Poisoning and envenomation linkage to evaluate outcomes and clinical variation in Australia (PAVLOVA): a longitudinal data-linkage cohort of acute poisonings, envenomations, and adverse drug reactions in New South Wales, Australia, 2011-2020.

INTRODUCTION: Poisoning is a leading cause of morbidity and mortality that is increasing in many countries. Better data are needed to understand epidemiology and outcomes of poisoning. This work describes a new poisoning data linkage cohort in New South Wales, Australia (population approximately 8 million). METHODS: This is a longitudinal health record linkage, 2011-2020, including data from: ambulance call-outs, emergency department presentations, hospital admissions, death registrations, the poisons centre, and four tertiary toxicology units. Individuals with poisoning, venomous animal/plant exposures, or adverse drug reaction events were included. RESULTS: There were 845,217 linkable events relating to 400,642 ambulance, 688,484 emergency department, 682,013 admission, 40,456 toxicology, and 11,879 death records. There were 572,841 people with events; the median age at the time of first event was 57 years, and 51.9% were female. Events leading to patient admission were most commonly adverse drug reactions (n = 511,263), intentional poisonings (n = 68,646), unintentional poisonings (n = 54,840) and animal/plant exposures (n = 11,092). Demographics varied by cause: intentional poisoning (median age 33 years, 64% female); unintentional poisoning/animals/plants (median age 43 years, 45%); and adverse drug reactions (median age 70 years, 54%). Adolescent females had highest rates of intentional poisoning, while unintentional poisoning had a bimodal distribution, highest in children <5 years old and males aged 20 to 50 years. Substance use disorders were documented comorbidities for 44% of intentional poisoning, 29% of unintentional poisoning, and 13% of adverse drug reaction-related admissions; mood disorders were documented for 54%, 17% and 10% of these admissions, respectively. DISCUSSION: Poisonings and hospitalised adverse drug reactions are common in New South Wales, affecting approximately 8% of the population in 10 years. This linkage improves understanding of poisoning risks and outcomes in Australia. CONCLUSIONS: This novel data linkage provides a unique opportunity to study poisoning across multiple settings for an individual over an extended period.

An integrated georeferenced dataset of public investments for soil defence in Italy.

The dataset collects and harmonizes financial data about public works in Italy, focusing on soil defence investments. The data are sourced from three distinct platforms: the Italian Ministry of Economics and Finance's open data platform OpenBDAP, the OpenCoesione website, concerned with interventions framed in cohesion policies financed by additional resources from the European and national budgets, and the ReNDiS database, provided by the Italian Institute for Environmental Protection and Research (ISPRA), that exclusively gathers information about public works in soil defence. The data records belonging to these three sources are linked by a unique project code (CUP), ensuring that there is no duplication of data. The OpenBDAP and OpenCoesione repositories report financial variables classified into various funds. In contrast, the ReNDiS database only provides the total amount of financial resources allocated to each intervention. Consequently, the merged dataset consolidates these financial variables into one, representing the total investment amount. For the first two databases this aggregate is derived by summing the financial flows from the different funds. Geographical referencing has been added to each intervention and each financial observation is associated with an Italian municipality. The database includes information on the region, province, and municipality for each record. Each database entry has also been equipped with the coordinates of the municipality's centroid and with the polygonal shape of the municipality area. Overall, the merged dataset encompasses 28 variables reporting three descriptive variables, one financial variable representing the total amount of financial resources, six geographic variables representing the codes and names of regions, provinces, and municipalities, sixteen variables referring to key dates of the process of public works, two geographical references variables respectively representing the centroids and the shape polygons of the municipality. This comprehensive dataset allows to analyse the spatial distribution of the resources allocated to soil defence investments. It offers insights to policymakers striving to allocate resources more efficiently, thereby fostering sustainable land management practices and ensuring the long-term health of the Italian ecosystems. The dataset can be complemented with additional information related to various concomitant aspects such as those pertaining to the environmental and socio-economic fields. This integration allows for broad analysis of the relationships between soil defence efforts and surrounding environmental and socio-economic contexts.

Linkability measures to assess the data characteristics for record linkage.

