Ethical challenges and opportunities for integrating predictive analytics in community-based overdose prevention

Summary

As predictive analytics become more widely integrated into local public health responses to the United States overdose epidemic, community-based substance use service providers have begun to adopt machine learning-based predictive tools to guide the allocation and delivery of overdose prevention services. While these tools hold promise for anticipating community overdose risk and enhancing the efficiency of overdose prevention resource distribution, outreach, and education efforts, their use in community settings raises substantial ethical and practical challenges. In this Viewpoint, we examine the application of predictive analytics to community-based overdose prevention through a public health ethics lens, drawing on principles of distributive justice, transparency, community participation, and implementation readiness. We outline five key ethical considerations for developers (i.e., institutional responsibility, oversimplification of complex social realities, data and algorithmic bias, community displacement in decision making, and equity trade-offs) and corresponding practical challenges for service providers. We offer five recommendations for developers, public health authorities, and frontline organizations to overcome challenges and ensure responsible, equity-driven implementation. As data-driven approaches to overdose prevention proliferate, ethical and participatory frameworks will be essential to ensure predictive tools strengthen, rather than undermine, community trust and health equity.

Introduction

The overdose epidemic remains a national emergency in the United States (US), with over 105,000 deaths nationally in 2023.¹ While overdose deaths nationally declined by 24% from October 2023 to September 2024,² they remain far above the levels of the pre-fentanyl waves of the epidemic.³ Likewise, in spite of overall national decreases, in some jurisdictions racial/ethnic and socioeconomic disparities in overdose death widened,^4,5 highlighting a need for public health authorities to consider how and to whom prevention resources are being distributed as a strategy to grow health equity. Although significant efforts have been directed toward overdose response efforts by local, state, and federal governments,⁶ the availability of public health resources—particularly harm reduction interventions—remains insufficient to fully address the crisis.⁷

The problem of scarce resources has forced public health officials to carefully evaluate how best to distribute overdose prevention tools across the regions they serve. Traditional decisions by local health authorities—such as naloxone distribution and street outreach—have been informed by community-level historical surveillance data and provider knowledge of fatal and non-fatal overdoses. However, changing trends in drug use and drug markets complicate traditional approaches, as historical overdose trends may fail to accurately represent present or future area-level overdose risk.⁸ To address these challenges, public health authorities and community-level service providers are adopting innovative, data-driven approaches to anticipate future overdose risks and guide the strategic delivery of prevention resources.

The promise of predictive analytics in overdose prevention

Predictive analytics, which forecast future trends based on multiple sources of data,⁹ have become increasingly used by healthcare providers to identify patient-level opioid use disorder and overdose risk.¹⁰ Their uses, however, in the context of community-based public health and harm reduction practice remain under developed, but hold significant promise.¹¹ Unlike traditional methods for area-level resource distribution that often rely on historical trends and static data, predictive models have the capacity to leverage machine learning to integrate large datasets (including environmental and social media data), identify patterns in those data, and forecast future risks at a highly granular level to inform community level resources for overdose prevention.^12,13 In this article, we use “predictive analytics” and “machine learning” interchangeably to refer to algorithmic models that can estimate future outcomes based on historical and real-time data inputs.

One emergent approach to small-area prediction¹⁴ uses machine learning to develop neighborhood overdose forecasts to inform community-level resource allocation by integrating local historical knowledge with real-time data.¹⁵ These methods have been applied in the US¹⁶ for neighborhood- and county-level predictions in Rhode Island,¹⁷ Massachusetts,¹⁸ Chicago,¹⁹ Los Angeles,²⁰ and New York City,²¹ among other jurisdictions.²² In one use case, in Rhode Island, community-based organizations and public health authorities jointly use overdose forecasts developed by predictive models to identify neighborhoods at highest risk of future overdose in six-month increments to facilitate longer-term resource planning at the organizational level.²² In another use case, in Ohio, the state health authority uses predictive analytics to forecast future 30-day overdose risk at the zip-code level to facilitate more rapid community-level preventive services targeting.²³ Such tools can supplement traditional surveillance reports and provider insight to better inform the deployment of naloxone distribution, outreach, and education campaigns.²⁴

