Learning optimal biomarker-guided treatment policy for chronic disorders.

Electroencephalogram (EEG) provides noninvasive measures of brain activity and is found to be valuable for the diagnosis of some chronic disorders. Specifically, pre-treatment EEG signals in the alpha and theta frequency bands have demonstrated some association with antidepressant response, which is well-known to have a low response rate. We aim to design an integrated pipeline that improves the response rate of patients with major depressive disorder by developing a treatment policy guided by the resting state pre-treatment EEG recordings and other treatment effects modifiers. First, we design an innovative automatic site-specific EEG preprocessing pipeline to extract features with stronger signals than raw data. We then estimate the conditional average treatment effect (CATE) using causal forests and use a doubly robust technique to improve efficiency in the estimation of the average treatment effect. We present evidence of heterogeneity in the treatment effect and the modifying power of the EEG features, as well as a significant average treatment effect, a result that cannot be obtained with conventional methods. Finally, we employ an efficient policy learning algorithm to learn an optimal depth-2 treatment assignment decision tree and compare its performance with Q-Learning and outcome-weighted learning via simulation studies and an application to a large multi-site, double-blind, randomized controlled clinical trial, EMBARC.

Estimation of Treatment Policy Estimands for Continuous Outcomes Using Off-Treatment Sequential Multiple Imputation.

The estimands framework outlined in ICH E9 (R1) describes the components needed to precisely define the effects to be estimated in clinical trials, which includes how post-baseline 'intercurrent' events (IEs) are to be handled. In late-stage clinical trials, it is common to handle IEs like 'treatment discontinuation' using the treatment policy strategy and target the treatment effect on outcomes regardless of treatment discontinuation. For continuous repeated measures, this type of effect is often estimated using all observed data before and after discontinuation using either a mixed model for repeated measures (MMRM) or multiple imputation (MI) to handle any missing data. In basic form, both these estimation methods ignore treatment discontinuation in the analysis and therefore may be biased if there are differences in patient outcomes after treatment discontinuation compared with patients still assigned to treatment, and missing data being more common for patients who have discontinued treatment. We therefore propose and evaluate a set of MI models that can accommodate differences between outcomes before and after treatment discontinuation. The models are evaluated in the context of planning a Phase 3 trial for a respiratory disease. We show that analyses ignoring treatment discontinuation can introduce substantial bias and can sometimes underestimate variability. We also show that some of the MI models proposed can successfully correct the bias, but inevitably lead to increases in variance. We conclude that some of the proposed MI models are preferable to the traditional analysis ignoring treatment discontinuation, but the precise choice of MI model will likely depend on the trial design, disease of interest and amount of observed and missing data following treatment discontinuation.

Application of hypothetical strategies in acute pain.

Since the publication of ICH E9 (R1), "Addendum to statistical principles for clinical trials: on choosing appropriate estimands and defining sensitivity analyses in clinical trials," there has been a lot of debate about the hypothetical strategy for handling intercurrent events. Arguments against the hypothetical strategy are twofold: (1) the clinical question has limited clinical/regulatory interest; (2) the estimation may need strong statistical assumptions. In this article, we provide an example of a hypothetical strategy handling use of rescue medications in the acute pain setting. We argue that the treatment effect of a drug that is attributable to the treatment alone is the clinical question of interest and is important to regulators. The hypothetical strategy is important when developing non-opioid treatment as it estimates the treatment effect due to treatment during the pre-specified evaluation period whereas the treatment policy strategy does not. Two widely acceptable and non-controversial clinical inputs are required to construct a reasonable estimator. More importantly, this estimator does not rely on additional strong statistical assumptions and is considered reasonable for regulatory decision making. In this article, we point out examples where estimators for a hypothetical strategy can be constructed without any strong additional statistical assumptions besides acceptable clinical inputs. We also showcase a new way to obtain estimation based on disease specific clinical knowledge instead of strong statistical assumptions. In the example presented, we clearly demonstrate the advantages of the hypothetical strategy compared to alternative strategies including the treatment policy strategy and a composite variable strategy.

