Spatial Patterns and Climate Drivers of Malaria in Three Border Areas of Brazil, Venezuela and Guyana, 2016-2018

Abstract IntroductionIn 2020, 77% of malaria cases in the Americas were concentrated in Venezuela, Brazil, and Colombia. These countries are characterized by a heterogeneous malaria landscape and malaria hotspots. Furthermore, the political unrest in Venezuela has led to significant cross-border population movement. Hence, the aim of this study was to describe spatial patterns and identify significant climatic drivers of malaria transmission along the Venezuela-Brazil-Guyana border, focusing on Bolivar state, Venezuela and Roraima state, Brazil.MethodsMalaria case data, stratified by species from 2016-2018, were obtained from the Brazilian Malaria Epidemiology Surveillance Information System, the Guyana Vector Borne Diseases Program, the Venezuelan Ministry of Health, and civil society organizations. Spatial autocorrelation in malaria incidence was explored using Getis-Ord (Gi*) statistics. A Poisson regression model was developed with a conditional autoregressive prior structure and posterior parameters were estimated using the Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. Climatic covariates were precipitation and minimum and maximum temperature. ResultsThere were 685,498 malaria cases during the study period. Plasmodium vivax was the predominant species (71.7%, 490,861). Malaria hotspots were located in eight municipalities along the Venezuela and Guyana international borders with Brazil. Plasmodium falciparum decreased by 1.6% (95% credible interval [CrI] 1.5%, 2.3%) and 9.6% (95% CrI 1.5%, 25.2%) per 1 cm increase in six-month lagged precipitation and each 1°C increase of minimum temperature without lag. Each 1°C increase of one-month lagged maximum temperature increased P. falciparum by 6.6% (95% CrI 4.8%, 21.7%). P. vivax cases decreased by 1.0% (95% CrI 1.0%, 1.1%) and 7.0% (95% CrI 6.5%, 7.5%) for each 1 cm increase of precipitation lagged at six-months and 1°C increase in minimum temperature lagged at six-months. There was no significant residual spatial clustering after accounting for climatic covariates.ConclusionMalaria hotspots were located along the Venezuela and Guyana international border with Roraima state, Brazil. In addition to population movement, climatic variables are important drivers of malaria transmission in these areas.

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Effect of climate change and deforestation on vector borne diseases in the North-Eastern Indian state of Mizoram bordering Myanmar

10.21203/rs.3.rs-126530/v1 ◽

2020 ◽

Author(s):

Balasubramani Karuppusamy ◽

Devojit Kumar Sarma ◽

Pachuau Lalmalsawma ◽

Lalfakzuala Pautu ◽

Krishanpal Karmodiya ◽

...

Keyword(s):

Climate Change ◽

Minimum Temperature ◽

Forest Cover ◽

Kendall Test ◽

Maximum Temperature ◽

Monsoon Period ◽

Vector Borne Diseases ◽

Sen’S Slope ◽

Mann Kendall Test ◽

Vector Borne

Abstract Background Malaria and dengue are the two major vector-borne diseases in Mizoram. Malaria is endemic in Mizoram, and dengue was first reported only in 2012. It is well documented that climate change has a direct influence on the incidence and spread of vector-borne diseases. The study was designed to study the trends and impact of climate variables (temperature, rainfall and humidity) in the monsoon period (May to September) and deforestation on the incidence of dengue and malaria in Mizoram. Methods Temperature, rainfall and humidity data of Mizoram from 1979–2013 were obtained from the National Centers for Environmental Prediction Climate Forecast System Reanalysis and analyzed. Forest cover data of Mizoram was extracted from India State of Forest Report (IFSR) and Land Processes Distributed Active Archive Centre. Percent tree cover datasets of Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer missions were also used to study the association between deforestation and incidence of vector-borne diseases. The study used non-parametric tests to estimate long-term trends in the climate (temperature, rainfall, humidity) and forest cover variables. The trend and its magnitude are estimated through Mann-Kendall test and Sen's slope method. Year-wise dengue and malaria data were obtained from the State Vector Borne Disease Control Program, Mizoram. Results The Mann-Kendall test indicates that compared to maximum temperature, minimum temperature during the monsoon period is increasing (p < 0.001). The Sen’s slope estimation also shows an average annual 0.020C (0.01–0.03 at 95% CI) monotonic increasing trend of minimum temperature. The residuals of Sen’s estimate show that temperature is increasing at an average of about 0.10C/year after 2007.Trends indicate that both rainfall and humidity are increasing (p <. 0.001); on an average, there is a 20.45 mm increase in monsoon rainfall per year (5.90–34.37 at 95% CI), while there is a 0.08% (0.02–0.18 at 95% CI) increase in relative humidity annually. IFSR data shows that there is an annual average decrease of 162 sq.km (272.81–37.53 at 95% CI, p < 0.001) in the dense forest cover. Mizoram in 2012 was the last state in India to report the incidence of dengue. Malaria transmission continues to be stable in Mizoram; compared to 2007, the cases have increased in 2019. Conclusion Over the study period, there is an ~ 0.80C rise in the minimum temperature in the monsoon season which could have facilitated the establishment of Aedes aegypti, the major dengue vector in Mizoram. In addition, the increase in rainfall and humidity may have also helped the biology of Ae. aegypti. Deforestation could be one of the major factors responsible for the consistently high number of malaria cases in Mizoram.

