The Effects of Climate Change Atmospheric Tempreture, Rainfall Pattern and Land Use Malaria Incidence Rates: A Retrospective Trend Analysis Over a Ten-Year Period 2010–2020 in Luapula and Southern Province of Zambia

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Likwa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3897357/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract This study examines the impact of climate change on the incidence of malaria in Zambia. The study focused on variations in prevalence influenced by climatic and environmental factors. This study adopted a retrospective comparative analytical approach utilizing 157 case records from each province. The survey investigated temperature, seasonal variations, and land use activities. The findings revealed a fluctuating trend in rainfall from 2010 to 2020, with a significant association between malaria incidence and rainfall (p value=0.041). In 2019, southern provinces experienced the highest percentage of drought (64%), resulting in reduced malaria transmission due to unfavorable environmental conditions for mosquito larvae. Demographic data highlight an urban‒rural divide, with Luapula Province exhibiting a greater number of malaria cases among children under five years of age. These results emphasize the critical role of climate change and local factors in malaria transmission dynamics. Epidemiology climate change malaria incidence Zambia prevalence climatic factors environmental factors retrospective comparative analytical approach case records temperature seasonal variations land use activities rainfall drought malaria transmission mosquito larvae demographic data urban‒rural divide children under five malaria control strategies Figures Figure 1 Figure 2 Figure 3 Figure 4 I. Climate change, characterized by shifts in global or regional climate patterns, has been attributed to increased atmospheric carbon dioxide from fossil fuel usage. This has led to various incremental health impacts over the past quarter-century. The resulting extreme weather events, such as floods, aridity, and heatwaves, directly affect human populations and indirectly contribute to unprecedented variations in disease patterns due to alterations in ecology and biotic systems [1]. Malaria, a pervasive parasitic disease, poses a substantial global health threat. Approximately 214 million cases and 438,000 deaths were reported worldwide in 2015, with Africa being the predominant region affected. The World Health Organization (WHO) warns that climate change could lead to an additional quarter of a million deaths annually by 2030-2050. This is attributed to malnutrition, malaria, diarrhoeal diseases, and heat stress [2]. Climate change patterns have already impacted malaria transmission in sub-Saharan Africa, which has the highest malaria burden. Other regions at risk include the Mediterranean, Southeast Asia, the Western Pacific, and the Americas. Notably, the top 11 countries contributing 85% of the global malaria burden, with the top six contributing more than 50%, are all located in sub-Saharan Africa [3]. Malaria is caused by five different parasites, with P. falciparum being the dominant species in Africa and accounting for the highest mortality worldwide [4]. The vector-borne nature of malaria underscores the significance of eco-environmental factors, including climate and landscape, in influencing both vector and parasite development and community exposure [5]. In Zambia, variations in malaria incidence persist across the country despite progress in some regions, such as the southern province. These variations are influenced by climate variables such as low altitude, high NDVI, and land surface temperature [6, 7]. The challenges faced by Zambia in malaria control, including a reversal in efforts in 2009-2010 due to decreased donor funding, emphasize the interconnectedness of socioeconomic factors and climate-induced health impacts [8]. II. This was a retrospective comparative study designed over 10 years in two provinces of Zambia. In this study, the patients were from Luapula Province, which has the highest incidence rate of malaria, while the control or comparison province was the southern province, which has the lowest incidence rate. The provinces were matched according to climatic variables to seasonal rainfall patterns, atmospheric air temperature, and man-made environmental activity. Therefore, this study will be conducted to determine the trends in malaria burden during the past 10 years at health centres in Luapula and southern provinces. The study population for the study included all individuals with suspected malaria who had visited the health centres for the first time from January 2010 to December 2020. 2.1 Data Extraction This comprehensive retrospective record review, spanning the years 2010 to 2020, employed diverse parameters, including the date of examination and the total number of clinically treated and confirmed malaria cases on a monthly and yearly basis [9]. The data collection involved interactions with municipal councils in each district, ensuring validation through observation[10]. The epidemiological data, crucially focusing on confirmed cases, were sourced from Zambia's Ministry of Health (MoH) via the National Malaria Elimination Centre (NMEC), emphasizing the reliance on laboratory diagnostic tests or rapid diagnostic test (RDT) results since 2010 [11]. Acknowledging the impact of local environmental factors on vector abundance and the significance of incident malaria cases in identifying residual transmission, environmental variables were obtained from satellite-based imagery datasets [12]. Climatic variables, particularly daily precipitation data and temperature variations, were acquired from the Zambia Meteorological Agency. The choice of primary climate variables, temperature and rainfall, was guided by a robust body of literature demonstrating their substantial influence on both short- and long-term changes in malaria transmission (13, 14, 15, 16, 17). 2.2 Data analysis The data collected for this study were entered into the Microsoft Office Excel Worksheet 2010 and analysed using STATA version 14 [18]. Descriptive statistics were used to calculate overall malaria incidence and trends in malaria transmission, with graphs generated to illustrate these trends over the ten-year study period [19]. Parametric and nonparametric statistical analyses were used to detect trends in climatic variables and determine the effects of climatic and sociodemographic factors on age-specific malaria incidence and control interventions [20]. Linear regression tests were implemented to explore trends and changes in trends and to regress malaria against environmental and intervention variables. This study specifically investigated the potential role of climate variables in malaria transmission dynamics at the subnational district level in Zambia from 2010 to 2020 [21]. Linear regression was also performed to assess the significance of malaria incidence trends over time, correlating malaria incidence rates with climatic variables such as temperature and rainfall. The land surface temperature, measured as the mean minimum, mean maximum, and mean average temperature for each month, was considered in this analysis. Pearson correlation coefficients were calculated to determine the association between malaria incidence and the selected climatic variables [22]. Additionally, larval control, a key aspect of the integrated malaria control approach in the two provinces, involved environmental management and chemical use. Understanding the environmental characteristics influencing the larval activity of principal malaria vectors was emphasized in the context of larval control as part of a broader vector management program [23]. 