Comments on 'standard and reference-based conditional mean imputation': Regulators and trial statisticians be aware!

Accurate frequentist performance of a method is desirable in confirmatory clinical trials, but is not sufficient on its own to justify the use of a missing data method. Reference-based conditional mean imputation, with variance estimation justified solely by its frequentist performance, has the surprising and undesirable property that the estimated variance becomes smaller the greater the number of missing observations; as explained under jump-to-reference it effectively forces the true treatment effect to be exactly zero for patients with missing data.

A Demonstration of Modified Treatment Policies to Evaluate Shifts in Mobility and COVID-19 Case Rates in US Counties.

Mixed evidence exists of associations between mobility data and coronavirus disease 2019 (COVID-19) case rates. We aimed to evaluate the county-level impact of reducing mobility on new COVID-19 cases in summer/fall of 2020 in the United States and to demonstrate modified treatment policies to define causal effects with continuous exposures. Specifically, we investigated the impact of shifting the distribution of 10 mobility indexes on the number of newly reported cases per 100,000 residents 2 weeks ahead. Primary analyses used targeted minimum loss-based estimation with Super Learner to avoid parametric modeling assumptions during statistical estimation and flexibly adjust for a wide range of confounders, including recent case rates. We also implemented unadjusted analyses. For most weeks, unadjusted analyses suggested strong associations between mobility indexes and subsequent new case rates. However, after confounder adjustment, none of the indexes showed consistent associations under mobility reduction. Our analysis demonstrates the utility of this novel distribution-shift approach to defining and estimating causal effects with continuous exposures in epidemiology and public health.

A framework for stakeholder engagement in the adoption of new anti-malarial treatments in Africa: a case study of Nigeria.

BACKGROUND: Recent reports of artemisinin partial resistance from Rwanda and Uganda are worrisome and suggest a future policy change to adopt new anti-malarials. This is a case study on the evolution, adoption, and implementation of new anti-malarial treatment policies in Nigeria. The main objective is to provide perspectives to enhance the future uptake of new anti-malarials, with an emphasis on stakeholder engagement strategies. METHODS: This case study is based on an analysis of policy documents and stakeholders' perspectives drawn from an empirical study conducted in Nigeria, 2019-2020. A mixed methods approach was adopted, including historical accounts, review of programme and policy documents, and 33 qualitative in-depth interviews and 6 focus group discussions. RESULTS: Based on policy documents reviewed, the adoption of artemisinin-based combination therapy (ACT) in Nigeria was swift due to political will, funding and support from global developmental partners. However, the implementation of ACT was met with resistance from suppliers, distributors, prescribers, and end-users, attributed to market dynamics, costs and inadequate stakeholder engagement. Deployment of ACT in Nigeria witnessed increased developmental partner support, robust data generation, ACT case-management strengthening and evidence on anti-malarial use in severe malaria and antenatal care management. A framework for effective stakeholder engagement for the future adoption of new anti-malarial treatment strategies was proposed. The framework covers the pathway from generating evidence on drug efficacy, safety and uptake; to making treatment accessible and affordable to end-users. It addresses which stakeholders to engage with and the content of engagement strategies with key stakeholders at different levels of the transition process. CONCLUSION: Early and staged engagement of stakeholders from global bodies to community level end-users is critical to the successful adoption and uptake of new anti-malarial treatment policies. A framework for these engagements was proposed as a contribution to enhancing the uptake of future anti-malarial strategies.

Retrieved-Dropout-Based multiple imputation for time-to-event data in cardiovascular outcome trials.

Recently, retrieved-dropout-based multiple imputation has been used in some therapeutic areas to address the treatment policy estimand, mostly for continuous endpoints. In this approach, data from subjects who discontinued study treatment but remained in study were used to construct a model for multiple imputation for the missing data of subjects in the same treatment arm who discontinued study. We extend this approach to time-to-event endpoints and provide a practical guide for its implementation. We use a cardiovascular outcome trial dataset to illustrate the method and compare the results with those from Cox proportional hazard and reference-based multiple imputation methods.

