Clinical Epidemiology最新文献

Background: In the presence of competing risks, when the baseline risk is unclear, if only the sub-distribution hazard ratio (SHR) is reported in the results, which is related to the cumulative incidence function, the survival disparity of events of interest between groups cannot be clarified. In contrast, the difference in restricted mean time lost (RMTLd), which is the difference in the areas under the cumulative incidence between two groups, can well compensate for the deficiencies of SHR and explain the effects on a time scale, facilitating clinical interpretation and communication.

Methods: The Surveillance, Epidemiology, and End Results (SEER) database was used to collect information on female patients with locally advanced breast cancer diagnosed between 2010 and 2015. The prognostic factors of breast cancer death were evaluated considering competing risk. Univariable and multivariable analyses were conducted to get SHR and RMTLd.

Results: SHR can indicate the direction of prognostic factors, while RMTLd can quantify prognostic effects and provide time-scale interpretation. For instance, in adjuvant radiotherapy, the SHR showed a protective effect, which can be quantified as an average increase of 4.15 months in survival time.

Discussion: In the presence of competing risks, the combined use of absolute measure RMTLd can more intuitively explain the prognostic effect, which is convenient for clinical practice and communication.

Introduction: The impact of type 2 diabetes (T2DM) on mortality following lung cancer diagnosis remains unclear, with conflicting evidence across studies. We aimed to assess differences in all-cause and cause-specific mortality between people with lung cancer with and without T2DM within a primary care population in England.

Methods: The study population was 69,674 people with incident lung cancer within the Clinical Practice Research Datalink (CPRD) Aurum primary care database (2010-2022). The study exposure was T2DM at cancer diagnosis, and the outcomes were all-cause and cause-specific mortality (cancer, cardio-vascular, respiratory). Cox models were fitted for each outcome adjusting for age, gender, smoking status, body mass index, calendar year and socioeconomic status (Index of Multiple Deprivation).

Results: After adjusting for age and gender, there was no evidence for a difference in all-cause mortality in people with T2DM compared with people without T2DM (IRR 0.98 95% CI 0.96, 1.01). After fully-adjusting for measured confounders, there was a small positive effect (IRR 1.07 95% CI 1.04, 1.09). After adjusting for age and gender, people with T2DM had lower rates of cancer-specific mortality compared to people without T2DM (IRR 0.96 95% CI 0.94, 0.98). However, after adjustment for all measured confounders there was a small positive association (IRR 1.05 95% CI 1.02, 1.07). In both age and gender adjusted and fully adjusted models people with T2DM had higher cardiovascular (fully adjusted HR 1.30 95% CI 1.15, 1.47) and respiratory disease mortality (fully adjusted HR 1.30 95% CI 1.15, 1.47).

Conclusion: There was robust evidence that people with T2DM had higher cardiovascular and respiratory disease mortality following lung cancer diagnosis. The relationships between T2DM and all-cause and cancer-specific mortality were highly sensitive to adjustment for confounding. Differences in studies on approaches to confounding and levels of missing data may contribute to the mixed findings on this association in the literature.

Purpose: COVID-19 has been extensively researched; however, the lack of standardized COVID-19 severity categorization in register-based research complicates comparison of studies. The WHO COVID-19 Clinical Progression Scale is a standardized disease severity tool for clinical data, though not adapted to data available in health registries. We aimed to develop and validate such a novel categorization with international applicability.

Methods: The WHO Clinical Progression Scale was translated to a severity index utilizing ICD- and procedure-codes from outpatient, inpatient, intensive care, and mortality registries using the adult Swedish population and SARS-CoV-2 positive-test data (January 2020 - July 2022). Cox proportional hazards were applied to determine whether increasing severity correlates with mortality in COVID-19 patients compared to the population.

Results: The WHO-Scale was translated to ten categories reflecting the increasing need for advanced care, encompassing 8,245,474 individuals including 1,981,946 SARS-CoV-2 infections. Fatal COVID-19 cases were older with more comorbidities. Those receiving mechanical ventilation and ECMO were younger with fewer comorbidities. Among survivors beyond 30 days, 90-day all-cause mortality increased with severity using category zero (no laboratory-verified SARS-CoV-2) as reference. Mortality was lowest for patients without health care adjusted for age, sex, comorbidities and socio-economic variables (adjusted hazard ratio (aHR) 1.18, 95% confidence interval (CI) 1.13-1.22). Those hospitalized >5 days had higher mortality (aHR 5.83, 5.5-6.17). Those requiring ECMO/ECLS had the highest mortality (aHR 593.54, 317.77-1108.65).

