Nuvey et al. BMC Public Health (2023) 23:1353 BMC Public Health https://doi.org/10.1186/s12889-023-16287-2 R E S E A R C H Open Access Relationship between animal health and livestock farmers’ wellbeing in Ghana: beyond zoonoses Francis Sena Nuvey1,2,3*, Daniel T. Haydon4, Jan Hattendorf1,5, Kennedy Kwasi Addo6, Gloria Ivy Mensah6, Günther Fink1,5, Jakob Zinsstag1,5 and Bassirou Bonfoh3 Abstract Introduction Livestock production is a key livelihood source for many people in developing countries. Poor control of livestock diseases hamper livestock productivity, threatening farmers’ wellbeing and food security. This study estimates the effect of livestock mortalities attributable to disease on the wellbeing of livestock farmers. Methods Overall, 350 ruminant livestock farmers were randomly selected from three districts located in the north, middle and southern belts of Ghana. Mixed-effect linear regression models were used to estimate the relationship between animal health and farmer wellbeing. Farmer wellbeing was assessed using the WHOQOL-BREF tool, as the mean quality-of-life in four domains (physical, psychological, social, and environmental). Animal health was assessed as annual livestock mortalities to diseases adjusted for herd size, and standardized in tropical livestock units to account for different ruminant livestock species. We adjusted for the potential confounding effect of farmers’ age, sex, educational attainment, farmland size, socio-economic status, perception of disease risk to herd, satisfaction with health, previous experience of disease outbreaks in herds, and social support availability by including these as fixed effects, and community as random effects, in a pre-specified model. Results Our results showed that farmers had a median score of 65.5 out of 100 (IQR: 56.6 to 73.2) on the wellbeing scale. The farmers’ reported on average (median) 10% (IQR: 0 to 23) annual herd mortalities to diseases. There was a significantly negative relationship between increasing level of animal disease-induced mortality in herds and farmers’ wellbeing. Specifically, our model predicted an expected difference in farmers’ wellbeing score of 7.9 (95%CI 1.50 to 14.39) between a farmer without any herd mortalities to diseases compared to a (hypothetical) farmer with 100% of herd mortalities caused by diseases in a farming year. Thus, there is a reduction of approximately 0.8 wellbeing points of farmers, for the average of 10% disease-induced herd mortalities experienced. Conclusions Disease-induced livestock mortalities have a significant negative effect on farmers’ wellbeing, particularly in the physical and psychological domains. This suggests that veterinary service policies addressing disease risks in livestock, could contribute to improving the wellbeing of livestock dependent populations, and public food security. *Correspondence: Francis Sena Nuvey francis.nuvey@swisstph.ch Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Nuvey et al. BMC Public Health (2023) 23:1353 Page 2 of 13 Keywords Wellbeing, Quality of life, Livestock farmers, Livestock diseases One health Introduction human and animal health, the majority of existing lit- Livestock production remains a key source of livelihood erature has predominantly focused on areas such as the for many people in developing countries, particularly potential for zoonotic diseases, impact of antimicrobial for rural dwellers [1]. Livestock production contributes usage on the development of pathogen resistance, and to public food security and revenues, as well as individ- the effect of animal diseases on food security [14–19]. It ual-level food resources, economic prosperity, and as an is worth noting that livestock farmers share strong bonds asset store against uncertainty [2, 3]. In spite of its value with their animals, with livestock fulfilling additional to society, livestock production is hampered by adverse social roles, including serving as companion animals for events including climate variabilities, conflicts, and ani- farmers [20, 21]. Hence, the impact of poor animal health mal disease outbreaks. These adversities negatively affect on livestock farmers can potentially extend beyond liveli- the productivity of the livestock sector [2]. hood loss, zoonotic infections, and food insecurity. Our In many sub-Saharan African countries including goal in this study therefore was to evaluate the average Ghana, transboundary animal diseases are highly preva- impact on a livestock farmer’s wellbeing that could be lent due to an inadequate adoption of disease prevention attributed to the health and mortality of animals in the and control measures, causing significant herd mor- farmer’s herd. talities [4]. The lack of adequate prevention of diseases in animals predisposes humans, and the ecosystem to Materials and methods heightened risks of zoonotic disease, antimicrobial resi- Description of study area due spread and related antimicrobial resistant pathogens This study was conducted in the Mion, Pru East and [5, 6]. Beyond these risks to human and ecosystem health, Kwahu Afram Plains South (KAPS) Districts, which are livestock mortalities could also affect the wellbeing of representative of the northern, middle and southern livestock dependent populations. Previous research has farming belts of Ghana. The districts lie in the Guinea shown a negative effect of animal disease-related mortali- Savannah, Transition and Deciduous forest Vegetation ties on livestock farmers’ psychological wellbeing [7, 8]. zones, which are the main livestock production zones in Although other dimensions of the wellbeing of livestock Ghana (Fig.  1) [22–24]. Agriculture contributed about farmers could be affected by poor animal health, there is one-fifth of the national gross domestic product of a dearth of evidence on the extent of these effects in the Ghana in 2019 with the livestock sector accounting for literature. 