Use of data linkage to measure the population health effect of non-health-care interventions

A systematic review and meta-analysis of data linkage between motor vehicle crash and hospital-based datasets

Motor vehicle crash data linkage has emerged as a vital tool to better understand the injury outcomes and the factors contributing to crashes. This systematic review and meta-analysis aims to explore the existing knowledge on data linkage between motor vehicle crashes and hospital-based datasets, summarize and highlight the findings of previous studies, and identify gaps in research. A comprehensive and systematic search of the literature yielded 54 studies for a qualitative analysis, and 35 of which were also considered for a quantitative meta-analysis. Findings highlight a range of viable methodologies for linking datasets, including manual, deterministic, probabilistic, and integrative methods. Designing a linkage method that integrates different algorithms and techniques is more likely to result in higher match rate and fewer errors. Examining the results of the meta-analysis reveals that a wide range of linkage rates were reported. There are several factors beyond the approach that affect the linkage rate including the size and coverage of both datasets and the linkage variables. Gender, age, crash type, and roadway geometry at the crash site were likely to be associated with a record's presence in a linked dataset. Linkage rate alone is not the only important metric and when linkage rate is used as a metric in research, both police and hospital rates should be reported. This study also highlights the importance of examining and accounting for population and bias introduced by linking two datasets.

Cohort profile: Born in Wales-a birth cohort with maternity, parental and child data linkage for life course research in Wales, UK

PURPOSE

Using Wales's national dataset for maternity and births as a core dataset, we have linked related datasets to create a more complete and comprehensive entire country birth cohort. Data of anonymised identified persons are linked on the individual level to data from health, social care and education data within the Secure Anonymised Information Linkage (SAIL) Databank. Each individual is assigned an encrypted Anonymised Linking Field; this field is used to link anonymised individuals across datasets. We present the descriptive data available in the core dataset, and the future expansion plans for the database beyond its initial development stage.

PARTICIPANTS

Descriptive information from 2011 to 2023 has been gathered from the National Community Child Health Database (NCCHD) in SAIL. This comprehensive dataset comprises over 400 000 child electronic records. Additionally, survey responses about health and well-being from a cross-section of the population including 2500 parents and 30 000 primary school children have been collected for enriched personal responses and linkage to the data spine.

FINDINGS TO DATE

The electronic cohort comprises all children born in Wales since 2011, with follow-up conducted until they finish primary school at age 11. The child cohort is 51%: 49% female: male, and 7.8% are from ethnic minority backgrounds. When considering age distribution, 26.8% of children are under the age of 5, while 63.2% fall within the age range of 5-11.

FUTURE PLANS

Born in Wales will expand by 30 000 new births annually in Wales (in NCCHD), while including follow-up data of children and parents already in the database. Supplementary datasets complement the existing linkage, including primary care, hospital data, educational attainment and social care. Future research includes exploring the long-term implications of COVID-19 on child health and development, and examining the impact of parental work environment on child health and development.

Ambient greenness, access to local green spaces, and subsequent mental health: a 10-year longitudinal dynamic panel study of 2·3 million adults in Wales

BACKGROUND

Living in greener areas, or close to green and blue spaces (GBS; eg, parks, lakes, or beaches), is associated with better mental health, but longitudinal evidence when GBS exposures precede outcomes is less available. We aimed to analyse the effect of living in or moving to areas with more green space or better access to GBS on subsequent adult mental health over time, while explicitly considering health inequalities.

METHODS

A cohort of the people in Wales, UK (≥16 years; n=2 341 591) was constructed from electronic health record data sources from Jan 1, 2008 to Oct 31, 2019, comprising 19 141 896 person-years of follow-up. Household ambient greenness (Enhanced Vegetation Index [EVI]), access to GBS (counts, distance to nearest), and common mental health disorders (CMD, based on a validated algorithm combining current diagnoses or symptoms of anxiety or depression [treated or untreated in the preceding 1-year period], or treatment of historical diagnoses from before the current cohort [up to 8 years previously, to 2000], where diagnosis preceded treatment) were record-linked. Cumulative exposure values were created for each adult, censoring for CMD, migration out of Wales, death, or end of cohort. Exposure and CMD associations were evaluated using multivariate logistic regression, stratified by area-level deprivation.

FINDINGS

After adjustment, exposure to greater ambient greenness over time (+0·1 increased EVI on a 0-1 scale) was associated with lower odds of subsequent CMD (adjusted odds ratio 0·80, 95% CI 0·80-0·81), where CMD was based on a combination of current diagnoses or symptoms (treated or untreated in the preceding 1-year period), or treatments. Ten percentile points more access to GBS was associated with lower odds of a later CMD (0·93, 0·93-0·93). Every additional 360 m to the nearest GBS was associated with higher odds of CMD (1·05, 1·04-1·05). We found that positive effects of GBS on mental health appeared to be greater in more deprived quintiles.

INTERPRETATION

Ambient exposure is associated with the greatest reduced risk of CMD, particularly for those who live in deprived communities. These findings support authorities responsible for GBS, who are attempting to engage planners and policy makers, to ensure GBS meets residents' needs.

FUNDING

National Institute for Health and Care Research Public Health Research programme.

COVID-19 vaccination in pregnancy: the impact of multimorbidity and smoking status on vaccine hesitancy, a cohort study of 25,111 women in Wales, UK

BACKGROUND

Multimorbidity, smoking status, and pregnancy are identified as three risk factors associated with more severe outcomes following a SARS-CoV-2 infection, thus vaccination uptake is crucial for pregnant women living with multimorbidity and a history of smoking. This study aimed to examine the impact of multimorbidity, smoking status, and demographics (age, ethnic group, area of deprivation) on vaccine hesitancy among pregnant women in Wales using electronic health records (EHR) linkage.

METHODS

This cohort study utilised routinely collected, individual-level, anonymised population-scale linked data within the Secure Anonymised Information Linkage (SAIL) Databank. Pregnant women were identified from 13th April 2021 to 31st December 2021. Survival analysis was employed to examine and compare the length of time to vaccination uptake in pregnancy by considering multimorbidity, smoking status, as well as depression, diabetes, asthma, and cardiovascular conditions independently. The study also assessed the variation in uptake by multimorbidity, smoking status, and demographics, both jointly and separately for the independent conditions, using hazard ratios (HR) derived from the Cox regression model.