Statistical methods for handling missing data to align with treatment policy strategy.

The International Council for Harmonization (ICH) E9(R1) addendum recommends choosing an appropriate estimand based on the study objectives in advance of trial design. One defining attribute of an estimand is the intercurrent event, specifically what is considered an intercurrent event and how it should be handled. The primary objective of a clinical study is usually to assess a product's effectiveness and safety based on the planned treatment regimen instead of the actual treatment received. The estimand using the treatment policy strategy, which collects and analyzes data regardless of the occurrence of intercurrent events, is usually utilized. In this article, we explain how missing data can be handled using the treatment policy strategy from the authors' viewpoint in connection with antihyperglycemic product development programs. The article discusses five statistical methods to impute missing data occurring after intercurrent events. All five methods are applied within the framework of the treatment policy strategy. The article compares the five methods via Markov Chain Monte Carlo simulations and showcases how three of these five methods have been applied to estimate the treatment effects published in the labels for three antihyperglycemic agents currently on the market.

Impute the missing data using retrieved dropouts.

BACKGROUND: In the past few decades various methods have been proposed to handle missing data of clinical studies, so as to assess the robustness of primary results. Some of the methods are based on the assumption of missing at random (MAR) which assumes subjects who discontinue the treatment will maintain the treatment effect after discontinuation. The agency, however, has expressed concern over methods based on this overly optimistic assumption, because it hardly holds for subjects discontinuing the investigational drug. Although in recent years a good number of sensitivity analyses based on missing not at random (MNAR) assumptions have been proposed, some use very conservative assumption on which it might be hard for sponsors and regulators to reach common ground. METHODS: Here we propose a multiple imputation method targeting at "treatment policy" estimand based on the MNAR assumption. This method can be used as the primary analysis, in addition to serving as a sensitivity analysis. It imputes missing data using information from retrieved dropouts defined as subjects who remain in the study despite occurrence of intercurrent events. Then imputed data long with completers and retrieved dropouts are analyzed altogether and finally multiple results are summarized into a single estimate. According to definition in ICH E9 (R1), this proposed approach fully aligns with the treatment policy estimand but its assumption is much more realistic and reasonable. RESULTS: Our approach has well controlled type I error rate with no loss of power. As expected, the effect size estimates take into account any dilution effect contributed by retrieved dropouts, conforming to the MNAR assumption. CONCLUSIONS: Although multiple imputation approaches are always used as sensitivity analyses, this multiple imputation approach can be used as primary analysis for trials with sufficient retrieved dropouts or trials designed to collect retrieved dropouts.

Reinforcement learning evaluation of treatment policies for patients with hepatitis C virus.

BACKGROUND: Evaluation of new treatment policies is often costly and challenging in complex conditions, such as hepatitis C virus (HCV) treatment, or in limited-resource settings. We sought to identify hypothetical policies for HCV treatment that could best balance the prevention of cirrhosis while preserving resources (financial or otherwise). METHODS: The cohort consisted of 3792 HCV-infected patients without a history of cirrhosis or hepatocellular carcinoma at baseline from the national Veterans Health Administration from 2015 to 2019. To estimate the efficacy of hypothetical treatment policies, we utilized historical data and reinforcement learning to allow for greater flexibility when constructing new HCV treatment strategies. We tested and compared four new treatment policies: a simple stepwise policy based on Aspartate Aminotransferase to Platelet Ratio Index (APRI), a logistic regression based on APRI, a logistic regression on multiple longitudinal and demographic indicators that were prespecified for clinical significance, and a treatment policy based on a risk model developed for HCV infection. RESULTS: The risk-based hypothetical treatment policy achieved the lowest overall risk with a score of 0.016 (90% CI 0.016, 0.019) while treating the most high-risk (346.4 ± 1.4) and the fewest low-risk (361.0 ± 20.1) patients. Compared to hypothetical treatment policies that treated approximately the same number of patients (1843.7 vs. 1914.4 patients), the risk-based policy had more untreated time per patient (7968.4 vs. 7742.9 patient visits), signaling cost reduction for the healthcare system. CONCLUSIONS: Off-policy evaluation strategies are useful to evaluate hypothetical treatment policies without implementation. If a quality risk model is available, risk-based treatment strategies can reduce overall risk and prioritize patients while reducing healthcare system costs.

