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Book ; Online: Challenges and Perspectives of Neurological Disorders

Kung, Woon-Man / Ningrum, Dina Nur Anggraini

2023

Keywords	Medicine ; Neurosciences ; atherosclerotic ; residual stenosis ; reocclusion ; endovascular treatment ; Alzheimer's disease ; mild cognitive impairment ; tau positron emission tomography ; deep learning radiomics ; glioblastoma ; natural killer cells ; immune function ; interferon ; gross total resection ; neuropsychological tests battery ; machine learning ; screening tool ; Florzolotau PET ; functional connectivity ; neuronal intranuclear inclusion disease ; NIID ; mitochondrial dysfunction ; MELAS ; reversible DWI hyperintensities ; virtual reality ; neurological ; cognitive ; rehabilitation ; diagnosis ; metaverse ; Erdheim-Chester disease ; histiocytosis ; neurohistiocytosis ; cerebellar ataxia ; myelin proteolipid protein ; protein structure prediction ; functional analysis ; multiple sclerosis ; amyotrophic lateral sclerosis ; NOTCH2NLC ; intermediate-length repeats ; nucleotide repeat expansion ; Tau PET ; radiomics ; Alzheimer's Disease ; Mild Cognitive Impairment ; n/a
Language	English
Size	1 electronic resource (146 pages)
Publisher	MDPI - Multidisciplinary Digital Publishing Institute
Publishing place	Basel
Document type	Book ; Online
Note	English
HBZ-ID	HT030377880
ISBN	9783036575025 ; 3036575022
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article: Challenges and Perspectives of Neurological Disorders.

Ningrum, Dina Nur Anggraini / Kung, Woon-Man

Brain sciences

2023 Volume 13, Issue 4

Abstract: Neurological disorders pose significant challenges to healthcare systems worldwide [ ... ]. ...

Abstract	Neurological disorders pose significant challenges to healthcare systems worldwide [...].
Language	English
Publishing date	2023-04-18
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2651993-8
ISSN	2076-3425
ISSN	2076-3425
DOI	10.3390/brainsci13040676
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the "New Normal" During the COVID-19 Pandemic in Indonesia.

Rahmanti, Annisa Ristya / Ningrum, Dina Nur Anggraini / Lazuardi, Lutfan / Yang, Hsuan-Chia / Li, Yu-Chuan Jack

Computer methods and programs in biomedicine

2021 Volume 205, Page(s) 106083

Abstract: Background: After two months of implementing a partial lockdown, the Indonesian government had announced the "New Normal" policy to prevent a further economic crash in the country. This policy received many critics, as Indonesia still experiencing a ... ...

Abstract	Background: After two months of implementing a partial lockdown, the Indonesian government had announced the "New Normal" policy to prevent a further economic crash in the country. This policy received many critics, as Indonesia still experiencing a fluctuated number of infected cases. Understanding public perception through effective risk communication can assist the government in relaying an appropriate message to improve people's compliance and to avoid further disease spread. Objective: This study observed how risk communication using social media platforms like Twitter could be adopted to measure public attention on COVID-19 related issues "New Normal". Method: From May 21 to June 18, 2020, we archived all tweets related to COVID-19 containing keywords: "#NewNormal", and "New Normal" using Drone Emprit Academy (DEA) engine. DEA search API collected all requested tweets and described the cumulative tweets for trend analysis, word segmentation, and word frequency. We further analyzed the public perception using sentiment analysis and identified the predominant tweets using emotion analysis. Result: We collected 284,216 tweets from 137,057 active users. From the trend analysis, we observed three stages of the changing trend of the public's attention on the "New Normal". Results from the sentiment analysis indicate that more than half of the population (52%) had a "positive" sentiment towards the "New Normal" issues while only 41% of them had a "negative" perception. Our study also demonstrated the public's sentiment trend has gradually shifted from "negative" to "positive" due to the influence of both the government actions and the spread of the disease. A more detailed analysis of the emotion analysis showed that the majority of the public emotions (77.6%) relied on the emotion of "trust", "anticipation", and "joy". Meanwhile, people were also surprised (8.62%) that the Indonesian government progressed to the "New Normal" concept despite a fluctuating number of cases. Conclusion: Our findings offer an opportunity for the government to use Twitter in the process of quick decision-making and policy evaluation during uncertain times in response to the COVID-19 pandemic.
MeSH term(s)	Attention ; COVID-19 ; Communicable Disease Control ; Communication ; Data Science ; Disease Outbreaks ; Humans ; Indonesia/epidemiology ; Pandemics ; SARS-CoV-2 ; Social Media
Language	English
Publishing date	2021-04-06
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2021.106083
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Development and validation of an instrument for measuring competencies on public health informatics of primary health care worker (PHIC4PHC) in Indonesia.

