Counties with Lower Insurance Coverage and Housing Problems Are Associated with Both Slower Vaccine Rollout and Higher COVID-19 Incidence

Donadio, Gregory; Choudhary, Mayank; Lindemer, Emily; Pawlowski, Colin; Soundararajan, Venky

doi:10.3390/vaccines9090973

Cited by 21 publications

(21 citation statements)

References 19 publications

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“…Demographic and social characteristics factors at the micro level were among the most frequent components affecting the distribution of Covid-19 vaccine. For example, according to some studies in the United States and the United Kingdom [ 39 , 49 ], the rate of vaccine injection is higher in women than men. Also, older people had greater access to vaccination [ 34 , 36 , 39 , 40 , 42 , 44 , 48 , 50 , 51 ].…”

Section: Discussionmentioning

confidence: 99%

“…For example, according to some studies in the United States and the United Kingdom [ 39 , 49 ], the rate of vaccine injection is higher in women than men. Also, older people had greater access to vaccination [ 34 , 36 , 39 , 40 , 42 , 44 , 48 , 50 , 51 ]. A study by Cardona et al in Maryland found that vaccination rates were lower in blacks [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

“…A study by Cardona et al in Maryland found that vaccination rates were lower in blacks [ 32 ]. Villagers have received fewer vaccinations than urban dwellers [ 39 , 46 , 49 ]. Vaccination rates were higher in people covered by insurance [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

“…Villagers have received fewer vaccinations than urban dwellers [ 39 , 46 , 49 ]. Vaccination rates were higher in people covered by insurance [ 39 ]. This indicates that in some countries, financial access has affected vaccine eligibility.…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Inequality in the distribution of Covid-19 vaccine: a systematic review

Bayati

Noroozi

Ghanbari-Jahromi

et al. 2022

Int J Equity Health

105

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Background The equality in the distribution of vaccines between and within countries along with follow sanitation tips and observe social distance, are effective strategies to rid the world of COVID-19 pandemic. Inequality in the distribution of COVID-19 vaccine, in addition to causing inequity to the population health, has a significant impact on the process of economic recovery. Methods All published original papers on the inequality of Covid-19 vaccine distribution and the factors affecting it were searched in PubMed, Web of Science, Scopus and ProQuest databases between December 2020 to 30 May 2022. Selection of articles, extraction of their data and qualitative assessment (by STROBE) were performed by two researchers separately. Data graphing form was used to extract detailed data from each study and then, the collected data were classified. Results A total of 4623 articles were evaluated. After removing duplicates and screening the title, abstract and full text of articles, 22 articles were selected and entered into the study. Fifteen (68.17%) studies were conducted in the United States, three (13.64%) in Europe, three (13.64%) in Asia and one (6.66%) in Oceania. Factors affecting the inequality in the distribution of COVID-19 vaccine were classified into macro and micro levels determinants. Conclusion Macro determinants of inequality in the Covid-19 vaccine distribution were consisted of economic (stability and country’s economic status, Gross Domestic Product (GDP) per capita, financial support and human development index), infrastructure and health system (appropriate information system, functional cold chains in vaccine transport, transport infrastructure, medical and non-medical facilities per capita, healthcare access and quality), legal and politics (vaccination allocation rules, health policies, political ideology and racial bias), and epidemiologic and demographic factors (Covid-19 incidence and deaths rate, life expectancy, vulnerability to Covid-19, working in medical setting, comorbidities, social vulnerability, incarceration and education index). Moreover, micro/ individual level factors were included in economic (household’s income, home ownership, employment, poverty, access to healthy food and residency in the deprived areas) and demographic and social characteristics (sex, age, race, ethnic, religion, disability, location (urban/rural) and insurance coverage).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Inequality in the distribution of Covid-19 vaccine: a systematic review

Bayati

Noroozi

Ghanbari-Jahromi

et al. 2022

Int J Equity Health

105

View full text Add to dashboard Cite

show abstract

“…Moreover, ref. [22] analyzes the influence of poverty and other socio-economic factors on the vaccination rate, with the study's results showing that negative socio-economic indicators, such as a lack of insurance, firearm fatalities, and housing problems, have a significantly negative influence on the vaccination rate.…”

Section: Discussionmentioning

confidence: 99%

The Evolution of AI-Driven Educational Systems during the COVID-19 Pandemic

et al. 2021

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COVID-19 had a major impact on education, substantially stimulating the need for digital remote education. From paperback books to PDFs, from physical classes to e-conferencing, and from various traditional mechanisms of information transmission to systems that are driven by artificial intelligence and use adaptive learning approaches, all of these had to be adopted by both teachers and students. This paper analyzes the use of an adaptive learning system before and during the COVID-19 pandemic from a statistical point of view on a set of data gathered in Romania during a pilot project. The main data inputs are the number of students that enrolled for a certain course, the percentage of students that successfully completed it, and information about their age group, location and main area of interest. Our study finds that the use of artificial intelligence has increased during the COVID-19 pandemic and, by analyzing the data obtained during the study, we managed to prove that artificial-intelligence-driven tools and systems have gained traction among all the parties involved in the educational process.

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Associations Between General Vaccine Hesitancy and Healthcare Access Among Arkansans

et al. 2022

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Background Vaccines provide protection against numerous diseases that can cause serious illness and death. However, vaccine hesitancy threatens to undermine progress in reducing preventable diseases and illness. Vaccine hesitancy has been shown to vary by sociodemographic characteristics. However, studies examining associations between healthcare access and vaccine hesitancy are lacking. Objective Using a statewide random sample of Arkansas adults, we examined the relationship between general vaccine hesitancy and healthcare access. Design From July 12 to 30, 2021, participants were contacted by landlines and cellular phones using random digit dialing. Participants A total of 1500 Arkansas adults were surveyed. Black/African American and Hispanic/Latinx adults were oversampled to ensure adequate representation. The survey had a cooperation rate of 20%. Main Measures The dependent variable was an ordinal measure of general vaccine hesitancy. Age, gender, race, education, relationship status, and rural/urban residence were included in the model. Healthcare access was measured across four domains: (1) health insurance coverage; (2) having a primary care provider (PCP); (3) forgoing care due to cost; and (4) time since last routine checkup. The relationship between general vaccine hesitancy and healthcare access was modeled using ordinal logistic regression, controlling for sociodemographic characteristics. Key Results Mean age was 48.5 years, 51.1% were women, 28% reported a race other than White, and 36.3% held a bachelor’s degree or higher. Those with a PCP and those with health insurance had approximately two-thirds the odds of being more hesitant ([OR=0.63, CI=0.47, 0.84] and [OR=0.68; CI=0.49, 0.94]) than those without a PCP and those without health insurance. Participants reporting a routine checkup in the last 2 years were almost half as likely to be more hesitant than those reporting a checkup more than 2 years prior (OR=0.58; CI=0.43, 0.79). Conclusions Results suggest improving access to health insurance, PCPs, and routine preventative care services may be critical to reducing vaccine hesitancy.

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Counties with Lower Insurance Coverage and Housing Problems Are Associated with Both Slower Vaccine Rollout and Higher COVID-19 Incidence

Cited by 21 publications

References 19 publications

Inequality in the distribution of Covid-19 vaccine: a systematic review

Inequality in the distribution of Covid-19 vaccine: a systematic review

The Evolution of AI-Driven Educational Systems during the COVID-19 Pandemic

Associations Between General Vaccine Hesitancy and Healthcare Access Among Arkansans

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