Evaluation of climatic and non-climatic influence on malaria prevalence in the Upper River Region of The Gambia

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Climate changes and non-climatic conditions can have a substantial influence on malaria prevalence, and further affect the coverage of preventive interventions. This work aimed at exploring the different risk factors, both climatic and non-climatic, associated with malaria. A descriptive research method using a parallel mixed method approach was adopted. Using a multistage approach, 381 households were selected from the region, and primary data was collected from household heads. Descriptive statistics were performed using StataSE18, and the significant influence of environmental and socioeconomic factors was analyzed using Chi-square (Χ 2 ). Thematic analysis for the qualitative part was carried out using Nvivo. Malaria is heavily influenced by rainfalls and floods and by some of the measured non-climatic factors. These results will provide individuals, professionals, government, and policymakers valuable information for better-targeting malaria control efforts. Knowledge Climate Change Prevalence Malaria Figures Figure 1 Figure 2 1 Introduction The effect of climate change on the burden of infectious diseases, particularly vector-borne diseases such as malaria, is currently debated[ 1 ]. Climate plays a major role on the malaria dynamics and distribution [ 2 ], and climate change will increase malaria transmission in certain geographical areas depending on demographic, socioeconomic, and ecological factors [ 3 , 4 ]. According to the IPCC WGII Sixth Assessment Report, the distribution and prevalence of malaria are influenced by rising temperatures and changing rainfall patterns (high confidence) [ 5 ], and Sub-Saharan Africa has an ideal climatic condition for endemic malaria transmission [ 6 ]. Projections on the influence of climate change on malaria estimated an increase in population at risk of 1.6 million by 2030 and 1.8 million by 2050 [ 7 ], although other factors can influence malaria transmission [ 8 ]. The development of the malaria parasite and its transmission [ 9 – 13 ] is accelerated by changing temperature, rainfall, flooding, moisture conditions of the environment, and other non-climatic factors[ 6 , 14 , 15 ]. The above-mentioned climatic variables favor the breeding, proliferation, mating, longevity, dispersal, blood-feeding behavior, and oviposition of mosquitoes [ 11 , 16 – 20 ]. The Gambia aims at eliminating malaria, i.e., interruption of local transmission, by 2030[ 21 ]. Nevertheless, malaria transmission is still ongoing despite a good coverage of control interventions, with the highest prevalence of infection in eastern Gambia, ie., 31.1% in the region's south bank and 36.8% north bank in [ 21 , 22 ]. Investigating both climatic and non-climatic factors becomes paramount for identifying the factors responsible for residual transmission so that control interventions may be targeted more efficiently. Studies have reported a significant effect of climatic variables on the longevity of mosquitoes and the development of malaria parasites in the mosquito, and, subsequently, malaria prevalence [ 7 , 17 ]. They have shown spatial and temporal variation in the prevalence of malaria infection using environmental temperature alongside rainfall and humidity. Nevertheless, the interaction between climatic factors and disease transmission is complicated and multifaceted, with mosquito survival, parasite development within the vector, and disease transmission potential restricted above and below certain temperature thresholds [ 16 ], [ 17 ]. Additionally, optimal ranges for climatic suitability vary depending on the vector species, pathogen, and region, with disease transmission further influenced by other social and ecological factors [ 3 ]. Some scholars state that malaria transmission and prevalence of infection are mainly influenced by temperature[ 12 ], while others argue that other factors such as the minimal temperatures, amount of rainfalls, and relative humidity are also important [ 23 ]. In addition to the complex relationship and overwhelming evidence that climatic variables affect substantially malaria transmission, deeper knowledge of the environmental, cultural, and socioeconomic elements that affect malaria at the household and societal levels is required [ 7 ]. This will necessitate an integrated strategy that takes into account climate change as well as other factors that influence malaria transmission [ 8 ]. This study used a mixed-method approach to capture comprehensive and detailed information on the knowledge of climatic influence on malaria, in addition to the major climatic and non-climatic influence on malaria transmission in the region. Determining the climatic variables and the socioeconomic factors associated with malaria prevalence in the region could help to decrease its burden in the Upper River Region of The Gambia. 2 Methodology Study area The study was conducted in the Upper River Region of The Gambia (URR) among household heads randomly selected in each of the seven districts in the region, namely Kantora, Tumana, Basse Fuladu East, Jimara, Wuli West, West East, and Sandu. The region has a land mass of about 2000 sq km and a population density of 116/Km 2 (GBoS, 2013). The Gambia lies in the tropical wet and dry or savanna climate zone, which has a distinct long dry season and short rainy season. URR is crossed by the river of The Gambia, a large, slow-moving waterway, characterized by tidal movements and saltwater intrusion as far as 200km upriver., creating breeding sites for malaria vectors. The estimated annual rainfall is between 800mm and 1200mm and the average number of rainy days ranges from 54 days in Banjul to 31 days in URR where there are often floods following the rains. The yearly average temperature is 31.85°C (89.33°F), about 2.3% higher than in other regions in The Gambia. In the dry season, the highest average temperature is between 33.22°C and 42.42°C, while in the wet season, the lowest average is between 19.48°C and 27.99°C, a conducive temperature that supports the development and transmission of P. falciparum . At temperatures below 20 o C P. falciparum cannot complete its life cycle and thus cannot be transmitted. Research design/ Data sampling techniques A parallel mixed-method approach, comprising equal strength of a qualitative strand and a quantitative strand, was used for the collection of primary data. The quantitative survey involved face-to-face interviews with selected household heads using structured questionnaires, while the qualitative study elicited information through Focus Group Discussions (FGDs), and Key Informant Interviews (KIIs) using a topic guide. For the survey, it was assumed a prevalence of 50%, and a margin of error of 5%, resulting in a sample size of 376 individuals, which was increased to 381 to account for unforeseen errors. A total of 91 individuals, irrespective of gender, were purposively selected for the qualitative study within the age bracket of 30–80 years, and included 31 Key Informants (Alkalo/chief), and 60 individuals (41 males and 19 females) for the FGDs. Each FGD involved a maximum of 10 participants. Seven (7) FGDs were organized across the region Pilot testing of the research instrument The research instruments were first reviewed by the researcher supervisors to ensure content validity and appropriateness, clarity, relevance, and suitability for the research. The instruments were further reviewed by the Research Ethics Committee at the University of The Gambia. A pilot testing was conducted in three selected settlements in the Basse district to check its suitability and adapt it if needed, to ensure the reliability and validity of the questionnaire and topic guide. The questionnaire was further revised after piloting and translated back into English language and checked for clarity. Moreover, the reliability of the questionnaire data was further tested using Cronbach alpha reliability statistics. Strategies to deal with validity threats in qualitative data This work employed various formats to deal with threats to validity that are relevant to qualitative research. The standards taken in the reduction of threats to descriptive and interpretative validity included: asking open-ended questions, verbatim transcripts of the interviews, presenting participant quotations without shortening, peer debriefing, collecting and analyzing quality data, and providing thick descriptions of the setting, participants, and themes. The triangulation employed both in data collection techniques and sampling strategies aids in solving the threat of both interpretative and theoretical validity [ 24 , 25 ]. Data processing To assess the knowledge of the influence of climate change on malaria, we employed a 5-point Likert scale (Strongly Agree = 5, Agree = 4, Neutral = 3, Disagree = 2, Strongly Disagree = 1). The true strength and rate of either agreement or disagreement were measured using the mean score of the total respondents to evaluate the certainty of the response and determine which climatic factors were perceived to have a greater influence on malaria. The true strength was measured by the degree to which respondents agreed or disagreed with the concept. However, the rate of perception in this regard is given by: the frequency of response in each scale divided by the total respondents' response, multiplied by the attributed scale value. The rate of perception is measured by the additional value above or below the attributed scale value from the mean score. Any value at the midpoint of 3.0 is considered neutral and is not measured. The percentage analysis was further used to evaluate the perception of the population on the major climatic factors associated with malaria prevalence. Multivariate regression was applied to investigate some of the socio-demographic factors influencing their perception. Moreover, the frequency, percentage, and statistically significant influence of non-climatic parameters (environmental, social, and economic factors) on the perceived climatic variables responsible for malaria prevalence in the regions were equally investigated using Pearson's Chi-square. The themes and sub-themes were extracted using NVivo 14 to analyse the qualitative data, whilst Stata 18 was employed to perform a descriptive analysis of the quantitative data. Ethical consideration The research was conducted based on the tenets of Climate Change and Education, at the University of the Gambia. The study was approved by the University of the Gambia Ethics Committee on the 28th of August 2023. Written consent was secured from the participants after an explanation on the study's aim, objectives, and procedures. Participation in the study was voluntary, and participants were free not to answer the survey, or to withdraw their participation without penalty. The individual answers were anonymized and referenced instead. 