ObjectiveTo systematically review the research conducted on prevalence of frailty and prefrailty among community-dwelling older adults in low-income and middle-income countries (LMICs) and to estimate the pooled prevalence of frailty and prefrailty in community-dwelling older adults in LMICs.DesignSystematic review and meta-analysis. PROSPERO registration number is CRD42016036083.Data sourcesMEDLINE, EMBASE, AMED, Web of Science, CINAHL and WHO Global Health Library were searched from their inception to 12 September 2017.SettingLow-income and middle-income countries.ParticipantsCommunity-dwelling older adults aged ≥60 years.ResultsWe screened 7057 citations and 56 studies were included. Forty-seven and 42 studies were included in the frailty and prefrailty meta-analysis, respectively. The majority of studies were from upper middle-income countries. One study was available from low-income countries. The prevalence of frailty varied from 3.9% (China) to 51.4% (Cuba) and prevalence of prefrailty ranged from 13.4% (Tanzania) to 71.6% (Brazil). The pooled prevalence of frailty was 17.4% (95% CI 14.4% to 20.7%, I2=99.2%) and prefrailty was 49.3% (95% CI 46.4% to 52.2%, I2=97.5%). The wide variation in prevalence rates across studies was largely explained by differences in frailty assessment method and the geographic region. These findings are for the studies with a minimum recruitment age 60, 65 and 70 years.ConclusionThe prevalence of frailty and prefrailty appears higher in community-dwelling older adults in upper middle-income countries compared with high-income countries, which has important implications for healthcare planning. There is limited evidence on frailty prevalence in lower middle-income and low-income countries.PROSPERO registration numberCRD42016036083.

Background-The incidence of myocardial infarction (MI) in Britain has fallen markedly in recent years. Few studies have investigated the extent to which this decline can be explained by concurrent changes in major cardiovascular risk factors. Methods and Results-The British Regional Heart Study examined changes in cardiovascular risk factors and MI incidence over 25 years from 1978 in a cohort of 7735 men. During this time, the age-adjusted hazard of MI decreased by 3.8% (95% confidence interval 2.6% to 5.0%) per annum, which corresponds to a 62% decline over the 25 years. At the same time, after adjustment for age, cigarette smoking prevalence, mean systolic blood pressure, and mean non-high-density lipoprotein (HDL) cholesterol decreased, whereas mean HDL cholesterol, mean body mass index, and physical activity levels rose. No significant change occurred in alcohol consumption. The fall in cigarette smoking explained the greatest part of the decline in MI incidence (23%), followed by changes in blood pressure (13%), HDL cholesterol (12%), and non-HDL cholesterol (10%). In combination, 46% (approximate 95% confidence interval 23% to 164%) of the decline in MI could be explained by these risk factor changes. Physical activity and alcohol consumption had little influence, whereas the increase in body mass index would have produced a rise in MI risk. Conclusions-Modest favorable changes in the major cardiovascular risk factors appear to have contributed to considerable reductions in MI incidence. This highlights the potential value of population-wide measures to reduce exposure to these risk factors in the prevention of coronary heart disease. (Circulation. 2008;117:598-604.)

BackgroundExisting 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.MethodsWe 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.ResultsDementia 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.ConclusionsRoutinely 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-016-0549-y) contains supplementary material, which is available to authorized users.

Importance People with Severe Mental Illness (SMI) including schizophrenia and bipolar disorder have excess cardiovascular disease (CVD). Risk prediction models, validated for the general population, may not accurately estimate cardiovascular risk in this group. Objectives To develop and validate a risk model exclusive to predicting CVD events in people with SMI, using established cardiovascular risk factors and additional variables. Design Prospective cohort and risk score development study. Setting UK Primary care Participants 38,824 people with a diagnosis of SMI (schizophrenia, bipolar disorder or other non-organic psychosis) aged 30-90 years. Median follow-up 5.6 years with 2,324 CVD events (6%). Main outcomes and measures Ten year risk of first cardiovascular event (myocardial infarction, angina pectoris, cerebrovascular accidents or major coronary surgery). Predictors included age, gender, height, weight, systolic blood pressure, diabetes, smoking, body mass index (BMI), lipid profile, social deprivation, SMI diagnosis, prescriptions of antidepressant , antipsychotics and reports of heavy alcohol use. Results We developed two risk models for people with SMI: The PRIMROSE BMI model and a lipid model. These mutually excluded lipids and BMI. From cross-validations, in terms of discrimination, for men, the PRIMROSE lipid model D statistic was 1.92 (1.80-2.03) and C statistic was 0.80 (0.76-0.83) compared to 1.74 (1.54-1.86) and 0.78 (0.75-0.82) for published Framingham risk scores; in women corresponding results were 1.87 (1.76-1.98) and 0.80 (0.76-0.83) for the PRIMROSE lipid model and 1.58 (1.48-1.68) and 0.76 (0.72-0.80) for Framingham. Discrimination statistics for the PRIMROSE BMI model were comparable to those for the PRIMROSE lipid model. Calibration plots suggested that both PRIMROSE models were superior to the Framingham models. Conclusion and relevance The PRIMROSE BMI and lipid CVD risk prediction models performed better in SMI than models which only include established CVD risk factors. Further work on their clinical and cost effectiveness is needed to ascertain the best thresholds for offering CVD interventions.