Associations between environmental covariates and temporal changes in malaria incidence in high transmission settings of Uganda: a distributed lag nonlinear analysis

Abstract Background Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in malaria incidence. The study investigated the effect of environmental covariates on malaria incidence in high transmission settings of Uganda. Methods This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period. Estimates of monthly malaria incidence (MI) were derived from MRCs’ catchment areas. Environmental data including monthly temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed lag nonlinear model was used to investigate the effect of environmental covariates on malaria incidence. Results Overall, the median (range) monthly temperature was 30 °C (26–47), rainfall 133.0 mm (3.0–247), NDVI 0.66 (0.24–0.80) and MI was 790 per 1000 person-years (73–3973). Temperature of 35 °C was significantly associated with malaria incidence compared to the median observed temperature (30 °C) at month lag 2 (IRR: 2.00, 95% CI: 1.42–2.83) and the increased cumulative IRR of malaria at month lags 1–4, with the highest cumulative IRR of 8.16 (95% CI: 3.41–20.26) at lag-month 4. Rainfall of 200 mm significantly increased IRR of malaria compared to the median observed rainfall (133 mm) at lag-month 0 (IRR: 1.24, 95% CI: 1.01–1.52) and the increased cumulative IRR of malaria at month lags 1–4, with the highest cumulative IRR of 1.99(95% CI: 1.22–2.27) at lag-month 4. Average NVDI of 0.72 significantly increased the cumulative IRR of malaria compared to the median observed NDVI (0.66) at month lags 2–4, with the highest cumulative IRR of 1.57(95% CI: 1.09–2.25) at lag-month 4. Conclusions In high-malaria transmission settings, high values of environmental covariates were associated with increased cumulative IRR of malaria, with IRR peaks at variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.

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Associations between environmental covariates and malaria incidence in high transmission settings of Uganda: A distributed non-linear lagged ecological analysis

10.21203/rs.3.rs-358891/v1 ◽

2021 ◽

Author(s):

Jaffer Okiring ◽

Isobel Routledge ◽

Adrienne Esptein ◽

Jane F. Namuganga ◽

Emmanuel V. Kamya ◽

...

Keyword(s):

Environmental Factors ◽

Malaria Transmission ◽

Malaria Incidence ◽

Environmental Data ◽

Environmental Covariates ◽

High Transmission ◽

Monthly Temperature ◽

Rate Of Increase ◽

Catchment Areas ◽

Non Linear

Abstract Background Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in the incidence of symptomatic malaria. The study aim was to investigate the associations between environmental covariates and malaria incidence in high transmission settings of Uganda.Methods This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period (January 2019 - December 2020). Estimates of monthly malaria incidence (MI) were derived from MRCs’ catchment areas. Environmental data including monthy average measures of temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed non-linear lagged model was used to investigate the quantitative relationship between environmental covariates and malaria incidence. Results Overall, the median (range) monthly temperature was 30oC (26-47), rainfall 133.0 mm (3.0-247), NDVI 0.66 (0.24-0.80) and MI was 790 per 1000 person-years (73-3973). A non-linear relationship between environmental covariates and malaria incidence was observed. An average monthly temperature of 35oC was associated with significant increases in malaria incidence compared to the median observed temperature (30oC) at month lag 2 (IRR: 2.00, 95% CI: 1.42-2.83) and the cumulative increases in MI significantly at month lags 1-4, with the highest cumulative IRR of 8.16 (95% CI: 3.41-20.26) at lag month 4. An average monthly rainfall of 200mm was associated with significant increases in malaria incidence compared to the median observed rainfall (133mm) at lag month 0 (IRR: 1.24, 95% CI: 1.01-1.52) and the cumulative IRR increases of malaria at month lags 1-4, with the highest cumulative IRR of 1.99(95% CI: 1.22-2.27) at lag month 4. An average NVDI of 0.72 was associated with significant cumulative increases in IRR of malaria as compared to the median observed NDVI (0.66) at month lag 2-4, with the highest cumulative IRR of 1.57(95% CI: 1.09-2.25) at lag month 4. The rate of increase in cumulative IRR of malaria was highest within lag months 1-2 as compared to lag months 3-4 for all the environmental covariates.Conclusions In high-malaria transmission settings, high values of environmental covariates were associated with cumulative increases in the incidence of malaria, with peak associations occurring after variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.