14% of this production [25]. The selected districts are Human wellbeing and productivity are closely inter- mainly rural and agrarian, with about one-third of the connected. Research has shown a strong two-way link livestock holdings of households being ruminant species. between productivity and wellbeing of people; better The ruminant livestock species mainly reared by farmers wellbeing has a strong and positive impact on productive include cattle, sheep, and goats. While the non-ruminant performance in work, while the productivity gains from livestock species reared, include poultry, pigs, and rabbits high performance also contribute to better wellbeing of [26]. people through higher incomes, life and job satisfaction [9]. It is essential therefore, that challenges affecting the Study design wellbeing of working people be addressed to foster better This study was a cross-sectional survey involving 350 productivity. Wellbeing could be measured either objec- ruminant livestock owners. The survey was conducted tively or subjectively. Objective measurements of wellbe- as a part of a larger project that employed a convergent ing are often implemented as aggregate population level parallel mixed-method design to assess the effectiveness indexes of wellbeing using different indicators such as the of veterinary interventions in Ghana. The full details of human development index [10], while subjective wellbe- the project design is provided in an earlier paper [27]. ing measures involve assessment of individual’s own per- In summary, the wellbeing of the ruminant livestock ception of their wellbeing [11]. The WHO Quality of Life farmers in the study was assessed using the WHO Qual- – BREF (WHOQOL – BREF) tool is often used to assess ity of life – BREF tool, and herd health was assessed as individual’s perception of their own wellbeing including the proportion of annual herd mortalities attributable their satisfaction with the level of functioning [12]. to diseases. We evaluate in this paper, the sensitivity of A livestock herd’s health is measured by the herd’s pro- farmer’s wellbeing to the level of disease-induced animal ductivity and ability to limit the incidence and effect of mortalities in the farmer’s herd, adjusting for other pre- economically important diseases [13]. Although previous specified covariates. research has highlighted significant connections between Nuvey et al. BMC Public Health (2023) 23:1353 Page 3 of 13 Fig. 1 Administrative map of Ghana showing the agro-ecological zones and study districts. The figure shows the district-level administrative and ago-ecological map of Ghana. It presents the distinct locations of the study districts (shaded areas to which arrows point) within the main agro-ecological zones. MION, PRU EAST, and KAPS denote the Mion, Pru East and Kwahu Afram Plains South Districts respectively Study population available prior to the study (2010 Population and Housing The study population included all ruminant livestock Census), there was about 80,880, 54,694, and 47,230 trop- farmers’ in the study area. We first obtained district maps ical livestock units (TLUs) of ruminant livestock species and created a sampling frame of villages within the study in the KAPS, Mion and Pru East Districts respectively, area. Based on the population and housing census data with an average of about 10 holdings per household. We Nuvey et al. BMC Public Health (2023) 23:1353 Page 4 of 13 randomly drew from the sampling frame, 15 villages in level available to farmers was assessed on a 5-point Likert the KAPS District, and 10 villages each in the Pru East scale of the level of support, the farmers received from and Mion Districts, proportional to the number of live- different facets of society including family, friends, law stock farming households per district [22–24]. A house- enforcement, credit institutions, community leaders and hold refers to a person or group of persons who normally religious leaders, to aid them in livestock farming. We live together and are catered for as one unit; members measured animal health using reported annual disease- may or may not be related. Any member of the household induced mortalities of livestock relative to a herd size, who takes responsibility for the upkeep of the house- and standardized in tropical livestock units [31]. The data hold’s livestock was eligible to participate in the study. was downloaded in Microsoft Excel format from ODK and imported into R version 4.1 [32] for analyses. Sample size and sampling technique The sample size determination and sampling procedure Data analyses for the survey is described