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1. Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study

Author

Oladeji, Olubusola, Zhang, Chi, Moradi, Tiam, Tarapore, Dharmesh, Stokes, Andrew C, Marivate, Vukosi, Sengeh, Moinina D, and Nsoesie, Elaine O

Subjects
Public aspects of medicine, RA1-1270
Abstract

BackgroundThe prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years. Behavioral data from digital sources, including social media and search engines, could be used for timely monitoring of behavioral risk factors. ObjectiveThe objective of our study was to propose the use of digital data from internet sources for monitoring changes in behavioral risk factors in Africa. MethodsWe obtained the adjusted volume of search queries submitted to Google for 108 terms related to diet, exercise, and disease from 2010 to 2016. We also obtained the obesity and overweight prevalence for 52 African countries from the World Health Organization (WHO) for the same period. Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. Out-of-sample predictions were used to assess and validate the model performance. ResultsThe study included 52 African countries. In 2016, the WHO reported an overweight prevalence ranging from 20.9% (95% credible interval [CI] 17.1%-25.0%) to 66.8% (95% CI 62.4%-71.0%) and an obesity prevalence ranging from 4.5% (95% CI 2.9%-6.5%) to 32.5% (95% CI 27.2%-38.1%) in Africa. The highest obesity and overweight prevalence were noted in the northern and southern regions. Google searches for diet-, exercise-, and obesity-related terms explained 97.3% (root-mean-square error [RMSE] 1.15) of the variation in obesity prevalence across all 52 countries. Similarly, the search data explained 96.6% (RMSE 2.26) of the variation in the overweight prevalence. The search terms yoga, exercise, and gym were most correlated with changes in obesity and overweight prevalence in countries with the highest prevalence. ConclusionsInformation-seeking patterns for diet- and exercise-related terms could indicate changes in attitudes toward and engagement in risk factors or healthy behaviors. These trends could capture population changes in risk factor prevalence, inform digital and physical interventions, and supplement official data from surveys.

Published
2021
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2. COVID-19 Misinformation Spread in Eight Countries: Exponential Growth Modeling Study

Author

Nsoesie, Elaine Okanyene, Cesare, Nina, Müller, Martin, and Ozonoff, Al

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundThe epidemic of misinformation about COVID-19 transmission, prevention, and treatment has been going on since the start of the pandemic. However, data on the exposure and impact of misinformation is not readily available. ObjectiveWe aim to characterize and compare the start, peak, and doubling time of COVID-19 misinformation topics across 8 countries using an exponential growth model usually employed to study infectious disease epidemics. MethodsCOVID-19 misinformation topics were selected from the World Health Organization Mythbusters website. Data representing exposure was obtained from the Google Trends application programming interface for 8 English-speaking countries. Exponential growth models were used in modeling trends for each country. ResultsSearches for “coronavirus AND 5G” started at different times but peaked in the same week for 6 countries. Searches for 5G also had the shortest doubling time across all misinformation topics, with the shortest time in Nigeria and South Africa (approximately 4-5 days). Searches for “coronavirus AND ginger” started at the same time (the week of January 19, 2020) for several countries, but peaks were incongruent, and searches did not always grow exponentially after the initial week. Searches for “coronavirus AND sun” had different start times across countries but peaked at the same time for multiple countries. ConclusionsPatterns in the start, peak, and doubling time for “coronavirus AND 5G” were different from the other misinformation topics and were mostly consistent across countries assessed, which might be attributable to a lack of public understanding of 5G technology. Understanding the spread of misinformation, similarities and differences across different contexts can help in the development of appropriate interventions for limiting its impact similar to how we address infectious disease epidemics. Furthermore, the rapid proliferation of misinformation that discourages adherence to public health interventions could be predictive of future increases in disease cases.

Published
2020
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4. Length of Stay prediction for Hospital Management using Domain Adaptation

Author

Momo, Lyse Naomi Wamba, Moorosi, Nyalleng, Nsoesie, Elaine O., Rademakers, Frank, and De Moor, Bart

Subjects
Computer Science - Machine Learning
Abstract

Inpatient length of stay (LoS) is an important managerial metric which if known in advance can be used to efficiently plan admissions, allocate resources and improve care. Using historical patient data and machine learning techniques, LoS prediction models can be developed. Ethically, these models can not be used for patient discharge in lieu of unit heads but are of utmost necessity for hospital management systems in charge of effective hospital planning. Therefore, the design of the prediction system should be adapted to work in a true hospital setting. In this study, we predict early hospital LoS at the granular level of admission units by applying domain adaptation to leverage information learned from a potential source domain. Time-varying data from 110,079 and 60,492 patient stays to 8 and 9 intensive care units were respectively extracted from eICU-CRD and MIMIC-IV. These were fed into a Long-Short Term Memory and a Fully connected network to train a source domain model, the weights of which were transferred either partially or fully to initiate training in target domains. Shapley Additive exPlanations (SHAP) algorithms were used to study the effect of weight transfer on model explanability. Compared to the benchmark, the proposed weight transfer model showed statistically significant gains in prediction accuracy (between 1% and 5%) as well as computation time (up to 2hrs) for some target domains. The proposed method thus provides an adapted clinical decision support system for hospital management that can ease processes of data access via ethical committee, computation infrastructures and time.

Published
2023

5. Guess Who’s Not Coming to Dinner? Evaluating Online Restaurant Reservations for Disease Surveillance

Author

Nsoesie, Elaine O, Buckeridge, David L, and Brownstein, John S

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundAlternative data sources are used increasingly to augment traditional public health surveillance systems. Examples include over-the-counter medication sales and school absenteeism. ObjectiveWe sought to determine if an increase in restaurant table availabilities was associated with an increase in disease incidence, specifically influenza-like illness (ILI). MethodsRestaurant table availability was monitored using OpenTable, an online restaurant table reservation site. A daily search was performed for restaurants with available tables for 2 at the hour and at half past the hour for 22 distinct times: between 11:00 am-3:30 pm for lunch and between 6:00-11:30 PM for dinner. In the United States, we examined table availability for restaurants in Boston, Atlanta, Baltimore, and Miami. For Mexico, we studied table availabilities in Cancun, Mexico City, Puebla, Monterrey, and Guadalajara. Time series of restaurant use was compared with Google Flu Trends and ILI at the state and national levels for the United States and Mexico using the cross-correlation function. ResultsDifferences in restaurant use were observed across sampling times and regions. We also noted similarities in time series trends between data on influenza activity and restaurant use. In some settings, significant correlations greater than 70% were noted between data on restaurant use and ILI trends. ConclusionsThis study introduces and demonstrates the potential value of restaurant use data for event surveillance.

Published
2014
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7. Use of Technology and Innovations in the COVID-19 Pandemic Response in Africa

Author

Maharana, Adyasha, Amutorine, Morine, Sengeh, Moinina David, and Nsoesie, Elaine O.

Subjects
Computer Science - Computers and Society, Computer Science - Social and Information Networks
Abstract

The use of technology has been ubiquitous in efforts to combat the ongoing public health crisis due to emergence and spread of the SARS-CoV-2 virus. African countries have made tremendous use of technology to disseminate information, counter the spread of COVID-19, and develop cutting-edge techniques to help with diagnosis, treatment and management of patients. The nature and outcomes of these efforts sometimes differ in Africa compared to other areas of the world due to its unique challenges and opportunities. Several countries have developed innovative technology-driven solutions to cater to a diverse population with varying access to technology. Much of the efforts are also earmarked by a flexible approach to problem solving, local tech entrepreneurship, and swift adoption of cutting-edge technology., Comment: 29 pages

Published
2020

9. Scraping Social Media Photos Posted in Kenya and Elsewhere to Detect and Analyze Food Types

Author

Wang, Kaihong, Jalal, Mona, Jefferson, Sankara, Zheng, Yi, Nsoesie, Elaine O., and Betke, Margrit

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Monitoring population-level changes in diet could be useful for education and for implementing interventions to improve health. Research has shown that data from social media sources can be used for monitoring dietary behavior. We propose a scrape-by-location methodology to create food image datasets from Instagram posts. We used it to collect 3.56 million images over a period of 20 days in March 2019. We also propose a scrape-by-keywords methodology and used it to scrape ~30,000 images and their captions of 38 Kenyan food types. We publish two datasets of 104,000 and 8,174 image/caption pairs, respectively. With the first dataset, Kenya104K, we train a Kenyan Food Classifier, called KenyanFC, to distinguish Kenyan food from non-food images posted in Kenya. We used the second dataset, KenyanFood13, to train a classifier KenyanFTR, short for Kenyan Food Type Recognizer, to recognize 13 popular food types in Kenya. The KenyanFTR is a multimodal deep neural network that can identify 13 types of Kenyan foods using both images and their corresponding captions. Experiments show that the average top-1 accuracy of KenyanFC is 99% over 10,400 tested Instagram images and of KenyanFTR is 81% over 8,174 tested data points. Ablation studies show that three of the 13 food types are particularly difficult to categorize based on image content only and that adding analysis of captions to the image analysis yields a classifier that is 9 percent points more accurate than a classifier that relies only on images. Our food trend analysis revealed that cakes and roasted meats were the most popular foods in photographs on Instagram in Kenya in March 2019., Comment: Another version of the paper was submitted to the ACM International Conference on Multimedia (ACMMM2019)

