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BackgroundThis study aimed to determine real-world prescribing patterns and determinants for Japanese patients with Parkinson's disease (PD), with a focus on patients ≥75 years.MethodsThis was a retrospective, observational, longitudinal study of patients with PD (≥30 years, ICD-10: G20 excluding Parkinson's syndrome) from three Japanese nationwide healthcare claim databases. Prescription drugs were tabulated using database receipt codes. Changes in treatment patterns were analyzed using network analysis. Factors associated with prescribing patterns and prescription duration were analyzed using multivariable analysis.ResultsOf 18 million insured people, 39,731 patients were eligible for inclusion (≥75-year group: 29,130;

PcBaSe Sweden is a data base for clinical epidemiological prostate cancer research based on linkages between the National Prostate Cancer Register (NPCR) of Sweden, a nationwide population-based quality database and other nationwide registries. In the period 1996-2023, 246 500 cases have been registered in NPCR with detailed data on tumour characteristics and primary treatment available https://statistik.incanet.se/npcr/. In addition, there are five controls per case. By use of the individually unique person identity number, the NPCR has been linked to the Swedish National Cancer Register, the Cause of Death Register, the Prescribed Drug Register, the National Patient Register, and the Acute Myocardial Infarction Register, the Register of the Total Population, the Longitudinal Integration database for health insurance and labour market studies (LISA), the Multi-Generation Register and several other population-based registers. Van Hemelrijck M, Garmo H, Wigertz A, Nilsson P, Stattin P. Cohort Profile Update: The National Prostate Cancer Register of Sweden and Prostate Cancer data Base-a refined prostate cancer trajectory, Int J Epidemiol, 2016 Feb;45(1):73-82. Purpose: To provide a platform for prostate cancer research. The data base allows for population-based observational studies with case-control, cohort, or longitudinal case only design that can be used for studies of pertinent issues of clinical importance.

Nonalcoholic fatty liver disease (NAFLD) is a spectrum of liver conditions that can progress to significant fibrosis and cirrhosis. There are an estimated 40-90 million individuals within the United States with NAFLD, 10- 30% of whom have NASH and may develop NASH-related cirrhosis. Identifying through non- invasive means those individuals who are at risk for progressive liver disease remains challenging.

The Nonalcoholic Steatohepatitis Clinical Research Network (NASH CRN) was initiated by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) in 2002 to conduct multicenter, collaborative studies on the etiology, contributing factors, natural history, complications and treatment of NASH.

The NAFLD Adult Database 2 continued the longitudinal follow-up of participants enrolled in earlier NASH CRN studies and recruited new participants with recent liver biopsies. Comprehensive data, including demographics, medical history, symptoms, medication use, alcohol use and routine laboratory studies were collected on all participants at entry and at annual visits for up to 10 years after enrollment. A liver biopsy was collected at baseline if not collected in a prior NASH CRN study. Study questionnaires administered at enrollment included Skinner Lifetime Drinking history, Alcohol Use Disorders Identification Test (AUDIT) and Beverage Questionnaire (BEVQ-15).

Specimens were collected every 48 weeks during follow-up. If liver biopsies were obtained as part of routine patient care, they were scored using the NASH CRN NAFLD Activity Score (NAS) and fibrosis score. The Interim drinking history (AUDIT-C) and BEVQ-15 were collected at follow-up visits.

Predicting health outcomes from longitudinal health histories is of central importance to healthcare. Observational healthcare databases such as patient diary databases provide a rich resource for patient-level predictive modeling. In this paper, we propose a Bayesian hierarchical vector autoregressive (VAR) model to predict medical and psychological conditions using multivariate time series data. Compared to the existing patient-specific predictive VAR models, our model demonstrated higher accuracy in predicting future observations in terms of both point and interval estimates due to the pooling effect of the hierarchical model specification. In addition, by adopting an elastic-net prior, our model offers greater interpretability about the associations between variables of interest on both the population level and the patient level, as well as between-patient heterogeneity. We apply the model to two examples: 1) predicting substance use craving, negative affect and tobacco use among college students, and 2) predicting functional somatic symptoms and psychological discomforts.

