":"","institution":"Disease Dynamics Unit, Department of Veterinary Medicine, Cambridge Veterinary School","correspondingAuthor":false,"prefix":"","firstName":"Abel","middleName":"W.","lastName":"Walekhwa","suffix":""}],"badges":[],"createdAt":"2025-08-12 09:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7353666/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7353666/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90884134,"identity":"8346f6a7-8887-40f1-ae56-77cf679060a2","added_by":"auto","created_at":"2025-09-09 09:59:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120027,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSelected study districts\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7353666/v1/2e9f4c8772e16c3e9e15c2b0.png"},{"id":90884135,"identity":"58344030-574d-47a6-a96d-703f85f2ded5","added_by":"auto","created_at":"2025-09-09 09:59:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99669,"visible":true,"origin":"","legend":"\u003cp\u003eKnowledge of community health workers on zoonoses, Mbarara, Kazo, Kiruhura, and Ibanda districts, Uganda, November 2024–March 2025; CCHF: Crimean Congo haemorrhagic fever; RVF: Rift Valley fever\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7353666/v1/a959bee9bed8e05fadc007b5.png"},{"id":96252737,"identity":"65d2e97d-6138-4246-97a8-ef68f2059dac","added_by":"auto","created_at":"2025-11-19 07:41:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1559198,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7353666/v1/289139e1-fc9b-4036-98fc-b9830c7208a3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Knowledge, perceptions, and practices of community health workers in zoonotic disease detection and reporting in Southwestern Uganda","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eZoonotic diseases (also called zoonoses) are diseases caused by viruses, bacteria, parasites, or fungi that can be transmitted between animals to humans, either directly (for example, through contact with bodily fluids or animal bites) or indirectly through vectors like mosquitoes, ticks, flukes, or through contaminated food or water (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). They pose significant public health risks, especially in regions with frequent human-animal interactions, and contribute to substantial economic losses, with Africa and Asia facing the highest disease burdens (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The World Health Organisation (WHO) has continuously reported that zoonotic diseases account for approximately two-thirds of newly emerging diseases (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Their impacts manifest in various ways, including animal disease and productivity losses, loss of income for livestock-dependent populations, and human morbidity and mortality. However, the spread can be prevented by recognizing the signs and symptoms of zoonotic diseases in both animals and humans and by seeking timely care and treatment.\u003c/p\u003e\u003cp\u003eGiven the cattle/livestock interactions in Uganda\u0026rsquo;s cattle corridor districts, the populations are particularly susceptible to key priority diseases, including anthrax, rabies, and viral Haemorrhagic fevers, such as Rift Valley fever (RVF), Crimean Congo Haemorrhagic fever (CCHF), and Ebola virus disease (EVD) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In the past five years, Uganda has experienced approximately 30 outbreaks of zoonotic diseases (EVD, CCHF, RVF, and Rabies) in about 20 districts, with the majority being in western Uganda (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). For the case of RVF, Uganda has registered 17 outbreaks between 2010 to 2024 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Notably, districts in southwestern Uganda have been grappling with cyclic zoonotic outbreaks over the past five years, indicating a persistent and escalating challenge that necessitates immediate attention and comprehensive intervention (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The recurrent outbreaks in the region signify a critical gap in existing interventions, highlighting the inadequacy of current strategies to break the transmission cycle and effectively engage communities in preventative measures.\u003c/p\u003e\u003cp\u003eThere has been support for engaging community health workers (CHWs) in providing basic health services, health education, and disease surveillance to enhance zoonotic disease detection through community-based surveillance (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The CHWs are always trained using the Ministry of Health system and structures. However, they do not receive annual or periodic refresher trainings, but different activities offer on-the-job mentorships and training to them (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond CHWs, healthcare workers in health facilities and community members often lack the necessary knowledge and resources to actively participate in zoonotic disease prevention efforts; for example, the local terms for RVF and CCHF are not widely known in the region (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Furthermore, there is a lack of community ownership in combating zoonotic diseases, which has contributed to delayed detection, hindering timely responses to potential outbreaks (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Previous studies have also indicated a delay in seeking care and treatment among communities and animal owners (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). These place the burden of zoonotic disease recognition and care-seeking on CHWs, who are in charge of both community health education and referral of sick community members and animals to facilities and animal health workers.