BACKGROUND: Linkage errors that occur according to linkage levels can adversely affect the accuracy and reliability of analysis results. This study aimed to identify the differences in results according to personally identifiable information linkage level, sample size, and analysis methods through empirical analysis.
METHODS: The difference between the results of linkage in directly identifiable information (DII) and indirectly identifiable information (III) linkage levels was set as III linkage based on name, date of birth, and sex and DII linkage based on resident registration number. The datasets linked at each level were named as databaseIII (DBIII) and databaseDII (DBDII), respectively. Considering the analysis results of the DII-linked dataset as the gold standard, descriptive statistics, group comparison, incidence estimation, treatment effect, and moderation effect analysis results were assessed.
RESULTS: The linkage rates for DBDII and DBIII were 71.1% and 99.7%, respectively. Regarding descriptive statistics and group comparison analysis, the difference in effect in most cases was \"none\" to \"very little.\" With respect to cervical cancer that had a relatively small sample size, analysis of DBIII resulted in an underestimation of the incidence in the control group and an overestimation of the incidence in the treatment group (hazard ratio [HR] = 2.62 [95% confidence interval (CI): 1.63-4.23] in DBIII vs. 1.80 [95% CI: 1.18-2.73] in DBDII). Regarding prostate cancer, there was a conflicting tendency with the treatment effect being over or underestimated according to the surveillance, epidemiology, and end results summary staging (HR = 2.27 [95% CI: 1.91-2.70] in DBIII vs. 1.92 [95% CI: 1.70-2.17] in DBDII for the localized stage; HR = 1.80 [95% CI: 1.37-2.36] in DBIII vs. 2.05 [95% CI: 1.67-2.52] in DBDII for the regional stage).
CONCLUSIONS: To prevent distortion of the analyses results in health and medical research, it is important to check that the patient population and sample size by each factor of interest (FOI) are sufficient when different data are linked using DBDII. In cases involving a rare disease or with a small sample size for FOI, there is a high likelihood that a DII linkage is unavoidable.

OBJECTIVE: The objective of the study was to assess the clinical predictive value of the dynamics of absolute lymphocyte count (ALC) for 90-day all-cause mortality in sepsis patients in intensive care unit (ICU).
METHODS: Retrospective cohort study using big data.
METHODS: This study was conducted using the Medical Information Mart for Intensive Care IV database V.2.0 database.
METHODS: The primary outcome was 90-day all-cause mortality.
METHODS: Patients were included if they were diagnosed with sepsis on the first day of ICU admission. Exclusion criteria were ICU stay under 24 hours; the absence of lymphocyte count on the first day; extremely high lymphocyte count (>10×109/L); history of haematolymphatic tumours, bone marrow or solid organ transplants; survival time under 72 hours and previous ICU admissions. The analysis ultimately included 17 329 sepsis patients.
RESULTS: The ALC in the non-survivors group was lower on days 1, 3, 5 and 7 after admission (p<0.001). The ALC on day 7 had the highest area under the curve (AUC) value for predicting 90-day mortality. The cut-off value of ALC on day 7 was 1.0×109/L. In the restricted cubic spline plot, after multivariate adjustments, patients with higher lymphocyte counts had a better prognosis. After correction, in the subgroups with Sequential Organ Failure Assessment score ≥6 or age ≥60 years, ALC on day 7 had the lowest HR value (0.79 and 0.81, respectively). On the training and testing set, adding the ALC on day 7 improved all prediction models\' AUC and average precision values.
CONCLUSIONS: Dynamic changes of ALC are closely associated with 90-day all-cause mortality in sepsis patients. Furthermore, the ALC on day 7 after admission is a better independent predictor of 90-day mortality in sepsis patients, especially in severely ill or young sepsis patients.