RESULTS

Within the population cohort, 8,203 (32.7%) received at least one dose of the COVID-19 vaccine during pregnancy, with 8,572 (34.1%) remaining unvaccinated throughout the follow-up period, and 8,336 (33.2%) receiving the vaccine postpartum. Women aged 30 years or older were more likely to have the vaccine in pregnancy. Those who had depression were slightly but significantly more likely to have the vaccine compared to those without depression (HR = 1.08, 95% CI 1.03 to 1.14, p = 0.002). Women living with multimorbidity were 1.12 times more likely to have the vaccine compared to those living without multimorbidity (HR = 1.12, 95% CI 1.04 to 1.19, p = 0.001). Vaccine uptakes were significantly lower among both current smokers and former smokers compared to never smokers (HR = 0.87, 95% CI 0.81 to 0.94, p < 0.001 and HR = 0.92, 95% CI 0.85 to 0.98, p = 0.015 respectively). Uptake was also lower among those living in the most deprived areas compared to those living in the most affluent areas (HR = 0.89, 95% CI 0.83 to 0.96, p = 0.002).

CONCLUSION

Younger women, living without multimorbidity, current and former smokers, and those living in the more deprived areas are less likely to have the vaccine, thus, a targeted approach to vaccinations may be required for these groups. Pregnant individuals living with multimorbidity exhibit a slight but statistically significant reduction in vaccine hesitancy towards COVID-19 during pregnancy.

Insights from linking police domestic abuse data and health data in South Wales, UK: a linked routine data analysis using decision tree classification

BACKGROUND

Exposure to domestic abuse can lead to long-term negative impacts on the victim's physical and psychological wellbeing. The 1998 Crime and Disorder Act requires agencies to collaborate on crime reduction strategies, including data sharing. Although data sharing is feasible for individuals, rarely are whole-agency data linked. This study aimed to examine the knowledge obtained by integrating information from police and health-care datasets through data linkage and analyse associated risk factor clusters.

METHODS

This retrospective cohort study analyses data from residents of South Wales who were victims of domestic abuse resulting in a Public Protection Notification (PPN) submission between Aug 12, 2015 and March 31, 2020. The study links these data with the victims' health records, collated within the Secure Anonymised Information Linkage databank, to examine factors associated with the outcome of an Emergency Department attendance, emergency hospital admission, or death within 12 months of the PPN submission. To assess the time to outcome for domestic abuse victims after the index PPN submission, we used Kaplan-Meier survival analysis. We used multivariable Cox regression models to identify which factors contributed the highest risk of experiencing an outcome after the index PPN submission. Finally, we created decision trees to describe specific groups of individuals who are at risk of experiencing a domestic abuse incident and subsequent outcome.

FINDINGS

After excluding individuals with multiple PPN records, duplicates, and records with a poor matching score or missing fields, the resulting clean dataset consisted of 8709 domestic abuse victims, of whom 6257 (71·8%) were female. Within a year of a domestic abuse incident, 3650 (41·9%) individuals had an outcome. Factors associated with experiencing an outcome within 12 months of the PPN included younger victim age (hazard ratio 1·183 [95% CI 1·053-1·329], p=0·0048), further PPN submissions after the initial referral (1·383 [1·295-1·476]; p<0·0001), injury at the scene (1·484 [1·368-1·609]; p<0·0001), assessed high risk (1·600 [1·444-1·773]; p<0·0001), referral to other agencies (1·518 [1·358-1·697]; p<0·0001), history of violence (1·229 [1·134-1·333]; p<0·0001), attempted strangulation (1·311 [1·148-1·497]; p<0·0001), and pregnancy (1·372 [1·142-1·648]; p=0·0007). Health-care data before the index PPN established that previous Emergency Department and hospital admissions, smoking, smoking cessation advice, obstetric codes, and prescription of antidepressants and antibiotics were associated with having a future outcome following a domestic abuse incident.

INTERPRETATION

The results indicate that vulnerable individuals are detectable in multiple datasets before and after involvement of the police. Operationalising these findings could reduce police callouts and future Emergency Department or hospital admissions, and improve outcomes for those who are vulnerable. Strategies include querying previous Emergency Department and hospital admissions, giving a high-risk assessment for a pregnant victim, and facilitating data linkage to identify vulnerable individuals.

FUNDING

National Institute for Health Research.

Healthcare use attributable to COVID-19: a propensity-matched national electronic health records cohort study of 249,390 people in Wales, UK

BACKGROUND

To determine the extent and nature of changes associated with COVID-19 infection in terms of healthcare utilisation, this study observed healthcare contact 1 to 4 and 5 to 24 weeks following a COVID-19 diagnosis compared to propensity-matched controls.

METHODS

Two hundred forty nine thousand three hundred ninety Welsh individuals with a positive reverse transcription-polymerase chain reaction (RT-PCR) test were identified from data from national PCR test results. After elimination criteria, 98,600 positive individuals were matched to test negative and never tested controls using propensity matching. Cohorts were split on test location. Tests could be taken in either the hospital or community. Controls were those who had tested negative in their respective environments. Survival analysis was utilised for first clinical outcomes which are grouped into primary and secondary. Primary outcomes include post-viral-illness and fatigue as an indication of long-COVID. Secondary outcomes include clinical terminology concepts for embolism, respiratory conditions, mental health conditions, fit notes, or hospital attendance. Increased instantaneous risk for positive individuals was quantified using hazard ratios (HR) from Cox regression, while absolute risk (AR) and relative risk were quantified using life table analysis.

RESULTS

Analysis was conducted using all individuals and stratified by test location. Cases are compared to controls from the same test location. Fatigue (HR: 1.77, 95% CI: 1.34-2.25, p =  < 0.001) and embolism (HR: 1.50, 95% CI: 1.15-1.97, p = 0.003) were more likely to occur in all positive individuals in the first 4 weeks; however, anxiety and depression (HR: 0.83, 95% CI: 0.73-0.95, p = 0.007) were less likely. Positive individuals continued to be more at risk of fatigue (HR: 1.47, 95% CI: 1.24-1.75, p =  < 0.001) and embolism (HR: 1.51, 95% CI: 1.13-2.02, p = 0.005) after 4 weeks. All positive individuals are also at greater risk of post-viral illness (HR: 4.57, 95% CI: 1.77-11.80, p = 0.002). Despite statistical association between testing positive and several conditions, life table analysis shows that only a small minority of the study population were affected.

CONCLUSIONS

Community COVID-19 disease is associated with increased risks of post-viral-illness, fatigue, embolism, and respiratory conditions. Despite elevated risks, the absolute healthcare burden is low. Subsequently, either very small proportions of people experience adverse outcomes following COVID-19 or they are not presenting to healthcare.

Breastfeeding initiation and duration through the COVID-19 pandemic, a linked population-level routine data study: the Born in Wales Cohort 2018-2021

OBJECTIVES

The WHO recommends exclusive breastfeeding for the first 6 months of life. This study aimed to examine the impact the pandemic had on breastfeeding uptake and duration, and whether intention to breastfeed is associated with longer duration of exclusive breastfeeding.

