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Background Electronic medical records contain information of value for research, but contain identifiable and often highly sensitive confidential information. Patient-identifiable information cannot in general be shared outside clinical care teams without explicit consent, but anonymisation/de-identification allows research uses of clinical data without explicit consent. Results This article presents CRATE (Clinical Records Anonymisation and Text Extraction), an open-source software system with separable functions: (1) it anonymises or de-identifies arbitrary relational databases, with sensitivity and precision similar to previous comparable systems; (2) it uses public secure cryptographic methods to map patient identifiers to research identifiers (pseudonyms); (3) it connects relational databases to external tools for natural language processing; (4) it provides a web front end for research and administrative functions; and (5) it supports a specific model through which patients may consent to be contacted about research. Conclusions Creation and management of a research database from sensitive clinical records with secure pseudonym generation, full-text indexing, and a consent-to-contact process is possible and practical using entirely free and open-source software.

Dementia is the leading cause of death in elderly Western populations. Preventative interventions that could delay dementia onset even modestly would provide a major public health impact. There are no disease-modifying treatments currently available. Lithium has been proposed as a potential treatment. We assessed the association between lithium use and the incidence of dementia and its subtypes. We conducted a retrospective cohort study comparing patients treated between January 1, 2005 and December 31, 2019, using data from electronic clinical records of secondary care mental health (MH) services in Cambridgeshire and Peterborough NHS Foundation Trust (CPFT), United Kingdom (catchment area population approximately 0.86 million). Eligible patients were those aged 50 years or over at baseline and who had at least 1 year follow-up, excluding patients with a diagnosis of mild cognitive impairment (MCI) or dementia before, or less than 1 year after, their start date. The intervention was the use of lithium. The main outcomes were dementia and its subtypes, diagnosed and classified according to the International Classification of Diseases-10th Revision (ICD-10). In this cohort, 29,618 patients (of whom 548 were exposed to lithium) were included. Their mean age was 73.9 years. A total of 40.2% were male, 33.3% were married or in a civil partnership, and 71.0% were of white ethnicity. Lithium-exposed patients were more likely to be married, cohabiting or in a civil partnership, to be a current/former smoker, to have used antipsychotics, and to have comorbid depression, mania/bipolar affective disorder (BPAD), hypertension, central vascular disease, diabetes mellitus, or hyperlipidemias. No significant difference between the 2 groups was observed for other characteristics, including age, sex, and alcohol-related disorders. In the exposed cohort, 53 (9.7%) patients were diagnosed with dementia, including 36 (6.8%) with Alzheimer disease (AD) and 13 (2.6%) with vascular dementia (VD). In the unexposed cohort, corresponding numbers were the following: dementia 3,244 (11.2%), AD 2,276 (8.1%), and VD 698 (2.6%). After controlling for sociodemographic factors, smoking status, other medications, other mental comorbidities, and physical comorbidities, lithium use was associated with a lower risk of dementia (hazard ratio [HR] 0.56, 95% confidence interval [CI] 0.40 to 0.78), including AD (HR 0.55, 95% CI 0.37 to 0.82) and VD (HR 0.36, 95% CI 0.19 to 0.69). Lithium appeared protective in short-term (≤1-year exposure) and long-term lithium users (>5-year exposure); a lack of difference for intermediate durations was likely due to lack of power, but there was some evidence for additional benefit with longer exposure durations. The main limitation was the handling of BPAD, the most common reason for lithium prescription but also a risk factor for dementia. This potential confounder would most likely cause an increase in dementia in the exposed group, whereas we found the opposite, and the sensitivity analysis confirmed the primary results. However, the specific nature of the group of patients exposed to lithium means that caution is needed in extending these findings to the general population. Another limitation is that our sample size of patients using lithium was small, reflected in the wide CIs for results relating to some durations of lithium exposure, although again sensitivity analyses remained consistent with our primary findings. We observed an association between lithium use and a decreased risk of developing dementia. This lends further support to the idea that lithium may be a disease-modifying treatment for dementia and that this is a promising treatment to take forwards to larger randomised controlled trials (RCTs) for this indication.

