Identifying facility-level and infrastructure-level bottlenecks in Japan's lecanemab rollout through an online survey of dementia specialists

Saki Nakashima; Kenichiro Sato; Yoshiki Niimi; Ryoko Ihara; Atsushi Iwata; Kiyotaka Nemoto; Tetsuaki Arai; Shinji Higashi; Ataru Igarashi; Kensaku Kasuga; Haruhiko Akiyama; Shuichi Awata; Manabu Ikeda; Tatsushi Toda; Takeshi Iwatsubo

doi:10.1177/25424823261470054

. 2026 Jul 25;10:25424823261470054. doi: 10.1177/25424823261470054

Identifying facility-level and infrastructure-level bottlenecks in Japan's lecanemab rollout through an online survey of dementia specialists

Saki Nakashima ^1,^2,^*, Kenichiro Sato ^2,^3,^4,^*, Yoshiki Niimi ^2,^4,^5,^✉, Ryoko Ihara ⁶, Atsushi Iwata ^6,⁷, Kiyotaka Nemoto ⁸, Tetsuaki Arai ⁹, Shinji Higashi ¹⁰, Ataru Igarashi ¹¹, Kensaku Kasuga ^12,¹³, Haruhiko Akiyama ¹⁴, Shuichi Awata ⁷, Manabu Ikeda ¹⁵, Tatsushi Toda ^1,¹⁶, Takeshi Iwatsubo ^2,^3,^4,^16,^✉

¹Department of Neurology, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan

²Unit for Early and Exploratory Clinical Development, The University of Tokyo Hospital, Bunkyo-ku, Japan

³Department of Neuropathology, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan

⁴Dementia Inclusion and Therapeutics, The University of Tokyo Hospital, Bunkyo-ku, Japan

⁵Department of Healthcare Economics and Health Policy, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan

⁶Department of Neurology, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Itabashi-ku, Japan

⁷Integrated Research Initiative for Living Well with Dementia, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Itabashi-ku, Japan

⁸Department of Medical Informatics and Management and Psychiatry, Institute of Medicine, University of Tsukuba, Tsukuba-shi, Japan

⁹Department of Psychiatry, Institute of Medicine, University of Tsukuba, Tsukuba-shi, Japan

¹⁰Department of Psychiatry, Ibaraki Medical Center, Tokyo Medical University, Inashiki-gun, Japan

¹¹Department of Public Health, Yokohama City University School of Medicine, Yokohama-shi, Japan

¹²Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata-shi, Japan

¹³Department of Diagnostic Innovation Science, National Center for Geriatrics and Gerontology, Obu-shi, Japan

¹⁴Department of Clinical Research, Yokohama Brain and Spine Center, Yokohama, Japan

¹⁵Department of Psychiatry, The University of Osaka Graduate School of Medicine, Osaka, Japan

¹⁶National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan

These authors equally contributed to this work

^✉

Yoshiki Niimi, Dementia Inclusion and Therapeutics, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655 Japan. Email: yoshikimailg@gmail.com

^✉

Takeshi Iwatsubo, Dementia Inclusion and Therapeutics, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655 Japan. Email: iwatsubo@m.u-tokyo.ac.jp

Roles

Saki Nakashima: Conceptualization, Data curation, Formal analysis, Investigation, Writing – original draft

Kenichiro Sato: Conceptualization, Data curation, Formal analysis, Writing – original draft

Yoshiki Niimi: Conceptualization, Data curation, Formal analysis, Validation, Writing – original draft

Ryoko Ihara: Conceptualization, Writing – review & editing

Atsushi Iwata: Conceptualization, Writing – review & editing

Kiyotaka Nemoto: Conceptualization, Writing – review & editing

Tetsuaki Arai: Conceptualization, Writing – review & editing

Shinji Higashi: Conceptualization, Writing – review & editing

Ataru Igarashi: Conceptualization, Writing – review & editing

Kensaku Kasuga: Conceptualization, Writing – review & editing

Haruhiko Akiyama: Conceptualization, Writing – review & editing

Shuichi Awata: Conceptualization, Writing – review & editing

Manabu Ikeda: Conceptualization, Supervision, Writing – review & editing

Tatsushi Toda: Conceptualization, Supervision, Writing – review & editing

Takeshi Iwatsubo: Conceptualization, Supervision, Writing – review & editing

PMCID: PMC13662313 PMID: 42876920

Abstract

Background

The approval of lecanemab has created new operational demands for Alzheimer's disease care. Japan's nationwide rollout under universal health insurance offers an opportunity to identify real-world bottlenecks in treatment delivery.

Objective

To identify facility-level and infrastructure-level factors associated with wait time to initial lecanemab infusion and treated patient volume among dementia specialists in Japan.

Methods

We performed a secondary analysis of an anonymous online survey of 296 dementia specialists certified to prescribe lecanemab in Japan approximately one year after approval. We examined associations between physician/facility characteristics, diagnostic and infusion resources, perceived implementation barriers, and two delivery metrics: wait time from first consultation to first infusion and number of treated patients per respondent. Bayesian accelerated failure-time and zero-truncated Poisson mixed-effects models were used.

Results

Approximately 80% of respondents reported average wait times of 3 months or less, whereas the median treated caseload was modest at 5 patients per physician. Perceived mismatch between treatment capacity and demand was associated with longer wait time (time ratio 1.323, 95% credible interval 1.045–1.682). Cerebrospinal fluid (CSF)-dominant amyloid-testing strategies tended to be associated with smaller caseloads, while in-house CSF testing and APOE testing were associated with larger caseloads. High-volume centers reported constrained outpatient infusion space. Lack of reimbursement for infusion-related tasks and APOE testing were perceived as major system-level barriers.

Conclusions

Japan's rollout may be constrained by some bottlenecks in diagnostics, infusion infrastructure, and uncompensated tasks. Appropriate policy interventions might help secure equitable access to the treatment.

