To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Research on sickness absence has typically focussed on single diagnoses, despite increasing recognition that long-term health conditions are highly multimorbid and clusters comprising coexisting mental and physical conditions are associated with poorer clinical and functional outcomes. The digitisation of sickness certification in the UK offers an opportunity to address sickness absence in a large primary care population.
Lambeth Datanet is a primary care database which collects individual-level data on general practitioner consultations, prescriptions, Quality and Outcomes Framework diagnostic data, sickness certification (fit note receipt) and demographic information (including age, gender, self-identified ethnicity, and truncated postcode). We analysed 326 415 people's records covering a 40-month period from January 2014 to April 2017.
We found significant variation in multimorbidity by demographic variables, most notably by self-defined ethnicity. Multimorbid health conditions were associated with increased fit note receipt. Comorbid depression had the largest impact on first fit note receipt, more than any other comorbid diagnoses. Highest rates of first fit note receipt after adjustment for demographics were for comorbid epilepsy and rheumatoid arthritis (HR 4.69; 95% CI 1.73–12.68), followed by epilepsy and depression (HR 4.19; 95% CI 3.60–4.87), chronic pain and depression (HR 4.14; 95% CI 3.69–4.65), cardiac condition and depression (HR 4.08; 95% CI 3.36–4.95).
Our results show striking variation in multimorbid conditions by gender, deprivation and ethnicity, and highlight the importance of multimorbidity, in particular comorbid depression, as a leading cause of disability among working-age adults.
How neighbourhood characteristics affect the physical safety of people with mental illness is unclear.
To examine neighbourhood effects on physical victimisation towards people using mental health services.
We developed and evaluated a machine-learning-derived free-text-based natural language processing (NLP) algorithm to ascertain clinical text referring to physical victimisation. This was applied to records on all patients attending National Health Service mental health services in Southeast London. Sociodemographic and clinical data, and diagnostic information on use of acute hospital care (from Hospital Episode Statistics, linked to Clinical Record Interactive Search), were collected in this group, defined as ‘cases’ and concurrently sampled controls. Multilevel logistic regression models estimated associations (odds ratios, ORs) between neighbourhood-level fragmentation, crime, income deprivation, and population density and physical victimisation.
Based on a human-rated gold standard, the NLP algorithm had a positive predictive value of 0.92 and sensitivity of 0.98 for (clinically recorded) physical victimisation. A 1 s.d. increase in neighbourhood crime was accompanied by a 7% increase in odds of physical victimisation in women and an 13% increase in men (adjusted OR (aOR) for women: 1.07, 95% CI 1.01–1.14, aOR for men: 1.13, 95% CI 1.06–1.21, P for gender interaction, 0.218). Although small, adjusted associations for neighbourhood fragmentation appeared greater in magnitude for women (aOR = 1.05, 95% CI 1.01–1.11) than men, where this association was not statistically significant (aOR = 1.00, 95% CI 0.95–1.04, P for gender interaction, 0.096). Neighbourhood income deprivation was associated with victimisation in men and women with similar magnitudes of association.
Neighbourhood factors influencing safety, as well as individual characteristics including gender, may be relevant to understanding pathways to physical victimisation towards people with mental illness.
Mood instability and sleep disturbance are common symptoms in people with mental illness. Both features are clinically important and associated with poorer illness trajectories. We compared clinical outcomes in people presenting to secondary mental health care with mood instability and/or sleep disturbance with outcomes in people without either mood instability or sleep disturbance.
Data were from electronic health records of 31,391 patients ages 16–65 years presenting to secondary mental health services between 2008 and 2016. Mood instability and sleep disturbance were identified using natural language processing. Prevalence of mood instability and sleep disturbance were estimated at baseline. Incidence rate ratios were estimates for clinical outcomes including psychiatric diagnoses, prescribed medication, and hospitalization within 2-years of presentation in persons with mood instability and/or sleep disturbance compared to individuals without either symptom.
