Title: Unraveling the “indirect effects” of interventions against malaria endemicity: A systematic scoping review Authors: Yura K. Ko1,2, Wataru Kagaya3, Chim W. Chan4, Mariko Kanamori5,6, Samuel M. Mbugua7,8,9, Alex K. Rotich7,8,10, Bernard N. Kanoi7,8, Mtakai Ngara1, Jesse Gitaka7,8, Akira Kaneko1,4 1. Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet 2. Department of Virology, Tohoku University Graduate School of Medicine 3. Department of Ecoepidemiology, Institute of Tropical Medicine (NEKKEN), Nagasaki University 4. Department of Virology and Parasitology, Graduate School of Medicine/ Osaka International Research Center for Infectious Diseases, Osaka Metropolitan University 5. Department of Public Health Sciences, Stockholm University 6. Institute for the Future of Human Society, Kyoto University 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 10. Department of Chemistry and Biochemistry, University of Eldoret Correspondence to: Yura K. Ko . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ Summary 1 There is an urgent need to maximize the effectiveness of existing malaria interventions and optimize the 2 deployment of novel countermeasures. When assessing the effects of interventions against malaria, it is 3 imperative to consider the interdependence of people and the resulting indirect effects, without which the 4 impact on health outcomes and their cost-effectiveness may be miscalculated. Here, we conducted a 5 scoping review of existing literature on the indirect effects of malaria interventions. We observed a recent 6 increase in both the number of reports and the variety of terms used to denote indirect effects. We further 7 classified eight categories of comparative analysis to identify the indirect effects, proposed common terms 8 for the indirect effects, and highlighted the potential benefits of mathematical models in estimating 9 indirect effects. Improving the study design and reporting the indirect effects of malaria interventions will 10 lead to better informed decisions by policymakers. 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ Introduction 27 The global fight against malaria has become increasingly challenging in recent years. Despite concerted 28 scale-up of intervention tools, such as the widespread distribution of long-lasting insecticidal nets 29 (LLINs), rapid diagnostic tests (RDTs), and artemisinin-based combination therapies (ACTs), the 30 estimated global case incidence of malaria in the past few years has stagnated at around 58 cases per 31 1,000 population at risk, while the global mortality rate has remained at approximately 14 per 100,000 32 population at risk1. Moreover, although malaria remains a leading cause of healthcare spending in 33 endemic countries2, the amount invested in 2022 fell short of the estimated USD 7.8 billion required 34 globally to achieve the Global Technical Strategy (GTS) targets set by the World Health Organization 35 (WHO)1. It is anticipated that high-income nations and other international funders will continue to 36 prioritize their efforts to address emerging diseases such as COVID-19 through 20243. In this context, 37 there is an urgent need to re-evaluate existing malaria interventions for more effective deployment, along 38 with the employment of novel countermeasures to reduce the malaria burden more efficiently and cost-39 effectively. 40 41 Malaria is a vector-borne disease transmitted by Anopheles mosquitoes. When measuring the effects of 42 interventions against such diseases, it is crucial to consider the interdependence in people, often referred 43 to as “dependent happenings”4. For instance, in malaria-endemic settings, a decline in the number of 44 malaria-infected individuals or mosquitoes will reduce parasite reservoirs and means of transmission in a 45 community, leading a lower possibility of infection among all community members. Consequently, 46 malaria control measures implemented in a community are expected to yield direct benefits for 47 individuals receiving the interventions and indirect benefits for both individuals receiving and not 48 receiving the interventions. Indirect effects can be defined as the unintended positive or negative 49 consequences of an intervention that influences disease transmission or health outcomes. Thus, without 50 proper consideration of the indirect effects, malaria interventions’ impacts on health outcomes and their 51 cost-effectiveness may be overestimated or underestimated. Therefore, adopting a comprehensive and 52 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ standardized approach to identify both direct and indirect effects is imperative to gain a detailed 53 understanding of intervention impacts. Moreover, evidence of indirect effects will influence policy 54 makers' decision making. If the direct effects are equivalent, an intervention that broadly benefits those 55 who do not receive the intervention is preferable to one that benefits only a limited number of people who 56 receive the intervention. 57 58 The concept of indirect effects of malaria intervention, especially LLINs, has long been well known. 