2.3 Data analysis The section shows the association between malaria incidence and climatic variables. After the data were cleaned with Excel, the data were imported and analysed using Stata version 14.0. In the bivariate analysis, logistic regression was used to assess the association between categorical variables and outcomes. Variables that were statistically significant at the 5% level. This study intends to compare and establish the effects of climatic change on malaria incidence rates over a ten-year period from 2010 to 2020 in selected provinces of Luapula and southern provinces in Zambia. Furthermore, this chapter discusses the demographic characteristics of malaria incidence and how changes in climatic and environmental variables influence the incidence of malaria. . III. Table TABLE I Correlations among Meteorological Factors Mansa district of Luapula Province Autocorrelation among Meteorological factors in Mansa district of Luapula Province Variables RH at 2 m Maximum Temperature Minimum Temperature Mean Temperature Minimum Precipitation Annual Rainfall RH at 2 m 1 -.524 -.561 .139 .657 * .657 * Max Temperature -.524 1 -.007 .598 -.679 * -.667 * Min Temperature -.561 -.007 1 -.806 ** .093 .077 Mean Temperature .139 .598 -.806 ** 1 -.477 -.456 Min Precipitation .657 * -.679 * .093 -.477 1 .998 ** Annual Rainfall .657 * -.667 * .077 -.456 .998 ** 1 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). Monze District of Southern Province Autocorrelation among Meteorological factors in Monze district of Southern Province Variables RH at 2 m Maximum Temperature Minimum Temperature Mean Temperature Minimum Precipitation Annual Rainfall RH at 2 m 1 -.140 .322 -.339 .813 ** .778 ** Max Temperature -.140 1 -.049 .580 -.147 -.212 Min Temperature .322 -.049 1 -.842 ** -.004 -.024 Mean Temperature -.339 .580 -.842 ** 1 -.077 -.096 Min Precipitation .813 ** -.147 -.004 -.077 1 .993 ** Annual Rainfall .778 ** -.212 -.024 -.096 .993 ** 1 **. Correlation is significant at the 0.01 level (2-tailed). Environmental Human Activities Influences Climate Change and the Malaria Incidence Rates in Luapula and Southern Province of Zambia. IV. Conclusion The study conducted in Luapula and southern provinces of Zambia over a ten-year period from 2010 to 2020 sheds light on the intricate relationships among climate change, environmental factors, and malaria incidence. In Luapula Province, a positive correlation was observed between malaria incidence rates and maximum and minimum temperatures. This positive association reflects the broader trend of climate change, with rising temperatures potentially facilitating the spread of malaria (Ashton et al., 2017). Furthermore, the negative correlation between malaria incidence and daily precipitation indicates that reduced rainfall is linked to increased malaria incidence. This aligns with the adverse effects of climate change, which include decreased precipitation and its impact on malaria transmission (World Health Organization, 2008). This study also highlights the influence of climate change on rainfall patterns, which has led to fluctuations over the years in Luapula Province. These fluctuations may be attributed to changing climate conditions, which affect malaria dynamics. (i) The effects of climate change on the malaria incidence rate in relation to rainfall and temperature variation patterns in Zambia over a ten-year period beginning in 2010 Luapula Province The results of the Pearson correlation test revealed a strong positive (0.043) correlation between the malaria incidence rate and maximum temperature. There was also a positive correlation (0.152) between the malaria incidence rate and minimum temperature. Similarly, there was a negative correlation (-0.623) between the malaria incidence rate and daily precipitation (mm) corrected between 2010 and 2020. Furthermore, analysis also indicated that there was a strong association between the malaria incidence rate and daily precipitation corrected between 2010 and 2020, with a p value of 0.041, which was statistically significant at 0.05. The data analysis also revealed a negative (-0.608) correlation between the malaria incidence rate and annual rainfall. The Pearson correlation coefficient also revealed a strong association between the malaria incidence rate and annual rainfall, with a p value of 0.047 indicating statistical significance at 0.05. Studies, such as those conducted by Ashton et al. (2017) and the World Health Organization (2008), have also highlighted the positive relationship between temperature and malaria incidence. Increasing temperatures can extend the geographical range of malaria vectors and accelerate the spread of the malaria parasite within mosquitoes, potentially leading to increased malaria transmission. The inverse relationship between malaria incidence and daily precipitation has been documented in previous studies, emphasizing the role of water availability in creating suitable breeding habitats for mosquito vectors. Reduced precipitation can lead to the creation of stagnant water bodies that are ideal for mosquito larvae to thrive, as mentioned by the World Health Organization (2008). The strong association between malaria incidence and annual rainfall, as indicated by the significant p value, underlines the importance of understanding long-term precipitation patterns in malaria-endemic regions. Such patterns can provide insights into the likelihood of malaria outbreaks, especially in areas where rainfall is a key driver of mosquito breeding, as supported by research on climate and malaria transmission dynamics (Ashton et al., 2017; World Health Organization, 2008). As a result, this study further revealed a high fluctuating trend in rainfall that was recorded from 2010 to 2020, with a 1058.0 mm mean annual rainfall, and the maximum total rainfall was observed in 2017 (1434.4 mm), while the minimum rainfall was observed in 2016 (801.6 mm). The results obtained showed an association between monthly malaria cases and the climatic/meteorological variables temperature and rainfall. Southern Province The Pearson correlation coefficient was calculated to determine the relationship between malaria incidence rates and meteorological/climatic factors. The results revealed that there was a negative correlation between the relative maximum temperature (-0.263) and minimum temperature (-0.276). On the other hand, there was a positive correlation (0.084) between the mean temperature and malaria incidence rate. There was also a strong positive correlation between malaria incidence rates and rainfall. The results from the Pearson correlation test between malaria incidence rates and daily precipitation corrected (mm/day) from 2010 to 2020 revealed that there was a positive correlation between malaria incidence rates and daily precipitation corrected (mm/day). Additionally, there was no statistically significant association between the Malaria incidence rate and minimum rainfall (mm/day), with a p value of 0.719 indicating a significant difference of 0.05. This negative relationship indicates that as temperatures increase, the incidence of malaria tends to decrease. These findings are consistent with those of several previous studies, which have shown that extreme heat can negatively impact mosquito survival and reduce malaria transmission (Shimaponda-Mataa et al.,2017). These findings contribute to the growing body of research investigating the links between meteorological and climatic factors and malaria incidence. These results reinforce the importance of considering these factors in malaria control strategies and adapting interventions to the specific weather conditions of different regions. Understanding the complex relationships between temperature and rainfall variables and malaria transmission is critical in developing effective and targeted measures for combating this disease, particularly in the face of climate change. The significance of these findings lies in their potential to inform evidence-based strategies for malaria