Introduction
Zoonoses are diseases that are naturally transmissible between humans and animals1. Globally, they account for; more than 60% of human infections, about 75% of emerging infections, 2.7 million human death yearly, 10% of the total DALYs lost and wildlife is at the spotlight for outbreaks2–6. Human health and animal health are inextricably linked and thus multi-sectoral efforts in disease surveillance is required to detect and respond timely to zoonoses so as to reduce their burden7.
In 2014, WHO and the United States jointly launched the Global Health Security Agenda (GHSA) which aims at building capacity for early detection and effective response to zoonoses through multisectoral collaboration8. Some collaborative efforts have been established between universities and research institutions for example Afrique One Consortium in Chad, Ivory Coast, Tanzania, Uganda, and Senegal that fosters OH9.
In 2012, Kenya established a Zoonotic Disease Unit (ZDU) to provide a multi-sectoral collaborative platform for better coordination of efforts geared towards bridging the gap between human and animal health in priority zoonoses prevention and control10. The ZDU unit comprises of a medical epidemiologist deployed from MOH, a veterinary epidemiologist deployed from the Ministry of Agriculture, Livestock and Fisheries (MALF), data analyst and an administrative assistant. In 2013, following devolution, ZDU established county and sub county platforms and surveillance officers in both animal and human health trained4 and in 2022, OH Strategic Plan for Prevention and Control of Zoonotic diseases in Kenya was launched11,12.
Multi-sectoral zoonotic disease prioritization with equal engagement from all sectors active in zoonoses surveillance is one of the most cost-effective ways to prevent, detect, and respond to public health threats13. In Sub-Saharan Africa, only 14% of the countries have multi-sectoral action plan for zoonoses and 32% have established inter-sectoral zoonotic surveillance systems14. The barriers towards effective MZDS are; lack of supportive policies, insufficient funding, conflicting departmental priorities and limited institutional capacities3,15,16. Therefore, early disease detection and control at the sources is delayed and risk widespread outbreaks and mortalities in both animals and humans resulting in disruptions in trade, economic losses and strain on public health resources17.
In the last three decades, Kenya has had sporadic outbreaks of priority zoonoses4. In 2007, over 300 people died of RVF with livestock loses of over US$9.3 million18. Nakuru county is a hotspot for Anthrax, Rabies and Brucellosis because of its proximity to wild life. Between 2014 and 2017, Anthrax infected 71 people with 2 deaths and over 700 animals died19. To detect and control zoonoses at the source, a MZDS is essential13. There is however very scanty information on utilization of MSDS in Nakuru County. The aim of this study was to determine the level of utilization of MZDS and its associated factors among animal and human healthcare workers in Nakuru county, Kenya.
Materials and methods
Type and period of study
Analytical cross-sectional study design was used and the study covered the period between August 20, 2023 and October 15, 2023.
Setting of the study
The study was conducted in Nakuru County, a third most popular county and located in Rift valley region of Kenya. It is bordered by; Baringo, Laikipia, Nyandarua, Kajiado, Narok, Bomet and Kericho counties. Has an area of 7,496.5km² and a population of 1,603,32520. It has physical features like; L. Naivasha a home of millions of flamingos, sanctuary to protect Rothschild giraffe and black rhinos, and Nakuru national park. The site is a hotspot for Anthrax, Brucellosis and Rabies21 and this formed the basis for purposive selection of the study area.
Inclusion Criteria. Human and Animal Healthcare workers who consented.
Exclusion Criteria. Healthcare workers that were not on duty over the period of the study.
Sampling
A census was conducted.
Data collection and tools
A semi-structured pretested interviewer-administered questionnaire was used in face-to-face interviews to collect quantitative data from 102 participants who serve as frontline workers on zoonotic disease surveillance activities at the sub-county levels. Key informant interview guide was used to collect data on institutional factors (Funding, space, Priorities, Staffing, MZDS plans, political will) from county head of veterinary services, county head of public health, County director of medical services, Data analyst and county emergency and operations center officer. Audio tape recording was used to maintain and capture their exact words. The sessions lasted 45–60 minutes. Saturation marked the end of interview sessions.
Data processing and analysis
Quantitative data was entered to excel file, cleaned and exported to R 4.3.1 Software for descriptive and inferential statistical analysis. The descriptive statistics were presented on tables. Hearing about MZDS and the regularity of carrying out joint data collection, analysis, interpretation and sharing with other sectors formed the basis for inferential statistics. Chi-square and independent t-test were used to measure association at a p value < 0.05 and 95% confidence interval. Qualitative data was analyzed manually using MS Excel spreadsheets. The transcripts were read again and again and listening to voice recordings repeatedly22. This helped to understand the concept and the words, phrases; sentences even sections were labeled and coded23. The concept to be coded depended on whether it is repeated several times, as the code would be so many, similar codes were grouped into categories which were used to generate themes24. The themes and categories were the main results of the study.
