Perceptions of Climate Change and Health Implications in Nigeria: A Planetary Health Perspective Using Afrobarometer Data

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Using a Planetary Health lens, this study explores public perceptions of climate change and its health implications in Nigeria, drawing on nationally representative data from Afrobarometer Round 9. It investigates how awareness, and health-related perception differ across region, hence shaping climate-responsive policies. A cross-sectional design was applied using secondary data from Afrobarometer’s Nigeria survey. The dataset includes geocoded responses capturing climate perceptions, experiences of extreme weather events, and perceived health effects. Descriptive statistics summarized demographic characteristics, while binary logistic regression examined predictors of climate change awareness and perceived health impacts. Spatial analysis using GIS tools in R mapped climate awareness across Nigeria’s geopolitical zones. Spatial autocorrelation (Moran’s I) and hotspot analysis identified clusters of awareness, while overlays of flood and drought risk revealed environmental vulnerabilities. Findings show substantial demographic and regional disparities. Mean age and gender composition varied across states. Education levels peaked in Anambra (1.85) but were lowest in Bayelsa and Zamfara. Climate change awareness ranged from 0–87.5%, highest in southern urbanized states and lowest in northern states. Perceptions of climate-related health impacts including heat illnesses, vector-borne diseases, respiratory disorders, and malnutrition ranged from 1.90–4.71 on a Likert scale. Logistic regression confirmed significant predictors of awareness to include gender with regional effects more pronounced in some states like Abuja, Benue and markedly lower in and Zamfara. The model showed excellent fit (χ² = 311.1, df = 39, p < 0.001; AIC = 1647.1). The study highlights key gaps in climate knowledge that hinder resilience. It recommends inclusive, education-focused, and gender-responsive climate communication strategies aligned with Planetary Health principles to advance sustainable and equitable climate action in Nigeria. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Having been increasing at a tremendous rate worldwide, climate change has covered areas of incidence of heat waves, flood, tropical storms, wildfires, drought and sea level rise, which have not been spared by any region (Hathaway & Maibach, 2018 ). The World Health Organisation (WHO) has stated that in this century, climate change has been named the largest threat to global health (Atwoli et al., 2021 ), and the abnormalities of the changes in temperature cause more than five million deaths annually worldwide (Zhao et al., 2021 ). Although various climate-related conditions are expected to increase the annual death rates caused by malnutrition, malaria, diarrhea, and heat stress by another 250,000 people between 2030 and 2050 (World Health Organization (WHO), 2024 ), climate-sensitive diseases have been observed to discriminate against the vulnerable communities, with the prevalence of vector-borne infections lumining with rainfall patterns and increases in temperatures. Malaria, as an example, is the most climate-sensitive one, with approximately 263 million instances and 597,000 deaths registered in the world in 2023 (WHO, 2024). As the most populated country in Africa comprising of more than 200 million people, Nigeria is rated as the second most vulnerable country in Africa to climatic change (The World Bank Group, 2021 ), such as rising temperature, rainfall, drought and desertification, rising seas, erosions, floods, frequent occurrence of extreme weather patterns, among others (Bolan et al., 2024 ). This is attributed to the diverse ecological zones (coastal areas, Sahel) as well as its high dependence on the climate sensitive sector like in the field of agriculture, which is the employer of 70 percent of the population (Gbode et al., 2019 ). Such changes are causing various dynamics, including the loss of biodiversity, loss of food and water security, rising poverty, conflict, displacement, economic imbalance, and adverse health conditions (Okon et al., 2021 ). As an example, the rise of sea-level in the area of coast has displaced 27–53 million people living in the area of coast (Okon et al., 2021 ). Nations such as Nigeria too experience the brunt of climate-sensitive illnesses including Malaria where the number of cases and deaths a year are approximately 68 million and 194,000 respectively marking almost the 27 per cent of the global burden, which is currently the highest burden of malaria worldwide (WHO Regional Office for Africa, 2023 ). This threat in Nigeria causes nearly 39 percent of the malaria deaths across the world among under-five (World Health Organization, 2022 ). Moreover, the country has not been spared by climate-related floods that displaced more than 2.1 million citizens, revealing an idea about the multiplicity of health hazards that climate change in Nigeria may cause (WHO Regional Office for Africa, 2023 ). All this and more is not easy to accept as the negative effects of climate change have led to at least 150,000 deaths each year, evidence of the serious health consequences (World Health Organisation, 2022). The risks to health due to climate change in Nigeria are demonstrated by various routes (Wright et al., 2024 ). The immediate effects are specific heat-related diseases, the injuries and deaths as the outcomes of extreme weather events, and the secondary effects come about through alterations in patterns of vector-borne diseases, water-based diseases, food scarcity and malnutrition (Landrigan et al., 2018 ; Ogbonna et al., 2007 ). Specifically, the nation has a problem with climate-sensitive illnesses like malaria, meningitis, cholera, and diarrheal diseases, which exhibit powerful climatic associations with variables of temperature, humidity, and rains patterns (Okon et al., 2021 ; Oluwatimilehin et al., 2022 ). In spite of these effects of the climate on health, climate-health awareness is variable, which restricts appropriate policy responses (Tran et al., 2021 ). Thus, public awareness regarding this risk is significant not only regarding the willingness of people, but also to advance climate adaptation and mitigation (Casson et al., 2023 ; van der Linden, 2015 ). Nigeria is also not exception to this challenge, but with regional diversity in it. Research is concerned with how people perceive climate change and health consequences of climate change in Nigeria which has provided vital information about the ideas and knowledge of the community. The research conducted among south-eastern and western regions has shown between 80–96 per cent prevalence when it comes to the general awareness of climate change, and bush burning/desertification is one of the primary causal factors such as (Joe-Ikechebelu et al., 2019 ; Odjugo & Ovuyovwiroye, 2013 ). Indeed, there is everything to show that the occurrence of climate change impacts in Nigeria is due to the causes attributable to the climate change, including temperature rise, increase in precipitation, rise in the level of the sea, extreme weather conditions, and, most of all, increase in health risks (Femi Monday, 2020 ; Haider, 2019 ). Nonetheless, irrespective of this consciousness, inequalities still exist in terms of the amounts of knowledge regarding the details of the causes and health consequences of climate change (Ebhuoma & Simatele, 2019). The relationship between climate perception and health awareness is complex and influenced by factors such as education level, occupation, access to information, and direct experience of climate impacts (Ogunbode et al., 2021). Rural communities, while often having direct experience of changing weather patterns and their impacts on agriculture and health, may lack comprehensive understanding of the broader climate change phenomenon and its long-term health implications (Uddin et al., 2020). This knowledge gap has important implications for adaptation planning and public health interventions. In the light of this, the concept of planetary health has emerged as a critical framework for understanding the interconnections between human health and the natural systems upon which civilization depends (Whitmee et al., 2015). This framework which was first articulated by The Lancet Commission, emphasizes the interdependence of human health and the health of nature, recognizing that sustainable solutions must address both human wellbeing and environmental sustainability. In the Nigerian context, planetary health considerations are particularly relevant given the country's dependence on natural resources and ecosystem services for livelihoods and health. Therefore, any disruptions like climate change will only increase zoonotic diseases, food insecurity, and respiratory illnesses among others (Lancet Countdown, 2023) . Globally, studies have examined perceptions of climate change and its health consequences among diverse populations. For instance, Heydari et al. (2023) explored the views of Iranian medical students through qualitative methods, while Hathaway and Maibach ( 2018 ) synthesized global literature on how health professionals perceive climate risks. Similarly, in China, Li et al. (2021) examined farmers’ perceptions of climate severity, and Casson et al. ( 2023 ) assessed health-related climate concerns in Canada. These studies underscore the growing recognition of climate change as a public health concern globally. However, within Africa, and particularly in Nigeria, evidence remains limited and often fragmented. Existing studies in Nigeria have focused mainly on youth perspectives within localized contexts (Joe-Ikechebelu et al., 2019 b), or they investigate health risks linked to climate change without examining broader population awareness or spatial patterns (Femi Monday, 2020 ; Omoruyi & Onafalujo, 2020). Other studies have emphasized observed climatic extremes (Gbode et al., 2019 ), or climate-health linkages in specific cities (Oluwatimilehin et al., 2022 ), but few have connected these insights to public understanding at a national scale. Moreover, while literature such as Okon et al. ( 2021 ) and Haider ( 2019 ) has called attention to the need for climate-responsive development in Nigeria, there remains a critical gap in understanding how perceptions of climate change and especially its health implications—are distributed across geographic regions and shaped by demographic factors. Furthermore, prior studies have not adopted a planetary health lens that explicitly links human health and environmental sustainability within the Nigerian context. To address these gaps, this study investigates the spatial patterns and demographic determinants of climate change awareness and perceived health impacts in Nigeria, using nationally representative data from Afrobarometer Round 9. By integrating a planetary health framework with spatial and multivariate analysis, this study provides a novel contribution to climate-health literature in Nigeria and offers insights for policy design tailored to sub-national differences in perception and vulnerability. Methodology To that end, this study used a cross-sectional study design to investigate spatial variations in the perceptions of the general populations in Nigeria regarding climate change and how it affects the health. The data were obtained in the Afrobarometer Round 9 survey, which is a nationally representative survey that contains response-level data on climate change awareness, socio-demographics and Positive and Negative Perceptions of Health Risks attributable to climate variability. 1,600 respondents of 36 states and the Federal Capital Territory (FCT) have been retained after cleaning the data. Data cleaning was done and missing data was discarded after which a logistic regression analysis was carried out, and spatial modeling. Statistical Analysis The three main indicators were created by means of the generation of spatial summaries Climate change awareness, Perceived extreme weather impacts and Perceived health impacts. The choropleth mapping was used to provide state-level aggregates. The Global Moran I and Local Indicators of Spatial Association (LISA) were calculated with the aim of evaluating the spatial dependency. The LISA clusters were rolled out in some of the regions such as the North-Central, South-South and South-West. 2.3.2 Logistic Regression Modelling Binary logistic regression models were estimated to examine the determinants of climate change awareness and perceived health impacts. Each model followed the form: $$\:logit\left(P(Y=1\right))={\beta\:}_{0}+{\beta\:}_{1}\left(gender\right)+{\beta\:}_{2}\left(age\right)+{\beta\:}_{3}\left(education\right)+\sum\:_{j=1}^{n}{\beta\:}_{j}{\left(region\right)}_{j}$$ RESULTS Descriptive statistics of Socio-demographic variables by Region The table gives the summary statistics of demographics of respondents in the 37 regions of Nigeria. Mean age is found to be very different. The highest level of education is in Anambra (1.85), Kogi (1.81) and Kwara (1.75) with the lowest level recorded at Bayelsa and Zamfara (1.00). Mean gender also differs slightly across states with Osun (43.4) and Ekiti (40.4) having the higher values implying a higher number of female respondents. Table 1 Regional Summary Statistics of Respondents in Nigeria Region Gender Age Education³ Abia 36.0 525.0 1.28 Adamawa 33.4 1756.0 1.16 Akwa Ibom 35.5 1083.0 1.18 Anambra 32.5 740.0 1.85 Bauchi 33.2 2184.0 1.35 Bayelsa 37.2 1383.0 1.00 Benue 34.0 3609.0 1.15 Borno 35.9 1372.0 1.15 Cross River 34.7 6461.0 1.06 Delta 37.3 3934.0 1.15 Ebonyi 34.9 621.0 1.08 Edo 37.3 4266.0 1.12 Ekiti 40.4 603.0 1.03 Enugu 40.3 582.0 1.25 FCT Abuja 33.9 2035.0 1.17 Gombe 35.4 4136.0 1.12 Imo 34.4 453.0 1.04 Jigawa 33.0 620.0 1.19 Kaduna 37.0 1443.0 1.25 Kano 31.8 620.0 1.21 Katsina 31.5 620.0 1.06 Kebbi 30.2 620.0 1.32 Kogi 37.6 2369.0 1.81 Kwara 33.4 3359.0 1.75 Lagos 37.1 623.0 1.20 Nasarawa 36.4 2158.0 1.38 Niger 33.1 2964.0 1.31 Ogun 35.2 570.0 1.04 Ondo 35.6 1060.0 1.05 Osun 43.4 606.0 1.02 Oyo 33.7 886.0 1.06 Plateau 38.9 5902.0 1.25 Rivers 34.9 3311.0 1.06 Sokoto 31.1 620.0 1.15 Taraba 33.8 5698.0 1.04 Yobe 32.5 1363.0 1.12 Zamfara 30.0 620.0 1.00 4.2. Spatial Distribution of Climate Change Awareness, Extreme Weather and Health Impacts 4.2.1 Spatial Distribution of Climate Change Awareness A choropleth map of the climate change awareness provided over the states is shown in Fig. 2 . Findings indicate that the percentage of awareness lies between 0 and 87.5 percent with difference in locations. States within the Southern region performed best in the awareness of the dementia care planning process especially Lagos, Rivers, Cross River, Enugu and Anambra, and they fit in the 43.44–87.50 percent group. Conversely on the other hand, the lowest rates of awareness could be found in the Northern states (0.00-15.35 per cent) amongst which are Zamfara, Kebbi, Yobe and Niger. Moreover, the coastal as well as the southeast regions are highly urbanized, more developed, and easily prone to floods, where the focus of awareness is high. The parts of central Nigeria displayed moderate levels of awareness whereas the northeastern states indicated low levels of awareness with some states showing inadequate information. 