PcBaSe Sweden is a data base for clinical epidemiological prostate cancer research based on linkages between the National Prostate Cancer Register (NPCR) of Sweden, a nationwide population-based quality database and other nationwide registries. In the period 1996-2009, 110 000 cases have been registered in NPCR with detailed data on tumour characteristics and primary treatment available. In addition, there are five controls per case.

By use of the individually unique person identity number, the NPCR has been linked to the Swedish National Cancer Register, the Cause of Death Register, the Prescribed Drug Register, the National Patient Register, and the Acute Myocardial Infarction Register, the Register of the Total Population, the Longitudinal Integration database for health insurance and labour market studies (LISA), the Multi-Generation Register and several other population-based registers.

Purpose:

To provide a platform for prostate cancer research. The data base allows for population-based observational studies with case-control, cohort, or longitudinal case only design that can be used for studies of pertinent issues of clinical importance.

Longitudinal studies of inflammatory biomarkers and depression.

BackgroundParkinson’s disease (PD) is a neurodegenerative disorder, with increasing prevalence among aging populations. Gender differences in PD extend to symptom presentation and treatment response, suggesting the need for gender-specific management strategies.MethodsThis gender-stratified analysis of a retrospective observational study used data from three nationwide Japanese healthcare databases. Patients aged ≥30 years diagnosed with PD between June 2016 and May 2021 were included. Patient demographics, prescribing patterns, and levodopa dosages were analyzed descriptively.ResultsOf 39,731 patients with PD identified, females (n = 22,724) outnumbered males (n = 17,007), especially in the ≥75 years group. Levodopa was the most commonly prescribed drug for both genders. The mean ± standard deviation maximum levodopa dose was numerically higher in males (520.0 ± 426.8 mg) compared with females (498.7 ± 424.2 mg). Usage of monoamine oxidase B inhibitors (MAOBI) was 24.0% in males and 18.9% in females. Among newly treated patients, >70% of both genders started treatment with levodopa monotherapy; a slightly higher proportion of males tended toward levodopa combination therapy. For both genders, concomitant drugs were most commonly MAOBI, non-ergot dopamine agonist, and zonisamide. However, females tended to receive a more diverse range of medications than these three drugs.ConclusionThis analysis highlights the high number of elderly female patients with PD in Japan. Slight gender differences in maximum levodopa dose and prescriptions for anti-PD drugs were observed. These findings emphasize the importance of personalized treatment approaches in PD management considering gender-specific differences in drug efficacy and side effects.Clinical trials registrationhttps://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000053425, identifier UMIN000046823.

Sub-group analysis of the pooled prevalence and odds ratios for the association between Trichomonas vaginalis and cervical neoplasia.

Cross-sectional and case-control studies on inflammatory biomarkers and depression.

The relationship between Trichomonas vaginalis and cervical neoplasia based on stage of abnormality.

BackgroundGlycemic disorder is closely related to the risk of pancreatic cancer, but previous studies focused on the influence of diabetes. The aim of this meta-analysis was to investigate the influence of prediabetes, an intermediate state between normoglycemia and diabetes, on the risk of pancreatic cancer.MethodsRelevant longitudinal observational studies were identified through a search of Medline, Embase, and Web of Science databases. To minimize the influence of between-study heterogeneity, a randomized-effects model was used to pool the results.ResultsNine cohort studies including 26,444,624 subjects were available for the meta-analysis. Among them, 2,052,986 (7.8%) had prediabetes at baseline, and the participants were followed for a mean duration of 5.9 years. It was found that, compared to people with normoglycemia, those with prediabetes had a higher incidence of pancreatic cancer (risk ratio [RR]: 1.42, 95% confidence interval: 1.36 to 1.49, p

Databases, websites and search terms used in the review.

Search Strategy for PubMed Database (PICO Frameworka).

Laboratory test results and EPO administered are median values and interquartile range (IQR).