prevention and control. (II) The role of climate change in the observed increase or decrease in Malaria in Zambia over a ten-year period from 2010 to 2020 The data analysis in this study revealed that there was a higher incidence of floods recorded in Luapula Province and a high incidence of droughts in the southern province of Zambia as of 2020. The results of the averaged VHI for Luapula Province showed that there was a positive correlation between land and vegetation. Furthermore, the data showed that there was a fluctuating trend in vegetation availability over the ten-year period. However, Luapula Province is more prone to harboring larvae for mosquitoes with favorable or suitable environmental conditions for survival, whereas in the southern province, the high number of droughts recorded has impacted the vegetation health index, leading to harsh environmental conditions for the survival and harboring of mosquito larvae. According to the data generated, the Vegetable Health Index for the yearly percentage of drought area for the province showed that in 2019, the southern province had the highest (64%) percentage of drought recorded during a ten-year period. In Luapula Province, the highest percentage recorded was 31% of the yearly percentage of drought area for the province, and drought was recorded in 2012. Similarly, Nygren et al. (2014) collected weekly malaria data from 2011 to 2013 and used the normalized difference vegetation index (NDVI), night surface temperature, rainfall, and night dew point to model health facility-level malaria transmission within the southern province. Their results revealed a significant association with environmental variables (dewing point, temperature, and NDVI) across low, moderate, and high transmission zones (Nygren et al., 2014). This study is important because of its contribution to the growing body of knowledge linking climate change, environmental conditions, and malaria transmission. By elucidating the distinct climate-related challenges faced by different regions within Zambia, this research provides critical insights for malaria control and adaptation strategies. Recognizing the impact of floods, droughts, and vegetation health on mosquito breeding and malaria transmission is vital for tailoring interventions to specific local conditions and effectively combating this disease in the context of a changing climate. 5.3 Role of environmental land use combined with climate change in the observed increase or decrease in Malaria incidence over a ten-year period from 2010 to 2020 in Zambia The study revealed that in 2010, the southern province had 216 kha of tree cover, extending over 2.5% of its land area. In 2021, 1.96 kha of tree cover was lost, equivalent to 564 kt of CO₂ emissions. In 2010, Luapula had 2.13 Mha of tree cover, extending over 43% of its land area. In 2021, 20.6 kha of tree cover was lost, equivalent to 7.81 Mt of CO₂ emissions. Furthermore, data obtained from ZamStats (2022) reveal that in Luapula Province, 194,520 people live in urban areas, while the majority of the population (797,407) lives in rural areas. In the southern province, 392,175 people live in urban areas, and 1,197,751 people (majority) live in rural areas. As of 2020, many settlements in Luapula Province have an estimated population density of 20 per km²; the total area of the province is 50,567 km² and characterized by its rural nature. In 2010, the total area of the southern province was 85,283 km², and the population density was 18.60 per km² (Grid3, 2022). These data imply that Luapula Province has a large surface area and vegetation that provide suitable conditions for mosquito larvae to survive, and the majority of the population lives in rural areas of the province and has more exposure to mosquito bites, as these areas have good VHIs that are between 40 and 60 over a ten-year period. However, in the southern province, there is a poor vegetation health index with high records of droughts due to urban settlements and high cases of deforestation, which have led to poor environmental conditions that have affected the survival of mosquito larvae in these areas. Furthermore, an example of a district-wide study was conducted in the Nchelenge district in Luapula Province using household-level cross-sectional surveys conducted every two months between 2012 and 2015 (Pinchoff et al., 2015, 2016). V. Conclusion In conclusion, a study conducted in Luapula and the southern provinces of Zambia from 2010 to 2020 highlights the intricate relationships among climate change, environmental factors, and malaria incidence. The findings revealed a positive correlation between malaria incidence rates and maximum and minimum temperatures, reflecting the broader trend of climate change. Additionally, a negative correlation was observed between malaria incidence and daily precipitation, indicating that reduced rainfall is linked to increased malaria incidence. This study also emphasized the influence of climate change on rainfall patterns, which has led to fluctuations over the years in Luapula Province. The results further revealed that there was a strong association between the malaria incidence rate and annual rainfall, highlighting the importance of understanding long-term precipitation patterns in malaria-endemic regions. These findings support previous research showing a positive relationship between temperature and malaria incidence, as rising temperatures can facilitate the spread of this disease. The inverse relationship between malaria incidence and daily precipitation is also consistent with the findings of previous studies, emphasizing the role of water availability in mosquito breeding habitats. The study's significance lies in its potential to inform evidence-based strategies for malaria prevention and control. By considering meteorological and climatic factors, interventions can be tailored to specific weather conditions, allowing for more effective measures against malaria transmission. Furthermore, the study highlights the impacts of floods, droughts, and vegetation health on mosquito breeding and malaria transmission, providing insights for adaptation strategies in the face of climate change. Overall, this research contributes to the growing body of knowledge on the complex relationships among climate change, environmental conditions, and malaria transmission. These findings underscore the need for proactive measures to address these factors in malaria control efforts. Declarations Acknowledgements I am greatly thankful to my God for seeing me through this programme. I extend my sincere gratitude to my Principal Supervisor Dr. Rosemary Ndonyo. We are grateful for her tireless guidance during this research and for her cosupervisor, Mr. Allan Mbewe. I am thankful to the Populations Studies Dept. postgraduate lecturers and technical staff for their support during the period of my study. Many thanks to the Ministry of Health (MoH) for allowing us to access the malaria data and for granting me permission to utilize part of the collected programme data for my dissertation write-up. I am greatly indebted to the National Malaria Control Centre Authors’ information Background for Manuscript: "The Effects of Climate Change on Malaria Incidence Rates in Luapula and Southern Province of Zambia: A Retrospective Trend Analysis Over a Ten-Year Period (2010-2020)" Joshua Kanjanga Phiri Contact Information: Address: P.O. Box 32696 Lusaka, Zambia Phone: +260-972-580-306 Email: [email protected] Profile: I am a dedicated and highly motivated individual seeking to establish my career in demography, statistics, analytics, and research. With approximately 8 years of experience spanning education and work, I have honed my skills in demographic methods, advanced research, statistical analysis, and the application of various software tools. My passion lies in contributing to sustainable development through innovative and practical solutions. References WHO. (2019). Climate change and health. WHO. (2014). World malaria report. WHO. (2019). Global Technical Strategy for Malaria 2016-2030. WHO. (2019). World malaria report. WHO. (2015). Climate, landscape, and housing structure in vector-borne disease. Riedel et al. (2010). Climate-based models for understanding and forecasting malaria epidemics. MoH. (2015). Ministry of Health report. Masaninga et al. (2012). Impact of malaria control efforts in Zambia. [Thomson et al., 2005]. Malaria epidemiology in an area of stable transmission in western Kenya: prospects for control. [Abiodun et al., 2016]. 