\u003c/p\u003e\u003cp\u003eCommunity Health Workers are an important intermediary between facilities and the community, as well as animal owners. Exploring their understanding and perspectives would inform zoonotic disease surveillance strengthening interventions in low-resourced settings like Uganda. We sought to assess the knowledge, perceptions, and practices of community health workers in zoonotic disease detection and reporting and associated factors in Mbarara, Kazo, Kiruhura, and Ibanda districts, Southwestern Uganda.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and site\u003c/h2\u003e\u003cp\u003eThis was a cross-sectional study conducted between November 2024 and March 2025 in four districts in the southwestern region of Uganda, i.e., Kiruhura, Ibanda, Kazo, and Mbarara (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These districts lie in the cattle corridor areas of Uganda (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). They were purposively selected as the areas with high populations of pastoralist communities, typically keeping large numbers of domestic ruminants (cattle, sheep, and goats). In all the selected districts, agro-pastoralism is the main economic and income-generating activity. Previous studies in the region have reported a high prevalence of zoonotic diseases such as RVF, CCHF, EVD, Anthrax, and Rabies (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe study was conducted among community health workers, including village health teams (VHTs) and community animal health workers (CAHWs). The VHTs were already in the healthcare system and had varying years of experience. Some of them were trained and deployed by the Ministry of Health in 2015, but others were recruited in subsequent years. Those in the animal health sector had also received modest training (about 14 days) and were deployed in 1994 by the Ministry of Agriculture, Animal Industry and Fisheries (MAAIF) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Both VHTs and CAHWs live and operate in their areas of residence and often get involved in different campaigns, activities from time to time. This helps them to apply their knowledge, skills, but also earn an income since they offer voluntary services, but activities such as campaigns provide a platform for them to engage and benefit (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The majority of them have formal education skills that enable them to read and write.\u003c/p\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eUsing Kish Leslie\u0026rsquo;s formula, a sample size of 423 CHWs was considered for the study after considering a 10% non-response rate and assuming a prevalence of 50%. The assumption of a 50% prevalence rate was chosen conservatively due to the lack of local data on zoonotic disease knowledge and practices among CHWs.\u003c/p\u003e\n\u003ch3\u003eSampling procedure\u003c/h3\u003e\n\u003cp\u003eWe randomly selected villages from each district, proportionate to the population size of the district. Within each selected village, a list of all CHWs was obtained, and systematic random sampling was used to select CHWs per village. The sampling interval was equal to the total number of CHWs in each village divided by the assigned sample size for that particular village. Interview appointments were then made with the selected CHWs. If a selected CHW was not available or had shifted from the village, the next CHW on the list was selected using the same systematic approach until the target sample size was reached.\u003c/p\u003e\n\u003ch3\u003eData collection procedures and tools\u003c/h3\u003e\n\u003cp\u003eInterview data were collected using a researcher-administered questionnaire. Before data collection, the questionnaire was pre-tested in Isingiro District, located in southwestern Uganda, which had also experienced recurrent zoonotic outbreaks. The pre-test aimed to assess the clarity, relevance, flow of questions, and appropriateness of translations, and the findings of the pre-test were used to adjust the tool. The final version of the questionnaire incorporated these refinements before deployment in the selected study districts. The questionnaire collected data on the following areas: CHWs\u0026rsquo; demographic characteristics such as sex, work experience (years), age, occupation, residence, household income, households served, and educational attainment. Knowledge of CHWs on zoonoses was assessed by their knowledge of diseases affecting animals and humans, the presenting signs and symptoms of anthrax, CCHF, RVF, EVD, and rabies, and ways these could be prevented.\u003c/p\u003e\u003cp\u003ePerceptions of CHWs were assessed with two questions that asked them if they felt health workers recognized their efforts when they reported suspected zoonotic diseases and if the community members were supportive as they carried out their work. Practices of CHWs in the detection, reporting, and referral of zoonotic diseases were assessed with questions that inquired if they kept records of zoonotic cases in humans and animals and if they submitted reports to higher levels periodically. Other variables under study included receiving training on detection, training of CHWs on their roles and responsibilities, and receiving refresher training.