Assessing The Impact of Cattle-Mediated Zooprophylaxis and Mosquito Species Composition on Malaria Endemicity in Ada West District, Ghana: A Quasi-Experimental Study

Assessing The Impact of Cattle-Mediated Zooprophylaxis and Mosquito Species Composition on Malaria Endemicity in Ada West District, Ghana: A Quasi-Experimental Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing The Impact of Cattle-Mediated Zooprophylaxis and Mosquito Species Composition on Malaria Endemicity in Ada West District, Ghana: A Quasi-Experimental Study Oscar VETSI, Boaz Ahulu, Michael Oppong Yeboah, Jerdu Bin-Eranaa Nuhu, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9396545/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Malaria remains a leading cause of morbidity and mortality in sub-Saharan Africa, with Ghana bearing a disproportionate burden of the disease. Zooprophylaxis, the deliberate or incidental diversion of blood-feeding mosquitoes from humans to livestock, has been proposed as a cost-effective, community-based vector control strategy. However, empirical evidence of its impact on mosquito species composition and malaria endemicity in Ghana is limited. This study assessed the effects of cattle-mediated zooprophylaxis on mosquito species composition and malaria endemicity in Ghana’s Ada West District. Methods A quasi-experimental design with a pre-post intervention control structure was employed. The study was conducted in communities within the Ada West District, comparing cattle-keeping (intervention) and non-cattle-keeping (control) communities from July 2025 to March 2026. A multistage sampling approach yielded 412 households and 1,648 participants in the study. Entomological data were collected using CDC light traps and pyrethrum spray catches, and mosquitoes were identified morphologically and by polymerase chain reaction (PCR). Malaria prevalence was determined using rapid diagnostic tests (RDT). Cattle density and management practices were assessed using structured household surveys. The Driver-Pressure-State-Impact-Response (DPSIR) framework guided conceptual integration. Data were analysed using multilevel logistic regression, chi-square tests, and a difference-in-differences (DiD) approach in Stata 17. Results A total of 5,842 mosquitoes were collected from traps. Anopheles gambiae sensu lato was the predominant species (67.3%), followed by An. funestus (21.4%), and Culex quinquefasciatus (11.3%). Intervention communities recorded significantly lower Anopheles density (2.1 vs. 4.8 per trap-night; p < 0.001) and malaria prevalence (11.2% vs. 24.7%; aOR: 0.38, 95% CI: 0.27–0.54; p < 0.001) than control communities. The DiD analysis revealed a net reduction in malaria prevalence of DiD = (11.2 − 22.8) − (24.7 − 23.9) = − 11.6 − 0.8 = − 12.4 percentage points (pp) attributable to zooprophylaxis (p = 0.003). Species diversity was significantly higher in the intervention communities (Shannon index H′ = 2.14 vs. 1.63; p = 0.021). Conclusions Cattle-mediated zooprophylaxis was associated with significant reductions in Anopheles density and malaria endemicity in the study area. These findings support the integration of strategic livestock management as a complementary approach within Ghana’s National Integrated Vector Management Program, particularly in the agro-pastoral communities of the Northern Region. Zooprophylaxis Malaria endemicity Mosquito species composition Anopheles gambiae Cattle Quasi-experimental Ghana DPSIR framework Vector control Figures Figure 1 Figure 2 Figure 3 Background Malaria continues to represent one of the most significant infectious disease challenges in sub-Saharan Africa. The World Health Organisation World Malaria Report 2024 estimated that Africa accounts for approximately 94% of all global malaria cases and 95% of malaria-related deaths ( 1 ). In Ghana, malaria remains hyperendemic and is a leading cause of outpatient visits, hospital admissions, and under-five mortality ( 2 ). The principal vectors, Anopheles gambiae sensu lato and Anopheles funestus , maintain year-round transmission in many ecological zones, including the coastal and riparian communities of the Greater Accra and Volta Regions ( 3 , 4 ). Conventional malaria vector control strategies, including indoor residual spraying (IRS) and long-lasting insecticidal nets (LLINs), have achieved measurable reductions in transmission intensity in several endemic settings ( 5 – 7 ). However, growing concerns regarding insecticide resistance in Anopheles populations, coupled with the limitations of single-strategy approaches, underscore the urgent need for complementary and ecologically grounded interventions ( 6 , 8 , 9 ). Zooprophylaxis, defined as the incidental or deliberate use of animals to divert blood-feeding vectors away from humans, has emerged as a promising low-cost strategy embedded in agricultural and pastoral livelihoods ( 10 ) The theoretical basis for zooprophylaxis is the host preference of malaria vectors ( 11 – 13 ). Anopheles arabiensis is a major member of the An. gambiae complex and demonstrates notable zoophily compared with the more anthropophilic An. gambiae sensu stricto ( 14 , 15 ). When cattle are present at significant densities, a proportion of mosquito blood meals is redirected from humans to bovine hosts, potentially reducing the entomological inoculation rate (EIR) and, consequently, the risk of malaria in adjacent human populations ( 16 , 17 ). However, empirical evidence regarding the effectiveness of zooprophylaxis has been inconsistent. Several studies conducted in East Africa and South Asia have reported significant reductions in malaria incidence associated with livestock presence ( 18 – 20 ), whereas others have raised concerns about zoopotentiation, the enhancement of vector breeding near livestock due to increased organic matter and standing water ( 21 , 22 ). This equivocality highlights the importance of investigating this phenomenon in specific ecological and epidemiological contexts. In Ghana, cattle herding is practiced extensively in communities along the Volta River Basin and coastal lowlands, including the Ada West District, an area characterised by high malaria transmission, extensive water bodies, and agro-pastoral livelihoods ( 23 – 25 ). Despite the ecological plausibility of zooprophylaxis in these settings, no rigorous study has examined its relationship with mosquito species composition and malaria endemicity in this geographical region to date. Additionally, the application of an integrated analytical framework, such as the DPSIR model, to elucidate the causal pathways linking livestock density to malaria outcomes has not been previously attempted in Ghana ( 23 , 26 , 27 ). Despite the high malaria burden in the Ada West District, a setting characterised by perennial transmission, extensive wetland breeding habitats, and widespread agro-pastoral livelihoods, there is a critical gap in the evidence regarding whether the coexistence of humans and cattle confers a measurable protective effect against malaria. Conventional vector control tools, including LLINs and IRS, have demonstrated limited and inconsistent impact in comparable coastal riparian settings, partly because of insecticide resistance and variable community adherence ( 28 ). Zooprophylaxis has been proposed as a complementary approach; however, its effectiveness in West African settings has not yet been rigorously evaluated. Specifically, no study has examined the differential effect of cattle presence on (i) the species composition and abundance of local Anopheles populations, (ii) the relative proportions of zoophilic An. arabiensis and the anthropophilic Anopheles gambiae sensu stricto; and (iii) community-level malaria endemicity in the Ada West District. Epidemiologically, the Ada West District reports persistently high malaria prevalence exceeding 20% in some communities, even after the sustained deployment of LLINs, indicating that additional or alternative strategies are warranted ( 29 ). Ecologically, the district’s riparian and wetland landscapes create favourable breeding habitats for Anopheles gambiae s.l. and An. funestus , and the co-existence of large cattle herds in the same communities presents a natural quasi-experimental opportunity to assess the zooprophylactic effect under real-world conditions in the Ada West District of Ghana. From a public health policy perspective, zooprophylaxis is intrinsically aligned with existing livelihoods and requires no additional costs for communities that own cattle. If proven effective, strategic guidance on cattle shelter placement relative to human dwellings could be incorporated into community health education at minimal cost. Furthermore, generating context-specific evidence from Ghana is necessary because findings from East Africa may not be directly transferable, given the differences in vector species composition, climate, housing types, and husbandry practices ( 30 – 32 ). This study was designed to assess (i) the impact of cattle-mediated zooprophylaxis on mosquito species composition and abundance, (ii) the relationship between zooprophylaxis exposure and malaria endemicity, and (iii) the determinants of malaria prevalence in communities with different livestock densities. Methods Study design A quasi-experimental design with pre-intervention and control group structures was employed to assess the impact of cattle-mediated zooprophylaxis on mosquito species composition and malaria endemicity. This design was adopted because full randomisation at the community level was neither ethically nor logistically feasible in the study area. Two categories of communities were defined: (i) intervention communities, cattle-keeping communities within the Ada West District (zooprophylaxis-exposed group), and (ii) control communities, non-cattle-keeping communities within the same district. Measurements were conducted at baseline (July 2025) and endline (January–March 2026), spanning both the peak and off-peak malaria transmission seasons. Study area This study was conducted in selected communities in the Ada West District of the Greater Accra Region, Ghana. The communities were stratified based on cattle ownership: those with a high cattle density constituted the intervention group, whereas those with negligible or no cattle presence constituted the control group. Both groups shared the same ecological zone, climate, health system, and socioeconomic environment, which substantially reduced the risk of confounding due to differences at the district level. The district is characterised by riparian and wetland ecosystems that create extensive mosquito breeding habitats and is classified as a malaria-endemic area with perennial transmission (Fig. 1 ). Study population and eligibility criteria The study population comprised three groups: (i) human residents of selected households, (ii) adult mosquito populations collected from households and peridomestic environments, and (iii) household-level cattle as the primary exposure variables. Human participants were eligible if they had resided in the study community for at least six months prior to baseline, provided informed consent, and belonged to households with a clearly defined cattle ownership status. Participants were excluded if they were part of transient or seasonal migrant populations, refused consent, or lived in communities that had undergone vector control interventions in the preceding six months. Description of Cattle Communities in Ada West District Table 1 presents the distribution of communities in the Ada West District where cattle are present in the study area. The communities are organised according to their respective areas and sub-districts, including Caesarkope, Amuyaokope Community-based Health Planning and Services (CHPS), Madavunu, Afiadenyigba, Koluedor, Koni Community-based Health Planning and Services (CHPS), Sege, Bornikope Sub-district, and Anyamam areas. Within each area, multiple communities were identified as locations where cattle were maintained. These communities span both community-based health planning and service (CHPS) zones and non-CHPS areas, reflecting the geographic spread of cattle across the districts. The table classifies the study locations based on the presence of cattle and provides a structured overview of the study settings. This categorisation was used to guide data collection and support the spatial organisation of the variables within the study. Table 1 Summary of Cattle Communities in Ada West District Area / Sub-district Communities Caesarkope Area Caesarkope; Caesarkope Panya; Mangoase; Odorkope; Tugahkope; Agbenyegahkope; Addodoadjikope Amuyaokope CHPS Amuyaokope; Badjorhey; JJ Farm; Ada Luta Madvunu Area Madavunu; Wonyi; Ayissah; Dogokope; Kuatsekope Afiadenyigba Area Afiadenyigba; Adictrekope; Kponya; English Kenya; Dorgobom Koluedor Koluedor; Koluedor Zongo Koni CHPS Narkietsekope; Koni–Alavanyo; Amartey Koni; Doratsekope Sege Area Sege; Sorkope; Nakomkope Bornikope Sub-district Bornikope; Toflokpo; Salom; Osoyaa; Anukpenya; Matsekope; Kpotsum Anyamam Area Anyamam; Okorhuesisi CHPS: Community-based Health Planning and Services. Sample size determination The sample size was calculated using the formula for comparing two independent proportions, assuming a baseline malaria prevalence of 25% in the control group and a 10 percentage-point reduction in the intervention group (15%), with a significance level of 5% (two-tailed) and 80% statistical power. A minimum of 178 households per group was required. After adjusting for a design effect of 1.5 due to cluster sampling and a 10% non-response rate, the final target was 206 households per arm (total: 412). With an average household size of four participants, the total sample was 1,648. Sampling technique A multistage sampling approach was used. In the first stage, the Ada West District was purposively selected based on the documented co-existence of cattle-keeping and non-cattle-keeping communities within the same ecological zone. In the second stage, communities were stratified by cattle ownership status (≥ 5 cattle per 10 households versus < 5 cattle per 10 households) using pre-existing agricultural census data [10] and randomly selected within strata. In the third stage, households were sampled using systematic random sampling methods. In the fourth stage, all eligible individuals from the sampled households were enrolled in the study. Data collection Entomological data collection Adult mosquitoes were collected using CDC miniature light traps (John W. Hock, Gainesville, FL, USA) and pyrethrum spray catches (PSC) were conducted in the sleeping rooms of the consenting households. Light traps were operated from 18:00 to 06:00 hours on three consecutive nights during each sampling visit. Larval sampling was conducted at identified breeding sites using the standard dipping technique (10 dips per breeding site). All mosquitoes were morphologically identified at the species level using an established dichotomous key. A random 20% subsample of Anopheles specimens was subjected to PCR-based molecular identification as described by Mulamba et al. ( 33 ). Mosquito density was expressed as the mean number of individuals per trap night (TN). Species diversity was quantified using the Shannon–Wiener (H′) and Pielou’s evenness (J′) indices. Malaria data collection Malaria prevalence was determined at baseline (July 2025) and endline (January–March 2026) using WHO-validated malaria rapid diagnostic tests (SD BIOLINE Malaria Ag P.f/Pan) ( 34 ). Thick and thin blood smears were prepared from all RDT-positive cases and a random 20% of RDT-negative cases and examined by two independent microscopists, with discordant results adjudicated by a third reviewer. The fever history in the preceding 48 hours and axillary temperature were recorded for each participant. Cattle exposure assessment Cattle density and management practices were assessed using structured household questionnaires administered by trained enumerators. The collected data included the number of cattle per household, distance between cattle shelters and the nearest human sleeping room, nocturnal cattle management practices, and the duration of cattle ownership. Community-level cattle density was computed as the number of cattle per 100 people using the 2021 census data. Environmental and sociodemographic data Environmental mapping was conducted using handheld GPS devices to record the coordinates of breeding sites, water bodies, vegetation and household locations. Sociodemographic data, including age, sex, household size, housing conditions, socioeconomic status (asset-based index), and ITN use, were collected using interviewer-administered questionnaires. Conceptual framework This study employed the Driver-Pressure-State-Impact-Response (DPSIR) framework to examine the ecological pathway through which cattle ownership influences malaria transmission in the Ada West District of Ghana, with malaria endemicity as the terminal outcome variable ( 35 ). Cattle ownership ( Driver ) , characterised by herd size and proximity of livestock to human dwellings, introduces an alternative vertebrate host into the peri-domestic environment, thereby diverting mosquito blood-feeding activity away from humans ( Pressure ) , as measured by shifts in the human blood index and indoor/outdoor biting ratios. Sustained over time, this behavioural diversion selectively favours zoophilic vector species, such as Anopheles arabiensis , over more anthropophilic counterparts, such as An. gambiae s.s. , reshaping the local vector community composition and abundance (state). These entomological changes reduce key transmission metrics, including the entomological inoculation rate, sporozoite rate, and vectorial capacity, thereby diminishing human–vector contact (Impact). The cumulative protective effect of this causal chain constitutes zooprophylaxis (response), an emergent ecological outcome rather than a deliberate intervention, quantified by comparing the malaria burden between cattle-owning and non-owning communities while controlling for relevant confounders. Importantly, the framework incorporates a feedback loop whereby demonstrated zooprophylactic protection may inform livestock management policies and vector control strategies, thereby modifying the original driver. Malaria endemicity, assessed through parasite prevalence, incidence rates, and transmission intensity classification, serves as the ultimate dependent variable, capturing the net downstream effect of all upstream components in the causal chain ( 26 , 36 ) (Fig. 2 ). Summary of DPSIR Components and Indicators The Table 2 below summarises the mapping of each DPSIR component to its corresponding study variable and key measurement indicators: Table 2 DPSIR Components and Indicators DPSIR Component Study Variable Key Indicators Driver Cattle ownership Ownership status, herd size, proximity of cattle to dwellings Pressure Altered mosquito host-seeking behaviour Human blood index, bovine blood index, indoor/outdoor biting ratios State Mosquito species composition and abundance An. gambiae s.l. sibling species ratios, An. funestus density, vector density per trap night Impact Malaria transmission dynamics Entomological inoculation rate, sporozoite rate, human biting rate, vectorial capacity Response Zooprophylaxis Comparative malaria risk between cattle-owning and non-owning communities, blood-meal diversion rates Outcome Malaria endemicity Parasite prevalence, malaria incidence rates, endemicity classification Data analysis All data were entered into REDCap and exported to Stata version 17.0 for further analysis. Descriptive statistics were computed for all the variables. Chi-square tests were used to assess the bivariate associations. Multilevel logistic regression with households nested within communities was used to estimate adjusted odds ratios (aOR) for malaria positivity, accounting for clustering effects. Variables with p < 0.20 in the bivariate analyses were included in the multivariable model; the final model retained variables with p < 0.05. PAST (PAleontological Statistics Software Package for Education and Data Analysis) was used to analyse and input the Shannon–Wiener (H′) and Pielou’s evenness (J′) indices. The difference-in-differences (DiD) approach was used to estimate the net causal impact of zooprophylaxis on malaria prevalence. Ethical considerations Ethical approval was obtained from the Ghana Health Service Ethics Review Committee and the University of Port Harcourt Ethics Review Board. Written informed consent was obtained Informed consent was obtained from all adult participants, and assent was obtained from children aged 8–17 years with parental consent. All RDT-positive malaria cases were referred for standard care at no cost to the study participants. This study was conducted in accordance with the principles of the Declaration of Helsinki. Results Study population and baseline characteristics A total of 412 households were enrolled, with 206 households in each group. Of the 1,648 eligible participants, 1,591 (96.5%) completed both assessments. The mean age of participants was 28.4 years (SD 17.6) in the intervention arm and 27.9 years (SD 16.8) in the control arm. Females constituted 53.2% and 52.8% of participants in the intervention and control arms, respectively. ITN use was comparable at baseline (64.3% vs. 66.1%; p = 0.67) between the two groups. The mean number of cattle per household was 7.4 (SD = 3.1). The baseline malaria prevalence was 22.8% in cattle-keeping communities and 23.9% in non-cattle-keeping communities (p = 0.74), confirming group comparability at baseline (Table 3 ). Table 3 Baseline sociodemographic and household characteristics of study participants, Ada West District (July 2025) Characteristic Cattle-keeping communities (n = 206) Non-cattle-keeping communities (n = 206) p-value Mean age, years (SD) 28.4 (17.6) 27.9 (16.8) 0.71 Female sex, n (%) 437 (53.2%) 422 (52.8%) 0.89 ITN use, n (%) 264 (64.3%) 272 (66.1%) 0.67 Mean cattle per household (SD) 7.4 (3.1) - - Improved housing, n (%) 148 (71.8%) 152 (73.8%) 0.62 Baseline malaria prevalence (%) 22.8% 23.9% 0.74 SD standard deviation, ITN insecticide-treated nets. p-values derived from independent-samples t-test (continuous variables) and chi-square test (categorical variables); Control communities were selected on the basis of no documented cattle-keeping per agricultural census records Mosquito species composition and abundance A total of 5,842 adult mosquitoes were collected across both sites during the six-month observation period: 2,614 from cattle-keeping communities and 3,228 from non-cattle-keeping communities. Anopheles gambiae s.l. was the predominant species (67.3%), followed by An. funestus (21.4%), and Culex quinquefasciatus (11.3%). Molecular analysis of Anopheles gambiae s.l. subsample confirmed that An. arabiensis constituted 43.7% of the mosquitoes in the intervention arm compared to 18.2% in the control arm (χ² = 47.3; P < 0.001). An. gambiae s.s. constituted 56.3% and 81.8% of the samples, respectively. Anopheles density was significantly lower in the intervention communities (2.1 vs. 4.8 per trap night; p < 0.001). Species diversity was significantly higher in cattle-keeping communities (Shannon H′ = 2.14 vs. 1.63; p = 0.021) than in other communities (Table 4 ). Table 4 Mosquito species composition and diversity indices by study arm, Ada West District (July 2025–March 2026) Parameter Cattle-keeping communities Non-cattle-keeping communities p-value Total mosquitoes collected, n 2,614 3,228 — An. gambiae s.l., % 64.1% 70.2% 0.03 An. arabiensis, % 43.7% 18.2% < 0.001 An. gambiae s.s., % 56.3% 81.8% < 0.001 An. funestus, % 24.8% 18.4% 0.08 Culex quinquefasciatus, % 11.1% 11.4% 0.91 Mean An. density/trap-night (95% CI) 2.1 (1.7–2.5) 4.8 (4.2–5.4) < 0.001 Shannon–Wiener index, H′ 2.14 1.63 0.021 Pielou’s evenness, J′ 0.79 0.61 0.018 An. Anopheles s.l. sensu lato; s.s. sensu stricto; CI, confidence interval. Proportions for An. arabiensis and An. gambiae s.s. are expressed as percentages of molecularly identified An. gambiae s.l. subsample Mosquito species diversity in cattle-keeping and non-cattle-keeping communities enhanced Four mosquito species were identified in the study communities: Anopheles gambiae sensu stricto, An. funestus , An. arabiensis , and Culex quinquefasciatus . The species composition differed markedly between the study groups (Fig. 1 A). In cattle-keeping communities, mosquito species were more evenly distributed, with Anopheles gambiae s.s. (36.1%), followed by Cx. quinquefasciatus (29.0%), An. arabiensis (24.8%), and An. funestus (11.1%). In contrast, Anopheles mosquitoes dominated the non-cattle-keeping communities. gambiae s.s. (42.4%) and Cx. quinquefasciatus (31.5%), with smaller proportions of An. funestus (12.8%), and An. arabiensis (13.4%). The Shannon diversity index (H') was significantly higher in cattle-keeping communities (H' = 2.14) than in non-cattle-keeping communities (H' = 1.63; p = 0.021; Fig. 1 B). Similarly, Pielou's evenness index (J') was significantly greater in cattle-keeping communities (J' = 0.79) than in non-cattle-keeping communities (J' = 0.61; p = 0.018; Fig. 1 C), indicating a more equitable distribution of individuals among species in communities with cattle than in those without cattle. Despite the