Evaluation for hierarchical diagnosis and treatment policy proposals in China: A novel multi-attribute group decision-making method with multi-parametric distance measures.

The policy 'hierarchical medical treatment system' promulgated by the State Council of China is an effective way to solve the problem of insufficient and unbalanced medical resources. In response, governments in different provinces explore a variety of different strategies to promote this policy, producing different results. To better strengthen the policy development, it is worthy to help policy-makers make decisions to elect the best one from different proposals. Thus, the aim of this paper is to develop a multi-attribute group decision-making (MAGDM) framework to better assist government select the optimal proposal. This study proposes a MAGDM method based on a family of q-rung orthopair fuzzy interaction power point Hamy mean operators to solve the above problem. To this end, new multi-parametric distance measures based on point operators in the framework of q-rung orthopair fuzzy set are proposed. With the help of the point distance measures, new power point operators average operator is also proposed. The results show that the proposed MAGDM method in this paper outperforms some existing methods and provides promising results for policy-makers seeking to identify the optimal hierarchical diagnosis and treatment policy (HDTP) proposal. Specifically, the results also revealed the best proposal for developing the HDTP proposals is Xiamen mode.

Estimation of a treatment policy estimand for time to event data using data collected post discontinuation of randomised treatment.

Discontinuation from randomised treatment is a common intercurrent event in clinical trials. When the target estimand uses a treatment policy strategy to deal with this intercurrent event, data after cessation of treatment is relevant to estimate the estimand and all efforts should be made to collect such data. Missing data may nevertheless occur due to participants withdrawing from the study and assumptions regarding the values for data that are missing are required for estimation. A missing-at-random assumption is commonly made in this setting, but it may not always be viewed as appropriate. Another potential approach is to assume missing values are similar to data collected after treatment discontinuation. This idea has been previously proposed in the context of recurrent event data. Here we extend this approach to time-to-event outcomes using the hazard function. We propose imputation models that allow for different hazard rates before and after treatment discontinuation and use the posttreatment discontinuation hazard to impute events for participants with missing follow-up periods due to study withdrawal. The imputation models are fitted as Andersen-Gill models. We illustrate the proposed methods with an example of a clinical trial in patients with chronic obstructive pulmonary disease.

Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory.

Recent clinical trials have shown that adaptive drug therapies can be more efficient than a standard cancer treatment based on a continuous use of maximum tolerated doses (MTD). The adaptive therapy paradigm is not based on a preset schedule; instead, the doses are administered based on the current state of tumour. But the adaptive treatment policies examined so far have been largely ad hoc. We propose a method for systematically optimizing adaptive policies based on an evolutionary game theory model of cancer dynamics. Given a set of treatment objectives, we use the framework of dynamic programming to find the optimal treatment strategies. In particular, we optimize the total drug usage and time to recovery by solving a Hamilton-Jacobi-Bellman equation. We compare MTD-based treatment strategy with optimal adaptive treatment policies and show that the latter can significantly decrease the total amount of drugs prescribed while also increasing the fraction of initial tumour states from which the recovery is possible. We conclude that the use of optimal control theory to improve adaptive policies is a promising concept in cancer treatment and should be integrated into clinical trial design.

Treatment policy estimands for recurrent event data using data collected after cessation of randomised treatment.