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Spatio-Temporal Dynamic of Malaria Incidence: A Comparison of Two Ecological Zones in Mali

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17134698 ◽

2020 ◽

Vol 17 (13) ◽

pp. 4698 ◽

Cited By ~ 1

Author(s):

François Freddy Ateba ◽

Issaka Sagara ◽

Nafomon Sogoba ◽

Mahamoudou Touré ◽

Drissa Konaté ◽

...

Keyword(s):

Malaria Transmission ◽

Malaria Incidence ◽

Additive Model ◽

Principal Component ◽

Environmental Data ◽

Ecological Zones ◽

Time Period ◽

Passive Case Detection ◽

Spatio Temporal ◽

The Relationship

Malaria transmission largely depends on environmental, climatic, and hydrological conditions. In Mali, malaria epidemiological patterns are nested within three ecological zones. This study aimed at assessing the relationship between those conditions and the incidence of malaria in Dangassa and Koila, Mali. Malaria data was collected through passive case detection at community health facilities of each study site from June 2015 to January 2017. Climate and environmental data were obtained over the same time period from the Goddard Earth Sciences (Giovanni) platform and hydrological data from Mali hydraulic services. A generalized additive model was used to determine the lagged time between each principal component analysis derived component and the incidence of malaria cases, and also used to analyze the relationship between malaria and the lagged components in a multivariate approach. Malaria transmission patterns were bimodal at both sites, but peak and lull periods were longer lasting for Koila study site. Temperatures were associated with malaria incidence in both sites. In Dangassa, the wind speed (p = 0.005) and river heights (p = 0.010) contributed to increasing malaria incidence, in contrast to Koila, where it was humidity (p < 0.001) and vegetation (p = 0.004). The relationships between environmental factors and malaria incidence differed between the two settings, implying different malaria dynamics and adjustments in the conception and plan of interventions.

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Lagged effects of rainfall on malaria: a case study of Meghalaya

10.21203/rs.3.rs-1170848/v1 ◽

2021 ◽

Author(s):

Holendro Singh Chungkham ◽

Strong P Marbaniang ◽

Hritiz Gogoi

Keyword(s):

Malaria Transmission ◽

Climatic Factors ◽

Malaria Incidence ◽

Climatic Conditions ◽

Structural Time Series ◽

Distributed Lag ◽

Department Of Health ◽

Non Linear ◽

Malaria Death ◽

Rainfall Level

Abstract Background: Meghalaya contributes about twenty per cent of India's total malaria death and is one of the high malaria endemic states in India, very susceptible to malaria transmission mainly due to favorable climatic conditions that mostly facilitate the transmission. In the relationship between malaria and meteorological factors, existing studies mainly focus on the interaction between different climatic factors, while interaction within one specific climatic predictor at different ag times has been largely neglected. This paper aims to explore the interaction of lagged rainfalls and their impact on malaria incidence. Methods: The district monthly malaria records from Jan 2005 to December 2017 was collected from the Department of Health Services (Malaria), Government of Meghalaya. The district monthly meteorological records from Jan 2005 to December 2017 was collected from the Directorate of Agriculture, Government of Meghalaya, in which average temperature (℃), humidity (%) and rainfall (mm) had been recorded. Monthly malaria cases and three climatic variables of 4 districts in Meghalaya from 2015 to 2017 were analysed with the varying coefficient-distributed lag non-linear model. The missing climatic values were imputed using Kalman Smoothing on structural time series using the package imputeTS in R. Results: During the period 2005-2017, a total of 309133 malaria cases were reported in all the districts under study. The monthly average rainfall ranges from a minimum of 181.79 mm in South Garo to a maximum of 367.87 in Jaintia. Also, South Garo and East Khasi are the hottest and the coolest place understudy with 26.96 and 16.86 degrees Celsius respectively. Rainfall levels in the first-month lag affect the non-linear patterns between the incidence of malaria and rainfall at each lag time. The low rainfall level at the first-month lag may promote malaria incidence as rainfall increases. However, for the high rainfall level at the first-month lag, malaria incidence decreases as rainfall increases. Conclusion: The interaction effect between lagged rainfalls on malaria incidence was observed in this study, and highlights its importance for future studies to better understand and predict malaria transmission.

