Showing papers by "Abraham D. Flaxman published in 2021"

Prevalence of Visual Acuity Loss or Blindness in the US: A Bayesian Meta-analysis.

Abstract: Importance Globally, more than 250 million people live with visual acuity loss or blindness, and people in the US fear losing vision more than memory, hearing, or speech. But it appears there are no recent empirical estimates of visual acuity loss or blindness for the US. Objective To produce estimates of visual acuity loss and blindness by age, sex, race/ethnicity, and US state. Data Sources Data from the American Community Survey (2017), National Health and Nutrition Examination Survey (1999-2008), and National Survey of Children’s Health (2017), as well as population-based studies (2000-2013), were included. Study Selection All relevant data from the US Centers for Disease Control and Prevention’s Vision and Eye Health Surveillance System were included. Data Extraction and Synthesis The prevalence of visual acuity loss or blindness was estimated, stratified when possible by factors including US state, age group, sex, race/ethnicity, and community-dwelling or group-quarters status. Data analysis occurred from March 2018 to March 2020. Main Outcomes or Measures The prevalence of visual acuity loss (defined as a best-corrected visual acuity greater than or equal to 0.3 logMAR) and blindness (defined as a logMAR of 1.0 or greater) in the better-seeing eye. Results For 2017, this meta-analysis generated an estimated US prevalence of 7.08 (95% uncertainty interval, 6.32-7.89) million people living with visual acuity loss, of whom 1.08 (95% uncertainty interval, 0.82-1.30) million people were living with blindness. Of this, 1.62 (95% uncertainty interval, 1.32-1.92) million persons with visual acuity loss are younger than 40 years, and 141 000 (95% uncertainty interval, 95 000-187 000) persons with blindness are younger than 40 years. Conclusions and Relevance This analysis of all available data with modern methods produced estimates substantially higher than those previously published.

Ensemble modelling in descriptive epidemiology: burden of disease estimation.

Abstract: Ensemble modelling is a quantitative method that combines information from multiple individual models and has shown great promise in statistical machine learning. Ensemble models have a theoretical claim to being models that make the 'best' predictions possible. Applications of ensemble models to health research have included applying ensemble models like the super learner and random forests to epidemiological prediction tasks. Recently, ensemble methods have been applied successfully in burden of disease estimation. This article aims to provide epidemiologists with a practical understanding of the mechanisms of an ensemble model and insight into constructing ensemble models that are grounded in the epidemiological dynamics of the prediction problem of interest. We summarize the history of ensemble models, present a user-friendly framework for conceptualizing and constructing ensemble models, walk the reader through a tutorial of applying the framework to an application in burden of disease estimation, and discuss further applications.

Impact of data visualization on decision-making and its implications for public health practice: a systematic literature review.

Abstract: Data visualization tools have the potential to support decision-making for public health professionals. This review summarizes the science and evidence regarding data visualization and its impact on decision-making behavior as informed by cognitive processes such as understanding, attitude, or perception.An electronic literature search was conducted using six databases, including reference list reviews. Search terms were pre-defined based on research questions.Sixteen studies were included in the final analysis. Data visualization interventions in this review were found to impact attitude, perception, and decision-making compared to controls. These relationships between the interventions and outcomes appear to be explained by mediating factors such as perceived trustworthiness and quality, domain-specific knowledge, basic beliefs shared by social groups, and political beliefs.Visualization appears to bring advantages by increasing the amount of information delivered and decreasing the cognitive and intellectual burden to interpret information for decision-making. However, understanding data visualization interventions specific to public health leaders' decision-making is lacking, and there is little guidance for understanding a participant's characteristics and tasks. The evidence from this review suggests positive effects of data visualization can be identified, depending on the control of confounding factors on attitude, perception, and decision-making.

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.

Improving methods to measure comparable mortality cause (IMMCMC) gold standard verbal autopsy dataset.

Abstract: Objectives Gold standard cause of death data is critically important to improve verbal autopsy (VA) methods in diagnosing cause of death where civil and vital registration systems are inadequate or poor. As part of a three-country research study-Improving Methods to Measure Comparable Mortality by Cause (IMMCMC) study-data were collected on clinicopathological criteria-based gold standard cause of death from hospital record reviews with matched VAs. The purpose of this data note is to make accessible a de-identified format of these gold standard VAs for interested researchers to improve the diagnostic accuracy of VA methods. Data description The study was conducted between 2011 and 2014 in the Philippines, Bangladesh, and Papua New Guinea. Gold standard diagnoses of underlying causes of death for deaths occurring in hospital were matched to VAs conducted using a standardized VA questionnaire developed by the Population Health Metrics Consortium. 3512 deaths were collected in total, comprised of 2491 adults (12 years and older), 320 children (28 days to 12 years), and 702 neonates (0-27 days).

Health impact and cost of COVID-19 prophylaxis with monoclonal antibodies

Abstract: Background The COVID-19 pandemic has led to over 600,000 deaths in the United States and continues to disrupt lives even as effective vaccines are available. We aimed to estimate the impact and health system cost of implementing post-exposure prophylaxis against household exposure to COVID-19 with monoclonal antibodies. Methods We developed a decision-analytical model analysis of results from a recent randomized controlled trial with complementary data on household demographic structure, vaccine coverage, and COVID-19 confirmed case counts for the representative month of May, 2021. The model population includes individuals of all ages in the United States by sex and race/ethnicity. Results In a month of similar intensity to May, 2021, in the USA, a monoclonal antibody post-exposure prophylaxis program reaching 50% of exposed unvaccinated household members aged 50+, would avert 1,813 (1,171 – 2,456) symptomatic infections, 526 (343 - 716) hospitalizations, and 83 (56 - 116) deaths. Assuming the unit cost of administering the intervention was US$ 1,264, this program would save the health system US$ 3,055,202 (−14,034,632 - 18,787,692). Conclusions Currently in the United States, health system and public health actors have an opportunity to improve health and reduce costs through COVID-19 post-exposure prophylaxis with monoclonal antibodies.