Conclusion: The novel COVID-19 severity index associated with all-cause 90-day mortality and aligned with previous literature. This index will enable comparative studies of COVID-19, which is important for public health policies and development of clinical guidelines. This is an innovative epidemiologic tool with potential applicability in all countries with centralised health registers. The index also has the potential to be used for other infectious diseases and in real-time data for modelling predictions.

Clinical algorithms are widely used tools for predicting, diagnosing, and managing diseases. However, race correction in these algorithms has faced increasing scrutiny for potentially perpetuating health disparities and reinforcing harmful stereotypes. This narrative review synthesizes historical, clinical, and methodological literature to examine the origins and consequences of race correction in clinical algorithms. We focus primarily on developments in the United States and the United Kingdom, where many race-based algorithms originated. Drawing on interdisciplinary sources, we discuss the persistence of race-based adjustments, the implications of their removal, and emerging strategies for bias mitigation and fairness in algorithm development. The practice began in the mid-19th century with the spirometer, which measured lung capacity and was used to reinforce racial hierarchies by characterizing lower lung capacity for Black people. Despite critiques that these differences reflect environmental exposure rather than inherited traits, the belief in race-based biological differences in lung capacity and other physiological functions, including cardiac, renal, and obstetric processes, persists in contemporary clinical algorithms. Concerns about race correction compounding health inequities have led many medical organizations to re-evaluate their algorithms, with some removing race entirely. Transitioning to race-neutral equations in areas like pulmonary function testing and obstetrics has shown promise in enhancing fairness without compromising accuracy. However, the impact of these changes varies across clinical contexts, highlighting the need for careful bias identification and mitigation. Future efforts should focus on incorporating diverse data sources, capturing true social and biological health determinants, implementing bias detection and fairness strategies, ensuring transparent reporting, and engaging with diverse communities. Educating students and trainees on race as a sociopolitical construct is also important for raising awareness and achieving health equity. Moving forward, regular monitoring, evaluation, and refinement of approaches in real-world settings are needed for clinical algorithms serve all patients equitably and effectively.

Purpose: No studies have validated psychiatric diseases diagnoses in Taiwan's National Health Insurance Research Database (NHIRD). We aimed to assess the interrater reliability of chart-review among psychiatrists, examine the validity of the diagnostic codes for psychotic disorders and affective diseases in the NHIRD against review-based diagnoses, and examine whether the change in the coding system from the ICD-9-CM to the ICD-10-CM affected the validity of the diagnostic codes.

Patients and methods: The study participants were psychiatric inpatients aged 18 to 65 years who were admitted in 2015 and 2017, respectively, to the main and three branch hospitals of National Taiwan University Hospital. A chart review was conducted among 48 purposively selected inpatients with discharge diagnoses in five core categories to assess interrater reliability. This chart-review procedure was then used to generate diagnostic codes for a stratified sampling of 727 inpatients with discharge diagnoses in 12 diagnostic categories of psychotic disorders and affective disorders to examine the validity of the diagnostic codes.

Results: The intraclass correlation coefficient reliability of schizophrenia and three broad categories of diagnoses indicated good interrater reliability. The positive predictive value and sensitivity of common diagnoses in the narrow category (eg, schizophrenia) or the broad category (eg, psychotic disorders, bipolar disorders, and major depressive disorders) were high-performing (≥ 0.70), whereas those of the diagnoses of low prevalence were modest. The validity indices of claims-based diagnoses using the ICD-10-CM tended to be better than those using the ICD-9-CM.

Conclusion: This first-ever study validating psychiatric diagnoses in Taiwan's NHIRD using a structured chart review suggests that the diagnostic codes of narrow categories of schizophrenia or other broad categories are recommended for high-performing validity indices. Intensive training for the coding plus the specific details requested by the ICD-10 may increase the validity of the claims-based databases for psychotic and affective disorders.

Background: Twin pregnancies, accounting for a rising proportion of births globally, present significant public health challenges in China. Birthweight discordance (BWD), a critical complication, remains understudied in its epidemiological context, particularly regarding its population-level associations with adverse neonatal outcomes.

Methods: This multi-center, retrospective cohort study leveraged data from 21 hospitals across 18 Chinese cities (2018-2020) to assess BWD and its epidemiological implications. Ordinal logistic regression with random effects was used to explore their association. BWD was defined as: [(larger birthweight - smaller birthweight) / larger birthweight] × 100% and categorized into four grades: I (≤15%), II (>15% to 20%), III (>20% to 25%), and IV (>25%).

