Reflections on modern methods: linkage error bias

Examining the quality and population representativeness of linked survey and administrative data: guidance and illustration using linked 1958 National Child Development Study and Hospital Episode Statistics data

Introduction

Recent years have seen an increase in linkages between survey and administrative data. It is important to evaluate the quality of such data linkages to discern the likely reliability of ensuing research. Evaluation of linkage quality and bias can be conducted using different approaches, but many of these are not possible when there is a separation of processes for linkage and analysis to help preserve privacy, as is typically the case in the UK (and elsewhere).

Objectives

We aimed to describe a suite of generalisable methods to evaluate linkage quality and population representativeness of linked survey and administrative data which remain tractable when users of the linked data are not party to the linkage process itself. We emphasise issues particular to longitudinal survey data throughout.

Methods

Our proposed approaches cover several areas: i) Linkage rates, ii) Selection into response, linkage consent and successful linkage, iii) Linkage quality, and iv) Linked data population representativeness. We illustrate these methods using a recent linkage between the 1958 National Child Development Study (NCDS; a cohort following an initial 17,415 people born in Great Britain in a single week of 1958) and Hospital Episode Statistics (HES) databases (containing important information regarding admissions, accident and emergency attendances and outpatient appointments at NHS hospitals in England).

Results

Our illustrative analyses suggest that the linkage quality of the NCDS-HES data is high and that the linked sample maintains an excellent level of population representativeness with respect to the single dimension we assessed.

Conclusions

Through this work we hope to encourage providers and users of linked data resources to undertake and publish thorough evaluations. We further hope that providing illustrative analyses using linked NCDS-HES data will improve the quality and transparency of research using this particular linked data resource.

Use of routine healthcare data in randomised implementation trials: a methodological mixed-methods systematic review

BACKGROUND

Routine data are increasingly used in randomised controlled trials evaluating healthcare interventions. They can aid participant identification, outcome assessment, and intervention delivery. Randomised implementation trials evaluate the effect of implementation strategies on implementation outcomes. Implementation strategies, such as reminders, are used to increase the uptake of evidence-based interventions into practice, while implementation outcomes, such as adoption, are key measures of the implementation process. The use of routine data in effectiveness trials has been explored; however, there are no reviews on implementation trials. We therefore aimed to describe how routine data have been used in randomised implementation trials and the design characteristics of these trials.

METHODS

We searched MEDLINE (Ovid) and Cochrane Central Register of Controlled Trials from Jan 2000 to Dec 2021 and manually searched protocols from trial registers. We included implementation trials and type II and type III hybrid effectiveness-implementation trials conducted using routine data. We extracted quantitative and qualitative data and narratively synthesised findings.

RESULTS

From 4206 titles, we included 80 trials, of which 22.5% targeted implementation of evidence-based clinical guidelines. Multicomponent implementation strategies were more commonly evaluated (70.0%) than single strategies. Most trials assessed adoption as the primary outcome (65.0%). The majority of trials extracted data from electronic health records (EHRs) (62.5%), and 91.3% used routine data for outcome ascertainment. Reported reasons for using routine data were increasing efficiency, assessing outcomes, reducing research burden, improving quality of care, identifying study samples, confirming findings, and assessing representativeness. Data quality, the EHR system, research governance, and external factors such as government policy could act either as facilitators or barriers.

CONCLUSIONS

Adherence to guidance on designing and reporting implementation studies, and specifically to harmonise the language used in describing implementation strategies and implementation outcomes, would aid identification of studies and data extraction. Routine healthcare data are widely used for participant identification, outcome assessment and intervention delivery. Researchers should familiarise themselves with the barriers and facilitators to using routine data, and efforts could be made to improve data quality to overcome some of the barriers.

REGISTRATION

PROSPERO CRD42022292321.

