UNASSIGNED: Nontyphoidal Salmonella infection is one of the most common foodborne illnesses, and its oncogenic potential has been documented in animal models. The primary goal of this study was to examine whether individuals who were exposed to enteric Salmonella infection are more likely to develop colorectal cancer (CRC) than the general population through the linkage of 2 statewide public health surveillance databases.
UNASSIGNED: We designed a 2-stage probabilistic linkage, starting with 17,587 records of enteric salmonellosis reported to Michigan Department of Health and Human Services between 1992 and 2020. These records did not include unique identifiers (such as Social Security number [SSN]). The initial linkage to LexisNexis address history was conducted to obtain information to calculate each person\'s time in Michigan as well as SSN for the second linkage. The linkage to the state cancer registry was performed to obtain the observed number of CRC cases, while the expected number of CRC cases was calculated according to corresponding state CRC incidence by age, sex, and calendar year.
UNASSIGNED: Ninety-three percent of the initially identified salmonellosis records were sent to LexisNexis linkage, which returned address history, death, and SSN for 97% of the records. Further linkage to the statewide cancer registry identified 98 incident CRC cases. Overall, the observed-to-expected (O/E) ratio was not different from unity (0.833; 95% CI, 0.627-1.003).
UNASSIGNED: While the new linkage strategy was found effective and should be applicable to other health conditions, we cannot rule out bias due to incomplete or underreporting of the infection in estimating the risk of CRC.

Large datasets in paediatric oncology are inherently rare. Therefore, it is paramount to fully exploit all available data, which are distributed over several resources, including biomaterials, images, clinical trials, and registries. With privacy-preserving record linkage (PPRL), personalised or pseudonymised datasets can be merged, without disclosing the patients\' identities. Although PPRL is implemented in various settings, use case descriptions are currently fragmented and incomplete. The present paper provides a comprehensive overview of current and future use cases for PPRL in paediatric oncology. We analysed the literature, projects, and trial protocols, identified use cases along a hypothetical patient journey, and discussed use cases with paediatric oncology experts. To structure PPRL use cases, we defined six key dimensions: distributed personalised records, pseudonymisation, distributed pseudonymised records, record linkage, linked data, and data analysis. Selected use cases were described (a) per dimension and (b) on a multi-dimensional level. While focusing on paediatric oncology, most aspects are also applicable to other (particularly rare) diseases. We conclude that PPRL is a key concept in paediatric oncology. Therefore, PPRL strategies should already be considered when starting research projects, to avoid distributed data silos, to maximise the knowledge derived from collected data, and, ultimately, to improve outcomes for children with cancer.

BACKGROUND: Nine in every thousand children born in the United Kingdom have congenital heart disease, and 250,000 adults are living with the condition. This study aims to investigate the associations between congenital heart disease and educational outcomes among school-aged children in Scotland.
METHODS: Routine health and education databases were linked to produce a cohort of all singleton children born in Scotland and attending a local authority run primary, secondary, or special school in Scotland at some point between 2009 and 2013. Children with congenital heart disease within this cohort were compared with children unaffected by congenital conditions. Outcomes investigated were special educational need (SEN), absenteeism, exclusion, academic attainment, and unemployment. All analyses were adjusted for sociodemographic and maternity confounders. Absenteeism was investigated as a mediating factor in the associations with attainment and unemployment.
RESULTS: Of the 715,850 children, 6,295 (0.9%) had congenital heart disease and 4,412 (6.1%) had isolated congenital heart disease. Congenital heart disease and isolated congenital heart disease were both significantly associated with subsequent special educational need (OR 3.45, 95% CI 3.26-3.65, p < 0.001 and OR 1.98, 95% CI 1.84-2.13, p < 0.001 respectively), absenteeism (IRR 1.13, 95% CI 1.10-1.16, p < 0.001 and IRR 1.10, 95% CI 1.06-1.13, p < 0.001 respectively), and low academic attainment (OR 1.69, 95% CI 1.39-2.07, p < 0.001 and OR 1.35, 95% CI 1.07-1.69, p = 0.011 respectively). Neither congenital heart disease nor isolated congenital heart disease were associated with school exclusion. Only congenital heart disease (OR 1.21, 95% CI 1.03-1.42, p = 0.022) but not isolated congenital heart disease was associated with unemployment. When days absent were included in the analyses investigating attainment and unemployment, the conclusions were not altered.
CONCLUSIONS: Children with congenital heart disease have greater special educational need, lower school attendance, attain lower examination grades and have greater unemployment compared to peers. In addition to healthcare support, affected children need educational support to avoid additional impact on their long-term wellbeing.

