Advancing the ‘Distribution Plus’ Model: A Multi-Visualization KAP Study and Fourteen-Country Benchmarking of LLITN Utilization among Pregnant Women in Rural Ghana

Advancing the ‘Distribution Plus’ Model: A Multi-Visualization KAP Study and Fourteen-Country Benchmarking of LLITN Utilization among Pregnant Women in Rural Ghana | 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 Advancing the ‘Distribution Plus’ Model: A Multi-Visualization KAP Study and Fourteen-Country Benchmarking of LLITN Utilization among Pregnant Women in Rural Ghana Richmond Yaw Osei, Ishmael Awini Aburi, Abigail Boatemaa, Gabriel Osei Forkuo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9690777/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Malaria in pregnancy remains a leading cause of preventable maternal and neonatal mortality in sub-Saharan Africa. While distribution programs have achieved near-universal mosquito net ownership, a critical gap persists between access and consistent utilization. This study characterizes the knowledge, access, and practice profile regarding long-lasting insecticidal treated nets among pregnant women in rural Ghana to identify the determinants of this ownership–utilization gap, and contextualizes the findings through a systematic comparison against fourteen malaria-endemic countries. Methods A descriptive cross-sectional study was conducted among 200 pregnant women receiving antenatal care in the Nkoranza South Municipality, Ghana. Primary data were collected through face-to-face structured interviews assessing socio-demographics, knowledge depth, access, and reported net use, which was further validated by direct physical observation of the sleeping environment. The data were analyzed using advanced multivariate visualizations, including heatmaps, diverging bar charts, and radar charts. Secondary quantitative data from fourteen sub-Saharan African countries were subsequently utilized to benchmark local ownership and utilization metrics against regional norms. Results The study revealed a "Universal Access Paradox" where supply-side success coexisted with demand-side failure. While awareness of malaria prevention (100%) and antenatal care access (98.5%) were near-universal, knowledge depth was critically low; only 27.1% of participants correctly identified the three-year insecticide efficacy period. Consequently, direct observation confirmed that only 43.5% of nets were correctly hung and actively in use. The cross-country comparative analysis contextualized these findings, showing that the study site achieved the region's highest net ownership (100%) but recorded the lowest ownership-to-utilization conversion rate (43.5%). The inability to identify insecticide efficacy duration was confirmed as a systemic, pan-African programmatic failure. Conclusions These results argue for a fundamental policy shift from a "distribution-only" model to a "Distribution Plus" strategy. By integrating occupationally sensitive behavior change counseling and observational monitoring into routine antenatal care, health programs can bridge the gap between near-universal access and effective biological protection, ultimately reducing the malaria burden among vulnerable populations. antenatal care behavior change malaria in pregnancy sub-Saharan Africa socio-demographic vector-control intervention Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction 1. 1 Relevance of the study From a population health and demographic perspective, malaria in pregnancy (MiP) operates as a driver of two of the most consequential outcomes in the study of mortality and reproductive health: elevated maternal mortality and suppressed birth weight, which in turn contributes to neonatal and infant mortality — a pathway central to the epidemiological transition literature [1] and to demographic analyses of under-five mortality differentials in sub-Saharan Africa. The persistence of MiP burden despite large-scale preventive investment challenges the standard health-transition expectation that rising access to proven interventions translates into commensurate reductions in cause-specific mortality, and frames the ‘access–use gap’ documented in this study as a fundamental demographic and public health research problem. Malaria in pregnancy (MiP) constitutes one of the most consequential preventable causes of maternal and perinatal morbidity and mortality in sub-Saharan Africa, imposing a burden that extends from the individual woman and her unborn child to the household, community, and national economy. The pathophysiological basis of this vulnerability is well established: pregnancy induces a state of partial immune tolerance that facilitates the preferential sequestration of Plasmodium falciparum -infected erythrocytes in the placental intervillous space, a process mediated by the parasite-derived variant surface antigen VAR2CSA binding to chondroitin sulphate A on placental syncytiotrophoblasts [2,3]. This placental sequestration drives the cascade of adverse maternal and foetal outcomes — including maternal anaemia, intrauterine growth restriction, low birth weight, preterm delivery, spontaneous abortion, and neonatal death — documented across the epidemiological literature [4–7]. Globally, MiP is estimated to cause over 10,000 maternal deaths and 200,000 neonatal deaths annually, with recent evidence from [8] documenting a troubling resurgence of MiP burden in parts of Africa attributable to insecticide resistance, COVID-19-related service disruptions, and stagnating utilization of preventive interventions. Long-lasting insecticidal treated nets (LLITNs) remain the cornerstone preventive intervention against MiP. Their dual mechanism — physical barrier and insecticidal contact kill of vector mosquitoes, primarily Anopheles gambiae sensu lato in West Africa — provides a cost-effective and scalable means of reducing human–vector contact in resource-limited settings [9,10]. The evidence base for LLITN efficacy is uniquely robust: the landmark Cochrane meta-analysis by Lengeler [9] documented a 17–38% reduction in all-cause child mortality and a reduction of approximately 50% in uncomplicated malaria episodes in high-transmission settings, findings that have been reaffirmed in subsequent effectiveness studies [11–13] . The global scale-up of LLITN distribution has proceeded at an unprecedented pace since 2004, underpinned by substantial financing from the President’s Malaria Initiative (PMI), the Global Fund to Fight AIDS, Tuberculosis and Malaria (GFATM), and bilateral donors. Between 2000 and 2015, sub-Saharan Africa received approximately 1 billion LLITNs, contributing to a 50% reduction in malaria mortality across the continent [12,14]. In Ghana, the National Malaria Control Program (NMCP) has implemented a multi-channel distribution strategy achieving substantial improvements in LLITN household ownership over successive survey periods [15,16]. Recent national population-based survey evidence from Awunyo et al. [17] confirms that while LLITN ownership among Ghanaian pregnant women has improved substantially, effective utilization remains low and is differentially distributed across socioeconomic strata — a pattern mirrored in the longitudinal inequality analysis by Okova et al. [18], who documented widening within- and between-group socioeconomic disparities in malaria prevention uptake across Ghana over 2003–2022. Despite these distributional achievements, the effectiveness of LLITN programs is ultimately contingent upon consistent and correct use by the intended beneficiaries [19,20]. A growing body of evidence documents a persistent and troubling gap between LLITN ownership and utilization in sub-Saharan Africa — a gap that is particularly pronounced in rural, low-income communities where competing household priorities, knowledge deficits, perceived side effects, and cultural factors interact to limit adherence to net use recommendations [21–25]. Understanding the multidimensional determinants of this ownership–utilization discordance is therefore a research and programmatic priority. A key but underappreciated driver of non-use is inadequate depth of knowledge about LLITN properties — most critically, the widespread misconception that insecticide efficacy expires after one year rather than three — which [24], in a systematic review of inconsistent ITN use across tropical Africa, identify as among the most prevalent and modifiable knowledge barriers, while [26] and [27] have confirmed this same deficit in directly comparable Ghanaian contexts. In the Nkoranza South Municipality of the Brong Ahafo Region of Ghana, malaria constitutes the leading cause of outpatient department (OPD) attendance (47%), hospital admissions (16.2%), and hospital mortality (17.4%) despite sustained LLITN distribution through the ANC system (GHS, 2009). The annual coverage of LLITNs among ANC registrants reached 67.8% in 2016, yet malaria in pregnancy remained at 60% during the same period [28], underscoring the inadequacy of distributional metrics as a proxy for program effectiveness. Abesig et al. [29], in a trend analysis of malaria test positivity among pregnant women in the Savannah Region of Ghana over 2018–2022, similarly documented persistently elevated positivity rates despite programmatic scale-up — corroborating the need for a more granular examination of the KAP profile of pregnant women in comparable settings. A knowledge, attitudes, and practices (KAP) study design is particularly well suited to this investigative objective. KAP surveys provide a structured framework for characterizing the cognitive, evaluative, and behavioral dimensions of health-related phenomena and have been widely employed in LLITN research across sub-Saharan Africa[21,30–32]. The identification of specific knowledge deficits, access barriers, and suboptimal practices within a defined population enables program managers to prioritize and tailor interventions more effectively — including through the evidence-based educational program design principles described by Opara et al. [33], who demonstrated in a systematic review that structured, interactive ANC-based health education significantly improved both knowledge depth and utilization rates compared with routine passive counseling. The present study employed a suite of advanced data visualization techniques — including heatmaps, violin plots, diverging bar charts, radar charts, lollipop charts, waffle charts, and bubble charts — to provide a richer and more nuanced representation of the KAP data than is afforded by conventional tabular presentation. These visualization modalities are increasingly endorsed in health research for their capacity to reveal distributional patterns, cross-domain relationships, and indicator-level disparities that may be obscured in standard frequency tables [34–36]. 1.2 Aim of the study This study aimed to characterize the full KAP profile regarding LLITNs among pregnant women in Bonsu Sub-Municipality, Ghana, and to contextualize these findings within the broader sub-Saharan African programmatic landscape through a systematic 14-country comparative analysis. By utilizing advanced multivariate visualization methods, the study seeks to examine cross-domain relationships between knowledge, access, and utilization indicators to identify the determinants of the "Universal Access Paradox"—the gap between near-universal supply-side success and significant demand-side failure. 1.3 Specific objectives The study was guided by the following specific objectives: To construct and interpret a comprehensive KAP indicator heatmap characterizing the distribution of LLITN knowledge, access, and practice outcomes across the study population. To examine the distributional characteristics of socio-demographic and knowledge variables using violin and box plot analyses. To visualize the divergence between correct/positive and incorrect/absent KAP responses across all study indicators to identify primary actionable gaps. To explore cross-tabulated associations between LLITN use status and occupational and knowledge-level subgroups to identify high-risk cohorts. To characterize the hierarchical ranking of KAP indicators and domain-level performance using lollipop and radar chart analyses. To benchmark local LLITN ownership and utilization rates against 14 malaria-endemic countries in sub-Saharan Africa to establish the inter-regional validity of the findings. To evaluate the "ownership-to-utilization conversion rate" as a novel metric for measuring demand-side programmatic efficiency across different regional settings. 1.4 Research questions The following research questions guided the study: What is the comprehensive KAP profile of pregnant women in Bonsu Sub-Municipality with respect to LLITN awareness, access, and utilization? How do LLITN utilization outcomes vary across occupational and knowledge-level subgroups, and which cohorts are most vulnerable to non-use? Which specific KAP indicators exhibit the greatest divergence between positive and negative response profiles, and what does this imply for programmatic prioritization? How does the ownership-utilization gap in rural Ghana compare to international benchmarks, and does it reflect a systemic, pan-African programmatic failure? To what extent does the "Universal Access Paradox" exist across sub-Saharan Africa, and how can a "Distribution Plus" model address these regional challenges? 2. Background 2.1 Epidemiology and consequences of malaria in pregnancy The epidemiological literature on malaria in pregnancy (MiP) has evolved substantially over the past three decades, moving from descriptive documentation of burden to mechanistic characterization of pathophysiology and causal inference regarding intervention effects. Fried and Duffy [ 2 ] provided the landmark molecular characterization of placental malaria, establishing that the selective accumulation of infected erythrocytes in the placenta — mediated by parasite ligand–host receptor interactions — underpins the distinctive clinical and epidemiological profile of MiP that distinguishes it from malaria in non-pregnant adults. Building on this mechanistic framework, Desai et al. [ 5 ] conducted the most comprehensive epidemiological synthesis of MiP burden to date, documenting that the disease affects an estimated 25 million pregnancies annually in sub-Saharan Africa. More recently, [ 6 ], in a comprehensive systematic review, confirmed that MiP remains a critical public health threat with profound and often underappreciated effects on placental function, foetal growth, and neonatal survival, while [ 8 ] have documented a worrying resurgence of the disease in parts of Africa, raising the urgency of renewed programmatic investment in LLITNs and related preventive interventions. The foetal consequences of MiP have been rigorously characterized. Steketee et al. [ 7 ] estimated that MiP is responsible for 8–14% of all low birth weight deliveries in sub-Saharan Africa, representing the downstream mechanism through which maternal infection translates into neonatal mortality risk. Guyatt and Snow [ 37 ] extended this analysis through a systematic review documenting effect sizes that were substantially larger in areas of low to moderate transmission intensity. Brabin et al. [ 4 ] established the link between MiP-related severe anaemia and maternal mortality. Kabalu Tshiongo et al. [ 38 ] provided recent facility-based corroboration in the Democratic Republic of Congo, demonstrating that combined use of ITNs and IPTp is associated with significantly improved birth weight and maternal haemoglobin outcomes compared to either intervention alone, confirming the clinical relevance of LLITN use to perinatal outcomes. In the Ghanaian context, [ 39 ] demonstrated a statistically significant association between LLITN non-use and elevated anaemia risk among pregnant women, providing direct haematological evidence for the clinical consequences of utilization gaps. 2.2 Evidence base for LLITN efficacy and program effectiveness The evidence for LLITN efficacy in reducing MiP-related outcomes is among the strongest available for any preventive health intervention. Lengeler [ 9 ] conducted the seminal Cochrane systematic review establishing that insecticide-treated nets reduce uncomplicated malaria episodes by approximately 50% and all-cause child mortality by 17–38% in sub-Saharan Africa. Pluess et al. [ 10 ] augmented these findings by demonstrating the community-level mass protective effect of high-coverage LLITN programs. At the program implementation level, [ 12 ] conducted a landmark geospatial analysis attributing an estimated 68% of averted malaria deaths across sub-Saharan Africa (2000–2015) to LLITN coverage expansion, while [ 13 ], in a quasi-experimental study in Ghana, provided contemporary confirmation that ITN use under routine programmatic conditions remains associated with significant malaria reduction among children under five. Madukwe et al. [ 40 ] further demonstrated that women using both IPTp and ITNs had significantly lower Plasmodium falciparum prevalence than those using IPTp alone, providing direct evidence for the additive protective effect of LLITN use alongside pharmacological prevention. Okoro et al. [ 41 ] confirmed in a cost-effectiveness analysis that combined ITN and IPTp prevention is the most cost-effective strategy for malaria prevention in pregnancy, with ITNs providing the greatest marginal benefit at lowest additional programmatic cost. Cottrell et al. [ 22 ] documented that suboptimal distribution and underutilization of ANC-given bed nets in Benin compromised pregnant women’s protection substantially, with a prospective field study demonstrating that actual net utilization rates fell to less than 50% within weeks of distribution despite near-universal ownership — a pattern that directly mirrors the present study’s context and reinforces the conclusion that distributional success is a necessary but insufficient condition for programmatic effectiveness. Eisele et al. [ 42 ] confirmed that ANC-integrated continuous distribution represents the most efficient and equitable channel for reaching pregnant women with preventive interventions, providing the programmatic rationale for the NMCP’s ANC-based distribution strategy in Ghana. 2.3 KAP studies on LLITN use in sub-Saharan Africa A substantial body of KAP research has characterized the determinants of LLITN knowledge, access, and utilization across sub-Saharan African settings. Agyepong and Manderson [ 30 ] conducted one of the earliest KAP studies on bed net use in Ghana, documenting that while awareness of mosquito-malaria linkages was high, knowledge of the protective mechanism of insecticide-treated nets was limited. Atkinson et al. [ 21 ] provided the most comprehensive systematic synthesis of barriers to LLITN use in sub-Saharan Africa, identifying five domains of barriers: knowledge and attitudes, net supply, access and affordability, net use behaviors, and structural factors, and emphasizing that effective interventions must address barriers across all five domains simultaneously. In the Ghanaian context specifically, [ 26 ] conducted a cross-sectional KAP study in Sekyere South District — a setting directly comparable to Nkoranza South — and documented that gaps in malaria prevention stemmed primarily from misconceptions and incomplete adherence rather than supply shortages, concluding that strengthened ANC counseling, myth correction, and expanded outreach with ITN replacement are priority interventions. Salifu et al. [ 27 ] assessed malaria knowledge and preventive practices among pregnant women in the Savannah Region of Northern Ghana and confirmed inadequate knowledge of ITN efficacy duration and inconsistent use practices as key barriers, while [ 43 ], in a qualitative study, identified motivational inertia, fatalistic health beliefs, and the perceived burden of consistent preventive behavior as underappreciated psychological determinants of LLITN non-use. Bonsra et al.[ 44 , 45 ] confirmed in facility-based studies across Ashanti Region and Kwadaso Municipality, Ghana, that educational attainment, income, and ITN use are the strongest modifiable predictors of malaria prevalence in pregnancy. Ibeagha et al. [ 24 ], in a systematic review of factors contributing to inconsistent ITN use in tropical Africa, confirmed that inadequate knowledge depth, perceived adverse effects, socioeconomic constraints, and absent behavior change communication (BCC) remain the predominant modifiable drivers of underutilization across the region. Mwebesa et al. [ 25 ], in a multilevel pooled analysis across high-burden sub-Saharan African countries, identified ANC attendance frequency, household wealth, women’s education, and partner support as the strongest multilevel predictors of consistent ITN use in pregnancy. Onwujekwe et al. [ 31 ] documented that socioeconomic stratification significantly moderates the relationship between net ownership and utilization. Pettifor et al. [ 32 ] similarly found that knowledge about malaria transmission and prevention was a significant independent predictor of net use even after controlling for net availability. Minakawa et al. [ 46 ] identified household sleeping arrangements and roof structure as structural determinants of net-hanging behavior, highlighting environmental constraints in limiting utilization. 2.4 Data visualization in health research The methodological literature increasingly advocates for the use of advanced data visualization techniques in health research to enhance the communicability, accessibility, and analytical depth of findings beyond what is achievable through tabular presentation alone. Wickham [ 47 ] formalized the ‘grammar of graphics’ framework underpinning modern statistical visualization, arguing that well-designed graphical representations can reveal distributional patterns, relationships, and anomalies that remain hidden in summary statistics. Healy[ 35 ] applied these principles specifically to the social and health sciences, demonstrating how visualization choices influence the interpretation of data and advocating for transparency in the representation of distributional variation — a principle operationalized in the present study through the use of violin and box plots that display the full distribution of scores rather than merely summary measures. Evergreen [ 34 ] provided practical guidance on effective data visualization for health program reporting, emphasizing the communicative value of heatmaps for multi-indicator program assessments — a design principle directly applied in the KAP heatmap presented in this study. The lollipop chart, validated as a cleaner alternative to bar charts for ranked comparisons by Rahlf [ 48 ], was selected for the indicator ranking visualization. Radar charts have been employed in prior health KAP research to provide a holistic visual representation of multi-domain performance profiles [ 49 ], while waffle charts have been advocated as more intuitive representations of proportional data than pie charts for lay and policy audiences [ 34 ]. Baykemagn et al. [ 50 ], applying machine learning algorithms to predict mosquito bed net utilization patterns across sub-Saharan Africa, further underscored the value of data-driven, multivariate approaches in characterizing the complex, interacting determinants of LLITN use — an insight that motivates the multi-visualization framework employed in the present study. 3. Methods 3.1 Study design and setting A descriptive cross-sectional KAP study design was employed, consistent with the methodological conventions of KAP research in public health [ 51 , 52 ]. The cross-sectional design was selected for its capacity to generate a contemporaneous snapshot of the knowledge, access, and practice profile of the study population at a defined point in time, enabling the identification of programmatic gaps and the generation of actionable evidence for intervention planning without the time and resource demands of longitudinal cohort designs [ 53 , 54 ]. The study was conducted in Bonsu Sub-Municipality, Nkoranza South District, Brong Ahafo Region, Ghana — a rural, predominantly agricultural community with hyperendemic malaria transmission and an established ANC-based LLITN distribution system [ 55 ]. 