Advancing cross-sectoral data linkage to understand and address the health impacts of social exclusion: Challenges and potential solutions

The use of administrative health data for research, monitoring, and quality improvement has proliferated in recent decades, leading to improvements in health across many disease areas and across the life course. However, not all populations are equally visible in administrative health data, and those that are less visible may be excluded from the benefits of associated research. Socially excluded populations - including the homeless, people with substance dependence, people involved in sex work, migrants or asylum seekers, and people with a history of incarceration - are typically characterised by health inequity. Yet people who experience social exclusion are often invisible within routinely collected administrative health data because information on their markers of social exclusion are not routinely recorded by healthcare providers. These circumstances make it difficult to understand the often complex health needs of socially excluded populations, evaluate and improve the quality of health services that they interact with, provide more accessible and appropriate health services, and develop effective and integrated responses to reduce health inequity. In this commentary we discuss how linking data from multiple sectors with administrative health data, often called cross-sectoral data linkage, is a key method for systematically identifying socially excluded populations in administrative health data and addressing other issues related to data quality and representativeness. We discuss how cross-sectoral data linkage can improve the representation of socially excluded populations in research, monitoring, and quality improvement initiatives, which can in turn inform coordinated responses across multiple sectors of service delivery. Finally, we articulate key challenges and potential solutions for advancing the use of cross-sectoral data linkage to improve the health of socially excluded populations, using international examples.

Retrospective, Observational Studies for Estimating Vaccine Effects on the Secondary Attack Rate of SARS-CoV-2

Coronavirus disease 2019 (COVID-19) vaccines are highly efficacious at preventing symptomatic infection, severe disease, and death. Most of the evidence that COVID-19 vaccines also reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is based on retrospective, observational studies. Specifically, an increasing number of studies are evaluating vaccine effectiveness against the secondary attack rate of SARS-CoV-2 using data available in existing health-care databases or contact-tracing databases. Since these types of databases were designed for clinical diagnosis or management of COVID-19, they are limited in their ability to provide accurate information on infection, infection timing, and transmission events. We highlight challenges with using existing databases to identify transmission units and confirm potential SARS-CoV-2 transmission events. We discuss the impact of common diagnostic testing strategies, including event-prompted and infrequent testing, and illustrate their potential biases in estimating vaccine effectiveness against the secondary attack rate of SARS-CoV-2. We articulate the need for prospective observational studies of vaccine effectiveness against the SARS-CoV-2 secondary attack rate, and we provide design and reporting considerations for studies using retrospective databases.

Lessons learned from using linked administrative data to evaluate the Family Nurse Partnership in England and Scotland

Introduction

"Big data" - including linked administrative data - can be exploited to evaluate interventions for maternal and child health, providing time- and cost-effective alternatives to randomised controlled trials. However, using these data to evaluate population-level interventions can be challenging.

Objectives

We aimed to inform future evaluations of complex interventions by describing sources of bias, lessons learned, and suggestions for improvements, based on two observational studies using linked administrative data from health, education and social care sectors to evaluate the Family Nurse Partnership (FNP) in England and Scotland.

Methods

We first considered how different sources of potential bias within the administrative data could affect results of the evaluations. We explored how each study design addressed these sources of bias using maternal confounders captured in the data. We then determined what additional information could be captured at each step of the complex intervention to enable analysts to minimise bias and maximise comparability between intervention and usual care groups, so that any observed differences can be attributed to the intervention.

Results

Lessons learned include the need for i) detailed data on intervention activity (dates/geography) and usual care; ii) improved information on data linkage quality to accurately characterise control groups; iii) more efficient provision of linked data to ensure timeliness of results; iv) better measurement of confounding characteristics affecting who is eligible, approached and enrolled.

Conclusions

Linked administrative data are a valuable resource for evaluations of the FNP national programme and other complex population-level interventions. However, information on local programme delivery and usual care are required to account for biases that characterise those who receive the intervention, and to inform understanding of mechanisms of effect. National, ongoing, robust evaluations of complex public health evaluations would be more achievable if programme implementation was integrated with improved national and local data collection, and robust quasi-experimental designs.

Comparing primary caregivers' reported injury data with routinely recorded injury data to assess predictors of childhood injury

BACKGROUND

Linking self-reported data collected from longitudinal studies with administrative health records is timely and cost-effective, provides the opportunity to augment information contained in each and can offset some of the limitations of both data sources. The aim of this study was to compare maternal-reported child injury data with administrative injury records and assess the level of agreement.

