Unraveling the “indirect effects” of interventions against malaria endemicity: A systematic scoping review

Unraveling the “indirect effects” of interventions against malaria endemicity: A systematic scoping review | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Unraveling the “indirect effects” of interventions against malaria endemicity: A systematic scoping review View ORCID Profile Yura K. Ko , View ORCID Profile Wataru Kagaya , Chim W. Chan , Mariko Kanamori , Samuel M. Mbugua , Alex K. Rotich , Bernard N. Kanoi , Mtakai Ngara , Jesse Gitaka , Akira Kaneko doi: https://doi.org/10.1101/2024.05.08.24307059 Yura K. Ko 1 Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet 2 Department of Virology, Tohoku University Graduate School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yura K. Ko For correspondence: yurako0603{at}gmail.com Wataru Kagaya 3 Department of Ecoepidemiology, Institute of Tropical Medicine (NEKKEN), Nagasaki University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Wataru Kagaya Chim W. Chan 4 Department of Virology and Parasitology, Graduate School of Medicine/ Osaka International Research Center for Infectious Diseases, Osaka Metropolitan University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mariko Kanamori 5 Department of Public Health Sciences, Stockholm University 6 Institute for the Future of Human Society, Kyoto University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Samuel M. Mbugua 7 Center for Research in Infectious Diseases, Directorate of Research and Innovation, Mount Kenya University 8 Centre for Malaria Elimination, Mount Kenya University 9 School of pharmacy and health sciences, United States International University Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alex K. Rotich 7 Center for Research in Infectious Diseases, Directorate of Research and Innovation, Mount Kenya University 8 Centre for Malaria Elimination, Mount Kenya University 10 Department of Chemistry and Biochemistry, University of Eldoret Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bernard N. Kanoi 7 Center for Research in Infectious Diseases, Directorate of Research and Innovation, Mount Kenya University 8 Centre for Malaria Elimination, Mount Kenya University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mtakai Ngara 1 Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jesse Gitaka 7 Center for Research in Infectious Diseases, Directorate of Research and Innovation, Mount Kenya University 8 Centre for Malaria Elimination, Mount Kenya University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Akira Kaneko 1 Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet 4 Department of Virology and Parasitology, Graduate School of Medicine/ Osaka International Research Center for Infectious Diseases, Osaka Metropolitan University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Summary There is an urgent need to maximize the effectiveness of existing malaria interventions and optimize the deployment of novel countermeasures. When assessing the effects of interventions against malaria, it is imperative to consider the interdependence of people and the resulting indirect effects, without which the impact on health outcomes and their cost-effectiveness may be miscalculated. Here, we conducted a scoping review of existing literature on the indirect effects of malaria interventions. We observed a recent increase in both the number of reports and the variety of terms used to denote indirect effects. We further classified eight categories of comparative analysis to identify the indirect effects, proposed common terms for the indirect effects, and highlighted the potential benefits of mathematical models in estimating indirect effects. Improving the study design and reporting the indirect effects of malaria interventions will lead to better informed decisions by policymakers. Introduction The global fight against malaria has become increasingly challenging in recent years. Despite concerted scale-up of intervention tools, such as the widespread distribution of long-lasting insecticidal nets (LLINs), rapid diagnostic tests (RDTs), and artemisinin-based combination therapies (ACTs), the estimated global case incidence of malaria in the past few years has stagnated at around 58 cases per 1,000 population at risk, while the global mortality rate has remained at approximately 14 per 100,000 population at risk 1 . Moreover, although malaria remains a leading cause of healthcare spending in endemic countries 2 , the amount invested in 2022 fell short of the estimated USD 7.8 billion required globally to achieve the Global Technical Strategy (GTS) targets set by the World Health Organization (WHO) 1 . It is anticipated that high-income nations and other international funders will continue to prioritize their efforts to address emerging diseases such as COVID-19 through 2024 3 . In this context, there is an urgent need to re-evaluate existing malaria interventions for more effective deployment, along with the employment of novel countermeasures to reduce the malaria burden more efficiently and cost-effectively. Malaria is a vector-borne disease transmitted by Anopheles mosquitoes. When measuring the effects of interventions against such diseases, it is crucial to consider the interdependence in people, often referred to as “dependent happenings” 4 . For instance, in malaria-endemic settings, a decline in the number of malaria-infected individuals or mosquitoes will reduce parasite reservoirs and means of transmission in a community, leading a lower possibility of infection among all community members. Consequently, malaria control measures implemented in a community are expected to yield direct benefits for individuals receiving the interventions and indirect benefits for both individuals receiving and not receiving the interventions. Indirect effects can be defined as the unintended positive or negative consequences of an intervention that influences disease transmission or health outcomes. Thus, without proper consideration of the indirect effects, malaria interventions’ impacts on health outcomes and their cost-effectiveness may be overestimated or underestimated. Therefore, adopting a comprehensive