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Anthropogenic forest loss and malaria prevalence: a comparative examination of the causes and disease consequences of deforestation in developing nations


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Malaria represents an infectious disease keenly tied to environmental conditions, as mosquitoes represent the disease vector. Many studies are beginning to document that changes in environmental conditions, such as deforestation, can greatly alter the density and activity of mosquito populations and therefore malaria rates. While numerous epidemiological studies examine the links between forest loss and mosquito proliferation in distinct locales, comparative assessments across multiple sites are lacking. We attempt to address this gap by imparting a cross-national analysis of less-developed, non-desert, malaria endemic nations. Using a structural equation model of 67 nations, we find positive associations between deforestation rates and malaria prevalence across nations. Our results also suggest that rural population growth and specialization in agriculture are two key influences on forest loss in developing nations. Thus, anthropogenic drivers of environmental degradation are important to consider in explaining cross-national variation in malaria rates.
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AIMS Environmental Science, 4(2): 217-231.
DOI: 10.3934/environsci.2017.2.217
Received: 31 October 2016
Accepted: 27 February 2017
Published: 01 March 2017
Research article
Anthropogenic forest loss and malaria prevalence: a comparative
examination of the causes and disease consequences of deforestation in
developing nations
Kelly F. Austin1,*, Megan O. Bellinger2 and Priyokti Rana2
1 Department of Sociology and Anthropology, Lehigh University
2 Integrated Degree in Engineering, Arts, and Sciences, Lehigh University
* Correspondence: Email:; Tel: +610 758-2103.
Abstract: Malaria represents an infectious disease keenly tied to environmental conditions, as
mosquitoes represent the disease vector. Many studies are beginning to document that changes in
environmental conditions, such as deforestation, can greatly alter the density and activity of mosquito
populations and therefore malaria rates. While numerous epidemiological studies examine the links
between forest loss and mosquito proliferation in distinct locales, comparative assessments across
multiple sites are lacking. We attempt to address this gap by imparting a cross-national analysis of
less-developed, non-desert, malaria endemic nations. Using a structural equation model of 67 nations,
we find positive associations between deforestation rates and malaria prevalence across nations. Our
results also suggest that rural population growth and specialization in agriculture are two key
influences on forest loss in developing nations. Thus, anthropogenic drivers of environmental
degradation are important to consider in explaining cross-national variation in malaria rates.
Keywords: malaria; deforestation; infectious disease
1. Introduction
The World Health Organization boasts that over the last 15 years, malaria incidence has decreased
by 37% and deaths from malaria declined by 60% globally [1]. While this is undoubtedly marked
progress, it is important to acknowledge that headway is not uniform across regions or countries, with
some nations, particularly in Sub-Saharan Africa and some areas of Southeast Asia, lagging behind in
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malaria improvements [1]. Furthermore, malaria represents an infectious disease keenly tied to
environmental conditions with mosquitoes as the disease vector [2,3]; thus, changes in environmental
conditions can greatly alter the density and activity of mosquito populations and therefore malaria
rates (e.g., [2]). As human alterations to the natural environment are only increasing in scale and
intensity over time [3,4], we cannot assume that progress in addressing this disease will continue with
manifest success. Instead, research needs to continue to investigate how human activities may be
altering ecosystem conditions in ways that enhance mosquito habitats and transmission of malaria.
Although a number of forms of environmental degradation or ecosystem alteration could affect
mosquito populations, deforestation is an increasingly studied factor in several locales across Asia,
Sub-Saharan Africa, and, most prominently, Latin America (e.g., [5-8]). While a number of case
studies identify links between forest loss and malaria or mosquito prevalence [5-9], whether
relationship exists across areas or regions remains insufficiently examined. We attempt to contribute to
this line of inquiry by imparting a cross-national analysis of less-developed, non-desert nations to
examine the potential association deforestation and malaria prevalence across countries.
Prior examinations of deforestation and malaria tend to focus on the epidemiological aspects of
ecosystem change and mosquito habitat proliferation [5,6,8-10]. While these studies provide key
evidence on the direct mechanisms that cause deforestation to lead to increased levels of larvae,
parasite concentrations, mosquito populations, or actual malaria rates [5,6,8-10], they fail also to
consider fully the wider socio-economic context in which forest loss occurs. Our study contributes to
this endeavor by simultaneously investigating the causes and disease consequences of deforestation in
developing nations. By illuminating some of the key anthropogenic forces behind deforestation, our
study contributes to broader understanding on how environmental degradation affects global trends in
infectious disease.
2. Background
2.1. Malaria: characteristics and global trends
In 2015, around 450,000 people died from malaria worldwide [1]. The burden of this disease
spreads across around 90 countries, with 88% of malaria cases concentrated in the Sub-Saharan
African region [1,11]. Over the last several decades, partially due to major initiatives and programs,
such as the WHO‘s ―Roll Back Malaria, Roll in Development‖, there have been major reductions in
malaria deaths and incidence [1,11]. However, this disease is still a leading threat to health and
premature death in several countries [1]. Poverty represents one of the greatest risks for malaria
cross-nationally [12]; undoubtedly, socioeconomic development fundamentally relates to the global
distribution of morbidity and mortality for many health issues (e.g., [13-18]).
Caused by a parasite in the genus Plasmodium, four of the species, P. falciparum, P. vivax, P.
malariae and P. ovale, lead to malaria in humans [19]. These parasites rely on two hosts throughout
their lifecycle: a mosquito and a human (or another animal) [19]. Only around 3040 of the 400 species
of Anopheles mosquito can act as vectors for the parasite, and only the bite of a female Anopheles
mosquito spreads the parasite from one person to another [19,20]. The different types of malaria cause
different variations of the disease in humans, with some forms being milder than other strains [19,20].
P. falciparum is the most prevalent species in Africa and causes the most deaths worldwide [1,11].
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Early diagnosis of malaria is crucial and essential for management of the disease [1,13]. The
WHO recognizes microscopy and rapid diagnostic tests as the current most common and effective
ways of diagnosing the disease [1,11]. However, the quality of this method can be inadequate due to
lack of sensitivity and insufficient expertise of personnel required to analyze results properly [1,11,21].
Especially in poor nations and rural areas, access to quality healthcare is limited [13,12,21]. Currently,
there are many drugs available to combat the infectious parasite, but some are not very effective [1,22,23].
The most effective and popular drug, artemisinin combination treatments (ACTs), contain an active
ingredient that is a derivative of artemisinin from the sweet wormwood tree [11,23]. Other drug
treatments, including quinine and chloroquine were also popular in past decades; however, the parasite
has developed resistance to these drug treatments [1,23]. In addition, recent studies already document
increased tolerance to ACTs in many regions, leading to concerns over the potentials of widespread
resistance [22].
As previously emphasized, patterns in malaria prevalence and international development are
closely linked, as rates are highest in the poorest nations and, conversely, eradication of malaria took
place decades ago in affluent nations [13,24,12,23]. Many studies in the areas of global health and
development emphasize that impoverished people lack knowledge of disease prevention techniques,
have little access to modern ‗Western‘ medicine, and face limited access to appropriate
preventative strategies, such as screened windows or insecticide-treated bed nets [13,24,21]. In
addition, many poor households lack adequate access to water or sanitation, increasing potential
mosquito habitats (e.g., [12,21,24-26]).
