Skip to main content
Download PDF

Malaria, relationship with climatic variables and deforestation in Colombia, Latin America and the Caribbean from 2000 to 2020: a systematic review

Malaria Journal volume 23, Article number: 347 (2024) Cite this article

Abstract

Background

This systematic review investigates the relationship between malaria incidence, climate variables, and deforestation in Colombia, Latin America, and the Caribbean from 2000 to 2020. Malaria, a significant public health issue in these regions, is influenced by ecological factors including climatic conditions and environmental changes, such as deforestation.

Methods

The review employs a comprehensive search strategy across PubMed, Web of Science, Embase, Scopus, Cochrane, and Scielo databases. It applies strict inclusion and exclusion criteria to ensure the relevance and quality of selected studies, focusing on analysing the relationship between climate variables, deforestation, and malaria incidence.

Results

Twenty-four articles were included in this review, fourteen of which assessed the relationship between climatic variables and malaria and ten between deforestation and malaria. The analysis reveals a nuanced understanding of malaria dynamics. A significant finding is the seasonal effect of climatic variables on malaria incidence. The study notes that increased rainfall is positively correlated with malaria incidence. Similarly, warmer temperatures are associated with increased malaria risks, and malaria rates can change by 10% to 80% for every degree of temperature increase, after adjusting for altitude. The impact of deforestation on malaria is complex, with positive and negative correlations observed, depending on the remaining forest cover.

Conclusions

The review highlights the multifaceted nature of malaria transmission, emphasizing the need for integrated approaches that consider both environmental and health perspectives. It underscores the importance of understanding the complex relationships between malaria incidence, climate variables, and deforestation.

Background

Malaria is one of the most important vector-borne diseases worldwide, with around 247 million new cases and 619,000 deaths reported in 2023 in 85 endemic countries [1]. There are currently five species of Plasmodium parasites that cause malaria in humans, of which Plasmodium. falciparum and Plasmodium vivax cause the majority of cases worldwide.

After the Africa and Asia regions, the Americas region is one of the most affected by the disease, where 18 of the 21 countries in the region are endemic for the disease. More than three-quarters of all cases occur in the Bolivarian Republic of Venezuela, Brazil, and Colombia. In the Americas region for the year 2021, 520,000 cases of malaria and 120 deaths were recorded, 74% of the reported cases were caused by P. vivax and 26% by P. falciparum. Compared to 2015, there was an increase of 8% in the number of cases and a 26% decrease in deaths [2].

The transmission of malaria is affected by ecological factors that influence the distribution and abundance of mosquito vectors and parasites. These factors can be classified as either extrinsic—including environmental components such as temperature, humidity, precipitation, and altitude, as well as social, cultural, economic, and political factors—or intrinsic, which involve interactions between humans, the vector, and the parasite [3]. Due to their geographical, environmental, and social characteristics, countries in Latin America and the Caribbean are particularly susceptible to the transmission and endemicity of malaria.

The epidemiology of malaria is complex and multifactorial, and understanding the association between climate, land use change, and malaria is essential. It depends on the ecology of the main prevalent vector species, the biology of the causative organism (Plasmodium), the resistance and immunity of the host, and climatic factors such as temperature, precipitation, humidity, and anthropogenic changes [4,5,6]. Regarding climatic factors, a positive association has been described between malaria incidence with seasonal rains causing an increase in vector abundance and greater malaria transmission in risk areas [7,8,9]. Additionally, climatic events such as El Niño and La Niña significantly influence climate on a broad scale, impacting temperature, precipitation patterns, and extreme weather events. El Niño typically results in warmer and drier conditions in some regions, while La Niña brings cooler and wetter conditions. These events can alter mosquito breeding sites and affect malaria transmission dynamics, necessitating consideration of these phenomena in studies examining climate and malaria in Latin America and the Caribbean [5, 6].

Deforestation is another factor that has been found to increase malaria cases, especially in the early stages of deforestation in the interior of the Amazon. This is because deforestation promotes the breeding habitat of the mosquito vector, such as Anopheles darlingi, which increases the survival and rate of human bites. However, as the forest is lost, the effects are attenuated [10, 11].

It is important to note that deforestation and malaria have bidirectional causal relationships, where deforestation increases malaria through ecological mechanisms, and malaria reduces deforestation through socioeconomic mechanisms. The strength of these relationships depends on the stage of land use transformation. More research is needed to fully understand the implications of land use change on malaria risk in optimal transmission [11,12,13]. Studies examining deforestation and its impact on malaria often employ a variety of methodologies. The variability in methods used to study deforestation, as highlighted in Tucker-Lima, includes satellite imagery analysis, field surveys, and modelling approaches [3]. These methodological differences can lead to varying conclusions about the impact of deforestation on malaria transmission, emphasizing the need for standardized approaches in future research.

Climatic factors have been well established as determinants of malaria transmission. However, the relationship between deforestation and land use to malaria is a more emergent field and there is no systematic review of his role as a contributor to malaria in Latin America. It is also unclear whether studies linking those factors to malaria transmission have taken into account the potential confounding effect of biological and climate factors.

The objective of this systematic review was to identify the characteristics of published knowledge for malaria and its relationship with climatic variables and deforestation in Colombia, Latin America, and the Caribbean, from 2000 to 2020.