Conflict-related intentional injuries in Baghdad, Iraq, 2003-2014: A modeling study and proposed method for calculating burden of injury in conflict.

Abstract: Background Previous research has focused on the mortality associated with armed conflict as the primary measure of the population health effects of war. However, mortality only demonstrates part of the burden placed on a population by conflict. Injuries and resultant disabilities also have long-term effects on a population and are not accounted for in estimates that focus solely on mortality. Our aim was to demonstrate a new method to describe the effects of both lives lost, and years of disability generated by a given conflict, with data from the US-led 2003 invasion and subsequent occupation of Iraq. Methods and findings Our data come from interviews conducted in 2014 in 900 Baghdad households containing 5,148 persons. The average household size was 5.72 persons. The majority of the population (55.8%) were between the ages of 19 and 60. Household composition was evenly divided between males and females. Household sample collection was based on methodology previously designed for surveying households in war zones. Survey questions were answered by the head of household or senior adult present. The questions included year the injury occurred, the mechanism of injury, the body parts injured, whether injury resulted in disability and, if so, the length of disability. We present this modeling study to offer an innovative methodology for measuring "years lived with disability" (YLDs) and "years of life lost" (YLLs) attributable to conflict-related intentional injuries, using the Global Burden of Disease (GBD) approach. YLDs were calculated with disability weights, and YLLs were calculated by comparing the age at death to the GBD standard life table to calculate remaining life expectancy. Calculations were also performed using Iraq-specific life expectancy for comparison. We calculated a burden of injury of 5.6 million disability-adjusted life years (DALYs) lost due to conflict-related injuries in Baghdad from 2003 to 2014. The majority of DALYs lost were attributable to YLLs, rather than YLDs, 4.99 million YLLs lost (95% uncertainty interval (UI) 3.87 million to 6.13 million) versus 616,000 YLDs lost (95% UI 399,000 to 894,000). Cause-based analysis demonstrated that more DALYs were lost to due to gunshot wounds (57%) than any other cause. Our study has several limitations. Recall bias regarding the reporting and attribution of injuries is possible. Second, we have no data past the time of the interview, so we assumed individuals with ongoing disability at the end of data collection would not recover, possibly counting more disability for injuries occurring later. Additionally, incomplete data could have led to misclassification of deaths, resulting in an underestimation of the total burden of injury. Conclusions In this study, we propose a methodology to perform burden of disease calculations for conflict-related injuries (expressed in DALYs) in Baghdad from 2003 to 2014. We go beyond previous reports of simple mortality to assess long-term population health effects of conflict-related intentional injuries. Ongoing disability is, in cross section, a relatively small 10% of the total burden. Yet, this small proportion creates years of demands on the health system, persistent limitations in earning capacity, and continuing burdens of care provision on family members.

Cost-Effectiveness of Multiple Micronutrients and Balanced-Energy Protein Supplementation During Antenatal Care in India, Pakistan, Mali and Tanzania: A Dynamic Microsimulation

Abstract: Background: Antenatal energy and micronutrient supplementation can improve birth outcomes and reduce disease burden but no studies have compared their cost-effectiveness. We compared the cost-effectiveness of multiple micronutrient supplementation (MMS), and balanced-energy protein (BEP) with iron and folic acid (IFA) to quantify their benefit in four countries with high prevalence of maternal undernutrition. Methods: Using estimates from the Global Burden of Disease 2017 Study, we simulated birth cohorts for two years in India, Pakistan, Mali, and Tanzania. We compared disability-adjusted life-years (DALYs) and incremental cost-effectiveness ratios (ICERs) in a baseline scenario with existing antenatal IFA coverage with scenarios where 90% of antenatal care attendees receive either MMS, BEP, or targeted-BEP (women with pre-pregnancy BMI <18·5 kg/m2 receive BEP containing MMS while women with BMI ≥18·5 kg/m2 receive MMS). Findings: ICERs for all scenarios were lowest in Pakistan, followed by Mali, India and Tanzania, in line with the baseline trend in attributable burden of low-birthweight. Targeted-BEP averts more DALYs than MMS alone while remaining cost-effective. ICERs for MMS compared to baseline were $9 (95%UI: $5-14) for Pakistan, $13 (95%UI: $7-21) for Mali, $13 (95%UI: $8-20) for India, $44 (95%UI: $20-80) for Tanzania. ICERs for targeted-BEP compared to baseline were $38 (95%UI: 23-54) for Pakistan, $51 (95%UI: 29-80) for Mali, $82 (95%UI: 56-115) for India, and $148 (95%UI: 77-250) for Tanzania. Interpretation: As countries start to invest in replacing IFA with MMS in alignment with recent WHO guidelines, co-implementing targeted-BEP offers a worthwhile and cost-effective strategy that should be considered at the same time to maximise benefits and synergize programme implementation. Funding Statement: Funded by the Bill and Melinda Gates Foundation for this project. Declaration of Interests: ADF has consulted recently for Janssen; SwissRe; Sanofi; Merck for Mothers; and Agathos, Ltd. Other authors have no conflicts of interest to disclose.