in detail in an earlier paper We performed descriptive analyses of the survey data, [27]. In summary, 350 livestock farmers were recruited comparing the distribution of responses by study district. from 38 villages in the three study districts, propor- The farmers’ herd sizes were converted to tropical live- tional to the size of ruminant livestock owning house- stock units (TLU) to standardize livestock holdings as holds using segmentation; where selected villages are follows: 1 TLU corresponds to 0.75 cattle and 0.1 small divided into smaller equal units called segments depend- ruminants (sheep and goats) [33]. The number of animal ing on size, and all eligible households recruited in one mortalities were also converted to TLUs. The relative randomly selected segment [28]. In selected segments wealth of households was determined using an index of of the study villages, all households who keep ruminant household’s ownership of selected assets, such as televi- livestock were eligible to be selected and the households sions, refrigerators and bicycles [34]. We determined the providing consent were recruited to participate in the severity of losses suffered as the proportion of a herd lost survey. For villages where sufficient households were not to different factors in TLUs. The social support available attained due to low number of livestock-owning house- to a farmer was the sum of the reported support level holds, the adjoining village was selected in an attempt to received from the different sources. We derived the dis- reach the desired sample size. Overall, the median num- ease risk to herd perception score as the sum of the Likert ber of farmers recruited per village was 10 farmers [inter- scale scores. One item score on the perception scale (Q4) quartile range (IQR) = 7 to 11]. is reversed to achieve a similar direction of the percep- tion score. For the wellbeing score, firstly three negatively Data collection and data management framed items (Q3, Q4, and Q26) were reversed to achieve The enumeration team visited the households keep- a similar direction of wellbeing scores. To obtain the ing ruminant livestock to administer the questionnaires scores for each wellbeing domain, the mean of all items between November 2021 and January 2022. The survey included within each wellbeing domain is calculated, and questionnaire was administered to the respondents’ face- multiplied by a factor of four and then transformed to to-face using tablets with Open Data Kit (ODK) appli- a scale from 0 to 100, according to the tool’s guidelines cation [29]. The data collected included social support [30]. We derived the overall wellbeing score as the aver- availability to farmers, farmers’ perception of disease risk age of the four wellbeing dimension scores [35]. to herd, farmers’ wellbeing and satisfaction with health, We performed univariable analyses, using linear regres- and other socio-demographic characteristics. The live- sion models to compare the relationship between farm- stock farmers’ wellbeing was assessed using the WHO ers’ wellbeing and the level of mortalities in their herds Quality of life – BREF (WHOQOL-BREF) tool [30]. The [categorized in three quantiles (tertiles): low, moderate WHOQOL-BREF is a 26-item 5-point Likert scale, with and severe] to all causes, and specifically to diseases. We scores ranging from 1 to 5; higher scores on the scale present the results using boxplots, comparing the average denote better wellbeing. Two of the items on the scale wellbeing scores between the levels of herd mortalities. assesses the study subject’s own perception of quality of In a pre-specified linear regression model, we evaluated life or wellbeing and overall satisfaction with health sta- the hypothesis that the level of animal disease-induced tus, and are excluded in the analysis for wellbeing. The 24 mortality in herds (herd health) is associated with farm- questions assess individual’s perception of their wellbeing ers’ overall wellbeing, accounting for the potential con- on the physical, psychological, social, and environmen- founding effects of other covariates in a linear mixed tal domains. Farmers’ perception of disease risk to herds effects model. The level of disease-induced herd mortal- was assessed on a five-item Likert scale with responses ity is derived as the number of animals lost to diseases ranging from 1 to 5; higher scores indicate higher risk relative to each farmers’ herd size (both in TLUs). The perception of the diseases to a herd. The social support covariates included in the model were farmers’ age, sex, Nuvey et al. BMC Public Health (2023) 23:1353 Page 5 of 13 educational attainment, farmland size, wealth status, sensitivity analysis to assess the robustness of the find- perception of disease risk to herds, overall satisfaction ings, and examined model residuals to determine if key with health, and level of social support received as fixed assumptions of model fit were met. effects, and village-level clusters as random effects in a linear mixed effect regression model. Values of p < 0.05 Results were considered statistically significant. We performed Socio-demographic characteristics of study respondents Table  1 presents the socio-demographic characteristics Table 1 Socio-demographic