Dynamic malaria hotspots in an open cohort in western Kenya

Mapping the stability of febrile illness hotspots in Punjab from 2012 to 2019- a spatial clustering and regression analysis

INTRODUCTION

Febrile illnesses (FI) represent a typical spectrum of diseases in low-resource settings, either in isolation or with other common symptoms. They contribute substantially to morbidity and mortality in India. The primary objective was to study the burden of FI based on Integrated Disease Surveillance Programme (IDSP) data in Punjab, analyze geospatial and temporal trends and patterns, and identify the potential hotspots for effective intervention.

METHODS

A retrospective ecological study used the district-level IDSP reports between 2012 and 2019. Diseases responsible for FI on a large scale, like Dengue, Chikungunya, Malaria (Plasmodium Falciparum, P. Vivax), Enteric fever, and Pyrexia of Unknown Origin (PUO), were included in the analysis. The digital map of Punjab was obtained from GitHub. Spatial autocorrelation and cluster analysis were done using Moran's I and Getis-Ord G* to determine hotspots of FI using the incidence and crude disease numbers reported under IDSP. Further, negative binomial regression was used to determine the association between Spatio-temporal and population variables per the census 2011. Stable hotspots were depicted using heat maps generated from district-wise yearly data.

RESULTS

PUO was the highest reported FI. We observed a rising trend in the incidence of Dengue, Chikungunya, and Enteric fever, which depicted occasional spikes during the study period. FI expressed significant inter-district variations and clustering during the start of the study period, with more dispersion in the latter part of the study period. P.Vivax malaria depicted stable hotspots in southern districts of Punjab. In contrast, P. Falciparum malaria, Chikungunya, and PUO expressed no spatial patterns. Enteric Fever incidence was high in central and northeastern districts but depicted no stable spatial patterns. Certain districts were common incidence hotspots for multiple diseases. The number of cases in each district has shown over-dispersion for each disease and has little dependence on population, gender, or residence as per regression analysis.

CONCLUSIONS

The study demonstrates that information obtained through IDSP can describe the spatial epidemiology of FI at crude spatial scales and drive concerted efforts against FI by identifying actionable points.

Comparative spatial–temporal analysis and predictive modeling of climate change-induced malaria vectors’ invasion in new hotspots in Kenya

Climate change/variability is a major driving factor among others that contribute to the spread of suitable malaria vectors’ geographical extent. The current study employed comparative spatial–temporal analysis using bioclimatic envelope modeling to predict and quantify the possible surge of suitable malaria vectors’ habitats in new hotspots in Kenya. BIOCLIM and BIOCLIM True/False models were run with model data from HADCM3, CCCMA and SCIRO IPCC future climatic projections under A2a scenario. Prediction and projection of the malaria vectors’ prevalence and distribution were done for the whole country. Spatial–temporal models were generated for the baseline climate, and projections were done to depict how the vectors are likely to be distributed by the years 2020, 2050 and 2080 under the influence of climate change. The results showed that the highest suitable malaria vectors’ habitats by area was 227, 092 km2 obtained when prediction was done with HADCM3 future climate by the year 2050. The least suitable habitats by area was 80, 060 km2 which was predicted with CCCMA projection by the year 2050. Ecological niche prediction from HADCM3 and CSIRO showed a similar trend although at different magnitudes. The prediction results portrayed high likelihood of shift in some suitable habitats that could turn unsuitable, while new hotspots are likely to emerge. The BIOCLIM prediction with the three future climate models showed that the current endemic zones of the lake region and south coastal strip of the Indian Ocean will still remain suitable habitats but with a decline shift in spatial extent by the year 2020, then start to expand by the years 2050–2080. Predictions from HADCM3 by the year 2050 has shown possible wide spread of malaria spatial extents in counties like Narok, Kajiado, Kitui, Makueni, Machakos, Meru, Marsabit, Isiolo, Samburu, Baringo, West Pokot, Turkana and Mandera, while a few others of lower extent might have some emerging isolated hotspots. Laikipia County might become unsuitable habitat for malaria vectors by the year 2050, and the case may remain the same by the year 2080. The malaria burden is likely to shift from Laikipia to the neighboring counties of Baringo, Isiolo, Meru and Turkana. In conclusion, malaria vectors are likely to spread in new continuous and isolated hotspots with future likely increase in malaria prevalence hence possible epidemic upsurge by the year 2050. Consequently, evidence-based scientific research can be utilized to guide policy for sustainable development in the health agenda.