BackgroundEffective post-stroke mobility, recovery, performance, and participation are key goals for stroke survivors. However, these outcomes may be hindered by post-stroke fatigue (PSF), which can affect numerous aspects of post-stroke mobility, recovery, performance, functioning, community participation, and return to work. This review aimed to assess the scientific evidence on the relationship between PSF and mobility function, functional recovery, functional performance, and participation-related outcomes among stroke survivors.MethodA comprehensive search of Cochrane Central, PubMed, Embase, and Web of Science (WoS) databases was conducted from inception to December 2023. Observational, cross-sectional, and longitudinal studies were included. The methodological quality of the included studies was assessed using the National Institute of Health’s quality assessment tool, while the risk of bias was assessed using the Quality in Prognostic Studies tool. A total of 28 studies (n = 2,495 participants, 1,626 men, mean age ranging from 52.5 ± 9.5 to 71.1 ± 9.9 years) were included. The data analysis was conducted using narrative and quantitative synthesis. Fixed and random effects meta-analyses were conducted to explore the relationships between PSF and relevant outcomes.ResultsChronic PSF was found to have significant negative correlations with mobility (meta r = −0.106, p 

Selected data on patients in the study and two control units in months 13–18 after start of CQI in the study unit.

BackgroundLow back pain (LBP) is a highly prevalent condition that substantially impairs individuals’ physical functioning. This highlights the need for effective management strategies to improve patient outcomes. It is, therefore, crucial to have knowledge of physical functioning prognostic factors that can predict outcomes to facilitate the development of targeted treatment plans aiming to achieve better patient outcomes. There is no synthesis of evidence for physical functioning measures as prognostic factors in the LBP population. The objective of this systematic review is to synthesize evidence for physical measures of physical functioning as prognostic factors to predict outcomes in LBP.MethodsThe protocol is registered in the International Prospective Register of Systematic Reviews and reported in line with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Prospective longitudinal observational studies investigating potential physical prognostic factors in LBP and/or low back-related leg pain population will be included, with no restriction on outcome. Searches will be performed in MEDLINE, EMBASE, Scopus, CINAHL databases, grey literature search using Open Grey System and ProQuest Dissertations and Theses, hand-searching journals, and reference lists of included studies. Two independent reviewers will evaluate the eligibility of studies, extract data, assess risk of bias and quality of evidence. Risk of bias will be assessed using the Quality in Prognostic Studies (QUIPS) tool. Adequacy of clinical, methodological, and statistical homogeneity among included studies will decide quantitative (meta-analysis) or qualitative analysis (narrative synthesis) focused on prognostic factors and strength of association with outcomes. Quality of cumulative evidence will be evaluated using a modified Grading of Recommendations Assessment, Development, and Evaluation (GRADE).DiscussionInformation about prognostic factors can be used to predict outcomes in LBP. Accurate outcome prediction is essential for identifying high-risk patients that allows targeted allocation of healthcare resources, ultimately reducing the healthcare burden.RegistrationPROSPERO, CRD42023406796.

Percentage of patients with various TSAT levels in the last 3 months of three studies over 14 years from 1997 to 2011.

Real-world healthcare data hold the potential to identify therapeutic solutions for progressive diseases by efficiently pinpointing safe and efficacious repurposing drug candidates. This approach circumvents key early clinical development challenges, particularly relevant for neurological diseases, concordant with the vision of the 21st Century Cures Act. However, to-date, these data have been utilized mainly for confirmatory purposes rather than as drug discovery engines. Here, we demonstrate the usefulness of real-world data in identifying drug repurposing candidates for disease-modifying effects, specifically candidate marketed drugs that exhibit beneficial effects on Parkinson’s disease (PD) progression. We performed an observational study in cohorts of ascertained PD patients extracted from two large medical databases, Explorys SuperMart (N = 88,867) and IBM MarketScan Research Databases (N = 106,395); and applied two conceptually different, well-established causal inference methods to estimate the effect of hundreds of drugs on delaying dementia onset as a proxy for slowing PD progression. Using this approach, we identified two drugs that manifested significant beneficial effects on PD progression in both datasets: rasagiline, narrowly indicated for PD motor symptoms; and zolpidem, a psycholeptic. Each confers its effects through distinct mechanisms, which we explored via a comparison of estimated effects within the drug classification ontology. We conclude that analysis of observational healthcare data, emulating otherwise costly, large, and lengthy clinical trials, can highlight promising repurposing candidates, to be validated in prospective registration trials, beneficial against common, late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.