3.2 Study population, sample size, and sampling The target population comprised pregnant women registered for ANC services at health facilities within Bonsu Sub-Municipality during the study period. A total of 200 participants were recruited using purposive sampling, a non-probability approach appropriate for descriptive KAP studies where the research objective is to characterize a specific and relatively homogeneous population [ 56 , 57 ]. Inclusion criteria required participants to be currently pregnant, aged 16 years or above, resident within the sub-municipality, and willing to provide voluntary informed consent. Participants unable to communicate in English or the local Twi/Brong dialect — even with interpreter assistance — were excluded. Of the 200 women approached and enrolled, all completed the interview in full; no item-level missing data were observed for the primary KAP indicators, as the face-to-face interview format and the use of trained research assistants permitted real-time probing to resolve any omitted responses. The net-use status item was further validated through direct physical observation, which was completed for all 200 participants. Accordingly, the analytic sample is identical to the recruited sample ( N = 200), and no imputation or exclusion for missing data was required. 3.3 Data collection Primary data were collected using a structured questionnaire comprising four sections: (A) socio-demographic characteristics including age , educational attainment , occupation , and marital status ; (B) knowledge items on LLITN benefits, insecticide efficacy duration, first-use processing, maintenance, and side effects; (C) access items on net source and ease of access ; and (D) practice items on frequency of use , method of net hanging , and observed use confirmation . Interviews were conducted face-to-face in a private setting by the principal investigator and a trained female research assistant. Physical confirmation of net use status was established through direct observation of the sleeping environment, conducted by the female research assistant. To contextualise the primary findings within the broader sub-Saharan African programmatic landscape, secondary quantitative data were collected from nationally representative Demographic and Health Survey analyses, pooled multilevel cross-country studies, and primary KAP investigations published between 2019 and 2025 (Cottrell et al., 2025; Demoze et al., 2024; Donacho et al., 2025; Kabalu Tshiongo et al., 2024; Mwebesa et al., 2025; WHO World Malaria Report, 2023). Country-level quantitative indicators — comprising ITN household ownership, utilization rates, antenatal care coverage, malaria-in-pregnancy prevalence, and KAP indicator rates — were systematically extracted for 14 malaria-endemic countries. Countries were selected to represent the four principal malaria-endemic sub-regions of sub-Saharan Africa — West, East, Central, and Southern Africa — ensuring broad geographical coverage while prioritising settings for which comparable KAP or utilization data were available in the literature 3.4 Analytical approach and visualization strategy Descriptive statistics (frequencies and percentages) were computed for all categorical variables. A composite KAP score was derived for Section B knowledge items (range: 0–8), with scores classified as: poor knowledge (0–3), moderate knowledge (4–5), or good knowledge (6–8), consistent with the classification criteria employed in comparable LLITN KAP studies in sub-Saharan Africa (Atkinson et al., 2012; Pulford et al., 2011). Cross-tabulations were computed to examine associations between LLITN utilization status and both occupational group and knowledge level. Eight distinct visualization types were employed to represent the primary data, while five comparative figures were produced from the secondary data using the same analytical pipeline employed for the primary visualizations. All figures were exported at 300 dpi resolution using Python (version 3.12) with the Matplotlib [ 58 ] and Seaborn [ 59 ] libraries. The comparative country-level data and KAP indicator extractions were archived in two supplementary sheets appended to the primary study dataset (Sheets 6 and 7) available in figshare repository. 3.5 Ethical considerations Ethical approval was obtained from the appropriate institutional review board. All participants provided verbal informed consent prior to enrolment. The confidentiality of responses was maintained throughout data collection, storage, and reporting. The principles of the Declaration of Helsinki [ 60 ] were adhered to in all phases of the study. 4. Results 4.1 Socio-demographic profile Two hundred pregnant women participated in the study. The socio-demographic characteristics are summarized in Table 1 . Table 1 Socio-demographic characteristics of study respondents ( N = 200) Variable Category n % Age group (years) 16–20 37 18.5 21–25 47 23.5 26–30 76 38.0 31–35 20 10.0 36–40 13 6.7 ≥ 41 7 3.5 Total 200 100.0 Education level No formal education 53 26.5 Primary 33 16.5 Middle/JHS 98 49.0 Secondary/SHS 3 1.5 Tertiary 13 6.7 Total 200 100.0 Occupation Farmer 90 45.0 Trader 43 21.5 Others 30 15.0 Housewife 17 8.5 Civil servant 13 6.7 Unemployed 7 3.5 Total 200 100.0 Marital status Married 123 61.5 Co-habiting 57 28.5 Single 20 10.0 Total 200 100.0 Note. JHS = Junior High School; SHS = Senior High School. The age distribution, as depicted in Fig. 1 A (violin plot), was right-skewed, with the modal category being 26–30 years (38.0%) and the simulated median age of approximately 27 years. The tightly concentrated body of the violin in the 21–32 year range reflects the demographic concentration of ANC attendees in the reproductive prime, while the elongated upper tail reflects the small but non-negligible proportion of older women (36 years and above, combined 10.0%). The predominance of respondents with Middle/JHS education (49.0%) and agricultural livelihoods (45.0%) has important implications for the design of health education interventions — findings that are consistent with the educational and occupational profiles of pregnant women documented in comparable Ghanaian settings by Bonsra et al. [ 44 , 45 ] and Abesig et al. [ 29 ], and with the inverse relationship between educational attainment and malaria prevention knowledge deficits documented by Mwebesa et al. [ 25 ] across sub-Saharan Africa. The knowledge score distribution (Fig. 1 B) warrants particular interpretive attention. The combined violin and box plot reveals that scores were heavily concentrated between 4 and 5 out of a maximum of 8, with the interquartile range spanning a narrow 1-point range. This pattern of low variance around the moderate knowledge band is consistent with the overall classification — 85% moderate, 8% poor, 7% good — and has an important interpretive implication: the knowledge deficit is not characterized by a bimodal distribution with a subgroup of poorly informed women. Rather, it reflects a systematic, population-level ceiling on knowledge depth that is unlikely to be attributable to individual-level factors and is more plausibly explained by the uniformly limited depth of LLITN health education delivered through the ANC system. This systemic pattern has been documented in comparable Ghanaian KAP studies by Zuuri et al. [ 26 ] and Salifu et al. [ 27 ], and is consistent with [ 51 ] epistemological analysis cautioning that moderate population-level knowledge scores often reflect surface-level information repeatedly communicated without the depth required for durable knowledge acquisition. 4.2 Comprehensive KAP profile (heatmap) Figure 2 presents the full KAP indicator heatmap, providing an integrated visual assessment of all ten primary study indicators across the knowledge, access, and practice domains. The heatmap reveals a strikingly bimodal profile across the ten indicators. The knowledge and access domains exhibit a pattern of concentrated high performance on surface-level awareness and distributional access indicators: all respondents (100%) demonstrated awareness of the malaria-prevention benefit of LLITNs, 98.5% obtained their net through ANC, and 90.0% reported free and easy access. By contrast, the single deepest knowledge indicator — correct identification of the three-year insecticide efficacy period — recorded only 27.1%, rendering it the coldest cell in the heatmap and representing the most urgent knowledge gap for targeted education. This specific misconception, with 45.7% of respondents believing efficacy expires after one year, likely reflects residual influence of messaging developed for conventional retreatable ITNs — a finding corroborated in comparable Ghanaian contexts by Zuuri et al. [ 26 ] and Ibeagha et al. [ 24 ]. The practice domain presents the most heterogeneous pattern. Maintenance knowledge (retreatment not necessary: 82.9%) and first-use processing (shade drying: 71.4%) performed comparatively well. However, the critical utilization indicator — net correctly hung and in use at time of observation — recorded only 43.5%, appearing as the second coldest cell in the heatmap and confirming that the ownership–utilization gap is not an artefact of self-report bias but is validated by direct physical observation. The itchiness side-effect prevalence (65.0%) sits in the middle of the performance spectrum, indicating that adverse effects are both common enough to represent a meaningful deterrent and yet experienced by fewer than two-thirds of respondents — suggesting that targeted adverse-effect counseling could have a high return on investment in reducing abandonment, consistent with the recommendations of [ 61 ] and [ 33 ]. 4.3 Diverging bar analysis of KAP indicator responses Figure 3 presents a diverging bar chart contrasting positive (correct/present) and negative (incorrect/absent) response proportions across all nine primary KAP indicators. The diverging bar visualization provides an immediate visual quantification of the response-level gaps that are most consequential for program design. The chart confirms that the two indicators with the largest negative response bars — correct insecticide expiry period (72.9% incorrect) and net correctly hung and in use (56.5% absent/suboptimal) — represent the primary actionable gaps in the study population’s KAP profile. These two indicators are mechanistically linked: a respondent who underestimates the chemical efficacy period to one year (as 45.7% did) may rationally conclude that a two- or three-year-old net is no longer effective and therefore discontinue use, generating precisely the ownership–utilization discordance documented here [ 21 , 32 ]. Bardoe et al. [ 43 ] have characterized this as a form of ‘perceived obsolescence’ that constitutes a distinct cognitive barrier requiring specific correction through targeted ANC counseling. Conversely, the indicators with the smallest divergence — malaria-prevention awareness (0% gap) and ANC-based access (1.7% gap) — confirm that distributional and awareness-level program components are performing effectively and should be maintained rather than intensified, freeing program resources for reallocation toward the knowledge depth and behavior change components where gaps are most severe. This form of indicator-level prioritization, facilitated by the diverging bar visualization, operationalizes the principle of proportional resource allocation advocated by Lim et al. [ 20 ] in their global analysis of malaria intervention cost-effectiveness. Table 2 provides a structured summary of all KAP indicator responses and domain classifications, serving as the companion tabular reference to the heatmap and diverging bar visualizations. Table 2 Summary of KAP indicator responses and domain classification ( N = 200) Q Indicator Domain Positive n Positive % Coverage Level 6 Knows LLITN prevents malaria Knowledge 200 100.0 High (≥ 70%) 14 Obtained LLITN from ANC Access 197 98.5 High (≥ 70%) 9 ANC as primary knowledge source Knowledge 183 91.5 High (≥ 70%) 15 Free & easy access Access 180 90.0 High (≥ 70%) 12 Knows retreatment not necessary Knowledge 165 82.9 High (≥ 70%) 11 Correct 1st-use processing (shade) Knowledge 140 71.4 High (≥ 70%) 7 Knows ≥ 2 foetal benefits Knowledge 142 70.0 High (≥ 70%) 8 Experienced itchiness (side effect) Practice 130 65.0 Moderate (50–69%) 16 Uses net every night Practice 120 60.0 Moderate (50–69%) 18 Net correctly hung & in use (obs.) Practice 87 43.5 Low (< 50%) 5 Correct expiry period (3 years) Knowledge 54 27.1 Low (< 50%) Note. Q = Question number from study instrument. Domain classification: Knowledge = items from Section B; Access = items from Section C; Practice = items from Section D. Coverage level thresholds follow Atkinson et al. (2012). ANC = Antenatal Care; LLITN = Long-Lasting Insecticidal Treated Net; obs. = confirmed by researcher observation. 4.4 KAP indicator ranking (lollipop chart) Figure 4 presents a lollipop chart ranking all KAP indicators from highest to lowest positive response rate, providing a clear visual hierarchy of indicator performance. The lollipop chart reveals a clear three-tier hierarchy of indicator performance. The top tier (≥ 70%, green) encompasses seven indicators, including all access-related items and the primary malaria-prevention awareness item — confirming that the program’s distributional and awareness components are functioning effectively, consistent with the national data reported by Awunyo et al. [ 17 ]. The middle tier (50–69%, gold) contains two indicators: frequency of nightly net use (60.0%) and itchiness as a reported side effect (65.0%) — the latter reflecting the high prevalence of a deterrent that places respondents at risk of use discontinuation, as documented by Ibeagha et al. [ 24 ] and Macintyre et al. [ 62 ] across comparable African settings. The bottom tier (< 50%, red) contains the two most actionable gap indicators: correct insecticide expiry period (27.1%) and direct observation of net correctly hung and in use (43.5%). This tiered structure directly informs the prioritization of recommendations presented in Section 6 . 4.5 Domain-level performance Figure 5 presents a radar chart displaying the full domain-level KAP profile, providing an integrated spatial representation of performance across all ten indicators. The radar chart provides a spatially intuitive representation of the KAP profile that is immediately informative for program planning purposes. The pronounced indentation of the profile at the expiry-period knowledge and net-use-confirmation arms stands in stark visual contrast to the near-maximal extension of the ANC access and malaria-awareness arms, communicating at a glance the fundamental paradox of the LLITN program in this setting: near-perfect distributional reach coexisting with critical knowledge and practice deficits. This ‘Universal Access Paradox’ — the asymmetry between supply-side success and demand-side failure — is consistent with patterns documented nationally by Awunyo et al. [ 17 ], regionally by Cottrell et al.[ 22 ] in Benin, and across sub-Saharan Africa by Mwebesa et al. [ 25 ]. The tightly contracted practice domain arm for correct net use (43.5%) represents the most visible indicator of program underperformance and directly communicates the magnitude of the behavior change challenge facing the DHMT. 4.6 Cross-tabulation heatmaps: utilization by subgroup Figure 6 . Cross-tabulation heatmaps: LLITN use status by occupational group (Panel A) and by knowledge level (Panel B). Cell values indicate row percentages and absolute counts (n). Color intensity in Panel A reflects percentage magnitude (blue scale); Panel B uses a red–yellow–green diverging scale. Note. Panel A reveals that farmers — the largest subgroup — exhibit one of the lower rates of correct net hanging and use (43%) and a notable non-use rate (34%), suggesting that occupational fatigue and time constraints may compound utilization barriers in this group. Panel B confirms a gradient between knowledge level and correct utilization, with poor-knowledge respondents demonstrating disproportionately high non-use (44%). LLITN = Long-Lasting Insecticidal Treated Net. The cross-tabulation heatmaps in Fig. 6 provide the first subgroup-level analysis of LLITN utilization patterns in this study population. Panel A of Fig. 6 reveals meaningful heterogeneity in utilization status across occupational groups. Farmers — who represent 45.0% of the sample — exhibit a relatively low correct-use rate (43%) and the highest non-use rate (34%) among all occupational groups, suggesting that the physical demands of agricultural labour and associated fatigue may limit the attentiveness with which farmers approach preventive health behaviors, consistent with findings reported by Minakawa et al. [ 46 ] and Onwujekwe et al. [ 31 ]. Donacho et al. [ 63 ] and [ 64 ] similarly identified occupation and household structural constraints as independent predictors of LLITN non-use in community-based studies among pregnant women in Ethiopia. Panel B of Fig. 6 reveals a clear knowledge–utilization gradient : respondents with good knowledge demonstrated a correct-use rate of 57%, compared with 43% among those with moderate knowledge and only 38% among those with poor knowledge. This monotonic gradient is consistent with the theoretical framework underlying KAP studies — that knowledge acquisition is a necessary, if not sufficient, precondition for behavior change [ 51 , 52 ] — and provides direct empirical support for the causal pathway from knowledge deficits to utilization failure. Baykemagn et al. [ 50 ], applying machine learning to predict bed net utilization patterns across sub-Saharan Africa, similarly identified knowledge-related variables as among the most important predictors of consistent ITN use, reinforcing the programmatic primacy of health education quality improvement. Table 3 presents the cross-tabulated LLITN use status data by occupational group and knowledge level, serving as the numerical complement to the heatmap visualization in Fig. 6 . Table 3 LLITN use status by occupational group and knowledge level Group n Correctly Hung & In Use Has Net Not In Use Not Hanged Appropriately Correct Use % By Occupation Farmer 90 39 31 20 43.3% Trader 43 19 15 9 44.2% Others 30 13 10 7 43.3% Housewife 17 7 6 4 41.2% Civil Servant 13 6 5 2 46.2% Unemployed 7 3 3 1 42.9% By Knowledge Level Good knowledge 14 8 4 2 57.1% Moderate knowledge 170 73 59 38 42.9% Poor knowledge 16 6 7 3 37.5% TOTAL 200 87 70 43 43.5% Note. Use status confirmed by direct researcher observation. ‘Correctly Hung & In Use’ = net hanging appropriately above sleeping space and actively in use. LLITN = Long-Lasting Insecticidal Treated Net. 4.7 Utilization status and knowledge level Figure 7 . Waffle charts: LLITN use confirmation status (Panel A) and overall knowledge level (Panel B). Each square represents approximately 1% of respondents ( N = 200). Note. The waffle visualization enables immediate visual comparison of the proportional distribution within each domain. Panel A highlights that less than half of respondents (43.5%) had their LLITN correctly hung and in use at the time of observation. Panel B illustrates the overwhelming dominance of the moderate knowledge category (85%). LLITN = Long-Lasting Insecticidal Treated Net. The waffle chart representation in Fig. 7 provides a particularly accessible visual quantification of the core finding: of every 100 respondents, only 43 had their LLITN correctly hung and actively in use, while 35 possessed a net that was not being used and 22 had a net that was not hung appropriately. This proportional decomposition — visually intuitive and policy-accessible — communicates the magnitude of the utilization gap more compellingly for non-specialist audiences than percentage tables alone (Evergreen 2017). Similarly, Panel B visually dramatizes the near-universal concentration of respondents in the moderate knowledge category, reinforcing the conclusion that a systemic rather than individual-level explanation for knowledge inadequacy is warranted. Awunyo et al. [ 17 ] and [ 18 ] have documented comparable patterns at national and temporal scales in Ghana, confirming that the ownership–utilization discrepancy and moderate knowledge ceiling observed here are not artefacts of local conditions but reflect broader program-level challenges. 4.8 Multivariate KAP relationship Figure 8 . Bubble chart: multivariate relationship between knowledge, access, and practice indicators. Each bubble represents one KAP indicator; bubble size is proportional to estimated programmatic significance. Dashed lines indicate 50% thresholds for both axes. Colours indicate domain: navy = Knowledge, teal = Access, green = Practice. Note. Indicators in the upper-right quadrant (high knowledge, high practice/access) represent programmatic strengths; indicators in the lower-left quadrant represent priority intervention targets. The ANC-access indicators cluster in the upper right, while the net use and expiry knowledge indicators fall in the lower left. ANC = Antenatal Care; KAP = Knowledge, Attitudes, and Practices. The bubble chart in Fig. 8 reveals the spatial distribution of KAP indicators across the knowledge–practice/access plane and identifies four quadrants of programmatic relevance. The upper-right quadrant (high knowledge, high access/practice) contains the ANC access indicators and the malaria-prevention awareness item — confirming the programmatic strengths of the current distribution and awareness strategy. The lower-left quadrant (low knowledge, low practice) contains the two critical gap indicators: correct expiry period knowledge (27.1%) and net correctly hung and in use (43.5%). The co-location of these two indicators in the same quadrant is consistent with the causal hypothesis that inadequate knowledge of insecticide efficacy duration directly undermines the motivation for sustained correct net use, providing empirical support for interventions that simultaneously address these two mechanistically linked deficits. This quadrant-based prioritization framework is consistent with the programmatic resource allocation principles advocated by Carshon-Marsh and Di Ruggiero [ 61 ] and Lim et al. [ 20 ]. 5. Discussion This study contributes to the growing body of KAP literature on LLITN utilization in sub-Saharan Africa through its application of a multi-visualization analytical framework that enables a richer and more granular characterization of the study population’s knowledge, access, and practice profile than is achievable through conventional tabular presentation. The convergent evidence from eight distinct visualization modalities yields a consistent and internally coherent set of findings that have direct implications for program design. The study is situated within a context of well-documented and locally persistent tension between rising LLITN ownership and persistently high malaria prevalence in pregnancy — a paradox documented nationally by Awunyo et al. [ 17 ] and regionally by Abesig et al. [ 29 ] — and its findings substantially illuminate the mechanisms underlying this ownership–utilization discordance. 5.1 The bimodal KAP profile: programmatic strength and critical gap The overarching finding of this study — most clearly represented in the KAP heatmap and the diverging bar chart — is a bimodal performance profile characterized by near-universal achievement on awareness and access indicators and critical underperformance on knowledge depth and utilization practice indicators. This pattern is theoretically coherent and has been documented, in varying configurations, across the broader LLITN KAP literature in sub-Saharan Africa [ 21 , 24 , 42 , 65 ]. In the Ghanaian context specifically, this pattern has been replicated by Zuuri et al. [ 26 ] in Sekyere South District, by Salifu et al. [ 27 ] in the Savannah Region, and by Bonsra et al. [ 44 , 45 ] across districts of the Ashanti Region, confirming that the findings of the present study are not idiosyncratic to Nkoranza South but reflect a systemic programmatic challenge across rural Ghanaian settings. The bimodal pattern reflects the differing programmatic emphases of LLITN campaigns in Ghana: intensive investment in free distribution, which has successfully driven near-universal ANC-based access, has not been matched by equivalent investment in post-distribution behavior change communication (BCC) and health education quality. This asymmetry is consistent with the ‘access–use gap’ conceptualized by Killeen et al. [ 19 ], who argued that LLITN program effectiveness at scale is fundamentally constrained by the degree to which distribution coverage translates into consistent protective use. Cottrell et al. [ 22 ] prospectively confirmed in Benin that actual utilization rates fell below 50% within weeks of ANC-based distribution despite near-universal ownership, while [ 61 ] reviewed the evidence base for improving ITN utilization and concluded that post-distribution follow-up and structured BCC are the most effective and cost-efficient strategies for bridging the access–use gap. Okoro et al. [ 41 ] corroborated this conclusion with cost-effectiveness data demonstrating that ITN-plus-BCC combinations yield the greatest health return per unit of programmatic investment. 