Published
2019

10. Detecting reports of unsafe foods in consumer product reviews.

Author

Maharana, Adyasha, Cai, Kunlin, Hellerstein, Joseph, Hswen, Yulin, Munsell, Michael, Staneva, Valentina, Verma, Miki, Vint, Cynthia, Wijaya, Derry, and Nsoesie, Elaine O

Subjects
artificial intelligence, consumer product safety, food and drug administration, food safety, machine learning
Abstract

Objectives:Access to safe and nutritious food is essential for good health. However, food can become unsafe due to contamination with pathogens, chemicals or toxins, or mislabeling of allergens. Illness resulting from the consumption of unsafe foods is a global health problem. Here, we develop a machine learning approach for detecting reports of unsafe food products in consumer product reviews from Amazon.com. Materials and Methods:We linked Amazon.com food product reviews to Food and Drug Administration (FDA) food recalls from 2012 to 2014 using text matching approaches in a PostGres relational database. We applied machine learning methods and over- and under-sampling methods to the linked data to automate the detection of reports of unsafe food products. Results:Our data consisted of 1 297 156 product reviews from Amazon.com. Only 5149 (0.4%) were linked to recalled food products. Bidirectional Encoder Representation from Transformations performed best in identifying unsafe food reviews, achieving an F1 score, precision and recall of 0.74, 0.78, and 0.71, respectively. We also identified synonyms for terms associated with FDA recalls in more than 20 000 reviews, most of which were associated with nonrecalled products. This might suggest that many more products should have been recalled or investigated. Discussion and Conclusion:Challenges to improving food safety include, urbanization which has led to a longer food chain, underreporting of illness and difficulty in linking contaminated food to illness. Our approach can improve food safety by enabling early identification of unsafe foods which can lead to timely recall thereby limiting the health and economic impact on the public.

Published
2019

13. Using Deep Learning to Examine the Association between the Built Environment and Neighborhood Adult Obesity Prevalence

Author

Maharana, Adyasha and Nsoesie, Elaine O.

Subjects
Computer Science - Computers and Society
Abstract

More than one-third of the adult population in the United States is obese. Obesity has been linked to factors such as, genetics, diet, physical activity and the environment. However, evidence indicating associations between the built environment and obesity has varied across studies and geographical contexts. Here, we used deep learning and approximately 150,000 high resolution satellite images to extract features of the built environment. We then developed linear regression models to consistently quantify the association between the extracted features and obesity prevalence at the census tract level for six cities in the United States. The extracted features of the built environment explained 72% to 90% of the variation in obesity prevalence across cities. Outof-sample predictions were considerably high with correlations greater than 80% between predicted and true obesity prevalence across all census tracts. This study supports a strong association between the built environment and obesity prevalence. Additionally, it also illustrates that features of the built environment extracted from satellite images can be useful for studying health indicators, such as obesity. Understanding the association between specific features of the built environment and obesity prevalence can lead to structural changes that could encourage physical activity and decreases in obesity prevalence.

Published
2017
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14. Demographics in Social Media Data for Public Health Research: Does it matter?

Author

Cesare, Nina, Grant, Christan, Hawkins, Jared B., Brownstein, John S., and Nsoesie, Elaine O.

Subjects
Computer Science - Computers and Society
Abstract

Social media data provides propitious opportunities for public health research. However, studies suggest that disparities may exist in the representation of certain populations (e.g., people of lower socioeconomic status). To quantify and address these disparities in population representation, we need demographic information, which is usually missing from most social media platforms. Here, we propose an ensemble approach for inferring demographics from social media data. Several methods have been proposed for inferring demographic attributes such as, age, gender and race/ethnicity. However, most of these methods require large volumes of data, which makes their application to large scale studies challenging. We develop a scalable approach that relies only on user names to predict gender. We develop three separate classifiers trained on data containing the gender labels of 7,953 Twitter users from Kaggle.com. Next, we combine predictions from the individual classifiers using a stacked generalization technique and apply the ensemble classifier to a dataset of 36,085 geotagged foodborne illness related tweets from the United States. Our ensemble approach achieves an accuracy, precision, recall, and F1 score of 0.828, 0.851, 0.852 and 0.837, respectively, higher than the individual machine learning approaches. The ensemble classifier also covers any user with an alphanumeric name, while the data matching approach, which achieves an accuracy of 0.917, only covers 67% of users. Application of our method to reports of foodborne illness in the United States highlights disparities in tweeting by gender and shows that counties with a high volume of foodborne-illness related tweets are heavily overrepresented by female Twitter users., Comment: Presented at the Data For Good Exchange 2017

Published
2017

15. Using Deep Learning and Satellite Imagery to Quantify the Impact of the Built Environment on Neighborhood Crime Rates

Author

Maharana, Adyasha, Nguyen, Quynh C., and Nsoesie, Elaine O.

Subjects
Computer Science - Computers and Society
Abstract

The built environment has been postulated to have an impact on neighborhood crime rates, however, measures of the built environment can be subjective and differ across studies leading to varying observations on its association with crime rates. Here, we illustrate an accurate and straightforward approach to quantify the impact of the built environment on neighborhood crime rates from high-resolution satellite imagery. Using geo-referenced crime reports and satellite images for three United States cities, we demonstrate how image features consistently identified using a convolutional neural network can explain up to 82% of the variation in neighborhood crime rates. Our results suggest the built environment is a strong predictor of crime rates, and this can lead to structural interventions shown to reduce crime incidence in urban settings.

Published
2017

16. How well can machine learning predict demographics of social media users?

Author

Cesare, Nina, Grant, Christan, Nguyen, Quynh, Lee, Hedwig, and Nsoesie, Elaine O.

Subjects
Computer Science - Social and Information Networks, Computer Science - Computers and Society
Abstract

The wide use of social media sites and other digital technologies have resulted in an unprecedented availability of digital data that are being used to study human behavior across research domains. Although unsolicited opinions and sentiments are available on these platforms, demographic details are usually missing. Demographic information is pertinent in fields such as demography and public health, where significant differences can exist across sex, racial and socioeconomic groups. In an attempt to address this shortcoming, a number of academic studies have proposed methods for inferring the demographics of social media users using details such as names, usernames, and network characteristics. Gender is the easiest trait to accurately infer, with measures of accuracy higher than 90 percent in some studies. Race, ethnicity and age tend to be more challenging to predict for a variety of reasons including the novelty of social media to certain age groups and a lack of significant deviations in user details across racial and ethnic groups. Although the endeavor to predict user demographics is plagued with ethical questions regarding privacy and data ownership, knowing the demographics in a data sample can aid in addressing issues of bias and population representation, so that existing societal inequalities are not exacerbated., Comment: 24 pages, 3 figures

Published
2017

19. The anthropogenic fingerprint on emerging infectious diseases

Author

Gibb, Rory, primary, Ryan, Sadie J., additional, Pigott, David, additional, Fernandez, Maria del Pilar, additional, Muylaert, Renata L., additional, Albery, Gregory F., additional, Becker, Daniel J., additional, Blackburn, Jason K., additional, Caceres-Escobar, Hernan, additional, Celone, Michael, additional, Eskew, Evan A., additional, Frank, Hannah K., additional, Han, Barbara A., additional, Hulland, Erin N., additional, Jones, Kate E., additional, Katz, Rebecca, additional, Kucharski, Adam, additional, Limmathurotsakul, Direk, additional, Lippi, Catherine A., additional, Longbottom, Joshua, additional, Martinez, Juan Fernando, additional, Messina, Jane P., additional, Nsoesie, Elaine O., additional, Redding, David W., additional, Romero-Alvarez, Daniel, additional, Schmid, Boris V., additional, Seifert, Stephanie N., additional, Sinchi, Anabel, additional, Trisos, Christopher H., additional, Wille, Michelle, additional, and Carlson, Colin J., additional

Published
2024
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21. Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks

Author

Ghosh, Saurav, Chakraborty, Prithwish, Nsoesie, Elaine O., Cohn, Emily, Mekaru, Sumiko R., Brownstein, John S., and Ramakrishnan, Naren

Subjects
Computer Science - Social and Information Networks, Computer Science - Computation and Language, Computer Science - Information Retrieval, Statistics - Machine Learning
Abstract