Keywords: Alzheimer's disease, disease-modifying therapy, infusion wait time, lecanemab, treatment barriers

Introduction

Alzheimer's disease (AD) is one of the leading causes of cognitive decline and disability in aging populations.^1,2 Japan already has nearly five million people living with dementia, and by 2060, about one-third of older Japanese citizens are projected to have dementia.^3,4 This growing public health burden has driven urgent interest in interventions that can delay or slow the progression of AD. Recent breakthroughs in disease-modifying therapies (DMTs) have made early diagnosis and treatment initiation increasingly important. Lecanemab is the first DMT to demonstrate clear clinical efficacy in early-stage AD.⁵ It received full regulatory approval in the United States in 2023, followed shortly thereafter by approval in Japan in September of the same year.^6,7 While this breakthrough offers an unprecedented opportunity to mitigate the burden of AD, it also places substantial new demands on healthcare systems worldwide.

The translation of DMTs into clinical practice is governed by strict guidelines, including the Appropriate Use Recommendations (AUR) in the United States⁸ and the Optimal Use Guidelines (OUG) in Japan,⁹ which define patient eligibility and facility requirements. Implementation involves practical challenges for facilities, including securing capacity for amyloid testing (positron emission tomography [PET] or cerebrospinal fluid [CSF]), ensuring sufficient MRI availability for safety monitoring, and organizing outpatient infusion spaces and staff. Each of these operational steps can become a rate-limiting factor, creating bottlenecks that impede timely and equitable delivery.

In Japan, approximately 6000 patients reportedly received lecanemab treatment during the first year after reimbursement.^10,11 This initial uptake underscores the urgent need to move beyond theoretical concerns and identify the real-world barriers emerging in clinical practice.^12,13 We previously surveyed dementia specialists in late 2024 and early 2025 regarding lecanemab implementation and identified infrastructure-level barriers between physicians who had prescribed lecanemab and those who had not.¹⁴ In the present study, we conducted a secondary analysis of these survey data to specifically identify potential facility-level and infrastructure-level bottlenecks associated with two key healthcare delivery metrics: wait time to treatment initiation and the patient volume per physician.

Methods

Study design

This is a secondary analysis of our previous study, the outline of which has been published elsewhere.¹⁴ The original study was a cross-sectional, anonymized online survey conducted in December 2024 and January 2025, when approximately one year had passed since the approval of lecanemab in Japan. The survey was conducted as an FY2024 Ministry of Health, Labour and Welfare (MHLW) Scientific Research Project in collaboration with relevant academic societies (i.e., the Japanese Society of Dementia Research and the Japanese Society of Psychogeriatrics). An invitation email containing a web survey (Google Form) link was sent to Japanese physicians who had completed the official training course to prescribe lecanemab under Japanese public health insurance.⁷ No personally identifiable information (e.g., name, e-mail address, or institutional affiliation) was collected. To maximize participation, the questionnaire was administered in three sections that were sent separately (December 23, December 30, and January 7), with a reminder sent on January 21. Data collection closed on February 19, 2025. Approximately 85% of respondents completed all sections, while the remaining 15% did not complete the first section.

The original survey is composed of 128 questions in total, which were designed by the study project team members, spanning several domains related to the implementation of DMT: respondent demographics, respondents’ actual experience of lecanemab prescription, their actual practice for eligibility determination, screening tests, and genetic testing. The survey also asked about respondents’ adverse event experiences and the follow-up practices for ineligible patients. A full English translation of the questions and responses is provided in the supplemental material of the original reporting literature.¹⁴ We hypothesized that limited diagnostic (amyloid testing, MRI) and infusion capacity would be associated with longer wait times and lower treated patient volume.

Requirements for DMT use under Japanese health insurance

The original survey was designed based on real-world practice as regulated by the OUG,^9,15 which specifies three categories of requirements for administering lecanemab: patient-based, facility-based, and physician-based requirements. Regarding patient-based requirements, eligible patients must have no specific brain MRI findings (e.g., vasogenic edema, five or more cerebral microbleeds, superficial hemosiderosis, or brain hemorrhage exceeding 1 cm in diameter); their cognition must be within an appropriate range (Mini-Mental State Examination ≥ 22 and Clinical Dementia Rating Global Score of 0.5 or 1), and findings indicative of amyloid pathology must be confirmed by amyloid PET or CSF test. In terms of requirements for medical facilities, only those with properly qualified personnel, imaging equipment (MRI field strength ≥ 1.5 Tesla [T]), and the availability of cognitive assessment are permitted to initiate infusions. Regarding physician-based requirements, the prescribing physician must be a certified physician in Neurology, Geriatrics, Psychiatry, or Neurosurgery, have at least 10 years of clinical experience diagnosing and managing dementia-related diseases, and be able to read brain MRI and to make appropriate decisions about treatment policies, especially in cases of amyloid-related imaging abnormalities (ARIA), i.e., treatment-associated MRI findings such as edema/effusion (ARIA-E) and microhemorrhage/superficial siderosis (ARIA-H).¹⁶ Additionally, a multidisciplinary medical team for DMT treatment must be in place, consisting of two or more full-time certified physicians, cognitive assessment specialists, MRI experts, and administrative support. For facilities administering infusions at six months or later, less stringent requirements may apply.

Questionnaire responses examined

Among these requirements, we evaluated the association between physician-based and facility-based requirements with two metrics of DMT implementation: the average wait time from first consultation to initial lecanemab infusion (Q20) and the total number of treated patients per respondent (Q24). For the purpose of our analysis, we included respondents who indicated that they had administered lecanemab to one or more patients, as well as those who provided valid answer to both Q20 and Q24.

