Multivariate Probit Research Articles

The recursive impact in the multivariate probit model: An application on farmers’ decisions for opting risk management strategies

AbstractThis study investigates the determinants of farmers’ risk management decisions in Khyber‐Pakhtunkhwa, Pakistan, using a recursive multivariate probit (RMVP) model. Employing data from 382 farmers collected through a multistage sampling process, the study compares the RMVP with the traditional multivariate probit (MVP) model, demonstrating the superior performance of the RMVP in capturing complex decision‐making patterns. Our rigorous statistical analysis demonstrates the significant impact of endogenous covariates on farmers’ risk management choices, revealing complementarity or substitutability among strategies. The study contributes to the literature by providing empirical evidence on the effectiveness of the RMVP model for understanding smallholder farmers’ risk management behavior and offering insights for policymakers to support resilient agricultural systems.

Value Chain Analysis of Haricot Bean in East Shewa Zone, Oromia Regional State, Ethiopia

The key objectives of the study were identifying haricot bean value chain actors, their functions and relationship among them, examining actor’s performance along the chain and analyzing factors affecting supply to the market and factors affecting market outlet choice in the study area. Both primary and secondary sources were used for data collection. The primary data was collected through interviewing 180 sample household farmers and 40 traders using semi-structured questionnaires. STATA version 15 Software was used for data analyzing. As the descriptive result indicate that, on average farmers allocate 0.72ha of land for haricot bean production, from which 21.72qt/ha of output gained. In the value chain of haricot bean input suppliers, farmers, collectors, traders, transporters and consumers are the major value chain actors who perform various value chain. From the study result, six marketing channels of haricot bean are exhibited in the study areas. Estimates of the multiple linear regression models indicate that the level of haricot bean supplied to the market is determined by educational level of household, haricot bean farming experience, land allocated for haricot bean, market price of haricot bean, TLU, access to credit, and access to market information. Therefore, policy makers should focus more on the abovementioned variables in the future intervention to increase volume of haricot bean supplied to the market. As the multivariate probit model result indicated that the market outlet choice of rural assemblers was positively and significantly influenced by distance to the nearest market center, distance to all weather road, and educational level whereas it’s negatively and significantly influenced by output level where as selling direct to consumers outlet choice was negatively and significantly affected by sex of household, educational level, farm experience of household in haricot bean, and output level. Urban traders/wholesalers market outlet was positively and significantly affected by sex of household, educational level, haricot bean farm experience, access to credit, access to market information, membership of cooperative, and output level whereas it’s negatively influenced by distance to the nearest market center, distance to all weather road and extension service. Based on the findings of this study, some relevant implications can be drawn that can assist to design appropriate intervention mechanisms to improve market outlets choice of haricot bean farmers in the study area.

Determinants of household participation in the cassava value-chain in South Africa

Cassava’s adaptability to different agroecological conditions, high yield, as well as its ability to thrive under harsh climatic conditions, makes it an essential food security crop. In South Africa, the cassava value chain is currently uncoordinated and underdeveloped, with a couple of smallholder farmers growing the crop for household consumption and as a source of income. Other farmers regard it as a secondary crop and hardly any producers grow it for industrial purposes. Hence, this study sought to analyze the determinants of household participation in the cassava value chain in South Africa. The study employed the multivariate probit model to analyze the determinants of household participation in the cassava value chain in South Africa, using a primary dataset collected through a simple sample method from smallholder farmers in KwaZulu-Natal, Mpumalanga, and Limpopo provinces. Results show that livestock ownership has a positive and significant effect on the likelihood of farmers participating in the value chain by growing cassava for household food consumption. Also, findings reveal that hiring labour in cassava production and an increase in the yield during the previous season increases the probability of farmers’ interest in selling cassava tubers along the value chain. Hence, the positive and statistically significant influence of hiring labour during cassava production in driving the farmers’ interest in selling cassava tubers and cuttings implies that the development of the cassava value chain presents great opportunities for creating jobs (employment) in the country. Also, policy interventions that ensure land tenure security and empower farmers to increase their cassava yields are bound to encourage further participation in the value chain with an interest in selling fresh tubers, among other derived products to generate income. Lastly, programmes that empower and encourage youth participation in the cassava value chain can increase the number of farmers interested in selling cassava products.