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Stratification At The Health District Level And Targeting of Malaria Control Interventions In Mali

10.21203/rs.3.rs-653056/v1 ◽

2021 ◽

Author(s):

Mady Cissoko ◽

Mahamadou Magassa ◽

Vincent Sanogo ◽

Abdoulaye Ouologuem ◽

Lansana Sangaré ◽

...

Keyword(s):

Malaria Control ◽

Malaria Incidence ◽

Control Strategies ◽

District Level ◽

Environmental Data ◽

World Health ◽

Maximum Temperature ◽

Low Transmission ◽

High Transmission ◽

Health Districts

Abstract IntroductionMalaria has been the leading cause of morbidity and mortality for several decades in Mali, with an increase from 2017 to 2020 (2,884,837 confirmed cases and 1,454 deaths). On the recommendation of the World Health Organization (WHO) and in the interests of efficient use of resources, Mali has begun a process of stratifying the health districts to target malaria control strategies.MethodMalaria, entomological and environmental data were collected through the local health information system (LHIS), the Demographic and Health Survey (DHS 2018), research institutions and MALI-METEO services. The WHO has recommended stratification at the district level consisted of assigning each district to one of 4 classes according to criteria based on incidence adjusted for attendance rate. Variables associated with monthly malaria incidence at the district level were identified using a general additive non-linear regression model.ResultsFrom 2017 to 2019, the median incidence across 75 health districts of Mali was 129.34 cases per 1,000 person-year (IQR=86.48). The results showed different periods of high malaria transmission in health districts level and durations varying from 2 to 6 months, showing a double peak for some health districts, which were located in the flooded areas. environmental variables such as rainfall, vegetation index (NDVI), maximum temperature and relative humidity were significantly associated at malaria incidence with a lag of around one month. A strata defines a geographical area with similar epidemiological, environmental and socio-economic factors. Stratification resulted in 12 health districts of very low transmission, 19 low transmission, 20 moderate transmission and 24 in high transmission areas. The number of rounds of season malaria chemoprevention will be based on the number of months in the high transmission period.ConclusionThis first stratification in Mali will allow targeting malaria control strategies. This approach will be dynamic and revised yearly in order to integrate information from the national epidemiological surveillance.

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Relationships between test positivity rate, total laboratory confirmed cases of malaria, and malaria incidence in high burden settings of Uganda: an ecological analysis

Malaria Journal ◽

10.1186/s12936-021-03584-7 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Jaffer Okiring ◽

Adrienne Epstein ◽

Jane F. Namuganga ◽

Victor Kamya ◽

Asadu Sserwanga ◽

...