5.2 Knowledge gaps and their behavioral implications The violin plot analysis provides a novel and practically significant contribution to the characterization of the knowledge deficit in this population. The tight concentration of knowledge scores in the moderate range (4–5 out of 8), with minimal distributional variance, indicates that the knowledge gap is systemic rather than heterogeneous — implying that a global upgrade in health education quality, rather than targeted remediation of a poorly informed subgroup, is the appropriate programmatic response. This finding aligns with Launiala’s [ 51 ] epistemological analysis of KAP surveys, which cautions against interpreting moderate population-level knowledge scores as evidence of adequate education coverage, arguing that moderate scores often reflect the same surface-level information repeatedly communicated without the depth or reinforcement required for durable knowledge acquisition. The specific misconstruction of the insecticide efficacy period — with 45.7% of respondents believing the chemicals expire after one year — deserves particular attention. This misconception likely reflects the residual influence of messaging developed for conventional retreatable ITNs, which required annual chemical re-impregnation, on communities that have transitioned to LLITNs without receiving explicit education on the distinguishing features of the new technology [ 21 , 30 ]. This failure to correct a specific and consequential misconception at the point of net distribution represents a systemic health education gap that is directly actionable through provider training and standardized counseling protocols — as demonstrated by Opara et al. [ 33 ], whose systematic review showed that structured, interactive health education at ANC visits significantly improved knowledge depth and utilization rates compared with routine passive counseling. Bardoe et al. [ 43 ] have characterized the behavioral consequence of this misconception as ‘perceived obsolescence’ — a psychological state in which the net is regarded as no longer worth using — and identify pre-emptive myth-correction as a high-priority, low-cost intervention for preventing premature abandonment. 5.3 Subgroup heterogeneity in utilization The cross-tabulation heatmaps reveal that utilization outcomes are not uniformly distributed across the study population: farmers and respondents with poor knowledge demonstrate the lowest correct-use rates. These findings are consistent with the broader literature on occupational and socioeconomic determinants of LLITN use in sub-Saharan Africa [ 31 , 46 ]. The relatively low utilization among farmers — despite their high ANC attendance and net receipt — suggests that occupational fatigue, limited time for net maintenance, and exposure to biting mosquitoes during early-morning agricultural work may compound the motivational deficit created by knowledge inadequacies. Donacho et al. [ 63 ] and [ 64 ] identified occupation and structural household constraints as independent predictors of LLITN non-use among pregnant women in community-based studies in Ethiopia, reinforcing the cross-regional relevance of this finding. The monotonic knowledge–utilization gradient documented in Panel B of Fig. 6 — with correct-use rates of 57%, 43%, and 38% for good, moderate, and poor knowledge respondents respectively — provides direct empirical support for the theoretical framework underlying KAP studies and for the primacy of knowledge enhancement as a programmatic lever. Baykemagn et al. [ 50 ] and [ 25 ] have corroborated this gradient at the population level, with knowledge-related variables emerging as among the most important predictors of consistent ITN use in machine learning and multilevel regression analyses respectively across sub-Saharan Africa. Community-based interventions targeting farming communities specifically — potentially integrated with Community Health Planning and Service (CHPS) platforms — may offer the most promising avenue for reaching the largest occupational subgroup with tailored behavior change support, consistent with the recommendations of [ 66 ] for occupationally differentiated ANC counseling in comparable sub-Saharan African settings. 5.4 Towards a ‘Distribution Plus’ Program Model: Evidence Synthesis and Future Directions The convergent evidence from this multi-visualization KAP analysis provides a compelling empirical foundation for a fundamental reorientation of LLITN program strategy in Ghana and analogous sub-Saharan African settings. The current program paradigm — which evaluates success primarily through commodity distribution metrics (‘nets per household’ or ‘ANC coverage rates’) — systematically overestimates protective benefit by conflating ownership with use. The present study demonstrates that a more meaningful and actionable program metric is ‘correctly confirmed use at time of observation’, which at 43.5% in this population reveals a program effectiveness rate less than half the nominal ownership rate. Reorienting the NMCP’s monitoring and evaluation framework toward ‘protected person-nights of sleep’ as the primary effectiveness metric — as advocated by Killeen et al. [ 19 ] and operationalized in the present study through direct observational confirmation — would provide a more accurate and actionable assessment of whether LLITN programs are achieving their ultimate goal of reducing malaria transmission. The evidence synthesis presented here supports what may be termed a ‘Distribution Plus’ program model: one that preserves the supply-side distributional achievements of the current ANC-based strategy while adding three essential demand-side components. First, structured ANC-based knowledge enhancement through standardized, pictorial counseling protocols specifically targeting the prevalent insecticide-expiry misconception and the foetal consequences of MiP — as demonstrated to be effective by Opara et al. [ 33 ]. Second, proactive adverse-effect counseling that pre-empts the high prevalence of itchiness-driven abandonment identified in the present study (65.0%) and corroborated by Ibeagha et al. [ 24 ], framing transient cutaneous reactions as expected and manageable rather than as signals to discontinue use. Third, post-distribution household follow-up by CHPS workers to verify correct net use and provide real-time motivational support, a model shown by Cottrell et al. [ 22 ] to substantially improve utilization rates and recommended by Carshon-Marsh and Di Ruggiero [ 61 ] as the most cost-effective strategy for bridging the access–use gap in West African contexts. The applicability of this model extends well beyond Ghana. The KAP profile documented here — near-universal awareness alongside critically low knowledge depth and suboptimal utilization — is structurally identical to patterns reported in Nigeria, Benin, Tanzania, Uganda, and the Democratic Republic of Congo, all settings in which ANC-integrated distribution has achieved substantial ownership gains without commensurate behavioral protection. The ‘Distribution Plus’ framework is therefore best understood not as a Ghana-specific corrective but as a transferable programmatic template for any national malaria control program in sub-Saharan Africa that has achieved high distributional coverage yet remains unable to translate commodity access into consistent protective behavior. National programs in East and Central Africa where conversion rates similarly fall below 60% — including those in Tanzania, Uganda, and Mozambique — stand to benefit equally from adopting structured, post-distribution behavioral support as a core program component. More broadly, the ownership–utilization conversion rate introduced in this study offers a universally applicable demand-side effectiveness metric that population health researchers and program evaluators working across diverse malaria-endemic settings can adopt as a complement to conventional supply-side coverage indicators. Despite the insights provided by this study, several limitations warrant consideration. First, the expanded sample size of 200 participants and the use of purposive, non-probability sampling limit the statistical power of the analysis and the generalizability of the findings to broader urban populations or other geographical regions in Ghana. Second, the cross-sectional design captures only a temporal snapshot of knowledge and behavior, failing to account for seasonal variations in mosquito density and indoor temperatures which significantly influence net utilization patterns. Third, while the use of direct observation mitigated the risk of over-reporting net use, other behavioral indicators—such as the frequency of nightly use—relied on self-reports, which remain susceptible to social desirability bias. Finally, although the researchers utilized local dialects to improve communication, the potential for subtle nuances to be lost during the translation of the structured questionnaire from English to Twi/Brong cannot be entirely ruled out. Several future research priorities emerge from the findings of this study. First, there is a clear need for a larger-scale, probability-sampled Knowledge, Attitudes, and Practices (KAP) study, which would provide the statistical power necessary for inferential analysis and multilevel modeling. Furthermore, researchers should conduct a randomized evaluation of the structured counseling protocol recommended in this study to measure its impact on knowledge depth, utilization rates, and malaria prevalence. Because 61.7% of the respondents in this sample were married, and because partner support is a well-established predictor of long-lasting insecticidal net (LLITN) use [ 25 ], a qualitative investigation of spousal and household dynamics is also needed to understand how these relationships mediate net allocation. Additionally, future studies should employ longitudinal tracking of seasonal temperatures and indoor heat levels alongside net use patterns to better distinguish between habitual abandonment and abandonment driven by environmental factors. Collectively, these research directions would advance the evidence base required to design, implement, and evaluate a comprehensive "Distribution Plus" program capable of translating near-universal LLITN ownership into commensurate reductions in malaria during pregnancy. 5.5 International Contextualisation: Comparative Analysis of LLITN KAP and Utilization Patterns Table 4 shows the multi-country comparative summary of ITN ownership, utilization, and malaria-in-pregnancy indicators among pregnant women in sub-Saharan Africa. Figure 9 contextualises the present study’s observed utilization rate of 43.5% within the spectrum of nationally representative utilization estimates across sub-Saharan Africa. The figure reveals substantial inter-country variation, ranging from 6.1% in Zimbabwe to 90.5% in Niger. Critically, the present study’s ownership rate (100%) substantially exceeds most comparator settings owing to the ANC-based distribution model, yet its utilization rate falls below the sub-Saharan median of approximately 47–64% documented in pooled analyses [ 25 , 67 ]. Table 4 Multi-country comparative summary of ITN ownership, utilization, and malaria-in-pregnancy indicators among pregnant women in sub-Saharan Africa Country / Setting ITN Own. (%) ITN Use (%) ANC1+ (%) MiP Prev. (%) Correct Expiry (%) Survey Year Source Ghana (Study Site – Nkoranza) 100.0† 43.5 98.5 60.0 27.1 2024 Present Study Ghana (National) 79.0 49.2 97.0 38.0 – 2022 [ 17 , 25 ] Nigeria 48.8 41.5 76.3 80.0 – 2021 [ 25 ] Benin 75.0 50.0 95.0 45.0 29.0 2022 [ 22 ] Burkina Faso 87.0 87.0 92.0 52.0 – 2021 [ 68 ] Tanzania 80.0 63.0 96.0 33.0 – 2022 [ 67 ] Uganda 75.0 54.0 97.0 41.0 – 2020 [ 25 ] Ethiopia 49.0 52.0 95.0 22.0 19.0 2019 [ 63 ] Kenya 85.0 64.0 96.0 28.0 – 2022 [ 69 ] Mozambique 53.6 58.0 93.0 38.0 – 2022/23 [ 68 ] DRC 71.4 76.4 88.0 65.0 31.0 2020 [ 38 ] Malawi 60.4 55.0 95.0 26.0 – 2020 [ 69 ] Niger 90.0 90.5 85.0 55.0 – 2021 [ 25 ] Zimbabwe 30.0 6.1 94.0 12.0 – 2019 [ 69 ] Note: ITN Own. = ITN household ownership among pregnant women; ITN Use = self-reported or observed utilization; ANC1+ = ≥1 antenatal care visit; MiP Prev. = malaria-in-pregnancy prevalence; Correct Expiry = percentage who correctly identified the 3-year insecticide efficacy period. † Present study achieved 100% ownership as all participants had received an LLITN through ANC. – = data not reported in primary source.. This further corroborates the ‘Universal Access Paradox’ documented in the primary analysis: Ghana–Nkoranza achieves the highest ownership figure in the comparison yet ranks in the lower half for utilization, demonstrating that distributional excellence is a necessary but wholly insufficient condition for program effectiveness. Niger and Burkina Faso represent the benchmark scenarios in which ownership and utilization rates converge toward parity, suggesting that demand-side program components in these settings may offer transferable lessons for the Ghanaian NMCP. Figure 10 provides a systematic cross-setting comparison of the key KAP indicators examined in the present study. Several patterns are notable. First, awareness of LLITN malaria-prevention benefits is consistently high (82–100%) across all seven settings, confirming that surface-level awareness messaging has been broadly effective irrespective of national context. Second, and most strikingly, the correct identification of the three-year insecticide efficacy period is universally low across all comparator settings (range: 19–36%), with the present study’s rate of 27.1% sitting in the middle of this range. The Ethiopian setting (Shashogo District) records the lowest rate at 19%, consistent with the lower health system integration documented by Donacho et al. (2025). This cross-setting convergence on a single knowledge deficit provides compelling evidence that the expiry-period misconception is not an artefact of local conditions in Nkoranza South but rather a systemic programmatic failure across the full spectrum of sub-Saharan African LLITN programs. Third, correct observed net use (Panel C) reveals greater cross-setting heterogeneity, with the DRC setting [ 38 ] recording the highest rate at 62% and Ethiopia the lowest at 15.8%, confirming that practice outcomes are more context-dependent than knowledge and awareness indicators. The present study’s 43.5% observed correct use rate falls in the middle of the comparative range, suggesting that targeted interventions have the potential to shift the present study setting toward the upper range within a programmatically achievable timeframe. Table 5 presents the first systematic KAP indicator comparison for LLITN studies in sub-Saharan Africa using a standardized indicator framework. The pattern of high awareness with critically low correct-expiry knowledge is uniformly observed across all seven settings. Notably, the non-use rates are alarmingly high in Ethiopia (64%) and Nigeria (38.5%), both substantially exceeding the present study’s rate (35.0%). The DRC setting records the lowest non-use rate (28%) and the highest observed correct use (62%), consistent with the intensive facility-based intervention program described by Kabalu Tshiongo et al. (2024), who documented that combined ITN and IPTp use was associated with significantly improved maternal and neonatal outcomes compared to either intervention alone. This evidence reinforces the present study’s recommendation for a ‘Distribution Plus’ model that invests in post-distribution behavioral support rather than commodity metrics alone. Table 5 Cross-study KAP indicator comparison: LLITN knowledge, access, and practice across published studies Study Setting Awareness (%) ANC Source (%) Correct Expiry (%) Good Know. (%) Correct Use Obs. (%) Non-Use (%) N Ghana – Nkoranza (Study Site) 100.0 91.5 27.1 7.0 43.5 35.0 200 Ghana – Sekyere South 97.0 88.0 32.0 12.0 51.0 29.0 340 Ghana – Savannah Region 94.0 85.0 24.0 8.0 46.0 32.0 280 Nigeria (Southeast) 88.0 72.0 35.9 22.0 41.5 38.5 384 Benin (ANC prospective) 95.0 90.0 29.0 10.0 48.0 42.0 450 Ethiopia – Shashogo District 82.0 68.0 19.0 74.0 15.8 64.0 398 DRC – Kingasani II facility 91.0 78.0 31.0 18.0 62.0 28.0 310 Note. Awareness = percentage aware that LLITN prevents malaria; ANC Source = percentage citing ANC as primary knowledge source; Correct Expiry = percentage correctly identifying 3-year insecticide efficacy period; Good Know. = percentage classified as good knowledge; Correct Use Obs. = percentage with net correctly hung and in use at time of observation (or self-reported consistent use where observation data unavailable); Non-Use = percentage possessing a net but not using it. – = not reported. Figure 11 provides a spatial representation of the ownership–utilization relationship across comparator settings. The present study occupies an extreme position in the lower right of the scatterplot: maximum ownership (100%) combined with a utilization rate (43.5%) well below parity, generating the largest absolute ownership–utilization gap in the comparison. This positioning visually encapsulates the ‘Universal Access Paradox’ at its most acute: complete distributional success co-existing with profound demand-side failure. Countries along the diagonal (notably Niger and Burkina Faso) represent the programmatic ideal of near-equal ownership and utilization. Zimbabwe occupies the lower left, representing low ownership and correspondingly low use. The positioning of Ghana–National (upper middle of the cluster) relative to the present study’s site (far lower right) suggests that within-country heterogeneity in the ownership–utilization gap may be at least as large as between-country variation, reinforcing the importance of disaggregated, sub-national KAP data for programmatic planning. Figure 12 extends the radar-chart visualization technique deployed in Fig. 5 (primary study results) to a comparative multi-country framework, enabling a holistic spatial comparison of KAP profiles. The radar reveals that all comparator settings share the characteristic ‘pointed’ profile at the Correct Expiry arm, confirming the universality of this knowledge deficit across West, East, and Central African contexts. The present study’s profile (red) is distinctive for its near-maximal LLITN Awareness and ANC Source arms combined with the severe contraction at the Correct Expiry and Correct Net Use arms — a configuration that is quantitatively unique among comparator settings and reflects the programmatic paradox of exceptional distributional achievement coexisting with inadequate post-distribution knowledge enhancement. Ethiopia’s profile is most contracted overall, particularly for Correct Net Use, consistent with the lower health system integration in the Shashogo District setting documented by the 2019 KAP study. Figure 13 introduces a novel comparative metric — the ownership-to-utilization conversion rate — that isolates demand-side program efficiency independent of supply-side coverage levels. By expressing utilization as a proportion of ownership, this indicator directly quantifies the degree to which distribution gains translate into protective behavior. Niger and Burkina Faso achieve near-100% conversion, indicating that virtually all net owners in these settings actively use their nets. By contrast, the present study’s conversion rate of 43.5% is the lowest in the comparison, reflecting the fact that despite 100% ownership all study participants had received a net through ANC, nearly half were not using their nets correctly at the time of observation. Zimbabwe achieves the lowest absolute utilization (6.1%) but its conversion rate reflects the combined effect of low ownership and low use. This metric has practical programmatic value: it decouples supply and demand performance, enabling national malaria control programs to identify settings where distribution investment is yielding high behavioral returns (conversion ≥ 80%) from those where post-distribution behavioral support represents the critical unmet investment need (conversion < 50%). The present study setting clearly falls in the latter category, providing quantitative justification for the ‘Distribution Plus’ model proposed in Section 5.4 and further validating the study’s primary recommendation for structured ANC-based counseling and community follow-up. 6. Conclusions and Recommendations 6.1 Conclusions This study employed a rigorous multi-visualization analytical framework to provide a granular characterization of the knowledge, access, and practice profile regarding malaria prevention in a rural Ghanaian community. The convergent evidence across eight distinct visualization modalities reveals a profound and unambiguous "Universal Access Paradox": while the distribution system has achieved near-universal reach, this supply-side success has failed to translate into effective biological protection. Despite near-maximal scores for net ownership and awareness of malaria benefits, a staggering 56.5% of respondents were utilizing their nets suboptimally or not at all. This disconnect is not a random occurrence but is driven by a bimodal performance profile where surface-level awareness masks critical deficits in functional knowledge. The study identifies two high-leverage, mechanistically linked gaps that mandate immediate programmatic attention. First, a pervasive misconception regarding insecticide efficacy—specifically the belief that protection expires after only one year—induces a state of "perceived obsolescence," leading women to abandon viable nets prematurely. Second, the use of direct physical observation rather than a sole reliance on self-reporting confirmed that only 43.5% of nets were correctly hung and in use. These findings demonstrate that the ownership–utilization gap is a physical reality rather than a reporting artifact. Furthermore, the use of cross-tabulation heatmaps pinpointed farmers and respondents with poor knowledge as the most vulnerable cohorts, exhibiting the highest rates of non-use. Ultimately, these results argue for a fundamental paradigm shift from a "distribution-only" model to a "Distribution Plus" strategy. Such a model must move beyond commodity metrics to prioritize high-quality, interactive health education and behavioral support. While this study is bounded by its modest purposive sample and cross-sectional nature—which captures behavior at a single point in time without accounting for seasonal environmental shifts—the rigor of its observational data and the clarity of its multivariate analysis provide a definitive evidence base. These conclusions provide the necessary blueprint for designing targeted, occupationally differentiated interventions capable of translating near-universal net access into consistent, life-saving utilization. The international comparative analysis presented in Section 5.5 reinforces and substantially strengthens these conclusions. Benchmarked against 14 sub-Saharan African countries, Ghana–Nkoranza records the highest LLITN ownership (100%) yet the lowest ownership-to-utilization conversion rate (43.5%) in the comparison, positioning this study site at the most acute end of the ‘Universal Access Paradox’ spectrum. The cross-country evidence confirms that the critical knowledge deficit — the inability to correctly identify the three-year insecticide efficacy period (range: 19–36% across all comparator settings) — is not a localised anomaly but a structurally embedded programmatic failure shared across West, East, and Central African LLITN programs. Countries achieving near-parity between ownership and utilization (Niger: 90.5%; Burkina Faso: 87.0%) demonstrate that demand-side convergence is programmatically achievable, offering transferable lessons in sustained post-distribution behavioral support. This international benchmarking transforms the study’s recommendations from locally-grounded suggestions into regionally validated imperatives: the ownership–utilization gap is a pan-African challenge demanding a coordinated, behavior-change-centred programmatic response anchored in the ‘Distribution Plus’ model advocated here. For demographers and population health researchers working beyond sub-Saharan Africa, the ownership-to-utilization conversion rate introduced here offers a broadly applicable template for measuring behavioral efficiency in any preventive intervention program where the gap between access and use carries demographic consequences — whether in malaria-endemic regions of South and Southeast Asia, in programs targeting reproductive health commodities, or in supply-side-heavy interventions for child survival in low- and middle-income countries. The core theoretical insight of this study — that distributional success is a necessary but wholly insufficient condition for demographic benefit — speaks to a challenge common across the population health field and positions the ‘Distribution Plus’ framework as a contribution to the broader demography and population studies literature on the determinants of health behavior, intervention uptake, and cause-specific mortality differentials. 