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Stata Statistical Software: Release 14. College Station, TX: StataCorp LP. Jackson, M. C., & Kongoli, C. (2014). Spatially Explicit Modelling of Malaria in Rural Zambia. PLoS ONE, 9(11), e114168. https://doi.org/10.1371/journal.pone.0114168 Smets, S., Mwalubunju, A., Van geertruyden, J. P., & Vansteelandt, S. (2013). A matched case–control study of the association between childhood malaria and acute renal failure in Malawi. PLoS ONE, 8(5), e63668. https://doi.org/10.1371/journal.pone.0063668 Bennett, A., Yukich, J., Miller, J. M., Keating, J., Moonga, H., Hamainza, B., & Steketee, R. W. (2016). The relative contribution of climate variability and vector control coverage to changes in malaria parasite prevalence in Zambia 2006–2012. Parasites & Vectors, 9(1), 431. https://doi.org/10.1186/s13071-016-1703-4 Molineaux, L. (1997). The epidemiology of human malaria as an explanation of its distribution, including some implications for its control. In Evolutionary Biology of Transient Unstable Populations (pp. 453–488). Springer. Ashton, M., et al. (2017). [Full Title of Ashton et al. Article], [Journal Name], [Volume Number](if applicable), [Page Numbers], DOI or URL (if applicable). World Health Organization. (2008). [Full Title of WHO Report or Publication], [Publication or Report Name], [Publication Date], URL (if applicable). Ashton, M., et al. (2017) - World Health Organization. (2008). [Full Title of Ashton et al. Article], [Journal Name], [Volume Number](if applicable), [Page Numbers], DOI or URL (if applicable). Shimaponda-Mataa, N., et al. (2017). [Full Title of Shimaponda-Mataa et al. Article], [Journal Name], [Volume Number](if applicable), [Page Numbers], DOI or URL (if applicable). Nygren, D., et al. (2014). [Full Title of Nygren et al. Article], [Journal Name], [Volume Number](if applicable), [Page Numbers], DOI or URL (if applicable). Pinchoff, J., et al. (2015, 2016). [Full Title of Pinchoff et al. Article], [Journal Name], [Volume Number](if applicable), [Page Numbers], DOI or URL (if applicable). Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-3897357","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269917622,"identity":"7204e786-89b2-4913-b79a-03fc372c6757","order_by":0,"name":"joshua phiri","email":"","orcid":"","institution":"university of Zambia","correspondingAuthor":false,"prefix":"","firstName":"joshua","middleName":"","lastName":"phiri","suffix":""},{"id":269917623,"identity":"2c1c034a-40be-4fae-98e8-a749b203083a","order_by":1,"name":"DR. Likwa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACPmY2EMUM5kgwVIDYzA14tbChajkDYjMS0MKArIWxDUQR0sLOlibxc4e1nLlE8sHblfNqo/nbgVp+VGzD57Bjkr1n0o0tZ6QlW57ddjx3xmHGBsaeM7fxaGFvu8Hbdjhxw40cM8nGbcdyG4BamBnb8Gu5+ReuZc6x3PmEtbAdu42wpaEmdwMRWtJ/y7alGxuceZZs2XDsQO5GoJaD+PzCz3/M2PBtm7WcwfHkgzcbaupy550/fPDBjwrcWhBAIAFEHgazDxChHmQfWF0dcYpHwSgYBaNgRAEA7i9ZFuaM+UQAAAAASUVORK5CYII=","orcid":"","institution":"university of Zambia","correspondingAuthor":true,"prefix":"DR.","firstName":"","middleName":"","lastName":"Likwa","suffix":""}],"badges":[],"createdAt":"2024-01-25 13:47:40","currentVersionCode":2,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3897357/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-3897357/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50389809,"identity":"ae5b5c0b-b1f3-40c8-bcb2-234c87abe8c2","added_by":"auto","created_at":"2024-01-30 18:31:57","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78006,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship of malaria incidence rates\u003c/p\u003e","description":"","filename":"f1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3897357/v2/91b1962a0a579fd91a2f97ad.jpg"},{"id":50389810,"identity":"0b8ae466-8e3c-4a2e-a6ae-1e7a58d26756","added_by":"auto","created_at":"2024-01-30 18:31:57","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59812,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of drought in southern provinces\u003c/p\u003e","description":"","filename":"f2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3897357/v2/db8c22634b8fc624341bce5d.jpg"},{"id":50389811,"identity":"21002fe9-9214-46de-b45e-e9514c12ecb9","added_by":"auto","created_at":"2024-01-30 18:31:57","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51409,"visible":true,"origin":"","legend":"\u003cp\u003eDrought Luapula percentage\u003c/p\u003e","description":"","filename":"f3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3897357/v2/4e360ac068c5db4c10e79c71.jpg"},{"id":50389812,"identity":"31343d54-0bd4-4390-8fe9-9eaa65f30b1b","added_by":"auto","created_at":"2024-01-30 18:31:57","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":95572,"visible":true,"origin":"","legend":"\u003cp\u003eAverage VHI of Luapula Province\u003c/p\u003e","description":"","filename":"f4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3897357/v2/ef53363ac8e8422370d76679.jpg"},{"id":50390143,"identity":"d701bb30-eea7-4c9b-bff3-9df9365af3ce","added_by":"auto","created_at":"2024-01-30 18:39:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":533356,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3897357/v2/8db2c307-b28d-4862-9633-72f305bd7659.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eThe Effects of Climate Change Atmospheric Tempreture, Rainfall Pattern and Land Use Malaria Incidence Rates: A Retrospective Trend Analysis Over a Ten-Year Period 2010–2020 in Luapula and Southern Province of Zambia\u003c/p\u003e","fulltext":[{"header":"I. ","content":"\u003cp\u003eClimate change, characterized by shifts in global or regional climate patterns, has been attributed to increased atmospheric carbon dioxide from fossil fuel usage. This has led to various incremental health impacts over the past quarter-century. The resulting extreme weather events, such as floods, aridity, and heatwaves, directly affect human populations and indirectly contribute to unprecedented variations in disease patterns due to alterations in ecology and biotic systems [1].\u003c/p\u003e\n\u003cp\u003eMalaria, a pervasive parasitic disease, poses a substantial global health threat. Approximately 214 million cases and 438,000 deaths were reported worldwide in 2015, with Africa being the predominant region affected. The World Health Organization (WHO) warns that climate change could lead to an additional quarter of a million deaths annually by 2030-2050. This is attributed to malnutrition, malaria, diarrhoeal diseases, and heat stress [2]. Climate change patterns have already impacted malaria transmission in sub-Saharan Africa, which has the highest malaria burden. Other regions at risk include the Mediterranean, Southeast Asia, the Western Pacific, and the Americas. Notably, the top 11 countries contributing 85% of the global malaria burden, with the top six contributing more than 50%, are all located in sub-Saharan Africa [3].\u003c/p\u003e\n\u003cp\u003eMalaria is caused by five different parasites, with P. falciparum being the dominant species in Africa and accounting for the highest mortality worldwide [4]. The vector-borne nature of malaria underscores the significance of eco-environmental factors, including climate and landscape, in influencing both vector and parasite development and community exposure [5].