However, their increased adoption raises ethical and practical challenges for harm reduction providers, the communities they serve, and neighborhood residents that must be addressed to ensure equitable and effective model use. Unlike many digital health tools, prediction for overdose prevention serves to allocate place-based resources rather than individual treatments; operates in the context of rapidly shifting drug markets and trends for which timeliness is paramount; intersects with the criminal legal system and patient privacy protections, heightening risks of model misuse; communicates risk of stigmatized health behaviors and outcomes, which may translate to stigma toward communities; and relies on data for which collection may be uneven across communities by racial and ethnic, socioeconomic, and geographic factors. Addressing these challenges is essential for predictive models to enhance, not undermine, public health equity, and community trust.

Ethical considerations and practical challenges in the use of predictive analytics to inform overdose prevention service delivery

The integration of predictive analytics into community-based overdose prevention raises several ethical considerations and practical challenges with real-world implications. As these novel tools proliferate across communities,²⁵ ethical issues must be addressed early to ensure responsible and equitable implementation. Our approach draws on the intervention-oriented framework for the ethical analysis of public health programs developed by Kass.²⁶ This framework positions ethical analysis as a professional responsibility for practitioners, guided by a series of considerations to guide the development and implementation of surveillance, prevention, and intervention activities. Following Kass's framework, our analysis considers core public health ethics concerns, including program effectiveness, the distribution of burdens and benefits, and how implementation can minimize harm and promote fairness. In addition to Kass's intervention-oriented framework, we draw on capabilities approaches from Sen and Nussbaum by highlighting whether overdose prediction expands real prevention opportunities for people who use drugs and the communities in which they live.^27,28 We also distinguish distributive justice from procedural justice across the collaborative roles of public health authorities, academic researchers, and community organizations. As such, summarized in the Table 1, we offer a normative, practice-oriented analysis through a discussion of five ethical considerations and corresponding practical challenges, with examples of real-world impact, that may arise as developers and community members integrate new, data-driven surveillance models to support targeted practice.

Table 1.

Ethical considerations and practical challenges to the use of predictive analytics for overdose prevention service delivery.

Ethical considerations	Practical challenges	Real-World impact	Implementation recommendation
Institutional responsibility for implementation readiness Health authorities expecting providers to use predictive analytics have a responsibility to offer foundational implementation support; duty of care and legal liability when forecasts misdirect resources or are ignored	Resource and capacity constraints Limited availability of funding, infrastructure, and personnel required to effectively implement predictive analytics; clarify accountability across stakeholder groups and actors	A small, community-based organization receives predictive overdose forecasts but lacks staff with the technical skills or time to interpret and apply them, resulting in unused or improperly used predictions	Capacity building Public health authority investments in training, infrastructure, and ongoing technical support for frontline harm reduction providers to apply predictive tools effectively; specify roles, audit rights, and implementation stopgaps
Oversimplification of complex social realities Risks of reducing multifaceted social and structural determinants of health into narrow, data-driven predictions; attend to drug market shifts and other rapid temporal changes	Adapting models to local contexts Need to tailor predictive tools to reflect unique community characteristics, which may require additional resources and data; model drift and surveillance lags may degrade performance	A model prioritizes outreach in areas with high emergency medical service calls, but misses encampments with high unreported overdoses due to criminalization or stigma, leading to under-service in high-need areas	Continuous evaluation Routinely review model performance between academic researchers and community partners, adjust based on feedback from field-based practitioners, and refresh model to account for temporal changes
Bias in data collection and modeling Historical or systemic inequities embedded in data may skew model outputs; protect privacy and disclosure risks and respect consent for secondary data use	Data quality and representativeness Incomplete or biased datasets may lead to biased results and misallocation of resources; small-area outputs may stigmatize neighborhoods	A model built with hospital overdose data fails to capture trends among unhoused or undocumented populations who avoid healthcare settings, resulting in skewed outreach priorities	Transparency and accountability Establishing clear governance structures between public health authorities, academic researchers, and community organizations and openly communicate about the design, data sources, uses, and limitations with end users and community members; restrict access to predictions to prevent community stigmatization
Community displacement in decision making Overreliance on predictive analytics may marginalize local expertise in resource allocation; recognize co-governance of data, predictions, and communication of predictions	Community trust and engagement Historically marginalized communities may be skeptical of top-down, hierarchical decision-making tools; ensure meaningful decision authority from community partners	A public health department begins to use predictive analytics to guide outreach without consulting peer navigators, resulting in limited responsiveness to hyperlocal changes to the drug supply	Participatory design Include harm reduction practitioners, people who use drugs, and other community members in model development, application decisions, and funding mechanisms alongside academic researchers and public health authorities
Balancing predictive analytics with health equity goals Risk that predictive tools unintentionally deprioritize marginalized populations for resource allocation; prioritize subgroup reporting and equitable allocation	Ensuring equitable resource allocation Logistical difficulties of designing and deploying predictive models to prioritize marginalized groups; assurance that high-need areas will not lose existing resources	A model consistently directs resources to neighborhoods with high rates of opioid use disorder treatment admission but overlooks communities with limited access to care, widening existing disparities	Equity-driven objectives Build models that prioritize equity outcomes to ensure fair distribution of prevention resources by community partners

