A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service

Comparing natural language processing representations of coded disease sequences for prediction in electronic health records

OBJECTIVE

Natural language processing (NLP) algorithms are increasingly being applied to obtain unsupervised representations of electronic health record (EHR) data, but their comparative performance at predicting clinical endpoints remains unclear. Our objective was to compare the performance of unsupervised representations of sequences of disease codes generated by bag-of-words versus sequence-based NLP algorithms at predicting clinically relevant outcomes.

MATERIALS AND METHODS

This cohort study used primary care EHRs from 6 286 233 people with Multiple Long-Term Conditions in England. For each patient, an unsupervised vector representation of their time-ordered sequences of diseases was generated using 2 input strategies (212 disease categories versus 9462 diagnostic codes) and different NLP algorithms (Latent Dirichlet Allocation, doc2vec, and 2 transformer models designed for EHRs). We also developed a transformer architecture, named EHR-BERT, incorporating sociodemographic information. We compared the performance of each of these representations (without fine-tuning) as inputs into a logistic classifier to predict 1-year mortality, healthcare use, and new disease diagnosis.

RESULTS

Patient representations generated by sequence-based algorithms performed consistently better than bag-of-words methods in predicting clinical endpoints, with the highest performance for EHR-BERT across all tasks, although the absolute improvement was small. Representations generated using disease categories perform similarly to those using diagnostic codes as inputs, suggesting models can equally manage smaller or larger vocabularies for prediction of these outcomes.

DISCUSSION AND CONCLUSION

Patient representations produced by sequence-based NLP algorithms from sequences of disease codes demonstrate improved predictive content for patient outcomes compared with representations generated by co-occurrence-based algorithms. This suggests transformer models may be useful for generating multi-purpose representations, even without fine-tuning.

"Spine to the future"-A narrative review of anatomy engagement

Anatomy has been integral to medical and health education for centuries, it has also had a significant role in wider public life, as an educational resource, a link to their health, and also as a darker deterrent. Historically, public engagement in anatomy is hallmarked by public dissections of convicted criminals across the globe. Artists, specifically non-medical men, such as Leonardo da Vinci, are reported to have participated in public dissection. Dissection would later rekindle public interest in anatomy as graverobbing led to the reform and regulation of anatomy in many countries. In recent years, there has been growing interest from the public in learning more about their bodies as health and well-being become of paramount importance, particularly following the COVID-19 pandemic. Anatomy sits in a prime position to direct and instigate conversations around health, well-being, and body image. Every human on earth possesses a perfect resource to look at and learn about. Models, art-based anatomical activities, and crafts provide active learning opportunities for the wider public around anatomy. Most recently, apps, games, and extended reality provide novel and insightful learning opportunities for the public relating to the body. Finally, training and resources must also be made available from institutions and professional bodies to anatomists to enable them to deliver engagement in an already congested and educationally heavy schedule. This resurgence of interest in anatomical public engagement sees anatomy re-enter the public spotlight, with more appropriate resources and educational settings to offer engagement with the aim of benefiting the public.

Identifying multi-resolution clusters of diseases in ten million patients with multimorbidity in primary care in England

BACKGROUND

Identifying clusters of diseases may aid understanding of shared aetiology, management of co-morbidities, and the discovery of new disease associations. Our study aims to identify disease clusters using a large set of long-term conditions and comparing methods that use the co-occurrence of diseases versus methods that use the sequence of disease development in a person over time.

METHODS

We use electronic health records from over ten million people with multimorbidity registered to primary care in England. First, we extract data-driven representations of 212 diseases from patient records employing (i) co-occurrence-based methods and (ii) sequence-based natural language processing methods. Second, we apply the graph-based Markov Multiscale Community Detection (MMCD) to identify clusters based on disease similarity at multiple resolutions. We evaluate the representations and clusters using a clinically curated set of 253 known disease association pairs, and qualitatively assess the interpretability of the clusters.

RESULTS

Both co-occurrence and sequence-based algorithms generate interpretable disease representations, with the best performance from the skip-gram algorithm. MMCD outperforms k-means and hierarchical clustering in explaining known disease associations. We find that diseases display an almost-hierarchical structure across resolutions from closely to more loosely similar co-occurrence patterns and identify interpretable clusters corresponding to both established and novel patterns.

CONCLUSIONS

Our method provides a tool for clustering diseases at different levels of resolution from co-occurrence patterns in high-dimensional electronic health records, which could be used to facilitate discovery of associations between diseases in the future.

Rising to the challenge of defining and operationalising multimorbidity in a UK hospital setting: the ADMISSION research collaborative

PURPOSE

Greater transparency and consistency when defining multimorbidity in different settings is needed. We aimed to: (1) adapt published principles that can guide the selection of long-term conditions for inclusion in research studies of multimorbidity in hospitals; (2) apply these principles and identify a list of long-term conditions; (3) operationalise this list by mapping it to International Classification of Diseases 10th revision (ICD-10) codes.

METHODS

Review by independent assessors and ratification by an interdisciplinary programme management group.

RESULTS

Agreement was reached that when defining multimorbidity in hospitals for research purposes all conditions must meet the following four criteria: (1) medical diagnosis; (2) typically present for ≥ 12 months; (3) at least one of currently active; permanent in effect; requiring current treatment, care or therapy; requiring surveillance; remitting-relapsing and requiring ongoing treatment or care, and; (4) lead to at least one of: significantly increased risk of death; significantly reduced quality of life; frailty or physical disability; significantly worsened mental health; significantly increased treatment burden (indicated by an increased risk of hospital admission or increased length of hospital stay). Application of these principles to two existing lists of conditions led to the selection of 60 conditions that can be used when defining multimorbidity for research focused on hospitalised patients. ICD-10 codes were identified for each of these conditions to ensure consistency in their operationalisation.

CONCLUSIONS

This work contributes to achieving the goal of greater transparency and consistency in the approach to the study of multimorbidity, with a specific focus on the UK hospital setting.

Prevalence of multimorbidity in survivors of 28 cancer sites: an English nationwide cross-sectional study

Multimorbidity, the presence of a chronic condition in addition to cancer, is of particular importance to cancer survivors. It has an impact on the progression, stage at diagnosis, prognosis, and treatment of cancer patients. Evidence is scarce on the prevalence of specific comorbidities in survivors of different cancers to inform prevention and management of multimorbidity. The objective of this study is to address this evidence gap by using large scale electronic health data from multiple linked UK healthcare databases to examine the prevalence of multimorbidity in 28 cancer sites. For this population-based cross-sectional study, we linked primary and secondary healthcare data from the UK Clinical Research Practice Datalink (CPRD) GOLD dataset and Hospital Episode Statistics (HES). We identified survivors of 28 common cancers aged 18 years or older at diagnosis who survived 2 years of cancer and compared their multimorbidity with matched controls without a history of cancer. To compare prevalence of individual comorbidity, multivariable logistic regression models, adjusted for confounding factors were used. Between January 1, 2010 and December 31, 2020, we identified 347,028 cancer survivors and 804,299 controls matched on age, sex and general practice. Cancer survivors had a higher prevalence of multimorbidity compared to non-cancer controls across all the cancer sites. Hypertension (56.2%), painful conditions (39.8%), osteoarthritis (38.0%), depression (31.8%) and constipation (31.4%) were the five most frequent chronic conditions reported. Compared to the controls, higher odds of constipation were found in survivors of 25 of the 28 cancer sites and higher odds of anaemia were found in 23 cancer sites. Prevalence of constipation, anaemia and painful conditions were higher after cancer diagnosis compared to before diagnosis. Since these comorbidities are not uniformly assessed as part of any of the comorbidity scales, they tend to be underreported among cancer survivors. The elevated risk of certain comorbidities in cancer survivors suggests the potential for preventative efforts in this population to lower disease burden and improve quality of life. Long-term conditions should not be viewed as the inevitable result of cancer diagnosis and treatment. We need to consider integrated management of chronic conditions tailored to specific cancers to improve cancer survivorship.

Effect of timeframes to define long term conditions and sociodemographic factors on prevalence of multimorbidity using disease code frequency in primary care electronic health records: retrospective study

Objective

To determine the extent to which the choice of timeframe used to define a long term condition affects the prevalence of multimorbidity and whether this varies with sociodemographic factors.

Design

Retrospective study of disease code frequency in primary care electronic health records.

Data sources

Routinely collected, general practice, electronic health record data from the Clinical Practice Research Datalink Aurum were used.

Main outcome measures

Adults (≥18 years) in England who were registered in the database on 1 January 2020 were included. Multimorbidity was defined as the presence of two or more conditions from a set of 212 long term conditions. Multimorbidity prevalence was compared using five definitions. Any disease code recorded in the electronic health records for 212 conditions was used as the reference definition. Additionally, alternative definitions for 41 conditions requiring multiple codes (where a single disease code could indicate an acute condition) or a single code for the remaining 171 conditions were as follows: two codes at least three months apart; two codes at least 12 months apart; three codes within any 12 month period; and any code in the past 12 months. Mixed effects regression was used to calculate the expected change in multimorbidity status and number of long term conditions according to each definition and associations with patient age, gender, ethnic group, and socioeconomic deprivation.

Results

9 718 573 people were included in the study, of whom 7 183 662 (73.9%) met the definition of multimorbidity where a single code was sufficient to define a long term condition. Variation was substantial in the prevalence according to timeframe used, ranging from 41.4% (n=4 023 023) for three codes in any 12 month period, to 55.2% (n=5 366 285) for two codes at least three months apart. Younger people (eg, 50-75% probability for 18-29 years v 1-10% for ≥80 years), people of some minority ethnic groups (eg, people in the Other ethnic group had higher probability than the South Asian ethnic group), and people living in areas of lower socioeconomic deprivation were more likely to be re-classified as not multimorbid when using definitions requiring multiple codes.

Conclusions

Choice of timeframe to define long term conditions has a substantial effect on the prevalence of multimorbidity in this nationally representative sample. Different timeframes affect prevalence for some people more than others, highlighting the need to consider the impact of bias in the choice of method when defining multimorbidity.

The implications of competing risks and direct treatment disutility in cardiovascular disease and osteoporotic fracture: risk prediction and cost effectiveness analysis

Background

Clinical guidelines commonly recommend preventative treatments for people above a risk threshold. Therefore, decision-makers must have faith in risk prediction tools and model-based cost-effectiveness analyses for people at different levels of risk. Two problems that arise are inadequate handling of competing risks of death and failing to account for direct treatment disutility (i.e. the hassle of taking treatments). We explored these issues using two case studies: primary prevention of cardiovascular disease using statins and osteoporotic fracture using bisphosphonates.

Objectives

Externally validate three risk prediction tools [QRISK®3, QRISK®-Lifetime, QFracture-2012 (ClinRisk Ltd, Leeds, UK)]; derive and internally validate new risk prediction tools for cardiovascular disease [competing mortality risk model with Charlson Comorbidity Index (CRISK-CCI)] and fracture (CFracture), accounting for competing-cause death; quantify direct treatment disutility for statins and bisphosphonates; and examine the effect of competing risks and direct treatment disutility on the cost-effectiveness of preventative treatments.