Identifiability and convergence behavior for Markov chain Monte Carlo using multivariate probit models

Multivariate probit models have been popularly utilized to analysis multivariate ordinal data. However, the identifiable multivariate probit models entail the covariance matrix for the underlying multivariate normal variables to be a correlation matrix, which brings a rigorous task to conduct efficient statistical analysis. Parameter expansion to make the identifiable model to be non-identifiable has been inevitably explored. However, the effect of the expanded parameters on the convergence of Markov chain Monte Carlo (MCMC) is seldomly investigated; in addition, the comparison of MCMC developed based on the identifiable model and that based on the non-identifiable model is hardly ever explored, especially for data with large sample sizes. In this paper, we conduct a thorough investigation to illustrate the effect of the expanded parameters on the convergence of MCMC and compare the behavior of MCMC between the identifiable and non-identifiable models. Our investigation provides a practical guide regarding the construction of non-identifiable models and development of corresponding MCMC sampling methods. We conduct our investigation using simulation studies and present an application using data from the Russia Longitudinal Monitoring Survey-Higher School of Economics (RLMS-HSE) study.

Exploring Drivers of Land Use/Land Cover Transformations in Goang Watershed Ethiopia: Integrating Local Community Perceptions with Remote Sensing Data

Land use/land cover (LULC) changes driven by human activities are major environmental challenges in many developing regions. This study assessed local community perceptions and household-level drivers of LULC change in northwest Ethiopia. The study combined remote sensing analysis of LULC change with household surveys to understand local perspectives and the socioeconomic, biophysical, and institutional factors influencing household engagement in deforestation activities. The household survey data was collected from 384 randomly selected household heads, as well as from focus group discussions and key informant participants. The remote sensing component used ERDAS IMAGINE 2014 software to classify satellite images and assess LULC changes over time. The quantitative socio-economic data was analyzed using descriptive statistics and econometric methods of the multivariate probit (MVP) model, while the qualitative data was presented using content analysis. The study revealed that population growth, poverty, and food insecurity were the major underlying driving factors, while agricultural land expansion, settlement growth, fuelwood collection, overgrazing, and forest fires were the major underlying causes of LULC change. The MVP model result indicated that gender, off-farm income, access to training, family size, educational level, and agroecology were key determinants of households’ participation in deforestation drivers such as forestland clearing for agriculture, fuelwood collection, timber harvesting, and overgrazing. These findings highlight the need for integrated land use policies and programs that address the socioeconomic and biophysical root causes of LULC change. This study recommends supporting alternative livelihoods, promoting fuel-efficient technologies, and tailoring interventions to different social groups and agroecological contexts of sustainable land use planning and natural resource management in the study site. Future studies should compare perceptions and drivers of land use change across different regions to identify common patterns and unique challenges. This approach will enhance understanding and inform targeted interventions for sustainable land management.

Does Adoption of Multiple Climate‐Smart Agriculture Practices Improve Rural Farm Households' Food Security in Ethiopia?

ABSTRACTClimate‐smart agriculture (CSA) is a set of economically and environmentally friendly practices that can address the issues of food security under the new realities of climate change. However, the adoption of these practices among smallholder farmers is still low, especially in developing countries like Ethiopia. Thus, the main objective of this study was to assess the factors determining rural farm households' adoption of CSA practices and their impact on food security in the Sidama region, Ethiopia. Primary data for this study were obtained from a randomly selected sample of 523 households using a pre‐tested, structured questionnaire. The collected data were analyzed using descriptive statistics, multivariate probit, and an endogenous switching regression model. The study found that, in the study area, conservation of agriculture is the most popular CSA practice, which is adopted by 96.2% of the adopters, followed by soil fertility management (82.2%), crop diversification (62.3%), small‐scale irrigation (47.9%), and crop–livestock integration (40.7%). The multivariate probit model revealed that the sex of the household head, age of the household head, educational level of the household head, extension contact, information on climate change, and distance to the market were identified as determinants for the adoption of CSA practices. Furthermore, the endogenous switching regression model analysis revealed that adoption of CSA practices increased rural farm household food security status. Therefore, this paper suggests that smallholder farmers should incorporate CSA practices to improve their food security.

