Identification of incident poisoning, fracture and burn events using linked primary care, secondary care and mortality data from England: implications for research and surveillance

Breaking It Down: A Systematic Review Unravelling the Impact of Attention Deficit Hyperactivity Disorder and Methylphenidate on Childhood Fractures

Limb fractures are a common cause of pediatric hospital admissions and surgeries, with a significant prevalence in the United Kingdom across all injury categories. Among psychiatric conditions in children, attention deficit hyperactivity disorder (ADHD) stands out as frequently associated with fractures, particularly those involving extremities. ADHD, with diagnoses prevalent among a significant proportion of school-age children and adolescents, has witnessed a growing global incidence. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist for our systematic literature search, using various databases and specific search terms related to ADHD and fractures. We considered articles from 2018 to 2023, focusing on English language papers with free full-text access. Our selection process used the PRISMA flowchart. We began with 1,890 articles and, after deduplication, title screening, abstract assessment, and quality evaluation included nine research papers in our review. Our primary focus was on examining fracture-related outcomes in individuals with ADHD compared to those without, considering medication status. These studies encompassed various designs, with a focus on the ADHD-fracture relationship and methylphenidate's (MPH) impact. Our study confirms that ADHD increases fracture risk and suggests that MPH may help mitigate this risk. Early ADHD detection is vital for nonpharmacological interventions. Orthopedic surgeons should proactively identify ADHD, while healthcare professionals should offer injury prevention guidance, particularly for at-risk groups.

Impact of the national home safety equipment scheme 'Safe At Home' on hospital admissions for unintentional injury in children under 5: a controlled interrupted time series analysis

BACKGROUND

Unintentional home injuries are a leading cause of preventable death in young children. Safety education and equipment provision improve home safety practices, but their impact on injuries is less clear. Between 2009 and 2011, a national home safety equipment scheme was implemented in England (Safe At Home), targeting high-injury-rate areas and socioeconomically disadvantaged families with children under 5. This provided a 'natural experiment' for evaluating the scheme's impact on hospital admissions for unintentional injuries.

METHODS

Controlled interrupted time series analysis of unintentional injury hospital admission rates in small areas (Lower Layer Super Output Areas (LSOAs)) in England where the scheme was implemented (intervention areas, n=9466) and matched with LSOAs in England and Wales where it was not implemented (control areas, n=9466), with subgroup analyses by density of equipment provision.

RESULTS

57 656 homes receiving safety equipment were included in the analysis. In the 2 years after the scheme ended, monthly admission rates declined in intervention areas (-0.33% (-0.47% to -0.18%)) but did not decline in control areas (0.04% (-0.11%-0.19%), p value for difference in trend=0.001). Greater reductions in admission rates were seen as equipment provision density increased. Effects were not maintained beyond 2 years after the scheme ended.

CONCLUSIONS

A national home safety equipment scheme was associated with a reduction in injury-related hospital admissions in children under 5 in the 2 years after the scheme ended. Providing a higher number of items of safety equipment appears to be more effective in reducing injury rates than providing fewer items.

Three Es of linked injury data: Episodes, Encounters and Events

BACKGROUND

Treatment and recovery times following injury can be lengthy, comprising multiple interactions with the hospital system for initial acute care, subsequent rehabilitation and possible re-presentation due to complications.

AIMS

This article aims to promote the use of consistent terminology in injury data linkage studies, suggest important factors to consider when managing linked injury data, and encourage thorough documentation and a robust discourse around different approaches to data management to ensure reproducibility, consistency and comparability of analyses arising from linked injury data.

APPROACH

This paper is presented in sections describing: (1) considerations for identifying injury cohorts, (2) considerations for grouping Episodes into Encounters and (3) considerations for grouping Encounters into Events. Summary tools are provided to aid researchers in the management of linked injury data.

DISCUSSION

Careful consideration of decisions made when identifying injury cohorts and grouping data into units of analysis (Episodes/Encounters/Events) is essential when using linked injury data. Choices made have the potential to significantly impact the epidemiological and clinical findings derived from linked injury data studies, which ultimately affect the quality of injury prevention initiatives and injury management policy and practice. It is intended that this paper will act as a call to action for injury linkage methodologists, and those using linked data, to critique approaches, share tools and engage in a robust discourse to further advance the use of linked injury data, and ultimately enhance the value of linked injury data for clinicians and health and social policymakers.

Incident Type 2 Diabetes and Risk of Fracture: A Comparative Cohort Analysis Using U.K. Primary Care Records

OBJECTIVE

To estimate risk of fracture in men and women with recent diagnosis of type 2 diabetes compared with individuals without diabetes.