METHODS

A deterministic linkage was undertaken to link injury-related data from the Growing up in New Zealand (GUiNZ) study to routinely collected injury records from New Zealand's Accident Compensation Corporation (ACC) for preschool children. The analyses compared: (i) the characteristics of mothers with linked data vs. those without, (ii) injury incidences from maternal recall with those recorded in ACC injury claims, and (iii) the demographic characteristics of concordant and discordant injury reports, including the validity and reliability of injury records from both data sources.

RESULTS

Of all mothers who responded to the injury questions in the GUiNZ study (n = 5836), more than 95% (n = 5637) agreed to have their child's record linked to routine administrative health records. The overall discordance in injury reports showed an increasing trend as children grew older (9% at 9 M to 29% at 54 M). The mothers of children with discordance between maternal injury reports and ACC records were more likely to be younger, of Pacific ethnicity, with lower educational attainment, and live in areas of high deprivation (p < 0.001). The level of agreement between maternal injury recall and ACC injury record decreased (κ = 0.83 to κ = 0.42) as the cohort moved through their preschool years.

CONCLUSIONS

In general, the findings of this study identified that there was underreporting and discordance of the maternal injury recall, which varied by the demographic characteristics of mothers and their child's age. Therefore, linking the routinely gathered injury data with maternal self-report child injury data has the potential to augment longitudinal birth cohort study data to investigate risk or protective factors associated with childhood injury.

Leveraging Administrative Data to Better Understand and Address Child Maltreatment: A Scoping Review of Data Linkage Studies

BACKGROUND

This scoping review aimed to overview studies that used administrative data linkage in the context of child maltreatment to improve our understanding of the value that data linkage may confer for policy, practice, and research.

METHODS

We searched MEDLINE, Embase, PsycINFO, CINAHL, and ERIC electronic databases in June 2019 and May 2020 for studies that linked two or more datasets (at least one of which was administrative in nature) to study child maltreatment. We report findings with numerical and narrative summary.

RESULTS

We included 121 studies, mainly from the United States or Australia and published in the past decade. Data came primarily from social services and health sectors, and linkage processes and data quality were often not described in sufficient detail to align with current reporting guidelines. Most studies were descriptive in nature and research questions addressed fell under eight themes: descriptive epidemiology, risk factors, outcomes, intergenerational transmission, predictive modelling, intervention/service evaluation, multi-sector involvement, and methodological considerations/advancements.

CONCLUSIONS

Included studies demonstrated the wide variety of ways in which data linkage can contribute to the public health response to child maltreatment. However, how research using linked data can be translated into effective service development and monitoring, or targeting of interventions, is underexplored in terms of privacy protection, ethics and governance, data quality, and evidence of effectiveness.

Thirty-three myths and misconceptions about population data: from data capture and processing to linkage

Databases covering all individuals of a population are increasingly used for research and decision-making. The massive size of such databases is often mistaken as a guarantee for valid inferences. However, population data have characteristics that make them challenging to use. Various assumptions on population coverage and data quality are commonly made, including how such data were captured and what types of processing have been applied to them. Furthermore, the full potential of population data can often only be unlocked when such data are linked to other databases. Record linkage often implies subtle technical problems, which are easily missed. We discuss a diverse range of myths and misconceptions relevant for anybody capturing, processing, linking, or analysing population data. Remarkably, many of these myths and misconceptions are due to the social nature of data collections and are therefore missed by purely technical accounts of data processing. Many are also not well documented in scientific publications. We conclude with a set of recommendations for using population data.

Clinical Indicators for Asthma-COPD Overlap: A Systematic Review and Meta-Analysis

Background

Some clinical indicators have been reported to be useful in differentiating asthma-chronic obstructive pulmonary disease (COPD) overlap (ACO) from pure asthma/COPD, but the results were inconsistent. This study aims to evaluate the diagnostic value of these indicators for ACO.

Methods

Databases of PubMed, EMBASE, Ovid and Web of Science were retrieved. Pooled standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated in random-effects models.