characteristics of the study of all study respondents (N = 350) stratified by district. respondents by study district The median age of the farmers completing the survey Characteristic KAPS MION PRU EAST was 45 years (IQR = 35 to 54 years). The median house- Median (IQR) Median Median hold size was 8 persons (IQR = 6 to 11 persons), with (IQR) (IQR) each household keeping on average (median) 2.5 TLUs Age (years) 46 (36, 56) 41 (34, 51) 46 (34, 57) of ruminant livestock per herd (IQR = 1.3 to 7.0 TLUs). Household size (persons) 7 (5, 10) 10 (7, 15) 8 (6, 13) More than 95% (333/350) of the respondents own the Health satisfaction score 75 (50, 75) 75 (50, 75) 75 (50, 75) livestock themselves. The farmers also cultivated on aver- % (n/N) % (n/N) % (n/N) age 7 acres of farmland (IQR = 3 to 15 acres) in addition Sex to rearing livestock. More than two-thirds (71%) of the Female 38% (57/149) 16% (16/98) 28% respondents were male, and about half of farmers had (29/103) received no formal education (51%). The wealth index Male 62% (92/149) 84% (82/98) 72% (74/103) analysis of households showed that in the Mion District, Educational attainment 67% of households were in the poorest two wealth quin- No formal education 28% (41/149) 87% (85/98) 51% tiles, while the same was true only for 42% of households (52/103) in KAPS and for 16% of households in the Pru East Dis- Up to 12 years education 48% (72/149) 6% (6/98) 28% tricts. On average, farmers ranked the social support (29/103) received in the study year at 6 out of 30 (IQR = 6 to 8). Higher education 24% (36/149) 7% (7/98) 21% The social support was received mainly from family and (22/103) friend sources (Fig. 2). Farmers scored on average, 19 out Wealth status of 25 (IQR = 17 to 21) on the disease risk perception scale. Poorest 14% (21/149) 42% (41/98) 8% (8/103) Effect of livestock mortality on livestock farmers’ wellbeing Below average 28% (41/149) 26% (25/98) 8% (8/103) The farmers reported a median of 0.5 TLUs (IQR = 0.1 Average 24% (36/149) 14% (14/98) 15% to 1.4 TLUs) of ruminant livestock mortalities per herd (16/103) in the study year (2021), corresponding to an average Above average 25% (37/149) 10% (10/98) 22% (median) of 19% mortality per herd (IQR = 6  to  37%). (23/103) Livestock diseases accounted for the majority of reported Least poor 9% (14/149) 8% (8/98) 47% herd mortalities. The farmers reported a median disease- (48/103) induced mortalities of 10% of the herds (IQR = 0 to 23%) Farm size (acres) (Fig. 3). About 45% (159/350) of farmers had past history Small (1st tertile: 0–5 acres) 63% (94/149) 16% (16/98) 28% of disease outbreaks in their herds, while 47% (164/350) (29/103) of them reported a disease outbreak in the study year Medium (2nd tertile: 6–11 21% (31/149) 43% (42/98) 21% acres) (22/103) (2021). Large (3rd tertile: 12–99 16% (24/149) 41% (40/98) 51% Table  2 presents the farmers’ scores on the physi- acres) (52/103) cal, psychological, social and environmental domains Herd size (TLU) of wellbeing, as well as a pooled overall wellbeing score. Small (1st tertile: 0.3–1.6 42% (62/149) 43% (42/98) 23% The farmers scored on average (median) 71.4 out of TLUs) (24/103) 100 (IQR = 57.1 to 85.7) on the physical, 70.8 out of 100 Medium (2nd tertile: 1.7–4.2 31% (46/149) 24% (24/98) 35% (IQR = 58.3 to 79.2) on the psychological, 66.7 out of 100 TLUs) (36/103) (IQR = 50.0 to 75.0) on the social and 56.3 out of 100 Large (3rd tertile: 4.3–181.9 27% (41/149) 33% (32/98) 42% (IQR = 43.8 to 65.6) on the environmental domains of TLUs) (43/103) wellbeing. The median overall wellbeing score was 65.5 For continuous variables, the median value with corresponding lower and upper quartile values (IQR) are presented in parentheses. Percentages (%) out of 100 (IQR = 56.6 to 73.2). The farmers ranked their are the proportions of ruminant livestock farmers within each characteristic overall satisfaction with health at 75 out of 100 on aver- explored per study district sub-sample (N). Numbers (n) of farmers, falling into each sub-category of characteristics within the study districts; Kwahu Afram age (IQR = 50 to 75). Plains South (KAPS), Mion and Pru East Districts Nuvey et al. BMC Public Health (2023) 23:1353 Page 6 of 13 Fig. 2 Sources and level of social support available to livestock farmers in Ghana The figure shows the distribution of support level received by farmers from different sources. Panel A presents the un-stratified distribution of support availability to farmers from the listed sources, while Panel B presents the stratified distribution of support received by study district. The height and gradi- ent of the color shows the proportion of farmers and the level of support received from each source respectively. For the gradient of the coloration, light coloration depicts no or very low support level from a source and deep coloration depicts very high support level. The y-axis shows the proportion of the farmers receiving support from a source We assessed the relationship between the level of mor- of herd mortality to diseases). While farmers with mod- tality in herds and overall farmer wellbeing. The levels of erate herd losses (between 1% and 18% of herd mortal- herd mortality to all causes and specifically to diseases, ity) also had significantly