Institutional responsibility for implementation readiness

The use of predictive analytics to forecast area-level health outcomes relies not only on timely data and accurate predictions, but also on implementation readiness at the community level.²⁹ To inform their overdose prevention efforts, community-based organizations that are the target end users of predictive analytics need the infrastructure and funding capacity to receive and apply those predictions in ways that meaningfully benefit practice. While considerable attention has been paid to improving the quality and timeliness of overdose surveillance and other drug-related indicators (e.g., prescription drug monitoring program data),³⁰ the data capacities of local harm reduction organizations and their abilities to integrate predictions into practice remain highly variable.³¹ Thus, oversight organizations (e.g., local or state health authorities) promoting the use of predictive analytics must ensure that recipient community organizations have the capacity to use such novel tools effectively.

Oversight organizations will, however, have to face the practical challenge of constrained resources and practitioner capacity. Even when public health data systems can facilitate real-time predictions, organizational capacity at the community level may remain constrained by limited funding, personnel shortages, and technological gaps.³² Without adequate investment in the local systems and workforce expected to use predictive tools, these models may be underutilized or misapplied, leading to inefficiency, missed opportunities for intervention, or degraded community trust.²⁴ To leverage predictive analytics for community-based service delivery, investments in infrastructure must be paralleled by investments in capacity building and workforce development.³³ The need for such investments to inform overdose prevention highlights an often unmet need for community-level public health infrastructure and trainings, particularly in marginalized communities that have been chronically underfunded.

Oversimplification of complex social realities

Predictive modeling necessarily simplifies complex public health dynamics into quantifiable features that may omit key social and behavioral processes at the individual and community levels.³⁴ While some degree of simplification is unavoidable, certain domain-specific social factors may not fit neatly into algorithmic predictions. In harm reduction service delivery, community-level overdose risk reflects a complex constellation of factors, including economic stability and histories of investment or disinvestment); healthcare and social services availability, access, and quality; and local drug market fluctuations.^35,36 These factors may be more dynamic and nuanced than the data used to measure them and may fail to reflect the lived experiences of risk and risk reduction strategies of people who use drugs in a given community.³⁷

