A Comparative Health Misinformation Need Assessment Analysis in Niger State, Nigeria

A Comparative Health Misinformation Need Assessment Analysis in Niger State, Nigeria | 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 A Comparative Health Misinformation Need Assessment Analysis in Niger State, Nigeria Anwuli Nwankwo, Kemisola Agbaoye, Sunday Oko, Abara Erim, Sonia Biose, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8398446/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background : Health misinformation poses critical threat to public health systems globally, with particularly devastating impacts in low- and middle-income countries where health literacy levels are limited, and communication infrastructure is inadequate. In Nigeria, Africa's most populous nation, the spread of false health information has undermined public health initiatives, eroded trust in health institutions, and contributed to preventable morbidity and mortality. Despite documented evidence of widespread misinformation, systematic understanding of the specific needs, gaps, and community perspectives regarding misinformation management remains limited. This study aimed to identify critical needs and gaps in managing health misinformation within Niger State, Nigeria, to inform the development of evidence-based, culturally sensitive, and sustainable interventions. Methodology: Mixed-methods triangulation design combined quantitative and qualitative approaches. A cross-sectional survey of 300 respondents from four local government areas was conducted using stratified random sampling. Data were collected in August 2023 (1 st to 31 st ) through structured questionnaires administered face-to-face and recorded digitally. Participants included community leaders, health workers, media practitioners, traders, and community members. Qualitative data consisted of eight focus group discussions, transcribed, and analyzed using Braun and Clarke’s thematic framework. Quantitative analysis used SPSS, while qualitative coding was conducted on Dedoose. Ethical approval was granted by the Niger State Research Ethics Committee. Results: Misinformation was highly prevalent, with community gatherings (63.0%) and social media (31.7%) as primary information sources. Although 53.6% reported confidence in identifying accurate information, qualitative data showed widespread difficulty distinguishing truth from falsehood. Only 23.3% knew of existing misinformation strategies, while 70.3% cited poor awareness education. Vaccine hesitancy was widespread, shaped by spiritual illness attributions and distrust of government health initiatives. Religious and traditional leaders held strong authority over communities, whereas health workers faced credibility challenges. Current interventions like, dialogues and disease surveillance rumour logs, had limited reach due to funding, poor attendance, and implementation gaps. Conclusion: Findings demonstrate that misinformation operates within socio-ecological systems defined by interpersonal networks, epistemic pluralism, structural vulnerabilities, and trust asymmetries. Effective responses must strengthen stakeholder partnerships, embed communication in traditional channels, enhance surveillance, build critical health literacy, address infrastructure deficits, and engage respectfully with existing belief systems to build trust. Health Misinformation Infodemic Management Health Literacy Trust in Healthcare Needs Assessment Stakeholder Involvement Community-Based Strategies Health Education Cultural Beliefs and Health Introduction The proliferation of health misinformation has emerged as one of the most pressing public health challenges of the digital age, with devastating consequences for healthcare systems worldwide (1) (2). In many low and middle-income countries, including Nigeria, the propagation of false health information is exacerbated by a complex interplay of factors, including varying levels of health literacy, diverse communication channels, and existing societal beliefs (3)(4)(5). The impact of such misinformation is particularly acute during health crises, where it can undermine crucial public health interventions (1). In Nigeria, Africa's most populous nation, the spread of false health information has undermined public health initiatives, eroded trust in health institutions, and contributed to preventable morbidity and mortality across diverse communities (1) (6)(7).Niger State, with its unique socio-cultural dynamics and diverse population, is highly susceptible to the circulation and impact of health misinformation (1). The challenges are compounded by systemic weaknesses in current management strategies, including limited real-time monitoring, inadequate fact-checking infrastructure, and deficiencies in public health communication (1) (7) (8).Misinformation spreads rapidly due to its emotional and sensational appeal, often drawing more attention than accurate facts (1). Globally, health misinformation poses a serious threat (9), disrupting public health efforts and leading to harmful outcomes (1). Health misinformation can be defined as an untrue or deceptive claim about health that lacks valid or scientific evidence (10). When health choices are based on such misinformation, it can cause emotional distress and create unrealistic expectations, leading to financial burdens, and encourage harmful actions (1). For instance, relying on unproven therapies can worsen illness and even increase the risk of death (1)(10). Health information is obtained from a variety of sources, such as: Healthcare providers, family, friends, literature, community gatherings, social media (1), newspapers, magazines, educational pamphlets, radio, television, and pharmaceutical commercials (11), which people pull together information on health and well-being (12). In regions with limited access to healthcare and low health literacy, health misinformation poses a major risk to the success of public health interventions (4) (13). Recent systematic research conducted in Niger State revealed substantial evidence of health misinformation circulation (1). The participants identified 35 rumours and instances of health misinformation in their communities during a comprehensive Health Misinformation Management Fellowship programme conducted between August 2023 and January 2024 (1). This study represents the most systematic effort to quantify health misinformation prevalence in the state. Health misinformation in Niger State, like elsewhere in Nigeria, spreads through various digital and traditional avenues, often boosted by community gatherings, social media, and influential people (1). The current gaps of health misinformation management are significantly impeded by a confluence of systemic weaknesses in health communication which leads to lack of accurate and timely communication, lack of education and understanding of health information (4), health disparity and lack of trust in the health system (1)(9)(14)(15). This widespread misinformation across multiple channels highlights the complicated nature of tackling the issue in Niger State. To effectively address this, interventions must be multi-faceted, involving diverse community members such as media professionals, civil society groups, traditional leaders, and religious figures, all working to foster trust and share accurate information (1)(7)(13)(16). This study aims to systematically identify critical gaps faced by Niger State in combating false health narratives. By conducting a comprehensive needs assessment, this study sought to ultimately provide evidence-based insights that can inform the development of more effective, culturally sensitive, and sustainable interventions to safeguard public health information management in Nigeria. Methodology A mixed-method approach for thorough description and deeper insights into the study was employed. Quantitative data alone cannot capture all the gaps, while qualitative data alone cannot establish the significant relationships between variables. Therefore, the integration of both methods provided a more comprehensive understanding of this complex phenomenon (17). A triangulation design was employed where quantitative surveys and qualitative interview were conducted to validate and corroborate findings about the gaps in managing health misinformation in Niger State, Nigeria. Study design and sampling strategy Quantitative survey: This survey was conducted in Niger State, Nigeria's largest state by land mass, predominantly rural, located in the North Central region of West Africa with approximately 6.7 million residents (18). It is bordered to the east by Kaduna State and the Federal Capital Territory, to the north by Kebbi State and Zamfara State, and to the south by Kogi and Kwara states, while its western border makes up part of the international border with Benin (19). Participants were selected from four (4) local government areas (LGAs) in Niger State which was informed by the Senatorial districts: Chanchaga, Lapai, Paikoro and Wushishi. Chanchaga LGA is an urban area that hosts Minna, the capital of Niger State that is driven by public service, commerce, small-scale manufacturing, education, and transportation. Lapai LGA is predominantly a rural area located in the southeastern part of Niger State. Infrastructure development is moderate, while outlying villages continue to face challenges in accessing roads, water, and healthcare. Paikoro LGA lies to the south of Chanchaga (20) and shares some peri-urban features due to its proximity to Minna. Although largely rural, certain parts of Paikoro are experiencing gradual urbanisation. The area is also known for its vibrant local markets, however, cultural practices such as pottery, weaving, and festivals are actively maintained within the local communities. Wushishi LGA is a rural area located in the north-central part of Niger State with low infrastructure presence. It represents a traditional rural economy with significant historical and cultural value but limited development (21). The quantitative method of this study adopted a cross-sectional design to capture data at a single point in time across diverse respondent categories. A stratified random sampling technique was employed to ensure representation across key stakeholder groups involved in community health and development. Data collection was conducted between 1st and 31st August 2023 , using a structured questionnaire administered through face-to-face interviews as shown in the supplementary file . Responses were recorded digitally using Kobo Toolbox , a mobile data collection platform, by a team of trained data collectors. A total of 300 participants were surveyed, including health care workers (HCWs), religious leaders, community/traditional leaders, Patent and Proprietary Medicine Vendors (PPMVs), media representatives, traditional medicine vendors (TMVs), and committee members . This diverse respondent pool was selected to provide a broad understanding of community perspectives and engagement with health-related misinformation. The sample size of 300 participants was calculated using Cochran's formula: n = (Z²pq)/e² where Z = 1.96 (95% confidence level), p = 0.50 (expected proportion), q = 0.50, and e = 0.05 (margin of error). The expected proportion of 0.50 was selected as the true prevalence of awareness regarding health misinformation management strategies was unknown prior to the study, and this value provides the most conservative sample size estimate. N = (1.96 2 ×0.50 ×0.50)/0.05 2 ×0.05 N = (3.8416 × 0.25) / 0.0025 N = 0.9604 / 0.0025 N = 384.16 N = 385 To account for the finite population size, the finite population correction was applied: n = n/[1+(n-1)/N], where N represents the estimated total number of key stakeholders (approximately 1,500) across the four study local government areas. n = 385 / [1 + (385-1)/1,500] n = 385 / [1 + 384/1,500] n = 385 / 1.256 n = 306 Rounded to n = 300 This adjustment resulted in a required sample size of 306 participants. However, the final sample size was set at 300, which satisfies the statistical requirements considering feasibility for data collection within the one-month study period (August 2023) and available resources. Qualitative survey: The qualitative component of the study utilised a purposive sampling technique to ensure the inclusion of participants with various stakeholders, including primary health care workers, health educators, media practitioners, traditional and religious leaders. A total of 8 focus group discussions (FGDs) were conducted across selected locations, allowing for rich, interactive dialogue and exploration of diverse perspectives. Data collection was facilitated using audio recording devices , ensuring accurate capture of participant responses. All recordings were subsequently transcribed verbatim using Microsoft Word’s voice transcription tool with human verification providing a reliable textual dataset for analysis. Data analysis Quantitative data, such as demographic information or categorical responses, was analyzed using SPSS, with results presented as frequencies and explained descriptively. Interview recordings from the qualitative data was transcribed into text documents using Dedoose software. These transcripts were then subjected to a thematic analysis, where initial codes were developed and subsequently grouped into broader themes based on their similarities. Data quality and assurance To protect the privacy and