\u003c/p\u003e\u003cp\u003eInterviews were conducted by six research assistants who had a training in public health. Before field data collection, research assistants were trained on the study protocol and ethical conduct of research. Written informed consent was obtained from the study participants before the interviews. To ensure cultural and linguistic accuracy, the questionnaire was translated into the local language (Runyankore) and then back-translated into English by bilingual experts\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData management and analysis\u003c/h2\u003e\u003cp\u003eAll electronic data files of the survey were downloaded in Microsoft Excel and exported to Stata version 17 (STATA Corp, College Station, Texas, USA for management and analysis. The data were cleaned by checking for inconsistencies, incompleteness, and outliers, edited to resolve identified inconsistencies, and coded.\u003c/p\u003e\u003cp\u003e We generated descriptive statistics to compute the frequencies, percentages, and summary statistics of individual characteristics, including sex, educational level, and level of income (which was converted from the reported local currency to USD at a rate of 1USD\u0026thinsp;=\u0026thinsp;UGX 3,760). Knowledge, perceptions, and practices of CHWs on anthrax, rabies, EVD, CCHF, and RVF were also assessed. Positive responses were scored with 1, while negative responses were scored with a 0 value. All scores were added to generate total scores. Bloom\u0026rsquo;s cut-off points were used to measure the knowledge, practices, and perception levels. Scores that were \u0026lt;\u0026thinsp;60% we considered to be poor, between 60%-80% were moderate, while those that were \u0026gt;\u0026thinsp;80% were considered to be good. At the bivariate level, modified Poisson regression was used to provide the unadjusted prevalence ratios. In multivariate analysis, we only included variables with a p-value of \u0026lt;\u0026thinsp;0.2 and known confounders such as age and sex, regardless of the p-value, and additional variables were added stepwise to build the model. Three of the diseases, i.e., CCHF, RVF, and EVD, were grouped as viral haemorrhagic fevers at this level. Multicollinearity among independent variables was assessed, and collinear variables were eliminated. We used the Bayesian Information Criterion (BIC) to determine the goodness of fit of the models and considered the one with the lowest BIC score for our final model.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eSocio-demographic characteristics of the study participants\u003c/h2\u003e\u003cp\u003eA total of 423 community health workers were recruited. The majority of the participants were female (59%, 251), and more than half were VHTs (62%, 263). Most participants resided in rural areas (79%, 333), reported a monthly household income of USD 53.2 or less (63%) and served more than fifty households (69%, 267) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemographic characteristics of the study participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (n\u0026thinsp;=\u0026thinsp;423)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCategory of community health workers\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnimal extension worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVillage health teams\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;53.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;53.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHouseholds served\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOwn mobile phone\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYears in service\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than a year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than a year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean Age (Standard deviation)\u0026thinsp;=\u0026thinsp;44.7 (4.5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eKnowledge of community health workers on zoonoses\u003c/h2\u003e\u003cp\u003eOur findings noted that most CHWs were knowledgeable about EVD (42%, 178) and anthrax (41%, 173), followed by Rift Valley fever (7%, 30) and rabies (6%, 25). Importantly, none of the respondents was knowledgeable about Crimean Congo haemorrhagic fever. Our study highlighted that the majority of the participants had low knowledge levels (95%, 402) about zoonotic diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea,b).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePerceptions and practices of the community health workers on zoonoses, Mbarara, Kazo, Kiruhura, and Ibanda districts, Uganda\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMost community health workers reported positive perceptions (78%, 331) and practices (60%, 256) related to zoonotic disease response. Most participants (91%, 386) felt recognized by health workers, and 85% (359) felt supported by their communities. In terms of practice, 94% (394) provided referrals to health or animal clinics, while 91% (386) gave feedback to the community and maintained records of the zoonotic cases in humans and animals (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePerceptions and practices of the community health workers on zoonoses, Mbarara, Kazo, Kiruhura, and Ibanda districts, Uganda, November 2024\u0026ndash;March 2025\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerception\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (n\u0026thinsp;=\u0026thinsp;423)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFelt recognized by health workers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFelt had community support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall positive perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePractise\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecords of zoonotic cases in the