59 Nevertheless, the description of indirect effects in the WHO guidelines for vector-borne mosquito control 60 only briefly states that community-level effects of ITNs have not always been observed5. In addition, the 61 scientific literature on malaria interventions that explicitly differentiate and thoroughly analyze their 62 indirect effects is currently limited6. A recent systematic review of the indirect effects of interventions on 63 health in low- and middle-income countries by Benjamin-Chung et al7. included only two malaria-related 64 studies. Moreover, the methodology of measuring the indirect effects greatly varies, and the terms 65 indicating the indirect effects are not standardized (e.g., community effects, spillover effects, mass effects, 66 herd effects, area-wide effects, spatial effects, and positive externalities). To address these knowledge 67 gaps, we conducted a scoping review to summarize how the indirect effects of malaria interventions were 68 analyzed and reported. 69 70 Methods 71 Search strategy and selection criteria 72 We followed the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-73 Analysis extension for scoping reviews (PRISMA-ScR)8. The study protocol is available at elsewhere 74 (https://inplasy.com/inplasy-2023-6-0025/). 75 76 Literature search 77 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ We searched PubMed, Web of Science, and EMBASE by title and abstracts. In addition, for grey 78 literature, we searched OAIster by keywords. Searches were conducted in June 2023. We set the search 79 terms as follows: ("malaria" OR "plasmodium") AND ("indirect effect*" OR "indirect protection" OR 80 "herd effect*" OR "herd protection" OR "community effect*" OR "communal effect*" OR "community-81 level effect*" OR "community protection" OR "communal protection" OR "community-level protection" 82 OR "peer effect*" OR "peer influence effect*" OR "mass effect*" OR "assembly effect*" OR "spillover 83 effect*" OR "contextual effect*" OR "free-rider" OR "free rider" OR "free-riding" OR "free riding" OR 84 "positive externality" OR "positive externalities" OR "dependent happenings") 85 86 Eligibility criteria 87 We included studies that were conducted to quantify the indirect effects of any interventions for all 88 species of malaria infection. We excluded non-original papers such as opinions and editorials. We only 89 targeted articles written in English. We defined indirect effects as the impact accrued by either the non-90 intervention or intervention group, stemming from alterations in malaria parasite or mosquito populations 91 within a community consequent to an intervention. It should be noted that simple group comparisons 92 between treatment and control (or baseline) groups/clusters are regarded as total effects. Studies that 93 reported only total effects were excluded from our review. However, if the treatment coverage in the 94 community was considerably low, the group comparisons between treatment and control were considered 95 indirect effects and were included in our review. 96 97 Study selection 98 We imported the data for each relevant publication into reference software (Rayyan, 99 https://www.rayyan.ai/). Prior to the initial screening, duplicate records were deleted automatically. In the 100 first review step, two reviewers (YKK, SMM) screened all records by title and abstract according to the 101 eligibility criteria. Any discrepancies in the process were resolved by discussion between both reviewers. 102 Once a record was selected, its full text was reviewed by at least two of five reviewers (YKK, WK, CWC, 103 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ MK, and AKR). Specific data (see the section “Data extraction and analysis”) were recorded and 104 summarized in a tabular form through this second review step. Any disagreement was addressed through 105 discussion. Additional reference and citation searches were also conducted. The reference lists of the 106 articles identified during the search were scanned manually, and eligible articles were included in the full-107 text reading. 108 109 Data extraction and analysis 110 We used a standardized data collection form that follows the PRISMA guidelines for scoping reviews8 to 111 obtain the following information from each record: title, name of authors, year of publication, region, 112 country, study type, malaria parasite species, type of interventions, type of outcomes, separate estimated 113 indirect effect for different conditions (yes/no), pre-specified to measure indirect effect (yes/no), 114 secondary analysis of previous study (yes/no), methods of indirect effects estimation, terms of indirect 115 effects, and if positive or negative indirect effects observed (yes/no). A detailed description of the 116 extracted data is in Supplementary Table 1. Standardized labels were made for each term for 117 inconsistencies of words, as listed in Supplementary Table 2. 118 119 Quality of study methodology for estimating indirect effect 120 We utilized the classification of risk of bias for indirect effect estimation proposed by Benjamin-Chung et 121 al7. We only assessed the risk of bias for field epidemiological studies, excluding mathematical modeling 122 studies and experimental hut trials. Each eligible study was classified as “very low”, “low”, “medium”, or 123 “high” in terms of the reliability of indirect effects estimation. 