4.2.2 Perceptions of Extreme Weather across Nigeria Figure 3 analysis portrays substantial spatial differences in public perception of extreme weather events in Nigeria, which is seen in the choropleth map as well with the distribution of mean Extreme Weather Scores at state level. Based on a Likert sort of scales, the scores were between 2.29 and 4.71, where greater scores would signify greater perceived frequency and severity of extreme weather events like floods, droughts and heatwaves. The North-Central, North-West, and South-South areas have the highest numbers of concerned regions with a mean of above 4.1 in places like Niger, Benue, Kaduna, Bayelsa, and Delta states which have very high fate of exposure to extreme weather occurrences. Instead, states in the South West (such as Ekiti, Ondo and Ogun) and parts of the South-East and North-East recorded more negligible levels of perception (less than 3.33), implying a lesser degree of concern or awareness by the people in the mentioned regions to climate and weather related occurrences. A lower score in these areas, however, may imply the lack of exposure or even a lapse in climate literacy and risk communication, although climate variability is not unaffected in these regions. 4.3.3 Perceived Health Impacts of Climate Change across Nigerian States The choropleth map of Fig. 4 presents the state-levels variations in Climate-Related Health Impact Scores of Nigeria, as per the survey responses of Afrobarometer. The score is used to reflect what people perceive about the impact of climate change on human health by including issues like heat illnesses, the spread of diseases by vectors, respiratory disorders, and name but the malnutrition that will come with the destruction of the environment. The scores were between 1.90–4.71 on the scale of likert kind of scale with a higher number indicating the higher perception of health impact. The information shows that there is significant geographical disparity with states in the North-Central like Niger, Benue, North-West, and South-South (such as Rivers, Bayelsa, Delta), and those portions of the South-West including Lagos, Ogun, demonstrating the most perceived health risks, with ratings above 4.25 points. Such areas have traditionally been susceptible to extreme weather conditions like floods and heat waves as well as synergistic effects such as inadequate urban air quality or escalating vectors that exacerbate climate-health relationships. In its turn, lower health impact scores (less than 3.13) were registered in the states of the South-East (e.g., Ebonyi, Anambra) and in some regions of the North-East, which implies a lower degree of public concern. This could possibly be low levels of exposure, underreporting of health outcomes or lower level of climate health literacy in these areas. In addition, there was low to moderate level of perception ( scores between 3.86 and 4.06) in various states distributed in the North-East, South-East and parts of the central zones that could be described as some form of awareness but that the climate-sensitive diseases were not a commonly experienced perceived illness. The states with missing data were few probably because of survey non-response or failure to sample the geographical areas. Based on planetary health, these results indicate that there is geographic variation in the distribution of climate-related health risks and perception in Nigeria. The hot-spots areas can be experiencing syndemic processes, in which environmental and social risk factors come together to increase the health burden. In the meantime, regions of low perception should be regarded as a potentially vulnerable area to early warning systems, risk communication, and climate-health education. 4.3. Local Spatial Clustering (LISA) of Climate-Related Perceptions In an attempt to analyze geographic patterns further, Local Indicators of Spatial Association (LISA) were employed to evaluate the spatial clustering of climate change awareness, experience of extreme weather, and perceived health effects. Figure 5 below: The first map on awareness cluster illustrates the LISA cluster map of High-High clusters of climate change awareness in the high-income southeast coastal states such as Lagos, Rivers, Cross River and Delta. These clusters show regions where focal state as well as its neighbors report about high awareness levels. A Low-Low cluster was found in the southeastern-central Nigeria, which implies spatially adjacent states of homogenous low awareness. Overall, most of northern Nigeria had non- significance clustering, which indicated the absence of a high level of spatial autocorrelation. equally, the extreme weather events (e.g., flooding, drought) also reported clusters spatially located in south-south region mainly. The situation was intense in the Delta, Bayelsa and Cross River states because they were all flood prone, and have dry season coastal erosion. Most of the rest of the country had only negligible or not otherwise significant clustering, indicating more clustered or localized weather experience of extreme weather. In addition, the climate-related health perception/s was also clustered in the southern regions mostly in Rivers and Delta and Lagos which met near equal prevalence and prevalence of the climate-related health issues (e.g. Malaria and cholera). Southeastern interior had a few Low-Low clusters whereas northern Nigeria had no high clustering in the majority. Fig: 5: LISA of climate change awareness, extreme weather experiences, and perceived health impact 4.4 Global Spatial Autocorrelation Analysis To test whether key climate perception variables follow spatial dependence, Table 2 below presents the statistic of Moran I which was calculated within a randomization structure with each variable; climate change awareness, extreme weather perceptions, and health-related consequences. Based on Table 2 , the value of Moran I measure is used to estimate the global spatial autocorrelation, it evaluates the degree by which measurements of a variable are near or far apart as well as randomly distributed on geographic units. Whenever the value of Moran is positive, it implies that there is a tendency of similar values (e.g. high with high, or low and also low) to be clustered as regards spatial location; whereas where Moran is negative there is dissimilarity (e.g. high values and low values) in respect of spatial location. As a measure of statistical significance, a p-value is reported in consideration of the null hypothesis that the data are spatially random. Moran P Statistics of climate change awareness given in Table 2 below was 0.107 (p = 0.049) and it was found to be a statistically significant positive spatial autocorrelation. This finding holds that more aware states have a tendency of being in close proximity with equally aware ones, and otherwise states with low states of awareness. This geoclustering upholds the trend that has been noted previously using LISA cluster maps, especially the High-High clusters which are heavily clustered to the South of Nigeria. In case of perceptions on extreme weather events, the Moran I was 0.042 (p = 0.563) which does not show significant spatial autocorrelation. This means that the views of extreme weather are spatially unstructured, probably are developed based on recent the local experiences instead of the influences of regional trends. Health consequences that were perceived according to climate change showed a Moran I value of 0.115 (p = 0.038) showing that there was a statistically significant positive spatial autocorrelation with it. This implies that the geographic concentration of the states where the respondents believe to be under strong health risks due to climatic changes. The presented results are consistent with visual trends and choropleth and cluster maps, which highlighted a high health impact score in the Niger Delta region and in named northern states. We can then conclude that both the awareness of climate change and perceptions of health effects have spatial clustering in the Nigerian states whilst perceptions of extreme weather are more in a random or local pattern. These geographical insights strengthen the need to integrate geographically specific intervention to increase awareness and resiliency in the climate-exposed areas. Table 2 Moran’s I Test Results for Spatial Autocorrelation of Climate Change Perceptions Variable Moran’s I Expected I Variance Standard Deviate p -value Spatial Autocorrelation Awareness 0.107 –0.029 0.00676 1.658 0.049 Significant positive Extreme Weather –0.042 –0.029 0.00673 –0.159 0.563 Not significant Health Impact 0.115 –0.029 0.00665 1.774 0.038 Significant positive 4.5 Determinants of Climate Change Awareness and Perceived Health Impact Findings from Table 3 reveal that result of the binary logistic regression model which was used to evaluate the socio-demographic and regional predictors of climate change awareness. The dependent variable was a binary formation of whether the respondents claimed to be aware about climate change or not. Gender, age, level of education and region of residence were used as independent variables and the perception of climatic health effects was a dichotomous variable describing whether the respondents indicated whether climatic change had effect or not. The statistical significance of the model was 62 (311.1, df = 39, p < 0.001) and possesses an excellent model fit (AIC = 1647.1). A number of predictors proved to be significant with a 5% level. According to the finding, gender was a statistically significant predictor (B = 0.015, p = 0.002) and therefore males were easier likely to know about climate change. The amount of the role that region performed as an explanation of the variability in awareness was substantial. Being aware happened to be significant in Abuja ( 2.266, p = 0.001). The rest of the effects were also statistically significant in Benue ( 1.008, p 0.034), Kaduna ( 1.043, p 0.020) and Kwara ( -1.678, p 0.019). On the contrary, states like Kano (b = -2.912, p < 0.001), Katsina (b = -3.114, p < 0.001) and Zamfara (b = -1.923, p = 0.006) had significantly lower chances of reporting awareness, implying great regional variation. The age and the education had no statistically significant impact on this model ( p > 0.3) although education theoretically would have some effect on awareness. There was lesser explanatory power (AIC = 229.2) and most of the predictors were not significant. The result of this study showed that none of the socio-demographic variables, including gender, age, and education significantly predicted perception of health impact (p > 0.2). In the case, states like Adamawa, Kano, and Lagos had the extremely large negative error (e.g., 0 269 19) with p > 0.99, and it indicates an inability of the model to be stable or even data imbalanced across the regional levels. Table 3 Regional Effects on Climate Change Awareness and Perceived Health Impacts Region Awareness: Estimate (SE) z p-value Health Impact: Estimate (SE) z p-value Adamawa 0.305 (0.505) 0.604 0.546 –19.77 (8484.0) –0.002 0.998 Akwa Ibom –1.132 (0.534) –2.119 0.034 * –19.85 (7561.0) –0.003 0.998 Anambra 0.104 (0.463) 0.225 0.822 –19.70 (6819.0) –0.003 0.998 Bauchi –1.046 (0.506) –2.066 0.039 * –19.72 (6904.0) –0.003 0.998 Bayelsa 0.595 (0.549) 1.084 0.279 1.035 (0.920) 1.125 0.261 Benue 1.008 (0.477) 2.114 0.034 * –19.77 (6909.0) –0.003 0.998 Borno –0.339 (0.469) –0.723 0.470 –19.84 (6921.0) –0.003 0.998 Cross River –0.787 (0.558) –1.411 0.158 –0.626 (1.320) –0.474 0.635 Delta –1.086 (0.511) –2.127 0.033 * –19.82 (6913.0) –0.003 0.998 Ebonyi –0.823 (0.592) –1.390 0.165 –19.85 (9810.0) –0.002 0.998 Edo –1.134 (0.540) –2.099 0.036 * –19.83 (7568.0) –0.003 0.998 