RESEARCH DESIGN AND METHODS

In this cohort study, we used routinely collected U.K. primary care data from The Health Improvement Network. In adults (>35 years) diagnosed with type 2 diabetes between 2004 and 2013, fractures sustained until 2019 were identified and compared with fractures sustained in individuals without diabetes. Multivariable models estimated time to first fracture following diagnosis of diabetes. Annual prevalence rates included at least one fracture in a given year.

RESULTS

Among 174,244 individuals with incident type 2 diabetes and 747,290 without diabetes, there was no increased risk of fracture among males with diabetes (adjusted hazard ratio [aHR] 0.97 [95% CI 0.94, 1.00]) and a small reduced risk among females (aHR 0.94 [95% CI 0.92, 0.96]). In those aged ≥85 years, those in the diabetes cohort were at significantly lower risk of incident fracture (males: aHR 0.85 [95% CI 0.71, 1.00]; females: aHR 0.85 [95% CI 0.78, 0.94]). For those in the most deprived areas, aHRs were 0.90 (95% CI 0.83, 0.98) for males and 0.91 (95% CI 0.85, 0.97) for females. Annual fracture prevalence rates, by sex, were similar for those with and without type 2 diabetes.

CONCLUSIONS

We found no evidence to suggest a higher risk of fracture following diagnosis of type 2 diabetes. After a diagnosis of type 2 diabetes, individuals should be encouraged to make positive lifestyle changes, including undertaking weight-bearing physical activities that improve bone health.

Concordance in the recording of stroke across UK primary and secondary care datasets: a population-based cohort study

Background

Previous work has demonstrated that the recording of acute health outcomes, such as myocardial infarction (MI), may be suboptimal in primary healthcare databases.

Aim

To assess the completeness and accuracy of the recording of stroke in UK primary care.

Design & setting

A population-based longitudinal cohort study.

Method

Cases of stroke were identified separately in Clinical Practice Research Datalink (CPRD) primary care records and linked Hospital Episode Statistics (HES). The recording of events in the same patient across the two datasets was compared. The reliability of strategies to identify fatal strokes in primary care and hospital records was also assessed.

Results

Of the 75 674 stroke events that were identified in either CPRD or HES data during the period of the study, 54 929 (72.6%) were recorded in CPRD and 51 013 (67.4%) were recorded in HES. Two-fifths (n = 30 268) of all recorded strokes were found in both datasets (allowing for a time window of 120 days). Among these 'matched' strokes the subtype was recorded accurately in approximately 75% of CPRD records (compared with coding in HES); however, 43.5% of ischaemic strokes in HES were coded as 'non-specific' strokes in CPRD data. Furthermore, 48.2% had same-day recordings, and 56.2% were date-matched within ±1 day.

Conclusion

The completeness and accuracy of stroke recording is improved by the use of linked hospital and primary care records. For studies that have a time-sensitive research question, the use of linked, as opposed to stand-alone, CPRD data is strongly recommended.

Delayed Denosumab Injections and Fracture Risk Among Patients With Osteoporosis : A Population-Based Cohort Study

BACKGROUND

Denosumab is effective for osteoporosis, but discontinuation leads to rapid reversal of its therapeutic effect.

OBJECTIVE

To estimate the risk for fracture among users of denosumab who delayed subsequent doses compared with users who received doses on time.

DESIGN

Population-based cohort study.

SETTING

The Health Improvement Network U.K. primary care database, 2010 to 2019.

PATIENTS

Persons aged 45 years or older who initiated denosumab therapy for osteoporosis.

MEASUREMENTS

Observational data were used to emulate an analysis of a hypothetical trial with 3 dosing intervals: subsequent denosumab injection given within 4 weeks after the recommended date ("on time"), delay by 4 to 16 weeks ("short delay"), and delay by more than 16 weeks ("long delay"). The primary outcome was a composite of all fracture types at 6 months after the recommended date. Secondary outcomes were major osteoporotic fracture, vertebral fracture, hip fracture, and nonvertebral fracture.

RESULTS

Investigators identified 2594 patients initiating denosumab therapy. The risk for composite fracture over 6 months was 27.3 in 1000 for on-time dosing, 32.2 in 1000 for short delay, and 42.4 in 1000 for long delay. Compared with on-time injections, short delay had a hazard ratio (HR) for composite fracture of 1.03 (95% CI, 0.63 to 1.69) and long delay an HR of 1.44 (CI, 0.96 to 2.17) (P for trend = 0.093). For vertebral fractures, short delay had an HR of 1.48 (CI, 0.58 to 3.79) and long delay an HR of 3.91 (CI, 1.62 to 9.45).

LIMITATION

Dosing schedules were not randomly assigned.

CONCLUSION

Although delayed administration of subsequent denosumab doses by more than 16 weeks is associated with increased risk for vertebral fracture compared with on-time dosing, evidence is insufficient to conclude that fracture risk is increased at other anatomical sites with long delay.