Methods

Databases and techniques for searching published studies

To investigate the impact of "climate variables" and "deforestation" on "malaria," a thorough search was conducted across six databases: Cochrane, PubMed, Scielo, Scopus, Embase, and Web of Science, covering the period from 2000 to 2020. The search employed a combination of controlled vocabulary (MeSH terms) and free terms, including spelling variations, synonyms, acronyms, and truncations. Additionally, tags were added in the title and abstract fields, with the use of proximity operators (ADJ) and Boolean operators (OR, AND) to broaden the search criteria. To ensure a comprehensive scope, articles in Spanish, English, and Portuguese from all Latin American and Caribbean countries were included, as indicated in Table 1.

Table 1 Search strategy
Full size table

Inclusion and exclusion criteria for published literature

Articles published in the period described, in English, Spanish, or Portuguese, were taken into account, selecting the titles and summaries of all the references obtained in the search. Studies were evaluated and classified by a team member into three categories.

Category 1, included articles: studies that contained original primary or secondary source information on malaria, climate variables, and deforestation. Category 2, included reviews. Items in Category 3 were excluded, comprising all articles on malaria that did not contain environmental data and/or deforestation or that, although including these variables, did not analyse associations between malaria and these variables. Descriptive ecological studies, case reports, editorials, letters of opinion, letters to the editor, as well as basic and/or clinical research were excluded from the search.

Full-text retrieval was performed on all references of relevant articles identified in Set 6 and Set 7 (see Table 1) that belonged to Category 1 and 2, to identify potentially relevant data.

The articles selected for full text reading were analysed and classified using the above criteria. Articles classified in Category 1 were selected and chosen for data extraction. A review of the bibliographic references of the articles classified in Category 2 was carried out to identify the articles that were not captured in the search strategy and that were potentially eligible.

Structured abstraction of relevant information

Eligible articles were distributed among the reviewers for the abstraction of relevant information. The variables extracted from the selected studies were: year of publication, year of the study, country of the study, name of the main author, name of the journal and digital object identifier DOI, title of the article, type of study, type of model employed, sources of environmental and deforestation data and main results. If the eligibility of an article was questioned, a second reviewer or all reviewers analysed the article to reach a consensus.

Information analysis

A descriptive analysis of the frequency of articles by country and subregion of Latin America and the Caribbean was carried out; as well as by the type of design employed, using the classification of ecological studies described by Morgenstern et al. [14], where these designs can be classified along two dimensions: the method of exposure measurement and the process of grouping. For the first dimension, a study is termed exploratory if the main exposure of interest is not measured, and analytic if the main exposure variable is measured and included in the analysis. This dimension is a continuum, as most ecological studies do not aim to test a single hypothesis. For the second dimension, the groups in an ecological study can be classified by place (multiple-group design), by time (time-trend design), or by both place and time (mixed design) [14]. The environmental and deforestation variables used in the different analyses and the types of statistical analyses used to relate patterns of malaria were described. The environmental variables found most frequently associated with epidemiological changes in malaria were identified and the strength of such associations was described. Additionally, the relationship between deforestation was described, as was the type of analysis, and it was established whether this association came from an analysis that controlled for possible confounder bias produced by other variables.

Results

Search strategy

Table 1 outlines the search strategy utilized in the databases examined for this review, with the outcomes of this strategy depicted in Fig. 1. After applying the combinations described in Set 4, a total of 900, 2140, 5606, and 55 articles were found in PubMed, Web of Science, Scopus, and Embase, respectively. Subsequently, the combination of climatic variables (Set 5) was applied, which obtained 92, 98, 411, and 6 results. After that, the “deforestation” variable (Set 6) was applied, and 20, 35, 67, and 3 articles were found. Finally, combining climate variables with deforestation (Set 7), 8, 9, 24, and 1 studies were obtained in those databases. These last three combinations (Set 5, Set 6, and Set 7) were considered for the selection by title. While for the Cochrane database, four articles were found using the combination of Set 4. No results were found for any combination in the Scielo database.

Fig. 1
figure 1

Search strategy flow diagram. Search strategy flow from databases

Full size image

Characteristics of the reviewed articles

From the PubMed, Web of Science, Scopus and Embase databases, a total of 524 articles were initially retrieved for consideration in this review. After reviewing the titles and abstracts and removing duplicates, 469 of these articles were excluded, 382 because they treat subjuct outside the main objetives of the review (vector biology, biomedical aspects of malaria, among others). Additionally, 29 articles were topic reviews, 21 were case reports, 9 were editorial letters, and 26 were excluded because they were specific to countries that were not within the focus of this study and, 2 were book chapter as show in detail in Fig. 2

Fig. 2
figure 2

PRISMA flow diagram. Study flow from literature search to data extration and analysis

Full size image

For full-text reading, 55 articles published between 2000 and 2020 were recovered. After reading the full text, 31 of the 55 studies were excluded, 18 because they did not use primary or secondary data, and 13 because did not establish the relationship between malaria and environmental variables and/or deforestation. The years 2011, and 2018 had the highest bibliographic production with four and five articles respectively, as shown in detail in Fig. 3. The study periods evaluated in the articles varied between 1 to 46 years as seen in Fig. 4. Twenty four studies were carried out in the Latin American Subregion (Ecuador, Bolivia, Brazil, Colombia, Peru, Suriname, Venezuela); two in the subregion of Central America (Panama).

Fig. 3
figure 3

Number of publications per year. Bar-chart showing the total number of included studies

Full size image
Fig. 4
figure 4

The study periods of studies analysed. Bar-chart showing the study period of the articles included for full-text reading by year of publication

Full size image

The most frequently used study designs were: 22 ecological studies and 2 cohort studies. According to the classification of ecological studies by Morgenstern et al. [14], the types of ecological studies found were 5 exploratory studies and 17 analytical studies: (6 multiple groups, 4 mixed, and 7 time series). The 24 articles were included, of which 22 were done in the Latin American subregion (Brazil, Colombia, Perú, Ecuador, Guyana, French Guiana, Suriname, and Venezuela) and 2 in Panama.