Ekiti –0.755 (0.536) –1.410 0.159 –20.03 (8428.0) –0.002 0.998 Enugu –0.923 (0.545) –1.693 0.090 . –19.98 (8429.0) –0.002 0.998 FCT Abuja 2.266 (0.716) 3.167 0.002 ** –19.78 (9775.0) –0.002 0.998 Gombe 0.124 (0.550) 0.226 0.822 –0.330 (1.295) –0.255 0.799 Imo –0.225 (0.466) –0.483 0.629 –1.160 (1.249) –0.929 0.353 Jigawa –16.27 (345.1) –0.047 0.962 –19.79 (6918.0) –0.003 0.998 Kaduna 1.043 (0.449) 2.324 0.020 * –1.457 (1.249) –1.166 0.244 Kano –2.912 (0.623) –4.674 < 0.001 *** –19.74 (4703.0) –0.004 0.997 Katsina –3.114 (0.803) –3.879 < 0.001 *** –19.79 (6003.0) –0.003 0.997 Kebbi –1.221 (0.550) –2.222 0.026 * –0.045 (1.051) –0.043 0.966 Kogi –0.920 (0.551) –1.670 0.095 . –19.74 (8328.0) –0.002 0.998 Kwara –1.678 (0.718) –2.337 0.019 * –19.65 (9687.0) –0.002 0.998 Lagos –1.005 (0.419) –2.395 0.017 * –19.91 (4367.0) –0.005 0.996 Nasarawa –0.642 (0.576) –1.115 0.265 –19.80 (9755.0) –0.002 0.998 Niger –1.654 (0.567) –2.915 0.004 ** –19.72 (6897.0) –0.003 0.998 Ogun –1.499 (0.544) –2.756 0.006 ** –1.168 (1.248) –0.936 0.349 Ondo –0.704 (0.505) –1.396 0.163 –0.994 (1.253) –0.794 0.428 Osun –1.093 (0.524) –2.085 0.037 * –1.171 (1.264) –0.926 0.354 Oyo –1.277 (0.488) –2.617 0.009 ** –19.83 (6006.0) –0.003 0.997 Plateau –0.413 (0.531) –0.778 0.436 0.048 (1.118) 0.043 0.966 Rivers –0.019 (0.443) –0.042 0.966 –0.673 (1.048) –0.642 0.521 Sokoto –1.876 (0.638) –2.942 0.003 ** –19.76 (7578.0) –0.003 0.998 Taraba –0.773 (0.603) –1.282 0.200 –19.73 (9807.0) –0.002 0.998 Yobe –0.195 (0.554) –0.352 0.725 –19.77 (9805.0) –0.002 0.998 Zamfara –1.923 (0.705) –2.727 0.006 ** –19.78 (8490.0) –0.002 0.998 Model Fit Summary Outcome Variable Null Deviance Residual Deviance AIC Sig. Predictors Awareness 1878.2 1567.1 1647.1 Gender, Kano, Katsina, Sokoto, Zamfara, FCT, etc. Health Impact 206.24 149.21 229.21 Only Intercept significant Note : p < 0.001 , p < 0.01 *, p < 0.05 Discussion of Findings This paper showed the spatial trends and demographic predictors of climate change awareness and perceived health consequences of climate change in Nigeria through a planetary health perspective. As the results show, there are marked regional differences in awareness, experience and perception which prove that people are not equally concerned and informed about the climate-related problems. Among the most notable outcomes, one must mention the fact that, there is a strong regional disparity in climate change awareness, with the highest awareness recorded in Southern and North-Central states, as opposed to the North-East and North-West. This geographical gap is similar to the one previously observed by Odjugo ( 2013 ) and which the author reported that Southern Nigerians are more concerned with climate change given the higher degree of education, access to media sources, and exposure to floods and sea level rise generated by climate changes. The high awareness in this region also conforms to other findings given by Joe-Ikechebelu et al., ( 2019 ) who established that the South-Easterners had greater climate literacy amongst the youth particularly in faith-based organizations. This finding implies that both analytical results support the same perception over the work conducted on this study and previous records on the role of localized exposure, education, and communication channels on awareness. Nevertheless, this experiment is different from other previously conducted studies in the regression analysis. In particular, although numerous factors, including demographic factors, such as education level and gender, are identified in the literature as determinants significantly related to the issue of climate change awareness, the present study did not find these factors to be significant, when regional location was taken into consideration. This deviation may imply that in the case of Nigeria, structural disparity in the regions of unequal infrastructure, dissimilar media penetration, and other individual countenance of environmental schooling is likely to supersede the weight of a single-level characteristics. In addition, some may argue that they could have benefited from this research because even though the researchers concluded that male respondents were more likely to report the awareness of the climate change situation, the results were negligible. This observation is in contrast to the information presented in international reviews (e.g., Wright et al., 2024 ), which argues that gendered vulnerability and care giving role commonly prompts women to be more aware of risk in the environment. The ambiguity might be an indication of sociocultural factors at work in Nigeria, which overwrite the common gender-perception trends in other societies. With respect to the perceptions of health impacts of the climate change, this research indicates evidential clusters on the map, involving a perception level being greater in flood-prone and coastal states such as Rivers, Bayelsa, Lagos and Delta. This can be corroborated with empirical data by Femi Monday ( 2020 ) documenting the rise in cases and societal-disease anxiety of vector-borne diseases, heat-related diseases, and respiratory illnesses in urban areas in the south of Nigeria. Moreover, as demonstrated by Gbode et al. ( 2019 ), these are also the regions where flooding, extreme rainfall, and humidity are the most dramatic types of climate extremes, which are all reported to increase disease burdens (Landrigan et al., 2018 ). The geographical co-location of exposure to climate extremes and perceived health risk promotes the planetary health message of Atwoli et al. ( 2021 ), who argue that the problem of the unequal and unfair health interference of climate-related risks is an urgent issue, including in low- and middle-income countries. Similarly, Bolan et al. ( 2024 ) highlight that, in the Global South, extreme weather events trigger the dissemination of pollutants; thus, increasing health risks in informal settlements environments strongly reflected in Nigeria in the coastal urban areas. However, these results can be partially criticized in light of the results given by Casson et al. ( 2023 ), who indicated that the demographic factors, particularly the ages and health status, have strong predictive power in climate-health matters in high-income countries such as Canada. This divergence can be associated with the variation in data context: whereas in Canada the respondents might be analyzing the climate-health relationship in terms of official health messaging, the Nigerian participants might be more experience-driven and locally determined, i.e., based on exposure at a lived level comparatively rather than institutionalized messaging. Notably, the discrepancy identified in the present study between high exposure and low awareness or concern evokes the claim by Okon et al. ( 2021 ) that the alarming health impacts of climate change remain largely ignored by climate policies, unnoticed by most Nigerians, and unadapted to despite the experience of such effects on human health in the country. This is of special concern because the degree of the perceived health risk and awareness is low in the North-East and North-West, which is likely to be an obstacle to premonitory adaptation. Planetary health-wise, the regional distribution of climate concern, not to mention the lack thereof in other regions, is an indicator of systematic difference in inequity of environmental health literacy and resilience. One must develop more equitable risk communication networks, health care infrastructure, and participatory bodies instead of simply cutting emissions levels or spending on adaptations to realize climate justice in Africa as Atwoli et al. ( 2021 ) and Wright et al. ( 2024 ) emphasize. The case study further supports the premise that any Nigerian adaptation to climate change should be regionally specific and address mitigation based on the experience of individual societies in line with the position of the WHO (2024) on climate-informed health planning in the most at-risk nations. Overall, the findings of this research are generally compatible with the current Nigerian and international evidence and current research on the geography of climate awareness and the vulnerability it presents, especially in terms of revealing the primary role of exposure and lived experience of a region. Nonetheless, this difference in the meaning of individual demographics threatens the generalisations present in the global north-based literature and demands a rather less universalistic approach to risk perception in Nigeria. These results contribute to increasingly large evidence base on spatially responsive and locally based public health and climate mitigation initiatives. Conclusion This study presents an uneven landscape of climate change awareness and its perceived health impacts across Nigeria. We found that while awareness was high in southern, more urbanized region, it remained low in parts of the north such as Zamfara and the south-southern part like Bayelsa, which also had lower educational status. Residents of urbanised areas were less likely to recognise climate change or connect it to health concerns, which presents deep social and geographic inequities. These disparities align with areas at greater environmental risk, suggesting that vulnerable populations may be least informed yet most exposed to climate-related health threats. The findings reveal that advancing climate resilience in Nigeria requires not only infrastructural or environmental interventions but also targeted communication strategies that are education-focused, gender-responsive, and regionally tailored. Strengthening climate-health literacy through community-based outreach and formal education systems will be vital to empower at-risk populations to adopt adaptive practices. Therefore, it is important that these strategies within a Planetary Health framework can enhance public engagement, bridge regional gaps, and accelerate equitable climate action. Which will help in integrating public perceptions into policy design and improve the responsiveness, effectiveness, and sustainability of Nigeria’s climate and health policies. Declarations Author Contributions: T.E.O & O.L.B conceptualized and designed the study, T.E.O developed the introduction while O.L.B developed the methodology, implemented the formal analysis and interpreted. T. E. O. wrote the discussion of findings and final review of the manuscript was done by T.E.O and O.L.B. All authors have read and agreed to publish this version of the manuscript. Funding : This research received no external funding. Clinical trial number : Not applicable. Institutional Review Board Statement: The study was based on the analysis of openly available data. Thus, ethical approval was not necessary. Acknowledgments : The authors are grateful to Afrobarometer, for granting the authors access to use Afrobarometer Dataset of Nigeria, (Round 9) which is available at http://www.afrobarometer.orgData. Conflicts of Interest: The authors declare no conflict of interest. References Atwoli, L., Baqui, A. H., Benfield, T., Bosurgi, R., Godlee, F., Hancocks, S., Horton, R., Laybourn-Langton, L., Monteiro, C. A., Norman, I., Patrick, K., Praities, N., Olde Rikkert, M. G. M., Rubin, E. J., Sahni, P., Smith, R., Talley, N., Turale, S., & Vázquez, D. (2021). Call for emergency action to limit global temperature increases, restore biodiversity, and protect health: Wealthy nations must do much more, much faster. Annals of Oncology , 32 (10), 1203–1205. https://doi.org/10.1016/j.annonc.2021.08.1743 Bolan, S., Padhye, L. P., Jasemizad, T., Govarthanan, M., Karmegam, N., Wijesekara, H., Amarasiri, D., Hou, D., Zhou, P., Biswal, B. K., Balasubramanian, R., Wang, H., Siddique, K. H. M., Rinklebe, J., Kirkham, M. B., & Bolan, N. (2024). Impacts of climate change on the fate of contaminants through extreme weather events. Science of The Total Environment , 909 , 168388. https://doi.org/10.1016/J.SCITOTENV.2023.168388 Casson, N., Cameron, L., Mauro, I., Friesen-Hughes, K., & Rocque, R. (2023). Perceptions of the health impacts of climate change among Canadians. BMC Public Health , 23 (1), 1–13. https://doi.org/10.1186/s12889-023-15105-z Femi Monday, I. (2020). Investigating Effects of Climate Change on Health Risks in Nigeria. Environmental Factors Affecting Human Health , 1–18. https://doi.org/10.5772/intechopen.86912 Gbode, I. E., Adeyeri, O. E., Menang, K. P., Intsiful, J. D. K., Ajayi, V. O., Omotosho, J. A., & Akinsanola, A. A. (2019). Observed changes in climate extremes in Nigeria. Meteorological Applications , 26 (4), 642–654. https://doi.org/10.1002/MET.1791 Haider, H. (2019). Climate change in Nigeria: impacts and responses. K4D Helpdesk Report , 1–38. http://www.rockfound.org/initiatives/climate/climate_change.shtml%0Awww.iied.org/HS/publications.html.