Questions related to physician-based and facility-based requirements were as follows: respondents’ board-certification in OUG-specified expertise (Q1), type of respondents’ facility (Q2), whether respondents’ facility is the Center for Dementia-Related Diseases (CDRD) (Q4), whether respondents’ facility is certified by the Accreditation Organization for Management of Radiologic Imaging (Q5), respondents’ board-certification in dementia treatment or psychogeriatrics (Q8), facility's availability of amyloid PET (Q10), facility's availability of amyloid CSF test (Q11), respondents’ inclination towards confirming amyloid positivity between amyloid PET and CSF (Q43), respondents’ preference towards the place of amyloid PET (Q50), respondents’ preference towards the place of amyloid CSF (Q51), whether respondent uses brain MRI field strength of 1.5 Tesla (T) or 3.0 T (Q56), whether respondent uses brain MRI sequence of T2* or susceptibility-weighted imaging (SWI), an MRI sequence sensitive to microhemorrhages and superficial siderosis, for identifying ARIA-H (Q57), whether a facility's practice includes apolipoprotein E (APOE) genotyping (e.g., ε4 status) when using lecanemab, which is relevant for AD and ARIA risk stratification (Q59), whether a facility's practice requires hospitalization when introducing lecanemab (Q64).

We also evaluated respondents’ impressions regarding outpatient resources at their own facility (Q65 for outpatient space, Q66 for outpatient staff, Q67 for continued-infusion facilities, and Q68 for discrepancy between actual capacity and the estimated demand), implementation challenges (Q127), and potential solutions (Q128) as answered by the respondents.

Statistical analyses

All statistical analyses were conducted using R version 4.3.1. Numerical variables were presented as median (interquartile range [IQR]), and categorical variables as counts and percentages (Supplemental Table 1). Multiple-choice responses were dichotomized (e.g., “selected/not selected” responses). For multivariate models, we used Bayesian hierarchical models. Covariates were pre-specified based on the OUG-defined facility- and physician-level requirements for lecanemab administration and on plausible resource constraints relevant to implementation (e.g., amyloid testing, MRI monitoring, infusion space/staffing). Because our objective was inference on implementation bottlenecks rather than purely predictive performance, we retained all pre-specified covariates to reduce confounding, while addressing multicollinearity by excluding variables with VIF > 5. The models were fitted using Hamiltonian Monte Carlo (HMC) implemented in Stan via the R package {brms} with default weakly informative priors (4 chains, 4000 iterations each, 1000 warm-up). Model convergence was assessed using R-hat below 1.01. Since the original survey was anonymous and did not collect institutional or personal identifiers, we created a pseudo-ID to account for potential clustering of responses from facilities with similar characteristics. This pseudo-ID was a composite identifier created by combining facility type (Q2), DRD center status (Q4), and prefecture of the respondent, and was used as a random effect in the hierarchical models.

For the time from a patient's first visit to their first infusion (Q20), we fitted a Bayesian Accelerated Failure Time (AFT) model¹⁷ with a random effect (frailty model) to account for interval-censored data. AFT model is a time-to-event model that estimates how covariates multiplicatively accelerate or decelerate the time to an event (here, time to first infusion). The time data were collected in discrete intervals (e.g., 0–1 month, 1–3 months), and the model was specified using a Weibull distribution for its flexibility. The model is specified as: $l n (T_{i j}) = β_{0} + \sum_{k = 1}^{p} β_{k} X_{i j k} + u_{j} + ϵ_{i j}$ , where T_ij is the event time for facility i within group j, β₀ is the intercept, X_k are the p covariates with corresponding coefficients β_k, u_j is the random intercept for pseudo-ID j (frailty term) assumed to be $u_{j} \sim N (0, σ_{u}^{2})$ , and ɛ_ij is the error term following a Gumbel distribution, which corresponds to the Weibull AFT parameterization. The results are presented as Time Ratios (TR), calculated by exponentiating the posterior median of the model coefficients (TR = e^β). The TR quantifies the direct effect of a covariate on the event time. A TR greater than 1.0 indicates that the covariate decelerates the time to event (prolongs the time to infusion), while a TR less than 1.0 indicates an acceleration of time to event (shorter time to infusion), compared to the reference group. The 95% credible interval (CrI) for the TR was used to assess statistical significance.

For the number of patients treated (Q24), we fitted a Bayesian zero-truncated Poisson mixed-effects model,¹⁸ which is used for count outcomes when zeros are structurally absent, such as physician-level treated patient counts restricted to respondents who treated at least one patient. This model was chosen because the outcome variable included only respondents who had treated at least one patient; thus the count distribution did not contain zeros. The model is specified as: $l n (u_{i j}) = β_{0} + \sum_{k = 1}^{p} β_{k} X_{i j k} + v_{j}$ , $Y_{i j} \sim T r u n c a t e d P o i s s o n (μ_{i j})$ , where μ_ij is the expected number of patients for facility i with a pseudo-ID of j, X_k are the covariates, and v_j is the random intercept for each pseudo-ID, assumed to be $v_{j} \sim N (0, σ_{v}^{2})$ . The results are presented as Incidence Rate Ratios (IRR), obtained by exponentiating the posterior median of the model coefficients (IRR = e^β). An IRR greater than 1.0 indicates that the covariate is associated with an increased count of treated patients, while an IRR less than 1.0 indicates a decreased count, compared to the reference group. The 95% CrI for the IRR was used to assess statistical significance.

Ethics

The original online survey and the current analysis have been approved by the University of Tokyo Graduate School of Medicine Institutional Ethics Committee (ID: 2024397NI). Online informed consent was obtained upon survey administration.

Results

Basic findings

Of the 719 respondents to the original survey, data from 296 respondents were used for the current analysis. This subgroup included those who indicated they had administered lecanemab to one or more patients and who provided valid answers to both Q20 and Q24. Regarding the outcome variables, the average wait time for approximately 80% of respondents was 3 months or less (Q20), and the median number of patients treated with lecanemab treatment was 5 (inter-quartile range 2–10) (Q24) (Supplemental Figure 1).