Ethical considerations
Ethical approval and a research permit were attained from: the ethics review board for Jomo Kenyatta University of Agriculture and Technology (JKUAT), Ref: JKU/ISERC/023/0842, JKUAT Board of postgraduate studies, Ref: JKU/2/11/HSH315R-0088/2022, National Council for Science, Technology and Innovation, Ref: NACOSTI/P/23/25534, and Nakuru county. Written informed consent was acquired from the participants. Completed questionnaires were kept under lock and key and accessed by only authorized people. Soft copies and all analyzed data were passworded.
Results
A total of 102 animal and public health surveillance officers from Nakuru county were consented and enrolled to the study. The number of participants; animal health workers 78.4% (N = 80) and public health officers 21.6% (N = 22) included in the study was 100% the total staff number, and at analysis, all the 102 entries were incorporated.
Demographic characteristics of respondents
Majority of the participants 65% (N = 66) were males. Most participants were aged 51–60 years (36.3%, N = 37), followed by 31–40 years (28.4%, N = 29). Their mean duration of service was 17.41 ±9.86 years. More than forty percent had diploma as their highest education (46.1%, N = 47). Table 1 below, shows other demographic characteristics.
Table 1. Demographic characteristics for the participants.
| Variable | Frequency (N = 102) | Percent (%) |
|---|
| Gender |
| Female | 36 | 35.0 |
| Male | 66 | 65.0 |
| Age |
| 20–30 | 18 | 17.6 |
| 31–40 | 29 | 28.4 |
| 41–50 | 18 | 17.6 |
| 51–60 | 37 | 36.3 |
| Time in service |
| Mean - 17.41, Minimum – 3, SD = 9.86, Maximum – 34 | | |
| Education level |
| Certificate | 30 | 29.4 |
| Diploma | 47 | 46.1 |
| Undergraduate | 21 | 20.6 |
| Postgraduate | 4 | 3.9 |
| Sector |
| Animal health | 80 | 78.4 |
| Human health | 22 | 21.6 |
Level of utilization of MZDS
Of the 102 participants, 73.5% (N=75) had heard about MZDS, among whom the level of utilization was 16% (N=12/75) and these were participants who frequently carried out joint data collection, analysis, interpretation and sharing with other sectors. The rest 84% (N=63/75) had either never utilized or only utilized it during outbreaks. A higher proportion of females (17.4%, 4/23) was involved in MZDS as compared to males (15.4%, 8/52). Knowing what MZDS entailed according to those who had heard about it had a higher utilization level (28.6%, 8/28) compared to those who did not know (8.5%, 4/47). Individuals with certificate level of education had no utilization (0%, 0/22) just like those with postgraduate (0%, 0/4), those with undergraduate had the highest utilization level of (29.4%, 5/17) followed by those with diploma (21.9%, 7/32). Participants who did not know of any organizational structure required for MZDS had no utilization (0%, 0/22), those who noted that a Subcounty one health committee, community health volunteers and the community had the highest utilization level (30%, 3/10) followed by those who noted that no particular structure is required had utilization level of (20.9, 9/43). Participants who noted that requiring a zoonotic disease outbreak for having a functional MZDS had the highest utilization level (66.7%, 2/3) followed by those who noted that requiring Commitment and participation by all sectors (37.5%, 6/16), then those who noted trained staff (20%, 1/5) and lastly those who noted budget, trained staff and office space (11.5%, 3/26). Respondents with frequent budgetary allocation had the highest utilization level (66.7%, 2/3) followed by those who have never had any budgetary allocation (44.4%, 4/9) and those who have never had any budgetary allocation with the lowest utilization level (9.5%, 6/57). Other utilization level for other variables is shown on the Table 2, below.
