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Background: Existing dementia risk scores require collection of additional data from patients, limiting their use in practice. Routinely collected healthcare data have the potential to assess dementia risk without the need to collect further information. Our objective was to develop and validate a 5-year dementia risk score derived from primary healthcare data.Methods: We used data from general practices in The Health Improvement Network (THIN) database from across the UK, randomly selecting 377 practices for a development cohort and identifying 930,395 patients aged 60-95 years without a recording of dementia, cognitive impairment or memory symptoms at baseline. We developed risk algorithm models for two age groups (60-79 and 80-95 years). An external validation was conducted by validating the model on a separate cohort of 264,224 patients from 95 randomly chosen THIN practices that did not contribute to the development cohort. Our main outcome was 5-year risk of first recorded dementia diagnosis. Potential predictors included sociodemographic, cardiovascular, lifestyle and mental health variables.Results: Dementia incidence was 1.88 (95% CI, 1.83-1.93) and 16.53 (95% CI, 16.15-16.92) per 1000 PYAR for those aged 60-79 (n = 6017) and 80-95 years (n = 7104), respectively. Predictors for those aged 60-79 included age, sex, social deprivation, smoking, BMI, heavy alcohol use, anti-hypertensive drugs, diabetes, stroke/TIA, atrial fibrillation, aspirin, depression. The discrimination and calibration of the risk algorithm were good for the 60-79 years model; D statistic 2.03 (95% CI, 1.95-2.11), C index 0.84 (95% CI, 0.81-0.87), and calibration slope 0.98 (95% CI, 0.93-1.02). The algorithm had a high negative predictive value, but lower positive predictive value at most risk thresholds. Discrimination and calibration were poor for the 80-95 years model.Conclusions: Routinely collected data predicts 5-year risk of recorded diagnosis of dementia for those aged 60-79, but not those aged 80+. This algorithm can identify higher risk populations for dementia in primary care. The risk score has a high negative predictive value and may be most helpful in 'ruling out' those at very low risk from further testing or intensive preventative activities. [ABSTRACT FROM AUTHOR]

BackgroundThe 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).MethodA 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.ResultsProviding 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.ConclusionsUsing 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. [ABSTRACT FROM PUBLISHER]

Background Although screening and brief intervention is effective at reducing alcohol consumption in primary care and is recommended by guidelines, there are numerous barriers to its delivery. Screening newly-registered patients for alcohol-use disorders provides an opportunity for systematic collection of alcohol consumption data. Aim To examine how alcohol screening data are recorded in primary care, the extent to which they are recorded, and whether reported levels of consumption differ from general population data. Design and setting Cross-sectional analysis, with data collected from patients in the year after registration. Method Data on alcohol consumption were collected from The Health Improvement Network (THIN) primary care database from patients aged ⩾18 years, newly registered with a general practice in 2007 to 2009, and compared with the Office for National Statistics Opinions (ONS Omnibus) survey. Results A total of 292 376 (76%) of the 382 609 newlyregistered patients had entries for alcohol consumption (units a week, Read Codes for level of consumption, and/or screening test). Only 25 975 (9%) were recorded as completing a validated screening test, most commonly AUDIT/AUDIT-C (16 004, 5%) or FAST (9419, 3%). Alcohol-use disorders are underreported in primary care (for example, higher risk drinking 1% males, 0.5% females) in comparison with the Opinions survey (8% males, 7% females). Conclusion Alcohol screening data are collected from most patients within 1 year of registration with a GP practice; however, use of a validated screening test is rarely documented and alcohol-use disorders are underreported. Further efforts are needed to encourage or incentivise the use of validated tests to improve the quality of data collected. [ABSTRACT FROM AUTHOR]

BackgroundThere 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.MethodFamily 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.ResultsThere 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.ConclusionsPredictA 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. [ABSTRACT FROM PUBLISHER]

Antipsychotics may confer long term benefits and risks, including cardiovascular disease (CVD) risk. Several studies using routine clinical data have reported associations between antipsychotics and CVD but potential confounding factors and unclear classification of drug exposure limits their interpretation.Method: We used data from The Health Improvement Network, a large UK primary care database to determine relative risks of (CVD) comparing similar groups of people only prescribed olanzapine versus either risperidone or quetiapine. We included participants over 18 between 1995 and 2011. To assess confounding factors we created propensity scores for being prescribed each antipsychotic. We used propensity score matching and Poisson regression to calculate the CVD incidence rate ratios for olanzapine versus the other two drugs.Results: We identified 18,319 people who received a single antipsychotic during follow-up (n=5090 risperidone, 7797 olanzapine and 4613 quetiapine). In unmatched analyses, the CVD incidence rate ratio (IRR) for olanzapine versus risperidone was 0.63 (0.51-0.77) but the propensity score matched IRR was 0.78 (0.61-1.02). In the unmatched olanzapine versus quetiapine analysis the IRR adjusted for age and sex for olanzapine was 1.52 (1.16-1.98) but the propensity score matched analysis gave an IRR of 1.08 (0.79-1.46).Conclusions: After propensity score matching, we found no statistical differences in CVD incidence between olanzapine and either risperidone or quetiapine. Analyses which did not account for confounding factors produced very different results. Researchers must address confounding factors when designing observational studies to assess adverse outcomes of drugs, including antipsychotics. [ABSTRACT FROM AUTHOR]

Background: Severe mental illness (SMI) is associated with excess cardiovascular disease (CVD) morbidity, but little is known on provision of preventative interventions. We investigated statin initiation for primary CVD prevention in individuals with and without SMI.Methods: We used primary care data from The Health Improvement Network from 2006 to 2015 for UK patients aged 30-99years with no pre-existing CVD conditions and selected individuals with schizophrenia (n=13,252) or bipolar disorder (n=11,994). In addition, we identified samples of individuals without schizophrenia (n=66,060) and bipolar disorder (n=59,765), but with similar age and gender distribution. Missing data on CVD covariates were estimated using multiple imputation. Statin prescribing differences between individuals with and without SMI were investigated using multivariable Poisson regression models.Results: Initiation of statin prescribing was between 2 and 3 fold higher in people aged 30-59years with SMI than in those without after adjusting for CVD covariates. The rates in those aged 60-74years with SMI were similar or slightly higher relative to those without SMI. The incidence rate ratio (IRR) was 1.15 (95% CI 1.03-1.28) for bipolar disorder and 1.00 (0.91-1.11) for schizophrenia. The rate of statin prescribing was lower (IRR 0.81 (0.66-0.98)) amongst the oldest (aged 75+years) with schizophrenia relative to those without schizophrenia.Conclusions: Despite higher rates of new statin prescriptions to younger individuals with SMI relative to individuals without SMI, there was evidence of lower rates of statin initiation for older individuals with schizophrenia, and this group may benefit from additional measures to prevent CVD. [ABSTRACT FROM AUTHOR]