In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include, applicability to a wide range of diseases, and ability to capture disease dynamics - including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China and India. We noted that temporal topic trends extracted from disease-related news reports successfully captured the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations., Comment: This paper has been submitted to a journal

Published
2016

25. Enhancing disease surveillance with novel data streams: challenges and opportunities

Author

Althouse, Benjamin M, Scarpino, Samuel V, Meyers, Lauren Ancel, Ayers, John W, Bargsten, Marisa, Baumbach, Joan, Brownstein, John S, Castro, Lauren, Clapham, Hannah, Cummings, Derek AT, Del Valle, Sara, Eubank, Stephen, Fairchild, Geoffrey, Finelli, Lyn, Generous, Nicholas, George, Dylan, Harper, David R, Hébert-Dufresne, Laurent, Johansson, Michael A, Konty, Kevin, Lipsitch, Marc, Milinovich, Gabriel, Miller, Joseph D, Nsoesie, Elaine O, Olson, Donald R, Paul, Michael, Polgreen, Philip M, Priedhorsky, Reid, Read, Jonathan M, Rodríguez-Barraquer, Isabel, Smith, Derek J, Stefansen, Christian, Swerdlow, David L, Thompson, Deborah, Vespignani, Alessandro, and Wesolowski, Amy

Subjects
Public Health, Health Sciences, Biotechnology, Good Health and Well Being, disease surveillance, novel data streams, digital surveillance
Abstract

Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.

Published
2015

26. Privacy-first health research with federated learning

Author

Sadilek, Adam, Liu, Luyang, Nguyen, Dung, Kamruzzaman, Methun, Serghiou, Stylianos, Rader, Benjamin, Ingerman, Alex, Mellem, Stefan, Kairouz, Peter, Nsoesie, Elaine O., MacFarlane, Jamie, Vullikanti, Anil, Marathe, Madhav, Eastham, Paul, Brownstein, John S., Arcas, Blaise Aguera y., Howell, Michael D., and Hernandez, John

Published
2021
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32. Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015–16: a modelling study

Author

Kraemer, Moritz U G, Faria, Nuno R, Reiner, Robert C, Jr, Golding, Nick, Nikolay, Birgit, Stasse, Stephanie, Johansson, Michael A, Salje, Henrik, Faye, Ousmane, Wint, G R William, Niedrig, Matthias, Shearer, Freya M, Hill, Sarah C, Thompson, Robin N, Bisanzio, Donal, Taveira, Nuno, Nax, Heinrich H, Pradelski, Bary S R, Nsoesie, Elaine O, Murphy, Nicholas R, Bogoch, Isaac I, Khan, Kamran, Brownstein, John S, Tatem, Andrew J, de Oliveira, Tulio, Smith, David L, Sall, Amadou A, Pybus, Oliver G, Hay, Simon I, and Cauchemez, Simon

Published
2017
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34. COVID-19 Mortality by Race and Ethnicity in US Metropolitan and Nonmetropolitan Areas, March 2020 to February 2022

Author

Lundberg, Dielle J., primary, Wrigley-Field, Elizabeth, additional, Cho, Ahyoung, additional, Raquib, Rafeya, additional, Nsoesie, Elaine O., additional, Paglino, Eugenio, additional, Chen, Ruijia, additional, Kiang, Mathew V., additional, Riley, Alicia R., additional, Chen, Yea-Hung, additional, Charpignon, Marie-Laure, additional, Hempstead, Katherine, additional, Preston, Samuel H., additional, Elo, Irma T., additional, Glymour, M. Maria, additional, and Stokes, Andrew C., additional

Published
2023
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35. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015

Author

Wang, Haidong, Naghavi, Mohsen, Allen, Christine, Barber, Ryan M, Bhutta, Zulfiqar A, Carter, Austin, Casey, Daniel C, Charlson, Fiona J, Chen, Alan Zian, Coates, Matthew M, Coggeshall, Megan, Dandona, Lalit, Dicker, Daniel J, Erskine, Holly E, Ferrari, Alize J, Fitzmaurice, Christina, Foreman, Kyle, Forouzanfar, Mohammad H, Fraser, Maya S, Fullman, Nancy, Gething, Peter W, Goldberg, Ellen M, Graetz, Nicholas, Haagsma, Juanita A, Hay, Simon I, Huynh, Chantal, Johnson, Catherine O, Kassebaum, Nicholas J, Kinfu, Yohannes, Kulikoff, Xie Rachel, Kutz, Michael, Kyu, Hmwe H, Larson, Heidi J, Leung, Janni, Liang, Xiaofeng, Lim, Stephen S, Lind, Margaret, Lozano, Rafael, Marquez, Neal, Mensah, George A, Mikesell, Joe, Mokdad, Ali H, Mooney, Meghan D, Nguyen, Grant, Nsoesie, Elaine, Pigott, David M, Pinho, Christine, Roth, Gregory A, Salomon, Joshua A, Sandar, Logan, Silpakit, Naris, Sligar, Amber, Sorensen, Reed J D, Stanaway, Jeffrey, Steiner, Caitlyn, Teeple, Stephanie, Thomas, Bernadette A, Troeger, Christopher, VanderZanden, Amelia, Vollset, Stein Emil, Wanga, Valentine, Whiteford, Harvey A, Wolock, Timothy, Zoeckler, Leo, Abate, Kalkidan Hassen, Abbafati, Cristiana, Abbas, Kaja M, Abd-Allah, Foad, Abera, Semaw Ferede, Abreu, Daisy M X, Abu-Raddad, Laith J, Abyu, Gebre Yitayih, Achoki, Tom, Adelekan, Ademola Lukman, Ademi, Zanfina, Adou, Arsène Kouablan, Adsuar, José C, Afanvi, Kossivi Agbelenko, Afshin, Ashkan, Agardh, Emilie Elisabet, Agarwal, Arnav, Agrawal, Anurag, Kiadaliri, Aliasghar Ahmad, Ajala, Oluremi N, Akanda, Ali Shafqat, Akinyemi, Rufus Olusola, Akinyemiju, Tomi F, Akseer, Nadia, Lami, Faris Hasan Al, Alabed, Samer, Al-Aly, Ziyad, Alam, Khurshid, Alam, Noore K M, Alasfoor, Deena, Aldhahri, Saleh Fahed, Aldridge, Robert William, Alegretti, Miguel Angel, Aleman, Alicia V, Alemu, Zewdie Aderaw, Alexander, Lily T, Alhabib, Samia, Ali, Raghib, Alkerwi, Ala'a, Alla, François, Allebeck, Peter, Al-Raddadi, Rajaa, Alsharif, Ubai, Altirkawi, Khalid A, Martin, Elena Alvarez, Alvis-Guzman, Nelson, Amare, Azmeraw T, Amegah, Adeladza Kofi, Ameh, Emmanuel A, Amini, Heresh, Ammar, Walid, Amrock, Stephen Marc, Andersen, Hjalte H, Anderson, Benjamin O, Anderson, Gregory M, Antonio, Carl Abelardo T, Aregay, Atsede Fantahun, Ärnlöv, Johan, Arsenijevic, Valentina S Arsic, Artaman, Al, Asayesh, Hamid, Asghar, Rana Jawad, Atique, Suleman, Avokpaho, Euripide Frinel G Arthur, Awasthi, Ashish, Azzopardi, Peter, Bacha, Umar, Badawi, Alaa, Bahit, Maria C, Balakrishnan, Kalpana, Banerjee, Amitava, Barac, Aleksandra, Barker-Collo, Suzanne L, Bärnighausen, Till, Barregard, Lars, Barrero, Lope H, Basu, Arindam, Basu, Sanjay, Bayou, Yibeltal Tebekaw, Bazargan-Hejazi, Shahrzad, Beardsley, Justin, Bedi, Neeraj, Beghi, Ettore, Belay, Haileeyesus Adamu, Bell, Brent, Bell, Michelle L, Bello, Aminu K, Bennett, Derrick A, Bensenor, Isabela M, Berhane, Adugnaw, Bernabé, Eduardo, Betsu, Balem Demtsu, Beyene, Addisu Shunu, Bhala, Neeraj, Bhalla, Ashish, Biadgilign, Sibhatu, Bikbov, Boris, Abdulhak, Aref A Bin, Biroscak, Brian J, Biryukov, Stan, Bjertness, Espen, Blore, Jed D, Blosser, Christopher D, Bohensky, Megan A, Borschmann, Rohan, Bose, Dipan, Bourne, Rupert R A, Brainin, Michael, Brayne, Carol E G, Brazinova, Alexandra, Breitborde, Nicholas J K, Brenner, Hermann, Brewer, Jerry D, Brown, Alexandria, Brown, Jonathan, Brugha, Traolach S, Buckle, Geoffrey Colin, Butt, Zahid A, Calabria, Bianca, Campos-Nonato, Ismael Ricardo, Campuzano, Julio Cesar, Carapetis, Jonathan R, Cárdenas, Rosario, Carpenter, David O, Carrero, Juan Jesus, Castañeda-Orjuela, Carlos A, Rivas, Jacqueline Castillo, Catalá-López, Ferrán, Cavalleri, Fiorella, Cercy, Kelly, Cerda, Jorge, Chen, Wanqing, Chew, Adrienne, Chiang, Peggy Pei-Chia, Chibalabala, Mirriam, Chibueze, Chioma Ezinne, Chimed-Ochir, Odgerel, Chisumpa, Vesper Hichilombwe, Choi, Jee-Young Jasmine, Chowdhury, Rajiv, Christensen, Hanne, Christopher, Devasahayam Jesudas, Ciobanu, Liliana G, Cirillo, Massimo, Cohen, Aaron J, Colistro, Valentina, Colomar, Mercedes, Colquhoun, Samantha M, Cooper, Cyrus, Cooper, Leslie Trumbull, Cortinovis, Monica, Cowie, Benjamin C, Crump, John A, Damsere-Derry, James, Danawi, Hadi, Dandona, Rakhi, Daoud, Farah, Darby, Sarah C, Dargan, Paul I, das Neves, José, Davey, Gail, Davis, Adrian C, Davitoiu, Dragos V, de Castro, E Filipa, de Jager, Pieter, Leo, Diego De, Degenhardt, Louisa, Dellavalle, Robert P, Deribe, Kebede, Deribew, Amare, Dharmaratne, Samath D, Dhillon, Preet K, Diaz-Torné, Cesar, Ding, Eric L, dos Santos, Kadine Priscila Bender, Dossou, Edem, Driscoll, Tim R, Duan, Leilei, Dubey, Manisha, Duncan, Bruce Bartholow, Ellenbogen, Richard G, Ellingsen, Christian Lycke, Elyazar, Iqbal, Endries, Aman Yesuf, Ermakov, Sergey Petrovich, Eshrati, Babak, Esteghamati, Alireza, Estep, Kara, Faghmous, Imad D A, Fahimi, Saman, Faraon, Emerito Jose Aquino, Farid, Talha A, Farinha, Carla Sofia e Sa, Faro, André, Farvid, Maryam S, Farzadfar, Farshad, Feigin, Valery L, 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2016
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36. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015

