Random effects models of tumour growth for investigating interval breast cancer.

In Nordic countries and across Europe, breast cancer screening participation is high. However, a significant number of breast cancer cases are still diagnosed due to symptoms between screening rounds, termed "interval cancers". Radiologists use the interval cancer proportion as a proxy for the screening false negative rate (ie, 1-sensitivity). Our objective is to enhance our understanding of interval cancers by applying continuous tumour growth models to data from a study involving incident invasive breast cancer cases. Building upon previous findings regarding stationary distributions of tumour size and growth rate distributions in non-screened populations, we develop an analytical expression for the proportion of interval breast cancer cases among regularly screened women. Our approach avoids relying on estimated background cancer rates. We make specific parametric assumptions concerning tumour growth and detection processes (screening or symptoms), but our framework easily accommodates alternative assumptions. We also show how our developed analytical expression for the proportion of interval breast cancers within a screened population can be incorporated into an approach for fitting tumour growth models to incident case data. We fit a model on 3493 cases diagnosed in Sweden between 2001 and 2008. Our methodology allows us to estimate the distribution of tumour sizes at the most recent screening for interval cancers. Importantly, we find that our model-based expected incidence of interval breast cancers aligns closely with observed patterns in our study and in a large Nordic screening cohort. Finally, we evaluate the association between screening interval length and the interval cancer proportion. Our analytical expression represents a useful tool for gaining insights into the performance of population-based breast cancer screening programs.

A Randomized Controlled Trial of a Pay-for-Performance Initiative to Reduce Costs of Care for High-Need Psychiatric Patients.

OBJECTIVE: Pay-for-performance (P4P) initiatives hold promise for improving health care delivery but are rarely applied to behavioral health or tested in randomized controlled trials (RCTs). This RCT examined the effectiveness of a P4P initiative to reduce total cost of 24-hour care among patients with high needs for psychiatric care in a large county in California. METHODS: From August 2016 to March 2022, a total of 652 adult residents of Santa Clara County, California, were enrolled in a P4P initiative (mean±SD age=46.7±13.3 years, 61% male, 51% White, and 60% diagnosed as having a bipolar or psychotic disorder). Participants were randomly assigned to usual full-service partnerships from the county (N=327) or a comparable level of care from a contractor who agreed to a schedule of financial penalties and rewards based on whether enrollees (N=325) used more or less care than a historical cohort of similar county patients. The primary outcome was total cost of 24-hour psychiatric services. Secondary outcomes were costs of each of the 24-hour care services. RESULTS: The proportion of the total sample that used 24-hour psychiatric services decreased over the 36-month study period. Intent-to-treat analyses revealed no differences between the two study conditions in total care costs during the follow-up period. No significant care utilization differences were observed between the two conditions in most of the individual 24-hour services. CONCLUSIONS: A P4P initiative for high-need patients was no more effective than usual care for reducing costs of 24-hour psychiatric care.

Estimated effectiveness and cost-effectiveness of opioid use disorder treatment under proposed U.S. regulatory relaxations: A model-based analysis.

AIM: To assess the effectiveness and cost-effectiveness of buprenorphine and methadone treatment in the U.S. if exemptions expanding coverage for substance use disorder services via telehealth and allowing opioid treatment programs to supply a greater number of take-home doses of medications for opioid use disorder (OUD) continue (Notice of Proposed Rule Making, NPRM). DESIGN SETTING AND PARTICIPANTS: Model-based analysis of buprenorphine and methadone treatment for a cohort of 100,000 individuals with OUD, varying treatment retention and overdose risk among individuals receiving and not receiving methadone treatment compared to the status quo (no NPRM). INTERVENTION: Buprenorphine and methadone treatment under NPRM. MEASUREMENTS: Fatal and nonfatal overdoses and deaths over five years, discounted lifetime per person QALYs and costs. FINDINGS: For buprenorphine treatment under the status quo, 1.21 QALYs are gained at a cost of $19,200/QALY gained compared to no treatment; with 20% higher treatment retention, 1.28 QALYs are gained at a cost of $17,900/QALY gained compared to no treatment, and the strategy dominates the status quo. For methadone treatment under the status quo, 1.11 QALYs are gained at a cost of $17,900/QALY gained compared to no treatment. In all scenarios, methadone provision cost less than $20,000/QALY gained compared to no treatment, and less than $50,000/QALY gained compared to status quo methadone treatment. CONCLUSIONS: Buprenorphine and methadone OUD treatment under NPRM are likely to be effective and cost-effective. Increases in overdose risk with take-home methadone would reduce health benefits. Clinical and technological strategies could mitigate this risk.

Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network.

BACKGROUND: Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability. METHODS: Electronic health records (EHRs) from 4 countries (United States, United Kingdom, Finland, and Korea) were mapped to the OMOP Common Data Model (CDM), 2008-2019. Machine learning (ML) models were developed to predict post-surgery prolonged opioid use (POU) risks using data collected 6 months before surgery. Both local and cross-site feature selection methods were applied in the development and external validation datasets. Models were developed using Observational Health Data Sciences and Informatics (OHDSI) tools and validated on separate patient cohorts. RESULTS: Model development included 41 929 patients, 14.6% with POU. The external validation included 31 932 (UK), 23 100 (US), 7295 (Korea), and 3934 (Finland) patients with POU of 44.2%, 22.0%, 15.8%, and 21.8%, respectively. The top-performing model, Lasso logistic regression, achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 during local validation and 0.69 (SD = 0.02) (averaged) in external validation. Models trained with cross-site feature selection significantly outperformed those using only features from the development site through external validation (P < .05). CONCLUSIONS: Using EHRs across four countries mapped to the OMOP CDM, we developed generalizable predictive models for POU. Our approach demonstrates the significant impact of cross-site feature selection in improving model performance, underscoring the importance of incorporating diverse feature sets from various clinical settings to enhance the generalizability and utility of predictive healthcare models.

A pilot, randomized clinical trial: Left dorsolateral prefrontal cortex intermittent theta burst stimulation improves treatment outcomes in veterans with alcohol use disorder.

BACKGROUND: Transcranial magnetic stimulation (TMS) offers a promising treatment avenue to modulate brain function in alcohol use disorder (AUD). To the best of our knowledge, this pilot study is the first randomized, double-blind, sham-controlled trial to deliver intermittent theta burst stimulation to the left dorsolateral prefrontal cortex (DLPFC) among US veterans with AUD. We hypothesized that 20 sessions of real TMS are tolerable and feasible. As a secondary line of inquiry, we hypothesized that, relative to sham TMS, individuals receiving real TMS would experience greater reductions in 6-month relapse rates, anhedonia, and alcohol cue-reactivity. METHODS: Veterans (n = 17, one woman) were enrolled in a double-blind, sham-controlled trial (2-3 sessions/day; 7-10 days; 600 pulses/session; 20 sessions). Pre- and posttreatment assessments included responses to self-report questionnaires and functional magnetic resonance imaging measures of alcohol cue-reactivity. Alcohol consumption was assessed for 6 months. Linear mixed-effects models were constructed to predict posttreatment craving, mood, and cue-reactivity. RESULTS: Individuals who received active iTBS (n = 8) were less likely to relapse within 3 months after treatment than the sham-treated group (n = 9) (OR = 12.0). Greater reductions in anhedonia were observed following active iTBS (Cohen's d = -0.59), relative to sham (d = -0.25). Alcohol cue-reactivity was reduced following active iTBS and increased following sham within the left insula (d = -0.19 vs. 0.51), left thalamus (d = -0.28 vs. 0.77), right insula (d = 0.18 vs. 0.52), and right thalamus (d = -0.06 vs. 0.62). CONCLUSIONS: Relative to sham, we demonstrate that 20 sessions of real left DLPFC iTBS reduced the likelihood of relapse for at least 3 months. The potential utility of this approach is underscored by observed decreases in anhedonia and alcohol cue-reactivity-strong predictors of relapse among veterans. These initial data offer a valuable set of effect sizes to inform future clinical trials in this patient population.

Does the advent of depot therapy represent a step change in our understanding of opioid use disorder and its treatment?

After years of minimal innovation in pharmacotherapeutics, impressive outcomes in the treatment of opioid use disorder are being obtained from a new way of delivering an old medication; long-acting injectable formulations of buprenorphine appear to produce compelling reductions in relapse to illicit opioid use not only during use but also following depot discontinuation. This commentary discusses potential mechanisms behind this observation, asks if the removal of the need for daily oral opioid agonist dosing furthers our understanding of addiction treatment and whether we should therefore consider expanding access to depot formulations.