Results

48 eligible studies were included. The pooled results indicated, compared with pure asthma, ACO patients had lower levels of forced expiratory volume in the first second (FEV₁)% predicted (pred) (SMD=−1.09, 95% CI −1.3 to −0.87), diffusion lung capacity for carbon monoxide (DLCO)% pred (SMD=−0.83, 95% CI −1.24 to −0.42), fractional exhaled nitric oxide (FeNO) (SMD=−0.23, 95% CI −0.36 to −0.11), and higher levels of induced sputum neutrophil (SMD = 0.51, 95% CI 0.21 to 0.81), circulating YKL-40 (SMD = 0.96, 95% CI 0.27 to 1.64). However, relative to COPD alone, ACO patients had higher levels of FEV₁% pred (SMD = 0.15, 95% CI 0.05 to 0.26), DLCO% pred (SMD = 0.38, 95% CI 0.16 to 0.6), FeNO (SMD = 0.59, 95% CI 0.40 to 0.78), serum total immunoglobulin (Ig)E (SMD = 0.42, 95% CI 0.1 to 0.75), blood eosinophil (SMD = 0.44, 95% CI 0.29 to 0.59), induced sputum eosinophil (SMD = 0.62, 95% CI 0.42 to 0.83), and lower levels of induced sputum neutrophil (SMD=−0.48, 95% CI −0.7 to −0.27), circulating YKL-40 (SMD=−1.09, 95% CI −1.92 to −0.26).

Conclusion

Compared with pure asthma/COPD, ACO patients have different levels of FEV₁% pred, DLCO% pred, FeNO, serum total IgE, blood eosinophil, induced sputum eosinophil/neutrophil, and circulating YKL-40, which could be helpful to establish a clinical diagnosis of ACO.

Interprofessional Collaboration in Fall Prevention: Insights from a Qualitative Study

(1) Background and objective: to explore the experiences of Swiss health care providers involved in a community fall prevention pilot project on barriers and facilitations in interprofessional cooperation between 2016 and 2017 in three regions of Switzerland. (2) Methods: semi-structured interviews with health care providers assessed their perspective on the evaluation of jointly developed tools for reporting fall risk, continuous training of the health care providers, sensitizing media campaigns, and others. (3) Results: One of the project's strengths is the interprofessional continuous trainings. These trainings allowed the health care providers to extend their network of health care providers, which contributed to an improvement of fall prevention. Challenges of the project were that the standardization of the interprofessional collaboration required additional efforts. These efforts are time consuming and, for some categories of health care providers, not remunerated by the Swiss health care system. (4) Conclusions: On a micro and meso level, the results of the present study indicate that the involved health care providers strongly support interprofessional collaboration in fall prevention. However, time and financial constraints challenge the implementation. On a macro level, potential ways to strengthen interprofessional collaboration are a core element in fall prevention.

Optimizing the Retrieval of the Vital Status of Cancer Patients for Health Data Warehouses by Using Open Government Data in France

Electronic Medical Records (EMR) and Electronic Health Records (EHR) are often missing critical information about the death of a patient, although it is an essential metric for medical research in oncology to assess survival outcomes, particularly for evaluating the efficacy of new therapeutic approaches. We used open government data in France from 1970 to September 2021 to identify deceased patients and match them with patient data collected from the Institut de Cancérologie de l’Ouest (ICO) data warehouse (Integrated Center of Oncology—the third largest cancer center in France) between January 2015 and November 2021. To meet our objective, we evaluated algorithms to perform a deterministic record linkage: an exact matching algorithm and a fuzzy matching algorithm. Because we lacked reference data, we needed to assess the algorithms by estimating the number of homonyms that could lead to false links, using the same open dataset of deceased persons in France. The exact matching algorithm allowed us to double the number of dates of death in the ICO data warehouse, and the fuzzy matching algorithm tripled it. Studying homonyms assured us that there was a low risk of misidentification, with precision values of 99.96% for the exact matching and 99.68% for the fuzzy matching. However, estimating the number of false negatives proved more difficult than anticipated. Nevertheless, using open government data can be a highly interesting way to improve the completeness of the date of death variable for oncology patients in data warehouses

Heparin versus citrate anticoagulation for continuous renal replacement therapy in intensive care: the RRAM observational study

BACKGROUND

In the UK, 10% of admissions to intensive care units receive continuous renal replacement therapy with regional citrate anticoagulation replacing systemic heparin anticoagulation over the last decade. Regional citrate anticoagulation is now used in > 50% of intensive care units, despite little evidence of safety or effectiveness.