3 Results 3.1 Knowledge of climate change's influence on malaria using the Likert scale 5-point Table 1 presents the knowledge of climate change/variables influence on malaria infection. The result reveals that a higher percentage of the respondent (55.12%) believe that climate change has an impact on malaria as they demonstrate their disagreement with the statement that” Climate change has no impact on malaria transmission”. However, only 10.24% of the population strongly disagree with the concept. Additionally, the total mean score value on this concept is seen at approximately 3.0 points which stands for neutral. The implication is that climate change may or may not influence malaria, despite the opinion of the majority. On the other hand, a higher percentage of respondents disagree with the concept that decreased rainfall (59.32%) and drought (39.37%) increased malaria transmission, slightly in conformity with their mean score value seen at 2.67 and 2.61 respectively. However, the strength of disagreement is somehow weak as the mean score is above the attributed point 2 with a rate of 0.67 and 0.61 respectively, approximately 3.0 means score value that stands for neutral. This also suggests uncertainty on the effect of the variables on malaria. The population was equally neutral on increased malaria transmission in increased humid temperatures, in complete alignment with the mean score value of approximately 3.0 points for neutral. Furthermore, a strong agreement was noted on the concept of the influence of warmer temperatures on mosquitoes' survival (38.85%), increased biting and abundance of mosquitoes (70.60%) during the rainy season in addition to the influence of flood on malaria prevalence (62.99%). A total mean score value of 3.82 (approx. 4.0) was noted on the concept of the influence of warmer temperatures on mosquito survival. This implies agreement with the concept, although the agreement is not very strong as the means score is -2 from the attributed value four (4) for agreement. The concept can be considered agreed on, but not strongly agreed. On the other hand, there is strong agreement on the concept of increased malaria prevalence during flooding (4.57), abundance of mosquitoes, and increased mosquito biting during the rainy season (4.69) as explained by their mean score value, approximately 5 points. The result demonstrates that knowledge of the influence of flood and rainfall on malaria is very certain among the population more than other accessed variables. Table 1 Knowledge of the influence of climate change variables on malaria Variables Percentage (Mean score value) Strongly agree Agree Neutral Disagree Strongly disagree Mean score Climate change has no impact on malaria transmission 8.66 (0.43) 24.67 (0.99) 11.55 (0.35) 44.88 (0.90) 10.24 (0.10) 2.77 Mosquitoes survive better at warmer temperatures 38.85 (1.94) 26.51 (1.0.6) 16.54 (0.50) 15.49 (0.31) 2.62 (.03) 3.84 Increasing humidity increases malaria prevalence 11.29 (0.56) 18.90 (0.76) 38.06 (1.14) 30.97 (0.62) 0.79 (.01) 3.09 Floods can increase malaria prevalence 62.99 (3.15) 34.12 (1.36) 1.05 (.03) 0.79 (.02) 1.05 (.01) 4.57 A decrease in rainfall leads to malaria prevalence 6.56(0.33) 21.26 (0.85) 8.66 (0.26) 59.32 (1.19) 4.20 (.04) 2.67 Droughts favor the transmission of malaria 2.10 (0.10) 14.44 (0.58) 34.91 (1.05) 39.37 (0.79) 9.19 (0.09) 2.61 Mosquito bites more and in abundance during raining season 70.60 (3.53) 28.08 (1.12) 0.52 (.02) 0.26 (.01) 0.52 (.01) 4.69 Source: Field data, 2023. Numbers in parentheses indicate the percentage of households while those without are the frequencies 3.2 Influence of Climatic variables on malaria prevalence in the region Figure 1 presents the perception of the population of the effects of climatic variables on malaria. Floods and increased rainfalls are perceived as favoring factors for malaria while average rainfalls, lower temperatures, and even higher temperatures are considered less important. Qualitative findings (Table 2 ) also identified rainfalls as a factor that significantly influences malaria prevalence in the region as highlighted in 37 coded files with 72 references, and floods with 44 references in 33 coded files. Temperature had 7 references from 4 coded files and humidity had just 3 references from 3 coded files. Table 2 Qualitative finding on climatic influence on malaria prevalence in URR Sub-themes NCFs NCRs Respondent’s statements Rainfall 37 72 There is too much malaria at this time (rainy season). I have been sick for the last ten days and have not gone to the farm since then. Too much rain causes malaria and this is the season for malaria. Flood 30 41 When there is a flood in the community, there is always lots of standing water around which serves as a breeding space for mosquitoes. Temperature 4 7 The increased temperature often led an increased number of the population that will be sick as a result of mosquitoes biting, but it reduces when the temperature is cold. Humid temperature 3 3 I think humidity also has little influence on malaria. Note: NCFs stands for the number of coding files and NCRs number of coding references The perception of the population on the climatic variables associated with the persistence of malaria in the region was significantly influenced by their age and the years lived in the region at a p-value of 0.004 and 0.002 respectively as shown in Table 3 . Table 3 Influence of Socio-demographic Factors on the Perception of the Population of the Climatic Variables Associated with Malaria Prevalence in the Region Perception coefficient P>|t| [95% conf. interval] District − .0000667 0.998 − .0491759 .0490426 Age .0125724 0.004 .0040511 .0210937 Occupation − .0336323 0.557 − .1460519 .0787874 Years lived in the region .1933408 0.002 .0703377 .3163439 constant 1.039902 0.003 .0703377 .3163439 3.3 Influence of non-climate factors on the prevalence of malaria. To further investigate the reasons for residual malaria transmission in the regions, non-climatic factors such as environmental, social, and economic factors, were also considered using both qualitative and quantitative methods (Table 4 and Fig. 2 ). Several environmental, social, and economic factors are perceived as important in increasing the malaria risk. Environmental pollutants, high population density, improper drainage systems, swamps, stagnant water, poor environmental hygiene, bushes, and garbage are all significantly associated with an increased malaria risk (Table 4 ). The quantitative result finding (Table 4 ) further aligns with the qualitative result ( Fig. 2 ). For example, during one of the FGDs respondents stated that: The dirty surroundings and stagnant water bodies are the cause of malaria in our communities. In those days, bed nets were not available but there was less malaria because of the clean environment (FGD Fatoto and Tumana). Among the social factors, agricultural development, population movement, urbanization, quality of health care, level of education, and employment status were thought associated with higher malaria risk. The results of the FGD further throw more light on these. Thus; In our region, maybe due to the vast land of bushes, we have lots of malaria in some villages, but for Bakadagi is very minimal (KII Jimara). I believe that doctors here are not professional because each time you go to the hospital the doctors will conclude is malaria without testing. People should be tested first before they come to that conclusion (KII Taibatou Wuli West). Poor hospital facilities and health personnel made most of the community hesitant to go to the hospital, they would rather stay home and treat themselves using local herbs (FGD Kantora). The region is close to rice farms, which also contribute to the increase in malaria especially during the rainy season. Malaria is more prevalent in the region than in CRR and LRR because of our rice cultivation. Not surprisingly poverty (low income, unemployment, or low salary) is also associated with a higher risk of malaria. Table 4 Influence of non-climatic factors on malaria prevalence in the region Has influence No influence Environmental factors Freq % Freq. (%) Pearson Chi2 P value Environmental pollutant 169 44.4 212 55.6 192.6464 0.000*** Population density 112 29.4 269 70.6 178.9893 0.000*** Improper drainage system 271 71.1 110 28.9 92.6178 0.000*** Swamps 303 79.5 78 20.5 54.0382 0.000*** Stagnant water 360 94.5 21 5.5 48.9049 0.000*** Poor environmental hygiene 360 94.5 21 5.5 17.0788 0.585 Bushes 265 69.6 116 30.5 70.9133 0.000*** Garbage 261 68.5 120 31.5 130.7062 0.000*** Social factors Agricultural development 190 49.87 191 50.1 40.3574 0.003*** Population movement 152 39.9 229 60.1 157.1531 0.000*** Urbanization 90 23.6 291 76.4 59.4107 0.000*** Poor health facility 365 95.8 16 4.2 40.3150 0.0003*** Poor healthcare 368 96.6 13 3.4 36.0731 0.0010** Unprofessional healthcare personnel 275 72.2 106 27.8 87.3375 0.000*** Educational background Employment status 110 92 28.9 24.2 271 289 71.1 75.9 155.4839 789133 0.000*** 0.000**** Economic factors Low income from business output 243 63.8 138 36.2 63.8812 0.000*** Unemployment 215 56.4 166 43.6 66.6847 0.000*** Low salary 172 45.1 209 54.9 67.4779 0.000*** 4 Discussion The result of the study reveals the knowledge of the population on the few concepts of climate change on malaria transmission. It also highlights the major perceived climatic variables responsible for the prevalence of malaria and other environmental, economic, and social factors that exacerbated the influence of climatic factors on the persistence of malaria in the region. Thus, the result shows that the population is aware of the influence of warmer temperatures on mosquito survival, consistent with [ 7 , 17 , 26 , 27 ] on the significant effect of warmer temperatures on vector survival and the development and maturation of the malaria parasite. In addition, a thermally stable lowland region is already warm enough for mosquito vectors to reproduce [ 2 , 28 ]. However, increasing temperatures are not perceived as a major contributing climatic factor to malaria transmission (Fig. 1 and Table 2 ). In the last few decades, the average temperature has been favorable for malaria transmission. The development of the malaria parasite in the vector occurs at temperatures between 18 and 32 o C [ 16 , 26 ], and any temperature outside this range would influence the parasite development [ 29 , 30 ]. The population further reiterates the point that malaria prevalence in the region is influenced also by the average and minimum temperature in the region. The findings of both the qualitative and quantitative components are consistent and disagree with some previous studies [ 12 ] [ 4 ] that reveal the influence of maximum temperature on malaria prevalence in their study regions. The association between hydrological parameters; (rainfall and flood) and the abundance and the biting rate of mosquitoes is well perceived by the population, and it is not surprising [ 29 , 31 ]. Indeed, the population knows that malaria occurs mainly during the rainy season or immediately after, and they have rightly observed that the burden of malaria increases with the abundance of stagnant water, particularly during floods. Moreover, the health education programs implemented by the Ministry of Health have probably shaped the opinion of the local population. Rainfall influences mosquito population dynamics [ 29 ] as it provides good breeding conditions for anopheles mosquitoes [ 32 ]. Regions with seasonal rainy periods and variations in rain patterns, especially in Sub-Sahara Africa where malaria is already considered endemic [ 33 ], will always experience a gradual increase in mosquito activity as soon as rainfall begins. Furthermore, floods also create suitable breeding sites for mosquitoes [ 31 ]therefore, there is a tendency for communities that experience flooding to experience a high malaria burden [ 34 ],[ 35 ]. The current research finding could also be attributed to green vegetation around the houses, workplaces, and construction sites, availability of water in ponds and ditches, and stagnant water in potholes and tires, all these conditions can be breeding sites for the vector in support of [ 31 , 34 ]. In addition, the peak of malaria transmission occurs in October-November, especially during the harvesting period also in agreement with [ 30 , 36 , 37 ]. The concept of the link between humid temperature and malaria is not well understood by the population, although such conditions are favorable for the survival of the malaria vectors and the development of malaria parasites in the vector [ 11 , 17 ]. A relative humidity equal to or greater than 60% encourages the breeding and proliferation of plasmodium parasites[ 16 , 38 ]. Importantly, the population's perception of these factors was significantly influenced by their age and the years of residence in the region at a p-value of 0.004 and 0.002 respectively. 84.8% of the respondents fall within the age bracket of 31–70 and 62% of them have lived in the region for more than 20 years. Therefore, most participants were able to appreciate the evolution of climate over time in the region and their perception can be considered reliable. Despite overwhelming evidence of the influence of hydrological parameters on malaria prevalence, other environmental and socioeconomic factors influence significantly its endemicity. Almost all the environmental and socioeconomic factors investigated in URR increase significantly the influence of climatic variables on malaria endemicity. As already reported by other investigations[ 39 , 40 ], environmental factors such as environmental pollutants, population density, improper drainage system, swamps, stagnant water, bushes around the house, and garbage promote malaria transmission. Pollutants such as plastic bags, rubbish, and others should be removed to maintain a clean environment unfavorable to vector breeding. An inadequate drainage system could result in floods, stagnant water, and ultimately an environment favorable for vector breeding and thus higher transmission. The nearness of bushes around the environment supports mosquito activities, as mosquitoes breed and multiply in such areas especially when it is damp or waterlogged, in agreement with [ 40 ]. The work by 40 reported a higher prevalence of malaria (23.23%) among the participants living in bushy areas than those living in non-bushy areas (9.23%). Furthermore, dirty environment and stagnated water are the top environmental factors influencing malaria prevalence in the region as noted in both qualitative and quantitative findings. A study by [ 40 ] also reported a higher prevalence (15.35%) of malaria among those residing in an area with stagnant water than those residing in an area with no stagnant water (9.47%). the finding also conforms with qualitative research findings by [ 41 ] where poor sanitation and poor drainage systems were considered the main factors contributing to mosquito breeding in the study area. Additionally, when households improperly dispose of waste, it increases the level of water pools of stagnant water, forming a breeding space for mosquitoes. This finding supports [ 42 ] that revealed high malaria incidence in locations closer to dumping sites. Social factors such as agricultural development, population movement, urbanization, poor health facilities and care, unprofessional health personnel, educational background, and employment status, have been identified to influence vector abundance and malaria prevalence in alignment with other research in different studied regions [ 6 , 15 , 39 , 43 ]. The statistical significance for all the access variables is seen at a p -value of less than < 0.05, suggesting a strong association with malaria prevalence in the region. Agricultural development, especially massive rice development projects in the region as revealed clearly in qualitative findings contributes to malaria increase in the region as wet or irrigated rice farms are suitable sites for vector breeding [ 20 , 44 , 45 ]. [ 20 ] show higher malaria prevalence in areas close to irrigated rice fields than in non-rice rice-growing areas [ 20 ]. This further implies that urban development and population movements are likely to influence any vector-borne diseases [ 39 ]. As new and modern development changes the outlook of rural patterns to urban settings like the building of gigantic infrastructures such as factories, industrial, construction of roads, and others, it creates highly heterogeneous socio-economic and environmental conditions conducive for malaria transmission. Malaria treatment depends on the availability of health facilities, good health services, and professional healthcare personnel [ 6 , 43 ]. Therefore, when access to health care is limited, malaria patients are not treated, with the risk of some of them evolving towards severe disease and death. The attitude of healthcare providers toward addressing and providing adequate medical attention to malaria patients, in addition to unprofessional healthcare personnel, will hinder the progress of the malaria reduction program, influence malaria prevalence, and expose more vulnerable populations to malaria risk. The statistically significant influence of three accessed economic factors: poor income from the business output, unemployment, and low salary on malaria prevalence in the region is proof that indeed there is a bilateral relationship between poverty and malaria [ 46 ]. Low income hinders the ability to provide effective prevention and protective measures such as living in a suitable house that is well-sealed from mosquitoes, use of insecticide-treated bed nets that will serve the whole household, and using a closed container for household water storage. The research finding agrees with [ 47 ] studies that reported a higher risk of malaria among those who engage in craft (22.8%) and unemployed (22.1%) compared with civil servants (8%). It further revealed a higher prevalence of lower-income earners (43.4%) than higher-income earners (5.1%). Unemployment was also a risk factor, with the highest prevalence (25.49%) in a work by [ 40 ] 5 Conclusion In conclusion, climate change is likely to affect malaria prevalence differently across regions, and a complete understanding of the dynamic of influence is the key to an effective adaptation strategy. The new result obtained by looking at the climatic variables associated with malaria prevalence in the region revealed no effect of temperature on malaria and brings to note rainfall and flood as the two major climatic factors along with interplay with poverty and other environmental and socioeconomic factors intensifying the vulnerability of this region to impact of this disease. This is a wake-up call for the government and the communities to engage in some discipline in ensuring proper environmental cleaning exercises every month, especially during the rainy season. The government should look into designing a good efficient drainage system, a better place for the disposition of rubbish in proximity to the community, and putting a well-trained officer for efficient waste management, professionalism in the healthcare sector, and the academic pedigree of the health personnel should be assessed before recruiting. The health personnel should also be encouraged to further learn and improve their skill. Government help is highly solicited for the rehabilitation of most of the health Centers in the region and support to household heads to encourage proactive measures in malaria treatment and prevention. As poor socioeconomic and environmental factors increase the risk of malaria infection, better socioeconomic conditions; good housing, good drainage system, standard health centers, professional health workers, education and awareness on the subject matter, and good environment cleaning habits, will reduce the malaria risk and then work towards achieving sustainable development goal (SD3). The strength of this work is the combination of both quantitative and qualitative work to determine the major climatic influence of malaria prevalence, and in looking at other non-climatic parameters capable of exacerbating malaria prevalence in a given region. The main weakness however lies in the inability to use a geographic information system (GIS) to map the districts in the region with the highest prevalence. Declarations Acknowledgments I wish to thank the West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), The Gambia for the PhD scholarship awarded to me through which I wrote this paper. I wish also to thank the German Federal Ministry of Education and Research for sponsoring my Ph.D. program and the host Universities in Germany; the University of Applied Sciences HAW Hamburg for hosting me throughout my stay in Germany. My profound gratitude goes to the sponsors and coordinators of short courses on Qualitative and Mixed Method Research Methodology, and Basic Epidemiology and Statistics, for offering me the opportunity and prerequisite knowledge from which I drew inspiration and skill to come up with this paper. Finally, I acknowledge the efforts of all my supervisors for their support throughout this research period and, for taking the time to read over the work before it was submitted. Author contribution Ugochinyere Agatha Okafor Chime conceptualized the idea, reviewed the literature, collected data analyzed data, and wrote the paper. Sidat Yaffa, Umberto D’Alessandro, Vincent Nduka Ojeh, Walter Leal Filho, and Iddisah Alhassah supervised and proofread the work. Ethical approval and informed consent statement: Ethical approval to conduct the study was obtained from the University of The Gambia Research Ethics Committee. All the respondents were informed about the confidentiality of the data. Competing interests The authors declare no competing interests. Funding No external funding was provided for this paper. Data availability The data will be made available on request. References Caminade C, McIntyre KM, Jones AE. Impact of recent and future climate change on vector-borne diseases. Ann N Y Acad Sci 2019; 1436: 157–173. Blanford JI, Blanford S, Crane RG, et al. Implications of temperature variation for malaria parasite development across Africa. 