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Vos, Theo, Allen, Christine, Arora, Megha, Barber, Ryan M, Bhutta, Zulfiqar A, Brown, Alexandria, Carter, Austin, Casey, Daniel C, Charlson, Fiona J, Chen, Alan Z, Coggeshall, Megan, Cornaby, Leslie, Dandona, Lalit, Dicker, Daniel J, Dilegge, Tina, Erskine, Holly E, Ferrari, Alize J, Fitzmaurice, Christina, Fleming, Tom, Forouzanfar, Mohammad H, Fullman, Nancy, Gething, Peter W, Goldberg, Ellen M, Graetz, Nicholas, Haagsma, Juanita A, Hay, Simon I, Johnson, Catherine O, Kassebaum, Nicholas J, Kawashima, Toana, Kemmer, Laura, Khalil, Ibrahim A, Kinfu, Yohannes, Kyu, Hmwe H, Leung, Janni, Liang, Xiaofeng, Lim, Stephen S, Lopez, Alan D, Lozano, Rafael, Marczak, Laurie, Mensah, George A, Mokdad, Ali H, Naghavi, Mohsen, Nguyen, Grant, Nsoesie, Elaine, Olsen, Helen, Pigott, David M, Pinho, Christine, Rankin, Zane, Reinig, Nikolas, Salomon, Joshua A, Sandar, Logan, Smith, Alison, Stanaway, Jeffrey, Steiner, Caitlyn, Teeple, Stephanie, Thomas, Bernadette A, Troeger, Christopher, Wagner, Joseph A, Wang, Haidong, Wanga, Valentine, Whiteford, Harvey A, Zoeckler, Leo, Abajobir, Amanuel Alemu, Abate, Kalkidan Hassen, Abbafati, Cristiana, Abbas, Kaja M, Abd-Allah, Foad, Abraham, Biju, Abubakar, Ibrahim, Abu-Raddad, Laith J, Abu-Rmeileh, Niveen M E, Ackerman, Ilana N, Adebiyi, Akindele Olupelumi, Ademi, Zanfina, Adou, Arsène Kouablan, Afanvi, Kossivi Agbelenko, Agardh, Emilie Elisabet, Agarwal, Arnav, Kiadaliri, Aliasghar Ahmad, Ahmadieh, Hamid, Ajala, Oluremi N, Akinyemi, Rufus Olusola, Akseer, Nadia, Al-Aly, Ziyad, Alam, Khurshid, Alam, Noore K M, Aldhahri, Saleh Fahed, Alegretti, Miguel Angel, Alemu, Zewdie Aderaw, Alexander, Lily T, Alhabib, Samia, Ali, Raghib, Alkerwi, Ala'a, Alla, François, Allebeck, Peter, Al-Raddadi, Rajaa, Alsharif, Ubai, Altirkawi, Khalid A, Alvis-Guzman, Nelson, Amare, Azmeraw T, Amberbir, Alemayehu, Amini, Heresh, Ammar, Walid, Amrock, Stephen Marc, Andersen, Hjalte H, Anderson, Gregory M, Anderson, Benjamin O, Antonio, Carl Abelardo T, Aregay, Atsede Fantahun, Ärnlöv, Johan, Artaman, Al, Asayesh, Hamid, Assadi, Reza, Atique, Suleman, Avokpaho, Euripide Frinel G Arthur, Awasthi, Ashish, Quintanilla, Beatriz Paulina Ayala, Azzopardi, Peter, Bacha, Umar, Badawi, Alaa, Balakrishnan, Kalpana, Banerjee, Amitava, Barac, Aleksandra, Barker-Collo, Suzanne L, Bärnighausen, Till, Barregard, Lars, Barrero, Lope H, Basu, Arindam, Bazargan-Hejazi, Shahrzad, Beghi, Ettore, Bell, Brent, Bell, Michelle L, Bennett, Derrick A, Bensenor, Isabela M, Benzian, Habib, Berhane, Adugnaw, Bernabé, Eduardo, Betsu, Balem Demtsu, Beyene, Addisu Shunu, Bhala, Neeraj, Bhatt, Samir, Biadgilign, Sibhatu, Bienhoff, Kelly, Bikbov, Boris, Biryukov, Stan, Bisanzio, Donal, Bjertness, Espen, Blore, Jed, Borschmann, Rohan, Boufous, Soufiane, Brainin, Michael, Brazinova, Alexandra, Breitborde, Nicholas J K, Brown, Jonathan, Buchbinder, Rachelle, Buckle, Geoffrey Colin, Butt, Zahid A, Calabria, Bianca, Campos-Nonato, Ismael Ricardo, Campuzano, Julio Cesar, Carabin, Hélène, 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Eshrati, Babak, Estep, Kara, Farid, Talha A, Farinha, Carla Sofia e Sa, Faro, André, Farvid, Maryam S, Farzadfar, Farshad, Feigin, Valery L, Felson, David T, Fereshtehnejad, Seyed-Mohammad, Fernandes, Jefferson G, Fernandes, Joao C, Fischer, Florian, Fitchett, Joseph R A, Foreman, Kyle, Fowkes, F Gerry R, Fox, Jordan, Franklin, Richard C, Friedman, Joseph, Frostad, Joseph, Fürst, Thomas, Futran, Neal D, Gabbe, Belinda, Ganguly, Parthasarathi, Gankpé, Fortuné Gbètoho, Gebre, Teshome, Gebrehiwot, Tsegaye Tewelde, Gebremedhin, Amanuel Tesfay, Geleijnse, Johanna M, Gessner, Bradford D, Gibney, Katherine B, Ginawi, Ibrahim Abdelmageem Mohamed, Giref, Ababi Zergaw, Giroud, Maurice, Gishu, Melkamu Dedefo, Giussani, Giorgia, Glaser, Elizabeth, Godwin, William W, Gomez-Dantes, Hector, Gona, Philimon, Goodridge, Amador, Gopalani, Sameer Vali, Gotay, Carolyn C, Goto, Atsushi, Gouda, Hebe N, Grainger, Rebecca, Greaves, Felix, Guillemin, Francis, Guo, Yuming, Gupta, Rahul, Gupta, Rajeev, Gupta, Vipin, Gutiérrez, Reyna A, Haile, Demewoz, Hailu, Alemayehu Desalegne, Hailu, Gessessew Bugssa, Halasa, Yara A, Hamadeh, Randah Ribhi, Hamidi, Samer, Hammami, Mouhanad, Hancock, Jamie, Handal, Alexis J, Hankey, Graeme J, Hao, Yuantao, Harb, Hilda L, Harikrishnan, Sivadasanpillai, Haro, Josep Maria, Havmoeller, Rasmus, Hay, Roderick J, Heredia-Pi, Ileana Beatriz, Heydarpour, Pouria, Hoek, Hans W, Horino, Masako, Horita, Nobuyuki, Hosgood, H Dean, Hoy, Damian G, Htet, Aung Soe, Huang, Hsiang, Huang, John J, Huynh, Chantal, Iannarone, Marissa, Iburg, Kim Moesgaard, Innos, Kaire, Inoue, Manami, Iyer, Veena J, Jacobsen, Kathryn H, Jahanmehr, Nader, Jakovljevic, Mihajlo B, Javanbakht, Mehdi, Jayaraman, Sudha P, Jayatilleke, Achala Upendra, Jee, Sun Ha, Jeemon, Panniyammakal, Jensen, Paul N, Jiang, Ying, Jibat, Tariku, Jimenez-Corona, Aida, Jin, Ye, Jonas, Jost B, Kabir, Zubair, Kalkonde, Yogeshwar, Kamal, Ritul, Kan, Haidong, Karch, André, Karema, Corine Kakizi, Karimkhani, Chante, Kasaeian, Amir, Kaul, Anil, Kawakami, Norito, Keiyoro, Peter Njenga, Kemp, Andrew Haddon, Keren, Andre, Kesavachandran, Chandrasekharan Nair, Khader, Yousef Saleh, Khan, Abdur Rahman, Khan, Ejaz Ahmad, Khang, Young-Ho, Khera, Sahil, Khoja, Tawfik Ahmed Muthafer, Khubchandani, Jagdish, Kieling, Christian, Kim, Pauline, Kim, Cho-il, Kim, Daniel, Kim, Yun Jin, Kissoon, Niranjan, Knibbs, Luke D, Knudsen, Ann Kristin, Kokubo, Yoshihiro, Kolte, Dhaval, Kopec, Jacek