and standardized approach to identify both direct and indirect effects is imperative to gain a detailed understanding of intervention impacts. Moreover, evidence of indirect effects will influence policy makers’ decision making. If the direct effects are equivalent, an intervention that broadly benefits those who do not receive the intervention is preferable to one that benefits only a limited number of people who receive the intervention. The concept of indirect effects of malaria intervention, especially LLINs, has long been well known. Nevertheless, the description of indirect effects in the WHO guidelines for vector-borne mosquito control only briefly states that community-level effects of ITNs have not always been observed 5 . In addition, the scientific literature on malaria interventions that explicitly differentiate and thoroughly analyze their indirect effects is currently limited 6 . A recent systematic review of the indirect effects of interventions on health in low- and middle-income countries by Benjamin-Chung et al 7 . included only two malaria-related studies. Moreover, the methodology of measuring the indirect effects greatly varies, and the terms indicating the indirect effects are not standardized (e.g., community effects, spillover effects, mass effects, herd effects, area-wide effects, spatial effects, and positive externalities). To address these knowledge gaps, we conducted a scoping review to summarize how the indirect effects of malaria interventions were analyzed and reported. Methods Search strategy and selection criteria We followed the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews (PRISMA-ScR) 8 . The study protocol is available at elsewhere ( https://inplasy.com/inplasy-2023-6-0025/ ). Literature search We searched PubMed, Web of Science, and EMBASE by title and abstracts. In addition, for grey literature, we searched OAIster by keywords. Searches were conducted in June 2023. We set the search terms as follows: (“malaria” OR “plasmodium”) AND (“indirect effect*” OR “indirect protection” OR “herd effect*” OR “herd protection” OR “community effect*” OR “communal effect*” OR “community-level effect*” OR “community protection” OR “communal protection” OR “community-level protection” OR “peer effect*” OR “peer influence effect*” OR “mass effect*” OR “assembly effect*” OR “spillover effect*” OR “contextual effect*” OR “free-rider” OR “free rider” OR “free-riding” OR “free riding” OR “positive externality” OR “positive externalities” OR “dependent happenings”) Eligibility criteria We included studies that were conducted to quantify the indirect effects of any interventions for all species of malaria infection. We excluded non-original papers such as opinions and editorials. We only targeted articles written in English. We defined indirect effects as the impact accrued by either the non-intervention or intervention group, stemming from alterations in malaria parasite or mosquito populations within a community consequent to an intervention. It should be noted that simple group comparisons between treatment and control (or baseline) groups/clusters are regarded as total effects. Studies that reported only total effects were excluded from our review. However, if the treatment coverage in the community was considerably low, the group comparisons between treatment and control were considered indirect effects and were included in our review. Study selection We imported the data for each relevant publication into reference software (Rayyan, https://www.rayyan.ai/ ). Prior to the initial screening, duplicate records were deleted automatically. In the first review step, two reviewers (YKK, SMM) screened all records by title and abstract according to the eligibility criteria. Any discrepancies in the process were resolved by discussion between both reviewers. Once a record was selected, its full text was reviewed by at least two of five reviewers (YKK, WK, CWC, MK, and AKR). Specific data (see the section “ Data extraction and analysis ”) were recorded and summarized in a tabular form through this second review step. Any disagreement was addressed through discussion. Additional reference and citation searches were also conducted. The reference lists of the articles identified during the search were scanned manually, and eligible articles were included in the full-text reading. Data extraction and analysis We used a standardized data collection form that follows the PRISMA guidelines for scoping reviews 8 to obtain the following information from each record: title, name of authors, year of publication, region, country, study type, malaria parasite species, type of interventions, type of outcomes, separate estimated indirect effect for different conditions (yes/no), pre-specified to measure indirect effect (yes/no), secondary analysis of previous study (yes/no), methods of indirect effects estimation, terms of indirect effects, and if positive or negative indirect effects observed (yes/no). A detailed description of the extracted data is in Supplementary Table 1. Standardized labels were made for each term for inconsistencies of words, as listed in Supplementary Table 2. Quality of study methodology for estimating indirect effect We utilized the classification of risk of bias for indirect effect estimation proposed by Benjamin-Chung et al 7 . We only assessed the risk of bias for field epidemiological studies, excluding mathematical modeling studies and experimental hut trials. Each eligible study was classified as “very low”, “low”, “medium”, or “high” in terms of the reliability of indirect effects estimation. Results Study selection Figure 1 illustrates a PRISMA flow diagram depicting the identification, screening, eligibility, and exclusion process of the studies. A total of 664 articles were identified through database searches (n = 570) and other sources (n = 94). Three hundred sixty-eight duplicate articles were removed. Thirty-eight articles met the eligibility criteria after review of titles and abstracts; 258 studies were excluded for one or more of the four following reasons: 1) different meanings of indirect effect, 2) not malaria-specific intervention, 3) not intervention study, and 4) not reporting indirect