Data source of the included articles

The main sources of information used in the selected articles after applying the inclusion and exclusion criteria were national databases for the malaria incidence variable and data from meteorological stations for the climatic variables as shown in Table 2.

Table 2 Data source
Full size table

Types of studies and statistical methods used for malaria risk analysis

The study designs used were: twenty-two ecologic studies and two cohorts [35, 37]. According to Morgenstern classification, five of the ecological studies were exploratory [23, 25, 28, 30, 31] and sixteen were analytic, seven studies were time series [20, 27, 29, 32, 33, 36, 38], six studies were multiple groups [11, 16,17,18,19, 24], and four mixed [15, 21, 22, 26].

Sixteen of the selected studies had malaria incidence as their main outcome and eight had malaria cases. The statistical approaches most frequently used for the analysis of this variable with the predictor variables were: negative binomial regression, Poisson regression, multivariate regression, Spearman's rank correlation coeffiecient, and Lineal regression as shown in Table 3.

Table 3 Statistical methods used for malaria risk analysis
Full size table

Covariates analysed to estimate malaria risk

The primary outcome studied in the articles was annual malaria incidence in twelve studies, total malaria cases in seven studies, monthly malaria incidence in three articles, and weekly malaria incidence in two studies. It should be noted that one article also applied similar methods to infectious diseases other than malaria. Regarding the predictor variables, precipitation and temperature were the most studied covariates, followed by land cover and El Niño-Southern Oscillation (ENSO). Twelve studies were carried out at the municipal level, eight at the district level and 4 at the country level. See Table 4.

Table 4 Variable used in malaria risk
Full size table

Relationship between climatic variables and malaria incidence

The articles included in this review show that the relationship between climatic variables such as precipitation, temperature, the El Niño phenomenon (ENSO), river level, among others, and the incidence of malaria has been widely studied in Latin America and the Caribbean.

The findings of the reviewed studies indicate that rainfall has a positive correlation with malaria rates in Colombia [15], Ecuador [15, 27], French Guyana [15, 16, 34, 35], Guyana [15], Peru [15, 26], Brazil [29, 32], and Venezuela [15].

One study noted that floods can trigger malaria epidemics in the coastal regions of northern Peru, while droughts in Colombia and Guyana favor the development of epidemics, and in Venezuela, droughts delay epidemics by a year. These variations underline the complex interaction between climate and malaria [15, 16]. Another study highlighted that regions such as Brazil, French Guiana, and Ecuador did not show a clear link between climate and malaria, suggesting that non-climatic factors such as fumigation, availability of medicines, and population migration play a larger role [15].

Several studies have highlighted the correlation between the El Niño phenomenon and the incidence of malaria [15, 16, 20, 29, 32, 36, 38]. In Colombia, a study found that a 1 °C change in ENSO variables could result in a 17.7% to 9.3% change in expected malaria cases. In specific regions, such as the Pacific and Atlantic regions of Colombia, this change could reach 22.9% and 23.4%, respectively, for a 1 °C change in the ENSO variables [20]. This important statistical relationship highlights the profound impact of the El Niño phenomenon on the incidence of malaria in these areas of this country.

The authors of several papers were able to establish some seasonal patterns in malaria incidence, with peaks typically occurring in January–February and July–August. A study focused on French Guiana found that malaria incidence showed a clear seasonal pattern, with significant increases during these peak periods. Another study noted that intra-annual variability in malaria cases often coincides with rainy seasons, contributing to higher transmission rates during these months [16, 34, 35]. This seasonal influence is crucial for planning and implementing effective malaria control measures.

Studies that analysed the temperature variable found that temperature changes significantly affect the risk of malaria. One study shows that for every degree increase in temperature, the incidence rate of malaria could increase by between 10 and 80%, particularly in higher altitude regions where temperature fluctuations are more pronounced [18]. Additionally, it was found that warmer temperatures are positively associated with a higher incidence of malaria, especially during December and January, the warmest months in many malaria-endemic countries in Latin America and the Caribbean [15, 21, 22]. These findings highlight the fundamental role of temperature in malaria transmission dynamics.

Studies employing multivariate analysis explored the combined effects of various climatic and environmental factors on malaria incidence. One study used boosted regression tree (BRT) models to identify high malaria-risk areas and found significant variations between different areas in terms of malaria risk for P. vivax and P. falciparum. The highest risk areas identified included Zone II (Loreto) with 56.5% high risk for P. vivax and 27.8% for P. falciparum. Adiotionally, this study observed that precipitation had a stronger effect on P. falciparum, while higher soil temperatures decreased the incidence of P. vivax [29]. Table 5 shows the main results of the reviewed articles that evaluated the relationship between malaria incidence and climatic variables.

Table 5 Main finding between malaria incidence and climatic variables
Full size table

Relationship between climatic variables and/or deforestation and malaria incidence

Brazil was the country with the most articles on this subject (n = 8). One study, conducted between 1997 and 2000, found that deforestation increase the risk of malaria by 33%, RR = 1.33, for every 4.3% increase in deforestation. They observed the deforestation in 1997 and during 2000–2006. In 1997, deforestation was concentrated in and around Mâncio Lima, with significant changes observed between 1997 and 2006, while the deforestation between 2001–2006 did not show a significant association with malaria risk, highlighting the importance of historical deforestation patterns in predicting current malaria risk [28].