OBJECTIVES: Accurate record linkage (RL) enables consolidation and de-duplication of data from disparate datasets, resulting in more comprehensive and complete patient data. However, conducting RL with low quality or unfit data can waste institutional resources on poor linkage results. We aim to evaluate data linkability to enhance the effectiveness of record linkage. MATERIALS AND METHODS: We describe a systematic approach using data fitness ("linkability") measures, defined as metrics that characterize the availability, discriminatory power, and distribution of potential variables for RL. We used the isolation forest algorithm to detect abnormal linkability values from 188 sites in Indiana and Colorado, and manually reviewed the data to understand the cause of anomalies. RESULT: We calculated 10 linkability metrics for 11 potential linkage variables (LVs) across 188 sites for a total of 20 680 linkability metrics. Potential LVs such as first name, last name, date of birth, and sex have low missing data rates, while Social Security Number vary widely in completeness among all sites. We investigated anomalous linkability values to identify the cause of many records having identical values in certain LVs, issues with placeholder values disguising data missingness, and orphan records. DISCUSSION: The fitness of a variable for RL is determined by its availability and its discriminatory power to uniquely identify individuals. These results highlight the need for awareness of placeholder values, which inform the selection of variables and methods to optimize RL performance. CONCLUSION: Evaluating linkability measures using the isolation forest algorithm to highlight anomalous findings can help identify fitness-for-use issues that must be addressed before initiating the RL process to ensure high-quality linkage outcomes.

Privacy-Preserving Linkage of Distributed Pseudonymised Datasets in a Virtual European Rare Disease Platform.

Secondary use of data for research purposes is especially important in rare diseases (RD), since, per definition, data are sparse. The European Joint Programme on Rare Diseases (EJP RD) aims at developing an RD infrastructure which supports the secondary use of data. Significant amounts of RD data are a) distributed and b) available only in pseudonymised format. Privacy-Preserving Record Linkage (PPRL) concerns the linking of such distributed datasets without disclosing the participant's identities. We present a concept for linking a PPRL Service to the EJP RD Virtual Platform (VP). Level 1 (resource discovery) connection is provided by running an FDP within the PPRL Service. On Level 2 (data discoverability), the PPRL Service can represent both, an individual and a catalog endpoint. Our solution can count patients in PPRL-supporting resources, count duplicates only once, and count only patients registered to multiple resources. Currently, we are preparing the deployment within the EJP RD VP.

A Hybrid-Approach for Privacy Preserving Record Linkage - A Case Study from Germany.

Over the last decade, the exponential growth in patient data volume and velocity has transformed it into a valuable resource for researchers. Yet, accessing comprehensive, unique patient data sets remains a challenge, particularly when individuals have received treatments across various practices and hospitals. Traditional record linkage methods fall short in adequately protecting patient privacy in these scenarios. Privacy Preserving Record Linkage (PPRL) offers a solution, employing techniques such as data cryptographic methods to identify common patients occurring in multiple datasets, while maintaining the privacy of other patients. This paper proposes an investigation into combined approaches of two common German PPRL tools, namely E-PIX and MainSEL. Each tool, while aiming for 'privacy preservation', employs distinct methods that offer unique advantages and drawbacks. Our research aims to explore these in a combined approach to leverage their respective strengths and mitigate their limitations. We anticipate that this synergistic approach will not only enhance data privacy but also allow for easier synchronisation of research data. This study is particularly pertinent in light of evolving privacy regulations and the increasing complexity of healthcare data management. By advancing PPRL methodologies, we aim to contribute to more robust, privacy-compliant data analysis practices in healthcare research.

Implementing privacy preserving record linkage: Insights from Australian use cases.

OBJECTIVE: To describe the use of privacy preserving linkage methods operationally in Australia, and to present insights and key learnings from their implementation. METHODS: Privacy preserving record linkage (PPRL) utilising Bloom filters provides a unique practical mechanism that allows linkage to occur without the release of personally identifiable information (PII), while still ensuring high accuracy. RESULTS: The methodology has received wide uptake within Australia, with four state linkage units with privacy preserving capability. It has enabled access to general practice and private pathology data amongst other, both much sought after datasets previous inaccessible for linkage. CONCLUSION: The Australian experience suggests privacy preserving linkage is a practical solution for improving data access for policy, planning and population health research. It is hoped interest in this methodology internationally continues to grow.