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A Bayesian spatio-temporal analysis of malaria in the Greater Accra Region of Ghana from 2015 to 2019.

10.21203/rs.3.rs-143819/v2 ◽

2021 ◽

Author(s):

Elorm Donkor ◽

Matthew Kelly ◽

Cecilia Eliason ◽

Charles Amotoh ◽

Darren Gray ◽

...

Keyword(s):

Malaria Transmission ◽

Incidence Rate ◽

Malaria Control ◽

Malaria Incidence ◽

Climatic Variables ◽

Maximum Temperature ◽

District Health ◽

South Central ◽

Malaria Incidence Rate ◽

Greater Accra Region

Abstract Efforts towards malaria control in Ghana have had positive impacts. However, these efforts need to be locally tailored to further accelerate progress. The aim of this study was to examine the climatic drivers of malaria transmission in the Greater Accra Region and identify inter-district variation of malaria burden. Monthly malaria cases for the Greater Accra Region were obtained from the Ghanaian District Health Information and Management System. Malaria cases were decomposed using the seasonal-trend decomposition, based on locally weighted regression to analyse the seasonality. A Poisson regression model with a conditional autoregressive prior structure was used to quantify associations between climatic variables and malaria risk, and spatial dependence. Posterior parameters were estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. A total of 1,105,370 malaria cases was recorded in the region from 2015–2019. The overall malaria incidence rate for the region was approximately 1 per 1,000,000 population. Malaria transmission was highly seasonal with an irregular inter-annual pattern. Malaria incidence was found to increase by 0.1% (95% credible interval [CrI]: 0.02–0.16%) for a 1°C rise in monthly maximum temperature lagged at 6 months and 0.2% (95% CrI: 0.5–0.3%) for 1°C rise in monthly minimum temperature without lag. No spatial dependency was observed after accounting for climatic variables. Only five districts located in the south-central part of the region had a malaria incidence rate that was lower than the regional average at > 95% probability level. The distribution of malaria cases was heterogeneous, seasonal and significantly associated with climatic variables. Targeted malaria control and prevention in high-risk districts at the appropriate time points could result in a significant reduction in malaria transmission in the Greater Accra Region.

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Effects of rainfall, temperature and topography on malaria incidence in elimination targeted district of Ethiopia

Malaria Journal ◽

10.1186/s12936-021-03641-1 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Desalegn Dabaro ◽

Zewdie Birhanu ◽

Abiyot Negash ◽

Dawit Hawaria ◽

Delenasaw Yewhalaw

Keyword(s):

Regression Model ◽

Malaria Transmission ◽

Malaria Elimination ◽

Meteorological Factors ◽

Malaria Incidence ◽

Meteorological Variables ◽

Maximum Temperature ◽

Negative Binomial Regression Model ◽

Regional State ◽

Cross Correlation Analysis

Abstract Background Climate and environmental factors could be one of the primary factors that drive malaria transmission and it remains to challenge the malaria elimination efforts. Hence, this study was aimed to evaluate the effects of meteorological factors and topography on the incidence of malaria in the Boricha district in Sidama regional state of Ethiopia. Methods Malaria morbidity data recorded from 2010 to 2017 were obtained from all public health facilities of Boricha District in the Sidama regional state of Ethiopia. The monthly malaria cases, rainfall, and temperature (minimum, maximum, and average) were used to fit the ARIMA model to compute the malaria transmission dynamics and also to forecast future incidence. The effects of the meteorological variables and altitude were assessed with a negative binomial regression model using R version 4.0.0. Cross-correlation analysis was employed to compute the delayed effects of meteorological variables on malaria incidence. Results Temperature, rainfall, and elevation were the major determinants of malaria incidence in the study area. A regression model of previous monthly rainfall at lag 0 and Lag 2, monthly mean maximum temperature at lag 2 and Lag 3, and monthly mean minimum temperature at lag 3 were found as the best prediction model for monthly malaria incidence. Malaria cases at 1801–1900 m above sea level were 1.48 times more likely to occur than elevation ≥ 2000 m. Conclusions Meteorological factors and altitude were the major drivers of malaria incidence in the study area. Thus, evidence-based interventions tailored to each determinant are required to achieve the malaria elimination target of the country.