Rachmani, Enny / Hsu, Chien-Yeh / Chang, Peter WuShou / Fuad, Anis / Nurjanah, Nurjanah / Shidik, Guruh Fajar / Ningrum, Dina Nur Anggraini / Lin, Ming-Chin

Primary health care research & development

2020 Volume 21, Page(s) e22

Abstract: Because of the increasing adoption and use of technology in primary health care (PHC), public health informatics competencies (PHIC) are becoming essential for public health workers. Unfortunately, no studies have measured PHIC in resource-limited ... ...

Abstract	Because of the increasing adoption and use of technology in primary health care (PHC), public health informatics competencies (PHIC) are becoming essential for public health workers. Unfortunately, no studies have measured PHIC in resource-limited setting. This paper describes the process of developing and validating Public Health Informatics Competencies for Primary Health Care (PHIC4PHC), an instrument for measuring PHC workers' competencies in public health informatics. Method: This study developed a questionnaire that had three stages: the Delphi technique, a pretest, and field test. Eleven academicians from a university and 13 PHC workers joined 2 rounds of group discussion in the first stage. The second stage comprised two pilot studies with 75 PHC workers in Semarang Municipality. The third stage involved validating the questionnaire with 462 PHC workers in Kendal District. This study used Pearson's product-moment correlation for the validity check and Cronbach's alpha coefficient for determining the internal consistency. This study used the K-means algorithm for clustering the results of the PHIC4PHC questionnaire. Results and Conclusion: PHIC4PHC is the first comprehensive PHIC questionnaire administered in a resource-limited setting, consisting of 11 indicators and 42 measurement items concerning knowledge of health information systems, skills required for health data management, ethical aspects of data sharing and health information literacy. The final results of PHIC4PHC were clustered into three classes based on the K-means algorithm. Overall, 45.7% PHC workers achieved medium competency, whereas 25.6% and 27.7% achieved low and high competency, respectively. Men had higher competency than women. The higher the worker's level of education, the higher the PHIC level; the longer the worker's work experience, the lower the PHIC score; and the greater the worker's age, the lower the PHIC score. Measuring and monitoring PHIC is vital to support successful health IT adoption in PHC.
MeSH term(s)	Adult ; Female ; Health Personnel ; Humans ; Indonesia ; Male ; Middle Aged ; Pilot Projects ; Primary Health Care ; Public Health Informatics
Language	English
Publishing date	2020-07-06
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2027892-5
ISSN	1477-1128 ; 1463-4236
ISSN (online)	1477-1128
ISSN	1463-4236
DOI	10.1017/S1463423620000018
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Deep Learning Classifier with Patient's Metadata of Dermoscopic Images in Malignant Melanoma Detection.

Ningrum, Dina Nur Anggraini / Yuan, Sheng-Po / Kung, Woon-Man / Wu, Chieh-Chen / Tzeng, I-Shiang / Huang, Chu-Ya / Li, Jack Yu-Chuan / Wang, Yao-Chin

Journal of multidisciplinary healthcare

2021 Volume 14, Page(s) 877–885

Abstract: Background: Incidence of skin cancer is one of the global burdens of malignancies that increase each year, with melanoma being the deadliest one. Imaging-based automated skin cancer detection still remains challenging owing to variability in the skin ... ...

