Evaluating spatially targeted HIV interventions and harm reduction services among people who inject drugs in a high-burden setting

Domain: chemistry
Published: 2026-02-24
Editorial outcome: minor_revision

Abstract
People who inject drugs (PWID) in India continue to experience high HIV incidence while coverage of HIV and harm reduction services within this population remains suboptimal in many settings, highlighting the need to identify novel service delivery points. To evaluate the effectiveness of spatially focused upscaling of interventions at observed venues where PWID injected drugs together, we developed an individual-based dynamic transmission model of HIV informed by detailed injection network, service engagement, and injection venue attendance data collected in a sociometric study of PWID (n=2512) in New Delhi, India. HIV incidence was simulated for different spatial targeting strategies and with increasing service coverage at injection venues according to UNAIDS/UNODC goals. We identified significant decreases in predicted HIV incidence when deploying interventions at frequently visited injection venues (from 6.8 cases/100 person-years to 2.7/100 PY for full service coverage at the most-visited venue, and further down to 1.3/100 PY for 12 most-visited venues). Prioritizing the most visited venues stratified by spatial clusters provided services to a larger number of individuals versus prioritizing the overall most visited venues, suggesting that service expansion at venues that are spatially distinct with minimal population overlap has a slightly larger impact on reducing HIV incidence.

Introduction
People who inject drugs (PWID) account for a disproportionate burden of human immunodeficiency virus (HIV) globally and are contributing to some of the fastest-growing epidemics in an era of continued progress toward HIV control1–3. A high HIV burden, between 6% to 10% prevalence, has been reported among PWID in India4 with significant regional variance5 and incidence rates higher by an order of 10 folds than other vulnerable populations such as female sex workers and men who have sex with men 6. Partially contributing to this high prevalence is low engagement in harm reduction services among PWID in India7. For example, even after an expansion in 2020-2021, medication for opioid use (MOUD) centers were estimated to cover less than 10% of local PWID in most states of India8, far below the goal of 50% coverage for people with opioid dependence by 2025 from the Global AIDS Strategy9,10. Factors that drove suboptimal coverage include criminalization of drug use, limited access to healthcare services, and economic instabilities resulting in many barriers to accessing care11,12. Although efforts continue to expand MOUD services alongside syringe service programs (SSP) have occurred in recent years, to date, there has been little impact on reducing HIV burden in this group4.

One of the challenges in the delivery of harm reduction services to PWID is how to optimize access and engagement when limited resources/programs are available. Commonly used strategies, such as decentralized service provision and peer-delivered harm reduction, can be effective but are often limited by their ability to reach either enough individuals or those at the highest risk13. The Joint United Nations Programme on HIV/AIDS (UNAIDS) 2025 targets for service coverage specify that 95% of individuals living with HIV should be diagnosed and that 95% of those diagnosed should be on sustained antiretroviral treatment (ART)10; United Nations Office on Drugs and Crime (UNODC) goal for SSP coverage is 200 sterile needles and syringes per PWID per year10, and 50% of individuals with opioid dependence should get access to MOUD according to the Global AIDS Strategy10. These goals require service delivery strategies that consistently cover a high percentage of the PWID population. Spatially targeting prevention programs have been proposed as a means to maximize efficiency since clusters of HIV cases tend to be heterogeneously distributed geographically14. Previously, we explored the spatial and network factors associated with high-risk injection practices and HIV transmission among a cohort of PWID in New Delhi, India15,16. We found that while needle-sharing partnerships were dynamic, there was strong geographic clustering of risk behaviors and HIV transmission by injection venues. Risk spaces other than PWID residences have been previously identified as loci of concentrated high-risk activities17, emphasizing the necessity to incorporate spatial factors into PWID network analyses. Understanding the heterogeneity and spatial distribution of PWID risk behaviors and linkage-to-care is indispensable to more efficiently deploy interventions to population clusters that are at high risk or disproportionately contributing to onward transmission. At the same time, leveraging the number of unique individuals at each injection venue and the geographical proximity of the venues could help optimize the deployment of any injection venue-focused strategies. Thus, we aim to explicitly incorporate injection venues as the primary sites for PWID harm reduction and HIV clinical service delivery in an HIV transmission model to a) project changes in HIV incidence by this spatially focused strategy and b) explore how different orders of prioritizing venues for service delivery translate to intervention effectiveness.

Here, we leverage a highly granular and comprehensive cross-sectional dataset of PWID with both their social (injection partnership) and spatial (injection venue attendance) networks explicitly mapped. We analyzed the injection venue attendance of a network-based cohort of PWID in New Delhi, India, and estimated their spatial patterns of injection-related risk behaviors and service access by injection venue attendance. We developed an individual-based network model of HIV transmission among a population of PWID with similar characteristics to the New Delhi cohort to assess spatial targeting strategies for service delivery. Injection partnership, venue attendance, demographics, risk behaviors, and baseline service engagements were informed by analyses of the empirical cohort data and dynamically modeled. We then evaluated the impact of scaling up interventions at injection venues, which increased the probability of service engagement for individuals who frequent these venues. We compared two different deployment strategies of either prioritizing venues for service expansion based on a) the number of PWID who frequent each venue (population-weighted) or b) a population-weighted strategy stratified by geographical locations. In all simulations, we evaluated the impacts of a range of service coverage levels and the number of venues with enhanced services on changes in HIV incidence in the simulated population compared to baseline, with service coverage as the status quo.