Achcar found that the deforestation rate in the Brazilian Amazon region had a behaviour similar to the pattern of malaria incidence, increasing from 1999 to 2004 and then decreasing. This temporal alignment highlights the complex interaction between environmental and epidemiological trends [33]. Terrazas found that during the study period, the crude rate in Amazonas was 4,142 cases per 100,000 inhabitants, with higher rates in Manaus and Río Negro compared to lower Amazonas. Additionally, 8% of malaria cases occurred in the indigenous population, where the highest incidence rates were observed. The multiple regression analysis showed a significant positive correlation between the average annual rate of deforestation and the incidence of malaria, explaining 35% of the variation in the incidence of malaria [31].

Research has shown that deforestation in one area can influence malaria rates in surrounding municipalities. Santos established that for every 100 km2 of deforestation, 7.26 additional malaria cases are registered per thousand inhabitants in the municipalities of the Brazilian Amazon. Furthermore, deforestation in neighbouring municipalities generates an average increase of 4.52 malaria cases per thousand inhabitants. The total effect of deforestation, considering direct and indirect effects, is 11.78 malaria cases per thousand inhabitants per 100 km2 of deforested forest [24]. Hanh in 2014 found that in timber-producing states, where 90% of deforestation has occurred, areas with a lower percentage of selectively logged had higher rates of malaria than areas with more selectively logged percent [30].

MacDonald explained that the annual loss of forests within a municipality leads to a significant increase in malaria incidence (β = 0.327, SE = 0.145, P = 0.024). Specifically, a 10% increase in deforestation would result in a 3.27% increase in malaria incidence, which translates to approximately 9,980 additional cases in 2008. The study found that in the "interior" regions of the Amazon, deforestation significantly increases the incidence of malaria, particularly for P. falciparum (β = 0.716, SE = 0.323, P = 0.027). However, no significant relationship was found in the "outer" regions where most of the forest has already been cleared.

Other studies point out that the relationship between deforestation and malaria is not always simple. Studies indicate that extreme levels of deforestation could reduce the incidence of malaria due to the degradation of mosquito habitats. For example, as deforestation exceeds 363 km2, the incidence of malaria tends to decrease. Furthermore, each 10% increase in municipal forest cover is associated with 4.32 additional cases of malaria per thousand inhabitants of the municipality. Population density is negatively related to malaria cases, possibly due to increased urbanization and reduced mosquito exposure. Furthermore, a 0.1-degree increase in average annual temperature results in 7.7 additional malaria cases, although no significant statistical relationship was found between rainfall and malaria cases [24].

The impact of forest fragmentation on malaria incidence is also significant. Smaller and more numerous forest patches are correlated with increased malaria cases, particularly patches smaller than 5 km2, which showed a correlation with malaria cases (0.81; P < 0.005). The regression analysis indicated that each km2 of deforestation corresponded to an increase of 27 new malaria cases (km2 = 0.78; F1,10 = 35.81; P < 0.001), while each km2 of impacted forest corresponded to an increase of 16 new cases. The statistical correlation between average monthly malaria cases and the three levels of forest use considered in this study differed, with a positive correlation for malaria and deforestation (r = 0.80; P = 0.002) [25].

Feged-Rivadeneira (2018) explored the relationship between spatial clusters, ethnicity, and deforestation alerts reported in the Colombian Environmental Information System (SIAC). They found two patterns of infection: an endemic one produced by P. vivax mainly in the indigenous and Afro-descendant population, and another of occupational risk produced by P. falciparum. All P. falciparum groups were located along the Pacific or Lower Cauca basin, suggesting that moderate deforestation favors malaria transmission. Furthermore, they found a high correlation between gold exploitation and malaria in several departments of Colombia [19]. De Oliveira Padilha et al. (2019) studied malaria in Brazil between 2009 and 2015 their findings indicated that in Rondônia, higher levels of deforestation were associated with a reduction in malaria cases, while in Acre, increased deforestation in the Brazilian Amazon is correlated with a higher incidence of malaria, suggesting that landscape modification is a crucial factor in the spread of the disease [22].

Piedrahita analysed how environmental and climatic factors, such as precipitation and NDVI, correlate with malaria incidence in the Colombian Pacific region. Although he did not directly address deforestation, the author suggests that NDVI and precipitation are significant factors in malaria incidence, implying that changes in vegetation cover due to deforestation could influence disease patterns, proposing that less vegetation can lead to an increase in malaria cases, while in areas with more vegetation may have a lower incidence [17].

Table 6  shows the main results of the reviewed articles that evaluated the relationship between malaria incidence, climatic variables, and deforestation.

Table 6 Main findings between malaria incidence, climatic variables, and deforestation
Full size table

Discussion

The review highlights limited exploration of the relationship between deforestation and malaria in Latin American literature, with most studies conducted in Brazil and two in Colombia. Historical deforestation patterns and specific local conditions are crucial for understanding malaria risk, as highlighted by Olson and Achcar [28, 33].

Olson found that malaria risk increased by 33% for a 4.3% increase in deforestation, between 1997 and 2000, while deforestation from 2001 to 2006 did not correlate with malaria risk [28]. Similarly, Achcar observed a temporal alignment between deforestation rates and malaria incidence, increasing from 1999 to 2004 and then declining, underscoring the complex interactions between environmental and epidemiological trends [33]. Baeza et al. propose that malaria risk may follow different trajectories in the short term, showing an increase in malaria incidence in the early stages of deforestation, followed by a decrease as the vector reaches endemic levels [39].