security of data, participant names were coded. When conducting interviews, data collectors used password-protected encrypted devices that were kept safe. To reduce the danger of data breaches, recordings were promptly moved to a secured cloud storage system, encrypted to protect data during transfer, and promptly erased from the encrypted devices. Thematic analysis The thematic analysis followed Braun and Clarke’s (22) six-phase framework: Familiarisation with the data - The transcriptions were read and re-read to immerse in the data. Initial notes and impressions were recorded to capture emerging ideas. Generating initial codes - The data was systematically coded using Dodoose software. Segments of text that appeared relevant to the research questions were identified and labelled. Coding was done inductively to allow themes to emerge naturally from the data. Searching for themes - The codes were examined to identify patterns and relationships. Similar codes were grouped together to form initial themes. The focus was on capturing broad patterns that addressed the research questions. Reviewing themes - The initial themes were reviewed and refined. This involved checking if the themes worked in relation to the coded extracts and the entire data set. Themes were modified, combined, or discarded based on their relevance and coherence. Defining and naming themes - Once the themes were finalised, each theme was defined and named. Clear definitions helped ensure that each theme captured a distinct aspect of the data. Sub-themes were also identified to provide a more detailed understanding of each major theme. Producing the report - The final step involved writing up the analysis. This included selecting vivid and compelling quotes from the transcriptions to illustrate each theme. The report was structured to provide a comprehensive understanding of the themes and their significance. Validation To ensure the reliability and validity of the analysis, several strategies were employed: Triangulation - Data from different focus groups and participant types were collected to identify common themes and discrepancies. Peer review: The initial codes and themes were reviewed by colleague's familiar with qualitative research to provide feedback and ensure the accuracy of the interpretation. Ethical considerations and approval: Objectives and voluntariness of the study were clearly explained to participants, and informed consent was obtained before data collection. Participants provided informed consent and were assured of their right to withdraw at any time. Responses were anonymised to ensure confidentiality and anonymity were strictly taken into consideration, and ethical approval was obtained from the Niger State Research Ethics Committee in Nigeria. Results Quantitative Survey: The sociodemographic characteristics as listed in Table 1 below showed more than half of the participants were female (54%), and the highest number of age groups were between the ages of 26 and 30 years old (24.7%). Most of them are businessmen and women (30.7%) who fall within the category of community members (21.7%), and many participants came from Chachanga (27.0%) and Wushishi (25.5%) local government areas (LGA) in Niger State, Nigeria. Table 1: Sociodemographic characteristics of participants Variables Frequency (n=300) Gender Male 138 (46%) Female 162 (54%) Age group in years 15 – 20 19 (6.3%) 21 – 25 15 (5.0%) 26 – 30 74 (24.7%) 31 – 35 60 (20.0%) 36 – 40 41 (13.7%) 41 – 45 49 (16.3%) 51 and above 42 (14.0%) Occupation Business entrepreneur 92 (30.7%) Civil servant 64 (21.3%) Healthcare workers 25 (8.3%) Religious leaders 22 (7.3%) Retired civil servants 6 (2.0%) Farmers 31 (10.3%) Journalist 1 (0.3%) Home maker 25 (8.3%) Student 29 (9.7%) Furniture maker 2 (0.7%) Community member 3 (1.0%) Categories of participants Primary Healthcare Workers 8 (2.7%) Health educator 3 (1.0%) Religious leaders 29 (9.7%) Community/traditional leaders 56 (18.7%) PPMV 23 (7.7%) Media 6 (2.0%) TMV 21 (7.0%) WHDC 8 (2.7%) Community members 65 (21.7%) Healthcare practitioners 43 (14.3%) Immunization officer 2 (0.7%) Security officer 1 (0.3%) Farmers 1 (0.3%) Others 34 (11.3%) Location Lapai 75 (25.0%) Paikoro 68 (22.7%) Chachanga 81 (27.0%) Wushishi 76 (25.3%) Approximately 63% obtained information from community gatherings, followed by social media (31.7%). Some respondents (38.3%) confidently identify accurate health information from false information, from community gatherings (66.3%) and social media (29.7%), as stated in Table 2. Table 2: Health misinformation awareness What platform do you frequently encounter health-related information? Social media 95 (31.7%) Websites 16 (5.3%) Community gatherings 189 (63.0%) How confident are you in identifying accurate health information from misleading or false information? Very confident 46 (15.3%) Confident 115 (38.3%) Neutral 69 (23.0%) Not confident 43 (14.3%) Not at all confident 27 (9.0%) What is the most common platform where health misinformation is often shared in your state/community? Social media 89 (29.7%) Website 11 (3.7%) Community gatherings 199 (66.3%) Others 1 (0.3%) Only about 23.3% are aware of strategies to counter health misinformation, and 34.7% of the participants believe the effectiveness of the existing strategies would help counter health misinformation. The most recommended strategy for combating health misinformation was conducting health education in community gatherings as shown in Table 3. Table 3: Existing health misinformation management strategies Are you aware of any existing strategies in the state/community to manage health misinformation? Yes 70 (23.3%) No 230 (76.7%) What are the existing strategies? Emir to educate his subjects and the community 5 (1.7%) Health education for religious leaders 1 (0.3%) Health education in community gatherings and house-to-house sensitization 48 (16.0%) Health education on radio programs 6 (2.0%) Health education in social gatherings 2 (0.7%) Town announcers 8 (2.7%) More than half of the participants (70.3%) believe there is a lack of awareness of education about health misinformation in the state/community and consider inadequate skills in identifying reliable health information sources as the most identified challenge/gap in the management of health misinformation as listed in Table 4. Table 4: Assessing education awareness and capacity building needs for community health misinformation management Is there a lack of awareness of education about health misinformation in the state/community? Yes 211 (70.3%) No 89 (29.7%) What specific areas do you think community health misinformation management capacity building should focus on? Enhancing skills in identifying reliable health information sources Yes No 149 (49.7%) 151 (50.3%) Improving abilities to critically evaluate health information online Yes No 102 (34.0%) 198 (66.0%) Strengthening data collection and analysis techniques Yes No 108 (36.0%) 192 (64.0%) Developing effective response strategies to address health misinformation Yes No 184 (61.3%) 116 (38.7%) Fostering collaboration with stakeholders for impactful interventions Yes No 219 (73.0%) 81 (27.0%) Qualitative Survey Nature of Health Misinformation: Participants identified widespread health misinformation influencing community health-seeking behaviour and clinical outcomes. Vaccine hesitancy emerged as particularly prevalent, with primary healthcare (PHC) workers reporting: "When we ask caregivers why they refuse vaccination, their fears about children being sick after the vaccination or fever are one of the major reasons." A health educator observed: "Most of the villagers have no scientific knowledge… when a child is convulsing, they say it is spiritual, 'iska,' something which is not right." Traditional healers corroborated this finding, noting persistent spiritual interpretations despite medical explanation: "Even if you tell them the bones are broken or the brain is affected, they don't listen… they want someone to 'remove the spirits.'" Another participant from the Niger PHC agency noted: “I was in a community and found a woman moving out with a child wearing a supervision of R.I.S.S. I asked if there was no health worker in that community, and they responded in the affirmative. However, they said this problem is not one that will be cured by modern medicine , so it's a spiritual thing. The condition of the child was convulsion and they were heading to a traditional herbalist." Sources and Spread of Misinformation: The spread of misinformation occurred primarily through interconnected channels – traditional social networks, digital platforms and religious institutions. Traditional Social Networks: Community leaders described rapid information diffusion through everyday social interactions: "Most of these misleading beliefs are promoted in the villages… once it starts, it spreads everywhere." Markets, ceremonies, and communal gathering spaces served as primary dissemination nodes, with one participant noting: "In the market, people sit down and talk… these rumours spread like wildfire." Digital Communication Channels: Health workers identified social media as an accelerant of misinformation spread: "WhatsApp voice notes and Facebook posts—people take them as facts immediately." Religious Messaging : Religious leaders emerged as particularly influential in shaping health beliefs. A religious leader confirmed: "When the sermons in the mosque mention something about immunization, the people follow it immediately." Sociocultural Determinants of Misinformation: Strong traditional belief system underpins many misconceptions across communities. Illnesses that present with sudden or dramatic symptoms such as seizures or mental distress, are commonly interpreted through a spiritual lens. A community leader explained: "If someone has convulsion or mental problem, the first thing they think is attack or witchcraft." A herbal vendor confirmed: "Native doctors are their first line of call before they go to hospital." Participants reported deep-seated scepticism toward government health initiatives. One community leader articulated this sentiment: "People believe the government only comes when there is immunization… they don't trust their intentions." PHC staff noted that participation often requires material incentives: "If there is no incentive like noodles, the turnout will be very low." Health-Seeking Behaviour Patterns : Health-seeking behaviour in many communities follows a pattern in which traditional, herbal, or spiritual remedies are tried before biomedical care is considered. PHC staff provided illustrative examples: "My neighbour's child had convulsion… they first went to the herbalist before thinking of hospital." Social networks actively reinforce alternative treatment pathways. Community leaders reported: "The family will say don't go to hospital; they will inject the child and worsen it." Confidence in herbal medicine remained high, with an herbal vendor stating: "They believe herbs work better… hospital is their last option." Trust Dynamics and Authority Structures : Trust emerges as a key determinant of how communities interpret and act upon health information. Religious leaders commanded unparalleled community authority. One religious leader stated: "Whatever the imam says is final in the community." A Community Leader affirmed this by saying, “if the community head says we shouldn’t allow immunization, nobody will accept it.” In contrast, health workers faced credibility challenges. PHC staff acknowledged: "The native doctors command more trust than our health workers ." Structural Vulnerabilities: Limited health literacy emerged as a fundamental vulnerability. Health educators highlighted: "They cannot differentiate truth from falsehood… anything they hear, they believe." Inadequate telecommunications infrastructure intensified information gaps. Surveillance officers reported: "Some communities have no network, no radio signal… they depend entirely on hearsay." Community leaders noted reliance on specific information brokers: "The VDC [Village Development Committee] members give them information because most people don't have phones." Current Intervention Strategies: Community dialogues and sensitization activities remained the primary intervention approach, though effectiveness was limited. Health educators admitted that: "Community dialogues help, but the attendance is always poor." Disease surveillance mechanisms demonstrated greater effectiveness. The State Disease Surveillance and Notification Officer (DSNO) reported: "We keep rumour logs and investigate them immediately." Surveillance officers confirmed: "Rumour management is very effective for us." Collaborative responses have proven effective during emergencies. PHC staff recalled: "During COVID, when government, religious leaders, and media worked together, confusion reduced." Despite these strategies, implementation suffered from inadequate funding. The State DSNO summarised: "Most of these activities are not deliberate; we lack resources to follow through." Gaps and Identified Opportunities for Intervention: Participants identified several opportunities for strengthening responses to health misinformation. Surveillance officers emphasised proactive monitoring: "State actors need training on how to track misinformation before it spreads." Strengthening health education emerged as another key opportunity. Health educators advocated for expanded presence: "We