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Biswas A, Tucker J, Bauhoff S. Performance of predictive algorithms in estimating the risk of being a zero-dose child in India, Mali and Nigeria. BMJ Glob Health 2023; 8:e012836. [PMID: 37821114 PMCID: PMC10583101 DOI: 10.1136/bmjgh-2023-012836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 08/29/2023] [Indexed: 10/13/2023] Open
Abstract
INTRODUCTION Many children in low-income and middle-income countries fail to receive any routine vaccinations. There is little evidence on how to effectively and efficiently identify and target such 'zero-dose' (ZD) children. METHODS We examined how well predictive algorithms can characterise a child's risk of being ZD based on predictor variables that are available in routine administrative data. We applied supervised learning algorithms with three increasingly rich sets of predictors and multiple years of data from India, Mali and Nigeria. We assessed performance based on specificity, sensitivity and the F1 Score and investigated feature importance. We also examined how performance decays when the model is trained on older data. For data from India in 2015, we further compared the inclusion and exclusion errors of the algorithmic approach with a simple geographical targeting approach based on district full-immunisation coverage. RESULTS Cost-sensitive Ridge classification correctly classifies most ZD children as being at high risk in most country-years (high specificity). Performance did not meaningfully increase when predictors were added beyond an initial sparse set of seven variables. Region and measures of contact with the health system (antenatal care and birth in a facility) had the highest feature importance. Model performance decreased in the time between the data on which the model was trained and the data to which it was applied (test data). The exclusion error of the algorithmic approach was about 9.1% lower than the exclusion error of the geographical approach. Furthermore, the algorithmic approach was able to detect ZD children across 176 more areas as compared with the geographical rule, for the same number of children targeted. INTERPRETATION Predictive algorithms applied to existing data can effectively identify ZD children and could be deployed at low cost to target interventions to reduce ZD prevalence and inequities in vaccination coverage.
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Affiliation(s)
- Arpita Biswas
- Center for Research on Computation and Society, Harvard University John A Paulson School of Engineering and Applied Sciences, Cambridge, Massachusetts, USA
| | - John Tucker
- Computer Science Department, Harvard College, Cambridge, Massachusetts, USA
| | - Sebastian Bauhoff
- Global Health and Population, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
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Detecting COVID-19 vaccine hesitancy in India: a multimodal transformer based approach. J Intell Inf Syst 2023; 60:157-173. [PMID: 36091222 PMCID: PMC9449921 DOI: 10.1007/s10844-022-00745-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/30/2022] [Accepted: 08/31/2022] [Indexed: 10/30/2022]
Abstract
COVID-19 has emerged as the greatest threat in recent times, causing extensive mortality and morbidity in the entire world. India is among the highly affected countries suffering severe disruptions due this pandemic. To overcome the adverse effects of COVID-19, vaccination has been identified as the most effective preventive measure globally. However, a growing amount of hesitancy has been observed among the general public regarding the efficacy and possible side-effects of vaccination. Such hesitancy may proved to be the greatest hindrance towards combating this deadly pandemic. This paper introduces a multimodal deep learning method for Indian Twitter user classification, leveraging both content-based and network-based features. To explore the fundamental features of different modalities, improvisations of transformer models, BERT and GraphBERT are utilized to encode the textual and network structure information. The proposed approach thus integrates multiple data representations, utilizing the advances in both transformer based deep learning as well as multimodal learning. Experimental results demonstrates the efficacy of proposed approach over state of the art approaches. Aggregated feature representations from multiple modalities embed additional information that improves the classification results. The findings of the proposed model has been further utilized to perform a study on the dynamics of COVID-19 vaccine hesitancy in India.
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Deiana C, Geraci A, Mazzarella G, Sabatini F. Perceived risk and vaccine hesitancy: Quasi-experimental evidence from Italy. HEALTH ECONOMICS 2022; 31:1266-1275. [PMID: 35318762 PMCID: PMC9314121 DOI: 10.1002/hec.4506] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 03/09/2022] [Accepted: 03/11/2022] [Indexed: 06/09/2023]
Abstract
In March 2021, Italian health authorities suspended the Vaxzevria vaccine (VA) for 4 days over reports of very rare blood disorders among recipients. We exploit the quasi-experimental setting arising from this break to study the drivers of vaccine hesitancy. Before the suspension, the VA vaccination trend followed the same pattern as Pfizer-Biontech (PB). After the suspension, VA and PB injections started to diverge, with VA daily decreasing by almost 60 doses per 100,000 inhabitants for the following 3 weeks. The resulting vaccination rate was 60 percent lower than the value that would have stemmed from the VA pre-suspension pattern. We show that the slowdown was weaker and less persistent in regions with higher COVID penetration and steadier and more pronounced in regions displaying greater attention to vaccine side effects as detected through Google searches. The public's interest in vaccine adverse events negatively correlates with COVID cases and deaths across regions.
