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Yang Y, Liu J, Sun C, Shi Y, Hsing JC, Kamya A, Keller CA, Antil N, Rubin D, Wang H, Ying H, Zhao X, Wu YH, Nguyen M, Lu Y, Yang F, Huang P, Hsing AW, Wu J, Zhu S. Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population. Eur Radiol 2023; 33:5894-5906. [PMID: 36892645 DOI: 10.1007/s00330-023-09515-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 10/21/2022] [Accepted: 02/03/2023] [Indexed: 03/10/2023]
Abstract
OBJECTIVES We aimed to develop and validate a deep learning system (DLS) by using an auxiliary section that extracts and outputs specific ultrasound diagnostic features to improve the explainable, clinical relevant utility of using DLS for detecting NAFLD. METHODS In a community-based study of 4144 participants with abdominal ultrasound scan in Hangzhou, China, we sampled 928 (617 [66.5%] females, mean age: 56 years ± 13 [standard deviation]) participants (2 images per participant) to develop and validate DLS, a two-section neural network (2S-NNet). Radiologists' consensus diagnosis classified hepatic steatosis as none steatosis, mild, moderate, and severe. We also explored the NAFLD detection performance of six one-section neural network models and five fatty liver indices on our data set. We further evaluated the influence of participants' characteristics on the correctness of 2S-NNet by logistic regression. RESULTS Area under the curve (AUROC) of 2S-NNet for hepatic steatosis was 0.90 for ≥ mild, 0.85 for ≥ moderate, and 0.93 for severe steatosis, and was 0.90 for NAFLD presence, 0.84 for moderate to severe NAFLD, and 0.93 for severe NAFLD. The AUROC of NAFLD severity was 0.88 for 2S-NNet, and 0.79-0.86 for one-section models. The AUROC of NAFLD presence was 0.90 for 2S-NNet, and 0.54-0.82 for fatty liver indices. Age, sex, body mass index, diabetes, fibrosis-4 index, android fat ratio, and skeletal muscle via dual-energy X-ray absorptiometry had no significant impact on the correctness of 2S-NNet (p > 0.05). CONCLUSIONS By using two-section design, 2S-NNet had improved the performance for detecting NAFLD with more explainable, clinical relevant utility than using one-section design. KEY POINTS • Based on the consensus review derived from radiologists, our DLS (2S-NNet) had an AUROC of 0.88 by using two-section design and yielded better performance for detecting NAFLD than using one-section design with more explainable, clinical relevant utility. • The 2S-NNet outperformed five fatty liver indices with the highest AUROCs (0.84-0.93 vs. 0.54-0.82) for different NAFLD severity screening, indicating screening utility of deep learning-based radiology may perform better than blood biomarker panels in epidemiology. • The correctness of 2S-NNet was not significantly influenced by individual's characteristics, including age, sex, body mass index, diabetes, fibrosis-4 index, android fat ratio, and skeletal muscle via dual-energy X-ray absorptiometry.
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Affiliation(s)
- Yang Yang
- Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jing Liu
- College of Computer Science and Technology, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, China
| | - Changxuan Sun
- Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yuwei Shi
- Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Julianna C Hsing
- Center for Policy, Outcomes, and Prevention, Stanford University School of Medicine, Stanford, CA, USA
| | - Aya Kamya
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Cody Auston Keller
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Neha Antil
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Daniel Rubin
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Hongxia Wang
- Department of Ultrasound, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haochao Ying
- Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, China
| | - Xueyin Zhao
- Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yi-Hsuan Wu
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, 780 Welch Road, CJ Huang Building, Suite 250D, Stanford, CA, 94305, USA
| | - Mindie Nguyen
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Division of Gastroenterology and Hepatology, Stanford University Medical Center, Stanford, CA, USA
| | - Ying Lu
- Department of Biomedical Data Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Fei Yang
- Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Pinton Huang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ann W Hsing
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, 780 Welch Road, CJ Huang Building, Suite 250D, Stanford, CA, 94305, USA.
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
| | - Jian Wu
- Second Affiliated Hospital School of Medicine, School of Public Health, and Institute of Wenzhou, Zhejiang University, Hangzhou, 310058, China.
| | - Shankuan Zhu
- Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China.