About two-thirds of respondents were board-certified in clinical neurology, followed by psychiatry, geriatrics, and neurosurgery (Q1) (Supplemental Table 1). Most worked in general or advanced-treatment hospitals (Q2) and were dementia specialists (Q8). Roughly half of facilities offered amyloid PET (Q10), while most performed CSF testing (Q11); about 50% used PET for pathology detection. Half of PET scans occurred at affiliated centers (Q50), whereas most CSF tests were in-house (Q51). Over 50% used 3.0 T MRI for ARIA monitoring (Q56) and 40% used SWI (Q57); only one-third conducted APOE testing (Q59), and about half initiated lecanemab during hospitalization (Q64). In outpatient infusion, ∼25% reported insufficient space or staff (Q65, Q66), and >50% said capacity fell short of demand (Q68). Key implementation challenges included lack of insurance coverage for APOE testing, inadequate incentives, poor cost–benefit, limited outpatient resources, restricted amyloid test reimbursement, and perceived low drug efficacy (Q127). Top solutions were securing APOE coverage, establishing new infusion and shared-decision fees, and improving drug efficacy and delivery (Q128).

Association between target outcomes and respondent/facility features

Multivariate model to identify associated factors are visualized in Figure 1. For the wait time from first consultation to initial lecanemab infusion (Figure 1A), shortages in each facility's patient treatment supply compared to demand (Q68) was the only factor associated with the length of wait time (TR 1.323, 95% CrI: 1.045–1.682).

For the number of treated patients (Figure 1B), respondents with certification in neurology, neurosurgery, or psychiatry had a larger number of lecanemab infusion experiences (IRR 2.977, 2.734, and 1.783, respectively) than those without (Q1). Regarding the facility's preference of amyloid test (PET versus CSF) (Q43), there was a trend towards a smaller number of cases as the reliance on amyloid PET decreased (e.g., IRR ∼ 0.5 when using CSF only or predominantly compared to when using PET alone). Those whose facilities conduct CSF test on their own facility (Q51) reported an increased caseloads compared to those who do not (IRR 1.686). Those who conduct APOE testing upon administration (Q59) reported an increased number of patients compared with those who do not perform it (IRR 1.981).

In terms of respondent impressions about outpatient resources, compared to respondents who perceive that their outpatient infusion space is sufficient (Q65), those who perceive it is tightened had a larger number of treated patients. Meanwhile, perception about their outpatient infusion staffing resource had no significant association with the patient caseload (Q66). Notably, those who do not perceive the infusion outsourcing system for continuous infusion (after six months) is sufficient had a smaller number of treated patients (Q67).

Association between target outcomes and challenges/solutions

Figure 2 demonstrates the univariate analysis results for the challenges in implementing lecanemab treatment in clinical practice (Q127) in the AFT model (Figure 2A: Q20, for the wait time from first consultation to initial lecanemab infusion) and in the Poisson model (Figure 2B: Q24, for the number of treated patients). Respondents who cited the limited efficacy of DMT showed a significantly longer wait time and a smaller number of experiences, while those who cited concerns for adverse event risk showed a significantly shorter wait time as well as a smaller number of cases. Those who raised the difficulty of OUG requirements for continuous infusion or the lack of coverage for APOE testing reported a significantly shorter wait time than those without.

Figure 3 demonstrates the univariate analyses results for the potential solutions to these challenges (Q128) in the AFT model (Figure 3A: Q20, for the wait time from first consultation to initial lecanemab infusion) and in the Poisson model (Figure 3B: Q24, for the number of treated patients). Those who raised the need for additional medical fee reimbursement for drug infusion, or the need for additional management fee reimbursement to facilitate affiliation/partnership with the continued infusion facilities, exhibited a significantly longer wait time and a smaller number of experiences.

Discussion

This analysis aimed to understand how Japanese dementia specialists are implementing lecanemab in daily clinical practice following its regulatory approval. Using an AFT model and a Bayesian zero-truncated Poisson model, we examined physician-based and facility-based factors associated with two important metrics: time to treatment initiation and treated patient volume per physician. While many barriers to DMT have been anticipated, our study provides empirical, post-launch evidence on which constraints are most strongly associated with access metrics in Japan, a setting with universal coverage and high imaging capacity.

Approximately 80% of specialist respondents reported an average wait time for infusion of three months or less after the first consultation, and less than five percent required waiting time ≥ 6 months (Supplemental Table 1, Q20). This speed is much faster than modelling studies that predicted a queue of about one-year in several European countries.¹⁹ In Japan, a universal insurance system¹⁰ and a high number of MRI scanners²⁰ might have supported this speed. We examined variables associated with shorter wait times; in the multivariable AFT model, the only significant factor associated with longer wait time was a perceived discrepancy between patient demand and treatment capacity (Q68). Our findings are also consistent with the international literature on health-system readiness for anti-amyloid therapies, which emphasizes that timely access depends on capacity across the care pathway, not drug approval alone.^21–23

Although the median caseload per physician was only five patients in the first year after reimbursement, this likely reflects capacity constraints along a multi-step implementation pathway (e.g., eligibility assessment, amyloid confirmation, MRI monitoring, scheduling of infusions, and follow-up), rather than a lack of demand. Meanwhile, multiple variables were associated with the number of patients treated. Specifically, physicians with board certification in neurology, neurosurgery, or psychiatry managed a significantly larger number of patients (Q1). Despite a trade-off between PET and CSF,²⁴ our multivariate analysis revealed that in-house CSF capability was associated with more treated patients (Q51) (IRR ≈ 1.7), while CSF-dominant strategies were associated with fewer treated patients (Q43) (IRR ≈ 0.5). This pattern may be consistent with higher throughput when a less invasive PET-first pathway is available. These approaches likely mitigate a major patient-side barrier. Given these constraints, it is understandable that many specialists are awaiting blood-based biomarkers (BBMs) (Q128); more than half supported the implementation of some BBMs like plasma p-tau, which has very high performance in detecting AD pathology²⁵ with good cost-performance.²⁶ Reimbursement for BBMs²⁷ might transfer the diagnostic burden from expensive imaging to primary care clinics.

