Spatial Modeling of COVID-19 Vaccine Hesitancy in the United States

Predictors of COVID-19 vaccine uptake among youth with bipolar disorder spectrum disorders and their caregivers

BACKGROUND

Little is known about rates of COVID-19 vaccine uptake among youth with bipolar spectrum disorders (BSD). As such, the aim of this study is to assess rates and predictors of COVID-19 vaccine uptake among youth with BSD and their caregivers in the United States.

METHODS

Youth and their main caregiver were recruited from a large pragmatic study cohort. Youth who were aged 8-22 at the time of this data collection, had a bipolar-spectrum disorder diagnosis, had overweight or obesity, and were treated with a second-generation antipsychotic were invited to participate in an online survey and interview assessing the impact of the COVID-19 pandemic.

RESULTS

A total of 453 surveys and 341 interviews were completed 07/2021-05/2022 by youth and their caregivers. Sixty-seven percent of caregivers and 63 % of youth reported receiving the COVID-19 vaccine. Vaccine uptake rates among youth and caregivers were highly correlated. Predictors of vaccine uptake among youth were older age and living in the Northeast Region of the United States. Predictors of caregiver vaccine uptake were male sex, higher annual household income and not having to quarantine due to COVID-19.

LIMITATIONS

The sample was small and not a full representation of a population with bipolar-spectrum disorders therefore, the results may not be generalizable. The study design and statistical method do not allow for causal inferences to be made.

CONCLUSIONS

These findings may aid in targeting interventions to maximize COVID-19 and other vaccine uptake in youth with bipolar disorders and their families.

Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review

BACKGROUND

Electronic health records (EHRs) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHRs in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.

OBJECTIVE

This study reviews advanced spatial analyses that used individual-level health data from EHRs within the United States to characterize patient phenotypes.

METHODS

We systematically evaluated English-language, peer-reviewed studies from the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on study design or specific health domains.

RESULTS

A substantial proportion of studies (>85%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86%) in publications was observed after 2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were limited.

CONCLUSIONS

This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support.

Preventive behavior intention for a viral outbreak among college students: The case of Mpox

OBJECTIVE

Examine college students' behavior intention to reduce viral outbreak transmission in the context of Mpox, and what preventive messaging strategies would be most effective in future transmissible disease outbreaks based on the Reasoned Action Approach (RAA).

PARTICIPANTS

Undergraduates at a mid-Atlantic U.S. University.

METHODS

An online survey (n = 266) conducted at the height of the recent Mpox outbreak in the U.S., asked about five target behaviors to reduce Mpox transmission, including RAA determinants for each behavior.

RESULTS

Highest intention was safe sex practices, lowest were vaccination and sexual abstinence, and sharing dishes and fabrics were in between. RAA determinants differed by target behavior, although attitudes were significantly positively associated with intention for all five behaviors.

CONCLUSIONS

College students are potentially open to preventative behaviors to reduce viral transmission during an outbreak. However, specific target behavior matters and messaging should focus on differing RAA determinants depending on target behavior.

A Study Examining the Impact of County-Level Demographic, Socioeconomic, and Political Affiliation Characteristics on COVID-19 Vaccination Patterns in Indiana

The COVID-19 vaccination campaign resulted in uneven vaccine uptake throughout the United States, particularly in rural areas, areas with socially and economically disadvantaged groups, and populations that exhibited vaccine hesitancy behaviors. This study examines how county-level sociodemographic and political affiliation characteristics differentially affected patterns of COVID-19 vaccinations in the state of Indiana every month in 2021. We linked county-level demographics from the 2016-2020 American Community Survey Five-Year Estimates and the Indiana Elections Results Database with county-level COVID-19 vaccination counts from the Indiana State Department of Health. We then created twelve monthly linear regression models to assess which variables were consistently being selected, based on the Akaike Information Criterion (AIC) and adjusted R-squared values. The vaccination models showed a positive association with proportions of Bachelor's degree-holding residents, of 40-59 year-old residents, proportions of Democratic-voting residents, and a negative association with uninsured and unemployed residents, persons living below the poverty line, residents without access to the Internet, and persons of Other Race. Overall, after April, the variables selected were consistent, with the model's high adjusted R2 values for COVID-19 cumulative vaccinations demonstrating that the county sociodemographic and political affiliation characteristics can explain most of the variation in vaccinations. Linking county-level sociodemographic and political affiliation characteristics with Indiana's COVID-19 vaccinations revealed inherent inequalities in vaccine coverage among different sociodemographic groups. Increased vaccine uptake could be improved in the future through targeted messaging, which provides culturally relevant advertising campaigns for groups less likely to receive a vaccine, and increasing access to vaccines for rural, under-resourced, and underserved populations.