The studies analysed showed that there are significant regional variations in the correlation between deforestation and malaria in the same country, as evidenced in the studies by Terrazas and Santos. Terrazas reported higher rates of malaria in Manaus and Río Negro, with a significant positive correlation between annual deforestation and malaria incidence, explaining 35% of the variation [31]. Santos further showed that for every 100 km2 of deforestation, 7.26 additional cases of malaria were recorded per thousand inhabitants, and neighboring municipalities also experienced an increase in malaria cases due to deforestation, indicating the broad regional impact of deforestation on malaria incidence [24]. While, to Oliveira-Padilha (2019), the relationship between deforestation and malaria can differ even within the same country. In Acre, a positive association between malaria incidence and deforestation was observed, while in Rondônia the opposite behaviour was observed, suggesting that the implications of deforestation on malaria risk are heterogeneous and depend on the level of activity anthropogenic [22].

The interaction between environmental changes and malaria incidence is complex as shown by MacDonald and other authors. MacDonald found that a 10% increase in deforestation resulted in a 3.27% increase in malaria incidence, particularly in the interior regions of the Amazon [11]. However, extreme levels of deforestation could reduce the incidence of malaria due to habitat degradation, and each 10% increase in municipal forest cover is associated with 4.32 additional malaria cases per thousand inhabitants [24]. Additionally, Chaves (2018) noted that smaller and more numerous forest patches were correlated with an increase in malaria cases, further complicating the relationship between deforestation and malaria [25]. Stefani (2013) supports this finding and shows that deforestation is strongly associated with an increased risk of malaria soon after forest clearing, but the risk may decrease as deforestation intensifies due to urbanization or large cultivated areas [10].

However, some studies, such as those by Hahn and Oliveira-Padilha, present contradictory findings on the impact of deforestation on the incidence of malaria. Hahn (2014) found that in timber-producing states, municipalities with low percentages of selectively forest clearing had the highest risk of malaria, while areas with high rates of selective logging had the lowest risk, which suggests that different types of deforestation activities could influence malaria risk. differently [30]. Oliveira-Padilha reported that, in Rondônia, higher levels of deforestation were associated with a reduction in malaria cases, while, in Acre, increased deforestation was correlated with a higher incidence of malaria, which indicating that landscape modification plays a crucial role in the spread of the disease [22]. While, Feged-Rivadeneira (2018) found no relationship between the intense transmission of malaria in endemic areas of the Colombian Pacific and deforestation patterns, but noted a direct association in areas with high gold exploitation, promoting the endemicity of malaria in those regions [19].

Regarding climatic variables, the review of various articles indicates a positive seasonal effect linked to increased precipitation and average temperature in the malaria incidence in Colombia and LAC (Latin America and the Caribbean) between 2000 and 2020. This relationship is evidenced by several studies, highlighting the significant impact of climatic variables on malaria transmission [15, 16, 21, 23, 27, 34, 35].

In French Guiana, four studies conducted during different periods emphasize the region's unique climatic conditions. Known as one of the most humid regions globally, French Guiana experiences annual rainfall between 2,000 and 4,000 mm, with a wet season extending from December to July [40]. These climatic factors significantly influence malaria transmission dynamics in the region [15, 16, 34, 35].

Studies by Hurtado and Amarilis, spanning 1980–2013 and 1980–2002 respectively, reveal differing patterns of malaria epidemics in Panama. Hurtado observed distinct seasonal malaria patterns, with cases primarily occurring in December, January, and February from 1980 to 2002, and significant increases in November and the dry season from 2003 to 2013 [21]. In contrast, Amarilis found maximum malaria cases in September and March without clear seasonality [38].

Multiple studies highlight the correlation between the El Niño phenomenon and malaria incidence. In Colombia, a 1 °C change in ENSO variables could lead to a 17.7% to 9.3% change in expected malaria cases, with even higher impacts in specific regions such as the Pacific and Atlantic, reaching 22.9% and 23.4%, respectively, for a 1 °C change [20]. This significant relationship underscores the profound influence of El Niño on malaria incidence in these areas [15, 16, 20, 29, 32, 36, 38].

Conversely, studies in Brazil, French Guiana, and Ecuador did not show a clear link between climate and malaria, suggesting that non-climatic factors such as fumigation, medicine availability, and population migration play a more substantial role in these regions [15]. These findings highlight the complex interplay of factors influencing malaria transmission and the need for region-specific studies.

The variability in vector species and their seasonal abundance significantly impacts malaria behaviour in the region. Entomological studies reveal that the abundance of Anopheles darlingi mosquitoes increase one month before the rainy season, as observed in French Guyana, Brazil, and Colombia [17, 25, 34]. In Colombia, Anopheles nuneztovari and Anopheles albimanus are associated with high-risk and low-risk malaria categories, respectively [17].

A longitudinal survey in Urabá-Bajo Cauca and Alto Sinú found An. nuneztovari to be the most abundant species at the beginning of the rainy season, while Anopheles pseudopunctipennis was predominant during dry periods [41]. These findings indicate that the seasonal abundance and species diversity of malaria vectors can vary significantly, influencing malaria transmission patterns.

The reviewed articles predominantly used ecological studies (22) and cohort studies (2) [35, 37]. These studies employed various designs, including exploratory and analytical approaches, with time series analysis and multiple group studies being common.

Common statistical methods included negative binomial regression [18, 20, 30], Poisson regression [20, 27, 33], multivariate regression [25, 28, 32], the Spearman rank correlation coefficient [16, 25, 29], and linear regression [17, 25, 31]. Precipitation and temperature were the most studied covariates, followed by land cover and El Niño-Southern Oscillation (ENSO).