Accelerating computation: A pairwise fitting technique for multivariate probit models

Fitting multivariate probit models via maximum likelihood presents considerable computational challenges, particularly in terms of computation time and convergence difficulties, even for small numbers of responses. These issues are exacerbated when dealing with ordinal data. An efficient computational approach is introduced, based on a pairwise fitting technique within a pseudo-likelihood framework. This methodology is applied to clinical case studies, specifically using a trivariate probit model. Additionally, the correlation structure among outcomes is allowed to depend on covariates, enhancing both the flexibility and interpretability of the model. By way of simulation and real data applications, the proposed approach demonstrates superior computational efficiency as the dimension of the outcome vector increases. The method's ability to capture covariate-dependent correlations makes it particularly useful in medical research, where understanding complex associations among health outcomes is of scientific importance.

Household Level Determinants of Wheat Packages Adoption in Tigray Region, Northern Ethiopia

Wheat has been considered as one of the strategic food security crops in Ethiopia. A number of research efforts have been done by different research institutions to improve the production and productivity of the crop and as a result a number of wheat production packages has been released and promoted all across the country, including Tigray. Yet, in Tigray, there is no adequate evidence on the adoption status of wheat packages and their determinant factors. Hence, this adoption study was initiated with the objective to analyze the status of wheat packages adoption, and determinant factors for further adoption. A multistage sampling technique was employed to select 493 households from five wheat growing districts of the region. Data were collected through the administration of semi-structured questionnaires and analyzed using both descriptive statistics and Multivariate Probit (MVP) model. The descriptive results described that, the average wheat packages adoption status was 67%, of these only 17% of farmers adopted all five packages. The result of MVP model revealed that sex, age, livestock size, extension services, average plot distance, off-farm income source, farmer perception on his status and credit access were the determinant factors which facilitate/and/or hinder wheat packages adoption in Tigray. Thus, the policy and development interventions should focus on improving institutional support and wealth creation opportunities to improve production and productivity of wheat producer farmers in Tigray.

Exploring Potential Adaptation Strategies for Smallholder Farmers to Climate Change: A Case Study at Abala Abaya, Wolaita Zone, South Ethiopia

Background: The frequency and intensity of extreme weather events are expected to increase as a result of climate change, which will contribute to changes in precipitation and temperature, and therefore will affect food security. Research issues related to increasing the sustainability of the agricultural sector, eliminating climate risks in agriculture are coming to the fore. This is especially relevant in arid regions. Objectives: This study aimed to assess the adaptation strategies of smallholder farmers to climate change in Abela Abaya, Wolaita Zone, Southern Ethiopia. Finding optimal practices adapted to random extreme events is fundamental for sustainable food production. In particular, the study attempted to understand the preferences of smallholder farmers in adaptation strategies to climate change in the study area; the main influencing factors determining the choice of adaptation strategies to climate change by smallholder farmers in the study area. Methods: To investigate this issue, qualitative and quantitative data were used within a descriptive research approach to assess potential adaptation strategies of smallholder farmers to climate change. Data on demographic, socio-economic, institutional, physical and psychological factors for adaptation strategies of smallholder farmers were collected through specially designed and pre-tested questionnaires, interviews and focus group discussions (FGDs). Rural counties for the study were selected using simple random sampling due to the same agroecology. The multivariate probit (MVP) model was used as it is a type of correlated binary response regression model that allows for the simultaneous identification and assessment of the impact of a set of independent factors on each of the possible approaches. Five adaptation methods are identified as dependent variables for the Multi-Variate Probit. These include Soil and Water Conservation (SWC) practices, use of drought-tolerant varieties, adjustment of planting dates, use of agroforestry, and implementation of water harvesting. The following were selected as independent variables for the current study. Results: Most farmers (96.7%) acknowledge the ongoing climate change and are concerned that their agriculture will not suffer under the new conditions. This is justified by the fact that farmers confirm their observations regarding the increase in air temperature, the increase in the frequency of plant diseases and the decrease in precipitation. At the same time, only 36.2% of the surveyed farmers reported that society is aware of the possible risks associated with global warming and is trying to adapt agricultural activities to new climatic conditions. In particular, the current study revealed for the first time that smallholder farmers in Abela Abaya primarily prefer approaches such as soil and water conservation (71.6%), adjustment of planting dates (59.4%), and agroforestry (44.5%). Conclusion: The study identified the most influential factors on the adoption of adapted agricultural practices in a specific region of Abala Abaya (Wolaita Zone, Southern Ethiopia), namely, household heads’ access to education, frequency of extension visits, access to climate information and land slope. Thus, decision makers can design and adopt appropriate programs based on the current results to preserve smallholder farming and maintain food security at the national level.