greater species diversity observed in cattle-keeping communities, the mean Anopheles density per trap night was significantly lower in these communities (2.1 mosquitoes per trap night) than in non-cattle-keeping communities (4.8 mosquitoes per trap night; p = 0.003; Fig. 1 D). This finding suggests that the presence of cattle may be associated with reduced Anopheles abundance, potentially through zooprophylactic effects, whereby cattle serve as alternative blood meal hosts, diverting mosquitoes away from human dwellings (Fig. 3 ). Malaria prevalence and zooprophylaxis effect At the endline, malaria prevalence was significantly lower in cattle-keeping communities (11.2%) than in non-cattle-keeping communities (24.7%) (χ² = 52.3; p < 0.001), representing an absolute reduction of 13.5 percentage points. In the multivariable analysis, residence in a high-cattle-density community was independently associated with significantly reduced malaria odds (aOR: 0.38, 95% CI: 0.27–0.54; p < 0.001), after controlling for ITN use, age, sex, housing type, and socioeconomic status. The DiD analysis yielded a net treatment effect of − 13.5 percentage points (95% CI: −17.8 to − 9.2; p = 0.003). Other significant predictors of malaria positivity included nonuse of ITNs (aOR: 2.71), children aged 2–5 years (aOR: 3.14), and poor housing conditions (aOR = 1.89) (Table 5 ). Cattle shelters located > 50 m from human sleeping quarters were also independently protective (aOR: 0.62, 95% CI: 0.44–0.87; p = 0.006). (Table 5 ). Table 5 Multilevel logistic regression of predictors of malaria prevalence at endline, Ada West District (January–March 2026) Variable Malaria prevalence (%) aOR (95% CI) p-value High cattle density (vs. low) 11.2% vs. 24.7% 0.38 (0.27–0.54) < 0.001 No ITN use (vs. ITN use) 29.8% vs. 14.1% 2.71 (1.98–3.71) < 0.001 Age 2–5 years (vs. adults) 38.4% vs. 14.7% 3.14 (2.09–4.72) < 0.001 Poor housing (vs. improved) 27.3% vs. 14.2% 1.89 (1.32–2.70) 50 m from house 10.4% vs. 18.7% 0.62 (0.44–0.87) 0.006 Low SES (vs. medium/high) 24.6% vs. 15.1% 1.54 (1.11–2.14) 0.009 Female sex (vs. male) 18.2% vs. 17.4% 0.97 (0.74–1.27) 0.83 aOR, adjusted odds ratio; CI, confidence interval; ITN, insecticide-treated net; SES, socioeconomic status. Multilevel logistic regression with households nested within communities. Reference categories shown in parentheses Difference-in-differences analysis The DiD analysis compared changes in malaria prevalence between cattle-keeping (intervention) and non-cattle-keeping (control) communities during the study period. At baseline, the prevalence was similar in both groups (22.8% vs. 23.9%), confirming comparability prior to the observation period. At the endline, malaria prevalence in cattle-keeping communities declined markedly from 22.8% to 11.2% (− 11.6 percentage points), whereas non-cattle-keeping communities experienced a marginal increase from 23.9% to 24.7% (+ 0.8 percentage points). The DiD estimate, representing the net effect after accounting for secular trends in the control group, was − 13.5 pp (95% CI: −17.8 to − 9.2; p = 0.003). The 95% confidence interval excludes zero, indicating that the observed difference is unlikely to be attributable to chance ( 37 ). These findings suggest that cattle-mediated zooprophylaxis played an important role in reducing malaria prevalence, most likely by diverting mosquito blood feeding from humans to bovine hosts (Table 6 ). Table 6 Difference-in-differences (DiD) analysis of the effect of cattle-mediated zooprophylaxis on malaria prevalence, Ada West District (July 2025–March 2026) Group Baseline prevalence (%) Endline prevalence (%) Change (percentage points) Cattle-keeping communities (Intervention) 22.8% 11.2% −11.6 Non-cattle-keeping communities (Control) 23.9% 24.7% + 0.8 Difference-in-differences (DiD) — — −13.5 (95% CI: −17.8 to − 9.2; p = 0.003) DiD, difference-in-differences; CI, confidence interval. The net treatment effect is calculated as (Intervention endline − Intervention baseline) − (Control endline − Control baseline) Larval survey results A total of 72 potential breeding sites were identified and sampled across both study arms: 34 in cattle-keeping and 38 in non-cattle-keeping communities. The breeding site types included temporary puddles and hoof prints, irrigation ditches, semi-permanent ponds and marshes, and drainage channels. Cattle-keeping communities had a higher proportion of temporary puddles and hoof-print pools (70.6%, n = 24) than non-cattle-keeping communities (31.6%, n = 12), reflecting the physical disturbance of the soil surface by the livestock. Conversely, semi-permanent ponds and marshes were more prevalent in the control communities (28.9% vs. 8.8%; p = 0.03). From 720 dip samples collected across the 72 sites (10 dip samples/site), 3,454 mosquito larvae were collected: 1,326 from cattle-keeping communities and 2,128 from non-cattle-keeping communities. The mean larval density was significantly lower in the intervention arm (3.9 per dip; 95% CI: 3.2–4.6) than in the control arm (5.6 per dip; 95% CI: 4.9–6.3; p < 0.001). The mean Anopheles larval density was 2.3 per dip (95% CI: 1.8–2.8) in cattle-keeping communities versus 4.0 per dip (95% CI: 3.4–4.6) in non-cattle-keeping communities (p < 0.001), representing a 42.5% reduction, which was consistent with the data from adult mosquito traps. Notably, Culex quinquefasciatus larvae were proportionally more abundant in the intervention arm (23.6% vs. 17.4%; p = 0.008), likely attributable to the increased organic matter from cattle dung in peridomestic breeding habitats, suggesting a degree of species-specific zoopotentiation (Table 7 ). Table 7 Larval survey findings by study arm, Ada West District (July 2025–March 2026) Parameter Cattle-keeping communities Non-cattle-keeping communities p-value Breeding sites sampled, n 34 38 — Total larvae collected, n 1,326 2,128 — Mean larval density/dip (95% CI) 3.9 (3.2–4.6) 5.6 (4.9–6.3) < 0.001 Mean Anopheles density/dip (95% CI) 2.3 (1.8–2.8) 4.0 (3.4–4.6) < 0.001 An. gambiae s.l. larvae, % 57.8% 66.4% 0.014 An. funestus larvae, % 18.6% 16.2% 0.24 Culex quinquefasciatus larvae, % 23.6% 17.4% 0.008 Temporary puddles/hoof prints, n (%) 24 (70.6%) 12 (31.6%) 0.001 Semi-permanent ponds/marshes, n (%) 3 (8.8%) 11 (28.9%) 0.03 Irrigation ditches/drainage, n (%) 7 (20.6%) 15 (39.5%) 0.09 An. Anopheles s.l. sensu lato; CI, confidence interval. Ten dips were taken at each breeding site using a standard dipping technique to collect larvae. Breeding site types were classified by field enumerators during sampling Discussion This study provides empirical evidence that cattle-mediated zooprophylaxis is associated with statistically significant reductions in both Anopheles mosquito density and malaria prevalence in Ghana’s Ada West District. The findings are broadly consistent with those reported from comparable agro-pastoral settings in Ethiopia, Kenya, and Tanzania ( 7 ), while contributing novel data specific to the West African epidemiological and ecological contexts of this study population. The most notable entomological finding was the differential composition of the Anopheles gambiae complex between the intervention and control communities. A higher proportion of An. arabiensis , the more zoophilic sibling species, in cattle-keeping communities in this region. gambiae s.s. suggests that cattle act as a selective host-diversion force favouring more zoophilic individuals ( 15 , 22 ). This was further supported by the elevated species diversity index in cattle-dense communities (H′ = 2.14 vs. 1.63), indicating that a more heterogeneous host environment sustains a more diverse and less anthropophilic mosquito assemblage ( 17 ). The 56.3% reduction in Anopheles trap density in the intervention arm was consistent with the meta-analytic estimate of a 35–65% reduction in human-biting rates in livestock-exposed communities reported by Pagès et al. ( 17 ). The DiD estimate of a 13.5 percentage-point (pp) net reduction in malaria prevalence attributable to zooprophylaxis was robust to covariate adjustment, suggesting that the observed effect was not merely a product of residual confounding by ITN use, socioeconomic status, or housing quality. The protective effect associated with a greater distance between cattle shelters and human sleeping quarters (aOR: 0.62) warrants careful interpretation of the results. Although physical separation may reduce the probability of mosquitoes transitioning between bovine and human hosts, it may also reflect broader and deliberate vector risk management behaviours. This finding aligns with the literature on optimal cattle placement as a zooprophylactic design parameter ( 8 ) and identifies an actionable spatial threshold in public health guidelines. The age gradient in malaria risk, with children aged 2–5 years having over three times the odds of malaria positivity compared with adults, was consistent with the established pattern of age-dependent immunity in endemic settings ( 23 ) and reinforced the importance of targeting interventions in this vulnerable sub-group. The significant association between poor housing conditions and malaria risk adds to the growing body of evidence linking structural housing improvements to reduced malaria transmission ( 5 ). The application of the DPSIR framework facilitated a system-level interpretation that went beyond conventional effect estimation. By conceptualising cattle ownership (driver), altered host-seeking pressure (pressure), changes in mosquito assemblage composition (state), and malaria burden (impact), this framework illuminates how livestock management practices can be leveraged as a public health response ( 4 , 7 ). This conceptualisation may be particularly useful for policymakers seeking to frame zooprophylaxis within the Integrated Vector Management (IVM) agenda. The limitations of this study include the quasi-experimental design, which cannot fully exclude unmeasured confounding; the single six-month study period, which precludes the assessment of longer-term mosquito species dynamics; and the inability to compute full entomological inoculation rates owing to logistical constraints on sporozoite rate determination. Future longitudinal studies with randomised community allocation and molecular xenomonitoring are recommended to confirm and extend these findings. Conclusions This study demonstrated that cattle-mediated zooprophylaxis was associated with significant reductions in both Anopheles mosquito density and malaria endemicity in communities in Ghana’s Ada West District, Ghana. The 13.5 pp reduction in malaria prevalence was accompanied by a demonstrable shift in the vector species composition toward the less anthropophilic Anopheles arabiensis and greater overall vector diversity, consistent with the theoretical basis for zooprophylaxis. These findings support the potential inclusion of strategic livestock management as a complementary vector control tool in Ghana’s integrated vector management program, particularly in rural agro-pastoral communities. Future research should determine the optimal cattle-to-human density ratios, evaluate synergies with insecticide-based interventions, and assess community acceptability and cost-effectiveness for the large-scale implementation of deliberate zooprophylaxis. Abbreviations aOR Adjusted odds ratio CDC Centers for Disease Control and Prevention CI Confidence interval DiD Difference-in-differences DPSIR Driver-Pressure-State-Impact-Response EIR Entomological inoculation rate GPS Global positioning system IRS Indoor residual spraying ITN Insecticide-treated net IVM Integrated Vector Management LLIN Long-lasting insecticidal net PAST PAleontological Statistics Software Package for Education and Data Analysis, PCR:Polymerase chain reaction PSC Pyrethrum spray catch RDT Rapid diagnostic test SD Standard deviation SES Socioeconomic status Declarations Ethics approval and consent to participate Ethical clearance was obtained from the Ghana Health Service Ethics Review Committee (GHS-ERC/026/825). Written informed consent was obtained from all adult participants, and assent was obtained from children aged 8–17 years with parental consent. All RDT-positive malaria cases were referred for standard care at no cost to study participants. This study was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare no conflicts of interest. Authors Details 1 Department of Public Health, Disease Control Unit, Ada West District, Sege, Accra, Ghana. 2 Oda Nursing and Midwifery Training College, Oda, Eastern Region, Ghana. 3 Akramaman Health Centre, Ga West Municipal Health Directorate, Amasaman, Accra, Ghana. 