Abstract	Background: Incidence of skin cancer is one of the global burdens of malignancies that increase each year, with melanoma being the deadliest one. Imaging-based automated skin cancer detection still remains challenging owing to variability in the skin lesions and limited standard dataset availability. Recent research indicates the potential of deep convolutional neural networks (CNN) in predicting outcomes from simple as well as highly complicated images. However, its implementation requires high-class computational facility, that is not feasible in low resource and remote areas of health care. There is potential in combining image and patient's metadata, but the study is still lacking. Objective: We want to develop malignant melanoma detection based on dermoscopic images and patient's metadata using an artificial intelligence (AI) model that will work on low-resource devices. Methods: We used an open-access dermatology repository of International Skin Imaging Collaboration (ISIC) Archive dataset consist of 23,801 biopsy-proven dermoscopic images. We tested performance for binary classification malignant melanomas vs nonmalignant melanomas. From 1200 sample images, we split the data for training (72%), validation (18%), and testing (10%). We compared CNN with image data only (CNN model) vs CNN for image data combined with an artificial neural network (ANN) for patient's metadata (CNN+ANN model). Results: The balanced accuracy for CNN+ANN model was higher (92.34%) than the CNN model (73.69%). Combination of the patient's metadata using ANN prevents the overfitting that occurs in the CNN model using dermoscopic images only. This small size (24 MB) of this model made it possible to run on a medium class computer without the need of cloud computing, suitable for deployment on devices with limited resources. Conclusion: The CNN+ANN model can increase the accuracy of classification in malignant melanoma detection even with limited data and is promising for development as a screening device in remote and low resources health care.
Language	English
Publishing date	2021-04-21
Publishing country	New Zealand
Document type	Journal Article
ZDB-ID	2453343-9
ISSN	1178-2390
ISSN	1178-2390
DOI	10.2147/JMDH.S306284
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Article: A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record.

Ningrum, Dina Nur Anggraini / Kung, Woon-Man / Tzeng, I-Shiang / Yuan, Sheng-Po / Wu, Chieh-Chen / Huang, Chu-Ya / Muhtar, Muhammad Solihuddin / Nguyen, Phung-Anh / Li, Jack Yu-Chuan / Wang, Yao-Chin

Journal of multidisciplinary healthcare

2021 Volume 14, Page(s) 2477–2485

Abstract: Purpose: To develop deep learning model (Deep-KOA) that can predict the risk of knee osteoarthritis (KOA) within the next year by using the previous three years nonimage-based electronic medical record (EMR) data.: Patients and methods: We randomly ... ...

Abstract	Purpose: To develop deep learning model (Deep-KOA) that can predict the risk of knee osteoarthritis (KOA) within the next year by using the previous three years nonimage-based electronic medical record (EMR) data. Patients and methods: We randomly selected information of two million patients from the Taiwan National Health Insurance Research Database (NHIRD) from January 1, 1999 to December 31, 2013. During the study period, 132,594 patients were diagnosed with KOA, while 1,068,464 patients without KOA were chosen randomly as control. We constructed a feature matrix by using the three-year history of sequential diagnoses, drug prescriptions, age, and sex. Deep learning methods of convolutional neural network (CNN) and artificial neural network (ANN) were used together to develop a risk prediction model. We used the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and precision to evaluate the performance of Deep-KOA. Then, we explored the important features using stepwise feature selection. Results: This study included 132,594 KOA patients, 83,111 females (62.68%), 49,483 males (37.32%), mean age 64.2 years, and 1,068,464 non-KOA patients, 545,902 females (51.09%), 522,562 males (48.91%), mean age 51.00 years. The Deep-KOA achieved an overall AUROC, sensitivity, specificity, and precision of 0.97, 0.89, 0.93, and 0.80 respectively. The discriminative analysis of Deep-KOA showed important features from several diseases such as disorders of the eye and adnexa, acute respiratory infection, other metabolic and immunity disorders, and diseases of the musculoskeletal and connective tissue. Age and sex were not found as the most discriminative features, with AUROC of 0.9593 (-0.76% loss) and 0.9644 (-0.25% loss) respectively. Whereas medications including antacid, cough suppressant, and expectorants were identified as discriminative features. Conclusion: Deep-KOA was developed to predict the risk of KOA within one year earlier, which may provide clues for clinical decision support systems to target patients with high risk of KOA to get precision prevention program.
Language	English
Publishing date	2021-09-11
Publishing country	New Zealand
Document type	Journal Article
ZDB-ID	2453343-9
ISSN	1178-2390
ISSN	1178-2390
DOI	10.2147/JMDH.S325179
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Article ; Online: Developing an Indonesia's health literacy short-form survey questionnaire (HLS-EU-SQ10-IDN) using the feature selection and genetic algorithm.

