2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Rift Valley fever knowledge, mitigation strategies and communication preferences among male and female livestock farmers in Eastern Province, Rwanda

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The Government of Rwanda reported an outbreak of Rift Valley fever (RVF) in the Eastern Province in 2018. To respond to the outbreak, vaccination and education campaigns about the disease were carried out. Because RVF cases continue to be detected in Rwanda and the disease impacts livelihoods and health, accurate knowledge and communication are imperative. The objectives of this study were to evaluate knowledge and risk perceptions of RVF transmission among livestock farmers in Nyagatare District, Eastern Province, Rwanda, and to compare RVF knowledge, risk perceptions, and farming practices between male and female livestock farmers. This cross-sectional, quantitative study was conducted in selected sectors of Nyagatare District in the Eastern Province of Rwanda in June 2019. A 34-question survey was used to ask about demographics, livestock ownership, risk perceptions about zoonotic diseases and livestock management, RVF knowledge, preferred communication sources and information sharing strategies, and protective strategies for RVF mitigation while working with livestock. Livestock farmers were interviewed at three milk collection centers, two village meeting points, a farm cooperative meeting, and during door-to-door visits in villages. In total, 123 livestock farmers were interviewed. The survey found that most livestock farmers lacked knowledge about epizootic and zoonotic transmission of RVF, more male livestock farmers were familiar with RVF and risk mitigation strategies, and female livestock farmers are not viewed as reliable sources of information. Additionally, most livestock farmers had not vaccinated their animals against RVF despite past vaccination campaigns. Radio was the most popular communication channel. These findings show that RVF knowledge and information sharing are inadequate among livestock farmers in Eastern Province. Therefore, vaccination and education campaigns may need to be reevaluated within the context of these trends in order to prepare for future RVF outbreaks.

          Author summary

          This study was conducted in order to evaluate RVF knowledge and awareness as well as communication and mitigation strategies among livestock farmers in Eastern Province, Rwanda. Rwanda declared an outbreak of RVF in 2018 and cases have continued to be detected. Thus, evaluating the status of knowledge, preventive strategies, and information sharing among livestock farmers is crucial in mitigating future outbreaks. Our team conducted a survey of knowledge, risk perceptions, mitigation strategies, and communication practices among livestock farmers from selected sectors within Nyagatare District and compared them between male and female livestock farmers in order to analyze gender-nuanced differences between these groups. Sectors were chosen for sampling based on their proximity to previous outbreak areas. Survey questionnaire results showed that knowledge and risk perceptions differed between male and female livestock farmers, and that they could be generally improved among all livestock farmers. Female livestock farmers and non-farming community members were viewed as unreliable sources of information which could impact information dissemination. Many farmers also reported that their livestock herds were not vaccinated for the disease despite previous vaccination campaigns. Communication strategies and information sources also differed between male and female livestock farmers, which highlights a need to consider gender in improving RVF vaccination and education campaign coverage. These findings pose implications for future community-based public health interventions as well as policy development for RVF control and mitigating future RVF outbreaks within Rwanda.

          Related collections

          Most cited references33

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Statistical notes for clinical researchers: Chi-squared test and Fisher's exact test