effect. Notably, among the studies excluded because of different meanings of indirect effect, 14 studies evaluated the indirect relationship between COVID-19 and malaria 9 – 22 , and one study was a causal mediation analysis 23 . Six articles were added from a manual search of reference lists of the 38 eligible articles from the initial screening. Of these 44 studies, 31 were included in this review after full-text reading. The reasons for exclusion in the full-text reading were 1) reporting total effect only (n = 7), 2) opinion or review article (n = 3), 3) estimating indirect effect in the context of mediation analysis (n = 2), and 4) not reporting indirect effect (n = 1). Download figure Open in new tab Figure 1: PRISMA flowchart of study selection Study characteristics Details of the 31 reviewed studies are summarized in Table 1 . Most studies were set in African countries (n = 24; 77%) and examined the indirect effects of interventions on P. falciparum (n = 18; 58%). Temporal trends in study types, intervention types, and terms used to describe indirect effects are illustrated in Figure 2 . Overall, until year 2000, very few studies purposefully reported indirect effects. Subsequently, there was a sharp increase in reporting from 2001 to 2005, followed by a gradual decline. Since 2016, there has been an upward trend once again ( Figure 2a ). The most common study type was mathematical modeling (n = 9; 29%), followed by cross-sectional surveys (n = 6; 19%) and re-analysis of cluster-randomized trials (n = 6; 19%) ( Figure 2a ). The most common interventions were insecticide-treated nets (ITNs) or LLINs (n = 17; 55 %). Until 2015, the focus was primarily on ITN/LLIN-related interventions. However, since 2016, reports on various interventions such as house modification, intermittent preventive treatment (IPT), seasonal malaria chemoprevention (SMC), and mass drug administration (MDA) have emerged. ( Figure 2b ) The most common terms used for indirect effects were “communal” or “community” effect/benefit/protection (n = 23; 74%), followed by “mass” or “mass killing” effect/benefit/protection (n =11; 36%). Until 2015, the use of communal/community effect and the mass effect dominated, but more recently, various terms have come into use, including herd effect, indirect effect, spatial effect, and spillover effect ( Figure 2c ). Among 21 studies eligible for quality assessment of evidence, 6 (29%) had high-quality evidence, 7 (33%) had moderate, 5 (24%) had low, and 3 (14%) had very low-quality evidence. Of studies with high-quality evidence, 5 (83%) used cluster-randomized designs. Download figure Open in new tab Figure 2: Time trend of study characteristics; a) study type, b) intervention type, c) term used to describe the concept of indirect effects. Note that for c), the total number of terms in the graph does not correspond to the total number of studies (n=31), as multiple terms can be used in a single paper. CRT: cluster randomized trial, Ento: entomological survey, ITN: insecticide-treated net, LLIN: long-lasting insecticide-treated net, IRS: indoor residual spray, IPT: intermittent preventive treatment, MDA: mass drug administration. For the study type, “Others” included analysis of passive case detection using surveillance data. For intervention type, “Others” included access to free antimalarials and target subsidies of ITNs. Regarding indirect effects terminology, “Others” included assembly effects, population effects, group-level effects, positive externality, and dependent happenings. View this table: View inline View popup Table 1: Characteristics of 31 included studies. Overview of methods for indirect effect analysis Among all included studies, each intervention’s indirect effect was evaluated in relation to reductions in malaria transmission. Figure 3 shows the categories of methods for indirect effect estimation identified through this review. In addition, a detailed description of the methods by intervention type is listed in Supplementary Table 3. Download figure Open in new tab Figure 3: Categories of indirect effect analysis methods. [1] comparison between no treatment in the treatment community and the control group, (1) comparison not conditional on treatment density nor geographical distance, (2) pre-post comparisons among those who did not receive the treatment, [2] Comparison conditional on treatment coverage or geographical distance, (1) comparisons within the control area according to distance to the treatment cluster. (2) comparisons within the treatment area according to the coverage among those who received the treatment. (3) comparisons within the treatment area according to the coverage, including both those who received treatment and those who did not. (4) comparisons within the treatment area according to the coverage among those who did not receive the treatment. (5) comparisons within the control area according to the coverage of the nearest treatment clusters, [3] comparisons conditional on other factors such as the repellent and killing effects of ITNs, pre-erythrocytic or blood-stage vaccine efficacy, endemicity of study area, and the connectedness between different areas. Type [3] only applies to mathematical modeling studies. If one of these did not apply, it was recorded as “Others”. Field studies (epidemiological and entomological studies) Among field studies, including epidemiological and entomological studies, 59% pre-specified analysis of indirect effects (n = 13). Comparisons of non-treatment populations in intervention communities with non-intervention communities or pre-post analyses of these populations ([1]-(1) and [1]-(2), respectively, in Figure 3 ) were employed by eight studies 24 – 31 . On the other hand, comparison among no-intervention individuals/groups according to distance to the treatment household or the treatment coverage within a certain distance range were employed by 16 studies ([2] in Figure 3 ) 24 , 27 , 30 , 32 – 44 . Comparisons conditioned on the distance to nearest intervention were reported in five studies 32 , 34 , 35 , 38 , 44 ([2]-(1) in Figure 3 ), all of which evaluated the impacts of ITN. There were two analytical approaches. One was to compare between groups