METHODS

A cohort study using routinely collected, linked healthcare data from the Secure Anonymised Information Linkage databank. All women who gave birth in Wales between 2018 and 2021 recorded in the Maternal Indicators dataset were asked about intention to breastfeed. These data were linked with the National Community Child Health Births and Breastfeeding dataset to examine breastfeeding rates.

RESULTS

Intention to breastfeed was associated with being 27.6 times more likely to continue to exclusively breastfeed for 6 months compared with those who did not intend to breastfeed (OR 27.6, 95% CI 24.9 to 30.7). Breastfeeding rates at 6 months were 16.6% prepandemic and 20.5% in 2020. When compared with a survey population, the initial intention to breastfeed/not breastfeed only changes for about 10% of women.

CONCLUSION

Women were more likely to exclusively breastfeed for 6 months during the pandemic compared with before or after the pandemic. Arguably, interventions which enable families to spend more time with their baby such as maternal and paternal leave may help improve breastfeeding duration. The biggest predictor of breastfeeding at 6 months was intention to breastfeed. Therefore, targeted interventions during pregnancy to encourage motivation to breastfeed could improve duration of breastfeeding.

A real-world data analysis of acetylsalicylic acid in FDA Adverse Event Reporting System (FAERS) database

BACKGROUND

Acetylsalicylic acid (Aspirin), one of the oldest medicines, is widely used in various clinical fields. However, numerous adverse events (AEs) have been reported. In this study, we aimed to investigate adverse drug reactions (ADRs) of aspirin using real-worlddata from the US Food and Drug Administration Adverse Event Reporting System (FAERS) database.

METHODS

We assessed the disproportionality of aspirin-related AEs by calculating measures such as reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagationneural network (BCPNN), and Gamma-Poisson Shrinker (GPS).

RESULTS

Out of 7,510,564 casereports in the FAERS database, 18644 reports of aspirin as the 'primary suspected (PS)' AEs were recorded. Disproportionality analyses identified 493 aspirin-related preferred terms (PTs) across 25 organ systems. Notably, unexpected significant AEs such as pallor (p=5.66E-33), dependence (p=6.45E-67), and compartment syndrome (p=1.95E-28) were observed, which were not mentioned in the drug's instructions.

CONCLUSION

Our findings align with clinical observations, highlighting potential new and unexpected ADR signals associated with aspirin. Further prospective clinical studies are necessary to confirm and elucidate the relationship between aspirin and these ADRs. This study offers a fresh and unique perspective for studying drug-AEs.

COVID-19 vaccination in pregnancy: views and vaccination uptake rates in pregnancy, a mixed methods analysis from SAIL and the Born-In-Wales Birth Cohort

BACKGROUND

Vaccine hesitancy amongst pregnant women has been found to be a concern during past epidemics. This study aimed to (1) estimate COVID-19 vaccination rates among pregnant women in Wales and their association with age, ethnicity, and area of deprivation, using electronic health record (EHR) data linkage, and (2) explore pregnant women's views on receiving the COVID-19 vaccine during pregnancy using data from a survey recruiting via social media (Facebook, Twitter), through midwives, and posters in hospitals (Born-In-Wales Cohort).

METHODS

This was a mixed-methods study utilising routinely collected linked data from the Secure Anonymised Information Linkage (SAIL) Databank (Objective 1) and the Born-In-Wales Birth Cohort participants (Objective 2). Pregnant women were identified from 13th April 2021 to 31st December 2021. Survival analysis was utilised to examine and compare the length of time to vaccination uptake in pregnancy, and variation in uptake by; age, ethnic group, and deprivation area was examined using hazard ratios (HR) from Cox regression. Survey respondents were women who had a baby during the COVID-19 pandemic or were pregnant between 1st November 2021 and 24th March 2022 and participating in Born-In-Wales. Codebook thematic analysis was used to generate themes from an open-ended question on the survey.

RESULTS

Population-level data linkage (objective 1): Within the population cohort, 8203 (32.7%) received at least one dose of the COVID-19 vaccine during pregnancy, 8572 (34.1%) remained unvaccinated throughout the follow-up period, and 8336 (33.2%) received the vaccine postpartum. Younger women (< 30 years) were less likely to have the vaccine, and those living in areas of high deprivation were also less likely to have the vaccine (HR = 0.88, 95% CI 0.82 to 0.95). Asian and Other ethnic groups were 1.12 and 1.18 times more likely to have the vaccine in pregnancy compared with White women (HR = 1.12, 95% CI 1.00 to 1.25) and (HR = 1.18, 95% CI 1.03 to 1.37) respectively. Survey responses (objective 2): 207 (69%) of participants stated that they would be happy to have the vaccine during pregnancy. The remaining 94 (31%) indicated they would not have the vaccine during pregnancy. Reasons for having the vaccine included protecting self and baby, perceived risk level, and receipt of sufficient evidence and advice. Reasons for vaccine refusal included lack of research about long-term outcomes for the baby, anxiety about vaccines, inconsistent advice/information, and preference to wait until after the pregnancy.

CONCLUSION

Potentially only 1 in 3 pregnant women would have the COVID-19 vaccine during pregnancy, even though 2 in 3 reported they would have the vaccination, thus it is critical to develop tailored strategies to increase its acceptance rate and decrease vaccine hesitancy. A targeted approach to vaccinations may be required for groups such as younger people and those living in higher deprivation areas.

Connected Bradford: a Whole System Data Linkage Accelerator

The richness of linked population data provides exciting opportunities to understand local health needs, identify and predict those in most need of support and evaluate health interventions. There has been extensive investment to unlock the potential of clinical data for health research in the UK. However, most of the determinants of our health are social, economic, education, environmental, housing, food systems and are influenced by local authorities. The Connected Bradford Whole System Data Linkage Accelerator was set up to link health, education, social care, environmental and other local government data to drive learning health systems, prevention and population health management. Data spanning a period of over forty years has been linked for 800,000 individuals using the pseudonymised NHS number and other data variables. This prospective data collection captures near real time activity. This paper describes the dataset and our Connected Bradford Whole System Data Accelerator Framework that covers public engagement; practitioner and policy integration; legal and ethical approvals; information governance; technicalities of data linkage; data curation and guardianship; data validity and visualisation.

Premature mortality in people affected by co-occurring homelessness, justice involvement, opioid dependence, and psychosis: a retrospective cohort study using linked administrative data

BACKGROUND

Homelessness, opioid dependence, justice involvement, and psychosis are each associated with an increased risk of poor health and commonly co-occur in the same individuals. Most existing studies of mortality associated with this co-occurrence rely on active follow-up methods prone to selection and retention bias, and focus on a limited set of specific exposures rather than taking a population-based approach. To address these limitations, we did a retrospective cohort study using linked administrative data.