community\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegularly submits reports\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvides referrals to the facility or animal clinic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGives feedback to the community\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall good practices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eFactors associated with knowledge, perceptions, and practices of community health workers on zoonoses\u003c/h2\u003e\u003cp\u003eIn terms of knowledge, CHWs who had received training on their roles were significantly more knowledgeable about zoonoses i.e. anthrax (adjusted prevalence ratio [aPR]\u0026thinsp;=\u0026thinsp;1.2, 95% CI: 1.1\u0026ndash;5.3), viral haemorrhagic fevers (VHFs) (aPR\u0026thinsp;=\u0026thinsp;2.2, 95% CI: 1.1\u0026ndash;5.5), and rabies (aPR\u0026thinsp;=\u0026thinsp;1.8, 95% CI: 1.6\u0026ndash;2.1) compared to those who had not received such training. Additionally, CHWs with a household income of less than or equal to USD 53.2 were significantly more likely to have more knowledge about VHFs (aPR\u0026thinsp;=\u0026thinsp;1.8, 95% CI: 1.1\u0026ndash;3.1). Training specifically on the detection of zoonoses was also associated with increased knowledge of VHFs (aPR\u0026thinsp;=\u0026thinsp;2.3, 95% CI: 1.2\u0026ndash;4.3).\u003c/p\u003e\u003cp\u003eRegarding perceptions, CHWs who had been trained on their roles were significantly more likely to feel recognized by health workers (aPR\u0026thinsp;=\u0026thinsp;9.7, 95% CI: 2.8\u0026ndash;33). Furthermore, lower household income was associated with greater perceived support from the community (aPR\u0026thinsp;=\u0026thinsp;4.2, 95% CI: 2.1\u0026ndash;8.6). CHWs residing in rural areas had higher odds of reporting having community support compared to their urban counterparts (aPR\u0026thinsp;=\u0026thinsp;3.7, 95% CI: 1.2\u0026ndash;12.0).\u003c/p\u003e\u003cp\u003eIn terms of practices, CHWs had significantly lower odds of recording zoonotic cases (aPR\u0026thinsp;=\u0026thinsp;0.16, 95% CI: 0.07\u0026ndash;0.38) and submitting reports (aPR\u0026thinsp;=\u0026thinsp;0.42, 95% CI: 0.21\u0026ndash;0.90) compared to the AEWs. Community health workers who had received training on roles had better practices (aPR\u0026thinsp;=\u0026thinsp;4.4, 95% CI: 1.5\u0026ndash;13) compared to those who hadn\u0026rsquo;t received training on roles and responsibilities (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eFactors associated with knowledge, perceptions, and practices of community health workers on zoonoses, Mbarara, Kazo. Kiruhura and Ibanda districts, Uganda, November 2024 to March 2025\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eKnowledge (aPR,95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003ePerception (aPR,95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003ePractices (aPR,95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnthrax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVHFs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRabies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRecognized by \u003c/p\u003e\u003cp\u003ehealth workers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCommunity support\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRecords cases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSubmits reports\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory of CHWs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAEW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVHT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.4 (0.69\u0026ndash;2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4 (0.68\u0026ndash;2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.74 (0.13\u0026ndash;4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.4 (0.68\u0026ndash;4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.7 (0.2\u0026ndash;12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16 (0.07\u0026ndash;0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.42 (0.21\u0026ndash;0.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.79 (0.33\u0026ndash;1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8 (1.1\u0026ndash;7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34 (0.02\u0026ndash;1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.5 (0.19\u0026ndash;3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.7 (1.2\u0026ndash;12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.4 (0.76-25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.2 (0.01\u0026ndash;3.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3 (0.19-10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.94 (0.49\u0026ndash;1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.45 (0.01\u0026ndash;8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12 (0.02\u0026ndash;1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.76 (0.29\u0026ndash;1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.24 (0.05\u0026ndash;1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.2 (0.58\u0026ndash;2.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTrained on roles\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2 (1.1\u0026ndash;5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.2 (1.1\u0026ndash;5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.8 (1.6\u0026ndash;2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.7 (2.8\u0026ndash;33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.53 (0.14\u0026ndash;2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.4 (1.5\u0026ndash;13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.69 (0.21\u0026ndash;2.