In the past, many clinical trials have withdrawn subjects from the study when they prematurely stopped their randomised treatment and have therefore only collected 'on-treatment' data. Thus, analyses addressing a treatment policy estimand have been restricted to imputing missing data under assumptions drawn from these data only. Many confirmatory trials are now continuing to collect data from subjects in a study even after they have prematurely discontinued study treatment as this event is irrelevant for the purposes of a treatment policy estimand. However, despite efforts to keep subjects in a trial, some will still choose to withdraw. Recent publications for sensitivity analyses of recurrent event data have focused on the reference-based imputation methods commonly applied to continuous outcomes, where imputation for the missing data for one treatment arm is based on the observed outcomes in another arm. However, the existence of data from subjects who have prematurely discontinued treatment but remained in the study has now raised the opportunity to use this 'off-treatment' data to impute the missing data for subjects who withdraw, potentially allowing more plausible assumptions for the missing post-study-withdrawal data than reference-based approaches. In this paper, we introduce a new imputation method for recurrent event data in which the missing post-study-withdrawal event rate for a particular subject is assumed to reflect that observed from subjects during the off-treatment period. The method is illustrated in a trial in chronic obstructive pulmonary disease (COPD) where the primary endpoint was the rate of exacerbations, analysed using a negative binomial model.

Propensity score method for analyzing the effect of labor induction in prolonged pregnancy.

INTRODUCTION: There is an ongoing debate on the optimal time of labor induction to reduce the risks associated with prolonged pregnancy. MATERIAL AND METHODS: Registry-based study of 212 716 term, singleton cephalic deliveries between 2006 and 2012 in Finland comparing the outcomes of labor induction with those of expectant management in five, three-day gestational age periods between 40 and 42 weeks (group 1: 40+0 -40+2 ; group 2: 40+3 -40+5 ; group 3: 40+6 -41+1 ; group 4: 41+2 -41+4 ; group 5: 41+5 -42+0 ). Using Poisson regression, induced deliveries in each of the gestational age periods were compared with all ongoing pregnancies. Propensity score matching was applied to reduce confounding by indication. RESULTS: In the gestational age groups 1 and 2, labor induction significantly decreased the risk of meconium aspiration syndrome [relative risk (RR) 0.40, 95% confidence interval (CI) 0.18-0.91 (group 1), RR 0.44, 95% CI 0.21-0.91 (group 2)] but increased the risk for prolonged hospitalization of a neonate [RR 1.30, 95% CI 1.10-1.54 (group 1) and RR 1.23, 95% CI 1.03-1.47 (group 2)]. In groups 3 and 4, labor induction significantly increased the risk for emergency cesarean section [RR 1.17, 95% CI 1.06-1.28 (group 3) and RR 1.19, 95% CI 1.09-1.29 (group 4)] but still reduced the risk for meconium aspiration syndrome. In group 5, labor induction did not affect the risk for any of the studied outcomes (operative delivery, obstetric trauma, neonatal mortality, respirator treatment, Apgar <7). CONCLUSIONS: Propensity score matching is a novel approach to studying the effect of labor induction. It highlighted the conflicting maternal and neonatal risks and benefits of the intervention, and supported expectant management as a valid option, at least until close to 42 weeks.

Estimands in clinical trials - broadening the perspective.

Defining the scientific questions of interest in a clinical trial is crucial to align its planning, design, conduct, analysis, and interpretation. However, practical experience shows that oftentimes specific choices in the statistical analysis blur the scientific question either in part or even completely, resulting in misalignment between trial objectives, conduct, analysis, and confusion in interpretation. The need for more clarity was highlighted by the Steering Committee of the International Council for Harmonization (ICH) in 2014, which endorsed a Concept Paper with the goal of developing a new regulatory guidance, suggested to be an addendum to ICH guideline E9. Triggered by these developments, we elaborate in this paper what the relevant questions in drug development are and how they fit with the current practice of intention-to-treat analyses. To this end, we consider the perspectives of patients, physicians, regulators, and payers. We argue that despite the different backgrounds and motivations of the various stakeholders, they all have similar interests in what the clinical trial estimands should be. Broadly, these can be classified into estimands addressing (a) lack of adherence to treatment due to different reasons and (b) efficacy and safety profiles when patients, in fact, are able to adhere to the treatment for its intended duration. We conclude that disentangling adherence to treatment and the efficacy and safety of treatment in patients that adhere leads to a transparent and clinical meaningful assessment of treatment risks and benefits. We touch upon statistical considerations and offer a discussion of additional implications. Copyright © 2016 John Wiley & Sons, Ltd.