METHODS

In this retrospective cohort study, we linked a population register of adults resident in Glasgow, UK, to administrative datasets from homelessness and criminal justice services; community pharmacies; and a clinical psychosis registry with data from April 1, 2010 to March 31, 2014. Linkage to death registrations from April 1, 2014 to March 31, 2019 provided follow-up data on premature mortality (age <75 years) from all causes, non-communicable diseases, and causes considered potentially avoidable through health-care or public health intervention. We estimated hazard ratios (HR) using Poisson regression, adjusting for age, gender, socioeconomic deprivation, and calendar time.

FINDINGS

Of 536 653 cohort members, 11 484 (2·1%) died during follow-up. All-cause premature mortality was significantly higher among people with multiple exposures than among people with single exposures, and among people with any exposure than among people with none (eg, homelessness plus other exposures vs no exposures: HR 8·4 [95% CI 7·3-9·5]; homelessness alone vs no exposures: HR 2·2 [1·9-2·5]). Avoidable premature mortality was highest among those with multiple exposures (eg, imprisonment plus other exposures vs no exposures: HR 10·5 [9·1-12·3]; imprisonment alone vs no exposures: HR 3·8 [3·0-4·8]). Premature mortality from non-communicable disease was higher among those with any exposures than among those with none, despite accounting for a lower proportion of deaths in the exposed group; although in some cases there was little difference between estimates for single versus multiple exposures.

INTERPRETATION

The co-occurrence of at least two of homelessness, opioid dependence, justice involvement, or psychosis is associated with very high rates of premature mortality, particularly from avoidable causes of death, including non-communicable disease. Responding to these findings demands wide-ranging efforts across health-care provision, public health, and social policy. Future work should examine the timing and sequencing of exposures to better understand the causal pathways underlying excess mortality.

FUNDING

Chief Scientist Office, Medical Research Council, NHS Research Scotland.

Record Linkage and Big Data-Enhancing Information and Improving Design

OBJECTIVE

To highlight the potential of multiple file record linkage. Linkage increases the value of existing information by supplying missing data or correcting errors in existing data, through generating important covariates, and by using family information to control for unmeasured variables and expand research opportunities.

STUDY DESIGN AND SETTING

Recent Manitoba papers highlight the use of linkage to produce better studies. Specific ways in which linkage helps deal with different substantive issues are described.

RESULTS

Wide data files-files containing considerable amounts of information on each individual-generated by linkage improve research by facilitating better design. Nonexperimental work in particular benefits from such linkages. Population registries are especially valuable in supplying family data to facilitate work across different substantive fields.

CONCLUSION

Several examples show how record linkage magnifies the value of information from individual projects. The results of observational studies become more defensible through the better designs facilitated by such linkage.

Connected Bradford: a Whole System Data Linkage Accelerator

The richness of linked population data provides exciting opportunities to understand local health needs, identify and predict those in most need of support and evaluate health interventions. There has been extensive investment to unlock the potential of clinical data for health research in the UK. However, most of the determinants of our health are social, economic, education, environmental, housing, food systems and are influenced by local authorities. The Connected Bradford Whole System Data Linkage Accelerator was set up to link health, education, social care, environmental and other local government data to drive learning health systems, prevention and population health management. Data spanning a period of over forty years has been linked for 800,000 individuals using the pseudonymised NHS number and other data variables. This prospective data collection captures near real time activity. This paper describes the dataset and our Connected Bradford Whole System Data Accelerator Framework that covers public engagement; practitioner and policy integration; legal and ethical approvals; information governance; technicalities of data linkage; data curation and guardianship; data validity and visualisation.

Protocol for the development of the Wales Multimorbidity e-Cohort (WMC): data sources and methods to construct a population-based research platform to investigate multimorbidity

INTRODUCTION

Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence.We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity.

METHODS AND ANALYSIS

The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity.Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation.

ETHICS AND DISSEMINATION

The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.

The SPOR-Canadian Data Platform: a national initiative to facilitate data rich multi-jurisdictional research

Administrative health data is recognized for its value for conducting population-based research that has contributed to numerous improvements in health. In Canada, each province and territory is responsible for administering its own publicly funded health care program, which has resulted in multiple sets of administrative health data. Challenges to using these data within each of these jurisdictions have been identified, which are further amplified when the research involves more than one jurisdiction. The benefits to conducting multi-jurisdictional studies has been recognized by the Canadian Institutes of Health Research (CIHR), which issued a call in 2017 for proposals that address the challenges. The grant led to the creation of Health Data Research Network Canada (HDRN), with a vision is to establish a distributed network that facilitates and accelerates multi-jurisdictional research in Canada. HDRN received funding for seven years that will be used to support the objectives and activities of an initiative called the Strategy for Patient-Oriented Research Canadian Data Platform (SPOR-CDP). In this paper, we describe the challenges that researchers face while using, or considering using, administrative health data to conduct multi-jurisdictional research and the various ways that the SPOR-CDP will attempt to address them. Our objective is to assist other groups facing similar challenges associated with undertaking multi-jurisdictional research.

Understanding and responding to COVID-19 in Wales: protocol for a privacy-protecting data platform for enhanced epidemiology and evaluation of interventions

INTRODUCTION

The emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.

METHODS AND ANALYSIS

Two privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection.

ETHICS AND DISSEMINATION

The Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.

How much does quality matter: the value of data

In a world of competing priorities, accurate production of information on the scale of the injury burden and the effectiveness of prevention-orientated interventions and policies is important; hence, data quality matters. This article surveys the literature about what is known about data quality in the injury field and developments to improve the quality and usability of information, particularly through triangulation of data sources, data linkage and unlocking the potential for more deeply phenotyped data through natural language processing.

A Profile of the SAIL Databank on the UK Secure Research Platform

BACKGROUND

The Secure Anonymised Information Linkage (SAIL) Databank is a national data safe haven of de identified datasets principally about the population of Wales, made available in anonymised form to researchers across the world. It was established to enable the vast arrays of data collected about individuals in the course of health and other public service delivery to be made available to answer important questions that could not otherwise be addressed without prohibitive effort. The SAIL Databank is the bedrock of other funded centres relying on the data for research.

APPROACH

SAIL is a data repository surrounded by a suite of physical, technical and procedural control measures embodying a proportionate privacy-by-design governance model, informed by public engagement, to safeguard the data and facilitate data utility. SAIL operates on the UK Secure Research Platform (SeRP), which is a customisable technology and analysis platform. Researchers access anonymised data via this secure research environment, from which results can be released following scrutiny for disclosure risk. SAIL data are being used in multiple research areas to evaluate the impact of health and social exposures and policy interventions.