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China.
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Hsing JC, Lin BJ, Pulendran U, Jani SG, Chiang WL, Chiang TL, Wang CJ. Development and Validation of Age-Specific Resilience Instruments for Early Childhood Assessment: A Taiwan Birth Cohort Study. Acad Pediatr 2022; 22:1142-1152. [PMID: 35691535 DOI: 10.1016/j.acap.2022.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 05/26/2022] [Accepted: 06/05/2022] [Indexed: 01/19/2023]
Abstract
BACKGROUND We sought to develop and validate age-specific instruments for measuring early childhood resilience at ages 3, 5 and 8 in the Taiwan Birth Cohort Study, a national longitudinal study. METHODS Using data from 18,553 mother-infant pairs, we conducted exploratory factor analysis (EFA) on a simple random half of our sample. We then used the remaining half of these data for confirmatory factor analysis (CFA) to further assess the fit of 3 CFA models (ie, first-order, second-order, and bifactor). Psychometric properties, distributions, and inter-item and inter-factor correlations of each instrument were also evaluated. RESULTS EFA and CFA showed that the bifactor model of resilience (which included a general resilience factor and 5 specific factors) had the best fit for all 3 resilience scales, with 19 items at year 3, 18 items at year 5, and 19 items at year 8. All 3 resilience scales showed good psychometric properties, including construct validity, internal consistency, and normal distributions. For predictive validity, we found that in the face of adversity (measured by the High Risk Family Score), individuals with high resilience scores at age 3 had better general health scores at ages 3, 5, and 8 compared to those with low resilience scores. CONCLUSIONS We describe the development and validation of age-appropriate survey instruments to assess resilience in young children at the population level. These instruments can be used to better understand how resilience can impact child health over time, and to identify key factors that can foster resilience.
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Affiliation(s)
- Julianna C Hsing
- Center for Policy, Outcomes, and Prevention, Stanford University School of Medicine (JC Hsing, B-J Lin, U Pulendran, SG Jani, W-L Chiang, and CJ Wang), Stanford, Calif; Department of Pediatrics, Stanford University School of Medicine (JC Hsing, B-J Lin, U Pulendran, SG Jani, W-L Chiang, and CJ Wang), Stanford, Calif; Department of Epidemiology and Population Health, Stanford University School of Medicine (JC Hsing), Stanford, Calif
| | - Bea-Jane Lin
- Center for Policy, Outcomes, and Prevention, Stanford University School of Medicine (JC Hsing, B-J Lin, U Pulendran, SG Jani, W-L Chiang, and CJ Wang), Stanford, Calif; Department of Pediatrics, Stanford University School of Medicine (JC Hsing, B-J Lin, U Pulendran, SG Jani, W-L Chiang, and CJ Wang), Stanford, Calif
| | - Uma Pulendran
- Center for Policy, Outcomes, and Prevention, Stanford University School of Medicine (JC Hsing, B-J Lin, U Pulendran, SG Jani, W-L Chiang, and CJ Wang), Stanford, Calif; Department of Pediatrics, Stanford University School of Medicine (JC Hsing, B-J Lin, U Pulendran, SG Jani, W-L Chiang, and CJ Wang), Stanford, Calif
| | - Shilpa G Jani
- Center for Policy, Outcomes, and Prevention, Stanford University School of Medicine (JC Hsing, B-J Lin, U Pulendran, SG Jani, W-L Chiang, and CJ Wang), Stanford, Calif; Department of Pediatrics, Stanford University School of Medicine (JC Hsing, B-J Lin, U Pulendran, SG Jani, W-L Chiang, and CJ Wang), Stanford, Calif
| | - Wan-Lin Chiang
- Center for Policy, Outcomes, and Prevention, Stanford University School of Medicine (JC Hsing, B-J Lin, U Pulendran, SG Jani, W-L Chiang, and CJ Wang), Stanford, Calif; Department of Pediatrics, Stanford University School of Medicine (JC Hsing, B-J Lin, U Pulendran, SG Jani, W-L Chiang, and CJ Wang), Stanford, Calif; College of Public Health, National Taiwan University (W-L Chiang and T-L Chiang), Taipei, Taiwan
| | - Tung-Liang Chiang
- College of Public Health, National Taiwan University (W-L Chiang and T-L Chiang), Taipei, Taiwan.