need more health educators in communities… people listen to them." Additional informant network was also recommended. Surveillance officers recommended strengthening community surveillance: "Informants are the first to hear rumours, they need training and support." However, participants identified gaps in policy and regulation, particularly concerning unregulated herbal advertising. A private practitioner noted: "Herbal vendors can advertise anything, but medical professionals cannot—there is no balance." Discussion This study employed methodological triangulation, combining quantitative survey (n = 300) with qualitative interview data to achieve convergence, complementarity, and expansion in understanding health misinformation dynamics. This mixed methods study revealed substantial convergence across multiple domains, strengthening the validity and credibility of findings. Community gatherings emerged as the dominant information platform in both datasets, 63.0% in the quantitative survey and repeatedly emphasized in qualitative interviews, as sites where information "spreads like wildfire.". This aligns with other literature documenting the primacy of interpersonal communication networks in rural areas in Africa (23) (24)(25)(26). Both data sources identified severely limited awareness of misinformation management strategies, 23.3% quantitative awareness rates and qualitative reports of poor dialogue attendance, corroborating research on weak health system responses to infodemics in low-resource settings (27). The quantitative finding that 70.3% perceived inadequate health misinformation education was substantiated by qualitative accounts of communities' inability to distinguish reliable information : "They cannot differentiate truth from falsehood… anything they hear, they believe." This convergence validates both the prevalence and nature of health literacy deficits while aligning with theoretical frameworks emphasizing critical information evaluation skills as essential health literacy competencies (28)(29). Trust hierarchies showed consistent patterns across methods. Quantitatively, 73.0% prioritized stakeholder collaboration, while qualitative data specified precisely which stakeholders commanded authority; religious leaders ( "Whatever the imam says is final" ) and traditional leaders ( "If the community head says we shouldn't allow immunization, nobody will accept it" ), contrasted with healthcare workers' credibility deficits ( "Native doctors command more trust than our health workers" ). This convergent validation of trust asymmetries has critical implications for intervention design, suggesting that effective communication strategies must work through rather than around existing authority structures (30). The quantitative finding that only 38.3% felt confident identifying accurate health information was enriched by qualitative accounts revealing the structural basis of this vulnerability, limited formal education, absent telecommunications infrastructure ( "Some communities have no network, no radio signal" ), and dependence on information intermediaries ( "VDC members give them information because most people don't have phones" ) (13)(31)(32). The low perceived effectiveness of existing strategies (34.7% quantitatively) gained explanatory power through qualitative accounts of implementation challenges, poor attendance, inadequate funding ( "We lack resources to follow through" ), and absence of trust in health messengers (33). This complementarity reveals that effectiveness deficits stem not from conceptual problems with interventions but from implementation failures and misalignment with community preferences and trust structures (33)(34). While the quantitative instrument assessed information sources and confidence levels, qualitative interviews uncovered the profound influence of supernatural illness attribution models that fundamentally reshape treatment-seeking pathways regardless of information access (35). The documented case of a mother bypassing available healthcare for a traditional herbalist to address her convulsing child's "spiritual" condition revealed how cultural beliefs can override both access to information and availability of biomedical care (36). The persistent rejection of biomedical explanations "Even if you tell them the bones are broken or the brain is affected, they don't listen" suggests misinformation operates not merely as knowledge deficits fixable through information provision, but as coherent alternative knowledge systems rooted in cultural epistemologies that define illness and causation (37)(38). This insight fundamentally challenges deficit-oriented models of health communication that assume information provision alone can change behaviour and aligns with cultural humility that emphasizes respectful engagement with alternative explanatory systems rather than dismissal (39)(40). The qualitative data also expanded the understanding of social influence mechanisms. While quantitative data showed community gatherings as primary information sources, qualitative accounts revealed how social networks actively discourage biomedical care ( "The family will say don't go to hospital; they will inject the child and worsen it" ), demonstrating that misinformation's power lies not only in false beliefs but in socially reinforced behavioral norms. This insight suggests interventions must address social network dynamics and normative influences, not merely individual knowledge (41)(42). 31.7% reported social media as a frequent information source and 29.7% identified it as a misinformation platform—substantial but minority proportions. However, qualitative data emphasized social media's role disproportionately, with health workers highlighting immediate uncritical acceptance of digital content: "WhatsApp voice notes and Facebook posts—people take them as facts immediately." This discrepancy indicates that social media’s influence is not primarily a function of how many people use it, but how rapidly and authoritatively content circulates within trusted networks (30) (33). Fast, emotionally charged messages appear credible, allowing misinformation to bypass experts and become part of community conversations (43) A small proportion of digitally connected individuals may function as information bridges, introducing content that then circulates through offline networks (44). The qualitative finding that "Village Development Committee members give them information because most people don't have phones" supports this bridging mechanism. Thus, social media's influence operates indirectly through trusted intermediaries who translate digital content into oral narratives, amplifying its impact beyond direct user penetration (33) (43). Limitations of the Study 1. Geographic Scope: The study was limited to four local government areas (Lapai, Paikoro, Chachanga, and Wushishi) in Niger State, Nigeria. Findings may not be generalizable to other regions of Nigeria or other contexts with different sociocultural, linguistic, or infrastructural characteristics. The specific nature of health misinformation and its management may vary significantly across different geographic and cultural settings. 2. Temporal Limitations: Data collection occurred at a specific point in time and may not capture seasonal variations in health-seeking behaviours, misinformation patterns, or the effectiveness of interventions. The dynamic nature of misinformation, particularly on digital platforms, means findings may quickly become outdated. The study does not track changes over time or assess longitudinal impacts of misinformation. 3. Language and Translation Issues: The document does not specify whether surveys and interviews were conducted in local languages and subsequently translated. Translation processes may have introduced interpretation biases or lost nuanced meanings, particularly regarding cultural concepts like "iska" (spirits) and traditional healing practices that may not have direct English equivalents. 4. Lack of Validation of Intervention Effectiveness: While the study identified existing strategies and recommendations from participants, it did not include empirical evaluation of intervention effectiveness. Claims about what works (e.g., surveillance systems, community dialogues) relied on participant perceptions rather than measured outcomes or comparative analysis of different approaches. 5. Limited Economic Analysis: The study did not examine economic factors influencing health-seeking behaviors and misinformation vulnerability, such as costs of biomedical care versus traditional healing, poverty levels, or economic incentives in the misinformation ecosystem (e.g., herbalists' financial interests in promoting alternative treatments). Despite these limitations, the study provides valuable insights through its mixed-methods triangulation approach and offers important groundwork for understanding health misinformation in this underexplored context. Future research should address these limitations through longitudinal designs, broader geographic scope, and deeper exploration of digital misinformation dynamics. Conclusion The combination of survey data and interview findings revealed that health misinformation in Niger State, Nigeria, is not simply a problem of false information circulating in communities. Instead, it is deeply rooted in how these communities are structured and function. Three main issues stand out from this study. First, community gatherings and face-to-face conversations remain the primary ways people share and receive health information, with social media serving to amplify rather than originate misinformation. Secondly, there is a significant disconnect between how confident people feel about identifying false health information (over half reported being confident) and their actual ability to do so, as revealed through interviews showing that many community members "cannot differentiate truth from falsehood." Thirdly and most importantly, the persistence of health misinformation is not because communities lack access to correct information, but because they trust traditional and religious leaders more than government health workers and biomedical explanations. This study shows that communities actively choose between different sources of health knowledge - biomedical, traditional, and spiritual - based on who they trust, what aligns with their cultural beliefs, and what has worked for them in the past. Current efforts to combat misinformation, such as community dialogues and health education sessions, exist but are poorly funded, inconsistently implemented, and often poorly attended. The few strategies that show promise, like tracking rumors through surveillance systems and coordinating responses during emergencies, demonstrate that change is possible when interventions are properly resourced and involve trusted community voices. Ultimately, addressing health misinformation in these communities requires more than correcting false information. It demands building genuine trust between health systems and communities, working with rather than against existing belief systems and traditional leaders, improving basic infrastructure like telecommunications and health facilities, and ensuring sustained engagement rather than sporadic campaigns. Until the underlying issues of trust, access, literacy, and resources are addressed, misinformation will continue to thrive regardless of how much accurate information is provided. Recommendations Based on this comprehensive need assessment, the following recommendations are crucial for enhancing health misinformation management in Niger State, Nigeria: 1. Multi-Stakeholder Collaborative Frameworks: Establish formal partnerships between health authorities, religious leaders, traditional leaders, and community influencers. Given that "whatever the imam says is final in the community," religious institutions should be engaged as partners rather than obstacles. These collaborations should be institutionalised with clear roles, resources, and accountability mechanisms rather than ad hoc arrangements during health emergencies. 2. Community- Centered Communication Strategies: Since community gatherings are where 63% encounter health information interventions should prioritise these traditional channels. Develop culturally appropriate health messaging that acknowledges spiritual beliefs while providing biomedical information. Train community health workers and volunteers to serve as trusted information intermediaries who can bridge epistemic frameworks. 3. Strengthened Surveillance and Rapid Response Systems: Build on the effectiveness of rumour tracking reported by disease surveillance officers. Establish community-based misinformation monitoring networks with trained informants who can identify and report emerging misinformation early. Allocate dedicated resources for rapid response teams that can address misinformation before widespread dissemination occurs. 4. Comprehensive Health Literacy Programming: Given that 70.3% identified lack of awareness education, implement sustained health literacy programmes focusing on critical evaluation skills rather than mere information transmission. These programmes should help community members understand how to assess information credibility across different sources and recognise common misinformation patterns. 