Dementia with Lewy bodies (DLBs) is a common cause of dementia but has higher mortality than Alzheimer's disease (AD). The reasons for this are unclear, but antidementia drugs (including acetylcholinesterase inhibitors [AChEIs] and memantine) symptomatically benefit people with DLB and might improve outcomes. We investigated whether AChEIs and/or memantine were associated with reduced hospital admissions and mortality. We performed a retrospective cohort study of those diagnosed with DLB between 1 January 2005 and 31 December 2019, using data from electronic clinical records of secondary care mental health services in Cambridgeshire and Peterborough NHS Foundation Trust (CPFT), United Kingdom (catchment area population approximately 0.86 million), as well as linked records from national Hospital Episode Statistics (HES) data. Eligible patients were those who started AChEIs or memantine within 3 months of their diagnosis (cases) and those who never used AChEIs or memantine (controls). Outcomes included admission, length of stay, and mortality. Cox proportional hazard and linear regression models were used. Of 592 patients with DLB, 219 never took AChEIs or memantine, 100 took AChEIs only, and 273 took both AChEIs and memantine. The cohorts were followed up for an average of 896 days, 981 days, and 1,004 days, respectively. There were no significant differences in the cohorts' baseline characteristics, except for socioeconomic status that was lower in patients who never took AChEIs or memantine (χ2 = 23.34, P = 0.003). After controlling for confounding by sociodemographic factors (age, sex, marital status, ethnicity, socioeconomic status), antipsychotic use, antidepressant use, cognitive status, physical comorbidity, anticholinergic burden, and global health performance, compared with patients who never took AChEIs or memantine, patients taking AChEIs only or taking both had a significantly lower risk of death (adjusted hazard ratio (HR) = 0.67, 95% CI = 0.48 to 0.93, p = 0.02; adjusted HR = 0.64, 95% CI = 0.50 to 0.83, P = 0.001, respectively). Those taking AChEIs or both AChEIs and memantine had significantly shorter periods of unplanned hospital admission for physical disorders (adjusted coefficient -13.48, 95% CI = [-26.87, -0.09], P = 0.049; adjusted coefficient -14.21, 95% CI = [-24.58, -3.85], P = 0.007, respectively), but no difference in length of stay for planned admissions for physical disorders, or for admissions for mental health disorders. No significant additional associations of memantine on admission, length of stay, and mortality were found (all P > 0.05). The main limitation was that this was a naturalistic study and possible confounds cannot be fully controlled, and there may be selection bias resulting from nonrandom prescription behaviour in clinical practice. However, we mimicked the intention-to-treat design of clinical trials, and the majority of baseline characters were balanced between cohorts. In addition, our series of sensitivity analyses confirmed the consistency of our results. In this study, we observed that use of AChEIs with or without memantine in DLB was associated with shorter duration of hospital admissions and decreased risk of mortality. Although our study was naturalistic, it supports further the use of AChEIs in DLB.

Background Interest in modifiable risk factors (MRFs) for dementia is high, given the personal, social, and economic impact of the disorder, especially in ageing societies such as the United Kingdom. Exploring the population attributable fraction (PAF) of dementia attributable to MRFs and how this may have changed over time remains unclear. Unravelling the temporal dynamics of MRFs is crucial for informing the development of evidence-based and effective public health policies. This investigation examined the temporal trajectories of MRFs for dementia in England. Methods We used data from the English Longitudinal Study of Ageing, a panel study over eight waves collected between 2004 and 2019 (76,904 interviews in total). We calculated the PAFs for twelve MRFs (including six early- to mid-life factors and six late-life factors), as recommended by the Lancet Commission, and the individual weighted PAFs (IW-PAFs) for each risk factor. Temporal trends were analysed to understand the changes in the overall PAF and IW-PAF over the study period. Subgroup analyses were conducted by sex and socioeconomic status (SES). Results The overall PAF for dementia MRFs changed from 46.73% in 2004/2005 to 36.79% in 2018/2019, though this trend was not statistically significant. During 2004–2019, hypertension, with an average IW-PAF of 8.21%, was the primary modifiable determinant of dementia, followed by obesity (6.16%), social isolation (5.61%), hearing loss (4.81%), depression (4.72%), low education (4.63%), physical inactivity (3.26%), diabetes mellitus (2.49%), smoking (2.0%), excessive alcohol consumption (1.16%), air pollution (0.42%), and traumatic brain injury (TBI) (0.26%). During 2004–2019, only IW-PAFs of low education, social isolation, and smoking showed significant decreasing trends, while IW-PAFs of other factors either did not change significantly or increased (including TBI, diabetes mellitus, and air pollution). Upon sex-specific disaggregation, a higher overall PAF for MRFs was found among women, predominantly associated with later-life risk factors, most notably social isolation, depression, and physical inactivity. Additionally, hearing loss, classified as an early- to mid-life factor, played a supplementary role in the identified sex disparity. A comparable discrepancy was evident upon PAF evaluation by SES, with lower income groups experiencing a higher dementia risk, largely tied to later-life factors such as social isolation, physical inactivity, depression, and smoking. Early- to mid-life factors, in particular, low education and obesity, were also observed to contribute to the SES-associated divergence in dementia risk. Temporal PAF and IW-PAF trends, stratified by sex and SES, revealed that MRF PAF gaps across sex or SES categories have persisted or increased. Conclusions In England, there was little change over time in the proportion of dementia attributable to known modifiable risk factors. The observed trends underscore the continuing relevance of these risk factors and the need for targeted public health strategies to address them.