This consideration reflects the practical challenge of model adaptation to different local contexts. Community-based harm reduction services must be flexible, dynamic, and responsive to the unique needs of the given community in which a service provider is embedded.³⁸ However, predictive models trained on national or regional datasets may not capture these hyperlocal distinctions. Incorporating these models into local-level harm reduction practice requires access to and use of both historical and time-sensitive unique, local drug use and overdose data.³⁹ Likewise, the temporal and geographic resolutions of predictions will need to differ between jurisdictions to ensure compatibility with provider capacity and activity. Adequate capacity building will thus need to draw on data sources and predictive modeling reflecting those local distinctions.⁴⁰

Bias in data collection and modeling

Axiomatic to predictive analytics is the understanding that models are only as good as the data on which they are trained.⁴¹ Biases related to overdose and other drug surveillance data sources can be due to a range of issues, including under- or over-reporting in certain communities,³⁰ historical disparities in drug enforcement,⁴² measurement error,⁴³ or systemic inequities in healthcare access.⁴⁴ This raises substantial ethical concerns, as biased data yielding misinformed predictions could perpetuate or exacerbate existing disparities in overdose prevention if predictions disincentive public investment in prevention.⁴⁵

This consideration is compounded by the practical challenge of data quality and representativeness. In the harm reduction context, state and local public health authorities may have fragmented or incomplete data about the populations served by community-based programs, as these programs disproportionately reach individuals with limited access to traditional healthcare and overrepresentation in the criminal legal system. Likewise, data may not exist for individuals who remain largely outside of those public sector systems (e.g., unhoused youth with limited health system contact⁴⁶ or suburban and rural communities with less criminal legal involvement than urban communities⁴⁷). To ensure accurate and equitable predictions, developers must generate models that maximize representation of a given provider's population of interest, including leveraging preventive service delivery data to improve community-level predictions. To support these goals, public health authorities will need to invest in data collection systems and strategies that actively include underrepresented populations.

Community displacement in decision-making

Incorporating predictive analytics as a decision support tool for community-based health and social service providers raises concerns regarding the potential displacement of localized expertise in service design and delivery decision making.⁴⁸ Harm reduction programs and syringe service organizations long have relied on deep, embedded relationships with people who use drugs in their communities, with resource allocation within a given neighborhood informed not only through public health data, but also the hyperlocal knowledge of frontline workers, community members, people who use drugs and those who are recipients of preventive services.^49,50 While predictive analytics may be used to inform service delivery and organizational decision making processes, its use has the potential to supplant the community expertise on which much of harm reduction infrastructure rests.

This raises the practical challenge of building community trust and engagement in predictive analytics and new modes of service delivery. Some providers and community members, particularly those from communities with histories of systemic neglect or punitive surveillance, may view predictive models with skepticism.^51,52 Without transparency in how models are developed and used, and without genuine opportunities to integrate hyperlocal knowledge as model inputs, these tools risk rejection or underutilization by the communities they intend to benefit. They also risk overlooking critical inputs and local context if modelers and health authorities adopt a strictly top-down approach.⁵³ Engaging communities in the development and deployment of predictive analytics, with clear communication channels between modelers, program operators, and community members, is critical to securing acceptance and legitimacy.

Balancing predictive analytics with health equity goals

Using predictive analytics as a decision support tool for overdose prevention introduces a risk of reinforcing existing racial and economic disparities in where and to whom resources are allocated.⁵⁴ For overdose prevention, modelers and practitioners must monitor how predictions prioritize service delivery to avoid diverting resources from marginalized communities and furthering disinvestment.⁵⁵ Ethical deployment requires a consistent loop: community knowledge should inform model inputs, while developers and health authorities must interpret outputs with explicit attention to shared equity goals. This includes both auditing predictions for disproportionate impacts across racial, ethnic, and socioeconomic lines using formal fairness metrics (e.g., demographic parity or equal opportunity),⁵⁶ and engaging community stakeholders to interpret results in ways that reflect local needs and lived experience.⁵⁷ Specific evaluation metrics may be used to judge the ethical success of prediction and concomitant deployment, such as model subgroup performance by race and ethnicity, geography, and socioeconomics; quantified distributive equity to ensure that resources align with burden and risk; and participation intensity among community-based organizations with access to predictive tools. Health equity must be a guiding principle at every stage of the modeling and implementation process, not an afterthought.

