2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC.

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(12)61728-0.pdf

Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010

https://hull-repository.worktribe.com/preview/1797952/1-s2.0-S0140673618322797-main.pdf

Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(12)61766-8.pdf

A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010

Abraham D. Flaxman and Theo Vos of the Institute for Health Metrics and Evaluation, University of Washington, discuss near-term applications for ML in population health and name their priorities for ongoing ML development.

/pdf/machine-learning-in-population-health-opportunities-and-20lpnxcmrk.pdf

Machine learning in population health: Opportunities and threats

Verbal autopsy (VA) is used to estimate the causes of death in areas with incomplete vital registration systems. The King and Lu method (KL) for direct estimation of cause-specific mortality fractions (CSMFs) from VA studies is an analysis technique that estimates CSMFs in a population without predicting individual-level cause of death as an intermediate step. In previous studies, KL has shown promise as an alternative to physician-certified verbal autopsy (PCVA). However, it has previously been impossible to validate KL with a large dataset of VAs for which the underlying cause of death is known to meet rigorous clinical diagnostic criteria. We applied the KL method to adult, child, and neonatal VA datasets from the Population Health Metrics Research Consortium gold standard verbal autopsy validation study, a multisite sample of 12,542 VAs where gold standard cause of death was established using strict clinical diagnostic criteria. To emulate real-world populations with varying CSMFs, we evaluated the KL estimations for 500 different test datasets of varying cause distribution. We assessed the quality of these estimates in terms of CSMF accuracy as well as linear regression and compared this with the results of PCVA. KL performance is similar to PCVA in terms of CSMF accuracy, attaining values of 0.669, 0.698, and 0.795 for adult, child, and neonatal age groups, respectively, when health care experience (HCE) items were included. We found that the length of the cause list has a dramatic effect on KL estimation quality, with CSMF accuracy decreasing substantially as the length of the cause list increases. We found that KL is not reliant on HCE the way PCVA is, and without HCE, KL outperforms PCVA for all age groups. Like all computer methods for VA analysis, KL is faster and cheaper than PCVA. Since it is a direct estimation technique, though, it does not produce individual-level predictions. KL estimates are of similar quality to PCVA and slightly better in most cases. Compared to other recently developed methods, however, KL would only be the preferred technique when the cause list is short and individual-level predictions are not needed.

/pdf/direct-estimation-of-cause-specific-mortality-fractions-from-3s4in6kec4.pdf

Direct estimation of cause-specific mortality fractions from verbal autopsies: multisite validation study using clinical diagnostic gold standards

We study a dynamically evolving random graph which adds vertices and edges using preferential attachment and is "attacked by an adversary". At time t, we add a new vertex xt and m random edges incident with xt, where m is constant. The neighbors of xt are chosen with probability proportional to degree. After adding the edges, the adversary is allowed to delete vertices. The only constraint on the adversarial deletions is that the total number of vertices deleted by time n must be no larger than δn, where δ is a constant. We show that if δ is sufficiently small then with high probability at time n the generated graph has a component of size Ω(n).

Adversarial deletion in a scale free random graph process

Consider a game in which edges of a graph are provided a pair at a time, and the player selects one edge from each pair, attempting to construct a graph with a component as large as possible. This game is in the spirit of recent papers on avoiding a giant component, but here we embrace it. We analyze this game in the offline and online setting, for arbitrary and random instances, which provides for interesting comparisons. For arbitrary instances, we find that the competitive ratio (the best possible solution value divided by best possible online solution value) is large. For "sparse" random instances the competitive ratio is also large, with high probability (whp): If the instance has 1/4(1 + e)n random edge pairs, with 0 < e ≤ 0.003, then any online algorithm generates a component of size O((log n)3/2) whp, while the optimal offline solution contains a component of size Ω(n) whp. For "dense" random instances, the average-case competitive ratio is much smaller. If the instance has ½(1 - e)n random edge pairs, with 0 < e ≤ 0.015, we give an online algorithm which finds a component of size Ω(n) whp.

/pdf/embracing-the-giant-component-31lc483ekk.pdf

Embracing the giant component

Systematic interviewer error is a potential issue in any health survey, and it can be especially pernicious in low- and middle-income countries, where survey teams may face problems of limited supervision, chaotic environments, language barriers, and low literacy. Survey teams in such environments could benefit from software that leverages mobile data collection tools to provide solutions for automated data quality control. As a first step in the creation of such software, we investigate and test several algorithms that find anomalous patterns in data. We validate the algorithms using one labeled data set and two unlabeled data sets from two community outreach programs in East Africa. In the labeled set, some of the data is known to be fabricated and some is believed to be relatively accurate. The unlabeled sets are from actual field operations. We demonstrate the feasibility of tools for automated data quality control by showing that the algorithms detect the fake data in the labeled set with a high sensitivity and specificity, and that they detect compelling anomalies in the unlabeled sets.

Abraham D. Flaxman

Papers

Machine learning in population health: Opportunities and threats

Direct estimation of cause-specific mortality fractions from verbal autopsies: multisite validation study using clinical diagnostic gold standards

Adversarial deletion in a scale free random graph process

Embracing the giant component

Automated quality control for mobile data collection