DISCUSSION

Lessons learned and their applications include: managing evolving legislative and regulatory requirements; employing multiple, tiered security mechanisms; working hard to increase analytical capacity efficiency; and developing a multi-faceted programme of public engagement. Further work includes: incorporating new data types; enabling alternative means of data access; and developing further efficiencies across our operations.

CONCLUSION

SAIL represents an ongoing programme of work to develop and maintain an extensive, whole population data resource for research. Its privacy-by-design model and UK SeRP technology have received international acclaim, and we continually endeavour to demonstrate trustworthiness to support data provider assurance and public acceptability in data use. We strive for further improvement and continue a mutual learning process with our contemporaries in this rapidly developing field.

Dynamic Urban Environmental Exposures on Depression and Suicide (NEEDS) in the Netherlands: a protocol for a cross-sectional smartphone tracking study and a longitudinal population register study

INTRODUCTION

Environmental exposures are intertwined with mental health outcomes. People are exposed to the environments in which they currently live, and to a multitude of environments along their daily movements and through their residential relocations. However, most research assumes that people are immobile, disregarding that such dynamic exposures also serve as stressors or buffers potentially associated with depression and suicide risk. The aim of the Dynamic Urban Environmental Exposures on Depression and Suicide (NEEDS) study is to examine how dynamic environmental exposures along people's daily movements and over their residential histories affect depression and suicide mortality in the Netherlands.

METHODS AND ANALYSIS

The research design comprises two studies emphasising the temporality of exposures. First, a cross-sectional study is assessing how daily exposures correlate with depression. A nationally representative survey was administered to participants recruited through stratified random sampling of the population aged 18-65 years. Survey data were enriched with smartphone-based data (eg, Global Positioning System tracking, Bluetooth sensing, social media usage, communication patterns) and environmental exposures (eg, green and blue spaces, noise, air pollution). Second, a longitudinal population register study is addressing the extent to which past environmental exposures over people's residential history affect suicide risk later in life. Statistical and machine learning-based models are being developed to quantify environment-health relations.

ETHICS AND DISSEMINATION

Ethical approval (FETC17-060) was granted by the Ethics Review Board of Utrecht University, The Netherlands. Project-related findings will be disseminated at conferences and in peer-reviewed journal papers. Other project outcomes will be made available through the project's web page, http://www.needs.sites.uu.nl.

Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond

The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well-being. However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency. We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team-based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit.

Including household effects in Big Data research: the experience of building a longitudinal residence algorithm using linked administrative data in Wales

The effect of the wider social-environment on physical and emotional health has long been an area of study. Extrapolating the impact of the individual's immediate environment, such as living with a smoker or caring for a chronically-ill child, would potentially reduce confounding effects in health-related research. Surveys, including the UK Census, are beginning to collect data on household composition. However, these surveys are expensive, time consuming, and, as such, are only completed by a subsection of the population. Large-scale, linked databanks, such as the SAIL Databank at Swansea University, which hold routinely collected secondary use clinical and administrative datasets, are broader in scope, both in terms of the nature of the data held, and the population. The SAIL databank includes demographic data and a geographic indicator that makes it possible to identify groups of people that share accommodation, and in some cases the familial relationships among them. This paper describes a method for creating households, including considerations for how that information can be securely shared for research purposes. This approach has broad implications in Wales and beyond, opening up possibilities for more detailed population-level research that includes consideration of residential social interactions.

Study protocol for investigating the impact of community home modification services on hospital utilisation for fall injuries: a controlled longitudinal study using data linkage

INTRODUCTION

This study will evaluate the effectiveness of home adaptations, both in preventing hospital admissions due to falls for older people, and improving timely discharge. Results will provide evidence for services at the interface between health and social care, informing policies seeking to promote healthy ageing through prudent healthcare and fall prevention.

METHODS AND ANALYSIS

All individuals living in Wales, UK, aged 60 years and over, will be included in the study using anonymised linked data from the Secure Anonymised Information Linkage Databank. We will use a national database of home modifications implemented by the charity organisation Care & Repair Cymru (C&R) from 2009 to 2017 to define an intervention cohort. We will use the electronic Frailty Index to assign individual levels of frailty (fit, mild, moderate or severe) and use these to create a comparator group (non-C&R) of people who have not received a C&R intervention. Coprimary outcomes will be quarterly numbers of emergency hospital admissions attributed to falls at home, and the associated length of stay. Secondary outcomes include the time in moving to a care home following a fall, and the indicative financial costs of care for individuals who had a fall. We will use appropriate multilevel generalised linear models to analyse the number of hospital admissions related to falls. We will use Cox proportional hazard models to compare the length of stay for fall-related hospital admissions and the time in moving to a care home between the C&R and non-C&R cohorts. We will assess the impact per frailty group, correct for population migration and adjust for confounding variables. Indicative costs will be calculated using financial codes for individual-level hospital stays. Results will provide evidence for services at the interface between health and social care, informing policies seeking to promote healthy ageing through prudent healthcare and prevention.

ETHICS AND DISSEMINATION

Information governance requirements for the use of record-linked data have been approved and only anonymised data will be used in our analysis. Our results will be submitted for publication in peer-reviewed journals. We will also work with lay members and the knowledge transfer team at Swansea University to create communication and dissemination materials on key findings.

Emergency hospital admissions associated with a non-randomised housing intervention meeting national housing quality standards: a longitudinal data linkage study

BACKGROUND

We investigated tenant healthcare utilisation associated with upgrading 8558 council houses to a national quality standard. Homes received multiple internal and external improvements and were analysed using repeated measures of healthcare utilisation.

METHODS

The primary outcome was emergency hospital admissions for cardiorespiratory conditions and injuries for residents aged 60 years and over. Secondary outcomes included each of the separate conditions, for tenants aged 60 and over, and for all ages. Council home address and intervention records for eight housing cointerventions were anonymously linked to demographic data, hospital admissions and deaths for individuals in a dynamic cohort. Counts of health events were analysed using multilevel regression models to investigate associations between receipt of each housing improvement, adjusting for potential confounding factors and regional trends.

RESULTS

Residents aged 60 years and over living in homes when improvements were made were associated with up to 39% fewer admissions compared with those living in homes that were not upgraded (incidence rate ratio=0.61, 95% CI 0.53 to 0.72). Reduced admissions were associated with electrical systems, windows and doors, wall insulation, and garden paths. There were small non-significant reductions for the primary outcome associated with upgrading heating, adequate loft insulation, new kitchens and new bathrooms.

