Abraham D. Flaxman

Bio: Abraham D. Flaxman is an academic researcher from Institute for Health Metrics and Evaluation. The author has contributed to research in topics: Population & Verbal autopsy. The author has an hindex of 66, co-authored 195 publications receiving 88582 citations. Previous affiliations of Abraham D. Flaxman include Microsoft & University of Queensland.

Estimation of Causes of Death in Suburban Nepal Using Verbal Autopsy.

Abstract: Background Nepal is in the midst of a disease transition, including a rapid increase of noncommunicable diseases. In order for health policy makers and planners to make informed programmatic and funding decisions, they need up to date and accurate data regarding cause of death throughout the country. Methods of improving cause of death reporting in Nepal are urgently required. Objective We sought to validate SmartVA-Analyze, an application which computer certifies verbal autopsies, to evaluate it as a method for collecting mortality data in Nepal. Method We conducted a medical record review of mortality cases at Dhulikhel Hospital, Kathmandu University Hospital. Cases with a verifiable underlying cause of death were used as gold standard reference cases. Verbal autopsies were conducted with caregivers of 48 gold standard cases. Result Of the 66 adult gold standard mortality cases reviewed, 76% were caused by cancer, cirrhosis, cardiovascular disease, COPD or injury. When assessing concordance between cause of death from verbal autopsy vs. gold standards, we found an overall agreement (Kappa) of 0.50. Kappa based on broader ICD-10 categories was 0.69. Cause-Specific Mortality Fraction Accuracy was 0.625, and disease specific measures of concordance varied widely, with sensitivities ranging from 0-100%. Conclusion Ongoing, countrywide mortality data collection is crucial for evidence-based priority setting in Nepal. Though not valid for all causes, we found SmartVA-Analyze to provide useful general cause of death data, particularly in settings where death certification is unavailable.

The lower tail of the random minimum spanning tree

Abstract: Consider a complete graph $K_n$ where the edges have costs given by independent random variables, each distributed uniformly between 0 and 1. The cost of the minimum spanning tree in this graph is a random variable which has been the subject of much study. This note considers the large deviation probability of this random variable. Previous work has shown that the log-probability of deviation by $\varepsilon$ is $-\Omega(n)$, and that for the log-probability of $Z$ exceeding $\zeta(3)$ this bound is correct; $\log {\rm Pr}[Z \geq \zeta(3) + \varepsilon] = -\Theta(n)$. The purpose of this note is to provide a simple proof that the scaling of the lower tail is also linear, $\log {\rm Pr}[Z \leq \zeta(3) - \varepsilon] = -\Theta(n)$.

Understanding data use and preference of data visualization for public health professionals: A qualitative study.

Abstract: OBJECTIVE This study aimed to assess public health professionals' use of data, information, and evidence and to understand perceptions and preferences regarding data visualization to inform future design of data visualization tools. DESIGN We conducted qualitative interviews with public health professionals who use data for decision making as part of community health assessment and program planning from state and local health departments across six states. RESULTS We identified four themes: 1) collection of data, information, and evidence; 2) management and analysis of data and information to inform decisions; 3) use of data to support public health practice; and 4) preferences for data visualization and how visualization is being used. Public health professionals use data, information, and evidence from various resources for communicating with co-workers, stakeholders, and the public, and decision making regarding their programs and services. CONCLUSION Data visualization tools can help public health professionals improve their understanding and communication, their education of stakeholders, and their decision making using data, information, and evidence. Public health professionals believe in the value of using data, information, and evidence. Opportunities exist in ways to support public health professionals' data use by adopting data visualization tools and by mitigating systematic challenges in public health information systems.

A Novel Method for Verifying War Mortality while Estimating Iraqi Deaths for the Iran-Iraq War through Operation Desert Storm (1980-1993).

Abstract: Objectives We estimated war-related Iraqi mortality for the period 1980 through 1993. Method To test our hypothesis that deaths reported by siblings (even dating back several decades) would correspond with war events, we compared sibling mortality reports with the frequency of independent news reports about violent historic events. We used data from a survey of 4,287 adults in 2000 Iraqi households conducted in 2011. Interviewees reported on the status of their 24,759 siblings. Death rates were applied to population estimates, 1980 to 1993. News report data came from the ProQuest New York Times database. Results About half of sibling-reported deaths across the study period were attributed to direct war-related injuries. The Iran-Iraq war led to nearly 200,000 adult deaths, and the 1990–1991 First Gulf War generated another approximately 40,000 deaths. Deaths during peace intervals before and after each war were significantly lower. We found a relationship between total sibling-reported deaths and the tally of war events across the period, p = 0.02. Conclusions We report a novel method to verify the reliability of epidemiological (household survey) estimates of direct war-related injury mortality dating back several decades.

Standards of Medical Care in Diabetes

Abstract: 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.