4 Lambussie District Health Directorate, Ghana Health Service, Lambussie, Upper West Region, Ghana. 5 Health Information Unit, Regional Health Directorate, Ghana Health Service, Central Region, Cape Coast, Ghana. 6 Allegheny County Health Department, Pittsburgh Pennsylvania, USA. 7 Ada West District Health Directorate, Ghana Health Service, Sege Accra, Ghana. 8 Department of Epidemiology, School of Public Health, University of Port Harcourt, Nigeria. Funding This study did not receive any specific funding from public, commercial, or not-for-profit funding agencies. Author Contribution VO conceived and designed the study, supervised data collection, conducted data analysis, and drafted the manuscript. AB and MOY contributed to the data collection and critically reviewed the manuscript. JBEN contributed to community engagement, participant recruitment and manuscript review. MAAM contributed to the data management, health information analysis, and manuscript review. MB, EK, RD, CBN, and RH served as data officers and analysts, contributing to field data collection, entry, and quality verification. AC and MOF provided field and institutional supervision and reviewed the manuscript. CTW provided academic supervision, contributed to the conceptual framework and reviewed the manuscript. All authors have read and approved the final manuscript Acknowledgement The authors express their sincere gratitude to Professor Charles Tobin-West for his invaluable academic guidance and support during this study. We are also grateful to the district director of Ada West for his support and to all the staff of the Ada West District Health Directorate for their logistical support and assistance at various stages of the fieldwork. We extend our appreciation to the community health workers and field enumerators who facilitated data collection and to the households and participants in the Ada West District, whose cooperation made this study possible. And to all Biomedical Scientists who supported in running the test and making sure all procedures were adhered to by international standard. Availability of data and materials The de-identified datasets used and analysed in the current study are available from the corresponding author upon reasonable request. References Gething PW, Battle KE, Bhatt S, Smith DL, Eisele TP, Cibulskis RE, et al. Declining malaria in Africa: Improving the measurement of progress. Malar J. 2014;13(1):39. 10.1186/1475-2875-13-39 . 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Cattle-related risk factors for malaria in southwest Ethiopia: A cross-sectional study. Malar J. 2022;21(1):179. 10.1186/s12936-022-04202-w . Mwalugelo YA, Mponzi WP, Muyaga LL, Mahenge HH, Katusi GC, Muhonja F, et al. Livestock keeping, mosquitoes and community viewpoints: a mixed methods assessment of relationships between livestock management, malaria vector biting risk and community perspectives in rural Tanzania. Malar J. 2024;23(1):213. 10.1186/s12936-024-05039-1 . Dev V. Long-Lasting Insecticidal Nets: An Evidence-Based Technology for Malaria Vector Control and Future Perspectives. In: Tyagi BK, editor. Genetically Modified and other Innovative Vector Control Technologies [Internet]. Singapore: Springer Singapore; 2021 [cited 2026 1 Apr]. pp. 297–309. Available from: https://link.springer.com/ 10.1007/978-981-16-2964-8_15 doi:10.1007/978-981-16-2964-8_15. Ghana Health Service G. Ada West Annual Report [End of Year Report] [Internet]. 2025. Available from: https://chimgh.org/ Wilcox BA, Echaubard P, De Garine-Wichatitsky M, Ramirez B. Vector-borne disease and climate change adaptation in African dryland social-ecological systems. Infect Dis Poverty. 2019;8(1):36. 10.1186/s40249-019-0539-3 . Obame-Nkoghe J, Agossou AE, Mboowa G, Kamgang B, Caminade C, Duke DC, et al. Climate-influenced vector-borne diseases in Africa: a call to empower the next generation of African researchers to find sustainable solutions. Infect Dis Poverty. 2024;13(1):26. 10.1186/s40249-024-01193-5 . Thomson MC, Muñoz ÁG, Cousin R, Shumake-Guillemot J. Climate drivers of vector-borne diseases in Africa and their relevance to control programs. Infect Dis Poverty. 2018;7(1):81. 10.1186/s40249-018-0460-1 . Mulamba C, Riveron JM, Ibrahim SS, Irving H, Barnes KG, Mukwaya LG et al. Widespread Pyrethroid and DDT Resistance in the Major Malaria Vector Anopheles funestus in East Africa Is Driven by Metabolic Resistance Mechanisms. Brooke B, editor. PLoS ONE. 2014;9(10):e110058. 10.1371/journal.pone.0110058 Ngalame AL, Watching D, Kibu OZ, Zeukóo EM, Nsagha DS. Assessment of the diagnostic performance of the SD Bioline Malaria antigen test for the diagnosis of malaria in the Tombel health district, Southwest region of Cameroon. Aninagyei E, editor. PLoS ONE. 2025;20(3):e0298992. 10.1371/journal.pone.0298992 Singh A, Rathour A, Gupta RK, Kumar DVR, Acharya S, Jamuna DKV. Employing the Drivers-Pressures-State-Impact-Response (DPSIR) Framework to Analyse Water Pollution Trends. NES. 2025;10(3):261–74. 10.28978/nesciences.1811122 . Zeru MA, Shibru S, Massebo F. Exploring the impact of cattle on human exposure to malaria mosquitoes in the Arba Minch area district of southwest Ethiopia. Parasites Vectors. 2020;13(1):322. 10.1186/s13071-020-04194-z . Chilton A, Rozema K. Difference-in-Differences. In: Trial by Numbers [Internet]. 1st edn. Oxford University PressNew York; 2024 [cited 2026 Apr 5]. pp. 104–31. Available from: https://academic.oup.com/book/56321/chapter/445422206 10.1093/oso/9780197747858.003.0006 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9396545","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625612553,"identity":"729e9d3d-fc4f-426b-afa5-b0330a652405","order_by":0,"name":"Oscar 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Service","correspondingAuthor":false,"prefix":"","firstName":"Maxwell","middleName":"","lastName":"Onasis-Fiadzo","suffix":""},{"id":625612565,"identity":"42a86529-46d3-4831-8b79-cdf8d17d3bf2","order_by":12,"name":"Charles Tobin-West","email":"","orcid":"","institution":"University of Port Harcourt","correspondingAuthor":false,"prefix":"","firstName":"Charles","middleName":"","lastName":"Tobin-West","suffix":""}],"badges":[],"createdAt":"2026-04-12 19:38:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9396545/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9396545/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107675868,"identity":"c86d2620-816c-4d41-8f5d-140ebbfb0f72","added_by":"auto","created_at":"2026-04-24 00:47:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":389303,"visible":true,"origin":"","legend":"\u003cp\u003eThe Profile of Ada West District, 2026 (\u003cem\u003e\u003cstrong\u003eSource;\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e District Heal\u003c/em\u003eth annual report, 2025)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9396545/v1/b2af9ad66e1c75810d1b69e6.png"},{"id":107675866,"identity":"f163395e-d84a-459a-9745-68bd7b933304","added_by":"auto","created_at":"2026-04-24 00:47:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154422,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConceptual framework guided by the Driver–Pressure–State–Impact–Response (DPSIR) model showing pathways linking cattle ownership to malaria transmission outcomes.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9396545/v1/6e9401966c41b8832fc74080.png"},{"id":107707650,"identity":"3331165d-f98d-47d1-b91f-ea159cba75cf","added_by":"auto","created_at":"2026-04-24 09:20:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMosquito species diversity in cattle-keeping and non-cattle-keeping communities in the Ada West District (July 2025–March 2026). (A) Species composition in each study arm. (B) Shannon–Wiener diversity index (H). (C) Pielou's evenness index, J′. Molecular identification of \u003c/em\u003eAnopheles gambiae\u003cem\u003e s.l. subsample was by PCR (20%). Pielou's evenness index (J′) measures how equally individuals are distributed across species, ranging from 0 (complete dominance by one species) to 1.0 (perfectly equal distribution of individuals across species).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9396545/v1/f72985800565b0c0958d7a3d.jpg"},{"id":109296551,"identity":"ad6db896-e3dc-431a-b526-5bb8ae94c1ca","added_by":"auto","created_at":"2026-05-15 08:48:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":951874,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9396545/v1/e043a842-036a-4e6c-b2f7-2d958f56f25a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing The Impact of Cattle-Mediated Zooprophylaxis and Mosquito Species Composition on Malaria Endemicity in Ada West District, Ghana: A Quasi-Experimental Study","fulltext":[{"header":"Background","content":"\u003cp\u003eMalaria continues to represent one of the most significant infectious disease challenges in sub-Saharan Africa. The World Health Organisation World Malaria Report 2024 estimated that Africa accounts for approximately 94% of all global malaria cases and 95% of malaria-related deaths (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In Ghana, malaria remains hyperendemic and is a leading cause of outpatient visits, hospital admissions, and under-five mortality (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The principal vectors, \u003cem\u003eAnopheles gambiae\u003c/em\u003e sensu lato and \u003cem\u003eAnopheles funestus\u003c/em\u003e, maintain year-round transmission in many ecological zones, including the coastal and riparian communities of the Greater Accra and Volta Regions (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConventional malaria vector control strategies, including indoor residual spraying (IRS) and long-lasting insecticidal nets (LLINs), have achieved measurable reductions in transmission intensity in several endemic settings (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, growing concerns regarding insecticide resistance in \u003cem\u003eAnopheles\u003c/em\u003e populations, coupled with the limitations of single-strategy approaches, underscore the urgent need for complementary and ecologically grounded interventions (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Zooprophylaxis, defined as the incidental or deliberate use of animals to divert blood-feeding vectors away from humans, has emerged as a promising low-cost strategy embedded in agricultural and pastoral livelihoods (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe theoretical basis for zooprophylaxis is the host preference of malaria vectors (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). \u003cem\u003eAnopheles arabiensis\u003c/em\u003e is a major member of the \u003cem\u003eAn. gambiae\u003c/em\u003e complex and demonstrates notable zoophily compared with the more anthropophilic \u003cem\u003eAn. gambiae\u003c/em\u003e sensu stricto (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). When cattle are present at significant densities, a proportion of mosquito blood meals is redirected from humans to bovine hosts, potentially reducing the entomological inoculation rate (EIR) and, consequently, the risk of malaria in adjacent human populations (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, empirical evidence regarding the effectiveness of zooprophylaxis has been inconsistent. Several studies conducted in East Africa and South Asia have reported significant reductions in malaria incidence associated with livestock presence (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), whereas others have raised concerns about zoopotentiation, the enhancement of vector breeding near livestock due to increased organic matter and standing water (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This equivocality highlights the importance of investigating this phenomenon in specific ecological and epidemiological contexts.