stratified by the distance category set at 100 – 400 m intervals, with the most distant group as the reference. In all studies, households without ITNs within 300 – 400 m of households with ITNs had the lowest risk of malaria-related outcomes (e.g. malaria parasitemia, mosquito density, anemia, and all-cause mortality). Another approach to measuring indirect effects by conditioning on distance was trend analysis, in which regression was performed with distance as an explanatory variable. Around year 2000, researchers simply incorporated distance into the model as a continuous variable 32 , 35 , but recently, Jarvis et al. have used a quadratic term to account for the nonlinearity called “distance decay” in spatial analysis 44 . The study reported that for every additional 100 m that a control household was from an intervention household, the all-cause mortality for children aged 6–59 months increased by 1.7% 44 . Regarding interventions conditional on treatment coverage, two patterns were observed: comparing among intervention populations ([2]-(2) and [2]-(3) in Figure 3 ) and among non-intervention populations ([2]-(4) and [2]-(5) in Figure 3 ). The definition of the areal unit for calculating intervention coverage varied from study to study, with a single cutoff determined by a 100-m to 3-km radius of the subject’s household 34 , 36 , 42 , 43 , multiple distances used in an exploratory manner 24 , 37 , 39 , and using primary sampling units 40 , 41 . There were also two approaches to analyzing indirect effects: one in which groups were stratified by intervention coverage and the other in which regression analysis was performed by incorporating intervention coverage as an explanatory variable. Several approaches other than the above-mentioned methodology were used to evaluate the indirect effects (categorized as “Others” in Table 1 ). Jarvis et al. (2019) showed that the treatment effects changed after reallocating the treatment and control cluster assignments based on the distance to the nearest treatment cluster 44 . Oduor et al. (2009) suggested positive indirect effects by confirming that the direct treatment effects were enhanced when spillovers to the neighboring sub-locations were accounted for 45 . In addition, Staedke et al. (2018) evaluated the effect of IPT in school children by comparing the reduction in malaria prevalence in all age groups between the intervention and control clusters 29 . The risk reduction was regarded as a community-level effect because the treatment coverage was considerably low (only school children among all age groups). Only two studies examined indirect effect heterogeneity 40 , 42 . Escamilla et al. (2017) reported that an increase in community bed net coverage was significantly associated with a decrease in malaria prevalence among children under five years and 5 – 19 year-olds, but no association was observed among adults older than 20 years 42 . In another study by Larsen et al. (2014), subgroup analyses were performed, stratified by rural versus urban areas and low versus high malaria transmission; however, no significant effect heterogeneity was observed 40 . In four studies, positive indirect effects were not observed, or negative indirect effects were observed with increased treatment coverage 37 , 39 , 41 , 42 . All four studies were observational studies. Among the field studies, 59% pre-specified analysis of indirect effects (n = 13). Mathematical modeling studies Among nine studies employing mathematical models 46 – 54 , two-thirds (n = 6) aimed to estimate the indirect effects of ITNs/LLINs, comparing outcomes before and after the intervention in the non-intervention group or altering parameters of intervention coverage through simulation. No mathematical modeling studies conducted a comparison based on distance conditioning, likely due to the infrequent use of spatial data in malaria transmission models. One notable characteristic of mathematical models is their ability to vary efficacy by changing more detailed parameters of interventions, such as the repellent and killing effects of ITNs 50 , vaccine target for pre-erythrocytic or blood-stage P. falciparum 52 , endemicity of study area 53 , and the connectedness between different areas 47 , 53 ([3] in Figure 3 ). Another distinctive method for estimating indirect effects involves using counterfactual hypothetical models. Unwin et al. (2023) disentangled the direct and indirect effects of ITNs 54 by maintaining the entomological inoculation rate (EIR) over time in certain scenarios, thereby breaking the link between current malaria endemicity and the human force of infection. Discussion To our knowledge, this is the first systematic scoping review on the indirect effects of malaria intervention. We reviewed studies whose titles or abstracts included terms indicative of indirect effects (except some articles from manual searches) and revealed that the number of such studies has increased in recent years, especially for interventions other than ITNs/LLINs. In addition, although not included in this review, an opinion piece 6 and a methodology study 55 have recently been published relating to the indirect effects of malaria intervention. Most recently, in 2023, a study intended to estimate both indirect and direct effects of reactive, focal chemoprevention and vector control interventions was made available as a preprint 56 . In light of the increasing interest in the indirect effects of malaria interventions, a scoping review summarizing previous studies is pertinent and salient. Several terms have been used to convey indirect effects. Apart from the “mass/mass killing” effect, which refers to the reduction of malaria transmission by decreasing the mosquito abundance or density through insecticides, other terms such as community effects, spillover effects, mass effects, and herd effects have been used interchangeably to denote indirect effects. Historically, indirect effects of malaria control interventions have often been labeled as community effects, especially for ITNs/LLINs (Supplementary Figure 1) and in the WHO vector control guideline 5 . In recent years, there has been more diversity in the terminology, particularly for interventions other