Education in particular signifies a key determinant of malaria incidence. For example, Dike et al. [27]
find that formal education teaches basic skills that remove cultural ideologies leading to misconceptions
that affect proper malaria management. In particular, people of higher education were more likely to
identify mosquitoes as the malaria disease vector and to use insecticide-treated bed nets [27]. Numerous
other studies of malaria report similar findings (e.g., [21,26,28]), suggesting that education also enhances
people‘s awareness of Western medicine techniques and propensity to seek proper treatment. The potential
importance of education in predicting cross-national malaria rates fits with other comparative global health
assessments, including examinations of HIV, life expectancy, and infant and child mortality, which find
that participation in schooling greatly improves health outcomes across nations (e.g., [14-18]).
While social and economic factors are important in shaping cross-national disease trends,
environmental factors are also especially relevant with mosquitoes as the vector [2]. Thus, prevalence
patterns are not only shaped by education, healthcare access, and poverty, but also the degree to which
environmental conditions exist or are created that can sustain or flourish mosquito habitats [2,3].
Location in tropical or sub-tropical zones alone is argued to account for a significant amount of
vulnerability to malaria, as the parasite quickly becomes weak and eliminated below 16 degrees
Celsius [29]. Additionally, the role of other environmental factors, such as deforestation, represents an
emerging area of inquiry (e.g., [7,8]). As deforestation results from human activities, this represents a
potential underlying anthropogenic cause of the malaria burden in many developing countries.
2.2. Links between forest loss and malaria
Today over 3 billion people worldwide remain at risk of acquiring malaria [13], and many of these
vulnerable people tend to live in areas where environmental factors facilitate Plasmodium growth and
prevalence. Mosquitoes fundamentally depend upon stagnant water and warm temperatures for their
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breeding and life cycle, explaining why malaria largely remains a tropical disease. Deforestation represents
an additional environmental factor that can influence malaria [2,6,8,9,30,31]. The goal of this section is to
draw on case study and epidemiological evidence from distinct studies to establish a rationale for
examining the relationship between deforestation and malaria prevalence at the cross-national level.
Deforestation can impact malaria prevalence by several mechanisms, including increasing the
amount of sunlight and standing water in some areas [2,6,9,32]. Although it is important to emphasize
that different sub-types of Anopheles mosquitoes prefer shadier versus more sunlit habitats, or may be
more or less sensitive to land use changes in general, typically, increasing standing water and sunlight
is favorable for most species [2,6,9,30,32]. Deforestation potentially contributes to these processes in
multiple ways. For example, primary growth forests tend to be heavily shaded with thick debris on the
ground, which absorbs water and often leaves any standing water acidic [32]. After clearing non-steep
terrain, the land usually become flatter and more likely to pool water, which is typically less acidic and
more conducive to Anopheles larvae development [32]. Not only does ponding more readily increase
the availability of breeding grounds for the malaria vector, but increases in sunlight resulting from
deforestation also promotes more ideal breeding grounds by warming temperatures [2,6-9]. When
agriculture replaces forested areas, the plants can still provide the bushy cover needed for some species
of Anopheles mosquito or stages of larvae development. Increases in mosquito reproduction
potentially impact rates of malaria transmission to nearby populations [2,6,9].
Another possible link between forest loss and malaria concerns biodiversity (e.g., [33,34]).
Disease ecologists find that higher levels of biodiversity generally take on a protective role for the
human population, in a so-called ―dilution effect.‖ If there is a wider variety of species available, the
proportion that are able to transmit the vector are reduced and thus transmission of diseases occurs less
frequently [33,34]. One of the primary effects of deforestation is a loss in biodiversity, as a single,
typically nonnative, crop often replaces a huge variety of vegetation and animal life [33]. Establishing
a connection between forest loss and malaria is not straightforward; there are many confounding
factors or factors specific to certain regions and locales. Thus, comparative assessments that examine
trends in forest loss and malaria across regions can help to establish whether we can generalize the
findings from epidemiological case studies to other areas endemic to the Plasmodium parasite.
Several studies conducted in sites in Asia indeed establish a link between malaria prevalence and
forest loss. For example, studies conducted in China and Myanmar determine that the pupation rate of
Anopheles minimus, a malaria vector, was highest in samples collected from deforested areas ([7,8]).
Similarly, a study carried out over the course of a year in Vietnam found that there was a statistically
significant relationship between percentage of forest cover loss and prevalence of multiple strains of
malaria [10]. In Sub-Saharan Africa, one study set in the highlands of Kenya in a case-control format
found that living on land without trees led to increased risk for malaria contraction [5].
South America, particularly the Amazon region, represents perhaps the most popular site to
examine the links between environmental degradation and malaria. For example, studies conducted in
the Peruvian Amazon, which has experienced deforestation due to small-scale farming, discovered that
Anopheles darlingi had a statistically significant preference for living and breeding in areas that had
experienced a loss of forest cover [6,9]. The observed connection between malaria rates and
deforestation persisted even when considering changes in population density [9]. A study that sampled
from over 800 water sources throughout the Amazon found that Anopheles darlingi sites were more
likely to be located in areas that had experienced alterations in land cover, such as reductions in forest
and the establishment of new croplands [6].
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These studies demonstrate that multiple researchers across different regions and variety of
Anopheles mosquito species identify a link between deforestation and malaria rates or mosquito
populations. In fact, Yasuoka and Levins [35] conduct a content analysis of case studies centered on
changes in ecology and malaria. They find around 60 examples from a variety of areas, including
Thailand, Nepal, India, China, Guyana, Uganda, and others, that demonstrate a link between
deforestation and land-use changes and growth in mosquito populations or malaria incidence [35].
It is important to emphasize that deforestation is not a natural phenomenon, but rather results
predominantly from human activities [4]. Expanding agricultural areas represents an underlying cause of
deforestation in the majority of these studies (e.g., [6,9,30]). Population growth within developing nations
can lead to increased pressure for food production, but also many developing nations are encouraged to
expand agricultural exports as a means for development (e.g., [36]). Aside from food demands, rural
population pressures overall are likely to spur forest loss [4]. Many developing nations where malaria is
most prevalent continue to have high rates of fertility and rural population growth [14]. Research also
identifies access to timber for building or for use as fuelwood as key causes of deforestation in developing
nations (e.g., [37,38]). Thus, rural population dynamics and pressures to increase food production are
likely to be key anthropogenic causes of deforestation. In our analysis, we engage an innovative
statistical method, structural equation modeling, in order to examine not only the potential role of
deforestation in explaining cross-national variation in malaria prevalence, but also to account for the
underlying causes of forest loss.
3. Materials and Methods
3.1. Sample
The sample is restricted to malaria-endemic nations. Malaria-endemic nations are nations that have
a constant and measurable incidence of malaria and are located in natural areas of transmission [1].
Countries that register malaria cases resulting from imported cases are not included in the analyses.