%0AH OW%0Ahttps://a ssets.publishing.service.g ov.uk/media/5dcd7a1aed915d 0719bf4542/675_Climate_Change_in_Nigeria.pdf Hathaway, J., & Maibach, E. W. (2018). Health Implications of Climate Change: a Review of the Literature About the Perception of the Public and Health Professionals. Current Environmental Health Reports , 5 (1), 197–204. https://doi.org/10.1007/s40572-018-0190-3 Joe-Ikechebelu, N. N., Azuike, E. C., & Nwankwo, B. E. (2019). Perception of Climate Change among Youths in a FBO in a South-Eastern Town in Nigeria: A Pilot Study. Community Medicine and Public Health Care , 6 (3), 1–7. https://doi.org/10.24966/cmph-1978/100050 Landrigan, P. J., Fuller, R., Acosta, N. J. R., Adeyi, O., Arnold, R., Basu, N. (Nil), Baldé, A. B., Bertollini, R., Bose-O’Reilly, S., Boufford, J. I., Breysse, P. N., Chiles, T., Mahidol, C., Coll-Seck, A. M., Cropper, M. L., Fobil, J., Fuster, V., Greenstone, M., Haines, A., … Zhong, M. (2018). The Lancet Commission on pollution and health. The Lancet , 391 (10119), 462–512. https://doi.org/10.1016/S0140-6736(17)32345-0 Odjugo, & Ovuyovwiroye, P. A. (2013). Analysis of climate change awareness in Nigeria. Scientific Research and Essays , 8 (26), 1203–1211. https://doi.org/10.5897/SRE11.2018 Ogbonna, D. N., Amangabara, G. T., & Ekere, T. O. (2007). Urban solid waste generation in Port Harcourt metropolis and its implications for waste management. Management of Environmental Quality: An International Journal , 18 (1), 71–88. https://doi.org/10.1108/14777830710717730 Okon, E. M., Falana, B. M., Solaja, S. O., Yakubu, S. O., Alabi, O. O., Okikiola, B. T., Awe, T. E., Adesina, B. T., Tokula, B. E., Kipchumba, A. K., & Edeme, A. B. (2021). Systematic review of climate change impact research in Nigeria: implication for sustainable development. Heliyon , 7 (9), e07941. https://doi.org/10.1016/j.heliyon.2021.e07941 Oluwatimilehin, I. A., Akerele, J. O., Oladeji, T. A., Omogbehin, M. H., & Atai, G. (2022). Assessment of the impact of climate change on the occurrences of malaria, pneumonia, meningitis, and cholera in Lokoja City, Nigeria. Regional Sustainability , 3 (4), 309–318. https://doi.org/10.1016/J.REGSUS.2022.11.007 The World Bank Group. (2021). Climate Risk profile: Nigeria (Vol. 3, Issue 4). https://www.who.int/health-topics/climate-change#tab=tab_1 Tran, B. X., Nguyen, T. H., Phung, D. T., Nguyen, L. H., Pham, H. Q., Vu, G. T., Le, H. T., Latkin, C. A., Ho, C. S. H., & Ho, R. C. M. (2021). Gaps in awareness of climate variability and its impacts on society among health professionals and community workers in Vietnam: Implications for COVID-19 and other epidemic response systems. International Journal of Disaster Risk Reduction , 59 , 102212. https://doi.org/10.1016/J.IJDRR.2021.102212 van der Linden, S. (2015). The social-psychological determinants of climate change risk perceptions: Towards a comprehensive model. Journal of Environmental Psychology , 41 , 112–124. https://doi.org/10.1016/J.JENVP.2014.11.012 WHO Regional Office for Africa. (2023). Report on malaria in Nigeria 2022. In Isbn: 978-929023498-2 . World Health Organisation, W. (2022). Food Safety. In: Facts sheets . World Health Organization. (2022). Regional data and trends briefing kit World malaria. World Malaria Report 2022 , December , 1–16. World Health Organization (WHO). (2024). Climate Change . https://www.who.int/health-topics/climate-change#tab=tab_1 Wright, C. Y., Kapwata, T., Naidoo, N., Asante, K. P., Arku, R. E., Cissé, G., Simane, B., Atuyambe, L., & Berhane, K. (2024). Climate Change and Human Health in Africa in Relation to Opportunities to Strengthen Mitigating Potential and Adaptive Capacity: Strategies to Inform an African “Brains Trust.” Annals of Global Health , 90 (1), 1–21. https://doi.org/10.5334/aogh.4260 Zhao, Q., Guo, Y., Ye, T., Gasparrini, A., Tong, S., Overcenco, A., Urban, A., Schneider, A., Entezari, A., Vicedo-Cabrera, A. M., Zanobetti, A., Analitis, A., Zeka, A., Tobias, A., Nunes, B., Alahmad, B., Armstrong, B., Forsberg, B., Pan, S. C., … Li, S. (2021). Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: a three-stage modelling study. The Lancet Planetary Health , 5 (7), e415–e425. https://doi.org/10.1016/S2542-5196(21)00081-4 Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":292747,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAn overview of climate-sensitive health risks, their exposure pathways and vulnerability factors. Climate change impacts health both directly and indirectly, and is strongly mediated by environmental, social and public health determinants. (Source: WHO)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7647558/v1/6b3cdc8850fc043e7e05962a.png"},{"id":95657540,"identity":"722169bb-37d9-4d09-b67d-19e468e5f3c4","added_by":"auto","created_at":"2025-11-11 16:21:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50035,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eClimate Change Awareness by Region in Nigeria\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7647558/v1/c4584c33c0bce1226e29a170.png"},{"id":95631180,"identity":"37f8010f-bcf7-4fb4-a090-51f878f08431","added_by":"auto","created_at":"2025-11-11 11:36:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":52577,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePerception of Extreme Weather by Region in Nigeria\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7647558/v1/8ea1d9ca03520d498e0e0677.png"},{"id":95631182,"identity":"068dd9b6-a109-410d-820e-348c43a89bf3","added_by":"auto","created_at":"2025-11-11 11:36:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51095,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePerceived Health Implications of Climate Change in Nigeria\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7647558/v1/46f47b0f708e840582fe9eeb.png"},{"id":95656805,"identity":"378681de-a2d9-4c67-b291-f07906dcb4bb","added_by":"auto","created_at":"2025-11-11 16:19:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1775860,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLISA \u003c/strong\u003eof climate change awareness, extreme weather experiences, and perceived health impact\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7647558/v1/6470077a926792cdd4600632.png"},{"id":104739301,"identity":"9faef192-f83f-4e2d-91f2-0dfe6863529a","added_by":"auto","created_at":"2026-03-16 16:01:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2960535,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7647558/v1/5a1fdbc5-59e1-4158-bb45-e714aae43630.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Perceptions of Climate Change and Health Implications in Nigeria: A Planetary Health Perspective Using Afrobarometer Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHaving been increasing at a tremendous rate worldwide, climate change has covered areas of incidence of heat waves, flood, tropical storms, wildfires, drought and sea level rise, which have not been spared by any region (Hathaway \u0026amp; Maibach, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The World Health Organisation (WHO) has stated that in this century, climate change has been named the largest threat to global health (Atwoli et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the abnormalities of the changes in temperature cause more than five million deaths annually worldwide (Zhao et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although various climate-related conditions are expected to increase the annual death rates caused by malnutrition, malaria, diarrhea, and heat stress by another 250,000 people between 2030 and 2050 (World Health Organization (WHO), \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), climate-sensitive diseases have been observed to discriminate against the vulnerable communities, with the prevalence of vector-borne infections lumining with rainfall patterns and increases in temperatures. Malaria, as an example, is the most climate-sensitive one, with approximately 263\u0026nbsp;million instances and 597,000 deaths registered in the world in 2023 (WHO, 2024).\u003c/p\u003e\u003cp\u003eAs the most populated country in Africa comprising of more than 200\u0026nbsp;million people, Nigeria is rated as the second most vulnerable country in Africa to climatic change (The World Bank Group, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), such as rising temperature, rainfall, drought and desertification, rising seas, erosions, floods, frequent occurrence of extreme weather patterns, among others (Bolan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is attributed to the diverse ecological zones (coastal areas, Sahel) as well as its high dependence on the climate sensitive sector like in the field of agriculture, which is the employer of 70 percent of the population (Gbode et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSuch changes are causing various dynamics, including the loss of biodiversity, loss of food and water security, rising poverty, conflict, displacement, economic imbalance, and adverse health conditions (Okon et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As an example, the rise of sea-level in the area of coast has displaced 27\u0026ndash;53\u0026nbsp;million people living in the area of coast (Okon et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nations such as Nigeria too experience the brunt of climate-sensitive illnesses including Malaria where the number of cases and deaths a year are approximately 68\u0026nbsp;million and 194,000 respectively marking almost the 27 per cent of the global burden, which is currently the highest burden of malaria worldwide (WHO Regional Office for Africa, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This threat in Nigeria causes nearly 39 percent of the malaria deaths across the world among under-five (World Health Organization, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, the country has not been spared by climate-related floods that displaced more than 2.1\u0026nbsp;million citizens, revealing an idea about the multiplicity of health hazards that climate change in Nigeria may cause (WHO Regional Office for Africa, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). All this and more is not easy to accept as the negative effects of climate change have led to at least 150,000 deaths each year, evidence of the serious health consequences (World Health Organisation, 2022).\u003c/p\u003e\u003cp\u003eThe risks to health due to climate change in Nigeria are demonstrated by various routes (Wright et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The immediate effects are specific heat-related diseases, the injuries and deaths as the outcomes of extreme weather events, and the secondary effects come about through alterations in patterns of vector-borne diseases, water-based diseases, food scarcity and malnutrition (Landrigan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ogbonna et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Specifically, the nation has a problem with climate-sensitive illnesses like malaria, meningitis, cholera, and diarrheal diseases, which exhibit powerful climatic associations with variables of temperature, humidity, and rains patterns (Okon et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Oluwatimilehin et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn spite of these effects of the climate on health, climate-health awareness is variable, which restricts appropriate policy responses (Tran et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, public awareness regarding this risk is significant not only regarding the willingness of people, but also to advance climate adaptation and mitigation (Casson et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; van der Linden, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Nigeria is also not exception to this challenge, but with regional diversity in it. Research is concerned with how people perceive climate change and health consequences of climate change in Nigeria which has provided vital information about the ideas and knowledge of the community. The research conducted among south-eastern and western regions has shown between 80\u0026ndash;96 per cent prevalence when it comes to the general awareness of climate change, and bush burning/desertification is one of the primary causal factors such as (Joe-Ikechebelu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Odjugo \u0026amp; Ovuyovwiroye, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Indeed, there is everything to show that the occurrence of climate change impacts in Nigeria is due to the causes attributable to the climate change, including temperature rise, increase in precipitation, rise in the level of the sea, extreme weather conditions, and, most of all, increase in health risks (Femi Monday, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Haider, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nonetheless, irrespective of this consciousness, inequalities still exist in terms of the amounts of knowledge regarding the details of the causes and health consequences of climate change (Ebhuoma \u0026amp; Simatele, 2019).\u003c/p\u003e\u003cp\u003eThe relationship between climate perception and health awareness is complex and influenced by factors such as education level, occupation, access to information, and direct experience of climate impacts (Ogunbode et al., 2021). Rural communities, while often having direct experience of changing weather patterns and their impacts on agriculture and health, may lack comprehensive understanding of the broader climate change phenomenon and its long-term health implications (Uddin et al., 2020). This knowledge gap has important implications for adaptation planning and public health interventions.\u003c/p\u003e\u003cp\u003eIn the light of this, the concept of planetary health has emerged as a critical framework for understanding the interconnections between human health and the natural systems upon which civilization depends (Whitmee et al., 2015). This framework which was first articulated by \u003cem\u003eThe Lancet\u003c/em\u003e Commission, emphasizes the interdependence of human health and the health of nature, recognizing that sustainable solutions must address both human wellbeing and environmental sustainability. In the Nigerian context, planetary health considerations are particularly relevant given the country's dependence on natural resources and ecosystem services for livelihoods and health. Therefore, any disruptions like climate change will only increase zoonotic diseases, food insecurity, and respiratory illnesses among others (Lancet Countdown, 2023) .