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Spatio-temporal patterns of childhood pneumonia in Bhutan: a Bayesian analysis

Scientific Reports ◽

10.1038/s41598-021-99137-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kinley Wangdi ◽

Kinley Penjor ◽

Tsheten Tsheten ◽

Chachu Tshering ◽

Peter Gething ◽

...

Keyword(s):

Relative Humidity ◽

Spatial Clustering ◽

Climatic Factors ◽

Temporal Trends ◽

Credible Interval ◽

Maximum Temperature ◽

Childhood Pneumonia ◽

Male Sex ◽

Spatio Temporal ◽

Spatial And Temporal Trends

AbstractPneumonia is one of the top 10 diseases by morbidity in Bhutan. This study aimed to investigate the spatial and temporal trends and risk factors of childhood pneumonia in Bhutan. A multivariable Zero-inflated Poisson regression model using a Bayesian Markov chain Monte Carlo simulation was undertaken to quantify associations of age, sex, altitude, rainfall, maximum temperature and relative humidity with monthly pneumonia incidence and to identify the underlying spatial structure of the data. Overall childhood pneumonia incidence was 143.57 and 10.01 per 1000 persons over 108 months of observation in children aged < 5 years and 5–14 years, respectively. Children < 5 years or male sex were more likely to develop pneumonia than those 5–14 years and females. Each 1 °C increase in maximum temperature was associated with a 1.3% (95% (credible interval [CrI] 1.27%, 1.4%) increase in pneumonia cases. Each 10% increase in relative humidity was associated with a 1.2% (95% CrI 1.1%, 1.4%) reduction in the incidence of pneumonia. Pneumonia decreased by 0.3% (CrI 0.26%, 0.34%) every month. There was no statistical spatial clustering after accounting for the covariates. Seasonality and spatial heterogeneity can partly be explained by the association of pneumonia risk to climatic factors including maximum temperature and relative humidity.

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Spatio-temporal patterns of pneumonia in Bhutan: A Bayesian analysis

10.21203/rs.3.rs-152074/v1 ◽

2021 ◽

Author(s):

Kinley Wangdi ◽

Kinley Penjor ◽

Tsheten Tsheten ◽

Chachu Tshering ◽

Peter Gething ◽

...

Keyword(s):

Risk Factors ◽

Relative Humidity ◽

Poisson Regression ◽

Spatial Clustering ◽

Climatic Factors ◽

Temporal Trends ◽

Credible Interval ◽

Maximum Temperature ◽

Spatio Temporal ◽

Spatial And Temporal Trends

Abstract Pneumonia is one of the top 10 diseases by morbity in Bhutan. This study aimed to investigate the spatial and temporal trends and risk factors of pneumonia in Bhutan. A multivariable Zero-inflated Poisson regression using a Bayesian Markov chain Monte Carlo simulation was undertaken to quantify associations of age, sex, rainfall, maximum temperature and relative humidity with monthly pneumonia incidence and identify underlying spatial structure of the data. Overall pneumonia incidence was 96.5 and 4.57 per 1,000 populations over nine years in people aged < 5 years and ≥ 5 years, respectively. Children < 5 years or being a female are more like to get pneumonia than ≥ 5 years and males. A 10mm increase in rainfall and 1°C increase in maximum temperature was associated with a 7.2% (95% (credible interval [CrI] 0.7%, 14.0%) and 28.6% (95% CrI 27.2%, 30.1%) increase in pneumonia cases. A 1% increase in relative humidity was associated with a decrease in the incidence of pneumonia by 8.6% (95% CrI 7.5%, 9.7%). There was no evidence of spatial clustering after accounting for the covariates. Seasonality and spatial heterogeneity can partly be explained by the association of pneumonia risk to climatic factors including rainfall, maximum temperature and relative humidity.

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A Bayesian spatio-temporal analysis of malaria in the Greater Accra Region of Ghana from 2015 to 2019.

10.21203/rs.3.rs-143819/v1 ◽

2021 ◽

Author(s):

Elorm Donkor ◽

Matthew Kelly ◽

Cecilia Eliason ◽

Charles Amotoh ◽

Prof. Darren J Gray ◽

...

