***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Background Malaria transmission is highly heterogeneous , generating malaria hotspots that can fuel malaria transmission across a wider area . Targeting hotspots may represent an efficacious strategy for reducing malaria transmission . We determined the impact of interventions targeted to serologically defined malaria hotspots on malaria transmission both inside hotspots and in surrounding communities . Methods and Findings Twenty-seven serologically defined malaria hotspots were detected in a survey conducted from 24 June to 31 July 2011 that included 17,503 individuals from 3,213 compounds in a 100-km2 area in Rachuonyo South District , Kenya . In a cluster-r and omized trial from 22 March to 15 April 2012 , we r and omly allocated five clusters to hotspot-targeted interventions with larviciding , distribution of long-lasting insecticide-treated nets , indoor residual spraying , and focal mass drug administration ( 2,082 individuals in 432 compounds ) ; five control clusters received malaria control following Kenyan national policy ( 2,468 individuals in 512 compounds ) . Our primary outcome measure was parasite prevalence in evaluation zones up to 500 m outside hotspots , determined by nested PCR ( nPCR ) at baseline and 8 wk ( 16 June–6 July 2012 ) and 16 wk ( 21 August–10 September 2012 ) post-intervention by technicians blinded to the intervention arm . Secondary outcome measures were parasite prevalence inside hotpots , parasite prevalence in the evaluation zone as a function of distance from the hotspot boundary , Anopheles mosquito density , mosquito breeding site productivity , malaria incidence by passive case detection , and the safety and acceptability of the interventions . Intervention coverage exceeded 87 % for all interventions . Hotspot-targeted interventions did not result in a change in nPCR parasite prevalence outside hotspot boundaries ( p ≥ 0.187 ) . We observed an average reduction in nPCR parasite prevalence of 10.2 % ( 95 % CI −1.3 to 21.7 % ) inside hotspots 8 wk post-intervention that was statistically significant after adjustment for covariates ( p = 0.024 ) , but not 16 wk post-intervention ( p = 0.265 ) . We observed no statistically significant trend in the effect of the intervention on nPCR parasite prevalence in the evaluation zone in relation to distance from the hotspot boundary 8 wk ( p = 0.27 ) or 16 wk post-intervention ( p = 0.75 ) . Thirty-six patients with clinical malaria confirmed by rapid diagnostic test could be located to intervention or control clusters , with no apparent difference between the study arms . In intervention clusters we caught an average of 1.14 female anophelines inside hotspots and 0.47 in evaluation zones ; in control clusters we caught an average of 0.90 female anophelines inside hotspots and 0.50 in evaluation zones , with no apparent difference between study arms . Our trial was not powered to detect subtle effects of hotspot-targeted interventions nor design ed to detect effects of interventions over multiple transmission seasons . Conclusions Despite high coverage , the impact of interventions targeting malaria vectors and human infections on nPCR parasite prevalence was modest , transient , and restricted to the targeted hotspot areas . Our findings suggest that transmission may not primarily occur from hotspots to the surrounding areas and that areas with highly heterogeneous but widespread malaria transmission may currently benefit most from an untargeted community-wide approach . Hotspot-targeted approaches may have more validity in setting s where human settlement is more nuclear . Trial registration Clinical Trials.gov Background Heterogeneity in malaria exposure complicates survival analyses of vaccine efficacy trials and confounds the association between immune correlates of protection and malaria infection in longitudinal studies . Analysis may be facilitated by taking into account the variability in individual exposure levels , but it is unclear how exposure can be estimated at an individual level . Method and Findings We studied three cohorts ( Chonyi , Junju and Ngerenya ) in Kilifi District , Kenya to assess measures of malaria exposure . Prospect i ve data were available on malaria episodes , geospatial coordinates , proximity to infected and uninfected individuals and residence in predefined malaria hotspots for 2,425 individuals .  