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold income (USD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;53.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;53.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3 (0.77\u0026ndash;2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.8 (1.1\u0026ndash;3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1 (0.29\u0026ndash;4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 (0.43\u0026ndash;3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.2 (2.1\u0026ndash;8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.91 (0.42\u0026ndash;1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.49 (0.26\u0026ndash;0.94)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHouseholds served\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;=50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3 (0.79\u0026ndash;2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.7 (0.97\u0026ndash;2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.6 (0.49\u0026ndash;5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.33 (0.09\u0026ndash;1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.89 (0.41\u0026ndash;1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.95 (0.46\u0026ndash;2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.63 (0.35\u0026ndash;1.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTrained on detection\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.9 (0.98\u0026ndash;3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.3 (1.2\u0026ndash;4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.8 (0.61-12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.98 (0.26\u0026ndash;3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.8 (0.69\u0026ndash;4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.46 (0.18\u0026ndash;1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.28 (0.14\u0026ndash;0.56)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRefresher training\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.76 (0.42\u0026ndash;1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72 (0.39\u0026ndash;1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89(0.32-24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.6 (0.39\u0026ndash;6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.3 (0.51\u0026ndash;3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.6 (0.68\u0026ndash;4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.8 (0.4\u0026ndash;5.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eVHF: Viral haemorrhagic fever; CHW: Community health worker; AEW: Animal extension worker; aPR: Adjusted prevalence ratios; CI: Confidence interval\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur findings revealed an overall lack of knowledge about zoonoses, despite relatively high levels of positive perceptions and practices among CHWs. Ebola virus disease and anthrax were the most recognized zoonotic diseases, whereas awareness of other zoonoses such as rabies, RVF, and CCHF was limited or non-existent. Training CHWs on their roles in reporting and detection of zoonoses was associated with good knowledge of zoonotic diseases among the CHWs. Furthermore, CHWs in the human health sector exhibited lower odds of recording zoonotic cases and submitting reports compared to animal extension workers.\u003c/p\u003e\u003cp\u003eOur study found that most of the participants had low levels of zoonotic disease knowledge. The low knowledge levels found in this study are consistent with findings from other East African countries, such as Kenya, where community-level awareness of zoonoses was similarly limited (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Given that CHWs act as the point of contact for health information and surveillance for the community, their limited awareness of zoonoses poses a risk for delayed detection, underreporting, and mismanagement of outbreaks, especially for diseases that may present with nonspecific febrile illnesses (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Strengthening training programs for CHWs could enhance their knowledge of zoonotic diseases and improve early detection and reporting at the community level.\u003c/p\u003e\u003cp\u003eDespite the observed gaps in zoonotic disease knowledge, most CHWs demonstrated positive perceptions of their roles and a sense of value and recognition, both from healthcare workers and the communities they served. These findings align with evidence from Tanzania, where CHWs who felt integrated into the formal health system demonstrated greater motivation and effectiveness (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Our study further revealed that CHWs who received training on their roles were more likely to feel recognized. As noted in the review of CHW programs, key features that enable positive CHW program outcomes include community embeddedness and continuous education (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Continuous capacity building of CHWs could sustain motivation and strengthen their contribution to zoonotic disease surveillance.\u003c/p\u003e\u003cp\u003eOur results revealed that CHWs who had been trained on their roles and responsibilities exhibited better zoonotic disease surveillance practices, including reporting and giving feedback to the community, compared to their counterparts. This finding reinforces the importance of clarity in the roles and responsibilities of CHWs\u0026rsquo; performance. Studies across low- and middle-income countries have consistently shown that role clarity through formal training enhances CHW performance and accountability (\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Without a clear understanding of their scope of work, CHWs may overlook critical tasks such as timely reporting of unusual animal deaths, fever cases in humans, or clusters of symptoms that are vital in preventing outbreaks.\u003c/p\u003e\u003cp\u003eOur findings also indicated that rural-based CHWs were significantly more likely to report strong support from their communities compared to those from urban areas and higher household income. Unlike urban CHWs, rural CHWs are often deeply embedded within the local social networks. They are more likely to be seen not just as health workers, but also as trusted neighbours and informal leaders (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Urban CHWs, on the other hand, due to higher population mobility, less cohesive communities, fragmented health-seeking behaviours, or competing job responsibilities, may not command the same level of trust or recognition (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, lower-income CHWs reported strong community support compared to those who earned high incomes. Lower-income CHWs may rely heavily on communal reciprocity and social capital, which can manifest as higher perceived community support (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). A study in low- and middle-income countries found that CHW effectiveness and community support were heavily dependent on contextual factors, including their embeddedness within community structures and their perceived social alignment (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Programs should recognize and harness the strong community support and social capital among lower-income CHWs to sustain their effectiveness.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStudy limitations\u003c/h2\u003e\u003cp\u003eThe cross-sectional nature of the study limits the ability to establish causal relationships. For example, while training was associated with higher knowledge, the design does not allow for the determination of whether training preceded or directly caused this outcome. However, the randomly selected participants across multiple districts enhanced the robustness of observed associations and provided a basis for future longitudinal studies. Our study was also conducted in purposively selected districts in southwestern Uganda, which may limit generalizability to other regions. However, focusing on high-risk areas for zoonotic outbreaks provides context-specific insights into CHW capacities in settings most vulnerable to zoonoses, where strengthening surveillance and response mechanisms is most critical.\u003c/p\u003e\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eWe found that community health workers in southwestern Uganda had low levels of knowledge about zoonotic diseases. Training on roles in disease detection and reporting, recognition by health workers, community support, rural residence, and lower incomes of CHWs were significantly associated with better knowledge, perceptions, and practices in zoonotic disease detection and reporting. To enhance the effectiveness of CHWs, national and district-level health systems should invest in comprehensive and continuous training programs that clarify roles, improve disease recognition, and build technical capacity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAEWs\u003c/strong\u003e – Animal Extension Workers\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eaPR\u0026nbsp;\u003c/strong\u003e– Adjusted Prevalence Ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCCHF\u003c/strong\u003e – Crimean Congo Haemorrhagic Fevers\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCHWs\u003c/strong\u003e – Community Health Workers\u003c/p\u003e\n\u003cp\u003eCAHWs – Community Animal Health Workers\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e – Confidence Intervals\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRVF\u003c/strong\u003e – Rift Valley Fever\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVHFs\u003c/strong\u003e – Viral Haemorrhagic Fevers\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVHTs\u003c/strong\u003e – Village Health Teams\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Makerere University School of Public Health Research Ethics Committee (MaKSPH), Reference number SPH-2024-671. Additionally, approval was granted by the Uganda National Council of Science and Technology (UNCST), Reference number HS5420ES. Written informed consent was obtained from all participants prior to data collection. Participants were informed of their right to withdraw from the study at any time without any consequences. For participants with hypertension, appropriate referrals were made to nearby public health facilities for further care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Korea Foundation for International Healthcare (KOFIH). \u0026nbsp;The funding body had no role in the design of the study, data collection, analysis, or interpretation of data, or in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRM, CM, DA, AWW, AB, ARA, PDK, SA, and FM conceptualized and designed the study. CM and AM collected the data while RM analysed and interpreted the data. CM and RM drafted the manuscript, and all authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the participants of the study, as well as the local leaders and community health workers in Mbarara, Kazo, Kiruhura, and Ibanda districts for their support during data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZ LPN, Charypkhan D, Hartnack S, Torgerson PR, R\u0026uuml;egg SR. The dual burden of animal and human zoonoses: A systematic review. PLoS Negl Trop Dis. 2022 Oct 14;16(10):e0010540. \u003c/li\u003e\n\u003cli\u003ePublic health emergencies of international concern: a historic overview | Journal of Travel Medicine | Oxford Academic [Internet]. [cited 2024 Aug 16]. 