Use Cases Requiring Privacy-Preserving Record Linkage in Paediatric Oncology.

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.

Statewide Linkage Study of Salmonella Infection and Colorectal Cancer Incidence in Michigan, United States.

Objective: 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. Materials and Methods: 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. Results: 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). Conclusions: 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.

High Healthcare Costs in Childhood Inflammatory Bowel Disease: Development of a Prediction Model Using Linked Clinical and Health Administrative Data.

BACKGROUND: The incidence of pediatric-onset inflammatory bowel disease (IBD) and the costs of caring for individuals with IBD are both increasing. We calculated the direct healthcare costs of pediatric IBD in the first year after diagnosis and developed a model to predict children who would have high costs (top 25th percentile). METHODS: Using data from the Canadian Children IBD Network inception cohort (≤16 years of age, diagnosed between 2013 and 2019) deterministically linked to health administrative data from Ontario, Canada, we estimated direct healthcare and medication costs accrued between 31 and 365 days after diagnosis. Candidate predictors included age at diagnosis, sex, rural/urban residence location, distance to pediatric center, neighborhood income quintile, IBD type, initial therapy, disease activity, diagnostic delay, health services utilization or surgery around diagnosis, regular primary care provider, and receipt of mental health care. Logistic regression with stepwise elimination was used for model building; 5-fold nested cross-validation optimized and improved model accuracy while limiting overfitting. RESULTS: The mean cost among 487 children with IBD was CA$15 168 ± 15 305. Initial treatment (anti-tumor necrosis factor therapy, aminosalicylates, or systemic steroids), having a mental health care encounter, undergoing surgery, emergency department visit at diagnosis, sex, and age were predictors of increased costs, while having a regular primary care provider was a predictor of decreased costs. The C-statistic for our model was 0.71. CONCLUSIONS: The cost of caring for children with IBD in the first year after diagnosis is immense and can be predicted based on characteristics at diagnosis. Efforts that mitigate rising costs without compromising quality of care are needed.

Cost of caring for children with IBD is high­CA$15 168 between 31 and 365 days from diagnosis in 487 Canadian children. Predictors of high costs included anti-tumor necrosis factor therapy and mental health care, with lower costs in those with a primary-care provider.

Privacy preserving record linkage for public health action: opportunities and challenges.

OBJECTIVES: To understand the landscape of privacy preserving record linkage (PPRL) applications in public health, assess estimates of PPRL accuracy and privacy, and evaluate factors for PPRL adoption. MATERIALS AND METHODS: A literature scan examined the accuracy, data privacy, and scalability of PPRL in public health. Twelve interviews with subject matter experts were conducted and coded using an inductive approach to identify factors related to PPRL adoption. RESULTS: PPRL has a high level of linkage quality and accuracy. PPRL linkage quality was comparable to that of clear text linkage methods (requiring direct personally identifiable information [PII]) for linkage across various settings and research questions. Accuracy of PPRL depended on several components, such as PPRL technique, and the proportion of missingness and errors in underlying data. Strategies to increase adoption include increasing understanding of PPRL, improving data owner buy-in, establishing governance structure and oversight, and developing a public health implementation strategy for PPRL. DISCUSSION: PPRL protects privacy by eliminating the need to share PII for linkage, but the accuracy and linkage quality depend on factors including the choice of PPRL technique and specific PII used to create encrypted identifiers. Large-scale implementations of PPRL linking millions of observations-including PCORnet, National Institutes for Health N3C, and the Centers for Disease Control and Prevention COVID-19 project have demonstrated the scalability of PPRL for public health applications. CONCLUSIONS: Applications of PPRL in public health have demonstrated their value for the public health community. Although gaps must be addressed before wide implementation, PPRL is a promising solution to data linkage challenges faced by the public health ecosystem.

Inverse probability weighting for self-selection bias correction in the investigation of social inequality in mortality.