UNASSIGNED: To assess the impact of the COVID-19 pandemic on the health condition of people ≥75 years of age and on their family caregivers in Spain.
UNASSIGNED: Multicentric, mixed method concurrent study.
UNASSIGNED: This work, which will be conducted within the primary care setting in 11 administrative regions of Spain, will include three coordinated studies with different methodologies. The first is a population-based cohort study that will use real-life data to analyze the rates and evolution of health needs, care provision, and services utilization before, during, and after the pandemic. The second is a prospective cohort study with 18 months of follow-up that will evaluate the impact of COVID-19 disease on mortality, frailty, functional and cognitive capacity, and quality of life of the participants. Finally, the third will be a qualitative study with a critical social approach to understand and interpret the social, political, and economic dimensions associated with the use of health services during the pandemic. We have followed the SPIRIT Checklist to address trial protocol and related documents. This research is being funded by the Instituto de Salud Carlos III since 2021 and was approved by its ethics committee (June 2022).
UNASSIGNED: The study findings will reveal the long-term impact of the COVID-19 pandemic on the older adults and their caregivers. This information will serve policymakers to adapt health policies to the needs of this population in situations of maximum stress, such as that produced by the COVID-19 pandemic.
UNASSIGNED: Identifier: NCT05249868 [ClinicalTrials.gov].

UNASSIGNED: The use of big data and large language models in healthcare can play a key role in improving patient treatment and healthcare management, especially when applied to large-scale administrative data. A major challenge to achieving this is ensuring that patient confidentiality and personal information is protected. One way to overcome this is by augmenting clinical data with administrative laboratory dataset linkages in order to avoid the use of demographic information.
UNASSIGNED: We explored an alternative method to examine patient files from a large administrative dataset in South Africa (the National Health Laboratory Services, or NHLS), by linking external data to the NHLS database using specimen barcodes associated with laboratory tests. This offers us with a deterministic way of performing data linkages without accessing demographic information. In this paper, we quantify the performance metrics of this approach.
UNASSIGNED: The linkage of the large NHLS data to external hospital data using specimen barcodes achieved a 95% success. Out of the 1200 records in the validation sample, 87% were exact matches and 9% were matches with typographic correction. The remaining 5% were either complete mismatches or were due to duplicates in the administrative data.
UNASSIGNED: The high success rate indicates the reliability of using barcodes for linking data without demographic identifiers. Specimen barcodes are an effective tool for deterministic linking in health data, and may provide a method of creating large, linked data sets without compromising patient confidentiality.

OBJECTIVE: Guideline adherence in radiotherapy is crucial for maintaining treatment quality and consistency, particularly in non-trial patient settings where most treatments occur. The study aimed to assess the impact of guideline changes on treatment planning practices and compare manual registry data accuracy with treatment planning data.
METHODS: This study utilised the DBCG RT Nation cohort, a collection of breast cancer radiotherapy data in Denmark, to evaluate adherence to guidelines from 2008 to 2016. The cohort included 7448 high-risk breast cancer patients. National guideline changes included, fractionation, introduction of respiratory gating, irradiation of the internal mammary lymph nodes, use of the simultaneous integrated boost technique and inclusion of the Left Anterior Descending coronary artery in delineation practice. Methods for structure name mapping, laterality detection, detection of temporal changes in population mean lung volume, and dose evaluation were presented and applied. Manually registered treatment characteristic data was obtained from the Danish Breast Cancer Database for comparison.
RESULTS: The study found immediate and consistent adherence to guideline changes across Danish radiotherapy centres. Treatment practices before guideline implementation were documented and showed a variation among centres. Discrepancies between manual registry data and actual treatment planning data were as high as 10% for some measures.
CONCLUSIONS: National guideline changes could be detected in the routine treatment data, with a high degree of compliance and short implementation time. Data extracted from treatment planning data files provides a more accurate and detailed characterisation of treatments and guideline adherence than medical register data.