than ITNs/LLINs. This diversity of terminology may create confusion and make it difficult for literature search on this topic. We propose using either community effects or spillover effects, a widely used term in general epidemiology 7 , 57 , when reporting indirect effects in malaria control, regardless of the type of intervention. We found that studies varied in their methodology for estimating indirect effects, although most can be typified into eight categories ( Figure 3 ). Since malaria parasites are transmitted via mosquitoes, it is appropriate to make comparisons conditional on distance to account for mosquito flight range or intervention density within that range. Comparisons between non-treatment groups conditional on distance from the treatment group were only conducted in studies on vector control such as ITNs/LLINs, while studies on interventions against parasites such as MDA, IPT/SMC, and vaccine were conditioned by treatment coverage (Supplementary Table 3). Future studies investigating the effectiveness of malaria interventions could draw on these methods, taking into account geographical characteristics and the feasibility of each study. When comparing the non-treated within intervention clusters, double-randomized trials 58 , which allow for the strongest inference by minimizing selection bias and unmeasured confounding, are considered the recommended approach 7 , 57 . However, we did not find any studies in our review that performed two-stage randomization. One possible reason is that a double-randomized trial is not always feasible, especially in the evaluation of malaria interventions. Because of the additional allocation of controls within the intervention cluster, more samples or reduced intervention coverage are needed to obtain sufficient power for the estimation of the indirect effect. In addition, in malaria, there are interventions that target subpopulations in the community, such as IPT, SMC, and vaccination targeting children or pregnant women. In these interventions, untargeted individuals in the treatment group and their counterparts in the control group (i.e., individuals who would be ineligible if they were assigned to the treatment group) may be comparable, effectively emulating a cluster-randomized trial design, which would not necessarily require a two-stage randomization. If using a cluster-randomized design or analyzing observational studies in which ineligible populations are not comparable to eligible populations, matching should be considered. It should be noted, however, that even with matching, unmeasured confounding may remain, and external validity may be reduced 57 , 59 . Other than changes in the number of malaria-infected individuals (drug or vaccine administration) or mosquitoes (vector control), indirect effects of interventions can manifest in two ways 7 : 1) individuals change their behavior because of the intervention and, in turn, influence the behavior of non-recipients in neighbors (social proximity), and 2) if a household member receives additional resources through the intervention, other household members will benefit because additional resources are available to the household (substitution). These indirect effects may not be trivial, and their relative magnitude may vary from setting to setting, which would necessitate intervention deployment plans tailor-made to suit area specificity, a lesson learned from the first Global Malaria Eradication Programme. We did not find studies reporting the indirect effects through these mechanisms that met our inclusion criteria. We excluded one study estimating the association between the proportion of nearby households receiving ITN subsidies and the probability of ITN use 60 because net usage was the only outcome reported. Future research on the impact of changes in individual behavior through programs such as conditional cash transfers 61 and subsidies based on malaria infection, morbidity, and mortality, especially when implemented alongside other malaria interventions, is warranted. Four studies either did not identify a positive indirect effect or reported a negative indirect effect 37 , 39 , 41 , 42 . There are several reasons for not observing positive indirect effects. First, indirect effects, in general, tend to be smaller than direct effects, studies designed to detect direct effects as primary objectives are often underpowered to detect indirect effects 7 . For instance, vector control measures reduce malaria transmission by reducing EIR in the community, but EIR and parasite prevalence are not linearly related 62 , and a substantial EIR reduction would be required to reduce malaria prevalence among non-recipients. Second, there is the potential confounder of residents’ behavior associated with both intervention compliance and the outcome. Residents’ compliance with interventions may depend on their perception of the risk of malaria transmission in the community and mosquito density 63 . For example, increasing community net usage is often associated with increasing mosquitos and malaria risk 64 . So, comparisons between non-recipients, especially when conditioned on coverage, may underestimate indirect effects. In addition, characteristics of non-recipients such as socio-economic status, healthcare access, and malaria preventive behavior may be different according to community treatment coverage, especially in an observational study setting 65 . Third, migration of infected individuals and mosquitoes between targeted and untargeted areas may have reduced the impact of the intervention in targeted areas 31 . No field studies conducted to date have taken into account these human and mosquito mobility to estimate indirect effects. Recently, there has been a substantial upsurge in the number of mathematical modeling studies on malaria 66 . In agent-based models, estimating the impact of an intervention in the non-intervention population is straightforward within any simulation, thus we had expected a greater number of modeling studies that estimated indirect effects. However, only nine mathematical modeling studies were included in our review. It is possible that our screening, based on keywords in titles and abstracts, excluded many of