| | - C Jason Wang
- Center for Policy, Outcomes, and Prevention, Stanford University School of Medicine (JC Hsing, B-J Lin, U Pulendran, SG Jani, W-L Chiang, and CJ Wang), Stanford, Calif; Department of Pediatrics, Stanford University School of Medicine (JC Hsing, B-J Lin, U Pulendran, SG Jani, W-L Chiang, and CJ Wang), Stanford, Calif.
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Jani SG, Ma J, Pulendran U, Hsing JC, Altamirano J, Shah S, Toomarian EY, Maldonado Y, Wang CHJ. Prospective Pilot Study Evaluating SARS-CoV-2 Transmission-Limiting Measures in an On-Site School. Acad Pediatr 2022; 22:671-679. [PMID: 34896273 PMCID: PMC8651529 DOI: 10.1016/j.acap.2021.11.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 11/18/2021] [Accepted: 11/30/2021] [Indexed: 12/24/2022]
Abstract
OBJECTIVES The purpose of our study is to evaluate the feasibility and reliability of a comprehensive set of preventive measures in limiting secondary transmission of COVID-19 in schools. METHODS A prospective cohort study was conducted to evaluate SARS-CoV-2 transmission in an independent K-8 school in San Mateo County, California. The research was conducted between September 14, 2020 through March 22, 2021 and consisted of: 1) demographic and epidemiological questionnaires; 2) daily symptom reporting; 3) weekly RT-PCR testing; and 4) periodic on-site qualitative observations. RESULTS One hundred eighty (79%) students and 63 (74%) on-site staff/contractors were enrolled. Participants reported symptoms in 144 (<1%) daily surveys of the 19,409 collected. Among those who reported symptoms and exposures, none tested positive during the 22-week study period. Of all participants, a total of 6 tested positive for SARS-CoV-2 at least once by RT-PCR; all were asymptomatic at time of testing. No in-school transmission occurred. Mask adherence was high among all grades, and incidents of improper mask use mostly occurred during noninstruction time. Physical distancing was well-enforced during class time and snack breaks, although adherence during noninstruction time waned as the school year progressed. CONCLUSIONS Our comprehensive, prospective study following COVID-19 transmission over 22 weeks in a K-8 school demonstrates that: 1) surveillance testing is important for detecting asymptomatic infections in schools; 2) monitoring symptoms may not be necessary and/or sufficient for COVID-19; and 3) younger children can adhere to key mitigation measures (eg, masking) which have the potential to limit transmission.
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Affiliation(s)
- Shilpa G Jani
- Center for Policy, Outcomes, and Prevention, and Division of General Pediatrics (SG Jani, J Ma, U Pulendran, JC Hsing, S Shah and CJ Wang), Stanford University School of Medicine, Stanford, Calif
| | - Jasmin Ma
- Center for Policy, Outcomes, and Prevention, and Division of General Pediatrics (SG Jani, J Ma, U Pulendran, JC Hsing, S Shah and CJ Wang), Stanford University School of Medicine, Stanford, Calif
| | - Uma Pulendran
- Center for Policy, Outcomes, and Prevention, and Division of General Pediatrics (SG Jani, J Ma, U Pulendran, JC Hsing, S Shah and CJ Wang), Stanford University School of Medicine, Stanford, Calif
| | - Julianna C Hsing
- Center for Policy, Outcomes, and Prevention, and Division of General Pediatrics (SG Jani, J Ma, U Pulendran, JC Hsing, S Shah and CJ Wang), Stanford University School of Medicine, Stanford, Calif; Department of Epidemiology and Population Health (JC Hsing), Stanford University School of Medicine, Stanford, Calif
| | - Jonathan Altamirano
- Division of Infectious Diseases, Department of Pediatrics (Y Maldonado), Stanford University School of Medicine, Stanford, Calif
| | - Soleil Shah
- Center for Policy, Outcomes, and Prevention, and Division of General Pediatrics (SG Jani, J Ma, U Pulendran, JC Hsing, S Shah and CJ Wang), Stanford University School of Medicine, Stanford, Calif
| | - Elizabeth Y Toomarian
- Graduate School of Education (EY Toomarian), Stanford University, Stanford, Calif; Synapse School (EY Toomarian), Menlo Park, Calif
| | - Yvonne Maldonado
- Division of Infectious Diseases, Department of Pediatrics (Y Maldonado), Stanford University School of Medicine, Stanford, Calif
| | - Chih-Hung Jason Wang
- Center for Policy, Outcomes, and Prevention, and Division of General Pediatrics (SG Jani, J Ma, U Pulendran, JC Hsing, S Shah and CJ Wang), Stanford University School of Medicine, Stanford, Calif; Center for Health Policy, Freeman-Spogli Institute for International Studies (CJ Wang), Stanford University, Stanford, Calif; Department of Health Policy (CJ Wang), Stanford University School of Medicine, Stanford, Calif.