A serious obstacle to the development of Natural Language Processing (NLP) methods in the clinical domain is the accessibility of textual data. The mental health domain is particularly challenging, partly because clinical documentation relies heavily on free text that is difficult to de-identify completely. This problem could be tackled by using artificial medical data. In this work, we present an approach to generate artificial clinical documents. We apply this approach to discharge summaries from a large mental healthcare provider and discharge summaries from an intensive care unit. We perform an extensive intrinsic evaluation where we (1) apply several measures of text preservation; (2) measure how much the model memorises training data; and (3) estimate clinical validity of the generated text based on a human evaluation task. Furthermore, we perform an extrinsic evaluation by studying the impact of using artificial text in a downstream NLP text classification task. We found that using this artificial data as training data can lead to classification results that are comparable to the original results. Additionally, using only a small amount of information from the original data to condition the generation of the artificial data is successful, which holds promise for reducing the risk of these artificial data retaining rare information from the original data. This is an important finding for our long-term goal of being able to generate artificial clinical data that can be released to the wider research community and accelerate advances in developing computational methods that use healthcare data.

Abstract Background Drug-induced alterations to the dopamine system in stimulant use disorder (SUD) are hypothesized to impair reinforcement learning (RL). Computational modeling enables the investigation of the latent processes of RL in SUD patients, which could elucidate the nature of their impairments. Methods We investigated RL in 44 SUD patients and 41 healthy control participants using a probabilistic RL task that assesses learning from reward and punishment separately. In an independent sample, we determined the modulatory role of dopamine in RL following a single dose of the dopamine D2/3 receptor antagonist amisulpride (400 mg) and the agonist pramipexole (0.5 mg) in a randomised, double-blind, placebo-controlled, crossover design. We analyzed task performance using computational modelling and hypothesized that RL impairments in SUD patients would be differentially modulated by a dopamine D2/3 receptor antagonist and agonist. Results Computational analyses in both samples revealed significantly reduced learning rates from punishment in SUD patients compared with healthy controls, whilst their reward learning rates were not measurably impaired. In addition, the dopaminergic receptor agents modulated RL parameters differentially in both groups. Both amisulpride and pramipexole impaired RL parameters in healthy participants, but ameliorated learning from punishment in SUD patients. Conclusion Our findings suggest that RL impairments seen in SUD patients are associated with altered dopamine function.

Background Epidemiological research may require linkage of information from multiple organizations. This can bring two problems: (1) the information governance desirability of linkage without sharing direct identifiers, and (2) a requirement to link databases without a common person-unique identifier. Methods We develop a Bayesian matching technique to solve both. We provide an open-source software implementation capable of de-identified probabilistic matching despite discrepancies, via fuzzy representations and complete mismatches, plus de-identified deterministic matching if required. We validate the technique by testing linkage between multiple medical records systems in a UK National Health Service Trust, examining the effects of decision thresholds on linkage accuracy. We report demographic factors associated with correct linkage. Results The system supports dates of birth (DOBs), forenames, surnames, three-state gender, and UK postcodes. Fuzzy representations are supported for all except gender, and there is support for additional transformations, such as accent misrepresentation, variation for multi-part surnames, and name re-ordering. Calculated log odds predicted a proband’s presence in the sample database with an area under the receiver operating curve of 0.997–0.999 for non-self database comparisons. Log odds were converted to a decision via a consideration threshold θ and a leader advantage threshold δ . Defaults were chosen to penalize misidentification 20-fold versus linkage failure. By default, complete DOB mismatches were disallowed for computational efficiency. At these settings, for non-self database comparisons, the mean probability of a proband being correctly declared to be in the sample was 0.965 (range 0.931–0.994), and the misidentification rate was 0.00249 (range 0.00123–0.00429). Correct linkage was positively associated with male gender, Black or mixed ethnicity, and the presence of diagnostic codes for severe mental illnesses or other mental disorders, and negatively associated with birth year, unknown ethnicity, residential area deprivation, and presence of a pseudopostcode (e.g. indicating homelessness). Accuracy rates would be improved further if person-unique identifiers were also used, as supported by the software. Our two largest databases were linked in 44 min via an interpreted programming language. Conclusions Fully de-identified matching with high accuracy is feasible without a person-unique identifier and appropriate software is freely available.