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Malaria transmission and prevalence in rice-growing versus non-rice-growing villages in Africa: a systematic review and meta-analysis. Lancet Planet Heal 2022; 6: e257–e269. Welfare S, International U. Republic of The Gambia Malaria Indicator Survey. Mwesigwa J, Okebe J, Affara M, et al. On – going malaria transmission in The Gambia despite high coverage of control interventions: a nationwide cross – sectional survey. Malar J 2015; 1–9. Ayanlade A, Sergi CM, Sakdapolrak P, et al. Resources, Environment and Sustainability Climate change engenders a better Early Warning System development across Sub-Saharan Africa : The malaria case. Resour Environ Sustain 2022; 10: 100080. Ihantola EM, Kihn LA. Threats to validity and reliability in mixed methods accounting research. Qual Res Account Manag 2011; 8: 39–58. Downing SM, Yudkowsky R. Assessment in Health Psychology . 2015. Epub ahead of print 2015. DOI: 10.1027/00452-000 . Silué, S.Dougba, N. D.Adjon Anderson, K. Alima D. Assessing Seasonal Climate Variability Impact on the Malaria Patient ’ s Cases in Assessing Seasonal Climate Variability Impact on the Malaria Patient ’ s Cases in the North of Côte d ’ Ivoire. Epub ahead of print 2021. DOI: 10.14738/aivp.96.11254 . Gatti L V., Basso LS, Miller JB, et al. Amazonia as a carbon source linked to deforestation and climate change. Nature 2021; 595: 388–393. Arab A, Jackson MC, Kongoli C. Modelling the effects of weather and climate on malaria distributions in West Africa. 2014; 1–9. Valerie T, Dornyo O, Amuzu S, et al. Estimating the Impact of Temperature and Rainfall on Malaria Incidence in Ghana from 2012 to 2017. Environ Model Assess 2022; 473–489. Fall P, Diouf I, Deme A, et al. Assessment of Climate-Driven Variations in Malaria Transmission in Senegal Using the VECTRI Model. Atmosphere (Basel) 2022; 13: 1–21. Androga DA. MAIN ARTICLE A literature review about the impact of climate change on malaria in South Sudan. 2020; 13: 2019–2021. Foley JE. Direct and Indirect Mechanisms for Climate Change to Impact Vector-Borne Disease . Elsevier Inc. Epub ahead of print 2018. DOI: 10.1016/b978-0-12-409548-9.11034-6 . Alexander J, Dongarwar D, Oduguwa E, et al. Temporal trends of gestational malaria in the United States. Parasite Epidemiol Control 2020; 11: e00191. Oluwatimilehin IA, Akerele JO, Oladeji TA, et al. Assessment of the impact of climate change on the occurrences of malaria, pneumonia, meningitis, and cholera in Lokoja City, Nigeria. Reg Sustain 2022; 3: 309–318. Global framework for the response to malaria in urban areas . 2022. Kotepui M, Kotepui KU. Impact of Weekly Climatic Variables on Weekly Malaria Incidence throughout Thailand: A Country-Based Six-Year. 2018. Canelas T, Castillo-salgado C, Baquero OS. Environmental and socioeconomic analysis of malaria transmission in the Brazilian Amazon, 2010–2015. 2019; 1–10. Diouf I, Fonseca BR, Caminade C, et al. Climate variability and malaria over West Africa. Am J Trop Med Hyg 2020; 102: 1037–1047. Palaniyandi M. The environmental Risk Factors Significant to Anopheles Species Vector Mosquito Profusion, P. falciparum, P. vivax Parasite Development, and Malaria Transmission, Using Remote Sensing and Gis : Review Article. 2021; 12: 162–171. Nyasa RB, Fotabe EL, Ndip RN. Trends in malaria prevalence and risk factors associated with the disease in Nkonghombeng; A typical rural setting in the equatorial rainforest of the South West Region of Cameroon. PLoS One 2021; 16: 1–20. Agyemang-Badu SY, Awuah E, Oduro-Kwarteng S, et al. Environmental Management and Sanitation as a Malaria Vector Control Strategy: A Qualitative Cross-Sectional Study Among Stakeholders, Sunyani Municipality, Ghana. Environ Health Insights; 17. Epub ahead of print 2023. DOI: 10.1177/11786302221146890 . Tukura ED, Ojeh VN, Philip AH, et al. Assessing the Potential Health Effect of Solid Waste Dump Site Located Close to Residential Areas in Jalingo, Taraba State Using Geospatial Techniques. World News Nat Sci 2018; 20: 160–175. Bayode T, Siegmund A. Social determinants of malaria prevalence among children under five years: A cross-sectional analysis of Akure, Nigeria. Sci African 2022; 16: e01196. Assi SB, Henry MC, Rogier C, et al. Inland valley rice production systems and malaria infection and disease in the forest region of western Côte d’Ivoire. Malar J ; 12. Epub ahead of print 2013. DOI: 10.1186/1475-2875-12-233 . Attu H, Adjei JK. Local knowledge and practices towards malaria in an irrigated farming community in Ghana. Malar J 2018; 1–8. Hutchins H, Power G, Ant T, et al. A survey of knowledge, attitudes and practices regarding malaria and bed nets on Bubaque Island, Guinea-Bissau. Malar J 2020; 19: 1–15. Akanbi FR. Socio-economic and demographic impact on malaria prevalence in Akoko South-west of Ondo state, Nigeria. Int J Infect Dis 2016; 45: 214. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5797924","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405410065,"identity":"cbc1a4d6-a322-40ad-9289-311f2e4556b8","order_by":0,"name":"Ugochinyere Agatha Okafor","email":"data:image/png;base64,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","orcid":"","institution":"University of The","correspondingAuthor":true,"prefix":"","firstName":"Ugochinyere","middleName":"Agatha","lastName":"Okafor","suffix":""},{"id":405410066,"identity":"5fe7cc9e-73a6-4ed8-8af3-3dd7f7026eb8","order_by":1,"name":"Umberto D’Alessandro","email":"","orcid":"","institution":"University of The","correspondingAuthor":false,"prefix":"","firstName":"Umberto","middleName":"","lastName":"D’Alessandro","suffix":""},{"id":405410067,"identity":"c19ec3e8-ba22-4026-b0e2-8fe712beeb01","order_by":2,"name":"Vincent Nduka Ojeh","email":"","orcid":"","institution":"University of The","correspondingAuthor":false,"prefix":"","firstName":"Vincent","middleName":"Nduka","lastName":"Ojeh","suffix":""},{"id":405410068,"identity":"4a2850e7-9c35-4a4a-9d55-39d191c68f96","order_by":3,"name":"Walter Leal Filho","email":"","orcid":"","institution":"University of The","correspondingAuthor":false,"prefix":"","firstName":"Walter","middleName":"Leal","lastName":"Filho","suffix":""},{"id":405410069,"identity":"617c77fc-780a-4fc7-8c41-398a6617d62e","order_by":4,"name":"Iddisah Alhassah","email":"","orcid":"","institution":"University of The","correspondingAuthor":false,"prefix":"","firstName":"Iddisah","middleName":"","lastName":"Alhassah","suffix":""},{"id":405410070,"identity":"b62d1d00-61cd-4f36-b12e-547aaaeeb264","order_by":5,"name":"Sidat Yaffa","email":"","orcid":"","institution":"University of The","correspondingAuthor":false,"prefix":"","firstName":"Sidat","middleName":"","lastName":"Yaffa","suffix":""}],"badges":[],"createdAt":"2025-01-09 15:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5797924/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5797924/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74625330,"identity":"1b879d75-a785-4c5a-ad5d-d43dbfbe6843","added_by":"auto","created_at":"2025-01-24 06:28:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55700,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eperception of climatic influence on malaria prevalence in the region.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5797924/v1/4644cd8dc323ee32d4c8e4e1.png"},{"id":74624288,"identity":"f715d6fe-740f-4bb4-8223-e6080c1ebdbd","added_by":"auto","created_at":"2025-01-24 06:20:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":166716,"visible":true,"origin":"","legend":"\u003cp\u003eQualitative findings on the influence of non-climatic factors on malaria prevalence in the region.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5797924/v1/eb28fb7c1103df329ae0894c.png"},{"id":74625472,"identity":"f71fde5d-47f6-4861-9f11-179e224d72f8","added_by":"auto","created_at":"2025-01-24 06:36:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1148893,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5797924/v1/591bab9c-0349-488d-82f4-8f13f30e4b92.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEvaluation of climatic and non-climatic influence on malaria prevalence in the Upper River Region of The Gambia\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe effect of climate change on the burden of infectious diseases, particularly vector-borne diseases such as malaria, is currently debated[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Climate plays a major role on the malaria dynamics and distribution [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and climate change will increase malaria transmission in certain geographical areas depending on demographic, socioeconomic, and ecological factors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. According to the IPCC WGII Sixth Assessment Report, the distribution and prevalence of malaria are influenced by rising temperatures and changing rainfall patterns (high confidence) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and Sub-Saharan Africa has an ideal climatic condition for endemic malaria transmission [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProjections on the influence of climate change on malaria estimated an increase in population at risk of 1.6\u0026nbsp;million by 2030 and 1.8\u0026nbsp;million by 2050 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], although other factors can influence malaria transmission [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The development of the malaria parasite and its transmission [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] is accelerated by changing temperature, rainfall, flooding, moisture conditions of the environment, and other non-climatic factors[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The above-mentioned climatic variables favor the breeding, proliferation, mating, longevity, dispersal, blood-feeding behavior, and oviposition of mosquitoes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The Gambia aims at eliminating malaria, i.e., interruption of local transmission, by 2030[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Nevertheless, malaria transmission is still ongoing despite a good coverage of control interventions, with the highest prevalence of infection in eastern Gambia, ie., 31.1% in the region's south bank and 36.8% north bank in [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Investigating both climatic and non-climatic factors becomes paramount for identifying the factors responsible for residual transmission so that control interventions may be targeted more efficiently.