Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya.
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ABSTRACT:
SUBMITTER: Sewe MO
PROVIDER: S-EPMC5453969 | biostudies-literature | 2017 Jun
REPOSITORIES: biostudies-literature
Publications
Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya.
Sewe Maquins Odhiambo MO Tozan Yesim Y Ahlm Clas C Rocklöv Joacim J
Scientific reports 20170601 1
Malaria surveillance data provide opportunity to develop forecasting models. Seasonal variability in environmental factors correlate with malaria transmission, thus the identification of transmission patterns is useful in developing prediction models. However, with changing seasonal transmission patterns, either due to interventions or shifting weather seasons, traditional modelling approaches may not yield adequate predictive skill. Two statistical models,a general additive model (GAM) and GAMB ...[more]
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| S-EPMC4845989 | biostudies-literature
Project description:BackgroundDespite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies.MethodsWe used remotely sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by Plasmodium falciparum and Plasmodium vivax from 2014 to 2017 in two geographic settings in Ethiopia. Geospatial datasets were derived from multiple sources and characterized climate, vegetation, land use, topography, and surface water. All data were summarized annually at the sub-district (kebele) level for each of the two study areas. We analyzed the associations between environmental data and malaria cases with Boosted Regression Tree (BRT) models.ResultsWe found considerable spatial variation in malaria occurrence. Spectral indices related to land cover greenness (NDVI) and moisture (NDWI) showed negative associations with malaria, as the highest malaria rates were found in landscapes with low vegetation cover and moisture during the months that follow the rainy season. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria. Settlement structure also played an important role, with different effects in the two study areas. Variables related to surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies, and height above nearest drainage did not have strong influences on malaria.ConclusionWe found different relationships between malaria and environmental conditions in two geographically distinctive areas. These results emphasize that studies of malaria-environmental relationships and predictive models of malaria occurrence should be context specific to account for such differences.
| S-EPMC8201719 | biostudies-literature
Project description:IntroductionThe effect of malaria exposure on mortality using health facility incidence data as a measure of transmission has not been well investigated. Health and demographic surveillance systems (HDSS) routinely capture data on mortality, interventions and other household related indicators, offering a unique platform for estimating and monitoring the incidence-mortality relationship in space and time.MethodsMortality data from the HDSS located in Western Kenya collected from 2007 to 2012 and linked to health facility incidence data were analysed using Bayesian spatio-temporal survival models to investigate the relation between mortality (all-cause/malaria-specific) and malaria incidence across all age groups. The analysis adjusted for insecticide-treated net (ITN) ownership, socio-economic status (SES), distance to health facilities and altitude. The estimates obtained were used to quantify excess mortality due to malaria exposure.ResultsOur models identified a strong positive relationship between slide positivity rate (SPR) and all-cause mortality in young children 1-4 years (HR = 4.29; 95% CI: 2.78-13.29) and all ages combined (HR = 1.55; 1.04-2.80). SPR had a strong positive association with malaria-specific mortality in young children (HR = 9.48; 5.11-37.94), however, in older children (5-14 years), it was associated with a reduction in malaria specific mortality (HR = 0.02; 0.003-0.33).ConclusionSPR as a measure of transmission captures well the association between malaria transmission intensity and all-cause/malaria mortality. This offers a quick and efficient way to monitor malaria burden. Excess mortality estimates indicate that small changes in malaria incidence substantially reduce overall and malaria specific mortality.
| S-EPMC5509217 | biostudies-literature
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| S-EPMC6177175 | biostudies-literature
Project description:As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071-2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios.
| S-EPMC5336225 | biostudies-literature
Project description:Genetic analyses of population structure can be placed in explicit environmental contexts if appropriate environmental data are available. Here, we use high-coverage and high-resolution oceanographic and genetic sequence data to assess population structure patterns and their potential environmental influences for humpback dolphins in the Western Indian Ocean. We analyzed mitochondrial DNA data from 94 dolphins from the coasts of South Africa, Mozambique, Tanzania and Oman, employing frequency-based and maximum-likelihood algorithms to assess population structure and migration patterns. The genetic data were combined with 13 years of remote sensing oceanographic data of variables known to influence cetacean dispersal and population structure. Our analyses show strong and highly significant genetic structure between all putative populations, except for those in South Africa and Mozambique. Interestingly, the oceanographic data display marked environmental heterogeneity between all sampling areas and a degree of overlap between South Africa and Mozambique. Our combined analyses therefore suggest the occurrence of genetically isolated populations of humpback dolphins in areas that are environmentally distinct. This study highlights the utility of molecular tools in combination with high-resolution and high-coverage environmental data to address questions not only pertaining to genetic population structure, but also to relevant ecological processes in marine species.