PRIMARY FUNDING SOURCE

National Clinical Research Center for Orthopedics, Sports Medicine & Rehabilitation.

Multinational Investigation of Fracture Risk with Antidepressant Use by Class, Drug, and Indication

OBJECTIVES

Antidepressants increase the risk of falls and fracture in older adults. However, risk estimates vary considerably even in comparable populations, limiting the usefulness of current evidence for clinical decision making. Our aim was to apply a common protocol to cohorts of older antidepressant users in multiple jurisdictions to estimate fracture risk associated with different antidepressant classes, drugs, doses, and potential treatment indications.

DESIGN

Retrospective (2009–2014) cohort study.

SETTING

Five jurisdictions in the United States, Canada, United Kingdom, and Taiwan.

PARTICIPANTS

Older antidepressant users—subjects were followed from first antidepressant prescription or dispensation to first fracture or until the end of follow‐up.

MEASUREMENTS

The risk of fractures with antidepressants was estimated by multivariable Cox proportional hazards models using time‐varying measures of antidepressant dose and use vs nonuse, adjusting for patient characteristics.

RESULTS

Between 42.9% and 55.6% of study cohorts were 75 years and older, and 29.3% to 45.4% were men. Selective serotonin reuptake inhibitors (SSRIs) (48.4%‐60.0%) were the predominant class used in North America compared with tricyclic antidepressants (TCAs) in the United Kingdom and Taiwan (49.6%‐53.6%). Fracture rates varied from 37.67 to 107.18 per 1,000. The SSRIs citalopram (hazard ratio [HR] = 1.23; 95% confidence interval [CI] = 1.11‐1.36 to HR = 1.43; 95% CI = 1.11‐1.84) and sertraline (HR = 1.36; 95% CI = 1.10‐1.68), the SNRI duloxetine (HR = 1.41; 95% CI = 1.06‐1.88), TCAs doxepin (HR = 1.36; 95% CI = 1.00‐1.86) and imipramine (HR = 1.16; 95% CI = 1.05‐1.28), and atypicals (HR = 1.34; 95% CI = 1.14‐1.58) increased fracture risk in some but not all jurisdictions. In the United States and the United Kingdom, fracture risk with all classes was higher when prescribed for depression than chronic pain, a trend that is likely explained by drug choice.

CONCLUSION

The fracture risk for patients may be reduced by selecting paroxetine, an SSRI with lower risk than citalopram, the SNRI venlafaxine over duloxetine, and the TCA amitriptyline over imipramine or doxepin. There is uncertainty about the risk associated with the atypical antidepressants. J Am Geriatr Soc 68:1494‐1503, 2020.

An Educational Review About Using Cost Data for the Purpose of Cost-Effectiveness Analysis

This paper provides an educational review covering the consideration of costs for cost-effectiveness analysis (CEA), summarising relevant methods and research from the published literature. Cost data are typically generated by applying appropriate unit costs to healthcare resource-use data for patients. Trial-based evaluations and decision analytic modelling represent the two main vehicles for CEA. The costs to consider will depend on the perspective taken, with conflicting recommendations ranging from focusing solely on healthcare to the broader 'societal' perspective. Alternative sources of resource-use are available, including medical records and forms completed by researchers or patients. Different methods are available for the statistical analysis of cost data, although consideration needs to be given to the appropriate methods, given cost data are typically non-normal with a mass point at zero and a long right-hand tail. The choice of covariates for inclusion in econometric models also needs careful consideration, focusing on those that are influential and that will improve balance and precision. Where data are missing, it is important to consider the type of missingness and then apply appropriate analytical methods, such as imputation. Uncertainty around costs should also be reflected to allow for consideration on the impacts of the CEA results on decision uncertainty. Costs should be discounted to account for differential timing, and are typically inflated to a common cost year. The choice of methods and sources of information used when accounting for cost information within CEA will have an effect on the subsequent cost-effectiveness results and how information is presented to decision makers. It is important that the most appropriate methods are used as overlooking the complicated nature of cost data could lead to inaccurate information being given to decision makers.