124 125 Results 126 Study selection 127 Figure 1 illustrates a PRISMA flow diagram depicting the identification, screening, eligibility, and 128 exclusion process of the studies. A total of 664 articles were identified through database searches (n = 129 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ 570) and other sources (n = 94). Three hundred sixty-eight duplicate articles were removed. Thirty-eight 130 articles met the eligibility criteria after review of titles and abstracts; 258 studies were excluded for one or 131 more of the four following reasons: 1) different meanings of indirect effect, 2) not malaria-specific 132 intervention, 3) not intervention study, and 4) not reporting indirect effect. Notably, among the studies 133 excluded because of different meanings of indirect effect, 14 studies evaluated the indirect relationship 134 between COVID-19 and malaria9–22, and one study was a causal mediation analysis23. Six articles were 135 added from a manual search of reference lists of the 38 eligible articles from the initial screening. Of 136 these 44 studies, 31 were included in this review after full-text reading. The reasons for exclusion in the 137 full-text reading were 1) reporting total effect only (n = 7), 2) opinion or review article (n = 3), 3) 138 estimating indirect effect in the context of mediation analysis (n = 2), and 4) not reporting indirect effect 139 (n = 1). 140 141 Study characteristics 142 Details of the 31 reviewed studies are summarized in Table 1. Most studies were set in African countries 143 (n = 24; 77%) and examined the indirect effects of interventions on P. falciparum (n = 18; 58%). 144 Temporal trends in study types, intervention types, and terms used to describe indirect effects are 145 illustrated in Figure 2. Overall, until year 2000, very few studies purposefully reported indirect effects. 146 Subsequently, there was a sharp increase in reporting from 2001 to 2005, followed by a gradual decline. 147 Since 2016, there has been an upward trend once again (Figure 2a). The most common study type was 148 mathematical modeling (n = 9; 29%), followed by cross-sectional surveys (n = 6; 19%) and re-analysis of 149 cluster-randomized trials (n = 6; 19%) (Figure 2a). The most common interventions were insecticide-150 treated nets (ITNs) or LLINs (n = 17; 55 %). Until 2015, the focus was primarily on ITN/LLIN-related 151 interventions. However, since 2016, reports on various interventions such as house modification, 152 intermittent preventive treatment (IPT), seasonal malaria chemoprevention (SMC), and mass drug 153 administration (MDA) have emerged. (Figure 2b) The most common terms used for indirect effects were 154 “communal” or “community” effect/benefit/protection (n = 23; 74%), followed by “mass” or “mass 155 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ killing” effect/benefit/protection (n =11; 36%). Until 2015, the use of communal/community effect and 156 the mass effect dominated, but more recently, various terms have come into use, including herd effect, 157 indirect effect, spatial effect, and spillover effect (Figure 2c). Among 21 studies eligible for quality 158 assessment of evidence, 6 (29%) had high-quality evidence, 7 (33%) had moderate, 5 (24%) had low, and 159 3 (14%) had very low-quality evidence. Of studies with high-quality evidence, 5 (83%) used cluster-160 randomized designs. 161 162 Overview of methods for indirect effect analysis 163 Among all included studies, each intervention's indirect effect was evaluated in relation to reductions in 164 malaria transmission. Figure 3 shows the categories of methods for indirect effect estimation identified 165 through this review. In addition, a detailed description of the methods by intervention type is listed in 166 Supplementary Table 3. 167 168 Field studies (epidemiological and entomological studies) 169 Among field studies, including epidemiological and entomological studies, 59% pre-specified analysis of 170 indirect effects (n = 13). Comparisons of non-treatment populations in intervention communities with 171 non-intervention communities or pre-post analyses of these populations ([1]-(1) and [1]-(2), respectively, 172 in Figure 3) were employed by eight studies24–31. On the other hand, comparison among no-intervention 173 individuals/groups according to distance to the treatment household or the treatment coverage within a 174 certain distance range were employed by 16 studies ([2] in Figure 3)24,27,30,32–44. 175 176 Comparisons conditioned on the distance to nearest intervention were reported in five studies32,34,35,38,44 177 ([2]-(1) in Figure 3), all of which evaluated the impacts of ITN. There were two analytical approaches. 178 One was to compare between groups stratified by the distance category set at 100 – 400 m intervals, with 179 the most distant group as the reference. In all studies, households without ITNs within 300 – 400 m of 180 households with ITNs had the lowest risk of malaria-related outcomes (e.g. malaria parasitemia, mosquito 181 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ density, anemia, and all-cause mortality). Another approach to measuring indirect effects by conditioning 182 on distance was trend analysis, in which regression was performed with distance as an explanatory 183 variable. Around year 2000, researchers simply incorporated distance into the model as a continuous 184 variable32,35, but recently, Jarvis et al. have used a quadratic term to account for the nonlinearity called 185 “distance decay” in spatial analysis44. The study reported that for every additional 100 m that a control 186 household was from an intervention household, the all-cause mortality for children aged 6–59 months 187 increased by 1.7%44. 