Category: Statistics

Res Health Serv Reg. 2024 May 15;3(1):8. doi: 10.1007/s43999-024-00044-y.

ABSTRACT

PURPOSE: The aim of this study was to examine geographic and socioeconomic variation in curative treatment and choice of treatment modality among elderly prostate cancer (PCa) patients.

METHODS: This register-based cohort study included all Norwegian men ≥ 70 years when diagnosed with non-metastatic, high-risk PCa in 2011-2020 (n = 10 807). Individual data were obtained from the Cancer Registry of Norway, the Norwegian Prostate Cancer Registry, and Statistics Norway. Multilevel logistic regression analysis was used to model variation across hospital referral areas (HRAs), incorporating clinical, demographic and socioeconomic factors.

RESULTS: Overall, 5186 (48%) patients received curative treatment (radical prostatectomy (RP) (n = 1560) or radiotherapy (n = 3626)). Geographic variation was found for both curative treatment (odds ratio 0.39-2.19) and choice of treatment modality (odds ratio 0.10-2.45). Odds of curative treatment increased with increasing income and education, and decreased for patients living alone, and with increasing age and frailty. Patients with higher income had higher odds of receiving RP compared to radiotherapy.

CONCLUSIONS: This study showed geographic and socioeconomic variation in treatment of elderly patients with non-metastatic, high-risk PCa, both in relation to overall curative treatment and choice of treatment modality. Further research is needed to explore clinical practices, the shared decision process and how socioeconomic factors influence the treatment of elderly patients with high-risk PCa.

PMID:39177854 | DOI:10.1007/s43999-024-00044-y

Res Health Serv Reg. 2022 Nov 30;1(1):13. doi: 10.1007/s43999-022-00013-3.

ABSTRACT

Despite decades of atlas production and use within multiple healthcare systems, and consistent reporting of geographical differences in the utilisation of services, significant levels of clinical variation persist. Drawing on over forty years of combined experience using atlases of clinical variation, we reflect on why that might be the case and explore the role of atlases have played in efforts to reduce inappropriate overuse, underuse and misuse of healthcare services. We contend that atlases are useful but, on their own, are not enough to drive change in clinical practice and improvement in patient outcomes. Building on four conceptual models we have published since 2017, we argue that atlases, with their focus on measuring healthcare utilisation by residents in different geographies, generally fail to provide sufficient information and statistical analyses to truly assess the nature of the variation and support action for change. They seldom use structures such as hospitals or teams as the unit of analysis to understand variation; they rarely feature the key elements of healthcare performance which underlie variation; they are mostly silent about how to assess whether the variation measured is warranted or truly unwarranted; nor do they identify evidence-based levers for change. This means that a stark choice confronts producers of atlases – to either continue with the current model and more explicitly rely on other players to undertake work to complete the ‘data to action’ cycle that is necessary to secure improvement; or to refine their offering – including more sophisticated performance measurement approaches, nuanced guides for interpretation of any differences found, support for the selection and application of levers for change that align with local context, and provision of evidence-based options for implementation.

PMID:39177847 | DOI:10.1007/s43999-022-00013-3

World J Urol. 2024 Aug 23;42(1):495. doi: 10.1007/s00345-024-05202-y.

ABSTRACT

OBJECTIVES: To develop and validate a prediction model for identifying non-prostate cancer (non-PCa) in biopsy-naive patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml to avoid unnecessary biopsy.

PATIENTS AND METHODS: Eligible patients who underwent transperineal biopsies at West China Hospital between 2018 and 2022 were included. The patients were randomly divided into training cohort (70%) and validation cohort (30%). Logistic regression was used to screen for independent predictors of non-PCa, and a nomogram was constructed based on the regression coefficients. The discrimination and calibration were assessed by the C-index and calibration plots, respectively. Decision curve analysis (DCA) and clinical impact curves (CIC) were applied to measure the clinical net benefit.

RESULTS: A total of 1580 patients were included, with 634 non-PCa. Age, prostate volume, prostate-specific antigen density (PSAD), apparent diffusion coefficient (ADC) and lesion zone were independent predictors incorporated into the optimal prediction model, and a corresponding nomogram was constructed ( https://nomogramscu.shinyapps.io/PI-RADS-4-5/ ). The model achieved a C-index of 0.931 (95% CI, 0.910-0.953) in the validation cohort. The DCA and CIC demonstrated an increased net benefit over a wide range of threshold probabilities. At biopsy-free thresholds of 60%, 70%, and 80%, the nomogram was able to avoid 74.0%, 65.8%, and 55.6% of unnecessary biopsies against 9.0%, 5.0%, and 3.6% of missed PCa (or 35.9%, 30.2% and 25.1% of foregone biopsies, respectively).