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4
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Altamirano J, Lopez M, Robinson IG, Chun LX, Tam GKY, Shaikh NJ, Hoyte EG, Carrington YJ, Jani SG, Toomarian EY, Hsing JC, Ma J, Pulendran U, Govindarajan P, Blomkalns AL, Pinsky BA, Wang CJ, Maldonado Y. Feasibility of Specimen Self-collection in Young Children Undergoing SARS-CoV-2 Surveillance for In-Person Learning. JAMA Netw Open 2022; 5:e2148988. [PMID: 35175340 PMCID: PMC8855233 DOI: 10.1001/jamanetworkopen.2021.48988] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
IMPORTANCE There is an urgent need to assess the feasibility of COVID-19 surveillance measures in educational settings. OBJECTIVE To assess whether young children can feasibly self-collect SARS-CoV-2 samples for surveillance testing over the course of an academic year. DESIGN, SETTING, AND PARTICIPANTS This prospective pilot cohort study was conducted from September 10, 2020, to June 10, 2021, at a K-8 school in San Mateo County, California. The research consisted of quantitative data collection efforts: (1) demographic data collected, (2) student sample self-collection error rates, and (3) student sample self-collection time durations. Students were enrolled in a hybrid learning model, a teaching model in which students were taught in person and online, with students having the option to attend virtually as needed. Data were collected under waiver of consent from students participating in weekly SARS-CoV-2 testing. MAIN OUTCOMES AND MEASURES Errors over time for self-collection of nasal swabs such as contaminated swabs and inadequate or shallow swabbing; time taken for sample collection. RESULTS Of 296 participants, 148 (50.0%) were boys and 148 (50.0%) were girls. A total of 87 participants (29.2%) identified as Asian; 2 (0.6%), Black or African American; 13 (4.4%), Hispanic/Latinx; 103 (34.6%), non-Hispanic White; 87 (29.2%), multiracial; and 6 (2.0%), other. The median school grade was fourth grade. From September 2020 to March 2021, a total of 4203 samples were obtained from 221 students on a weekly basis, while data on error rates were collected. Errors occurred in 2.7% (n = 107; 95% CI, 2.2%-3.2%) of student encounters, with the highest rate occurring on the first day of testing (20 [10.2%]). There was an overall decrease in error rates over time. From April to June 2021, a total of 2021 samples were obtained from 296 students on a weekly basis while data on encounter lengths were collected. Between April and June 2021, 193 encounters were timed. The mean duration of each encounter was 70 seconds (95% CI, 66.4-73.7 seconds). CONCLUSIONS AND RELEVANCE Mastery of self-collected lower nasal swabs is possible for children 5 years and older. Testing duration can be condensed once students gain proficiency in testing procedures. Scalability for larger schools is possible if consideration is given to the resource-intensive nature of the testing and the setting's weather patterns.