Regional variations in vaccination against COVID-19 in Germany

Vaccination willingness against COVID-19 is generally perceived as low. Moreover, there is large heterogeneity across and within countries. As a whole, Germany has average vaccination rates compared to other industrialized countries. However, vaccination rates in the 16 different German federal states differ by more than 20 percentage points. We describe variation in vaccination rates on the level of the 400 German counties using data on all vaccinations carried out until December 2022. Around 52-72% of that variation can be explained by regional differences in demographic characteristics, housing, education and political party preferences. We find indications that the remaining part may be due to differences in soft factors such as risk aversion, trust in the German government, trust in science, and beliefs in conspiracy theories regarding the origins of the Corona virus. We conclude that improving the trust in science and the fight against conspiracy theories may possibly be effective tools to improve vaccination rates and effectively fight pandemics.

Optimizing the Selection of Mass Vaccination Sites: Access and Equity Consideration

In the early phases of the COVID-19 pandemic, vaccine accessibility was limited, impacting large metropolitan areas such as Los Angeles County, which has over 10 million residents but only nine initial vaccination sites, which resulted in people experiencing long travel times to get vaccinated. We developed a mixed-integer linear model to optimize site selection, considering equitable access for vulnerable populations. Analyzing 277 zip codes between December 2020 and May 2021, our model incorporated factors such as car ownership, ethnic group disease vulnerability, and the Healthy Places Index, alongside travel times by car and public transit. Our optimized model significantly outperformed actual site allocations for all ethnic groups. We observed that White populations faced longer travel times, likely due to their residences being in more remote, less densely populated areas. Conversely, areas with higher Latino and Black populations, often closer to the city center, benefited from shorter travel times in our model. However, those without cars experienced greater disadvantages. While having many vaccination sites might improve access for those dependent on public transit, that advantage is diminished if people must search among many sites to find a location with available vaccines.

Socioeconomic determinants of COVID-19 vaccine acceptance

The aim of the paper is to evaluate the relative importance of the set of socioeconomic characteristics of population on collective decision on COVID-19 vaccine acceptance. We apply cross-section OLS methods to the municipal-level non-survey data for 145 municipalities in Serbia, on the COVID-19 vaccination rate and socioeconomic characteristics of the population, to evaluate the determinants of cross-municipal variation in vaccine uptake decision. Using the estimated coefficients from the OLS regressions, we apply the standardized beta method to evaluate the relative importance of each factor. Vaccine acceptance in municipalities rises with the average level of education (especially in the female population), age and employment, while being negatively linked to religiosity of people and the proportion of rural population. We also find some evidence on the positive impact of the overall trust in government. Education level has the single largest impact, shaping around 37% of (explained) variation in the vaccination rate across municipalities, a rise in the proportion of people with higher degree by 1% being associated with increase in vaccination rate by 0.36%. Age of population explains 21%, urban-rural structure 13% and religiosity 11% of variation in vaccine acceptance, while employment status and trust in government each explain around 9% of variation in vaccine uptake across municipalities. Effective vaccination promotion strategy should be focused on younger, less-educated, unemployed cohorts, as well as on rural areas and should involve representatives of mainstream religions. Fostering education and strengthening trust in government are some of the key structural factors that may promote efficient collective behaviour in this respect.

Socio-demographic determinants of COVID-19 vaccine uptake in Ontario: Exploring differences across the Health Region model

The COVID-19 pandemic continues to be a worldwide public health concern. Although vaccines against this disease were rapidly developed, vaccination uptake has not been equal across all the segments of the population, particularly in the case of underrepresented groups. However, there are also differences in vaccination across geographical areas, which might be important to consider in the development of future public health vaccination policies. In this study, we examined the relationship between vaccination status (having received the first dose of a COVID-19 vaccine), socio-economic strata, and the Health Regions for individuals in Ontario, Canada. Our results show that between October of 2021 and January of 2022, individuals from underrepresented communities were three times less likely to be vaccinated than White/Caucasian individuals across the province of Ontario, and that in some cases, within these groups, individuals in low-income brackets had significantly higher odds of vaccination when compared to their peers in high income brackets. Finally, we identified significantly lower odds of vaccination in the Central, East and West Health Regions of Ontario within certain underrepresented groups. This study shows that there is an ongoing need to better understand and address differences in vaccination uptake across diverse segments of the population of Ontario that the pandemic has largely impacted.

Geographic disparities and predictors of COVID-19 vaccination in Missouri: a retrospective ecological study

Background

Limited information is available on geographic disparities of COVID-19 vaccination in Missouri and yet this information is essential for guiding efforts to improve vaccination coverage. Therefore, the objectives of this study were to (a) investigate geographic disparities in the proportion of the population vaccinated against COVID-19 in Missouri and (b) identify socioeconomic and demographic predictors of the identified disparities.

Methods

The COVID-19 vaccination data for time period January 1 to December 31, 2021 were obtained from the Missouri Department of Health. County-level data on socioeconomic and demographic factors were downloaded from the 2020 American Community Survey. Proportions of county population vaccinated against COVID-19 were computed and displayed on choropleth maps. Global ordinary least square regression model and local geographically weighted regression model were used to identify predictors of proportions of COVID-19 vaccinated population.