Determinants of coping strategies among agropastoralists in Kitui and Isiolo counties, Kenya

Agropastoralists in Kenya’s Arid and Semi-arid Lands (ASALs) face various shocks that threaten their livelihoods and expose them to significant health and economic risks. While existing studies often focus on climate-related shocks, they frequently overlook other challenges faced by these communities. A comprehensive understanding of how agropastoralists manage diverse shocks is essential to developing effective vulnerability reduction strategies. This study examined the major shocks experienced by agropastoral households in Kenya’s ASALs, their primary coping strategies, and the determinants informing the choice of the strategies adopted. Using cross-sectional data from 371 households in Isiolo (203) and Kitui (168) counties, this study applied descriptive analysis and multivariate probit (MVP) models to assess coping strategies and influencing factors. From 2016 to 2021, households faced shocks, such as droughts, livestock pests and diseases, crop pests, theft, and intercommunal conflicts. Regional variations were observed: crop pests were common in Kitui, whereas Isiolo experienced higher theft and conflict rates. Coping strategies include income diversification, asset sales, increased farm labor, and reduced food expenditure. Severe droughts and conflicts led to migration, while floods caused extreme measures such as withdrawing children from school. Key factors influencing coping strategies include household demographics, farm characteristics, and market proximity. Households with more adults, higher literacy, or greater income from crops and livestock are less likely to adopt costly strategies. This study recommends integrated interventions to enhance resilience to both climate and non-climate shocks.

Determinants of health prevention behaviors in Portugal

Abstract Background One of the main causes of death in developed societies are chronic non-communicable diseases, often originated by risky behaviors as tobacco consumption, poor diet, excessive alcohol consumption and lack of physical activity. Changing lifestyles can thus lead to a reduction in chronic diseases and in their economic costs. The objective of this study is to understand what the main factors associated with health behaviors are. Methods We used the data collected through the Portuguese National Health Surveys from 2014 and 2019. The sample analyzed was composed of individuals aged 15 or more years old living in the national territory at the time the surveys were conducted. Data on demographics, socio-economic variables and health related variables was used. A maximum likelihood estimation was performed using a multivariate probit model, by using health behaviors (weekly exercise, healthy diet, non-smoking, and prudent alcohol consumption) as the outcomes of the analysis. Results The results point to a strong association between social class and health behaviors, particularly between higher income and healthy diet. It is important to highlight that having an appointment with a General Practitioner is positively associated with all healthy behaviors. For all behaviors but healthy diet, the coefficients are statistically significant at a 5% significance level and non-smoking displays the highest one (0.135). Conclusions This study presents the first step to understand the determinants of health prevention behaviors, which is important to design policies targeting lifestyles. One of its main conclusions indicates that social class (income and education) and GP appointments are associated with health behaviors. Further research would be needed to study the underlying mechanisms through which some of these factors impact behaviors. This presentation was supported by National Funds through FCT, I.P., within CINTESIS, R&D Unit (reference UIDB/4255/2020). Key messages • This study is an important step in the formulation of policies targeting lifestyles since it allows us to understand which factors are associated with health behaviors. • From this study one can conclude that social class characteristics, particularly income, and access to GP appointments are associated positively with health behaviors.