\u003c/p\u003e \u003cp\u003eIn Ghana, cattle herding is practiced extensively in communities along the Volta River Basin and coastal lowlands, including the Ada West District, an area characterised by high malaria transmission, extensive water bodies, and agro-pastoral livelihoods (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Despite the ecological plausibility of zooprophylaxis in these settings, no rigorous study has examined its relationship with mosquito species composition and malaria endemicity in this geographical region to date. Additionally, the application of an integrated analytical framework, such as the DPSIR model, to elucidate the causal pathways linking livestock density to malaria outcomes has not been previously attempted in Ghana (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the high malaria burden in the Ada West District, a setting characterised by perennial transmission, extensive wetland breeding habitats, and widespread agro-pastoral livelihoods, there is a critical gap in the evidence regarding whether the coexistence of humans and cattle confers a measurable protective effect against malaria. Conventional vector control tools, including LLINs and IRS, have demonstrated limited and inconsistent impact in comparable coastal riparian settings, partly because of insecticide resistance and variable community adherence (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Zooprophylaxis has been proposed as a complementary approach; however, its effectiveness in West African settings has not yet been rigorously evaluated. Specifically, no study has examined the differential effect of cattle presence on (i) the species composition and abundance of local \u003cem\u003eAnopheles\u003c/em\u003e populations, (ii) the relative proportions of zoophilic \u003cem\u003eAn. arabiensis\u003c/em\u003e and the anthropophilic Anopheles gambiae sensu stricto; and (iii) community-level malaria endemicity in the Ada West District.\u003c/p\u003e \u003cp\u003eEpidemiologically, the Ada West District reports persistently high malaria prevalence exceeding 20% in some communities, even after the sustained deployment of LLINs, indicating that additional or alternative strategies are warranted (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Ecologically, the district\u0026rsquo;s riparian and wetland landscapes create favourable breeding habitats for \u003cem\u003eAnopheles gambiae\u003c/em\u003e s.l. and \u003cem\u003eAn. funestus\u003c/em\u003e, and the co-existence of large cattle herds in the same communities presents a natural quasi-experimental opportunity to assess the zooprophylactic effect under real-world conditions in the Ada West District of Ghana. From a public health policy perspective, zooprophylaxis is intrinsically aligned with existing livelihoods and requires no additional costs for communities that own cattle. If proven effective, strategic guidance on cattle shelter placement relative to human dwellings could be incorporated into community health education at minimal cost. Furthermore, generating context-specific evidence from Ghana is necessary because findings from East Africa may not be directly transferable, given the differences in vector species composition, climate, housing types, and husbandry practices (\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study was designed to assess (i) the impact of cattle-mediated zooprophylaxis on mosquito species composition and abundance, (ii) the relationship between zooprophylaxis exposure and malaria endemicity, and (iii) the determinants of malaria prevalence in communities with different livestock densities.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy design\u003c/h2\u003e\n \u003cp\u003eA quasi-experimental design with pre-intervention and control group structures was employed to assess the impact of cattle-mediated zooprophylaxis on mosquito species composition and malaria endemicity. This design was adopted because full randomisation at the community level was neither ethically nor logistically feasible in the study area. Two categories of communities were defined: (i) intervention communities, cattle-keeping communities within the Ada West District (zooprophylaxis-exposed group), and (ii) control communities, non-cattle-keeping communities within the same district. Measurements were conducted at baseline (July 2025) and endline (January\u0026ndash;March 2026), spanning both the peak and off-peak malaria transmission seasons.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStudy area\u003c/h3\u003e\n\u003cp\u003eThis study was conducted in selected communities in the Ada West District of the Greater Accra Region, Ghana. The communities were stratified based on cattle ownership: those with a high cattle density constituted the intervention group, whereas those with negligible or no cattle presence constituted the control group. Both groups shared the same ecological zone, climate, health system, and socioeconomic environment, which substantially reduced the risk of confounding due to differences at the district level. The district is characterised by riparian and wetland ecosystems that create extensive mosquito breeding habitats and is classified as a malaria-endemic area with perennial transmission (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eStudy population and eligibility criteria\u003c/h3\u003e\n\u003cp\u003eThe study population comprised three groups: (i) human residents of selected households, (ii) adult mosquito populations collected from households and peridomestic environments, and (iii) household-level cattle as the primary exposure variables. Human participants were eligible if they had resided in the study community for at least six months prior to baseline, provided informed consent, and belonged to households with a clearly defined cattle ownership status. Participants were excluded if they were part of transient or seasonal migrant populations, refused consent, or lived in communities that had undergone vector control interventions in the preceding six months.\u003c/p\u003e\n\u003ch3\u003eDescription of Cattle Communities in Ada West District\u003c/h3\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the distribution of communities in the Ada West District where cattle are present in the study area. The communities are organised according to their respective areas and sub-districts, including Caesarkope, Amuyaokope Community-based Health Planning and Services (CHPS), Madavunu, Afiadenyigba, Koluedor, Koni Community-based Health Planning and Services (CHPS), Sege, Bornikope Sub-district, and Anyamam areas. Within each area, multiple communities were identified as locations where cattle were maintained. These communities span both community-based health planning and service (CHPS) zones and non-CHPS areas, reflecting the geographic spread of cattle across the districts. The table classifies the study locations based on the presence of cattle and provides a structured overview of the study settings. This categorisation was used to guide data collection and support the spatial organisation of the variables within the study.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of Cattle Communities in Ada West District\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea / Sub-district\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCommunities\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaesarkope Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaesarkope; Caesarkope Panya; Mangoase; Odorkope; Tugahkope; Agbenyegahkope; Addodoadjikope\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmuyaokope CHPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmuyaokope; Badjorhey; JJ Farm; Ada Luta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMadvunu Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMadavunu; Wonyi; Ayissah; Dogokope; Kuatsekope\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfiadenyigba Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfiadenyigba; Adictrekope; Kponya; English Kenya; Dorgobom\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKoluedor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKoluedor; Koluedor Zongo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKoni CHPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNarkietsekope; Koni\u0026ndash;Alavanyo; Amartey Koni; Doratsekope\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSege Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSege; Sorkope; Nakomkope\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBornikope Sub-district\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBornikope; Toflokpo; Salom; Osoyaa; Anukpenya; Matsekope; Kpotsum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnyamam Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnyamam; Okorhuesisi\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\u003cem\u003eCHPS: Community-based Health Planning and Services.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eSample size determination\u003c/h3\u003e\n\u003cp\u003eThe sample size was calculated using the formula for comparing two independent proportions, assuming a baseline malaria prevalence of 25% in the control group and a 10 percentage-point reduction in the intervention group (15%), with a significance level of 5% (two-tailed) and 80% statistical power. A minimum of 178 households per group was required. After adjusting for a design effect of 1.5 due to cluster sampling and a 10% non-response rate, the final target was 206 households per arm (total: 412). With an average household size of four participants, the total sample was 1,648.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eSampling technique\u003c/h2\u003e\n \u003cp\u003eA multistage sampling approach was used. In the first stage, the Ada West District was purposively selected based on the documented co-existence of cattle-keeping and non-cattle-keeping communities within the same ecological zone. In the second stage, communities were stratified by cattle ownership status (\u0026ge;\u0026thinsp;5 cattle per 10 households versus \u0026lt;\u0026thinsp;5 cattle per 10 households) using pre-existing agricultural census data [10] and randomly selected within strata. In the third stage, households were sampled using systematic random sampling methods. In the fourth stage, all eligible individuals from the sampled households were enrolled in the study.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eEntomological data collection\u003c/h2\u003e\n \u003cp\u003eAdult mosquitoes were collected using CDC miniature light traps (John W. Hock, Gainesville, FL, USA) and pyrethrum spray catches (PSC) were conducted in the sleeping rooms of the consenting households. Light traps were operated from 18:00 to 06:00 hours on three consecutive nights during each sampling visit. Larval sampling was conducted at identified breeding sites using the standard dipping technique (10 dips per breeding site). All mosquitoes were morphologically identified at the species level using an established dichotomous key. A random 20% subsample of \u003cem\u003eAnopheles\u003c/em\u003e specimens was subjected to PCR-based molecular identification as described by Mulamba \u003cem\u003eet al.\u003c/em\u003e (\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e). Mosquito density was expressed as the mean number of individuals per trap night (TN). Species diversity was quantified using the Shannon\u0026ndash;Wiener (H\u0026prime;) and Pielou\u0026rsquo;s evenness (J\u0026prime;) indices.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eMalaria data collection\u003c/h2\u003e\n \u003cp\u003eMalaria prevalence was determined at baseline (July 2025) and endline (January\u0026ndash;March 2026) using WHO-validated malaria rapid diagnostic tests (SD BIOLINE Malaria Ag P.f/Pan) (\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e). Thick and thin blood smears were prepared from all RDT-positive cases and a random 20% of RDT-negative cases and examined by two independent microscopists, with discordant results adjudicated by a third reviewer. The fever history in the preceding 48 hours and axillary temperature were recorded for each participant.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eCattle exposure assessment\u003c/h2\u003e\n \u003cp\u003eCattle density and management practices were assessed using structured household questionnaires administered by trained enumerators. The collected data included the number of cattle per household, distance between cattle shelters and the nearest human sleeping room, nocturnal cattle management practices, and the duration of cattle ownership. Community-level cattle density was computed as the number of cattle per 100 people using the 2021 census data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eEnvironmental and sociodemographic data\u003c/h2\u003e\n \u003cp\u003eEnvironmental mapping was conducted using handheld GPS devices to record the coordinates of breeding sites, water bodies, vegetation and household locations. Sociodemographic data, including age, sex, household size, housing conditions, socioeconomic status (asset-based index), and ITN use, were collected using interviewer-administered questionnaires.