\u003c/p\u003e \u003cp\u003eStudies have reported a significant effect of climatic variables on the longevity of mosquitoes and the development of malaria parasites in the mosquito, and, subsequently, malaria prevalence [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. They have shown spatial and temporal variation in the prevalence of malaria infection using environmental temperature alongside rainfall and humidity. Nevertheless, the interaction between climatic factors and disease transmission is complicated and multifaceted, with mosquito survival, parasite development within the vector, and disease transmission potential restricted above and below certain temperature thresholds [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, optimal ranges for climatic suitability vary depending on the vector species, pathogen, and region, with disease transmission further influenced by other social and ecological factors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSome scholars state that malaria transmission and prevalence of infection are mainly influenced by temperature[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], while others argue that other factors such as the minimal temperatures, amount of rainfalls, and relative humidity are also important [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition to the complex relationship and overwhelming evidence that climatic variables affect substantially malaria transmission, deeper knowledge of the environmental, cultural, and socioeconomic elements that affect malaria at the household and societal levels is required [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This will necessitate an integrated strategy that takes into account climate change as well as other factors that influence malaria transmission [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study used a mixed-method approach to capture comprehensive and detailed information on the knowledge of climatic influence on malaria, in addition to the major climatic and non-climatic influence on malaria transmission in the region. Determining the climatic variables and the socioeconomic factors associated with malaria prevalence in the region could help to decrease its burden in the Upper River Region of The Gambia.\u003c/p\u003e"},{"header":"2 Methodology","content":"\u003cp\u003e \u003cb\u003eStudy area\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study was conducted in the Upper River Region of The Gambia (URR) among household heads randomly selected in each of the seven districts in the region, namely Kantora, Tumana, Basse Fuladu East, Jimara, Wuli West, West East, and Sandu. The region has a land mass of about 2000 sq km and a population density of 116/Km\u003csup\u003e2\u003c/sup\u003e (GBoS, 2013).\u003c/p\u003e \u003cp\u003eThe Gambia lies in the tropical wet and dry or savanna climate zone, which has a distinct long dry season and short rainy season. URR is crossed by the river of The Gambia, a large, slow-moving waterway, characterized by tidal movements and saltwater intrusion as far as 200km upriver., creating breeding sites for malaria vectors. The estimated annual rainfall is between 800mm and 1200mm and the average number of rainy days ranges from 54 days in Banjul to 31 days in URR where there are often floods following the rains.\u003c/p\u003e \u003cp\u003eThe yearly average temperature is 31.85\u0026deg;C (89.33\u0026deg;F), about 2.3% higher than in other regions in The Gambia. In the dry season, the highest average temperature is between 33.22\u0026deg;C and 42.42\u0026deg;C, while in the wet season, the lowest average is between 19.48\u0026deg;C and 27.99\u0026deg;C, a conducive temperature that supports the development and transmission of \u003cem\u003eP. falciparum\u003c/em\u003e. At temperatures below 20\u003csup\u003eo\u003c/sup\u003eC \u003cem\u003eP. falciparum\u003c/em\u003e cannot complete its life cycle and thus cannot be transmitted.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResearch design/ Data sampling techniques\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA parallel mixed-method approach, comprising equal strength of a qualitative strand and a quantitative strand, was used for the collection of primary data. The quantitative survey involved face-to-face interviews with selected household heads using structured questionnaires, while the qualitative study elicited information through Focus Group Discussions (FGDs), and Key Informant Interviews (KIIs) using a topic guide.\u003c/p\u003e \u003cp\u003eFor the survey, it was assumed a prevalence of 50%, and a margin of error of 5%, resulting in a sample size of 376 individuals, which was increased to 381 to account for unforeseen errors. A total of 91 individuals, irrespective of gender, were purposively selected for the qualitative study within the age bracket of 30\u0026ndash;80 years, and included 31 Key Informants (Alkalo/chief), and 60 individuals (41 males and 19 females) for the FGDs. Each FGD involved a maximum of 10 participants. Seven (7) FGDs were organized across the region\u003c/p\u003e \u003cp\u003e \u003cb\u003ePilot testing of the research instrument\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe research instruments were first reviewed by the researcher supervisors to ensure content validity and appropriateness, clarity, relevance, and suitability for the research. The instruments were further reviewed by the Research Ethics Committee at the University of The Gambia. A pilot testing was conducted in three selected settlements in the Basse district to check its suitability and adapt it if needed, to ensure the reliability and validity of the questionnaire and topic guide. The questionnaire was further revised after piloting and translated back into English language and checked for clarity. Moreover, the reliability of the questionnaire data was further tested using Cronbach alpha reliability statistics.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStrategies to deal with validity threats in qualitative data\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis work employed various formats to deal with threats to validity that are relevant to qualitative research. The standards taken in the reduction of threats to descriptive and interpretative validity included: asking open-ended questions, verbatim transcripts of the interviews, presenting participant quotations without shortening, peer debriefing, collecting and analyzing quality data, and providing thick descriptions of the setting, participants, and themes. The triangulation employed both in data collection techniques and sampling strategies aids in solving the threat of both interpretative and theoretical validity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eData processing\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo assess the knowledge of the influence of climate change on malaria, we employed a 5-point Likert scale (Strongly Agree\u0026thinsp;=\u0026thinsp;5, Agree\u0026thinsp;=\u0026thinsp;4, Neutral\u0026thinsp;=\u0026thinsp;3, Disagree\u0026thinsp;=\u0026thinsp;2, Strongly Disagree\u0026thinsp;=\u0026thinsp;1). The true strength and rate of either agreement or disagreement were measured using the mean score of the total respondents to evaluate the certainty of the response and determine which climatic factors were perceived to have a greater influence on malaria. The true strength was measured by the degree to which respondents agreed or disagreed with the concept. However, the rate of perception in this regard is given by: \u003cem\u003ethe frequency of response in each scale divided by the total respondents' response, multiplied by the attributed scale value.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe rate of perception is measured by the additional value above or below the attributed scale value from the mean score. Any value at the midpoint of 3.0 is considered neutral and is not measured. The percentage analysis was further used to evaluate the perception of the population on the major climatic factors associated with malaria prevalence. Multivariate regression was applied to investigate some of the socio-demographic factors influencing their perception. Moreover, the frequency, percentage, and statistically significant influence of non-climatic parameters (environmental, social, and economic factors) on the perceived climatic variables responsible for malaria prevalence in the regions were equally investigated using Pearson's Chi-square.\u003c/p\u003e \u003cp\u003eThe themes and sub-themes were extracted using NVivo 14 to analyse the qualitative data, whilst Stata 18 was employed to perform a descriptive analysis of the quantitative data.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEthical consideration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe research was conducted based on the tenets of Climate Change and Education, at the University of the Gambia. The study was approved by the University of the Gambia Ethics Committee on the 28th of August 2023. Written consent was secured from the participants after an explanation on the study's aim, objectives, and procedures. Participation in the study was voluntary, and participants were free not to answer the survey, or to withdraw their participation without penalty. The individual answers were anonymized and referenced instead.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Knowledge of climate change\u0026apos;s influence on malaria using the Likert scale 5-point\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the knowledge of climate change/variables influence on malaria infection. The result reveals that a higher percentage of the respondent (55.12%) believe that climate change has an impact on malaria as they demonstrate their disagreement with the statement that\u0026rdquo; Climate change has no impact on malaria transmission\u0026rdquo;. However, only 10.24% of the population strongly disagree with the concept. Additionally, the total mean score value on this concept is seen at approximately 3.0 points which stands for neutral. The implication is that climate change may or may not influence malaria, despite the opinion of the majority. On the other hand, a higher percentage of respondents disagree with the concept that decreased rainfall (59.32%) and drought (39.37%) increased malaria transmission, slightly in conformity with their mean score value seen at 2.67 and 2.61 respectively. However, the strength of disagreement is somehow weak as the mean score is above the attributed point 2 with a rate of 0.67 and 0.61 respectively, approximately 3.0 means score value that stands for neutral. This also suggests uncertainty on the effect of the variables on malaria. The population was equally neutral on increased malaria transmission in increased humid temperatures, in complete alignment with the mean score value of approximately 3.0 points for neutral.\u003c/p\u003e\n \u003cp\u003eFurthermore, a strong agreement was noted on the concept of the influence of warmer temperatures on mosquitoes\u0026apos; survival (38.85%), increased biting and abundance of mosquitoes (70.60%) during the rainy season in addition to the influence of flood on malaria prevalence (62.99%). A total mean score value of 3.82 (approx. 