Malaria-endemic nations overwhelmingly have GDP per capita estimates in the lower three quartiles of
the income classification of countries [1], thus largely representing a sample of less-developed nations.
We exclude desert nations as deforestation is only relevant and measurable in countries with significant
forest stock. Our sample includes 67 non-desert, less-developed nations for which data are reported for
the key variables in the analysis, including the deforestation rate and malaria prevalence. For a complete
list of the countries included in the analyses, see Table 1.
3.2. Analytic strategy
To examine the association between deforestation and malaria prevalence, as well as the underlying
predictors of deforestation, we utilize structural equation models (SEMs). SEM is particularly useful in
this context based on its efficient ability to model direct and indirect effects (e.g., [39]). While most
comparative analyses utilize direct effects approaches, this strategy prevents modeling the mediating
and interrelationships outlined above, such as the potential pathway involving rural population growth,
deforestation, and malaria. Structural equation modeling also allows us to utilize latent or composite
constructs of multi-dimensional concepts, such as health resources, and circumvent issues of
multicollinearity, which often occur with cross-national data [40].
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Table 1. Countries included in the Analyses (N = 67).
Gambia, The
Papua New Guinea
Sao Tome & Principe
Burkina Faso
Sierra Leone
Cabo Verde
Solomon Islands
South Africa
Central African Rep.
Congo, Dem. Rep.
Congo, Rep.
Cote d'Ivoire
Dominican Rep.
El Salvador
Notes: ASAsia, LALatin America, PACPacific Islands, SSASub-Saharan Africa
Another key aspect of SEMs involves the estimation of fit statistics that enable the researcher to
judge the fit of the model as a whole to the data provided and compare equally plausible models [39].
An additional feature of SEM is its utilization of maximum likelihood (ML) missing value routine that
calculates pathway coefficients based on all available data points; when cases are missing information
on select variables, those cases are dropped from those pathway estimations, but retained for others
when the data are available [40]. Thus, SEM allows us to maximize our sample of nations by retaining
cases that might be missing data on one or two control variables included in the models [40].
In SEM, we simultaneously estimate a system of linear equations that correspond to our hypotheses
about the correlations in our observed data [39]. For empirical identification, it is important that the
models estimate normally [39]. We utilize SEM software in AMOS and Mplus; in both statistical
packages, the path diagram displayed in Figures 1 and 2 all estimate normally. The assumptions of SEM
include multivariate normality, completely random missing data, sufficiently large sample, and the
correct model specification [40]. To protect against the negative consequences of multivariate
non-normality we also ran the analyses using the robust ML estimator (MLR) implemented in Mplus [41].
These estimates with the MLR estimator are robust to non-normality [41]. The results were consistent with
those achieved with the ML procedure. Although data should be completely missing at random, the use of
the maximum likelihood (ML) estimator also provides consistent estimates under the assumption of
missing at random, which is an easier condition to satisfy [42]. Additionally, we fail to see any pattern to
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the missing data to suggest that the data are not missing at random. Third, the positive asymptotic
properties of the ML estimation procedure (consistency and efficiency) are known when the sample size is
relatively large. As our sample is relatively small (N = 67), we re-ran our analyses with bootstrap standard
errors as well as a robust ML (MLR) procedure and obtained estimates and model fit statistics for
Figures 1 & 2 that are consistent with the ML estimator. Model specification errors occur with the
omission of relevant variables. If this occurs, the errors and the exogenous variables in the model correlate,
leading to biased estimates [40]. To avoid this, we draw on prior research to select a wide range of
variables. Our review of the literature engages perspectives on development, health, and the environment
into our structural model. We test all theoretically informed paths as shown in Figures 1 and 2 below.
3.3. Variables included in the analysis
The key dependent variable in the analysis is the malaria prevalence rate. We created the malaria
prevalence variable using data on the estimated number of malaria cases from the World Health
Organization [1] and total population level from the World Bank [43] for the year 2013. We weighted
the number of malaria cases for each nation by its total population, and then multiplied by 100,000 to
form the prevalence rate.
As the malaria parasite requires particular climate conditions, latitude represents an important
exogenous, environmental condition to consider. We transformed the latitude scores into absolute
values to capture distance from the equator [43].
We examine deforestation as a prominent form of environmental degradation that is likely to be
directly associated with increasing malaria prevalence. The natural deforestation rate represents an annual
percent change score, calculated using FAO estimates of natural forest area, from 2012 to 2013 [43]. These
data come from the Global Forest Resource Assessment (GFRA) and represent point estimates for natural
forest stock measured in thousand square hectares for 2012 and 2013. The natural forest area measure
includes land area that is more than 0.5 hectares which contains trees higher than 5 meters and a
canopy cover of more than 10%. We multiply the change score by -1 to capture rate of forest loss.
We consider both agriculture and rural population growth as key drivers of forest loss. We therefore
include a measure of rural population growth, which accounts for the annual percent change in the rural
population for the year 2012 [43]. We measure specialization in agriculture using data on the percent of the
economy that comes from agricultural production, or agriculture as a percent of GDP [43].
To assess the influence of economic development, we include GDP per capita. This represents
the total annual output of a country‘s economy divided by its population, measured in current
international dollars for the year 2012 [43]. More specifically, GDP per capita is the total market value
of all final goods and services produced in a country in a given year, equal to total consumer,
investment, and government spending, divided by the mid-year population. It is converted into current
international dollars using Purchasing Power Parity (PPP) rates, providing a standard measure
allowing for comparisons of real price levels between countries [43].
Public health conditions are important non-economic factors that are likely to influence malaria
rates in less-developed nations. We therefore measure public health conditions using a latent construct
comprised of the following indicators: number of physicians, the fertility rate, access to clean water,
and secondary schooling for the year 2012. Physicians, secondary schooling, access to clean water and
the fertility rate represent some of the most prominent predictors of malaria rates, as well as other
health outcomes in developing nations (e.g., [14-16]). The variable medical doctors measures the
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number of physicians in a nation per 100,000 people. The fertility rate is an estimate of the number of
children an average woman would have if current age-specific fertility rates remained constant during
her reproductive years. Percent access to clean water refers to the percentage of the population using an
improved drinking water source. Improved drinking water sources include piped water located inside
the user‘s dwelling, plot, or yard, and other improved drinking water sources, such as public taps or
standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection.
Secondary school enrollment represents a gross enrollment ratio, which calculates the ratio of total
enrollment, regardless of age, to the population of the age group that officially corresponds to
secondary level education. We acquired all of these measures from the World Bank [43].
Additionally, we include a regional indicator for Sub-Saharan Africa, as many Sub-Saharan
African nations have an especially high level of malaria and the highest concentrations of P.
falciparum, which is one of the severest strains of the parasite. We measure this as a dummy variable,
where countries coded with a ‗1‘ indicate location in Sub-Saharan Africa and those with a ‗0‘ indicate
that a nation is located in a different region of the world [43].