Antibody levels to the malaria antigens AMA1 and MSP1142 were available for 291 children from Junju . We calculated distance-weighted local prevalence of malaria infection within 1 km radius as a marker of individual 's malaria exposure . We used multivariable modified Poisson regression model to assess the discriminatory power of these markers for malaria infection ( i.e. asymptomatic parasitaemia or clinical malaria ) . The area under the receiver operating characteristic ( ROC ) curve was used to assess the discriminatory power of the models . Local malaria prevalence within 1 km radius and AMA1 and MSP1142 antibodies levels were independently associated with malaria infection . Weighted local malaria prevalence had an area under ROC curve of 0.72 ( 95%CI : 0.66–0.73 ) , 0.71 ( 95%CI : 0.69–0.73 ) and 0.82 ( 95%CI : 0.80–0.83 ) among cohorts in Chonyi , Junju and Ngerenya respectively . In a small subset of children from Junju , a model incorporating weighted local malaria prevalence with AMA1 and MSP1142 antibody levels provided an AUC of 0.83 ( 95%CI : 0.79–0.88 ) . Conclusion We have proposed an approach to estimating the intensity of an individual 's malaria exposure in the field . The weighted local malaria prevalence can be used as individual marker of malaria exposure in malaria vaccine trials and longitudinal studies of natural immunity to malaria Background Malaria is major public health problem in Senegal . In some parts of the country , it occurs almost permanently with a seasonal increase during the rainy season . There is evidence to suggest that the prevalence of malaria in Senegal has decreased considerably during the past few years . Recent data from the Senegalese National Malaria Control Programme ( NMCP ) indicates that the number of malaria cases decrease from 1,500,000 in 2006 to 174,339 in 2010 . With the decline of malaria morbidity in Senegal , the characterization of the new epidemiological profile of this disease is crucial for public health decision makers . Methods SaTScan ™ software using the Kulldorf method of retrospective space-time permutation and the Bernoulli purely spatial model was used to identify malaria clusters using confirmed malaria cases in 74 villages . ArcMAp was used to map malaria hotspots . Logistic regression was used to investigate risk factors for malaria hotspots in Keur Soce health and demographic surveillance site . Results A total of 1,614 individuals in 440 r and omly selected households were enrolled . The overall malaria prevalence was 12 % . The malaria prevalence during the study period varied from less than 2 % to more than 25 % from one village to another . The results showed also that rooms located between 50 m to 100 m away from livestock holding place [ adjusted O.R = 0.7 , P = 0.044 , 95 % C.I ( 1.02 - 7.42 ) ] , bed net use [ adjusted O.R = 1.2 , P = 0.024 , 95 % C.I ( 1.02 –1.48 ) ] , are good predictors for malaria hotspots in the Keur Soce health and demographic surveillance site . The socio economic status of the household also predicted on hotspots patterns . The less poor household are 30 % less likely to be classified as malaria hotspots area compared to the poorest household [ adjusted O.R = 0.7 , P = 0.014 , 95 % C.I ( 0.47 – 0.91 ) ] . Conclusion The study investigated risk factors for malaria hotspots in small communities in the Keur Soce site . The result showed considerable variation of malaria prevalence between villages which can not be detected in aggregated data . The data presented in this paper are the first step to underst and ing malaria in the Keur Soce site from a micro-geographic perspective Background Malaria is a leading cause of disease burden in Ug and a , although surprisingly few contemporary , age-stratified data exist on malaria epidemiology in the country . This report presents results from a total population survey of malaria infection and intervention coverage in a rural area of eastern Ug and a , with a specific focus on how risk factors differ between demographic groups in this population . Methods In 2008 , a cross-sectional survey was conducted in four contiguous villages in Mul and a , sub-county in Tororo district , eastern Ug and a , to investigate the epidemiology and risk factors of Plasmodium species infection . All permanent residents were invited to participate , with blood smears collected from 1,844 individuals aged between six months and 88 years ( representing 78 % of the population ) . Demographic , household and socio-economic characteristics were combined with environmental data using a Geographical Information System . Hierarchical models were used to explore patterns of malaria infection and identify individual , household and environmental risk factors . Results Overall , 709 individuals were infected with Plasmodium , with prevalence highest among 5 - 9 year olds ( 63.5 % ) . Thin films from a r and om sample of 20 % of parasite positive participants showed that 94.0 % of infections were Plasmodium falciparum and 6.0 % were P. malariae ; no other species or mixed infections were seen . In total , 68 % of households owned at least one mosquito although only 27 % of school-aged children reported sleeping under a net the previous night . In multivariate analysis , infection risk was highest amongst children aged 5 - 9 years and remained high in older children . Risk of infection was lower for those that reported sleeping under a bed net the previous night and living more than 750 m from a rice-growing area . After accounting for clustering within compounds , there was no evidence for an association between infection prevalence and socio-economic status , and no evidence for spatial clustering . Conclusion These findings demonstrate that mosquito net usage remains inadequate and is strongly associated with risk of malaria  among school-aged children .  