Keyword(s):

Malaria Transmission ◽

Incidence Rate ◽

Malaria Control ◽

Malaria Incidence ◽

Climatic Variables ◽

Maximum Temperature ◽

District Health ◽

South Central ◽

Malaria Incidence Rate ◽

Greater Accra Region

Abstract Background: Efforts towards malaria control in Ghana have had positive impacts. However, these efforts need to be locally tailored to further accelerate progress. The aim of this study was to examine the climatic drivers of malaria transmission in the Greater Accra Region and identify inter-district variation of malaria burden.Methodology: Monthly malaria cases for the Greater Accra Region were obtained from the Ghanaian District Health Information and Management System from 2015 to 2019. Malaria cases were decomposed using the seasonal-trend decomposition, based on locally weighted regression to analyze the seasonality. A Poisson regression model with a conditional autoregressive prior structure was used to quantify associations between climatic variables and malaria risk, and spatial dependence. Posterior parameters were estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling.Results: A total of 1,105,370 malaria cases was recorded in the region from 2015–2019. The overall malaria incidence rate for the region was approximately 1 per 1,000,000 population. Malaria transmission was highly seasonal with an irregular inter-annual pattern during the study period. Malaria incidence was found to increase by 0.1% (95% credible interval [CrI]: 0.02–0.16%) for a 1°C rise in monthly mean maximum temperature lagged at 6 months and 0.2% (95% CrI: 0.5–0.3%) for 1°C rise in monthly mean minimum temperature without lag. No spatial dependency was observed after accounting for climatic variables. Only five districts located in the south-central part of the region had a malaria incidence rate that was lower than the regional average at > 95% probability level.Conclusion: The distribution of malaria cases was heterogeneous, seasonal and significantly associated with climatic variables. Targeted malaria control and prevention in high-risk districts at the appropriate time points could result in a significant reduction in malaria transmission in the Greater Accra Region.

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Spatiotemporal Evolution of Fractional Vegetation Cover and Its Response to Climate Change Based on MODIS Data in the Subtropical Region of China

Remote Sensing ◽

10.3390/rs13050913 ◽

2021 ◽

Vol 13 (5) ◽

pp. 913

Author(s):

Hua Liu ◽

Xuejian Li ◽

Fangjie Mao ◽

Meng Zhang ◽

Di’en Zhu ◽

...

Keyword(s):

Vegetation Cover ◽

Minimum Temperature ◽

Maximum Temperature ◽

Distribution Area ◽

Spatiotemporal Pattern ◽

Climate Factors ◽

Important Indicator ◽

Fractional Vegetation Cover ◽

Annual Minimum Temperature ◽

Total Study Area

The subtropical vegetation plays an important role in maintaining the structure and function of global ecosystems, and its contribution to the global carbon balance are receiving increasing attention. The fractional vegetation cover (FVC) as an important indicator for monitoring environment change, is widely used to analyze the spatiotemporal pattern of regional and even global vegetation. China is an important distribution area of subtropical vegetation. Therefore, we first used the dimidiate pixel model to extract the subtropical FVC of China during 2001–2018 based on MODIS land surface reflectance data, and then used the linear regression analysis and the variation coefficient to explore its spatiotemporal variations characteristics. Finally, the partial correlation analysis and the partial derivative model were used to analyze the influences and contributions of climate factors on FVC, respectively. The results showed that (1) the subtropical FVC had obvious spatiotemporal heterogeneity; the FVC high-coverage and medium-coverage zones were concentratedly and their combined area accounted for more than 70% of the total study area. (2) The interannual variation in the average subtropical FVC from 2001 to 2018 showed a significant growth trend. (3) In 76.28% of the study area, the regional FVC showed an increasing trend, and the remaining regional FVC showed a decreasing trend. However, the overall fluctuations in the FVC (increasing or decreasing) in the region were relatively stable. (4) The influences of climate factors to the FVC exhibited obvious spatial differences. More than half of all pixels exhibited the influence of the average annual minimum temperature and the annual precipitation had positive on FVC, while the average annual maximum temperature had negative on FVC. (5) The contributions of climate changes to FVC had obvious heterogeneity, and the average annual minimum temperature was the main contribution factor affecting the dynamic variations of FVC.

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Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China

Forest Ecosystems ◽

10.1186/s40663-021-00309-9 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Peixin Ren ◽

Zelin Liu ◽

Xiaolu Zhou ◽

Changhui Peng ◽

Jingfeng Xiao ◽

...