4.0) was noted on the concept of the influence of warmer temperatures on mosquito survival. This implies agreement with the concept, although the agreement is not very strong as the means score is -2 from the attributed value four (4) for agreement. The concept can be considered agreed on, but not strongly agreed. On the other hand, there is strong agreement on the concept of increased malaria prevalence during flooding (4.57), abundance of mosquitoes, and increased mosquito biting during the rainy season (4.69) as explained by their mean score value, approximately 5 points.\u003c/p\u003e\n \u003cp\u003eThe result demonstrates that knowledge of the influence of flood and rainfall on malaria is very certain among the population more than other accessed variables.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eKnowledge of the influence of climate change variables on malaria\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003ePercentage (Mean score value)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStrongly agree\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStrongly disagree\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean score\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClimate change has no impact on malaria transmission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.66 (0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.67 (0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.55 (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.88 (0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.24 (0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMosquitoes survive better at warmer temperatures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.85 (1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.51 (1.0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.54 (0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.49 (0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.62 (.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncreasing humidity increases malaria prevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.29 (0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.90 (0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.06 (1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.97 (0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 (.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFloods can increase malaria prevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.99 (3.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.12 (1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05 (.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 (.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA decrease in rainfall leads to malaria prevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.56(0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.26 (0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.66 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.32 (1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.20 (.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDroughts favor the transmission of malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.10 (0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.44 (0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.91 (1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.37 (0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.19 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMosquito bites more and in abundance during raining season\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.60 (3.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.08 (1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52 (.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26 (.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52 (.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eSource: Field data, 2023. Numbers in parentheses indicate the percentage of households while those without are the frequencies\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e3.2 Influence of Climatic variables on malaria prevalence in the region\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the perception of the population of the effects of climatic variables on malaria. Floods and increased rainfalls are perceived as favoring factors for malaria while average rainfalls, lower temperatures, and even higher temperatures are considered less important.\u003c/p\u003e\n\u003cp\u003eQualitative findings (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) also identified rainfalls as a factor that significantly influences malaria prevalence in the region as highlighted in 37 coded files with 72 references, and floods with 44 references in 33 coded files. Temperature had 7 references from 4 coded files and humidity had just 3 references from 3 coded files.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eQualitative finding on climatic influence on malaria prevalence in URR\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSub-themes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNCFs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNCRs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRespondent\u0026rsquo;s statements\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eThere is too much malaria at this time (rainy season). I have been sick for the last ten days and have not gone to the farm since then. Too much rain causes malaria and this is the season for malaria.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eWhen there is a flood in the community, there is always lots of standing water around which serves as a breeding space for mosquitoes.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eThe increased temperature often led an increased number of the population that will be sick as a result of mosquitoes biting, but it reduces when the temperature is cold.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHumid temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eI think humidity also has little influence on malaria.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cem\u003eNote: NCFs stands for the number of coding files and NCRs number of coding references\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe perception of the population on the climatic variables associated with the persistence of malaria in the region was significantly influenced by their age and the years lived in the region at a p-value of 0.004 and 0.002 respectively as shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInfluence of Socio-demographic Factors on the Perception of the Population of the Climatic Variables Associated with Malaria Prevalence in the Region\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePerception\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ecoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u0026gt;|t|\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e[95% conf. interval]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistrict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.0000667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.0491759 .0490426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.0125724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.0040511 .0210937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.0336323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.1460519 .0787874\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears lived in the region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.1933408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.0703377 .3163439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003econstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.039902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.0703377 .3163439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Influence of non-climate factors on the prevalence of malaria.\u003c/h2\u003e\n \u003cp\u003eTo further investigate the reasons for residual malaria transmission in the regions, non-climatic factors such as environmental, social, and economic factors, were also considered using both qualitative and quantitative methods (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cstrong\u003eand\u003c/strong\u003e Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Several environmental, social, and economic factors are perceived as important in increasing the malaria risk. Environmental pollutants, high population density, improper drainage systems, swamps, stagnant water, poor environmental hygiene, bushes, and garbage are all significantly associated with an increased malaria risk (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The quantitative result finding (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) further aligns with the qualitative result \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). For example, during one of the FGDs respondents stated that:\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cem\u003eThe dirty surroundings and stagnant water bodies are the cause of malaria in our communities. In those days, bed nets were not available but there was less malaria because of the clean environment (FGD Fatoto and Tumana).\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eAmong the social factors, agricultural development, population movement, urbanization, quality of health care, level of education, and employment status were thought associated with higher malaria risk. The results of the FGD further throw more light on these. Thus;\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cem\u003eIn our region, maybe due to the vast land of bushes, we have lots of malaria in some villages, but for Bakadagi is very minimal (KII Jimara).\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eI believe that doctors here are not professional because each time you go to the hospital the doctors will conclude is malaria without testing. People should be tested first before they come to that conclusion (KII Taibatou Wuli West).\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePoor hospital facilities and health personnel made most of the community hesitant to go to the hospital, they would rather stay home and treat themselves using local herbs (FGD Kantora). The region is close to rice farms, which also contribute to the increase in malaria especially during the rainy season. Malaria is more prevalent in the region than in CRR and LRR because of our rice cultivation.\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eNot surprisingly poverty (low income, unemployment, or low salary) is also associated with a higher risk of malaria.