Cultural engagement and prevalence of pain in socially isolated older people: a longitudinal modified treatment policy approach.

Background: It remains uncertain whether cultural engagement positively influences the reduction of pain risk, particularly depending on the social isolation status. The aim of this study was to examine the impact of cultural engagement on the reduction of pain prevalence over a 6-year follow-up period among older people, particularly those experiencing different dimensions of social isolation. Methods: This study was a prospective longitudinal study. We analysed the English Longitudinal Study of Ageing cohort, consisting of 6468 community-dwelling adults aged ≥50 years old who provided data in waves 6 (2012-2013), 7 (2014-2015), 8 (2016-2017), and 9 (2018-2019). Self-reported cultural engagement (going to museums, art galleries, exhibitions, the theatre, concerts, or the opera) measured in waves 6-8 was used as the exposure variable. Meanwhile self-reported moderate-to-severe pain in wave 9 was used as the outcome variable. Social isolation was considered in waves 6-8, and the possibility of effect modification was captured by assessing each component of the social isolation index: not married or cohabiting with a partner, fewer than monthly contact with children/other immediate family/friends, and not engaging in any organisations, religious groups, or committees. Findings: The estimated pain prevalence was 29.2% (95% confidence interval, 28.1-30.3; reference) after adjusting for time-variant, time-invariant, and loss to follow-up factors. Cultural engagement led to a reduction in pain prevalence to 24.1% for all individuals, representing a decrease of 5.1% (95% confidence interval, 0.6-9.6; P-value, 0.03). In older people who were not married or cohabiting, cultural engagement resulted in a decrease in pain prevalence to 25.8%, a reduction of 3.4% (95% confidence interval, 0.4-6.4; P-value, 0.01). For those with less frequent contact with close family members, the pain prevalence decreased to 25.3%, a reduction of 3.9% (95% confidence interval, 0.2-7.6; P-value, 0.03). Meanwhile, other dimensions of social isolation did not show a significant reduction in pain prevalence. Interpretation: Cultural engagement may help to reduce the risk of pain in socially isolated older adults. Those who were single or living alone and had less frequent contact with immediate family were particularly vulnerable. While cultural engagement might help certain socially isolated older people feel better, its effectiveness varies, highlighting the need for targeted interventions. Funding: The Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number (22K17648, Ikeda).

Implementing age-friendly policies in China: Assessing the impact on older adults' self-rated health.

Using prefecture-level policy documents (2008-2018) and the China Health and Retirement Longitudinal Study (2011-2018), this study used fixed-effects regressions to examine the associations between the maturity of two age-friendly policies, i.e., old age care (OAC) and preferential treatment (PT) policies for older adults, and the self-rated health (SRH) of older adults. We use policy duration and policy density to measure policy maturity. The results showed positive relationships exist between the density of OAC and PT policy and older adults' SRH, whereas long policy duration often relates to lower SRH. Policy duration and policy density work synergistically. Furthermore, heterogeneity analyses indicated that older adults aged over 75 years, male, those with physical or mental impairment, and living in rural areas and in the first- and second-tier cities benefit significantly from denser OAC policy. The SRH of older adults suffering from physical disabilities or mental problems and living in rural areas is positively associated with denser PT policy. From a policy perspective, our findings suggest that age-friendly policies should be updated over time and be place- and characteristic-tailored.