Rachmani, Enny / Hsu, Chien-Yeh / Nurjanah, Nurjanah / Chang, Peter Wushou / Shidik, Guruh Fajar / Noersasongko, Edi / Jumanto, Jumanto / Fuad, Anis / Ningrum, Dina Nur Anggraini / Kurniadi, Arif / Lin, Ming-Chin

Computer methods and programs in biomedicine

2019 Volume 182, Page(s) 105047

Abstract: Background and objective: Measuring health literacy becomes more important because its association with health status and healthcare outcomes. Studies have developed at least 133 measurement tools for health literacy. HLS-EU-Q47 is a questionnaire ... ...

Abstract	Background and objective: Measuring health literacy becomes more important because its association with health status and healthcare outcomes. Studies have developed at least 133 measurement tools for health literacy. HLS-EU-Q47 is a questionnaire consisting of 12 sub-dimensions and 47 questions developed by the Europe Health Literacy Consortium. Many countries in Europe and Asia have used HLS-EU-Q47 as a tool for measuring health literacy in the general public. Indonesia has conducted general health literacy survey using HLS-EU-Q47 but finding the difficulties because of the time-consuming interview. A shorter version of HLS-EU-Q47 is needed to apply in health literacy researches in Indonesia. This paper reports the results of feature reduction to develop a short Indonesian version HLS-EU questionnaire and measures the accuracy of the model compared with other short form like HLS-EU-SQ16 or HLS-SF12. Method: The analysis was performed on a population-based dataset from Indonesia-Semarang Health Literacy Survey for which there were specific target variables as the classification of health literacy level. All attributes were assessed as potential targets in the models derived from the full dataset and its subsets. The feature selection methods with genetic algorithm were used as the filter as well as validation (cross validation) and classification (k-NN:k-nearest neighbor). The predictive accuracy of health literacy level and the complexity of models based on the reduced datasets were compared among the methods and other short versions such as HLS-EU-SQ16, HLS-SF12. Result: The accuracy of the existing short form models were 90.64% with the HLS-EU-SQ16 and 88.67% with the HLS-SF12. This study proposed a model with 10 features as the construct of a short Indonesian-version (proposed as the HLS-EU-SQ10-IDN) since the model was with higher accuracy than the HLS-SF12, but fewer features for measuring general health literacy index. Moreover, the short version only completed part of 12 dimensions of the full questionnare. Conclusion: A data mining technique using feature selection with combination of genetic algorithm and k-NN algorithm was applied to develop a short version questionnaire and proved to have better accuracy, as compared with the short version developed by traditional statistical technique.
MeSH term(s)	Adult ; Algorithms ; Female ; Health Literacy ; Humans ; Indonesia ; Male ; Middle Aged ; Surveys and Questionnaires ; Young Adult
Language	English
Publishing date	2019-08-26
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2019.105047
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Correlation between Diabetes Mellitus and Knee Osteoarthritis: A Dry-To-Wet Lab Approach.

Dubey, Navneet Kumar / Ningrum, Dina Nur Anggraini / Dubey, Rajni / Deng, Yue-Hua / Li, Yu-Chuan / Wang, Peter D / Wang, Joseph R / Syed-Abdul, Shabbir / Deng, Win-Ping

International journal of molecular sciences

2018 Volume 19, Issue 10

Abstract: Recent years have witnessed an increased prevalence of knee osteoarthritis (KOA) among diabetes mellitus (DM) patients-conditions which might share common risk factors such as obesity and advanced aging. Therefore, we conducted dry-to-wet lab research ... ...