Infection risk amongst adults is influenced by proximity to potential mosquito breeding grounds . Taken together , these findings emphasize the importance of increasing net coverage , especially among school-aged children Background Indonesia is among those countries committed to malaria eradication , with a continuously decreasing incidence of malaria . However , at district level the situation is different . This study presents a case of malaria resurgence Kokap Subdistrict of the Kulon Progo District in Yogyakarta Province , Java after five years of low endemicity . This study also aims to describe the community perceptions and health services delivery situation that contribute to this case . Methods All malaria cases ( 2007–2011 ) in Kulon Progo District were stratified to annual parasite incidence ( API ) . Two-hundred and twenty-six cases during an outbreak ( May 2011 to April 2012 ) were geocoded by household addresses using a geographic information system ( GIS ) technique and clusters were identified by SaTScan software analysis ( Arc GIS 10.1 ) . Purposive r and om sampling was conducted on respondents living inside the clusters to identify community perceptions and behaviour related to malaria . Interviews were conducted with malaria health officers to underst and the challenges of malaria surveillance and control . Results After experiencing three consecutive years with API less than 1 per thous and , malaria in Kokap subdistrict increased almost ten times higher than API in the district level and five times higher than national API . Malaria cases were found in all five villages in 2012 . One primary and two secondary malaria clusters in Hargotirto and Kalirejo villages were identified during the 2011–2012 outbreak . Most of the respondents were positively aware with malaria signs and activities of health workers to prevent malaria , although some social economic activities could not be hindered . Return transmigrants or migrant workers entering to their villages , reduced numbers of village malaria workers and a surge in malaria cases in the neighbouring district contributed to the resurgence . Conclusion Community perception , awareness and participation could constitute a solid foundation for malaria elimination in Kokap . However , decreasing number of village malaria workers and ineffective communication between primary health centres ( PHCs ) within boundary areas with similar malaria problems needs attention . De central ization policy was allegedly the reason for the less integrated malaria control between districts , especially in the cross border areas . Malaria resurgence needs attention particularly when it occurs in an area that is entering the elimination phase Background As malaria transmission declines , continued improvements of prevention and control interventions will increasingly rely on accurate knowledge of risk factors and an ability to define high-risk areas and population s at risk for focal targeting of interventions . This paper explores the independent association between living in a hotspot and prospect i ve risk of malaria infection . Methods Malaria infection status defined by nPCR and AMA-1 status in year 1 were used to define geographic hotspots using two geospatial statistical methods ( SaTScan and Kernel density smoothing ) . Other malaria risk factors for malaria infection were explored by fitting a multivariable model . Results This study demonstrated that residing in infection hotspot of malaria transmission is an independent predictor of malaria infection in the future . Conclusion It is likely that targeting such hotspots with better coverage and improved malaria control strategies will result in more cost-efficient uses of re sources to move towards malaria elimination Background Malaria control strategies are design ed as a solution for either the whole region or the whole country and are assumed to suit every 
    Output:
    
Conclusions Several variables associated with individual-level malaria infection were identified , but there was limited evidence that these factors explain variation in malaria risk at village or hotspot level .