Book Review: The economics of poverty by Martin Ravallion

In a post for From Poverty to Power, Oxfam inequality number cruncher Deborah Hardoon reviews The Economics of Poverty by Martin Ravallion

Two view learning: SVM-2K, theory and practice

Kernel methods make it relatively easy to define complex highdimensional feature spaces. This raises the question of how we can identify the relevant subspaces for a particular learning task. When two views of the same phenomenon are available kernel Canonical Correlation Analysis (KCCA) has been shown to be an effective preprocessing step that can improve the performance of classification algorithms such as the Support Vector Machine (SVM). This paper takes this observation to its logical conclusion and proposes a method that combines this two stage learning (KCCA followed by SVM) into a single optimisation termed SVM-2K. We present both experimental and theoretical analysis of the approach showing encouraging results and insights

Revision rates after primary hip and knee replacement in England between 2003 and 2006

<b>Background</b>: Hip and knee replacement are some of the most frequently performed surgical procedures in the world. Resurfacing of the hip and unicondylar knee replacement are increasingly being used. There is relatively little evidence on their performance. To study performance of joint replacement in England, we investigated revision rates in the first 3 y after hip or knee replacement according to prosthesis type. <b>Methods and Findings</b>: We linked records of the National Joint Registry for England and Wales and the Hospital Episode Statistics for patients with a primary hip or knee replacement in the National Health Service in England between April 2003 and September 2006. Hospital Episode Statistics records of succeeding admissions were used to identify revisions for any reason. 76,576 patients with a primary hip replacement and 80,697 with a primary knee replacement were included (51% of all primary hip and knee replacements done in the English National Health Service). In hip patients, 3-y revision rates were 0.9% (95% confidence interval [CI] 0.8%–1.1%) with cemented, 2.0% (1.7%–2.3%) with cementless, 1.5% (1.1%–2.0% CI) with “hybrid” prostheses, and 2.6% (2.1%–3.1%) with hip resurfacing (p < 0.0001). Revision rates after hip resurfacing were increased especially in women. In knee patients, 3-y revision rates were 1.4% (1.2%–1.5% CI) with cemented, 1.5% (1.1%–2.1% CI) with cementless, and 2.8% (1.8%–4.5% CI) with unicondylar prostheses (p < 0.0001). Revision rates after knee replacement strongly decreased with age. <b>Interpretation</b>: Overall, about one in 75 patients needed a revision of their prosthesis within 3 y. On the basis of our data, consideration should be given to using hip resurfacing only in male patients and unicondylar knee replacement only in elderly patients

Prevalence of frailty and prefrailty among community-dwelling older adults in low-income and middle-income countries: a systematic review and meta-analysis

OBJECTIVE: To 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. DESIGN: Systematic review and meta-analysis. PROSPERO registration number is CRD42016036083. DATA SOURCES: MEDLINE, EMBASE, AMED, Web of Science, CINAHL and WHO Global Health Library were searched from their inception to 12 September 2017. SETTING: Low-income and middle-income countries. PARTICIPANTS: Community-dwelling older adults aged ≥60 years. RESULTS: We 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. CONCLUSION: The 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

Ranking algorithms for implicit feedback

This report presents novel algorithms to use eye movements as an implicit relevance feedback in order to improve the performance of the searches. The algorithms are evaluated on "Transport Rank Five" Dataset which were previously collected in Task 8.3. We demonstrated that simple linear combination or tensor product of eye movement and image features can improve the retrieval accuracy

Predicting dementia risk in primary care: development and validation of the Dementia Risk Score using routinely collected data

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

Joint Correlational and Discriminative Ensemble Classifier Learning for Dementia Stratification Using Shallow Brain Multiplexes

The demented brain wiring undergoes several changes with dementia progression. However, in early dementia stages, particularly early mild cognitive impairment (eMCI), these remain challenging to spot. Hence, developing accurate diagnostic techniques for eMCI identification is critical for early intervention to prevent the onset of Alzheimer’s Disease (AD). There is a large body of machine-learning based research developed for classifying different brain states (e.g., AD vs MCI). These works can be fundamentally grouped into two categories. The first uses correlational methods, such as canonical correlation analysis (CCA) and its variants, with the aim to identify most correlated features for diagnosis. The second includes discriminative methods, such as feature selection methods and linear discriminative analysis (LDA) and its variants to identify brain features that distinguish between two brain states. However, existing methods examine these correlational and discriminative brain data independently, which overlooks the complementary information provided by both techniques, which could prove to be useful in the classification of patients with dementia. On the other hand, how early dementia affects cortical brain connections in morphology remains largely unexplored. To address these limitations, we propose a joint correlational and discriminative ensemble learning framework for eMCI diagnosis that leverages a novel brain network representation, derived from the cortex. Specifically, we devise ‘the shallow convolutional brain multiplex’ (SCBM), which not only measures the similarity in morphology between pairs of brain regions, but also encodes the relationship between two morphological brain networks. Then, we represent each individual brain using a set of SCBMs, which are used to train joint ensemble CCA-SVM and LDA-based classifier. Our framework outperformed several state-of-the-art methods by 3-7% including independent correlational and discriminative methods.</p

Optimal SBP targets in routine clinical care

Objective: Compare outcomes of intensive treatment of SBP to less than 120 mmHg versus standard treatment to less than 140 mmHg in the US clinical Systolic Blood Pressure Intervention Trial (SPRINT) with similar hypertensive patients managed in routine primary care in the United Kingdom. Methods: Hypertensive patients aged 50–90 without diabetes or chronic kidney disease (CKD) were selected in SPRINT and The Health Improvement Network (THIN) database. Patients were enrolled in 2010–2013 and followed-up to 2015 (SPRINT N = 4112; THIN N = 8631). Cox's proportional hazards regressions were fitted to estimate the hazard of all-cause mortality or CKD (main adverse effect) associated with intensive treatment, adjusted for sex, age, ethnicity, smoking, blood pressure, cardiovascular disease, aspirin, statin, number of antihypertensive drugs at baseline, change in number of antihypertensive drugs at trial entry, and clinical site. Results: Almost half of the patients had intensive treatment (43–45%). In SPRINT, intensive treatment was associated with a decreased hazard of mortality of 0.63 (0.43–0.92), while in THIN with an increased hazard of 1.66 (1.28–2.15). In THIN, this effect was time-dependent. Intensive treatment was associated with an increased hazard of CKD of 2.67 (1.74–4.11) in SPRINT and 1.35 (1.08–1.70) in THIN. In THIN, this effect differed by the number of antihypertensive drugs prescribed at baseline. Conclusion: It appears that intensive treatment of SBP may be harmful in the general population where all have access to routine healthcare as with the UK National Health Services, but could be beneficial in high-risk patients who are closely monitored