          When we try to compare proportions of a categorical outcome according to different independent groups, we can consider several statistical tests such as chi-squared test, Fisher's exact test, or z-test. The chi-squared test and Fisher's exact test can assess for independence between two variables when the comparing groups are independent and not correlated. The chi-squared test applies an approximation assuming the sample is large, while the Fisher's exact test runs an exact procedure especially for small-sized samples. Chi-squared test 1. Independency test The chi-squared test is used to compare the distribution of a categorical variable in a sample or a group with the distribution in another one. If the distribution of the categorical variable is not much different over different groups, we can conclude the distribution of the categorical variable is not related to the variable of groups. Or we can say the categorical variable and groups are independent. For example, if men have a specific condition more than women, there is bigger chance to find a person with the condition among men than among women. We don't think gender is independent from the condition. If there is equal chance of having the condition among men and women, we will find the chance of observing the condition is the same regardless of gender and can conclude their relationship as independent. Examples 1 and 2 in Table 1 show perfect independent relationship between condition (A and B) and gender (male and female), while example 3 represents a strong association between them. In example 3, women had a greater chance to have the condition A (p = 0.7) compared to men (p = 0.3). The chi-squared test performs an independency test under following null and alternative hypotheses, H0 and H1, respectively. H0: Independent (no association) H1: Not independent (association) The test statistic of chi-squared test: χ 2 = ∑ ( 0 - E ) 2 E ~ χ 2 with degrees of freedom (r - 1)(c - 1), Where O and E represent observed and expected frequency, and r and c is the number of rows and columns of the contingency table. The first step of the chi-squared test is calculation of expected frequencies (E). E is calculated under the assumption of independent relation or, in other words, no association. Under independent relationship, the cell frequencies are determined only by marginal proportions, i.e., proportion of A (60/200 = 0.3) and B (1400/200 = 0.7) in example 2. In example 2, the expected frequency of the male and A cell is calculated as 30 that is the proportion of 0.3 (proportion of A) in 100 Males. Similarly, the expected frequency of the male and A cell is 50 that is the proportion of 0.5 (proportion of A = 100/200 = 0.5) in 100 Males in example 3 (Table 1). Expected frequency (E) of Male & A = Number of A * Number of Male Total number = p A * p male * total number The second step is obtaining (O - E)2/E for each cell and summing up the values over each cell. The final summed value follows chi-squared distribution. For the ‘male and A’ cell in example 3, (O - E)2/E = (30 - 50)2/50 = 8. Chi-squared statistic calculated = ∑ ( 0 - E ) 2 E = 8 + 8 + 8 + 8 = 32 in example 3. For examples 1 and 2, the chi-squared statistics equal zero. A big difference between observed value and expected value or a large chi-squared statistic implies that the assumption of independency applied in calculation of expected value is irrelevant to the observed data that is being tested. The degrees of freedom is one as the data has two rows and two columns: (r - 1) * (c - 1) = (2 - 1) * (2 - 1) = 1. The final step is making conclusion referring to the chi-squared distribution. We reject the null hypothesis of independence if the calculated chi-squared statistic is larger than the critical value from the chi-squared distribution. In the chi-squared distribution, the critical values are 3.84, 5.99, 7.82, and 9.49, with corresponding degrees of freedom of 1, 2, 3, and 4, respectively, at an alpha level