AIM

The aim of the Renal Replacement Anticoagulant Management study was to evaluate the clinical and health economic impacts of intensive care units moving from systemic heparin anticoagulation to regional citrate anticoagulation for continuous renal replacement therapy.

DESIGN

This was an observational comparative effectiveness study.

SETTING

The setting was NHS adult general intensive care units in England and Wales.

PARTICIPANTS

Participants were adults receiving continuous renal replacement therapy in an intensive care unit participating in the Intensive Care National Audit & Research Centre Case Mix Programme national clinical audit between 1 April 2009 and 31 March 2017.

INTERVENTIONS

Exposure - continuous renal replacement therapy in an intensive care unit after completion of transition to regional citrate anticoagulation. Comparator - continuous renal replacement therapy in an intensive care unit before starting transition to regional citrate anticoagulation or had not transitioned.

OUTCOME MEASURES

Primary effectiveness - all-cause mortality at 90 days. Primary economic - incremental net monetary benefit at 1 year. Secondary outcomes - mortality at hospital discharge, 30 days and 1 year; days of renal, cardiovascular and advanced respiratory support in intensive care unit; length of stay in intensive care unit and hospital; bleeding and thromboembolic events; prevalence of end-stage renal disease at 1 year; and estimated lifetime incremental net monetary benefit.

DATA SOURCES

Individual patient data from the Intensive Care National Audit & Research Centre Case Mix Programme were linked with the UK Renal Registry, Hospital Episode Statistics (for England), Patient Episodes Data for Wales and Civil Registrations (Deaths) data sets, and combined with identified periods of systemic heparin anticoagulation and regional citrate anticoagulation (survey of intensive care units). Staff time and consumables were obtained from micro-costing. Continuous renal replacement therapy system failures were estimated from the Post-Intensive Care Risk-adjusted Alerting and Monitoring data set. EuroQol-3 Dimensions, three-level version, health-related quality of life was obtained from the Intensive Care Outcomes Network study.

RESULTS

Out of the 188 (94.9%) units that responded to the survey, 182 (96.8%) use continuous renal replacement therapy. After linkage, data were available from 69,001 patients across 181 intensive care units (60,416 during periods of systemic heparin anticoagulation use and 8585 during regional citrate anticoagulation use). The change to regional citrate anticoagulation was not associated with a step change in 90-day mortality (odds ratio 0.98, 95% confidence interval 0.89 to 1.08). Secondary outcomes showed step increases in days of renal support (difference in means 0.53 days, 95% confidence interval 0.28 to 0.79 days), advanced cardiovascular support (difference in means 0.23 days, 95% confidence interval 0.09 to 0.38 days) and advanced respiratory support (difference in means, 0.53 days, 95% CI 0.03 to 1.03 days) with a trend toward fewer bleeding episodes (odds ratio 0.90, 95% confidence interval 0.76 to 1.06) with transition to regional citrate anticoagulation. The micro-costing study indicated that regional citrate anticoagulation was more expensive and was associated with an estimated incremental net monetary loss (step change) of -£2376 (95% confidence interval -£1912 to £911). The estimated likelihood of cost-effectiveness at 1 year was less than 0.1%.

LIMITATIONS

Lack of patient-level treatment data means that the results represent average effects of changing to regional citrate anticoagulation in intensive care units. Administrative data are subject to variation in data quality over time, which may contribute to observed trends.

CONCLUSIONS

The introduction of regional citrate anticoagulation has not improved outcomes for patients and is likely to have substantially increased costs. This study demonstrates the feasibility of evaluating effects of changes in practice using routinely collected data.

FUTURE WORK

(1) Prioritise other changes in clinical practice for evaluation and (2) methodological research to understand potential implications of trends in data quality.

TRIAL REGISTRATION

This trial is registered as ClinicalTrials.gov NCT03545750.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 13. See the NIHR Journals Library website for further project information.