5. Infrastructure Development for Information Access: Address the fundamental infrastructure gaps where communities have "no network, no radio signal." Invest in expanding telecommunications coverage, establishing community radio stations, and creating offline information access points. Ensure health information is available in formats accessible to populations with limited literacy and connectivity. 6. Trust-Building Initiatives: Develop long-term programmes to rebuild trust between communities and health systems. This should include consistent presence of health workers in communities beyond immunisation campaigns, transparency in health system operations, and demonstrated responsiveness to community health concerns. Move beyond transactional relationships requiring material incentives to authentic engagement. 7. Regulatory Framework for Health Information: Establish regulations governing health claims in both traditional and digital media. As noted regarding herbal advertising asymmetry, create balanced frameworks that protect against dangerous misinformation while respecting medical pluralism. Develop enforcement mechanisms for false health claims regardless of source. 8. Capacity Building for Health Workers and Educators: Invest in training programmes for health workers on effective community engagement, cultural competency, and communication strategies that respect local belief systems. Address the finding that health workers face credibility challenges compared to traditional healers by enhancing interpersonal skills and community relationship-building. 9. Integration Rather Than Displacement of Traditional Medicine: Recognise that traditional medicine is deeply embedded in health-seeking behaviours. Rather than positioning biomedical care in opposition, explore integration models where traditional and biomedical practitioners collaborate, with appropriate safeguards. This approach may increase community acceptance of biomedical information and reduce delays in seeking appropriate care. 10. Sustainable Funding Mechanisms: Address the fundamental constraint identified by the State DSNO: "we lack resources to follow through." Advocate for dedicated budget lines for misinformation management within health system financing. Explore innovative funding mechanisms including public-private partnerships, international development support, and domestic resource mobilisation. Abbreviation DSNO – Disease Surveillance and Notification Officer LGAs – Local Government Areas HCW – Health Care Workers PPMV – Patent and Proprietary Medicine Vendors TMV – Traditional Medicine Vendors FGD – Focus Group Discussion SPSS – Statistical Package for Social Sciences PHC – Primary Health Care VDC – Village Development Committee WHDC - Ward Health Development Committee Declarations Ethics approval and consent to participate The research was conducted in accordance with the Declaration of Helsinki, approved by the National Health Research Ethics Committee of Nigeria (NHREC) with the ethics code number NHREC/01/01/2007-12/10/2023 and informed consent was obtained from all subjects including parents or legal guardians of participants under the age of 16. Consent for publication Not applicable Availability of data and materials Data is provided within the manuscript and the supplementary information file Competing interests The authors declare no competing interests Funding This research was conducted as part of the project funded by Gates Foundation Authors' contributions Anwuli Nwankwo conducted data analysis, wrote and reviewed the manuscript. Sunday Oko, Abara Erim, and Sonia Biose conducted data analysis, and the needs assessment study. Kemisola Agbaoye, Solomon Oladimeji, and Muhammed Gaya Yahaya participated in data collection. Vivianne Ihekweazu and Kemisola Agbaoye provided strategic leadership and oversight to the conceptualisation and implementation of the fellowship and approved the final manuscript. Acknowledgements The authors are grateful to the Niger State Primary Health Care Board and the Ministry of Secondary and Tertiary Health Care for their support in the study. 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J Commun Healthc [Internet]. 2022 Jul 3 [cited 2025 Dec 2];15(3):155–7. Available from: https://www.tandfonline.com/doi/pdf/10.1080/17538068.2022.2123676 Opara UC, Iheanacho PN, Li H, Petrucka P. Facilitating and limiting factors of cultural norms influencing use of maternal health services in primary health care facilities in Kogi State, Nigeria; a focused ethnographic research on Igala women. BMC Pregnancy Childbirth [Internet]. 2024 Dec 1 [cited 2025 Dec 2];24(1). Available from: https://pubmed.ncbi.nlm.nih.gov/39192210/ Opara UC, Iheanacho PN, Petrucka P. Cultural and religious structures influencing the use of maternal health services in Nigeria: a focused ethnographic research. Reproductive Health 2024 21:1 [Internet]. 2024 Dec 18 [cited 2025 Dec 2];21(1):188-. Available from: https://link.springer.com/article/10.1186/s12978-024-01933-8 View of Social Media and Fake News in Nigeria: A Speech Act Analysis of WhatsApp Messages on Coronavirus [Internet]. [cited 2025 Dec 2]. 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In many low and middle-income countries, including Nigeria, the propagation of false health information is exacerbated by a complex interplay of factors, including varying levels of health literacy, diverse communication channels, and existing societal beliefs (3)(4)(5). The impact of such misinformation is particularly acute during health crises, where it can undermine crucial public health interventions (1). In Nigeria, Africa's most populous nation, the spread of false health information has undermined public health initiatives, eroded trust in health institutions, and contributed to preventable morbidity and mortality across diverse communities (1) (6)(7).Niger State, with its unique socio-cultural dynamics and diverse population, is highly susceptible to the circulation and impact of health misinformation (1). The challenges are compounded by systemic weaknesses in current management strategies, including limited real-time monitoring, inadequate fact-checking infrastructure, and deficiencies in public health communication (1) (7) (8).Misinformation spreads rapidly due to its emotional and sensational appeal, often drawing more attention than accurate facts (1). Globally, health misinformation poses a serious threat (9), disrupting public health efforts and leading to harmful outcomes (1). Health misinformation can be defined as an untrue or deceptive claim about health that lacks valid or scientific evidence (10). When health choices are based on such misinformation, it can cause emotional distress and create unrealistic expectations, leading to financial burdens, and encourage harmful actions (1). For instance, relying on unproven therapies can worsen illness and even increase the risk of death (1)(10). Health information is obtained from a variety of sources, such as: Healthcare providers, family, friends, literature, community gatherings, social media (1), newspapers, magazines, educational pamphlets, radio, television, and pharmaceutical commercials (11), which people pull together information on health and well-being (12). In regions with limited access to healthcare and low health literacy, health misinformation poses a major risk to the success of public health interventions (4) (13). Recent systematic research conducted in Niger State revealed substantial evidence of health misinformation circulation (1). The participants identified 35 rumours and instances of health misinformation in their communities during a comprehensive Health Misinformation Management Fellowship programme conducted between August 2023 and January 2024 (1). This study represents the most systematic effort to quantify health misinformation prevalence in the state. Health misinformation in Niger State, like elsewhere in Nigeria, spreads through various digital and traditional avenues, often boosted by community gatherings, social media, and influential people (1). The current gaps of health misinformation management are significantly impeded by a confluence of systemic weaknesses in health communication which leads to lack of accurate and timely communication, lack of education and understanding of health information (4), health disparity and lack of trust in the health system (1)(9)(14)(15). This widespread misinformation across multiple channels highlights the complicated nature of tackling the issue in Niger State. To effectively address this, interventions must be multi-faceted, involving diverse community members such as media professionals, civil society groups, traditional leaders, and religious figures, all working to foster trust and share accurate information (1)(7)(13)(16). This study aims to systematically identify critical gaps faced by Niger State in combating false health narratives. By conducting a comprehensive needs assessment, this study sought to ultimately provide evidence-based insights that can inform the development of more effective, culturally sensitive, and sustainable interventions to safeguard public health information management in Nigeria.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eA mixed-method approach for thorough description and deeper insights into the study was employed. Quantitative data alone cannot capture all the gaps, while qualitative data alone cannot establish the significant relationships between variables. Therefore, the integration of both methods provided a more comprehensive understanding of this complex phenomenon\u0026nbsp;(17).\u0026nbsp;A triangulation design was employed where quantitative surveys and qualitative interview were conducted to validate and corroborate findings about the gaps in managing health misinformation in Niger State, Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy design and sampling strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative survey:\u003c/strong\u003e This survey was conducted in Niger State, Nigeria's largest state by land mass, predominantly rural, located in the North Central region of West Africa with approximately 6.7 million residents (18). It is bordered to the east by Kaduna State and the Federal Capital Territory, to the north by Kebbi State and Zamfara State, and to the south by Kogi and Kwara states, while its western border makes up part of the international border with Benin (19). Participants were selected from four (4) local government areas (LGAs) in Niger State which was informed by the Senatorial districts: Chanchaga, Lapai, Paikoro and Wushishi. Chanchaga LGA is an urban area that hosts Minna, the capital of Niger State that is driven by public service, commerce, small-scale manufacturing, education, and transportation. Lapai LGA is predominantly a rural area located in the southeastern part of Niger State. Infrastructure development is moderate, while outlying villages continue to face challenges in accessing roads, water, and healthcare. Paikoro LGA lies to the south of Chanchaga (20) and shares some peri-urban features due to its proximity to Minna. Although largely rural, certain parts of Paikoro are experiencing gradual urbanisation. The area is also known for its vibrant local markets, however, cultural practices such as pottery, weaving, and festivals are actively maintained within the local communities. \u003cstrong\u003eWushishi LGA\u003c/strong\u003e is a rural area located in the north-central part of Niger State with low infrastructure presence. It represents a traditional rural economy with significant historical and cultural value but limited development (21). The quantitative method of this study adopted a \u003cstrong\u003ecross-sectional design\u003c/strong\u003e to capture data at a single point in time across diverse respondent categories. A \u003cstrong\u003estratified random sampling technique\u003c/strong\u003e was employed to ensure representation across key stakeholder groups involved in community health and development. Data collection was conducted between \u003cstrong\u003e1st and 31st August 2023\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e using a \u003cstrong\u003estructured questionnaire\u003c/strong\u003e administered through \u003cstrong\u003eface-to-face interviews as shown in the supplementary file\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Responses were recorded digitally using \u003cstrong\u003eKobo Toolbox\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e a mobile data collection platform, by a team of trained data collectors. A total of \u003cstrong\u003e300 participants\u003c/strong\u003e were surveyed, including \u003cstrong\u003ehealth care workers (HCWs), religious leaders, community/traditional leaders, Patent and Proprietary Medicine Vendors (PPMVs), media representatives, traditional medicine vendors (TMVs),\u003c/strong\u003e and \u003cstrong\u003ecommittee members\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e This diverse respondent pool was selected to provide a broad understanding of community perspectives and engagement with health-related misinformation.