COVID-19: Evidenced Health Disparity
Definition
:1. Introduction
2. Defining Health Disparity
Organization, Country | Definition |
---|---|
National Institute of Minority Health Disparities [14] | A health difference, determined on the basis of one or more health outcomes that adversely affect disadvantaged populations. |
Healthy People 2020 [16] | A particular type of health difference closely linked with social, economic, and environmental disadvantages. |
Centers for Disease Control and Prevention [17] | Preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health experienced by socially disadvantaged racial, ethnic, and other population groups and communities. |
Institute of Medicine [18] | A health service disparity between population groups is determined as differences in treatment or access not justified by differences in the health status or preferences of the groups. |
National Health Service [15] | Unfair and avoidable differences in health across the population and between different groups within society. |
3. Intersectionality Framework and Health Disparity
3.1. COVID-19 Disparity
3.2. COVID-19 and Environmental Injustice
3.3. COVID-19 and Incidence of Violence
4. Determinants of Health Disparities
4.1. Social and Structural Determinants of Health
4.2. Structural Racism
4.3. Age and Gender Disparity
4.4. Case Study of COVID-19
5. Implication for Achieving Global and Sustainable Health
6. Measuring Health Disparity
7. The Role of Geospatial and Machine Learning Techniques in Health Disparity
7.1. GIS and Health Data Linkage
7.2. Disease Epidemiology, Machine Learning, and Artificial Intelligence
8. Conclusions and Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Entry Link on the Encyclopedia Platform
References
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Study | Location | Health Outcomes | Intersectionality Framework | Common Variables of Interest | Data Analytical Techniques | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Violence | Health Care | Public Policies | Mobility | Racial/Ethnic Heterogeneity | Sociodemographic | Environmental Injustice | Health Disparity | Geographic/Temporal Disparity | GIS/Spatial Statistics | AI, Machine, and Deep Learning | Aspatial | ||||
Chaudhuri et al., 2021 | UK | Age-adjusted COVID-19 morality | x | x | x | x | SAR | OLS | |||||||
Iyanda et al., 2020 | Global | COVID-19 outbreak | x | x | x | x | x | MGWR | OLS | ||||||
Iyanda et al., 2021 | USA | COVID-19 case fatal ratio | x | x | x | x | GWR | Poisson | |||||||
Louis-Jean et al., 2020 | USA | COVID-19 | x | x | |||||||||||
Allen Et al., 2020 | USA | COVID-19 confirmed cases, deaths | x | x | x | x | x | Thematic mapping | OLS | ||||||
Abedi et al., 2020 | USA | COVID-19 infection rate | x | x | x | x | x | Map overlay | OLS, Pearson correlation, Forest Plot | ||||||
Chen et al., 2020 | China | COVID-19 Mortality rate | x | Remote sensing | Difference-in-Difference | ||||||||||
Adams, 2020 | Canada | COVID-19 | x | x | x | x | Polynomial regression | ||||||||
Lippi et al., 2020 | Italy | COVID-19 infection | x | x | Pearson’s correlation | ||||||||||
Berman & Ebisu, 2020 | USA | COVID-19 infection | x | x | x | t-test | |||||||||
Travaglio et al., 2021 | UK/England | COVID-19 mortality | x | x | x | Heatmap | GLM, BLR | ||||||||
Arimiyaw et al., 2020 | SSA region | COVID-19 | X | x | |||||||||||
Fattorini & Regoli, 2020 | Italy | COVID-19 | x | x | x | Thematic mapping | Pearson’s correlation | ||||||||
Bashir et al., 2020 | Germany | COVID-19 confirmed cases, deaths, recoveries | x | x | wavelet transform coherence; correlation | ||||||||||
Bashir et al., 2020 | California, USA | COVID-19 confirmed cases, deaths | x | x | x | Thematic mapping | Spearman’s/Kendall correlation | ||||||||
Chakraborty, 2021 | USA | COVID-19 incidence rate | x | x | x | x | x | LISA | OLS, GEE | ||||||
Terrel & James, 2020 | Louisiana, USA | COVID-19 deaths | x | x | x | x | x | Spearman correlation; Shapiro-Wilks’s test | |||||||
Martinez Dy & Jayawarna, 2020 | UK | COVID-19 impacts | x | x | |||||||||||
Krzysztofowicz & Osińska-Skotak, 2021 | Poland | COVID-19 Vaccination | Thiessen Polygon | ||||||||||||
Bachtiger et al., 2020 | Generalized | COVID-19 | x | x | |||||||||||
Punn et al., 2020 | Global | COVID-19 confirmed cases, death, recovery | x | ||||||||||||
Cavaljal et al., 2018 | Philippines | Dengue | x | ||||||||||||
Alimadadi et al., 2020 | Generalized | COVID-19 | x | ||||||||||||
Kushwaha et al., 2020 | COVID-19 | x | x | ||||||||||||
Pinter et al., 2020 | Hungary | COVID-19 | x | ||||||||||||
Li et al., 2021 | Multi-country | x | x | x | x | ||||||||||
Kuo & Fu, 2021 | USA | COVID-19 infection | x | ||||||||||||
Mollalo et al., 2018 | Iran | Sandfly; Cutaneous leishmaniasis | x | x | Pearson’s correlation | ||||||||||
Mele & Magazzino, 2020 | India | COVID-19 | x | ||||||||||||
Biana, 2020 | Philippines | COVID-19 | x | x | x | x | x | ||||||||
Sonu et al., 2021 | USA | COVID-19 | x | x | x | ||||||||||
Wilson et al., 2020 | USA | COVID-19 | x | x | x | x | |||||||||
Reicher & Stott, 2020 | UK, USA, France | COVID-19 | x | x | |||||||||||
Corpuz 2021 | Philippines | COVID-19 | x | x | x | ||||||||||
Njoku et al., 2021 | USA | COVID-19 | x | x | x | x | |||||||||
Joseph-Salisbury et al., 2021 | UK | COVID-19 | x | x | x | ||||||||||
Gibson et al., 2021 | USA | COVID-19 | x | x | x | ||||||||||
Coyne & Yatsyshina, 2020 | Generalized | COVID-19 | x | x | |||||||||||
Bailey et al., 2020 | Generalized | COVID-19 | x | x | x | x | |||||||||
Elias et al., 2021 | Generalized |
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Iyanda, A.; Boakye, K.; Lu, Y. COVID-19: Evidenced Health Disparity. Encyclopedia 2021, 1, 744-763. https://doi.org/10.3390/encyclopedia1030057
Iyanda A, Boakye K, Lu Y. COVID-19: Evidenced Health Disparity. Encyclopedia. 2021; 1(3):744-763. https://doi.org/10.3390/encyclopedia1030057
Chicago/Turabian StyleIyanda, Ayodeji, Kwadwo Boakye, and Yongmei Lu. 2021. "COVID-19: Evidenced Health Disparity" Encyclopedia 1, no. 3: 744-763. https://doi.org/10.3390/encyclopedia1030057