A, Kosen, Soewarta, Kotsakis, Georgios A, Koul, Parvaiz A, Koyanagi, Ai, Kravchenko, Michael, Defo, Barthelemy Kuate, Bicer, Burcu Kucuk, Kudom, Andreas A, Kuipers, Ernst J, Kumar, G Anil, Kutz, Michael, Kwan, Gene F, Lal, Aparna, Lalloo, Ratilal, Lallukka, Tea, Lam, Hilton, Lam, Jennifer O, Langan, Sinead M, Larsson, Anders, Lavados, Pablo M, Leasher, Janet L, Leigh, James, Leung, Ricky, Levi, Miriam, Li, Yichong, Li, Yongmei, Liang, Juan, Liu, Shiwei, Liu, Yang, Lloyd, Belinda K, Lo, Warren D, Logroscino, Giancarlo, Looker, Katharine J, Lotufo, Paulo A, Lunevicius, Raimundas, Lyons, Ronan A, Mackay, Mark T, Magdy, Mohammed, Razek, Abd El, Mahdavi, Mahdi, Majdan, Marek, Majeed, Azeem, Malekzadeh, Reza, Marcenes, Wagner, Margolis, David Joel, Martinez-Raga, Jose, Masiye, Felix, Massano, João, McGarvey, Stephen Theodore, McGrath, John J, McKee, Martin, McMahon, Brian J, Meaney, Peter A, Mehari, Alem, Mejia-Rodriguez, Fabiola, Mekonnen, Alemayehu B, Melaku, Yohannes Adama, Memiah, Peter, Memish, Ziad A, Mendoza, Walter, Meretoja, Atte, Meretoja, Tuomo J, Mhimbira, Francis Apolinary, Millear, Anoushka, Miller, Ted R, Mills, Edward J, Mirarefin, Mojde, Mitchell, Philip B, Mock, Charles N, Mohammadi, Alireza, Mohammed, Shafiu, Monasta, Lorenzo, Hernandez, Julio Cesar Montañez, Montico, Marcella, Mooney, Meghan D, Moradi-Lakeh, Maziar, Morawska, Lidia, Mueller, Ulrich O, Mullany, Erin, Mumford, John Everett, Murdoch, Michele E, Nachega, Jean B, Nagel, Gabriele, Naheed, Aliya, Naldi, Luigi, Nangia, Vinay, Newton, John N, Ng, Marie, Ngalesoni, Frida Namnyak, Nguyen, Quyen Le, Nisar, Muhammad Imran, Pete, Patrick Martial Nkamedjie, Nolla, Joan M, Norheim, Ole F, Norman, Rosana E, Norrving, Bo, Nunes, Bruno P, Ogbo, Felix Akpojene, Oh, In-Hwan, Ohkubo, Takayoshi, Olivares, Pedro R, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Ortiz, Alberto, Osman, Majdi, Ota, Erika, PA, Mahesh, Park, Eun-Kee, Parsaeian, Mahboubeh, de Azeredo Passos, Valéria Maria, Caicedo, Angel J Paternina, Patten, Scott B, Patton, George C, Pereira, David M, Perez-Padilla, Rogelio, Perico, Norberto, Pesudovs, Konrad, Petzold, Max, Phillips, Michael Robert, Piel, Frédéric B, Pillay, Julian David, Pishgar, Farhad, Plass, Dietrich, Platts-Mills, James A, Polinder, Suzanne, Pond, Constance D, Popova, Svetlana, Poulton, Richie G, Pourmalek, Farshad, Prabhakaran, Dorairaj, Prasad, Noela M, Qorbani, Mostafa, Rabiee, Rynaz H S, Radfar, Amir, Rafay, Anwar, Rahimi, Kazem, Rahimi-Movaghar, Vafa, Rahman, Mahfuzar, Rahman, Mohammad Hifz Ur, Rahman, Sajjad Ur, Rai, Rajesh Kumar, Rajsic, Sasa, Ram, Usha, Rao, Puja, Refaat, Amany H, Reitsma, Marissa B, Remuzzi, Giuseppe, Resnikoff, Serge, Reynolds, Alex, Ribeiro, Antonio L, Blancas, Maria Jesus Rios, Roba, Hirbo Shore, Rojas-Rueda, David, Ronfani, Luca, Roshandel, Gholamreza, Roth, Gregory A, Rothenbacher, Dietrich, Roy, Ambuj, Sagar, Rajesh, Sahathevan, Ramesh, Sanabria, Juan R, Sanchez-Niño, Maria Dolores, Santos, Itamar S, Santos, João Vasco, Sarmiento-Suarez, Rodrigo, Sartorius, Benn, Satpathy, Maheswar, Savic, Miloje, Sawhney, Monika, Schaub, Michael P, Schmidt, Maria Inês, Schneider, Ione J C, Schöttker, Ben, Schwebel, David C, Scott, James G, Seedat, Soraya, Sepanlou, Sadaf G, Servan-Mori, Edson E, Shackelford, Katya A, Shaheen, Amira, Shaikh, Masood Ali, Sharma, Rajesh, Sharma, Upasana, Shen, Jiabin, Shepard, Donald S, Sheth, Kevin N, Shibuya, Kenji, Shin, Min-Jeong, Shiri, Rahman, Shiue, Ivy, Shrime, Mark G, Sigfusdottir, Inga Dora, Silva, Diego Augusto Santos, Silveira, Dayane Gabriele Alves, Singh, Abhishek, Singh, Jasvinder A, Singh, Om Prakash, Singh, Prashant Kumar, Sivonda, Anna, Skirbekk, Vegard, Skogen, Jens Christoffer, Sligar, Amber, Sliwa, Karen, Soljak, Michael, Søreide, Kjetil, Sorensen, Reed J D, Soriano, Joan B, Sposato, Luciano A, Sreeramareddy, Chandrashekhar T, Stathopoulou, Vasiliki, Steel, Nicholas, Stein, Dan J, Steiner, Timothy J, Steinke, Sabine, Stovner, Lars, Stroumpoulis, Konstantinos, Sunguya, Bruno F, Sur, Patrick, Swaminathan, Soumya, Sykes, Bryan L, Szoeke, Cassandra E I, Tabarés-Seisdedos, Rafael, Takala, Jukka S, Tandon, Nikhil, Tanne, David, Tavakkoli, Mohammad, Taye, Bineyam, Taylor, Hugh R, Ao, Braden J Te, Tedla, Bemnet Amare, Terkawi, Abdullah Sulieman, Thomson, Alan J, Thorne-Lyman, Andrew L, Thrift, Amanda G, Thurston, George D, Tobe-Gai, Ruoyan, Tonelli, Marcello, Topor-Madry, Roman, Topouzis, Fotis, Tran, Bach Xuan, Truelsen, Thomas, Dimbuene, Zacharie Tsala, Tsilimbaris, Miltiadis, Tura, Abera Kenay, Tuzcu, Emin Murat, Tyrovolas, Stefanos, Ukwaja, Kingsley N, Undurraga, Eduardo A, Uneke, Chigozie Jesse, Uthman, Olalekan A, van Gool, Coen H, Varakin, Yuri Y, Vasankari, Tommi, Venketasubramanian, Narayanaswamy, Verma, Raj Kumar, Violante, Francesco S, Vladimirov, Sergey K, Vlassov, Vasiliy Victorovich, Vollset, Stein Emil, Wagner, Gregory R, Waller, Stephen G, Wang, Linhong, Watkins, David A, Weichenthal, Scott, Weiderpass, Elisabete, Weintraub, Robert G, Werdecker, Andrea, Westerman, Ronny, White, Richard A, Williams, Hywel C, Wiysonge, Charles Shey, Wolfe, Charles D A, Won, Sungho, Woodbrook, Rachel, Wubshet, Mamo, Xavier, Denis, Xu, Gelin, Yadav, Ajit Kumar, Yan, Lijing L, Yano, Yuichiro, Yaseri, Mehdi, Ye, Pengpeng, Yebyo, Henock Gebremedhin, Yip, Paul, Yonemoto, Naohiro, Yoon, Seok-Jun, Younis, Mustafa Z, Yu, Chuanhua, Zaidi, Zoubida, Zaki, Maysaa El Sayed, Zeeb, Hajo, Zhou, Maigeng, Zodpey, Sanjay, Zuhlke, Liesl Joanna, and Murray, Christopher J L