Results
Spatial patterns of PWID risky behaviors were heterogeneous and concentrated at a small number of injection venues
Based on responses from 2512 participants, a total of 110 injection venues with >10 unique visitors were identified at baseline (Fig. 1). Participants reported frequenting a mean of 4.4 venues (IQR=2, 6). To explore possible differential injection venue attendance by demographic or behavioral characteristics, we analyzed several variables reflecting injection-related risk behaviors and the uptake of harm reduction or clinical services stratified by whether the participant visited the top 5 populated venues, which we considered as popular venues amongst our cohort. These top 5 venues were cumulatively visited by ~66% of our baseline PWID cohort. Participants who reported attendance at popular venues tended to be younger and initiated injection drug use at a younger age; these individuals also reported having a larger number of injection partners and visiting more injection venues (Table 1). In terms of risk behaviors, individuals visiting these popular venues had higher injection frequencies and reported that a higher proportion of their injections were with others. They were also less likely to have ever been tested for HIV while also more likely to be living with HIV. Harm reduction service engagement was less clear by injection venue as participants frequenting popular venues were less likely to access MOUD but more likely to access SSP (all p<0.05). When considering individual injection venue populations and behavioral characteristics of their visitors, however, there was no significant linear relationship between venue population and variables commonly related to HIV transmission (Fig. S1).

Injection venues were spatially clustered, allowing for spatial targeting of interventions
Three spatial clusters of injection venues were identified by matching venues to postal code areas of New Delhi and calculating the total number of injection venues within each postal code area (Fig. 2a). We compared the number of injection venues in each postal code area versus their neighboring postal codes and identified three geographically disparate areas (with postal codes as units) that contained 35, 89, and 8 unique injection venues and spanned an area of 72, 148, and 11 square kilometers, respectively (see Fig. 2a).

PWID residences were often near their reported injection venues, although some of the most popular venues were also frequented by PWID who lived further away (Fig. S2–3). Despite the overlapping residence locations of PWID from all 3 clusters, each cluster included at least one unique postal code area of residence, and over 75% of resident PWID frequented that cluster. Higher percentages of PWID living in the northwest postal code areas of New Delhi visited injection venues in Spatial Cluster #1. Most PWID living in the central postal code areas visited venues in Cluster #2, as did most PWID from the peripheral postal code areas. Cluster #3 was one single postal code area, but its venues were almost exclusively frequented by PWID living in that postal code area, who did not visit any other venues in Clusters #1 or #2.

By adding up unique participants who reported visiting each venue in their survey responses, we compared the numbers of unique PWID covered by scaling interventions spatially: we considered two different approaches to expand services to injection venues (Fig. 2b, c). The population-weighted approach sequentially increased service coverage at overall popular venues frequented by the most individuals, while the spatially-stratified approach sequentially covered the most popular venues by each geographical cluster (see Methods). As shown in Fig. 3, deploying services in the order of the spatially-stratified approach consistently reached a larger population than the population-weighted approach when covering no more than 9 injection venues. The relationship was flipped when more than 9 venues were covered since the spatially-stratified approach included more venues that were in remote locations. Even when compared to a Greedy Algorithm selection process, where services are expanded to maximize the number of unique number of PWID with improved care, the spatial-stratified approach yielded smaller gaps in PWID coverage from the Greedy Algorithm for the first several venues covered (Fig. S4).

Model simulations with service coverage ladder and two expansion approaches
We leveraged survey data from the cohort in India to calculate estimates of injection partnerships, injection venue attendance, risk behaviors, and service access. We calculated venue-specific risk behaviors and service access parameters for the model that would reflect the characteristics of the sub-population who visited a particular injection venue (Table S1). A model schematic (Fig. S5), table of parameters (Table S2), and ERGM diagnostics and goodness-of-fit (Fig. S6) are provided in Supplementary Materials. All simulations started with the same prevalence of people living with HIV observed in the cohort (37% prevalence) for all locations. We then explored scenarios with expansions in both 1) the number of injection venues to scale up service coverage for PWID who frequented the venue and 2) the coverage levels (50%/75%/100% to UNAID/UNODC goals for each service). For each scenario, we ran 50 simulations to account for stochasticity. We present the mean HIV incidence 2 years after service deployment to quantify the effectiveness of combinations of service coverage and different expansion strategies.

Without scaling up any interventions, the HIV incidence of the simulated population stabilized at 6.8 cases per 100 person-years, with a 95% confidence interval of [6.5, 7.2]. When increased coverage of a package of services (SSP, MOUD, HIV testing, and ART prescription) was simulated, unsurprisingly, HIV incidence decreased as more injection venues were covered (Fig. 4). Taking the spatially-stratified approach as an example (Fig. 4a), when 50% of all UNAID/UNODC goals were reached, the resulting HIV incidence decreased to 3.8 per 100 PY [3.4, 4.2] when interventions were expanded to only the most populated venue; HIV incidence further dropped to 2.9 per 100 PY [2.6, 3.3] when 12 out of 110 injection venues were covered. When 100% of all UNAID/UNODC goals were reached, HIV incidence was 2.7 per PY [2.5, 3.0] when services were scaled up at the most populated venue and could go down to 1.3 per 100 PY [1.1, 1.5] when 12 venues were covered.

The population-weighted venue expansion approach led to similar results (Fig. 4b). Across the range of service coverage combinations, scaling SSP coverage resulted in the largest marginal decrease in HIV incidence. Improving SSP coverage closer to the UNODC goals (50% to 75%, or 75% to 100%) decreased HIV incidence by ~10%.