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Affiliation(s)
- Jonathan Altamirano
- Division of Infectious Diseases, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California
| | - Marcela Lopez
- Division of Infectious Diseases, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - India G. Robinson
- Division of Infectious Diseases, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Leanne X. Chun
- Division of Infectious Diseases, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Grace K.-Y. Tam
- Division of Infectious Diseases, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Nuzhat J. Shaikh
- Division of Infectious Diseases, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Elisabeth G. Hoyte
- Division of Allergy, Immunology, and Rheumatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Yuan Jin Carrington
- Division of Infectious Diseases, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Shilpa G. Jani
- Center for Policy, Outcomes, and Prevention, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Elizabeth Y. Toomarian
- Graduate School of Education, Stanford University, Stanford, California
- Synapse School, Menlo Park, California
| | - Julianna C. Hsing
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California
- Center for Policy, Outcomes, and Prevention, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Jasmin Ma
- Center for Policy, Outcomes, and Prevention, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Uma Pulendran
- Center for Policy, Outcomes, and Prevention, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Prasanthi Govindarajan
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California
| | - Andra L. Blomkalns
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California
| | - Benjamin A. Pinsky
- Department of Pathology, Stanford University School of Medicine, Stanford, California
| | - C. Jason Wang
- Center for Policy, Outcomes, and Prevention, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
- Department of Health Policy, Stanford University School of Medicine, Stanford, California
| | - Yvonne Maldonado
- Division of Infectious Diseases, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California
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Yarney J, Ohene Oti NO, Calys-Tagoe BNL, Gyasi RK, Agyeman Duah I, Akoto-Aidoo C, McGuire V, Hsing JC, Parkin M, Tettey Y, Hsing AW. Establishing a Cancer Registry in a Resource-Constrained Region: Process Experience From Ghana. JCO Glob Oncol 2021; 6:610-616. [PMID: 32302237 PMCID: PMC7193799 DOI: 10.1200/jgo.19.00387] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
PURPOSE In a review of cancer incidence across continents (GLOBOCAN 2012), data sources from Ghana were classified as Frequencies, the lowest classification for inclusion, signifying the worst data quality for inclusion in the analysis. Recognizing this deficiency, the establishment of a population-based cancer registry was proposed as part of a broader cancer control plan. METHODS The registry was examined under the following headings: policy, data source, and administrative structure; external support and training; and definition of geographic coverage. RESULTS The registry was set up based on the Ghana policy document on the strategy for cancer control. The paradigm shift ensured subscription to one data collection software (CanReg 5) in the country. The current approach consists of trained registrars based in the registry who conduct active data abstraction at the departments and units of the hospital and pathologic services. To ensure good governance, an administrative structure was created, including an advisory board, a technical committee, and registry staff. External support for the establishment of the Accra Cancer Registry has come mainly from Stanford University and the African Cancer Registry Network, in collaboration with the University of Ghana. Unlike previous attempts, this registry has a well-defined population made up of nine municipal districts. CONCLUSION The Accra Cancer Registry was established as a result of the lessons learned from failed previous attempts and aim to provide a model for setting up other cancer registries in Ghana. It will eventually be the focal point where all the national data can be collated.
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Affiliation(s)
- Joel Yarney
- Accra Cancer Registry, Accra, Ghana.,National Centre for Radiotherapy and Nuclear Medicine, Accra, Ghana
| | - Naomi O Ohene Oti
- Accra Cancer Registry, Accra, Ghana.,National Centre for Radiotherapy and Nuclear Medicine, Accra, Ghana
| | - Benedict N L Calys-Tagoe
- Accra Cancer Registry, Accra, Ghana.,Department of Community Health, University of Ghana School of Public Health, Accra, Ghana
| | - Richard K Gyasi