Design, participants, main outcome measures, data sources

Discrimination and calibration of risk prediction models (Clinical Practice Research Datalink participants: aged 25-84 years for cardiovascular disease and aged 30-99 years for fractures); direct treatment disutility was elicited in online stated-preference surveys (people with/people without experience of statins/bisphosphonates); costs and quality-adjusted life-years were determined from decision-analytic modelling (updated models used in National Institute for Health and Care Excellence decision-making).

Results

CRISK-CCI has excellent discrimination, similar to that of QRISK3 (Harrell's c = 0.864 vs. 0.865, respectively, for women; and 0.819 vs. 0.834, respectively, for men). CRISK-CCI has systematically better calibration, although both models overpredict in high-risk subgroups. People recommended for treatment (10-year risk of ≥ 10%) are younger when using QRISK-Lifetime than when using QRISK3, and have fewer observed events in a 10-year follow-up (4.0% vs. 11.9%, respectively, for women; and 4.3% vs. 10.8%, respectively, for men). QFracture-2012 underpredicts fractures, owing to under-ascertainment of events in its derivation. However, there is major overprediction among people aged 85-99 years and/or with multiple long-term conditions. CFracture is better calibrated, although it also overpredicts among older people. In a time trade-off exercise (n = 879), statins exhibited direct treatment disutility of 0.034; for bisphosphonates, it was greater, at 0.067. Inconvenience also influenced preferences in best-worst scaling (n = 631). Updated cost-effectiveness analysis generates more quality-adjusted life-years among people with below-average cardiovascular risk and fewer among people with above-average risk. If people experience disutility when taking statins, the cardiovascular risk threshold at which benefits outweigh harms rises with age (≥ 8% 10-year risk at 40 years of age; ≥ 38% 10-year risk at 80 years of age). Assuming that everyone experiences population-average direct treatment disutility with oral bisphosphonates, treatment is net harmful at all levels of risk.

Limitations

Treating data as missing at random is a strong assumption in risk prediction model derivation. Disentangling the effect of statins from secular trends in cardiovascular disease in the previous two decades is challenging. Validating lifetime risk prediction is impossible without using very historical data. Respondents to our stated-preference survey may not be representative of the population. There is no consensus on which direct treatment disutilities should be used for cost-effectiveness analyses. Not all the inputs to the cost-effectiveness models could be updated.

Conclusions

Ignoring competing mortality in risk prediction overestimates the risk of cardiovascular events and fracture, especially among older people and those with multimorbidity. Adjustment for competing risk does not meaningfully alter cost-effectiveness of these preventative interventions, but direct treatment disutility is measurable and has the potential to alter the balance of benefits and harms. We argue that this is best addressed in individual-level shared decision-making.

Study registration

This study is registered as PROSPERO CRD42021249959.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 15/12/22) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 4. See the NIHR Funding and Awards website for further award information.

Metabolic syndrome increases osteoarthritis risk: findings from the UK Biobank prospective cohort study

OBJECTIVE

The association between Metabolic Syndrome (MetS), its components, and the risk of osteoarthritis (OA) has been a topic of conflicting evidence in different studies. The aim of this present study is to investigate the association between MetS, its components, and the risk of OA using data from the UK Biobank.

METHODS

A prospective cohort study was conducted in the UK Biobank to assess the risk of osteoarthritis (OA) related to MetS. MetS was defined according to the criteria set by the International Diabetes Federation (IDF). Additionally, lifestyle factors, medications, and the inflammatory marker C-reactive protein (CRP) were included in the model. Cox proportional hazards regression was used to calculate hazard ratios (HR) and 95% confidence intervals (CI). The cumulative risk of OA was analyzed using Kaplan-Meier curves and log-rank tests. To explore potential nonlinear associations between MetS components and OA risk, a restricted cubic splines (RCS) model was employed. In addition, the polygenic risk score (PRS) of OA was calculated to characterize individual genetic risk.

RESULTS

A total of 45,581 cases of OA were identified among 370,311 participants, with a median follow-up time of 12.48 years. The study found that individuals with MetS had a 15% higher risk of developing OA (HR = 1.15, 95%CI:1.12-1.19). Additionally, central obesity was associated with a 58% increased risk of OA (HR = 1.58, 95%CI:1.5-1.66), while hyperglycemia was linked to a 13% higher risk (HR = 1.13, 95%CI:1.1-1.15). Dyslipidemia, specifically in triglycerides (HR = 1.07, 95%CI:1.05-1.09) and high-density lipoprotein (HR = 1.05, 95%CI:1.02-1.07), was also found to be slightly associated with OA risk. When stratified by PRS, those in the high PRS group had a significantly higher risk of OA compared to those with a low PRS, whereas no interaction was found between MetS and PRS on OA risks. Furthermore, the presence of MetS significantly increased the risk of OA by up to 35% in individuals with elevated CRP levels (HR = 1.35, 95% CI:1.3-1.4).

CONCLUSION

MetS and its components have been found to be associated with an increased risk of OA, particularly in individuals with elevated levels of CRP. These findings highlight the significance of managing MetS as a preventive and intervention measure for OA.

Developing and evaluating pediatric phecodes (Peds-Phecodes) for high-throughput phenotyping using electronic health records

OBJECTIVE

Pediatric patients have different diseases and outcomes than adults; however, existing phecodes do not capture the distinctive pediatric spectrum of disease. We aim to develop specialized pediatric phecodes (Peds-Phecodes) to enable efficient, large-scale phenotypic analyses of pediatric patients.

MATERIALS AND METHODS

We adopted a hybrid data- and knowledge-driven approach leveraging electronic health records (EHRs) and genetic data from Vanderbilt University Medical Center to modify the most recent version of phecodes to better capture pediatric phenotypes. First, we compared the prevalence of patient diagnoses in pediatric and adult populations to identify disease phenotypes differentially affecting children and adults. We then used clinical domain knowledge to remove phecodes representing phenotypes unlikely to affect pediatric patients and create new phecodes for phenotypes relevant to the pediatric population. We further compared phenome-wide association study (PheWAS) outcomes replicating known pediatric genotype-phenotype associations between Peds-Phecodes and phecodes.

RESULTS

The Peds-Phecodes aggregate 15 533 ICD-9-CM codes and 82 949 ICD-10-CM codes into 2051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 vs 192 out of 687 SNPs, P < .001).

DISCUSSION

We introduce Peds-Phecodes, a high-throughput EHR phenotyping tool tailored for use in pediatric populations. We successfully validated the Peds-Phecodes using genetic replication studies. Our findings also reveal the potential use of Peds-Phecodes in detecting novel genotype-phenotype associations for pediatric conditions. We expect that Peds-Phecodes will facilitate large-scale phenomic and genomic analyses in pediatric populations.

CONCLUSION

Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior PheWAS outcomes compared to phecodes.

Pan-cancer analyses of the associations between 109 pre-existing conditions and cancer treatment patterns across 19 adult cancers

Comorbidities present considerable challenges to cancer treatment and care. However, little is known about the effect of comorbidity on cancer treatment decisions across a wide range of cancer types and treatment modalities. Harnessing a cohort of 280,543 patients spanning 19 site-specific cancers, we explored pan-cancer frequencies of 109 comorbidities. Multinomial logistic regression was used to analyse the relationship between comorbidities and cancer treatment types, while binomial logistic regression examined the association between comorbidities and chemotherapy drug types, adjusting for demographic and clinical factors. Patients with comorbidity exhibited lower odds of receiving chemotherapy and multimodality treatment. End-stage renal disease was significantly associated with a decreased odds of receiving chemotherapy and surgery. Patients with prostate cancer who have comorbid non-acute cystitis, obstructive and reflux uropathy, urolithiasis, or hypertension were less likely to receive chemotherapy. Among patients with breast cancer, dementia, left bundle branch block, peripheral arterial disease, epilepsy, Barrett's oesophagus, ischaemic stroke, unstable angina and asthma were associated with lower odds of receiving multimodal chemotherapy, radiotherapy and surgery. Comorbidity is also consistently associated with the lower odds of receiving chemotherapy when comparing across 10 drug classes. Patients with comorbid dementia, intracerebral haemorrhage, subarachnoid haemorrhage, oesophageal varices, liver fibrosis sclerosis and cirrhosis and secondary pulmonary hypertension were less likely to receive antimetabolites. Comorbidity can influence the effectiveness and tolerability of cancer treatment and ultimately, prognosis. Multi-specialty collaborative care is essential for the management of comorbidity during cancer treatment, including prophylactic measures to manage toxicities.

Obesity, metabolic health and clinical outcomes after incident cardiovascular disease: A nationwide population-based cohort study

BACKGROUND

The association between metabolic syndrome and increased cardiovascular disease (CVD) risk is well-established. However, in patients with incident CVD, the relationship between obesity, metabolic health, and subsequent CVD and mortality outcomes are less well-established. This study investigated the association between body mass index (BMI), metabolic health and the risk of subsequent cardiovascular mortality and morbidity outcomes in patients with incident CVD events.

METHODS

This cohort study identified 130 685 patients from the nationwide Clinical Practice Research Datalink (CPRD GOLD) and Hospital Episode Statistics (HES) databases in the United Kingdom. Patients were ≥18 years with incident CVD [coronary heart disease (CHD), stroke, or peripheral vascular disease (PVD)] between 1 January 1998 and 31 December 2017. BMI (in kg/m2 ) was categorized as underweight (<18.5), normal (18.5-24.9), overweight (25.0-29.9) and obese (≥30). Within each BMI category, patients were grouped by increasing count of 1, 2 or 3 metabolic risk factors [RF] (dyslipidaemia, diabetes mellitus and hypertension) and were regarded as metabolically unhealthy while absence of these factors was considered metabolically healthy (MH). Multivariable Cox regression was used to assess the risk (hazard ratio with 95% confidence interval) of subsequent outcomes (non-fatal CHD, stroke, PVD, incident heart failure, CVD-mortality and all-cause mortality) in BMI subgroups with incremental count of metabolic RFs.

RESULTS

During a median follow-up of 13.0 years, a higher BMI was associated with reduced risk for stroke, PVD, CVD-mortality and all-cause mortality within each metabolic risk category, while increasing metabolic RFs within each BMI subgroup accounted for increasing risks. When compared with patients with normal BMI and no RF, CVD-mortality risk in overweight patients with no RF was 0.76 (0.70-0.84), and in obese patients with no RF was 0.85 (0.76-0.96). The respective risk for all-cause mortality in patients with overweight and no RF was 0.69 (0.65-0.72), and in obese patients with no RF was 0.75 (0.70-0.79). Subsequent outcomes of stroke and PVD showed similar trends. In contrast, the risk of subsequent non-fatal CHD events and incident HF increased with higher BMI and with incremental metabolic risk factors within each BMI category. Underweight was constantly associated with increased risk for all outcomes regardless of the presence of metabolic RFs except for non-fatal CHD events.

CONCLUSIONS

In patients with incident CVD, overweight and obesity were related to a more favourable prognosis for subsequent stroke, PVD and mortality (CVD-related and all-cause) irrespective of the presence of other metabolic risk factors.