4. Results
Table 2 displays the bivariate correlation matrix and univariate statistics for all of the variables
used in the analyses. The magnitude of the bivariate correlation coefficients in Table 2 demonstrates
that many of the predictor variables in the sample are highly correlated, such as the indicators for
public health conditions variables. This further warrants the use of the SEM analytical technique given
its superior handling of inter-correlated independent variables through the creation of latent constructs
and direct and indirect pathways that circumvents the tendency to bias coefficient estimates [39].
Table 2. Correlation matrix and univariate statistics
1. Malaria Prevalence
2. Latitude
3. GDP per capita
4. Sub-Saharan Africa
5. Secondary Schooling
6. Medical Doctors
7. Clean Water
8. Fertility Rate
9. Agriculture % GDP
10. Rural Pop Growth
11. Deforest-ation Rate
Standard Deviation
A preliminary step in the empirical assessment of our complete SEM was to validate empirically
whether public health conditions represent latent factors that can be appropriately estimated secondary
school enrollments, trained medical doctors, access to clean water, and the average fertility rate. To test
this, we initially construct a confirmatory factor analysis (CFA) of the measures and analyze the
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overall and component measures of fit. By empirical standards, we find evidence at both the
component and overall model levels to support our predictions that these indicators can be used to
measure public health conditions. This fits with our substantive interpretations of prior health and
development research discussed earlier.
Figures 1 and 2 present the SEM results of malaria prevalence. We test all theoretically and
substantively informed paths in the path diagram displayed in Figure 1, and then eliminate all
non-significant relationships to present the most parsimonious model in Figure 2. Before turning to a
discussion of results, we note the model fit statistics indicate an excellent fit of both models to the data.
Specifically, for Figure 1, in accordance with empirical standards the chi-square test statistic is
non-significant (χ2 = 33.15 with df = 31); the values of the Incremental Fit Index (0.980),
Tucker-Lewis Index (0.965), and the Confirmatory Fit Index (0.979) all exceed 0.90; and the root
mean squared error of approximation (RMSEA) value (0.046) is below the suggested threshold of .10
for smaller samples [39].
Notes: ***p < 0.001, **p < 0.01, *p < 0.05 (two-tailed tests); standardized coefficients reported.
Figure 1. SEM predicting malaria prevalence, saturated Model.
The results from the trimmed model (where non-significant paths are eliminated) presented in
Figure 2 are also well within range of the empirical standards for SEMs. The chi-square test statistic is
non-significant (χ2 = 45.08 with df = 37); the values of the Incremental Fit Index (0.968),
Tucker-Lewis Index (0.966), and the Confirmatory Fit Index (0.981) all exceed 0.90; and the root
mean squared error of approximation (RMSEA) value (0.048) is below the appropriate threshold.
It is common when using SEMs to eliminate non-significant paths for the sake of parsimony.
When looking across the saturated model in Figure 1 and the trimmed model in Figure 2, there are a
few substantial differences in the path coefficients. The only changes are a small reduction in the
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magnitudes for effects on malaria prevalence and deforestation in the more saturated model. As the
substantive findings are essentially consistent across the two models, and as the fit statistics are
acceptable in the second model as well as the first, we prefer to focus our discussion of the results on
the findings presented in Figure 2. In the path diagrams, standardized regression coefficients are
reported and flagged for statistical significance. We also include Table 3, which displays the
standardized regression coefficients in addition to the unstandardized regression coefficients and
standard errors for the model in Figure 2. However, we keep our interpretation of the results focused on
Figure 2, as the path diagram facilitates accessible interpretations.
Notes: ***p <0.001, **p < 0.01, *p < 0.05 (two-tailed tests); standardized coefficients reported.
Figure 2. SEM predicting malaria prevalence, trimmed model.
The results presented in Figure 2 suggest that deforestation is associated with increased
prevalence of malaria in developing nations, which is consistent with our predictions (0.24). In
addition, we find that economic specialization in agriculture (0.27) and rural population growth (0.28)
are positively associated with the deforestation rate. Thus, not only do we find empirical evidence for
links between deforestation and malaria across nations, but also that population growth in rural areas
and increased emphasis on agriculture contribute to deforestation.
In addition, we find that public health conditions have a significant, negative association with
malaria rates in less-developed nations. In particular, secondary school enrollments, medical doctors,
access to clean water, and the average fertility rate together represent a set of public health conditions
that are robustly associated with declines in malaria prevalence (0.66). In addition to public health
conditions, latitude also explains cross-national variation in malaria prevalence, where nations located
further from the equator tend to have lower rates of prevalence (0.18).
Our results also suggest that GDP per capita or level of economic development and location in
Sub-Saharan Africa are important underlying factors contributing to variations in the malaria burden
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across developing nations in indirect ways. It is important to emphasize that the results across Figures 1
and 2 demonstrate that each of these variables has an indirect association to malaria prevalence.
Specifically, more economically developed nations tend to have improved public health conditions
(0.37), which then reduce the malaria burden. In addition, more affluent nations tend to have
significantly lower levels of rural population growth (0.51) and increased economic specialization in
agricultural production (0.70), which in turn are associated with deforestation rates.
Table 3. Regression results for SEM depicted in Figure 2 predicting malaria prevalence.
Latitude Malaria Prevalence
Public Health Conditions Malaria Prevalence
Deforestation Rate Malaria Prevalence
Rural Pop Growth Deforestation Rate
Agriculture % GDP Deforestation Rate
Rural Pop Growth Public Health Conditions
GDP per capita Public Health Conditions
GDP per capita Rural Pop Growth
GDP per capita Agriculutre % GDP
Sub-Saharan Africa Public Health Conditions
Sub-Saharan Africa Rurual Pop Growth
Public Health Conditions Secondary Schooling
Public Health Conditions Medical Doctors
Public Health Conditions Clean Water
Public Health Conditions Fertility Rate
Notes: ***p < 0.001, **p < 0.01, *p < 0.05 (two-tailed tests); standardized coefficients flagged for statistical
significance; unstandardized coefficients reported in in italics; standard errors reported in parentheses.
We also find in Figure 2 that Sub-Saharan African nations tend to have considerably higher rates
of rural population growth than other nations (0.27), and that Sub-Saharan African nations also have
much weaker public health conditions in comparison to other nations (0.50). Our results also suggest
that nations experiencing high levels of rural population growth are more likely to have poorer public
health conditions (0.26). This fits with prior research identifying that rural populations face
especially poor access to and quality of a variety of health and social resources, including health
facilities and education [21].
Overall, our results suggest that deforestation is an important factor in explaining cross-national
variation in malaria prevalence. We also find that anthropogenic forces related to specialization in
agriculture and rural population pressures are associated with heightened levels of deforestation,
thereby suggesting that human activities contribute to some level of malaria prevalence. In considering
the relative weight of impact of latitude versus deforestation in the model, we find in our analyses that
forest loss is more relevant in explaining cross-national variability in malaria rates than being located
close to a tropical zone. Thus, human-induced environmental changes may be more significant in
predicting malaria prevalence than the truly natural or inherent environmental conditions.