ObjectivesUK National Health Service/Health and Social Care (NHS/HSC) data are variably shared between healthcare organisations for direct care, and increasingly de-identified for research. Few large-scale studies have examined public opinion on sharing, including of mental health (MH) versus physical health (PH) data. We measured data sharing preferences.Design/setting/interventions/outcomesPre-registered anonymous online survey, measuring expressed preferences, recruiting February to September 2020. Participants were randomised to one of three framing statements regarding MH versus PH data.ParticipantsOpen to all UK residents. Participants numbered 29 275; 40% had experienced an MH condition.ResultsMost (76%) supported identifiable data sharing for direct clinical care without explicit consent, but 20% opposed this. Preference for clinical/identifiable sharing decreased with geographical distance and was slightly less for MH than PH data, with small framing effects. Preference for research/de-identified data sharing without explicit consent showed the same small PH/MH and framing effects, plus greater preference for sharing structured data than de-identified free text. There was net support for research sharing to the NHS, academic institutions, and national research charities, net ambivalence about sharing to profit-making companies researching treatments, and net opposition to sharing to other companies (similar to sharing publicly). De-identified linkage to non-health data was generally supported, except to data held by private companies. We report demographic influences on preference. A majority (89%) supported a single NHS mechanism to choose uses of their data. Support for data sharing increased during COVID-19.ConclusionsSupport for healthcare data sharing for direct care without explicit consent is broad but not universal. There is net support for the sharing of de-identified data for research to the NHS, academia, and the charitable sector, but not the commercial sector. A single national NHS-hosted system for patients to control the use of their NHS data for clinical purposes and for research would have broad support.Trial registration numberISRCTN37444142.

Background Older adults who live alone and have difficulties in activities of daily living (ADLs) may have been more vulnerable during the COVID-19 pandemic. However, little is known about pandemic-related changes in ADL assistance (such as home care, domiciliary care) and its international variation. We examined international patterns and changes in provision of ADL assistance, and related these to country-level measures including national income and health service expenditure. Methods We analysed data covering 29 countries from three longitudinal cohort studies (Health and Retirement Study, English Longitudinal Study of Aging, and Survey of Health, Ageing and Retirement in Europe). Eligible people were aged ≥50 years and living alone. Outcomes included ADL difficulty status (assessed via six basic ADLs and five instrumental ADLs) and receipt of ADL assistance. Wealth-related inequality and need-related inequity in ADL assistance were measured using Erreygers’ corrected concentration index (ECI). Correlations were estimated between prevalence/inequality/inequity in ADL assistance and national health-related indicators. We hypothesized these measures would be associated with health system factors such as affordability and availability of ADL assistance, as well as active ageing awareness. Results During COVID-19, 18.4% of older adults living alone reported ADL difficulties (ranging from 8.8% in Switzerland to 29.2% in the USA) and 56.8% of those reporting difficulties received ADL assistance (ranging from 38.7% in the UK to 79.8% in Lithuania). Females were more likely to receive ADL assistance than males in 16/29 countries; the sex gap increased further during the pandemic. Wealth-related ECIs indicated socioeconomic equality in ADL assistance within 24/39 countries before the pandemic, and significant favouring of the less wealthy in 18/29 countries during the pandemic. Needs-related ECIs indicated less equity in assistance with ADLs during the pandemic than before. Our hypotheses on the association between ADL provision measures and health system factors were confirmed before COVID-19, but unexpectedly disconfirmed during COVID-19. Conclusion This study revealed an unequal (and in some countries, partly needs-mismatched) response from countries to older adults living alone during the COVID-19 pandemic. The findings might inform future research about, and policies for, older adults living alone, particularly regarding social protection responses during crises.

Serotonin is involved in a wide range of mental capacities essential for navigating the social world, including emotion and impulse control. Much recent work on serotonin and social functioning has focused on decision-making. Here we investigated the influence of serotonin on human emotional reactions to social conflict. We used a novel computerised task that required mentally simulating social situations involving unjust harm and found that depleting the serotonin precursor tryptophan—in a double-blind randomised placebo-controlled design—enhanced emotional responses to the scenarios in a large sample of healthy volunteers (n = 73), and interacted with individual differences in trait personality to produce distinctive human emotions. Whereas guilt was preferentially elevated in highly empathic participants, annoyance was potentiated in those high in trait psychopathy, with medium to large effect sizes. Our findings show how individual differences in personality, when combined with fluctuations of serotonin, may produce diverse emotional phenotypes. This has implications for understanding vulnerability to psychopathology, determining who may be more sensitive to serotonin-modulating treatments, and casts new light on the functions of serotonin in emotional processing.