Prevalence of chronic pain or analgesic use in children and young people and its long-term impact on substance misuse, mental illness, and prescription opioid use: a retrospective longitudinal cohort study

Background

Epidemiological studies suggest chronic and recurrent pain affects around a quarter of children, while 8% report intense and frequent pain. The long-term implications of chronic pain in childhood are uncertain. Using electronic health records (EHRs) we used both disease codes and medicines prescription records to investigate the scale of chronic pain and long-term analgesic use in children and young people (CYP), and if chronic pain and/or use of analgesic medicines at an early age is associated with substance misuse, use of prescription opioids, and poor mental health in adulthood.

Methods

We conducted a cohort study using data from IQVIA Medical Research Data UK. We identified individuals aged 2-24 with exposure to either a diagnostic code indicating chronic pain (diagnosis-exposed), repeat prescription for medicines commonly used to treat pain (prescription-exposed), or both. Follow-up began at 25, and the unexposed population acted as comparators. We calculated hazard ratios (HR) for mental health and substance misuse outcomes, and rate ratios (RR) for opioid prescriptions in adulthood. Additionally, we investigated which diagnoses, if any, were over-represented in the prescription-exposed subgroup.

Findings

The cohort constituted 853,625 individuals; 146,431 had one or more of the exposures of interest (diagnosis-exposed = 115,101, prescription-exposed = 20,298, both-exposed = 11,032), leaving 707,194 as comparators. Median age at index exposure was 18.7 years (IQR 14.7-22.3). On average during follow-up, the pooled exposed group had, respectively, a 31% and 17% higher risk of adverse mental health and substance misuse outcomes (adjusted HR [95% CI] of 1.31 [1.29-1.32] and 1.17 [1.11-1.24]). Exposed individuals also received prescription opioids at double the rate of unexposed individuals on average during follow-up (adjusted RR 2.01 [95% CI 1.95-2.10]). Outcomes varied between exposure subgroups, with prescription- and both-exposure tending to have worse outcomes. Unlike these two subgroups, in the diagnosis-exposed subgroup we did not detect a greater risk of substance misuse.

Interpretation

Chronic pain in CYP is associated with increased prescription opioid use and adverse mental health outcomes in adulthood, as is repeat prescription for analgesic medicines, but only the latter is also associated with substance misuse in adulthood. It is essential to avoid the harms of under-treating pain in CYP while giving due consideration to the risks posed by analgesic medicines. Early recognition of chronic pain in CYP and utilising non-pharmacological management options may help minimise overprescribing, and long-term reliance on dependence-forming-drugs.

Funding

AL is an NIHR funded academic clinical fellow, and was supported by funding from UCLH Charities while carrying out this work. RS and DS are part of the Advanced Pain Discovery Platform and were supported by a UKRI and Versus Arthritis grant (MR/W002566/1) as part of the Consortium Against Pain Inequality. AW was supported by the Wellcome Trust (220558/Z/20/Z).

Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland

There is still limited understanding of how chronic conditions co-occur in patients with multimorbidity and what are the consequences for patients and the health care system. Most reported clusters of conditions have not considered the demographic characteristics of these patients during the clustering process. The study used data for all registered patients that were resident in Fife or Tayside, Scotland and aged 25 years or more on 1st January 2000 and who were followed up until 31st December 2018. We used linked demographic information, and secondary care electronic health records from 1st January 2000. Individuals with at least two of the 31 Elixhauser Comorbidity Index conditions were identified as having multimorbidity. Market basket analysis was used to cluster the conditions for the whole population and then repeatedly stratified by age, sex and deprivation. 318,235 individuals were included in the analysis, with 67,728 (21·3%) having multimorbidity. We identified five distinct clusters of conditions in the population with multimorbidity: alcohol misuse, cancer, obesity, renal failure, and heart failure. Clusters of long-term conditions differed by age, sex and socioeconomic deprivation, with some clusters not present for specific strata and others including additional conditions. These findings highlight the importance of considering demographic factors during both clustering analysis and intervention planning for individuals with multiple long-term conditions. By taking these factors into account, the healthcare system may be better equipped to develop tailored interventions that address the needs of complex patients.

Cardiovascular disease incidence rates: a study using routinely collected health data

BACKGROUND

There is substantial evidence that systemic anticancer therapies and radiotherapy can increase the long-term risk of cardiovascular disease (CVD). Optimal management decisions for cancer patients therefore need to take into account the likely risks from a proposed treatment option, as well as its likely benefits. For CVD, the magnitude of the risk depends on the incidence of the disease in the general population to which the patient belongs, including variation with age and sex, as well as on the treatment option under consideration. The aim of this paper is to provide estimates of CVD incidence rates in the general population of England for use in cardio-oncology and in other relevant clinical, research and health policy contexts.

METHODS

We studied a population-based representative cohort, consisting of 2,633,472 individuals, derived by electronic linkage of records from primary care with those of admitted-patient care in England during April 1, 2010, to April 1, 2015. From 38 individual CVDs available via the linked dataset we identified five relevant categories of CVD whose risk may be increased by cancer treatments: four of heart disease and one of stroke.

RESULTS

We calculated incidence rates by age-group and sex for all relevant CVD categories combined, for the four relevant categories of heart disease combined, and for the five relevant CVD categories separately. We present separate incidence rates for all 38 individual CVDs available via the linked dataset. We also illustrate how our data can be used to estimate absolute CVD risks in a range of people with Hodgkin lymphoma treated with chemotherapy and radiotherapy.

CONCLUSIONS

Our results provide population-based CVD incidence rates for a variety of uses, including the estimation of absolute risks of CVD from cancer treatments, thus helping patients and clinicians to make appropriate individualized cancer treatment decisions. Graphical Abstract: Cardiovascular incidence rates for use in cardio-oncology and elsewhere: A presentation of age- and sex-specific cardiovascular disease (CVD) incidence rates for use in calculation of absolute cardiovascular risks of cancer treatments, and in other clinical, research and health policy contexts. Abbreviations - CVD: cardiovascular disease; y: years.

Age at Type 2 Diabetes Diagnosis and Cause-Specific Mortality: Observational Study of Primary Care Patients in England

OBJECTIVE

To examine the associations between age at type 2 diabetes diagnosis and the relative and absolute risk of all-cause and cause-specific mortality in England.

RESEARCH DESIGN AND METHODS

In this cohort study using primary care data from the Clinical Practice Research Datalink, we identified 108,061 individuals with newly diagnosed type 2 diabetes (16-50 years of age), matched to 829,946 individuals without type 2 diabetes. We estimated all-cause and cause-specific mortality (cancer, cardiorenal, other [noncancer or cardiorenal]) by age at diagnosis, using competing-risk survival analyses adjusted for key confounders.

RESULTS

Comparing individuals with versus without type 2 diabetes, the relative risk of death decreased with an older age at diagnosis: the hazard ratio for all-cause mortality was 4.32 (95% CI 3.35-5.58) in individuals diagnosed at ages 16-27 years compared with 1.53 (95% CI 1.46-1.60) at ages 48-50 years. Smaller relative risks by increasing age at diagnosis were also observed for cancer, cardiorenal, and noncancer or cardiorenal death. Irrespective of age at diagnosis, the 10-year absolute risk of all-cause and cause-specific mortality were higher in individuals with type 2 diabetes; yet, the absolute differences were small.

CONCLUSIONS

Although the relative risk of death in individuals with versus without type 2 was higher at younger ages, the 10-year absolute risk of all investigated causes of death was small and similar in the two groups. Further multidecade studies could help estimate the long-term risk of complications and death in individuals with early-onset type 2 diabetes.

Impact of socio-economic conditions and perinatal factors on risk of becoming a child looked after: a whole population cohort study using routinely collected data in Wales

OBJECTIVES

Between 1997 and 2021, the number of children looked after (CLA) in Wales, UK, increased steadily, with stark inequalities. We aimed to assess how deprivation and maternal and child perinatal characteristics influence the risk of becoming CLA in Wales.

STUDY DESIGN

We constructed a prospective longitudinal cohort of children born in Wales between April 2006 and March 2021 (n = 395,610) using linked administrative records.

METHODS

Survival models examined the risk of CLA from birth by small-area deprivation and maternal and child perinatal characteristics. Population attributable fractions quantify the potential impact of action on modifiable risk factors.

RESULTS

Children from the most deprived fifth of the population were 3.4 times more likely to enter care than those in the least deprived (demographic adjusted hazard ratios [aHRs] 3.40, 95% confidence interval [CI] 3.08, 3.74). Maternal mental health problems in pregnancy (fully aHR, 2.03, 95% CI 1.88, 2.19) and behavioural factors, such as smoking (aHR 2.46, 95% CI 2.34-2.60), alcohol problems (aHR 2.35, 95% CI 1.70-3.23) and substance use in pregnancy (aHR 5.72, 95% CI 5.03-6.51), as well as child congenital anomalies (aHR 1.46, 95% CI 1.16-1.84), low birth weight (aHR 1.28, 95% CI 1.17, 1.39) and preterm birth (aHR 1.16, 95% CI 1.06, 1.26), were associated with higher risk of CLA status. The risk of CLA in the population may be reduced by 35% (95% CI 0.33, 0.38) if children in the two most deprived fifths of the population experienced the conditions of those in the least deprived.

CONCLUSIONS

Deprivation and perinatal maternal health are important modifiable risk factors for children becoming CLA. Our analysis provides insight into the mechanisms of intergenerational transfer of disadvantage in a vulnerable section of the child population and identifies targets for public health action.

Integration of clinical demographics and routine laboratory analysis parameters for early prediction of gestational diabetes mellitus in the Chinese population

Gestational diabetes mellitus (GDM) is one of the most common complications in pregnancy, impairing both maternal and fetal health in short and long term. As early interventions are considered desirable to prevent GDM, this study aims to develop a simple-to-use nomogram based on multiple common risk factors from electronic medical health records (EMHRs). A total of 924 pregnant women whose EMHRs were available at Peking University International Hospital from January 2022 to October 2022 were included. Clinical demographics and routine laboratory analysis parameters at 8-12 weeks of gestation were collected. A novel nomogram was established based on the outcomes of multivariate logistic regression. The nomogram demonstrated powerful discrimination (the area under the receiver operating characteristic curve = 0.7542), acceptable agreement (Hosmer-Lemeshow test, P = 0.3214) and favorable clinical utility. The C-statistics of 10-Fold cross validation, Leave one out cross validation and Bootstrap were 0.7411, 0.7357 and 0.7318, respectively, indicating the stability of the nomogram. A novel nomogram based on easily-accessible parameters was developed to predict GDM in early pregnancy, which may provide a paradigm for repurposing clinical data and benefit the clinical management of GDM. There is a need for prospective multi-center studies to validate the nomogram before employing the nomogram in real-world clinical practice.

South Asian, Black and White ethnicity and the effect of potentially modifiable risk factors for dementia: A study in English electronic health records

INTRODUCTION

We aimed to investigate ethnic differences in the associations of potentially modifiable risk factors with dementia.

METHODS

We used anonymised data from English electronic primary care records for adults aged 65 and older between 1997 and 2018. We used Cox regression to investigate main effects for each risk factor and interaction effects between each risk factor and ethnicity.