ATLAS: an automated association test using probabilistically linked health records with application to genetic studies

OBJECTIVE

Large amounts of health data are becoming available for biomedical research. Synthesizing information across databases may capture more comprehensive pictures of patient health and enable novel research studies. When no gold standard mappings between patient records are available, researchers may probabilistically link records from separate databases and analyze the linked data. However, previous linked data inference methods are constrained to certain linkage settings and exhibit low power. Here, we present ATLAS, an automated, flexible, and robust association testing algorithm for probabilistically linked data.

MATERIALS AND METHODS

Missing variables are imputed at various thresholds using a weighted average method that propagates uncertainty from probabilistic linkage. Next, estimated effect sizes are obtained using a generalized linear model. ATLAS then conducts the threshold combination test by optimally combining P values obtained from data imputed at varying thresholds using Fisher's method and perturbation resampling.

RESULTS

In simulations, ATLAS controls for type I error and exhibits high power compared to previous methods. In a real-world genetic association study, meta-analysis of ATLAS-enabled analyses on a linked cohort with analyses using an existing cohort yielded additional significant associations between rheumatoid arthritis genetic risk score and laboratory biomarkers.

DISCUSSION

Weighted average imputation weathers false matches and increases contribution of true matches to mitigate linkage error-induced bias. The threshold combination test avoids arbitrarily choosing a threshold to rule a match, thus automating linked data-enabled analyses and preserving power.

CONCLUSION

ATLAS promises to enable novel and powerful research studies using linked data to capitalize on all available data sources.

Clinical data for paediatric research: the Swiss approach : Proceedings of the National Symposium in Bern, Switzerland, Dec 5-6, 2019

BACKGROUND AND PURPOSE

Continuous improvement of health and healthcare system is hampered by inefficient processes of generating new evidence, particularly in the case of rare diseases and paediatrics. Currently, most evidence is generated through specific research projects, which typically require extra encounters with patients, are costly and entail long delays between the recognition of specific needs in healthcare and the generation of necessary evidence to address those needs. The Swiss Personalised Health Network (SPHN) aims to improve the use of data obtained during routine healthcare encounters by harmonizing data across Switzerland and facilitating accessibility for research. The project "Harmonising the collection of health-related data and biospecimens in paediatric hospitals throughout Switzerland (SwissPedData)" was an infrastructure development project funded by the SPHN, which aimed to identify and describe available data on child health in Switzerland and to agree on a standardised core dataset for electronic health records across all paediatric teaching hospitals. Here, we describe the results of a two-day symposium that aimed to summarise what had been achieved in the SwissPedData project, to put it in an international context, and to discuss the next steps for a sustainable future. The target audience included clinicians and researchers who produce and use health-related data on children in Switzerland.

KEY HIGHLIGHTS

The symposium consisted of state-of-the-art lectures from national and international keynote speakers, workshops and plenary discussions. This manuscript summarises the talks and discussions in four sections: (I) a description of the Swiss Personalized Health Network and the results of the SwissPedData project; (II) examples of similar initiatives from other countries; (III) an overview of existing health-related datasets and projects in Switzerland; and (IV) a summary of the lessons learned and future prospective from workshops and plenary discussions.

IMPLICATIONS

Streamlined processes linking initial collection of information during routine healthcare encounters, standardised recording of this information in electronic health records and fast accessibility for research are essential to accelerate research in child health and make it affordable. Ongoing projects prove that this is feasible in Switzerland and elsewhere. International collaboration is vital to success. The next steps include the implementation of the SwissPedData core dataset in the clinical information systems of Swiss hospitals, the use of this data to address priority research questions, and the acquisition of sustainable funding to support a slim central infrastructure and local support in each hospital. This will lay the foundation for a national paediatric learning health system in Switzerland.

Linking education and hospital data in England: linkage process and quality

INTRODUCTION

Linkage of administrative data for universal state education and National Health Service (NHS) hospital care would enable research into the inter-relationships between education and health for all children in England.

OBJECTIVES

We aim to describe the linkage process and evaluate the quality of linkage of four one-year birth cohorts within the National Pupil Database (NPD) and Hospital Episode Statistics (HES).