these studies. This also supports the importance of our proposal on standardizing the terms used to refer to an indirect effect. An advantage of mathematical modeling is the ability to examine changes in indirect effects not only by varying the coverage of the intervention but also by adjusting other parameters, such as deterrent and insecticidal effects in the case of ITNs/LLINs, simultaneously. It would be beneficial to take advantage of mathematical models and consider parameters for which data are not reliably quantified. For example, the main advantage of house modification is that once installed, it remains semi-permanent. Therefore, its effect is less susceptible to variations in human behavior 67 , such as repurposing and inconsistent uses of LLINs 68 . Incorporating such behaviors into the model and estimating the indirect effects on those who do not receive the intervention will have important implications for the widespread implementation of the intervention. One limitation of our study is that the search strategy may not have captured all relevant articles. We searched for keywords in the titles and abstracts, potentially missing studies that only reported the indirect effects of malaria interventions within the full text of the article. While efforts were made to manually include references cited for indirect effects, they were unlikely to be complete. Additionally, Benjamin-Chung et al. noted evidence of publication bias reporting for indirect effects 7 . Nonetheless, this review aimed to pave the way for improved design and reporting of future research on the indirect effects of malaria interventions. By highlighting this critical area, we hope to contribute to a more appropriate evaluation of intervention effectiveness. In conclusion, our review notes an increase in the number of studies that measured the indirect effects of malaria interventions in recent years. We outline eight comparative schemes by which indirect effects of malaria interventions can potentially be quantified, and propose standardized terms for describing indirect effects. We further support the use of mathematical models to inform the evaluation of indirect effects of malaria interventions. Incorporating assessment of indirect effects in future trials and studies may provide insights to optimize the deployment of existing and new interventions, a critical pillar in the current fight against malaria globally. In addition, evidence about the cost-effectiveness of interventions, taking into account the indirect effects, will lead to better-informed decisions by policymakers. Data Availability All data produced in the present study are available upon reasonable request to the authors Declarations Acknowledgments We are grateful to Dr. Masaru Nagashima for his thoughtful input from his expertise in development economics research. Contributions YKK developed the original concept. YKK and SMM conducted the first literature screening. YKK, WK, CWC, MK, and AKR conducted the full-text reading. YKK drafted the first draft of the manuscript and YKK, WK, CWC, MK, AKR, BNK, MN, JG, and AK contributed to the revisions. All authors reviewed and approved the final manuscript. Funding YKK and MK were financially supported by the Japan Society for the Promotion of Science. AK and JG received support from JICA/AMED joint research project (SATREPS) (Grant no. 20JM0110020H0002), Hitachi Fund Support for Research Related to Infectious Diseases, and Sumitomo Chemical Corporation. The funding bodies play no role in the study. Competing Interests The authors declare no competing interests. References ↵ World Health Organization . World malaria report 2023 . 2023 . ↵ Patel D , Patel KF , Patel K , Patel P , Patel S , Bansal R . Assessment of out of pocket expenditure for treatment of malaria in Surat city . National journal of community medicine 2016 ; 7 : 741 – 4 . OpenUrl ↵ Rannan-Eliya RP . Financing malaria . PLOS Glob Public Health 2022 ; 2 : e0000609 . OpenUrl ↵ Halloran ME , Hudgens MG . Dependent Happenings: A Recent Methodological Review . Curr Epidemiol Rep 2016 ; 3 : 297 – 305 . OpenUrl ↵ World Health Organization . GUIDELINES FOR MALARIA VECTOR CONTROL . 2019 . ↵ McCann RS , Cohee LM , Goupeyou-Youmsi J , Laufer MK . Maximizing Impact: Can Interventions to Prevent Clinical Malaria Reduce Parasite Transmission? Trends Parasitol 2020 ; 36 : 906 – 13 . OpenUrl ↵ Benjamin-Chung J , Abedin J , Berger D , et al. Spillover effects on health outcomes in low- and middle-income countries: a systematic review . Int J Epidemiol 2017 ; 46 : 1251 – 76 . OpenUrl PubMed ↵ Tricco AC , Lillie E , Zarin W , et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation . Ann Intern Med 2018 ; 169 : 467 – 73 . OpenUrl CrossRef PubMed ↵ Feldman M , Vernaeve L , Tibenderana J , et al. Navigating the COVID-19 Crisis to Sustain Community-Based Malaria Interventions in Cambodia . Glob Health Sci Pract 2021 ; 9 : 344 – 54 . OpenUrl Abstract / FREE Full Text Fleischman E , Hutchinson AH , Paracha NZ , Kumarasinghe C , Patel E . The Indirect Costs of the SARS-CoV-2 Pandemic: A Case of Severe Malaria in Brooklyn . Cureus 2020 ; 12 : e12331 . OpenUrl Langdon A , Abdlaziz I , Rhodes K , Clarke J . Case of myocarditis secondary to severe Plasmodium falciparum infection . BMJ Case Rep 2022 ; 15 . DOI: 10.1136/bcr-2022-249363 . OpenUrl CrossRef Weiss DJ , Bertozzi-Villa A , Rumisha SF , et al. Indirect effects of the COVID-19 pandemic on malaria intervention coverage, morbidity, and mortality in Africa: a geospatial modelling analysis . Lancet Infect Dis 2021 ; 21 : 59 – 69 . OpenUrl CrossRef PubMed Velavan TP , Meyer CG , Esen M , Kremsner PG , Ntoumi F , PANDORA-ID-NET and CANTAM consortium . COVID-19 and syndemic challenges in “Battling the Big Three”: HIV, TB and malaria . Int J Infect Dis 2021 ; 106 : 29 – 32 . OpenUrl Heuschen A-K , Lu G , Razum O , et al. Public health-relevant consequences of the COVID-19 pandemic on malaria in sub-Saharan Africa: a scoping review . Malar J 2021 ; 20 : 339 . OpenUrl CrossRef PubMed Heuschen A-K , Abdul-Mumin A , Abubakari A , et al. Effects of the COVID-19 pandemic on general health and malaria control in Ghana: a qualitative study with mothers and health care