Published
2016
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37. Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015

Author

Kassebaum, Nicholas J, Arora, Megha, Barber, Ryan M, Bhutta, Zulfiqar A, Brown, Jonathan, Carter, Austin, Casey, Daniel C, Charlson, Fiona J, Coates, Matthew M, Coggeshall, Megan, Cornaby, Leslie, Dandona, Lalit, Dicker, Daniel J, Erskine, Holly E, Ferrari, Alize J, Fitzmaurice, Christina, Foreman, Kyle, Forouzanfar, Mohammad H, Fullman, Nancy, Gething, Peter W, Goldberg, Ellen M, Graetz, Nicholas, Haagsma, Juanita A, Hay, Simon I, Johnson, Catherine O, Kemmer, Laura, Khalil, Ibrahim A, Kinfu, Yohannes, Kutz, Michael J, Kyu, Hmwe H, Leung, Janni, Liang, Xiaofeng, Lim, Stephen S, Lozano, Rafael, Mensah, George A, Mikesell, Joe, Mokdad, Ali H, Mooney, Meghan D, Naghavi, Mohsen, Nguyen, Grant, Nsoesie, Elaine, Pigott, David M, Pinho, Christine, Rankin, Zane, Reinig, Nikolas, Salomon, Joshua A, Sandar, Logan, Smith, Alison, Sorensen, Reed J D, Stanaway, Jeffrey, Steiner, Caitlyn, Teeple, Stephanie, Troeger, Chris, Truelsen, Thomas, VanderZanden, Amelia, Wagner, Joseph A, Wanga, Valentine, Whiteford, Harvey A, Zhou, Maigeng, Zoeckler, Leo, Abajobir, Amanuel Alemu, Abate, Kalkidan Hassen, Abbafati, Cristiana, Abbas, Kaja M, Abd-Allah, Foad, Abraham, Biju, Abubakar, Ibrahim, Abu-Raddad, Laith J, Abu-Rmeileh, Niveen M E, Achoki, Tom, Ackerman, Ilana N, Adebiyi, Akindele Olupelumi, Adedeji, Isaac Akinkunmi, Adsuar, José C, Afanvi, Kossivi Agbelenko, Afshin, Ashkan, Agardh, Emilie Elisabet, Agarwal, Arnav, Agarwal, Sanjay Kumar, Ahmed, Muktar Beshir, Kiadaliri, Aliasghar Ahmad, Ahmadieh, Hamid, Akseer, Nadia, Al-Aly, Ziyad, Alam, Khurshid, Alam, Noore K M, Aldhahri, Saleh Fahed, Alegretti, Miguel Angel, Aleman, Alicia V, Alemu, Zewdie Aderaw, Alexander, Lily T, Ali, Raghib, Alkerwi, Ala'a, Alla, François, Allebeck, Peter, Allen, Christine, Alsharif, Ubai, Altirkawi, Khalid A, Martin, Elena Alvarez, Alvis-Guzman, Nelson, Amare, Azmeraw T, Amberbir, Alemayehu, Amegah, Adeladza Kofi, Amini, Heresh, Ammar, Walid, Amrock, Stephen Marc, Anderson, Gregory M, Anderson, Benjamin O, Antonio, Carl Abelardo T, Anwari, Palwasha, Ärnlöv, Johan, Arsenijevic, Valentina S Arsic, Artaman, Al, Asayesh, Hamid, Asghar, Rana Jawad, Avokpaho, Euripide Frinel G Arthur, Awasthi, Ashish, Quintanilla, Beatriz Paulina Ayala, Azzopardi, Peter, Bacha, Umar, Badawi, Alaa, Balakrishnan, Kalpana, Banerjee, Amitava, Barac, Aleksandra, Barker-Collo, Suzanne L, Bärnighausen, Till, Barregard, Lars, Barrero, Lope H, Basu, Sanjay, Bayou, Tigist Assefa, Beardsley, Justin, Bedi, Neeraj, Beghi, Ettore, Bell, Brent, Bell, Michelle L, Benjet, Corina, Bennett, Derrick A, Bensenor, Isabela M, Berhane, Adugnaw, Bernabé, Eduardo, Betsu, Balem Demtsu, Beyene, Addisu Shunu, Bhala, Neeraj, Bhansali, Anil, Bhatt, Samir, Biadgilign, Sibhatu, Bienhoff, Kelly, Bikbov, Boris, Abdulhak, Aref A Bin, Biryukov, Stan, Bisanzio, Donal, Bjertness, Espen, Blore, Jed D, Borschmann, Rohan, Boufous, Soufiane, Bourne, Rupert R A, Brainin, Michael, Brazinova, Alexandra, Breitborde, Nicholas J K, Brugha, Traolach S, Buchbinder, Rachelle, Buckle, Geoffrey Colin, Butt, Zahid A, Calabria, Bianca, Campos-Nonato, Ismael Ricardo, Campuzano, Julio Cesar, Carabin, Hélène, Carapetis, Jonathan R, Cárdenas, Rosario, Carrero, Juan Jesus, Castañeda-Orjuela, Carlos A, Rivas, Jacqueline Castillo, Catalá-López, Ferrán, Cavalleri, Fiorella, Chang, Jung-Chen, Chiang, Peggy Pei-Chia, Chibalabala, Mirriam, Chibueze, Chioma Ezinne, Chisumpa, Vesper Hichilombwe, Choi, Jee-Young Jasmine, Choudhury, Lincoln, Christensen, Hanne, Ciobanu, Liliana G, Colistro, Valentina, Colomar, Mercedes, Colquhoun, Samantha M, Cortinovis, Monica, Crump, John A, Damasceno, Albertino, Dandona, Rakhi, Dargan, Paul I, das Neves, José, Davey, Gail, Davis, Adrian C, Leo, Diego De, Degenhardt, Louisa, Gobbo, Liana C Del, Derrett, Sarah, Jarlais, Don C Des, deVeber, Gabrielle A, Dharmaratne, Samath D, Dhillon, Preet K, Ding, Eric L, Doyle, Kerrie E, Driscoll, Tim R, Duan, Leilei, Dubey, Manisha, Duncan, Bruce Bartholow, Ebrahimi, Hedyeh, Ellenbogen, Richard G, Elyazar, Iqbal, Endries, Aman Yesuf, Ermakov, Sergey Petrovich, Eshrati, Babak, Esteghamati, Alireza, Estep, Kara, Fahimi, Saman, Farid, Talha A, Farinha, Carla Sofia e Sa, Faro, André, Farvid, Maryam S, Farzadfar, Farshad, Feigin, Valery L, Fereshtehnejad, Seyed-Mohammad, Fernandes, Jefferson G, Fernandes, Joao C, Fischer, Florian, Fitchett, Joseph R A, Foigt, Nataliya, Fowkes, F Gerry R, Franklin, Richard C, Friedman, Joseph, Frostad, Joseph, Fürst, Thomas, Futran, Neal D, Gabbe, Belinda, Gankpé, Fortuné Gbètoho, Garcia-Basteiro, Alberto L, Gebrehiwot, Tsegaye Tewelde, Gebremedhin, Amanuel Tesfay, Geleijnse, Johanna M, Gibney, Katherine B, Gillum, Richard F, Ginawi, Ibrahim Abdelmageem Mohamed, Giref, Ababi Zergaw, Giroud, Maurice, Gishu, Melkamu Dedefo, Giussani, Giorgia, Godwin, William W, Gomez-Dantes, Hector, Gona, Philimon, Goodridge, Amador, Gopalani, Sameer Vali, Gotay, Carolyn C, Goto, Atsushi, Gouda, Hebe N, Gugnani, Harish, Guo, Yuming, Gupta, Rahul, Gupta, Rajeev, Gupta, Vipin, Gutiérrez, Reyna A, Hafezi-Nejad, Nima, Haile, Demewoz, Hailu, Alemayehu Desalegne, Hailu, Gessessew Bugssa, Halasa, Yara A, Hamadeh, Randah Ribhi, Hamidi, Samer, Hammami, Mouhanad, Handal, Alexis J, Hankey, Graeme J, Harb, Hilda L, Harikrishnan, Sivadasanpillai, Haro, Josep Maria, Hassanvand, Mohammad Sadegh, Hassen, Tahir Ahmed, Havmoeller, Rasmus, Hay, Roderick J, Hedayati, Mohammad T, Heredia-Pi, Ileana Beatriz, Heydarpour, Pouria, Hoek, Hans W, Hoffman, Daniel J, Horino, Masako, Horita, Nobuyuki, Hosgood, H Dean, Hoy, Damian G, Hsairi, Mohamed, Huang, Hsiang, Huang, John J, Iburg, Kim Moesgaard, Idrisov, Bulat T, Innos, Kaire, Inoue, Manami, Jacobsen, Kathryn H, Jauregui, Alejandra, Jayatilleke, Achala Upendra, Jeemon, Panniyammakal, Jha, Vivekanand, Jiang, Guohong, Jiang, Ying, Jibat, Tariku, Jimenez-Corona, Aida, Jin, Ye, Jonas, Jost