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

Background: Healthcare workers are at the forefront of the COVID-19 pandemic and it is essential to monitor the relative infection rate of this group, as compared to workers in other occupations. This study aimed to produce estimates of the relative incidence ratio between healthcare workers and workers in non-healthcare occupations. Methods: Analysis of cross-sectional data from a daily, web-based survey of 1,788,795 Facebook users from September 6, 2020 to October 18, 2020. Participants were Facebook users in the United States aged 18 and above who were tested for COVID-19 because of an employer or school requirement in the past 14 days. The exposure variable was a self-reported history of working in healthcare in the past four weeks and the main outcome was a self-reported positive test for COVID-19. Results: On October 18, 2020, in the United States, there was a relative COVID-19 incidence ratio of 0.7 (95% UI 0.6 to 0.8) between healthcare workers and workers in non-healthcare occupations. Conclusions: Currently in the United States, healthcare workers have a substantially and significantly lower COVID-19 incidence rate than workers in non-healthcare occupations.

The relative incidence of COVID-19 in healthcare workers versus non-healthcare workers: evidence from a web-based survey of Facebook users in the United States

OBJECTIVE Local health departments (LHDs) have essential roles to play in ensuring the promotion of physical activity (PA) in their communities in order to reduce obesity. Little research exists, however, regarding the existence of these PA interventions across communities and how these interventions may impact community health. DESIGN In this exploratory study, we used cluster analysis to identify the structure of co-occurring PA interventions, followed by regression analysis to quantify the association between the patterns of PA interventions and prevalence of PA and obesity at a population level. SETTING Our study setting included local health jurisdictions in Colorado, Florida, Minnesota, New Jersey, Tennessee, and Washington. PARTICIPANTS Participating jurisdictions were those 218 local health jurisdictions (mostly counties) from which LHD leaders had provided data in 2013 for the Multi-Network Practice and Outcome Variation Examination Study. MAIN OUTCOME MEASURES We obtained unique public health activities data on PA interventions conducted in 2012 from 218 LHDs in 6 participating states. We categorized jurisdictions using cluster analysis, based on PA intervention approaches indicated by LHD leaders as available in their communities and then examined associations between categories and prevalence of obesity and of residents engaged in PA. RESULTS We identified 5 distinct PA intervention categories representing community-wide approaches-Comprehensive Approach, Built Environment, Personal Health, School-Based Interventions, and No Apparent Activities. Prevalence rates of obesity and PA among jurisdictions in the intervention clusters were significantly different from jurisdictions with No Apparent Activities, with more population-level approaches most significantly related to beneficial outcomes. CONCLUSION Our findings suggest the importance of standardized public health services data for generating evidence regarding health-related outcomes. The intervention categories we identified appear to reflect broad, local community-wide prevention approaches and demonstrated that population-level PA interventions can be testable and may have particularly beneficial relationships to community health. Widespread adoption of such standardized data depicting local public health prevention activity could support monitoring practice change, performance improvement, comparisons across communities that could reduce unnecessary variation, and the generation of evidence for public health practice and policy-making.

Five Community-wide Approaches to Physical Activity Promotion: A Cluster Analysis of These Activities in Local Health Jurisdictions in 6 States.

Many resource-poor countries lack the capacity to accurately track vital registration data, such as cause of death, which are crucial inputs to health and development decision making. Verbal autopsy provides a means to ascertain cause of death in the poorest countries through the means of a standard questionnaire, but because doctors are scarce and their time is better spent treating the ill, methods of classifying deaths based on questionnaire input have become increasingly important. In this paper we present preliminary work on the use of machine learning algorithms to classify cause of death in developing countries.

/pdf/machine-learning-methods-for-verbal-autopsy-in-developing-1k7m04ud2x.pdf

Machine learning methods for verbal autopsy in developing countries

Let I be a random 3CNF formula generated by choosing a truth assignment p for variables x1, xn uniformly at random and including every clause with i literals set true by p with probability pi, independently. We show that for any constants 0 ≤ η2,η3 ≤ 1 there is a constant dmin so that for all d ≥ dmin a spectral algorithm similar to the graph coloring algorithm of Alon and Kahale will find a satisfying assignment with high probability for p1 = d-n2, p2 = η2d-n2, and p3 = η3d-n2. Appropriately setting the ηi's yields natural distributions on satisfiable 3CNFs, not-all-equal-sat 3CNFs, and exactly-one-sat 3CNFs. © 2008 Wiley Periodicals, Inc. Random Struct. Alg., 2008

A spectral technique for random satisfiable 3CNF formulas

Objective
To assess the changes in patient outcome prediction and hospital performance ranking when incorporating diagnoses as risk adjusters rather than comorbidity indices.

Data Sources
Healthcare Cost and Utilization Project State Inpatient Databases for New York State, 2005–2009.

Study Design
Conducted tree-based classification for mortality and readmission by incorporating discrete patient diagnoses as predictors, comparing with traditional comorbidity indices such as those used for Centers for Medicare and Medicaid Services (CMS) outcome models.

Principal Findings
Diagnosis codes as predictors increased predictive accuracy 5.6 percent (95% CI: 4.5–6.9 percent) relative to CMS condition categories for heart failure 30-day mortality. Most other outcomes exhibited statistically significant accuracy gains and facility ranking shifts. Sensitivity analysis showed improvements even when predictors were limited to only the diagnoses included in CMS models.

Conclusions
Discretizing patient severity information beyond the levels of traditional comorbidity indices improves patient outcome predictions and substantially shifts facility rankings.

/pdf/improving-hospital-performance-rankings-using-discrete-4hjhb7enu6.pdf

Abraham D. Flaxman

Papers

The relative incidence of COVID-19 in healthcare workers versus non-healthcare workers: evidence from a web-based survey of Facebook users in the United States

Five Community-wide Approaches to Physical Activity Promotion: A Cluster Analysis of These Activities in Local Health Jurisdictions in 6 States.

Machine learning methods for verbal autopsy in developing countries

A spectral technique for random satisfiable 3CNF formulas

Improving Hospital Performance Rankings Using Discrete Patient Diagnoses for Risk Adjustment of Outcomes.