Notably, these ecological studies have presented further methodological development, such as the control of extreme values in time series, which reduces estimation bias [42, 43]. This is important for subsequent studies that include these variables in spatiotemporal analyses and propose optimal altitude and temperature ranges for disease transmission, helping suggest more specific control measures.

The ecological studies, the sources of information are mainly secondary sources, in which data validation must be carried out in order to build robust models that allow estimating the dynamics of different diseases transmitted by vectors such as malaria and their behaviour with other variables [44]. Strengthening national surveillance systems and standardizing data collection protocols are essential for accurate malaria risk estimation.

The studies reviewed were primarily conducted in Brazil (11), French Guiana (4), Colombia (4), Ecuador (2), Peru (2), Panama (2), Venezuela (2), Guyana (1), and Suriname (1). This regional distribution shows the need for more comprehensive studies across Latin America.

The studies reviewed indicate regional variations and complex interactions between environmental factors and malaria incidence. Tailored approaches to malaria management in deforested areas are needed, considering the diverse and sometimes contradictory deforestation effects on malaria transmission. Integrating satellite imagery and remote sensing data can improve understanding and management of the spread and intensity of malaria [3, 19, 23].

Deforestation influences malaria incidence, with impacts varying by region, period, and environmental factors. The complex interactions between deforestation and malaria underscore the need for targeted control measures and robust data analysis methodologies to effectively manage malaria risk in deforested areas.

Conclusions

This review of articles examined the relationship between climate variables, deforestation, and malaria incidence in Colombia, Latin America, and the Caribbean between 2000 and 2020, evidencing the following findings.

The relationship between climatic factors such as precipitation, temperature, and the El Niño phenomenon (ENSO) and malaria incidence is well documented in the region studied. Rainfall shows a positive correlation with malaria rates in countries such as Colombia, Ecuador, French Guiana, Guyana, Peru, Brazil, and Venezuela. Variations in climate, such as floods and droughts, significantly affect malaria transmission, highlighting the complex interplay between climate and malaria dynamics. Several studies highlight the strong influence of the El Niño phenomenon on malaria incidence.

Malaria incidence follows seasonal patterns, with peaks occurring during and after rainy seasons. This seasonal variability is crucial for planning and implementing effective malaria control measures. Temperature changes significantly affect the risk of malaria.

The relationship between deforestation and malaria is complex and region-specific. While some studies indicate that deforestation increases the risk of malaria by altering mosquito habitats, others suggest that extreme deforestation may reduce the incidence of malaria due to habitat degradation. There are important regional variations in how climatic factors and deforestation impact malaria incidence. In Brazil, for example, higher levels of deforestation are associated with different malaria risks depending on the region and period. Similarly, in Colombia, deforestation and gold exploitation are linked to malaria endemicity in certain areas.

The studies reviewed predominantly used ecological and cohort study designs, employing various statistical methods to analyse the data. Negative binomial regression, Poisson regression, multivariate regression, and linear regression were commonly used to explore the relationships between climate variables, deforestation, and malaria incidence.

The regional distribution of the studies indicates the need for more extensive research in Latin America. Integrating satellite imagery and remote sensing data can improve understanding and management of the spread and intensity of malaria.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Data sharing is not applicable to this article as no datasets were generated or analyzed specifically for this manuscript beyond the results discussed.

References

  1. WHO. Malaria. Geneva, World Health Organization, 2023.

  2. WHO. World malaria report 2021: regional data and trends [Internet]. Geneva, World Health Organization, 2021 [cited 2023 Jul 31]. p. 1–15. Available from: https://www.who.int/publications/m/item/WHO-UCN-GMP-2021.09

  3. Tucker Lima JM, Vittor A, Rifai S, Valle D. Does deforestation promote or inhibit malaria transmission in the Amazon? A systematic literature review and critical appraisal of current evidence. Philos Trans R Soc Lond B Biol Sci. 2017;372:20160125.

    Article  PubMed  PubMed Central  Google Scholar 

  4. De Castro MC, Monte-Mór RL, Sawyer DO, Singer BH. Malaria risk on the Amazon frontier. Proc Natl Acad Sci USA. 2006;103:2452–7.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Chaves LF, Ramírez Rojas M, Prado M, Garcés JL, Salas Peraza D, Marín RR. Health policy impacts on malaria transmission in Costa Rica. Parasitology. 2020;147:999–1007.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Ikeda T, Behera SK, Morioka Y, Minakawa N, Hashizume M, Tsuzuki A, et al. Seasonally lagged effects of climatic factors on malaria incidence in South Africa. Sci Rep. 2017;7:2458.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Stuckey EM, Smith T, Chitnis N. Seasonally dependent relationships between indicators of malaria transmission and disease provided by mathematical model simulations. PLoS Comput Biol. 2014;10: e1003812.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Briët OJT, Vounatsou P, Amerasinghe PH. Malaria seasonality and rainfall seasonality in Sri Lanka are correlated in space. Geospat Health. 2008;2:183–90.

    Article  PubMed  Google Scholar 

  9. Organización Panamericana de la Salud, Organización Mundial de la Salud. Plan de acción para la eliminación de la malaria 2021–2025. [Internet]. 2022 Dec 15 [cited 2023 Aug 7]; OPS/CDE/VT/22–0005:1–36. Available from: https://iris.paho.org/handle/10665.2/56859

  10. Stefani A, Dusfour I, Corrêa APS, Cruz MCB, Dessay N, Galardo AKR, et al. Land cover, land use and malaria in the Amazon: a systematic literature review of studies using remotely sensed data. Malar J. 2013;12:192.