Familial risk of postpartum psychosis.

Objective: Postpartum psychosis, a mood disorder triggered by childbirth, is one of the most severe psychiatric conditions, with high risks of suicide and infanticide if untreated. While it is evident that genetic factors play a crucial role in disorder risk, the exact extent of their importance is yet to be determined. Methods: This cohort study consisted of 1,648,759 women from the Swedish nationwide registers, of whom 2,514 (0.15%) experienced postpartum psychosis within three months of their first-ever childbirth. We estimated the relative recurrence risk of postpartum psychosis for female full siblings and cousins as a measure of familial, genetic, and environmental risk. Results: Relative recurrence risk of postpartum psychosis in full siblings was 10.69 (95% CI=6.60-16.26) when adjusted for year of and age at childbirth. Although cousins showed an elevated relative recurrence risk, these results did not reach statistical significance (1.78, 95% CI=0.70-3.62). Despite the higher familial risk of postpartum psychosis among full siblings, the absolute risk for women with an affected sibling is relatively low, estimated at 1.55% within the entire population. Conclusions: The observed increased risk of postpartum psychosis in full siblings suggests both genetic and shared environmental influences. However, the lack of significant results in cousins hampers a definitive distinction between these factors. Furthermore, despite increased relative recurrence risk in siblings, their overall likelihood of developing postpartum psychosis remains low. Our study underscores the need for further research to better understand the intricate interplay of genetics and environment in the development of postpartum psychosis.

Breast Cancer Incidence After a False-Positive Mammography Result.

Importance: False-positive mammography results are common. However, long-term outcomes after a false-positive result remain unclear. Objectives: To examine long-term outcomes after a false-positive mammography result and to investigate whether the association of a false-positive mammography result with cancer differs by baseline characteristics, tumor characteristics, and time since the false-positive result. Design, Setting, and Participants: This population-based, matched cohort study was conducted in Sweden from January 1, 1991, to March 31, 2020. It included 45 213 women who received a first false-positive mammography result between 1991 and 2017 and 452 130 controls matched on age, calendar year of mammography, and screening history (no previous false-positive result). The study also included 1113 women with a false-positive result and 11 130 matched controls with information on mammographic breast density from the Karolinska Mammography Project for Risk Prediction of Breast Cancer study. Statistical analysis was performed from April 2022 to February 2023. Exposure: A false-positive mammography result. Main Outcomes and Measures: Breast cancer incidence and mortality. Results: The study cohort included 497 343 women (median age, 52 years [IQR, 42-59 years]). The 20-year cumulative incidence of breast cancer was 11.3% (95% CI, 10.7%-11.9%) among women with a false-positive result vs 7.3% (95% CI, 7.2%-7.5%) among those without, with an adjusted hazard ratio (HR) of 1.61 (95% CI, 1.54-1.68). The corresponding HRs were higher among women aged 60 to 75 years at the examination (HR, 2.02; 95% CI, 1.80-2.26) and those with lower mammographic breast density (HR, 4.65; 95% CI, 2.61-8.29). In addition, breast cancer risk was higher for women who underwent a biopsy at the recall (HR, 1.77; 95% CI, 1.63-1.92) than for those without a biopsy (HR, 1.51; 95% CI, 1.43-1.60). Cancers after a false-positive result were more likely to be detected on the ipsilateral side of the false-positive result (HR, 1.92; 95% CI, 1.81-2.04) and were more common during the first 4 years of follow-up (HR, 2.57; 95% CI, 2.33-2.85 during the first 2 years; HR, 1.93; 95% CI, 1.76-2.12 at >2 to 4 years). No statistical difference was found for different tumor characteristics (except for larger tumor size). Furthermore, associated with the increased risk of breast cancer, women with a false-positive result had an 84% higher rate of breast cancer death than those without (HR, 1.84; 95% CI, 1.57-2.15). Conclusions and Relevance: This study suggests that the risk of developing breast cancer after a false-positive mammography result differs by individual characteristics and follow-up. These findings can be used to develop individualized risk-based breast cancer screening after a false-positive result.

Leadership Styles Experienced During Military Service Predict Later Anhedonic Depressive Symptoms and Self-Efficacy in Veterans With Alcohol Use Disorder.