Impact of Initial Treatment Policies on Long-term Complications and Costs in Japanese Patients with Type 2 Diabetes: A Real-World Database Study.

INTRODUCTION: Type 2 diabetes (T2D) represents a remarkable disease burden in Japan, and the cost-effectiveness of pharmacotherapy is an important consideration. In this study, we compared the long-term effects of the type of initial medication, as well as the initial frequency of clinic visits, on the occurrence of T2D-related complications. Additionally, we compared the medical costs associated with each treatment pattern. METHODS: We analyzed electronic health record data collected from multiple primary care clinics in Japan. Patients were selected based on being primarily prescribed either biguanides (BG) or DPP-4 inhibitors (DPP-4i) during a 3-month baseline period, both of which are commonly used as first-choice medications in Japan. We then followed the onset of T2D-related complications and conducted survival analyses. Additionally, we calculated the accumulated medical costs up to the onset of an event or loss to follow-up, and summarized the annual costs per patient for each treatment pattern. RESULTS: A total of 416 Japanese patients with T2D who initiated treatment between January 2015 and September 2021 were included. The median follow-up period was 2.69 years. The survival analysis showed that the use of DPP-4is and frequent visits from the beginning of treatment did not offer a benefit in suppressing the onset of complications later on. On the other hand, it was found that the annual medical costs for the group using DPP-4i with frequent visits were about 1.9 times higher than for the group using BGs with less frequent visits. CONCLUSIONS: The results suggest that for Japanese patients with T2D, the use of BGs along with relatively long follow-up intervals in the beginning of treatment can remarkably reduce medical costs while providing a level of complication suppression equivalent to that of the use of DPP-4is or frequent visits.

Co-located Heroin Assisted Treatment within primary care: A preliminary analysis of the implications for healthcare access, cost, and treatment delivery in the UK.

BACKGROUND: The UK is experiencing its highest rate of drug related deaths in 25 years. Poor and inconsistent access to healthcare negatively impacts health outcomes for people who use drugs. Innovation in models of care which promote access and availability of physical treatment is fundamental. Heroin Assisted Treatment (HAT) is a treatment modality targeted at the most marginalised people who use drugs, at high risk of mortality and morbidity. The first service-provider initiated HAT service in the UK ran between October 2019 and November 2022 in Middlesbrough, England. The service was co-located within a specialist primary care facility offering acute healthcare treatment alongside injectable diamorphine. METHODS: Analysis of anonymised health records for healthcare costs (not including drug treatment) took place using descriptive statistics prior and during engagement with HAT, at both three (n=15) and six (n=12) months. Primary outcome measures were incidents of wound care, skin and soft tissue infections (SSTIs), overdose (OD) events, unplanned overnight stays in hospital, treatment engagement (general and within hospital care settings) and ambulance incidents. Secondary outcome measures were costs associated with these events. RESULTS: A shift in healthcare access for participants during HAT engagement was observed. HAT service attendance appeared to support health promoting preventative care, and reduce reactive reliance on emergency healthcare systems. At three and six months, engagement for preventative wound care and treatment for SSTIs increased at the practice. Unplanned emergency healthcare interactions for ODs, overnight hospital stays, serious SSTIs, and ambulance incidents reduced, and there was an increase in treatment engagement (i.e. a reduction in appointments which were not engaged with). There was a decrease in treatment engagement in hospital settings. Changes in healthcare utilisation during HAT translated to a reduction in healthcare costs of 58% within six months compared to the same timeframe from the period directly prior to commencing HAT. CONCLUSION: This exploratory study highlights the potential for innovative harm reduction interventions such as HAT, co-located with primary care services, to improve healthcare access and engagement for a high-risk population. Increased uptake of primary healthcare services translated to reductions in emergency healthcare use and associated costs. Although costs of HAT provision are substantial, the notable cost-savings in health care should be an important consideration in service implementation planning.