Only 28% of respondents felt their outpatient infusion space was “enough,” while 22% said it was “tightened” or “exhausted” (Supplemental Table 1, Q65), and staffing showed same tendency (Supplemental Table 1, Q66). Importantly, our multivariate Poisson model indicates a strong, counterintuitive correlation: physicians who perceive their outpatient infusion staff as “tightened” are managing more patients (IRR ≈ 1.2) compared to those who feel they have enough staff (Q66). This suggests that resource bottlenecks are emerging precisely where adoption is highest. Furthermore, a crucial finding relates to long-term capacity: facilities where the system for outsourcing continuous infusions (after six months) was not perceived as sufficient were associated with a smaller number of treated patients (Q67).

This reality of overloaded high-volume centers is not matched by economic incentives. As identified in our survey, “insufficient incentive” was one of the most frequently cited barriers to implementation (more than half of respondents, Q127), and robust support exists for creating new fee codes—for services like shared decision-making, infusion management, and coordinating with partner facilities—with each exceeding 50% endorsement (Q128). This indicates that without dedicated reimbursement, hospitals have little financial motivation to expand infusion capacity or hire specialized staff for a patient population whose primary drug cost is fixed externally.

These findings may have several implications for the sustainable expansion of DMT delivery. Importantly, our survey does not directly evaluate policy interventions; therefore, the following points are presented as potential implications based on observed associations and respondents’ reported barriers and endorsed solutions (Q127–Q128). Respondents frequently cited limited incentives and endorsed additional reimbursement items, suggesting that reimbursement mechanisms supporting key operational tasks, such as infusion-day adjustment, ARIA monitoring, and liaison with a partner facility, may warrant consideration. In addition, the lack of national coverage for APOE genotyping was commonly reported as a barrier, and APOE testing was associated with a higher number of patients in our analysis (IRR ≈ 2.0).²⁸ Currently, Japanese physicians who want to conduct APOE testing have to rely on their own clinical research or a nationwide clinical research called AD-DMT registry (https://addmt.ncnp.go.jp), which collects nationwide real-world data for safety and efficacy of DMTs in Japan with a similar framework as ALZ-NET in the United States.²⁹ Respondents also expressed strong expectations for BBMs. If validated and reimbursed within routine care pathways, BBMs may reduce reliance on imaging- or procedure-intensive diagnostic steps and potentially improve throughput; economic evaluations should consider pathway-level outcomes, in addition to per-test costs. Finally, our findings suggest that strengthening regional capacity and shared-care pathways for maintenance infusions (after six months) may warrant consideration to mitigate concentration of workload at high-volume centers and improve regional equity.³⁰

This study has some typical limitations of an anonymous self-report survey. First, case numbers might include double-counting when several respondents belonged to the same facility. Second, response bias and limited representativeness are possible. Although the survey targeted trained/certified prescribers, the analytic sample (n = 296) reflects respondents with treated experience and valid responses to Q20 and Q24, and thus, early adopters and better-prepared centers may have been over-represented. As a result, wait times may be underestimated and implementation capacity overestimated relative to the broader population of trained prescribers and eligible facilities, particularly smaller or less specialized centers that have not yet initiated lecanemab. Therefore, system-level interpretations should be viewed as empirical signals from engaged implementing sites rather than fully generalizable estimates for all eligible facilities nationwide. Third, the cross-sectional design cannot conclude causality. In addition, because this secondary analysis was designed to focus on implementation bottlenecks using Q20 and Q24 as delivery metrics, the analytic dataset does not allow robust estimation of patient-level adverse event rates. Accordingly, evaluating real-world safety outcomes, including ARIA, alongside access metrics remains an important next step. Still, the sample covers about 40% of the Japanese specialists who prescribed lecanemab in the first year, providing informative empirical evidence on early implementation patterns. Finally, because institutional identifiers were not collected, we constructed a pseudo-ID to partially account for clustering. This approach provides an approximate adjustment under anonymity constraints, but it cannot perfectly capture true facility-level dependence: different institutions may share the same pseudo-ID, and a single institution could be represented by multiple pseudo-IDs if responses differ in the component variables. Accordingly, some residual within-facility correlation or misclassification may persist.

To conclude, Japan's dementia care system leveraged universal insurance, abundant imaging, and specialist enthusiasm to introduce lecanemab rapidly. However, structural weaknesses—such as the logistical challenges of amyloid testing, strained infusion resources, a lack of clear pathways for long-term outsourcing, and unpaid coordination and genetic testing—now hinder access. Clinicians advocate dedicated infusion reimbursements, national APOE test coverage, and swift BBM adoption. Implementing these measures might help make DMT treatment available for patients who need it, without the treatment being limited to only flagship hospitals.

Supplemental Material

sj-docx-1-alr-10.1177_25424823261470054 - Supplemental material for Identifying facility-level and infrastructure-level bottlenecks in Japan's lecanemab rollout through an online survey of dementia specialists

sj-docx-1-alr-10.1177_25424823261470054.docx^{(84.1KB, docx)}

Supplemental material, sj-docx-1-alr-10.1177_25424823261470054 for Identifying facility-level and infrastructure-level bottlenecks in Japan's lecanemab rollout through an online survey of dementia specialists by Saki Nakashima, Kenichiro Sato, Yoshiki Niimi, Ryoko Ihara, Atsushi Iwata, Kiyotaka Nemoto, Tetsuaki Arai, Shinji Higashi, Ataru Igarashi, Kensaku Kasuga, Haruhiko Akiyama, Shuichi Awata, Manabu Ikeda, Tatsushi Toda and Takeshi Iwatsubo in Journal of Alzheimer's Disease Reports

Acknowledgements

This survey was carried out by the FY2024 Ministry of Health, Labour and Welfare (MHLW) Scientific Research Project titled “A Study on Responses to Social Issues Arising from Advances in Dementia Medicine” (principal investigator (PI): Tetsuaki Arai), in collaboration with the Japanese Society of Dementia Research (president: Takeshi Iwatsubo) and the Japanese Society of Psychogeriatrics (president: Manabu Ikeda). The authors’ affiliation, “Dementia Inclusion and Therapeutics,” is an endowed department at the University of Tokyo Hospital funded by Effissimo Capital Management Pte Ltd

Footnotes

ORCID iDs: Kenichiro Sato https://orcid.org/0000-0003-4351-9526

Atsushi Iwata https://orcid.org/0000-0001-7308-5314

Ataru Igarashi https://orcid.org/0000-0001-6307-6916

Kensaku Kasuga https://orcid.org/0000-0002-2178-0769

Ethical considerations: The original online survey and the current analysis have been approved by the University of Tokyo Graduate School of Medicine Institutional Ethics Committee (ID: 2024397NI). Online informed consent was obtained upon survey administration.