CONCLUSION

Results suggest that hospital admissions can be avoided through improving whole home quality standards. This is the first large-scale longitudinal evaluation of a whole home intervention that has evaluated multiple improvement elements using individual-level objective routine health data.

Creating individual level air pollution exposures in an anonymised data safe haven: a platform for evaluating impact on educational attainment

Introduction

There is a lack of evidence on the adverse effects of air pollution on cognition for people with air quality-related health conditions. We propose that educational attainment, as a proxy for cognition, may increase with improved air quality. This study will explore whether asthma and seasonal allergic rhinitis, when exacerbated by acute exposure to air pollution, is associated with educational attainment.

Objective

To describe the preparation of individual and household-level linked environmental and health data for analysis within an anonymised safe haven. Also to introduce our statistical analysis plan for our study: COgnition, Respiratory Tract illness and Effects of eXposure (CORTEX).

Methods

We imported daily air pollution and aeroallergen data, and individual level education data into the SAIL databank, an anonymised safe haven for person-based records. We linked individual-level education, socioeconomic and health data to air quality data for home and school locations, creating tailored exposures for individuals across a city. We developed daily exposure data for all pupils in repeated cross sectional exam cohorts (2009-2015).

Conclusion

We have used the SAIL databank, an innovative, data safe haven to create individual-level exposures to air pollution and pollen for multiple daily home and school locations. The analysis platform will allow us to evaluate retrospectively the impact of air quality on attainment for multiple cross-sectional cohorts of pupils. Our methods will allow us to distinguish between the pollution impacts on educational attainment for pupils with and without respiratory health conditions. The results from this study will further our understanding of the effects of air quality and respiratory-related health conditions on cognition.

Highlights

Toward dynamic urban environmental exposure assessments in mental health research

It is increasingly recognized that mental disorders are affected by both personal characteristics and environmental exposures. The built, natural, and social environments can either contribute to or buffer against metal disorders. Environmental exposure assessments related to mental health typically rely on neighborhoods within which people currently live. In this article, I call into question such neighborhood-based exposure assessments at one point in time, because human life unfolds over space and across time. To circumvent inappropriate exposure assessments and to better grasp the etiologies of mental disease, I argue that people are exposed to multiple health-supporting and harmful exposures not only during their daily lives, but also over the course of their lives. This article aims to lay a theoretical foundation elucidating the impact of dynamic environmental exposures on mental health outcomes. I examine, first, the possibilities and challenges for mental health research to integrate people's environmental exposures along their daily paths and, second, how exposures over people's residential history might affect mental health later in life. To push the borders of scientific inquiries, I stress that only such mobility-based approaches facilitate an exploration of exposure duration, exposure sequences, and exposure accumulation.

Mortality, morbidity and health in developed societies: a review of data sources

The purpose of this paper is to review the major sources of data on mortality, morbidity and health in Europe and in other developed regions in order to examine their potential for analysing mortality and morbidity levels and trends. The review is primarily focused on routinely collected information covering a whole country. No attempt is made to draw up an inventory of sources by country; the paper deals instead with the pros and cons of each source for mortality and morbidity studies in demography. While each source considered separately can already yield useful, though partial, results, record linkage among data sources can significantly improve the analysis. Record linkage can also lead to the detection of possible causal associations that could eventually be confirmed. More generally, Big Data can reveal changing mortality and morbidity trends and patterns that could lead to preventive measures being taken rather than more costly curative ones.

Life course epidemiology: Modeling educational attainment with administrative data

Background

Understanding the processes across childhood and adolescence that affect later life inequalities depends on many variables for a large number of individuals measured over substantial time periods. Linkable administrative data were used to generate birth cohorts and to study pathways of inequity in childhood and early adolescence leading to differences in educational attainment. Advantages and disadvantages of using large administrative data bases for such research were highlighted.

Methods

Children born in Manitoba, Canada between 1982 and 1995 were followed until age 19 (N = 89,763), with many time-invariant measures serving as controls. Five time-varying predictors of high school graduation—three social and two health—were modelled using logistic regression and a framework for examining predictors across the life course. For each time-varying predictor, six temporal patterns were tested: full, accumulation of risk, sensitive period, and three critical period models.

Results

Predictors measured in early adolescence generated the highest odds ratios, suggesting the importance of adolescence. Full models provided the best fit for the three time-varying social measures. Residence in a low-income neighborhood was a particularly influential predictor of not graduating from high school. The transmission of risk across developmental periods was also highlighted; exposure in one period had significant implications for subsequent life stages.

Conclusion

This study advances life course epidemiology, using administrative data to clarify the relationships among several measures of social behavior, cognitive development, and health. Analyses of temporal patterns can be useful in studying such other outcomes as educational achievement, teen pregnancy, and workforce participation.

Ensuring Privacy When Integrating Patient-Based Datasets: New Methods and Developments in Record Linkage

In an era where the volume of structured and unstructured digital data has exploded, there has been an enormous growth in the creation of data about individuals that can be used for understanding and treating disease. Joining these records together at an individual level provides a complete picture of a patient's interaction with health services and allows better assessment of patient outcomes and effectiveness of treatment and services. Record linkage techniques provide an efficient and cost-effective method to bring individual records together as patient profiles. These linkage procedures bring their own challenges, especially relating to the protection of privacy. The development and implementation of record linkage systems that do not require the release of personal information can reduce the risks associated with record linkage and overcome legal barriers to data sharing. Current conceptual and experimental privacy-preserving record linkage (PPRL) models show promise in addressing data integration challenges. Enhancing and operationalizing PPRL protocols can help address the dilemma faced by some custodians between using data to improve quality of life and dealing with the ethical, legal, and administrative issues associated with protecting an individual's privacy. These methods can reduce the risk to privacy, as they do not require personally identifying information to be shared. PPRL methods can improve the delivery of record linkage services to the health and broader research community.

Outcomes of an investment in administrative data infrastructure: An example of capacity building at the Manitoba Centre for Health Policy

Using the Manitoba Centre for Health Policy as an example, this commentary discusses how even small investments in population health data can create a multitude of research benefits. The authors highlight that through infrastructure development such as acquiring databases, facilitating access to data and developing data management practices, new, innovative research can be achieved at relatively low cost.

The Digital House of Care: information solutions for integrated care

Purpose

The purpose of this paper is to describe the development of a digital tool in an English county striving towards a vision of integrated information that is used to underpin an increasingly integrated future of health and social care delivery.

Design/methodology/approach

It discusses the policy context nationally, the origins and implementation of the initiative, the authors’ experiences and viewpoint highlighting key challenges and learning, as well as examples of new work undertaken.