To further explore the effectiveness of the two expansion approaches, we first analyzed the number of unique individuals covered when the number of covered venues increased, using SSP as an example (Fig. S7). The overall marginal advantage of the two strategies closely reflects the number of unique PWID covered in Fig. 3. When SSP is scaled up, regardless of the target coverage (% to goal), the spatially-stratified approach yielded significantly more unique individuals covered by SSP compared to the population-weighted approach. When services were expanded to more than 4 venues, the differences between the two strategies were negligible (Fig. S7).

Finally, we evaluated how the two expansion strategies impacted the decrease in HIV incidence within the simulated population (Fig. 5). For simplicity, here we only compared three levels of overall interventions: low/medium/high levels, as in all services (SSP, MOUD, HIV testing, and ART prescription) being all scaled up to 50%/75%/100% of their respective goals. As expected, HIV incidence decreased as service coverage improved at more injection venues. Across all three coverage levels, service delivery to the most populated injection venues led to the largest marginal decrease in HIV incidence, followed by decreases at slower rates when interventions were expanded to less populated venues. Scaling up all interventions in all 110 venues with >10 visitors decreased HIV incidence to 2.6 per 100 PY [2.4, 2.9] for the low service coverage level and 0.5 per 100 PY [0.4, 0.6] for the high coverage level.

In terms of changes in HIV incidence per venue covered, we estimated a decrease of 4.1 per 100 PY per venue in HIV incidence when covering the first venue, 0.82/100 PY/venue when covering 6 venues, and 0.04/100 PY/venue when covering all 110 venues. Unlike the unique numbers of individuals who benefited from either expansion strategy (Fig. 3), the relative difference in HIV incidence between the two expansion strategies was smaller and less statistically significant (Fig. 5). Expanding services from 2 to 4 total venues yielded a slight marginal advantage for the spatially-stratified approach, but the difference was not statistically significant. The largest marginal decrease in HIV incidence when adopting the spatially-stratified approach was when the 2 top venues were targeted, with an additional 0.27 per 100 PY decrease in HIV incidence. When services were expanded to more than 4 venues, there was no substantial advantage by either expansion strategy in this specific PWID population. While neither service strategy was able to reach the entire PWID cohort even with 12 venues being deployed interventions, the individuals who were not reached in our simulation showed lower risks of HIV transmission, compared to those who were reached (Table S3).

Given the highly skewed visitor counts among injection venues with the most popular venue being frequented by 48.5% of our PWID cohort, we considered a counter-factual scenario where the most popular venue did not exist and thus interventions could not be scaled up. This scenario led to fewer individuals being reached by targeting a few injection venues, as well as a smaller difference in the unique numbers of PWID covered by the two service expansion strategies, with the spatially-stratified strategy reaching more individuals until 8 injection venues were covered (Fig. S8). Like Fig. 5, Fig. S9 demonstrates the decrease in HIV incidence if various levels of upscaled services were deployed to an increasing number of injection venues, assuming the most popular venue identified from our data did not exist. Decreases in HIV incidence by upscaled interventions were smaller when injection venue attendance was more scattered; for example, HIV incidence ranged from 2.3 to 3.5 cases per 100 PY when 6 venues were covered by either service deployment approach (Fig. S9) compared to 1.9–3.2 cases per 100 PY with the most popular venue being covered first (Fig. 5). When only few venues (2-3) were covered, the spatially-stratified approach resulted in significantly lower HIV incidence (p<0.05) compared to the population-weighted approach, but the difference became negligible when more than 3 venues were covered.

Methods
Study overview
Our study used empirical data from the baseline cohort of The Spatial Network Study, an ongoing longitudinal cohort of PWID in New Delhi, India, since 2017. All participants provided written informed consent. Details about the study population and recruitment have been previously published15,16. The model was informed by survey data from 2512 participants who had their baseline visits between November 2017 and July 2019. Variables used included basic demographic characteristics (age, gender, etc.), injection venue(s) visited, drug-use behaviors including frequency of injection, injection with others, syringe sharing, service access rates including MOUD and SSP, HIV testing, and ART adherence. We also used test results from biospecimens, including HIV antibody titer and, if positive, HIV RNA level. As part of the study, each participant was asked to provide information about partners they had injected drugs with more than once in the past month. Unique referral cards were given to the participant for each injection partner nominated; if the named injection partner came back with the referral card, a network tie would be recorded. As a result, a sociometric network was built from 10 initial index participants and 2502 subsequent recruits. An additional list of confirmed deaths, migrations, and suspected deaths with corresponding participant IDs was used to estimate the mortality rate of the cohort. The study protocol was approved by institutional review boards at Johns Hopkins Medicine (IRB00110421) and the YRGaitonde Centre for AIDS Research and Education in India (YRG292). All participants provided written informed consent.

Network model structure
We adapted and extended the R package EpiModel modeling framework (Version 2.4.0)24 to build a dynamic network-based model of HIV transmission. We simulated a population like the cohort, with 2512 individuals with the same demographic characteristics and similar sociospatial networks as in their baseline surveys. Model parameters were informed by cross-sectional analyses of the survey data (Table S2), when possible, as well as estimates from existing literature (details about the data source, uncertainty, and calibration of parameter values can be found in the Supplementary Material). A temporal exponential random graph model (ERGM) was used to simulate the dynamic and heterogeneous nature of injection partnerships, where nodes were individuals and edges/ties represented injection partnerships. We estimated an ERGM with a) network degrees associated with each node’s HIV infection status, number of injection venues they visited, and whether they were an index node, and b) assortative mixing associated with nodal homophily of popular venues both nodes visited and their HIV infection status. At each time point, individuals could enter the population and join the injection network or leave the population permanently based on an all-cause mortality rate specific to this cohort. Each individual newly entering the simulated population was assigned several injection venues to frequent based on a distribution of the number of venues visited by the surveyed cohort; the exact venues visited were randomly sampled with the probability ratio proportional to the population of each venue. Stochasticity was incorporated into the model using distributions of demographic and drug-use characteristics (see Table S2) for new PWID entering the population, venue-specific service engagement where the probability of engagement was informed by the data (Table S1) or simulated increases in service attendance, and the probabilistic formation and dissolution of social and spatial ties at each time point, with the dissolution rate from survey data. Simulations were run for 2 years after additional service expansion at injection venues (at year 1).