Consent to participate: Online informed consent had been obtained on the introductory page of the original survey.

Consent for publication: Not applicable

Author c ontributions: Please replace this text with the Manuscript Author Contributions sent in the Manuscript metadata/information from Sage Track.

Author contribution(s): Saki Nakashima: Conceptualization; Data curation; Formal analysis; Investigation; Writing – original draft.

Kenichiro Sato: Conceptualization; Data curation; Formal analysis; Writing – original draft.

Yoshiki Niimi: Conceptualization; Data curation; Formal analysis; Validation; Writing – original draft.

Ryoko Ihara: Conceptualization; Writing – review & editing.

Atsushi Iwata: Conceptualization; Writing – review & editing.

Kiyotaka Nemoto: Conceptualization; Writing – review & editing.

Tetsuaki Arai: Conceptualization; Writing – review & editing.

Shinji Higashi: Conceptualization; Writing – review & editing.

Ataru Igarashi: Conceptualization; Writing – review & editing.

Kensaku Kasuga: Conceptualization; Writing – review & editing.

Haruhiko Akiyama: Conceptualization; Writing – review & editing.

Shuichi Awata: Conceptualization; Writing – review & editing.

Manabu Ikeda: Conceptualization; Supervision; Writing – review & editing.

Tatsushi Toda: Conceptualization; Supervision; Writing – review & editing.

Takeshi Iwatsubo: Conceptualization; Supervision; Writing – review & editing.

Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by MHLW Special Research Program Grant Number JPMH23CA2008 (T.A, Y.N, A. Iwata, K.N, S.H, A. Igarashi, K.K), JSPS KAKENHI Grant Number JP24K10653 (R.I) and JP25K19014 (K.S), AMED Grant Number JP23dk0207048 (T.I) and JP24dk0207054 (Y.N) and JP24dk0207068 (T.I) and JP25dk0207075 (K.S).

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: A.I. receives honorarium and clinical trial grant from Eisai. Other authors have no conflict of interest to disclose related to the content of the manuscript.

Supplemental material: Supplemental material for this article is available online.

Data availability statement

The survey data we used for this analysis are not available for data sharing.

References

1.Jack CR, Jr, Bennett DA, Blennow K, et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement 2018; 14: 535–562. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Jack CR, Jr. Advances in Alzheimer’s disease research over the past two decades. Lancet Neurol 2022; 21: 866–869. [DOI] [PubMed] [Google Scholar]
3.Nakahori N, Sekine M, Yamada M, et al. Future projections of the prevalence of dementia in Japan: results from the Toyama Dementia Survey. BMC Geriatr 2021; 21: 602. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Kasajima M, Eggleston K, Kusaka S, et al. Projecting prevalence of frailty and dementia and the economic cost of care in Japan from 2016 to 2043: a microsimulation modelling study. Lancet Public Health 2022; 7: e458–e468. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.van Dyck CH, Swanson CJ, Aisen P, et al. Lecanemab in early Alzheimer’s disease. N Engl J Med 2023; 388: 9–21. [DOI] [PubMed] [Google Scholar]
6.Jin M, Noble JM. What’s in it for me? Contextualizing the potential clinical impacts of lecanemab, donanemab, and other anti-β-amyloid monoclonal antibodies in early Alzheimer’s disease. eNeuro 2024; 11: ENEURO.0088–24.2024. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Sato K, Niimi Y, Ihara R, et al. Public perceptions related to healthcare preparedness to anti-amyloid therapies for Alzheimer’s disease in Japan. Alzheimers Res Ther 2024; 16: 205. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Cummings J, Apostolova L, Rabinovici GD, et al. Lecanemab: appropriate use recommendations. J Prev Alzheimers Dis 2023; 10: 362–377. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Ministry of Health, Labour, and Welfare. LEQEMBI Optimal Use Guideline [Article in Japanese], https://www.mhlw.go.jp/content/12404000/001178607.pdf (2023, accessed 13 December 2023).
10.Kinoshita S and Kishimoto T. Addressing the barriers to promoting lecanemab use: Preventing Intergenerational Conflicts to Build a Sustainable Healthcare System. BMJ 2025; 389: r796. [Google Scholar]
11.Mix Online. Eisai FY2025 Q3 Financial Results Briefing. [Article in Japanese], https://www.mixonline.jp/tabid55.html?artid=77869 (2025, accessed 9 February 2025).
12.Shields LBE, Hust H, Cooley SD, et al. Initial experience with lecanemab and lessons learned in 71 patients in a regional medical center. J Prev Alzheimers Dis 2024; 11: 1549–1562. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.O'Donnell AJ, Fortunato AT, Spitznogle BL, et al. Implementation of lecanemab for Alzheimer’s disease within the veterans health administration: facilitators and barriers. J Am Geriatr Soc 2025; 73: 3178–3183. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Sato K, Niimi Y, Ihara R, et al. Real-world lecanemab adoption in Japan 1 year after launch: insights from 311 specialists on infrastructure and reimbursement barriers. Alzheimers Dement 2025; 21: e70652. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Iwata A. How will the emergence of lecanemab change dementia treatment? Geriatr Gerontol Int 2024; 24: 841–844. [DOI] [PubMed] [Google Scholar]
16.Sperling RA, Jack CR, Jr, Black SE, et al. Amyloid-related imaging abnormalities in amyloid-modifying therapeutic trials: recommendations from the Alzheimer's Association Research Roundtable Workgroup. Alzheimers Dement 2011; 7: 367–385. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Crowther MJ, Royston P, Clements M. A flexible parametric accelerated failure time model and the extension to time-dependent acceleration factors. Biostatistics 2023; 24: 811–831. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Joyce GF, Carrera MP, Goldman DP, et al. Physician prescribing behavior and its impact on patient-level outcomes. Am J Manag Care 2011; 17: e462–e471. [PMC free article] [PubMed] [Google Scholar]
19.Hlavka JP, Mattke S, Liu JL. Assessing the preparedness of the health care system infrastructure in six European countries for an Alzheimer’s treatment. Rand Health Q 2019; 8: 2. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.OECD Data. Magnetic resonance imaging (MRI) units, https://data.oecd.org/healtheqt/magnetic-resonance-imaging-mri-units.htm (2024, accessed 13 May 2024).
21.Belder CRS, Schott JM, Fox NC. Preparing for disease-modifying therapies in Alzheimer’s disease. Lancet Neurol 2023; 22: 782–783. [DOI] [PubMed] [Google Scholar]
22.Pittock RR, Aakre JA, Castillo AM, et al. Eligibility for anti-amyloid treatment in a population-based study of cognitive aging. Neurology 2023; 101: e1837–e1849. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Wahlberg K, Winblad B, Cole A, et al. People get ready! A new generation of Alzheimer’s therapies may require new ways to deliver and pay for healthcare. J Intern Med 2024; 295: 281–291. [DOI] [PubMed] [Google Scholar]
24.Contador J, Vargas-Martínez AM, Sánchez-Valle R, et al. Cost-effectiveness of Alzheimer’s disease CSF biomarkers and amyloid-PET in early-onset cognitive impairment diagnosis. Eur Arch Psychiatry Clin Neurosci 2023; 273: 243–252. [DOI] [PubMed] [Google Scholar]
25.Khalafi M, Dartora WJ, McIntire LBJ, et al. Diagnostic accuracy of phosphorylated tau217 in detecting Alzheimer’s disease pathology among cognitively impaired and unimpaired: a systematic review and meta-analysis. Alzheimers Dement 2025; 21: e14458. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Sato K, Niimi Y, Ihara R, et al. Blood-based biomarker prescreening with different testing combinations and cutoffs: a simulation study examining efficacy and cost-effectiveness in Alzheimer’s disease prevention studies. Alzheimers Dement (N Y) 2025; 11: e70065. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.FDA. FDA clears first blood test used in diagnosing Alzheimer’s disease: new test provides less invasive option, reduces reliance on PET scans and increases diagnosis accessibility, https://www.fda.gov/news-events/press-announcements/fda-clears-first-blood-test-used-diagnosing-alzheimers-disease (2025, accessed 18 May 2025).
28.Sato K, Niimi Y, Ihara R, et al. Advertisement by medical facilities as an opportunity route of APOE genetic testing in Japan: a website analysis. J Community Genet 2024; 15: 195–204. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Alzheimer’s Association launches ALZ-NET: a long-term data collection and sharing network for new treatments. Alzheimers Dement 2022; 18: 1694–1695. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Ohashi K, Takahashi-Iwata I, Jieyu Z, et al. Are there shortages and regional disparities in lecanemab treatment facilities? A cross-sectional study. Health Serv Insights 2024; 17: 11786329241299312. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