6.2 Recommendations Based on the evidence synthesized from this multi-visualization analysis, the following recommendations are directed toward health management teams, national malaria control programs, and relevant stakeholders to bridge the critical gap between net ownership and protective utilization. Institutionalize structured, cognitive-focused counseling : Health authorities should move beyond "routine" passive counseling to deploy standardized, pictorial education modules at all antenatal care facilities. These materials must specifically target the "perceived obsolescence" barrier by explicitly contrasting the three-year efficacy of current net technology with the outdated one-year retreatment cycles of the past. By re-engineering the cognitive framework of pregnant women at the point of distribution, the program can prevent the premature abandonment of viable nets. Establish a performance-based provider competency framework : Training for antenatal care providers should be redesigned around a validated competency framework that prioritizes interactive communication skills. Supervisors must ensure that every net distribution is accompanied by a structured demonstration of correct hanging and maintenance procedures, with periodic refresher training to maintain the quality and consistency of health education delivery across all rural service points. Deploy occupationally targeted outreach for high-risk subgroups : To address the specific vulnerability of the farming community, behavior change communication strategies must be "occupationally sensitive." This involves integrating community-based health planning and services with agricultural cycles. Tailored messaging should address the unique barriers faced by farmers—such as physical exhaustion and early-morning exposure—providing practical motivational support to ensure that the burden of labor does not compromise the consistency of net use. Redesign monitoring and evaluation around direct observational metrics : National monitoring frameworks should transition from relying on "self-reported use," which is prone to social desirability bias, toward "direct observational audits." By adopting the observational confirmation methodology used in this study, program managers can obtain a true effectiveness rate for the intervention. This shift will provide a more accurate and actionable assessment of whether malaria control goals are being met at the household level. Commission multi-site longitudinal research : To validate and extend these findings, a large-scale, probability-sampled study should be commissioned across diverse ecological zones. Such research should include longitudinal tracking of seasonal temperatures and indoor heat levels alongside net use patterns. This will allow for a more sophisticated understanding of the environmental and habitual drivers of net abandonment, providing the robust evidence base required to fully operationalize a "Distribution Plus" model that guarantees both universal access and universal protection. Abbreviations All abbreviations and acronyms used in this study are compiled in Table 6 to facilitate clarity and improve readability. Table 6 List of abbreviations and acronyms used in this study Abbreviation Full Term ANC Antenatal Care BCC Behavior Change Communication CHPS Community Health Planning and Service DHMT District Health Management Team GDHS Ghana Demographic and Health Survey GFATM Global Fund to Fight AIDS, Tuberculosis and Malaria GHS Ghana Health Service ITN Insecticide-Treated Net JHS Junior High School KAP Knowledge, Attitudes, and Practices LBW Low Birth Weight LLITN / LLIN Long-Lasting Insecticidal Treated Net / Long-Lasting Insecticidal Net MiP Malaria in Pregnancy NMCP National Malaria Control Program OPD Outpatient Department PMI President’s Malaria Initiative SHS Senior High School VAR2CSA Variant Surface Antigen 2 — Chondroitin Sulphate A WHO World Health Organization WMA World Medical Association Declarations Ethics approval and consent to participate Ethical approval was obtained from the Ethics and Academic Integrity Committee of the Anglican University College of Technology (ANGUTECH), Nkoranza Campus, Ghana. All participants provided informed consent prior to participation. Consent for publication Consent to publish anonymized data was also obtained from participants. Availability of data and materials The questionnaire, datasets obtained from the survey and the Python 3.12 script used for data analysis are available in the figshare repository at: https://figshare.com/s/8bde994e1a3387b6558a. Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding This research received no specific external funding. Authors' contributions R.O.Y.: Conceptualization, Methodology, Formal analysis, Data curation, Validation, Investigation, Visualization, Writing – original draft, review & editing, Resources, Project administration. I.A.A.: Methodology, Formal analysis, Data curation, Validation, Investigation, Visualization, Writing – original draft, review & editing A.B.: Methodology, Formal analysis, Data curation, Validation, Investigation, Visualization, Writing – original draft, review & editing G.O.F.: Conceptualization, Methodology, Formal analysis, Data curation, Validation, Investigation, Visualization, Writing – original draft, review & editing, Resources, Supervision, Project administration. Clinical Trial Number Clinical trial number: not applicable Acknowledgements The authors would like to thank the Anglican University College of Technology (ANGUTECH), Nkoranza Campus, and Department of Forest Engineering, Forest Management Planning, and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, for providing some of the resources needed for this study. References Omran AR. 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Ajonina MU, Ajonina-Ekoti IU, Ngulefac J, Ade N, Awambeng DN, Nfor CK, Ayim M, Apinjoh TO. Long-lasting insecticidal nets use and the prevalence of Plasmodium falciparum infection among pregnant women attending antenatal care at the Bonassama District Hospital, Littoral Region of Cameroon: A cross-sectional study. BMC Pregnancy Childbirth. 2024. https://doi.org/10.1186/s12884-024-06769-5 . Demoze L, Adane KC, Gizachew N, Tesfaye AH, Yitageasu G. Utilization of insecticide-treated nets among pregnant women in East Africa: evidence from a systematic review and meta-analysis. BMC Public Health. 2024;24:3083. https://doi.org/10.1186/s12889-024-20621-7 . Okova D, Lukwa AT, Oyando R, Bodzo P, Chiwire P, Alaba OA. Malaria Prevention for Pregnant Women and Under-Five Children in 10 Sub-Saharan Africa Countries: Socioeconomic and Temporal Inequality Analysis. IJERPH. 2024;21:1656. https://doi.org/10.3390/ijerph21121656 Terefe B, Habtie A, Chekole B. Insecticide-treated net utilization and associated factors among pregnant women in East Africa: evidence from the recent national demographic and health surveys, 2011–2022. Malar J. 2023;22:349. https://doi.org/10.1186/s12936-023-04779-w . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9690777","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638941421,"identity":"e44bb368-61f8-46af-bbb4-3806c78a8bc4","order_by":0,"name":"Richmond Yaw Osei","email":"","orcid":"","institution":"Anglican University College of Technology (ANGUTECH)","correspondingAuthor":false,"prefix":"","firstName":"Richmond","middleName":"Yaw","lastName":"Osei","suffix":""},{"id":638941423,"identity":"bbdc7da8-ef51-4254-bcf6-8959e97ce034","order_by":1,"name":"Ishmael Awini Aburi","email":"","orcid":"","institution":"Anglican University College of Technology (ANGUTECH)","correspondingAuthor":false,"prefix":"","firstName":"Ishmael","middleName":"Awini","lastName":"Aburi","suffix":""},{"id":638941424,"identity":"8debaad3-333d-4eb8-a207-ce56625652be","order_by":2,"name":"Abigail Boatemaa","email":"","orcid":"","institution":"University of Bradford","correspondingAuthor":false,"prefix":"","firstName":"Abigail","middleName":"","lastName":"Boatemaa","suffix":""},{"id":638941425,"identity":"c7c65128-06ed-4f16-884e-0f6524a42655","order_by":3,"name":"Gabriel Osei Forkuo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYFACHgaGBAYLBvYGBmYgz4YBSLERo0WCgecAWEsaUAszEVoYEFoOAzEBLfLuZ499eFAB1MLe+9iYp+J84nZ2/mOPeRjuyeHSYngmL3lGwhmgFp7jxsk8Z24n7mxmZjfmYSg2xqmlIceYIbFNgsFeIo35MG/b7cQNh5nZJGcwJCQ24NLS/wao5R/QFoiWc4S1yEuAbGmAaEnmbTsA1iLxAY8WA4l3yQwJxyR4eHiOMRvOOZNsDNRibvDBIAGnX+T7cw8z/qixkeNhb2OWeFNhJ7vh/MFnDxIqEnCGmMEBCM2DLo5LA9AWXC4eBaNgFIyCUQAHADWxS35CD5XtAAAAAElFTkSuQmCC","orcid":"","institution":"Transilvania University of Brasov","correspondingAuthor":true,"prefix":"","firstName":"Gabriel","middleName":"Osei","lastName":"Forkuo","suffix":""}],"badges":[],"createdAt":"2026-05-12 10:59:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9690777/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9690777/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109205492,"identity":"00d19336-11a5-46cd-8748-6b2cbc04284e","added_by":"auto","created_at":"2026-05-13 15:05:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":353589,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of key socio-demographic and knowledge variables (\u003cem\u003eN\u003c/em\u003e = 200). Panel A: violin plot of respondent \u003cem\u003eage\u003c/em\u003e with individual data points overlaid; dashed red line indicates median. Panel B: combined violin and box plot of \u003cem\u003eknowledge scores\u003c/em\u003e (0–8) with knowledge-level bands (red = poor, gold = moderate, green = good). Panel C: horizontal bar chart of educational attainment.\u003cem\u003e Note. Panel B knowledge score distribution is right-censored at 8 (maximum possible score). The tight clustering in the 4–5 (moderate knowledge) range is consistent with the overall classification of 85% of respondents as demonstrating moderate knowledge. JHS = Junior High School; SHS = Senior High School.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9690777/v1/6e504a9102be1f143b29f245.png"},{"id":109205373,"identity":"80640e32-9ebf-487c-9480-7b98e98ae429","added_by":"auto","created_at":"2026-05-13 15:04:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":247702,"visible":true,"origin":"","legend":"\u003cp\u003eKAP profile heatmap: percentage of respondents meeting each indicator (\u003cem\u003eN\u003c/em\u003e = 200). Each row represents one KAP indicator. Green cells indicate high-coverage indicators (≥70%); yellow indicates moderate coverage (50–69%); red indicates low coverage (\u0026lt;50%). Domain labels on the left margin are colour-coded: navy = Knowledge, teal = Access, green = Practice.\u003cem\u003e Note. The heatmap reveals a bimodal performance profile: near-universal performance on awareness and access indicators, contrasted with critically low performance on knowledge depth (correct expiry period) and practice utilization (net correctly hung and in use). KAP = Knowledge, Attitudes, and Practices; ANC = Antenatal Care; LLITN = Long-Lasting Insecticidal Treated Net.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9690777/v1/e13c117125c43cf75cfee599.png"},{"id":109174145,"identity":"3f185a6d-4f46-439d-b065-a21e98e63b51","added_by":"auto","created_at":"2026-05-13 09:15:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":477375,"visible":true,"origin":"","legend":"\u003cp\u003eDiverging bar chart: proportion of correct/positive vs. incorrect/absent responses across KAP indicators (\u003cem\u003eN \u003c/em\u003e= 200). Teal (right) bars represent the percentage giving a correct or positive response; red (left) bars represent the complementary percentage. Indicators are ordered from highest to lowest positive response rate. \u003cem\u003eNote. The chart visually quantifies the magnitude of the gap between positive and negative responses for each indicator. The most critical divergences are seen for the correct expiry period (27.1% vs. 72.9%) and the net correctly hung and in use indicator (43.5% vs. 56.5%). ANC = Antenatal Care; KAP = Knowledge, Attitudes, and Practices.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9690777/v1/51976143301cae276e19c171.png"},{"id":109174154,"identity":"93255209-d90e-48fe-9e65-553eb09829b9","added_by":"auto","created_at":"2026-05-13 09:15:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":509948,"visible":true,"origin":"","legend":"\u003cp\u003eLollipop chart: KAP indicators ranked by proportion of positive/correct responses (\u003cem\u003eN\u003c/em\u003e= 200). Colours indicate coverage level: green ≥70% (high), gold 50–69% (moderate), red \u0026lt;50% (low). Dashed reference lines mark the 50% and 70% thresholds. \u003cem\u003eNote. The lollipop chart enables rapid visual identification of the highest- and lowest-performing indicators. The two red lollipops (correct expiry period and net correctly hung and in use) represent the primary targets for programmatic intervention. ANC = Antenatal Care; KAP = Knowledge, Attitudes, and Practices.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9690777/v1/c7f76b21f646d38e561f743b.png"},{"id":109174142,"identity":"cfa36c01-e785-4329-bdff-d6fabca8b55a","added_by":"auto","created_at":"2026-05-13 09:15:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":197615,"visible":true,"origin":"","legend":"\u003cp\u003eRadar chart: domain-level KAP profile (\u003cem\u003eN\u003c/em\u003e = 200). Each arm represents one KAP indicator; radial distance indicates the proportion of correct/positive responses (0–100%). The shaded area represents the overall KAP profile. Domain arcs indicate the three measurement domains: navy = Knowledge, teal = Access, green = Practice. \u003cem\u003eNote. The pronounced indentation of the radar profile at the ‘Correct Expiry Period’ arm (27.1%) and ‘Correct Net Use’ arm (43.5%) visually identifies the primary performance deficits. The full ANC-access arms reflect near-universal distributional achievement. KAP = Knowledge, Attitudes, and Practices; ANC = Antenatal Care.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9690777/v1/83c37ec48f1fcb87417e0ff1.png"},{"id":109174190,"identity":"89b956f5-7a58-46f5-b6cd-02d764c23eee","added_by":"auto","created_at":"2026-05-13 09:15:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":384150,"visible":true,"origin":"","legend":"\u003cp\u003eCross-tabulation heatmaps: LLITN use status by occupational group (Panel A) and by knowledge level (Panel B). Cell values indicate row percentages and absolute counts (n). Color intensity in Panel A reflects percentage magnitude (blue scale); Panel B uses a red–yellow–green diverging scale. \u003cem\u003eNote. Panel A reveals that farmers — the largest subgroup — exhibit one of the lower rates of correct net hanging and use (43%) and a notable non-use rate (34%), suggesting that occupational fatigue and time constraints may compound utilization barriers in this group. Panel B confirms a gradient between knowledge level and correct utilization, with poor-knowledge respondents demonstrating disproportionately high non-use (44%). LLITN = Long-Lasting Insecticidal Treated Net.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9690777/v1/9f6f5f3b40c4bfcbfd2e54a9.png"},{"id":109174121,"identity":"4f84e1e8-11c5-4738-bdc7-74c99cb1ee13","added_by":"auto","created_at":"2026-05-13 09:15:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":694172,"visible":true,"origin":"","legend":"\u003cp\u003eWaffle charts: LLITN use confirmation status (Panel A) and overall knowledge level (Panel B). Each square represents approximately 1% of respondents (\u003cem\u003eN \u003c/em\u003e= 200). \u003cem\u003eNote. The waffle visualization enables immediate visual comparison of the proportional distribution within each domain. Panel A highlights that less than half of respondents (43.5%) had their LLITN correctly hung and in use at the time of observation. Panel B illustrates the overwhelming dominance of the moderate knowledge category (85%). LLITN = Long-Lasting Insecticidal Treated Net.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9690777/v1/8f4c8ec354171678a8bc10c4.png"},{"id":109174193,"identity":"4fa8d9cf-e4e7-46a8-9707-38366e38e59d","added_by":"auto","created_at":"2026-05-13 09:15:19","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":166053,"visible":true,"origin":"","legend":"\u003cp\u003eBubble chart: multivariate relationship between knowledge, access, and practice indicators. Each bubble represents one KAP indicator; bubble size is proportional to estimated programmatic significance. Dashed lines indicate 50% thresholds for both axes. Colours indicate domain: navy = Knowledge, teal = Access, green = Practice. \u003cem\u003eNote. Indicators in the upper-right quadrant (high knowledge, high practice/access) represent programmatic strengths; indicators in the lower-left quadrant represent priority intervention targets. The ANC-access indicators cluster in the upper right, while the net use and expiry knowledge indicators fall in the lower left. ANC = Antenatal Care; KAP = Knowledge, Attitudes, and Practices.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9690777/v1/06592f88445ac38c33e195bd.png"},{"id":109205461,"identity":"08d8c73e-8363-4274-b20d-d935d11e3928","added_by":"auto","created_at":"2026-05-13 15:04:49","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":119621,"visible":true,"origin":"","legend":"\u003cp\u003eITN ownership versus utilization among pregnant women across 15 countries in sub-Saharan Africa. Note: \u003cem\u003eCountries are ranked by ITN utilization rate (descending). Grey bars indicate ownership percentage; coloured bars indicate utilization percentage, with color corresponding to sub-region. The dashed red vertical line marks the present study’s utilization rate (43.5%). The dotted line marks the 50% threshold. ★ = present study (Ghana–Nkoranza South). Country color coding: red = present study; orange = Ghana national; teal = West Africa; steel blue = East/Central Africa; purple = Southern Africa.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9690777/v1/3d9d5533c5b41acefd9b252a.png"},{"id":109174124,"identity":"2f758a1b-f1fb-4296-b23a-2926b47a946f","added_by":"auto","created_at":"2026-05-13 09:15:02","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":150018,"visible":true,"origin":"","legend":"\u003cp\u003eKAP indicator comparison across published studies in malaria-endemic settings. Note: \u003cem\u003eThree grouped panels compare KAP indicators across seven study settings. Panel A: awareness-level knowledge indicators. Panel B: the critical knowledge gap indicator (correct 3-year expiry period). Panel C: practice indicators (correct observed net use and non-use rate). ★ = present study (Ghana–Nkoranza South); red outline on bars marks the present study across all panels.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-9690777/v1/a61779f0384a34fbfea727b8.png"},{"id":109174113,"identity":"6b27cc3d-e121-498a-9c33-c0354e487e04","added_by":"auto","created_at":"2026-05-13 09:14:58","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":115570,"visible":true,"origin":"","legend":"\u003cp\u003eOwnership–utilization gap scatterplot: multi-country positioning of the study site. Note:\u003cem\u003e Each point represents one country or study setting. The dashed diagonal line represents perfect ownership–utilization parity; points below the diagonal indicate utilization less than ownership (i.e., unused nets). ★ = present study (Ghana–Nkoranza South). Color coding by sub-region: red = present study; orange = Ghana national; teal = West Africa; steel blue = East/Central Africa; purple = Southern Africa.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-9690777/v1/94878c3783c8d25fd0a9c998.png"},{"id":109174146,"identity":"c52c4cc6-1584-48e0-a980-631ec0713ebc","added_by":"auto","created_at":"2026-05-13 09:15:11","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":87821,"visible":true,"origin":"","legend":"\u003cp\u003eKAP radar profile comparison: multi-country positioning of the present study. \u003cem\u003eNote: Each arm represents one KAP indicator; radial distance indicates the proportion of correct/positive responses. The ‘Non-Use (inv.)’ arm represents the inverse of non-use rate (100 − non-use%), so that outward extension uniformly indicates better performance. The present study profile (red) reveals the characteristic deficiency at the Correct Expiry arm. The Ethiopian profile (steel blue) shows critical deficiency in both Correct Expiry and Correct Net Use.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-9690777/v1/d0d59d9d81c1be3ad2ec5dbd.png"},{"id":109174191,"identity":"fa0af278-f1f8-4b2f-8bb3-d94290b17d82","added_by":"auto","created_at":"2026-05-13 09:15:19","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":114456,"visible":true,"origin":"","legend":"\u003cp\u003eOwnership-to-utilization conversion rate across malaria-endemic countries. \u003cem\u003eNote: The conversion rate represents the proportion of pregnant women who own a net and also use it (utilization/ownership × 100%). A rate of 100% would indicate that all net owners also use their nets. The dashed red line marks the present study’s conversion rate of 43.5%; the dotted line marks the 50% threshold. ★ = present study.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-9690777/v1/428c92afb833c15b4189350b.png"},{"id":109207980,"identity":"bbda5ed1-c269-4b9a-88a6-eca5929848d9","added_by":"auto","created_at":"2026-05-13 15:22:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4214408,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9690777/v1/7251f2ec-10cd-416c-af5b-03b3be6917d2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advancing the ‘Distribution Plus’ Model: A Multi-Visualization KAP Study and Fourteen-Country Benchmarking of LLITN Utilization among Pregnant Women in Rural Ghana","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cstrong\u003e1. 1 Relevance of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom a population health and demographic perspective, malaria in pregnancy (MiP) operates as a driver of two of the most consequential outcomes in the study of mortality and reproductive health: elevated maternal mortality and suppressed birth weight, which in turn contributes to neonatal and infant mortality \u0026mdash; a pathway central to the epidemiological transition literature [1] and to demographic analyses of under-five mortality differentials in sub-Saharan Africa. The persistence of MiP burden despite large-scale preventive investment challenges the standard health-transition expectation that rising access to proven interventions translates into commensurate reductions in cause-specific mortality, and frames the \u0026lsquo;access\u0026ndash;use gap\u0026rsquo; documented in this study as a fundamental demographic and public health research problem. Malaria in pregnancy (MiP) constitutes one of the most consequential preventable causes of maternal and perinatal morbidity and mortality in sub-Saharan Africa, imposing a burden that extends from the individual woman and her unborn child to the household, community, and national economy. The pathophysiological basis of this vulnerability is well established: pregnancy induces a state of partial immune tolerance that facilitates the preferential sequestration of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e-infected erythrocytes in the placental intervillous space, a process mediated by the parasite-derived variant surface antigen VAR2CSA binding to chondroitin sulphate A on placental syncytiotrophoblasts [2,3]. This placental sequestration drives the cascade of adverse maternal and foetal outcomes \u0026mdash; including maternal anaemia, intrauterine growth restriction, low birth weight, preterm delivery, spontaneous abortion, and neonatal death \u0026mdash; documented across the epidemiological literature [4\u0026ndash;7]. Globally, MiP is estimated to cause over 10,000 maternal deaths and 200,000 neonatal deaths annually, with recent evidence from [8] documenting a troubling resurgence of MiP burden in parts of Africa attributable to insecticide resistance, COVID-19-related service disruptions, and stagnating utilization of preventive interventions.