Simulating disease transmission and service uptake
Given that our data mainly focused on details of the drug injection network compared to the IDU sexual network, we focused on parenteral transmission through sharing injection paraphernalia among PWID, as equipment sharing was identified as a substantial contributor to PWID morbidity and mortality globally5. The baseline HIV infection prevalence (defined by detectable HIV antibody titer) within the initial population was 37%15. Transmission can occur when an individual living with HIV (the ego) injects drugs with their network member(s) or vice versa. The number of injection events done with others was determined by the ego’s monthly injection frequency and whether the ego had access to MOUD that could decrease their frequency of drug use, multiplied by a proportion of injections shared with others by the ego (informed from cohort data). The injection dyad was more likely to inject together if they went to at least one common injection venue. A base probability of syringe sharing was applied to each injection partnership and proportionally decreased if the ego accessed SSP and had access to clean syringes (see Supplementary Information for additional details and values). Thus, each injection event with shared injection paraphernalia had a non-zero infectivity that was defined by an ego-specific transmission risk. The risk of HIV transmission was higher when a newly infected ego was in the acute phase (see Supplementary Information). Finally, initiation of ART after diagnosis decreased the base transmission probability by different rates with or without successful viral suppression. Rates of all service engagements and HIV viral suppression at baseline were based on the current uptake within the cohort (see Table S1).

Modeled scenarios and outcomes
We assessed the relative and marginal benefit of a range of integrated harm reduction and HIV response services, increased up to the goals set by UNAID/UNODC (Fig. 4). Based on the cost-of-service delivery, we assumed that services would be increased starting with the least expensive (SSP) to the most expensive (ART prescription), with MOUD and HIV testing in between. For example, a 50% goal for SSP coverage needed to be reached before a 50% goal for MOUD coverage could be reached. We considered a range of service coverage percentages in each scenario, corresponding to 50%, 75%, or 100% completion of the respective service coverage goals (Fig. 4). In addition, we investigated the number of venues where these services would be deployed. We considered increased services from 1 to 12 injection venues and then 110 venues out of 110 total venues that were visited by more than 10 unique participants. Since covering 12 venues could theoretically cover >75% unique PWID in the cohort, we primarily focused on increasing venues from 1 to 12. In the model, we assumed that all individuals who frequented a venue would have an increased probability of service engagement if that venue had an expansion of that specific service; we did not incorporate correlations between engagement of one service with another.

