scholarly journals Spatio-temporal variation of malaria hotspots in central Senegal, 2008-2012

2020 ◽  
Author(s):  
Sokhna DIENG ◽  
El Hadj Ba ◽  
Badara Cissé ◽  
Kankoe Sallah ◽  
Abdoulaye Guindo ◽  
...  
Keyword(s):  
Temporal Variation ◽  
Incidence Rate ◽  
Malaria Incidence ◽  
Vegetation Type ◽  
Local Level ◽  
Entire Study ◽  
Entire Area ◽  
Low Transmission ◽  
Malaria Incidence Rate ◽  
Spatio Temporal

Abstract Background: In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation.Methods: This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model.Results : The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR=0.48, IC95%: 0.33-0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown.Conclusion : In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed.Trial registrationThe data used in this work were obtained from a clinical trial registered at www.clinicaltrials.gov under # NCT 00712374.

scholarly journals Spatio-temporal variation of malaria hotspots in central Senegal, 2008-2012

2019 ◽  
Author(s):  
Sokhna DIENG ◽  
El Hadj Ba ◽  
Badara Cissé ◽  
Kankoe Sallah ◽  
Abdoulaye Guindo ◽  
...  
Keyword(s):  
Temporal Variation ◽  
Incidence Rate ◽  
Malaria Incidence ◽  
Vegetation Type ◽  
Control Strategies ◽  
Entire Study ◽  
Entire Area ◽  
Low Transmission ◽  
Malaria Incidence Rate ◽  
Spatio Temporal

Abstract Background In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal, and to identify the meteorological, environmental, and preventive factors that influence this variation. Methods The weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population 523,908) during a trial of Seasonal Malaria Chemoprevention (SMC), were analysed. Data on weekly rainfall and annual vegetation types were obtained for each village from remote sensing data. The time series of weekly cumulative malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected for each period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. Results The cumulative malaria incidence rate for the entire area ranged from 0 to 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR=0.48, IC95%: 0.33-0.68). According to the spatial interpolation, 2 zones located in the south of the study area had the highest risk of being a hotspot (ORmin=1.90, 95%CI: 1.02-3.56; ORmax=60.65, 95%CI: 26.86-136.95). The association between rainfall and hotspot status was non-linear and depended on vegetation type and the amount of rainfall. Conclusion In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. Our analysis shows also the importance of adapting control strategies to the local context and dynamic patterns. Moreover, the issue of spatial hotspots and foci of malaria persistence during LTPs needs to be further addressed.

scholarly journals Spatio-temporal variation of malaria hotspots in central Senegal, 2008-2012

2020 ◽  
Author(s):  
Sokhna DIENG ◽  
El Hadj Ba ◽  
Badara Cissé ◽  
Kankoe Sallah ◽  
Abdoulaye Guindo ◽  
...  
Keyword(s):  
Temporal Variation ◽  
Incidence Rate ◽  
Malaria Incidence ◽  
Vegetation Type ◽  
Local Context ◽  
Entire Study ◽  
Entire Area ◽  
Low Transmission ◽  
Malaria Incidence Rate ◽  
Spatio Temporal

Abstract Background In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal, and to identify the meteorological, environmental, and preventive factors that influence this variation. Methods The weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population 523,908) during a trial of Seasonal Malaria Chemoprevention (SMC), were analysed. Data on weekly rainfall and annual vegetation types were obtained for each village from remote sensing data. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. Results The malaria incidence rate for the entire area ranged from 0 to 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR=0.48, IC95%: 0.33-0.68). The association between rainfall and hotspot status was non-linear and depended on vegetation type and the amount of rainfall. The association between village location in the study area and the hotspot status was also showed. Conclusion In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. Knowing the similar environmental and meteorological particularities of hotspots, surveillance on these factors could lead targeted public health interventions in local context. Moreover, the issue of spatial hotspots and foci of malaria persistence during LTPs needs to be further addressed.