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4. Conclusions & limitations
Nearly 130 million hectares of forestan area almost equivalent in size to South Africa - have
been lost since 1990, according to a recent FAO report [4]. Not only does deforestation contribute to
profound impacts on climate change, biodiversity loss, and changing weather patterns, but this study
builds on a body of evidence that deforestation also influences malaria transmission. While isolated
studies have begun to identify links between forest loss and mosquito proliferation or malaria
prevalence in certain locales (e.g., [5-8]) this study adds to the research on this phenomena by finding
an association between deforestation and malaria prevalence at the cross-national level.
In addition, we extend our analysis to also to consider some of the most prominent causes of forest
loss in developing nations. Our findings suggest that rural population growth and economic
specialization in agriculture are significant in increasing levels of forest loss in many developing nations.
Therefore, not only are there potential links between environmental change and malaria, but we consider
the anthropogenic underpinnings of this form of degradation. Rural population pressures not only affect
forests, but public health conditions in developing nations as well. Perhaps in some areas, growing rural
populations who tend to live closer to the natural habitats of mosquitoes may experience further
compounding risks due to their lack of health resources and proximity to deforested areas.
One limitation of our study is sample size. Sample size is inherently restricted as it is only
appropriate to include malaria endemic, non-desert nations in the analyses. Given those parameters, a
sample of nearly 70 nations is actually robust, and our model adheres to the model fit and other
parameters appropriate for SEMs. We also re-ran our analyses with bootstrap standard errors as well as a
robust ML (MLR) procedure and obtained estimates and model fit statistics for Figures 1 & 2 that were
consistent with the ML estimator. Although the use of cross-national data represents a key contribution
of this research, there are notable limitations with this approach. Cross-national analyses cannot account
for the particular sub-national ecological factors that may affect malaria prevalence, such as soil
conditions, level of rainfall, or temperature variations, as well as the particular vector susceptibilities and
preferences that vary by region or sub-species of mosquito. These represent topics more appropriate for
the epidemiological field studies we draw on, and instead we prefer to focus on determining if
comparative analysis uncovers broad patterns linking deforestation to malaria across nations. In so doing,
we acknowledge that we cannot assume that deforestation will cause increased malaria rates in every
nation, region, or area, but rather emphasize that there appears to be a pattern across countries between
deforestation and malaria prevalence, where the nations with higher rates of deforestation also tend to
have higher rates of malaria, net of other factors. Our results help to expand the generalizability of case
studies exploring forest loss and mosquito proliferation across different sites (e.g., [5-10]). Certainly,
given this complex topic, we hope to encourage further research examining how forest loss influences
trends in malaria and other mosquito-borne diseases in and across diverse settings.
Our study makes an interdisciplinary contribution by bringing together ideas and insights from
across fields of global development and sociology, global health, epidemiology, environmental science,
and human ecology. We consider many of the social, demographic, economic, and environmental
predictors found to be important in predicting both malaria prevalence and deforestation from across
these disciplines. The use of structural equation models (SEMs) allows us to test the interconnections
and complex pathways among these different types of indicators, and serves to illuminate how
international inequalities frame the proximate predictors of malaria rates in developing nations, such
as forest loss and public health conditions.
AIMS Environmental Science Volume 4, Issue 2, 217-231.
Importantly, we find there is no direct association between GDP per capita and Sub-Saharan Africa
and malaria prevalence. Although many studies make direct links between rising affluence and malaria
management, our results suggest that economic growth will be most effective if channeled to improving
public health conditions or addressing rural population growth. Similarly, the indirect associations
involving Sub-Saharan Africa imply that there is nothing inherent about this region that explain
disproportionately high malaria rates; rather our results suggest that Sub-Saharan African nations suffer
from a lack of public health services and extreme levels of rural population growth. Our results also find
links between GDP per capita and economic specialization in agriculture, where poorer nations in our
sample are much more likely to generate a larger share of their economic revenues from food production,
which in turn are associated with increased deforestation rates and malaria prevalence. Although we do
not focus on exports specifically, it is important to keep in mind that a notable amount of food produced
in poor nations is destined for markets in affluent nations [44]. Developing nations are often encouraged
by supra national organizations and core governments to specialize in agriculture as a means to
development [36]; perhaps these policy recommendations should be interpreted with caution given the
potentially contaminant influences on forests and malaria rates.
Although there have been major improvements in malaria prevention, diagnosis, and treatment in
many nations over the last several decades [1], malaria remains a leading cause of death and threat to
health in many regions and countries across the Global South [1,12]. Some patterns in climate change,
deforestation, and other human-induced changes to the natural environment could alter and amplify
malaria transmission (e.g., [31]). In addition to the influence of environmental changes, resistance to
both insecticides and antimalarial medications is increasing [22,23]. As human alterations to the
natural environment are only intensifying, understanding and mitigating the underlying anthropogenic
causes of malaria transmission deserves vigilant attention.
Conflict of interest
The authors declare there is no conflict of interest.
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© 2017 Kelly F. Austin et al., licensee AIMS Press. This is an
open access article distributed under the terms of the Creative
Commons Attribution License
... Many dozens of species in the genus Plasmodium are known to naturally infect some mammals, birds, and reptiles [10][11][12][13]. Just four of those species are considered adapted to humans as primary intermediate hosts [14,15], a relationship hereby referred to as natural, and all are considered pathogenic. A fifth malaria species also successfully infects humans but not as its natural host; Plasmodium knowlesi, a non-human primate (NHP) Plasmodium that occurs in macaques, but also infects humans [16,17]. ...
... The anti-malarial treatment selected to treat malaria in orang-utans at IAR RRC were based on guidance for therapies used in human malaria [14,57]: ...
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Background Plasmodial species naturally infecting orang-utans, Plasmodium pitheci and Plasmodium silvaticum , have been rarely described and reportedly cause relatively benign infections. Orang-utans at Rescue Rehabilitation Centres (RRC) across the orang-utan natural range suffer from malaria illness. However, the species involved and clinical pathology of this illness have not been described in a systematic manner. The objective of the present study was to identify the Plasmodium species infecting orang-utans under our care, define the frequency and character of malaria illness among the infected, and establish criteria for successful diagnosis and treatment. Methods During the period 2017–2021, prospective active surveillance of malaria among 131 orang-utans resident in a forested RRC in West Kalimantan (Indonesia) was conducted. A total of 1783 blood samples were analysed by microscopy and 219 by nucleic acid based (PCR) diagnostic testing. Medical records of inpatient orang-utans at the centre from 2010 to 2016 were also retrospectively analysed for instances of symptomatic malaria. Results Active surveillance revealed 89 of 131 orang-utans were positive for malaria at least once between 2017 and 2021 (period prevalence = 68%). During that period, 14 cases (affecting 13 orang-utans) developed clinical malaria (0.027 attacks/orang-utan-year). Three other cases were found to have occurred from 2010–2016. Sick individuals presented predominantly with fever, anaemia, thrombocytopenia, and leukopenia. All had parasitaemias in excess of 4000/μL and as high as 105,000/μL, with severity of illness correlating with parasitaemia. Illness and parasitaemia quickly resolved following administration of artemisinin-combined therapies. High levels of parasitaemia also sometimes occurred in asymptomatic cases, in which case, parasitaemia cleared spontaneously. Conclusions This study demonstrated that P. pitheci very often infected orang-utans at this RRC. In about 14% of infected orang-utans, malaria illness occurred and ranged from moderate to severe in nature. The successful clinical management of acute pitheci malaria is described. Concerns are raised about this infection potentially posing a threat to this endangered species in the wild.