188 189 Regarding interventions conditional on treatment coverage, two patterns were observed: comparing 190 among intervention populations ([2]-(2) and [2]-(3) in Figure 3) and among non-intervention populations 191 ([2]-(4) and [2]-(5) in Figure 3). The definition of the areal unit for calculating intervention coverage 192 varied from study to study, with a single cutoff determined by a 100-m to 3-km radius of the subject's 193 household34,36,42,43, multiple distances used in an exploratory manner24,37,39, and using primary sampling 194 units40,41. There were also two approaches to analyzing indirect effects: one in which groups were 195 stratified by intervention coverage and the other in which regression analysis was performed by 196 incorporating intervention coverage as an explanatory variable. 197 198 Several approaches other than the above-mentioned methodology were used to evaluate the indirect 199 effects (categorized as “Others” in Table 1). Jarvis et al. (2019) showed that the treatment effects changed 200 after reallocating the treatment and control cluster assignments based on the distance to the nearest 201 treatment cluster44. Oduor et al. (2009) suggested positive indirect effects by confirming that the direct 202 treatment effects were enhanced when spillovers to the neighboring sub-locations were accounted for45. In 203 addition, Staedke et al. (2018) evaluated the effect of IPT in school children by comparing the reduction 204 in malaria prevalence in all age groups between the intervention and control clusters29. The risk reduction 205 was regarded as a community-level effect because the treatment coverage was considerably low (only 206 school children among all age groups). 207 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ 208 Only two studies examined indirect effect heterogeneity40,42. Escamilla et al. (2017) reported that an 209 increase in community bed net coverage was significantly associated with a decrease in malaria 210 prevalence among children under five years and 5 – 19 year-olds, but no association was observed among 211 adults older than 20 years42. In another study by Larsen et al. (2014), subgroup analyses were performed, 212 stratified by rural versus urban areas and low versus high malaria transmission; however, no significant 213 effect heterogeneity was observed40. In four studies, positive indirect effects were not observed, or 214 negative indirect effects were observed with increased treatment coverage37,39,41,42. All four studies were 215 observational studies. Among the field studies, 59% pre-specified analysis of indirect effects (n = 13). 216 217 Mathematical modeling studies 218 Among nine studies employing mathematical models46–54, two-thirds (n = 6) aimed to estimate the 219 indirect effects of ITNs/LLINs, comparing outcomes before and after the intervention in the non-220 intervention group or altering parameters of intervention coverage through simulation. No mathematical 221 modeling studies conducted a comparison based on distance conditioning, likely due to the infrequent use 222 of spatial data in malaria transmission models. One notable characteristic of mathematical models is their 223 ability to vary efficacy by changing more detailed parameters of interventions, such as the repellent and 224 killing effects of ITNs50, vaccine target for pre-erythrocytic or blood-stage P. falciparum52, endemicity of 225 study area53, and the connectedness between different areas47,53 ([3] in Figure 3). 226 227 Another distinctive method for estimating indirect effects involves using counterfactual hypothetical 228 models. Unwin et al. (2023) disentangled the direct and indirect effects of ITNs54 by maintaining the 229 entomological inoculation rate (EIR) over time in certain scenarios, thereby breaking the link between 230 current malaria endemicity and the human force of infection. 231 232 Discussion 233 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ To our knowledge, this is the first systematic scoping review on the indirect effects of malaria 234 intervention. We reviewed studies whose titles or abstracts included terms indicative of indirect effects 235 (except some articles from manual searches) and revealed that the number of such studies has increased in 236 recent years, especially for interventions other than ITNs/LLINs. In addition, although not included in this 237 review, an opinion piece6 and a methodology study55 have recently been published relating to the indirect 238 effects of malaria intervention. Most recently, in 2023, a study intended to estimate both indirect and 239 direct effects of reactive, focal chemoprevention and vector control interventions was made available as a 240 preprint56. In light of the increasing interest in the indirect effects of malaria interventions, a scoping 241 review summarizing previous studies is pertinent and salient. 