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Afzal A, Shariff MA, Perez-Gutierrez V, Khalid A, Pili C, Pillai A, Venugopal U, Kasubhai M, Kanna B, Poole BD, Pickett BE, Redd DS, Menon V. Impact of Local and Demographic Factors on Early COVID-19 Vaccine Hesitancy among Health Care Workers in New York City Public Hospitals. Vaccines (Basel) 2022; 10:vaccines10020273. [PMID: 35214729 PMCID: PMC8879070 DOI: 10.3390/vaccines10020273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/04/2022] [Accepted: 02/07/2022] [Indexed: 12/04/2022] Open
Abstract
Despite the development of several effective vaccines, SARS-CoV-2 continues to spread, causing serious illness among the unvaccinated. Healthcare professionals are trusted sources of information about vaccination, and therefore understanding the attitudes and beliefs of healthcare professionals regarding the vaccines is of utmost importance. We conducted a survey-based study to understand the factors affecting COVID-19 vaccine attitudes among health care professionals in NYC Health and Hospitals, at a time when the vaccine was new, and received 3759 responses. Machine learning and chi-square analyses were applied to determine the factors most predictive of vaccine hesitancy. Demographic factors, education, role at the hospital, perceptions of the pandemic itself, and location of work and residence were all found to significantly contribute to vaccine attitudes. Location of residence was examined for both borough and neighborhood, and was found to have a significant impact on vaccine receptivity. Interestingly, this borough-level data did not correspond to the number or severity of cases in the respective boroughs, indicating that local social or other influences likely have a substantial impact. Local and demographic factors should be strongly considered when preparing pro-vaccine messages or campaigns.
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Affiliation(s)
- Afsheen Afzal
- Department of Medicine, NYC Health and Hospitals/Lincoln, Bronx, NY 10451, USA; (A.A.); (M.A.S.); (V.P.-G.); (A.K.); (A.P.); (U.V.); (M.K.); (B.K.)
| | - Masood A. Shariff
- Department of Medicine, NYC Health and Hospitals/Lincoln, Bronx, NY 10451, USA; (A.A.); (M.A.S.); (V.P.-G.); (A.K.); (A.P.); (U.V.); (M.K.); (B.K.)
| | - Victor Perez-Gutierrez
- Department of Medicine, NYC Health and Hospitals/Lincoln, Bronx, NY 10451, USA; (A.A.); (M.A.S.); (V.P.-G.); (A.K.); (A.P.); (U.V.); (M.K.); (B.K.)
| | - Amnah Khalid
- Department of Medicine, NYC Health and Hospitals/Lincoln, Bronx, NY 10451, USA; (A.A.); (M.A.S.); (V.P.-G.); (A.K.); (A.P.); (U.V.); (M.K.); (B.K.)
| | - Christina Pili
- Research Administration, NYC Health and Hospitals/Central Office, New York, NY 10013, USA;
| | - Anjana Pillai
- Department of Medicine, NYC Health and Hospitals/Lincoln, Bronx, NY 10451, USA; (A.A.); (M.A.S.); (V.P.-G.); (A.K.); (A.P.); (U.V.); (M.K.); (B.K.)
| | - Usha Venugopal
- Department of Medicine, NYC Health and Hospitals/Lincoln, Bronx, NY 10451, USA; (A.A.); (M.A.S.); (V.P.-G.); (A.K.); (A.P.); (U.V.); (M.K.); (B.K.)
| | - Moiz Kasubhai
- Department of Medicine, NYC Health and Hospitals/Lincoln, Bronx, NY 10451, USA; (A.A.); (M.A.S.); (V.P.-G.); (A.K.); (A.P.); (U.V.); (M.K.); (B.K.)
| | - Balavenkatesh Kanna
- Department of Medicine, NYC Health and Hospitals/Lincoln, Bronx, NY 10451, USA; (A.A.); (M.A.S.); (V.P.-G.); (A.K.); (A.P.); (U.V.); (M.K.); (B.K.)
| | - Brian D. Poole
- Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA; (B.E.P.); (D.S.R.)
- Correspondence: (B.D.P.); (V.M.); Tel.: +1-801-422-8092 (B.D.P.)
| | - Brett E. Pickett
- Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA; (B.E.P.); (D.S.R.)
| | - David S. Redd
- Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA; (B.E.P.); (D.S.R.)
| | - Vidya Menon
- Department of Medicine, NYC Health and Hospitals/Lincoln, Bronx, NY 10451, USA; (A.A.); (M.A.S.); (V.P.-G.); (A.K.); (A.P.); (U.V.); (M.K.); (B.K.)