Abstract	Recent years have witnessed an increased prevalence of knee osteoarthritis (KOA) among diabetes mellitus (DM) patients-conditions which might share common risk factors such as obesity and advanced aging. Therefore, we conducted dry-to-wet lab research approaches to assess the correlation of type 1 DM (T1DM) and type 2 DM (T2DM) with KOA among all age and genders of Taiwanese population. The strength of association (odds ratio: OR) was analyzed using a phenome-wide association study portal. Populations of 37,353 T1DM and 1,218,254 T2DM were included. We observed a significant association of KOA with T1DM (OR: 1.40 (1.33⁻1.47),
MeSH term(s)	Aged ; Aged, 80 and over ; Animals ; Biomarkers ; Cartilage, Articular/metabolism ; Cartilage, Articular/pathology ; Diabetes Mellitus, Experimental ; Diabetes Mellitus, Type 1/complications ; Diabetes Mellitus, Type 1/epidemiology ; Diabetes Mellitus, Type 1/metabolism ; Diabetes Mellitus, Type 2/complications ; Diabetes Mellitus, Type 2/epidemiology ; Diabetes Mellitus, Type 2/metabolism ; Disease Models, Animal ; Female ; Humans ; Knee Joint/metabolism ; Knee Joint/pathology ; Male ; Mice ; Middle Aged ; Obesity/complications ; Obesity/metabolism ; Odds Ratio ; Osteoarthritis, Knee/epidemiology ; Osteoarthritis, Knee/etiology ; Osteoarthritis, Knee/metabolism ; Osteoarthritis, Knee/pathology ; Phenotype ; Proteoglycans/metabolism ; Risk Assessment
Chemical Substances	Biomarkers ; Proteoglycans
Language	English
Publishing date	2018-10-03
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms19103021
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Diseases, Injuries, and Risk Factors in Child and Adolescent Health, 1990 to 2017: Findings From the Global Burden of Diseases, Injuries, and Risk Factors 2017 Study.

Reiner, Robert C / Olsen, Helen Elizabeth / Ikeda, Chad Thomas / Echko, Michelle M / Ballestreros, Katherine E / Manguerra, Helen / Martopullo, Ira / Millear, Anoushka / Shields, Chloe / Smith, Alison / Strub, Bryan / Abebe, Molla / Abebe, Zegeye / Adhena, Beyene Meressa / Adhikari, Tara Ballav / Akibu, Mohammed / Al-Raddadi, Rajaa M / Alvis-Guzman, Nelson / Antonio, Carl Abelardo T /

Aremu, Olatunde / Asgedom, Solomon Weldegebreal / Asseffa, Netsanet Abera / Avila-Burgos, Leticia / Barac, Aleksandra / Bärnighausen, Till W / Bassat, Quique / Bensenor, Isabela M / Bhutta, Zulfiqar A / Bijani, Ali / Bililign, Nigus / Cahuana-Hurtado, Lucero / Malta, Deborah Carvalho / Chang, Jung-Chen / Charlson, Fiona J / Dharmaratne, Samath Dhamminda / Doku, David Teye / Edessa, Dumessa / El-Khatib, Ziad / Erskine, Holly E / Ferrari, Alize J / Fullman, Nancy / Gupta, Rahul / Hassen, Hamid Yimam / Hay, Simon I / Ilesanmi, Olayinka Stephen / Jacobsen, Kathryn H / Kahsay, Amaha / Kasaeian, Amir / Kassa, Tesfaye Dessale / Kebede, Seifu / Khader, Yousef Saleh / Khan, Ejaz Ahmad / Khan, Mohammed Nuruzzaman / Khang, Young-Ho / Khubchandani, Jagdish / Kinfu, Yohannes / Kochhar, Sonali / Kokubo, Yoshihiro / Koyanagi, Ai / Defo, Barthelemy Kuate / Lal, Dharmesh Kumar / Kumsa, Fekede Asefa / Larson, Heidi J / Leung, Janni / Mamun, Abdullah A / Mehata, Suresh / Melku, Mulugeta / Mendoza, Walter / Mezgebe, Haftay Berhane / Miller, Ted R / Moges, Nurilign Abebe / Mohammed, Shafiu / Mokdad, Ali H / Monasta, Lorenzo / Neupane, Subas / Nguyen, Huong Lan Thi / Ningrum, Dina Nur Anggraini / Nirayo, Yirga Legesse / Nong, Vuong Minh / Ogbo, Felix Akpojene / Olagunju, Andrew T / Olusanya, Bolajoko Olubukunola / Olusanya, Jacob Olusegun / Patton, George C / Pereira, David M / Pourmalek, Farshad / Qorbani, Mostafa / Rafay, Anwar / Rai, Rajesh Kumar / Ram, Usha / Ranabhat, Chhabi Lal / Renzaho, Andre M N / Rezai, Mohammad Sadegh / Ronfani, Luca / Roth, Gregory A / Safiri, Saeid / Sartorius, Benn / Scott, James G / Shackelford, Katya Anne / Sliwa, Karen / Sreeramareddy, Chandrashekhar / Sufiyan, Mu'awiyyah Bable / Terkawi, Abdullah Sulieman / Topor-Madry, Roman / Tran, Bach Xuan / Ukwaja, Kingsley Nnanna / Uthman, Olalekan A / Vollset, Stein Emil / Weldegwergs, Kidu Gidey / Werdecker, Andrea / Whiteford, Harvey A / Wijeratne, Tissa / Yonemoto, Naohiro / Yotebieng, Marcel / Zuhlke, Liesl J / Kyu, Hmwe Hmwe / Naghavi, Mohsen / Vos, Theo / Murray, Christopher J L / Kassebaum, Nicholas J