of 0.5. Larger chi-square statistics than these critical values of specific corresponding degrees of freedom lead to the rejection of null hypothesis of independence. In examples 1 and 2, the chi-squared statistic is zero which is smaller than the critical value of 3.84, concluding independent relationship between gender and condition. However, data in example 3 have a large chi-squared statistic of 32 which is larger than 3.84; it is large enough to reject the null hypothesis of independence, concluding a significant association between two variables. The chi-squared test needs an adequate large sample size because it is based on an approximation approach. The result is relevant only when no more than 20% of cells with expected frequencies < 5 and no cell have expected frequency < 1.1 2. Effect size As the significant test does not tell us the degree of effect, displaying effect size is helpful to show the magnitude of effect. There are three different measures of effect size for chi-squared test, Phi (φ), Cramer's V (V), and odds ratio (OR). Among them φ and OR can be used as the effect size only in 2 × 2 contingency tables, but not for bigger tables. φ = χ 2 n V = χ 2 n · d f , where n is total number of observation, and df is degrees of freedom calculated by (r - 1) * (c - 1). Here, r and c are the numbers of rows and columns of the contingency table. In example 3, we can calculate them as φ = χ 2 n = 32 200 = 0.4 , V = χ 2 n · d f = 32 200 · 1 = 0.4 , and O R = 70 · 70 30 · 30 = 5.44 . Referring to Table 2, the effect size V = 0.4 is interpreted medium to large. If number of rows and/or columns are larger than 2, only Cramer's V is available. 3. Post-hoc pairwise comparison of chi-squared test The chi-squared test assesses a global question whether relation between two variables is independent or associated. If there are three or more levels in either variable, a post-hoc pairwise comparison is required to compare the levels of each other. Let's say that there are three comparative groups like control, experiment 1, and experiment 2 and we try to compare the prevalence of a certain disease. If the chi-squared test concludes that there is significant association, we may want to know if there is any significant difference in three compared pairs, between control and experiment 1, between control and experiment 2, and between experiment 1 and experiment 2. We can reduce the table into multiple 2 × 2 contingency tables and perform the chi-squared test with applying the Bonferroni corrected alpha level (corrected α = 0.05/3 compared pairs = 0.017). Fisher's exact test Fisher's exact test is practically applied only in analysis of small samples but actually it is valid for all sample sizes. While the chi-squared test relies on an approximation, Fisher's exact test is one of exact tests. Especially when more than 20% of cells have expected frequencies < 5, we need to use Fisher's exact test because applying approximation method is inadequate. Fisher's exact test assesses the null hypothesis of independence applying hypergeometric distribution of the numbers in the cells of the table. Many packages provide the results of Fisher's exact test for 2 × 2 contingency tables but not for bigger contingency tables with more rows or columns. For example, the SPSS statistical package automatically provides an analytical result of Fisher's exact test as well as chi-squared test only for 2 × 2 contingency tables. For Fisher's exact test of bigger contingency tables, we can use web pages providing such analyses. For example, the web page ‘Social Science Statistics’ (http://www.socscistatistics.com/tests/chisquare2/Default2.aspx) permits performance of Fisher exact test for up to 5 × 5 contingency tables. The procedure of chi-squared test and Fisher's exact test using IBM SPSS Statistics for Windows Version 23.0 (IBM Corp., Armonk, NY, USA) is as follows:
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Risk perceptions and health behavior.