\u003c/p\u003e\n\u003cp\u003eThe sample size of 300 participants was calculated using Cochran's formula:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003en = (Z²pq)/e²\u003c/p\u003e\n\u003cp\u003ewhere Z = 1.96 (95% confidence level), p = 0.50 (expected proportion), q = 0.50, and e = 0.05 (margin of error). The expected proportion of 0.50 was selected as the true prevalence of awareness regarding health misinformation management strategies was unknown prior to the study, and this value provides the most conservative sample size estimate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eN = (1.96\u003csup\u003e2\u003c/sup\u003e ×0.50 ×0.50)/0.05\u003csup\u003e2\u003c/sup\u003e×0.05\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eN = (3.8416 × 0.25) / 0.0025\u003c/p\u003e\n\u003cp\u003eN = 0.9604 / 0.0025\u003c/p\u003e\n\u003cp\u003eN = 384.16\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eN = 385\u003c/p\u003e\n\u003cp\u003eTo account for the finite population size, the finite population correction was applied:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003en = n/[1+(n-1)/N], where N represents the estimated total number of key stakeholders (approximately 1,500) across the four study local government areas.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003en = 385 / [1 + (385-1)/1,500]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003en = 385 / [1 + 384/1,500]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003en = 385 / 1.256\u0026nbsp;\u003c/p\u003e\n\u003cp\u003en = 306\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRounded to n = 300\u003c/p\u003e\n\u003cp\u003eThis adjustment resulted in a required sample size of 306 participants. However, the final sample size was set at 300, which satisfies the statistical requirements considering feasibility for data collection within the one-month study period (August 2023) and available resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative survey:\u003c/strong\u003e The qualitative component of the study utilised a \u003cstrong\u003epurposive sampling technique\u003c/strong\u003eto ensure the inclusion of participants with various stakeholders, including primary health care workers, health educators, media practitioners, traditional and religious leaders. A total of 8 \u003cstrong\u003efocus group discussions (FGDs)\u003c/strong\u003e were conducted across selected locations, allowing for rich, interactive dialogue and exploration of diverse perspectives. Data collection was facilitated using \u003cstrong\u003eaudio recording devices\u003c/strong\u003e, ensuring accurate capture of participant responses. All recordings were subsequently \u003cstrong\u003etranscribed verbatim using Microsoft Word’s voice transcription tool\u003c/strong\u003e with human verification providing a reliable textual dataset for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuantitative data, such as demographic information or categorical responses, was analyzed using SPSS, with results presented as frequencies and explained descriptively. Interview recordings from the qualitative data was transcribed into text documents using Dedoose software. These transcripts were then subjected to a thematic analysis, where initial codes were developed and subsequently grouped into broader themes based on their similarities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData quality and assurance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo protect the privacy and security of data, participant names were coded. When conducting interviews, data collectors used password-protected encrypted devices that were kept safe. To reduce the danger of data breaches, recordings were promptly moved to a secured cloud storage system, encrypted to protect data during transfer, and promptly erased from the encrypted devices. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThematic analysis\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eThe thematic analysis followed Braun and Clarke’s (22) six-phase framework: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFamiliarisation with the data - The transcriptions were read and re-read to immerse in the data. Initial notes and impressions were recorded to capture emerging ideas. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenerating initial codes - The data was systematically coded using Dodoose software. Segments of text that appeared relevant to the research questions were identified and labelled. Coding was done inductively to allow themes to emerge naturally from the data. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSearching for themes - The codes were examined to identify patterns and relationships. Similar codes were grouped together to form initial themes. The focus was on capturing broad patterns that addressed the research questions. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReviewing themes - The initial themes were reviewed and refined. This involved checking if the themes worked in relation to the coded extracts and the entire data set. Themes were modified, combined, or discarded based on their relevance and coherence. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDefining and naming themes - Once the themes were finalised, each theme was defined and named. Clear definitions helped ensure that each theme captured a distinct aspect of the data. Sub-themes were also identified to provide a more detailed understanding of each major theme. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProducing the report - The final step involved writing up the analysis. This included selecting vivid and compelling quotes from the transcriptions to illustrate each theme. The report was structured to provide a comprehensive understanding of the themes and their significance. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eTo ensure the reliability and validity of the analysis, several strategies were employed: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTriangulation - Data from different focus groups and participant types were collected to identify common themes and discrepancies. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePeer review: The initial codes and themes were reviewed by colleague's familiar with qualitative research to provide feedback and ensure the accuracy of the interpretation. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical considerations and approval:\u003c/strong\u003e Objectives and voluntariness of the study were clearly explained to participants, and informed consent was obtained before data collection. Participants provided informed consent and were assured of their right to withdraw at any time. Responses were anonymised to ensure confidentiality and anonymity were strictly taken into consideration, and ethical approval was obtained from the Niger State Research Ethics Committee in Nigeria. \u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eQuantitative Survey:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sociodemographic characteristics as listed in \u003cstrong\u003e\u003cem\u003eTable 1\u003c/em\u003e\u003c/strong\u003e below showed more than half of the participants were female (54%), and the highest number of age groups were between the ages of 26 and 30 years old (24.7%). Most of them are businessmen and women (30.7%) who fall within the category of community members (21.7%), and many participants came from Chachanga (27.0%) and Wushishi (25.5%) local government areas (LGA) in Niger State, Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Sociodemographic characteristics of participants \u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (n=300)\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 586px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eMale \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e138 (46%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eFemale \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e162 (54%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 586px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group in years\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e15 \u0026ndash; 20 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e19 (6.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e21 \u0026ndash; 25 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e15 (5.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e26 \u0026ndash; 30 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e74 (24.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e31 \u0026ndash; 35 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e60 (20.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e36 \u0026ndash; 40 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e41 (13.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e41 \u0026ndash; 45 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e49 (16.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e51 and above \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e42 (14.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 586px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eBusiness entrepreneur \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e92 (30.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eCivil servant \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e64 (21.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eHealthcare workers \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e25 (8.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eReligious leaders \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e22 (7.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eRetired civil servants \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e6 (2.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eFarmers \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e31 (10.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eJournalist \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e1 (0.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eHome maker \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e25 (8.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eStudent \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e29 (9.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eFurniture maker \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e2 (0.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eCommunity member \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e3 (1.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 586px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories of participants\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003ePrimary Healthcare Workers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e8 (2.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eHealth educator \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e3 (1.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eReligious leaders \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e29 (9.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eCommunity/traditional leaders \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e56 (18.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003ePPMV \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e23 (7.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eMedia \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e6 (2.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eTMV \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e21 (7.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eWHDC \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e8 (2.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eCommunity members \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e65 (21.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eHealthcare practitioners \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e43 (14.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eImmunization officer \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e2 (0.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eSecurity officer \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e1 (0.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eFarmers \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e1 (0.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eOthers \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e34 (11.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 586px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eLapai \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e75 (25.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003ePaikoro \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e68 (22.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eChachanga \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e81 (27.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eWushishi \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e76 (25.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eApproximately 63% obtained information from community gatherings, followed by social media (31.7%). Some respondents (38.3%) confidently identify accurate health information from false information, from community gatherings (66.3%) and social media (29.7%), as stated in \u003cstrong\u003e\u003cem\u003eTable 2.\u003c/em\u003e\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Health misinformation awareness\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 586px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhat platform do you frequently encounter health-related information?\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eSocial media \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e95 (31.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eWebsites \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e16 (5.