242 243 Several terms have been used to convey indirect effects. Apart from the "mass/mass killing" effect, which 244 refers to the reduction of malaria transmission by decreasing the mosquito abundance or density through 245 insecticides, other terms such as community effects, spillover effects, mass effects, and herd effects have 246 been used interchangeably to denote indirect effects. Historically, indirect effects of malaria control 247 interventions have often been labeled as community effects, especially for ITNs/LLINs (Supplementary 248 Figure 1) and in the WHO vector control guideline5. In recent years, there has been more diversity in the 249 terminology, particularly for interventions other than ITNs/LLINs. This diversity of terminology may 250 create confusion and make it difficult for literature search on this topic. We propose using either 251 community effects or spillover effects, a widely used term in general epidemiology7,57, when reporting 252 indirect effects in malaria control, regardless of the type of intervention. 253 254 We found that studies varied in their methodology for estimating indirect effects, although most can be 255 typified into eight categories (Figure 3). Since malaria parasites are transmitted via mosquitoes, it is 256 appropriate to make comparisons conditional on distance to account for mosquito flight range or 257 intervention density within that range. Comparisons between non-treatment groups conditional on 258 distance from the treatment group were only conducted in studies on vector control such as ITNs/LLINs, 259 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ while studies on interventions against parasites such as MDA, IPT/SMC, and vaccine were conditioned 260 by treatment coverage (Supplementary Table 3). Future studies investigating the effectiveness of malaria 261 interventions could draw on these methods, taking into account geographical characteristics and the 262 feasibility of each study. 263 264 When comparing the non-treated within intervention clusters, double-randomized trials58, which allow for 265 the strongest inference by minimizing selection bias and unmeasured confounding, are considered the 266 recommended approach7,57. However, we did not find any studies in our review that performed two-stage 267 randomization. One possible reason is that a double-randomized trial is not always feasible, especially in 268 the evaluation of malaria interventions. Because of the additional allocation of controls within the 269 intervention cluster, more samples or reduced intervention coverage are needed to obtain sufficient power 270 for the estimation of the indirect effect. In addition, in malaria, there are interventions that target 271 subpopulations in the community, such as IPT, SMC, and vaccination targeting children or pregnant 272 women. In these interventions, untargeted individuals in the treatment group and their counterparts in the 273 control group (i.e., individuals who would be ineligible if they were assigned to the treatment group) may 274 be comparable, effectively emulating a cluster-randomized trial design, which would not necessarily 275 require a two-stage randomization. If using a cluster-randomized design or analyzing observational 276 studies in which ineligible populations are not comparable to eligible populations, matching should be 277 considered. It should be noted, however, that even with matching, unmeasured confounding may remain, 278 and external validity may be reduced57,59. 279 280 Other than changes in the number of malaria-infected individuals (drug or vaccine administration) or 281 mosquitoes (vector control), indirect effects of interventions can manifest in two ways7: 1) individuals 282 change their behavior because of the intervention and, in turn, influence the behavior of non-recipients in 283 neighbors (social proximity), and 2) if a household member receives additional resources through the 284 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ intervention, other household members will benefit because additional resources are available to the 285 household (substitution). These indirect effects may not be trivial, and their relative magnitude may vary 286 from setting to setting, which would necessitate intervention deployment plans tailor-made to suit area 287 specificity, a lesson learned from the first Global Malaria Eradication Programme. We did not find studies 288 reporting the indirect effects through these mechanisms that met our inclusion criteria. We excluded one 289 study estimating the association between the proportion of nearby households receiving ITN subsidies 290 and the probability of ITN use60 because net usage was the only outcome reported. Future research on the 291 impact of changes in individual behavior through programs such as conditional cash transfers61 and 292 subsidies based on malaria infection, morbidity, and mortality, especially when implemented alongside 293 other malaria interventions, is warranted. 294 295 Four studies either did not identify a positive indirect effect or reported a negative indirect effect37,39,41,42. 