INTRODUCTION: Lifetime and past-year alcohol use disorder (AUD) prevalence is significantly higher in US Armed Services Veterans than in non-veterans across adulthood. This study examined the associations of perceived transformational leadership styles (TLS) experienced during military service and anhedonic depression and self-efficacy related to confidence to abstain or reduce alcohol consumption in Veterans seeking treatment for AUD. The ramifications of perceived leadership styles on multiple aspects of follower psychiatric functioning, including depressive and PTSD symptomatology, during and after military service, may be substantial and enduring. Higher anhedonic depression and lower abstinence self-efficacy are related to increased risk of relapse after treatment. We predicted Veterans, in treatment for AUD, who reported higher perceived levels of transformational leadership during military service, demonstrate lower anhedonic depressive symptoms and higher alcohol abstinence self-efficacy. MATERIALS AND METHODS: Veterans with AUD (n = 60; 50 ± 14 years of age) were recruited from residential treatment at the VA Palo Alto Health Care System. All procedures were approved by the VA Palo Alto Health Care System and Stanford University institutional review boards. A series of mediation analyses were completed with The Multifactor Leadership Questionnaire measures of TLS (average across leadership measures [transformational leadership average; TLS average]) as predictor and the Alcohol Abstinence Self-Efficacy Scale, Mood and Anxiety Symptom Questionnaire, anhedonic depression subscale, as dependent measures. PTSD Checklist for DSM-5 score was tested as a mediator variable. RESULTS: Higher reported perceived TLS average during military service was significantly related to lower anhedonic depressive symptoms. Higher TLS average was related to higher self-efficacy to resist alcohol use in contexts involving experience of physical issues and withdrawal/cravings and urges. These relationships were not mediated by PTSD symptomatology or duration of military service, age, education, time since military service, military branch, combat exposure, or current psychiatric diagnosis. CONCLUSIONS: The significant associations of perceived TLS during military service with anhedonic depression and alcohol use self-efficacy are clinically relevant because these measures are associated with relapse risk after AUD treatment. Further study of the implications of perceived TLS during military service for AUD and other substance use disorder treatment outcome is warranted in Veterans.

Drug-related deaths among housed and homeless individuals in the UK and the USA: comparative retrospective cohort study.

BACKGROUND: The UK and USA currently report their highest number of drug-related deaths since records began, with higher rates among individuals experiencing homelessness. AIMS: Given that overdose prevention in homeless populations may require unique strategies, we evaluated whether substances implicated in death differed between (a) housed decedents and those experiencing homelessness and (b) between US and UK homeless populations. METHOD: We conducted an internationally comparative retrospective cohort study utilising multilevel multinomial regression modelling of coronial/medical examiner-verified drug-related deaths from 1 January 2012 to 31 December 2021. UK data were available for England, Wales and Northern Ireland; US data were collated from eight county jurisdictions. Data were available on decedent age, sex, ethnicity, housing status and substances implicated in death. RESULTS: Homeless individuals accounted for 16.3% of US decedents versus 3.4% in the UK. Opioids were implicated in 66.3 and 50.4% of all studied drug-related deaths in the UK and the USA respectively. UK homeless decedents had a significantly increased risk of having only opioids implicated in death compared with only non-opioids implicated (relative risk ratio RRR = 1.87, 95% CI 1.76-1.98, P < 0.001); conversely, US homeless decedents had a significantly decreased risk (RRR = 0.37, 95% CI 0.29-0.48, P < 0.001). Methamphetamine was implicated in two-thirds (66.7%) of deaths among US homeless decedents compared with 0.4% in the UK. CONCLUSIONS: Both the rate and type of drug-related deaths differ significantly between homeless and housed populations in the UK and USA. The two countries also differ in drugs implicated in death. Targeted programmes for country-specific implicated drug profiles appear warranted.

Monitoring medication and illicit drug consumption in a prison by wastewater-based epidemiology: Impact of COVID-19 restrictions.