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eCommunity gatherings \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e189 (63.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 586px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHow confident are you in identifying accurate health information from misleading or false information?\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eVery confident \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e46 (15.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eConfident \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e115 (38.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eNeutral \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e69 (23.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eNot confident \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e43 (14.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eNot at all confident \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e27 (9.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 586px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhat is the most common platform where health misinformation is often shared in your state/community?\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eSocial media \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e89 (29.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eWebsite \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e11 (3.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eCommunity gatherings \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e199 (66.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eOthers \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e1 (0.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOnly about 23.3% are aware of strategies to counter health misinformation, and 34.7% of the participants believe the effectiveness of the existing strategies would help counter health misinformation. The most recommended strategy for combating health misinformation was conducting health education in community gatherings as shown in\u003cstrong\u003e\u003cem\u003e\u0026nbsp;Table 3.\u003c/em\u003e\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eExisting\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ehealth misinformation management strategies\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"584\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 584px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAre you aware of any existing strategies in the state/community to manage health misinformation?\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eYes \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e70 (23.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eNo \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e230 (76.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 584px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhat are the existing strategies?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eEmir to educate his subjects and the community \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e5 (1.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eHealth education for religious leaders \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e1 (0.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eHealth education in community gatherings and house-to-house sensitization \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e48 (16.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eHealth education on radio programs \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e6 (2.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eHealth education in social gatherings \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e2 (0.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eTown announcers \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e8 (2.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMore than half of the participants (70.3%) believe there is a lack of awareness of education about health misinformation in the state/community and consider inadequate skills in identifying reliable health information sources as the most identified challenge/gap in the management of health misinformation as listed in \u003cstrong\u003e\u003cem\u003eTable 4.\u003c/em\u003e \u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Assessing education awareness and capacity building needs for community health misinformation management\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 586px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIs there a lack of awareness of education about health misinformation in the state/community?\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eYes \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e211 (70.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eNo \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e89 (29.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 586px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhat specific areas do you think community health misinformation management capacity building should focus on?\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eEnhancing skills in identifying reliable health information sources \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e149 (49.7%) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e151 (50.3%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eImproving abilities to critically evaluate health information online \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e102 (34.0%) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e198 (66.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eStrengthening data collection and analysis techniques \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e108 (36.0%) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e192 (64.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eDeveloping effective response strategies to address health misinformation \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e184 (61.3%) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e116 (38.7%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eFostering collaboration with stakeholders for impactful interventions \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e219 (73.0%) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e81 (27.0%) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative Survey\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003eNature of Health Misinformation:\u003c/strong\u003e Participants identified widespread health misinformation influencing community health-seeking behaviour and clinical outcomes. Vaccine hesitancy emerged as particularly prevalent, with primary healthcare (PHC) workers reporting: \u003cem\u003e\u0026quot;When we ask caregivers why they refuse vaccination, their fears about children being sick after the vaccination or fever are one of the major reasons.\u0026quot;\u003c/em\u003e A health educator observed: \u003cem\u003e\u0026quot;Most of the villagers have no scientific knowledge\u0026hellip; when a child is convulsing, they say it is spiritual, \u0026apos;iska,\u0026apos; something which is not right.\u0026quot;\u003c/em\u003e Traditional healers corroborated this finding, noting persistent spiritual interpretations despite medical explanation: \u003cem\u003e\u0026quot;Even if you tell them the bones are broken or the brain is affected, they don\u0026apos;t listen\u0026hellip; they want someone to \u0026apos;remove the spirits.\u0026apos;\u0026quot; \u0026nbsp;Another participant from the Niger PHC agency noted: \u0026ldquo;I was in a community and found a woman moving out with a child wearing a supervision of R.I.S.S. I asked if there was no health worker in that community, and they responded in the affirmative. However, they said this problem is not one that will be cured by modern medicine\u003c/em\u003e\u003cem\u003e, so it\u0026apos;s a spiritual thing. The condition of the child was convulsion and they were heading to a traditional herbalist.\u0026quot;\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSources and Spread of Misinformation:\u003c/strong\u003e The spread of misinformation occurred primarily through interconnected channels \u0026ndash; traditional social networks, digital platforms and religious institutions.\u003c/p\u003e\n\u003cp\u003eTraditional Social Networks:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eCommunity leaders described rapid information diffusion through everyday social interactions: \u003cem\u003e\u0026quot;Most of these misleading beliefs are promoted in the villages\u0026hellip; once it starts, it spreads everywhere.\u0026quot;\u003c/em\u003e Markets, ceremonies, and communal gathering spaces served as primary dissemination nodes, with one participant noting: \u003cem\u003e\u0026quot;In the market, people sit down and talk\u0026hellip; these rumours spread like wildfire.\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDigital Communication Channels:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHealth workers identified social media as an accelerant of misinformation spread: \u003cem\u003e\u0026quot;WhatsApp voice notes and Facebook posts\u0026mdash;people take them as facts immediately.\u0026quot;\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReligious Messaging\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eReligious leaders emerged as particularly influential in shaping health beliefs. A religious leader confirmed: \u003cem\u003e\u0026quot;When the sermons in the mosque mention something about immunization, the people follow it immediately.\u0026quot;\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSociocultural Determinants of Misinformation:\u003c/strong\u003e Strong traditional belief system underpins many misconceptions across communities. Illnesses that present with sudden or dramatic symptoms such as seizures or mental distress, are commonly interpreted through a spiritual lens. A community leader explained: \u003cem\u003e\u0026quot;If someone has convulsion or mental problem, the first thing they think is attack or witchcraft.\u0026quot;\u003c/em\u003e A herbal vendor confirmed: \u003cem\u003e\u0026quot;Native doctors are their first line of call before they go to hospital.\u0026quot;\u003c/em\u003e Participants reported deep-seated scepticism toward government health initiatives. One community leader articulated this sentiment: \u003cem\u003e\u0026quot;People believe the government only comes when there is immunization\u0026hellip; they don\u0026apos;t trust their intentions.\u0026quot;\u003c/em\u003e PHC staff noted that participation often requires material incentives: \u003cem\u003e\u0026quot;If there is no incentive like noodles, the turnout will be very low.\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth-Seeking Behaviour Patterns\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eHealth-seeking behaviour in many communities follows a pattern in which traditional, herbal, or spiritual remedies are tried before biomedical care is considered.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ePHC staff provided illustrative examples: \u003cem\u003e\u0026quot;My neighbour\u0026apos;s child had convulsion\u0026hellip; they first went to the herbalist before thinking of hospital.\u0026quot;\u003c/em\u003e Social networks actively reinforce alternative treatment pathways. Community leaders reported: \u003cem\u003e\u0026quot;The family will say don\u0026apos;t go to hospital; they will inject the child and worsen it.\u0026quot;\u003c/em\u003e Confidence in herbal medicine remained high, with an herbal vendor stating: \u003cem\u003e\u0026quot;They believe herbs work better\u0026hellip; hospital is their last option.\u0026quot;\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrust Dynamics and Authority Structures\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eTrust emerges as a key determinant of how communities interpret and act upon health information.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eReligious leaders commanded unparalleled community authority. One religious leader stated: \u003cem\u003e\u0026quot;Whatever the imam says is final in the community.\u0026quot;\u003c/em\u003e A Community Leader affirmed this by saying, \u003cem\u003e\u0026ldquo;if the community head says we shouldn\u0026rsquo;t allow immunization, nobody will accept it.\u0026rdquo;\u003c/em\u003e In contrast, health workers faced credibility challenges. PHC staff acknowledged: \u003cem\u003e\u0026quot;The native doctors command more trust than our health workers\u003c/em\u003e\u003cem\u003e.\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural Vulnerabilities:\u0026nbsp;\u003c/strong\u003eLimited health literacy emerged as a fundamental vulnerability. Health educators highlighted: \u003cem\u003e\u0026quot;They cannot differentiate truth from falsehood\u0026hellip; anything they hear, they believe.\u0026quot;\u003c/em\u003e Inadequate telecommunications infrastructure intensified information gaps. Surveillance officers reported: \u003cem\u003e\u0026quot;Some communities have no network, no radio signal\u0026hellip; they depend entirely on hearsay.\u0026quot;\u003c/em\u003e Community leaders noted reliance on specific information brokers: \u003cem\u003e\u0026quot;The VDC [Village Development Committee] members give them information because most people don\u0026apos;t have phones.