(1) Background: An online survey-based observational cross-sectional study aimed at elucidating the experience and attitudes of an unstructured population regarding diagnostic imaging. (2) Methods: Invitations to participate were distributed using mixed-mode design to deidentified residents aged 18 years and older. Main outcome measures included morbidity structure and incidence of diagnostic imaging administrations. (3) Results: Respondents (n = 1069) aged 44.3 ± 14.4 years; 32.8% suffered from cardiovascular diseases (CVD); 9.5% had chronic respiratory pathology; 28.9% considered themselves healthy. Respondents with COVID-19 history (49.7%) reported higher rates of computed tomography (CT) (p < 0.0001), magnetic resonance imaging (MRI) (p < 0.001), and ultrasound (p < 0.05). COVID-19 history in CVD respondents shifted imaging administrations towards CT and MRI (p < 0.05). Every tenth respondent received MRI, CT, and ultrasound on a paid basis; 29.0% could not pay for diagnostic procedures; 13.1% reported unavailable MRI. Professional status significantly affected the pattern of diagnostic modalities (p < 0.05). MRI and CT availability differed between respondents in urban and rural areas (p < 0.0001). History of technogenic events predisposed responders to overestimate diagnostic value of fluorography (p < 0.05). (4) Conclusions: Preparedness to future pandemics requires the development of community-based outreach programs focusing on people\'s awareness regarding medical imaging safety and diagnostic value.

OBJECTIVE: To develop a machine learning-based prediction model for identifying hyperuricemic participants at risk of developing gout.
METHODS: A retrospective nationwide Israeli cohort study used the Clalit Health Insurance database of 473 124 individuals to identify adults 18 years or older with at least two serum urate measurements exceeding 6.8 mg/dl between January 2007 and December 2022. Patients with a prior gout diagnosis or on gout medications were excluded. Patients\' demographic characteristics, community and hospital diagnoses, routine medication prescriptions and laboratory results were used to train a risk prediction model. A machine learning model, XGBoost, was developed to predict the risk of gout. Feature selection methods were used to identify relevant variables. The model\'s performance was evaluated using the receiver operating characteristic area under the curve (ROC AUC) and precision-recall AUC. The primary outcome was the diagnosis of gout among hyperuricemic patients.
RESULTS: Among the 301 385 participants with hyperuricemia included in the analysis, 15 055 (5%) were diagnosed with gout. The XGBoost model had a ROC-AUC of 0.781 (95% CI 0.78-0.784) and precision-recall AUC of 0.208 (95% CI 0.195-0.22). The most significant variables associated with gout diagnosis were serum uric acid levels, age, hyperlipidemia, non-steroidal anti-inflammatory drugs and diuretic purchases. A compact model using only these five variables yielded a ROC-AUC of 0.714 (95% CI 0.706-0.723) and a negative predictive value (NPV) of 95%.
CONCLUSIONS: The findings of this cohort study suggest that a machine learning-based prediction model had relatively good performance and high NPV for identifying hyperuricemic participants at risk of developing gout.