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eConceptual framework\u003c/h2\u003e\n \u003cp\u003eThis study employed the Driver-Pressure-State-Impact-Response (DPSIR) framework to examine the ecological pathway through which cattle ownership influences malaria transmission in the Ada West District of Ghana, with malaria endemicity as the terminal outcome variable (\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eCattle ownership \u003cstrong\u003e(\u003c/strong\u003eDriver\u003cstrong\u003e)\u003c/strong\u003e, characterised by herd size and proximity of livestock to human dwellings, introduces an alternative vertebrate host into the peri-domestic environment, thereby diverting mosquito blood-feeding activity away from humans \u003cstrong\u003e(\u003c/strong\u003ePressure\u003cstrong\u003e)\u003c/strong\u003e, as measured by shifts in the human blood index and indoor/outdoor biting ratios. Sustained over time, this behavioural diversion selectively favours zoophilic vector species, such as \u003cem\u003eAnopheles arabiensis\u003c/em\u003e, over more anthropophilic counterparts, such as \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e, reshaping the local vector community composition and abundance\u003c/p\u003e\n \u003cp\u003e(state). These entomological changes reduce key transmission metrics, including the entomological inoculation rate, sporozoite rate, and vectorial capacity, thereby diminishing human\u0026ndash;vector contact (Impact). The cumulative protective effect of this causal chain constitutes zooprophylaxis (response), an emergent ecological outcome rather than a deliberate intervention, quantified by comparing the malaria burden between cattle-owning and non-owning communities while controlling for relevant confounders.\u003c/p\u003e\n \u003cp\u003eImportantly, the framework incorporates a feedback loop whereby demonstrated zooprophylactic protection may inform livestock management policies and vector control strategies, thereby modifying the original driver. Malaria endemicity, assessed through parasite prevalence, incidence rates, and transmission intensity classification, serves as the ultimate dependent variable, capturing the net downstream effect of all upstream components in the causal chain (\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eSummary of DPSIR Components and Indicators\u003c/h2\u003e\n \u003cp\u003eThe Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e below summarises the mapping of each DPSIR component to its corresponding study variable and key measurement indicators:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDPSIR Components and Indicators\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDPSIR Component\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStudy Variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKey Indicators\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDriver\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCattle ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOwnership status, herd size, proximity of cattle to dwellings\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltered mosquito host-seeking behaviour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman blood index, bovine blood index, indoor/outdoor biting ratios\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eState\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMosquito species composition and abundance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAn. gambiae s.l. sibling species ratios, An. funestus density, vector density per trap night\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eImpact\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalaria transmission dynamics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEntomological inoculation rate, sporozoite rate, human biting rate, vectorial capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZooprophylaxis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComparative malaria risk between cattle-owning and non-owning communities, blood-meal diversion rates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalaria endemicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParasite prevalence, malaria incidence rates, endemicity classification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eData analysis\u003c/h2\u003e\n \u003cp\u003eAll data were entered into REDCap and exported to Stata version 17.0 for further analysis. Descriptive statistics were computed for all the variables. Chi-square tests were used to assess the bivariate associations. Multilevel logistic regression with households nested within communities was used to estimate adjusted odds ratios (aOR) for malaria positivity, accounting for clustering effects. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in the bivariate analyses were included in the multivariable model; the final model retained variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. PAST (PAleontological Statistics Software Package for Education and Data Analysis) was used to analyse and input the Shannon\u0026ndash;Wiener (H\u0026prime;) and Pielou\u0026rsquo;s evenness (J\u0026prime;) indices. The difference-in-differences (DiD) approach was used to estimate the net causal impact of zooprophylaxis on malaria prevalence.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eEthical considerations\u003c/h2\u003e\n \u003cp\u003eEthical approval\u0026nbsp;was obtained from the Ghana Health Service Ethics Review Committee and the University of Port Harcourt Ethics Review Board. Written informed consent was obtained\u003c/p\u003e\n \u003cp\u003eInformed consent\u0026nbsp;was obtained from all adult participants, and assent was obtained from children aged 8\u0026ndash;17 years with parental consent. All RDT-positive malaria cases were referred for standard care at no cost to the study participants. This study was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStudy population and baseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 412 households were enrolled, with 206 households in each group. Of the 1,648 eligible participants, 1,591 (96.5%) completed both assessments. The mean age of participants was 28.4 years (SD 17.6) in the intervention arm and 27.9 years (SD 16.8) in the control arm. Females constituted 53.2% and 52.8% of participants in the intervention and control arms, respectively. ITN use was comparable at baseline (64.3% vs. 66.1%; p\u0026thinsp;=\u0026thinsp;0.67) between the two groups. The mean number of cattle per household was 7.4 (SD\u0026thinsp;=\u0026thinsp;3.1). The baseline malaria prevalence was 22.8% in cattle-keeping communities and 23.9% in non-cattle-keeping communities (p\u0026thinsp;=\u0026thinsp;0.74), confirming group comparability at baseline (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\u003eBaseline sociodemographic and household characteristics of study participants, Ada West District (July 2025)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCattle-keeping communities (n\u0026thinsp;=\u0026thinsp;206)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-cattle-keeping communities (n\u0026thinsp;=\u0026thinsp;206)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age, years (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.4 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.9 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e437 (53.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e422 (52.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITN use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264 (64.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272 (66.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean cattle per household (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.4 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved housing, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e148 (71.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (73.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline malaria prevalence (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSD standard deviation, ITN insecticide-treated nets. p-values derived from independent-samples t-test (continuous variables) and chi-square test (categorical variables); Control communities were selected on the basis of no documented cattle-keeping per agricultural census records\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMosquito species composition and abundance\u003c/h2\u003e \u003cp\u003eA total of 5,842 adult mosquitoes were collected across both sites during the six-month observation period: 2,614 from cattle-keeping communities and 3,228 from non-cattle-keeping communities. \u003cem\u003eAnopheles gambiae\u003c/em\u003e s.l. was the predominant species (67.3%), followed by \u003cem\u003eAn. funestus\u003c/em\u003e (21.4%), and \u003cem\u003eCulex quinquefasciatus\u003c/em\u003e (11.3%). Molecular analysis of Anopheles gambiae s.l. subsample confirmed that \u003cem\u003eAn. arabiensis\u003c/em\u003e constituted 43.7% of the mosquitoes in the intervention arm compared to 18.2% in the control arm (χ\u0026sup2; = 47.3; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e constituted 56.3% and 81.8% of the samples, respectively. \u003cem\u003eAnopheles\u003c/em\u003e density was significantly lower in the intervention communities (2.1 vs. 4.8 per trap night; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Species diversity was significantly higher in cattle-keeping communities (Shannon H\u0026prime; = 2.14 vs. 1.63; p\u0026thinsp;=\u0026thinsp;0.021) than in other communities (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMosquito species composition and diversity indices by study arm, Ada West District (July 2025\u0026ndash;March 2026)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCattle-keeping communities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-cattle-keeping communities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal mosquitoes collected, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAn. gambiae s.l., %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAn. arabiensis, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAn. gambiae s.s., %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAn. funestus, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCulex quinquefasciatus, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean An. density/trap-night (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1 (1.7\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8 (4.2\u0026ndash;5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon\u0026ndash;Wiener index, H\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePielou\u0026rsquo;s evenness, J\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAn. Anopheles s.l. sensu lato; s.s. sensu stricto; CI, confidence interval. Proportions for An. arabiensis and An. gambiae s.s. are expressed as percentages of molecularly identified An. gambiae s.l. subsample\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMosquito species diversity in cattle-keeping and non-cattle-keeping communities enhanced\u003c/h2\u003e \u003cp\u003eFour mosquito species were identified in the study communities: \u003cem\u003eAnopheles gambiae\u003c/em\u003e sensu stricto, \u003cem\u003eAn. funestus\u003c/em\u003e, \u003cem\u003eAn. arabiensis\u003c/em\u003e, and \u003cem\u003eCulex quinquefasciatus\u003c/em\u003e. The species composition differed markedly between the study groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In cattle-keeping communities, mosquito species were more evenly distributed, with \u003cem\u003eAnopheles gambiae\u003c/em\u003e s.s. (36.1%), followed by \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e (29.0%), \u003cem\u003eAn. arabiensis\u003c/em\u003e (24.8%), and \u003cem\u003eAn. funestus\u003c/em\u003e (11.1%). In contrast, Anopheles mosquitoes dominated the non-cattle-keeping communities. \u003cem\u003egambiae\u003c/em\u003e s.s. (42.4%) and \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e (31.5%), with smaller proportions of An. funestus (12.8%), and \u003cem\u003eAn. arabiensis\u003c/em\u003e (13.4%).\u003c/p\u003e \u003cp\u003eThe Shannon diversity index (H') was significantly higher in cattle-keeping communities (H' = 2.14) than in non-cattle-keeping communities (H' = 1.63; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Similarly, Pielou's evenness index (J') was significantly greater in cattle-keeping communities (J' = 0.79) than in non-cattle-keeping communities (J' = 0.61; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), indicating a more equitable distribution of individuals among species in communities with cattle than in those without cattle.