Recommendations for the ethical integration of predictive analytics into overdose prevention service delivery

To address the ethical and practical challenges associated with the integration of predictive analytics to inform community-based harm reduction service delivery, we propose a series of five recommendations to guide responsible and equitable implementation by algorithm developers, health authorities, and service providers (Table 1). We also note potential scenarios of “real-world impact” to reflect typical use cases drawn from our and the field's collective experiences with implementing these approaches to overdose prevention.

Notably, predictive analytics are not always the best or only option. Depending on local contexts and capacities, simpler approaches—such as enhanced or real-time descriptive surveillance, rules-based thresholds, participatory hotspot mapping with community organizations, or universal and proportional increases in baseline services—may be sufficient to inform resource allocation. As such, we do not necessarily presume that prediction adds value but instead offer a framework to guide implementation in those instances for which prediction is appropriate. While our examples are US-centric, the recommendations we posit below may function as guardrails for public health authorities and academic researchers across global contexts, with localized adaptation of these recommendations to meet the drug policy and use environment, privacy law, data access and governance, and community-based prevention infrastructure of other countries and regions. Our recommendations aim to ensure that predictive tools complement, rather than replace, community-driven harm reduction efforts while promoting transparency, equity, and accountability in both modeling and prevention practice.

First, participatory design is essential in developing predictive models that align with the needs and realities of harm reduction practitioners and the communities they serve.⁵⁸ Involving harm reduction practitioners, people who use drugs, and other community stakeholders early in the modeling processes to inform the selection of model inputs, evaluation criteria, and interpretation of predictions is one approach to ensure these tools reflect the on-the-ground needs and realities of communities and providers. Our recommendations draw on the long histories of community-engaged and community-based research, which emphasize shared problem framing, co-ownership of data, and joint decision-making.⁵⁹ Participatory approaches to complex modeling represent an emerging approach in health policy and decision science^60,61 that can identify best practices for the co-development of predictive analytics for public health and harm reduction.⁶² Community engagement can enhance model relevance, increase community and practitioner buy-in, and mitigate the risk of displacing local expertise. Given potential tensions between local knowledge and technical modeling, structured deliberation processes—such as participatory system dynamics—can support mutual understanding and co-creation. In the context of overdose prediction, participatory approaches can provide concrete practices that translate public health ethics into on-the-ground implementation.

Providing access to model assumptions, data sources, and decision-making criteria can enhance public confidence and prevent misuse.⁶³ Transparency and accountability should be ethical priorities to build trust in predictive models. Indeed, our approach maps to core principles of ethical AI, which our framework seeks to implicitly operationalize in the public health practice context, including: fairness, accountability, privacy, and human oversight.⁶⁴ Public health authorities, research partners, and community organizations should jointly establish governance structures that clarify which parties develop, implement, maintain, and oversee predictive tools.⁶⁵ Key stakeholders from within each of these groups may include, but are not limited to: policymakers and policy managers and analytical and surveillance staff from public health authorities; administrative and programmatic leaders and clinical and peer staff from community-based organizations; scholars and technicians from academic research organizations; and residents, leaders, first responders, and people who use drugs from local communities impacted by predictions.

As predictive overdose prevention collaborations expand, parties can look to established models data governance and legal authority to guide implementation and ensure that ethical governance aligns with the existing legal landscape shaping overdose data.⁶⁶ These include confidentiality and privacy laws (e.g., HIPAA and 42 CRF Part 2), state and local data sharing statutes, and public records requirements (i.e., sunshine policies). Where applicable, emerging algorithmic accountability or procurement standards can require documentation of model purpose, data provenance, impact assessments, and appeal pathways. Across contexts and data sources, clear chains of responsibility and accountability can ensure that each party's role complements the others, avoiding top-down, non-collaborative decision making. These roles can be formalized by naming community organizations as funded partners to any work, budgeting paid time for peer and front-line staff to participate in model development and co-interpretation, and sharing model authorship and ownership across the process. Openly communicating the purpose, methods, and limitations of predictive models can enable stakeholders to critically assess their validity and impact.⁶⁷