Malaria hotspots explained from the perspective of ecological theory underlying insect foraging

Hotspots constitute the major reservoir for residual malaria transmission, with higher malaria incidence than neighbouring areas, and therefore, have the potential to form the cornerstone for successful intervention strategies. Detection of malaria hotspots is hampered by their heterogenous spatial distribution, and the laborious nature and low sensitivity of the current methods used to assess transmission intensity. We adopt ecological theory underlying foraging in herbivorous insects to vector mosquito host seeking and modelling of fine-scale landscape features at the village level. The overall effect of environmental variables on the density of indoor mosquitoes, sporozoite infected mosquitoes, and malaria incidence, was determined using generalized linear models. Spatial analyses were used to identify hotspots for malaria incidence, as well as malaria vector density and associated sporozoite prevalence. We identify household occupancy and location as the main predictors of vector density, entomological inoculation rate and malaria incidence. We propose that the use of conventional vector control and malaria interventions, integrated with their intensified application targeting predicted hotspots, can be used to reduce malaria incidence in endemic and residual malaria settings.

Lack of Consistent Malaria Incidence Hotspots in a Highland Kenyan Area During a 10-Year Period of Very Low and Unstable Transmission

The use of spatial data in malaria elimination strategies is important to understand whether targeted interventions against malaria can be used, particularly in areas with limited resources. We previously documented consistent areas of increased malaria incidence in the epidemic-prone area of Kipsamoite in highland Kenya from 2001 to 2004. In this area and a neighboring subcounty (Kapsisiywa), malaria incidence decreased substantially in 2005, going from peak incidence of 31.7 per 1,000 persons in June 2004 to peak incidence of 7.4 per 1,000 persons in May 2005. Subsequently, the use of indoor residual spraying and artemisinin combination therapy malaria treatment led to a possible interruption of malaria transmission for a 13-month period from 2007 to 2008, after which the incidence returned to very low levels until an epidemic in April-July 2013. In the present study, we used novel kernel density estimation methods to determine whether areas of increased malaria incidence were consistent in six periods of peak incidence from 2003 to 2013, and to assess patterns of incidence in the period before versus. after the period of possible interruption. Areas of highest incidence differed during peak malaria transmission periods over the years 2003-2013, and differed before and after the potential malaria interruption. In this epidemic-prone region with very low malaria transmission, consistent malaria "hotspots" identified in a time of higher transmission are no longer present. Ongoing assessment of spatial malaria epidemiology to identify and target current areas of elevated malaria risk may be important in campaigns to control or eliminate malaria in epidemic-prone areas.

Leveraging risk maps of malaria vector abundance to guide control efforts reduces malaria incidence in Eastern Province, Zambia

Although transmission of malaria and other mosquito-borne diseases is geographically heterogeneous, in sub-Saharan Africa risk maps are rarely used to determine which communities receive vector control interventions. We compared outcomes in areas receiving different indoor residual spray (IRS) strategies in Eastern Province, Zambia: (1) concentrating IRS interventions within a geographical area, (2) prioritizing communities to receive IRS based on predicted probabilities of Anopheles funestus, and (3) prioritizing communities to receive IRS based on observed malaria incidence at nearby health centers. Here we show that the use of predicted probabilities of An. funestus to guide IRS implementation saw the largest decrease in malaria incidence at health centers, a 13% reduction (95% confidence interval = 5-21%) compared to concentrating IRS geographically and a 37% reduction (95% confidence interval = 30-44%) compared to targeting IRS based on health facility incidence. These results suggest that vector control programs could produce better outcomes by prioritizing IRS according to malaria-vector risk maps.

Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period

In moving toward malaria elimination, finer scale malaria risk maps are required to identify hotspots for implementing surveillance–response activities, allocating resources, and preparing health facilities based on the needs and necessities at each specific area. This study aimed to demonstrate the use of multi-criteria decision analysis (MCDA) in conjunction with geographic information systems (GISs) to create a spatial model and risk maps by integrating satellite remote-sensing and malaria surveillance data from 18 counties of Yunnan Province along the China–Myanmar border. The MCDA composite and annual models and risk maps were created from the consensus among the experts who have been working and know situations in the study areas. The experts identified and provided relative factor weights for nine socioeconomic and disease ecology factors as a weighted linear combination model of the following: ([Forest coverage × 0.041] + [Cropland × 0.086] + [Water body × 0.175] + [Elevation × 0.297] + [Human population density × 0.043] + [Imported case × 0.258] + [Distance to road × 0.030] + [Distance to health facility × 0.033] + [Urbanization × 0.036]). The expert-based model had a good prediction capacity with a high area under curve. The study has demonstrated the novel integrated use of spatial MCDA which combines multiple environmental factors in estimating disease risk by using decision rules derived from existing knowledge or hypothesized understanding of the risk factors via diverse quantitative and qualitative criteria using both data-driven and qualitative indicators from the experts. The model and fine MCDA risk map developed in this study could assist in focusing the elimination efforts in the specifically identified locations with high risks.

Spatio-temporal variation of malaria hotspots in Central Senegal, 2008-2012

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 registration

The data used in this work were obtained from a clinical trial registered on July 10, 2008 at www.clinicaltrials.gov under NCT00712374.

Application of Functional Data Analysis to Identify Patterns of Malaria Incidence, to Guide Targeted Control Strategies

We introduce an approach based on functional data analysis to identify patterns of malaria incidence to guide effective targeting of malaria control in a seasonal transmission area. Using functional data method, a smooth function (functional data or curve) was fitted from the time series of observed malaria incidence for each of 575 villages in west-central Senegal from 2008 to 2012. These 575 smooth functions were classified using hierarchical clustering (Ward’s method), and several different dissimilarity measures. Validity indices were used to determine the number of distinct temporal patterns of malaria incidence. Epidemiological indicators characterizing the resulting malaria incidence patterns were determined from the velocity and acceleration of their incidences over time. We identified three distinct patterns of malaria incidence: high-, intermediate-, and low-incidence patterns in respectively 2% (12/575), 17% (97/575), and 81% (466/575) of villages. Epidemiological indicators characterizing the fluctuations in malaria incidence showed that seasonal outbreaks started later, and ended earlier, in the low-incidence pattern. Functional data analysis can be used to identify patterns of malaria incidence, by considering their temporal dynamics. Epidemiological indicators derived from their velocities and accelerations, may guide to target control measures according to patterns.

A Cross-Sectional Population Study of Geographic, Age-Specific, and Household Risk Factors for Asymptomatic Plasmodium falciparum Malaria Infection in Western Kenya