Incidence of injuries in Norway: linking primary and secondary care data

Aims: Most studies of injury incidence underestimate the total burden of injury, as they do not include injuries treated in primary care. The aim of this study was to measure the total incidence of medically treated injuries in Norway. We further investigated the epidemiology of injuries treated in primary and secondary care. Methods: We collected individual-level data on injury diagnoses from the Norwegian Patient Registry and the national registry dataset for reimbursement of primary care providers for the period 2009-2014, and estimated the annual incidence of patients registered with an injury diagnosis in either or both of these registries. We also converted ICD-10 codes in secondary care into ICPC-2 codes to compare the types of injuries treated in primary and secondary care. Results: The annual incidence of medically treated injuries in Norway was 125 patients per 1000 inhabitants. Fifty-five per cent of injured patients received treatment exclusively in primary care. We observed stable time trends over the six-year period. Incidence rates were higher in primary care for the youngest children and in middle adulthood, but were higher in secondary care for older people. Overall, injury incidence was higher for men, but women became more injury prone with age. We only observed this gender reversal in secondary care. With the exception of fractures, all injury types were predominantly treated in primary care. Conclusions: A substantial proportion of injured patients in Norway are treated exclusively in primary care. The demographic profile of these patients differs from those treated in secondary care.

Incidence of medically attended paediatric burns across the UK

Objective

Childhood burns represent a burden on health services, yet the full extent of the problem is difficult to quantify. We estimated the annual UK incidence from primary care (PC), emergency attendances (EA), hospital admissions (HA) and deaths.

Methods

The population was children (0–15 years), across England, Wales, Scotland and Northern Ireland (NI), with medically attended burns 2013–2015. Routinely collected data sources included PC attendances from Clinical Practice Research Datalink 2013–2015), EAs from Paediatric Emergency Research in the United Kingdom and Ireland (PERUKI, 2014) and National Health Services Wales Informatics Services, HAs from Hospital Episode Statistics, National Services Scotland and Social Services and Public Safety (2014), and mortality from the Office for National Statistics, National Records of Scotland and NI Statistics and Research Agency 2013–2015. The population denominators were based on Office for National Statistics mid-year population estimates.

Results

The annual PC burns attendance was 16.1/10 000 persons at risk (95% CI 15.6 to 16.6); EAs were 35.1/10 000 persons at risk (95% CI 34.7 to 35.5) in England and 28.9 (95% CI 27.5 to 30.3) in Wales. HAs ranged from 6.0/10 000 person at risk (95% CI 5.9 to 6.2) in England to 3.1 in Wales and Scotland (95% CI 2.7 to 3.8 and 2.7 to 3.5, respectively) and 2.8 (95% CI 2.4 to 3.4) in NI. In England, Wales and Scotland, 75% of HAs were aged <5 years. Mortality was low with 0.1/1 000 000 persons at risk (95% CI 0.06 to 0.2).

Conclusions

With an estimated 19 574 PC attendances, 37 703 EAs (England and Wales only), 6639 HAs and 1–6 childhood deaths annually, there is an urgent need to improve UK childhood burns prevention.

Self-reported and routinely collected electronic healthcare resource-use data for trial-based economic evaluations: the current state of play in England and considerations for the future

BACKGROUND

Randomised controlled trials (RCTs) are generally regarded as the "gold standard" for providing quantifiable evidence around the effectiveness and cost-effectiveness of new healthcare technologies. In order to perform the economic evaluations associated with RCTs, there is a need for accessible and good quality resource-use data; for the purpose of discussion here, data that best reflect the care received. Traditionally, researchers have developed questionnaires for resource-use data collection. However, the evolution of routinely collected electronic data within care services provides new opportunities for collecting data without burdening patients or caregivers (e.g. clinicians). This paper describes the potential strengths and limitations of each data collection method and then discusses aspects for consideration before choosing which method to use.

MAIN TEXT

We describe electronic data sources (large observational datasets, commissioning data, and raw data extraction) that may be suitable data sources for informing clinical trials and the current status of self-reported instruments for measuring resource-use. We assess the methodological risks and benefits, and compare the two methodologies. We focus on healthcare resource-use; however, many of the considerations have relevance to clinical questions. Patient self-report forms a pragmatic and cheap method that is largely under the control of the researcher. However, there are known issues with the validity of the data collected, loss to follow-up may be high, and questionnaires suffer from missing data. Routinely collected electronic data may be more accurate and more practical if large numbers of patients are involved. However, datasets often incur a cost and researchers are bound by the time for data approval and extraction by the data holders.

CONCLUSIONS

Owing to the issues associated with electronic datasets, self-reported methods may currently be the preferred option. However, electronic hospital data are relatively more accessible, informative, standardised, and reliable. Therefore in trials where secondary care constitutes a major driver of patient care, detailed electronic data may be considered superior to self-reported methods; with the caveat of requiring data sharing agreements with third party providers and potentially time-consuming extraction periods. Self-reported methods will still be required when a 'societal' perspective (e.g. quantifying informal care) is desirable for the intended analysis.