Keyword(s):

Regression Models ◽

Malaria Incidence ◽

Temporal Changes ◽

Linear Regression Models ◽

High Burden ◽

Exponential Regression ◽

Malaria Morbidity ◽

Laboratory Test Results ◽

Combining Data ◽

Catchment Areas

Abstract Background Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. However, there are limited data on the accuracy of TPR and TCM for predicting temporal changes in malaria incidence, especially in high burden settings. Methods This study leveraged data from 5 malaria reference centres (MRCs) located in high burden settings over a 15-month period from November 2018 through January 2020 as part of an enhanced health facility-based surveillance system established in Uganda. Individual level data were collected from all outpatients including demographics, laboratory test results, and village of residence. Estimates of malaria incidence were derived from catchment areas around the MRCs. Temporal relationships between monthly aggregate measures of TPR and TCM relative to estimates of malaria incidence were examined using linear and exponential regression models. Results A total of 149,739 outpatient visits to the 5 MRCs were recorded. Overall, malaria was suspected in 73.4% of visits, 99.1% of patients with suspected malaria received a diagnostic test, and 69.7% of those tested for malaria were positive. Temporal correlations between monthly measures of TPR and malaria incidence using linear and exponential regression models were relatively poor, with small changes in TPR frequently associated with large changes in malaria incidence. Linear regression models of temporal changes in TCM provided the most parsimonious and accurate predictor of changes in malaria incidence, with adjusted R2 values ranging from 0.81 to 0.98 across the 5 MRCs. However, the slope of the regression lines indicating the change in malaria incidence per unit change in TCM varied from 0.57 to 2.13 across the 5 MRCs, and when combining data across all 5 sites, the R2 value reduced to 0.38. Conclusions In high malaria burden areas of Uganda, site-specific temporal changes in TCM had a strong linear relationship with malaria incidence and were a more useful metric than TPR. However, caution should be taken when comparing changes in TCM across sites.

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The impact of environmental variables on the spread of COVID-19 in the Republic of Korea

Scientific Reports ◽

10.1038/s41598-021-85493-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yong Kwan Lim ◽

Oh Joo Kweon ◽

Hye Ryoun Kim ◽

Tae-Hyoung Kim ◽

Mi-Kyung Lee

Keyword(s):

Environmental Factors ◽

Virus Disease ◽

Environmental Parameters ◽

Environmental Data ◽

Republic Of Korea ◽

Metropolitan Region ◽

Health Concern ◽

Ozone Level ◽

The Republic ◽

The Impact

AbstractCorona virus disease 2019 (COVID-19) has been declared a global pandemic and is a major public health concern worldwide. In this study, we aimed to determine the role of environmental factors, such as climate and air pollutants, in the transmission of COVID-19 in the Republic of Korea. We collected epidemiological and environmental data from two regions of the Republic of Korea, namely Seoul metropolitan region (SMR) and Daegu-Gyeongbuk region (DGR) from February 2020 to July 2020. The data was then analyzed to identify correlations between each environmental factor with confirmed daily COVID-19 cases. Among the various environmental parameters, the duration of sunshine and ozone level were found to positively correlate with COVID-19 cases in both regions. However, the association of temperature variables with COVID-19 transmission revealed contradictory results when comparing the data from SMR and DGR. Moreover, statistical bias may have arisen due to an extensive epidemiological investigation and altered socio-behaviors that occurred in response to a COVID-19 outbreak. Nevertheless, our results suggest that various environmental factors may play a role in COVID-19 transmission.

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Asymptomatic malaria and anaemia among pregnant women during high and low malaria transmission seasons in Burkina Faso: household-based cross-sectional surveys in Burkina Faso, 2013 and 2017

Malaria Journal ◽

10.1186/s12936-021-03703-4 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Toussaint Rouamba ◽

Sékou Samadoulougou ◽

Mady Ouédraogo ◽

Hervé Hien ◽

Halidou Tinto ◽

...