Mood instability was present in 9.58%, and sleep disturbance in 26.26% of patients within 1-month of presenting to secondary mental health services. Compared with individuals without either symptom, those with mood instability and sleep disturbance showed significantly increased incidence of prescription of any psychotropic medication (incidence rate ratios [IRR] = 7.04, 95% confidence intervals [CI] 6.53–7.59), and hospitalization (IRR = 5.32, 95% CI 5.32, 4.67–6.07) within 2-years of presentation. Incidence rates of most clinical outcomes were considerably increased among persons with both mood instability and sleep disturbance, relative to persons with only one symptom.
Mood instability and sleep disturbance are present in a wide range of mental disorders, beyond those in which they are conventionally considered to be symptoms. They are associated with poor outcomes, particularly when they occur together. The poor prognosis associated with mood instability and sleep disorder may be, in part, because they are often treated as secondary symptoms. Mood instability and sleep disturbance need better recognition as clinical targets for treatment in their own right.
Repetitive Transcranial Magnetic Stimulation (rTMS) research in psychiatry mostly excludes left-handed participants. We recruited left-handed people with a bulimic disorder and found that stimulation of the left prefrontal cortex may result in different effects in left- and right-handed people. This highlights the importance of handedness and cortex lateralisation for rTMS.
A recent systematic review found a high prevalence of violence and mental distress among women trafficked for sexual exploitation; no data were identified for trafficked men and children.
To describe the clinical characteristics of trafficked people in contact with a large inner city mental health service compared with a non-trafficked cohort.
To investigate whether, compared with a non-trafficked cohort, trafficked people would be significantly more likely to have co-morbid disorders and have significantly smaller improvements in functioning at the end of an episode of care.
Study population: mental health service users who had been trafficked for exploitation and a non-trafficked service user cohort matched for gender and age. Data source: The South London and Maudsley NHS Trust (SLaM) Biomedical Research Centre Case Register Interactive Search (CRIS) database of anonymised full patient records (2006–2012).
We identified case records of 135 people who had been trafficked. 104 (77%) were female; age at first SLaM contact ranged from 8 to 49 years (mean 23.6, SD 8.0). 38 (28%) of the trafficked service users received psychiatric care from an emergency department. Depression (28.1%, n = 38), PTSD (19.3%, n = 26), non-affective psychoses (12.6%, n = 17) were the most frequently recorded diagnoses among trafficked service users. Further analysis is in progress and scheduled for completion by March 2013.
Significant numbers of trafficked people were seen in an inner-city mental health service; services therefore need to understand their complex needs.
Higher all-cause mortality and shorter life expectancies for people with severe mental illness (SMI, including schizophrenia, schizoaffective disorder, and bipolar disorder) have been frequently reported. Cancer contributes a substantial proportion of mortality (20 to 30%) as the second or third leading cause of death among people with SMI. Outcomes of cancer incidence studies in SMI were considerably heterogeneous, varying by cancer types and mental disorders.
To compare the incidence of overall and each type of cancer between people with SMI in southeast London and general population in UK.
Using the anonymised linkage between a regional monopoly secondary mental health service provider covering four southeast London boroughs and a population-based cancer register, we carried out the comparisons of cancer incidences between people with SMI and general population by age- and gender-standardisation in 2011.
Among SMI subjects with cancer (N=105), the most common cancer types were lung and colorectal cancer followed by breast cancer for women and prostate cancer for men in this area. Standardised incidence ratios (SIRs) for all cancers in SMI were 1.19 (95% CI: 0.97-1.44) overall, 2.43 (95% CI: 1.98-2.94) in men (n=61), and 0.98 (95% CI: 0.71-1.31) in women (n=44). Based on relatively small case numbers, raised SIRs were found for lung cancer in men (SIR=7.57, 95% CI: 3.04-15.6) and women (SIR=7.61, 95% CI: 2.79-16.6), and in women for colorectal (SIR=7.85, 95%CI: 2.55-18.32) and breast cancer (SIR=7.86, 95% CI: 4.58-12.59).