UNASSIGNED: To achieve scientific goals, researchers often require integration of data from a primary electronic health record (EHR) system and one or more ancillary EHR systems used during the same patient care encounter. Although studies have demonstrated approaches for linking patient identity records across different EHR systems, little is known about linking patient encounter records across primary and ancillary EHR systems.
UNASSIGNED: We compared a patients-first approach versus an encounters-first approach for linking patient encounter records across multiple EHR systems.
UNASSIGNED: We conducted a retrospective observational study of 348,904 patients with 533,283 encounters from 2010 to 2020 across our institution\'s primary EHR system and an ancillary EHR system used in perioperative settings. For the patients-first approach and the encounters-first approach, we measured the number of patient and encounter links created as well as runtime.
UNASSIGNED: While the patients-first approach linked 43% of patients and 49% of encounters, the encounters-first approach linked 98% of patients and 100% of encounters. The encounters-first approach was 20 times faster than the patients-first approach for linking patients and 33% slower for linking encounters.
UNASSIGNED: Findings suggest that common patient and encounter identifiers shared among EHR systems via automated interfaces may be clinically useful but not \"research-ready\" and thus require an encounters-first linkage approach to enable secondary use for scientific purposes. Based on our search, this study is among the first to demonstrate approaches for linking patient encounters across multiple EHR systems. Enterprise data warehouse for research efforts elsewhere may benefit from an encounters-first approach.

Variation in availability, format, and standardization of patient attributes across health care organizations impacts patient-matching performance. We report on the changing nature of patient-matching features available from 2010-2020 across diverse care settings. We asked 38 health care provider organizations about their current patient attribute data-collection practices. All sites collected name, date of birth (DOB), address, and phone number. Name, DOB, current address, social security number (SSN), sex, and phone number were most commonly used for cross-provider patient matching. Electronic health record queries for a subset of 20 participating sites revealed that DOB, first name, last name, city, and postal codes were highly available (>90%) across health care organizations and time. SSN declined slightly in the last years of the study period. Birth sex, gender identity, language, country full name, country abbreviation, health insurance number, ethnicity, cell phone number, email address, and weight increased over 50% from 2010 to 2020. Understanding the wide variation in available patient attributes across care settings in the United States can guide selection and standardization efforts for improved patient matching in the United States.

BACKGROUND: The global prevalence of diabetes is similar in men and women; however, there is conflicting evidence regarding sex differences in diabetes-related complications. The aim of this study was to investigate sex differences in incident microvascular and macrovascular complications among adults with diabetes.
METHODS: This prospective cohort study linked data from the 45 and Up Study, Australia, to administrative health records. The study sample included 25 713 individuals (57% men), aged ≥45 years, with diabetes at baseline. Incident cardiovascular disease (CVD), eye, lower limb, and kidney complications were determined using hospitalisation data and claims for medical services. Multivariable Cox proportional hazards models were used to assess the association between sex and incident complications.
RESULTS: Age-adjusted incidence rates per 1000 person years for CVD, eye, lower limb, and kidney complications were 37, 52, 21, and 32, respectively. Men had a greater risk of CVD (adjusted hazard ratio (aHR) 1.51, 95% CI 1.43 to 1.59), lower limb (aHR 1.47, 95% CI 1.38 to 1.57), and kidney complications (aHR 1.55, 95% CI 1.47 to 1.64) than women, and a greater risk of diabetic retinopathy (aHR 1.14, 95% CI 1.03 to 1.26). Over 10 years, 44%, 57%, 25%, and 35% of men experienced a CVD, eye, lower limb, or kidney complication, respectively, compared with 31%, 61%, 18%, and 25% of women. Diabetes duration (<10 years vs ≥10 years) had no substantial effect on sex differences in complications.
CONCLUSIONS: Men with diabetes are at greater risk of complications, irrespective of diabetes duration. High rates of complications in both sexes highlight the importance of targeted complication screening and prevention strategies from diagnosis.

BACKGROUND: Growing up with parental alcohol use disorder (AUD) is a risk factor for psychiatric disorders. This study investigated the risk of mood disorders and of anxiety disorders in the adult children of parents with AUD, adjusted for sociodemographic factors.
METHODS: Individual-level register data on the total population were linked to follow children of parents with AUD from 1973 to 2018 to assess their risk of mood disorders and of anxiety disorders. AUD, mood disorders and anxiety disorders were defined with International Statistical Classification of Diseases and Related Health Problems codes from the National Patient Register. HRs of outcomes were calculated with Cox regression. Model 1 was adjusted for the child\'s sex, parental education and death of a parent. Model 2 was adjusted for those factors and parental diagnosis of mood or anxiety disorder.
RESULTS: Those with ≥1 parent with AUD (99 723 of 2 421 479 children) had a higher risk of mood disorder and of anxiety disorder than those whose parents did not have AUD (HR mood 2.32, 95% CI 2.26 to 2.39; HR anxiety 2.66, 95% CI 2.60 to 2.72). The risk remained elevated after adjustment for sociodemographic factors and parental psychiatric diagnosis (HR mood 1.67, 95% CI 1.63 to 1.72; HR anxiety 1.74, 95% CI 1.69 to 1.78). The highest risks were associated with AUD in both parents, followed by AUD in mothers and then in fathers.
CONCLUSIONS: Adult children of parents with AUD have a raised risk of mood and anxiety disorders even after adjustment for sociodemographic factors and parental mood or anxiety disorder. These population-level findings can inform future policies and interventions.