    Article  PubMed  PubMed Central  Google Scholar 

  11. MacDonald AJ, Mordecai EA. Amazon deforestation drives malaria transmission, and malaria burden reduces forest clearing. Proc Natl Acad Sci USA. 2019;116:22212–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Pereira Á, Pérez M. Epidemiología y tratamiento del paludismo Offarm. 2002;21:110–4.

    Google Scholar 

  13. Cella W, Silva DCBD, Melo GC De, Tadei WP, Sampaio V de S, Pimenta P, et al. Do climate changes alter the distribution and transmission of malaria? Evidence assessment and recommendations for future studies. Rev Soc Bras Med Trop. 2019;52:e20190308.

  14. Morgenstern H. Ecologic studies in epidemiology: concepts, principles, and methods. Ann Rev Public Health. 1995;15:61–81.

    Article  Google Scholar 

  15. Gagnon AS, Smoyer-Tomic KE, Bush ABG. The El Niño southern oscillation and malaria epidemics in South America. Int J Biometeorol. 2002;46:81–9.

    Article  PubMed  Google Scholar 

  16. Girod R, Roux E, Berger F, Stefani A, Gaborit P, Carinci R, et al. Unravelling the relationships between Anopheles darlingi (Diptera: Culicidae) densities, environmental factors and malaria incidence: understanding the variable patterns of malarial transmission in French Guiana (South America). Ann Trop Med Parasitol. 2011;105:107–22.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Piedrahita S, Altamiranda-Saavedra M, Correa MM. Spatial fine-resolution model of malaria risk for the Colombian Pacific region. Trop Med Int Health. 2020;25:1024–31.

    Article  PubMed  Google Scholar 

  18. Siraj AS, Santos-Vega M, Bouma MJ, Yadeta D, Ruiz Carrascal D, Pascual M. Altitudinal changes in malaria incidence in highlands of Ethiopia and Colombia. Science. 2014;343:1154–8.

    Article  PubMed  CAS  Google Scholar 

  19. Feged-Rivadeneira A, Ángel A, González-Casabianca F, Rivera C. Malaria intensity in Colombia by regions and populations. PLoS ONE. 2018;13: e0203673.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Mantilla G, Oliveros H, Barnston AG. The role of ENSO in understanding changes in Colombia’s annual malaria burden by region, 1960–2006. Malar J. 2009;8:6.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Hurtado LA, Cáceres L, Chaves LF, Calzada JE. When climate change couples social neglect: malaria dynamics in Panamá. Emerg Microbes Infect. 2014;3: e27.

    Article  PubMed  PubMed Central  Google Scholar 

  22. de Oliveira Padilha MA, de Oliveira MJ, Romano G, de Lima MVM, Alonso WJ, Sallum MAM, et al. Comparison of malaria incidence rates and socioeconomic-environmental factors between the states of Acre and Rondônia: a spatio-temporal modelling study. Malar J. 2019;18:306.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Bauch SC, Birkenbach AM, Pattanayak SK, Sills EO. Public health impacts of ecosystem change in the Brazilian Amazon. Proc Natl Acad Sci USA. 2015;112:7414–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Santos AS, Almeida AN. The impact of deforestation on malaria infections in the Brazilian Amazon. Ecol Econom. 2018;154:247–56.

    Article  Google Scholar 

  25. Chaves LSM, Conn JE, López RVM, Sallum MAM. Abundance of impacted forest patches less than 5 km2 is a key driver of the incidence of malaria in Amazonian Brazil. Sci Rep. 2018;8:7077.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Solano-Villarreal E, Valdivia W, Pearcy M, Linard C, Pasapera-Gonzales JJJ, Moreno-Gutierrez D, et al. Malaria risk assessment and mapping using satellite imagery and boosted regression trees in the Peruvian Amazon. Sci Rep. 2019;9:15173.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Gunderson AK, Kumar RE, Recalde-Coronel C, Vasco LE, Valle-Campos A, Mena CF, et al. Malaria transmission and spillover across the Peru-Ecuador border: a spatiotemporal analysis. Int J Environ Res Public Health. 2020;17:7434.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Olson SH, Gangnon R, Silveira GA, Patz JA. Deforestation and malaria in Mâncio Lima county. Brazil Emerg Infect Dis. 2010;16:1108–15.

    Article  PubMed  Google Scholar 

  29. Wolfarth BR, Filizola N, Tadei WP, Durieux L. Epidemiological analysis of malaria and its relationships with hydrological variables in four municipalities of the State of Amazonas. Brazil Hydrol Sci J. 2013;58:1495–504.

    Article  Google Scholar 

  30. Hahn MB, Gangnon RE, Barcellos C, Asner GP, Patz JA. Influence of deforestation, logging, and fire on malaria in the Brazilian Amazon. PLoS ONE. 2014;9: e85725.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Terrazas WCM, Sampaio V de S, de Castro DB, Pinto RC, de Albuquerque BC, Sadahiro M, et al. Deforestation, drainage network, indigenous status, and geographical differences of malaria in the State of Amazonas. Malar J. 2015;14:379.

  32. Coutinho PEG, Candido LA, Tadei WP, da Silva Junior UL, Correa HKM, Guzzo Coutinho PE, et al. An analysis of the influence of the local effects of climatic and hydrological factors affecting new malaria cases in riverine areas along the Rio Negro and surrounding Puraquequara Lake, Amazonas. Brazil Environ Monit Assess. 2018;190:311.