- Correspondence: (B.D.P.); (V.M.); Tel.: +1-801-422-8092 (B.D.P.)
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Riad A, Huang Y, Abdulqader H, Morgado M, Domnori S, Koščík M, Mendes JJ, Klugar M, Kateeb E. Universal Predictors of Dental Students' Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach. Vaccines (Basel) 2021; 9:1158. [PMID: 34696266 PMCID: PMC8539257 DOI: 10.3390/vaccines9101158] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/01/2021] [Accepted: 10/08/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND young adults represent a critical target for mass-vaccination strategies of COVID-19 that aim to achieve herd immunity. Healthcare students, including dental students, are perceived as the upper echelon of health literacy; therefore, their health-related beliefs, attitudes and behaviors influence their peers and communities. The main aim of this study was to synthesize a data-driven model for the predictors of COVID-19 vaccine willingness among dental students. METHODS a secondary analysis of data extracted from a recently conducted multi-center and multi-national cross-sectional study of dental students' attitudes towards COVID-19 vaccination in 22 countries was carried out utilizing decision tree and regression analyses. Based on previous literature, a proposed conceptual model was developed and tested through a machine learning approach to elicit factors related to dental students' willingness to get the COVID-19 vaccine. RESULTS machine learning analysis suggested five important predictors of COVID-19 vaccination willingness among dental students globally, i.e., the economic level of the country where the student lives and studies, the individual's trust of the pharmaceutical industry, the individual's misconception of natural immunity, the individual's belief of vaccines risk-benefit-ratio, and the individual's attitudes toward novel vaccines. CONCLUSIONS according to the socio-ecological theory, the country's economic level was the only contextual predictor, while the rest were individual predictors. Future research is recommended to be designed in a longitudinal fashion to facilitate evaluating the proposed model. The interventions of controlling vaccine hesitancy among the youth population may benefit from improving their views of the risk-benefit ratio of COVID-19 vaccines. Moreover, healthcare students, including dental students, will likely benefit from increasing their awareness of immunization and infectious diseases through curricular amendments.
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Affiliation(s)
- Abanoub Riad
- Department of Public Health, Faculty of Medicine, Masaryk University, 625 00 Brno, Czech Republic; (M.K.); (M.K.)
- International Association of Dental Students (IADS), 1216 Geneva, Switzerland; (H.A.); (M.M.); (S.D.)
- Czech National Centre for Evidence-Based Healthcare and Knowledge Translation (Cochrane Czech Republic, Czech EBHC: JBI Centre of Excellence, Masaryk University GRADE Centre), Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic
| | - Yi Huang
- Department of Psychology, Faculty of Social Studies, Masaryk University, 602 00 Brno, Czech Republic;
- Institute for Research of Children, Youth and Family, Faculty of Social Studies, Masaryk University, 602 00 Brno, Czech Republic
| | - Huthaifa Abdulqader
- International Association of Dental Students (IADS), 1216 Geneva, Switzerland; (H.A.); (M.M.); (S.D.)
| | - Mariana Morgado
- International Association of Dental Students (IADS), 1216 Geneva, Switzerland; (H.A.); (M.M.); (S.D.)
- Clinical Research Unit (CRU), Egas Moniz Cooperativa de Ensino Superior, 2829-511 Almada, Portugal;
| | - Silvi Domnori
- International Association of Dental Students (IADS), 1216 Geneva, Switzerland; (H.A.); (M.M.); (S.D.)
| | - Michal Koščík
- Department of Public Health, Faculty of Medicine, Masaryk University, 625 00 Brno, Czech Republic; (M.K.); (M.K.)
| | - José João Mendes
- Clinical Research Unit (CRU), Egas Moniz Cooperativa de Ensino Superior, 2829-511 Almada, Portugal;
| | - Miloslav Klugar
- Department of Public Health, Faculty of Medicine, Masaryk University, 625 00 Brno, Czech Republic; (M.K.); (M.K.)
- Czech National Centre for Evidence-Based Healthcare and Knowledge Translation (Cochrane Czech Republic, Czech EBHC: JBI Centre of Excellence, Masaryk University GRADE Centre), Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic
| | - Elham Kateeb
- Oral Health Research and Promotion Unit, Faculty of Dentistry, Al-Quds University, Jerusalem 510 00, Palestine;
- Public Health Committee, World Dental Federation (FDI), 1216 Geneva, Switzerland
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