\u0026quot;\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCurrent Intervention Strategies:\u003c/strong\u003e Community dialogues and sensitization activities remained the primary intervention approach, though effectiveness was limited. Health educators admitted that: \u003cem\u003e\u0026quot;Community dialogues help, but the attendance is always poor.\u0026quot;\u003c/em\u003e Disease surveillance mechanisms demonstrated greater effectiveness. The State Disease Surveillance and Notification Officer (DSNO) reported: \u003cem\u003e\u0026quot;We keep rumour logs and investigate them immediately.\u0026quot;\u003c/em\u003e Surveillance officers confirmed: \u003cem\u003e\u0026quot;Rumour management is very effective for us.\u0026quot;\u0026nbsp;\u003c/em\u003eCollaborative responses have proven effective during emergencies. PHC staff recalled: \u003cem\u003e\u0026quot;During COVID, when government, religious leaders, and media worked together, confusion reduced.\u0026quot;\u003c/em\u003e Despite these strategies, implementation suffered from inadequate funding. The State DSNO summarised: \u003cem\u003e\u0026quot;Most of these activities are not deliberate; we lack resources to follow through.\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGaps and Identified Opportunities for Intervention:\u0026nbsp;\u003c/strong\u003eParticipants identified several opportunities for strengthening responses to health misinformation.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSurveillance officers emphasised proactive monitoring: \u003cem\u003e\u0026quot;State actors need training on how to track misinformation before it spreads.\u0026quot;\u0026nbsp;\u003c/em\u003eStrengthening health education emerged as another key opportunity.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHealth educators advocated for expanded presence: \u003cem\u003e\u0026quot;We need more health educators in communities\u0026hellip; people listen to them.\u0026quot;\u003c/em\u003e Additional informant network was also recommended.\u0026nbsp;Surveillance officers recommended strengthening community surveillance: \u003cem\u003e\u0026quot;Informants are the first to hear rumours, they need training and support.\u0026quot;\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHowever, participants identified gaps in policy and regulation, particularly concerning unregulated herbal advertising. A private practitioner noted: \u003cem\u003e\u0026quot;Herbal vendors can advertise anything, but medical professionals cannot\u0026mdash;there is no balance.\u0026quot;\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study employed methodological triangulation, combining quantitative survey (n = 300) with qualitative interview data to achieve convergence, complementarity, and expansion in understanding health misinformation dynamics.\u0026nbsp;This mixed methods study revealed substantial convergence across multiple domains, strengthening the validity and credibility of findings. Community gatherings emerged as the dominant information platform in both datasets, 63.0% in the quantitative survey and repeatedly emphasized in qualitative interviews, as sites where information \u003cem\u003e\u0026quot;spreads like wildfire.\u0026quot;. This\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ealigns with other literature documenting the primacy of interpersonal communication networks in rural areas in Africa (23) (24)(25)(26). Both data sources identified severely limited awareness of misinformation management strategies, 23.3% quantitative awareness rates and qualitative reports of poor dialogue attendance, corroborating research on weak health system responses to infodemics in low-resource settings (27). The quantitative finding that 70.3% perceived inadequate health misinformation education was substantiated by qualitative accounts of communities\u0026apos; inability to distinguish reliable information\u003cem\u003e: \u0026quot;They cannot differentiate truth from falsehood\u0026hellip; anything they hear, they believe.\u0026quot;\u003c/em\u003e This convergence validates both the prevalence and nature of health literacy deficits while aligning with theoretical frameworks emphasizing critical information evaluation skills as essential health literacy competencies (28)(29).\u003c/p\u003e\n\u003cp\u003eTrust hierarchies showed consistent patterns across methods. Quantitatively, 73.0% prioritized stakeholder collaboration, while qualitative data specified precisely which stakeholders commanded authority; religious leaders (\u003cem\u003e\u0026quot;Whatever the imam says is final\u0026quot;\u003c/em\u003e) and traditional leaders (\u003cem\u003e\u0026quot;If the community head says we shouldn\u0026apos;t allow immunization, nobody will accept it\u0026quot;\u003c/em\u003e), contrasted with healthcare workers\u0026apos; credibility deficits (\u003cem\u003e\u0026quot;Native doctors command more trust than our health workers\u0026quot;\u003c/em\u003e). This convergent validation of trust asymmetries has critical implications for intervention design, suggesting that effective communication strategies must work through rather than around existing authority structures (30). The quantitative finding that only 38.3% felt confident identifying accurate health information was enriched by qualitative accounts revealing the structural basis of this vulnerability, limited formal education, absent telecommunications infrastructure (\u003cem\u003e\u0026quot;Some communities have no network, no radio signal\u0026quot;\u003c/em\u003e), and dependence on information intermediaries (\u003cem\u003e\u0026quot;VDC members give them information because most people don\u0026apos;t have phones\u0026quot;\u003c/em\u003e) (13)(31)(32). The low perceived effectiveness of existing strategies (34.7% quantitatively) gained explanatory power through qualitative accounts of implementation challenges, poor attendance, inadequate funding (\u003cem\u003e\u0026quot;We lack resources to follow through\u0026quot;\u003c/em\u003e), and absence of trust in health messengers (33). This complementarity reveals that effectiveness deficits stem not from conceptual problems with interventions but from implementation failures and misalignment with community preferences and trust structures (33)(34).\u003c/p\u003e\n\u003cp\u003eWhile the quantitative instrument assessed information sources and confidence levels, qualitative interviews uncovered the profound influence of supernatural illness attribution models that fundamentally reshape treatment-seeking pathways regardless of information access (35). The documented case of a mother bypassing available healthcare for a traditional herbalist to address her convulsing child\u0026apos;s \u0026quot;spiritual\u0026quot; condition revealed how cultural beliefs can override both access to information and availability of biomedical care (36). The persistent rejection of biomedical explanations \u003cem\u003e\u0026quot;Even if you tell them the bones are broken or the brain is affected, they don\u0026apos;t listen\u0026quot;\u003c/em\u003e suggests misinformation operates not merely as knowledge deficits fixable through information provision, but as coherent alternative knowledge systems rooted in cultural epistemologies that define illness and causation (37)(38). This insight fundamentally challenges deficit-oriented models of health communication that assume information provision alone can change behaviour and aligns with cultural humility that emphasizes respectful engagement with alternative explanatory systems rather than dismissal (39)(40). The qualitative data also expanded the understanding of social influence mechanisms. While quantitative data showed community gatherings as primary information sources, qualitative accounts revealed how social networks actively discourage biomedical care (\u003cem\u003e\u0026quot;The family will say don\u0026apos;t go to hospital; they will inject the child and worsen it\u0026quot;\u003c/em\u003e), demonstrating that misinformation\u0026apos;s power lies not only in false beliefs but in socially reinforced behavioral norms. This insight suggests interventions must address social network dynamics and normative influences, not merely individual knowledge (41)(42).\u003c/p\u003e\n\u003cp\u003e31.7% reported social media as a frequent information source and 29.7% identified it as a misinformation platform\u0026mdash;substantial but minority proportions. However, qualitative data emphasized social media\u0026apos;s role disproportionately, with health workers highlighting immediate uncritical acceptance of digital content: \u003cem\u003e\u0026quot;WhatsApp voice notes and Facebook posts\u0026mdash;people take them as facts immediately.\u0026quot;\u0026nbsp;\u003c/em\u003eThis discrepancy indicates that social media\u0026rsquo;s influence is not primarily a function of how many people use it, but how rapidly and authoritatively content circulates within trusted networks (30) (33).\u0026nbsp;Fast, emotionally charged messages appear credible, allowing misinformation to bypass experts and become part of community conversations (43)\u0026nbsp;A small proportion of digitally connected individuals may function as information bridges, introducing content that then circulates through offline networks (44). The qualitative finding that \u003cem\u003e\u0026quot;Village Development Committee members give them information because most people don\u0026apos;t have phones\u0026quot;\u003c/em\u003e supports this bridging mechanism. Thus, social media\u0026apos;s influence operates indirectly through trusted intermediaries who translate digital content into oral narratives, amplifying its impact beyond direct user penetration (33) (43).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Geographic Scope: The study was limited to four local government areas (Lapai, Paikoro, Chachanga, and Wushishi) in Niger State, Nigeria. Findings may not be generalizable to other regions of Nigeria or other contexts with different sociocultural, linguistic, or infrastructural characteristics. The specific nature of health misinformation and its management may vary significantly across different geographic and cultural settings.\u003c/p\u003e\n\u003cp\u003e2. Temporal Limitations: Data collection occurred at a specific point in time and may not capture seasonal variations in health-seeking behaviours, misinformation patterns, or the effectiveness of interventions. The dynamic nature of misinformation, particularly on digital platforms, means findings may quickly become outdated. The study does not track changes over time or assess longitudinal impacts of misinformation.\u003c/p\u003e\n\u003cp\u003e3. Language and Translation Issues: The document does not specify whether surveys and interviews were conducted in local languages and subsequently translated. Translation processes may have introduced interpretation biases or lost nuanced meanings, particularly regarding cultural concepts like \u0026quot;iska\u0026quot; (spirits) and traditional healing practices that may not have direct English equivalents.\u003c/p\u003e\n\u003cp\u003e4. Lack of Validation of Intervention Effectiveness: While the study identified existing strategies and recommendations from participants, it did not include empirical evaluation of intervention effectiveness. Claims about what works (e.g., surveillance systems, community dialogues) relied on participant perceptions rather than measured outcomes or comparative analysis of different approaches.\u003c/p\u003e\n\u003cp\u003e5. Limited Economic Analysis: The study did not examine economic factors influencing health-seeking behaviors and misinformation vulnerability, such as costs of biomedical care versus traditional healing, poverty levels, or economic incentives in the misinformation ecosystem (e.g., herbalists\u0026apos; financial interests in promoting alternative treatments).\u003c/p\u003e\n\u003cp\u003eDespite these limitations, the study provides valuable insights through its mixed-methods triangulation approach and offers important groundwork for understanding health misinformation in this underexplored context. Future research should address these limitations through longitudinal designs, broader geographic scope, and deeper exploration of digital misinformation dynamics.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe combination of survey data and interview findings revealed that health misinformation in Niger State, Nigeria, is not simply a problem of false information circulating in communities. Instead, it is deeply rooted in how these communities are structured and function. Three main issues stand out from this study. First, community gatherings and face-to-face conversations remain the primary ways people share and receive health information, with social media serving to amplify rather than originate misinformation. Secondly, there is a significant disconnect between how confident people feel about identifying false health information (over half reported being confident) and their actual ability to do so, as revealed through interviews showing that many community members \u0026quot;cannot differentiate truth from falsehood.