lower physical (69.4 vs. 74.6, was categorized into tertiles (three quantiles); low, mod- p = 0.02) and psychological (66.7 vs. 70.6, p = 0.04) wellbe- erate and severe, based on the distribution of proportions ing scores compared to the farmers with low level of loss. of herd mortalities. Figure  4 presents the relationship Table  3 presents the results of the linear mixed effect between farmers’ wellbeing in all domains and the three regression model, with fixed effects for disease-related levels of herd mortalities (low, moderate and severe) to herd mortalities relative to the herd sizes (all in TLUs), all causes. Farmers with severe herd mortalities (more farmers’ age, sex, educational attainment, farmland size, than 31% of herd mortality) had significantly lower lev- wealth index, social support level received, overall satis- els of overall (mean score of 60.5 versus 66.5, p < 0.001), faction with health, and perception of disease risk to herd physical (64.1 vs. 73.4, p < 0.001), psychological (64.6 vs. and village-level clusters as random effects. 69.2, p = 0.04), and social (60.2 vs. 68.2, p < 0.001) wellbe- There was a significantly negative relationship between ing, compared to farmers with low level of loss (less than increasing levels of disease-induced herd mortalities 1% of herd mortality). and farmers’ overall wellbeing. Specifically, our model The relationship between levels of herd mortalities predicted an expected difference in farmers’ wellbeing specific to diseases and farmers’ wellbeing is presented score of 7.9 (95%CI 1.50 to 14.39) between a farmer with- in Fig.  5. The level of disease-induced herd mortalities out any animal mortalities compared to a hypothetical was significantly associated with farmers’ overall, physi- farmer with 100% of animal mortalities to diseases. Thus, cal, and psychological wellbeing. The farmers with severe there is a reduction of approximately 0.8 wellbeing points herd losses (more than 18% of herd mortality to diseases), of farmers, for the average of 10% disease-induced herd had significantly lower overall (mean score of 61.7 versus mortalities experienced (Fig.  6). A likelihood-ratio test 66.9, p = 0.002), physical (65.1 vs. 74.6, p < 0.001), and psy- showed that the model including disease-induced herd chological (65.7 vs. 70.6, p = 02) wellbeing scores, com- mortalities provided a better fit for the data than a model pared to the farmers with low level of losses (less than 1% without it, 𝒳2[1] = 6.13, p = 0.01. Excluding livestock Nuvey et al. BMC Public Health (2023) 23:1353 Page 7 of 13 Fig. 3 Factors causing animal mortality in ruminant livestock herds in Ghana Presents the distribution of the proportion of farmers’ herds lost to different factors. The y-axis shows the proportion of herd mortalities for each specified factor depicted by different colors for a livestock farmer and stratified by study district. The position of each dot on the y-axis denotes each individual farmer’s level of reported losses to a factor Table 2 Summary of overall wellbeing and wellbeing domain farmers who did not own animals in their herds did not scores by study district change the results and conclusions (Additional file 1). In Domain Num- KAPS MION PRU EAST addition, including the other causes of animal mortalities ber of items relative to the herd size did not change significantly the Median Median Median effect size (Additional file 2). (IQR) (IQR) (IQR) Overall wellbeing 24 65.1 (55.8, 67.0 (60.3, 64.8 (55.4, Discussion 72.5) 76.3) 72.2) In this study, we aimed to estimate the average effect on Physical 7 71.4 (53.6, 82.1 (67.9, 67.9 (53.6, the wellbeing of a livestock farmer that can be attributed 82.1) 89.3) 78.6) to disease-induced mortalities in the farmer’s herd. To Psychological 6 66.7 (58.3, 75.0 (62.5, 66.7 (58.3, achieve this goal, we adopted a cross-sectional survey 79.2) 83.3) 75.0) design, in which we measured farmers’ wellbeing and Social 3 66.7 (58.3, 66.7 (58.3, 58.3 (50.0, 75.0) 83.3) 75.0) annual herd mortalities and evaluated this association, Environment 8 53.1 (43.8, 50.0 (40.6, 59.4 (50.0, accounting for specified covariates, using linear mixed 62.5) 62.5) 68.8) effect models. Our results suggest that the level of animal Wellbeing domains include physical, psychological, social and environmental disease-induced herd mortalities have a large and nega- quality of life of farmers assessed using the WHO Quality of life – BREF tool; tive effect on farmers’ wellbeing significantly different a 24-item 5-point Likert scale. Overall wellbeing is the average of scores in all the domains of wellbeing. Median wellbeing scores with corresponding from zero, particularly in the physical and psychologi- interquartile ranges (IQR) stratified by study district are presented cal domains of wellbeing. The effect size did not change significantly after the inclusion of other causes of live- stock mortality including theft, conflict, accidents and weather-related herd mortalities and control variables in the model. Nuvey et al. BMC Public Health (2023) 23:1353 Page 8 of 13 Fig. 4 Relationship between herd mortality to all causes and farmers’ wellbeing Shows the relationship between the level of herd mortality to all causes and farmers’ wellbeing in all domains. The