RESULTS

We included 865,674 people with 8,479,973 person years of follow up. Hypertension, dyslipidaemia, obesity and diabetes were more common in people from minority ethnic groups than White people. The impact of hypertension, obesity, diabetes, low HDL and sleep disorders on dementia risk was increased in South Asian people compared to White people. The impact of hypertension was greater in Black compared to White people.

DISCUSSION

Dementia prevention efforts should be targeted towards people from minority ethnic groups and tailored to risk factors of particular importance.

Impact of individual, household, and area characteristics on health and social care outcomes for people with multimorbidity: Protocol for a multilevel analysis

BACKGROUND

Multimorbidity is one of the greatest challenges facing health and social care systems globally. It is associated with high rates of health service use, adverse healthcare events, and premature death. Despite its importance, little is known about the effects of contextual determinants such as household and area characteristics on health and care outcomes for people with multimorbidity. This study protocol presents a plan for the examination of associations between individual, household, and area characteristics with important health and social care outcomes.

METHODS

The study will use a cross-section of data from the SAIL Databank on 01 January 2019 and include all people alive and registered with a Welsh GP. The cohort will be stratified according to the presence or absence of multimorbidity, defined as two or more long-term conditions. Multilevel models will be used to examine covariates measured for individuals, households, and areas to account for social processes operating at different levels. The intra-class correlation coefficient will be calculated to determine the strength of association at each level of the hierarchy. Model outcomes will be any emergency department attendance, emergency hospital or care home admission, or mortality, within the study follow-up period.

DISCUSSION

Household and area characteristics might act as protective or risk factors for health and care outcomes for people with multimorbidity, in which case results of the analyses can be used to guide clinical and policy responses for effective targeting of limited resources.

Reynolds N, R. Barnes M, Hull S, Finer S, Mathur R. Ethnic differences in early onset multimorbidity and associations with health service use, long-term prescribing, years of life lost, and mortality: A cross-sectional study using clustering in the UK Clinical Practice Research Datalink

BACKGROUND

The population prevalence of multimorbidity (the existence of at least 2 or more long-term conditions [LTCs] in an individual) is increasing among young adults, particularly in minority ethnic groups and individuals living in socioeconomically deprived areas. In this study, we applied a data-driven approach to identify clusters of individuals who had an early onset multimorbidity in an ethnically and socioeconomically diverse population. We identified associations between clusters and a range of health outcomes.

METHODS AND FINDINGS

Using linked primary and secondary care data from the Clinical Practice Research Datalink GOLD (CPRD GOLD), we conducted a cross-sectional study of 837,869 individuals with early onset multimorbidity (aged between 16 and 39 years old when the second LTC was recorded) registered with an English general practice between 2010 and 2020. The study population included 777,906 people of White ethnicity (93%), 33,915 people of South Asian ethnicity (4%), and 26,048 people of Black African/Caribbean ethnicity (3%). A total of 204 LTCs were considered. Latent class analysis stratified by ethnicity identified 4 clusters of multimorbidity in White groups and 3 clusters in South Asian and Black groups. We found that early onset multimorbidity was more common among South Asian (59%, 33,915) and Black (56% 26,048) groups compared to the White population (42%, 777,906). Latent class analysis revealed physical and mental health conditions that were common across all ethnic groups (i.e., hypertension, depression, and painful conditions). However, each ethnic group also presented exclusive LTCs and different sociodemographic profiles: In White groups, the cluster with the highest rates/odds of the outcomes was predominantly male (54%, 44,150) and more socioeconomically deprived than the cluster with the lowest rates/odds of the outcomes. On the other hand, South Asian and Black groups were more socioeconomically deprived than White groups, with a consistent deprivation gradient across all multimorbidity clusters. At the end of the study, 4% (34,922) of the White early onset multimorbidity population had died compared to 2% of the South Asian and Black early onset multimorbidity populations (535 and 570, respectively); however, the latter groups died younger and lost more years of life. The 3 ethnic groups each displayed a cluster of individuals with increased rates of primary care consultations, hospitalisations, long-term prescribing, and odds of mortality. Study limitations include the exclusion of individuals with missing ethnicity information, the age of diagnosis not reflecting the actual age of onset, and the exclusion of people from Mixed, Chinese, and other ethnic groups due to insufficient power to investigate associations between multimorbidity and health-related outcomes in these groups.

CONCLUSIONS

These findings emphasise the need to identify, prevent, and manage multimorbidity early in the life course. Our work provides additional insights into the excess burden of early onset multimorbidity in those from socioeconomically deprived and diverse groups who are disproportionately and more severely affected by multimorbidity and highlights the need to ensure healthcare improvements are equitable.

Identifying potential biases in code sequences in primary care electronic healthcare records: a retrospective cohort study of the determinants of code frequency

OBJECTIVES

To determine whether the frequency of diagnostic codes for long-term conditions (LTCs) in primary care electronic healthcare records (EHRs) is associated with (1) disease coding incentives, (2) General Practice (GP), (3) patient sociodemographic characteristics and (4) calendar year of diagnosis.

DESIGN

Retrospective cohort study.

SETTING

GPs in England from 2015 to 2022 contributing to the Clinical Practice Research Datalink Aurum dataset.

PARTICIPANTS

All patients registered to a GP with at least one incident LTC diagnosed between 1 January 2015 and 31 December 2019.

PRIMARY AND SECONDARY OUTCOME MEASURES

The number of diagnostic codes for an LTC in (1) the first and (2) the second year following diagnosis, stratified by inclusion in the Quality and Outcomes Framework (QOF) financial incentive programme.

RESULTS

3 113 724 patients were included, with 7 723 365 incident LTCs. Conditions included in QOF had higher rates of annual coding than conditions not included in QOF (1.03 vs 0.32 per year, p<0.0001). There was significant variation in code frequency by GP which was not explained by patient sociodemographics. We found significant associations with patient sociodemographics, with a trend towards higher coding rates in people living in areas of higher deprivation for both QOF and non-QOF conditions. Code frequency was lower for conditions with follow-up time in 2020, associated with the onset of the COVID-19 pandemic.

CONCLUSIONS

The frequency of diagnostic codes for newly diagnosed LTCs is influenced by factors including patient sociodemographics, disease inclusion in QOF, GP practice and the impact of the COVID-19 pandemic. Natural language processing or other methods using temporally ordered code sequences should account for these factors to minimise potential bias.

Identifying populations with chronic pain in primary care: developing an algorithm and logic rules applied to coded primary care diagnostic and medication data

BACKGROUND

Estimates of chronic pain prevalence using coded primary care data are likely to be substantially lower than estimates derived from community surveys. Most primary care studies have estimated chronic pain prevalence using data searches confined to analgesic medication prescriptions. Increasingly, following recent NICE guideline recommendations, patients and doctors opt for non-drug treatment of chronic pain thus excluding these patients from prevalence estimates based on medication codes. We aimed to develop and test an algorithm combining medication codes with selected diagnostic codes to estimate chronic pain prevalence using coded primary care data.

METHODS

Following a scoping review 4 criteria were developed to identify cohorts of people with chronic pain. These were (1) people with one of 12 ('tier 1') conditions that almost always results in the individual having chronic pain (2) people with one of 20 ('tier 2') conditions included when there are also 3 or more prescription-only analgesics issued in the last 12 months (3) chronic neuropathic pain, or (4) 4 or more prescription-only analgesics issued in the last 12 months. These were translated into 8 logic rules which included 1,932 SNOMED CT codes.

RESULTS

The algorithm was run on primary care data from 41 GP Practices in Lambeth. The total population consisted of 386,238 GP registered adults ≥ 18 years as of the 31st March 2021. 64,135 (16.6%) were identified as people with chronic pain. This definition demonstrated notably high rates in Black ethnicity females, and higher rates in the most deprived, and older population.

CONCLUSIONS

Estimates of chronic pain prevalence using structured healthcare data have previously shown lower prevalence estimates for chronic pain than reported in community surveys. This has limited the ability of researchers and clinicians to fully understand and address the complex multifactorial nature of chronic pain. Our study demonstrates that it may be possible to establish more representative prevalence estimates using structured data than previously possible. Use of logic rules offers the potential to move systematic identification and population-based management of chronic pain into mainstream clinical practice at scale and support improved management of symptom burden for people experiencing chronic pain.

Comorbidity Patterns in Patients Newly Diagnosed With Colorectal Cancer: Network-Based Study

BACKGROUND

Patients with colorectal cancer (CRC) often present with multiple comorbidities, and many of these can affect treatment and survival. However, previous comorbidity studies primarily focused on diseases in commonly used comorbidity indices. The comorbid status of CRC patients with respect to the entire spectrum of chronic diseases has not yet been investigated.

OBJECTIVE

This study aimed to systematically analyze all chronic diagnoses and diseases co-occurring, using a network-based approach and large-scale administrative health data, and provide a complete picture of the comorbidity pattern in patients newly diagnosed with CRC from southwest China.

METHODS

In this retrospective observational study, the hospital discharge records of 678 hospitals from 2015 to 2020 in Sichuan Province, China were used to identify new CRC cases in 2020 and their history of diseases. We examined all chronic diagnoses using ICD-10 (International Classification of Diseases, 10th Revision) codes at 3 digits and focused on chronic diseases with >1% prevalence in at least one subgroup (1-sided test, P<.025), which resulted in a total of 66 chronic diseases. Phenotypic comorbidity networks were constructed across all CRC patients and different subgroups by sex, age (18-59, 60-69, 70-79, and ≥80 years), area (urban and rural), and cancer site (colon and rectum), with comorbidity as a node and linkages representing significant correlations between multiple comorbidities.

RESULTS

A total of 29,610 new CRC cases occurred in Sichuan, China in 2020. The mean patient age at diagnosis was 65.6 (SD 12.9) years, and 75.5% (22,369/29,610) had at least one comorbidity. The most prevalent comorbidities were hypertension (8581/29,610, 29.0%; 95% CI 28.5%-29.5%), hyperplasia of the prostate (3816/17,426, 21.9%; 95% CI 21.3%-22.5%), and chronic obstructive pulmonary disease (COPD; 4199/29,610, 14.2%; 95% CI 13.8%-14.6%). The prevalence of single comorbidities was different in each subgroup in most cases. Comorbidities were closely associated, with disorders of lipoprotein metabolism and hyperplasia of the prostate mediating correlations between other comorbidities. Males and females shared 58.3% (141/242) of disease pairs, whereas male-female disparities occurred primarily in diseases coexisting with COPD, cerebrovascular diseases, atherosclerosis, heart failure, or renal failure among males and with osteoporosis or gonarthrosis among females. Urban patients generally had more comorbidities with higher prevalence and more complex disease coexistence relationships, whereas rural patients were more likely to have co-existing severe diseases, such as heart failure comorbid with the sequelae of cerebrovascular disease or COPD.

CONCLUSIONS

Male-female and urban-rural disparities in the prevalence of single comorbidities and their complex coexistence relationships in new CRC cases were not due to simple coincidence. The results reflect clinical practice in CRC patients and emphasize the importance of measuring comorbidity patterns in terms of individual and coexisting diseases in order to better understand comorbidity patterns.