| S-EPMC3182502 | biostudies-other
Project description:Malaria is one of the most severe problems faced by the world even today. Understanding the causative factors such as age, sex, social factors, environmental variability etc. as well as underlying transmission dynamics of the disease is important for epidemiological research on malaria and its eradication. Thus, development of suitable modeling approach and methodology, based on the available data on the incidence of the disease and other related factors is of utmost importance. In this study, we developed a simple non-linear regression methodology in modeling and forecasting malaria incidence in Chennai city, India, and predicted future disease incidence with high confidence level. We considered three types of data to develop the regression methodology: a longer time series data of Slide Positivity Rates (SPR) of malaria; a smaller time series data (deaths due to Plasmodium vivax) of one year; and spatial data (zonal distribution of P. vivax deaths) for the city along with the climatic factors, population and previous incidence of the disease. We performed variable selection by simple correlation study, identification of the initial relationship between variables through non-linear curve fitting and used multi-step methods for induction of variables in the non-linear regression analysis along with applied Gauss-Markov models, and ANOVA for testing the prediction, validity and constructing the confidence intervals. The results execute the applicability of our method for different types of data, the autoregressive nature of forecasting, and show high prediction power for both SPR and P. vivax deaths, where the one-lag SPR values plays an influential role and proves useful for better prediction. Different climatic factors are identified as playing crucial role on shaping the disease curve. Further, disease incidence at zonal level and the effect of causative factors on different zonal clusters indicate the pattern of malaria prevalence in the city. The study also demonstrates that with excellent models of climatic forecasts readily available, using this method one can predict the disease incidence at long forecasting horizons, with high degree of efficiency and based on such technique a useful early warning system can be developed region wise or nation wise for disease prevention and control activities.
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Project description:BackgroundMalaria and schistosomiasis present considerable disease burden in tropical and sub-tropical areas and severity is worsened by co-infections in areas where both diseases are endemic. Although pathogenesis of these infections separately is well studied, there is limited information on the pathogenic disease mechanisms and clinical disease outcomes in co-infections. In this study, we investigated the prevalence of malaria and schistosomiasis co-infections, and the hematologic and blood chemistry abnormalities in asymptomatic adults in a rural fishing community in western Kenya.MethodsThis sub-study used samples and data collected at enrollment from a prospective observational cohort study (RV393) conducted in Kisumu County, Kenya. The presence of malaria parasites was determined using microscopy and real-time-PCR, and schistosomiasis infection by urine antigen analysis (CCA). Hematological analysis and blood chemistries were performed using standard methods. Statistical analyses were performed to compare demographic and infection data distribution, and hematologic and blood chemistry parameters based on different groups of infection categories. Clinically relevant hematologic conditions were analyzed using general linear and multivariable Poisson regression models.ResultsFrom February 2017 to May 2018, we enrolled 671 participants. The prevalence of asymptomatic Plasmodium falciparum was 28.2% (157/556) and schistosomiasis 41.2% (229/562), with 18.0% (100/556) of participants co-infected. When we analyzed hematological parameters using Wilcoxon rank sum test to evaluate median (IQR) distribution based on malarial parasites and/or schistosomiasis infection status, there were significant differences in platelet counts (p = 0.0002), percent neutrophils, monocytes, eosinophils, and basophils (p < 0.0001 each). Amongst clinically relevant hematological abnormalities, eosinophilia was the most prevalent at 20.6% (116/562), whereas thrombocytopenia was the least prevalent at 4.3% (24/562). In univariate model, Chi-Square test performed for independence between participant distribution in different malaria parasitemia/schistosomiasis infection categories within each clinical hematological condition revealed significant differences for thrombocytopenia and eosinophilia (p = 0.006 and p < 0.0001, respectively), which was confirmed in multivariable models. Analysis of the pairwise mean differences of liver enzyme (ALT) and kidney function (Creatinine Clearance) indicated the presence of significant differences in ALT across the infection groups (parasite + /CCA + vs all other groups p < .003), but no differences in mean Creatinine Clearance across the infection groups.ConclusionsOur study demonstrates the high burden of asymptomatic malaria parasitemia and schistosomiasis infection in this rural population in Western Kenya. Asymptomatic infection with malaria or schistosomiasis was associated with laboratory abnormalities including neutropenia, leukopenia and thrombocytopenia. These abnormalities could be erroneously attributed to other diseases processes during evaluation of diseases processes. Therefore, evaluating for co-infections is key when assessing individuals with laboratory abnormalities. Additionally, asymptomatic infection needs to be considered in control and elimination programs given high prevalence documented here.