Specific pattern of elevated risks of cancer incidence were found for people with SMI.
Compared to the general population, people with schizophrenia have a substantially higher risk of premature mortality which translates into a 10–15 year reduction in life expectancy. The aim of this investigation was to determine if symptoms (including aggression, hallucinations or delusions, and depression) or the environmental and functional status of people with schizophrenia contribute to the high mortality risk observed in this patient group.
We identified cases of schizophrenia, aged ≥15 years in a large secondary mental healthcare case register linked to national mortality tracing. We modelled the effect of specific symptoms, activities of daily living (ADLs), living conditions, occupational and recreational activities (Health of the Nation Outcome Scale [HoNOS] subscales) on all-cause mortality over a 4-year observation period (2007-10) using Cox regression.
We identified 4270 schizophrenia cases (170 deaths) in the observation period. After controlling for a broad range of covariates, mortality was not significantly associated with hallucinations and delusions or overactive-aggressive behaviour, but was associated with subclinical depression (adjusted HR 1.5; 95% CI 1.1-2.2) and ADL impairment (adjusted HR 1.8; 95% CI 1.2-2.9).
Severity of symptoms, such as delusions and hallucinations, was less important in predicting mortality than subclinical depression and difficulties carrying out activities of daily living. The overall picture appears to be one where the highest all-cause mortality risk is in service users who are least visible to clinical teams.
Longitudinal cognitive change before and after acetyl cholinesterase inhibitor (AChEI) treatment initiation in Alzheimer's disease has never been described previously in a representative clinical population.
To model longitudinal changes in cognitive function for before and after AChEI prescription.
To further investigate differences in response by cognitive function at treatment initiation.
A retrospective longitudinal analysis was carried out of all 1843 patients from the South London and Maudsley NHS Foundation Trust (a large mental health provider to a catchment population of approximately 1.2 m) who were prescribed AChEIs between 2003–10 and had a minimum of one MMSE score within 1 year before treatment initiation and one MMSE score within 3 years after this. Manually extracted MMSE scores were analyzed over this period using three-piece linear mixed models.
Rates of MMSE change were −1.9 (95% CI −2.3,−1.4) in the year before treatment initiation, +1.3 (0.9,1.7) in the 6 months after treatment initiation, and −2.4 (−2.6,−2.3) from 6 months to 3 years. The difference between pre-treatment and 6-month-post-treatment slopes was −0.6 (−1.8,0.6) at baseline (treatment initiation) MMSE of 25 or over, +2.7 (1.7,3.7) at MMSE 21–24, and +4.6 (3.6,5.7) at MMSE 10–20.
In this naturalistic sample, a clear cognitive response to AChEI treatment was observed over the first six months followed by an unchanged decline. Response was substantially higher for patients with lower MMSE scores at treatment initiation.
The symptoms of bipolar disorder are sometimes misrecognised for unipolar depression and inappropriately treated with antidepressants. This may be associated with increased risk of developing mania. However, the extent to which this depends on what type of antidepressant is prescribed remains unclear.
To investigate the association between different classes of antidepressants and subsequent onset of mania/bipolar disorder in a real-world clinical setting.
Data on prior antidepressant therapy were extracted from 21,012 adults with unipolar depression receiving care from the South London and Maudsley NHS Foundation Trust (SLaM). multivariable Cox regression analysis (with age and gender as covariates) was used to investigate the association of antidepressant therapy with risk of developing mania/bipolar disorder.