professionals . Malar J 2023 ; 22 : 78 . OpenUrl Buonsenso D , Iodice F , Cinicola B , Raffaelli F , Sowa S , Ricciardi W . Management of Malaria in Children Younger Than 5 Years Old During Coronavirus Disease 2019 Pandemic in Sierra Leone: A Lesson Learned? Front Pediatr 2020 ; 8 : 587638 . OpenUrl Burt JF , Ouma J , Lubyayi L , et al. Indirect effects of COVID-19 on maternal, neonatal, child, sexual and reproductive health services in Kampala , Uganda. BMJ Global Health 2021 ; 6 : e006102 . OpenUrl Baral S , Rao A , Twahirwa Rwema JO , et al. Competing Health Risks Associated with the COVID-Pandemic and Early Response: A Scoping Review . medRxiv 2021 ; published online Dec 15. DOI:10.1101/2021.01.07.21249419. Altare C , Kostandova N , Gankpe GF , et al. The first year of the COVID-19 pandemic in humanitarian settings: epidemiology, health service utilization, and health care seeking behavior in Bangui and surrounding areas, Central African Republic . Confl Health 2023 ; 17 : 24 . OpenUrl Druetz T , Cooper S , Bicaba F , et al. Change in childbearing intention, use of contraception, unwanted pregnancies, and related adverse events during the COVID-19 pandemic: Results from a panel study in rural Burkina Faso . PLOS Glob Public Health 2022 ; 2 : e0000174 . OpenUrl Menendez C , Gonzalez R , Donnay F , Leke RGF . Avoiding indirect effects of COVID-19 on maternal and child health . The Lancet Global Health 2020 ; 8 : e863 – 4 . OpenUrl ↵ Bertoli F , Veritti D , Danese C , et al. Ocular Findings in COVID-19 Patients: A Review of Direct Manifestations and Indirect Effects on the Eye . J Ophthalmol 2020 ; 2020 : 4827304 . OpenUrl ↵ Okoh OM , Olapeju B , Oyedokun-Adebagbo F , et al. The role of ideation on the effect of an SBC intervention on consistent bed net use among caregivers of children under 5 years in Nigeria: a multilevel mediation analysis . BMC Public Health 2021 ; 21 : 1660 . OpenUrl ↵ Howard SC , Omumbo J , Nevill C , Some ES , Donnelly CA , Snow RW . Evidence for a mass community effect of insecticide-treated bednets on the incidence of malaria on the Kenyan coast . Trans R Soc Trop Med Hyg 2000 ; 94 : 357 – 60 . OpenUrl CrossRef PubMed Maxwell CA , Msuya E , Sudi M , Njunwa KJ , Carneiro IA , Curtis CF . Effect of community-wide use of insecticide-treated nets for 3-4 years on malarial morbidity in Tanzania . Trop Med Int Health 2002 ; 7 : 1003 – 8 . OpenUrl CrossRef PubMed Web of Science Charlwood JD , Alcântara J , Pinto J , et al. Do bednets reduce malaria transmission by exophagic mosquitoes? Trans R Soc Trop Med Hyg 2005 ; 99 : 901 – 4 . OpenUrl CrossRef PubMed ↵ Killeen GF , Tami A , Kihonda J , et al. Cost-sharing strategies combining targeted public subsidies with private-sector delivery achieve high bednet coverage and reduced malaria transmission in Kilombero Valley, southern Tanzania . BMC Infect Dis 2007 ; 7 : 121 . OpenUrl CrossRef PubMed Cissé B , Ba EH , Sokhna C , et al. Effectiveness of Seasonal Malaria Chemoprevention in Children under Ten Years of Age in Senegal: A Stepped-Wedge Cluster-Randomised Trial . PLoS Med 2016 ; 13 : e1002175 . OpenUrl CrossRef PubMed ↵ Staedke SG , Maiteki-Sebuguzi C , Rehman AM , et al. Assessment of community-level effects of intermittent preventive treatment for malaria in schoolchildren in Jinja, Uganda (START-IPT trial): a cluster-randomised trial . The Lancet Global Health 2018 ; 6 : e668 – 79 . OpenUrl ↵ Mwanga EP , Mmbando AS , Mrosso PC , et al. Eave ribbons treated with transfluthrin can protect both users and non-users against malaria vectors . Malar J 2019 ; 18 : 314 . OpenUrl ↵ Hast MA , Chaponda M , Muleba M , et al. The Impact of 3 Years of Targeted Indoor Residual Spraying With Pirimiphos-Methyl on Malaria Parasite Prevalence in a High-Transmission Area of Northern Zambia . Am J Epidemiol 2019 ; 188 : 2120 – 30 . OpenUrl CrossRef ↵ Binka FN , Indome F , Smith T . Impact of spatial distribution of permethrin-impregnated bed nets on child mortality in rural northern Ghana . Am J Trop Med Hyg 1998 ; 59 : 80 – 5 . OpenUrl Abstract Ilboudo-Sanogo E , Cuzin-Ouattara N , Diallo DA , et al. Insecticide-treated materials, mosquito adaptation and mass effect: entomological observations after five years of vector control in Burkina Faso . Trans R Soc Trop Med Hyg 2001 ; 95 : 353 – 60 . OpenUrl CrossRef PubMed ↵ Hawley WA , Phillips-Howard PA , ter Kuile FO , et al. Community-wide effects of permethrin-treated bed nets on child mortality and malaria morbidity in western Kenya . Am J Trop Med Hyg 2003 ; 68 : 121 – 7 . OpenUrl Abstract / FREE Full Text ↵ Gimnig JE , Kolczak MS , Hightower AW , et al. Effect of permethrin-treated bed nets on the spatial distribution of malaria vectors in western Kenya . Am J Trop Med Hyg 2003 ; 68 : 115 – 20 . OpenUrl Abstract / FREE Full Text ↵ Abdulla S , Gemperli A , Mukasa O , et al. Spatial effects of the social marketing of insecticide-treated nets on malaria morbidity . Trop Med Int Health 2005 ; 10 : 11 – 8 . OpenUrl CrossRef PubMed Web of Science ↵ Gosoniu L , Vounatsou P , Tami A , Nathan R , Grundmann H , Lengeler C . Spatial effects of mosquito bednets on child mortality . BMC Public Health 2008 ; 8 : 356 . OpenUrl CrossRef PubMed ↵ Klinkenberg E , Onwona-Agyeman KA , McCall PJ , et al. Cohort trial reveals community impact of insecticide-treated nets on malariometric indices in urban Ghana . Trans R Soc Trop Med Hyg 2010 ; 104 : 496 – 503 . OpenUrl CrossRef PubMed ↵ Komazawa O , Kaneko S , K’Opiyo J , et al. Are long-lasting insecticidal nets effective for preventing childhood deaths among non-net users? A community-based cohort study in western Kenya . PLoS One 2012 ; 7 : e49604 . OpenUrl PubMed ↵ Larsen DA , Hutchinson P , Bennett A , et al. Community coverage with insecticide-treated mosquito nets and observed associations with all-cause child mortality and malaria parasite infections . Am J Trop Med Hyg 2014 ; 91 : 950 – 8 . OpenUrl Abstract / FREE Full Text ↵ Buchwald AG , Coalson JE , Cohee LM , et al. Insecticide-treated net effectiveness at preventing Plasmodium falciparum infection varies by age and season . Malar J 2017 ; 16 : 32 . OpenUrl CrossRef ↵ Escamilla V , Alker A , Dandalo L , et al. Effects of community-level bed net coverage