Injury among children and young people with and without attention-deficit hyperactivity disorder in the community: The risk of fractures, thermal injuries, and poisonings

BACKGROUND

Injuries commonly cause morbidity and mortality in children and young people (CYP). Attention-deficit hyperactivity disorder (ADHD) is the commonest neurobehavioural disorder in CYP and is associated with increased injury risk. However, large, population-based estimates of the risk of specific injuries are lacking. We aimed to provide estimates of the risk of fractures, thermal injuries, and poisonings in CYP with and without ADHD.

METHODS

In this population-based cohort study, we used primary and secondary care medical records from England from the Clinical Practice Research Datalink. There were 15,126 CYP with ADHD frequency-matched to 263,724 without, aged 3-17 years at diagnosis. The risk of (a) fractures, (b) thermal injuries, and (c) poisonings in CYP with ADHD was compared with those without.

RESULTS

The absolute rate of injury per thousand person-years at risk in CYP with versus without ADHD was fracture 28.9 (95% CI [27.5, 30.3]) versus 18.7 (95% CI [18.5, 19.0]), long bone fracture 17.7 (95% CI [16.7, 18.8]) versus 11.8 (95% CI [11.6, 12.0]), thermal injuries 4.4 (95% CI [3.9, 4.9]) versus 2.2 (95% CI [2.1, 2.3]), and poisonings 6.3 (95% CI [5.7, 6.9]) versus 1.9 (95% CI [1.9, 2.0]). Adjusting for age, sex, geographical region, deprivation, and calendar year, CYP with ADHD had 25% increase in risk of fracture (hazard ratio [HR] = 1.25; 95% CI [1.19, 1.31]), 21% increase in risk of long bone fracture (HR = 1.21; 95% CI [1.13, 1.28]), double the risk of thermal injury (HR = 2.00; 95% CI [1.76, 2.27]), and almost four times the risk of poisoning (HR = 3.72; 95% CI [3.32, 4.17]).

CONCLUSIONS

CYP with ADHD are at greater risk of fracture, thermal injury, and poisoning compared with those without. Paediatricians and health care professionals should provide injury prevention advice at diagnosis and reviews.

Approach to record linkage of primary care data from Clinical Practice Research Datalink to other health-related patient data: overview and implications

Record linkage is increasingly used to expand the information available for public health research. An understanding of record linkage methods and the relevant strengths and limitations is important for robust analysis and interpretation of linked data. Here, we describe the approach used by Clinical Practice Research Datalink (CPRD) to link primary care data to other patient level datasets, and the potential implications of this approach for CPRD data analysis. General practice electronic health record software providers separately submit de-identified data to CPRD and patient identifiers to NHS Digital, excluding patients who have opted-out from contributing data. Data custodians for external datasets also send patient identifiers to NHS Digital. NHS Digital uses identifiers to link the datasets using an 8-stage deterministic methodology. CPRD subsequently receives a de-identified linked cohort file and provides researchers with anonymised linked data and metadata detailing the linkage process. This methodology has been used to generate routine primary care linked datasets, including data from Hospital Episode Statistics, Office for National Statistics and National Cancer Registration and Analysis Service. 10.6 million (M) patients from 411 English general practices were included in record linkage in June 2018. 9.1M (86%) patients were of research quality, of which 8.0M (88%) had a valid NHS number and were eligible for linkage in the CPRD standard linked dataset release. Linking CPRD data to other sources improves the range and validity of research studies. This manuscript, together with metadata generated on match strength and linkage eligibility, can be used to inform study design and explore potential linkage-related selection and misclassification biases.

Approach to record linkage of primary care data from Clinical Practice Research Datalink to other health-related patient data: overview and implications

Record linkage is increasingly used to expand the information available for public health research. An understanding of record linkage methods and the relevant strengths and limitations is important for robust analysis and interpretation of linked data. Here, we describe the approach used by Clinical Practice Research Datalink (CPRD) to link primary care data to other patient level datasets, and the potential implications of this approach for CPRD data analysis. General practice electronic health record software providers separately submit de-identified data to CPRD and patient identifiers to NHS Digital, excluding patients who have opted-out from contributing data. Data custodians for external datasets also send patient identifiers to NHS Digital. NHS Digital uses identifiers to link the datasets using an 8-stage deterministic methodology. CPRD subsequently receives a de-identified linked cohort file and provides researchers with anonymised linked data and metadata detailing the linkage process. This methodology has been used to generate routine primary care linked datasets, including data from Hospital Episode Statistics, Office for National Statistics and National Cancer Registration and Analysis Service. 10.6 million (M) patients from 411 English general practices were included in record linkage in June 2018. 9.1M (86%) patients were of research quality, of which 8.0M (88%) had a valid NHS number and were eligible for linkage in the CPRD standard linked dataset release. Linking CPRD data to other sources improves the range and validity of research studies. This manuscript, together with metadata generated on match strength and linkage eligibility, can be used to inform study design and explore potential linkage-related selection and misclassification biases.