Keyword(s):

Climate Change ◽

Minimum Temperature ◽

Vegetation Index ◽

Climatic Factors ◽

Time Lag ◽

Evergreen Forest ◽

Maximum Temperature ◽

Daily Minimum Temperature ◽

Study Region ◽

Cumulative Precipitation

Abstract Background Vegetation phenology research has largely focused on temperate deciduous forests, thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions. Results Using satellite solar-induced chlorophyll fluorescence (SIF) and MODIS enhanced vegetation index (EVI) data, we applied two methods to evaluate temporal and spatial patterns of the end of the growing season (EGS) in subtropical vegetation in China, and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation. Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods (dynamic threshold method and derivative method) was later than that derived from gross primary productivity (GPP) based on the eddy covariance technique, and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks, respectively. We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation (accounting for more than 73% and 62% of the study areas, respectively), but negatively correlated with preseason maximum temperature (accounting for more than 59% of the study areas). In addition, EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors, and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests, shrub and grassland. Conclusions Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China. We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region. These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China, and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.

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The effect of average temperature on suicide rates in five urban California counties, 1999–⁠2019: an ecological time series analysis

BMC Public Health ◽

10.1186/s12889-021-11001-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sierra Cheng ◽

Rebecca Plouffe ◽

Stephanie M. Nanos ◽

Mavra Qamar ◽

David N. Fisman ◽

...

Keyword(s):

Public Health ◽

Suicide Rate ◽

Minimum Temperature ◽

The United States ◽

Additive Models ◽

Maximum Temperature ◽

Mortality Data ◽

Average Temperature ◽

Suicide Rates ◽

Average Maximum

Abstract Background Suicide is among the top 10 leading causes of premature morality in the United States and its rates continue to increase. Thus, its prevention has become a salient public health responsibility. Risk factors of suicide transcend the individual and societal level as risk can increase based on climatic variables. The purpose of the present study is to evaluate the association between average temperature and suicide rates in the five most populous counties in California using mortality data from 1999 to 2019. Methods Monthly counts of death by suicide for the five counties of interest were obtained from CDC WONDER. Monthly average, maximum, and minimum temperature were obtained from nCLIMDIV for the same time period. We modelled the association of each temperature variable with suicide rate using negative binomial generalized additive models accounting for the county-specific annual trend and monthly seasonality. Results There were over 38,000 deaths by suicide in California’s five most populous counties between 1999 and 2019. An increase in average temperature of 1 °C corresponded to a 0.82% increase in suicide rate (IRR = 1.0082 per °C; 95% CI = 1.0025–1.0140). Estimated coefficients for maximum temperature (IRR = 1.0069 per °C; 95% CI = 1.0021–1.0117) and minimum temperature (IRR = 1.0088 per °C; 95% CI = 1.0023–1.0153) were similar. Conclusion This study adds to a growing body of evidence supporting a causal effect of elevated temperature on suicide. Further investigation into environmental causes of suicide, as well as the biological and societal contexts mediating these relationships, is critical for the development and implementation of new public health interventions to reduce the incidence of suicide, particularly in the face increasing temperatures due to climate change.

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Effects of Cropland Expansion on Temperature Extremes in Western India from 1982 to 2015

Land ◽

10.3390/land10050489 ◽

2021 ◽

Vol 10 (5) ◽

pp. 489

Author(s):

Jinxiu Liu ◽

Weihao Shen ◽

Yaqian He

Keyword(s):

Land Use ◽

Heat Flux ◽

Land Cover ◽

Minimum Temperature ◽

Negative Impact ◽

Maximum Temperature ◽

Daily Minimum Temperature ◽

Daily Maximum ◽

Temperature Extremes ◽

Western India

India has experienced extensive land cover and land use change (LCLUC). However, there is still limited empirical research regarding the impact of LCLUC on climate extremes in India. Here, we applied statistical methods to assess how cropland expansion has influenced temperature extremes in India from 1982 to 2015 using a new land cover and land use dataset and ECMWF Reanalysis V5 (ERA5) climate data. Our results show that during the last 34 years, croplands in western India increased by ~33.7 percentage points. This cropland expansion shows a significantly negative impact on the maxima of daily maximum temperature (TXx), while its impacts on the maxima of daily minimum temperature and the minima of daily maximum and minimum temperature are limited. It is estimated that if cropland expansion had not taken place in western India over the 1982 to 2015 period, TXx would likely have increased by 0.74 (±0.64) °C. The negative impact of croplands on reducing the TXx extreme is likely due to evaporative cooling from intensified evapotranspiration associated with croplands, resulting in increased latent heat flux and decreased sensible heat flux. This study underscores the important influences of cropland expansion on temperature extremes and can be applicable to other geographic regions experiencing LCLUC.

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