\u003c/p\u003e\n\u003cp\u003eIn Zambia, variations in malaria incidence persist across the country despite progress in some regions, such as the southern province. These variations are influenced by climate variables such as low altitude, high NDVI, and land surface temperature [6, 7]. The challenges faced by Zambia in malaria control, including a reversal in efforts in 2009-2010 due to decreased donor funding, emphasize the interconnectedness of socioeconomic factors and climate-induced health impacts [8].\u003c/p\u003e"},{"header":"II. ","content":"\u003cp\u003eThis was a retrospective comparative study designed over 10 years in two provinces of Zambia. In this study, the patients were from Luapula Province, which has the highest incidence rate of malaria, while the control or comparison province was the southern province, which has the lowest incidence rate. The provinces were matched according to climatic variables to seasonal rainfall patterns, atmospheric air temperature, and man-made environmental activity. Therefore, this study will be conducted to determine the trends in malaria burden during the past 10 years at health centres in Luapula and southern provinces. The study population for the study included all individuals with suspected malaria who had visited the health centres for the first time from January 2010 to December 2020.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.1 Data Extraction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis comprehensive retrospective record review, spanning the years 2010 to 2020, employed diverse parameters, including the date of examination and the total number of clinically treated and confirmed malaria cases on a monthly and yearly basis [9]. The data collection involved interactions with municipal councils in each district, ensuring validation through observation[10]. The epidemiological data, crucially focusing on confirmed cases, were sourced from Zambia\u0026apos;s Ministry of Health (MoH) via the National Malaria Elimination Centre (NMEC), emphasizing the reliance on laboratory diagnostic tests or rapid diagnostic test (RDT) results since 2010 [11]. Acknowledging the impact of local environmental factors on vector abundance and the significance of incident malaria cases in identifying residual transmission, environmental variables were obtained from satellite-based imagery datasets [12]. Climatic variables, particularly daily precipitation data and temperature variations, were acquired from the Zambia Meteorological Agency. The choice of primary climate variables, temperature and rainfall, was guided by a robust body of literature demonstrating their substantial influence on both short- and long-term changes in malaria transmission (13, 14, 15, 16, 17).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2 Data analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe data collected for this study were entered into the Microsoft Office Excel Worksheet 2010 and analysed using STATA version 14 [18]. Descriptive statistics were used to calculate overall malaria incidence and trends in malaria transmission, with graphs generated to illustrate these trends over the ten-year study period [19]. Parametric and nonparametric statistical analyses were used to detect trends in climatic variables and determine the effects of climatic and sociodemographic factors on age-specific malaria incidence and control interventions [20]. Linear regression tests were implemented to explore trends and changes in trends and to regress malaria against environmental and intervention variables. This study specifically investigated the potential role of climate variables in malaria transmission dynamics at the subnational district level in Zambia from 2010 to 2020 [21]. Linear regression was also performed to assess the significance of malaria incidence trends over time, correlating malaria incidence rates with climatic variables such as temperature and rainfall. The land surface temperature, measured as the mean minimum, mean maximum, and mean average temperature for each month, was considered in this analysis. Pearson correlation coefficients were calculated to determine the association between malaria incidence and the selected climatic variables [22]. Additionally, larval control, a key aspect of the integrated malaria control approach in the two provinces, involved environmental management and chemical use. Understanding the environmental characteristics influencing the larval activity of principal malaria vectors was emphasized in the context of larval control as part of a broader vector management program [23].\u003c/p\u003e\n\u003carticle\u003e\u003cem\u003e2.3 Data analysis\u003c/em\u003e\u003c/article\u003e\n\u003cp\u003eThe section shows the association between malaria incidence and climatic variables. After the data were cleaned with Excel, the data were imported and analysed using Stata version 14.0. In the bivariate analysis, logistic regression was used to assess the association between categorical variables and outcomes. Variables that were statistically significant at the 5% level. This study intends to compare and establish the effects of climatic change on malaria incidence rates over a ten-year period from 2010 to 2020 in selected provinces of Luapula and southern provinces in Zambia. Furthermore, this chapter discusses the demographic characteristics of malaria incidence and how changes in climatic and environmental variables influence the incidence of malaria.\u003c/p\u003e\n\u003cp\u003e.\u003c/p\u003e"},{"header":"III. Table","content":"\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTABLE I\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelations\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eamong Meteorological Factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMansa district of Luapula Province\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAutocorrelation among Meteorological factors in Mansa district of Luapula Province\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" colspan=\"2\"\u003e\n \u003cp\u003eRH at 2 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eMaximum Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eMinimum Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eMean Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eMinimum \u0026nbsp;Precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003eAnnual Rainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003eRH at 2 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e-.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e.657\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e.657\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003eMax Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e-.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e-.679\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e-.667\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003eMin Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e-.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e-.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-.806\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003eMean Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-.806\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e-.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e-.456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003eMin Precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e.657\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e-.679\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e.998\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003eAnnual Rainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e.657\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e-.667\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e.998\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"bottom\"\u003e\n \u003cp\u003e*. Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"bottom\"\u003e\n \u003cp\u003e**. Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMonze District of Southern Province\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eAutocorrelation among \u0026nbsp; \u0026nbsp; Meteorological factors in Monze district of Southern Province\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" colspan=\"2\"\u003e\n \u003cp\u003eRH at 2 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eMaximum Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eMinimum Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eMean Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eMinimum \u0026nbsp;Precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003eAnnual Rainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eRH at 2 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e.813\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.778\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eMax Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eMin Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-.842\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eMean Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-.842\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eMin Precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.813\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.993\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eAnnual Rainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.778\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e.993\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003e**. Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental Human Activities Influences Climate Change and the Malaria Incidence Rates in Luapula and Southern Province of Zambia.\u003c/strong\u003e\u003c/p\u003e"},{"header":"IV. Conclusion","content":"\u003cp\u003eThe study conducted in Luapula and southern provinces of Zambia over a ten-year period from 2010 to 2020 sheds light on the intricate relationships among climate change, environmental factors, and malaria incidence. In Luapula Province, a positive correlation was observed between malaria incidence rates and maximum and minimum temperatures. This positive association reflects the broader trend of climate change, with rising temperatures potentially facilitating the spread of malaria (Ashton et al., 2017). Furthermore, the negative correlation between malaria incidence and daily precipitation indicates that reduced rainfall is linked to increased malaria incidence. This aligns with the adverse effects of climate change, which include decreased precipitation and its impact on malaria transmission (World Health Organization, 2008). This study also highlights the influence of climate change on rainfall patterns, which has led to fluctuations over the years in Luapula Province. These fluctuations may be attributed to changing climate conditions, which affect malaria dynamics.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(i) The effects of climate change on the malaria incidence rate in relation to rainfall and temperature variation patterns in Zambia over a ten-year period beginning in 2010\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Luapula Province\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of the Pearson correlation test revealed a strong positive (0.043) correlation between the malaria incidence rate and maximum temperature. There was also a positive correlation (0.152) between the malaria incidence rate and minimum temperature. Similarly, there was a negative correlation (-0.623) between the malaria incidence rate and daily precipitation (mm) corrected between 2010 and 2020. Furthermore, analysis also indicated that there was a strong association between the malaria incidence rate and daily precipitation corrected between 2010 and 2020, with a p value of 0.041, which was statistically significant at 0.05. The data analysis also revealed a negative (-0.608) correlation between the malaria incidence rate and annual rainfall. The Pearson correlation coefficient also revealed a strong association between the malaria incidence rate and annual rainfall, with a p value of 0.047 indicating statistical significance at 0.05.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudies, such as those conducted by Ashton et al. (2017) and the World Health Organization (2008), have also highlighted the positive relationship between temperature and malaria incidence. Increasing temperatures can extend the geographical range of malaria vectors and accelerate the spread of the malaria parasite within mosquitoes, potentially leading to increased malaria transmission. The inverse relationship between malaria incidence and daily precipitation has been documented in previous studies, emphasizing the role of water availability in creating suitable breeding habitats for mosquito vectors. Reduced precipitation can lead to the creation of stagnant water bodies that are ideal for mosquito larvae to thrive, as mentioned by the World Health Organization (2008). The strong association between malaria incidence and annual rainfall, as indicated by the significant p value, underlines the importance of understanding long-term precipitation patterns in malaria-endemic regions. Such patterns can provide insights into the likelihood of malaria outbreaks, especially in areas where rainfall is a key driver of mosquito breeding, as supported by research on climate and malaria transmission dynamics (Ashton et al., 2017; World Health Organization, 2008). As a result, this study further revealed a high fluctuating trend in rainfall that was recorded from 2010 to 2020, with a 1058.0 mm mean annual rainfall, and the maximum total rainfall was observed in 2017 (1434.4 mm), while the minimum rainfall was observed in 2016 (801.6 mm). The results obtained showed an association between monthly malaria cases and the climatic/meteorological variables temperature and rainfall.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSouthern Province\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Pearson correlation coefficient was calculated to determine the relationship between malaria incidence rates and meteorological/climatic factors. The results revealed that there was a negative correlation between the relative maximum temperature (-0.263) and minimum temperature (-0.276). On the other hand, there was a positive correlation (0.084) between the mean temperature and malaria incidence rate. There was also a strong positive correlation between malaria incidence rates and rainfall. The results from the Pearson correlation test between malaria incidence rates and daily precipitation corrected (mm/day) from 2010 to 2020 revealed that there was a positive correlation between malaria incidence rates and daily precipitation corrected (mm/day). Additionally, there was no statistically significant association between the Malaria incidence rate and minimum rainfall (mm/day), with a p value of 0.719 indicating a significant difference of 0.05.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis negative relationship indicates that as temperatures increase, the incidence of malaria tends to decrease. These findings are consistent with those of several previous studies, which have shown that extreme heat can negatively impact mosquito survival and reduce malaria transmission (Shimaponda-Mataa et al.,2017). These findings contribute to the growing body of research investigating the links between meteorological and climatic factors and malaria incidence. These results reinforce the importance of considering these factors in malaria control strategies and adapting interventions to the specific weather conditions of different regions. Understanding the complex relationships between temperature and rainfall variables and malaria transmission is critical in developing effective and targeted measures for combating this disease, particularly in the face of climate change. The significance of these findings lies in their potential to inform evidence-based strategies for malaria prevention and control.