Results: Among 6437 twin pairs, 73.6% were classified as Grade I (no BWD), while 10.7%, 7.1%, and 8.6% constituted Grades II, III, and IV discordance, respectively. Dose-response relationships emerged: each incremental BWD elevated risks of small vulnerable newborns (aOR = 1.83, 95% CI 1.76-1.90), small for gestational age (aOR = 1.23, 95% CI 1.18-1.29), low birthweight (LBW, aOR = 1.16, 95% CI 1.13-1.20), very LBW (aOR = 1.63, 95% CI 1.53-1.73) and extreme LBW (aOR = 1.82, 95% CI 1.61-2.05). Smaller twins exhibited disproportionately higher adverse outcome rates than larger twins. Sensitivity analyses confirmed robustness across specific subgroups.

Conclusion: BWD exceeding 20% affects 15.7% of live-born twins in China, mirroring rates in high-income settings. BWD demonstrates strong dose-response relationships with adverse outcomes, validating its utility for twin health stratification. These findings call for integrating BWD assessment into prenatal surveillance and risk-adapted care to reduce neonatal morbidity/mortality, urging clinicians and policymakers to prioritize perinatal outcome equity.

Background: Socioeconomic differences in health have become an increasing public health concern and priority, leading to a growing number of studies investigating the relationship between socioeconomic position and health outcomes. However, variability in methodological practices hampers the comparability of findings and leads to inefficiencies, as researchers invest substantial resources in selecting appropriate variables and methods. To address these challenges, the SEPLINE initiative was established to develop a methodological guideline aimed at enhancing the comparability, quality, and feasibility of socioeconomic research using Danish registry data.

Methods: The guideline was developed through a consensus-driven approach involving an interdisciplinary group of stakeholders from Danish universities, research institutions, and data warehouses. The guideline addresses socioeconomic position as an exposure based on data from Danish registries, with the cancer continuum applied as a case outcome to illustrate its application. The development process included two collaborative workshops informed by a pre-workshop questionnaire. Workshop I (spring 2024) focused on socioeconomic indicators, data collection, and data management, featuring expert presentations and group discussions. Workshop II (fall 2024) addressed analytical methods, including causal inference challenges and income/wealth assessment methods. Insights from these workshops were integrated into iterative refinements of the guideline.

Conclusions and implications: The guideline provides a structured framework for conducting socioeconomic epidemiological research using Danish registry data, offering specific information on data sources and recommendations about variable selection, measurement timing, and data handling. While tailored to Danish registry-based cancer research, the guideline's methodological principles have broader applicability to other diseases and international contexts. By emphasizing transparency, theoretical grounding, and methodological rigor, SEPLINE aims to advance the study of social determinants of health. Researchers are encouraged to use the guideline as a relevant starting point and adapt it to their specific study populations and research questions, ensuring its relevance across diverse settings.

Objective: Given the lack of data on long-term outcomes among patients with sepsis, this study aimed to examine all-cause 2-year mortality and factors associated with mortality in adults admitted to an emergency department with sepsis.

Study design: Prospective cohort study.

Methods: This study included all emergency department patients admitted with sepsis to Slagelse Hospital, Denmark, between October 1, 2017, and March 31, 2018. Data on patients with infectious diseases was prospectively extracted from electronic health records during the study period. Sepsis was defined as a Sequential Organ Failure Assessment (SOFA) score ≥ 2 from baseline. The outcome was 2-year all-cause mortality. The Kaplan-Meier method was used to estimate the mortality. Cox regression analyses were used to compute adjusted hazard ratios (aHR) with 95% confidence intervals for prognostic factors associated with mortality.

Results: A total of 714 patients (58.4% men) with a median age of 75 years were diagnosed with sepsis. After two years, 354 (49.6%; 45.9-53.3) patients had died. Factors associated with elevated mortality risk included age (< 65 years as reference) 65-85 years (aHR 1.89; 1.35-2.64) or age > 85 years (aHR 2.99; 2.07-4.31); SOFA score > 4 (aHR 2.45; 1.82-3.30) (score of 2 as reference); and history of malignancy (aHR 1.91; 1.44-2.53), ischemic heart disease (aHR 1.38; 1.03-1.84), dementia (aHR 1.84; 1.34-2.53), previous sepsis admission (aHR 1.45; 1.15-1.82), new-onset atrial fibrillation (aHR 1.56; 1.05-2.34), and mildly decreased (6.9-7.9 mmmol/L) hemoglobin values (aHR 1.68; 1.29-2.19) and significantly decreased (<6.9 mmol/L) hemoglobin values (aHR 2.30; 1.74-3.02) with normal range (≥ 8mmol/L) as reference. Skin infection was associated with diminished mortality risk (aHR 0.50; 0.29-0.85) compared to patients with other sources of infection.