B, Kabir, Zubair, Kajungu, Dan K, Kalkonde, Yogeshwar, Kamal, Ritul, Kan, Haidong, Kandel, Amit, Karch, André, Karema, Corine Kakizi, Karimkhani, Chante, Kasaeian, Amir, Katibeh, Marzieh, Kaul, Anil, Kawakami, Norito, Kazi, Dhruv S, Keiyoro, Peter Njenga, Kemp, Andrew Haddon, Kengne, Andre Pascal, Keren, Andre, Kesavachandran, Chandrasekharan Nair, Khader, Yousef Saleh, Khan, Abdur Rahman, Khan, Ejaz Ahmad, Khang, Young-Ho, Khoja, Tawfik Ahmed Muthafer, Khubchandani, Jagdish, Kieling, Christian, Kim, Cho-il, Kim, Daniel, Kim, Yun Jin, Kissoon, Niranjan, Kivipelto, Miia, Knibbs, Luke D, Knudsen, Ann Kristin, Kokubo, Yoshihiro, Kolte, Dhaval, Kopec, Jacek A, Koul, Parvaiz A, Koyanagi, Ai, Defo, Barthelemy Kuate, Kuchenbecker, Ricardo S, Bicer, Burcu Kucuk, Kuipers, Ernst J, Kumar, G Anil, Kwan, Gene F, Lalloo, Ratilal, Lallukka, Tea, Larsson, Anders, Latif, Asma Abdul, Lavados, Pablo M, Lawrynowicz, Alicia Elena Beatriz, Leasher, Janet L, Leigh, James, Leung, Ricky, Li, Yichong, Li, Yongmei, Lipshultz, Steven E, Liu, Patrick Y, Liu, Yang, Lloyd, Belinda K, Logroscino, Giancarlo, Looker, Katharine J, Lotufo, Paulo A, Lucas, Robyn M, Lunevicius, Raimundas, Lyons, Ronan A, Razek, Hassan Magdy Abd El, Mahdavi, Mahdi, Majdan, Marek, Majeed, Azeem, Malekzadeh, Reza, Malta, Deborah Carvalho, Marcenes, Wagner, Martinez-Raga, Jose, Masiye, Felix, Mason-Jones, Amanda J, Matzopoulos, Richard, Mayosi, Bongani M, McGrath, John J, McKee, Martin, Meaney, Peter A, Mehari, Alem, Melaku, Yohannes Adama, Memiah, Peter, Memish, Ziad A, Mendoza, Walter, Meretoja, Atte, Meretoja, Tuomo J, Mesfin, Yonatan Moges, Mhimbira, Francis Apolinary, Millear, Anoushka, Miller, Ted R, Mills, Edward J, Mirarefin, Mojde, Mirrakhimov, Erkin M, Mitchell, Philip B, Mock, Charles N, Mohammad, Karzan Abdulmuhsin, Mohammadi, Alireza, Mohammed, Shafiu, Monasta, Lorenzo, Hernandez, Julio Cesar Montañez, Montico, Marcella, Moradi-Lakeh, Maziar, Mori, Rintaro, Mueller, Ulrich O, Mumford, John Everett, Murdoch, Michele E, Murthy, Gudlavalleti Venkata Satyanarayana, Nachega, Jean B, Naheed, Aliya, Naldi, Luigi, Nangia, Vinay, Newton, John N, Ng, Marie, Ngalesoni, Frida Namnyak, Nguyen, Quyen Le, Nisar, Muhammad Imran, Pete, Patrick Martial Nkamedjie, Nolla, Joan M, Norheim, Ole F, Norman, Rosana E, Norrving, Bo, Obermeyer, Carla Makhlouf, Ogbo, Felix Akpojene, Oh, In-Hwan, Oladimeji, Olanrewaju, Olivares, Pedro R, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Oren, Eyal, Ortiz, Alberto, Ota, Erika, Oyekale, Abayomi Samuel, PA, Mahesh, Park, Eun-Kee, Parsaeian, Mahboubeh, Patten, Scott B, Patton, George C, Pedro, João Mário, Pereira, David M, Perico, Norberto, Pesudovs, Konrad, Petzold, Max, Phillips, Michael Robert, Piel, Frédéric B, Pillay, Julian David, Pishgar, Farhad, Plass, Dietrich, Polinder, Suzanne, Popova, Svetlana, Poulton, Richie G, Pourmalek, Farshad, Prasad, Noela M, Qorbani, Mostafa, Rabiee, Rynaz H S, Radfar, Amir, Rafay, Anwar, Rahimi, Kazem, Rahimi-Movaghar, Vafa, Rahman, Mahfuzar, Rahman, Mohammad Hifz Ur, Rahman, Sajjad Ur, Rai, Dheeraj, Rai, Rajesh Kumar, Rajsic, Sasa, Raju, Murugesan, Ram, Usha, Ranganathan, Kavitha, Refaat, Amany H, Reitsma, Marissa B, Remuzzi, Giuseppe, Resnikoff, Serge, Reynolds, Alex, Ribeiro, Antonio L, Ricci, Stefano, Roba, Hirbo Shore, Rojas-Rueda, David, Ronfani, Luca, Roshandel, Gholamreza, Roth, Gregory A, Roy, Ambuj, Sackey, Ben Benasco, Sagar, Rajesh, Sanabria, Juan R, Sanchez-Niño, Maria Dolores, Santos, Itamar S, Santos, João Vasco, Sarmiento-Suarez, Rodrigo, Sartorius, Benn, Satpathy, Maheswar, Savic, Miloje, Sawhney, Monika, Schmidt, Maria Inês, Schneider, Ione J C, Schutte, Aletta E, Schwebel, David C, Seedat, Soraya, Sepanlou, Sadaf G, Servan-Mori, Edson E, Shahraz, Saeid, Shaikh, Masood Ali, Sharma, Rajesh, She, Jun, Sheikhbahaei, Sara, Shen, Jiabin, Sheth, Kevin N, Shibuya, Kenji, Shigematsu, Mika, Shin, Min-Jeong, Shiri, Rahman, Sigfusdottir, Inga Dora, Silva, Diego Augusto Santos, Silverberg, Jonathan I, Simard, Edgar P, Singh, Abhishek, Singh, Jasvinder A, Singh, Prashant Kumar, Skirbekk, Vegard, Skogen, Jens Christoffer, Soljak, Michael, Søreide, Kjetil, Sreeramareddy, Chandrashekhar T, Stathopoulou, Vasiliki, Steel, Nicholas, Stein, Dan J, Stein, Murray B, Steiner, Timothy J, Stovner, Lars Jacob, Stranges, Saverio, Stroumpoulis, Konstantinos, Sunguya, Bruno F, Sur, Patrick J, Swaminathan, Soumya, Sykes, Bryan L, Szoeke, Cassandra E I, Tabarés-Seisdedos, Rafael, Tandon, Nikhil, Tanne, David, Tavakkoli, Mohammad, Taye, Bineyam, Taylor, Hugh R, Ao, Braden J Te, Tegegne, Teketo Kassaw, Tekle, Dejen Yemane, Terkawi, Abdullah Sulieman, Tessema, Gizachew Assefa, Thakur, J S, Thomson, Alan J, Thorne-Lyman, Andrew L, Thrift, Amanda G, Thurston, George D, Tobe-Gai, Ruoyan, Tonelli, Marcello, Topor-Madry, Roman, Topouzis, Fotis, Tran, Bach Xuan, Dimbuene, Zacharie Tsala, Tsilimbaris, Miltiadis, Tura, Abera Kenay, Tuzcu, Emin Murat, Tyrovolas, Stefanos, Ukwaja, Kingsley N, Undurraga, Eduardo A, Uneke, Chigozie Jesse, Uthman, Olalekan A, van Gool, Coen H, van Os, Jim, Vasankari, Tommi, Vasconcelos, Ana Maria Nogales, Venketasubramanian, Narayanaswamy, Violante, Francesco S, Vlassov, Vasiliy Victorovich, Vollset, Stein Emil, Wagner, Gregory R, Wallin, Mitchell T, Wang, Linhong, Weichenthal, Scott, Weiderpass, Elisabete, Weintraub, Robert G, Werdecker, Andrea, Westerman, Ronny, Wijeratne, Tissa, Wilkinson, James D, Williams, Hywel C, Wiysonge, Charles Shey, Woldeyohannes, Solomon Meseret, Wolfe, Charles D A, Won, Sungho, Xu, Gelin, Yadav, Ajit Kumar, Yakob, Bereket, Yan, Lijing L, Yano, Yuichiro, Yaseri, Mehdi, Ye, Pengpeng, Yip, Paul, Yonemoto, Naohiro, Yoon, Seok-Jun, Younis, Mustafa Z, Yu, Chuanhua, Zaidi, Zoubida, Zaki, Maysaa El Sayed, Zeeb, Hajo, Zodpey, Sanjay, Zonies, David, Zuhlke, Liesl Joanna, Vos, Theo, Lopez, Alan D, and Murray, Christopher J L