- Accra Cancer Registry, Accra, Ghana.,Department of Pathology, University of Ghana, Accra, Ghana
| | - Isaac Agyeman Duah
- Accra Cancer Registry, Accra, Ghana.,National Centre for Radiotherapy and Nuclear Medicine, Accra, Ghana
| | - Charles Akoto-Aidoo
- Accra Cancer Registry, Accra, Ghana.,National Centre for Radiotherapy and Nuclear Medicine, Accra, Ghana
| | - Valerie McGuire
- Department of Health Policy and Research, Stanford University School of Medicine, Stanford, CA
| | - Julianna C Hsing
- Department of Health Policy and Research, Stanford University School of Medicine, Stanford, CA
| | - Max Parkin
- Nuffield Department of Population Health, Oxford University, Oxford, United Kingdom.,African Cancer Registry Network, Oxford, United Kingdom
| | - Yao Tettey
- Department of Pathology, University of Ghana, Accra, Ghana
| | - Ann W Hsing
- Department of Pediatrics, Center of Policy, Outcomes, and Prevention, Stanford University School of Medicine, Stanford, CA
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Jani SG, Nguyen AD, Abraham Z, Scala M, Blumenfeld YJ, Morton J, Nguyen M, Ma J, Hsing JC, Moiwa-Grant M, Profit J, Wang CJ. PretermConnect: Leveraging mobile technology to mitigate social disadvantage in the NICU and beyond. Semin Perinatol 2021; 45:151413. [PMID: 33888330 PMCID: PMC8923031 DOI: 10.1016/j.semperi.2021.151413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Preterm birth (PTB) - delivery prior to 37-weeks gestation - disproportionately affects low-income and minority populations and leads to substantial infant morbidity and mortality. The time following a PTB represents an optimal window for targeted interventions that encourage mothers to prioritize their own health and that of their babies. Healthcare teams can leverage digital strategies to address maternal and infant needs in this postpartum period, both in the neonatal intensive care unit and beyond. We therefore developed PretermConnect, a mobile app designed to educate, engage, and empower women at risk for PTB. This article describes the participant-centered design approach of PretermConnect, with preliminary findings from focus groups and co-design sessions in different community settings and suggested future directions for mobile technologies in population health. Apps such as PretermConnect can mitigate social disadvantage by serving as remote monitoring tools, providing social support, preventing recurrent PTB and lowering infant mortality rates.
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Affiliation(s)
- Shilpa G. Jani
- Center for Policy, Outcomes and Prevention, and Division of General Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Audrey D. Nguyen
- Center for Policy, Outcomes and Prevention, and Division of General Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA,David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Zara Abraham
- Center for Policy, Outcomes and Prevention, and Division of General Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Melissa Scala
- Division of Neonatal and Developmental Medicine, Stanford University School of Medicine/Lucile Packard Children’s Hospital, Stanford, CA, USA
| | - Yair J. Blumenfeld
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA
| | - Jane Morton
- Adjunct Clinical Professor of Pediatrics Emerita, Stanford Medical Center, Stanford, CA, USA
| | - Monique Nguyen
- Center for Policy, Outcomes and Prevention, and Division of General Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jasmin Ma
- Center for Policy, Outcomes and Prevention, and Division of General Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Julianna C. Hsing
- Center for Policy, Outcomes and Prevention, and Division of General Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Manafoh Moiwa-Grant
- Center for Policy, Outcomes and Prevention, and Division of General Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jochen Profit
- Perinatal Epidemiology and Health Outcomes Research Unit, Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA,California Perinatal Quality Care Collaborative, Palo Alto, CA, USA
| | - C. Jason Wang
- Center for Policy, Outcomes and Prevention, and Division of General Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA,Corresponding Author: C. Jason Wang, Mailing address: 117 Encina Commons, Stanford, CA