Drug consumption in prisons is a concern for the safety of incarcerated people and staff. Typically, drug use prevalence in prisons is estimated through urinalysis and intelligence operations, which can be intrusive and stressful. An alternative approach, wastewater-based epidemiology (WBE), was used in this study to estimate the consumption of licit and illicit drugs for the entire population of a prison in Australia. Wastewater samples were collected from March to December 2020, covering periods of no restrictions and periods when prison access was restricted to prevent the transmission of COVID-19. Target biomarkers were analysed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). The average consumption of common illicit drugs (MDMA, methamphetamine and cocaine) over the sampling period in the prison (0.5 - 4.5 mg/1000 people/day) was two to three orders of magnitude lower than in the community population (254 - 1000 mg/1000 people/day). Comparison of WBE estimates against pharmacy dispensing data suggested potential illicit buprenorphine consumption at the prison. Methamphetamine and buprenorphine use decreased when no visitors were allowed (18% - 72% decrease for methamphetamine; about half decrease for buprenorphine) and increased once these restrictions were eased (22% - 39% increase for methamphetamine; 44% - 67% increase for buprenorphine). The changes in drug use may be attributed in part to a reduction of drug trafficking into the prison from visitors or non-essential staffs and in part to the reduced contribution of urine from staff who used toilets within the prison. This study provided useful information on the scale of illicit drug use and extra-medical use of licit drugs in prison, and its changes under different security conditions.

Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring.

BACKGROUND: Treatment of surgical pain is a common reason for opioid prescriptions. Being able to predict which patients are at risk for opioid abuse, dependence, and overdose (opioid-related adverse outcomes [OR-AE]) could help physicians make safer prescription decisions. We aimed to develop a machine-learning algorithm to predict the risk of OR-AE following surgery using Medicaid data with external validation across states. METHODS: Five machine learning models were developed and validated across seven US states (90-10 data split). The model output was the risk of OR-AE 6-months following surgery. The models were evaluated using standard metrics and area under the receiver operating characteristic curve (AUC) was used for model comparison. We assessed calibration for the top performing model and generated bootstrap estimations for standard deviations. Decision curves were generated for the top-performing model and logistic regression. RESULTS: We evaluated 96,974 surgical patients aged 15 and 64. During the 6-month period following surgery, 10,464 (10.8%) patients had an OR-AE. Outcome rates were significantly higher for patients with depression (17.5%), diabetes (13.1%) or obesity (11.1%). The random forest model achieved the best predictive performance (AUC: 0.877; F1-score: 0.57; recall: 0.69; precision:0.48). An opioid disorder diagnosis prior to surgery was the most important feature for the model, which was well calibrated and had good discrimination. CONCLUSIONS: A machine learning models to predict risk of OR-AE following surgery performed well in external validation. This work could be used to assist pain management following surgery for Medicaid beneficiaries and supports a precision medicine approach to opioid prescribing.

Long-Term Effects of Increasing Buprenorphine Treatment Seeking, Duration, and Capacity on Opioid Overdose Fatalities: A Model-based Analysis.

OBJECTIVES: Because buprenorphine treatment of opioid use disorder reduces opioid overdose deaths (OODs), expanding access to care is an important policy and clinical care goal. Policymakers must choose within capacity limitations whether to expand the number of people with opioid use disorder who are treated or extend duration for existing patients. This inherent tradeoff could be made less acute with expanded buprenorphine treatment capacity. METHODS: To inform such decisions, we used a validated simulation model to project the effects of increasing buprenorphine treatment-seeking, average episode duration, and capacity (patients per provider) on OODs in the United States from 2023 to 2033, varying the start time to assess the effects of implementation delays. RESULTS: Results show that increasing treatment duration alone could cost lives in the short term by reducing capacity for new admissions yet save more lives in the long term than accomplished by only increasing treatment seeking. Increasing provider capacity had negligible effects. The most effective 2-policy combination was increasing capacity and duration simultaneously, which would reduce OODs up to 18.6% over a decade. By 2033, the greatest reduction in OODs (≥20%) was achieved when capacity was doubled and average duration reached 2 years, but only if the policy changes started in 2023. Delaying even a year diminishes the benefits. Treatment-seeking increases were equally beneficial whether they began in 2023 or 2025 but of only marginal benefit beyond what capacity and duration achieved. CONCLUSIONS: If policymakers only target 2 policies to reduce OODs, they should be to increase capacity and duration, enacted quickly and aggressively.

Understanding predictors of mental health and substance use treatment utilization among US adults: A repeated cross-sectional study.