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInfluence of non-climatic factors on malaria prevalence in the region\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 32.4536%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 13.136%;\"\u003e\n \u003cp\u003eHas influence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003eNo influence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 12.1425%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 7.8374%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnvironmental factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003eFreq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003eFreq.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003ePearson Chi2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003eEnvironmental pollutant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e44.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e55.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e192.6464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003ePopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e70.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e178.9893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003eImproper drainage system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e71.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e92.6178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003eSwamps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e79.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e54.0382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003eStagnant water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e94.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e48.9049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003ePoor environmental hygiene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e94.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e17.0788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003eBushes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e69.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e70.9133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003eGarbage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e68.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e130.7062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\" style=\"width: 57.8424%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003eAgricultural development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e49.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e50.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e40.3574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.003***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003ePopulation movement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e39.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e60.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e157.1531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003eUrbanization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e76.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e59.4107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003ePoor health facility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e95.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e40.3150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.0003***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003ePoor healthcare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e96.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e36.0731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.0010**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003eUnprofessional healthcare personnel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e72.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e87.3375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003eEducational background\u003c/p\u003e\n \u003cp\u003eEmployment status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003cp\u003e24.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e71.1\u003c/p\u003e\n \u003cp\u003e75.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e155.4839\u003c/p\u003e\n \u003cp\u003e789133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003cp\u003e0.000****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\" style=\"width: 57.8424%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEconomic factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003eLow income from business output\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e63.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e36.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e63.8812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003eUnemployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e56.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e43.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e66.6847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.4536%;\"\u003e\n \u003cp\u003eLow salary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.9609%;\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.1751%;\"\u003e\n \u003cp\u003e45.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.6232%;\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6297%;\"\u003e\n \u003cp\u003e54.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.1425%;\"\u003e\n \u003cp\u003e67.4779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.8374%;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe result of the study reveals the knowledge of the population on the few concepts of climate change on malaria transmission. It also highlights the major perceived climatic variables responsible for the prevalence of malaria and other environmental, economic, and social factors that exacerbated the influence of climatic factors on the persistence of malaria in the region. Thus, the result shows that the population is aware of the influence of warmer temperatures on mosquito survival, consistent with [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] on the significant effect of warmer temperatures on vector survival and the development and maturation of the malaria parasite. In addition, a thermally stable lowland region is already warm enough for mosquito vectors to reproduce [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, increasing temperatures are not perceived as a major contributing climatic factor to malaria transmission (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the last few decades, the average temperature has been favorable for malaria transmission. The development of the malaria parasite in the vector occurs at temperatures between 18 and 32\u003csup\u003eo\u003c/sup\u003eC [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and any temperature outside this range would influence the parasite development [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The population further reiterates the point that malaria prevalence in the region is influenced also by the average and minimum temperature in the region. The findings of both the qualitative and quantitative components are consistent and disagree with some previous studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] that reveal the influence of maximum temperature on malaria prevalence in their study regions.\u003c/p\u003e \u003cp\u003eThe association between hydrological parameters; (rainfall and flood) and the abundance and the biting rate of mosquitoes is well perceived by the population, and it is not surprising [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Indeed, the population knows that malaria occurs mainly during the rainy season or immediately after, and they have rightly observed that the burden of malaria increases with the abundance of stagnant water, particularly during floods. Moreover, the health education programs implemented by the Ministry of Health have probably shaped the opinion of the local population.\u003c/p\u003e \u003cp\u003eRainfall influences mosquito population dynamics [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] as it provides good breeding conditions for anopheles mosquitoes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Regions with seasonal rainy periods and variations in rain patterns, especially in Sub-Sahara Africa where malaria is already considered endemic [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], will always experience a gradual increase in mosquito activity as soon as rainfall begins. Furthermore, floods also create suitable breeding sites for mosquitoes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]therefore, there is a tendency for communities that experience flooding to experience a high malaria burden [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e],[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The current research finding could also be attributed to green vegetation around the houses, workplaces, and construction sites, availability of water in ponds and ditches, and stagnant water in potholes and tires, all these conditions can be breeding sites for the vector in support of [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In addition, the peak of malaria transmission occurs in October-November, especially during the harvesting period also in agreement with [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe concept of the link between humid temperature and malaria is not well understood by the population, although such conditions are favorable for the survival of the malaria vectors and the development of malaria parasites in the vector [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A relative humidity equal to or greater than 60% encourages the breeding and proliferation of plasmodium parasites[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Importantly, the population's perception of these factors was significantly influenced by their age and the years of residence in the region at a p-value of 0.004 and 0.002 respectively. 84.8% of the respondents fall within the age bracket of 31\u0026ndash;70 and 62% of them have lived in the region for more than 20 years. Therefore, most participants were able to appreciate the evolution of climate over time in the region and their perception can be considered reliable.\u003c/p\u003e \u003cp\u003eDespite overwhelming evidence of the influence of hydrological parameters on malaria prevalence, other environmental and socioeconomic factors influence significantly its endemicity. Almost all the environmental and socioeconomic factors investigated in URR increase significantly the influence of climatic variables on malaria endemicity. As already reported by other investigations[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], environmental factors such as environmental pollutants, population density, improper drainage system, swamps, stagnant water, bushes around the house, and garbage promote malaria transmission. Pollutants such as plastic bags, rubbish, and others should be removed to maintain a clean environment unfavorable to vector breeding.\u003c/p\u003e \u003cp\u003eAn inadequate drainage system could result in floods, stagnant water, and ultimately an environment favorable for vector breeding and thus higher transmission. The nearness of bushes around the environment supports mosquito activities, as mosquitoes breed and multiply in such areas especially when it is damp or waterlogged, in agreement with [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The work by \u003csup\u003e40\u003c/sup\u003e reported a higher prevalence of malaria (23.23%) among the participants living in bushy areas than those living in non-bushy areas (9.23%).\u003c/p\u003e \u003cp\u003eFurthermore, dirty environment and stagnated water are the top environmental factors influencing malaria prevalence in the region as noted in both qualitative and quantitative findings. A study by [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] also reported a higher prevalence (15.35%) of malaria among those residing in an area with stagnant water than those residing in an area with no stagnant water (9.47%). the finding also conforms with qualitative research findings by [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] where poor sanitation and poor drainage systems were considered the main factors contributing to mosquito breeding in the study area. Additionally, when households improperly dispose of waste, it increases the level of water pools of stagnant water, forming a breeding space for mosquitoes. This finding supports [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] that revealed high malaria incidence in locations closer to dumping sites.\u003c/p\u003e \u003cp\u003eSocial factors such as agricultural development, population movement, urbanization, poor health facilities and care, unprofessional health personnel, educational background, and employment status, have been identified to influence vector abundance and malaria prevalence in alignment with other research in different studied regions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The statistical significance for all the access variables is seen at a \u003cb\u003ep\u003c/b\u003e-value of less than \u0026lt;\u0026thinsp;0.05, suggesting a strong association with malaria prevalence in the region. Agricultural development, especially massive rice development projects in the region as revealed clearly in qualitative findings contributes to malaria increase in the region as wet or irrigated rice farms are suitable sites for vector breeding [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] show higher malaria prevalence in areas close to irrigated rice fields than in non-rice rice-growing areas [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This further implies that urban development and population movements are likely to influence any vector-borne diseases [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. As new and modern development changes the outlook of rural patterns to urban settings like the building of gigantic infrastructures such as factories, industrial, construction of roads, and others, it creates highly heterogeneous socio-economic and environmental conditions conducive for malaria transmission.\u003c/p\u003e \u003cp\u003eMalaria treatment depends on the availability of health facilities, good health services, and professional healthcare personnel [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Therefore, when access to health care is limited, malaria patients are not treated, with the risk of some of them evolving towards severe disease and death. The attitude of healthcare providers toward addressing and providing adequate medical attention to malaria patients, in addition to unprofessional healthcare personnel, will hinder the progress of the malaria reduction program, influence malaria prevalence, and expose more vulnerable populations to malaria risk.\u003c/p\u003e \u003cp\u003eThe statistically significant influence of three accessed economic factors: poor income from the business output, unemployment, and low salary on malaria prevalence in the region is proof that indeed there is a bilateral relationship between poverty and malaria [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Low income hinders the ability to provide effective prevention and protective measures such as living in a suitable house that is well-sealed from mosquitoes, use of insecticide-treated bed nets that will serve the whole household, and using a closed container for household water storage. The research finding agrees with [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] studies that reported a higher risk of malaria among those who engage in craft (22.8%) and unemployed (22.1%) compared with civil servants (8%). It further revealed a higher prevalence of lower-income earners (43.4%) than higher-income earners (5.1%). Unemployment was also a risk factor, with the highest prevalence (25.49%) in a work by [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, climate change is likely to affect malaria prevalence differently across regions, and a complete understanding of the dynamic of influence is the key to an effective adaptation strategy. The new result obtained by looking at the climatic variables associated with malaria prevalence in the region revealed no effect of temperature on malaria and brings to note rainfall and flood as the two major climatic factors along with interplay with poverty and other environmental and socioeconomic factors intensifying the vulnerability of this region to impact of this disease.\u003c/p\u003e \u003cp\u003eThis is a wake-up call for the government and the communities to engage in some discipline in ensuring proper environmental cleaning exercises every month, especially during the rainy season. The government should look into designing a good efficient drainage system, a better place for the disposition of rubbish in proximity to the community, and putting a well-trained officer for efficient waste management, professionalism in the healthcare sector, and the academic pedigree of the health personnel should be assessed before recruiting. The health personnel should also be encouraged to further learn and improve their skill. Government help is highly solicited for the rehabilitation of most of the health Centers in the region and support to household heads to encourage proactive measures in malaria treatment and prevention.\u003c/p\u003e \u003cp\u003eAs poor socioeconomic and environmental factors increase the risk of malaria infection, better socioeconomic conditions; good housing, good drainage system, standard health centers, professional health workers, education and awareness on the subject matter, and good environment cleaning habits, will reduce the malaria risk and then work towards achieving sustainable development goal (SD3).\u003c/p\u003e \u003cp\u003eThe strength of this work is the combination of both quantitative and qualitative work to determine the major climatic influence of malaria prevalence, and in looking at other non-climatic parameters capable of exacerbating malaria prevalence in a given region. The main weakness however lies in the inability to use a geographic information system (GIS) to map the districts in the region with the highest prevalence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI wish to thank the\u0026nbsp;West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), The Gambia for the PhD scholarship awarded to me through which I wrote this paper. I wish also to thank the German Federal Ministry of Education and Research for sponsoring my Ph.D. program and the host Universities in Germany; the University of Applied Sciences HAW Hamburg for hosting me throughout my stay in Germany. My profound gratitude goes to the sponsors and coordinators of short courses on Qualitative and Mixed Method Research Methodology, and Basic Epidemiology and Statistics, for offering me the opportunity and prerequisite knowledge from which I drew inspiration and skill to come up with this paper. Finally, I acknowledge the efforts of all my supervisors for their support throughout this research period and, for taking the time to read over the work before it was submitted.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUgochinyere Agatha Okafor Chime\u0026nbsp;conceptualized the idea, reviewed the literature, collected data analyzed data, and wrote the paper.\u003c/p\u003e\n\u003cp\u003eSidat Yaffa, Umberto D\u0026rsquo;Alessandro, Vincent Nduka Ojeh, Walter Leal Filho, and\u0026nbsp;Iddisah Alhassah\u0026nbsp;supervised and proofread the work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and informed consent statement:\u0026nbsp;\u003c/strong\u003eEthical approval to conduct the study was obtained from the University of The Gambia Research Ethics Committee. \u0026nbsp;All the respondents were informed about the confidentiality of the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was provided for this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCaminade C, McIntyre KM, Jones AE. 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Int J Infect Dis 2016; 45: 214.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Knowledge, Climate Change, Prevalence, Malaria","lastPublishedDoi":"10.21203/rs.3.rs-5797924/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5797924/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite the scale-up of intervention, malaria remains a burden in the Upper River Region of The Gambia. Climate changes and non-climatic conditions can have a substantial influence on malaria prevalence, and further affect the coverage of preventive interventions. This work aimed at exploring the different risk factors, both climatic and non-climatic, associated with malaria. A descriptive research method using a parallel mixed method approach was adopted. Using a multistage approach, 381 households were selected from the region, and primary data was collected from household heads. Descriptive statistics were performed using StataSE18, and the significant influence of environmental and socioeconomic factors was analyzed using Chi-square (Χ\u003csup\u003e2\u003c/sup\u003e). Thematic analysis for the qualitative part was carried out using Nvivo.\u003c/p\u003e \u003cp\u003eMalaria is heavily influenced by rainfalls and floods and by some of the measured non-climatic factors. These results will provide individuals, professionals, government, and policymakers valuable information for better-targeting malaria control efforts.\u003c/p\u003e","manuscriptTitle":"Evaluation of climatic and non-climatic influence on malaria prevalence in the Upper River Region of The Gambia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-24 06:20:16","doi":"10.21203/rs.3.rs-5797924/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ee7ddeff-6692-4486-bb94-82c81fb5dafb","owner":[],"postedDate":"January 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-24T06:20:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-24 06:20:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5797924","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5797924","identity":"rs-5797924","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}