BACKGROUND: Empirical evaluation of inverse probability weighting (IPW) for self-selection bias correction is inaccessible without the full source population. We aimed to: (i) investigate how self-selection biases frequency and association measures and (ii) assess self-selection bias correction using IPW in a cohort with register linkage. METHODS: The source population included 17 936 individuals invited to the Copenhagen Aging and Midlife Biobank during 2009-11 (ages 49-63 years). Participants counted 7185 (40.1%). Register data were obtained for every invited person from 7 years before invitation to the end of 2020. The association between education and mortality was estimated using Cox regression models among participants, IPW participants and the source population. RESULTS: Participants had higher socioeconomic position and fewer hospital contacts before baseline than the source population. Frequency measures of participants approached those of the source population after IPW. Compared with primary/lower secondary education, upper secondary, short tertiary, bachelor and master/doctoral were associated with reduced risk of death among participants (adjusted hazard ratio [95% CI]: 0.60 [0.46; 0.77], 0.68 [0.42; 1.11], 0.37 [0.25; 0.54], 0.28 [0.18; 0.46], respectively). IPW changed the estimates marginally (0.59 [0.45; 0.77], 0.57 [0.34; 0.93], 0.34 [0.23; 0.50], 0.24 [0.15; 0.39]) but not only towards those of the source population (0.57 [0.51; 0.64], 0.43 [0.32; 0.60], 0.38 [0.32; 0.47], 0.22 [0.16; 0.29]). CONCLUSIONS: Frequency measures of study participants may not reflect the source population in the presence of self-selection, but the impact on association measures can be limited. IPW may be useful for (self-)selection bias correction, but the returned results can still reflect residual or other biases and random errors.

The accuracy of parent/carer proxy-reporting of caries experience in children and association with socioeconomic circumstances: a cross-sectional data linkage study.

OBJECTIVES: To compare parent/carer proxy-reported dental caries experience of their 5-year-old child with epidemiological survey clinician examination of caries experience in the same children. To determine any differences in the accuracy by area-based socioeconomic group. METHODS: A cross-sectional data linkage study linked data from the Growing Up in Scotland (GUS) study and the National Dental Inspection Programme (NDIP) school epidemiology survey. Parent/carer proxy-reported caries experience was compared with clinician-measured caries experience on n=3008 children, and data were stratified by home-residential area-based socioeconomic deprivation levels (Scottish Index of Multiple Deprivation (SIMD)). Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated overall and stratified by SIMD. RESULTS: Overall, parent/carer proxy-reporting had low sensitivity (42.3% 95%CI: 39.0, 45.7) that decreased with decreasing deprivation (SIMD-1(most deprived): 49.4% to SIMD-5 (least deprived): 37.2%). Specificity remained consistently high overall and across area-based socioeconomic deprivation levels (overall=96.2%, 95%CI: 95.3, 97.0; SIMD-1: 94.4% SIMD-5: 97.8%). In children whose parents/carers reported them to have caries experience (GUS) a high percentage were found to have caries experience (NDIP) (PPV=81.8%, 95%CI: 78.2, 84.9). CONCLUSION: Parent/carer proxy-reporting of caries experience in 5-year-old children had very low sensitivity and was lowest in children from the least deprived areas. In contrast, parents/carers who reported their child had caries experience did so reasonably accurately. This study concludes that proxy reporting caries experience is not sufficiently sensitive to replace clinician examination in assessing dental caries experience in surveys of child populations and highlights the importance of data linkage to routine datasets.

Educational outcomes among children with congenital heart disease compared to peers: a Scotland-wide record-linkage study of 715,850 schoolchildren.

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. CONCLUSION: 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.

Sex differences in risk of incident microvascular and macrovascular complications: a population-based data-linkage study among 25 713 people with diabetes.

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.

Taipei Medical University Clinical Research Database: a collaborative hospital EHR database aligned with international common data standards.