| S-EPMC8431856 | biostudies-literature
Project description:BackgroundThe gold standard for diagnosing Plasmodium falciparum infection is microscopic examination of Giemsa-stained peripheral blood smears. The effectiveness of this procedure for infection surveillance and malaria control may be limited by a relatively high parasitaemia detection threshold. Persons with microscopically undetectable infections may go untreated, contributing to ongoing transmission to mosquito vectors. The purpose of this study was to determine the magnitude and determinants of undiagnosed submicroscopic P. falciparum infections in a rural area of western Kenya.MethodsA health facility-based survey was conducted, and 367 patients seeking treatment for symptoms consistent with uncomplicated malaria in Homa Bay County were enrolled. The frequency of submicroscopic P. falciparum infection was measured by comparing the prevalence of infection based on light microscopic inspection of thick blood smears versus real-time polymerase chain reaction (RT-PCR) targeting P. falciparum 18S rRNA gene. Long-lasting insecticidal net (LLIN) use, participation in nocturnal outdoor activities, and gender were considered as potential determinants of submicroscopic infections.ResultsMicroscopic inspection of blood smears was positive for asexual P. falciparum parasites in 14.7% (54/367) of cases. All of these samples were confirmed by RT-PCR. 35.8% (112/313) of blood smear negative cases were positive by RT-PCR, i.e., submicroscopic infection, resulting in an overall prevalence by RT-PCR alone of 45.2% compared to 14.7% for blood smear alone. Females had a higher prevalence of submicroscopic infections (35.6% or 72 out of 202 individuals, 95% CI 28.9-42.3) compared to males (24.2%, 40 of 165 individuals, 95% CI 17.6-30.8). The risk of submicroscopic infections in LLIN users was about half that of non-LLIN users (OR = 0.59). There was no difference in the prevalence of submicroscopic infections of study participants who were active in nocturnal outdoor activities versus those who were not active (OR = 0.91). Patients who participated in nocturnal outdoor activities and use LLINs while indoors had a slightly higher risk of submicroscopic infection than those who did not use LLINs (OR = 1.48).ConclusionMicroscopic inspection of blood smears from persons with malaria symptoms for asexual stage P. falciparum should be supplemented by more sensitive diagnostic tests in order to reduce ongoing transmission of P. falciparum parasites to local mosquito vectors.
| S-EPMC8685826 | biostudies-literature
Project description:Malaria hotspots, defined as areas where transmission intensity exceeds the average level, become more pronounced as transmission declines. Targeting hotspots may accelerate reductions in transmission and could be pivotal for malaria elimination. Determinants of hotspot location, particularly of their movement, are poorly understood. We used spatial statistical methods to identify foci of incidence of self-reported malaria in a large census population of 64,000 people, in 8,290 compounds over a 2.5-year study period. Regression models examine stability of hotspots and identify static and dynamic correlates with their location. Hotspot location changed over short time-periods, rarely recurring in the same area. Hotspots identified in spring versus fall season differed in their stability. Households located in a hotspot in the fall were more likely to be located in a hotspot the following fall (RR?=?1.77, 95% CI: 1.66-1.89), but the opposite was true for compounds in spring hotspots (RR?=?0.15, 95% CI: 0.08-0.28). Location within a hotspot was related to environmental and static household characteristics such as distance to roads or rivers. Human migration into a household was correlated with risk of hotspot membership, but the direction of the association differed based on the origin of the migration event.
| S-EPMC5766583 | biostudies-other