JAMA pediatrics

2019 Volume 173, Issue 6, Page(s) e190337

Abstract: Importance: Understanding causes and correlates of health loss among children and adolescents can identify areas of success, stagnation, and emerging threats and thereby facilitate effective improvement strategies.: Objective: To estimate mortality ... ...

Abstract	Importance: Understanding causes and correlates of health loss among children and adolescents can identify areas of success, stagnation, and emerging threats and thereby facilitate effective improvement strategies. Objective: To estimate mortality and morbidity in children and adolescents from 1990 to 2017 by age and sex in 195 countries and territories. Design, setting, and participants: This study examined levels, trends, and spatiotemporal patterns of cause-specific mortality and nonfatal health outcomes using standardized approaches to data processing and statistical analysis. It also describes epidemiologic transitions by evaluating historical associations between disease indicators and the Socio-Demographic Index (SDI), a composite indicator of income, educational attainment, and fertility. Data collected from 1990 to 2017 on children and adolescents from birth through 19 years of age in 195 countries and territories were assessed. Data analysis occurred from January 2018 to August 2018. Exposures: Being under the age of 20 years between 1990 and 2017. Main outcomes and measures: Death and disability. All-cause and cause-specific deaths, disability-adjusted life years, years of life lost, and years of life lived with disability. Results: Child and adolescent deaths decreased 51.7% from 13.77 million (95% uncertainty interval [UI], 13.60-13.93 million) in 1990 to 6.64 million (95% UI, 6.44-6.87 million) in 2017, but in 2017, aggregate disability increased 4.7% to a total of 145 million (95% UI, 107-190 million) years lived with disability globally. Progress was uneven, and inequity increased, with low-SDI and low-middle-SDI locations experiencing 82.2% (95% UI, 81.6%-82.9%) of deaths, up from 70.9% (95% UI, 70.4%-71.4%) in 1990. The leading disaggregated causes of disability-adjusted life years in 2017 in the low-SDI quintile were neonatal disorders, lower respiratory infections, diarrhea, malaria, and congenital birth defects, whereas neonatal disorders, congenital birth defects, headache, dermatitis, and anxiety were highest-ranked in the high-SDI quintile. Conclusions and relevance: Mortality reductions over this 27-year period mean that children are more likely than ever to reach their 20th birthdays. The concomitant expansion of nonfatal health loss and epidemiological transition in children and adolescents, especially in low-SDI and middle-SDI countries, has the potential to increase already overburdened health systems, will affect the human capital potential of societies, and may influence the trajectory of socioeconomic development. Continued monitoring of child and adolescent health loss is crucial to sustain the progress of the past 27 years.
MeSH term(s)	Adolescent ; Adolescent Health/trends ; Age Distribution ; Child ; Child Health/trends ; Child, Preschool ; Female ; Global Burden of Disease/trends ; Global Health/trends ; Humans ; Infant ; Infant, Newborn ; Male ; Morbidity/trends ; Quality-Adjusted Life Years ; Risk Factors ; Sex Distribution ; Socioeconomic Factors ; Spatio-Temporal Analysis ; Wounds and Injuries/epidemiology ; Wounds and Injuries/etiology ; Young Adult
Language	English
Publishing date	2019-06-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2701223-2
ISSN	2168-6211 ; 2168-6203
ISSN (online)	2168-6211
ISSN	2168-6203
DOI	10.1001/jamapediatrics.2019.0337
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Article ; Online: Global Mortality From Firearms, 1990-2016.