\u003c/p\u003e\u003cp\u003eGlobally, studies have examined perceptions of climate change and its health consequences among diverse populations. For instance, Heydari et al. (2023) explored the views of Iranian medical students through qualitative methods, while Hathaway and Maibach (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) synthesized global literature on how health professionals perceive climate risks. Similarly, in China, Li et al. (2021) examined farmers\u0026rsquo; perceptions of climate severity, and Casson et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) assessed health-related climate concerns in Canada. These studies underscore the growing recognition of climate change as a public health concern globally. However, within Africa, and particularly in Nigeria, evidence remains limited and often fragmented. Existing studies in Nigeria have focused mainly on youth perspectives within localized contexts (Joe-Ikechebelu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003eb), or they investigate health risks linked to climate change without examining broader population awareness or spatial patterns (Femi Monday, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Omoruyi \u0026amp; Onafalujo, 2020). Other studies have emphasized observed climatic extremes (Gbode et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), or climate-health linkages in specific cities (Oluwatimilehin et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but few have connected these insights to public understanding at a national scale.\u003c/p\u003e\u003cp\u003eMoreover, while literature such as Okon et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Haider (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) has called attention to the need for climate-responsive development in Nigeria, there remains a critical gap in understanding how perceptions of climate change and especially its health implications\u0026mdash;are distributed across geographic regions and shaped by demographic factors. Furthermore, prior studies have not adopted a planetary health lens that explicitly links human health and environmental sustainability within the Nigerian context.\u003c/p\u003e\u003cp\u003eTo address these gaps, this study investigates the spatial patterns and demographic determinants of climate change awareness and perceived health impacts in Nigeria, using nationally representative data from Afrobarometer Round 9. By integrating a planetary health framework with spatial and multivariate analysis, this study provides a novel contribution to climate-health literature in Nigeria and offers insights for policy design tailored to sub-national differences in perception and vulnerability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eTo that end, this study used a cross-sectional study design to investigate spatial variations in the perceptions of the general populations in Nigeria regarding climate change and how it affects the health. The data were obtained in the Afrobarometer Round 9 survey, which is a nationally representative survey that contains response-level data on climate change awareness, socio-demographics and Positive and Negative Perceptions of Health Risks attributable to climate variability. 1,600 respondents of 36 states and the Federal Capital Territory (FCT) have been retained after cleaning the data. Data cleaning was done and missing data was discarded after which a logistic regression analysis was carried out, and spatial modeling.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe three main indicators were created by means of the generation of spatial summaries Climate change awareness, Perceived extreme weather impacts and Perceived health impacts. The choropleth mapping was used to provide state-level aggregates. The Global Moran I and Local Indicators of Spatial Association (LISA) were calculated with the aim of evaluating the spatial dependency. The LISA clusters were rolled out in some of the regions such as the North-Central, South-South and South-West.\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Logistic Regression Modelling\u003c/h2\u003e\u003cp\u003eBinary logistic regression models were estimated to examine the determinants of climate change awareness and perceived health impacts. Each model followed the form:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:logit\\left(P(Y=1\\right))={\\beta\\:}_{0}+{\\beta\\:}_{1}\\left(gender\\right)+{\\beta\\:}_{2}\\left(age\\right)+{\\beta\\:}_{3}\\left(education\\right)+\\sum\\:_{j=1}^{n}{\\beta\\:}_{j}{\\left(region\\right)}_{j}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\n\u003ch3\u003eDescriptive statistics of Socio-demographic variables by Region\u003c/h3\u003e\n\u003cp\u003eThe table gives the summary statistics of demographics of respondents in the 37 regions of Nigeria. Mean age is found to be very different. The highest level of education is in Anambra (1.85), Kogi (1.81) and Kwara (1.75) with the lowest level recorded at Bayelsa and Zamfara (1.00). Mean gender also differs slightly across states with Osun (43.4) and Ekiti (40.4) having the higher values implying a higher number of female respondents.\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\u003eRegional Summary Statistics of Respondents in Nigeria\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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 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colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3609.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBorno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1372.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross River\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6461.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3934.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEbonyi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e621.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEdo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4266.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEkiti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e603.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnugu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e582.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFCT Abuja\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2035.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGombe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4136.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e453.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJigawa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e620.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKaduna\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1443.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e620.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKatsina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e620.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKebbi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e620.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKogi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2369.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKwara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3359.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLagos\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e623.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNasarawa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2158.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.38\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\u003e33.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2964.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOgun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e570.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOndo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1060.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOsun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e606.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOyo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e886.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlateau\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5902.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRivers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3311.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSokoto\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e620.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTaraba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5698.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1363.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZamfara\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\u003e620.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e4.2. Spatial Distribution of Climate Change Awareness, Extreme Weather and Health Impacts\u003c/b\u003e\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e4.2.1 Spatial Distribution of Climate Change Awareness\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eA choropleth map of the climate change awareness provided over the states is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Findings indicate that the percentage of awareness lies between 0 and 87.5 percent with difference in locations. States within the Southern region performed best in the awareness of the dementia care planning process especially Lagos, Rivers, Cross River, Enugu and Anambra, and they fit in the 43.44\u0026ndash;87.50 percent group. Conversely on the other hand, the lowest rates of awareness could be found in the Northern states (0.00-15.35 per cent) amongst which are Zamfara, Kebbi, Yobe and Niger. Moreover, the coastal as well as the southeast regions are highly urbanized, more developed, and easily prone to floods, where the focus of awareness is high. The parts of central Nigeria displayed moderate levels of awareness whereas the northeastern states indicated low levels of awareness with some states showing inadequate information.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Perceptions of Extreme Weather across Nigeria\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e analysis portrays substantial spatial differences in public perception of extreme weather events in Nigeria, which is seen in the choropleth map as well with the distribution of mean Extreme Weather Scores at state level. Based on a Likert sort of scales, the scores were between 2.29 and 4.71, where greater scores would signify greater perceived frequency and severity of extreme weather events like floods, droughts and heatwaves. The North-Central, North-West, and South-South areas have the highest numbers of concerned regions with a mean of above 4.1 in places like Niger, Benue, Kaduna, Bayelsa, and Delta states which have very high fate of exposure to extreme weather occurrences. Instead, states in the South West (such as Ekiti, Ondo and Ogun) and parts of the South-East and North-East recorded more negligible levels of perception (less than 3.33), implying a lesser degree of concern or awareness by the people in the mentioned regions to climate and weather related occurrences. A lower score in these areas, however, may imply the lack of exposure or even a lapse in climate literacy and risk communication, although climate variability is not unaffected in these regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e4.3.3 Perceived Health Impacts of Climate Change across Nigerian States\u003c/h2\u003e\u003cp\u003eThe choropleth map of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the state-levels variations in Climate-Related Health Impact Scores of Nigeria, as per the survey responses of Afrobarometer. The score is used to reflect what people perceive about the impact of climate change on human health by including issues like heat illnesses, the spread of diseases by vectors, respiratory disorders, and name but the malnutrition that will come with the destruction of the environment. The scores were between 1.90\u0026ndash;4.71 on the scale of likert kind of scale with a higher number indicating the higher perception of health impact.\u003c/p\u003e\u003cp\u003eThe information shows that there is significant geographical disparity with states in the North-Central like Niger, Benue, North-West, and South-South (such as Rivers, Bayelsa, Delta), and those portions of the South-West including Lagos, Ogun, demonstrating the most perceived health risks, with ratings above 4.25 points. Such areas have traditionally been susceptible to extreme weather conditions like floods and heat waves as well as synergistic effects such as inadequate urban air quality or escalating vectors that exacerbate climate-health relationships.\u003c/p\u003e\u003cp\u003eIn its turn, lower health impact scores (less than 3.13) were registered in the states of the South-East (e.g., Ebonyi, Anambra) and in some regions of the North-East, which implies a lower degree of public concern. This could possibly be low levels of exposure, underreporting of health outcomes or lower level of climate health literacy in these areas. In addition, there was low to moderate level of perception ( scores between 3.86 and 4.06) in various states distributed in the North-East, South-East and parts of the central zones that could be described as some form of awareness but that the climate-sensitive diseases were not a commonly experienced perceived illness. The states with missing data were few probably because of survey non-response or failure to sample the geographical areas.