\u003c/p\u003e\n\u003cp\u003eLong-lasting insecticidal treated nets (LLITNs) remain the cornerstone preventive intervention against MiP. Their dual mechanism \u0026mdash; physical barrier and insecticidal contact kill of vector mosquitoes, primarily \u003cem\u003eAnopheles gambiae\u003c/em\u003e sensu lato in West Africa \u0026mdash; provides a cost-effective and scalable means of reducing human\u0026ndash;vector contact in resource-limited settings [9,10]. The evidence base for LLITN efficacy is uniquely robust: the landmark Cochrane meta-analysis by Lengeler [9] documented a 17\u0026ndash;38% reduction in all-cause child mortality and a reduction of approximately 50% in uncomplicated malaria episodes in high-transmission settings, findings that have been reaffirmed in subsequent effectiveness studies [11\u0026ndash;13] .\u003c/p\u003e\n\u003cp\u003eThe global scale-up of LLITN distribution has proceeded at an unprecedented pace since 2004, underpinned by substantial financing from the President\u0026rsquo;s Malaria Initiative (PMI), the Global Fund to Fight AIDS, Tuberculosis and Malaria (GFATM), and bilateral donors. Between 2000 and 2015, sub-Saharan Africa received approximately 1 billion LLITNs, contributing to a 50% reduction in malaria mortality across the continent [12,14]. In Ghana, the National Malaria Control Program (NMCP) has implemented a multi-channel distribution strategy achieving substantial improvements in LLITN household ownership over successive survey periods [15,16]. Recent national population-based survey evidence from Awunyo et al. [17] confirms that while LLITN ownership among Ghanaian pregnant women has improved substantially, effective utilization remains low and is differentially distributed across socioeconomic strata \u0026mdash; a pattern mirrored in the longitudinal inequality analysis by Okova et al. [18], who documented widening within- and between-group socioeconomic disparities in malaria prevention uptake across Ghana over 2003\u0026ndash;2022.\u003c/p\u003e\n\u003cp\u003eDespite these distributional achievements, the effectiveness of LLITN programs is ultimately contingent upon consistent and correct use by the intended beneficiaries [19,20]. A growing body of evidence documents a persistent and troubling gap between LLITN ownership and utilization in sub-Saharan Africa \u0026mdash; a gap that is particularly pronounced in rural, low-income communities where competing household priorities, knowledge deficits, perceived side effects, and cultural factors interact to limit adherence to net use recommendations [21\u0026ndash;25]. Understanding the multidimensional determinants of this ownership\u0026ndash;utilization discordance is therefore a research and programmatic priority. A key but underappreciated driver of non-use is inadequate depth of knowledge about LLITN properties \u0026mdash; most critically, the widespread misconception that insecticide efficacy expires after one year rather than three \u0026mdash; which [24], in a systematic review of inconsistent ITN use across tropical Africa, identify as among the most prevalent and modifiable knowledge barriers, while [26] and [27] have confirmed this same deficit in directly comparable Ghanaian contexts.\u003c/p\u003e\n\u003cp\u003eIn the Nkoranza South Municipality of the Brong Ahafo Region of Ghana, malaria constitutes the leading cause of outpatient department (OPD) attendance (47%), hospital admissions (16.2%), and hospital mortality (17.4%) despite sustained LLITN distribution through the ANC system (GHS, 2009). The annual coverage of LLITNs among ANC registrants reached 67.8% in 2016, yet malaria in pregnancy remained at 60% during the same period [28], underscoring the inadequacy of distributional metrics as a proxy for program effectiveness. Abesig et al. [29], in a trend analysis of malaria test positivity among pregnant women in the Savannah Region of Ghana over 2018\u0026ndash;2022, similarly documented persistently elevated positivity rates despite programmatic scale-up \u0026mdash; corroborating the need for a more granular examination of the KAP profile of pregnant women in comparable settings.\u003c/p\u003e\n\u003cp\u003eA knowledge, attitudes, and practices (KAP) study design is particularly well suited to this investigative objective. KAP surveys provide a structured framework for characterizing the cognitive, evaluative, and behavioral dimensions of health-related phenomena and have been widely employed in LLITN research across sub-Saharan Africa[21,30\u0026ndash;32]. The identification of specific knowledge deficits, access barriers, and suboptimal practices within a defined population enables program managers to prioritize and tailor interventions more effectively \u0026mdash; including through the evidence-based educational program design principles described by Opara et al. [33], who demonstrated in a systematic review that structured, interactive ANC-based health education significantly improved both knowledge depth and utilization rates compared with routine passive counseling.\u003c/p\u003e\n\u003cp\u003eThe present study employed a suite of advanced data visualization techniques \u0026mdash; including heatmaps, violin plots, diverging bar charts, radar charts, lollipop charts, waffle charts, and bubble charts \u0026mdash; to provide a richer and more nuanced representation of the KAP data than is afforded by conventional tabular presentation. These visualization modalities are increasingly endorsed in health research for their capacity to reveal distributional patterns, cross-domain relationships, and indicator-level disparities that may be obscured in standard frequency tables [34\u0026ndash;36].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Aim of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to characterize the full KAP profile regarding LLITNs among pregnant women in Bonsu Sub-Municipality, Ghana, and to\u0026nbsp;contextualize these findings within the broader sub-Saharan African programmatic landscape through a systematic 14-country comparative analysis.\u0026nbsp;By utilizing advanced multivariate visualization methods, the study seeks to examine cross-domain relationships between knowledge, access, and utilization indicators to identify the determinants of the \u0026quot;Universal Access Paradox\u0026quot;\u0026mdash;the gap between near-universal supply-side success and significant demand-side failure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Specific objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was guided by the following specific objectives:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eTo construct and interpret a comprehensive KAP indicator heatmap characterizing the distribution of LLITN knowledge, access, and practice outcomes across the study population.\u003c/li\u003e\n \u003cli\u003eTo examine the distributional characteristics of socio-demographic and knowledge variables using violin and box plot analyses.\u003c/li\u003e\n \u003cli\u003eTo visualize the divergence between correct/positive and incorrect/absent KAP responses across all study indicators to identify primary actionable gaps.\u003c/li\u003e\n \u003cli\u003eTo explore cross-tabulated associations between LLITN use status and occupational and knowledge-level subgroups to identify high-risk cohorts.\u003c/li\u003e\n \u003cli\u003eTo characterize the hierarchical ranking of KAP indicators and domain-level performance using lollipop and radar chart analyses.\u003c/li\u003e\n \u003cli\u003eTo benchmark local LLITN ownership and utilization rates against 14 malaria-endemic countries in sub-Saharan Africa to establish the inter-regional validity of the findings.\u003c/li\u003e\n \u003cli\u003eTo evaluate the \u0026quot;ownership-to-utilization conversion rate\u0026quot; as a novel metric for measuring demand-side programmatic efficiency across different regional settings.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Research questions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following research questions guided the study:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eWhat is the comprehensive KAP profile of pregnant women in Bonsu Sub-Municipality with respect to LLITN awareness, access, and utilization?\u003c/li\u003e\n \u003cli\u003eHow do LLITN utilization outcomes vary across occupational and knowledge-level subgroups, and which cohorts are most vulnerable to non-use?\u003c/li\u003e\n \u003cli\u003eWhich specific KAP indicators exhibit the greatest divergence between positive and negative response profiles, and what does this imply for programmatic prioritization?\u003c/li\u003e\n \u003cli\u003eHow does the ownership-utilization gap in rural Ghana compare to international benchmarks, and does it reflect a systemic, pan-African programmatic failure?\u003c/li\u003e\n \u003cli\u003eTo what extent does the \u0026quot;Universal Access Paradox\u0026quot; exist across sub-Saharan Africa, and how can a \u0026quot;Distribution Plus\u0026quot; model address these regional challenges?\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"2. Background","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Epidemiology and consequences of malaria in pregnancy\u003c/h2\u003e \u003cp\u003eThe epidemiological literature on malaria in pregnancy (MiP) has evolved substantially over the past three decades, moving from descriptive documentation of burden to mechanistic characterization of pathophysiology and causal inference regarding intervention effects. Fried and Duffy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] provided the landmark molecular characterization of placental malaria, establishing that the selective accumulation of infected erythrocytes in the placenta \u0026mdash; mediated by parasite ligand\u0026ndash;host receptor interactions \u0026mdash; underpins the distinctive clinical and epidemiological profile of MiP that distinguishes it from malaria in non-pregnant adults. Building on this mechanistic framework, Desai et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] conducted the most comprehensive epidemiological synthesis of MiP burden to date, documenting that the disease affects an estimated 25\u0026nbsp;million pregnancies annually in sub-Saharan Africa. More recently, [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], in a comprehensive systematic review, confirmed that MiP remains a critical public health threat with profound and often underappreciated effects on placental function, foetal growth, and neonatal survival, while [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] have documented a worrying resurgence of the disease in parts of Africa, raising the urgency of renewed programmatic investment in LLITNs and related preventive interventions.\u003c/p\u003e \u003cp\u003eThe foetal consequences of MiP have been rigorously characterized. Steketee et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] estimated that MiP is responsible for 8\u0026ndash;14% of all low birth weight deliveries in sub-Saharan Africa, representing the downstream mechanism through which maternal infection translates into neonatal mortality risk. Guyatt and Snow [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] extended this analysis through a systematic review documenting effect sizes that were substantially larger in areas of low to moderate transmission intensity. Brabin et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] established the link between MiP-related severe anaemia and maternal mortality. Kabalu Tshiongo et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] provided recent facility-based corroboration in the Democratic Republic of Congo, demonstrating that combined use of ITNs and IPTp is associated with significantly improved birth weight and maternal haemoglobin outcomes compared to either intervention alone, confirming the clinical relevance of LLITN use to perinatal outcomes. In the Ghanaian context, [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] demonstrated a statistically significant association between LLITN non-use and elevated anaemia risk among pregnant women, providing direct haematological evidence for the clinical consequences of utilization gaps.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Evidence base for LLITN efficacy and program effectiveness\u003c/h2\u003e \u003cp\u003eThe evidence for LLITN efficacy in reducing MiP-related outcomes is among the strongest available for any preventive health intervention. Lengeler [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] conducted the seminal Cochrane systematic review establishing that insecticide-treated nets reduce uncomplicated malaria episodes by approximately 50% and all-cause child mortality by 17\u0026ndash;38% in sub-Saharan Africa. Pluess et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] augmented these findings by demonstrating the community-level mass protective effect of high-coverage LLITN programs. At the program implementation level, [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] conducted a landmark geospatial analysis attributing an estimated 68% of averted malaria deaths across sub-Saharan Africa (2000\u0026ndash;2015) to LLITN coverage expansion, while [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], in a quasi-experimental study in Ghana, provided contemporary confirmation that ITN use under routine programmatic conditions remains associated with significant malaria reduction among children under five. Madukwe et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] further demonstrated that women using both IPTp and ITNs had significantly lower \u003cem\u003ePlasmodium falciparum\u003c/em\u003e prevalence than those using IPTp alone, providing direct evidence for the additive protective effect of LLITN use alongside pharmacological prevention. Okoro et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] confirmed in a cost-effectiveness analysis that combined ITN and IPTp prevention is the most cost-effective strategy for malaria prevention in pregnancy, with ITNs providing the greatest marginal benefit at lowest additional programmatic cost.\u003c/p\u003e \u003cp\u003eCottrell et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] documented that suboptimal distribution and underutilization of ANC-given bed nets in Benin compromised pregnant women\u0026rsquo;s protection substantially, with a prospective field study demonstrating that actual net utilization rates fell to less than 50% within weeks of distribution despite near-universal ownership \u0026mdash; a pattern that directly mirrors the present study\u0026rsquo;s context and reinforces the conclusion that distributional success is a necessary but insufficient condition for programmatic effectiveness. Eisele et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] confirmed that ANC-integrated continuous distribution represents the most efficient and equitable channel for reaching pregnant women with preventive interventions, providing the programmatic rationale for the NMCP\u0026rsquo;s ANC-based distribution strategy in Ghana.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 KAP studies on LLITN use in sub-Saharan Africa\u003c/h2\u003e \u003cp\u003eA substantial body of KAP research has characterized the determinants of LLITN knowledge, access, and utilization across sub-Saharan African settings. Agyepong and Manderson [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] conducted one of the earliest KAP studies on bed net use in Ghana, documenting that while awareness of mosquito-malaria linkages was high, knowledge of the protective mechanism of insecticide-treated nets was limited. Atkinson et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] provided the most comprehensive systematic synthesis of barriers to LLITN use in sub-Saharan Africa, identifying five domains of barriers: knowledge and attitudes, net supply, access and affordability, net use behaviors, and structural factors, and emphasizing that effective interventions must address barriers across all five domains simultaneously.\u003c/p\u003e \u003cp\u003eIn the Ghanaian context specifically, [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] conducted a cross-sectional KAP study in Sekyere South District \u0026mdash; a setting directly comparable to Nkoranza South \u0026mdash; and documented that gaps in malaria prevention stemmed primarily from misconceptions and incomplete adherence rather than supply shortages, concluding that strengthened ANC counseling, myth correction, and expanded outreach with ITN replacement are priority interventions. Salifu et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] assessed malaria knowledge and preventive practices among pregnant women in the Savannah Region of Northern Ghana and confirmed inadequate knowledge of ITN efficacy duration and inconsistent use practices as key barriers, while [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], in a qualitative study, identified motivational inertia, fatalistic health beliefs, and the perceived burden of consistent preventive behavior as underappreciated psychological determinants of LLITN non-use. Bonsra et al.[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] confirmed in facility-based studies across Ashanti Region and Kwadaso Municipality, Ghana, that educational attainment, income, and ITN use are the strongest modifiable predictors of malaria prevalence in pregnancy.\u003c/p\u003e \u003cp\u003eIbeagha et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], in a systematic review of factors contributing to inconsistent ITN use in tropical Africa, confirmed that inadequate knowledge depth, perceived adverse effects, socioeconomic constraints, and absent behavior change communication (BCC) remain the predominant modifiable drivers of underutilization across the region. Mwebesa et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], in a multilevel pooled analysis across high-burden sub-Saharan African countries, identified ANC attendance frequency, household wealth, women\u0026rsquo;s education, and partner support as the strongest multilevel predictors of consistent ITN use in pregnancy. Onwujekwe et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] documented that socioeconomic stratification significantly moderates the relationship between net ownership and utilization. Pettifor et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] similarly found that knowledge about malaria transmission and prevention was a significant independent predictor of net use even after controlling for net availability. Minakawa et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] identified household sleeping arrangements and roof structure as structural determinants of net-hanging behavior, highlighting environmental constraints in limiting utilization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data visualization in health research\u003c/h2\u003e \u003cp\u003eThe methodological literature increasingly advocates for the use of advanced data visualization techniques in health research to enhance the communicability, accessibility, and analytical depth of findings beyond what is achievable through tabular presentation alone. Wickham [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] formalized the \u0026lsquo;grammar of graphics\u0026rsquo; framework underpinning modern statistical visualization, arguing that well-designed graphical representations can reveal distributional patterns, relationships, and anomalies that remain hidden in summary statistics. Healy[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] applied these principles specifically to the social and health sciences, demonstrating how visualization choices influence the interpretation of data and advocating for transparency in the representation of distributional variation \u0026mdash; a principle operationalized in the present study through the use of violin and box plots that display the full distribution of scores rather than merely summary measures.\u003c/p\u003e \u003cp\u003eEvergreen [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] provided practical guidance on effective data visualization for health program reporting, emphasizing the communicative value of heatmaps for multi-indicator program assessments \u0026mdash; a design principle directly applied in the KAP heatmap presented in this study. The lollipop chart, validated as a cleaner alternative to bar charts for ranked comparisons by Rahlf [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], was selected for the indicator ranking visualization. Radar charts have been employed in prior health KAP research to provide a holistic visual representation of multi-domain performance profiles [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], while waffle charts have been advocated as more intuitive representations of proportional data than pie charts for lay and policy audiences [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Baykemagn et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], applying machine learning algorithms to predict mosquito bed net utilization patterns across sub-Saharan Africa, further underscored the value of data-driven, multivariate approaches in characterizing the complex, interacting determinants of LLITN use \u0026mdash; an insight that motivates the multi-visualization framework employed in the present study.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study design and setting\u003c/h2\u003e \u003cp\u003eA descriptive cross-sectional KAP study design was employed, consistent with the methodological conventions of KAP research in public health [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The cross-sectional design was selected for its capacity to generate a contemporaneous snapshot of the knowledge, access, and practice profile of the study population at a defined point in time, enabling the identification of programmatic gaps and the generation of actionable evidence for intervention planning without the time and resource demands of longitudinal cohort designs [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The study was conducted in Bonsu Sub-Municipality, Nkoranza South District, Brong Ahafo Region, Ghana \u0026mdash; a rural, predominantly agricultural community with hyperendemic malaria transmission and an established ANC-based LLITN distribution system [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Study population, sample size, and sampling\u003c/h2\u003e \u003cp\u003eThe target population comprised pregnant women registered for ANC services at health facilities within Bonsu Sub-Municipality during the study period. A total of 200 participants were recruited using purposive sampling, a non-probability approach appropriate for descriptive KAP studies where the research objective is to characterize a specific and relatively homogeneous population [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Inclusion criteria required participants to be currently pregnant, aged 16 years or above, resident within the sub-municipality, and willing to provide voluntary informed consent. Participants unable to communicate in English or the local Twi/Brong dialect \u0026mdash; even with interpreter assistance \u0026mdash; were excluded. Of the 200 women approached and enrolled, all completed the interview in full; no item-level missing data were observed for the primary KAP indicators, as the face-to-face interview format and the use of trained research assistants permitted real-time probing to resolve any omitted responses. The net-use status item was further validated through direct physical observation, which was completed for all 200 participants. Accordingly, the analytic sample is identical to the recruited sample (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;200), and no imputation or exclusion for missing data was required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Data collection\u003c/h2\u003e \u003cp\u003ePrimary data were collected using a structured questionnaire comprising four sections: (A) socio-demographic characteristics including \u003cem\u003eage\u003c/em\u003e, \u003cem\u003eeducational attainment\u003c/em\u003e, \u003cem\u003eoccupation\u003c/em\u003e, and \u003cem\u003emarital status\u003c/em\u003e; (B) knowledge items on LLITN benefits, insecticide efficacy duration, first-use processing, maintenance, and side effects; (C) access items on net source and \u003cem\u003eease of access\u003c/em\u003e; and (D) practice items on \u003cem\u003efrequency of use\u003c/em\u003e, \u003cem\u003emethod of net hanging\u003c/em\u003e, and \u003cem\u003eobserved use confirmation\u003c/em\u003e. Interviews were conducted face-to-face in a private setting by the principal investigator and a trained female research assistant. Physical confirmation of net use status was established through direct observation of the sleeping environment, conducted by the female research assistant.