296 There are several reasons for not observing positive indirect effects. First, indirect effects, in general, tend 297 to be smaller than direct effects, studies designed to detect direct effects as primary objectives are often 298 underpowered to detect indirect effects7. For instance, vector control measures reduce malaria 299 transmission by reducing EIR in the community, but EIR and parasite prevalence are not linearly related62, 300 and a substantial EIR reduction would be required to reduce malaria prevalence among non-recipients. 301 Second, there is the potential confounder of residents' behavior associated with both intervention 302 compliance and the outcome. Residents' compliance with interventions may depend on their perception of 303 the risk of malaria transmission in the community and mosquito density63. For example, increasing 304 community net usage is often associated with increasing mosquitos and malaria risk64. So, comparisons 305 between non-recipients, especially when conditioned on coverage, may underestimate indirect effects. In 306 addition, characteristics of non-recipients such as socio-economic status, healthcare access, and malaria 307 preventive behavior may be different according to community treatment coverage, especially in an 308 observational study setting65. Third, migration of infected individuals and mosquitoes between targeted 309 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ and untargeted areas may have reduced the impact of the intervention in targeted areas31. No field studies 310 conducted to date have taken into account these human and mosquito mobility to estimate indirect effects. 311 312 Recently, there has been a substantial upsurge in the number of mathematical modeling studies on 313 malaria66. In agent-based models, estimating the impact of an intervention in the non-intervention 314 population is straightforward within any simulation, thus we had expected a greater number of modeling 315 studies that estimated indirect effects. However, only nine mathematical modeling studies were included 316 in our review. It is possible that our screening, based on keywords in titles and abstracts, excluded many 317 of these studies. This also supports the importance of our proposal on standardizing the terms used to 318 refer to an indirect effect. An advantage of mathematical modeling is the ability to examine changes in 319 indirect effects not only by varying the coverage of the intervention but also by adjusting other parameters, 320 such as deterrent and insecticidal effects in the case of ITNs/LLINs, simultaneously. It would be 321 beneficial to take advantage of mathematical models and consider parameters for which data are not 322 reliably quantified. For example, the main advantage of house modification is that once installed, it 323 remains semi-permanent. Therefore, its effect is less susceptible to variations in human behavior67, such 324 as repurposing and inconsistent uses of LLINs68. Incorporating such behaviors into the model and 325 estimating the indirect effects on those who do not receive the intervention will have important 326 implications for the widespread implementation of the intervention. 327 328 One limitation of our study is that the search strategy may not have captured all relevant articles. We 329 searched for keywords in the titles and abstracts, potentially missing studies that only reported the indirect 330 effects of malaria interventions within the full text of the article. While efforts were made to manually 331 include references cited for indirect effects, they were unlikely to be complete. Additionally, Benjamin-332 Chung et al. noted evidence of publication bias reporting for indirect effects7. Nonetheless, this review 333 aimed to pave the way for improved design and reporting of future research on the indirect effects of 334 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ malaria interventions. By highlighting this critical area, we hope to contribute to a more appropriate 335 evaluation of intervention effectiveness. 336 337 In conclusion, our review notes an increase in the number of studies that measured the indirect effects of 338 malaria interventions in recent years. We outline eight comparative schemes by which indirect effects of 339 malaria interventions can potentially be quantified, and propose standardized terms for describing indirect 340 effects. We further support the use of mathematical models to inform the evaluation of indirect effects of 341 malaria interventions. Incorporating assessment of indirect effects in future trials and studies may provide 342 insights to optimize the deployment of existing and new interventions, a critical pillar in the current fight 343 against malaria globally. In addition, evidence about the cost-effectiveness of interventions, taking into 344 account the indirect effects, will lead to better-informed decisions by policymakers. 345 346 Declarations 347 Acknowledgments 348 We are grateful to Dr. Masaru Nagashima for his thoughtful input from his expertise in development 349 economics research. 350 351 Contributions 352 YKK developed the original concept. YKK and SMM conducted the first literature screening. YKK, WK, 353 CWC, MK, and AKR conducted the full-text reading. YKK drafted the first draft of the manuscript and 354 YKK, WK, CWC, MK, AKR, BNK, MN, JG, and AK contributed to the revisions. All authors reviewed 355 and approved the final manuscript. 