\u003c/p\u003e\u003cp\u003eBased on planetary health, these results indicate that there is geographic variation in the distribution of climate-related health risks and perception in Nigeria. The hot-spots areas can be experiencing syndemic processes, in which environmental and social risk factors come together to increase the health burden. In the meantime, regions of low perception should be regarded as a potentially vulnerable area to early warning systems, risk communication, and climate-health education.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.3. \u003cb\u003eLocal Spatial Clustering (LISA) of Climate-Related Perceptions\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eIn an attempt to analyze geographic patterns further, Local Indicators of Spatial Association (LISA) were employed to evaluate the spatial clustering of climate change awareness, experience of extreme weather, and perceived health effects. Figure\u0026nbsp;5 below: The first map on awareness cluster illustrates the LISA cluster map of High-High clusters of climate change awareness in the high-income southeast coastal states such as Lagos, Rivers, Cross River and Delta. These clusters show regions where focal state as well as its neighbors report about high awareness levels. A Low-Low cluster was found in the southeastern-central Nigeria, which implies spatially adjacent states of homogenous low awareness. Overall, most of northern Nigeria had non- significance clustering, which indicated the absence of a high level of spatial autocorrelation.\u003c/p\u003e\u003cp\u003eequally, the extreme weather events (e.g., flooding, drought) also reported clusters spatially located in south-south region mainly. The situation was intense in the Delta, Bayelsa and Cross River states because they were all flood prone, and have dry season coastal erosion. Most of the rest of the country had only negligible or not otherwise significant clustering, indicating more clustered or localized weather experience of extreme weather. In addition, the climate-related health perception/s was also clustered in the southern regions mostly in Rivers and Delta and Lagos which met near equal prevalence and prevalence of the climate-related health issues (e.g. Malaria and cholera). Southeastern interior had a few Low-Low clusters whereas northern Nigeria had no high clustering in the majority.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFig: 5: LISA\u003c/b\u003e of climate change awareness, extreme weather experiences, and perceived health impact\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.4 \u003cb\u003eGlobal Spatial Autocorrelation Analysis\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eTo test whether key climate perception variables follow spatial dependence, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below presents the statistic of Moran I which was calculated within a randomization structure with each variable; climate change awareness, extreme weather perceptions, and health-related consequences.\u003c/p\u003e\u003cp\u003eBased on Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the value of Moran I measure is used to estimate the global spatial autocorrelation, it evaluates the degree by which measurements of a variable are near or far apart as well as randomly distributed on geographic units. Whenever the value of Moran is positive, it implies that there is a tendency of similar values (e.g. high with high, or low and also low) to be clustered as regards spatial location; whereas where Moran is negative there is dissimilarity (e.g. high values and low values) in respect of spatial location. As a measure of statistical significance, a p-value is reported in consideration of the null hypothesis that the data are spatially random.\u003c/p\u003e\u003cp\u003eMoran P Statistics of climate change awareness given in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below was 0.107 (p\u0026thinsp;=\u0026thinsp;0.049) and it was found to be a statistically significant positive spatial autocorrelation. This finding holds that more aware states have a tendency of being in close proximity with equally aware ones, and otherwise states with low states of awareness. This geoclustering upholds the trend that has been noted previously using LISA cluster maps, especially the High-High clusters which are heavily clustered to the South of Nigeria. In case of perceptions on extreme weather events, the Moran I was 0.042 (p\u0026thinsp;=\u0026thinsp;0.563) which does not show significant spatial autocorrelation. This means that the views of extreme weather are spatially unstructured, probably are developed based on recent the local experiences instead of the influences of regional trends.\u003c/p\u003e\u003cp\u003eHealth consequences that were perceived according to climate change showed a Moran I value of 0.115 (p\u0026thinsp;=\u0026thinsp;0.038) showing that there was a statistically significant positive spatial autocorrelation with it. This implies that the geographic concentration of the states where the respondents believe to be under strong health risks due to climatic changes. The presented results are consistent with visual trends and choropleth and cluster maps, which highlighted a high health impact score in the Niger Delta region and in named northern states. We can then conclude that both the awareness of climate change and perceptions of health effects have spatial clustering in the Nigerian states whilst perceptions of extreme weather are more in a random or local pattern. These geographical insights strengthen the need to integrate geographically specific intervention to increase awareness and resiliency in the climate-exposed areas.\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\u003eMoran\u0026rsquo;s I Test Results for Spatial Autocorrelation of Climate Change Perceptions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eMoran\u0026rsquo;s I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExpected I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVariance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStandard Deviate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSpatial Autocorrelation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAwareness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSignificant positive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtreme Weather\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth Impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSignificant positive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Determinants of Climate Change Awareness and Perceived Health Impact\u003c/h2\u003e\u003cp\u003eFindings from Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reveal that result of the binary logistic regression model which was used to evaluate the socio-demographic and regional predictors of climate change awareness. The dependent variable was a binary formation of whether the respondents claimed to be aware about climate change or not. Gender, age, level of education and region of residence were used as independent variables and the perception of climatic health effects was a dichotomous variable describing whether the respondents indicated whether climatic change had effect or not.\u003c/p\u003e\u003cp\u003eThe statistical significance of the model was 62 (311.1, df\u0026thinsp;=\u0026thinsp;39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and possesses an excellent model fit (AIC\u0026thinsp;=\u0026thinsp;1647.1). A number of predictors proved to be significant with a 5% level. According to the finding, gender was a statistically significant predictor (B\u0026thinsp;=\u0026thinsp;0.015, p\u0026thinsp;=\u0026thinsp;0.002) and therefore males were easier likely to know about climate change. The amount of the role that region performed as an explanation of the variability in awareness was substantial. Being aware happened to be significant in Abuja ( 2.266, p\u0026thinsp;=\u0026thinsp;0.001). The rest of the effects were also statistically significant in Benue ( 1.008, p 0.034), Kaduna ( 1.043, p 0.020) and Kwara ( -1.678, p 0.019). On the contrary, states like Kano (b = -2.912, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Katsina (b = -3.114, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Zamfara (b = -1.923, p\u0026thinsp;=\u0026thinsp;0.006) had significantly lower chances of reporting awareness, implying great regional variation. The age and the education had no statistically significant impact on this model ( p\u0026thinsp;\u0026gt;\u0026thinsp;0.3) although education theoretically would have some effect on awareness.\u003c/p\u003e\u003cp\u003eThere was lesser explanatory power (AIC\u0026thinsp;=\u0026thinsp;229.2) and most of the predictors were not significant. The result of this study showed that none of the socio-demographic variables, including gender, age, and education significantly predicted perception of health impact (p\u0026thinsp;\u0026gt;\u0026thinsp;0.2). In the case, states like Adamawa, Kano, and Lagos had the extremely large negative error (e.g., 0 269 19) with p\u0026thinsp;\u0026gt;\u0026thinsp;0.99, and it indicates an inability of the model to be stable or even data imbalanced across the regional levels.\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\u003eRegional Effects on Climate Change Awareness and Perceived Health Impacts\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAwareness: Estimate (SE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHealth Impact: Estimate (SE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdamawa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.305 (0.505)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.77 (8484.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAkwa Ibom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;1.132 (0.534)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;2.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.034 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.85 (7561.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnambra\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.104 (0.463)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.70 (6819.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBauchi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;1.046 (0.506)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;2.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.039 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.72 (6904.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c3\"\u003e\u003cp\u003e2.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.034 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.77 (6909.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBorno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.339 (0.469)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.84 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colname=\"c7\"\u003e\u003cp\u003e0.635\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;1.086 (0.511)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;2.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.033 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.82 (6913.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEbonyi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.823 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colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.83 (7568.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEkiti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.755 (0.536)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;1.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;20.03 (8428.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnugu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.923 (0.545)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;1.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.090 .\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.98 (8429.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFCT Abuja\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.266 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colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;0.330 (1.295)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.799\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.225 (0.466)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;1.160 (1.249)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.74 (4703.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKatsina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;3.114 (0.803)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;3.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.79 (6003.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKebbi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;1.221 (0.550)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;2.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.026 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;0.045 (1.051)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKogi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.920 (0.551)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;1.670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.095 .