\u003c/p\u003e \u003cp\u003eDespite the greater species diversity observed in cattle-keeping communities, the mean Anopheles density per trap night was significantly lower in these communities (2.1 mosquitoes per trap night) than in non-cattle-keeping communities (4.8 mosquitoes per trap night; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). This finding suggests that the presence of cattle may be associated with reduced Anopheles abundance, potentially through zooprophylactic effects, whereby cattle serve as alternative blood meal hosts, diverting mosquitoes away from human dwellings (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMalaria prevalence and zooprophylaxis effect\u003c/h2\u003e \u003cp\u003eAt the endline, malaria prevalence was significantly lower in cattle-keeping communities (11.2%) than in non-cattle-keeping communities (24.7%) (χ\u0026sup2; = 52.3; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), representing an absolute reduction of 13.5 percentage points. In the multivariable analysis, residence in a high-cattle-density community was independently associated with significantly reduced malaria odds (aOR: 0.38, 95% CI: 0.27\u0026ndash;0.54; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), after controlling for ITN use, age, sex, housing type, and socioeconomic status. The DiD analysis yielded a net treatment effect of \u0026minus;\u0026thinsp;13.5 percentage points (95% CI: \u0026minus;17.8 to \u0026minus;\u0026thinsp;9.2; p\u0026thinsp;=\u0026thinsp;0.003). Other significant predictors of malaria positivity included nonuse of ITNs (aOR: 2.71), children aged 2\u0026ndash;5 years (aOR: 3.14), and poor housing conditions (aOR\u0026thinsp;=\u0026thinsp;1.89) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Cattle shelters located\u0026thinsp;\u0026gt;\u0026thinsp;50 m from human sleeping quarters were also independently protective (aOR: 0.62, 95% CI: 0.44\u0026ndash;0.87; p\u0026thinsp;=\u0026thinsp;0.006). (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultilevel logistic regression of predictors of malaria prevalence at endline, Ada West District (January\u0026ndash;March 2026)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eMalaria prevalence (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eaOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh cattle density (vs. low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.2% vs. 24.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38 (0.27\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo ITN use (vs. ITN use)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.8% vs. 14.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.71 (1.98\u0026ndash;3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 2\u0026ndash;5 years (vs. adults)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.4% vs. 14.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.14 (2.09\u0026ndash;4.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor housing (vs. improved)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.3% vs. 14.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.89 (1.32\u0026ndash;2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCattle shelter\u0026thinsp;\u0026gt;\u0026thinsp;50 m from house\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.4% vs. 18.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62 (0.44\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow SES (vs. medium/high)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.6% vs. 15.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.54 (1.11\u0026ndash;2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex (vs. male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.2% vs. 17.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97 (0.74\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eaOR, adjusted odds ratio; CI, confidence interval; ITN, insecticide-treated net; SES, socioeconomic status. Multilevel logistic regression with households nested within communities. Reference categories shown in parentheses\u003c/em\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eDifference-in-differences analysis\u003c/h2\u003e \u003cp\u003eThe DiD analysis compared changes in malaria prevalence between cattle-keeping (intervention) and non-cattle-keeping (control) communities during the study period. At baseline, the prevalence was similar in both groups (22.8% vs. 23.9%), confirming comparability prior to the observation period. At the endline, malaria prevalence in cattle-keeping communities declined markedly from 22.8% to 11.2% (\u0026minus;\u0026thinsp;11.6 percentage points), whereas non-cattle-keeping communities experienced a marginal increase from 23.9% to 24.7% (+\u0026thinsp;0.8 percentage points). The DiD estimate, representing the net effect after accounting for secular trends in the control group, was \u0026minus;\u0026thinsp;13.5 pp (95% CI: \u0026minus;17.8 to \u0026minus;\u0026thinsp;9.2; p\u0026thinsp;=\u0026thinsp;0.003). The 95% confidence interval excludes zero, indicating that the observed difference is unlikely to be attributable to chance (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). These findings suggest that cattle-mediated zooprophylaxis played an important role in reducing malaria prevalence, most likely by diverting mosquito blood feeding from humans to bovine hosts (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifference-in-differences (DiD) analysis of the effect of cattle-mediated zooprophylaxis on malaria prevalence, Ada West District (July 2025\u0026ndash;March 2026)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline prevalence (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEndline prevalence (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange (percentage points)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCattle-keeping communities (Intervention)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;11.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-cattle-keeping communities (Control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDifference-in-differences (DiD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;13.5 (95% CI: \u0026minus;17.8 to \u0026minus;\u0026thinsp;9.2; p\u0026thinsp;=\u0026thinsp;0.003)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eDiD, difference-in-differences; CI, confidence interval. The net treatment effect is calculated as (Intervention endline\u0026thinsp;\u0026minus;\u0026thinsp;Intervention baseline) \u0026minus; (Control endline\u0026thinsp;\u0026minus;\u0026thinsp;Control baseline)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eLarval survey results\u003c/h2\u003e \u003cp\u003eA total of 72 potential breeding sites were identified and sampled across both study arms: 34 in cattle-keeping and 38 in non-cattle-keeping communities. The breeding site types included temporary puddles and hoof prints, irrigation ditches, semi-permanent ponds and marshes, and drainage channels. Cattle-keeping communities had a higher proportion of temporary puddles and hoof-print pools (70.6%, n\u0026thinsp;=\u0026thinsp;24) than non-cattle-keeping communities (31.6%, n\u0026thinsp;=\u0026thinsp;12), reflecting the physical disturbance of the soil surface by the livestock. Conversely, semi-permanent ponds and marshes were more prevalent in the control communities (28.9% vs. 8.8%; p\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e \u003cp\u003eFrom 720 dip samples collected across the 72 sites (10 dip samples/site), 3,454 mosquito larvae were collected: 1,326 from cattle-keeping communities and 2,128 from non-cattle-keeping communities. The mean larval density was significantly lower in the intervention arm (3.9 per dip; 95% CI: 3.2\u0026ndash;4.6) than in the control arm (5.6 per dip; 95% CI: 4.9\u0026ndash;6.3; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The mean \u003cem\u003eAnopheles\u003c/em\u003e larval density was 2.3 per dip (95% CI: 1.8\u0026ndash;2.8) in cattle-keeping communities versus 4.0 per dip (95% CI: 3.4\u0026ndash;4.6) in non-cattle-keeping communities (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), representing a 42.5% reduction, which was consistent with the data from adult mosquito traps. Notably, \u003cem\u003eCulex quinquefasciatus\u003c/em\u003e larvae were proportionally more abundant in the intervention arm (23.6% vs. 17.4%; p\u0026thinsp;=\u0026thinsp;0.008), likely attributable to the increased organic matter from cattle dung in peridomestic breeding habitats, suggesting a degree of species-specific zoopotentiation (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLarval survey findings by study arm, Ada West District (July 2025\u0026ndash;March 2026)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCattle-keeping communities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-cattle-keeping communities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreeding sites sampled, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal larvae collected, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean larval density/dip (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.9 (3.2\u0026ndash;4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.6 (4.9\u0026ndash;6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Anopheles density/dip (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3 (1.8\u0026ndash;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (3.4\u0026ndash;4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAn. gambiae s.l. larvae, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAn. funestus larvae, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCulex quinquefasciatus larvae, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporary puddles/hoof prints, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemi-permanent ponds/marshes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrrigation ditches/drainage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAn. Anopheles s.l. sensu lato; CI, confidence interval. Ten dips were taken at each breeding site using a standard dipping technique to collect larvae. Breeding site types were classified by field enumerators during sampling\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides empirical evidence that cattle-mediated zooprophylaxis is associated with statistically significant reductions in both \u003cem\u003eAnopheles\u003c/em\u003e mosquito density and malaria prevalence in Ghana\u0026rsquo;s Ada West District. The findings are broadly consistent with those reported from comparable agro-pastoral settings in Ethiopia, Kenya, and Tanzania (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), while contributing novel data specific to the West African epidemiological and ecological contexts of this study population.\u003c/p\u003e \u003cp\u003eThe most notable entomological finding was the differential composition of \u003cem\u003ethe\u003c/em\u003e Anopheles gambiae complex between the intervention and control communities. A higher proportion of \u003cem\u003eAn. arabiensis\u003c/em\u003e, the more zoophilic sibling species, in cattle-keeping communities \u003cem\u003ein this region. gambiae\u003c/em\u003e s.s. suggests that cattle act as a selective host-diversion force favouring more zoophilic individuals (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This was further supported by the elevated species diversity index in cattle-dense communities (H\u0026prime; = 2.14 vs. 1.63), indicating that a more heterogeneous host environment sustains a more diverse and less anthropophilic mosquito assemblage (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe 56.3% reduction in \u003cem\u003eAnopheles\u003c/em\u003e trap density in the intervention arm was consistent with the meta-analytic estimate of a 35\u0026ndash;65% reduction in human-biting rates in livestock-exposed communities reported by Pag\u0026egrave;s et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The DiD estimate of a 13.5 percentage-point (pp) net reduction in malaria prevalence attributable to zooprophylaxis was robust to covariate adjustment, suggesting that the observed effect was not merely a product of residual confounding by ITN use, socioeconomic status, or housing quality.\u003c/p\u003e \u003cp\u003eThe protective effect associated with a greater distance between cattle shelters and human sleeping quarters (aOR: 0.62) warrants careful interpretation of the results. Although physical separation may reduce the probability of mosquitoes transitioning between bovine and human hosts, it may also reflect broader and deliberate vector risk management behaviours. This finding aligns with the literature on optimal cattle placement as a zooprophylactic design parameter (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) and identifies an actionable spatial threshold in public health guidelines.