Table 2. Level of utilization of MZDS.
| Variable | Level of Utilization of MZDS |
|---|
Utilized
(16%, N = 12) | Didn’t utilize
(84%, N = 63) |
|---|
| Demographic Characteristics |
|---|
| Age | 20–30 | 1 (12.5%) | 7 (87.5%) |
| 31–40 | 4 (18.2%) | 18 (81.8%) |
| 41–50 | 4 (26.7%) | 11 (73.3%) |
| 51–60 | 3 (10.0%) | 27 (90.0%) |
| Sector | Animal health | 5 (9.4%) | 48 (90.6%) |
| Public health | 7 (31.8%) | 15 (68.2%) |
| Awareness on MZDS |
|---|
| Respondents who had heard about of (MZDS) | 12 (16%) | 63 (84%) |
| Respondents who listed the five priority-zoonotic diseases | Correct | 2 (25%) | 6 (75%) |
| Incorrect | 10 (14.9%) | 57 (85.1%) |
| Ranking priority zoonoses in-terms of severity, epidemic & socio-economic burden | Correct | 1 (33.3%) | 2 (66.7%) |
| Incorrect | 1 (15.3%) | 61 (84.7% |
| Attitudes |
|---|
| MZDS is important for early disease detection & response | Agree | 12 (16%) | 63 (84%) |
| I am an important actor for MZDS | Agree | 12 (16.2%) | 62 (83.8%) |
| Neutral | 0 (0) | 1 (100%) |
| Sensitization on MZDS by devolved | Agree | 6 (50%) | 6 (50%) |
| County ZDU is adequate | Neutral | 4 (8.5%) | 43 (91.5%) |
| Don’t Agree | 2 (12.5%) | 14 (87.5%) |
| With initiation of MZDS, infrastructures for zoonoses surveillance has improved | Agree | 10 (41.7%) | 14 (58.3%) |
| Neutral | 2 (6.1%) | 31 (93.9%) |
| Don’t Agree | 0 (0) | 18 (100%) |
| Results and information on MZDS is adequately shared to all sectors | Agree | 6 (37.5) | 10(62.5%) |
| Neutral | 3 (7%) | 40 (93%) |
| Don’t Agree | 3 (18.8%) | 13 (81.3%) |
| Practice |
|---|
| Proportion of Respondents who received information on MZDS | Never | 1 (12.5%) | 7 (87.5%) |
| Rarely | 5 (11.4%) | 39 (88.6%) |
| Frequently | 6 (26.1%) | 17 (73.9%) |
| Participation in any training on MZDS | Never | 3 (15.8%) | 16 (84.2%) |
| Rarely | 4 (8.2%) | 45 (91.8%) |
| Frequently | 5 (71.4%) | 2 (28.6%) |
| Regularity of storing data and information on MZDS in your sub-county (N = 51) | Never | 0 (0) | 14 (100%) |
| Rarely | 4 (8.9%) | 41 (91.1%) |
| Frequently | 8 (50%) | 8 (50%) |
| Proportion of respondents who notify a health counterpart in the sub-county | Frequently | 12 (17.1%) | 58 (82.9%) |
| Rarely | 0 (0) | 5 (100%) |
| Community involvement in zoonoses surveillance activities. | Never | 0 (0) | 4 (100%) |
| Rarely | 1 (11.1%) | 8 (88.9%) |
| Frequently | 11 (17.7%) | 51 (82.3%) |
Demographic factors associated with utilization of MZDS
Education level (χ =3.889, df = 3, p = 0.026) (χ =3.889, df = 3, p = 0.026) and sector (χ =1.657, df = 3, p = 0.023) were significantly associated with utilization of MZDS. Individuals with diploma and undergraduate education were more likely involved in MZDS as opposed to those with certificate and postgraduate education. Individuals from public health were also more likely involved as opposed to individuals from animal health. All other variables considered in this study are shown on Table 3, below.
Table 3. Association between demographic variables and utilization of MZDS.