356 357 Funding 358 YKK and MK were financially supported by the Japan Society for the Promotion of Science. AK and JG 359 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ received support from JICA/AMED joint research project (SATREPS) (Grant no. 20JM0110020H0002), 360 Hitachi Fund Support for Research Related to Infectious Diseases, and Sumitomo Chemical Corporation. 361 The funding bodies play no role in the study. 362 363 Competing Interests 364 The authors declare no competing interests. 365 366 References 367 1 World Health Organization. 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(which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ Figure 1: PRISMA flowchart of study selection . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ 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. . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ 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”. . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059doi: medRxiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ Table 1: Characteristics of 31 included studies. Authors, country of interest Type of malaria Study type Intervention Pre-specified indirect effects* Term of indirect effct Methods Indirect effect Quality Title and Abstract Main text Binka et al. P.f Re-analysis of CRT ITN/LLIN y - mass effect [2]-(1) Positive high 1998, Ghana Howard et al. - Re-analysis of CRT ITN/LLIN n mass community effect, mass effect mass effect, mass community effect, mass killing effect [1]-(1), [2]-(3), [2]-(4) Positive high 2000, Kenya Ilboudo- Sanogo et al. P.f Ento House modification n mass effect mass killing effect, mass effect [2]-(3) Positive low 2001, Burkina Faso Maxwell et al. P.f Cross- sectional ITN/LLIN y mass killing benefit, community-wide effects mass effect, community benefit, the effect of mass mosquito killing [1]-(1) Positive low 2002, Tanzania Hawley et al. P.f, P.m, Re-analysis of CRT ITN/LLIN n community wide effects, community effect, area-wide effects beneficial community effect, coomunity wide effect, area-wide effects [2]-(1), [2]-(5) Positive high 2003, Kenya P.o Gimnig et al. P.f, P.m, Ento ITN/LLIN n community-wide suppression community effect [2]-(1) Positive high 2003, Kenya P.o . C C -B Y -N C -N D 4.0 International license It is m ade available under a is the author/funder, w ho has granted m edR xiv a license to display the preprint in perpetuity. (w h ich w as n o t certified b y p eer review ) T he copyright holder for this preprint this version posted M ay 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059 doi: m edR xiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ Charlwood et al. P.f, P.m, Others (Passive surveillance) Untreated n mass effect community-wide effect [1]-(2) Positive very low 2005, Sao Tome and Prıncipe P.v, P.o bed net Abdulla et al. P.f Cross- sectional ITN/LLIN n spatial effects spatial effect, coverage effect [2]-(3) Positive moderate 2005, Tanzania Killeen et al. P.f Ento Target subsidies of ITN y community-level protection community-level effects, mass effects, communinal protection [1]-(2), [2]-(5) Positive moderate 2007, Tanzania Gosoniu et al. P.f Cohort ITN/LLIN y Spatial effects, community effect benefit, community effect indirect effects, spatital effects, community effects, community-wide effect, mass effect, community-level protection [2]-(4) No positive low 2008, Tanzania Oduor et al. - Others (Passive surveillance) Access to free antimalarials y spillover effects spillover effects Others Positive low 2009, Kenya Klinkenberg et al. P.f Cohort ITN/LLIN y community impact, commuinty effect, mass effect commuinty effect, mass effect, spatial protective effect, spatial effect, community protective effect [2]-(1) Positive moderate 2010, Ghana . C C -B Y -N C -N D 4.0 International license It is m ade available under a is the author/funder, w ho has granted m edR xiv a license to display the preprint in perpetuity. (w h ich w as n o t certified b y p eer review ) T he copyright holder for this preprint this version posted M ay 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059 doi: m edR xiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ Komazawa et al. - Cohort ITN/LLIN y community effects community effects, communal effects [2]-(4) Negative very low 2012, Kenya Larsen et al. - Cross- sectional ITN/LLIN y community-level protection commuinty-wide protection, community-level protection, area- wide effects [2]-(2), [2]-(4) Positive moderate 2014, 17 African countries Cisse et al. P.f CRT IPT/SMC y - indirect effects, herd effect [1]-(1) Positive high 2016, Senegal Buchwald et al. P.f Cross- sectional ITN/LLIN n community-level effect community effect [2]-(3) No positive very low 2017, Malawi Escamilla et al. P.f Cross- sectional ITN/LLIN y community-level effects, indirect preotective effects community-level effects, herd effects, indirect preotective effects, community protective effect, community-wide benefts [2]-(2), [2]-(3), [2]-(4) Positive/ No positive low 2017, Malawi Staedke et al. P.f CRT IPT/SMC y community-level effects, community- level benefits community-level benefits Others, [1]-(1) Positive moderate 2018, Uganda Parker et al. P.f Re-analysis of CRT MDA n herd protection, herd effect