Evaluating Metformin Strategies for Cancer Prevention: A Target Trial Emulation Using Electronic Health Records

BACKGROUND

Metformin users appear to have a substantially lower risk of cancer than nonusers in many observational studies. These inverse associations may be explained by common flaws in observational analyses that can be avoided by explicitly emulating a target trial.

METHODS

We emulated target trials of metformin therapy and cancer risk using population-based linked electronic health records from the UK (2009-2016). We included individuals with diabetes, no history of cancer, no recent prescription for metformin or other glucose-lowering medication, and hemoglobin A1c (HbA1c) <64 mmol/mol (<8.0%). Outcomes included total cancer and 4 site-specific cancers (breast, colorectal, lung, and prostate). We estimated risks using pooled logistic regression with adjustment for risk factors via inverse-probability weighting. We emulated a second target trial among individuals regardless of diabetes status. We compared our estimates with those obtained using previously applied analytic approaches.

RESULTS

Among individuals with diabetes, the estimated 6-year risk differences (metformin - no metformin) were -0.2% (95% CI = -1.6%, 1.3%) in the intention-to-treat analysis and 0.0% (95% CI = -2.1%, 2.3%) in the per-protocol analysis. The corresponding estimates for all site-specific cancers were close to zero. Among individuals regardless of diabetes status, these estimates were also close to zero and more precise. By contrast, previous analytic approaches yielded estimates that appeared strongly protective.

CONCLUSIONS

Our findings are consistent with the hypothesis that metformin therapy does not meaningfully influence cancer incidence. The findings highlight the importance of explicitly emulating a target trial to reduce bias in the effect estimates derived from observational analyses.

A population-based study exploring phenotypic clusters and clinical outcomes in stroke using unsupervised machine learning approach

Individuals developing stroke have varying clinical characteristics, demographic, and biochemical profiles. This heterogeneity in phenotypic characteristics can impact on cardiovascular disease (CVD) morbidity and mortality outcomes. This study uses a novel clustering approach to stratify individuals with incident stroke into phenotypic clusters and evaluates the differential burden of recurrent stroke and other cardiovascular outcomes. We used linked clinical data from primary care, hospitalisations, and death records in the UK. A data-driven clustering analysis (kamila algorithm) was used in 48,114 patients aged ≥ 18 years with incident stroke, from 1-Jan-1998 to 31-Dec-2017 and no prior history of serious vascular events. Cox proportional hazards regression was used to estimate hazard ratios (HRs) for subsequent adverse outcomes, for each of the generated clusters. Adverse outcomes included coronary heart disease (CHD), recurrent stroke, peripheral vascular disease (PVD), heart failure, CVD-related and all-cause mortality. Four distinct phenotypes with varying underlying clinical characteristics were identified in patients with incident stroke. Compared with cluster 1 (n = 5,201, 10.8%), the risk of composite recurrent stroke and CVD-related mortality was higher in the other 3 clusters (cluster 2 [n = 18,655, 38.8%]: hazard ratio [HR], 1.07; 95% CI, 1.02-1.12; cluster 3 [n = 10,244, 21.3%]: HR, 1.20; 95% CI, 1.14-1.26; and cluster 4 [n = 14,014, 29.1%]: HR, 1.44; 95% CI: 1.37-1.50). Similar trends in risk were observed for composite recurrent stroke and all-cause mortality outcome, and subsequent recurrent stroke outcome. However, results were not consistent for subsequent risk in CHD, PVD, heart failure, CVD-related mortality, and all-cause mortality. In this proof of principle study, we demonstrated how a heterogenous population of patients with incident stroke can be stratified into four relatively homogenous phenotypes with differential risk of recurrent and major cardiovascular outcomes. This offers an opportunity to revisit the stratification of care for patients with incident stroke to improve patient outcomes.

Preterm birth and the risk of multimorbidity in adolescence: a multiregister-based cohort study

BACKGROUND

Multimorbidity affects people of all ages, but the risk factors of multimorbidity in adolescence are unclear. The aim of this study was to examine preterm birth (<37 weeks) as a shared risk factor for multiple health outcomes and the role of gestational age (degree of prematurity) in the development of increasingly complex multimorbidity (two, three, or four health outcomes) in adolescence (age 10-18 years).

METHODS

We used population-wide data from Finland (1 187 610 adolescents born 1987-2006) and Norway (555 431 adolescents born 1998-2007). Gestational age at birth was ascertained from medical birth registers and categorised as 23-27 weeks (extremely preterm), 28-31 weeks (very preterm), 32-33 weeks (moderately preterm), 34-36 weeks (late preterm), 37-38 weeks (early term), 39-41 weeks (term, reference category) and 42-44 weeks (post-term). Children who died or emigrated before their 10th birthday, and those with missing or implausible data on gestational age, birthweight, or covariates, were excluded. Health outcomes at age 10-18 years were ascertained from specialised health care and mortality registers. We calculated hazard ratios (HRs) and population attributable fractions (PAFs) with 95% CIs for multiple health outcomes during adolescence.

FINDINGS

Individuals were followed up from age 10 to 18 years (mean follow-up: 6 years, SD: 3 years). Preterm birth was associated with increased risks of 20 hospital-treated malignant, cardiovascular, endocrinological, neuropsychiatric, respiratory, genitourinary, and congenital health outcomes, after correcting for multiple testing and ignoring small effects (HR <1·2). Confounder-adjusted HRs comparing preterm with term-born adolescents were 2·29 (95% CI 2·19-2·39) for two health outcomes (PAF 9·0%; 8·3-9·6), and 4·22 (3·66-4·87) for four health outcomes (PAF 22·7%; 19·4-25·8) in the Finnish data. Results in the Norwegian data showed a similar pattern. We observed a consistent dose-response relationship between an earlier gestational age and elevated risks of increasingly complex multimorbidity in both datasets.

INTERPRETATION

Preterm birth is associated with increased risks of diverse multimorbidity patterns at age 10-18 years. Adolescents with a preterm-born background could benefit from diagnostic vigilance directed at multimorbidity and a multidisciplinary approach to health care.

FUNDING

European Union Horizon 2020, Academy of Finland, Foundation for Pediatric Research, Sigrid Jusélius Foundation, Signe and Ane Gyllenberg Foundation.

Trajectories in chronic disease accrual and mortality across the lifespan in Wales, UK (2005-2019), by area deprivation profile: linked electronic health records cohort study on 965,905 individuals

Background

Understanding and quantifying the differences in disease development in different socioeconomic groups of people across the lifespan is important for planning healthcare and preventive services. The study aimed to measure chronic disease accrual, and examine the differences in time to individual morbidities, multimorbidity, and mortality between socioeconomic groups in Wales, UK.

Methods

Population-wide electronic linked cohort study, following Welsh residents for up to 20 years (2000-2019). Chronic disease diagnoses were obtained from general practice and hospitalisation records using the CALIBER disease phenotype register. Multi-state models were used to examine trajectories of accrual of 132 diseases and mortality, adjusted for sex, age and area-level deprivation. Restricted mean survival time was calculated to measure time spent free of chronic disease(s) or mortality between socioeconomic groups.

Findings

In total, 965,905 individuals aged 5-104 were included, from a possible 2.9 m individuals following a 5-year clearance period, with an average follow-up of 13.2 years (12.7 million person-years). Some 673,189 (69.7%) individuals developed at least one chronic disease or died within the study period. From ages 10 years upwards, the individuals living in the most deprived areas consistently experienced reduced time between health states, demonstrating accelerated transitions to first and subsequent morbidities and death compared to their demographic equivalent living in the least deprived areas. The largest difference were observed in 10 and 20 year old males developing multimorbidity (-0.45 years (99% CI: -0.45, -0.44)) and in 70 year old males dying after developing multimorbidity (-1.98 years (99% CI: -2.01, -1.95)).

Interpretation

This study adds to the existing literature on health inequalities by demonstrating that individuals living in more deprived areas consistently experience accelerated time to diagnosis of chronic disease and death across all ages, accounting for competing risks.

Funding

UK Medical Research Council, Health Data Research UK, and Administrative Data Research Wales.

Long-term healthcare use after postoperative complications: an analysis of linked primary and secondary care routine data

Background

Postoperative complications are associated with reduced long-term survival. We characterise healthcare use changes after sentinel postoperative complications.

Methods

We linked primary and secondary care records of patients undergoing elective surgery at four East London hospitals (2012-7) with at least 90 days follow-up. Complication codes (wound infection, urinary tract infection, pneumonia, new stroke, and new myocardial infarction) recorded within 90 days of surgery were identified from primary or secondary care. Outcomes were change in healthcare contact days in the 2 yr before and after surgery, and 2 yr mortality. We report rate ratios (RaR) with 95% confidence intervals and adjusted for baseline healthcare use and confounders using negative binomial regression.

Results

We included 49 913 patients (median age 49 yr [inter-quartile range {IQR}: 34-64]), 27 958 (56.0%) were female. Amongst 3883 (7.8%) patients with complications (median age 58 [IQR: 43-72]), there were 18.4 days per year in contact with healthcare before surgery and 25.3 days after surgery (RaR: 1.38 [1.37-1.39]). Patients without complications (median age 48 [IQR: 33-63]) had 12.3 days per year in contact with healthcare before surgery and 14.0 days after surgery (RaR: 1.14 [1.14-1.15]). The adjusted incidence rate ratio of days in contact with healthcare associated with complications was 1.67 (1.49-1.87). More patients (391; 10.1%) with complications died within 2 yr than those without (1428; 3.1%).

Conclusions

Patients with postoperative complications are older with greater healthcare use before surgery. However, their absolute and relative increases in healthcare use after surgery are greater than patients without complications.

Developing and Evaluating Pediatric Phecodes (Peds-Phecodes) for High-Throughput Phenotyping Using Electronic Health Records

Objective

Pediatric patients have different diseases and outcomes than adults; however, existing phecodes do not capture the distinctive pediatric spectrum of disease. We aim to develop specialized pediatric phecodes (Peds-Phecodes) to enable efficient, large-scale phenotypic analyses of pediatric patients.

Materials and Methods

We adopted a hybrid data- and knowledge-driven approach leveraging electronic health records (EHRs) and genetic data from Vanderbilt University Medical Center to modify the most recent version of phecodes to better capture pediatric phenotypes. First, we compared the prevalence of patient diagnoses in pediatric and adult populations to identify disease phenotypes differentially affecting children and adults. We then used clinical domain knowledge to remove phecodes representing phenotypes unlikely to affect pediatric patients and create new phecodes for phenotypes relevant to the pediatric population. We further compared phenome-wide association study (PheWAS) outcomes replicating known pediatric genotype-phenotype associations between Peds-Phecodes and phecodes.

Results

The Peds-Phecodes aggregate 15,533 ICD-9-CM codes and 82,949 ICD-10-CM codes into 2,051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 versus 192 out of 687 SNPs, p<0.001).

Discussion

We introduce Peds-Phecodes, a high-throughput EHR phenotyping tool tailored for use in pediatric populations. We successfully validated the Peds-Phecodes using genetic replication studies. Our findings also reveal the potential use of Peds-Phecodes in detecting novel genotype-phenotype associations for pediatric conditions. We expect that Peds-Phecodes will facilitate large-scale phenomic and genomic analyses in pediatric populations.