Poisoning substances taken by young people: a population-based cohort study

Background

Globally, poisonings account for most medically-attended self-harm. Recent data on poisoning substances are lacking, but are needed to inform self-harm prevention.

Aim

To assess poisoning substance patterns and trends among 10–24-year-olds across England

Design and setting

Open cohort study of 1 736 527 young people, using linked Clinical Practice Research Datalink, Hospital Episode Statistics, and Office for National Statistics mortality data, from 1998 to 2014.

Method

Poisoning substances were identified by ICD-10 or Read Codes. Incidence rates and adjusted incidence rate ratios (aIRR) were calculated for poisoning substances by age, sex, index of multiple deprivation, and calendar year.

Results

In total, 40 333 poisoning episodes were identified, with 57.8% specifying the substances involved. The most common substances were paracetamol (39.8%), alcohol (32.7%), non-steroidal anti-inflammatory drugs (NSAIDs) (11.6%), antidepressants (10.2%), and opioids (7.6%). Poisoning rates were highest at ages 16–18 years for females and 19–24 years for males. Opioid poisonings increased fivefold from 1998–2014 (females: aIRR 5.30, 95% confidence interval (CI) = 4.08 to 6.89; males: aIRR 5.11, 95% CI = 3.37 to 7.76), antidepressant poisonings three-to fourfold (females: aIRR 3.91, 95% CI = 3.18 to 4.80, males: aIRR 2.70, 95% CI = 2.04 to 3.58), aspirin/NSAID poisonings threefold (females: aIRR 2.84, 95% CI = 2.40 to 3.36, males: aIRR 2.76, 95% CI = 2.05 to 3.72) and paracetamol poisonings threefold in females (aIRR 2.87, 95% CI = 2.58 to 3.20). Across all substances poisoning incidence was higher in more disadvantaged groups, with the strongest gradient for opioid poisonings among males (aIRR 3.46, 95% CI = 2.24 to 5.36).

Conclusion

It is important that GPs raise awareness with families of the substances young people use to self-harm, especially the common use of over-the-counter medications. Quantities of medication prescribed to young people at risk of self-harm and their families should be limited, particularly analgesics and antidepressants.

Differing patterns in intentional and unintentional poisonings among young people in England, 1998-2014: a population-based cohort study

Background

Accurate and up to date data on changes in poisoning incidence among young people are lacking. Recent linkage of UK primary care, hospital and mortality data allows these to be quantified to inform service delivery.

Methods

An open cohort study of 1 736 527 young people aged 10-24 between 1998 and 2014 was conducted using linked data from the Clinical Practice Research Datalink, Hospital Episode Statistics and Office for National Statistics deaths. Incidence rates (IRs) by poisoning intent were calculated by age, sex, deprivation and year.

Results

Total poisoning IRs increased by 25% from 1998/99 to 2013/14 [adjusted incidence rate ratio (aIRR) 1.25, 95% CI: 1.20-1.30]. Patterns differed markedly by intent. Intentional poisoning rates increased by 52% while unintentional rates remained unchanged. Intentional rates increased almost exclusively among females, gradually between 1998/99 and 2013/14 among 16-18 (88% increase) and 19-24 (36% increase) year olds but only increased among 10-15 year olds in the last 2 years (79% increase). A 2-fold increased risk of poisoning for the most compared to least deprived quintile existed (aIRR 2.21, 95% CI: 2.02-2.23) and remained over time.

Conclusions

Commissioning of primary and secondary prevention services needs to address the growing problem of intentional poisonings among young people.

Performing studies using the UK Clinical Practice Research Datalink: to link or not to link?

The Clinical Practice Research Datalink (CPRD) is a repository of electronic medical records collected during routine primary care clinical practice in the UK, and is one of the most widely used sources of real-world data for healthcare research. Although CPRD provides access to comprehensive longitudinal patient records, the data does not fully capture diagnoses or outcomes occurring in secondary care and/or mortality. We provide here an overview of CPRD and the potential bias when using unlinked data in certain situations. Linkage of CPRD to other datasets can help to overcome these limitations. We discuss when to consider linkage to secondary care, disease-specific data sources or the official mortality data when conducting research using CPRD data.

Alcohol Misuse and Injury Outcomes in Young People Aged 10-24

PURPOSE

The burden of alcohol-attributable disease is a global problem. Young people often present to emergency health-care services with alcohol intoxication but little is known about how best to intervene at that point to improve future health outcomes. This study aimed to assess whether young people with an alcohol-specific hospital admission are at increased risk of injury following discharge.