\u0026quot; Thirdly and most importantly, the persistence of health misinformation is not because communities lack access to correct information, but because they trust traditional and religious leaders more than government health workers and biomedical explanations. This study shows that communities actively choose between different sources of health knowledge - biomedical, traditional, and spiritual - based on who they trust, what aligns with their cultural beliefs, and what has worked for them in the past. Current efforts to combat misinformation, such as community dialogues and health education sessions, exist but are poorly funded, inconsistently implemented, and often poorly attended. The few strategies that show promise, like tracking rumors through surveillance systems and coordinating responses during emergencies, demonstrate that change is possible when interventions are properly resourced and involve trusted community voices. Ultimately, addressing health misinformation in these communities requires more than correcting false information. It demands building genuine trust between health systems and communities, working with rather than against existing belief systems and traditional leaders, improving basic infrastructure like telecommunications and health facilities, and ensuring sustained engagement rather than sporadic campaigns. Until the underlying issues of trust, access, literacy, and resources are addressed, misinformation will continue to thrive regardless of how much accurate information is provided.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on this comprehensive need assessment, the following recommendations are crucial for enhancing health misinformation management in Niger State, Nigeria:\u003c/p\u003e\n\u003cp\u003e1. Multi-Stakeholder Collaborative Frameworks: Establish formal partnerships between health authorities, religious leaders, traditional leaders, and community influencers. Given that \u0026quot;whatever the imam says is final in the community,\u0026quot; religious institutions should be engaged as partners rather than obstacles. These collaborations should be institutionalised with clear roles, resources, and accountability mechanisms rather than ad hoc arrangements during health emergencies.\u003c/p\u003e\n\u003cp\u003e2. Community- Centered Communication Strategies:\u0026nbsp;Since community gatherings are where 63% encounter health information interventions should prioritise these traditional channels. Develop culturally appropriate health messaging that acknowledges spiritual beliefs while providing biomedical information. Train community health workers and volunteers to serve as trusted information intermediaries who can bridge epistemic frameworks.\u003c/p\u003e\n\u003cp\u003e3. Strengthened Surveillance and Rapid Response Systems: Build on the effectiveness of rumour tracking reported by disease surveillance officers. Establish community-based misinformation monitoring networks with trained informants who can identify and report emerging misinformation early. Allocate dedicated resources for rapid response teams that can address misinformation before widespread dissemination occurs.\u003c/p\u003e\n\u003cp\u003e4. Comprehensive Health Literacy Programming: Given that 70.3% identified lack of awareness education, implement sustained health literacy programmes focusing on critical evaluation skills rather than mere information transmission. These programmes should help community members understand how to assess information credibility across different sources and recognise common misinformation patterns.\u003c/p\u003e\n\u003cp\u003e5. Infrastructure Development for Information Access: Address the fundamental infrastructure gaps where communities have \u0026quot;no network, no radio signal.\u0026quot; Invest in expanding telecommunications coverage, establishing community radio stations, and creating offline information access points. Ensure health information is available in formats accessible to populations with limited literacy and connectivity.\u003c/p\u003e\n\u003cp\u003e6. Trust-Building Initiatives: Develop long-term programmes to rebuild trust between communities and health systems. This should include consistent presence of health workers in communities beyond immunisation campaigns, transparency in health system operations, and demonstrated responsiveness to community health concerns. Move beyond transactional relationships requiring material incentives to authentic engagement.\u003c/p\u003e\n\u003cp\u003e7. Regulatory Framework for Health Information: Establish regulations governing health claims in both traditional and digital media. As noted regarding herbal advertising asymmetry, create balanced frameworks that protect against dangerous misinformation while respecting medical pluralism. Develop enforcement mechanisms for false health claims regardless of source.\u003c/p\u003e\n\u003cp\u003e8. Capacity Building for Health Workers and Educators: Invest in training programmes for health workers on effective community engagement, cultural competency, and communication strategies that respect local belief systems. Address the finding that health workers face credibility challenges compared to traditional healers by enhancing interpersonal skills and community relationship-building.\u003c/p\u003e\n\u003cp\u003e9. Integration Rather Than Displacement of Traditional Medicine: Recognise that traditional medicine is deeply embedded in health-seeking behaviours. Rather than positioning biomedical care in opposition, explore integration models where traditional and biomedical practitioners collaborate, with appropriate safeguards. This approach may increase community acceptance of biomedical information and reduce delays in seeking appropriate care.\u003c/p\u003e\n\u003cp\u003e10. Sustainable Funding Mechanisms: Address the fundamental constraint identified by the State DSNO: \u0026quot;we lack resources to follow through.\u0026quot; Advocate for dedicated budget lines for misinformation management within health system financing. Explore innovative funding mechanisms including public-private partnerships, international development support, and domestic resource mobilisation.\u003c/p\u003e"},{"header":"Abbreviation","content":"\u003cp\u003eDSNO \u0026ndash; Disease Surveillance and Notification Officer\u003c/p\u003e\n\u003cp\u003eLGAs \u0026ndash; Local Government Areas\u003c/p\u003e\n\u003cp\u003eHCW \u0026ndash; Health Care Workers\u003c/p\u003e\n\u003cp\u003ePPMV \u0026ndash; Patent and Proprietary Medicine Vendors\u003c/p\u003e\n\u003cp\u003eTMV \u0026ndash; Traditional Medicine Vendors\u003c/p\u003e\n\u003cp\u003eFGD \u0026ndash; Focus Group Discussion\u003c/p\u003e\n\u003cp\u003eSPSS \u0026ndash; Statistical Package for Social Sciences\u003c/p\u003e\n\u003cp\u003ePHC \u0026ndash; Primary Health Care\u003c/p\u003e\n\u003cp\u003eVDC \u0026ndash; Village Development Committee\u003c/p\u003e\n\u003cp\u003eWHDC - Ward Health Development Committee\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe research was conducted in accordance with the Declaration of Helsinki, approved by the National Health Research Ethics Committee of Nigeria (NHREC) with the ethics code number NHREC/01/01/2007-12/10/2023 and informed consent was obtained from all subjects including parents or legal guardians of\u0026nbsp;participants under the age of 16.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eData is provided within the manuscript and the supplementary information file\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis research was conducted as part of the project funded by Gates Foundation\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAnwuli Nwankwo conducted data analysis, wrote and reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSunday Oko, Abara Erim, and Sonia Biose conducted data analysis, and the needs assessment study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKemisola Agbaoye, Solomon Oladimeji, and Muhammed Gaya Yahaya participated in data collection.\u003c/p\u003e\n\u003cp\u003eVivianne Ihekweazu and Kemisola Agbaoye provided strategic leadership and oversight to the conceptualisation and implementation of the fellowship and approved the final manuscript.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAcknowledgements\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe authors are grateful to the Niger State Primary Health Care Board and the Ministry of Secondary and Tertiary Health Care for their support in the study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eErim A, Oko S, Biose S, Agbaoye K, Nzedibe OE, Nwankwo A, et al. 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Available from: http://jdc.journals.unisel.edu.my/index.php/jdc/article/view/224/165\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Health Misinformation, Infodemic Management, Health Literacy, Trust in Healthcare, Needs Assessment, Stakeholder Involvement, Community-Based Strategies, Health Education, Cultural Beliefs and Health","lastPublishedDoi":"10.21203/rs.3.rs-8398446/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8398446/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Health misinformation poses critical threat to public health systems globally, with particularly devastating impacts in low- and middle-income countries where health literacy levels are limited, and communication infrastructure is inadequate. In Nigeria, Africa's most populous nation, the spread of false health information has undermined public health initiatives, eroded trust in health institutions, and contributed to preventable morbidity and mortality. Despite documented evidence of widespread misinformation, systematic understanding of the specific needs, gaps, and community perspectives regarding misinformation management remains limited. This study aimed to identify critical needs and gaps in managing health misinformation within Niger State, Nigeria, to inform the development of evidence-based, culturally sensitive, and sustainable interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003e Mixed-methods triangulation design combined quantitative and qualitative approaches. A cross-sectional survey of 300 respondents from four local government areas was conducted using stratified random sampling. Data were collected in August 2023 (1\u003csup\u003est\u003c/sup\u003e to 31\u003csup\u003est\u003c/sup\u003e) through structured questionnaires administered face-to-face and recorded digitally. Participants included community leaders, health workers, media practitioners, traders, and community members. Qualitative data consisted of eight focus group discussions, transcribed, and analyzed using Braun and Clarke’s thematic framework. Quantitative analysis used SPSS, while qualitative coding was conducted on Dedoose. Ethical approval was granted by the Niger State Research Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Misinformation was highly prevalent, with community gatherings (63.0%) and social media (31.7%) as primary information sources. Although 53.6% reported confidence in identifying accurate information, qualitative data showed widespread difficulty distinguishing truth from falsehood. Only 23.3% knew of existing misinformation strategies, while 70.3% cited poor awareness education. Vaccine hesitancy was widespread, shaped by spiritual illness attributions and distrust of government health initiatives. Religious and traditional leaders held strong authority over communities, whereas health workers faced credibility challenges. Current interventions like, dialogues and disease surveillance rumour logs, had limited reach due to funding, poor attendance, and implementation gaps.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Findings demonstrate that misinformation operates within socio-ecological systems defined by interpersonal networks, epistemic pluralism, structural vulnerabilities, and trust asymmetries. Effective responses must strengthen stakeholder partnerships, embed communication in traditional channels, enhance surveillance, build critical health literacy, address infrastructure deficits, and engage respectfully with existing belief systems to build trust.\u003c/p\u003e","manuscriptTitle":"A Comparative Health Misinformation Need Assessment Analysis in Niger State, Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 12:39:24","doi":"10.21203/rs.3.rs-8398446/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-27T14:20:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171346406695508871276856004212632394727","date":"2026-02-26T10:43:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226931645045964257523520414837677687119","date":"2026-02-25T05:32:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T20:15:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129714951664837025745767687966472997233","date":"2026-02-23T23:15:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266909428009064045529241324025462546628","date":"2026-02-11T00:01:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-08T11:11:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-15T01:28:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-05T03:50:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-03T19:46:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-01-03T19:40:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"308a9ac0-822b-4326-b2c2-1c217a5ec12c","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T12:39:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 12:39:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8398446","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8398446","identity":"rs-8398446","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}