Complementarities in R&D and Innovation Decisions: An Enquiry on an Extensive Sample of Latin American Companies

This study uses an extensive sample of company data referring to seven Latin American countries to analyze the potential influence of simultaneity and heterogeneity in determining decisions to commit to R&D and innovation outputs. First, the analysis focuses on the decisions about spending on in-house and external research. Second, the outcomes of three different innovations: process, product, and organizational. Choices were considered simultaneously by employing a multivariate probit estimation model. In so doing, the enquiry considered the possible systematic interdependencies among the decisions. The results indicate that the two R&D decisions are interdependent. However, a different picture emerged in relation to innovation outputs, in which the correlations coefficients were small with unexpected signs. This may indicate a structural weakness of the technological production system in Latin America, wherein the synergies, complementarities, and complex relationships usually involved in the process of innovation are not fully developed.

Consumer acceptance of bacteriophage technology for microbial control

As global concerns and awareness of the threat posed by antimicrobial resistance (AMR) grow, the One Health initiative provides a framework to advance public understanding of and willingness to address AMR. The use of bacteriophages as a non-antibiotic means to control bacterial infections aligns with this framework, but it is unknown whether the use of new antibiotic alternatives, and bacteriophages in particular, will find acceptance among consumers. Within the context of poultry production and consumption, we sampled 1497 Pakistani consumers studying their knowledge of and familiarity with antibiotics, bacteriophages and AMR, whether they perceived AMR as a threat, and their willingness to accept alternative microbial control technologies. Additionally, we tested the impact information about bacteriophages had on the different concepts as well as respondents’ perception of different chicken quality attributes. Using both Probit and Multivariate Probit models we found that familiarity with antibiotics and bacteriophages is significantly associated with the acceptance of alternative microbial control technologies and perceptions of resulting products. Moreover, we find that providing information about the technology does not significantly alter respondent’s perception of most quality attributes.

Affect heuristics and peer effect on substance use among adolescents in Brazil

Significant changes are observed during adolescence with respect to health-related habits and risky behaviors. Among the behavioral changes, the consumption of substances such as alcoholic beverages, cigarettes, and illicit drugs, and social interactions are relevant factors for the intake of such substances. This study aims to analyze the relationship between the joint decisions of substance use by adolescents and their peers from the perspective of the affect heuristics. To this end, a multivariate probit model was estimated, based on microdata from the 2019 National School Health Survey. The results showed that substance use by adolescents was positively related to substance use by their peers; therefore, having friends who consume alcohol, illicit drugs, and cigarettes or having parents who consume alcohol or cigarettes increases the likelihood of an adolescent consuming these substances. The study also identified that the substance consumption decision was governed by an unobserved common factor compatible with the affect heuristic.

Farmland Degradation in the Czech Republic: Drivers and Barriers of Mitigation Strategies in Agricultural Soils

ABSTRACTIn line with Sustainable Development Goal (SDG) 15.3, which aims to restore degraded land and soil, and the priorities of the Common Agriculture Policy (CAP) to mitigate environmental issues caused by intensive agriculture, understanding the factors influencing farmers' adoption of soil conservation practices (SCPs) is crucial. However, there is still a limited understanding of these specific factors, particularly in the context of the Czech Republic. This study investigates the perceptions and determinants influencing the adoption of SCPs among farmers in the Czech Republic. We analyzed 358 randomly selected farm households using probit and multivariate probit models. Our results show that a significant number of farmers perceive soil degradation as an important problem, attributing it to factors such as low soil nutrient content, declining soil humus, water and wind erosion. The results of our analysis showed positive associations for perceived effectiveness and profitability, indicating that farmers tend to adopt practices such as minimum tillage, mulching, mixed cropping, cover cropping, and continuous soil cover when these methods are perceived to be effective and profitable. Conversely, negative associations are found for perceptions of soil degradation and education, suggesting potential barriers to adoption with higher levels of perceived soil degradation and education. The study also highlights the complex interplay of information sources on adoption, with both positive and negative trends. In light of these findings, we propose recommendations, including that awareness campaigns should be tailored to address perceptions of soil degradation, and that the use of peer networks and information dissemination from research institutions can bridge the gap between scientific recommendations and on‐farm practices. Policy makers and agricultural extension services should work together to develop targeted strategies that take into account regional differences in the factors influencing adoption and ultimately promote widespread adoption of SCPs.