METHODS

A cohort study was conducted using a general population of 10- to 24-year-olds identified using primary care medical records with linked hospital admission records between 1998 and 2013. Exposed individuals had an alcohol-specific admission. Unexposed individuals did not and were frequency matched by age (±5 years) and general practice (ratio 10:1). Incidence rates of injury-related hospital admission post discharge were calculated, and hazard ratios (HR) were estimated by Cox regression.

RESULTS

The cohort comprised 11,042 exposed and 110,656 unexposed individuals with 4,944 injury-related admissions during follow-up (2,092 in exposed). Injury rates were six times higher in those with a prior alcohol admission (73.92 per 1,000 person-years, 95% confidence interval (CI) 70.82-77.16 vs. 12.36, 11.91-12.81). The risk of an injury admission was highest in the month following an alcohol-specific admission (adjusted HR = 15.62, 95% CI 14.08-17.34), and remained higher compared to those with no previous alcohol-specific admission at 1 year (HR 5.28 (95% CI 4.97-5.60)) and throughout follow-up.

CONCLUSIONS

Young people with an alcohol-specific admission are at increased risk of subsequent injury requiring hospitalization, especially immediately post discharge, indicating a need for prompt intervention as soon as alcohol misuse behaviors are identified.

Linked data systems for injury surveillance and targeted prevention planning: Identifying geographical differences in injury in Western Australia, 2009-2012

ISSUE ADDRESSED

Injuries are a leading preventable cause of disease burden in Australia. Understanding how injuries vary by geographical location is important to guide health promotion planning. Therefore, the geographical and temporal distribution of injury across Western Australia from 2009 to 2012 is explored.

METHODS

Three Western Australian health datasets were linked and the expected number of injury cases per postcode calculated. A Standardised Injury Ratio was calculated by comparing the observed and expected number of injury cases. Priority areas and associated injury mechanisms were identified by postcode based on injury rates and temporal trends.

RESULTS

Injury levels varied across health region, health district and postcode. All nonmetropolitan regions had at least one health district classified as High or Medium-High priority. In contrast, neither metropolitan health region had health districts in these categories. Adopting the finer postcode level of analysis showed localised injury priority areas, even within health districts not classified as High or Medium-High injury areas. Postcodes classified as High or Medium-High injury priority were located alongside those with lower priority categories.

CONCLUSION

Injury prevention priority areas had consistent trends both geographically and over time. Finer scale analysis can provide public health policy makers with more robust information to plan, evaluate and support a range of injury prevention programs. SO WHAT?: The use of linked data systems and spatial analysis can assist health promotion decision-makers and practitioners by demonstrating area-based differences in injury prevention allowing effective targeting of limited resources to populations at the highest risk of injury.

Economic Evaluations Alongside Efficient Study Designs Using Large Observational Datasets: the PLEASANT Trial Case Study

BACKGROUND

Large observational datasets such as Clinical Practice Research Datalink (CPRD) provide opportunities to conduct clinical studies and economic evaluations with efficient designs.

OBJECTIVES

Our objectives were to report the economic evaluation methodology for a cluster randomised controlled trial (RCT) of a UK NHS-delivered public health intervention for children with asthma that was evaluated using CPRD and describe the impact of this methodology on results.

METHODS

CPRD identified eligible patients using predefined asthma diagnostic codes and captured 1-year pre- and post-intervention healthcare contacts (August 2012 to July 2014). Quality-adjusted life-years (QALYs) 4 months post-intervention were estimated by assigning utility values to exacerbation-related contacts; a systematic review identified these utility values because preference-based outcome measures were not collected. Bootstrapped costs were evaluated 12 months post-intervention, both with 1-year regression-based baseline adjustment (BA) and without BA (observed).

RESULTS

Of 12,179 patients recruited, 8190 (intervention 3641; control 4549) were evaluated in the primary analysis, which included patients who received the protocol-defined intervention and for whom CPRD data were available. The intervention's per-patient incremental QALY loss was 0.00017 (bias-corrected and accelerated 95% confidence intervals [BCa 95% CI] -0.00051 to 0.00018) and cost savings were £14.74 (observed; BCa 95% CI -75.86 to 45.19) or £36.07 (BA; BCa 95% CI -77.11 to 9.67), respectively. The probability of cost savings was much higher when accounting for BA versus observed costs due to baseline cost differences between trial arms (96.3 vs. 67.3%, respectively).

CONCLUSION

Economic evaluations using data from a large observational database without any primary data collection is feasible, informative and potentially efficient. Clinical Trials Registration Number: ISRCTN03000938.

Recording of hospitalizations for acute exacerbations of COPD in UK electronic health care records

BACKGROUND

Accurate identification of hospitalizations for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) within electronic health care records is important for research, public health, and to inform health care utilization and service provision. We aimed to develop a strategy to identify hospitalizations for AECOPD in secondary care data and to investigate the validity of strategies to identify hospitalizations for AECOPD in primary care data.