Conclusion: Sepsis is associated with a poor prognosis. Our findings underscore the prognostic effects of age, SOFA score, and specific comorbidities on 2-year mortality among patients with sepsis.

Purpose: Hydrocephalus is a neurological condition characterized by an accumulation of cerebrospinal fluid (CSF) with no cure and limited treatments. There is a significant gap in hydrocephalus research where patients lack opportunities to voice their perspectives on their condition. The Hydrocephalus Association Patient-Powered Interactive Engagement Registry (HAPPIER) database captures the lived experiences of those affected by hydrocephalus and provides a platform for researchers to access these data or distribute their own surveys, ultimately aiming to improve patient-centered care and outcomes. This publication introduces the registry by highlighting the demographics, etiology, treatments, symptom profiles, and diagnosed comorbidities of the participants.

Methods: The Hydrocephalus Association and a 10-member steering committee developed HAPPIER. Other patient registries, existing surveys and assessments, and University of Utah Data Center faculty guided survey development. The Hydrocephalus Association recruited participants using social and traditional media, medical referrals, and advertisements at events.

Results: Of the 691 survey participants with hydrocephalus, 451 (65.3%) responded for themselves. The majority of the registry was female (55.0%), white (86.0%), and from the United States and territories (87.7%). Most were diagnosed between 0-11 months (46.2%), with congenital hydrocephalus as the most reported etiology (43.8%). Participants reported a shunt(s) as the most prevalent treatment (71.2%) and headaches as the most frequent symptom (60.3%), while 69.9% of participants reported being diagnosed with movement impairments and 70.8% with other health conditions.

Conclusion: HAPPIER is a novel database that addresses gaps in data on non-clinical outcomes of hydrocephalus, which are critical to clinical care and understanding hydrocephalus. Patient perspectives and outcomes remain historically underrepresented. By directly engaging individuals living with hydrocephalus and their caregivers, HAPPIER incorporates essential patient perspectives through planned longitudinal data collection and patient surveys. These data are open to investigators interested in analyzing the collected data.

Purpose: Multi-database studies may provide heterogeneous results despite using common protocols, leading to challenges in interpretation, but also providing an opportunity to gain insights on populations or healthcare systems. The objectives of these analyses were to develop a framework for exploring sources of statistical heterogeneity and apply it to the multi-database EXACOS-CV (EXAcerbations of COPD and their OutcomeS on CardioVascular diseases) program.

Methods: A conceptual framework to systematically assess sources of statistical heterogeneity in multi-database studies was developed. This framework distinguishes between methodological diversity and true clinical variation. Methodological diversity includes differences in study design and database selection, while true variation considers population and healthcare differences. Possible sources of methodological diversity were identified via a novel checklist and explored. In turn, hypotheses were generated about true variation. The framework and checklist were applied to EXACOS-CV cohort studies in Germany, Canada, the Netherlands, and Spain which deviated least from the common protocol and so were included. Focus was on adjusted hazard ratios (aHR) for post-exacerbation associations with decompensated heart failure (HF) and all-cause death, for which results were most and least heterogeneous, respectively.

Results: Across EXACOS-CV studies, the adjusted hazard ratios (aHR) for HF in the first 1-7 days post-exacerbation, compared to non-exacerbation periods, ranged from 2.6 (95% CI, 2.3, 2.9) in Germany to 72.3 (64.4, 81.2) in Canada, and the association with death, relative to non-exacerbation periods, ranged from 3.5 (2.4, 5.3) in the Netherlands to 22.1 (19.9, 24.4) in Spain. Completed methodological diversity checklists linked differences in aHRs to possible variation in ability to capture pre-existing cardiovascular comorbidities across studies, as well as differences in confounder measurement. Standardizing adjusted models across studies did not fully explain heterogeneity, suggesting other contributing factors. Heterogeneity may result from genuine variation in prevalence of CV disease. It was hypothesized that patients with pre-existing CV disease have more accurate diagnoses and management of post-exacerbation CV events, possibly leading to lower risks of such events.

Conclusion: Multi-database studies can provide directional insights on the study research question while considering healthcare system and population differences. The developed framework aids assessment of heterogeneity sources.