Conclusion

Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior PheWAS outcomes compared to phecodes.

Impact of data source choice on multimorbidity measurement: a comparison study of 2.3 million individuals in the Welsh National Health Service

BACKGROUND

Measurement of multimorbidity in research is variable, including the choice of the data source used to ascertain conditions. We compared the estimated prevalence of multimorbidity and associations with mortality using different data sources.

METHODS

A cross-sectional study of SAIL Databank data including 2,340,027 individuals of all ages living in Wales on 01 January 2019. Comparison of prevalence of multimorbidity and constituent 47 conditions using data from primary care (PC), hospital inpatient (HI), and linked PC-HI data sources and examination of associations between condition count and 12-month mortality.

RESULTS

Using linked PC-HI compared with only HI data, multimorbidity was more prevalent (32.2% versus 16.5%), and the population of people identified as having multimorbidity was younger (mean age 62.5 versus 66.8 years) and included more women (54.2% versus 52.6%). Individuals with multimorbidity in both PC and HI data had stronger associations with mortality than those with multimorbidity only in HI data (adjusted odds ratio 8.34 [95% CI 8.02-8.68] versus 6.95 (95%CI 6.79-7.12] in people with ≥ 4 conditions). The prevalence of conditions identified using only PC versus only HI data was significantly higher for 37/47 and significantly lower for 10/47: the highest PC/HI ratio was for depression (14.2 [95% CI 14.1-14.4]) and the lowest for aneurysm (0.51 [95% CI 0.5-0.5]). Agreement in ascertainment of conditions between the two data sources varied considerably, being slight for five (kappa < 0.20), fair for 12 (kappa 0.21-0.40), moderate for 16 (kappa 0.41-0.60), and substantial for 12 (kappa 0.61-0.80) conditions, and by body system was lowest for mental and behavioural disorders. The percentage agreement, individuals with a condition identified in both PC and HI data, was lowest in anxiety (4.6%) and highest in coronary artery disease (62.9%).

CONCLUSIONS

The use of single data sources may underestimate prevalence when measuring multimorbidity and many important conditions (especially mental and behavioural disorders). Caution should be used when interpreting findings of research examining individual and multiple long-term conditions using single data sources. Where available, researchers using electronic health data should link primary care and hospital inpatient data to generate more robust evidence to support evidence-based healthcare planning decisions for people with multimorbidity.

Genetic risk, adherence to healthy lifestyle and acute cardiovascular and thromboembolic complications following SARS-COV-2 infection

Current understanding of determinants for COVID-19-related cardiovascular and thromboembolic (CVE) complications primarily covers clinical aspects with limited knowledge on genetics and lifestyles. Here, we analysed a prospective cohort of 106,005 participants from UK Biobank with confirmed SARS-CoV-2 infection. We show that higher polygenic risk scores, indicating individual's hereditary risk, were linearly associated with increased risks of post-COVID-19 atrial fibrillation (adjusted HR 1.52 [95% CI 1.44 to 1.60] per standard deviation increase), coronary artery disease (1.57 [1.46 to 1.69]), venous thromboembolism (1.33 [1.18 to 1.50]), and ischaemic stroke (1.27 [1.05 to 1.55]). These genetic associations are robust across genders, key clinical subgroups, and during Omicron waves. However, a prior composite healthier lifestyle was consistently associated with a reduction in all outcomes. Our findings highlight that host genetics and lifestyle independently affect the occurrence of CVE complications in the acute infection phrase, which can guide tailored management of COVID-19 patients and inform population lifestyle interventions to offset the elevated cardiovascular burden post-pandemic.

Independent Associations of Incident Epilepsy and Enzyme-Inducing and Non-Enzyme-Inducing Antiseizure Medications With the Development of Osteoporosis

Importance

Both epilepsy and enzyme-inducing antiseizure medications (eiASMs) having varying reports of an association with increased risks for osteoporosis.

Objective

To quantify and model the independent hazards for osteoporosis associated with incident epilepsy and eiASMS and non-eiASMs.

Design, Setting, and Participants

This open cohort study covered the years 1998 to 2019, with a median (IQR) follow-up of 5 (1.7-11.1) years. Data were collected for 6275 patients enrolled in the Clinical Practice Research Datalink and from hospital electronic health records. No patients who met inclusion criteria (Clinical Practice Research Datalink-acceptable data, aged 18 years or older, follow-up after the Hospital Episode Statistics patient care linkage date of 1998, and free of osteoporosis at baseline) were excluded or declined.

Exposure

Incident adult-onset epilepsy using a 5-year washout and receipt of 4 consecutive ASMs.

Main Outcomes and Measures

The outcome was incident osteoporosis as determined through Cox proportional hazards or accelerated failure time models where appropriate. Incident epilepsy was treated as a time-varying covariate. Analyses controlled for age, sex, socioeconomic status, cancer, 1 or more years of corticosteroid use, body mass index, bariatric surgery, eating disorders, hyperthyroidism, inflammatory bowel disease, rheumatoid arthritis, smoking status, falls, fragility fractures, and osteoporosis screening tests. Subsequent analyses (1) excluded body mass index, which was missing in 30% of patients; (2) applied propensity score matching for receipt of an eiASM; (3) restricted analyses to only those with incident onset epilepsy; and (4) restricted analyses to patients who developed epilepsy at age 65 years or older. Analyses were performed between July 1 and October 31, 2022, and in February 2023 for revisions.

Results

Of 8 095 441 adults identified, 6275 had incident adult-onset epilepsy (3220 female [51%] and 3055 male [49%]; incidence rate, 62 per 100 000 person-years) with a median (IQR) age of 56 (38-73) years. When controlling for osteoporosis risk factors, incident epilepsy was independently associated with a 41% faster time to incident osteoporosis (time ratio [TR], 0.59; 95% CI, 0.52-0.67; P < .001). Both eiASMs (TR, 0.91; 95% CI, 0.87-0.95; P < .001) and non-eiASMs (TR, 0.77; 95% CI, 0.76-0.78; P < .001) were also associated with significant increased risks independent of epilepsy, accounting for 9% and 23% faster times to development of osteoporosis, respectively. The independent associations among epilepsy, eiASMs, and non-eiASMs remained consistent in propensity score-matched analyses, cohorts restricted to adult-onset epilepsy, and cohorts restricted to late-onset epilepsy.

Conclusions and Relevance

These findings suggest that epilepsy is independently associated with a clinically meaningful increase in the risk for osteoporosis, as are both eiASMs and non-eiASMs. Routine screening and prophylaxis should be considered in all people with epilepsy.

Stratification of diabetes in the context of comorbidities, using representation learning and topological data analysis

Diabetes is a heterogenous, multimorbid disorder with a large variation in manifestations, trajectories, and outcomes. The aim of this study is to validate a novel machine learning method for the phenotyping of diabetes in the context of comorbidities. Data from 9967 multimorbid patients with a new diagnosis of diabetes were extracted from Clinical Practice Research Datalink. First, using BEHRT (a transformer-based deep learning architecture), the embeddings corresponding to diabetes were learned. Next, topological data analysis (TDA) was carried out to test how different areas in high-dimensional manifold correspond to different risk profiles. The following endpoints were considered when profiling risk trajectories: major adverse cardiovascular events (MACE), coronary artery disease (CAD), stroke (CVA), heart failure (HF), renal failure (RF), diabetic neuropathy, peripheral arterial disease, reduced visual acuity and all-cause mortality. Kaplan Meier curves were plotted for each derived phenotype. Finally, we tested the performance of an established risk prediction model (QRISK) by adding TDA-derived features. We identified four subgroups of patients with diabetes and divergent comorbidity patterns differing in their risk of future cardiovascular, renal, and other microvascular outcomes. Phenotype 1 (young with chronic inflammatory conditions) and phenotype 2 (young with CAD) included relatively younger patients with diabetes compared to phenotypes 3 (older with hypertension and renal disease) and 4 (older with previous CVA), and those subgroups had a higher frequency of pre-existing cardio-renal diseases. Within ten years of follow-up, 2592 patients (26%) experienced MACE, 2515 patients (25%) died, and 2020 patients (20%) suffered RF. QRISK3 model's AUC was augmented from 67.26% (CI 67.25-67.28%) to 67.67% (CI 67.66-67.69%) by adding specific TDA-derived phenotype and the distances to both extremities of the TDA graph improving its performance in the prediction of CV outcomes. We confirmed the importance of accounting for multimorbidity when risk stratifying heterogenous cohort of patients with new diagnosis of diabetes. Our unsupervised machine learning method improved the prediction of clinical outcomes.

Hospital admissions linked to SARS-CoV-2 infection in children and adolescents: cohort study of 3.2 million first ascertained infections in England

OBJECTIVE

To describe hospital admissions associated with SARS-CoV-2 infection in children and adolescents.

DESIGN

Cohort study of 3.2 million first ascertained SARS-CoV-2 infections using electronic health care record data.

SETTING

England, July 2020 to February 2022.

PARTICIPANTS

About 12 million children and adolescents (age <18 years) who were resident in England.

MAIN OUTCOME MEASURES

Ascertainment of a first SARS-CoV-2 associated hospital admissions: due to SARS-CoV-2, with SARS-CoV-2 as a contributory factor, incidental to SARS-CoV-2 infection, and hospital acquired SARS-CoV-2.

RESULTS

3 226 535 children and adolescents had a recorded first SARS-CoV-2 infection during the observation period, and 29 230 (0.9%) infections involved a SARS-CoV-2 associated hospital admission. The median length of stay was 2 (interquartile range 1-4) days) and 1710 of 29 230 (5.9%) SARS-CoV-2 associated admissions involved paediatric critical care. 70 deaths occurred in which covid-19 or paediatric inflammatory multisystem syndrome was listed as a cause, of which 55 (78.6%) were in participants with a SARS-CoV-2 associated hospital admission. SARS-CoV-2 was the cause or a contributory factor in 21 000 of 29 230 (71.8%) participants who were admitted to hospital and only 380 (1.3%) participants acquired infection as an inpatient and 7855 (26.9%) participants were admitted with incidental SARS-CoV-2 infection. Boys, younger children (<5 years), and those from ethnic minority groups or areas of high deprivation were more likely to be admitted to hospital (all P<0.001). The covid-19 vaccination programme in England has identified certain conditions as representing a higher risk of admission to hospital with SARS-CoV-2: 11 085 (37.9%) of participants admitted to hospital had evidence of such a condition, and a further 4765 (16.3%) of participants admitted to hospital had a medical or developmental health condition not included in the vaccination programme's list.

CONCLUSIONS

Most SARS-CoV-2 associated hospital admissions in children and adolescents in England were due to SARS-CoV-2 or SARS-CoV-2 was a contributory factor. These results should inform future public health initiatives and research.