Factors affecting farmers' choice to adopt risk management strategies: The application of multivariate and multinomial probit models

This study investigates the factors that impact farmers' adoption of risk management strategies (RMS) in Pakistan during times of uncertainty. The study examines farmers' RMS adoption choices using both multinomial probit (MNP) and multivariate probit (MVP). Data were collected from 382 sampled farmers from four districts in Khyber-Pakhtunkhwa (KP) province of Pakistan via a multistage sampling technique. This study utilizes the MNP model, considering the assumption of Independence of Irrelevant Alternatives (IIA) and incorporating correlated error terms. The objective is to understand farmers' behavior in risky situations and determine if there is heterogeneity. Results are compared with the MVP model to assess robustness and gain insights into farmers' decision-making processes.The research findings reveal that our results are robust, and farmers behave homogeneously in different settings of the provision of risk management strategies. Farmers adopt RMS individually or in combination to mitigate the adverse effects of natural calamities on their livelihood. The risk-averse farmers, who perceive weather-related risks as a threat, access credits and information, and have farms close to a river are more likely to adopt RMS, irrespective of the format of the strategies available. Moreover, the predicted probabilities and correlation of the RMS and RM categories further strengthened our model estimation. These findings provide insights into the behavior of farmers in adopting RMS which are helpful for policymakers and stakeholders in developing strategies to mitigate the impacts of natural calamities on farmers.

Small but innovative: Unveiling the impact of micro-entrepreneurs' personality traits on a spectrum of innovations

This article investigates whether a micro-entrepreneur's personality influences their innovativeness. Using the Big Five theory and a broad definition of innovation from the 4th version of the Oslo Manual (two types and nine categories of innovations in total), the study analyses data from 1,848 Polish micro-entrepreneurs. Since innovativeness is a complex process with several interdependencies, and previous research shows that introducing one type of innovation is not independent of introducing other types, Multivariate Probit (MVP) regression was used to estimate the models. The results allow two conclusions to be drawn. Firstly, in the case of product innovations implemented by micro-entrepreneurs, three personality traits have a positive impact. These are Openness to experience, Conscientiousness and Extroversion. Secondly, in the case of business process innovations, all 7 types are positively influenced by two personality traits, namely Openness to experience and Extroversion.

Relationships and determinants of adoption of sustainable land management technologies in Eastern and Southern Highlands of Ethiopia

Degraded land is a major danger to agricultural productivity and food security in Ethiopia. Sustainable land management technologies (SLMTs), through targeted interventions, have been applied to address this problem. Notwithstanding some of the known agricultural and environmental benefits connected with the adoption of some SLMTs, the adoption rate remains below expectations. Therefore, this study aims to analyze the relationships and determinants of the adoption of SLMTs in eastern and southern Ethiopia. Data were collected from 384 sampled households selected using multi-stage sampling techniques. Descriptive statistics and multivariate probit (MVP) model were used to analyze data. Estimated results of MVP revealed that the adoption of SLMTs is positively and significantly influenced by education level, livestock, membership in a social group, extension contacts, access to SLM information, training, credit, land tenure, and slope, while age, farm distance, and the number of active family members due to youth migration abroad had negative effects. The widely adopted SLMTs on farmlands were inorganic fertilizer (49.5%), soil bunds (48.4%), bench terraces (47.2%), intercropping (46.1%), and manure (38%). This study concluded that farmers invested a blend of SLMTs, some of which complemented and others substituted for one another. Therefore, policy-makers and planners should focus on fortifying human and local institutions’ capacity to implement SLM and consider SLM technology interdependencies before communicating with farmers.