OBJECTIVE: The objective of this paper is to provide a comprehensive overview of the development and features of the Taipei Medical University Clinical Research Database (TMUCRD), a repository of real-world data (RWD) derived from electronic health records (EHRs) and other sources. METHODS: TMUCRD was developed by integrating EHRs from three affiliated hospitals, including Taipei Medical University Hospital, Wan-Fang Hospital and Shuang-Ho Hospital. The data cover over 15 years and include diverse patient care information. The database was converted to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) for standardisation. RESULTS: TMUCRD comprises 89 tables (eg, 29 tables for each hospital and 2 linked tables), including demographics, diagnoses, medications, procedures and measurements, among others. It encompasses data from more than 4.15 million patients with various medical records, spanning from the year 2004 to 2021. The dataset offers insights into disease prevalence, medication usage, laboratory tests and patient characteristics. DISCUSSION: TMUCRD stands out due to its unique advantages, including diverse data types, comprehensive patient information, linked mortality and cancer registry data, regular updates and a swift application process. Its compatibility with the OMOP CDM enhances its usability and interoperability. CONCLUSION: TMUCRD serves as a valuable resource for researchers and scholars interested in leveraging RWD for clinical research. Its availability and integration of diverse healthcare data contribute to a collaborative and data-driven approach to advancing medical knowledge and practice.

Linking Patient Encounters across Primary and Ancillary Electronic Health Record Systems: A Comparison of Two Approaches.

Background: 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. Objectives: We compared a patients-first approach versus an encounters-first approach for linking patient encounter records across multiple EHR systems. Methods: 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. Results: 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. Conclusion: 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.

SARS-CoV-2 vaccine uptake and risks of severe COVID-19 disease among people prescribed opioid agonist therapy in Scotland.

BACKGROUND: There is limited evidence quantifying the risk of severe COVID-19 disease among people with opioid dependence. We examined vaccine uptake and severe disease (admission to critical care or death with COVID-19) among individuals prescribed opioid agonist therapy (OAT). METHOD: A case-control design was used to examine vaccine uptake in those prescribed OAT compared with the general population, and the association between severe disease and OAT. In both analyses, 10 controls from the general population were matched (to each OAT recipient and COVID-19 case, respectively) according to socio-demographic factors. Conditional logistic regression was used to estimate rate ratios (RR) for severe disease. RESULTS: Vaccine uptake was markedly lower in the OAT cohort (dose 1: 67%, dose 2: 53% and dose 3: 31%) compared with matched controls (76%, 72% and 57%, respectively). Those prescribed OAT within the last 5 years, compared with those not prescribed, had increased risk of severe COVID-19 (RR 3.38, 95% CI 2.75 to 4.15), particularly in the fourth wave (RR 6.58, 95% CI 4.20 to 10.32); adjustment for comorbidity and vaccine status attenuated this risk (adjusted RR (aRR) 2.43, 95% CI 1.95 to 3.02; wave 4 aRR 3.78, 95% CI 2.30 to 6.20). Increased risk was also observed for those prescribed OAT previously (>3 months ago) compared with recently (aRR 1.74, 95% CI 1.11 to 2.71). CONCLUSIONS: The widening gap in vaccine coverage for those prescribed OAT, compared with the general population, is likely to have exacerbated the risk of severe COVID-19 in this population over the pandemic. However, continued OAT use may have provided protection from severe COVID-19 among those with opioid dependence.

Risk of severe mood and anxiety disorders in the adult children of parents with alcohol use disorder: a nationwide cohort study.

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. CONCLUSION: 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.

Variable selection for latent class analysis in the presence of missing data with application to record linkage.

The Fellegi-Sunter model is a latent class model widely used in probabilistic linkage to identify records that belong to the same entity. Record linkage practitioners typically employ all available matching fields in the model with the premise that more fields convey greater information about the true match status and hence result in improved match performance. In the context of model-based clustering, it is well known that such a premise is incorrect and the inclusion of noisy variables could compromise the clustering. Variable selection procedures have therefore been developed to remove noisy variables. Although these procedures have the potential to improve record matching, they cannot be applied directly due to the ubiquity of the missing data in record linkage applications. In this paper, we modify the stepwise variable selection procedure proposed by Fop, Smart, and Murphy and extend it to account for missing data common in record linkage. Through simulation studies, our proposed method is shown to select the correct set of matching fields across various settings, leading to better-performing algorithms. The improved match performance is also seen in a real-world application. We therefore recommend the use of our proposed selection procedure to identify informative matching fields for probabilistic record linkage algorithms.