\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.74 (8328.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKwara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;1.678 (0.718)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;2.337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.019 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.65 (9687.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLagos\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;1.005 (0.419)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;2.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.017 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.91 (4367.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNasarawa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.642 (0.576)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;1.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.80 (9755.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;1.654 (0.567)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;2.915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004 **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.72 (6897.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOgun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;1.499 (0.544)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;2.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006 **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;1.168 (1.248)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOndo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.704 (0.505)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;1.396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;0.994 (1.253)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.428\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOsun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;1.093 (0.524)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;2.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.037 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;1.171 (1.264)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.354\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOyo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;1.277 (0.488)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;2.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009 **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.83 (6006.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlateau\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.413 (0.531)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.048 (1.118)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRivers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.019 (0.443)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;0.673 (1.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.521\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSokoto\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;1.876 (0.638)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;2.942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003 **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.76 (7578.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTaraba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.773 (0.603)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;1.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.73 (9807.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.195 (0.554)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.77 (9805.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZamfara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;1.923 (0.705)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;2.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006 **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;19.78 (8490.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel Fit Summary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOutcome Variable\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNull Deviance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eResidual Deviance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e\u003cb\u003eSig. Predictors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAwareness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1878.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1567.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1647.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eGender, Kano, Katsina, Sokoto, Zamfara, FCT, etc.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth Impact\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e206.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e229.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eOnly Intercept significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e*, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion of Findings","content":"\u003cp\u003eThis paper showed the spatial trends and demographic predictors of climate change awareness and perceived health consequences of climate change in Nigeria through a planetary health perspective. As the results show, there are marked regional differences in awareness, experience and perception which prove that people are not equally concerned and informed about the climate-related problems.\u003c/p\u003e\u003cp\u003eAmong the most notable outcomes, one must mention the fact that, there is a strong regional disparity in climate change awareness, with the highest awareness recorded in Southern and North-Central states, as opposed to the North-East and North-West. This geographical gap is similar to the one previously observed by Odjugo (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and which the author reported that Southern Nigerians are more concerned with climate change given the higher degree of education, access to media sources, and exposure to floods and sea level rise generated by climate changes. The high awareness in this region also conforms to other findings given by Joe-Ikechebelu et al., (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) who established that the South-Easterners had greater climate literacy amongst the youth particularly in faith-based organizations. This finding implies that both analytical results support the same perception over the work conducted on this study and previous records on the role of localized exposure, education, and communication channels on awareness.\u003c/p\u003e\u003cp\u003eNevertheless, this experiment is different from other previously conducted studies in the regression analysis. In particular, although numerous factors, including demographic factors, such as education level and gender, are identified in the literature as determinants significantly related to the issue of climate change awareness, the present study did not find these factors to be significant, when regional location was taken into consideration. This deviation may imply that in the case of Nigeria, structural disparity in the regions of unequal infrastructure, dissimilar media penetration, and other individual countenance of environmental schooling is likely to supersede the weight of a single-level characteristics.\u003c/p\u003e\u003cp\u003eIn addition, some may argue that they could have benefited from this research because even though the researchers concluded that male respondents were more likely to report the awareness of the climate change situation, the results were negligible. This observation is in contrast to the information presented in international reviews (e.g., Wright et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which argues that gendered vulnerability and care giving role commonly prompts women to be more aware of risk in the environment. The ambiguity might be an indication of sociocultural factors at work in Nigeria, which overwrite the common gender-perception trends in other societies.\u003c/p\u003e\u003cp\u003eWith respect to the perceptions of health impacts of the climate change, this research indicates evidential clusters on the map, involving a perception level being greater in flood-prone and coastal states such as Rivers, Bayelsa, Lagos and Delta. This can be corroborated with empirical data by Femi Monday (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) documenting the rise in cases and societal-disease anxiety of vector-borne diseases, heat-related diseases, and respiratory illnesses in urban areas in the south of Nigeria. Moreover, as demonstrated by Gbode et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), these are also the regions where flooding, extreme rainfall, and humidity are the most dramatic types of climate extremes, which are all reported to increase disease burdens (Landrigan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe geographical co-location of exposure to climate extremes and perceived health risk promotes the planetary health message of Atwoli et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who argue that the problem of the unequal and unfair health interference of climate-related risks is an urgent issue, including in low- and middle-income countries. Similarly, Bolan et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlight that, in the Global South, extreme weather events trigger the dissemination of pollutants; thus, increasing health risks in informal settlements environments strongly reflected in Nigeria in the coastal urban areas.\u003c/p\u003e\u003cp\u003eHowever, these results can be partially criticized in light of the results given by Casson et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who indicated that the demographic factors, particularly the ages and health status, have strong predictive power in climate-health matters in high-income countries such as Canada. This divergence can be associated with the variation in data context: whereas in Canada the respondents might be analyzing the climate-health relationship in terms of official health messaging, the Nigerian participants might be more experience-driven and locally determined, i.e., based on exposure at a lived level comparatively rather than institutionalized messaging.\u003c/p\u003e\u003cp\u003eNotably, the discrepancy identified in the present study between high exposure and low awareness or concern evokes the claim by Okon et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) that the alarming health impacts of climate change remain largely ignored by climate policies, unnoticed by most Nigerians, and unadapted to despite the experience of such effects on human health in the country. This is of special concern because the degree of the perceived health risk and awareness is low in the North-East and North-West, which is likely to be an obstacle to premonitory adaptation.\u003c/p\u003e\u003cp\u003ePlanetary health-wise, the regional distribution of climate concern, not to mention the lack thereof in other regions, is an indicator of systematic difference in inequity of environmental health literacy and resilience. One must develop more equitable risk communication networks, health care infrastructure, and participatory bodies instead of simply cutting emissions levels or spending on adaptations to realize climate justice in Africa as Atwoli et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Wright et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) emphasize. The case study further supports the premise that any Nigerian adaptation to climate change should be regionally specific and address mitigation based on the experience of individual societies in line with the position of the WHO (2024) on climate-informed health planning in the most at-risk nations.\u003c/p\u003e\u003cp\u003eOverall, the findings of this research are generally compatible with the current Nigerian and international evidence and current research on the geography of climate awareness and the vulnerability it presents, especially in terms of revealing the primary role of exposure and lived experience of a region. Nonetheless, this difference in the meaning of individual demographics threatens the generalisations present in the global north-based literature and demands a rather less universalistic approach to risk perception in Nigeria. These results contribute to increasingly large evidence base on spatially responsive and locally based public health and climate mitigation initiatives.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents an uneven landscape of climate change awareness and its perceived health impacts across Nigeria. We found that while awareness was high in southern, more urbanized region, it remained low in parts of the north such as Zamfara and the south-southern part like Bayelsa, which also had lower educational status. Residents of urbanised areas were less likely to recognise climate change or connect it to health concerns, which presents deep social and geographic inequities. These disparities align with areas at greater environmental risk, suggesting that vulnerable populations may be least informed yet most exposed to climate-related health threats.\u003c/p\u003e\u003cp\u003eThe findings reveal that advancing climate resilience in Nigeria requires not only infrastructural or environmental interventions but also targeted communication strategies that are education-focused, gender-responsive, and regionally tailored. Strengthening climate-health literacy through community-based outreach and formal education systems will be vital to empower at-risk populations to adopt adaptive practices.