scholarly journals Five-year Surveillance of Malaria in Hotspot and Ivermectin Mass-drug Administration Zone of Amhara Regional State, Ethiopia

2021 ◽  
Author(s):  
Chalachew Yenew ◽  
Sileshi Mulatu ◽  
Asaye Alamneh
Keyword(s):  
Data Analysis ◽  
Incidence Rate ◽  
Drug Administration ◽  
Mass Drug Administration ◽  
Malaria Incidence ◽  
Reduction Rate ◽  
Supportive Supervision ◽  
Standard Format ◽  
Regional State ◽  
Malaria Incidence Rate

Abstract Objectives: Evaluate the five-year surveillance of malaria in the hotspot and Ivermectin mass-drug administration Zone of Amhara Regional State, Ethiopia.Methods: - A descriptive prevalence study design was employed and incorporated 25 study health institutions into the survey using the purposive sampling technique. Data were obtained by the standard format of systematic evaluation of four surveillance units from January to August 2020 through observation, document review, and interviewing surveillance officers and focal persons using a semi-structured Survey and generated the statistical analysis, tabular, and graphical output using the open-source statistical program R. Results: - Average report fullness and aptness were 97.9% and 96% subsequently. The average annual malaria incidence rate declined in terms of place and time, from the year 2015 to 2019 with an average reduction rate of 5.5% and the average annual parasitic incidence rate was 52%. The study identifies high endemicity of malaria due to no program-specific supportive supervision of public health emergency management and no routine data analysis.Conclusions: This result revealed that the malaria incidence rate showed a remarkable decline. However, the annual parasitic incidence rate remains constant. The study also indicated that ivermectin did not affect malaria elimination. Hence, the districts and sub-city health offices should conduct regular surveillance data analysis, perform supportive supervision, avail budgets, and further laboratory investigations to investigate the effect of ivermectin on the parasites under laboratory conditions.

scholarly journals Bayesian spatio-temporal distributed lag modeling for delayed climatic effects on sparse malaria incidence data

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chawarat Rotejanaprasert ◽  
Nattwut Ekapirat ◽  
Prayuth Sudathip ◽  
Richard J. Maude
Keyword(s):  
Climatic Factors ◽  
Malaria Incidence ◽  
Ground Truth ◽  
Real Data ◽  
Delayed Effect ◽  
Climatic Effects ◽  
Geographic Scale ◽  
Low Transmission ◽  
Spatio Temporal

Abstract Background In many areas of the Greater Mekong Subregion (GMS), malaria endemic regions have shrunk to patches of predominantly low-transmission. With a regional goal of elimination by 2030, it is important to use appropriate methods to analyze and predict trends in incidence in these remaining transmission foci to inform planning efforts. Climatic variables have been associated with malaria incidence to varying degrees across the globe but the relationship is less clear in the GMS and standard methodologies may not be appropriate to account for the lag between climate and incidence and for locations with low numbers of cases. Methods In this study, a methodology was developed to estimate the spatio-temporal lag effect of climatic factors on malaria incidence in Thailand within a Bayesian framework. A simulation was conducted based on ground truth of lagged effect curves representing the delayed relation with sparse malaria cases as seen in our study population. A case study to estimate the delayed effect of environmental variables was used with malaria incidence at a fine geographic scale of sub-districts in a western province of Thailand. Results From the simulation study, the model assumptions which accommodated both delayed effects and excessive zeros appeared to have the best overall performance across evaluation metrics and scenarios. The case study demonstrated lagged climatic effect estimation of the proposed modeling with real data. The models appeared to be useful to estimate the shape of association with malaria incidence. Conclusions A new method to estimate the spatiotemporal effect of climate on malaria trends in low transmission settings is presented. The developed methodology has potential to improve understanding and estimation of past and future trends in malaria incidence. With further development, this could assist policy makers with decisions on how to more effectively distribute resources and plan strategies for malaria elimination.

scholarly journals Trends of Malaria on Hotspot and Ivermectin Mass-drug Administration Zone of Amhara Regional State, 2020