Keyword(s):

Burkina Faso ◽

Pregnant Women ◽

Malaria Transmission ◽

Asymptomatic Malaria ◽

Cross Sectional ◽

Transmission Season ◽

Low Transmission ◽

High Transmission ◽

Asymptomatic Carriage ◽

Low Malaria Transmission

Abstract Background Malaria in endemic countries is often asymptomatic during pregnancy, but it has substantial consequences for both the mother and her unborn baby. During pregnancy, anaemia is an important consequence of malaria infection. In Burkina Faso, the intensity of malaria varies according to the season, albeit the prevalence of malaria and anaemia as well as their risk factors, during high and low malaria transmission seasons is underexplored at the household level. Methods Data of 1751 pregnant women from October 2013 to March 2014 and 1931 pregnant women from April 2017 to June 2017 were drawn from two cross-sectional household surveys conducted in 24 health districts of Burkina Faso. Pregnant women were tested for malaria in their household after consenting. Asymptomatic carriage was defined as a positive result from malaria rapid diagnostic tests in the absence of clinical symptoms of malaria. Anaemia was defined as haemoglobin level less than 11 g/dL in the first and third trimester and less than 10.5 g/dL in the second trimester of pregnancy. Results Prevalence of asymptomatic malaria in pregnancy was estimated at 23.9% (95% CI 20.2–28.0) during the high transmission season (October–November) in 2013. During the low transmission season, it was 12.7% (95% CI 10.9–14.7) between December and March in 2013–2014 and halved (6.4%; 95% CI 5.3–7.6) between April and June 2017. Anaemia prevalence was estimated at 59.4% (95% CI 54.8–63.8) during the high transmission season in 2013. During the low transmission season, it was 50.6% (95% CI 47.7–53.4) between December and March 2013–2014 and 65.0% (95% CI 62.8–67.2) between April and June, 2017. Conclusion This study revealed that the prevalence of malaria asymptomatic carriage and anaemia among pregnant women at the community level remain high throughout the year. Thus, more efforts are needed to increase prevention measures such as IPTp–SP coverage in order to reduce anaemia and contribute to preventing low birth weight and poor pregnancy outcomes.

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Mapping Malaria Transmission Risk in Northern Morocco Using Entomological and Environmental Data

Malaria Research and Treatment ◽

10.4061/2011/391463 ◽

2011 ◽

Vol 2011 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

E. Adlaoui ◽

C. Faraj ◽

M. El Bouhmi ◽

A. El Aboudi ◽

S. Ouahabi ◽

...

Keyword(s):

Plasmodium Vivax ◽

Malaria Transmission ◽

Satellite Imagery ◽

Malaria Vector ◽

Environmental Changes ◽

Temporal Variations ◽

Environmental Data ◽

Transmission Risk ◽

Cultivation Period ◽

Very High

Malaria resurgence risk in Morocco depends, among other factors, on environmental changes as well as the introduction of parasite carriers. The aim of this paper is to analyze the receptivity of the Loukkos area, large wetlands in Northern Morocco, to quantify and to map malaria transmission risk in this region using biological and environmental data. This risk was assessed on entomological risk basis and was mapped using environmental markers derived from satellite imagery. Maps showing spatial and temporal variations of entomological risk for Plasmodium vivax and P. falciparum were produced. Results showed this risk to be highly seasonal and much higher in rice fields than in swamps. This risk is lower for Afrotropical P. falciparum strains because of the low infectivity of Anopheles labranchiae, principal malaria vector in Morocco. However, it is very high for P. vivax mainly during summer corresponding to the rice cultivation period. Although the entomological risk is high in Loukkos region, malaria resurgence risk remains very low, because of the low vulnerability of the area.

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Exploring the Impact of Climate Variability on Malaria Transmission Using a Dynamic Mosquito-Human Malaria Model

The Open Infectious Diseases Journal ◽

10.2174/1874279301810010088 ◽

2018 ◽

Vol 10 (1) ◽

pp. 88-100 ◽

Cited By ~ 5

Author(s):

Gbenga J. Abiodun ◽

Peter J. Witbooi ◽

Kazeem O. Okosun ◽

Rajendra Maharaj

Keyword(s):