\u003c/p\u003e \u003cp\u003eThe age gradient in malaria risk, with children aged 2\u0026ndash;5 years having over three times the odds of malaria positivity compared with adults, was consistent with the established pattern of age-dependent immunity in endemic settings (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and reinforced the importance of targeting interventions in this vulnerable sub-group. The significant association between poor housing conditions and malaria risk adds to the growing body of evidence linking structural housing improvements to reduced malaria transmission (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe application of the DPSIR framework facilitated a system-level interpretation that went beyond conventional effect estimation. By conceptualising cattle ownership (driver), altered host-seeking pressure (pressure), changes in mosquito assemblage composition (state), and malaria burden (impact), this framework illuminates how livestock management practices can be leveraged as a public health response (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This conceptualisation may be particularly useful for policymakers seeking to frame zooprophylaxis within the Integrated Vector Management (IVM) agenda.\u003c/p\u003e \u003cp\u003eThe limitations of this study include the quasi-experimental design, which cannot fully exclude unmeasured confounding; the single six-month study period, which precludes the assessment of longer-term mosquito species dynamics; and the inability to compute full entomological inoculation rates owing to logistical constraints on sporozoite rate determination. Future longitudinal studies with randomised community allocation and molecular xenomonitoring are recommended to confirm and extend these findings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrated that cattle-mediated zooprophylaxis was associated with significant reductions in both \u003cem\u003eAnopheles\u003c/em\u003e mosquito density and malaria endemicity in communities in Ghana\u0026rsquo;s Ada West District, Ghana. The 13.5 pp reduction in malaria prevalence was accompanied by a demonstrable shift in the vector species composition toward the less anthropophilic \u003cem\u003eAnopheles arabiensis\u003c/em\u003e and greater overall vector diversity, consistent with the theoretical basis for zooprophylaxis. These findings support the potential inclusion of strategic livestock management as a complementary vector control tool in Ghana\u0026rsquo;s integrated vector management program, particularly in rural agro-pastoral communities. Future research should determine the optimal cattle-to-human density ratios, evaluate synergies with insecticide-based interventions, and assess community acceptability and cost-effectiveness for the large-scale implementation of deliberate zooprophylaxis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eaOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted odds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCenters for Disease Control and Prevention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDiD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifference-in-differences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDPSIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDriver-Pressure-State-Impact-Response\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEntomological inoculation rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal positioning system\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIndoor residual spraying\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eITN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsecticide-treated net\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntegrated Vector Management\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLLIN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLong-lasting insecticidal net\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePAleontological Statistics Software Package for Education and Data Analysis, PCR:Polymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePyrethrum spray catch\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRDT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRapid diagnostic test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e Ethical clearance was obtained from the Ghana Health Service Ethics Review Committee (GHS-ERC/026/825). Written informed consent was obtained from all adult participants, and assent was obtained from children aged 8\u0026ndash;17 years with parental consent. All RDT-positive malaria cases were referred for standard care at no cost to study participants. This study was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthors Details\u003c/h2\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003eDepartment of Public Health, Disease Control Unit, Ada West District, Sege, Accra, Ghana.\u003c/p\u003e \u003cp\u003e \u003csup\u003e2\u003c/sup\u003eOda Nursing and Midwifery Training College, Oda, Eastern Region, Ghana.\u003c/p\u003e \u003cp\u003e \u003csup\u003e3\u003c/sup\u003eAkramaman Health Centre, Ga West Municipal Health Directorate, Amasaman, Accra, Ghana.\u003c/p\u003e \u003cp\u003e \u003csup\u003e4\u003c/sup\u003eLambussie District Health Directorate, Ghana Health Service, Lambussie, Upper West Region, Ghana.\u003c/p\u003e \u003cp\u003e \u003csup\u003e5\u003c/sup\u003eHealth Information Unit, Regional Health Directorate, Ghana Health Service, Central Region, Cape Coast, Ghana.\u003c/p\u003e \u003cp\u003e \u003csup\u003e6\u003c/sup\u003eAllegheny County Health Department, Pittsburgh Pennsylvania, USA.\u003c/p\u003e \u003cp\u003e \u003csup\u003e7\u003c/sup\u003eAda West District Health Directorate, Ghana Health Service, Sege Accra, Ghana.\u003c/p\u003e \u003cp\u003e \u003csup\u003e8\u003c/sup\u003eDepartment of Epidemiology, School of Public Health, University of Port Harcourt, Nigeria.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study did not receive any specific funding from public, commercial, or not-for-profit funding agencies.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eVO conceived and designed the study, supervised data collection, conducted data analysis, and drafted the manuscript. AB and MOY contributed to the data collection and critically reviewed the manuscript. JBEN contributed to community engagement, participant recruitment and manuscript review. MAAM contributed to the data management, health information analysis, and manuscript review. MB, EK, RD, CBN, and RH served as data officers and analysts, contributing to field data collection, entry, and quality verification. AC and MOF provided field and institutional supervision and reviewed the manuscript. CTW provided academic supervision, contributed to the conceptual framework and reviewed the manuscript. All authors have read and approved the final manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors express their sincere gratitude to Professor Charles Tobin-West for his invaluable academic guidance and support during this study. We are also grateful to the district director of Ada West for his support and to all the staff of the Ada West District Health Directorate for their logistical support and assistance at various stages of the fieldwork. We extend our appreciation to the community health workers and field enumerators who facilitated data collection and to the households and participants in the Ada West District, whose cooperation made this study possible. And to all Biomedical Scientists who supported in running the test and making sure all procedures were adhered to by international standard.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe de-identified datasets used and analysed in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGething PW, Battle KE, Bhatt S, Smith DL, Eisele TP, Cibulskis RE, et al. 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Oxford University PressNew York; 2024 [cited 2026 Apr 5]. pp. 104\u0026ndash;31. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://academic.oup.com/book/56321/chapter/445422206\u003c/span\u003e\u003cspan address=\"https://academic.oup.com/book/56321/chapter/445422206\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/oso/9780197747858.003.0006\u003c/span\u003e\u003cspan address=\"10.1093/oso/9780197747858.003.0006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"Zooprophylaxis, Malaria endemicity, Mosquito species composition, Anopheles gambiae, Cattle, Quasi-experimental, Ghana, DPSIR framework, Vector control","lastPublishedDoi":"10.21203/rs.3.rs-9396545/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9396545/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMalaria remains a leading cause of morbidity and mortality in sub-Saharan Africa, with Ghana bearing a disproportionate burden of the disease. Zooprophylaxis, the deliberate or incidental diversion of blood-feeding mosquitoes from humans to livestock, has been proposed as a cost-effective, community-based vector control strategy. However, empirical evidence of its impact on mosquito species composition and malaria endemicity in Ghana is limited. This study assessed the effects of cattle-mediated zooprophylaxis on mosquito species composition and malaria endemicity in Ghana\u0026rsquo;s Ada West District.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA quasi-experimental design with a pre-post intervention control structure was employed. The study was conducted in communities within the Ada West District, comparing cattle-keeping (intervention) and non-cattle-keeping (control) communities from July 2025 to March 2026. A multistage sampling approach yielded 412 households and 1,648 participants in the study. Entomological data were collected using CDC light traps and pyrethrum spray catches, and mosquitoes were identified morphologically and by polymerase chain reaction (PCR). Malaria prevalence was determined using rapid diagnostic tests (RDT). Cattle density and management practices were assessed using structured household surveys. The Driver-Pressure-State-Impact-Response (DPSIR) framework guided conceptual integration. Data were analysed using multilevel logistic regression, chi-square tests, and a difference-in-differences (DiD) approach in Stata 17.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 5,842 mosquitoes were collected from traps. \u003cem\u003eAnopheles gambiae\u003c/em\u003e sensu lato was the predominant species (67.3%), followed by \u003cem\u003eAn. funestus\u003c/em\u003e (21.4%), and \u003cem\u003eCulex quinquefasciatus\u003c/em\u003e (11.3%). Intervention communities recorded significantly lower \u003cem\u003eAnopheles\u003c/em\u003e density (2.1 vs. 4.8 per trap-night; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and malaria prevalence (11.2% vs. 24.7%; aOR: 0.38, 95% CI: 0.27\u0026ndash;0.54; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than control communities. The DiD analysis revealed a net reduction in malaria prevalence of DiD = (11.2\u0026thinsp;\u0026minus;\u0026thinsp;22.8) \u0026minus; (24.7\u0026thinsp;\u0026minus;\u0026thinsp;23.9)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;11.6\u0026thinsp;\u0026minus;\u0026thinsp;0.8\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;12.4 percentage points (pp) attributable to zooprophylaxis (p\u0026thinsp;=\u0026thinsp;0.003). Species diversity was significantly higher in the intervention communities (Shannon index H\u0026prime; = 2.14 vs. 1.63; p\u0026thinsp;=\u0026thinsp;0.021).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCattle-mediated zooprophylaxis was associated with significant reductions in \u003cem\u003eAnopheles\u003c/em\u003e density and malaria endemicity in the study area. These findings support the integration of strategic livestock management as a complementary approach within Ghana\u0026rsquo;s National Integrated Vector Management Program, particularly in the agro-pastoral communities of the Northern Region.\u003c/p\u003e","manuscriptTitle":"Assessing The Impact of Cattle-Mediated Zooprophylaxis and Mosquito Species Composition on Malaria Endemicity in Ada West District, Ghana: A Quasi-Experimental Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 00:47:45","doi":"10.21203/rs.3.rs-9396545/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":"de002e64-553b-4252-b29e-87f174dc58a3","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-14T23:41:42+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T23:53:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 00:47:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9396545","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9396545","identity":"rs-9396545","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}