We compared two strategies for choosing the order in which venues would be prioritized to receive all scaled-up services. For the first strategy, we ranked venues based on the venue visitor size (population-weighted approach), which was calculated as the total number of unique PWID who frequented this venue (Fig. 2b). In the second strategy, we ranked venues based on geographically stratified venue population to target the most populated venue(s) that were in nonadjacent geographic clusters (spatially-stratified approach, Fig. 2c). The effects of scaled-up interventions with either targeting strategy were measured as the decrease in HIV incidence in the simulated population compared to baseline, where no additional interventions were delivered. Predicted HIV incidence from our model was out of individuals who were alive in the current step of simulations. The baseline scenario assumed that service coverage stayed the same for the entire duration of the simulation at the estimated current coverage based on the proportion of visitors of a certain venue who reported access to each service. Our model was calibrated to a stable HIV prevalence and incidence within the simulated population in the baseline scenario.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Wang and colleagues leverage data from a network of people who inject drugs (PWID) in New Delhi to understand spatial targeting possibilities for HIV and harm reduction services. Specifically, they are interested in how to minimize the number of intervention sites, while maximally reducing incidence. The data source is detailed and unique: a sociometric study of ~2500 PWID. Within this population, they have detailed behavioral, social network, and injection venue data that they use to parameterize a mathematical model. They examine the projected HIV incidence rate with varying interventions (e.g. SPSS, MOUD), number of injection venues, degree of scale-up (25-100%), and type of targeting approaches (largest venues versus spatial stratification). A central finding is that if services were scaled up to only the single largest venue, a decrease in incidence from 6.8 to 2.7 per 100 PYs could be achieved. Going from 1 venue to 12 resulted in additional (but more marginal) declines to 1.5 per 100PYs. These findings stem from the insight that risk behaviors and vulnerabilities are spatially concentrated. Strengths of this analysis include: 1) a very detailed empirically-derived dataset that parameterizes the models, 2) novelty of the work, and 3) interesting findings—I especially love figure 5. However, I do have some concerns and questions that I describe below.\n1) Research question: The research question is implied, but not directly stated in the abstract or introduction. Please provide a direct articulation of the question.\n2) Generalizability: The very strength of this analysis (the very detailed multi-faceted empirically grounded assumptions) is also its Achille’s heal: replicability. This seems very difficult to replicate without substantial funding and very specific expertise. Are there implications for other regions in India or beyond? The finding that offering services at only a few sites is remarkable. Are there any reasons to believe that this finding could extend beyond New Delhi?\n3) Rationale for risk stratification: One implicit assumption is that risk stratification is needed for this population. However, with an HIV incidence rate of 6.8 per 100 person years, it seems the entire population is high risk. Perhaps, some reframing of how to reach the entire population or >90% of the population most efficiently is needed.\n4) Use of venues, rather than networks: The 2500-person sample was recruited through only 10 initial index participants! Given that this peer recruitment approach was successful for research, would it also be applicable for intervention provision (rather than venue-based intervention)?\n5) Feasibility: Programmatically, is it feasible for these services to be offered at any of these venues? The authors treat all of them as potential sites for intervention, but as someone unfamiliar with this setting, it is not clear if this is the case. Are certain venues too busy, unhygienic, dangerous to offer services?\n6) Marginalization: Even if most people are reached, not everyone is. Who is left behind? How are people who congregate at these most populous sites different from those who do not?", "reviewer_2": "This manuscript investigates whether spatially targeted interventions at injection venues may reduce HIV incidence among people who inject drugs (PWID) in India. The authors leverage detailed sociometric and spatial data from New Delhi and develop an individual-based model to compare intervention strategies. The use of detailed individual and spatial injecting data is novel and a highly unique data source. The use of agent-based network modeling to assess the impact of network-targeted interventions is a methodologically strong approach. The findings are timely given service coverage goals and ongoing challenges in reaching PWID populations in low- and middle-income countries. The paper is clearly written and outlined. I have the following queries which I think if addressed would improve the clarity of the analysis:\nAbstract\n• The primary research question was not stated in the abstract.\n• It would be helpful to include quantitative findings in the abstract to understand some level of magnitude\nIntroduction\n• There is high novelty in your approach of segmenting distinct needle-sharing networks and then using a venue-based intervention strategy to maximize coverage. However, I don’t feel that the introduction is really highlighting the uniqueness of the research, as well as the unique dataset. This should be emphasized.\n• It would be helpful to state the current status quo in terms of interventions. And why the status quo can’t be scaled up to reach the same proportion of individuals as the venue-based approach suggested.\n• Minor: I was not clear on what the following means: “clusters of HIV cases tend to be heterogeneously distributed geographically.”\n• Minor: Line 68 stated that “there was strong geographic clustering of risk behaviors and HIV transmission by injection venues”, but I am not clear what is meant by clustering.\n• Minor: Line 71 stated that “… factors into PWID network analyses,” but I am not clear what the network analyses are at this point.\n• Minor: Line 71 stated that “Understanding the heterogeneity …”, but I am not clear what is meant by heterogeneity. Is the heterogeneity across venues?\nResults\n• The authors conducted an analysis looking at the top 5 venues. However, that analysis is never really mentioned again. I believe their point was that large venues differ from smaller venues. It would be clearer if they generated plots and regressions where the x-axis / independent variable was venue size and the y-axis / dependent variable was a characteristic of interest.\n• The identification of the 3 spatial clusters was interesting. My understanding is that the authors were attempting to find distinct needle-sharing networks. If this is the case, it would be more appropriate to use network community detection to address this problem.\n• Lines 122-131 discussed the connection between PWID residence and clusters. However, I did not see what PWID residence was of importance for their analysis. A clarification would be helpful.\n• I found the spatial stratification of venue scale-up very interesting. Given the number of unique PWID served was important, why not investigate a greedy algorithm for selecting ‘x’ venues based on the ‘x’ venues with the most unique PWID.\n• Starting on Line 205, I was not clear on the counter-factual scenario.