            Risk perceptions - or an individual's perceived susceptibility to a threat - are a key component of many health behavior change theories. Risk perceptions are often targeted in health behavior change interventions, and recent meta-analytic evidence suggests that interventions that successfully engage and change risk perceptions produce subsequent increases in health behaviors. Here, we review recent literature on risk perceptions and health behavior, including research on the formation of risk perceptions, types of risk perceptions (including deliberative, affective, and experiential), accuracy of risk perceptions, and associations and interactions among types of risk perceptions. Taken together, existing research suggests that disease risk perceptions are a critical determinant of health behavior, although the nature of the association among risk perceptions and health behavior may depend on the profile of different types of risk perceptions and the accuracy of such perceptions.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              What have we learned about communication inequalities during the H1N1 pandemic: a systematic review of the literature

              Background During public health emergencies, public officials are busy in developing communication strategies to protect the population from existing or potential threats. However, a population’s social and individual determinants (i.e. education, income, race/ethnicity) may lead to inequalities in individual or group-specific exposure to public health communication messages, and in the capacity to access, process, and act upon the information received by specific sub-groups- a concept defined as communication inequalities. The aims of this literature review are to: 1) characterize the scientific literature that examined issues related to communication to the public during the H1N1 pandemic, and 2) summarize the knowledge gained in our understanding of social determinants and their association with communication inequalities in the preparedness and response to an influenza pandemic. Methods Articles were searched in eight major communication, social sciences, and health and medical databases of scientific literature and reviewed by two independent reviewers by following the PRISMA guidelines. The selected articles were classified and analyzed in accordance with the Structural Influence Model of Public Health Emergency Preparedness Communications. Results A total of 118 empirical studies were included for final review. Among them, 78% were population-based studies and 22% were articles that employed information environment analyses techniques. Consistent results were reported on the association between social determinants of communication inequalities and emergency preparedness outcomes. Trust in public officials and source of information, worry and levels of knowledge about the disease, and routine media exposure as well as information-seeking behaviors, were related to greater likelihood of adoption of recommended infection prevention practices. When addressed in communication interventions, these factors can increase the effectiveness of the response to pandemics. Conclusions Consistently across studies, a number of potential predictors of behavioral compliance to preventive recommendations during a pandemic were identified. Our findings show the need to include such evidence found in the development of future communication campaigns to ensure the highest rates of compliance with recommended protection measures and reduce communication inequalities during future emergencies.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Negl Trop Dis
                PLoS Negl Trop Dis
                plos
                PLoS Neglected Tropical Diseases
                Public Library of Science (San Francisco, CA USA )
                1935-2727
                1935-2735
                23 August 2021
                August 2021
                : 15
                : 8
                : e0009705
                Affiliations
                [1 ] Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine at Tufts University, North Grafton, Massachusetts, United States of America
                [2 ] Center for One Health, University of Global Health Equity, Kigali, Rwanda
                [3 ] School of Veterinary Medicine, College of Agriculture, Animal Sciences and Veterinary Medicine, University of Rwanda, Nyagatare, Rwanda
                National Institutes of Health, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-6122-5332
                https://orcid.org/0000-0002-3299-4658
                https://orcid.org/0000-0003-4926-2661
                Article
                PNTD-D-21-00602
                10.1371/journal.pntd.0009705
                8412303
                34424895
                feba9fc8-6e33-4ae3-b156-e4d4181005f5
                © 2021 Smith et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 28 April 2021
                : 4 August 2021
                Page count
                Figures: 1, Tables: 6, Pages: 15
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100008176, Cummings Foundation;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100008176, Cummings Foundation;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100008176, Cummings Foundation;
                Award Recipient :
                Funded by: University of Global Health Equity, Rwanda
                Award Recipient :
                Funded by: University of Global Health Equity, Rwanda
                Award Recipient :
                Funded by: University of Global Health Equity, Rwanda
                Award Recipient :
                Funded by: university of rwanda-sweden program
                Award ID: 51160027
                Award Recipient :
                L. S., E. N., and J. H. A. were funded by Cummings Foundation ( https://www.cummingsfoundation.org/). J.H. A., J. S., and A. S. were funded by the University of Global Health Equity ( https://ughe.org/). A. S. was also funded by the University of Rwanda (UR)-Sweden program (SIDA Research Grant Number 51160027, ORG Prevalence and Risk Factors for the Rift Valley fever in Rwanda) ( https://ursweden.ur.ac.rw/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Agriculture
                Animal Management
                Livestock
                Medicine and health sciences
                Medical conditions
                Tropical diseases
                Neglected tropical diseases
                Rift Valley fever
                Medicine and health sciences
                Medical conditions
                Infectious diseases
                Viral diseases
                Rift Valley fever
                Medicine and health sciences
                Medical conditions
                Infectious diseases
                Zoonoses
                Rift Valley fever
                Biology and Life Sciences
                Immunology
                Vaccination and Immunization
                Medicine and Health Sciences
                Immunology
                Vaccination and Immunization
                Medicine and Health Sciences
                Public and Occupational Health
                Preventive Medicine
                Vaccination and Immunization
                People and Places
                Population Groupings
                Professions
                Agricultural Workers
                People and Places
                Geographical Locations
                Africa
                Rwanda
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Biology and Life Sciences
                Veterinary Science
                Veterinary Medicine
                Livestock Care
                Biology and Life Sciences
                Veterinary Science
                Veterinary Diseases
                Custom metadata
                vor-update-to-uncorrected-proof
                2021-09-02
                All relevant data are within the manuscript and its Supporting Information files.

                Infectious disease & Microbiology
                Infectious disease & Microbiology

                Comments

                Comment on this article