Climate Variability ◽

Malaria Transmission ◽

Malaria Incidence ◽

Principal Component ◽

Wavelet Coherence ◽

Human Malaria ◽

Kwazulu Natal ◽

The Impact ◽

Wavelet Coherence Analysis ◽

Malaria Model

Introduction: The reasons for malaria resurgence mostly in Africa are yet to be well understood. Although the causes are often linked to regional climate change, it is important to understand the impact of climate variability on the dynamics of the disease. However, this is almost impossible without adequate long-term malaria data over the study areas. Methods: In this study, we develop a climate-based mosquito-human malaria model to study malaria dynamics in the human population over KwaZulu-Natal, one of the epidemic provinces in South Africa, from 1970-2005. We compare the model output with available observed monthly malaria cases over the province from September 1999 to December 2003. We further use the model outputs to explore the relationship between the climate variables (rainfall and temperature) and malaria incidence over the province using principal component analysis, wavelet power spectrum and wavelet coherence analysis. The model produces a reasonable fit with the observed data and in particular, it captures all the spikes in malaria prevalence. Results: Our results highlight the importance of climate factors on malaria transmission and show the seasonality of malaria epidemics over the province. Results from the principal component analyses further suggest that, there are two principal factors associated with climates variables and the model outputs. One of the factors indicate high loadings on Susceptible, Exposed and Infected human, while the other is more correlated with Susceptible and Recovered humans. However, both factors reveal the inverse correlation between Susceptible-Infected and Susceptible-Recovered humans respectively. Through the spectrum analysis, we notice a strong annual cycle of malaria incidence over the province and ascertain a dominant of one year periodicity. Consequently, our findings indicate that an average of 0 to 120-day lag is generally noted over the study period, but the 120-day lag is more associated with temperature than rainfall. This is consistence with other results obtained from our analyses that malaria transmission is more tightly coupled with temperature than with rainfall in KwaZulu-Natal province.

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Heterogeneous exposure and hotspots for malaria vectors at three study sites in Uganda

Gates Open Research ◽

10.12688/gatesopenres.12838.2 ◽

2018 ◽

Vol 2 ◽

pp. 32 ◽

Cited By ~ 8

Author(s):

Su Yun Kang ◽

Katherine E. Battle ◽

Harry S. Gibson ◽

Laura V. Cooper ◽

Kilama Maxwell ◽

...

Keyword(s):

Malaria Transmission ◽

Malaria Vector ◽

Malaria Control ◽

Negative Binomial ◽

Indoor Residual Spraying ◽

Housing Quality ◽

Environmental Covariates ◽

Vector Density ◽

Vector Abundance ◽

Study Sites

Background: Heterogeneity in malaria transmission has household, temporal, and spatial components. These factors are relevant for improving the efficiency of malaria control by targeting heterogeneity. To quantify variation, we analyzed mosquito counts from entomological surveillance conducted at three study sites in Uganda that varied in malaria transmission intensity. Mosquito biting or exposure is a risk factor for malaria transmission. Methods: Using a Bayesian zero-inflated negative binomial model, validated via a comprehensive simulation study, we quantified household differences in malaria vector density and examined its spatial distribution. We introduced a novel approach for identifying changes in vector abundance hotspots over time by computing the Getis-Ord statistic on ratios of household biting propensities for different scenarios. We also explored the association of household biting propensities with housing and environmental covariates. Results: In each site, there was evidence for hot and cold spots of vector abundance, and spatial patterns associated with urbanicity, elevation, or other environmental covariates. We found some differences in the hotspots in rainy vs. dry seasons or before vs. after the application of control interventions. Housing quality explained a portion of the variation among households in mosquito counts. Conclusion: This work provided an improved understanding of heterogeneity in malaria vector density at the three study sites in Uganda and offered a valuable opportunity for assessing whether interventions could be spatially targeted to be aimed at abundance hotspots which may increase malaria risk. Indoor residual spraying was shown to be a successful measure of vector control interventions in Tororo, Uganda. Cement walls, brick floors, closed eaves, screened airbricks, and tiled roofs were features of a house that had shown reduction of household biting propensity. Improvements in house quality should be recommended as a supplementary measure for malaria control reducing risk of infection.

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