The burden of Plasmodium falciparum (Pf) malaria in Kenya is decreasing; however, it is still one of the top 10 causes of morbidity, particularly in regions of western Kenya. Between April 2015 and June 2016, we enrolled 965 apparently healthy children aged 0-15 years in former Nyanza and Western Provinces in Kenya to characterize the demographic, geographic, and household risk factors of asymptomatic malaria as part of an epidemiologic study to investigate the risk factors for endemic Burkitt lymphoma. The children were sampled using a stratified, multistage cluster sampling survey design. Malaria was assessed by rapid diagnostic test (RDT) and thick-film microscopy (TFM). Primary analyses of Pf malaria prevalence (pfPR) are based on RDT. Associations between weighted pfPR and potential risk factors were evaluated using logistic regression, accounting for the survey design. Plasmodium falciparum malaria prevalence was 36.0% (27.5%, 44.5%) by RDT and 22.3% (16.0%, 28.6%) by TFM. Plasmodium falciparum malaria prevalence was positively associated with living in the lake-endemic area (adjusted odds ratio [aOR] 3.46; 95% confidence interval [95% CI] 1.63, 7.37), paternal occupation as peasant farmer (aOR 1.87; 1.08, 3.26) or manual laborer (aOR 1.83; 1.00, 3.37), and keeping dogs (aOR 1.62; 0.98-2.69) or cows (aOR 1.52; 0.96-2.40) inside or near the household. Plasmodium falciparum malaria prevalence was inversely associated with indoor residual insecticide spraying (IRS) (aOR 0.44; 0.19, 1.01), having a household connected to electricity (aOR 0.47; 0.22, 0.98), and a household with two (aOR 0.45; 0.22, 0.93) or ≥ three rooms (aOR 0.41; 0.18, 0.93). We report high but geographically heterogeneous pfPR in children in western Kenya and significant associations with IRS and household-level socioeconomic factors.

Malaria Hotspots: Is There Epidemiological Evidence for Fine-Scale Spatial Targeting of Interventions?

As data at progressively granular spatial scales become available, the temptation is to target interventions to areas with higher malaria transmission - so-called hotspots - with the aim of reducing transmission in the wider community. This paper reviews literature to determine if hotspots are an intrinsic feature of malaria epidemiology and whether current evidence supports hotspot-targeted interventions. Hotspots are a consistent feature of malaria transmission at all endemicities. The smallest spatial unit capable of supporting transmission is the household, where peri-domestic transmission occurs. Whilst the value of focusing interventions to high-burden areas is evident, there is currently limited evidence that local-scale hotspots fuel transmission. As boundaries are often uncertain, there is no conclusive evidence that hotspot-targeted interventions accelerate malaria elimination.

A population-based study of the prevalence and risk factors of low-grade Plasmodium falciparum malaria infection in children aged 0-15 years old in northern Tanzania

OBJECTIVES

Northern Tanzania experiences significant malaria-related morbidity and mortality, but accurate data are scarce. We update the data on patterns of low-grade Plasmodium falciparum malaria infection among children in northern Tanzania.

METHODS

Plasmodium falciparum malaria prevalence (pfPR) was assessed in a representative sample of 819 children enrolled in 94 villages in northern Tanzania between October 2015 and August 2016, using a complex survey design. Individual- and household-level risk factors for pfPR were elicited using structured questionnaires. pfPR was assessed using rapid diagnostic tests (RDTs) and thick film microscopy (TFM). Associations with pfPR, based on RDT, were assessed using adjusted odds ratios (aOR) and confidence intervals (CI) from weighted survey logistic regression models.

RESULTS

Plasmodium falciparum malaria prevalence (pfPR) was 39.5% (95% CI: 31.5, 47.5) by RDT and 33.4% (26.0, 40.6) by TFM. pfPR by RDT was inversely associated with higher-education parents, especially mothers (5-7 years of education: aOR 0.55; 95% CI: 0.31, 0.96, senior secondary education: aOR 0.10; 95% CI: 0.02, 0.55), living in a house near the main road (aOR 0.34; 95% CI: 0.15, 0.76), in a larger household (two rooms: aOR 0.40; 95% CI: 0.21, 0.79, more than two rooms OR 0.35; 95% CI: 0.20, 0.62). Keeping a dog near or inside the house was positively associated with pfPR (aOR 2.01; 95% CI: 1.26, 3.21). pfPR was not associated with bed-net use or indoor residual spraying.

CONCLUSIONS

Nearly 40% of children in northern Tanzania had low-grade malaria antigenaemia. Higher parental education and household metrics but not mosquito bed-net use were inversely associated with pfPR.