... We use a multi-model inference hierarchical modelling framework to retrospectively assess relationships between malaria infection status and agricultural covariates while controlling for a number of important individual (age and sex of child), household (education level of the mother, wealth of household, access to improved sanitation and water sources, whether the child slept in a bed-net and whether the dwelling was sprayed with insecticide within the last 12 months) 36 and extrinsic or environmental confounders (population density, forest cover, forest loss, temperature, precipitation and elevation) 18,[37][38][39][40][41][42][43][44] . ...
... Forest cover and forest loss. Forest loss and forest cover have similarly been considered important factors in malaria ecology across sub-Saharan Africa 15,18,42,43 . In addition, agriculture is considered the leading driver of forest loss in sub-Saharan Africa 87 . ...
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Agriculture in Africa is rapidly expanding but with this comes potential disbenefits for the environment and human health. Here, we retrospectively assess whether childhood malaria in sub-Saharan Africa varies across differing agricultural land uses after controlling for socio-economic and environmental confounders. Using a multi-model inference hierarchical modelling framework, we found that rainfed cropland was associated with increased malaria in rural (OR 1.10, CI 1.03–1.18) but not urban areas, while irrigated or post flooding cropland was associated with malaria in urban (OR 1.09, CI 1.00–1.18) but not rural areas. In contrast, although malaria was associated with complete forest cover (OR 1.35, CI 1.24–1.47), the presence of natural vegetation in agricultural lands potentially reduces the odds of malaria depending on rural–urban context. In contrast, no associations with malaria were observed for natural vegetation interspersed with cropland (veg-dominant mosaic). Agricultural expansion through rainfed or irrigated cropland may increase childhood malaria in rural or urban contexts in sub-Saharan Africa but retaining some natural vegetation within croplands could help mitigate this risk and provide environmental co-benefits.
... In our study period deforestation happened in the year 1996 i.e., forest cover growth rate (-0.43) per square km in the negative sign that indicates deforestation and simultaneously malaria cases reached its pick in the decade. Deforestation leads to high incidence of malaria because of pools of water in the forest open for the sunlight, that gives the sufficient temperature and humidity to Anopheles misquotes for breeding and development of malaria parasites(Austin et al., 2017). ...
... Developing rural regions, which are shaped by human activities, also have a great potential to increase the abundance of mosquitoes (Chaves et al. 2021). The reason behind it is sometimes the increased frequency of human movement in the region (Marshall 2018), and it has additionally been reported that socioeconomic changes, lack of proper housing and sanitation, and limited access to health facilities may also intensify the transmission of malaria (Austin et al. 2017). With cases of the Covid pandemic, which is still mutating and very much active throughout the world (Doug et al. 2020), co-infections with mosquito borne diseases such as Dengue are also expected to strain the healthcare systems to near the breaking point in countries such as Brazil (Lorenz et al. 2020). ...
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Combating vector-borne diseases requires a multidisciplinary approach for best results. Here we undertook field surveys in the potential mosquito breeding habitats in a rapidly urbanizing rural region in West Bengal, India. Secondly, we measured the knowledge about the mosquito vectors from students of a local college and we introduced a multilingual mobile software application “Mosa Nirmul” (EradicationOfMosquitoes) with an AI and game element and measured their perception and future scopes. The seasonal survey about the possible breeding grounds of mosquitoes revealed the presence of the larva of all three common genera of disease carrying mosquitoes Aedes , Culex and Anopheles . The presence was noted in a various types water and garbage sources. Their prevalence was found to be the highest in the post-monsoon season. In the second study, the college students demonstrated reasonably competent knowledge about the basics of protection against mosquitoes, but the score was lower when the questions were about the breeding grounds of mosquitoes. In the third study, the software application was received on the positive side, with a mean score of 3.98/5.. The AI module was also understood by many with about half of them were confident to collect data by themselves for related projects, with a score of mean score of 3.56. The surveys showed that mosquito vectors are still a threat in rural areas. However, with the positive results from the usage of the app, existing baseline knowledge, and willingness to adopt modern technologies, many factors looked promising for the future.
... An analysis by Austin, Bellinger, and Rana of 67 developing nations where malaria is endemic showed that deforestation is associated with increased prevalence of malaria-across different regions and different species of mosquitoes. 121 The study cites several ways in which deforestation can impact malaria prevalence, including increases in sunlight and standing water that provide favorable breeding sites for competent mosquito vectors; loss of biodiversity; and increases in human exposure to vector mosquitoes. While the study identifies a broad pattern linking deforestation to malaria, it also notes that the analysis does not consider regional or local ecological factors and mosquito species characteristics that may affect malaria prevalence. ...
Technical Report
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The Vitality of Forests: Illustrating the evidence connecting forests and human health, is intended to better justify why the public, policymakers, and private sector should be interested in forests’ role beyond their recreational, carbon sequestration, or biodiversity conservation potential. The evidence demonstrates that public health and forests are entwined—at the local, regional, and global scale—and that across each of nature’s contributions to human health, forest conservation, protection, and management can improve human lives.
... The effect of the proportion of forest in the landscape was assessed on vector [153] or human [76,[154][155][156] infection (group 25). Studies focusing on deforestation were very heterogeneous in the type of exposure they assessed: rate of forest loss [157], percentage of forest cover lost during the past year and the past 5 years [154], comparison between stages of forest [158], percentage of 4 stages of forest indicating deforestation [159]. One study tested the effect of the distance to the nearest forest [160] and two studies compared epidemiology in dense forest and in other habitats (fringe forest [161]; villages [162]). ...
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Background The control and prevention of vector-borne and zoonotic diseases is often based on the reduction of host or vector populations, involving but not limited to preventative culling and use of insecticides. Yet, destructive interventions such as these have shown several limitations including ineffectiveness on arthropods and negative impacts on ecosystems. An alternative strategy would be to rely on the natural ecosystem functions and their careful management to regulate such diseases. The goal of our work was to evaluate existing scientific evidence on potential links between ecosystem components/functions and 14 vector-borne and zoonotic diseases impacting human health and answer the question: “What evidence exists on the impact of specific ecosystem components and functions on infectious diseases?”. Methods We searched for scientific articles published in English and French and screened them in a 3-round process (title, abstract and full-text). Articles were retained, without any geographical limitation, if they matched the following eligibility criteria: an exposure/intervention linked to changes in biological communities, habitats, or landscapes; an outcome consisting of any measure of infection in vector, animal or human hosts; and the presence of a comparator, in time and/or in space. The results are presented as a systematic map, followed by a narrative review where the amount of papers allowed for synthesis. Results Searches in 5 scientific publication databases allowed to retrieve 9723 unique articles, among which 207 were retained after the screening process. The amount of relevant literature was highly variable depending on diseases, and the types of exposures also varied greatly among studies focusing on the same disease. A hundred articles presented in the map were unique in their “disease x exposure” combination and thus not eligible for further narrative description. The remaining 107 articles were organized in 34 “disease x exposure” groups, encompassing 9 out of the 14 initial diseases. The groups were composed of 2 to 16 articles and were examined to provide a description of the current state of knowledge for those diseases. Conclusion Studies investigating the interaction between infectious diseases and ecosystems components and functions are still very scarce, and certain diseases are much more studied than others. Out of 14 diseases, 8 generated less than 10 relevant articles, while 2 diseases (Lyme disease and West Nile disease) represented 44% of all relevant studies. Although several vector-borne diseases included in the review represent a major health issue in the world, such as malaria or dengue, they have been exclusively studied under the prism of land-use, and we were unable to find relevant studies that tested the regulatory role of animal biodiversity-related functions. The role of predation in the regulation of vector and host populations has rarely been studied, with the exception of schistosomiasis. The dilution and amplification effects were addressed in several studies focusing on the composition of ecological communities. This map is a first step and could be upgraded in order to guide future research projects with the aim to conduct meta-analysis and build a robust evidence base to inform decision-making.