Findings

In all, 12 health and care organisations have participated in this project. The ability for local commissioners and providers of services to now understand “flow” both between and within services at a granular level is unique. Costs are modest, and the opportunities for refining and better targeting as well as validating services are significant, thus demonstrating a return on investment. Key learning includes how organisational development was equally as important as the implementation of innovative new software, that change management from grass roots to strategic leaders is vital, and that the whole system is greater than the sum of its otherwise in-silo parts.

Practical implications

Data linkage initiatives, whether local, regional or national in scale, need to be programme managed. A robust governance and accountability framework must be in place to realise the benefits of such as a solution, and IT infrastructure is paramount.

Social implications

Organisational development, collaborative as well as distributed leadership, and managing a change in culture towards health and care information is critical in order to create a supportive environment that fosters learning across organisational boundaries.

Originality/value

This paper draws on the recent experience of achieving large-scale data integration across the boundaries of health and social care, to help plan and commission services more effectively. This rich, multi-agency intelligence has already begun to change the way in which the system considers service planning, and learning from this county’s approach may assist others considering similar initiatives.

Residential Moving and Preventable Hospitalizations

OBJECTIVES

To investigate the association between moving home in the first year of life and subsequent emergency admissions for potentially preventable hospitalizations.

METHODS

We undertook a cohort analysis of linked anonymized data on 237 842 children in the Welsh Electronic Cohort for Children. We included children born in Wales between April 1, 1999 and December 31, 2008. The exposure was the number of residential moves from birth up to 1 year. The main outcome was emergency admissions for potentially preventable hospitalizations (PPH) between the age of 1 and 5 years.

RESULTS

After adjustment for confounders, we identified that moving home frequently in the first year of life was associated with an increased risk of emergency PPH between the ages of 1 and 5 when compared with not moving. We found significant differences associated with ≥2 moves for the following: ear, nose, and throat infections (incidence risk ratio [IRR], 1.44; 95% confidence interval [CI], 1.29-1.61); convulsions/epilepsy (IRR, 1.58; 95% CI, 1.23-2.04); injuries (IRR, 1.33; 95% CI, 1.18-1.51); dehydration/gastroenteritis (IRR, 1.51; 95% CI, 1.21-1.88); asthma (IRR, 1.61; 95% CI, 1.19-2.16); influenza/pneumonia (IRR, 1.15; 95% CI, 1.00-1.32); and dental conditions (IRR, 1.30; 95% CI, 1.03-1.64) for ≥1 moves.

CONCLUSIONS

Children who move home in the first year of life are at substantially increased risk of emergency admissions for PPH in early childhood. Additional research that focuses on enhancing health and social support services for highly mobile families, educating parents about safety risks, and improving housing quality is warranted.

Effects of an air pollution personal alert system on health service usage in a high-risk general population: a quasi-experimental study using linked data

BACKGROUND

There is no evidence to date on whether an intervention alerting people to high levels of pollution is effective in reducing health service utilisation. We evaluated alert accuracy and the effect of a targeted personal air pollution alert system, airAware, on emergency hospital admissions, emergency department attendances, general practitioner contacts and prescribed medications.

METHODS

Quasi-experimental study describing accuracy of alerts compared with pollution triggers; and comparing relative changes in healthcare utilisation in the intervention group to those who did not sign-up. Participants were people diagnosed with asthma, chronic obstructive pulmonary disease (COPD) or coronary heart disease, resident in an industrial area of south Wales and registered patients at 1 of 4 general practices. Longitudinal anonymised record linked data were modelled for participants and non-participants, adjusting for differences between groups.

RESULTS

During the 2-year intervention period alerts were correctly issued on 208 of 248 occasions; sensitivity was 83.9% (95% CI 78.8% to 87.9%) and specificity 99.5% (95% CI 99.3% to 99.6%). The intervention was associated with a 4-fold increase in admissions for respiratory conditions (incidence rate ratio (IRR) 3.97; 95% CI 1.59 to 9.93) and a near doubling of emergency department attendance (IRR=1.89; 95% CI 1.34 to 2.68).

CONCLUSIONS

The intervention was associated with increased emergency admissions for respiratory conditions. While findings may be context specific, evidence from this evaluation questions the benefits of implementing near real-time personal pollution alert systems for high-risk individuals.

Effects of an air pollution personal alert system on health service usage in a high-risk general population: a quasi-experimental study using linked data

BACKGROUND

There is no evidence to date on whether an intervention alerting people to high levels of pollution is effective in reducing health service utilisation. We evaluated alert accuracy and the effect of a targeted personal air pollution alert system, airAware, on emergency hospital admissions, emergency department attendances, general practitioner contacts and prescribed medications.

METHODS

Quasi-experimental study describing accuracy of alerts compared with pollution triggers; and comparing relative changes in healthcare utilisation in the intervention group to those who did not sign-up. Participants were people diagnosed with asthma, chronic obstructive pulmonary disease (COPD) or coronary heart disease, resident in an industrial area of south Wales and registered patients at 1 of 4 general practices. Longitudinal anonymised record linked data were modelled for participants and non-participants, adjusting for differences between groups.

RESULTS

During the 2-year intervention period alerts were correctly issued on 208 of 248 occasions; sensitivity was 83.9% (95% CI 78.8% to 87.9%) and specificity 99.5% (95% CI 99.3% to 99.6%). The intervention was associated with a 4-fold increase in admissions for respiratory conditions (incidence rate ratio (IRR) 3.97; 95% CI 1.59 to 9.93) and a near doubling of emergency department attendance (IRR=1.89; 95% CI 1.34 to 2.68).

CONCLUSIONS

The intervention was associated with increased emergency admissions for respiratory conditions. While findings may be context specific, evidence from this evaluation questions the benefits of implementing near real-time personal pollution alert systems for high-risk individuals.

Does frequent residential mobility in early years affect the uptake and timeliness of routine immunisations? An anonymised cohort study

BACKGROUND

There are conflicting findings regarding the impact of residential mobility on immunisation status. Our aim was to determine whether there was any association between residential mobility and take up of immunisations and whether they were delayed in administration.

METHODS

We carried out a cohort analysis of children born in Wales, UK. Uptake and time of immunisation were collected electronically. We defined frequent movers as those who had moved: 2 or more times in the period prior to the final scheduled on-time date (4 months) for 5 in 1 vaccinations; and 3 or more times in the period prior to the final scheduled on-time date (12 months) for MMR, pneumococcal and meningitis C vaccinations. We defined immunisations due at 2-4 months delayed if they had not been given by age 1; and those due at 12-13 months as delayed if they had not been given by age 2.