Naghavi, Mohsen / Marczak, Laurie B / Kutz, Michael / Shackelford, Katya Anne / Arora, Megha / Miller-Petrie, Molly / Aichour, Miloud Taki Eddine / Akseer, Nadia / Al-Raddadi, Rajaa M / Alam, Khurshid / Alghnam, Suliman A / Antonio, Carl Abelardo T / Aremu, Olatunde / Arora, Amit / Asadi-Lari, Mohsen / Assadi, Reza / Atey, Tesfay Mehari / Avila-Burgos, Leticia / Awasthi, Ashish /

Ayala Quintanilla, Beatriz Paulina / Barker-Collo, Suzanne Lyn / Bärnighausen, Till Winfried / Bazargan-Hejazi, Shahrzad / Behzadifar, Masoud / Behzadifar, Meysam / Bennett, James R / Bhalla, Ashish / Bhutta, Zulfiqar A / Bilal, Arebu Issa / Borges, Guilherme / Borschmann, Rohan / Brazinova, Alexandra / Campuzano Rincon, Julio Cesar / Carvalho, Félix / Castañeda-Orjuela, Carlos A / Dandona, Lalit / Dandona, Rakhi / Dargan, Paul I / De Leo, Diego / Dharmaratne, Samath Dhamminda / Ding, Eric L / Phuc Do, Huyen / Doku, David Teye / Doyle, Kerrie E / Driscoll, Tim Robert / Edessa, Dumessa / El-Khatib, Ziad / Endries, Aman Yesuf / Esteghamati, Alireza / Faro, Andre / Farzadfar, Farshad / Feigin, Valery L / Fischer, Florian / Foreman, Kyle J / Franklin, Richard Charles / Fullman, Nancy / Futran, Neal D / Gebrehiwot, Tsegaye Tewelde / Gutiérrez, Reyna Alma / Hafezi-Nejad, Nima / Haghparast Bidgoli, Hassan / Hailu, Gessessew Bugssa / Haro, Josep Maria / Hassen, Hamid Yimam / Hawley, Caitlin / Hendrie, Delia / Híjar, Martha / Hu, Guoqing / Ilesanmi, Olayinka Stephen / Jakovljevic, Mihajlo / James, Spencer L / Jayaraman, Sudha / Jonas, Jost B / Kahsay, Amaha / Kasaeian, Amir / Keiyoro, Peter Njenga / Khader, Yousef / Khalil, Ibrahim A / Khang, Young-Ho / Khubchandani, Jagdish / Ahmad Kiadaliri, Aliasghar / Kieling, Christian / Kim, Yun Jin / Kosen, Soewarta / Krohn, Kristopher J / Kumar, G Anil / Lami, Faris Hasan / Lansingh, Van C / Larson, Heidi Jane / Linn, Shai / Lunevicius, Raimundas / Magdy Abd El Razek, Hassan / Magdy Abd El Razek, Muhammed / Malekzadeh, Reza / Carvalho Malta, Deborah / Mason-Jones, Amanda J / Matzopoulos, Richard / Memiah, Peter T N / Mendoza, Walter / Meretoja, Tuomo J / Mezgebe, Haftay Berhane / Miller, Ted R / Mohammed, Shafiu / Moradi-Lakeh, Maziar / Mori, Rintaro / Nand, Devina / Tat Nguyen, Cuong / Le Nguyen, Quyen / Ningrum, Dina Nur Anggraini / Akpojene Ogbo, Felix / Olagunju, Andrew T / Patton, George C / Phillips, Michael R / Polinder, Suzanne / Pourmalek, Farshad / Qorbani, Mostafa / Rahimi-Movaghar, Afarin / Rahimi-Movaghar, Vafa / Rahman, Mahfuzar / Rai, Rajesh Kumar / Ranabhat, Chhabi Lal / Rawaf, David Laith / Rawaf, Salman / Rowhani-Rahbar, Ali / Safdarian, Mahdi / Safiri, Saeid / Sagar, Rajesh / Salama, Joseph S / Sanabria, Juan / Santric Milicevic, Milena M / Sarmiento-Suárez, Rodrigo / Sartorius, Benn / Satpathy, Maheswar / Schwebel, David C / Seedat, Soraya / Sepanlou, Sadaf G / Shaikh, Masood Ali / Sharew, Nigussie Tadesse / Shiue, Ivy / Singh, Jasvinder A / Sisay, Mekonnen / Skirbekk, Vegard / Soares Filho, Adauto Martins / Stein, Dan J / Stokes, Mark Andrew / Sufiyan, Mu'awiyyah Babale / Swaroop, Mamta / Sykes, Bryan L / Tabarés-Seisdedos, Rafael / Tadese, Fentaw / Tran, Bach Xuan / Thanh Tran, Tung / Ukwaja, Kingsley Nnanna / Vasankari, Tommi Juhani / Vlassov, Vasily / Werdecker, Andrea / Ye, Pengpeng / Yip, Paul / Yonemoto, Naohiro / Younis, Mustafa Z / Zaidi, Zoubida / El Sayed Zaki, Maysaa / Hay, Simon I / Lim, Stephen S / Lopez, Alan D / Mokdad, Ali H / Vos, Theo / Murray, Christopher J L