... Such human movement promotes propagation of Plasmodium to new areas, and persistence in others where the disease is decreasing, as demonstrated in Burkina Faso, Africa [37]. The dynamics of socioecological change, poor housing, inadequate access to water and sanitation, and lack of or poor access to health facilities by the inhabitants also serve to intensify malaria transmission [38]. ...
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Inter-relationships among mosquito vectors, Plasmodium parasites, human ecology, and biotic and abiotic factors, drive malaria risk. Specifically, rural landscapes shaped by human activities have a great potential to increase the abundance of malaria vectors, putting many vulnerable people at risk. Understanding at which point the abundance of vectors increases in the landscape can help to design policies and interventions for effective and sustainable control. Using a dataset of adult female mosquitoes collected at 79 sites in malaria endemic areas in the Brazilian Amazon, this study aimed to (1) verify the association among forest cover percentage (PLAND), forest edge density (ED), and variation in mosquito diversity; and to (2) test the hypothesis of an association between landscape structure (i.e., PLAND and ED) and Nyssorhynchus darlingi (Root) dominance. Mosquito collections were performed employing human landing catch (HLC) (peridomestic habitat) and Shannon trap combined with HLC (forest fringe habitat). Nyssorhynchus darlingi abundance was used as the response variable in a generalized linear mixed model, and the Shannon diversity index (H’) of the Culicidae community, PLAND, and the distance house-water drainage were used as predictors. Three ED categories were also used as random effects. A path analysis was used to understand comparative strengths of direct and indirect relationships among Amazon vegetation classes, Culicidae community, and Ny . darlingi abundance. Our results demonstrate that Ny . darlingi is negatively affected by H´ and PLAND of peridomestic habitat, and that increasing these variables (one-unit value at β 0 = 768) leads to a decrease of 226 ( P < 0.001) and 533 ( P = 0.003) individuals, respectively. At the forest fringe, a similar result was found for H’ (β 1 = -218; P < 0.001) and PLAND (β 1 = -337; P = 0.04). Anthropogenic changes in the Amazon vegetation classes decreased mosquito biodiversity, leading to increased Ny . darlingi abundance. Changes in landscape structure, specifically decreases in PLAND and increases in ED, led to Ny . darlingi becoming the dominant species, increasing malaria risk. Ecological mechanisms involving changes in landscape and mosquito species composition can help to understand changes in the epidemiology of malaria.
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The study was conducted in gutter, swamps, and large water bodies and Culex (Diptera: Culicidae) larvae were sampled weekly from May to November 2019. Three Culex vectors identified in the areas namely, Culex quinquefasciatus, Cx. nigripalpus and Cx. salinarius. Habitats-wised showed large water habitats had the abundance of Culex followed by gutters and swamps. Based on the monthly abundance rate, the peak abundance of the immature stages was June and July and declined in November in both habitats. While the age survival rate differed significantly from one another across the months in all the habitat types. On the age distribution within the habitat types, L4 was the highest, in gutters, while in swamps, and large water habitats, L1 larvae were the most abundant, respectively. While between the habitats, gutters had a significantly higher abundance of L1, L2, L3, and L4, while swamp and larger water habitats were insignificant (p > 0.05) from one another for L1 and L4 except for the L3 immature stage that was differentiated with larger water being the most abundant. The findings imply that anthropogenic changes to the ecosystem are causing a severe hazard of Culex-Borne Diseases to public health in Niger State.
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As countries in the Greater Mekong Sub-region (GMS) increasingly focus their malaria control and elimination efforts on reducing forest-related transmission, greater understanding of the relationship between deforestation and malaria incidence will be essential for programs to assess and meet their 2030 elimination goals. Leveraging village-level health facility surveillance data and forest cover data in a spatio-temporal modeling framework, we found evidence that deforestation is associated with short-term increases, but long-term decreases in confirmed malaria case incidence in Lao People's Democratic Republic (Lao PDR). We identified strong associations with deforestation measured within 30 km of villages but not with deforestation in the near (10 km) and immediate (1 km) vicinity. Results appear driven by deforestation in densely forested areas and were more pronounced for infections with Plasmodium falciparum ( P. falciparum ) than for Plasmodium vivax ( P. vivax ). These findings highlight the influence of forest activities on malaria transmission in the GMS.
Factors expected to change concurrently with forest loss—such as economic activity and air pollution—shape human health in different ways, making it difficult to ex ante predict the net impact of deforestation. This paper investigates the infant mortality effects of prenatal exposure to high biomass forest loss in Indonesia, a country with rich forest reserves increasingly being subjected to high levels of deforestation. Indonesia officially bans clearing in areas with high biomass natural forests (referred to henceforth as ‘protected forests’), yet these forests face illegal logging. The analysis uses a fixed effects approach, essentially tracking how mortality responds to protected forest cover changes over time within districts. Results suggest that protected forest loss favors survival among all infants. However, there is variation in the protected forest loss-infant mortality relationship by pregnancy order or gravidity—while children born from women’s higher order pregnancies are less likely to die when exposed to deforestation, children born from first pregnancies experience an increase in their risk of death. Potential mechanisms such as overall air pollution, economic activity and perinatal health care do not appear to explain the gravidity-specific effects of deforestation in protected areas. However, the observed pattern of results suggests that effects are being channeled through malaria—the disease, which is likely to increase with forest loss, tends to disproportionately infect women during their first pregnancy, thus causing greater harm to the children born from these pregnancies.
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Background: Many developing countries are experiencing rapid ecological changes such as deforestation and shifting agricultural practices. These environmental changes may have an important consequence on malaria due to their impact on vector survival and reproduction. Despite intensive deforestation and malaria transmission in the China-Myanmar border area, the impact of deforestation on malaria vectors in the border area is unknown. Methods: We conducted life table studies on Anopheles minimus larvae to determine the pupation rate and development time in microcosms under deforested, banana plantation, and forested environments. Results: The pupation rate of An. minimus was 3.8 % in the forested environment. It was significantly increased to 12.5 % in banana plantations and to 52.5 % in the deforested area. Deforestation reduced larval-to-pupal development time by 1.9-3.3 days. Food supplementation to aquatic habitats in forested environments and banana plantations significantly increased larval survival rate to a similar level as in the deforested environment. Conclusion: Deforestation enhanced the survival and development of An. minimus larvae, a major malaria vector in the China-Myanmar border area. Experimental determination of the life table parameters on mosquito larvae under a variety of environmental conditions is valuable to model malaria transmission dynamics and impact by climate and environmental changes.