    Article  PubMed  Google Scholar 

  33. Achcar JA, Martinez EZ, de Souza AD, Tachibana VM, Flores EF. Use of Poisson spatiotemporal regression models for the Brazilian Amazon forest: malaria count data. Rev Soc Bras Med Trop. 2011;44:749–54.

    Article  PubMed  Google Scholar 

  34. Stefani A, Hanf M, Nacher M, Girod R, Carme B. Environmental, entomological, socioeconomic and behavioural risk factors for malaria attacks in Amerindian children of Camopi. French Guiana Malar J. 2011;10:246.

    Article  PubMed  Google Scholar 

  35. Basurko C, Hanf M, Han-Sze R, Rogier S, Héritier P, Grenier C, et al. Influence of climate and river level on the incidence of malaria in Cacao. French Guiana Malar J. 2011;10:26.

    Article  PubMed  Google Scholar 

  36. Delgado-Petrocelli L, Córdova K, Camardiel A, Aguilar VH, Hernández D, Ramos S. Analysis of the El Niño/La niña-Southern oscillation variability and malaria in the Estado Sucre. Venezuela Geospat Health. 2012;6(Suppl 3):S51–7.

    Article  PubMed  Google Scholar 

  37. Stefani A, Roux E, Fotsing JM, Carme B. Studying relationships between environment and malaria incidence in Camopi (French Guiana) through the objective selection of buffer-based landscape characterisations. Int J Health Geogr. 2011;10:65.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Hurtado LA, Calzada JE, Rigg CA, Castillo M, Fernando CL. Climatic fluctuations and malaria transmission dynamics, prior to elimination, in Guna Yala. Republica de Panama Malar J. 2018;17:85.

    Article  PubMed  Google Scholar 

  39. Baeza A, Santos-Vega M, Dobson AP, Pascual M. The rise and fall of malaria under land-use change in frontier regions. Nat Ecol Evol. 2017;1:108.

    Article  PubMed  Google Scholar 

  40. Centre Spatial Guyanais. Available from: https://centrespatialguyanais.cnes.fr/fr/

  41. Naranjo-Diaz N, Rosero DA, Rua-Uribe G, Luckhart S, Correa MM. Abundance, behavior and entomological inoculation rates of anthropophilic anophelines from a primary Colombian malaria endemic area. Parasit Vectors. 2013;6:61.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Sagaró del Campo NM, Matamoros LZ. [Historical changes of the statistical techniques and the methodologies for the study of the causality in medical sciences](in Spanish). Medisan. 2019;23:534.

  43. Rojas-Torres L. Robustez de los índices de ajuste del análisis factorial confirmatorio a los valores extremos. Revista de Matemática: Teoría y Aplicaciones. 2020;27:383–404.

    Google Scholar 

  44. Odhiambo JN, Kalinda C, MacHaria PM, Snow RW, Sartorius B. Spatial and spatio-temporal methods for mapping malaria risk: a systematic review. BMJ Glob Health. 2020;5: e002919.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This research was funded by the "Convocatoria permanente de apoyo económico para la publicación de artículos de investigación en revistas internacionales indexadas Medline, Pubmed, Web of Science Core Collection, ubicadas en los cuartiles 1 y 2 SJR o JCR." Additionally, it received support from "Convocatoria Nº 785 de Doctorados Nacionales de 2017 de Minciencias de Colombia", and Fogarty International Center (FIC). U.S. National Institutes of Health. Award Number D43TW006589. These funding sources had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript. No additional external funding was received for this study.

Author information

Author notes

  1. Ana B. Vásquez-Rodríguez, Neal Alexander and Fernando de la Hoz Restrepo have contributed equally to this work.

Authors and Affiliations

  1. Grupo de Epidemiología y Evaluación en Salud Pública/Departamento de Salud Pública/Facultad de Medicina, Universidad Nacional de Colombia, Carrera 45 26-86, 111321, Bogotá, D.C., Colombia

    Carol B. Colonia, Ana B. Vásquez-Rodríguez & Fernando de la Hoz Restrepo

  2. Centro Internacional de Entrenamiento e Investigaciones Médicas, CIDEIM, Calle 18 122-135, 760031, Cali, Colombia

    Neal Alexander

Authors
  1. Carol B. Colonia
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ana B. Vásquez-Rodríguez
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Neal Alexander
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Fernando de la Hoz Restrepo
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

[CBC, NA, FDLH] contributed to the conceptualization and design. [CBC, ABVR] data analysis of this study. [CBC] drafted the manuscript. [NA, FDLH] contributed to reviewing and editing. All authors read and approved the final manuscript. The authors declare that they have no competing interests.

CBC and ABVR: design of the work; analysis and interpretation data, prepared figures and wrote the main manuscript text. NA and FDLAHR: design of the work; supervision and editing.

Corresponding author

Correspondence to Fernando de la Hoz Restrepo.

Ethics declarations

Ethics approval and consent to participate

Not applicable. This research does not involve human participants, human data, or animal studies.

Consent for publication

Not applicable. This manuscript does not contain any individual person’s data.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Colonia, C.B., Vásquez-Rodríguez, A.B., Alexander, N. et al. Malaria, relationship with climatic variables and deforestation in Colombia, Latin America and the Caribbean from 2000 to 2020: a systematic review. Malar J 23, 347 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12936-024-05140-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12936-024-05140-5

Keywords

Malaria Journal

ISSN: 1475-2875

Contact us