\u003c/p\u003e\u003cp\u003eTherefore, it is important that these strategies within a Planetary Health framework can enhance public engagement, bridge regional gaps, and accelerate equitable climate action. Which will help in integrating public perceptions into policy design and improve the responsiveness, effectiveness, and sustainability of Nigeria\u0026rsquo;s climate and health policies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e T.E.O \u0026amp; O.L.B conceptualized and designed the study, T.E.O developed the introduction while O.L.B developed the methodology, implemented the formal analysis and interpreted. T. E. O. wrote the discussion of findings and final review of the manuscript was done by T.E.O and O.L.B. All authors have read and agreed to publish this version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e The study was based on the analysis of openly available data. Thus, ethical approval was not necessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e: The authors are grateful to Afrobarometer, for granting the authors access to use Afrobarometer Dataset of Nigeria, (Round 9) which is available at http://www.afrobarometer.orgData.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAtwoli, L., Baqui, A. H., Benfield, T., Bosurgi, R., Godlee, F., Hancocks, S., Horton, R., Laybourn-Langton, L., Monteiro, C. A., Norman, I., Patrick, K., Praities, N., Olde Rikkert, M. G. M., Rubin, E. J., Sahni, P., Smith, R., Talley, N., Turale, S., \u0026amp; V\u0026aacute;zquez, D. (2021). Call for emergency action to limit global temperature increases, restore biodiversity, and protect health: Wealthy nations must do much more, much faster. \u003cem\u003eAnnals of Oncology\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(10), 1203\u0026ndash;1205. https://doi.org/10.1016/j.annonc.2021.08.1743\u003c/li\u003e\n\u003cli\u003eBolan, S., Padhye, L. P., Jasemizad, T., Govarthanan, M., Karmegam, N., Wijesekara, H., Amarasiri, D., Hou, D., Zhou, P., Biswal, B. K., Balasubramanian, R., Wang, H., Siddique, K. H. M., Rinklebe, J., Kirkham, M. B., \u0026amp; Bolan, N. (2024). Impacts of climate change on the fate of contaminants through extreme weather events. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, \u003cem\u003e909\u003c/em\u003e, 168388. https://doi.org/10.1016/J.SCITOTENV.2023.168388\u003c/li\u003e\n\u003cli\u003eCasson, N., Cameron, L., Mauro, I., Friesen-Hughes, K., \u0026amp; Rocque, R. (2023). Perceptions of the health impacts of climate change among Canadians. \u003cem\u003eBMC Public Health\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 1\u0026ndash;13. https://doi.org/10.1186/s12889-023-15105-z\u003c/li\u003e\n\u003cli\u003eFemi Monday, I. (2020). Investigating Effects of Climate Change on Health Risks in Nigeria. \u003cem\u003eEnvironmental Factors Affecting Human Health\u003c/em\u003e, 1\u0026ndash;18. https://doi.org/10.5772/intechopen.86912\u003c/li\u003e\n\u003cli\u003eGbode, I. E., Adeyeri, O. E., Menang, K. P., Intsiful, J. D. K., Ajayi, V. O., Omotosho, J. A., \u0026amp; Akinsanola, A. A. (2019). Observed changes in climate extremes in Nigeria. \u003cem\u003eMeteorological Applications\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(4), 642\u0026ndash;654. https://doi.org/10.1002/MET.1791\u003c/li\u003e\n\u003cli\u003eHaider, H. (2019). Climate change in Nigeria: impacts and responses. \u003cem\u003eK4D Helpdesk Report\u003c/em\u003e, 1\u0026ndash;38. http://www.rockfound.org/initiatives/climate/climate_change.shtml%0Awww.iied.org/HS/publications.html.%0AH\nOW%0Ahttps://a\nssets.publishing.service.g\nov.uk/media/5dcd7a1aed915d\n0719bf4542/675_Climate_Change_in_Nigeria.pdf\u003c/li\u003e\n\u003cli\u003eHathaway, J., \u0026amp; Maibach, E. W. (2018). Health Implications of Climate Change: a Review of the Literature About the Perception of the Public and Health Professionals. \u003cem\u003eCurrent Environmental Health Reports\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), 197\u0026ndash;204. https://doi.org/10.1007/s40572-018-0190-3\u003c/li\u003e\n\u003cli\u003eJoe-Ikechebelu, N. N., Azuike, E. C., \u0026amp; Nwankwo, B. E. (2019). Perception of Climate Change among Youths in a FBO in a South-Eastern Town in Nigeria: A Pilot Study. \u003cem\u003eCommunity Medicine and Public Health Care\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(3), 1\u0026ndash;7. https://doi.org/10.24966/cmph-1978/100050\u003c/li\u003e\n\u003cli\u003eLandrigan, P. J., Fuller, R., Acosta, N. J. R., Adeyi, O., Arnold, R., Basu, N. (Nil), Bald\u0026eacute;, A. B., Bertollini, R., Bose-O\u0026rsquo;Reilly, S., Boufford, J. I., Breysse, P. N., Chiles, T., Mahidol, C., Coll-Seck, A. M., Cropper, M. L., Fobil, J., Fuster, V., Greenstone, M., Haines, A., \u0026hellip; Zhong, M. (2018). The Lancet Commission on pollution and health. \u003cem\u003eThe Lancet\u003c/em\u003e, \u003cem\u003e391\u003c/em\u003e(10119), 462\u0026ndash;512. https://doi.org/10.1016/S0140-6736(17)32345-0\u003c/li\u003e\n\u003cli\u003eOdjugo, \u0026amp; Ovuyovwiroye, P. A. (2013). Analysis of climate change awareness in Nigeria. \u003cem\u003eScientific Research and Essays\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(26), 1203\u0026ndash;1211. https://doi.org/10.5897/SRE11.2018\u003c/li\u003e\n\u003cli\u003eOgbonna, D. N., Amangabara, G. T., \u0026amp; Ekere, T. O. (2007). Urban solid waste generation in Port Harcourt metropolis and its implications for waste management. \u003cem\u003eManagement of Environmental Quality: An International Journal\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 71\u0026ndash;88. https://doi.org/10.1108/14777830710717730\u003c/li\u003e\n\u003cli\u003eOkon, E. M., Falana, B. M., Solaja, S. O., Yakubu, S. O., Alabi, O. O., Okikiola, B. T., Awe, T. E., Adesina, B. T., Tokula, B. E., Kipchumba, A. K., \u0026amp; Edeme, A. B. (2021). Systematic review of climate change impact research in Nigeria: implication for sustainable development. \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(9), e07941. https://doi.org/10.1016/j.heliyon.2021.e07941\u003c/li\u003e\n\u003cli\u003eOluwatimilehin, I. A., Akerele, J. O., Oladeji, T. A., Omogbehin, M. H., \u0026amp; Atai, G. (2022). Assessment of the impact of climate change on the occurrences of malaria, pneumonia, meningitis, and cholera in Lokoja City, Nigeria. \u003cem\u003eRegional Sustainability\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(4), 309\u0026ndash;318. https://doi.org/10.1016/J.REGSUS.2022.11.007\u003c/li\u003e\n\u003cli\u003eThe World Bank Group. (2021). \u003cem\u003eClimate Risk profile: Nigeria\u003c/em\u003e (Vol. 3, Issue 4). https://www.who.int/health-topics/climate-change#tab=tab_1\u003c/li\u003e\n\u003cli\u003eTran, B. X., Nguyen, T. H., Phung, D. T., Nguyen, L. H., Pham, H. Q., Vu, G. T., Le, H. T., Latkin, C. A., Ho, C. S. H., \u0026amp; Ho, R. C. M. (2021). Gaps in awareness of climate variability and its impacts on society among health professionals and community workers in Vietnam: Implications for COVID-19 and other epidemic response systems. \u003cem\u003eInternational Journal of Disaster Risk Reduction\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e, 102212. https://doi.org/10.1016/J.IJDRR.2021.102212\u003c/li\u003e\n\u003cli\u003evan der Linden, S. (2015). The social-psychological determinants of climate change risk perceptions: Towards a comprehensive model. \u003cem\u003eJournal of Environmental Psychology\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e, 112\u0026ndash;124. https://doi.org/10.1016/J.JENVP.2014.11.012\u003c/li\u003e\n\u003cli\u003eWHO Regional Office for Africa. (2023). Report on malaria in Nigeria 2022. In \u003cem\u003eIsbn: 978-929023498-2\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eWorld Health Organisation, W. (2022). \u003cem\u003eFood Safety. In: Facts sheets\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2022). Regional data and trends briefing kit World malaria. \u003cem\u003eWorld Malaria Report 2022\u003c/em\u003e, \u003cem\u003eDecember\u003c/em\u003e, 1\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO). (2024). \u003cem\u003eClimate Change\u003c/em\u003e. https://www.who.int/health-topics/climate-change#tab=tab_1\u003c/li\u003e\n\u003cli\u003eWright, C. Y., Kapwata, T., Naidoo, N., Asante, K. P., Arku, R. E., Ciss\u0026eacute;, G., Simane, B., Atuyambe, L., \u0026amp; Berhane, K. (2024). Climate Change and Human Health in Africa in Relation to Opportunities to Strengthen Mitigating Potential and Adaptive Capacity: Strategies to Inform an African \u0026ldquo;Brains Trust.\u0026rdquo; \u003cem\u003eAnnals of Global Health\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e(1), 1\u0026ndash;21. https://doi.org/10.5334/aogh.4260\u003c/li\u003e\n\u003cli\u003eZhao, Q., Guo, Y., Ye, T., Gasparrini, A., Tong, S., Overcenco, A., Urban, A., Schneider, A., Entezari, A., Vicedo-Cabrera, A. M., Zanobetti, A., Analitis, A., Zeka, A., Tobias, A., Nunes, B., Alahmad, B., Armstrong, B., Forsberg, B., Pan, S. C., \u0026hellip; Li, S. (2021). Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: a three-stage modelling study. \u003cem\u003eThe Lancet Planetary Health\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(7), e415\u0026ndash;e425. https://doi.org/10.1016/S2542-5196(21)00081-4\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7647558/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7647558/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change poses significant threats to human health, ecosystems, and socioeconomic stability, particularly in low- and middle-income countries such as Nigeria. Using a Planetary Health lens, this study explores public perceptions of climate change and its health implications in Nigeria, drawing on nationally representative data from Afrobarometer Round 9. It investigates how awareness, and health-related perception differ across region, hence shaping climate-responsive policies.\u003c/p\u003e\u003cp\u003eA cross-sectional design was applied using secondary data from Afrobarometer\u0026rsquo;s Nigeria survey. The dataset includes geocoded responses capturing climate perceptions, experiences of extreme weather events, and perceived health effects. Descriptive statistics summarized demographic characteristics, while binary logistic regression examined predictors of climate change awareness and perceived health impacts. Spatial analysis using GIS tools in R mapped climate awareness across Nigeria\u0026rsquo;s geopolitical zones. Spatial autocorrelation (Moran\u0026rsquo;s I) and hotspot analysis identified clusters of awareness, while overlays of flood and drought risk revealed environmental vulnerabilities.\u003c/p\u003e\u003cp\u003eFindings show substantial demographic and regional disparities. Mean age and gender composition varied across states. Education levels peaked in Anambra (1.85) but were lowest in Bayelsa and Zamfara. Climate change awareness ranged from 0\u0026ndash;87.5%, highest in southern urbanized states and lowest in northern states. Perceptions of climate-related health impacts including heat illnesses, vector-borne diseases, respiratory disorders, and malnutrition ranged from 1.90\u0026ndash;4.71 on a Likert scale. Logistic regression confirmed significant predictors of awareness to include gender with regional effects more pronounced in some states like Abuja, Benue and markedly lower in and Zamfara. The model showed excellent fit (χ\u0026sup2; = 311.1, df\u0026thinsp;=\u0026thinsp;39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; AIC\u0026thinsp;=\u0026thinsp;1647.1).\u003c/p\u003e\u003cp\u003eThe study highlights key gaps in climate knowledge that hinder resilience. It recommends inclusive, education-focused, and gender-responsive climate communication strategies aligned with Planetary Health principles to advance sustainable and equitable climate action in Nigeria.\u003c/p\u003e","manuscriptTitle":"Perceptions of Climate Change and Health Implications in Nigeria: A Planetary Health Perspective Using Afrobarometer Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 11:35:56","doi":"10.21203/rs.3.rs-7647558/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-27T08:43:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-14T09:57:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T19:41:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185818252772001602446530496818311977511","date":"2025-11-02T04:37:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200920050856014742943935175215724405273","date":"2025-10-31T11:23:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-31T06:34:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-13T15:47:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-07T07:11:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-07T07:09:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2025-09-18T09:01:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ee658ea-c2e9-4eb0-a679-1ca825648d27","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:00:44+00:00","versionOfRecord":{"articleIdentity":"rs-7647558","link":"https://doi.org/10.1186/s12982-026-01612-y","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2026-03-09 15:57:38","publishedOnDateReadable":"March 9th, 2026"},"versionCreatedAt":"2025-11-11 11:35:56","video":"","vorDoi":"10.1186/s12982-026-01612-y","vorDoiUrl":"https://doi.org/10.1186/s12982-026-01612-y","workflowStages":[]},"version":"v1","identity":"rs-7647558","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7647558","identity":"rs-7647558","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}