overall wellbeing is the average of wellbeing scores in the physical, psychological, social and environmental domains. The level of herd mortalities are reported animal deaths on farms due to all causes relative to a farmer’s herd size in the study year. The level of herd mortality is categorized into tertiles (three quantiles) of severity: low (less than 1% of herd mortality), moderate (1 to 30% of herd mortality) and severe (more than 31% of herd mortality). The box plots show the average wellbe- ing scores with corresponding interquartile ranges for farmers within each level of herd mortality, with the levels of herd mortalities distinguished by colors. The dashed lines show significant results of hypothesis testing of the relationship between farmers’ wellbeing and higher levels of herd mortalities compared to low loss levels using a linear regression model. *, ***, denote 5%, and 0.1% significance levels respectively These results underscore the need to consider the wellbeing is large and significant. Few studies have high- interdependencies between human, animal and ecosys- lighted the strong link between poor animal health and tem health, beyond zoonosis spread in health research. the psychological wellbeing of livestock dependent popu- There exists substantial evidence supporting the impact lations [7, 8, 36, 37]. on global health security, of pathogen spread between The effect of the severity of herd mortalities to diseases the animal, human, and environmental interfaces, in the was more pronounced on the physical and psychologi- absence of adequate control measures [6]. These health cal domains of health compared to the other wellbeing impact evaluations usually have a biomedical physical domains (i.e. social and environmental wellbeing). This health focus. Thus, the observed impact could be even finding is intuitive given the extensive nature of farm- larger when the multidimensionality of health is fully ing in the study area [26] and the relative emotional and considered. We have demonstrated in this study that security attachment between farmers and their livestock the impact of poor animal health on farmers’ overall [7, 21, 38]. Other sources of herd mortality including Nuvey et al. BMC Public Health (2023) 23:1353 Page 9 of 13 Fig. 5 Relationship between herd mortality to diseases and farmers’ wellbeing shows the relationship between the level of herd mortality specifically to only diseases and farmers’ wellbeing in all domains. The overall wellbeing is the average score of wellbeing scores in the physical, psychological, social and environmental domains. The level of herd mortalities are reported disease- induced animal deaths on farms relative to a farmer’s herd size in the study year. The level of herd mortality is categorized into tertiles (three quantiles) of severity: low (less than 1% of herd mortality), moderate (1 to 18% of herd mortality) and severe (more than 18% of herd mortality). The box plots show the average wellbeing scores with corresponding interquartile ranges for farmers within each level of herd mortality, with the levels of disease-induced herd mortalities distinguished by colors. The dashed lines show significant results of hypothesis testing of the relationship between farmers’ wellbeing and higher levels of herd mortalities to diseases compared to low loss levels using a linear regression model. *, **, ***, denote 5%, 1%, and 0.1% significance levels respectively livestock theft, conflict, and weather-related losses would preventive measures particularly vaccination to sus- affect more the social and environmental domains of tainably address disease-induced livestock mortalities wellbeing, compared to disease-induced losses as shown [43–46]. Our findings in the earlier studies of the larger in our results. The extent of these associations could be project showed that the main diseases causing livestock assessed in future studies. In-depth studies from an eco- mortalities are Contagious Bovine Pleuropneumonia system perspective, of the relationship between ecosys- and Food and Mouth Disease in cattle, and Peste des tem challenges, and human and animal wellbeing, are Petits Ruminants in small ruminants (sheep and goats) needed. [27]. Vaccination utilization by farmers to protect herds Disease-induced livestock mortalities remain a signifi- against these diseases was also very low [47] although cant barrier to the productivity and trade in the livestock observed as the key intervention that reduces the mor- sector in many African countries including Ghana [4]. talities [43]. There is thus the need for transdisciplinary Similar to our results, previous research in other coun- strategies that improve high quality vaccine adoption, tries identified animal diseases as a significant source given the availability of effective vaccines to control these of livestock herd mortalities for households [39–42]. diseases [48]. The evidence from our work suggests that, Based on this impact of diseases on herds, studies have addressing animal health challenges through veteri- emphasized the effectiveness and profitability of applying nary service policies could contribute to improving the Nuvey et al. BMC Public Health (2023) 23:1353 Page 10 of 13 Table 3 Mixed effects model