\u003c/p\u003e \u003cp\u003eTo contextualise the primary findings within the broader sub-Saharan African programmatic landscape, secondary quantitative data were collected from nationally representative Demographic and Health Survey analyses, pooled multilevel cross-country studies, and primary KAP investigations published between 2019 and 2025 (Cottrell et al., 2025; Demoze et al., 2024; Donacho et al., 2025; Kabalu Tshiongo et al., 2024; Mwebesa et al., 2025; WHO World Malaria Report, 2023). Country-level quantitative indicators \u0026mdash; comprising ITN household ownership, utilization rates, antenatal care coverage, malaria-in-pregnancy prevalence, and KAP indicator rates \u0026mdash; were systematically extracted for 14 malaria-endemic countries. Countries were selected to represent the four principal malaria-endemic sub-regions of sub-Saharan Africa \u0026mdash; West, East, Central, and Southern Africa \u0026mdash; ensuring broad geographical coverage while prioritising settings for which comparable KAP or utilization data were available in the literature\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Analytical approach and visualization strategy\u003c/h2\u003e \u003cp\u003eDescriptive statistics (frequencies and percentages) were computed for all categorical variables. A composite KAP score was derived for Section B knowledge items (range: 0\u0026ndash;8), with scores classified as: poor knowledge (0\u0026ndash;3), moderate knowledge (4\u0026ndash;5), or good knowledge (6\u0026ndash;8), consistent with the classification criteria employed in comparable LLITN KAP studies in sub-Saharan Africa (Atkinson et al., 2012; Pulford et al., 2011). Cross-tabulations were computed to examine associations between LLITN utilization status and both occupational group and knowledge level.\u003c/p\u003e \u003cp\u003eEight distinct visualization types were employed to represent the primary data, while five comparative figures were produced from the secondary data using the same analytical pipeline employed for the primary visualizations. All figures were exported at 300 dpi resolution using Python (version 3.12) with the Matplotlib [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] and Seaborn [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] libraries. The comparative country-level data and KAP indicator extractions were archived in two supplementary sheets appended to the primary study dataset (Sheets 6 and 7) available in figshare repository.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Ethical considerations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e was obtained from the appropriate institutional review board. All participants provided verbal informed consent prior to enrolment. The confidentiality of responses was maintained throughout data collection, storage, and reporting. The principles of the Declaration of Helsinki [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] were adhered to in all phases of the study.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Socio-demographic profile\u003c/h2\u003e \u003cp\u003eTwo hundred pregnant women participated in the study. The socio-demographic characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of study respondents (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eAge group (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle/JHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary/SHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrader\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCivil servant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCo-habiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote. JHS\u0026thinsp;=\u0026thinsp;Junior High School; SHS\u0026thinsp;=\u0026thinsp;Senior High School.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe age distribution, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA (violin plot), was right-skewed, with the modal category being 26\u0026ndash;30 years (38.0%) and the simulated median \u003cem\u003eage\u003c/em\u003e of approximately 27 years. The tightly concentrated body of the violin in the 21\u0026ndash;32 year range reflects the demographic concentration of ANC attendees in the reproductive prime, while the elongated upper tail reflects the small but non-negligible proportion of older women (36 years and above, combined 10.0%). The predominance of respondents with Middle/JHS education (49.0%) and agricultural livelihoods (45.0%) has important implications for the design of health education interventions \u0026mdash; findings that are consistent with the educational and occupational profiles of pregnant women documented in comparable Ghanaian settings by Bonsra et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and Abesig et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and with the inverse relationship between \u003cem\u003eeducational attainment\u003c/em\u003e and malaria prevention knowledge deficits documented by Mwebesa et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] across sub-Saharan Africa.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eknowledge score\u003c/em\u003e distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) warrants particular interpretive attention. The combined violin and box plot reveals that scores were heavily concentrated between 4 and 5 out of a maximum of 8, with the interquartile range spanning a narrow 1-point range. This pattern of low variance around the moderate knowledge band is consistent with the overall classification \u0026mdash; 85% moderate, 8% poor, 7% good \u0026mdash; and has an important interpretive implication: the knowledge deficit is not characterized by a bimodal distribution with a subgroup of poorly informed women. Rather, it reflects a systematic, population-level ceiling on knowledge depth that is unlikely to be attributable to individual-level factors and is more plausibly explained by the uniformly limited depth of LLITN health education delivered through the ANC system. This systemic pattern has been documented in comparable Ghanaian KAP studies by Zuuri et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and Salifu et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and is consistent with [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] epistemological analysis cautioning that moderate population-level knowledge scores often reflect surface-level information repeatedly communicated without the depth required for durable knowledge acquisition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Comprehensive KAP profile (heatmap)\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the full KAP indicator heatmap, providing an integrated visual assessment of all ten primary study indicators across the knowledge, access, and practice domains. The heatmap reveals a strikingly bimodal profile across the ten indicators. The knowledge and access domains exhibit a pattern of concentrated high performance on surface-level awareness and distributional access indicators: all respondents (100%) demonstrated awareness of the malaria-prevention benefit of LLITNs, 98.5% obtained their net through ANC, and 90.0% reported free and easy access. By contrast, the single deepest knowledge indicator \u0026mdash; correct identification of the three-year insecticide efficacy period \u0026mdash; recorded only 27.1%, rendering it the coldest cell in the heatmap and representing the most urgent knowledge gap for targeted education. This specific misconception, with 45.7% of respondents believing efficacy expires after one year, likely reflects residual influence of messaging developed for conventional retreatable ITNs \u0026mdash; a finding corroborated in comparable Ghanaian contexts by Zuuri et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and Ibeagha et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe practice domain presents the most heterogeneous pattern. Maintenance knowledge (retreatment not necessary: 82.9%) and first-use processing (shade drying: 71.4%) performed comparatively well. However, the critical utilization indicator \u0026mdash; net correctly hung and in use at time of observation \u0026mdash; recorded only 43.5%, appearing as the second coldest cell in the heatmap and confirming that the ownership\u0026ndash;utilization gap is not an artefact of self-report bias but is validated by direct physical observation. The itchiness side-effect prevalence (65.0%) sits in the middle of the performance spectrum, indicating that adverse effects are both common enough to represent a meaningful deterrent and yet experienced by fewer than two-thirds of respondents \u0026mdash; suggesting that targeted adverse-effect counseling could have a high return on investment in reducing abandonment, consistent with the recommendations of [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] and [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Diverging bar analysis of KAP indicator responses\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a diverging bar chart contrasting positive (correct/present) and negative (incorrect/absent) response proportions across all nine primary KAP indicators. The diverging bar visualization provides an immediate visual quantification of the response-level gaps that are most consequential for program design. The chart confirms that the two indicators with the largest negative response bars \u0026mdash; correct insecticide expiry period (72.9% incorrect) and net correctly hung and in use (56.5% absent/suboptimal) \u0026mdash; represent the primary actionable gaps in the study population\u0026rsquo;s KAP profile. These two indicators are mechanistically linked: a respondent who underestimates the chemical efficacy period to one year (as 45.7% did) may rationally conclude that a two- or three-year-old net is no longer effective and therefore discontinue use, generating precisely the ownership\u0026ndash;utilization discordance documented here [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Bardoe et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] have characterized this as a form of \u0026lsquo;perceived obsolescence\u0026rsquo; that constitutes a distinct cognitive barrier requiring specific correction through targeted ANC counseling.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConversely, the indicators with the smallest divergence \u0026mdash; malaria-prevention awareness (0% gap) and ANC-based access (1.7% gap) \u0026mdash; confirm that distributional and awareness-level program components are performing effectively and should be maintained rather than intensified, freeing program resources for reallocation toward the knowledge depth and behavior change components where gaps are most severe. This form of indicator-level prioritization, facilitated by the diverging bar visualization, operationalizes the principle of proportional resource allocation advocated by Lim et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] in their global analysis of malaria intervention cost-effectiveness.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a structured summary of all KAP indicator responses and domain classifications, serving as the companion tabular reference to the heatmap and diverging bar visualizations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of KAP indicator responses and domain classification (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive \u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositive %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoverage Level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnows LLITN prevents malaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eHigh (\u0026ge;\u0026thinsp;70%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObtained LLITN from ANC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eHigh (\u0026ge;\u0026thinsp;70%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANC as primary knowledge source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eHigh (\u0026ge;\u0026thinsp;70%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFree \u0026amp; easy access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eHigh (\u0026ge;\u0026thinsp;70%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnows retreatment not necessary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eHigh (\u0026ge;\u0026thinsp;70%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrect 1st-use processing (shade)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eHigh (\u0026ge;\u0026thinsp;70%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnows\u0026thinsp;\u0026ge;\u0026thinsp;2 foetal benefits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eHigh (\u0026ge;\u0026thinsp;70%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperienced itchiness (side effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePractice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eModerate (50\u0026ndash;69%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUses net every night\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePractice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eModerate (50\u0026ndash;69%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNet correctly hung \u0026amp; in use (obs.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePractice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eLow (\u0026lt;\u0026thinsp;50%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrect expiry period (3 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eLow (\u0026lt;\u0026thinsp;50%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote. Q\u0026thinsp;=\u0026thinsp;Question number from study instrument. Domain classification: Knowledge\u0026thinsp;=\u0026thinsp;items from Section B; Access\u0026thinsp;=\u0026thinsp;items from Section C; Practice\u0026thinsp;=\u0026thinsp;items from Section D. Coverage level thresholds follow Atkinson et al. (2012). ANC\u0026thinsp;=\u0026thinsp;Antenatal Care; LLITN\u0026thinsp;=\u0026thinsp;Long-Lasting Insecticidal Treated Net; obs. = confirmed by researcher observation.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4 KAP indicator ranking (lollipop chart)\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents a lollipop chart ranking all KAP indicators from highest to lowest positive response rate, providing a clear visual hierarchy of indicator performance. The lollipop chart reveals a clear three-tier hierarchy of indicator performance. The top tier (\u0026ge;\u0026thinsp;70%, green) encompasses seven indicators, including all access-related items and the primary malaria-prevention awareness item \u0026mdash; confirming that the program\u0026rsquo;s distributional and awareness components are functioning effectively, consistent with the national data reported by Awunyo et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The middle tier (50\u0026ndash;69%, gold) contains two indicators: frequency of nightly net use (60.0%) and itchiness as a reported side effect (65.0%) \u0026mdash; the latter reflecting the high prevalence of a deterrent that places respondents at risk of use discontinuation, as documented by Ibeagha et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and Macintyre et al. [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] across comparable African settings. The bottom tier (\u0026lt;\u0026thinsp;50%, red) contains the two most actionable gap indicators: correct insecticide expiry period (27.1%) and direct observation of net correctly hung and in use (43.5%). This tiered structure directly informs the prioritization of recommendations presented in Section \u003cspan refid=\"Sec27\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Domain-level performance\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents a radar chart displaying the full domain-level KAP profile, providing an integrated spatial representation of performance across all ten indicators. The radar chart provides a spatially intuitive representation of the KAP profile that is immediately informative for program planning purposes. The pronounced indentation of the profile at the expiry-period knowledge and net-use-confirmation arms stands in stark visual contrast to the near-maximal extension of the ANC access and malaria-awareness arms, communicating at a glance the fundamental paradox of the LLITN program in this setting: near-perfect distributional reach coexisting with critical knowledge and practice deficits. This \u0026lsquo;Universal Access Paradox\u0026rsquo; \u0026mdash; the asymmetry between supply-side success and demand-side failure \u0026mdash; is consistent with patterns documented nationally by Awunyo et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], regionally by Cottrell et al.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] in Benin, and across sub-Saharan Africa by Mwebesa et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The tightly contracted practice domain arm for correct net use (43.5%) represents the most visible indicator of program underperformance and directly communicates the magnitude of the behavior change challenge facing the DHMT.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Cross-tabulation heatmaps: utilization by subgroup\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Cross-tabulation heatmaps: LLITN use status by occupational group (Panel A) and by knowledge level (Panel B). Cell values indicate row percentages and absolute counts (n). Color intensity in Panel A reflects percentage magnitude (blue scale); Panel B uses a red\u0026ndash;yellow\u0026ndash;green diverging scale. \u003cem\u003eNote. Panel A reveals that farmers \u0026mdash; the largest subgroup \u0026mdash; exhibit one of the lower rates of correct net hanging and use (43%) and a notable non-use rate (34%), suggesting that occupational fatigue and time constraints may compound utilization barriers in this group. Panel B confirms a gradient between knowledge level and correct utilization, with poor-knowledge respondents demonstrating disproportionately high non-use (44%). LLITN\u0026thinsp;=\u0026thinsp;Long-Lasting Insecticidal Treated Net.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe cross-tabulation heatmaps in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e provide the first subgroup-level analysis of LLITN utilization patterns in this study population. \u003cb\u003ePanel A\u003c/b\u003e of Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e reveals meaningful heterogeneity in utilization status across occupational groups. Farmers \u0026mdash; who represent 45.0% of the sample \u0026mdash; exhibit a relatively low correct-use rate (43%) and the highest non-use rate (34%) among all occupational groups, suggesting that the physical demands of agricultural labour and associated fatigue may limit the attentiveness with which farmers approach preventive health behaviors, consistent with findings reported by Minakawa et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and Onwujekwe et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Donacho et al. [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] and [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] similarly identified occupation and household structural constraints as independent predictors of LLITN non-use in community-based studies among pregnant women in Ethiopia.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePanel B\u003c/b\u003e of Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e reveals a clear \u003cem\u003eknowledge\u0026ndash;utilization gradient\u003c/em\u003e: respondents with good knowledge demonstrated a correct-use rate of 57%, compared with 43% among those with moderate knowledge and only 38% among those with poor knowledge. This monotonic gradient is consistent with the theoretical framework underlying KAP studies \u0026mdash; that knowledge acquisition is a necessary, if not sufficient, precondition for behavior change [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] \u0026mdash; and provides direct empirical support for the causal pathway from knowledge deficits to utilization failure. Baykemagn et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], applying machine learning to predict bed net utilization patterns across sub-Saharan Africa, similarly identified knowledge-related variables as among the most important predictors of consistent ITN use, reinforcing the programmatic primacy of health education quality improvement.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the cross-tabulated LLITN use status data by occupational group and knowledge level, serving as the numerical complement to the heatmap visualization in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLLITN use status by occupational group and \u003cem\u003eknowledge level\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCorrectly Hung \u0026amp; In Use\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHas Net Not In Use\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Hanged Appropriately\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCorrect Use %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eBy Occupation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e43.3%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrader\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e44.2%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e43.3%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e41.2%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCivil Servant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e46.2%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e42.9%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBy Knowledge Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e57.1%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e42.9%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e37.5%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTOTAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e43.5%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote. Use status confirmed by direct researcher observation. \u0026lsquo;Correctly Hung \u0026amp; In Use\u0026rsquo; = net hanging appropriately above sleeping space and actively in use. LLITN\u0026thinsp;=\u0026thinsp;Long-Lasting Insecticidal Treated Net.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Utilization status and knowledge level\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Waffle charts: LLITN use confirmation status (Panel A) and overall knowledge level (Panel B). Each square represents approximately 1% of respondents (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;200). \u003cem\u003eNote. The waffle visualization enables immediate visual comparison of the proportional distribution within each domain. Panel A highlights that less than half of respondents (43.5%) had their LLITN correctly hung and in use at the time of observation. Panel B illustrates the overwhelming dominance of the moderate knowledge category (85%). LLITN\u0026thinsp;=\u0026thinsp;Long-Lasting Insecticidal Treated Net.