on malaria morbidity in Lilongwe, Malawi . Malar J 2017 ; 16 : 142 . OpenUrl PubMed ↵ Parker DM , Tun STT , White LJ , et al. Potential herd protection against Plasmodium falciparum infections conferred by mass antimalarial drug administrations . Elife 2019 ; 8 : e41023 . OpenUrl CrossRef ↵ Jarvis CI , Multerer L , Lewis D , et al. Spatial Effects of Permethrin-Impregnated Bed Nets on Child Mortality: 26 Years on, a Spatial Reanalysis of a Cluster Randomized Trial . Am J Trop Med Hyg 2019 ; 101 : 1434 – 41 . OpenUrl ↵ Oduor J , Kamau A , Mathenge E . Evaluating the impact of microfranchising the distribution of anti-malarial drugs in Kenya on malaria mortality and morbidity . Journal of Development Effectiveness 2009 ; 1 : 353 – 77 . OpenUrl ↵ Struchiner CJ , Halloran ME , Robins JM , Spielman A . The behaviour of common measures of association used to assess a vaccination programme under complex disease transmission patterns--a computer simulation study of malaria vaccines . Int J Epidemiol 1990 ; 19 : 187 – 96 . OpenUrl CrossRef PubMed ↵ Killeen GF , Knols BGJ , Gu W . Taking malaria transmission out of the bottle: implications of mosquito dispersal for vector-control interventions . Lancet Infect Dis 2003 ; 3 : 297 – 303 . OpenUrl CrossRef PubMed Web of Science Killeen GF , Smith TA , Ferguson HM , et al. Preventing childhood malaria in Africa by protecting adults from mosquitoes with insecticide-treated nets . PLoS Med 2007 ; 4 : e229 . OpenUrl CrossRef PubMed Killeen GF , Smith TA . Exploring the contributions of bed nets, cattle, insecticides and excitorepellency to malaria control: a deterministic model of mosquito host-seeking behaviour and mortality . Trans R Soc Trop Med Hyg 2007 ; 101 : 867 – 80 . OpenUrl CrossRef PubMed ↵ Killeen GF , Chitnis N , Moore SJ , Okumu FO . Target product profile choices for intra-domiciliary malaria vector control pesticide products: repel or kill? Malar J 2011 ; 10 : 207 . OpenUrl CrossRef PubMed Okumu FO , Kiware SS , Moore SJ , Killeen GF . Mathematical evaluation of community level impact of combining bed nets and indoor residual spraying upon malaria transmission in areas where the main vectors are Anopheles arabiensis mosquitoes . Parasit Vectors 2013 ; 6 : 17 . OpenUrl CrossRef PubMed Web of Science ↵ Wenger EA , Eckhoff PA . A mathematical model of the impact of present and future malaria vaccines . Malar J 2013 ; 12 : 126 . OpenUrl CrossRef PubMed ↵ Tun STT , Parker DM , Aguas R , White LJ . The assembly effect: the connectedness between populations is a double-edged sword for public health interventions . Malar J 2021 ; 20 : 189 . OpenUrl ↵ Unwin HJT , Sherrard-Smith E , Churcher TS , Ghani AC . Quantifying the direct and indirect protection provided by insecticide treated bed nets against malaria . Nat Commun 2023 ; 14 : 676 . OpenUrl ↵ Multerer L , Glass TR , Vanobberghen F , Smith T . Analysis of contamination in cluster randomized trials of malaria interventions . Trials 2021 ; 22 : 613 . OpenUrl ↵ Benjamin-Chung J , Li H , Nguyen A , et al. Targeted malaria elimination interventions reduce Plasmodium falciparum infections up to 3 kilometers away . medRxiv 2023 ; published online Nov 30. DOI:10.1101/2023.09.19.23295806. ↵ Benjamin-Chung J , Arnold BF , Berger D , et al. Spillover effects in epidemiology: parameters, study designs and methodological considerations . Int J Epidemiol 2018 ; 47 : 332 – 47 . OpenUrl CrossRef PubMed ↵ Clemens J , Shin S , Ali M . New approaches to the assessment of vaccine herd protection in clinical trials . Lancet Infect Dis 2011 ; 11 : 482 – 7 . OpenUrl CrossRef PubMed Web of Science ↵ Freemantle N , Marston L , Walters K , Wood J , Reynolds MR , Petersen I . Making inferences on treatment effects from real world data: propensity scores, confounding by indication, and other perils for the unwary in observational research . BMJ 2013 ; 347 : f6409 . OpenUrl FREE Full Text ↵ Bhattacharya D , Dupas P , Kanaya S . Estimating the Impact of Means-tested Subsidies under Treatment Externalities with Application to Anti-Malarial Bednets . https://www.nber.org › papers https://www.nber.org › papers. 2013 ; published online Feb. DOI:10.3386/w18833. ↵ Matsumoto T , Nagashima M , Kagaya W , Kongere J , Gitaka J , Kaneko A. Evaluation of a financial incentive intervention on malaria prevalence among the residents in Lake Victoria basin, Kenya: study protocol for a cluster-randomized controlled trial . Trials 2024 ; 25 : 165 . OpenUrl ↵ Amoah B , McCann RS , Kabaghe AN , et al. Identifying Plasmodium falciparum transmission patterns through parasite prevalence and entomological inoculation rate . Elife 2021 ; 10 . DOI: 10.7554/eLife.65682 . OpenUrl CrossRef PubMed ↵ Koenker HM , Loll D , Rweyemamu D , Ali AS . A good night’s sleep and the habit of net use: perceptions of risk and reasons for bed net use in Bukoba and Zanzibar . Malar J 2013 ; 12 : 203 . OpenUrl CrossRef PubMed ↵ Msellemu D , Shemdoe A , Makungu C , et al. The underlying reasons for very high levels of bed net use, and higher malaria infection prevalence among bed net users than non-users in the Tanzanian city of Dar es Salaam: a qualitative study . Malar J 2017 ; 16 : 423 . OpenUrl ↵ Larsen DA , Keating J , Miller J , et al. Barriers to insecticide-treated mosquito net possession 2 years after a mass free distribution campaign in Luangwa District, Zambia . PLoS One 2010 ; 5 : e13129 . OpenUrl CrossRef PubMed ↵ Smith NR , Trauer JM , Gambhir M , et al. Agent-based models of malaria transmission: a systematic review . Malar J 2018 ; 17 : 299 . OpenUrl CrossRef ↵ Kagaya W , Chan CW , Kongere J , et al. Evaluation of the protective efficacy of Olyset®Plus ceiling net on reducing malaria prevalence in children in Lake Victoria Basin, Kenya: study protocol for a cluster-randomized controlled trial . Trials 2023 ; 24 : 354 . OpenUrl ↵ Larson PS , Minakawa N , Dida GO , Njenga SM , Ionides EL , Wilson ML. Insecticide-treated net use before and after mass distribution in a fishing community along Lake Victoria, Kenya: successes and unavoidable pitfalls . Malar J 2014 ; 13 : 466 . OpenUrl View the discussion thread. Back to top Previous Next Posted May 08, 2024. 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