sj-docx-1-alr-10.1177_25424823261470054.docx^{(84.1KB, docx)}

Data Availability Statement

The survey data we used for this analysis are not available for data sharing.

[bibr1-25424823261470054] 1.Jack CR, Jr, Bennett DA, Blennow K, et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement 2018; 14: 535–562. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr2-25424823261470054] 2.Jack CR, Jr. Advances in Alzheimer’s disease research over the past two decades. Lancet Neurol 2022; 21: 866–869. [DOI] [PubMed] [Google Scholar]

[bibr3-25424823261470054] 3.Nakahori N, Sekine M, Yamada M, et al. Future projections of the prevalence of dementia in Japan: results from the Toyama Dementia Survey. BMC Geriatr 2021; 21: 602. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr4-25424823261470054] 4.Kasajima M, Eggleston K, Kusaka S, et al. Projecting prevalence of frailty and dementia and the economic cost of care in Japan from 2016 to 2043: a microsimulation modelling study. Lancet Public Health 2022; 7: e458–e468. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr5-25424823261470054] 5.van Dyck CH, Swanson CJ, Aisen P, et al. Lecanemab in early Alzheimer’s disease. N Engl J Med 2023; 388: 9–21. [DOI] [PubMed] [Google Scholar]

[bibr6-25424823261470054] 6.Jin M, Noble JM. What’s in it for me? Contextualizing the potential clinical impacts of lecanemab, donanemab, and other anti-β-amyloid monoclonal antibodies in early Alzheimer’s disease. eNeuro 2024; 11: ENEURO.0088–24.2024. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr7-25424823261470054] 7.Sato K, Niimi Y, Ihara R, et al. Public perceptions related to healthcare preparedness to anti-amyloid therapies for Alzheimer’s disease in Japan. Alzheimers Res Ther 2024; 16: 205. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr8-25424823261470054] 8.Cummings J, Apostolova L, Rabinovici GD, et al. Lecanemab: appropriate use recommendations. J Prev Alzheimers Dis 2023; 10: 362–377. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr9-25424823261470054] 9.Ministry of Health, Labour, and Welfare. LEQEMBI Optimal Use Guideline [Article in Japanese], https://www.mhlw.go.jp/content/12404000/001178607.pdf (2023, accessed 13 December 2023).

[bibr10-25424823261470054] 10.Kinoshita S and Kishimoto T. Addressing the barriers to promoting lecanemab use: Preventing Intergenerational Conflicts to Build a Sustainable Healthcare System. BMJ 2025; 389: r796. [Google Scholar]

[bibr11-25424823261470054] 11.Mix Online. Eisai FY2025 Q3 Financial Results Briefing. [Article in Japanese], https://www.mixonline.jp/tabid55.html?artid=77869 (2025, accessed 9 February 2025).