| Variable | Utilized MZDS (16%) | Didn’t utilize MZDS (84%) | Chi-square value | Df | p-value |
|---|
| Gender |
| Female (n=23) | 4(17.4%) | 19 (82.6%) | | | |
| Male (n=52) | 8 (15.4%) | 44 (84.6%) | 0.997 | 1 | 0.318 |
| Age |
| 20-30 | 1 (12.5%) | 7 (87.5%) | | | |
| 31-40 | 4 (18.2%) | 18 (81.8%) | | | |
| 41-50 | 4 (26.7%) | 11 (73.3%) | | | |
| 51-60 | 3 (10.0%) | 27 (90.0%) | 2.200 | 3 | 0.520 |
| Education level |
| Certificate | 0 (0) | 22 (100%) | | | |
| Diploma | 7 (21.9%) | 25 (78.1%) | | | |
| Undergraduate | 5 (29.4%) | 12 (70.6%) | | | |
| Postgraduate | 0 (0) | 4 (100%) | 3.986 | 3 | 0.026 |
| Sector |
| Animal health | 5 (9.4%) | 48 (90.6%) | | | |
| Public health | 7 (31.8%) | 15 (68.2%) | 1.657 | 1 | 0.026 |
| Time spent in service | 16.08 | 19.81 | 1.206 | 73 | 0.232 |
Association between awareness, attitude and practices towards MZDS and utilization of MZDS
Utilization of MZDS was associated with being awareness of what it entails (t= 2.269, df = 15.05, p = 0.038). Participants who utilized MZDS had a higher mean score (mean= 0.6825) than those who did not utilize (mean= 0.3333). Agreeing that that sensitization on MZDS by devolved County Zoonotic Disease Unit (ZDU) was adequate (t = 2.466, df = 73, p = 0.016) was significantly associated with utilization of MZDS. Those who had never engaged in joint work had a higher mean score (2.333), which leans towards disagreeing than those who engaged on joint work (1.923). Individuals who had been trained on MZDS had a higher utilization level (t=2.227, df = 3, p = 0.029). Those who believed that infrastructure for MZDS has improved following devolution (t= 4.209, df= 73, p = 0.001) also had a higher utilization level. Having a budget and storing data on MZDS were associated with higher utilization levels. Other factors were not significant as shown on Table 4, below.
Table 4. Association between awareness, attitudes and practices towards MZDS and utilization of MZDS.
| Variable | Utilized MZDS (16%) | Didn’t utilize MZDS (84%) | t-test | Df | p-value |
|---|
| Awareness |
| Organizational structure of MSDS at subcounty? |
| Correct | 3 (4.0%) | 7 (9.3%) | | | |
| Incorrect | 9 (12.0%) | 56 (74.7%) | -1.017 | 3.132 | 0.327 |
| Respondents who correctly listed the five priority zoonotic diseases in Kenya |
| Correct | 2 (25%) | 6 (75%) | | | |
| Incorrect | 10 (14.9%) | 57 (85.1%) | -0.728 | 3 | 0.469 |
| Ranking priority zoonoses in-terms of severity, epidemic potential & socio-economic burden |
| Correct | 1 (33.3%) | 2 (66.7%) | | | |
| Incorrect | 11 (15.3%) | 61 (84.7%) | -0.829 | 3 | 0.410 |
| Requirements for MZDS to be functional |
| Correct | 12 (16%) | 60 (80%) | | | |
| Incorrect | 0 (0%) | 3 (4.0%) | -1.762 | 2 | 0.083 |
| Attitude |
| I am an important actor in Multisectoral approach |
| Agree | 12 (16.2%) | 62 (83.8%) | | | |
| Neutral | 0 (0) | 1 (100%) | 0.434 | 73 | 0.666 |
| Multisectoral approach has easily been implemented in my area of work. |
| Agree | 7 (29.2%) | 17 (70.8%) | | | |
| Neutral | 3 (7.3%) | 38 (92.7%) | | | |
| Don’t Agree | 2 (20%) | 8 (80%) | 1.342 | 73 | 0.184 |
| Information on MZDS is adequately shared to all sectors |
| Agree | 6 (37.5%) | 10 (62.5%) | | | |
| Neutral | 3 (7%) | 40 (93%) | | | |
| Don’t Agree | 3 (18.8%) | 13 (81.3%) | 1.448 | 73 | 0.152 |
| Practices |
| Proportion of Respondents who received information on MZDS |
| Never | 1 (12.5%) | 7 (87.5%) | | | |
| Rarely | 5 (11.4%) | 39 (88.6%) | | | |
| Frequently | 6 (26.1%) | 17 (73.9%) | -1.339 | 3 | 0.185 |
| Regularity of storing data and information on MZDS |
| Never | 0 (0) | 14 (100%) | | | |
| Rarely | 4 (8.9%) | 41 (91.1%) | | | |
| Frequently | 8 (50%) | 8 (50%) | -4.211 | 3 | 0.001 |
| Budget for MZDS in the subcounty |
| Never | 6 (8.0%) | 57 (76.0%) | | | |
| Rarely | 4 (5.3%) | 5 (6.7%) | | | |
| Frequently | 2 (2.7%) | 1 (1.3%) | -2.422 | 11.9 | 0.032 |
Institutional factors associated with utilization of MZDS
Results from the qualitative data indicated that there was no; proper organizational structure, budgetary allocation and infrastructure for MZDS. One key informant argued that “… multi-sectoral collaboration has no separate resources, departments use their own routine resources e.g., staff, vehicles, computers, emails and budget”. On organizational structure, there were no goals, workplans or policy frameworks on MZDS. one interviewee reported that “… there is no well-defined organization structure, however, the directors of the two departments take the lead”. On political support, MZDS was still more technical and political support was very low. On suggestions on how to improve MZDS, there is need to provide a budgetary allocation and capacity building. One argued that “… there is need for proper budgetary allocation and support for technical group”.
Discussion
The Level of MZDS among animal and human health care workers in Nakuru County
The level of MZDS was 16%, which is low in a zoonotic prone area. The rate reported in other sub-Saharan countries with multisectoral collaborations is 32%14 which is higher compared to findings of our study. However, Munyua et al., 2019, noted that MZDS in Kenya are generally low due to limited funding and competing priorities. This study further identified other factors like awareness, education level, sensitization and trainings, sector where participants belong and data storage as other factors were associated with the low utilization. Given that MZDS is key for early zoonotic disease detection and response and that the overall utilization level is less than twenty percent, the initiative should be addressed much more seriously by all sectors to enhance timely sharing of zoonotic information25.
Demographic characteristics associated with MZDS
Nakuru county animal and health staff benefit from the Field Epidemiology Training Program (FETP) which is a postgraduate training and it’s likely that it was a motivation for higher utilization amongst those with undergrade training. Human health workers were more likely to involved in collaboration which deviates from the previously reported evidence by 26 who noted that animal health workers were twice more likely to collaborate compared to human health workers. The deviation may be because trainings on collaboration were born from COVID-19 outbreak and the lead team was human health. Human health has partners like Africa Medical Research Foundation (AMREF), CDC who fund their workplans. Since animal health workers are directly in contact with animals, there is great need to bridge the gaps of their MZDS under-utilization.
Awareness, attitude and practice factors associated with MZDS
Knowing what MZDS entails was associated with higher utilization however, less than a half of those who knew what MZDS entails utilized it. This might be explained by the fact that was no budgetary allocation for collaboration activities at the county and so no accountability. Sensitization on MZDS by devolved County ZDU was found to be adequate and associated with higher utilization of MZDS. Those who had ever trained in MZDS were more likely to utilize MZDS and Infrastructure for MZDS had improved following devolution. 27 noted that awareness of health and veterinary departments was very key for MZDS. 15 also reported that knowledge attained from trainings had significant effect on utilization of MZDS. Nakuru county has partners like AMREF and ZDU which occasionally offer trainings and sensitization to the animal and healthcare workers in Nakuru county with focus on bi-sectoral collaboration at sub-county level using Community Event-Based Surveillance System (CEBs)28. With only 10% frequently receiving trainings and the rest either never or only been trained once in the last three years, it is possible to increase individual participations in MZDS by stepping up trainings. Data storage by participants was associated with MZDS. This could be explained by the fact that trainings and sensitizations provide an avenue for participants to store data.
Institutional factors for MZDS
There is no financing for MZDS and as reported by 16, Kenya’s challenges for MZDS are insufficient funding and competing priorities. Sectors in cooperate MZDS activities along their sector workplans and since there is no particular budget and accountability, less can be devoted to MZDS. There were no MZDS goals, workplans and organizational structure and this is in line with the findings of 26 who reported lack of plans and networks by institutions are limiting inter-sectoral collaborations. There also exists more of informal communications between sectors for example, visits, phone calls are predominantly used for information sharing. These findings agree with 25 who noted that lack of effective coordination and commitment to OH preparedness and response efforts, lack of an agreed mechanism for sharing information across sectors and partners were key hindrances to MZDS.
Limitations
Being a cross-sectional study, the association from the subset analysis cannot be assumed to mean cause-effect relationship but rather an indicator of factors that are commonly identified with the comparative sub-groups. Another limitation, no confirmation was done whether indeed the respondents utilized the MZDZ, thus totally relying respondents’ sincerity.
Conclusion
MZDS should be a major issue of concern in sub-Saharan Africa for early disease detection. Zoonoses can aggravate fast causing a myriad of sequels, and hence the need for early detection, timely response and control. From the current study, there is a low level of MZDS in Nakuru County. The factors for the low utilization include lack of proper coordination and organizational and financing mechanisms. However, Sensitization and trainings on MZDS were associated with more utilization of MZDS.
Recommendations
The National government and the county government of Nakuru need to pay more attention towards MZDS activities through financing MZDS activities, sensitization and training staff, and providing physical infrastructure like mobility means, office space and its infrastructure.
Acknowledgments
Peter Ngugi, animal health staff-Nakuru county and James Maino, University of Eldoret guided data collection and quantitative data analysis respectively.
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