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe waffle chart representation in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e provides a particularly accessible visual quantification of the core finding: of every 100 respondents, only 43 had their LLITN correctly hung and actively in use, while 35 possessed a net that was not being used and 22 had a net that was not hung appropriately. This proportional decomposition \u0026mdash; visually intuitive and policy-accessible \u0026mdash; communicates the magnitude of the utilization gap more compellingly for non-specialist audiences than percentage tables alone (Evergreen 2017). Similarly, Panel B visually dramatizes the near-universal concentration of respondents in the moderate knowledge category, reinforcing the conclusion that a systemic rather than individual-level explanation for knowledge inadequacy is warranted. Awunyo et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] have documented comparable patterns at national and temporal scales in Ghana, confirming that the ownership\u0026ndash;utilization discrepancy and moderate knowledge ceiling observed here are not artefacts of local conditions but reflect broader program-level challenges.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.8 Multivariate KAP relationship\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Bubble chart: multivariate relationship between knowledge, access, and practice indicators. Each bubble represents one KAP indicator; bubble size is proportional to estimated programmatic significance. Dashed lines indicate 50% thresholds for both axes. Colours indicate domain: navy\u0026thinsp;=\u0026thinsp;Knowledge, teal\u0026thinsp;=\u0026thinsp;Access, green\u0026thinsp;=\u0026thinsp;Practice. \u003cem\u003eNote. Indicators in the upper-right quadrant (high knowledge, high practice/access) represent programmatic strengths; indicators in the lower-left quadrant represent priority intervention targets. The ANC-access indicators cluster in the upper right, while the net use and expiry knowledge indicators fall in the lower left. ANC\u0026thinsp;=\u0026thinsp;Antenatal Care; KAP\u0026thinsp;=\u0026thinsp;Knowledge, Attitudes, and Practices.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe bubble chart in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e reveals the spatial distribution of KAP indicators across the \u003cem\u003eknowledge\u0026ndash;practice/access\u003c/em\u003e plane and identifies four quadrants of programmatic relevance. The upper-right quadrant (high knowledge, high access/practice) contains the ANC access indicators and the malaria-prevention awareness item \u0026mdash; confirming the programmatic strengths of the current distribution and awareness strategy. The lower-left quadrant (low knowledge, low practice) contains the two critical gap indicators: correct expiry period knowledge (27.1%) and net correctly hung and in use (43.5%). The co-location of these two indicators in the same quadrant is consistent with the causal hypothesis that inadequate knowledge of insecticide efficacy duration directly undermines the motivation for sustained correct net use, providing empirical support for interventions that simultaneously address these two mechanistically linked deficits. This quadrant-based prioritization framework is consistent with the programmatic resource allocation principles advocated by Carshon-Marsh and Di Ruggiero [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] and Lim et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study contributes to the growing body of KAP literature on LLITN utilization in sub-Saharan Africa through its application of a multi-visualization analytical framework that enables a richer and more granular characterization of the study population\u0026rsquo;s knowledge, access, and practice profile than is achievable through conventional tabular presentation. The convergent evidence from eight distinct visualization modalities yields a consistent and internally coherent set of findings that have direct implications for program design. The study is situated within a context of well-documented and locally persistent tension between rising LLITN ownership and persistently high malaria prevalence in pregnancy \u0026mdash; a paradox documented nationally by Awunyo et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and regionally by Abesig et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] \u0026mdash; and its findings substantially illuminate the mechanisms underlying this ownership\u0026ndash;utilization discordance.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.1 The bimodal KAP profile: programmatic strength and critical gap\u003c/h2\u003e \u003cp\u003eThe overarching finding of this study \u0026mdash; most clearly represented in the KAP heatmap and the diverging bar chart \u0026mdash; is a bimodal performance profile characterized by near-universal achievement on awareness and access indicators and critical underperformance on knowledge depth and utilization practice indicators. This pattern is theoretically coherent and has been documented, in varying configurations, across the broader LLITN KAP literature in sub-Saharan Africa [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. In the Ghanaian context specifically, this pattern has been replicated by Zuuri et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] in Sekyere South District, by Salifu et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] in the Savannah Region, and by Bonsra et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] across districts of the Ashanti Region, confirming that the findings of the present study are not idiosyncratic to Nkoranza South but reflect a systemic programmatic challenge across rural Ghanaian settings.\u003c/p\u003e \u003cp\u003eThe bimodal pattern reflects the differing programmatic emphases of LLITN campaigns in Ghana: intensive investment in free distribution, which has successfully driven near-universal ANC-based access, has not been matched by equivalent investment in post-distribution behavior change communication (BCC) and health education quality. This asymmetry is consistent with the \u0026lsquo;access\u0026ndash;use gap\u0026rsquo; conceptualized by Killeen et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], who argued that LLITN program effectiveness at scale is fundamentally constrained by the degree to which distribution coverage translates into consistent protective use. Cottrell et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] prospectively confirmed in Benin that actual utilization rates fell below 50% within weeks of ANC-based distribution despite near-universal ownership, while [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] reviewed the evidence base for improving ITN utilization and concluded that post-distribution follow-up and structured BCC are the most effective and cost-efficient strategies for bridging the access\u0026ndash;use gap. Okoro et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] corroborated this conclusion with cost-effectiveness data demonstrating that ITN-plus-BCC combinations yield the greatest health return per unit of programmatic investment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Knowledge gaps and their behavioral implications\u003c/h2\u003e \u003cp\u003eThe violin plot analysis provides a novel and practically significant contribution to the characterization of the knowledge deficit in this population. The tight concentration of \u003cem\u003eknowledge scores\u003c/em\u003e in the moderate range (4\u0026ndash;5 out of 8), with minimal distributional variance, indicates that the knowledge gap is systemic rather than heterogeneous \u0026mdash; implying that a global upgrade in health education quality, rather than targeted remediation of a poorly informed subgroup, is the appropriate programmatic response. This finding aligns with Launiala\u0026rsquo;s [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] epistemological analysis of KAP surveys, which cautions against interpreting moderate population-level knowledge scores as evidence of adequate education coverage, arguing that moderate scores often reflect the same surface-level information repeatedly communicated without the depth or reinforcement required for durable knowledge acquisition.\u003c/p\u003e \u003cp\u003eThe specific misconstruction of the insecticide efficacy period \u0026mdash; with 45.7% of respondents believing the chemicals expire after one year \u0026mdash; deserves particular attention. This misconception likely reflects the residual influence of messaging developed for conventional retreatable ITNs, which required annual chemical re-impregnation, on communities that have transitioned to LLITNs without receiving explicit education on the distinguishing features of the new technology [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This failure to correct a specific and consequential misconception at the point of net distribution represents a systemic health education gap that is directly actionable through provider training and standardized counseling protocols \u0026mdash; as demonstrated by Opara et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], whose systematic review showed that structured, interactive health education at ANC visits significantly improved knowledge depth and utilization rates compared with routine passive counseling. Bardoe et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] have characterized the behavioral consequence of this misconception as \u0026lsquo;perceived obsolescence\u0026rsquo; \u0026mdash; a psychological state in which the net is regarded as no longer worth using \u0026mdash; and identify pre-emptive myth-correction as a high-priority, low-cost intervention for preventing premature abandonment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Subgroup heterogeneity in utilization\u003c/h2\u003e \u003cp\u003eThe cross-tabulation heatmaps reveal that utilization outcomes are not uniformly distributed across the study population: farmers and respondents with poor knowledge demonstrate the lowest correct-use rates. These findings are consistent with the broader literature on occupational and socioeconomic determinants of LLITN use in sub-Saharan Africa [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The relatively low utilization among farmers \u0026mdash; despite their high ANC attendance and net receipt \u0026mdash; suggests that occupational fatigue, limited time for net maintenance, and exposure to biting mosquitoes during early-morning agricultural work may compound the motivational deficit created by knowledge inadequacies. Donacho et al. [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] and [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] identified occupation and structural household constraints as independent predictors of LLITN non-use among pregnant women in community-based studies in Ethiopia, reinforcing the cross-regional relevance of this finding.\u003c/p\u003e \u003cp\u003eThe monotonic \u003cem\u003eknowledge\u0026ndash;utilization gradient\u003c/em\u003e documented in Panel B of Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u0026mdash; with correct-use rates of 57%, 43%, and 38% for good, moderate, and poor knowledge respondents respectively \u0026mdash; provides direct empirical support for the theoretical framework underlying KAP studies and for the primacy of knowledge enhancement as a programmatic lever. Baykemagn et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] have corroborated this gradient at the population level, with knowledge-related variables emerging as among the most important predictors of consistent ITN use in machine learning and multilevel regression analyses respectively across sub-Saharan Africa. Community-based interventions targeting farming communities specifically \u0026mdash; potentially integrated with Community Health Planning and Service (CHPS) platforms \u0026mdash; may offer the most promising avenue for reaching the largest occupational subgroup with tailored behavior change support, consistent with the recommendations of [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] for occupationally differentiated ANC counseling in comparable sub-Saharan African settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Towards a \u0026lsquo;Distribution Plus\u0026rsquo; Program Model: Evidence Synthesis and Future Directions\u003c/h2\u003e \u003cp\u003eThe convergent evidence from this multi-visualization KAP analysis provides a compelling empirical foundation for a fundamental reorientation of LLITN program strategy in Ghana and analogous sub-Saharan African settings. The current program paradigm \u0026mdash; which evaluates success primarily through commodity distribution metrics (\u0026lsquo;nets per household\u0026rsquo; or \u0026lsquo;ANC coverage rates\u0026rsquo;) \u0026mdash; systematically overestimates protective benefit by conflating ownership with use. The present study demonstrates that a more meaningful and actionable program metric is \u0026lsquo;correctly confirmed use at time of observation\u0026rsquo;, which at 43.5% in this population reveals a program effectiveness rate less than half the nominal ownership rate. Reorienting the NMCP\u0026rsquo;s monitoring and evaluation framework toward \u0026lsquo;protected person-nights of sleep\u0026rsquo; as the primary effectiveness metric \u0026mdash; as advocated by Killeen et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and operationalized in the present study through direct observational confirmation \u0026mdash; would provide a more accurate and actionable assessment of whether LLITN programs are achieving their ultimate goal of reducing malaria transmission.\u003c/p\u003e \u003cp\u003eThe evidence synthesis presented here supports what may be termed a \u0026lsquo;Distribution Plus\u0026rsquo; program model: one that preserves the supply-side distributional achievements of the current ANC-based strategy while adding three essential demand-side components. First, structured ANC-based knowledge enhancement through standardized, pictorial counseling protocols specifically targeting the prevalent insecticide-expiry misconception and the foetal consequences of MiP \u0026mdash; as demonstrated to be effective by Opara et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Second, proactive adverse-effect counseling that pre-empts the high prevalence of itchiness-driven abandonment identified in the present study (65.0%) and corroborated by Ibeagha et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], framing transient cutaneous reactions as expected and manageable rather than as signals to discontinue use. Third, post-distribution household follow-up by CHPS workers to verify correct net use and provide real-time motivational support, a model shown by Cottrell et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] to substantially improve utilization rates and recommended by Carshon-Marsh and Di Ruggiero [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] as the most cost-effective strategy for bridging the access\u0026ndash;use gap in West African contexts. The applicability of this model extends well beyond Ghana. The KAP profile documented here \u0026mdash; near-universal awareness alongside critically low knowledge depth and suboptimal utilization \u0026mdash; is structurally identical to patterns reported in Nigeria, Benin, Tanzania, Uganda, and the Democratic Republic of Congo, all settings in which ANC-integrated distribution has achieved substantial ownership gains without commensurate behavioral protection. The \u0026lsquo;Distribution Plus\u0026rsquo; framework is therefore best understood not as a Ghana-specific corrective but as a transferable programmatic template for any national malaria control program in sub-Saharan Africa that has achieved high distributional coverage yet remains unable to translate commodity access into consistent protective behavior. National programs in East and Central Africa where conversion rates similarly fall below 60% \u0026mdash; including those in Tanzania, Uganda, and Mozambique \u0026mdash; stand to benefit equally from adopting structured, post-distribution behavioral support as a core program component. More broadly, the ownership\u0026ndash;utilization conversion rate introduced in this study offers a universally applicable demand-side effectiveness metric that population health researchers and program evaluators working across diverse malaria-endemic settings can adopt as a complement to conventional supply-side coverage indicators.\u003c/p\u003e \u003cp\u003eDespite the insights provided by this study, several limitations warrant consideration. First, the expanded sample size of 200 participants and the use of purposive, non-probability sampling limit the statistical power of the analysis and the generalizability of the findings to broader urban populations or other geographical regions in Ghana. Second, the cross-sectional design captures only a temporal snapshot of knowledge and behavior, failing to account for seasonal variations in mosquito density and indoor temperatures which significantly influence net utilization patterns. Third, while the use of direct observation mitigated the risk of over-reporting net use, other behavioral indicators\u0026mdash;such as the frequency of nightly use\u0026mdash;relied on self-reports, which remain susceptible to social desirability bias. Finally, although the researchers utilized local dialects to improve communication, the potential for subtle nuances to be lost during the translation of the structured questionnaire from English to Twi/Brong cannot be entirely ruled out.\u003c/p\u003e \u003cp\u003eSeveral future research priorities emerge from the findings of this study. First, there is a clear need for a larger-scale, probability-sampled Knowledge, Attitudes, and Practices (KAP) study, which would provide the statistical power necessary for inferential analysis and multilevel modeling. Furthermore, researchers should conduct a randomized evaluation of the structured counseling protocol recommended in this study to measure its impact on knowledge depth, utilization rates, and malaria prevalence. Because 61.7% of the respondents in this sample were married, and because partner support is a well-established predictor of long-lasting insecticidal net (LLITN) use [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], a qualitative investigation of spousal and household dynamics is also needed to understand how these relationships mediate net allocation. Additionally, future studies should employ longitudinal tracking of seasonal temperatures and indoor heat levels alongside net use patterns to better distinguish between habitual abandonment and abandonment driven by environmental factors. Collectively, these research directions would advance the evidence base required to design, implement, and evaluate a comprehensive \"Distribution Plus\" program capable of translating near-universal LLITN ownership into commensurate reductions in malaria during pregnancy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.5 International Contextualisation: Comparative Analysis of LLITN KAP and Utilization Patterns\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the multi-country comparative summary of ITN ownership, utilization, and malaria-in-pregnancy indicators among pregnant women in sub-Saharan Africa. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e contextualises the present study\u0026rsquo;s observed utilization rate of 43.5% within the spectrum of nationally representative utilization estimates across sub-Saharan Africa. The figure reveals substantial inter-country variation, ranging from 6.1% in Zimbabwe to 90.5% in Niger. Critically, the present study\u0026rsquo;s ownership rate (100%) substantially exceeds most comparator settings owing to the ANC-based distribution model, yet its utilization rate falls below the sub-Saharan median of approximately 47\u0026ndash;64% documented in pooled analyses [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMulti-country comparative summary of ITN ownership, utilization, and malaria-in-pregnancy indicators among pregnant women in sub-Saharan Africa\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry / Setting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eITN Own. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eITN Use (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eANC1+ (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMiP Prev. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCorrect Expiry (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSurvey Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGhana (Study Site \u0026ndash; Nkoranza)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.0\u0026dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePresent Study\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGhana (National)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e 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class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTanzania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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colname=\"c2\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMozambique\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2022/23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalawi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZimbabwe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote: ITN Own. = ITN household ownership among pregnant women; ITN Use\u0026thinsp;=\u0026thinsp;self-reported or observed utilization; ANC1+ = \u0026ge;1 antenatal care visit; MiP Prev. = malaria-in-pregnancy prevalence; Correct Expiry\u0026thinsp;=\u0026thinsp;percentage who correctly identified the 3-year insecticide efficacy period. \u0026dagger; Present study achieved 100% ownership as all participants had received an LLITN through ANC. \u0026ndash; = data not reported in primary source..\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis further corroborates the \u0026lsquo;Universal Access Paradox\u0026rsquo; documented in the primary analysis: Ghana\u0026ndash;Nkoranza achieves the highest ownership figure in the comparison yet ranks in the lower half for utilization, demonstrating that distributional excellence is a necessary but wholly insufficient condition for program effectiveness. Niger and Burkina Faso represent the benchmark scenarios in which ownership and utilization rates converge toward parity, suggesting that demand-side program components in these settings may offer transferable lessons for the Ghanaian NMCP.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e provides a systematic cross-setting comparison of the key KAP indicators examined in the present study. Several patterns are notable. First, awareness of LLITN malaria-prevention benefits is consistently high (82\u0026ndash;100%) across all seven settings, confirming that surface-level awareness messaging has been broadly effective irrespective of national context. Second, and most strikingly, the correct identification of the three-year insecticide efficacy period is universally low across all comparator settings (range: 19\u0026ndash;36%), with the present study\u0026rsquo;s rate of 27.1% sitting in the middle of this range. The Ethiopian setting (Shashogo District) records the lowest rate at 19%, consistent with the lower health system integration documented by Donacho et al. (2025). This cross-setting convergence on a single knowledge deficit provides compelling evidence that the expiry-period misconception is not an artefact of local conditions in Nkoranza South but rather a systemic programmatic failure across the full spectrum of sub-Saharan African LLITN programs. Third, correct observed net use (Panel C) reveals greater cross-setting heterogeneity, with the DRC setting [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] recording the highest rate at 62% and Ethiopia the lowest at 15.8%, confirming that practice outcomes are more context-dependent than knowledge and awareness indicators. The present study\u0026rsquo;s 43.5% observed correct use rate falls in the middle of the comparative range, suggesting that targeted interventions have the potential to shift the present study setting toward the upper range within a programmatically achievable timeframe.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the first systematic KAP indicator comparison for LLITN studies in sub-Saharan Africa using a standardized indicator framework. The pattern of high awareness with critically low correct-expiry knowledge is uniformly observed across all seven settings. Notably, the non-use rates are alarmingly high in Ethiopia (64%) and Nigeria (38.5%), both substantially exceeding the present study\u0026rsquo;s rate (35.0%). The DRC setting records the lowest non-use rate (28%) and the highest observed correct use (62%), consistent with the intensive facility-based intervention program described by Kabalu Tshiongo et al. (2024), who documented that combined ITN and IPTp use was associated with significantly improved maternal and neonatal outcomes compared to either intervention alone. This evidence reinforces the present study\u0026rsquo;s recommendation for a \u0026lsquo;Distribution Plus\u0026rsquo; model that invests in post-distribution behavioral support rather than commodity metrics alone.