2020 ◽  
Author(s):  
Chalachew Yenew ◽  
Sileshi Mulatu
Keyword(s):  
Public Health ◽  
Data Analysis ◽  
Incidence Rate ◽  
Drug Administration ◽  
Mass Drug Administration ◽  
Surveillance System ◽  
Malaria Incidence ◽  
Health Facilities ◽  
Supportive Supervision ◽  
Malaria Incidence Rate

Abstract Background:- Public health surveillance (PHS) is the continuing organized gathering, investigation, elucidation, and well-timed distribution of health-related information for activities and program evaluation. Conducting a surveillance system evaluation is crucial for monitoring the efficacy and effectiveness of intervention programs in health care systems. This study aimed to Evaluate the Trends of Malaria in the hotspot and Ivermectin mass-drug administration Zone of Amhara Regional State, Ethiopia, 2020.Methods: - A descriptive prevalence study design was used to evaluate the surveillance system of the Awi zone selected woreda. 25 study sources were incorporated in the survey (5 District Health Offices (5HOs), 10 Health Centers (10HCs), and 10 Health Posts (10 HPs). Purposive sampling techniques were utilized. Data were obtained by communicable diseases control the standard format of systematic evaluation of four surveillance units from January to August 2020 through observation, document review, and interviewing surveillance officers and focal persons using a semi-structured Survey.Results: - Average report fullness and aptness were 97.9% and 96% respectively. The average annual malaria incidence rate was a decline from the year 2015 to 2019 with an average reduction rate of 5.5% and the average annual parasitic incidence rate was 52 (22-199). In 2019/2020, 43131 Malaria cases were reported in the zone. Supervisions were made as integrated supportive supervision in the last six months. However, there was no program specific supportive supervision of public health emergency management. Data analysis was not routinely practiced in both visited districts and was not used for decision making.Conclusions: This result revealed that the malaria incidence rate showed a remarkable decline. However, the annual parasitic incidence rate remains constant. This indicates that ivermectin did not affect malaria elimination. The structure of the surveillance information transfer as of Kebel to Zone was well organized. However, coordination and supervision of the surveillanc activities were not frequent. From those supervised health facilities, most of them are not receiving feedback. There was no budget line, written feedback, epidemic and preparedness, and a response plan regular based on supportive supervision at all visited health facilities. Depending on this, we recommend that districts and sub-city health offices should conduct regular surveillance data analysis, perform supportive supervision, avail budgets and mitigate resource constraints and improve data quality on the job training and supportive supervision. Further laboratory investigations should be done to investigate the effect of ivermectin on the parasites under laboratory conditions.

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

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.

scholarly journals The Impact of Meteorological Parameters on the Pattern of Malaria Incidence Rate in the Northwest, Ethiopia; Repeated Cross-sectional Study Design With Bayesian Approach

2021 ◽  
Author(s):  
Asefa Adimasu ◽  
Adhanom Gebreegziabher Baraki ◽  
Kassahun Alemu Gelaye ◽  
Dawit Wendimsigegn
Keyword(s):  
Incidence Rate ◽  
Study Design ◽  
Meteorological Parameters ◽  
Malaria Incidence ◽  
Cross Sectional Study ◽  
Northwest Ethiopia ◽  
Maximum Temperature ◽  
Sectional Study ◽  
Cross Sectional ◽  
Malaria Incidence Rate