JAMA

2018 Volume 320, Issue 8, Page(s) 792–814

Abstract: Importance: Understanding global variation in firearm mortality rates could guide prevention policies and interventions.: Objective: To estimate mortality due to firearm injury deaths from 1990 to 2016 in 195 countries and territories.: Design, ... ...

Abstract	Importance: Understanding global variation in firearm mortality rates could guide prevention policies and interventions. Objective: To estimate mortality due to firearm injury deaths from 1990 to 2016 in 195 countries and territories. Design, setting, and participants: This study used deidentified aggregated data including 13 812 location-years of vital registration data to generate estimates of levels and rates of death by age-sex-year-location. The proportion of suicides in which a firearm was the lethal means was combined with an estimate of per capita gun ownership in a revised proxy measure used to evaluate the relationship between availability or access to firearms and firearm injury deaths. Exposures: Firearm ownership and access. Main outcomes and measures: Cause-specific deaths by age, sex, location, and year. Results: Worldwide, it was estimated that 251 000 (95% uncertainty interval [UI], 195 000-276 000) people died from firearm injuries in 2016, with 6 countries (Brazil, United States, Mexico, Colombia, Venezuela, and Guatemala) accounting for 50.5% (95% UI, 42.2%-54.8%) of those deaths. In 1990, there were an estimated 209 000 (95% UI, 172 000 to 235 000) deaths from firearm injuries. Globally, the majority of firearm injury deaths in 2016 were homicides (64.0% [95% UI, 54.2%-68.0%]; absolute value, 161 000 deaths [95% UI, 107 000-182 000]); additionally, 27% were firearm suicide deaths (67 500 [95% UI, 55 400-84 100]) and 9% were unintentional firearm deaths (23 000 [95% UI, 18 200-24 800]). From 1990 to 2016, there was no significant decrease in the estimated global age-standardized firearm homicide rate (-0.2% [95% UI, -0.8% to 0.2%]). Firearm suicide rates decreased globally at an annualized rate of 1.6% (95% UI, 1.1-2.0), but in 124 of 195 countries and territories included in this study, these levels were either constant or significant increases were estimated. There was an annualized decrease of 0.9% (95% UI, 0.5%-1.3%) in the global rate of age-standardized firearm deaths from 1990 to 2016. Aggregate firearm injury deaths in 2016 were highest among persons aged 20 to 24 years (for men, an estimated 34 700 deaths [95% UI, 24 900-39 700] and for women, an estimated 3580 deaths [95% UI, 2810-4210]). Estimates of the number of firearms by country were associated with higher rates of firearm suicide (P < .001; R2 = 0.21) and homicide (P < .001; R2 = 0.35). Conclusions and relevance: This study estimated between 195 000 and 276 000 firearm injury deaths globally in 2016, the majority of which were firearm homicides. Despite an overall decrease in rates of firearm injury death since 1990, there was variation among countries and across demographic subgroups.
MeSH term(s)	Adolescent ; Adult ; Age Distribution ; Aged ; Aged, 80 and over ; Child ; Child, Preschool ; Female ; Firearms/statistics & numerical data ; Global Health/statistics & numerical data ; Homicide/statistics & numerical data ; Humans ; Infant ; Infant, Newborn ; Male ; Middle Aged ; Mortality/trends ; Sex Distribution ; Suicide/statistics & numerical data ; Wounds, Gunshot/mortality ; Young Adult
Language	English
Publishing date	2018-10-16
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2958-0
ISSN	1538-3598 ; 0254-9077 ; 0002-9955 ; 0098-7484
ISSN (online)	1538-3598
ISSN	0254-9077 ; 0002-9955 ; 0098-7484
DOI	10.1001/jama.2018.10060
Database	MEDical Literature Analysis and Retrieval System OnLINE

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