Determinants of the factors affecting the governance structure choice of the pineapple value chain: A case study on market-based agroforestry in Bangladesh

This study examines the governance structures selected by pineapple farmers in the agroforestry system of Bangladesh and the determinants that impact their choices for these structures. To evaluate the factors influencing farmers' decisions to participate in spot markets, bilateral agreements, or association transactions, the study utilizes both quantitative and qualitative approaches, including surveys of 200 farmers, 15 focus group discussions (FGDs), and 2 key informant interviews (KIIs). In order to choose individual participants, the study employed a stratified random sampling technique. Utilising a multivariate probit (MVP) model, the study reveals crucial factors like age, education, farming experience, farm size, market certainty, weather influence, market connection, and pre-delivery conditions. Analysis indicates that spot market is the most favored governance structure (GS) among the three options in the agroforestry system, followed by bilateral transactions and association agreements. Evidence suggests that younger and less educated farmers tend to favor associations, whereas more experienced farmers tend to favor bilateral partnerships. Farms of bigger size typically refrain from joining groups, and unfavorable weather circumstances decrease the probability of engaging in spot markets. The study also demonstrates that robust market links exert an impact on the selection of governance frameworks. Considering the results, the study suggests enhancing training programs to facilitate farmers' understanding and involvement in associations, motivating farmer associations to appeal to larger farms and small farmers, and enhancing market information systems to establish robust market connections.

Low knowledge levels and high willingness to use oral Pre-Exposure Prophylaxis (PrEP) among Key Populations in Kampala, Uganda: Implications for targeted educational interventions.

Preexposure prophylaxis (PrEP) reduces new human immunodeficiency virus(HIV) infections by up to 96% and is recommended for key populations by the World Health Organization. Understanding the knowledge and willingness to use PrEP is essential for effective implementation. This study assessed these factors and identifiedcharacteristics associated with differences in knowledge among key populations in Kampala, Uganda. We administered a cross-sectional survey to a systematic sample of 497 participants from fisherfolk (283, 56.9%), men who have sex with men (MSM) (93, 18.7%), and female sex worker (FSW) (121, 24.4%) communities in Kampala Central, where PrEP had not yet been rolled out. Data on sociodemographic characteristics, PrEP awareness, and HIV-related behavioralfactors were collected. Knowledge about PrEP was measured using an adopted questionnaire comprising five key questions about PrEP knowledge, graded as no knowledge, some knowledge and good knowledge. Ordered probit regression models were used to analyze the associations of independent factors with PrEP knowledge levels. Participants had a mean age of 29±7.6 years. Ofthese, 257 (51.7%) reported having sex with women, 157 (31.6%) with men and 83 (16.7%) with both men and women. Self-reported HIV-positive status was 6.4% in fisherfolk, 11.8% in MSM and 27.3% in FSW. PrEP awareness stood at 62.4% overall, with the highest awareness in FSW (73.6%) and the lowest in fisherfolk (54.1%). Willingness to use PrEP was high across all groups (77.7%), although it was lower among FSW (66.9%). Multivariate probit analysis highlighted key independent factors associated with PrEP knowledge among fisherfolks and HIV-related concerns (Adj. Coeff = 0.54, 95% CI: 0.11, 0.97) and lack of PrEP awareness (Adj. Coeff = -0.99, 95% CI:-1.28, -0.70); among MSM, lack of PrEP awareness (Adj. Coeff = -1.74, 95% CI:-2.38, -1.10); and in FSW, tertiary education (Adj. Coeff = 1.53, 95% CI: 0.07, 2.99) and marital status (Adj. Coeff = 0.57, 95% CI=0.09, 1.05). MEshighlighted the influence of these independent factors at each knowledge level. Despite the high willingness to use PrEP, significant knowledge gaps exist, particularly concerning dosage and use duration, which are influenced by factors such as HIV concern and educational attainment. Tailored educational initiatives may bridge these gaps and enhance willingness to use PrEP.