Abstract Background: Commonly the incidence of malaria was determined by some meteorological parameters. However, updated evidences were not reported in the study area and recently malaria is reported as an epidemic disease in Ethiopia particularly in Amhara regional state. Therefore the study was aimed to estimate malaria incidence proportion linked with some meteorological parameters.Methods: A repeated cross-sectional study design was done in 8 districts of northwest Ethiopia. All malaria patients who visited the local health institutions in the study area were the study participants. A monthly malaria surveillance data were retrieved from 8 districts of North Gondar zone health department and metrological data were obtained from west Amhara metrology agency office monthly reported databases. Data was clean and analyzed by using R2 win bugs software. The bayesian generalized negative binomial regression model was fitted for parameter estimation. Results: The overall average cumulative annual malaria incidence rate during the study period was 29.9 per 100 populations. In this study relative humidity (IRR; 1.04 (95% BCI, 1.01-1.05), normalized difference vegetation index [IRR; 2.74(95% BCI, 1.35-5.58)], altitude [IRR; 0.97(95% BCI, 0.95 - 0.99], average maximum temperature [IRR; 1.07(95% BCI (1.05 - 1.09)] and average minimum temperature [IRR; 1.04 (95% BCI (1.02-1.07)] were the statistically significant predictors. However, monthly rainfall, length of a sunshine hour, monthly wind speed was not associated with malaria incidence. Conclusion: The research showed a greater incidence of malaria in the study area when compared to the national statistics. Climatic variability changes the pattern of the malaria incidence in the study area.

Trends in Malaria Indicators After Scale-Up of Community-Based Malaria Management in Afghanistan

2021 ◽  
Author(s):  
Sayed Daoud Mahmoodi ◽  
Abdul Alim Atarud ◽  
Ahmad Walid Sadiqi ◽  
Sarah Gallalee ◽  
Willi McFarland ◽  
...  
Keyword(s):  
Incidence Rate ◽  
Death Rate ◽  
Malaria Incidence ◽  
Scale Up ◽  
Community Based ◽  
Cross Sectional ◽  
Malaria Incidence Rate ◽  
Malaria Death ◽  
Malaria Test ◽  
Timely Treatment

Abstract Objectives: The Community-Based Malaria Management (CBMM) strategy, introduced in 2013 and expanded to all health facilities and health posts in Afghanistan by 2016, aimed to deliver rapid diagnostic testing and more timely treatment to all communities nationwide. In this study, we compared the trends in several malaria outcome indicators before and after the expansion of the CBMM strategy.Study Design: Cross-sectional analysis of surveillance data Methods: Generalized estimating equation (GEE) models with a Poisson distribution were used to assess trends of three key outcomes before (2012-2015) and after (2016-2019) CBMM expansion. These outcomes were annual malaria incidence rate (both all and confirmed malaria incidence), malaria death rate, and malaria test positivity rate. Additional variables assessed included annual blood examination rates (ABER) and malaria confirmation rate.Results: Average malaria incidence rates decreased from 13.1 before CBMM expansion to 10.0 per 1000 persons per year after CBMM expansion (P<0.001). The time period after CBMM was expanded witnessed a 339% increase in confirmed malaria incidence as compared to the period before (IRR 3.39, 95% CI 2.18, 5.27; P<0.001). In the period since the expansion of CBMM (2016-2019), overall malaria incidence rate declined by 19% each year (IRR 0.81, 95% CI 0.71,0.92; P=0.001) and the malaria death rate declined by 85% each year (IRR 0.15, 95% CI 0.12, 0.20; P<0.001). In comparing the before period to the after period, the ABER increased from 2.3 to 3.5 per 100 person/year, the malaria test positivity rate increased from 12.2% to 20.5%, and the confirmation rate increased from 21% before to 71% after CBMM.Conclusions: Afghanistan’s CBMM expansion to introduce rapid diagnostic tests and provide more timely treatment for malaria through all levels of care temporally correlates with significant improvement in multiple indicators of malaria control.

scholarly journals Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

2019 ◽  
Vol 16 (24) ◽  
pp. 5078
Author(s):  
Sucharita Gopal ◽  
Yaxiong Ma ◽  
Chen Xin ◽  
Joshua Pitts ◽  
Lawrence Were
Keyword(s):  
Incidence Rate ◽  
Southern Oscillation ◽  
Lake Victoria ◽  
Malaria Incidence ◽  
Well Being ◽  
Enso Event ◽  
Primary Objective ◽  
Demographic And Health Surveys ◽  
Cold Spot ◽  
Malaria Incidence Rate

The United Nations’ Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used—Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino–southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.

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