RESULTS

Uptake rates of routine immunisations and whether they were given within the specified timeframe were high for both groups. There was no increased risk (odds ratios (95% confidence intervals) between frequent movers compared to non-movers for the uptake of: primary MMR 1.08 (0.88-1.32); booster Meningitis C 1.65 (0.93-2.92); booster pneumococcal 1.60 (0.59-4.31); primary 5 in 1 1.28 (0.92-1.78); and timeliness: primary MMR 0.92 (0.79-1.07); booster Meningitis C 1.26 (0.77-2.07); booster pneumococcal 1.69 (0.23-12.14); and primary 5 in 1 1.04 (0.88-1.23).

DISCUSSION

Findings suggest that children who move home frequently are not adversely affected in terms of the uptake of immunisations and whether they were given within a specified timeframe. Both were high and may reflect proactive behaviour in the primary healthcare setting to meet Government coverage rates for immunisation.

All Wales Injury Surveillance System revised: development of a population-based system to evaluate single-level and multilevel interventions

Background

Injury surveillance has been established since the 1990s, but is still largely based upon single-source data from sentinel sites. The growth of electronic health records and developments in privacy protecting linkage technologies provide an opportunity for more sophisticated surveillance systems.

Objective

To describe the evolution of an injury surveillance system to support the evaluation of interventions, both simple and complex in terms of organisation.

Methods

The paper describes the evolution of the system from one that relied upon data only from emergency departments to one that include multisource data and are now embedded in a total population privacy protecting data linkage system. Injury incidence estimates are compared by source and data linkage used to aid understanding of data quality issues. Examples of applications, challenges and solutions are described.

Results

The age profile and estimated incidence of injuries recorded in general practice, emergency departments and hospital admissions differ considerably. Data linkage has enabled the evaluation of complex interventions and measurement of longer-term impact of a wide range of exposures.

Conclusions

Embedding injury surveillance within privacy protecting data linkage environment can transform the utility of a traditional single-source surveillance system to a multisource system. It also facilitates greater involvement in the evaluation of simple and complex healthcare and non-healthcare interventions and contributes to the growing evidence basis underlying the science of injury prevention and control.

Accuracy of Probabilistic Linkage Using the Enhanced Matching System for Public Health and Epidemiological Studies

Background

The Enhanced Matching System (EMS) is a probabilistic record linkage program developed by the tuberculosis section at Public Health England to match data for individuals across two datasets. This paper outlines how EMS works and investigates its accuracy for linkage across public health datasets.

Methods

EMS is a configurable Microsoft SQL Server database program. To examine the accuracy of EMS, two public health databases were matched using National Health Service (NHS) numbers as a gold standard unique identifier. Probabilistic linkage was then performed on the same two datasets without inclusion of NHS number. Sensitivity analyses were carried out to examine the effect of varying matching process parameters.

Results

Exact matching using NHS number between two datasets (containing 5931 and 1759 records) identified 1071 matched pairs. EMS probabilistic linkage identified 1068 record pairs. The sensitivity of probabilistic linkage was calculated as 99.5% (95%CI: 98.9, 99.8), specificity 100.0% (95%CI: 99.9, 100.0), positive predictive value 99.8% (95%CI: 99.3, 100.0), and negative predictive value 99.9% (95%CI: 99.8, 100.0). Probabilistic matching was most accurate when including address variables and using the automatically generated threshold for determining links with manual review.

Conclusion

With the establishment of national electronic datasets across health and social care, EMS enables previously unanswerable research questions to be tackled with confidence in the accuracy of the linkage process. In scenarios where a small sample is being matched into a very large database (such as national records of hospital attendance) then, compared to results presented in this analysis, the positive predictive value or sensitivity may drop according to the prevalence of matches between databases. Despite this possible limitation, probabilistic linkage has great potential to be used where exact matching using a common identifier is not possible, including in low-income settings, and for vulnerable groups such as homeless populations, where the absence of unique identifiers and lower data quality has historically hindered the ability to identify individuals across datasets.

Dynamic consent: a possible solution to improve patient confidence and trust in how electronic patient records are used in medical research

With one million people treated every 36 hours, routinely collected UK National Health Service (NHS) health data has huge potential for medical research. Advances in data acquisition from electronic patient records (EPRs) means such data are increasingly digital and can be anonymised for research purposes. NHS England’s care.data initiative recently sought to increase the amount and availability of such data. However, controversy and uncertainty following the care.data public awareness campaign led to a delay in rollout, indicating that the success of EPR data for medical research may be threatened by a loss of patient and public trust. The sharing of sensitive health care data can only be done through maintaining such trust in a constantly evolving ethicolegal and political landscape. We propose that a dynamic consent model, whereby patients can electronically control consent through time and receive information about the uses of their data, provides a transparent, flexible, and user-friendly means to maintain public trust. This could leverage the huge potential of the EPR for medical research and, ultimately, patient and societal benefit.

The communities first (ComFi) study: protocol for a prospective controlled quasi-experimental study to evaluate the impact of area-wide regeneration on mental health and social cohesion in deprived communities

INTRODUCTION

Recent systematic reviews have highlighted the dearth of evidence on the effectiveness of regeneration on health and health inequalities. 'Communities First' is an area-wide regeneration scheme to improve the lives of people living in the most deprived areas in Wales (UK). This study will evaluate the impact of Communities First on residents' mental health and social cohesion.

METHODS AND ANALYSIS

A prospective controlled quasi-experimental study of the association between residence in Communities First regeneration areas in Caerphilly county borough and change in mental health and social cohesion. The study population is the 4226 residents aged 18-74 years who responded to the Caerphilly Health and Social Needs Study in 2001 (before delivery) and 2008 (after delivery of Communities First). Data on the location, type and cost of Communities First interventions will be extracted from records collected by Caerphilly county borough council. The primary outcome is the change in mental health between 2001 and 2008. Secondary outcomes are changes: in common mental disorder case status (using survey and general practice data), social cohesion and mental health inequalities. Multilevel models will examine change in mental health and social cohesion between Communities First and control areas, adjusting for individual and household level confounding factors. Further models will examine the effects of (1) different types of intervention, (2) contamination across areas, (3) length of residence in a Communities First area, and (4) population migration. We will carry out a cost-consequences analysis to summarise the outcomes generated for participants, as well as service utilisation and utility gains.

ETHICS AND DISSEMINATION

This study has had approval from the Information Governance Review Panel at Swansea University (Ref: 0266 CF). Findings will be disseminated through peer-review publications, international conferences, policy and practice partners in local and national government, and updates on our study website (http://medicine.cardiff.ac.uk/clinical-study/communities-first-regeneration-programme/).