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Hsing JC, Ma J, Barrero-Castillero A, Jani SG, Pulendran UP, Lin BJ, Thomas-Uribe M, Wang CJ. Influence of Health Beliefs on Adherence to COVID-19 Preventative Practices: International, Social Media-Based Survey Study. J Med Internet Res 2021; 23:e23720. [PMID: 33571103 PMCID: PMC7919844 DOI: 10.2196/23720] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 12/16/2020] [Accepted: 02/01/2021] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Health behavior is influenced by culture and social context. However, there are limited data evaluating the scope of these influences on COVID-19 response. OBJECTIVE This study aimed to compare handwashing and social distancing practices in different countries and evaluate practice predictors using the health belief model (HBM). METHODS From April 11 to May 1, 2020, we conducted an online, cross-sectional survey disseminated internationally via social media. Participants were adults aged 18 years or older from four different countries: the United States, Mexico, Hong Kong (China), and Taiwan. Primary outcomes were self-reported handwashing and social distancing practices during COVID-19. Predictors included constructs of the HBM: perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, and cues to action. Associations of these constructs with behavioral outcomes were assessed by multivariable logistic regression. RESULTS We analyzed a total of 71,851 participants, with 3070 from the United States, 3946 from Mexico, 1201 from Hong Kong (China), and 63,634 from Taiwan. Of these countries, respondents from the United States adhered to the most social distancing practices (χ23=2169.7, P<.001), while respondents from Taiwan performed the most handwashing (χ23=309.8, P<.001). Multivariable logistic regression analyses indicated that self-efficacy was a positive predictor for handwashing (odds ratio [OR]United States 1.58, 95% CI 1.21-2.07; ORMexico 1.5, 95% CI 1.21-1.96; ORHong Kong 2.48, 95% CI 1.80-3.44; ORTaiwan 2.30, 95% CI 2.21-2.39) and social distancing practices (ORUnited States 1.77, 95% CI 1.24-2.49; ORMexico 1.77, 95% CI 1.40-2.25; ORHong Kong 3.25, 95% CI 2.32-4.62; ORTaiwan 2.58, 95% CI 2.47-2.68) in all countries. Handwashing was positively associated with perceived susceptibility in Mexico, Hong Kong, and Taiwan, while social distancing was positively associated with perceived severity in the United States, Mexico, and Taiwan. CONCLUSIONS Social media recruitment strategies can be used to reach a large audience during a pandemic. Self-efficacy was the strongest predictor for handwashing and social distancing. Policies that address relevant health beliefs can facilitate adoption of necessary actions for preventing COVID-19. Our findings may be explained by the timing of government policies, the number of cases reported in each country, individual beliefs, and cultural context.
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Affiliation(s)
- Julianna C Hsing
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, United States
- Center for Policy, Outcomes, and Prevention, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States
| | - Jasmin Ma
- Center for Policy, Outcomes, and Prevention, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States
| | - Alejandra Barrero-Castillero
- Division of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Shilpa G Jani
- Center for Policy, Outcomes, and Prevention, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States
| | - Uma Palam Pulendran
- Center for Policy, Outcomes, and Prevention, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States
| | - Bea-Jane Lin
- Center for Policy, Outcomes, and Prevention, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States
| | - Monika Thomas-Uribe
- Department of Pediatrics, University of California San Francisco - Fresno, Fresno, CA, United States
| | - C Jason Wang
- Center for Policy, Outcomes, and Prevention, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States
- Center for Health Policy, Freeman-Spogli Institute for International Studies, Stanford University, Stanford, CA, United States
- Center for Primary Care Outcomes Research, Stanford University School of Medicine, Stanford, CA, United States
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Abstract
Telehealth has improved delivery of health care worldwide by improving access to and the quality of health care and by improving the global shortage of health professionals through collaboration and training. Although many telehealth efforts have been reported in adult health care settings, it is important to examine telehealth efforts in the pediatric setting. Children who are most commonly ill and malnourished are often those of underserved populations of the developing world. This article examines current uses of pediatric telehealth in a global setting and discusses key approaches to how telehealth may become successfully integrated and scaled in those settings.
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Affiliation(s)
- Julianna C Hsing
- Department of Epidemiology and Population Health, Stanford University School of Medicine, 150 Governor's Lane, Stanford, CA 94305, USA; Center for Policy, Outcomes and Prevention, Stanford University School of Medicine, 117 Encina Commons, Stanford, CA 94305, USA.