Background: Understanding discrepancies in mental health and substance use treatment utilization can help identify inequities in access to health services. We investigate mental health and substance use treatment utilization as function of demographic and social determinants, as well as pre-existing mental health and substance use disorders. Methods: In this repeated cross-sectional study, we used the 2017-2019 National Survey on Drug Use and Health data on US adults above age 18. Two logistic regression models were conducted, using predictors of age, gender, race/Hispanicity, sexual identity, education, insurance, family income, and past year mental health and substance use disorders, with outcomes of mental health or substance use treatment utilization. Weighted estimates of substance use disorders and insurance types and Pearson's correlation tests of vulnerability among age, gender, and treatment type were reported. Findings: Racial minorities, uninsured populations, sexual minorities, and females had lower odds of receiving mental health treatment, while older populations, lower income groups, and dual eligible enrollees had higher odds. Individuals with substance use disorders but no mental illness had higher odds of receiving mental health treatment. Those utilizing mental health treatment were mostly of high income, privately insured, and using cannabis, cocaine, and opioids. Older populations, men, and Medicaid only enrollees had higher odds of receiving substance use disorder treatment, whereas racial minorities had lower odds. Distribution of income, insurance type, and substance use were more widespread than mental health treatment. Interpretation: Mental health treatment can be used as an avenue for substance use treatment, particularly opioid use disorders. It is important to target vulnerable populations, like racial minorities and uninsured populations to improve access to mental health and substance use treatment.

Influence of mammographic density and compressed breast thickness on true mammographic sensitivity: a cohort study.

Understanding the detectability of breast cancer using mammography is important when considering nation-wide screening programmes. Although the role of imaging settings on image quality has been studied extensively, their role in detectability of cancer at a population level is less well studied. We wish to quantify the association between mammographic screening sensitivity and various imaging parameters. Using a novel approach applied to a population-based breast cancer screening cohort, we specifically focus on sensitivity as defined in the classical diagnostic testing literature, as opposed to the screen-detected cancer rate, which is often used as a measure of sensitivity for monitoring and evaluating breast cancer screening. We use a natural history approach to model the presence and size of latent tumors at risk of detection at mammography screening, and the screening sensitivity is modeled as a logistic function of tumor size. With this approach we study the influence of compressed breast thickness, x-ray exposure, and compression pressure, in addition to (percent) breast density, on the screening test sensitivity. When adjusting for all screening parameters in addition to latent tumor size, we find that percent breast density and compressed breast thickness are statistically significant factors for the detectability of breast cancer. A change in breast density from 6.6 to 33.5% (the inter-quartile range) reduced the odds of detection by 61% (95% CI 48-71). Similarly, a change in compressed breast thickness from 46 to 66 mm reduced the odds by 42% (95% CI 21-57). The true sensitivity of mammography, defined as the probability that an examination leads to a positive result if a tumour is present in the breast, is associated with compressed breast thickness after accounting for mammographic density and tumour size. This can be used to guide studies of setups aimed at improving lesion detection. Compressed breast thickness-in addition to breast density-should be considered when assigning complementary screening modalities and personalized screening intervals.

Spectrum and Frequency of Germline FANCM Protein-Truncating Variants in 44,803 European Female Breast Cancer Cases.

FANCM germline protein truncating variants (PTVs) are moderate-risk factors for ER-negative breast cancer. We previously described the spectrum of FANCM PTVs in 114 European breast cancer cases. In the present, larger cohort, we report the spectrum and frequency of four common and 62 rare FANCM PTVs found in 274 carriers detected among 44,803 breast cancer cases. We confirmed that p.Gln1701* was the most common PTV in Northern Europe with lower frequencies in Southern Europe. In contrast, p.Gly1906Alafs*12 was the most common PTV in Southern Europe with decreasing frequencies in Central and Northern Europe. We verified that p.Arg658* was prevalent in Central Europe and had highest frequencies in Eastern Europe. We also confirmed that the fourth most common PTV, p.Gln498Thrfs*7, might be a founder variant from Lithuania. Based on the frequency distribution of the carriers of rare PTVs, we showed that the FANCM PTVs spectra in Southwestern and Central Europe were much more heterogeneous than those from Northeastern Europe. These findings will inform the development of more efficient FANCM genetic testing strategies for breast cancer cases from specific European populations.