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(II) The role of climate change in the observed increase or decrease in Malaria in Zambia over a ten-year period from 2010 to 2020\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data analysis in this study revealed that there was a higher incidence of floods recorded in Luapula Province and a high incidence of droughts in the southern province of Zambia as of 2020. The results of the averaged VHI for Luapula Province showed that there was a positive correlation between land and vegetation. Furthermore, the data showed that there was a fluctuating trend in vegetation availability over the ten-year period. However, Luapula Province is more prone to harboring larvae for mosquitoes with favorable or suitable environmental conditions for survival, whereas in the southern province, the high number of droughts recorded has impacted the vegetation health index, leading to harsh environmental conditions for the survival and harboring of mosquito larvae.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the data generated, the Vegetable Health Index for the yearly percentage of drought area for the province showed that in 2019, the southern province had the highest (64%) percentage of drought recorded during a ten-year period. In Luapula Province, the highest percentage recorded was 31% of the yearly percentage of drought area for the province, and drought was recorded in 2012.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilarly, Nygren et al. (2014) collected weekly malaria data from 2011 to 2013 and used the normalized difference vegetation index (NDVI), night surface temperature, rainfall, and night dew point to model health facility-level malaria transmission within the southern province. Their results revealed a significant association with environmental variables (dewing point, temperature, and NDVI) across low, moderate, and high transmission zones (Nygren et al., 2014). This study is important because of its contribution to the growing body of knowledge linking climate change, environmental conditions, and malaria transmission. By elucidating the distinct climate-related challenges faced by different regions within Zambia, this research provides critical insights for malaria control and adaptation strategies. Recognizing the impact of floods, droughts, and vegetation health on mosquito breeding and malaria transmission is vital for tailoring interventions to specific local conditions and effectively combating this disease in the context of a changing climate.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5.3 Role of environmental land use combined with climate change in the observed increase or decrease in Malaria incidence over a ten-year period from 2010 to 2020 in Zambia\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study revealed that in 2010, the southern province had 216 kha of tree cover, extending over 2.5% of its land area. In 2021, 1.96 kha of tree cover was lost, equivalent to 564 kt of CO₂ emissions. In 2010, Luapula had 2.13 Mha of tree cover, extending over 43% of its land area. In 2021, 20.6 kha of tree cover was lost, equivalent to 7.81 Mt of CO₂ emissions.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, data obtained from ZamStats (2022) reveal that in Luapula Province, 194,520 people live in urban areas, while the majority of the population (797,407) lives in rural areas. In the southern province, 392,175 people live in urban areas, and 1,197,751 people (majority) live in rural areas.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs of 2020, many settlements in Luapula Province have an estimated population density of 20 per km\u0026sup2;; the total area of the province is 50,567 km\u0026sup2; and characterized by its rural nature. In 2010, the total area of the southern province was 85,283 km\u0026sup2;, and the population density was 18.60 per km\u0026sup2; (Grid3, 2022). These data imply that Luapula Province has a large surface area and vegetation that provide suitable conditions for mosquito larvae to survive, and the majority of the population lives in rural areas of the province and has more exposure to mosquito bites, as these areas have good VHIs that are between 40 and 60 over a ten-year period.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, in the southern province, there is a poor vegetation health index with high records of droughts due to urban settlements and high cases of deforestation, which have led to poor environmental conditions that have affected the survival of mosquito larvae in these areas.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, an example of a district-wide study was conducted in the Nchelenge district in Luapula Province using household-level cross-sectional surveys conducted every two months between 2012 and 2015 (Pinchoff et al., 2015, 2016).\u003c/p\u003e"},{"header":"V. Conclusion","content":"\u003cp\u003eIn conclusion, a study conducted in Luapula and the southern provinces of Zambia from 2010 to 2020 highlights the intricate relationships among climate change, environmental factors, and malaria incidence. The findings revealed a positive correlation between malaria incidence rates and maximum and minimum temperatures, reflecting the broader trend of climate change. Additionally, a negative correlation was observed between malaria incidence and daily precipitation, indicating that reduced rainfall is linked to increased malaria incidence. This study also emphasized the influence of climate change on rainfall patterns, which has led to fluctuations over the years in Luapula Province.\u003c/p\u003e\n\u003cp\u003eThe results further revealed that there was a strong association between the malaria incidence rate and annual rainfall, highlighting the importance of understanding long-term precipitation patterns in malaria-endemic regions. These findings support previous research showing a positive relationship between temperature and malaria incidence, as rising temperatures can facilitate the spread of this disease. The inverse relationship between malaria incidence and daily precipitation is also consistent with the findings of previous studies, emphasizing the role of water availability in mosquito breeding habitats.\u003c/p\u003e\n\u003cp\u003eThe study\u0026apos;s significance lies in its potential to inform evidence-based strategies for malaria prevention and control. By considering meteorological and climatic factors, interventions can be tailored to specific weather conditions, allowing for more effective measures against malaria transmission. Furthermore, the study highlights the impacts of floods, droughts, and vegetation health on mosquito breeding and malaria transmission, providing insights for adaptation strategies in the face of climate change.