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Background Despite being one of the world’s most affected regions, only little is known about the social and spatial distributions of malaria in Indonesian Papua. Existing studies tend to be descriptive in nature; their inferences are prone to confounding and selection biases. At the same time, there remains limited malaria-cartographic activity in the region. Analysing a subset (N = 22,643) of the National Basic Health Research 2007 dataset (N = 987,205), this paper aims to quantify the district-specific risk of malaria in Papua and to understand how socio-demographic/economic factors measured at individual and district levels are associated with individual’s probability of contracting the disease. Methods We adopt a Bayesian hierarchical logistic regression model that accommodates not only the nesting of individuals within the island’s 27 administrative units but also the spatial autocorrelation among these locations. Both individual and contextual characteristics are included as predictors in the model; a normal conditional autoregressive prior and an exchangeable one are assigned to the random effects. Robustness is then assessed through sensitivity analyses using alternative hyperpriors. Results We find that rural Papuans as well as those who live in poor, densely forested, lowland districts are at a higher risk of infection than their counterparts. We also find age and gender differentials in malaria prevalence, if only to a small degree. Nine districts are estimated to have higher-than-expected malaria risks; the extent of spatial variation on the island remains notable even after accounting for socio-demographic/economic risk factors. Conclusions Although we show that malaria is geography-dependent in Indonesian Papua, it is also a disease of poverty. This means that malaria eradication requires not only biological (proximal) interventions but also social (distal) ones.
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Time is divided by geologists according to marked shifts in Earth's state. Recent global environmental changes suggest that Earth may have entered a new human-dominated geological epoch, the Anthropocene. Here we review the historical genesis of the idea and assess anthropogenic signatures in the geological record against the formal requirements for the recognition of a new epoch. The evidence suggests that of the various proposed dates two do appear to conform to the criteria to mark the beginning of the Anthropocene: 1610 and 1964. The formal establishment of an Anthropocene Epoch would mark a fundamental change in the relationship between humans and the Earth system.
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Although seldom noted in scholarly accounts, malaria represents a leading cause of death and underdevelopment in poor nations. Enormous cross-national variation in malaria rates across its endemic zones suggest the importance of large-scale factors in explaining comparative disease trends. While the biological vulnerability of women and children to malaria is often acknowledged, the literature has yet to investigate how gender inequalities contribute to patterns of malaria prevalence. Utilizing structural equation modeling on a sample of 90 less-developed nations and engaging insights from gender stratification perspectives, we consider the influence of both legal economic status and social dimensions of women’s status on malaria rates. We find that women’s legal economic status has an indirect relationship on malaria rates by enhancing women’s social standing and strengthening general health provisions. The results suggest that addressing issues of gender inequality in poor nations is central to tackling this persistent pandemic.
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Human activity is rapidly transforming most of Earth's natural systems. How this transformation is impacting human health, whose health is at greatest risk, and the magnitude of the associated disease burden are relatively new subjects within the field of environmental health. We discuss what is known about the human health implications of changes in the structure and function of natural systems and propose that these changes are affecting human health in a variety of important ways. We identify several gaps and limitations in the research that has been done to date and propose a more systematic and comprehensive approach to applied research in this field. Such efforts could lead to a more robust understanding of the human health impacts of accelerating environmental change and inform decision making in the land-use planning, environmental conservation, and public health policy realms.
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Although AIDS is a leading cause of death worldwide, the consequences of the pandemic are remarkably unequally distributed cross-nationally. This unequal global distribution of AIDS deaths should be of interest to sociologists because of the potential role of structural forces in accounting for these disparities. Yet, there has been relatively little sociological research on this topic. Using underutilized cross-national data on AIDS deaths, this study examines the macro-level sources of variation in AIDS death rates across 115 countries. The analysis focuses on secondary school enrollment as a key structural factor in reducing AIDS deaths. After controlling for the prevalence of HIV infection, secondary school enrollment has the second largest and most robust effect on AIDS death rates. Economic development also reduces AIDS death rates, but this effect is mediated by secondary school enrollment. Schooling matters partly because it reduces HIV prevalence, but education still continues to have a significant negative effect on AIDS deaths even after controlling for prevalence. The education effect is robust in a variety of sensitivity analyses. Ultimately, this study underscores the role of structural factors, particularly education, for understanding the AIDS crisis.
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Throughout the majority of the twentieth century, global inequality in health out-comes declined as most of the world experienced substantial economic gains and the availability of medicine, sanitation, and health interventions spread, especially to less-developed nations (Madzumdar 2001; Riley 2005; Soares 2007). During this period, the largest improvements in life expectancy were evidenced in the poorest of nations, where life expectancy scores have historically been the lowest (Clark ABSTRACT: Researchers note a recent trend of increasing inequality in cross-national life expectancy rates, largely due to conditions in the poorest of nations. Threats to life expectancy in less-developed nations include poverty, warfare, intense hunger, and disease, particularly AIDS/ HIV. This article utilizes structural equation models for a sample of less-developed nations and a subsample of Sub-Saharan African nations to test interrelationships among predictors. Findings indicate modernization to be the most robust predictor of life expectancy across less-developed nations and HIV to be the strongest determinant of life expectancy in Sub-Saharan African nations. Somewhat surprisingly, warfare and hunger do not have direct impacts on life expectancy among less-developed nations; however, important linkages among warfare, hunger, and disease are evidenced in the Sub-Saharan African sample, along with a notable positive influence of modernization on HIV rates. The findings demonstrate the significance of HIV on cross-national life expectancy scores and illuminate unique dynamics in Sub-Saharan Africa.
World Development Indicators 2014 provides a compilation of relevant, high-quality, and internationally comparable statistics about global development and the fight against poverty. It is intended to help users of all kinds—policymakers, students, analysts, professors, program managers, and citizens—find and use data related to all aspects of development, including those that help monitor and understand progress toward the two goals. • Six themes are used to organize indicators—world view, people, environment, economy, states and markets, and global links. As in past editions, World view reviews global progress toward the Millennium Development Goals (MDGs) and provides key indicators related to poverty. • A complementary online data analysis tool is available this year to allow readers to further investigate global, regional, and country progress on the MDGs: • Each of the remaining sections includes an introduction; six stories highlighting specific global, regional or country trends; and a table of the most relevant and popular indicators for that theme, together with a discussion of indicator compilation methodology.
Model Notation, Covariances, and Path Analysis. Causality and Causal Models. Structural Equation Models with Observed Variables. The Consequences of Measurement Error. Measurement Models: The Relation Between Latent and Observed Variables. Confirmatory Factor Analysis. The General Model, Part I: Latent Variable and Measurement Models Combined. The General Model, Part II: Extensions. Appendices. Distribution Theory. References. Index.