CONCLUSION: The developed nomogram has favorable predictive capability and clinical utility can help identify non-PCa to support clinical decision-making and reduce unnecessary prostate biopsies.

PMID:39177844 | DOI:10.1007/s00345-024-05202-y

Biometrics. 2024 Jul 1;80(3):ujae078. doi: 10.1093/biomtc/ujae078.

ABSTRACT

Interval-censored failure time data frequently arise in various scientific studies where each subject experiences periodical examinations for the occurrence of the failure event of interest, and the failure time is only known to lie in a specific time interval. In addition, collected data may include multiple observed variables with a certain degree of correlation, leading to severe multicollinearity issues. This work proposes a factor-augmented transformation model to analyze interval-censored failure time data while reducing model dimensionality and avoiding multicollinearity elicited by multiple correlated covariates. We provide a joint modeling framework by comprising a factor analysis model to group multiple observed variables into a few latent factors and a class of semiparametric transformation models with the augmented factors to examine their and other covariate effects on the failure event. Furthermore, we propose a nonparametric maximum likelihood estimation approach and develop a computationally stable and reliable expectation-maximization algorithm for its implementation. We establish the asymptotic properties of the proposed estimators and conduct simulation studies to assess the empirical performance of the proposed method. An application to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study is provided. An R package ICTransCFA is also available for practitioners. Data used in preparation of this article were obtained from the ADNI database.

PMID:39177025 | DOI:10.1093/biomtc/ujae078

Cancer Med. 2024 Aug;13(16):e70032. doi: 10.1002/cam4.70032.

ABSTRACT

The large real-world EHR dataset Flatiron has shown that race was not significantly associated with poorer survival in patients with DLBCL. Medicaid insurance status was significantly associated with poorer overall survival and time to second-line therapy or death due to any cause in patients with DLBCL aged <65 years.

PMID:39177019 | DOI:10.1002/cam4.70032

Euro Surveill. 2024 Aug;29(34). doi: 10.2807/1560-7917.ES.2024.29.34.2400139.

ABSTRACT

BackgroundIn 2022, a global monkeypox virus (MPXV) clade II epidemic occurred mainly among men who have sex with men. Until early 1980s, European smallpox vaccination programmes were part of worldwide smallpox eradication efforts. Having received smallpox vaccine > 20 years ago may provide some cross-protection against MPXV.AimTo assess the effectiveness of historical smallpox vaccination against laboratory-confirmed mpox in 2022 in Europe.MethodsEuropean countries with sufficient data on case vaccination status and historical smallpox vaccination coverage were included. We selected mpox cases born in these countries during the height of the national smallpox vaccination campaigns (latest 1971), male, with date of onset before 1 August 2022. We estimated vaccine effectiveness (VE) and corresponding 95% CI for each country using logistic regression as per the Farrington screening method. We calculated a pooled estimate using a random effects model.ResultsIn Denmark, France, the Netherlands and Spain, historical smallpox vaccination coverage was high (80-90%) until the end of the 1960s. VE estimates varied widely (40-80%, I2 = 82%), possibly reflecting different booster strategies. The pooled VE estimate was 70% (95% CI: 23-89%).ConclusionOur findings suggest residual cross-protection by historical smallpox vaccination against mpox caused by MPXV clade II in men with high uncertainty and heterogeneity. Individuals at high-risk of exposure should be offered mpox vaccination, following national recommendations, regardless of prior smallpox vaccine history, until further evidence becomes available. There is an urgent need to conduct similar studies in sub-Saharan countries currently affected by the MPXV clade I outbreak.

PMID:39176988 | DOI:10.2807/1560-7917.ES.2024.29.34.2400139

Euro Surveill. 2024 Aug;29(34). doi: 10.2807/1560-7917.ES.2024.29.34.2300503.

ABSTRACT

BackgroundVaccine uptake differs between social groups. Mobile vaccination units (MV-units) were deployed in the Netherlands by municipal health services in neighbourhoods with low uptake of COVID-19 vaccines.AimWe aimed to evaluate the impact of MV-units on vaccine uptake in neighbourhoods with low vaccine uptake.MethodsWe used the Dutch national-level registry of COVID-19 vaccinations (CIMS) and MV-unit deployment registrations containing observations in 253 neighbourhoods where MV-units were deployed and 890 contiguous neighbourhoods (total observations: 88,543 neighbourhood-days). A negative binomial regression with neighbourhood-specific temporal effects using splines was used to study the effect.ResultsDuring deployment, the increase in daily vaccination rate in targeted neighbourhoods ranged from a factor 2.0 (95% confidence interval (CI): 1.8-2.2) in urbanised neighbourhoods to 14.5 (95% CI: 11.6-18.0) in rural neighbourhoods. The effects were larger in neighbourhoods with more voters for the Dutch conservative Reformed Christian party but smaller in neighbourhoods with a higher proportion of people with non-western migration backgrounds. The absolute increase in uptake over the complete intervention period ranged from 0.22 percentage points (95% CI: 0.18-0.26) in the most urbanised neighbourhoods to 0.33 percentage point (95% CI: 0.28-0.37) in rural neighbourhoods.ConclusionDeployment of MV-units increased daily vaccination rate, particularly in rural neighbourhoods, with longer travel distance to permanent vaccination locations. This public health intervention shows promise to reduce geographic and social health inequalities, but more proactive and long-term deployment is required to identify its potential to substantially contribute to overall vaccination rates at country level.