\u003c/p\u003e\n\u003cp\u003eOverall, this research contributes to the growing body of knowledge on the complex relationships among climate change, environmental conditions, and malaria transmission. These findings underscore the need for proactive measures to address these factors in malaria control efforts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch1\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/h1\u003e\n\u003cp\u003eI am greatly thankful to my God for seeing me through this programme. I extend my sincere gratitude to my Principal Supervisor Dr. Rosemary Ndonyo. We are grateful for her tireless guidance during this research and for her cosupervisor, Mr. Allan Mbewe. I am thankful to the Populations Studies Dept. postgraduate lecturers and technical staff for their support during the period of my study. Many thanks to the Ministry of Health (MoH) for allowing us to access the malaria data and for granting me permission to utilize part of the collected programme data for my dissertation write-up. I am greatly indebted to the National Malaria Control Centre\u003c/p\u003e\n\u003ch1\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/h1\u003e\n\u003cp\u003eBackground for Manuscript: \u0026quot;The Effects of Climate Change on Malaria Incidence Rates in Luapula and Southern Province of Zambia: A Retrospective Trend Analysis Over a Ten-Year Period (2010-2020)\u0026quot;\u003c/p\u003e\n\u003cp\u003eJoshua Kanjanga Phiri\u003c/p\u003e\n\u003cp\u003eContact Information: Address: P.O. Box 32696 Lusaka, Zambia Phone: +260-972-580-306 Email: \u003ca href=\"mailto:[email protected]\" target=\"_blank\"\[email protected]\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eProfile: I am a dedicated and highly motivated individual seeking to establish my career in demography, statistics, analytics, and research. With approximately 8 years of experience spanning education and work, I have honed my skills in demographic methods, advanced research, statistical analysis, and the application of various software tools. My passion lies in contributing to sustainable development through innovative and practical solutions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWHO. (2019). Climate change and health.\u003c/li\u003e\n \u003cli\u003eWHO. (2014). World malaria report.\u003c/li\u003e\n \u003cli\u003eWHO. (2019). Global Technical Strategy for Malaria 2016-2030.\u003c/li\u003e\n \u003cli\u003eWHO. (2019). World malaria report.\u003c/li\u003e\n \u003cli\u003eWHO. (2015). Climate, landscape, and housing structure in vector-borne disease.\u003c/li\u003e\n \u003cli\u003eRiedel et al. (2010). Climate-based models for understanding and forecasting malaria epidemics.\u003c/li\u003e\n \u003cli\u003eMoH. (2015). Ministry of Health report.\u003c/li\u003e\n \u003cli\u003eMasaninga et al. (2012). Impact of malaria control efforts in Zambia.\u003c/li\u003e\n \u003cli\u003e[Thomson et al., 2005]. Malaria epidemiology in an area of stable transmission in western Kenya: prospects for control.\u003c/li\u003e\n \u003cli\u003e[Abiodun et al., 2016]. Modelling the influence of climate variability on malaria transmission in two climatic zones in Nigeria.\u003c/li\u003e\n \u003cli\u003e[Blanford et al., 2013]. Implications of temperature variation for malaria parasite development across Africa.\u003c/li\u003e\n \u003cli\u003e[Col\u0026oacute;n-Gonz\u0026aacute;lez et al., 2016]. 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College Station, TX: StataCorp LP.\u003c/li\u003e\n \u003cli\u003eJackson, M. C., \u0026amp; Kongoli, C. (2014). Spatially Explicit Modelling of Malaria in Rural Zambia. PLoS ONE, 9(11), e114168. \u003ca href=\"https://doi.org/10.1371/journal.pone.0114168\"\u003ehttps://doi.org/10.1371/journal.pone.0114168\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eSmets, S., Mwalubunju, A., Van geertruyden, J. P., \u0026amp; Vansteelandt, S. (2013). A matched case\u0026ndash;control study of the association between childhood malaria and acute renal failure in Malawi. PLoS ONE, 8(5), e63668. \u003ca href=\"https://doi.org/10.1371/journal.pone.0063668\"\u003ehttps://doi.org/10.1371/journal.pone.0063668\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eBennett, A., Yukich, J., Miller, J. M., Keating, J., Moonga, H., Hamainza, B., \u0026amp; Steketee, R. W. (2016). The relative contribution of climate variability and vector control coverage to changes in malaria parasite prevalence in Zambia 2006\u0026ndash;2012. Parasites \u0026amp; Vectors, 9(1), 431. \u003ca href=\"https://doi.org/10.1186/s13071-016-1703-4\"\u003ehttps://doi.org/10.1186/s13071-016-1703-4\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eMolineaux, L. (1997). The epidemiology of human malaria as an explanation of its distribution, including some implications for its control. In Evolutionary Biology of Transient Unstable Populations (pp. 453\u0026ndash;488). Springer.\u003c/li\u003e\n \u003cli\u003eAshton, M., et al. (2017). [Full Title of Ashton et al. Article], [Journal Name], [Volume Number](if applicable), [Page Numbers], DOI or URL (if applicable).\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. (2008). [Full Title of WHO Report or Publication], [Publication or Report Name], [Publication Date], URL (if applicable).\u003c/li\u003e\n \u003cli\u003eAshton, M., et al. (2017) - World Health Organization. (2008). [Full Title of Ashton et al. Article], [Journal Name], [Volume Number](if applicable), [Page Numbers], DOI or URL (if applicable).\u003c/li\u003e\n \u003cli\u003eShimaponda-Mataa, N., et al. (2017). [Full Title of Shimaponda-Mataa et al. Article], [Journal Name], [Volume Number](if applicable), [Page Numbers], DOI or URL (if applicable).\u003c/li\u003e\n \u003cli\u003eNygren, D., et al. (2014). [Full Title of Nygren et al. Article], [Journal Name], [Volume Number](if applicable), [Page Numbers], DOI or URL (if applicable).\u003c/li\u003e\n \u003cli\u003ePinchoff, J., et al. (2015, 2016). [Full Title of Pinchoff et al. Article], [Journal Name], [Volume Number](if applicable), [Page Numbers], DOI or URL (if applicable).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Norwegain Programme for capacity Development in Higher Education and research for Development ","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":"climate change, malaria incidence, Zambia, prevalence, climatic factors, environmental factors, retrospective comparative analytical approach, case records, temperature, seasonal variations, land use activities, rainfall, drought, malaria transmission, mosquito larvae, demographic data, urban‒rural divide, children under five, malaria control strategies","lastPublishedDoi":"10.21203/rs.3.rs-3897357/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3897357/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the impact of climate change on the incidence of malaria in Zambia. The study focused on variations in prevalence influenced by climatic and environmental factors. This study adopted a retrospective comparative analytical approach utilizing 157 case records from each province. The survey investigated temperature, seasonal variations, and land use activities. The findings revealed a fluctuating trend in rainfall from 2010 to 2020, with a significant association between malaria incidence and rainfall (p value=0.041). In 2019, southern provinces experienced the highest percentage of drought (64%), resulting in reduced malaria transmission due to unfavorable environmental conditions for mosquito larvae. Demographic data highlight an urban‒rural divide, with Luapula Province exhibiting a greater number of malaria cases among children under five years of age. These results emphasize the critical role of climate change and local factors in malaria transmission dynamics.\u003c/p\u003e","manuscriptTitle":"The Effects of Climate Change Atmospheric Tempreture, Rainfall Pattern and Land Use Malaria Incidence Rates: A Retrospective Trend Analysis Over a Ten-Year Period 2010–2020 in Luapula and Southern Province of Zambia","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2024-01-30 18:31:52","doi":"10.21203/rs.3.rs-3897357/v2","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}},{"code":1,"date":"2024-01-29 16:36:19","doi":"10.21203/rs.3.rs-3897357/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":"2dcb87ad-55ec-43f6-9eb4-757615c33d13","owner":[],"postedDate":"January 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28467109,"name":"Epidemiology"}],"tags":[],"updatedAt":"2024-01-29T16:36:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-30 18:31:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-3897357","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3897357","identity":"rs-3897357","version":["v2"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}