Published
2016
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46. What matters for health? Public views from eight countries

Author

Abdalla, Salma M, primary, Hernandez, Mark, additional, Fazaludeen Koya, Shaffi, additional, Rosenberg, Samuel B, additional, Robbins, Grace, additional, Magana, Laura, additional, Nsoesie, Elaine Okanyene, additional, Sabin, Lora, additional, and Galea, Sandro, additional

Published
2022
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47. Fatal police violence by race and state in the USA, 1980–2019: a network meta-regression

Author

Sharara, Fablina, Wool, Eve E., Bertolacci, Gregory J., Weaver, Nicole Davis, Balassyano, Shelly, Watson, Alexandrea, Dippenaar, Ilse N., Cunningham, Matthew, Fuller, John E., Marczak, Laurie B., Johnson, Catherine Owens, Roth, Gregory A., Abedi, Aidin, Acuna, Juan Manuel, Advani, Shailesh M., Agasthi, Pradyumna, Alahdab, Fares, Al-Aly, Ziyad, Antony, Catherine M., Assari, Shervin, Awan, Asma Tahir, Basu, Sanjay, Bell, Arielle Wilder, Braithwaite, Dejana, Burkart, Katrin, Chew, Adrienne, Cho, Daniel Youngwhan, Chowdhury, Mohiuddin Ahsanul Kabir, Dai, Xiaochen, Daoud, Farah, Dharmaratne, Samath Dhamminda, Dwyer-Lindgren, Laura, Elgendy, Islam Y., Fares, Jawad, Farwati, Medhat, Fisher, James L., Gholamrezanezhad, Ali, Gopalani, Sameer Vali, Grada, Ayman, Guha, Avirup, Hafezi-Nejad, Nima, Haider, Mohammad Rifat, Hay, Simon I., Hossain, Md Mahbub, Ikuta, Kevin S., Islam, Jessica Y., Jabbarinejad, Roxana, Khatab, Khaled, Khubchandani, Jagdish, Kim, Daniel, Kimokoti, Ruth W., Kisa, Adnan, Krishnamoorthy, Vijay, Krohn, Kris J., Lawrence, Wayne R., Legrand, Kate E., Lim, Stephen S., Lin, Ziqiang, Liu, Xuefeng, Mahmoudi, Morteza, Martinez-Piedra, Ramon, Mehta, Kala M., Miller, Ted R., Moitra, Modhurima, Mokdad, Ali H., Morrison, Shane Douglas, Murray, Christopher J. L., Napoles, Anna Maria, Nsoesie, Elaine Okanyene, Odell, Christopher M., Pawar, Shrikant, Perez-Stable, Eliseo J., Pilz, Tessa M., Ram, Pradhum, Reiner, Robert C., Roberts, Nicholas L. S., Salehi, Sana, Sanabria, Juan, Schwebel, David C., Sheikh, Aziz, Sigfusdottir, Inga Dora, Singh, Jasvinder A., Spurlock, Emma Elizabeth, Sykes, Bryan L., Tadbiri, Hooman, Tleyjeh, Imad I., Tsai, Alexander C., Vos, Theo, Wu, Chenkai, Yandrapalli, Srikanth, Ismaeel Yunusa, Zand, Ramin, Zhang, Wangjian, and Naghavi, Mohsen

Subjects
Adult, Aged, 80 and over, Male, Adolescent, Databases, Factual, General Medicine, Articles, Middle Aged, Violence, United States, Police, Black or African American, Young Adult, Cause of Death, Child, Preschool, Humans, Female, Public Health, Child, Homicide, Aged
Abstract

Summary Background The burden of fatal police violence is an urgent public health crisis in the USA. Mounting evidence shows that deaths at the hands of the police disproportionately impact people of certain races and ethnicities, pointing to systemic racism in policing. Recent high-profile killings by police in the USA have prompted calls for more extensive and public data reporting on police violence. This study examines the presence and extent of under-reporting of police violence in US Government-run vital registration data, offers a method for correcting under-reporting in these datasets, and presents revised estimates of deaths due to police violence in the USA. Methods We compared data from the USA National Vital Statistics System (NVSS) to three non-governmental, open-source databases on police violence: Fatal Encounters, Mapping Police Violence, and The Counted. We extracted and standardised the age, sex, US state of death registration, year of death, and race and ethnicity (non-Hispanic White, non-Hispanic Black, non-Hispanic of other races, and Hispanic of any race) of each decedent for all data sources and used a network meta-regression to quantify the rate of under-reporting within the NVSS. Using these rates to inform correction factors, we provide adjusted estimates of deaths due to police violence for all states, ages, sexes, and racial and ethnic groups from 1980 to 2019 across the USA. Findings Across all races and states in the USA, we estimate 30 800 deaths (95% uncertainty interval [UI] 30 300–31 300) from police violence between 1980 and 2018; this represents 17 100 more deaths (16 600–17 600) than reported by the NVSS. Over this time period, the age-standardised mortality rate due to police violence was highest in non-Hispanic Black people (0·69 [95% UI 0·67–0·71] per 100 000), followed by Hispanic people of any race (0·35 [0·34–0·36]), non-Hispanic White people (0·20 [0·19–0·20]), and non-Hispanic people of other races (0·15 [0·14– 0·16]). This variation is further affected by the decedent's sex and shows large discrepancies between states. Between 1980 and 2018, the NVSS did not report 55·5% (54·8–56·2) of all deaths attributable to police violence. When aggregating all races, the age-standardised mortality rate due to police violence was 0·25 (0·24–0·26) per 100 000 in the 1980s and 0·34 (0·34–0·35) per 100 000 in the 2010s, an increase of 38·4% (32·4–45·1) over the period of study. Interpretation We found that more than half of all deaths due to police violence that we estimated in the USA from 1980 to 2018 were unreported in the NVSS. Compounding this, we found substantial differences in the age-standardised mortality rate due to police violence over time and by racial and ethnic groups within the USA. Proven public health intervention strategies are needed to address these systematic biases. State-level estimates allow for appropriate targeting of these strategies to address police violence and improve its reporting. Funding Bill & Melinda Gates Foundation, National Institute on Minority Health and Health Disparities, and National Heart, Lung, and Blood Institute.

Published
2021

50. Reconstruction of Zika virus introduction in Brazil

Author

Zinszer, Kate, Morrison, Kathryn, Brownstein, John S., Marinho, Fatima, Santos, Alexandre F., and Nsoesie, Elaine O.

Subjects
Disease transmission -- Research, Zika virus -- Risk factors, Health
Abstract

Autochthonous transmission of Zika virus has been confirmed in 67 countries worldwide and in 46 countries or territories in the Americas (1,2). It is believed that Zika virus was introduced [...]

Published
2017
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