| | - C Jason Wang
- Center for Policy, Outcomes and Prevention, Stanford University School of Medicine, 117 Encina Commons, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Stanford, CA 94305, USA
| | - Paul H Wise
- Center for Policy, Outcomes and Prevention, Stanford University School of Medicine, 117 Encina Commons, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Stanford, CA 94305, USA
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Hsing JC, Nguyen MH, Yang B, Min Y, Han SS, Pung E, Winter SJ, Zhao X, Gan D, Hsing AW, Zhu S, Wang CJ. Associations Between Body Fat, Muscle Mass, and Nonalcoholic Fatty Liver Disease: A Population-Based Study. Hepatol Commun 2019; 3:1061-1072. [PMID: 31388627 PMCID: PMC6671685 DOI: 10.1002/hep4.1392] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 05/28/2019] [Indexed: 12/16/2022] Open
Abstract
Nonalcoholic fatty liver disease (NAFLD) is one of the most common forms of liver disease worldwide and has emerged as a significant public health concern in China. A better understanding of the etiology of NAFLD can inform effective management strategies for this disease. We examined factors associated with NAFLD in two districts of Hangzhou, China, focusing on the relationship of regional body fat distribution, muscle mass, and NAFLD. We used baseline data to carry out a cross‐sectional analysis among 3,589 participants from the Wellness Living Laboratory (WELL) China study, a longitudinal population‐based study that aims to investigate and promote well‐being among the Chinese population. NAFLD was defined using the widely validated fatty liver index (FLI). Multivariate logistic regressions were performed to assess independent associations between NAFLD and metabolic risk factors (e.g., insulin resistance) and dual x‐ray absorptiometry (DXA)‐derived measures (e.g., android fat ratio [AFR] and skeletal muscle index [SMI]). Of the 3,589 participants, 476 (13.3%) were classified as having FLI‐defined NAFLD (FLI ≥60). Among those, 58.0% were men. According to our analysis, AFR (odds ratio [OR], 10.0; 95% confidence interval [CI], 5.8‐18.5), insulin resistance (OR, 4.0; 95% CI, 3.0‐5.3), high alanine aminotransferase levels (OR, 7.6; 95% CI, 5.8‐10.0), smoking (OR, 2.0; 95% CI, 1.4‐3.0), and male sex (OR, 2.9; 95% CI, 2.0‐4.2) were positively associated with NAFLD risk, while SMI (OR, 0.1; 95% CI, 0.07‐0.13) was inversely associated with NAFLD risk. Conclusion: In addition to known metabolic risk factors, DXA‐derived AFR and SMI may provide additional insights to the understanding of NAFLD. Interventions that aim to decrease AFR and increase SMI may be important to reduce the burden of NAFLD in this population.
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Affiliation(s)
- Julianna C Hsing
- Center for Policy, Outcomes and Prevention Stanford University School of Medicine Stanford CA.,Division of General Pediatrics Stanford University School of Medicine Stanford CA
| | - Mindie H Nguyen
- Division of Gastroenterology and Hepatology, Department of Medicine Stanford University School of Medicine Stanford CA.,Stanford Cancer Institute Stanford University School of Medicine Stanford CA
| | - Baiyu Yang
- Stanford Cancer Institute Stanford University School of Medicine Stanford CA
| | - Yan Min
- Stanford Prevention Research Center Stanford University School of Medicine Stanford CA
| | - Summer S Han
- Stanford Cancer Institute Stanford University School of Medicine Stanford CA.,Quantitative Sciences Unit, Department of Medicine Stanford University School of Medicine Stanford CA.,Department of Neurosurgery Stanford University School of Medicine Stanford CA
| | - Emily Pung
- Department of Public Health University of Maryland College Park MD
| | - Sandra J Winter
- Stanford Prevention Research Center Stanford University School of Medicine Stanford CA
| | - Xueyin Zhao
- Chronic Disease Research Institute, Department of Nutrition and Food Hygiene, School of Public Health Zhejiang University Hangzhou China.,Women's Hospital, School of Medicine Zhejiang University Hangzhou China
| | - Da Gan
- Chronic Disease Research Institute, Department of Nutrition and Food Hygiene, School of Public Health Zhejiang University Hangzhou China.,Women's Hospital, School of Medicine Zhejiang University Hangzhou China
| | - Ann W Hsing
- Stanford Cancer Institute Stanford University School of Medicine Stanford CA.,Stanford Prevention Research Center Stanford University School of Medicine Stanford CA
| | - Shankuan Zhu
- Chronic Disease Research Institute, Department of Nutrition and Food Hygiene, School of Public Health Zhejiang University Hangzhou China.,Women's Hospital, School of Medicine Zhejiang University Hangzhou China
| | - C Jason Wang
- Center for Policy, Outcomes and Prevention Stanford University School of Medicine Stanford CA.,Division of General Pediatrics Stanford University School of Medicine Stanford CA
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