Exome-wide evidence of compound heterozygous effects across common phenotypes in the UK Biobank

Exome-sequencing association studies have successfully linked rare protein-coding variation to risk of thousands of diseases. However, the relationship between rare deleterious compound heterozygous (CH) variation and their phenotypic impact has not been fully investigated. Here, we leverage advances in statistical phasing to accurately phase rare variants (MAF ~ 0.001%) in exome sequencing data from 175,587 UK Biobank (UKBB) participants, which we then systematically annotate to identify putatively deleterious CH coding variation. We show that 6.5% of individuals carry such damaging variants in the CH state, with 90% of variants occurring at MAF < 0.34%. Using a logistic mixed model framework, systematically accounting for relatedness, polygenic risk, nearby common variants, and rare variant burden, we investigate recessive effects in common complex diseases. We find six exome-wide significant (P < 1.68 × 10 - 7 ) and 17 nominally significant (P < 5.25 × 10 - 5 ) gene-trait associations. Among these, only four would have been identified without accounting for CH variation in the gene. We further incorporate age-at-diagnosis information from primary care electronic health records, to show that genetic phase influences lifetime risk of disease across 20 gene-trait combinations (FDR < 5%). Using a permutation approach, we find evidence for genetic phase contributing to disease susceptibility for a collection of gene-trait pairs, including FLG-asthma (P = 0.00205 ) and USH2A-visual impairment (P = 0.0084 ). Taken together, we demonstrate the utility of phasing large-scale genetic sequencing cohorts for robust identification of the phenome-wide consequences of compound heterozygosity.

UK Biobank retinal imaging grading: methodology, baseline characteristics and findings for common ocular diseases

BACKGROUND/OBJECTIVES

This study aims to describe the grading methods and baseline characteristics for UK Biobank (UKBB) participants who underwent retinal imaging in 2009-2010, and to characterise individuals with retinal features suggestive of age-related macular degeneration (AMD), glaucoma and retinopathy.

METHODS

Non-mydriatic colour fundus photographs and macular optical coherence tomography (OCT) scans were manually graded by Central Administrative Research Facility certified graders and quality assured by clinicians of the Network of Ophthalmic Reading Centres UK. Captured retinal features included those associated with AMD (≥1 drusen, pigmentary changes, geographic atrophy or exudative AMD; either imaging modality), glaucoma (≥0.7 cup-disc ratio, ≥0.2 cup-disc ratio difference between eyes, other abnormal disc features; photographs only) and retinopathy (characteristic features of diabetic retinopathy with or without microaneurysms; either imaging modality). Suspected cases of these conditions were characterised with reference to diagnostic records, physical and biochemical measurements.

RESULTS

Among 68,514 UKBB participants who underwent retinal imaging, the mean age was 57.3 years (standard deviation 8.2), 45.7% were men and 90.6% were of White ethnicity. A total of 64,367 participants had gradable colour fundus photographs and 68,281 had gradable OCT scans in at least one eye. Retinal features suggestive of AMD and glaucoma were identified in 15,176 and 2184 participants, of whom 125 (0.8%) and 188 (8.6%), respectively, had a recorded diagnosis. Of 264 participants identified to have retinopathy with microaneurysms, 251 (95.1%) had either diabetes or hypertension.

CONCLUSIONS

This dataset represents a valuable addition to what is currently available in UKBB, providing important insights to both ocular and systemic health.

Characterizing subgroup performance of probabilistic phenotype algorithms within older adults: a case study for dementia, mild cognitive impairment, and Alzheimer's and Parkinson's diseases

Objective

Biases within probabilistic electronic phenotyping algorithms are largely unexplored. In this work, we characterize differences in subgroup performance of phenotyping algorithms for Alzheimer's disease and related dementias (ADRD) in older adults.

Materials and methods

We created an experimental framework to characterize the performance of probabilistic phenotyping algorithms under different racial distributions allowing us to identify which algorithms may have differential performance, by how much, and under what conditions. We relied on rule-based phenotype definitions as reference to evaluate probabilistic phenotype algorithms created using the Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation framework.

Results

We demonstrate that some algorithms have performance variations anywhere from 3% to 30% for different populations, even when not using race as an input variable. We show that while performance differences in subgroups are not present for all phenotypes, they do affect some phenotypes and groups more disproportionately than others.

Discussion

Our analysis establishes the need for a robust evaluation framework for subgroup differences. The underlying patient populations for the algorithms showing subgroup performance differences have great variance between model features when compared with the phenotypes with little to no differences.

Conclusion

We have created a framework to identify systematic differences in the performance of probabilistic phenotyping algorithms specifically in the context of ADRD as a use case. Differences in subgroup performance of probabilistic phenotyping algorithms are not widespread nor do they occur consistently. This highlights the great need for careful ongoing monitoring to evaluate, measure, and try to mitigate such differences.

Hospital-wide natural language processing summarising the health data of 1 million patients

Electronic health records (EHRs) represent a major repository of real world clinical trajectories, interventions and outcomes. While modern enterprise EHR's try to capture data in structured standardised formats, a significant bulk of the available information captured in the EHR is still recorded only in unstructured text format and can only be transformed into structured codes by manual processes. Recently, Natural Language Processing (NLP) algorithms have reached a level of performance suitable for large scale and accurate information extraction from clinical text. Here we describe the application of open-source named-entity-recognition and linkage (NER+L) methods (CogStack, MedCAT) to the entire text content of a large UK hospital trust (King's College Hospital, London). The resulting dataset contains 157M SNOMED concepts generated from 9.5M documents for 1.07M patients over a period of 9 years. We present a summary of prevalence and disease onset as well as a patient embedding that captures major comorbidity patterns at scale. NLP has the potential to transform the health data lifecycle, through large-scale automation of a traditionally manual task.

Stigmatization, panic disorder, and death anxiety among patients of Covid-19: Fourth wave of pandemic in Pakistan

BACKGROUND

In Pakistan, the fourth wave of COVID-19 is causing an increasing number of positive cases. This fourth wave may be a risky aspect of mental health issues for COVID-19 patients. This quantitative study is designed to understand the stigmatization, and panic disorder and to explore the mediating role of death anxiety among patients of COVID-19 during the fourth wave of novel coronavirus.

METHODS

The study was conducted using a correlational research design. The survey was carried out by utilizing a questionnaire with a convenient sample technique. The sample of the study was comprised of 139 patients with COVID-19. Data were collected through Stigma Scale for Chronic Illnesses (SSCI), The Panic Disorder Severity Scale (PDSS), and Death Anxiety Inventory.

RESULTS

Results show that stigma is significantly positively related to panic disorder and death anxiety. Furthermore, panic disorder is also significantly positively related to death anxiety. Results also indicate that stigmatization is a significant positive predictor for death anxiety and panic disorder. Moreover, results indicate that death anxiety has a mediating role in the relationship between stigmatization and panic disorder with age and gender as covariates.

CONCLUSION

This study would be helpful for people around the world to understand this threatening contagious virus so they wouldn't stigmatize infected ones. Additional research is required for the sustainable improvement of anxiety over time.

Age, sex, and socioeconomic differences in multimorbidity measured in four ways: UK primary care cross-sectional analysis

BACKGROUND

Multimorbidity poses major challenges to healthcare systems worldwide. Definitions with cut-offs in excess of ≥2 long-term conditions (LTCs) might better capture populations with complexity but are not standardised.

AIM

To examine variation in prevalence using different definitions of multimorbidity.

DESIGN AND SETTING

Cross-sectional study of 1 168 620 people in England.

METHOD

Comparison of multimorbidity (MM) prevalence using four definitions: MM2+ (≥2 LTCs), MM3+ (≥3 LTCs), MM3+ from 3+ (≥3 LTCs from ≥3 International Classification of Diseases, 10th revision chapters), and mental-physical MM (≥2 LTCs where ≥1 mental health LTC and ≥1 physical health LTC are recorded). Logistic regression was used to examine patient characteristics associated with multimorbidity under all four definitions.

RESULTS

MM2+ was most common (40.4%) followed by MM3+ (27.5%), MM3+ from 3+ (22.6%), and mental-physical MM (18.9%). MM2+, MM3+, and MM3+ from 3+ were strongly associated with oldest age (adjusted odds ratio [aOR] 58.09, 95% confidence interval [CI] = 56.13 to 60.14; aOR 77.69, 95% CI = 75.33 to 80.12; and aOR 102.06, 95% CI = 98.61 to 105.65; respectively), but mental-physical MM was much less strongly associated (aOR 4.32, 95% CI = 4.21 to 4.43). People in the most deprived decile had equivalent rates of multimorbidity at a younger age than those in the least deprived decile. This was most marked in mental-physical MM at 40-45 years younger, followed by MM2+ at 15-20 years younger, and MM3+ and MM3+ from 3+ at 10-15 years younger. Females had higher prevalence of multimorbidity under all definitions, which was most marked for mental-physical MM.

CONCLUSION

Estimated prevalence of multimorbidity depends on the definition used, and associations with age, sex, and socioeconomic position vary between definitions. Applicable multimorbidity research requires consistency of definitions across studies.

Cumulative burden of 144 conditions, critical care hospitalisation and premature mortality across 26 adult cancers

A comprehensive evaluation of the total burden of morbidity endured by cancer survivors remains unavailable. This study quantified the burden of 144 health conditions and critical care admissions across 26 adult cancers and treatment modalities in 243,767 adults. By age 60, top conditions ranked by fold difference (cumulative burden in survivors divided by cumulative burden in controls) were haematology, immunology/infection and pulmonary conditions. Patients who had all three forms of treatment (chemotherapy, radiotherapy and surgery) experienced a high cumulative burden of late morbidities compared with patients who received radiotherapy alone. The top five cancers with the highest cumulative burden of critical care admissions by age 60 were bone (12.4 events per 100 individuals [CI: 11.6-13.1]), brain (9.0 [7.5-10.5]), spinal cord and nervous system (7.2 [6.7-7.8]), testis (6.7 [4.9-8.4]) and Hodgkin lymphoma (4.4 [3.6-5.1]). Conditions that were associated with high excess years-of-life-lost were haematological conditions (9.6 years), pulmonary conditions (8.6 years) and immunological conditions or infections (7.8 years). As the population of cancer survivors continues to grow, our results indicate that it is important to tackle long-term health consequences through enacting data-driven policies.

Socioeconomic gradient in mortality of working age and older adults with multiple long-term conditions in England and Ontario, Canada

BACKGROUND

There is currently mixed evidence on the influence of long-term conditions and deprivation on mortality. We aimed to explore whether number of long-term conditions contribute to socioeconomic inequalities in mortality, whether the influence of number of conditions on mortality is consistent across socioeconomic groups and whether these associations vary by working age (18-64 years) and older adults (65 + years). We provide a cross-jurisdiction comparison between England and Ontario, by replicating the analysis using comparable representative datasets.

METHODS

Participants were randomly selected from Clinical Practice Research Datalink in England and health administrative data in Ontario. They were followed from 1 January 2015 to 31 December 2019 or death or deregistration. Number of conditions was counted at baseline. Deprivation was measured according to the participant's area of residence. Cox regression models were used to estimate hazards of mortality by number of conditions, deprivation and their interaction, with adjustment for age and sex and stratified between working age and older adults in England (N = 599,487) and Ontario (N = 594,546).