In total, 91,110 person-years of follow-up data were analysed (mean follow-up: 4.3 years). The overall incidence rate of mania/bipolar disorder was 10.9 per 1000 person-years. The peak incidence of mania/bipolar disorder was seen in patients aged between 26 and 35 years (12.3 per 1000 person-years). The most frequently prescribed antidepressants were SSRIs (35.5%), mirtazapine (9.4%), venlafaxine (5.6%) and TCAs (4.7%). Prior antidepressant treatment was associated with an increased incidence of mania/bipolar disorder ranging from 13.1 to 19.1 per 1000 person-years. Multivariable analysis indicated a significant association with SSRIs (hazard ratio 1.34, 95% CI 1.18–1.52) and venlafaxine (1.35, 1.07–1.70).
In people with unipolar depression, antidepressant treatment is associated with an increased risk of subsequent mania/bipolar disorder. These findings highlight the importance of considering risk factors for mania when treating people with depression.
Disclosure of interest
The authors have not supplied their declaration of competing interest.
Mood instability is an important problem but has received relatively little research attention. Natural language processing (NLP) is a novel method, which can used to automatically extract clinical data from electronic health records (EHRs).
To extract mood instability data from EHRs and investigate its impact on people with mental health disorders.
Data on mood instability were extracted using NLP from 27,704 adults receiving care from the South London and Maudsley NHS Foundation Trust (SLaM) for affective, personality or psychotic disorders. These data were used to investigate the association of mood instability with different mental disorders and with hospitalisation and treatment outcomes.
Mood instability was documented in 12.1% of people included in the study. It was most frequently documented in people with bipolar disorder (22.6%), but was also common in personality disorder (17.8%) and schizophrenia (15.5%). It was associated with a greater number of days spent in hospital (B coefficient 18.5, 95% CI 12.1–24.8), greater frequency of hospitalisation (incidence rate ratio 1.95, 1.75–2.17), and an increased likelihood of prescription of antipsychotics (2.03, 1.75–2.35).
Using NLP, it was possible to identify mood instability in a large number of people, which would otherwise not have been possible by manually reading clinical records. Mood instability occurs in a wide range of mental disorders. It is generally associated with poor clinical outcomes. These findings suggest that clinicians should screen for mood instability across all common mental health disorders. The data also highlight the utility of NLP for clinical research.
Disclosure of interest
The authors have not supplied their declaration of competing interest.
There are often substantial delays before diagnosis and initiation of treatment in people bipolar disorder. Increased delays are a source of considerable morbidity among affected individuals.
To investigate the factors associated with delays to diagnosis and treatment in people with bipolar disorder.
Retrospective cohort study using electronic health record data from the South London and Maudsley NHS Foundation Trust (SLaM) from 1364 adults diagnosed with bipolar disorder. The following predictor variables were analysed in a multivariable Cox regression analysis on diagnostic delay and treatment delay from first presentation to SLaM: age, gender, ethnicity, compulsory admission to hospital under the UK Mental Health Act, marital status and other diagnoses prior to bipolar disorder.
The median diagnostic delay was 62 days (interquartile range: 17–243) and median treatment delay was 31 days (4–122). Compulsory hospital admission was associated with a significant reduction in both diagnostic delay (hazard ratio 2.58, 95% CI 2.18–3.06) and treatment delay (4.40, 3.63–5.62). Prior diagnoses of other psychiatric disorders were associated with increased diagnostic delay, particularly alcohol (0.48, 0.33–0.41) and substance misuse disorders (0.44, 0.31–0.61). Prior diagnosis of schizophrenia and psychotic depression were associated with reduced treatment delay.
Some individuals experience a significant delay in diagnosis and treatment of bipolar disorder, particularly those with alcohol/substance misuse disorders. These findings highlight a need to better identify the symptoms of bipolar disorder and offer appropriate treatment sooner in order to facilitate improved clinical outcomes. This may include the development of specialist early intervention services.
Disclosure of interest
The authors have not supplied their declaration of competing interest.
Despite their use in clinical practice, there is little evidence to support the use of therapist written goodbye letters as therapeutic tools. However, preliminary evidence suggests that goodbye letters may have benefits in the treatment of anorexia nervosa (AN).