\nDiscussion\n• Lines 230-234 state that it was shown that popular injection venues were associated with certain characteristics. However, I don’t believe this was shown. It was shown that the top 5 venues collectively had different characteristics than the remaining.\nMethods\n• There was not discussion if the population was re-weighted based on the cohort was derived from an RDS sample\n• The terms used in the ERGM could have been clearer in the main text\n• I believe the model only includes injecting related transmission and not sexual transmission – the authors could be more explicit about this in the methods. Relatedly, the statement “we focused on parental transmission as…equipment sharing was identified as the leading contributor to PWID morbidity and mortality globally” is a bit of a strange statement/phrasing and should be clarified. There are many places where sexual transmission among PWID plays an important role in HIV transmission (and likely, overall PWID morbidity/mortality, such as in sub-saharan Africa), although I appreciate this may not be as relevant in India.\n• It would be useful to present diagnostics that the ERGM model fit the data well.\n• It would be useful if the authors could present a figure in the supplement to confirm what happens to incidence and prevalence in the status quo projections. Is the model at steady state?"}, "author_response": "RESPONSE TO REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nWang and colleagues leverage data from a network of people who inject drugs (PWID) in New Delhi to\nunderstand spatial targeting possibilities for HIV and harm reduction services. Specifically, they are interested in\nhow to minimize the number of intervention sites, while maximally reducing incidence. The data source is\ndetailed and unique: a sociometric study of ~2500 PWID. Within this population, they have detailed behavioral,\nsocial network, and injection venue data that they use to parameterize a mathematical model. They examine the\nprojected HIV incidence rate with varying interventions (e.g. SPSS, MOUD), number of injection venues, degree\nof scale-up (25-100%), and type of targeting approaches (largest venues versus spatial stratification). A central\nfinding is that if services were scaled up to only the single largest venue, a decrease in incidence from 6.8 to 2.7\nper 100 PYs could be achieved. Going from 1 venue to 12 resulted in additional (but more marginal) declines to\n1.5 per 100PYs. These findings stem from the insight that risk behaviors and vulnerabilities are spatially\nconcentrated. Strengths of this analysis include: 1) a very detailed empirically-derived dataset that parameterizes\nthe models, 2) novelty of the work, and 3) interesting findings—I especially love figure 5. However, I do have\nsome concerns and questions that I describe below.\n1) Research question: The research question is implied, but not directly stated in the abstract or introduction.\nPlease provide a direct articulation of the question.\nThank you for pointing this out, and we apologize for the oversight. The research question is added to the\nabstract (Lines 22-24 in the marked-up manuscript) and emphasized again in the introduction (Lines 81-\n85).\n2) Generalizability: The very strength of this analysis (the very detailed multi-faceted empirically grounded\nassumptions) is also its Achille’s heel: replicability. This seems very difficult to replicate without substantial\nfunding and very specific expertise. Are there implications for other regions in India or beyond? The finding that\noffering services at only a few sites is remarkable. Are there any reasons to believe that this finding could extend\nbeyond New Delhi?\nWe agree that such detailed data in a cohort is expensive and complicated to replicate. However, we have\n(and so have others) included surveys on injection venues and sexual venues (among men who have sex\nwith men) as part of cross-sectional studies of respondent-driven sampling. We observed similar spatial\nheterogeneity even in these less complex and easy-to-replicate studies. This could be easily integrated\ninto RDS for surveillance, which is the standard for quantifying disease burden in PWID and other key\npopulations. Once such data are available, simulations could be widely applicable to other PWID settings,\nespecially highly aggregated/clustered networks. Injection venues, especially those visited by PWID\nliving nearby, as in our cohort, are more stable than the dynamic drug-use dyadic relationships from\nPWID social networks and can avoid bias due to network homophily; thus, offering services to large\nvenues is more likely to guarantee effectiveness. In addition, further decisions on which venues to\nprioritize for service delivery do not necessarily require data as granular as what we used to build the\nmodel. For example, a field survey to quantify the density of injection venues in different areas can likely\nlead to similar spatial stratification to maximize the unique numbers of PWID reached, without the need\nto collect detailed social network data.\n3) Rationale for risk stratification: One implicit assumption is that risk stratification is needed for this population.\nHowever, with an HIV incidence rate of 6.8 per 100 person years, it seems the entire population is high risk.\nPerhaps, some reframing of how to reach the entire population or >90% of the population most efficiently is\nneeded.\nWe apologize for the confusion in the wording. No risk stratification was done on the population, and\nTable 1 was generated to highlight the heterogeneity of injection venues in terms of visitor behavioral\nrisks. However, it is true that even with the spatially-stratified approach covering 15 injection venues, no\nmore than 78% PWID could be reached by interventions. This could be because our empirical network\noriginated from 10 seeds and thus formed an extensive branch of a partial network, resulting in some\nindividuals being at the very periphery of the network and becoming harder to reach than in reality.\n4) Use of venues, rather than networks: The 2500-person sample was recruited through only 10 initial index\nparticipants! Given that this peer recruitment approach was successful for research, would it also be applicable for\nintervention provision (rather than venue-based intervention)?\nOur proposed means of service provision is mobile service trucks that can consistently reach venue\nvisitors without relying on certain drug-use partnerships and can move if venue locations change, whereas\nsocial networks are less predictable with their dynamic dissolution and formation. In addition, many of\nthe drug use ties identified in this network were one individual buying drugs from the other, with some\nco-injections occurring at the same time. Such a social network structure may be less reliable for\ndeploying peer-based interventions.\n5) Feasibility: Programmatically, is it feasible for these services to be offered at any of these venues? The authors\ntreat all of them as potential sites for intervention, but as someone unfamiliar with this setting, it is not clear if this\nis the case. Are certain venues too busy, unhygienic, dangerous to offer services?\nThe popular venues identified by our PWID cohort are all suitable to deploy interventions. Trained\noutreach workers and/or mobile vans are able to access these venues and deliver HIV testing and\nsyringes/needles at the field-level. Further, we have recently established a facility within 200 meters of\nthe most frequented venue (Venue #40 in our study) that is providing medications for opioid use disorder,\ntesting for HIV, HBV and HCV, as well as other social support services. There is also a clinician on-site\nand we facilitate ART currently but could easily expand to delivery novel agents such as long-acting\nARVs.