[bibr12-25424823261470054] 12.Shields LBE, Hust H, Cooley SD, et al. Initial experience with lecanemab and lessons learned in 71 patients in a regional medical center. J Prev Alzheimers Dis 2024; 11: 1549–1562. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr13-25424823261470054] 13.O'Donnell AJ, Fortunato AT, Spitznogle BL, et al. Implementation of lecanemab for Alzheimer’s disease within the veterans health administration: facilitators and barriers. J Am Geriatr Soc 2025; 73: 3178–3183. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr14-25424823261470054] 14.Sato K, Niimi Y, Ihara R, et al. Real-world lecanemab adoption in Japan 1 year after launch: insights from 311 specialists on infrastructure and reimbursement barriers. Alzheimers Dement 2025; 21: e70652. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr15-25424823261470054] 15.Iwata A. How will the emergence of lecanemab change dementia treatment? Geriatr Gerontol Int 2024; 24: 841–844. [DOI] [PubMed] [Google Scholar]

[bibr16-25424823261470054] 16.Sperling RA, Jack CR, Jr, Black SE, et al. Amyloid-related imaging abnormalities in amyloid-modifying therapeutic trials: recommendations from the Alzheimer's Association Research Roundtable Workgroup. Alzheimers Dement 2011; 7: 367–385. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr17-25424823261470054] 17.Crowther MJ, Royston P, Clements M. A flexible parametric accelerated failure time model and the extension to time-dependent acceleration factors. Biostatistics 2023; 24: 811–831. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr18-25424823261470054] 18.Joyce GF, Carrera MP, Goldman DP, et al. Physician prescribing behavior and its impact on patient-level outcomes. Am J Manag Care 2011; 17: e462–e471. [PMC free article] [PubMed] [Google Scholar]

[bibr19-25424823261470054] 19.Hlavka JP, Mattke S, Liu JL. Assessing the preparedness of the health care system infrastructure in six European countries for an Alzheimer’s treatment. Rand Health Q 2019; 8: 2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr20-25424823261470054] 20.OECD Data. Magnetic resonance imaging (MRI) units, https://data.oecd.org/healtheqt/magnetic-resonance-imaging-mri-units.htm (2024, accessed 13 May 2024).

[bibr21-25424823261470054] 21.Belder CRS, Schott JM, Fox NC. Preparing for disease-modifying therapies in Alzheimer’s disease. Lancet Neurol 2023; 22: 782–783. [DOI] [PubMed] [Google Scholar]

[bibr22-25424823261470054] 22.Pittock RR, Aakre JA, Castillo AM, et al. Eligibility for anti-amyloid treatment in a population-based study of cognitive aging. Neurology 2023; 101: e1837–e1849. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr23-25424823261470054] 23.Wahlberg K, Winblad B, Cole A, et al. People get ready! A new generation of Alzheimer’s therapies may require new ways to deliver and pay for healthcare. J Intern Med 2024; 295: 281–291. [DOI] [PubMed] [Google Scholar]

[bibr24-25424823261470054] 24.Contador J, Vargas-Martínez AM, Sánchez-Valle R, et al. Cost-effectiveness of Alzheimer’s disease CSF biomarkers and amyloid-PET in early-onset cognitive impairment diagnosis. Eur Arch Psychiatry Clin Neurosci 2023; 273: 243–252. [DOI] [PubMed] [Google Scholar]

[bibr25-25424823261470054] 25.Khalafi M, Dartora WJ, McIntire LBJ, et al. Diagnostic accuracy of phosphorylated tau217 in detecting Alzheimer’s disease pathology among cognitively impaired and unimpaired: a systematic review and meta-analysis. Alzheimers Dement 2025; 21: e14458. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr26-25424823261470054] 26.Sato K, Niimi Y, Ihara R, et al. Blood-based biomarker prescreening with different testing combinations and cutoffs: a simulation study examining efficacy and cost-effectiveness in Alzheimer’s disease prevention studies. Alzheimers Dement (N Y) 2025; 11: e70065. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr27-25424823261470054] 27.FDA. FDA clears first blood test used in diagnosing Alzheimer’s disease: new test provides less invasive option, reduces reliance on PET scans and increases diagnosis accessibility, https://www.fda.gov/news-events/press-announcements/fda-clears-first-blood-test-used-diagnosing-alzheimers-disease (2025, accessed 18 May 2025).

[bibr28-25424823261470054] 28.Sato K, Niimi Y, Ihara R, et al. Advertisement by medical facilities as an opportunity route of APOE genetic testing in Japan: a website analysis. J Community Genet 2024; 15: 195–204. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr29-25424823261470054] 29.Alzheimer’s Association launches ALZ-NET: a long-term data collection and sharing network for new treatments. Alzheimers Dement 2022; 18: 1694–1695. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr30-25424823261470054] 30.Ohashi K, Takahashi-Iwata I, Jieyu Z, et al. Are there shortages and regional disparities in lecanemab treatment facilities? A cross-sectional study. Health Serv Insights 2024; 17: 11786329241299312. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Identifying facility-level and infrastructure-level bottlenecks in Japan's lecanemab rollout through an online survey of dementia specialists

Saki Nakashima

Kenichiro Sato

Yoshiki Niimi

Ryoko Ihara

Atsushi Iwata

Kiyotaka Nemoto

Tetsuaki Arai

Shinji Higashi

Ataru Igarashi

Kensaku Kasuga

Haruhiko Akiyama

Shuichi Awata

Manabu Ikeda

Tatsushi Toda

Takeshi Iwatsubo

Roles

Abstract

Background

Objective

Methods

Results

Conclusions

Introduction

Methods

Study design

Requirements for DMT use under Japanese health insurance

Questionnaire responses examined

Statistical analyses

Ethics

Results

Basic findings

Association between target outcomes and respondent/facility features

Figure 1.

Association between target outcomes and challenges/solutions

Figure 2.

Figure 3.

Discussion

Supplemental Material

Acknowledgements

Footnotes

Data availability statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases