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This presentation describes and analyses patient characteristics and service usage over one year of a cohort of people with a diagnosis of severe mental illness across England, including contacts with primary and secondary care and continuity- of-care.
Data were collected from primary care patient notes (n=1150) by trained nurses from 64 practices in England, covering all service contacts from 1st April 2008 to 31st March 2009.
The estimated national rate of patients seen only in primary care was 31.1% and rates of schizophrenia and bipolar disorder were 56.8% and 37.9%. Patients had 7,961 consultations within primary care and 1,993 contacts with mental health services (20% of the total). Of those seen in secondary care, 61% had at most two secondary care contacts recorded in primary care notes. Median consultation rates with GPs were lower than have been reported for previous years and were only slightly above the general population. Relational continuity in primary care was poor for 21% of patients (Modified Modified Continuity Index =< 0.5), and for almost a third of new referrals to mental health services the primary care record contained no information on the referral outcome.
Primary care is centrally involved in the care of people with serious mental illness, but primary care and cross- boundary continuity is poor for a substantial proportion. Research is needed to determine the impact of poor continuity on patient outcomes, and above all, the impact of new collaborative ways of working at the primary/secondary care interface
Cardiovascular risk prediction tools are important for cardiovascular disease (CVD) prevention, however, which algorithms are appropriate for people with severe mental illness (SMI) is unclear.
To determine the cost-effectiveness using the net monetary benefit (NMB) approach of two bespoke SMI-specific risk algorithms compared to standard risk algorithms for primary CVD prevention in those with SMI, from an NHS perspective.
A microsimulation model was populated with 1000 individuals with SMI from The Health Improvement Network Database, aged 30–74 years without CVD. Four cardiovascular risk algorithms were assessed; (1) general population lipid, (2) general population BMI, (3) SMI-specific lipid and (4) SMI-specific BMI, compared against no algorithm. At baseline, each cardiovascular risk algorithm was applied and those high-risk (> 10%) were assumed to be prescribed statin therapy, others received usual care. Individuals entered the model in a ‘healthy’ free of CVD health state and with each year could retain their current health state, have cardiovascular events (non-fatal/fatal) or die from other causes according to transition probabilities.
The SMI-specific BMI and general population lipid algorithms had the highest NMB of the four algorithms resulting in 12 additional QALYs and a cost saving of approximately £37,000 (US$ 58,000) per 1000 patients with SMI over 10 years.
The general population lipid and SMI-specific BMI algorithms performed equally well. The ease and acceptability of use of a SMI-specific BMI algorithm (blood tests not required) makes it an attractive algorithm to implement in clinical settings.
Disclosure of interest
The authors have not supplied their declaration of competing interest.
Nearly half of care home residents with advanced dementia have clinically significant agitation. Little is known about costs associated with these symptoms toward the end of life. We calculated monetary costs associated with agitation from UK National Health Service, personal social services, and societal perspectives.
Prospective cohort study.
Thirteen nursing homes in London and the southeast of England.
Seventy-nine people with advanced dementia (Functional Assessment Staging Tool grade 6e and above) residing in nursing homes, and thirty-five of their informal carers.
Data collected at study entry and monthly for up to 9 months, extrapolated for expression per annum. Agitation was assessed using the Cohen-Mansfield Agitation Inventory (CMAI). Health and social care costs of residing in care homes, and costs of contacts with health and social care services were calculated from national unit costs; for a societal perspective, costs of providing informal care were estimated using the resource utilization in dementia (RUD)-Lite scale.
After adjustment, health and social care costs, and costs of providing informal care varied significantly by level of agitation as death approached, from £23,000 over a 1-year period with no agitation symptoms (CMAI agitation score 0–10) to £45,000 at the most severe level (CMAI agitation score >100). On average, agitation accounted for 30% of health and social care costs. Informal care costs were substantial, constituting 29% of total costs.
With the increasing prevalence of dementia, costs of care will impact on healthcare and social services systems, as well as informal carers. Agitation is a key driver of these costs in people with advanced dementia presenting complex challenges for symptom management, service planners, and providers.
The prevention of depression is a key public health policy priority. PredictD is the first risk algorithm for the prediction of the onset of major depression. Our aim in this study was to model the cost-effectiveness of PredictD in depression prevention in general practice (GP).
A decision analytical model was developed to determine the cost-effectiveness of two approaches, each of which was compared to treatment as usual (TAU) over 12 months: (1) the PredictD risk algorithm plus a low-intensity depression prevention programme; and (2) a universal prevention programme in which there was no initial identification of those at risk. The model simulates the incidence of depression and disease progression over 12 months and calculates the net monetary benefit (NMB) from the National Health Service (NHS) perspective.
Providing patients with PredictD and a depression prevention programme prevented 15 (17%) cases of depression in a cohort of 1000 patients over 12 months and had the highest probability of being the optimal choice at a willingness to pay (WTP) of £20 000 for a quality-adjusted life year (QALY). Universal prevention was strongly dominated by PredictD plus a depression prevention programme in that universal prevention resulted in less QALYs than PredictD plus prevention for a greater cost.
Using PredictD to identify primary-care patients at high risk of depression and providing them with a low-intensity prevention programme is potentially cost-effective at a WTP of £20 000 per QALY.
Several studies have reported weak associations between religious or spiritual belief and psychological health. However, most have been cross-sectional surveys in the USA, limiting inference about generalizability. An international longitudinal study of incidence of major depression gave us the opportunity to investigate this relationship further.
Data were collected in a prospective cohort study of adult general practice attendees across seven countries. Participants were followed at 6 and 12 months. Spiritual and religious beliefs were assessed using a standardized questionnaire, and DSM-IV diagnosis of major depression was made using the Composite International Diagnostic Interview (CIDI). Logistic regression was used to estimate incidence rates and odds ratios (ORs), after multiple imputation of missing data.
The analyses included 8318 attendees. Of participants reporting a spiritual understanding of life at baseline, 10.5% had an episode of depression in the following year compared to 10.3% of religious participants and 7.0% of the secular group (p < 0.001). However, the findings varied significantly across countries, with the difference being significant only in the UK, where spiritual participants were nearly three times more likely to experience an episode of depression than the secular group [OR 2.73, 95% confidence interval (CI) 1.59–4.68]. The strength of belief also had an effect, with participants with strong belief having twice the risk of participants with weak belief. There was no evidence of religion acting as a buffer to prevent depression after a serious life event.
These results do not support the notion that religious and spiritual life views enhance psychological well-being.
PredictD is a risk algorithm that was developed to predict risk of onset of major depression over 12 months in general practice attendees in Europe and validated in a similar population in Chile. It was the first risk algorithm to be developed in the field of mental disorders. Our objective was to extend predictD as an algorithm to detect people at risk of major depression over 24 months.
Participants were 4190 adult attendees to general practices in the UK, Spain, Slovenia and Portugal, who were not depressed at baseline and were followed up for 24 months. The original predictD risk algorithm for onset of DSM-IV major depression had already been developed in data arising from the first 12 months of follow-up. In this analysis we fitted predictD to the longer period of follow-up, first by examining only the second year (12–24 months) and then the whole period of follow-up (0–24 months).
The instrument performed well for prediction of major depression from 12 to 24 months [c-index 0.728, 95% confidence interval (CI) 0.675–0.781], or over the whole 24 months (c-index 0.783, 95% CI 0.757–0.809).
The predictD risk algorithm for major depression is accurate over 24 months, extending it current use of prediction over 12 months. This strengthens its use in prevention efforts in general medical settings.
The different incidence rates of, and risk factors for, depression in different countries argue for the need to have a specific risk algorithm for each country or a supranational risk algorithm. We aimed to develop and validate a predictD-Spain risk algorithm (PSRA) for the onset of major depression and to compare the performance of the PSRA with the predictD-Europe risk algorithm (PERA) in Spanish primary care.
A prospective cohort study with evaluations at baseline, 6 and 12 months. We measured 39 known risk factors and used multi-level logistic regression and inverse probability weighting to build the PSRA. In Spain (4574), Chile (2133) and another five European countries (5184), 11 891 non-depressed adult primary care attendees formed our at-risk population. The main outcome was DSM-IV major depression (CIDI).
Six variables were patient characteristics or past events (sex, age, sex×age interaction, education, physical child abuse, and lifetime depression) and six were current status [Short Form 12 (SF-12) physical score, SF-12 mental score, dissatisfaction with unpaid work, number of serious problems in very close persons, dissatisfaction with living together at home, and taking medication for stress, anxiety or depression]. The C-index of the PSRA was 0.82 [95% confidence interval (CI) 0.79–0.84]. The Integrated Discrimination Improvement (IDI) was 0.0558 [standard error (s.e.)=0.0071, Zexp=7.88, p<0.0001] mainly due to the increase in sensitivity. Both the IDI and calibration plots showed that the PSRA functioned better than the PERA in Spain.
The PSRA included new variables and afforded an improved performance over the PERA for predicting the onset of major depression in Spain. However, the PERA is still the best option in other European countries.
There are no risk models for the prediction of anxiety that may help in prevention. We aimed to develop a risk algorithm for the onset of generalized anxiety and panic syndromes.
Family practice attendees were recruited between April 2003 and February 2005 and followed over 24 months in the UK, Spain, Portugal and Slovenia (Europe4 countries) and over 6 months in The Netherlands, Estonia and Chile. Our main outcome was generalized anxiety and panic syndromes as measured by the Patient Health Questionnaire. We entered 38 variables into a risk model using stepwise logistic regression in Europe4 data, corrected for over-fitting and tested it in The Netherlands, Estonia and Chile.
There were 4905 attendees in Europe4, 1094 in Estonia, 1221 in The Netherlands and 2825 in Chile. In the algorithm four variables were fixed characteristics (sex, age, lifetime depression screen, family history of psychological difficulties); three current status (Short Form 12 physical health subscale and mental health subscale scores, and unsupported difficulties in paid and/or unpaid work); one concerned country; and one time of follow-up. The overall C-index in Europe4 was 0.752 [95% confidence interval (CI) 0.724–0.780]. The effect size for difference in predicted log odds between developing and not developing anxiety was 0.972 (95% CI 0.837–1.107). The validation of predictA resulted in C-indices of 0.731 (95% CI 0.654–0.809) in Estonia, 0.811 (95% CI 0.736–0.886) in The Netherlands and 0.707 (95% CI 0.671–0.742) in Chile.
PredictA accurately predicts the risk of anxiety syndromes. The algorithm is strikingly similar to the predictD algorithm for major depression, suggesting considerable overlap in the concepts of anxiety and depression.
Factors associated with depression are usually identified from cross-sectional studies.
We explore the relative roles of onset and recovery in determining these associations.
Hazard ratios for onset and recovery were estimated for 39 risk factors from a cohort study of 10 045 general practice attendees whose depression status was assessed at baseline, 6 and 12 months.
Risk factors have a stronger relative effect on the rate of onset than recovery. The strongest risk factors for both onset and maintenance of depression tend to be time-dependent. With the exception of female gender the strength of a risk factor's effect on onset is highly predictive of its impact on recovery.
Preventive measures will achieve a greater reduction in the prevalence of depression than measures designed to eliminate risk factors post onset. The strength of time-dependent risk factors suggests that it is more productive to focus on proximal rather than distal factors.
There is evidence that the prevalence of common mental disorders varies
To compare prevalence of common mental disorders in general practice
attendees in six European countries.
Unselected attendees to general practices in the UK, Spain, Portugal,
Slovenia, Estonia and The Netherlands were assessed for major depression,
panic syndrome and other anxiety syndrome. Prevalence of DSM–IV major
depression, other anxiety syndrome and panic syndrome was compared
between the UK and other countries after taking account of differences in
demographic factors and practice consultation rates.
Prevalence was estimated in 2344 men and 4865 women. The highest
prevalence for all disorders occurred in the UK and Spain, and lowest in
Slovenia and The Netherlands. Men aged 30–50 and women aged 18–30 had the
highest prevalence of major depression; men aged 40–60 had the highest
prevalence of anxiety, and men and women aged 40–50 had the highest
prevalence of panic syndrome. Demographic factors accounted for the
variance between the UK and Spain but otherwise had little impact on the
significance of observed country differences.
These results add to the evidence for real differences between European
countries in prevalence of psychological disorders and show that the
burden of care on general practitioners varies markedly between
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