\n6) Marginalization: Even if most people are reached, not everyone is. Who is left behind? How are people who\ncongregate at these most populous sites different from those who do not?\nWe compared demographic, drug use risks, and service engagement variables of our PWID cohort\nstratified by whether they can be reached by either service delivery strategies when 12 venues were\ncovered (see Table S3 in Supplementary Materials and Lines 224-227 in manuscript). Individuals who\nwould not have received services when venue-based interventions were deployed were more likely to be\nolder, inject less frequently overall and with others, visit fewer injection venues, have been tested for HIV\ninfection, and were less likely to be living with HIV. In summary, the ones who exclusively visited less-\nfrequented venues also had lower risks of HIV transmission.\nReviewer #2 (Remarks to the Author):\nThis manuscript investigates whether spatially targeted interventions at injection venues may reduce HIV\nincidence among people who inject drugs (PWID) in India. The authors leverage detailed sociometric and spatial\ndata from New Delhi and develop an individual-based model to compare intervention strategies. The use of\ndetailed individual and spatial injecting data is novel and a highly unique data source. The use of agent-based\nnetwork modeling to assess the impact of network-targeted interventions is a methodologically strong approach.\nThe findings are timely given service coverage goals and ongoing challenges in reaching PWID populations in\nlow- and middle-income countries. The paper is clearly written and outlined. I have the following queries which I\nthink if addressed would improve the clarity of the analysis:\nAbstract\n• The primary research question was not stated in the abstract.\nThank you for pointing this out, and we apologize for the oversight. The research question is added to the\nabstract (Lines 22-24 in the marked-up manuscript) and emphasized again in the introduction (Lines 81-\n85).\n• It would be helpful to include quantitative findings in the abstract to understand some level of magnitude\nThe key quantitative finding is added to the abstract (Lines 32-34).\nIntroduction\n• There is high novelty in your approach of segmenting distinct needle-sharing networks and then using a venue-\nbased intervention strategy to maximize coverage. However, I don’t feel that the introduction is really\nhighlighting the uniqueness of the research, as well as the unique dataset. This should be emphasized.\nThank you for the suggestion. We further emphasized the uniqueness and granularity of our empirical\ndataset in the introduction (Line 86-88) and the uniqueness of the venue-based modeling approach in the\ndiscussion (Lines 325-328). While not directly identifying needle-sharing sub-networks (by social ties) in\nthis study, the distinct spatial clusters of injection venues differentially cover subgroups of unique PWID\nin our cohort.\n• It would be helpful to state the current status quo in terms of interventions. And why the status quo can’t be\nscaled up to reach the same proportion of individuals as the venue-based approach suggested.\nThe status quo for the various services are:\nMedication for Opioid Use Disorder (MOUD) – established physical brick and mortar facilities deliver\nMOUD. Clients are required to go to these facilities daily for their buprenorphine. The distance from the\nvenues to these facilities is variable.\nSyringe Service Program (SSP) – this happens at the field-level by outreach workers; given the limited\nnumber of outreach workers there is no approach to saturate the most “hyperendemic” venues vs. sub-\nsaturate all venues. In an ideal scenario, the goal would be to saturate all venues.\nHIV testing: clients currently need to go to physical integrated testing and counseling centers to be\nscreened for HIV. Distance from venue to these facilities varies. PWID are also hesitant to visit many of\nthese venues for perceived fear of discrimination.\nART: Clients who screen positive for HIV need to visit government ART centers to be initiated on ART\nand collect refills.\nDistance and other social factors (perceived stigma) limit the ability to scale-up the status quo and have\nan impact on the field. Many of these services lend themselves to field-based delivery. We believe that\nthese models suggest that to eliminate HIV among PWID in this setting, current models will not work\neven if scaled up as they do not penetrate these populations. An alternate approach of targeting venues as\nthese models suggest will likely lead to better return on investment and elimination of HIV among PWID.\n• Minor: I was not clear on what the following means: “clusters of HIV cases tend to be heterogeneously\ndistributed geographically.”\n• Minor: Line 68 stated that “there was strong geographic clustering of risk behaviors and HIV transmission by\ninjection venues”, but I am not clear what is meant by clustering.\n• Minor: Line 71 stated that “… factors into PWID network analyses,” but I am not clear what the network\nanalyses are at this point.\n• Minor: Line 71 stated that “Understanding the heterogeneity …”, but I am not clear what is meant by\nheterogeneity. Is the heterogeneity across venues?\nWithin a PWID cohort with high HIV prevalence, usually infections are not evenly distributed\ngeographically. Districts or census tracks can have much higher HIV burden (clusters of HIV cases) due\nto a higher concentration of individuals living with HIV, with riskier drug use behaviors, or being more\nsocially connected. Such skewed distribution (geographical heterogeneity) could be due to the existence\nof injection venues where PWID inject drugs with alters that do or do not previously know and have\nhigher risks of sharing injecting paraphernalia. Many network analysis studies focus on the drive of tie\nformation; geographical location of drug injection could be incorporated to evaluate homophily between\ntwo nodes to predict potential tie formation between two PWID.\nResults\n• The authors conducted an analysis looking at the top 5 venues. However, that analysis is never really mentioned\nagain. I believe their point was that large venues differ from smaller venues. It would be clearer if they generated\nplots and regressions where the x-axis / independent variable was venue size and the y-axis / dependent variable\nwas a characteristic of interest.\nTable 1 aimed to highlight that geographical hotspots of PWID are associated with disproportionally\nhigher risks of HIV transmission, thus highlighting the necessity of deploying services to these locations.\nWe added a figure comparing drug use and service engagement characteristics of venue visitors (monthly\ninjection frequency, probability of syringe sharing, number of drug use partners, whether living with HIV,\naccess to MOUD, and whether ever tested for HIV) against injection venue size, as suggested (Figure S1,\nLines 123-126 in main text). However, there was no clear relationship between visitor size of venues and\nprobability of risky behavior or service engagement of these visitors. This is because many smaller\nvenues near big ones were visited by the same individuals. These results reflect our emphasis on spatial\nclusters of injection venues instead of focusing on individual venues, given the high visitor overlaps.\n• The identification of the 3 spatial clusters was interesting. My understanding is that the authors were attempting\nto find distinct needle-sharing networks. If this is the case, it would be more appropriate to use network\ncommunity detection to address this problem.