predicting the effect of level of epidemic in the United Kingdom led to larger men- herd mortalities to diseases on farmer wellbeing scores adjusting tal health and suicide problems [49]. In this particular for other covariates instance, a ring vaccination and quarantine policy might Parameter Estimate 95% CI p-value have been more appropriate. Fixed effects Our study had some limitations. The nature of the Proportion of herd mortality * -7.94 -14.39 0.02 design does not enable us to determine the temporal rela- – -1.50 Satisfaction with health 0.27 0.23– < 0.001 tionship between poor animal health (disease-induced 0.32 mortalities) and farmer wellbeing. Furthermore, in our Social support received 0.84 0.47– < 0.001 attempt to measure reliably the impact of diseases on 1.22 farmers’ herds, we relied on only disease-induced herd Perception of disease risk to 0.41 0.01– 0.04 mortalities. Thus, the impact of diseases resulting in only herd 0.80 morbidity without the death of the infected animals was Age (years) -0.10 -0.17 0.01 not accounted for in our measurements. We argue how- – -0.02 ever that, the observed impact on farmers’ wellbeing is Farm size (acres) 0.06 -0.02– 0.17 likely to be larger, if disease-induced morbidities should 0.14 Sex be considered. Future studies implementing interven- [ref = female] Male 1.50 -0.72– 0.19 tions to reduce disease incidence using randomized 3.71 controlled trials could evaluate the extent of this rela- Education level [ref = no formal tionship more definitively, as well as assess the pathways education] of the impact. Our study focused on ruminant livestock Up to 12 years 0.01 -2.36– 0.99 farmers, however, based on our engagements with the 2.37 farmers in our study who also own other species such Higher education 2.27 -0.53– 0.11 as poultry and pigs, we understand that they experience 5.07 similar challenges with diseases among these other spe- Wealth index [ref = poorest] cies. Thus, future studies could further explore this miss- Below average 0.08 -2.93– 0.96 ing perspective in our study. Additionally, despite efforts 3.10 Average 2.80 -0.36– 0.08 to obtain a representative sample of the different agro- 5.95 ecological zones in Ghana, our study did not account Above average 2.92 -0.38– 0.08 for the two other minority agro-ecological zones namely 6.23 the Evergreen and Coastal Savannah zones. Even though Least poor 2.92 -0.53– 0.09 these zones are not typical areas for livestock production 6.38 in Ghana, their inclusion would have improved the repre- History of disease outbreak sentativeness of our findings with diversification and the [ref = No] crop production as adaptations options. In spite of this Yes -0.01 -2.21– 0.99 missing perspective, we do not expect the parameters 2.20 Random effects evaluated to be markedly different in these agro-ecolog- Within cluster standard 8.78 8.02– … ical zones. Our study thus, has provided valuable infor- deviation 9.56 mation on the relationship between poor animal health Between cluster standard 2.08 0.00– … and the wellbeing of livestock dependent populations, deviation 3.44 making a strong case for improvements in performance Marginal R2/ Conditional R2 0.47 / 0.50 of veterinary services, for better animal health. * Proportion of herd mortality refers to livestock mortalities to diseases relative to herd size standardized in tropical livestock units. Estimates are the mean changes in overall wellbeing scores of ruminant livestock farmers attributable Conclusion to changes in parameters, with their corresponding 95% confidence intervals Our study has shown that diseases are the main cause (95% CI) and p-values. Overall wellbeing is the average of scores in all the of animal mortalities for ruminant livestock farmers in wellbeing domains including physical, psychological, social and environmental wellbeing assessed using the WHO Quality of life – BREF tool. “ref” denotes the Ghana. The poor health of the livestock herds has a sig- reference level for categorical variables in the model. Marginal and conditional nificant influence on the wellbeing of the livestock farm- R2 are the model variance explained by the fixed effect, and both fixed and random effects respectively ers. Given that, the main diseases accounting for these mortalities have effective vaccines for their control, and vaccination utilization is low among the farmers, our wellbeing of livestock dependent populations. However, findings suggest that improvements in veterinary poli- it should be noted that disease control policies should be cies and service delivery, which address disease risks in adequate to the farming systems. For example the mass livestock, would contribute to better wellbeing of live- culling of livestock during the Foot and Mouth Disease stock dependent populations. This study exemplifies Nuvey et al. BMC Public Health (2023) 23:1353 Page 11 of 13 Fig. 6 Effect of herd mortalities to diseases on farmers’ wellbeing Shows the actual and predicted relationship between the severity of disease-induced animal mortalities and farmers’ overall wellbeing. The overall well- being is the average score of wellbeing scores in the physical, psychological, social, and environmental domains. Panel A shows the relationship between 10% increments in relative herd