Third, predictive models should consistently remain oriented toward equity-driven objectives throughout their development and implementation to support harm reduction practitioners and the communities they serve. Considerable progress has been made toward the incorporation of health equity principles throughout the predictive model development and evaluation processes in clinical and other public health settings.⁶⁸ In the community-based harm reduction context, ethical attention should extend beyond model performance to the structural consequences of prediction-driven resource allocation, considering explicitly how forecasts may influence the distribution and timing of resources prior to implementation.⁶⁹

Fourth, continuous evaluation is necessary to ensure that predictive models remain effective, fair, and responsive to evolving community needs.⁷⁰ Regular evaluations held jointly among public health scientists, health authorities, and community-based harm reduction providers should examine how predictive model outputs advance outlined goals, evaluate the impacts of prediction implementation on communities, identify unintended consequences, and refine approaches accordingly.⁷¹ Iterative updates over time can help mitigate biases, improve accuracy, and ensure that predictive analytics successfully functions as a decision support tool for providers aiming to respond to shifting overdose patterns, reduce overdose deaths, and build health equity.

Finally, capacity building is crucial to ensure that community-based harm reduction organizations and public health practitioners can effectively use predictive analytics.⁷² Investments by public health authorities in training programs, technical support, infrastructure development, and knowledge-sharing initiatives for community-based providers in collaboration with model developers will enable practitioners to integrate these tools into decision-making processes. Without adequate capacity, the barriers to using such novel tools may be insurmountable, so community-level capacity remains, in some respects, a first step for jurisdictions as they consider how predictive analytics could be used to enhance harm reduction service provision.⁷³

Likewise, effective co-production and co-utilization of models and model outputs requires a concrete workforce skillset that, through robust and deliberate capacity building, is attainable for community-based organizations.⁷⁴ Such capacity building could include data literacy component to ensure understanding of basic model outputs, data governance and rights, and their organization's policies for and authority to set acceptable uses. Conversely, it is critical that model developers bring and cultivate skills in translation of technical content for the community-based sector, including facilitation skills for co-interpretation of model outputs, engagement and understanding of the communities and geographies within which they are working, and cultural humility to gauge the limits of a model-based approach to prevention. Prior work has demonstrated that such data literacy training is feasible within community-based harm reduction organizations and is central to the success of collaborative public health modeling.^24,75

By integrating these recommendations, public health practitioners can harness the potential of predictive analytics while meeting ethical challenges inherent to these new approaches head on. While our analysis centers on overdose prevention—and thus may not capture domain-specific constraints in other applications of public health injury prevention (e.g., community and interpersonal violence prevention, fall and drowning prevention, or traffic injury prevention)⁷⁶—the core ethical architecture that we outline is portable to other areas of public health importance. Likewise, the rapid pace of technological advancement may outpace specific implementation recommendations. As such, our recommendations are principles-based with the intention to guide adaptation as specific technology advances. Additionally, as a Viewpoint, our contribution is limited here to a practice-oriented framework; future work should advance more comprehensive implementation guidance for practitioners and researchers. Centering community needs and health equity goals can aid in the leveraging of novel predictive approaches to overdose and injury prevention as capacity building vehicles for community-based organizations. Overall, such data-driven tools hold great promise to enhance harm reduction efforts, but care must be taken to ensure that they contribute to more equitable and effective overdose prevention strategies.

Contributors

BA: Investigation; project administration; writing–original draft; writing–review and editing; funding acquisition AU: Investigation; writing–review and editing BC: Investigation; supervision; writing–review and editing; funding acquisition CF: Investigation; supervision; writing–original draft; writing–review and editing; funding acquisition.

Declaration of interests

The authors declare no conflicts of interest.