\nWe did not attempt to find distinct needle-sharing networks in our analysis. The identification of injection\nvenue clusters was to consider the geographical proximity of naturally formed hotspots of co-drug use,\nwhich could serve as areas where PWID could be reached with services. By providing harm reduction and\nclinical services at these locations, a large number of unique PWID could be reached in a more cost-\neffective manner. While venue-specific needle-sharing sub-networks are likely to form as individuals are\ninjecting drugs at the same location, some PWID were on the receiving end of syringe sharing from\nsomeone they did not know, or even by picking up used syringes from the ground. The social sub-\nnetworks thus do not fully highlight the significance of our venue-specific service delivery based on\nspatial clusters, which is a more direct strategy of service provision at the spot of drug injection\noccurrence, bypassing the instability or inconsistency of drug-use relationships in one’s network and\nreaching the most unique individuals with fewer service spot deployments.\n• Lines 122-131 discussed the connection between PWID residence and clusters. However, I did not see what\nPWID residence was of importance for their analysis. A clarification would be helpful.\nThe significance of relating venue clusters to PWID residence is clarified in the discussion (Lines 262-\n263). In summary, the fact that each injection venue cluster tends to be visited by PWID living nearby,\nsome even exclusively (cluster #3), highlights that a spatially-stratified approach of service delivery can\nprovide services to PWID harder to reach by a centralized delivery method based on venue popularity.\nThe association between residence and injection cluster also implies that these injection venues are\nrelatively stable; even with the risk of dissolution, they are more likely to form again somewhere nearby.\n• I found the spatial stratification of venue scale-up very interesting. Given the number of unique PWID served\nwas important, why not investigate a greedy algorithm for selecting ‘x’ venues based on the ‘x’ venues with the\nmost unique PWID.\nThank you for the suggestion. We implemented the Greedy Algorithm and added a figure into\nSupplementary Materials (Figure S4, Lines 156-160 in the main text) comparing the percentages of\nunique PWID in the cohort reached by different service delivery strategies (population-weighted,\nspatially-stratified, or by a greedy algorithm to maximize coverage). For the first several venues covered,\nthe spatially-stratified approach yielded smaller gaps in PWID coverage from the greedy algorithm, but\nunsurprisingly, both population-weighted and spatially-stratified approaches plateaued sooner than the\ngreedy algorithm when more venues were covered. While theoretically maximizing PWID reached, the\ngreedy algorithm approach lacks generalizability as it is highly specific to the dataset and requires\ninformation on all identifiable visitors of every recorded venue, while the spatially-stratified approach\nbalances the cost and yield by showing that general knowledge of venue clustering could potentially\nreduce repeated PWID coverage of intervention and increase service delivery efficiency.\n• Starting on Line 205, I was not clear on the counter-factual scenario.\nThe counter-factual scenario acts as a sensitivity analysis to evaluate the effectiveness of our intervention\nmethod when the most popular injection venue did not exist. This analysis was necessary because of the\nunique network structure observed in this cohort where the injection venue visitor counts are highly\nskewed and the most popular venue was able to cover almost 50% of the population. This may not be the\ncase for other more scattered PWID networks, and we showed the decrease in HIV incidence of our\nvenue-targeting method and an advantage of the spatial-stratified service deployment strategy for such a\nmore scattered sociospatial network.\nDiscussion\n• Lines 230-234 state that it was shown that popular injection venues were associated with certain characteristics.\nHowever, I don’t believe this was shown. It was shown that the top 5 venues collectively had different\ncharacteristics than the remaining.\nWe apologize for the lack of clarity in our description of venue popularity. Since the top 5 venues were\nidentified based on ranking their visitor sizes, i.e. the number of unique PWID who ever reported\nfrequenting this location, we defined them as the popular venues among our PWID cohort (explained now\nin Lines 112-113). Thus, Table 1 and the corresponding text in the discussion section both aim to\nhighlight that the injection venues that PWID frequently visited also disproportionally hosted the subset\nof PWID with higher HIV transmission risks.\nMethods\n• There was not discussion if the population was re-weighted based on the cohort was derived from an RDS\nsample\nThe 10 initial index recruits were selected from a previous respondent-driven sampling survey accrued for\na cluster-randomized trial, but the remainder of our baseline cohort were recruited via a name generator\nnetwork referral methodology. Instead of a fixed number of alters, participants were asked to list all the\nalters they injected drugs with in the prior 1 month, and a coupon was given to every alter named. When\nan alter returned with a coupon, they got reconnected into the network by identity matching, recruited as\nan ego, and were given more coupons for every alter they could name. Thus, the network by design has\nincorporated the heterogeneity in ego network sizes and does not need to be re-weighted.\n• The terms used in the ERGM could have been clearer in the main text\nMore complete descriptions of ERGM estimation have been added to the main text (Lines 372-379).\n• I believe the model only includes injecting related transmission and not sexual transmission – the authors could\nbe more explicit about this in the methods. Relatedly, the statement “we focused on parental transmission as…equipment sharing was identified as the leading contributor to PWID morbidity and mortality globally” is a\nbit of a strange statement/phrasing and should be clarified. There are many places where sexual transmission\namong PWID plays an important role in HIV transmission (and likely, overall PWID morbidity/mortality, such as\nin sub-saharan Africa), although I appreciate this may not be as relevant in India.\nWe now state more explicitly in the methods about modeling just the drug injecting network and not the\nsexual transmission (Lines 395-398) as routes for disease acquisition. We agree that sexual transmission\nof HIV could be playing a much more significant role among PWID in other settings compared to our\ncohort where very few individuals reported any sexual relationship with their drug injecting alters (Lines\n317-319). We changed the ‘the leading contributor’ from original text to ‘a substantial contributor’ to\navoid confusion (Line 398).\n• It would be useful to present diagnostics that the ERGM model fit the data well.\nA figure showing ERGM diagnostics and goodness-of-fit was added to the Supplementary Material\n(Figure S6, Line 168 in the main text).\n• It would be useful if the authors could present a figure in the supplement to confirm what happens to incidence\nand prevalence in the status quo projections. Is the model at steady state?\nWe calibrated our model to give a steady incidence/prevalence with the status quo intervention coverage\n(Lines 448-449). It suits our purpose of evaluating the impacts of different hypothetical intervention\nstrategies."}, {"round": 2, "reviewer_comments": {"reviewer_2": "Comments were thoroughly addressed."}, "author_response": ""}]

Decision letter