UNASSIGNED: The potential for secondary use of health data to improve healthcare is currently not fully exploited. Health data is largely kept in isolated data silos and key infrastructure to aggregate these silos into standardized bodies of knowledge is underdeveloped. We describe the development, implementation, and evaluation of a federated infrastructure to facilitate versatile secondary use of health data based on Health Data Space nodes.
UNASSIGNED: Our proposed nodes are self-contained units that digest data through an extract-transform-load framework that pseudonymizes and links data with privacy-preserving record linkage and harmonizes into a common data model (OMOP CDM). To support collaborative analyses a multi-level feature store is also implemented. A feasibility experiment was conducted to test the infrastructures potential for machine learning operations and deployment of other apps (e.g., visualization). Nodes can be operated in a network at different levels of sharing according to the level of trust within the network.
UNASSIGNED: In a proof-of-concept study, a privacy-preserving registry for heart failure patients has been implemented as a real-world showcase for Health Data Space nodes at the highest trust level, linking multiple data sources including (a) electronical medical records from hospitals, (b) patient data from a telemonitoring system, and (c) data from Austria\'s national register of deaths. The registry is deployed at the tirol kliniken, a hospital carrier in the Austrian state of Tyrol, and currently includes 5,004 patients, with over 2.9 million measurements, over 574,000 observations, more than 63,000 clinical free text notes, and in total over 5.2 million data points. Data curation and harmonization processes are executed semi-automatically at each individual node according to data sharing policies to ensure data sovereignty, scalability, and privacy. As a feasibility test, a natural language processing model for classification of clinical notes was deployed and tested.
UNASSIGNED: The presented Health Data Space node infrastructure has proven to be practicable in a real-world implementation in a live and productive registry for heart failure. The present work was inspired by the European Health Data Space initiative and its spirit to interconnect health data silos for versatile secondary use of health data.

Conducting clinical trials (CTs) has become increasingly costly and complex in terms of designing and operationalizing. These challenges exist in running CTs on novel therapies, particularly in oncology and rare diseases, where CTs increasingly target narrower patient groups. In this study, we describe external control arms (ECA) and other relevant tools, such as virtualization and decentralized clinical trials (DCTs), and the ability to follow the clinical trial subjects in the real world using tokenization. ECAs are typically constructed by identifying appropriate external sources of data, then by cleaning and standardizing it to create an analysis-ready data file, and finally, by matching subjects in the external data with the subjects in the CT of interest. In addition, ECA tools also include subject-level meta-analysis and simulated subjects\' data for analyses. By implementing the recent advances in digital health technologies and devices, virtualization, and DCTs, realigning of CTs from site-centric designs to virtual, decentralized, and patient-centric designs can be done, which reduces the patient burden to participate in the CTs and encourages diversity. Tokenization technology allows linking the CT data with real-world data (RWD), creating more comprehensive and longitudinal outcome measures. These tools provide robust ways to enrich the CT data for informed decision-making, reduce the burden on subjects and costs of trial operations, and augment the insights gained for the CT data.

BACKGROUND: Advancements in linking publicly available census records with vital and administrative records have enabled novel investigations in epidemiology and social history. However, in the absence of unique identifiers, the linkage of the records may be uncertain or only be successful for a subset of the census cohort, resulting in missing data. For survival analysis, differential ascertainment of event times can impact inference on risk associations and median survival.
METHODS: We modify some existing approaches that are commonly used to handle missing survival times to accommodate this imperfect linkage situation including complete case analysis, censoring, weighting, and several multiple imputation methods. We then conduct simulation studies to compare the performance of the proposed approaches in estimating the associations of a risk factor or exposure in terms of hazard ratio (HR) and median survival times in the presence of missing survival times. The effects of different missing data mechanisms and exposure-survival associations on their performance are also explored. The approaches are applied to a historic cohort of residents in Ambler, PA, established using the 1930 US census, from which only 2,440 out of 4,514 individuals (54%) had death records retrievable from publicly available data sources and death certificates. Using this cohort, we examine the effects of occupational and paraoccupational asbestos exposure on survival and disparities in mortality by race and gender.
RESULTS: We show that imputation based on conditional survival results in less bias and greater efficiency relative to a complete case analysis when estimating log-hazard ratios and median survival times. When the approaches are applied to the Ambler cohort, we find a significant association between occupational exposure and mortality, particularly among black individuals and males, but not between paraoccupational exposure and mortality.
CONCLUSIONS: This investigation illustrates the strengths and weaknesses of different imputation methods for missing survival times due to imperfect linkage of the administrative or registry data. The performance of the methods may depend on the missingness process as well as the parameter being estimated and models of interest, and such factors should be considered when choosing the methods to address the missing event times.