PMID:39176986 | DOI:10.2807/1560-7917.ES.2024.29.34.2300503

Drug Alcohol Rev. 2024 Aug 23. doi: 10.1111/dar.13933. Online ahead of print.

ABSTRACT

INTRODUCTION: More evidence for patterns of healthcare utilisation and associated costs among people receiving opioid agonist therapy (OAT) is needed. We investigated primary and secondary healthcare usage and costs among methadone and buprenorphine recipients in England.

METHODS: We conducted a cohort study using the Clinical Practice Research Datalink GOLD and Aurum databases of patients who were prescribed OAT between 1 January 2007 and 31 July 2019. The cohort was linked to Hospital Episode Statistics admitted patient care, outpatient and emergency department data, neighbourhood- and practice-level Index of Multiple Deprivation quintiles and mortality records. Negative binomial regression models were applied to estimate weighted rate ratios (wRR) of healthcare utilisation. Total and mean costs were calculated using Unit Costs of Health and Social Care and the National Healthcare Service Payment by Results National Tariffs.

RESULTS: Among 12,639 patients observed over 39,016 person-years, we found higher rate of hospital admissions (wRR 1.18; 1.08-1.28) among methadone compared with buprenorphine recipients. The commonest hospital discharge diagnoses among methadone patients were infectious diseases (19.2%), mental and behavioural disorders (17.0%) and drug-related poisoning (16.5%); the three commonest among buprenorphine patients were mental and behavioural diseases (21.5%), endocrine (13.8%) and genitourinary system diseases (13.1%). Methadone patients had similar mean costs compared with buprenorphine patients (cost difference: £539.01; 432.11-1006.69).

DISCUSSION AND CONCLUSIONS: Differences in healthcare utilisation frequency for methadone versus buprenorphine recipients were observed. The differences in associated costs were mainly driven by hospital admissions. These findings offer valuable insights for optimising care strategies and resource allocation for OAT recipients.

PMID:39176979 | DOI:10.1111/dar.13933

Stud Health Technol Inform. 2024 Aug 22;316:1069-1073. doi: 10.3233/SHTI240595.

ABSTRACT

This comparative study examines the transition from isolated registries to a consolidated data-centric approach at University Hospital Schleswig-Holstein, focusing on migrating the Atrioventricular Valve Intervention Registry (AVIR) from REDCap to a Medical Data Integration Center based openEHR registry. Through qualitative analysis, we identify key disparities and strategic decisions guiding this transition. While REDCap has historical utility, its limitations in automated data integration and traceability highlight the advantages of a data-centric approach, which include streamlined data (integration) management at a single-point-of-truth based on e.g., centralized consent management. Our findings lay the groundwork for the AVIR project and a proof-of-concept data-centric registry, reflecting a broader industry trend towards data-centric healthcare initiatives.

PMID:39176974 | DOI:10.3233/SHTI240595

Stud Health Technol Inform. 2024 Aug 22;316:1038-1042. doi: 10.3233/SHTI240588.

ABSTRACT

The effective management of human resources in nursing is fundamental to ensuring high-quality care. The necessary staffing levels can be derived from the nursing-related health status. Our approach is based on the use of artificial intelligence (AI) and machine learning (ML) to recognize key workload-driving predictors from routine data in the first step and derive recommendations for staffing levels in the second step. The precedent analysis was a multi-center study with data provided by three hospitals. The SPI (Self Care Index = sum score of 10 functional/cognitive items of the epaAC (epaAC = nursing assessment tool for AcuteCare (abbreviated from the German-language effiziente Pflege-Analyse AcuteCare))) was identified as a strong predictor of nursing workload. The SPI alone explains the variance in minutes with an adjusted R2 of 40% to 66%. With the addition of further predictors such as “fatigue” or “pain intensity”, the adjusted R2 can be increased by up to 17%. The resulting model can be used as a foundation for data-based personnel controlling using AI-based prediction models.

PMID:39176968 | DOI:10.3233/SHTI240588