CONCLUSIONS: There is a lack of research from high-income countries with various health care and funding systems regarding barriers and facilitators in low vision services (LVS) access. Furthermore, very few studies on LVS provision have used claims data.
OBJECTIVE: This study aimed to investigate which patient characteristics predict receiving multidisciplinary LVS (MLVS) in the Netherlands, a high-income country, based on health care claims data.
METHODS: Data from a Dutch national health insurance claims database (2015 to 2018) of patients with eye diseases causing potentially severe visual impairment were retrieved. Patients received MLVS (n = 8766) and/or ophthalmic treatment in 2018 (reference, n = 565,496). MLVS is provided by professionals from various clinical backgrounds, including nonprofit low vision optometry. Patient characteristics (sociodemographic, clinical, contextual, general health care utilization) were assessed as potential predictors using a multivariable logistic regression model, which was internally validated with bootstrapping.
RESULTS: Predictors for receiving MLVS included prescription of low vision aids (odds ratio [OR], 8.76; 95% confidence interval [CI], 7.99 to 9.61), having multiple ophthalmic diagnoses (OR, 3.49; 95% CI, 3.30 to 3.70), receiving occupational therapy (OR, 2.32; 95% CI, 2.15 to 2.51), mental comorbidity (OR, 1.17; 95% CI, 1.10 to 1.23), comorbid hearing disorder (OR, 1.98; 95% CI, 1.86 to 2.11), and receiving treatment in both a general hospital and a specialized ophthalmic center (OR, 1.23; 95% CI, 1.10 to 1.37), or by a general practitioner (OR, 1.23; 95% CI, 1.18 to 1.29). Characteristics associated with lower odds included older age (OR, 0.30; 95% CI, 0.28 to 0.32), having a low social economic status (OR, 0.91; 95% CI, 0.86 to 0.97), physical comorbidity (OR, 0.87; 95% CI, 0.82 to 0.92), and greater distance to an MLVS (OR, 0.95; 95% CI, 0.92 to 0.98). The area under the curve of the model was 0.75 (95% CI, 0.75 to 0.76; optimism = 0.0008).
CONCLUSIONS: Various sociodemographic, clinical, and contextual patient characteristics, as well as factors related to patients\' general health care utilization, were found to influence MLVS receipt as barriers or facilitators. Eye care practitioners should have attention for socioeconomically disadvantaged older patients when considering MLVS referral.

During the 5th Science, Technology, and Innovation Basic Plan, the Japanese government proposed a novel societal concept -Society 5.0- that promoted a healthcare system characterized by its capability to provide unintrusive, predictive, longitudinal care through the integration of cyber and physical space. The role of Society 5.0 in managing our quality of vision will become more important in the modern digitalized and aging society, both of which are known risk factors for developing dry eye. Dry eye is the most common ocular surface disease encountered in Japan with symptoms including increased dryness, eye discomfort, and decreased visual acuity. Owing to its complexity, implementation of P4 (predictive, preventive, personalized, participatory) medicine in managing dry eye requires a comprehensive understanding of its pathology, as well as a strategy to visualize and stratify its risk factors. Using DryEyeRhythm®, a mobile health (mHealth) smartphone software (app), we established a route to collect holistic medical big data on dry eye, such as the subjective symptoms and lifestyle data for each individual. The studies to date aided in determining the risk factors for severe dry eye, the association between major depressive disorder and dry eye exacerbation, eye drop treatment adherence, app-based stratification algorithms based on symptomology, blink detection biosensoring as a dry eye-related digital phenotype, and effectiveness of app-based dry eye diagnosis support compared to traditional methods. These results contribute to elucidating disease pathophysiology and promoting preventive and effective measures to counteract dry eye through mHealth.

This study aims to analyse and determine the effect of Big Data, the Internet of Things (IoT), and physical-cyber system variables on human factors in refinery industry operators and the influence of human factors and managerial initiatives on sustainable manufacturing. The method used in this study is a quantitative method using partial least square-structural equation modelling (PLS-SEM). The respondents in this study were workers of Indonesia\'s upstream oil and gas sector. The results of this study indicate that Big Data, IoT, and Physical Cyber Systems (PCS) have a positive and significant effect on the human factor. In addition, there is a significant relationship between human factors and sustainable manufacturing. Furthermore, it is also found that there is a relationship between managerial initiatives and sustainable manufacturing. However, the managerial initiative cannot moderate the human factor and sustainable manufacturing.
This research explores the profound influence of Industry 4.0 technologies, encompassing big data, IoT, and physical-cyber systems (PCS), on the human aspects of Indonesia’s oil and gas sector. The outcomes of this study offer valuable perspectives on how Industry 4.0 technologies can revolutionise the Indonesian oil and gas industry while underscoring the significance of factoring in human elements and sustainable practices within this dynamic landscape.