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCross-study KAP indicator comparison: LLITN knowledge, access, and practice across published studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy Setting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAwareness (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANC Source (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorrect Expiry (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGood Know. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCorrect Use Obs. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon-Use (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGhana \u0026ndash; Nkoranza (Study Site)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGhana \u0026ndash; Sekyere South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGhana \u0026ndash; Savannah Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNigeria (Southeast)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenin (ANC prospective)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthiopia \u0026ndash; Shashogo District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRC \u0026ndash; Kingasani II facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote. Awareness\u0026thinsp;=\u0026thinsp;percentage aware that LLITN prevents malaria; ANC Source\u0026thinsp;=\u0026thinsp;percentage citing ANC as primary knowledge source; Correct Expiry\u0026thinsp;=\u0026thinsp;percentage correctly identifying 3-year insecticide efficacy period; Good Know. = percentage classified as good knowledge; Correct Use Obs. = percentage with net correctly hung and in use at time of observation (or self-reported consistent use where observation data unavailable); Non-Use\u0026thinsp;=\u0026thinsp;percentage possessing a net but not using it. \u0026ndash; = not reported.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e provides a spatial representation of the ownership\u0026ndash;utilization relationship across comparator settings. The present study occupies an extreme position in the lower right of the scatterplot: maximum ownership (100%) combined with a utilization rate (43.5%) well below parity, generating the largest absolute ownership\u0026ndash;utilization gap in the comparison. This positioning visually encapsulates the \u0026lsquo;Universal Access Paradox\u0026rsquo; at its most acute: complete distributional success co-existing with profound demand-side failure. Countries along the diagonal (notably Niger and Burkina Faso) represent the programmatic ideal of near-equal ownership and utilization. Zimbabwe occupies the lower left, representing low ownership and correspondingly low use. The positioning of Ghana\u0026ndash;National (upper middle of the cluster) relative to the present study\u0026rsquo;s site (far lower right) suggests that within-country heterogeneity in the ownership\u0026ndash;utilization gap may be at least as large as between-country variation, reinforcing the importance of disaggregated, sub-national KAP data for programmatic planning.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e extends the radar-chart visualization technique deployed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (primary study results) to a comparative multi-country framework, enabling a holistic spatial comparison of KAP profiles. The radar reveals that all comparator settings share the characteristic \u0026lsquo;pointed\u0026rsquo; profile at the Correct Expiry arm, confirming the universality of this knowledge deficit across West, East, and Central African contexts. The present study\u0026rsquo;s profile (red) is distinctive for its near-maximal LLITN Awareness and ANC Source arms combined with the severe contraction at the Correct Expiry and Correct Net Use arms \u0026mdash; a configuration that is quantitatively unique among comparator settings and reflects the programmatic paradox of exceptional distributional achievement coexisting with inadequate post-distribution knowledge enhancement. Ethiopia\u0026rsquo;s profile is most contracted overall, particularly for Correct Net Use, consistent with the lower health system integration in the Shashogo District setting documented by the 2019 KAP study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e introduces a novel comparative metric \u0026mdash; the ownership-to-utilization conversion rate \u0026mdash; that isolates demand-side program efficiency independent of supply-side coverage levels. By expressing utilization as a proportion of ownership, this indicator directly quantifies the degree to which distribution gains translate into protective behavior. Niger and Burkina Faso achieve near-100% conversion, indicating that virtually all net owners in these settings actively use their nets. By contrast, the present study\u0026rsquo;s conversion rate of 43.5% is the lowest in the comparison, reflecting the fact that despite 100% ownership all study participants had received a net through ANC, nearly half were not using their nets correctly at the time of observation. Zimbabwe achieves the lowest absolute utilization (6.1%) but its conversion rate reflects the combined effect of low ownership and low use. This metric has practical programmatic value: it decouples supply and demand performance, enabling national malaria control programs to identify settings where distribution investment is yielding high behavioral returns (conversion\u0026thinsp;\u0026ge;\u0026thinsp;80%) from those where post-distribution behavioral support represents the critical unmet investment need (conversion\u0026thinsp;\u0026lt;\u0026thinsp;50%). The present study setting clearly falls in the latter category, providing quantitative justification for the \u0026lsquo;Distribution Plus\u0026rsquo; model proposed in Section \u003cspan refid=\"Sec25\" class=\"InternalRef\"\u003e5.4\u003c/span\u003e and further validating the study\u0026rsquo;s primary recommendation for structured ANC-based counseling and community follow-up.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions and Recommendations","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Conclusions\u003c/h2\u003e \u003cp\u003eThis study employed a rigorous multi-visualization analytical framework to provide a granular characterization of the knowledge, access, and practice profile regarding malaria prevention in a rural Ghanaian community. The convergent evidence across eight distinct visualization modalities reveals a profound and unambiguous \"Universal Access Paradox\": while the distribution system has achieved near-universal reach, this supply-side success has failed to translate into effective biological protection. Despite near-maximal scores for net ownership and awareness of malaria benefits, a staggering 56.5% of respondents were utilizing their nets suboptimally or not at all. This disconnect is not a random occurrence but is driven by a bimodal performance profile where surface-level awareness masks critical deficits in functional knowledge. The study identifies two high-leverage, mechanistically linked gaps that mandate immediate programmatic attention. First, a pervasive misconception regarding insecticide efficacy—specifically the belief that protection expires after only one year—induces a state of \"perceived obsolescence,\" leading women to abandon viable nets prematurely. Second, the use of direct physical observation rather than a sole reliance on self-reporting confirmed that only 43.5% of nets were correctly hung and in use. These findings demonstrate that the ownership–utilization gap is a physical reality rather than a reporting artifact. Furthermore, the use of cross-tabulation heatmaps pinpointed farmers and respondents with poor knowledge as the most vulnerable cohorts, exhibiting the highest rates of non-use. Ultimately, these results argue for a fundamental paradigm shift from a \"distribution-only\" model to a \"Distribution Plus\" strategy. Such a model must move beyond commodity metrics to prioritize high-quality, interactive health education and behavioral support. While this study is bounded by its modest purposive sample and cross-sectional nature—which captures behavior at a single point in time without accounting for seasonal environmental shifts—the rigor of its observational data and the clarity of its multivariate analysis provide a definitive evidence base. These conclusions provide the necessary blueprint for designing targeted, occupationally differentiated interventions capable of translating near-universal net access into consistent, life-saving utilization. The international comparative analysis presented in Section \u003cspan class=\"InternalRef\"\u003e5.5\u003c/span\u003e reinforces and substantially strengthens these conclusions. Benchmarked against 14 sub-Saharan African countries, Ghana–Nkoranza records the highest LLITN ownership (100%) yet the lowest ownership-to-utilization conversion rate (43.5%) in the comparison, positioning this study site at the most acute end of the ‘Universal Access Paradox’ spectrum. The cross-country evidence confirms that the critical knowledge deficit — the inability to correctly identify the three-year insecticide efficacy period (range: 19–36% across all comparator settings) — is not a localised anomaly but a structurally embedded programmatic failure shared across West, East, and Central African LLITN programs. Countries achieving near-parity between ownership and utilization (Niger: 90.5%; Burkina Faso: 87.0%) demonstrate that demand-side convergence is programmatically achievable, offering transferable lessons in sustained post-distribution behavioral support. This international benchmarking transforms the study’s recommendations from locally-grounded suggestions into regionally validated imperatives: the ownership–utilization gap is a pan-African challenge demanding a coordinated, behavior-change-centred programmatic response anchored in the ‘Distribution Plus’ model advocated here. For demographers and population health researchers working beyond sub-Saharan Africa, the ownership-to-utilization conversion rate introduced here offers a broadly applicable template for measuring behavioral efficiency in any preventive intervention program where the gap between access and use carries demographic consequences — whether in malaria-endemic regions of South and Southeast Asia, in programs targeting reproductive health commodities, or in supply-side-heavy interventions for child survival in low- and middle-income countries. The core theoretical insight of this study — that distributional success is a necessary but wholly insufficient condition for demographic benefit — speaks to a challenge common across the population health field and positions the ‘Distribution Plus’ framework as a contribution to the broader demography and population studies literature on the determinants of health behavior, intervention uptake, and cause-specific mortality differentials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Recommendations\u003c/h2\u003e \u003cp\u003eBased on the evidence synthesized from this multi-visualization analysis, the following recommendations are directed toward health management teams, national malaria control programs, and relevant stakeholders to bridge the critical gap between net ownership and protective utilization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInstitutionalize structured, cognitive-focused counseling\u003c/b\u003e: Health authorities should move beyond \"routine\" passive counseling to deploy standardized, pictorial education modules at all antenatal care facilities. These materials must specifically target the \"perceived obsolescence\" barrier by explicitly contrasting the three-year efficacy of current net technology with the outdated one-year retreatment cycles of the past. By re-engineering the cognitive framework of pregnant women at the point of distribution, the program can prevent the premature abandonment of viable nets.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEstablish a performance-based provider competency framework\u003c/b\u003e: Training for antenatal care providers should be redesigned around a validated competency framework that prioritizes interactive communication skills. Supervisors must ensure that every net distribution is accompanied by a structured demonstration of correct hanging and maintenance procedures, with periodic refresher training to maintain the quality and consistency of health education delivery across all rural service points.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDeploy occupationally targeted outreach for high-risk subgroups\u003c/b\u003e: To address the specific vulnerability of the farming community, behavior change communication strategies must be \"occupationally sensitive.\" This involves integrating community-based health planning and services with agricultural cycles. Tailored messaging should address the unique barriers faced by farmers—such as physical exhaustion and early-morning exposure—providing practical motivational support to ensure that the burden of labor does not compromise the consistency of net use.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRedesign monitoring and evaluation around direct observational metrics\u003c/b\u003e: National monitoring frameworks should transition from relying on \"self-reported use,\" which is prone to social desirability bias, toward \"direct observational audits.\" By adopting the observational confirmation methodology used in this study, program managers can obtain a true effectiveness rate for the intervention. This shift will provide a more accurate and actionable assessment of whether malaria control goals are being met at the household level.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCommission multi-site longitudinal research\u003c/b\u003e: To validate and extend these findings, a large-scale, probability-sampled study should be commissioned across diverse ecological zones. Such research should include longitudinal tracking of seasonal temperatures and indoor heat levels alongside net use patterns. This will allow for a more sophisticated understanding of the environmental and habitual drivers of net abandonment, providing the robust evidence base required to fully operationalize a \"Distribution Plus\" model that guarantees both universal access and universal protection.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAll abbreviations and acronyms used in this study are compiled in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e to facilitate clarity and improve readability.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab6\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of abbreviations and acronyms used in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eFull Term\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eANC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAntenatal Care\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eBCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBehavior Change Communication\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eCHPS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCommunity Health Planning and Service\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eDHMT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDistrict Health Management Team\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eGDHS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGhana Demographic and Health Survey\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eGFATM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGlobal Fund to Fight AIDS, Tuberculosis and Malaria\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eGHS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGhana Health Service\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eITN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eInsecticide-Treated Net\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eJHS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eJunior High School\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eKAP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eKnowledge, Attitudes, and Practices\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eLBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLow Birth Weight\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eLLITN / LLIN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLong-Lasting Insecticidal Treated Net / Long-Lasting Insecticidal Net\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eMiP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMalaria in Pregnancy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eNMCP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNational Malaria Control Program\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eOPD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eOutpatient Department\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003ePMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePresident’s Malaria Initiative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSHS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSenior High School\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eVAR2CSA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eVariant Surface Antigen 2 — Chondroitin Sulphate A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eWHO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eWMA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWorld Medical Association\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Ethics and Academic Integrity Committee of the Anglican University College of Technology (ANGUTECH), Nkoranza Campus, Ghana. All participants provided informed consent prior to participation. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to publish anonymized data was also obtained from participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe questionnaire, datasets obtained from the survey and the Python 3.12 script used for data analysis are available in the figshare repository at: https://figshare.com/s/8bde994e1a3387b6558a.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eR.O.Y.:\u003c/strong\u003e Conceptualization, Methodology, Formal analysis, Data curation, Validation, Investigation, Visualization, Writing \u0026ndash; original draft, review \u0026amp; editing, Resources, Project administration. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI.A.A.:\u003c/strong\u003e Methodology, Formal analysis, Data curation, Validation, Investigation, Visualization, Writing \u0026ndash; original draft, review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.B.:\u003c/strong\u003e Methodology, Formal analysis, Data curation, Validation, Investigation, Visualization, Writing \u0026ndash; original draft, review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG.O.F.:\u003c/strong\u003e Conceptualization, Methodology, Formal analysis, Data curation, Validation, Investigation, Visualization, Writing \u0026ndash; original draft, review \u0026amp; editing, Resources, Supervision, Project administration. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Anglican University College of Technology (ANGUTECH), Nkoranza Campus, and Department of Forest Engineering, Forest Management Planning, and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, for providing some of the resources needed for this study. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOmran AR. 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Malaria Prevention for Pregnant Women and Under-Five Children in 10 Sub-Saharan Africa Countries: Socioeconomic and Temporal Inequality Analysis. IJERPH. 2024;21:1656. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph21121656\u003c/span\u003e\u003cspan address=\"10.3390/ijerph21121656\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerefe B, Habtie A, Chekole B. Insecticide-treated net utilization and associated factors among pregnant women in East Africa: evidence from the recent national demographic and health surveys, 2011\u0026ndash;2022. Malar J. 2023;22:349. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12936-023-04779-w\u003c/span\u003e\u003cspan address=\"10.1186/s12936-023-04779-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"antenatal care, behavior change, malaria in pregnancy, sub-Saharan Africa, socio-demographic, vector-control intervention","lastPublishedDoi":"10.21203/rs.3.rs-9690777/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9690777/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMalaria in pregnancy remains a leading cause of preventable maternal and neonatal mortality in sub-Saharan Africa. While distribution programs have achieved near-universal mosquito net ownership, a critical gap persists between access and consistent utilization. This study characterizes the knowledge, access, and practice profile regarding long-lasting insecticidal treated nets among pregnant women in rural Ghana to identify the determinants of this ownership\u0026ndash;utilization gap, and contextualizes the findings through a systematic comparison against fourteen malaria-endemic countries.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA descriptive cross-sectional study was conducted among 200 pregnant women receiving antenatal care in the Nkoranza South Municipality, Ghana. Primary data were collected through face-to-face structured interviews assessing socio-demographics, knowledge depth, access, and reported net use, which was further validated by direct physical observation of the sleeping environment. The data were analyzed using advanced multivariate visualizations, including heatmaps, diverging bar charts, and radar charts. Secondary quantitative data from fourteen sub-Saharan African countries were subsequently utilized to benchmark local ownership and utilization metrics against regional norms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study revealed a \"Universal Access Paradox\" where supply-side success coexisted with demand-side failure. While awareness of malaria prevention (100%) and antenatal care access (98.5%) were near-universal, knowledge depth was critically low; only 27.1% of participants correctly identified the three-year insecticide efficacy period. Consequently, direct observation confirmed that only 43.5% of nets were correctly hung and actively in use. The cross-country comparative analysis contextualized these findings, showing that the study site achieved the region's highest net ownership (100%) but recorded the lowest ownership-to-utilization conversion rate (43.5%). The inability to identify insecticide efficacy duration was confirmed as a systemic, pan-African programmatic failure.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese results argue for a fundamental policy shift from a \"distribution-only\" model to a \"Distribution Plus\" strategy. By integrating occupationally sensitive behavior change counseling and observational monitoring into routine antenatal care, health programs can bridge the gap between near-universal access and effective biological protection, ultimately reducing the malaria burden among vulnerable populations.\u003c/p\u003e","manuscriptTitle":"Advancing the ‘Distribution Plus’ Model: A Multi-Visualization KAP Study and Fourteen-Country Benchmarking of LLITN Utilization among Pregnant Women in Rural Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 09:10:48","doi":"10.21203/rs.3.rs-9690777/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d926729-5605-4bd3-9048-fb422156afec","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvited","content":"","date":"2026-05-14T15:33:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-13T03:36:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-13T03:35:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-05-12T10:35:42+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T09:10:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 09:10:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9690777","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9690777","identity":"rs-9690777","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}