FINDINGS

There is a deprivation gradient in mortality between those living in the most deprived areas compared to the least deprived areas in England and Ontario. Number of conditions at baseline was associated with increasing mortality. The association was stronger in working age compared with older adults respectively in England (HR = 1.60, 95% CI 1.56,1.64 and HR = 1.26, 95% CI 1.25,1.27) and Ontario (HR = 1.69, 95% CI 1.66,1.72 and HR = 1.39, 95% CI 1.38,1.40). Number of conditions moderated the socioeconomic gradient in mortality: a shallower gradient was seen for persons with more long-term conditions.

CONCLUSIONS

Number of conditions contributes to higher mortality rate and socioeconomic inequalities in mortality in England and Ontario. Current health care systems are fragmented and do not compensate for socioeconomic disadvantages, contributing to poor outcomes particularly for those managing multiple long-term conditions. Further work should identify how health systems can better support patients and clinicians who are working to prevent the development and improve the management of multiple long-term conditions, especially for individuals living in socioeconomically deprived areas.

Prevalence of co-morbidity and history of recent infection in patients with neuromuscular disease: A cross-sectional analysis of United Kingdom primary care data

BACKGROUND

People with neuromuscular disease (NMD) experience a broader range of chronic diseases and health symptoms compared to the general population. However, no comprehensive analysis has directly quantified this to our knowledge.

METHODS

We used a large UK primary care database (Clinical Practice Research Datalink) to compare the prevalence of chronic diseases and other health conditions, including recent infections between 23,876 patients with NMD ever recorded by 2019 compared to 95,295 age-sex-practice matched patients without NMD. Modified Poisson regression estimated Prevalence Ratios (PR) to summarise the presence of the disease/condition ever (or for infections in 2018) in NMD patients versus non-NMD patients.

RESULTS

Patients with NMD had significantly higher rates for 16 of the 18 conditions routinely recorded in the primary care Quality and Outcomes Framework (QOF). Approximately 1-in-10 adults with NMD had ≥4 conditions recorded (PR = 1.39, 95%CI 1.33-1.45). Disparities were more pronounced at younger ages (18-49). For other (non-QOF) health conditions, significantly higher recorded levels were observed for rarer events (pulmonary embolism PR = 1.96 95%CI 1.76-2.18, hip fractures PR = 1.65 95%CI 1.47-1.85) as well as for more common primary care conditions (constipation PR = 1.52 95%CI 1.46-1.57, incontinence PR = 1.52 95%CI 1.44-1.60). The greatest co-morbidity burden was in patients with a myotonic disorder. Approximately 1-in-6 (17.1%) NMD patients had an infection recorded in the preceding year, with the risk of being hospitalised with an infection nearly double (PR = 1.92, 95%CI 1.79-2.07) compared to non-NMD patients.

CONCLUSION

The burden of chronic co-morbidity among patients with NMD is extremely high compared to the general population, and they are also more likely to present in primary and secondary care for acute events such as infections.

Identifying subtypes of chronic kidney disease with machine learning: development, internal validation and prognostic validation using linked electronic health records in 350,067 individuals

BACKGROUND

Although chronic kidney disease (CKD) is associated with high multimorbidity, polypharmacy, morbidity and mortality, existing classification systems (mild to severe, usually based on estimated glomerular filtration rate, proteinuria or urine albumin-creatinine ratio) and risk prediction models largely ignore the complexity of CKD, its risk factors and its outcomes. Improved subtype definition could improve prediction of outcomes and inform effective interventions.

METHODS

We analysed individuals ≥18 years with incident and prevalent CKD (n = 350,067 and 195,422 respectively) from a population-based electronic health record resource (2006-2020; Clinical Practice Research Datalink, CPRD). We included factors (n = 264 with 2670 derived variables), e.g. demography, history, examination, blood laboratory values and medications. Using a published framework, we identified subtypes through seven unsupervised machine learning (ML) methods (K-means, Diana, HC, Fanny, PAM, Clara, Model-based) with 66 (of 2670) variables in each dataset. We evaluated subtypes for: (i) internal validity (within dataset, across methods); (ii) prognostic validity (predictive accuracy for 5-year all-cause mortality and admissions); and (iii) medications (new and existing by British National Formulary chapter).

FINDINGS

After identifying five clusters across seven approaches, we labelled CKD subtypes: 1. Early-onset, 2. Late-onset, 3. Cancer, 4. Metabolic, and 5. Cardiometabolic. Internal validity: We trained a high performing model (using XGBoost) that could predict disease subtypes with 95% accuracy for incident and prevalent CKD (Sensitivity: 0.81-0.98, F1 score:0.84-0.97). Prognostic validity: 5-year all-cause mortality, hospital admissions, and incidence of new chronic diseases differed across CKD subtypes. The 5-year risk of mortality and admissions in the overall incident CKD population were highest in cardiometabolic subtype: 43.3% (42.3-42.8%) and 29.5% (29.1-30.0%), respectively, and lowest in the early-onset subtype: 5.7% (5.5-5.9%) and 18.7% (18.4-19.1%).

MEDICATIONS

Across CKD subtypes, the distribution of prescription medication classes at baseline varied, with highest medication burden in cardiometabolic and metabolic subtypes, and higher burden in prevalent than incident CKD.

INTERPRETATION

In the largest CKD study using ML, to-date, we identified five distinct subtypes in individuals with incident and prevalent CKD. These subtypes have relevance to study of aetiology, therapeutics and risk prediction.

FUNDING

AstraZeneca UK Ltd, Health Data Research UK.

Impact of the COVID-19 pandemic on incidence of tics in children and young people: a population-based cohort study

BACKGROUND

Since the onset of the coronavirus (COVID-19) pandemic, clinicians have reported an increase in presentations of sudden and new onset tics particularly affecting teenage girls. This population-based study aimed to describe and compare the incidence of tics in children and young people in primary care before and during the COVID-19 pandemic in England.

METHODS

We used information from the UK Clinical Practice Research Datalink (CPRD) Aurum dataset and included males and females aged 4-11 years and 12-18 years between Jan 1, 2015, and Dec 31, 2021. We grouped the pre-pandemic period (2015-2019) and presented the pandemic years (2020, 2021) separately. We described the characteristics of children and young people with a first record of a motor or vocal tic in each time period. Incidence rates of tics by age-sex groups in 2015-2019, 2020, and 2021 were calculated. Negative binomial regression models were used to calculate incidence rate ratios.

FINDINGS

We included 3,867,709 males and females aged 4-18 years. Over 14,734,062 person-years of follow-up, 11,245 people had a first tic record during the whole study period. The characteristics of people with tics differed over time, with the proportion of females aged 12-18 years and the proportion with mental health conditions including anxiety increasing during the pandemic. Tic incidence rates per 10,000 person-years were highest for 4-11-year-old males in all three time periods (13.4 [95% confidence interval 13.0-13.8] in 2015-2019; 13.2 [12.3-14.1] in 2020; 15.1 [14.1-16.1] in 2021) but increased markedly during the pandemic in 12-18-year-old females, from 2.5 (2.3-2.7) in 2015-2019, to 10.3 (9.5-11.3) in 2020 and 13.1 (12.1-14.1) in 2021. There were smaller increases in incidence rates in 12-18-year-old males (4.6 [4.4-4.9] in 2015-2019; 4.7 [4.1-5.3] in 2020; 6.2 [5.5-6.9] in 2021) and 4-11-year-old females (4.9 [4.7-5.2] in 2015-2019; 5.7 [5.1-6.4] in 2020; 7.6 [6.9-8.3] in 2021). Incidence rate ratios comparing 2020 and 2021 with 2015-2019 were highest in the 12-18-year-old female subgroup (4.2 [3.6-4.8] in 2020; 5.3 [4.7-6.0] in 2021).

INTERPRETATION

The incidence of tics in children and young people increased across all age and sex groups during the COVID-19 pandemic, with a differentially large effect in teenage girls (a greater than four-fold increase). Furthermore, in those with tic symptoms, proportions with mental health disorders including anxiety increased during the pandemic. Further research is required on the social and contextual factors underpinning this rise in onset of tics in teenage girls.

FUNDING

National Institute for Health Research Nottingham Biomedical Research Centre.

Maternal and child outcomes for pregnant women with pre-existing multiple long-term conditions: protocol for an observational study in the UK

INTRODUCTION

One in five pregnant women has multiple pre-existing long-term conditions in the UK. Studies have shown that maternal multiple long-term conditions are associated with adverse outcomes. This observational study aims to compare maternal and child outcomes for pregnant women with multiple long-term conditions to those without multiple long-term conditions (0 or 1 long-term conditions).

METHODS AND ANALYSIS

Pregnant women aged 15-49 years old with a conception date between 2000 and 2019 in the UK will be included with follow-up till 2019. The data source will be routine health records from all four UK nations (Clinical Practice Research Datalink (England), Secure Anonymised Information Linkage (Wales), Scotland routine health records and Northern Ireland Maternity System) and the Born in Bradford birth cohort. The exposure of two or more pre-existing, long-term physical or mental health conditions will be defined from a list of health conditions predetermined by women and clinicians. The association of maternal multiple long-term conditions with (a) antenatal, (b) peripartum, (c) postnatal and long-term and (d) mental health outcomes, for both women and their children will be examined. Outcomes of interest will be guided by a core outcome set. Comparisons will be made between pregnant women with and without multiple long-term conditions using modified Poisson and Cox regression. Generalised estimating equation will account for the clustering effect of women who had more than one pregnancy episode. Where appropriate, multiple imputation with chained equation will be used for missing data. Federated analysis will be conducted for each dataset and results will be pooled using random-effects meta-analyses.

ETHICS AND DISSEMINATION

Approval has been obtained from the respective data sources in each UK nation. Study findings will be submitted for publications in peer-reviewed journals and presented at key conferences.

COVID-19, Mental Health, and Chronic Illnesses: A Syndemic Perspective

BACKGROUND

The COVID-19 pandemic is an epidemiological and psychological crisis; what it does to the body is quite well known by now, and more research is underway, but the syndemic impact of COVID-19 and mental health on underlying chronic illnesses among the general population is not completely understood.

METHODS

We carried out a literature review to identify the potential impact of COVID-19 and related mental health issues on underlying comorbidities that could affect the overall health of the population.

RESULTS

Many available studies have highlighted the impact of COVID-19 on mental health only, but how complex their interaction is in patients with comorbidities and COVID-19, the absolute risks, and how they connect with the interrelated risks in the general population, remain unknown. The COVID-19 pandemic can be recognized as a syndemic due to; synergistic interactions among different diseases and other health conditions, increasing overall illness burden, emergence, spread, and interactions between infectious zoonotic diseases leading to new infectious zoonotic diseases; this is together with social and health interactions leading to increased risks in vulnerable populations and exacerbating clustering of multiple diseases.

CONCLUSION

There is a need to develop evidence to support appropriate and effective interventions for the overall improvement of health and psychosocial wellbeing of at-risk populations during this pandemic. The syndemic framework is an important framework that can be used to investigate and examine the potential benefits and impact of codesigning COVID-19/non-communicable diseases (NCDs)/mental health programming services which can tackle these epidemics concurrently.