METHODS

We used multi-step deterministic linkage algorithms to link longitudinal records from state schools to the chronology of records in the NHS Personal Demographics Service (PDS; linkage stage 1), and HES (linkage stage 2). We calculated linkage rates and compared pupil characteristics in linked and unlinked samples for each stage of linkage and each cohort (1990/91, 1996/97, 1999/00, and 2004/05).

RESULTS

Of the 2,287,671 pupil records, 2,174,601 (95%) linked to HES. Linkage rates improved over time (92% in 1990/91 to 99% in 2004/05). Ethnic minority pupils and those living in more deprived areas were less likely to be matched to hospital records, but differences in pupil characteristics between linked and unlinked samples were moderate to small.

CONCLUSION

We linked nearly all pupils to at least one hospital record. The high coverage of the linkage represents a unique opportunity for wide-scale analyses across the domains of health and education. However, missed links disproportionately affected ethnic minorities or those living in the poorest neighbourhoods: selection bias could be mitigated by increasing the quality and completeness of identifiers recorded in administrative data or the application of statistical methods that account for missed links.

HIGHLIGHTS

Longitudinal administrative records for all children attending state school and acute hospital services in England have been used for research for more than two decades, but lack of a shared unique identifier has limited scope for linkage between these databases.We applied multi-step deterministic linkage algorithms to 4 one-year cohorts of children born 1 September-31 August in 1990/91, 1996/97, 1999/00 and 2004/05. In stage 1, full names, date of birth, and postcode histories from education data in the National Pupil Database were linked to the NHS Personal Demographic Service. In stage 2, NHS number, postcode, date of birth and sex were linked to hospital records in Hospital Episode Statistics.Between 92% and 99% of school pupils linked to at least one hospital record. Ethnic minority pupils and pupils who were living in the most deprived areas were least likely to link. Ethnic minority pupils were less likely than white children to link at the first step in both algorithms.Bias due to linkage errors could lead to an underestimate of the health needs in disadvantaged groups. Improved data quality, more sensitive linkage algorithms, and/or statistical methods that account for missed links in analyses, should be considered to reduce linkage bias.

National administrative record linkage between specialist community drug and alcohol treatment data (the National Drug Treatment Monitoring System (NDTMS)) and inpatient hospitalisation data (Hospital Episode Statistics (HES)) in England: design, method and evaluation

OBJECTIVES

The creation and evaluation of a national record linkage between substance misuse treatment, and inpatient hospitalisation data in England.

DESIGN

A deterministic record linkage using personal identifiers to link the National Drug Treatment Monitoring System (NDTMS) curated by Public Health England (PHE), and Hospital Episode Statistics (HES) Admitted Patient Care curated by National Health Service (NHS) Digital.

SETTING AND PARTICIPANTS

Adults accessing substance misuse treatment in England between 1 April 2018 and 31 March 2019 (n=268 251) were linked to inpatient hospitalisation records available since 1 April 1997.

OUTCOME MEASURES

Using a gold-standard subset, linked using NHS number, we report the overall linkage sensitivity and precision. Predictors for linkage error were identified, and inverse probability weighting was used to interrogate any potential impact on the analysis of length of hospital stay.

RESULTS

79.7% (n=213 814) people were linked to at least one HES record, with an estimated overall sensitivity of between 82.5% and 83.3%, and a precision of between 90.3% and 96.4%. Individuals were more likely to link if they were women, white and aged between 46 and 60. Linked individuals were more likely to have an average length of hospital stay ≥5 days if they were men, older, had no fixed residential address or had problematic opioid use. These associations did not change substantially after probability weighting, suggesting they were not affected by bias from linkage error.

CONCLUSIONS

Linkage between substance misuse treatment and hospitalisation records offers a powerful new tool to evaluate the impact of treatment on substance related harm in England. While linkage error can produce misleading results, linkage bias appears to have little effect on the association between substance misuse treatment and length of hospital admission. As subsequent analyses are conducted, potential biases associated with the linkage process should be considered in the interpretation of any findings.