This study aimed to examine whether therapist written goodbye letters were associated with improvements in body mass index (BMI) and eating disorder symptomology in patients with AN after treatment.
Participants were adults with AN (n = 41) who received The Maudsley Model of Anorexia Treatment for Adults (MANTRA) in a clinical trial evaluating two AN out-patient treatments. As part of MANTRA, therapists wrote goodbye letters to patients. A rating scheme was developed to rate letters for structure and quality. Linear regression analyses were used to examine associations between goodbye letter scores and outcomes after treatment.
Higher quality letters and letters that adopted a more affirming stance were associated with greater improvements in BMI at 12 months. Neither the overall quality nor the style of goodbye letters were associated with improvements in BMI at 24 months or reductions in eating disorder symptomology at either 12 or 24 months.
The results highlight the potential importance of paying attention to the overall quality of therapist written goodbye letters in the treatment of AN, and adopting an affirming stance.
The density of information in digital health records offers new potential opportunities for automated prediction of cost-relevant outcomes.
We investigated the extent to which routinely recorded data held in the electronic health record (EHR) predict priority service outcomes and whether natural language processing tools enhance the predictions. We evaluated three high priority outcomes: in-patient duration, readmission following in-patient care and high service cost after first presentation.
We used data obtained from a clinical database derived from the EHR of a large mental healthcare provider within the UK. We combined structured data with text-derived data relating to diagnosis statements, medication and psychiatric symptomatology. Predictors of the three different clinical outcomes were modelled using logistic regression with performance evaluated against a validation set to derive areas under receiver operating characteristic curves.
In validation samples, the full models (using all available data) achieved areas under receiver operating characteristic curves between 0.59 and 0.85 (in-patient duration 0.63, readmission 0.59, high service use 0.85). Adding natural language processing-derived data to the models increased the variance explained across all clinical scenarios (observed increase in r2 = 12–46%).
EHR data offer the potential to improve routine clinical predictions by utilising previously inaccessible data. Of our scenarios, prediction of high service use after initial presentation achieved the highest performance.
We conducted unmanned aerial vehicle lidar missions in the Maya Lowlands between June 2017 and June 2018 to develop appropriate methods, procedures, and standards for drone lidar surveys of ancient Maya settlements and landscapes. Three site locations were tested within upper Usumacinta River region using Phoenix Lidar Systems: Piedras Negras, Guatemala, was tested in 2017, and Budsilha and El Infiernito, both in Mexico, were tested in 2018. These sites represent a range of natural and cultural contexts, which make them ideal to evaluate the usefulness of the technology in the field. Results from standard digital elevation and surface models demonstrate the utility of deploying drone lidar in the Maya Lowlands and throughout Latin America. Drone survey can be used to target and efficiently document ancient landscapes and settlement. Such an approach is adaptive to fieldwork and is cost effective but still requires planning and thoughtful evaluation of samples. Future studies will test and evaluate the methods and techniques for filtering and processing these data.
Implementation of high-quality, dispatcher-assisted cardiopulmonary resuscitation (DA-CPR) is critical to improving survival from out-of-hospital cardiac arrest (OHCA). However, despite some studies demonstrating the use of a metronome in a stand-alone setting, no research has yet demonstrated the effectiveness of a metronome tool in improving DA-CPR in the context of a realistic 911 call or using instructions that have been tested in real-world emergency calls.
Use of the metronome tool will increase the proportion of callers able to perform CPR within the target rate without affecting depth.
The prospective, randomized, controlled study involved simulated 911 cardiac arrest calls made by layperson-callers and handled by certified emergency medical dispatchers (EMDs) at four locations in Salt Lake City, Utah USA. Participants were randomized into two groups. In the experimental group, layperson-callers received CPR pre-arrival instructions with metronome assistance. In the control group, layperson-callers received only pre-arrival instructions. The primary outcome measures were correct compression rate (counts per minute [cpm]) and depth (mm).