population-level effect, community-level effect, group-level effect, herd effect [2]-(2), [2]-(4) Positive moderate 2019, Myanmar . C C -B Y -N C -N D 4.0 International license It is m ade available under a is the author/funder, w ho has granted m edR xiv a license to display the preprint in perpetuity. (w h ich w as n o t certified b y p eer review ) T he copyright holder for this preprint this version posted M ay 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059 doi: m edR xiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ Mwanga et al. - Ento House modification y communal protection communal protection, community level protection, communal benefit, communal level benefit [1]-(1), [2]-(4) Positive - 2019, Tanzania Hast et al. P.f Cross- sectional IRS y indirect effects indirect effects [1]-(2) Positive moderate 2019, Zambia Jarvis et al. - Re-analysis of CRT ITN/LLIN y Spatial Effects, spatial indirect effects, spillover, spillover effect, spatial spillover effect, indirect benefit positive spillovers, spatial indirect effects, spatial effects, spillover effect, indirect benefit, spatial spillovers, positive spatial spillover effect, mass killing effects [2]-(1), Others Positive high 2019, Ghana Struchiner et al. - Modeling Malaria Vaccine - dependent happenings, indirect effects indirect effects, dependent happenings, the secondary effects of herd immunity [1]-(1) Positive - 1990 Killeen et al. - Modeling ITN/LLIN - community-level protection community-level protection, community-level effect, communal effects, mass effect [2]-(3), [2]-(5), [3] Positive - 2003, Tanzania . C C -B Y -N C -N D 4.0 International license It is m ade available under a is the author/funder, w ho has granted m edR xiv a license to display the preprint in perpetuity. (w h ich w as n o t certified b y p eer review ) T he copyright holder for this preprint this version posted M ay 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059 doi: m edR xiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ Killeen et al. - Modeling ITN/LLIN - community-level impacts, community- level protection, community-wide benefits, communal benefits community-wide benefits, communal benefits [2]-(2), [2]-(4) Positive - 2007, Tanzania Killeen et al. - Modeling ITN/LLIN - - Community-level effect, community-level protection, communiy-wide protection, communal protection [2]-(2), [2]-(4) Positive - 2007 Killeen et al. - Modeling ITN/LLIN and IRS - communal protection positive externality, community-level impact, communal protection, community-level benefits [2]-(2), [2]-(4), [3] Positive/Negative - 2011 Okumu et al. - Modeling ITN/LLIN and IRS - community-level protection, communal protection, community protection community-levels effect, community-level protection, communal protection, community level impact [1]-(1) Positive - 2013, Tanzania Wenger et al. P.f Modeling Malaria Vaccine - community-level protection community-level effect, community effect, [1]-(2), [2]-(3), [3] Positive - 2013 . C C -B Y -N C -N D 4.0 International license It is m ade available under a is the author/funder, w ho has granted m edR xiv a license to display the preprint in perpetuity. (w h ich w as n o t certified b y p eer review ) T he copyright holder for this preprint this version posted M ay 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059 doi: m edR xiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/ Tun et al. - Modeling MDA - assembly effect, herd effect, community effect community-level effect, herd effect, spill-over effect, assembly effect [1]-(2), [2]-(3), [3] Positive - 2021 Unwin et al. P.f Modeling ITN/LLIN - indirect protection, community protection, indirect benefits indirect protection, community protection, indirect benefits, community effect, community benefits, mass community effect, indirect effect Others, [2]-(3) Positive - 2023 P.f: Plasmodium falciparum, P.m: Plasmodium malariae, P.o: Plasmodium ovale, P.v: Plasmodium vivax, 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 categories of indirect effect estimation 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 treatment area according to the coverage among those who received the treatment. (2) comparisons within the treatment area according to the coverage among those who did not receive the treatment. (3) comparisons within the control area according to distance to the treatment cluster. (4) comparisons within the control area according to the coverage of the nearest treatment clusters. (5) comparisons within the treatment area according to the coverage, including both those who received treatment and those who did not, [3] comparisons conditional on other factors such as the repellent and killing effects of ITNs, pre-erythrocytic or blood-stage vaccines, 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”. *y: yes, n: no . C C -B Y -N C -N D 4.0 International license It is m ade available under a is the author/funder, w ho has granted m edR xiv a license to display the preprint in perpetuity. (w h ich w as n o t certified b y p eer review ) T he copyright holder for this preprint this version posted M ay 8, 2024. ; https://doi.org/10.1101/2024.05.08.24307059 doi: m edR xiv preprint https://doi.org/10.1101/2024.05.08.24307059 http://creativecommons.org/licenses/by-nc-nd/4.0/