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ESTABLISHMENT OF AN ANIMAL HEALTH INDUSTRY ASSOCIATION (AHIA) UGANDA [Internet]. 2021 Mar. Available from: https://galvdox.galvmed.org/application/files/8516/6627/6505/171_Uganda_Report_GALVMED_AHIA_March_3_2021_-_FINAL.pdf\u003c/li\u003e\n\u003cli\u003eAbdi IH, Affognon HD, Wanjoya AK, Onyango-Ouma W, Sang R. Knowledge, Attitudes and Practices (KAP) on rift valley fever among pastoralist communities of Ijara District, North Eastern Kenya. PLoS Negl Trop Dis. 2015;9(11):e0004239. \u003c/li\u003e\n\u003cli\u003eMajiwa HO. Knowledge, Attitudes, and Practices on Zoonotic Diseases and Control Among Actors in Livestock Trade in Busia County, Western Kenya [Internet] [PhD Thesis]. University of Nairobi; 2023 [cited 2025 May 1]. Available from: https://erepository.uonbi.ac.ke/handle/11295/165134\u003c/li\u003e\n\u003cli\u003eNations F and AO of the U, Health WO for A, Organization WH. 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Available from: https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD010414.pub2/abstract\u003c/li\u003e\n\u003cli\u003eGatua JG. Decentralization, social connections and primary health care: Evidence from Kenya. World Dev. 2024;178:106562. \u003c/li\u003e\n\u003cli\u003eKok MC, Broerse JEW, Theobald S, Ormel H, Dieleman M, Taegtmeyer M. Performance of community health workers: situating their intermediary position within complex adaptive health systems. Hum Resour Health [Internet]. 2017 Dec [cited 2025 May 1];15(1). Available from: https://human-resources-health.biomedcentral.com/articles/10.1186/s12960-017-0234-z\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Community Health Workers, One Health, Training, Zoonoses, detection, reporting, Uganda","lastPublishedDoi":"10.21203/rs.3.rs-7353666/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7353666/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCommunity health workers (CHWs) serve as the first point of contact between the healthcare system and communities, making them pivotal for early detection and effective response to zoonotic disease outbreaks. Southwestern Uganda is a known hotspot for recurrent zoonotic disease outbreaks, yet limited evidence exists on the capacity of CHWs in this region to detect and respond to these threats. We assessed the knowledge, perceptions, and practices of CHWs in zoonotic disease detection and reporting and factors associated with these outcomes in Mbarara, Kazo, Kiruhura, and Ibanda districts, Southwestern Uganda.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a cross-sectional study between November 2024 and March 2025. We included CHWs from both human and animal health, using systematic simple random sampling. We administered face-to-face interviews using standardized questionnaires. Responses were scored as 1 for positive and 0 for negative, with total scores categorized using Bloom\u0026rsquo;s cut-off points: \u0026lt;60% as poor, 60\u0026ndash;80% as moderate, and \u0026gt;\u0026thinsp;80% as good. Modified Poisson regression was used to identify factors associated with knowledge, perceptions, and practices of CHWs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 423 participants were included in the study. The majority (95%; n\u0026thinsp;=\u0026thinsp;402) had low knowledge levels on zoonoses, with none reporting awareness of Crimean Congo Haemorrhagic Fever (CCHF). Community health workers who received training on their roles in detection and reporting were significantly more knowledgeable about anthrax (adjusted prevalence ratio [aPR]\u0026thinsp;=\u0026thinsp;1.2, 95% CI: 1.1\u0026ndash;5.3), viral haemorrhagic fevers (aPR\u0026thinsp;=\u0026thinsp;2.2, 95% CI: 1.1\u0026ndash;5.5), and rabies (aPR\u0026thinsp;=\u0026thinsp;1.8, 95% CI: 1.6\u0026ndash;2.1). CHWs in the human health sector had lower odds of recording zoonotic cases (aPR\u0026thinsp;=\u0026thinsp;0.16, 95%CI: 0.07\u0026ndash;0.38) and submitting reports (aPR\u0026thinsp;=\u0026thinsp;0.42, 95% CI: 0.21\u0026ndash;0.90) than animal extension workers. Trained CHWs reported better practices (aPR\u0026thinsp;=\u0026thinsp;4.4, 95% CI:1.5\u0026ndash;13) compared to those who were not trained.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eWe found that CHWs had low knowledge on zoonoses, with no participants aware of CCHF. Training on roles in detection and reporting was associated with improved knowledge and practices. CHWs in the animal sector were more likely to record and report zoonotic cases than their human health counterparts. Our findings highlight the need for targeted, cross-sectoral training programs to strengthen CHW capacity for early detection and reporting on zoonotic diseases in high-risk areas.\u003c/p\u003e","manuscriptTitle":"Knowledge, perceptions, and practices of community health workers in zoonotic disease detection and reporting in Southwestern Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 09:59:00","doi":"10.21203/rs.3.rs-7353666/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed9fef28-0245-4eaa-94ff-01bc66837b75","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-18T13:09:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 09:59:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7353666","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7353666","identity":"rs-7353666","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}