METHODS

We identified patients with chronic obstructive pulmonary disease (COPD) in the Clinical Practice Research Datalink (CPRD) with linked Hospital Episodes Statistics (HES) data. We used discharge summaries for recent hospitalizations for AECOPD to develop a strategy to identify the recording of hospitalizations for AECOPD in HES. We then used the HES strategy as a reference standard to investigate the positive predictive value (PPV) and sensitivity of strategies for identifying AECOPD using general practice CPRD data. We tested two strategies: 1) codes for hospitalization for AECOPD and 2) a code for AECOPD other than hospitalization on the same day as a code for hospitalization due to unspecified reason.

RESULTS

In total, 27,182 patients with COPD were included. Our strategy to identify hospitalizations for AECOPD in HES had a sensitivity of 87.5%. When compared with HES, using a code suggesting hospitalization for AECOPD in CPRD resulted in a PPV of 50.2% (95% confidence interval [CI] 48.5%-51.8%) and a sensitivity of 4.1% (95% CI 3.9%-4.3%). Using a code for AECOPD and a code for hospitalization due to unspecified reason resulted in a PPV of 43.3% (95% CI 42.3%-44.2%) and a sensitivity of 5.4% (95% CI 5.1%-5.7%).

CONCLUSION

Hospitalization for AECOPD can be identified with high sensitivity in the HES database. The PPV and sensitivity of strategies to identify hospitalizations for AECOPD in primary care data alone are very poor. Primary care data alone should not be used to identify hospitalizations for AECOPD. Instead, researchers should use data that are linked to data from secondary care.

Differing patterns in thermal injury incidence and hospitalisations among 0-4 year old children from England

OBJECTIVE

To describe patterns in thermal injury incidence and hospitalisations by age, gender, calendar year and socioeconomic status among 0-4 year olds in England for the period 1998-2013.

PARTICIPANTS

708,050 children with linked primary care and hospitalisation data from the Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics (HES), respectively.

ANALYSIS

Incidence rates of all thermal injuries (identified in CPRD and/or HES), hospitalised thermal injuries, and serious thermal injuries (hospitalised for ≥72h). Adjusted incidence rate ratios (IRR) and 95% confidence intervals (95%CI), estimated using Poisson regression.

RESULTS

Incidence rates of all thermal injuries, hospitalised thermal injuries, and serious thermal injuries were 59.5 per 10,000 person-years (95%CI 58.4-60.6), 11.3 (10.8-11.8) and 2.15 (1.95-2.37), respectively. Socioeconomic gradients, between the most and least deprived quintiles, were steepest for serious thermal injuries (IRR 3.17, 95%CI 2.53-3.96). Incidence of all thermal injuries (IRR 0.64, 95%CI 0.58-0.70) and serious thermal injuries (IRR 0.44, 95%CI 0.33-0.59) reduced between 1998/9 and 2012/13. Incidence rates of hospitalised thermal injuries did not significantly change over time.

CONCLUSION

Incidence of all thermal injuries and those hospitalised for ≥72h reduced over time. Steep socioeconomic gradients support continued targeting of preventative interventions to those living in the most deprived areas.

Epidemiology of poisonings, fractures and burns among 0-24 year olds in England using linked health and mortality data

BACKGROUND

Understanding patterns of injury in England is challenging due to a lack of national injury surveillance data. Through recent linkage of a large primary care research database to hospitalization and mortality data, we describe the epidemiology of poisonings, fractures and burns over a 14-year period.

METHODS

We used linked English primary care, hospitalisation and mortality data from the Clinical Practice Research Datalink, Hospital Episode Statistics and Office for National Statistics between 1998 and 2011 to establish a cohort of 2,106,420 0-24 year olds. Incidence rates, per 10 000 person-years (PY) were estimated by age, sex, calendar year and socioeconomic status. Using Poisson regression we estimated incidence rate ratios, adjusting for age and sex.

RESULTS

Age patterns of injury incidence varied by injury type, with peaks at age 2 (74.3/10 000 PY) and 18 (74.7/10 000 PY) for poisonings, age 13 for fractures (305.1/10 000 PY) and age 1 for burns (116.8/10 000 PY). Over time, fracture incidence increased, whereas poisoning incidence increased only among 15-24 year olds and burns incidence reduced. Poisoning and burns incidence increased with deprivation, with the steepest socioeconomic gradient for poisonings among 20-24 year olds (IRR 2.63, 95% confidence interval 2.24-3.09).

CONCLUSION

Differing patterns according to age and injury type reflect differences in underlying injury mechanisms, highlighting the importance of developing tailored preventative interventions across the life course. Inequalities in injury occurrences support the targeting of preventative interventions to children and young people living in the most deprived areas.