mortalities to diseases and farmers overall wellbeing without accounting for the potential confounding effect of other covariates. Panel B shows the estimated marginal effect at different levels of disease-induced livestock mortalities, conditional on the other co-variates in the pre-specified linear mixed effect linear regression model. The slope of the marginal effect line with confidence intervals around the point estimates shows the extent and direction of the relationship between the levels of disease-induced herd mortalities and livestock farmers’ overall wellbeing Nuvey et al. BMC Public Health (2023) 23:1353 Page 12 of 13 the benefits of integrated human and animal health Data availabilityAll data generated or analyzed during this study are included in this published studies through a One Health approach, which cannot article [and its supplementary information files]. be achieved if human and animal health are studied in separation. Declarations Supplementary Information Ethics approval and consent to participateThe study was reviewed and approved by the Ghana Health Service Ethics The online version contains supplementary material available at https://doi. Review Committee (approval number: GHS-ERC 006/09/20). In the study org/10.1186/s12889-023-16287-2. districts, permission was obtained from all the relevant authorities prior to data collection. The study participants provided written informed consent Table S1: Mixed effects model predicting the effect of level of herd mor- and the data generated are kept as confidential records. All the methods were talities to all causes on farmer wellbeing adjusting for other covariates. S1 carried out in accordance with relevant guidelines and regulations (Such as Figure: Effect of the level of herd mortalities to diseases on farmers’ well- Declaration of Helsinki). being (N = 333) The figure shows the actual and predicted relationship between the level of animal mortalities to diseases and farmers’ overall Consent for publication wellbeing. The overall wellbeing is the average score of wellbeing scores in Not applicable. physical, psychological, social, and environmental domains. Panel A shows the relationship between 10 percentage increments in relative herd mor- Competing interest talities to diseases and farmers overall wellbeing without accounting for The authors declare that they have no competing interests. the potential confounding effect of other covariates. Panel B presents the estimated marginal effect at different levels of disease-induced livestock Author details mortalities, conditional on the other co-variates in the pre-specified linear 1Swiss Tropical and Public Health Institute, Kreuzstrasse 2, Allschwil mixed effect model. The slope of the marginal effect line with confidence 4123, Switzerland intervals around the point estimates shows the extent and direction of the 2Faculty of Medicine, University of Basel, Klingelbergstrasse 61, Basel relationship between the levels of disease-induced herd mortalities and 4056, Switzerland livestock farmers’ overall wellbeing 3Centre Suisse de Recherches Scientifiques en Côte d’Ivoire, Abidjan, BP 1303, Côte d’Ivoire Table S2: Mixed effects model predicting the effect of level of herd 4School of Biodiversity, One Health and Veterinary Medicine, College of mortalities to all causes on farmer wellbeing adjusting for other covari- Medical, Veterinary and Life Sciences, University of Glasgow, ates. S2 Figure: Effect of the level of herd mortalities suffered on farmers’ Glasgow G12 8QQ, Scotland wellbeing The figure shows the actual and predicted relationship between 5Faculty of Science, University of Basel, Klingelbergstrasse 50, Basel the level of animal mortalities to all causes and farmers’ overall wellbeing. 4056, Switzerland The overall wellbeing is the average score of wellbeing scores in physical, 6Department of Bacteriology, Noguchi Memorial Institute for Medical psychological, social, and environmental domains. Panel A shows the Research, University of Ghana, P.O. Box LG 581, Accra, Ghana relationship between 10 percentage increments in relative herd mortali- ties to all causes and farmers overall wellbeing without accounting for the potential confounding effect of other covariates. Panel B presents the Received: 20 March 2023 / Accepted: 11 July 2023 estimated marginal effect at different levels of livestock mortalities expe- rienced, conditional on the other co-variates in the pre-specified linear mixed effect model. The slope of the marginal effect line with confidence intervals around the point estimates shows the extent and direction of the relationship between the levels of herd mortalities to all causes and livestock farmers’ overall wellbeing References 1. Herrero M, Grace D, Njuki J, Johnson N, Enahoro D, Silvestri S, et al. The roles of livestock in developing countries. Animal. 2013;7(s1):3–18. Acknowledgements 2. UNICEF, WFP, WHO. The state of Food Security and Nutrition in the World We would like to acknowledge the livestock farmers and agricultural 2021. Transforming food systems for food security, improved nutrition and department staff in all the study districts for their participation in this study. affordable healthy diets for all. 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