A total of 148 layperson-callers (57.4% assigned to experimental group) participated in the study. There was a statistically significant association between the number of participants who achieved the target compression rate and experimental study group (P=.003), and the experimental group had a significantly higher median compression rate than the control group (100 cpm and 89 cpm, respectively; P=.013). Overall, there was no significant correlation between compression rate and depth.
An automated software metronome tool is effective in getting layperson-callers to achieve the target compression rate and compression depth in a realistic DA-CPR scenario.
Scott G, Barron T, Gardett I, Broadbent M, Downs H, Devey L, Hinterman EJ, Clawson J, Olola C. Can a software-based metronome tool enhance compression rate in a realistic 911 call scenario without adversely impacting compression depth for dispatcher-assisted CPR? Prehosp Disaster Med. 2018;33(4):399–405
In cases of non-fatal self-harm, suicide notes are a major risk factor for repeated self-harm and suicide. Suicide notes can now be left on new media services, emails or text messages, as well as on paper.
In a group of people who had harmed themselves, we aimed to compare new media note-leavers with paper note-leavers and characterise these groups demographically and by risk factors.
Clinical notes of patients who presented with non-fatal self-harm to two London emergency departments were anonymously searched for mentions of new media use. These were categorised and risk factors were compared for those who had left a new media note, a paper note, or no note to establish differences in risk of note-leaving.
New media note-leaving was associated with younger age and substance use; both risk factors for repeated self-harm. However, suicidal intent remained highest in paper note-leavers.
Paper note-leavers remain at greatest risk, however new media note leaving is still correlated with risk factors related to repeated self-harm and suicide. Clinicians should enquire about new media use during emergency department assessments of self-harm.
Early recognition of an acute myocardial infarction (AMI) can increase the patient’s likelihood of survival. As the first point of contact for patients accessing medical care through emergency services, emergency medical dispatchers (EMDs) represent the earliest potential identification point for AMIs. The objective of the study was to determine how AMI cases were coded and prioritized at the dispatch point, and also to describe the distribution of these cases by patient age and gender.
No studies currently exist that describe the EMD’s ability to correctly triage AMIs into Advanced Life Support (ALS) response tiers.
The retrospective descriptive study utilized data from three sources: emergency medical dispatch, Emergency Medical Services (EMS), and emergency departments (EDs)/hospitals. The primary outcome measure was the distributions of AMI cases, as categorized by Chief Complaint Protocol, dispatch priority code and level, and patient age and gender. The EMS and ED/hospital data came from the Utah Department of Health (UDoH), Salt Lake City, Utah. Dispatch data came from two emergency communication centers covering the entirety of Salt Lake City and Salt Lake County, Utah.
Overall, 89.9% of all the AMIs (n=606) were coded in one of the three highest dispatch priority levels, all of which call for ALS response (called CHARLIE, DELTA, and ECHO in the studied system). The percentage of AMIs significantly increased for patients aged 35 years and older, and varied significantly by gender, dispatch level, and chief complaint. A total of 85.7% of all deaths occurred among patients aged 55 years and older, and 88.9% of the deaths were handled in the ALS-recommended priority levels.
Acute myocardial infarctions may present as a variety of clinical symptoms, and the study findings demonstrated that more than one-half were identified as having chief complaints of Chest Pain or Breathing Problems at the dispatch point, followed by Sick Person and Unconscious/Fainting. The 35-year age cutoff for assignment to higher priority levels is strongly supported. The Falls and Sick Person Protocols offer opportunities to capture atypical AMI presentations.
ClawsonJJ, GardettI, ScottG, FivazC, BarronT, BroadbentM, OlolaC. Hospital-Confirmed Acute Myocardial Infarction: Prehospital Identification Using the Medical Priority Dispatch System. Prehosp Disaster Med. 2018;33(1):29–35.