Assessing data linkage quality in cohort studies

Background: Linkage of administrative data sources provides an efficient means of collecting detailed data on how individuals interact with cross-sectoral services, society, and the environment. These data can be used to supplement conventional cohort studies, or to create population-level electronic cohorts generated solely from administrative data. However, errors occurring during linkage (false matches/missed matches) can lead to bias in results from linked data.

Aim: This paper provides guidance on evaluating linkage quality in cohort studies.

Methods: We provide an overview of methods for linkage, describe mechanisms by which linkage error can introduce bias, and draw on real-world examples to demonstrate methods for evaluating linkage quality.

Results: Methods for evaluating linkage quality described in this paper provide guidance on (i) estimating linkage error rates, (ii) understanding the mechanisms by which linkage error might bias results, and (iii) information that should be shared between data providers, linkers and users, so that approaches to handling linkage error in analysis can be implemented.

Conclusion: Linked administrative data can enhance conventional cohorts and offers the ability to answer questions that require large sample sizes or hard-to-reach populations. Care needs to be taken to evaluate linkage quality in order to provide robust results.

An Italian Network of Population-Based Birth Cohorts to Evaluate Social and Environmental Risk Factors on Pregnancy Outcomes: The LEAP Study

In Italy, few multicentre population-based studies on pregnancy outcomes are available. Therefore, we established a network of population-based birth cohorts in the cities of Turin, Reggio Emilia, Modena, Bologna, and Rome (northern and central Italy), to study the role of socioeconomic factors and air pollution exposure on term low birthweight, preterm births and the prevalence of small for gestational age. In this article, we will report the full methodology of the study and the first descriptive results. We linked 2007–2013 delivery certificates with municipal registry data and hospital records, and selected singleton livebirths from women who lived in the cities for the entire pregnancy, resulting in 211,853 births (63% from Rome, 21% from Turin and the remaining 16% from the three cities in Emilia-Romagna Region). We have observed that the association between socioeconomic characteristics and air pollution exposure varies by city and pollutant, suggesting a possible effect modification of both the city and the socioeconomic position on the impact of air pollution on pregnancy outcomes. This is the largest Italian population-based birth cohort, not distorted by selection mechanisms, which has also the advantage of being sustainable over time and easily transferable to other areas. Results from the ongoing multivariable analyses will provide more insight on the relative impact of different strands of risk factors and on their interaction, as well as on the modifying effect of the contextual characteristics. Useful recommendations for strategies to prevent adverse pregnancy outcomes may eventually derive from this study.

Evaluating the real-world implementation of the Family Nurse Partnership in England: protocol for a data linkage study

INTRODUCTION

Almost 20 000 babies are born to teenage mothers each year in England, with poorer outcomes for mothers and babies than among older mothers. A nurse home visitation programme in the USA was found to improve a wide range of outcomes for young mothers and their children. However, a randomised controlled trial in England found no effect on short-term primary outcomes, although cognitive development up to age 2 showed improvement. Our study will use linked routinely collected health, education and social care data to evaluate the real-world effects of the Family Nurse Partnership (FNP) on child outcomes up to age 7, with a focus on identifying whether the FNP works better for particular groups of families, thereby informing programme targeting and resource allocation.

METHODS AND ANALYSIS

We will construct a retrospective cohort of all women aged 13-24 years giving birth in English NHS hospitals between 2010 and 2017, linking information on mothers and children from FNP programme data, Hospital Episodes Statistics and the National Pupil Database. To assess the effectiveness of FNP, we will compare outcomes for eligible mothers ever and never enrolled in FNP, and their children, using two analysis strategies to adjust for measured confounding: propensity score matching and analyses adjusting for maternal characteristics up to enrolment/28 weeks gestation. Outcomes of interest include early childhood development, childhood unplanned hospital admissions for injury or maltreatment-related diagnoses and children in care. Subgroup analyses will determine whether the effect of FNP varied according to maternal characteristics (eg, age and education).

ETHICS AND DISSEMINATION

The Nottingham Research Ethics Committee approved this study. Mothers participating in FNP were supportive of our planned research. Results will inform policy-makers for targeting home visiting programmes. Methodological findings on the accuracy and reliability of cross-sectoral data linkage will be of interest to researchers.