Results

Counties located in eastern Missouri tended to have high proportions of COVID-19 vaccinated population while low proportions were observed in the southernmost part of the state. Counties with low proportions of population vaccinated against COVID-19 tended to have high percentages of Hispanic/Latino population (p = 0.046), individuals living below the poverty level (p = 0.049), and uninsured (p = 0.015) populations. The strength of association between proportion of COVID-19 vaccinated population and percentage of Hispanic/Latino population varied by geographic location.

Conclusion

The study findings confirm geographic disparities of proportions of COVID-19 vaccinated population in Missouri. Study findings are useful for guiding programs geared at improving vaccination coverage and uptake by targeting resources to areas with low proportions of vaccinated individuals.

Hesitancy or Resistance? Differential Changes in COVID-19 Vaccination Intention Between Black and White Americans

The literature on COVID-19 vaccination has rarely taken a macro and longitudinal approach to investigate the nuanced racial and ethnic differences in vaccine hesitancy and refusal. To fill this gap, this study examines the relationships between race, time, and COVID-19 vaccine hesitancy and refusal using state-level data from the US Census Household Pulse Survey, 2020 US Decennial Census, and other sources (i.e., American Community Survey, Human Development Index database, and Centers for Disease Control and Prevention). Four longitudinal Generalized Estimating Equations (GEEs) were estimated to analyze how time-variant and time-invariant measures, and time itself influenced COVID-19 vaccine hesitancy and refusal rates, controlling for the effect of other relevant covariates. The results provide descriptive evidence that COVID-19 vaccine hesitancy had decreased in the USA, but vaccine refusal remained stable between January and October 2021. The GEEs further indicated that the proportion of the Black population was positively associated with both vaccine hesitancy and refusal rates, while the proportion of the White population was positively associated with the vaccine refusal rate but not associated with the vaccine hesitancy rate. In addition, over the 10-month period, COVID-19 vaccine hesitancy and refusal in the Black population declined rapidly, but vaccine refusal in the White population stayed fairly stable. More research and practical efforts are needed to understand and inform the public about these important but overlooked trends.

Geospatially clustered low COVID-19 vaccine rates among adolescents in socially vulnerable US counties

COVID-19 vaccinations are widely available across the United States (U.S.), yet little is known about the spatial clustering of COVID-19 vaccinations. This study aimed to test for geospatial clustering of COVID-19 vaccine rates among adolescents aged 12-17 across the U.S. counties and to compare these clustering patterns by sociodemographic characteristics. County-level data on COVID-19 vaccinations and sociodemographic characteristics were obtained from the COVID-19 Community Profile Report up to April 14, 2022. A total of 3,108 counties were included in the analysis. Global Moran's I statistic and Anselin Local Moran's analysis were used, and clustering patterns were compared to sociodemographic variables using t-tests. Counties with low COVID-19 vaccinated clusters were more likely, when compared to unclustered counties, to have higher numbers of individuals in poverty and uninsured individuals, and higher values of Social Vulnerability Index (SVI) and COVID-19 Community Vulnerability Index (CCVI). While high COVID-19 vaccinated clusters, compared to neighboring counties, had lower numbers of Black population, individuals in poverty, and uninsured individuals, and lower values of SVI and CCVI, but a higher number of Hispanic population. This study emphasizes the importance of addressing systemic barriers, such as poverty and lack of health insurance, which were found to be associated with low COVID-19 vaccination coverage.

COVID-19 vaccine hesitancy worldwide and its associated factors: a systematic review and meta-analysis

Introduction

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has taken a toll on humans, and the development of effective vaccines has been a promising tool to end the pandemic. However, for a vaccination program to be successful, a considerable proportion of the community must be vaccinated. Hence, public acceptance of coronavirus disease 2019 (COVID-19) vaccines has become the key to controlling the pandemic. Recent studies have shown vaccine hesitancy increasing over time. This systematic review aims to evaluate the COVID-19 vaccine hesitancy rate and related factors in different communities.

Method

A comprehensive search was performed in MEDLINE (via PubMed), Scopus, and Web of Science from January 1, 2019 to January 31, 2022. All relevant descriptive and observational studies (cross-sectional and longitudinal) on vaccine hesitancy and acceptance were included in this systematic review. In the meta-analysis, odds ratio (OR) was used to assess the effects of population characteristics on vaccine hesitancy, and event rate (acceptance rate) was the effect measure for overall acceptance. Publication bias was assessed using the funnel plot, Egger's test, and trim-and-fill methods.

Result

A total of 135 out of 6,417 studies were included after screening. A meta-analysis of 114 studies, including 849,911 participants, showed an overall acceptance rate of 63.1%. In addition, men, married individuals, educated people, those with a history of flu vaccination, those with higher income levels, those with comorbidities, and people living in urban areas were less hesitant.

Conclusion

Increasing public awareness of the importance of COVID-19 vaccines in overcoming the pandemic is crucial. Being men, living in an urban region, being married or educated, having a history of influenza vaccination, having a higher level of income status, and having a history of comorbidities are associated with higher COVID-19 vaccine acceptance.

Hybrid health regimes: Access to primary care physicians and COVID-19 vaccine uptake across municipalities in Romania

This study analyses COVID-19 vaccine uptake at the municipal level in Romania using the global health regimes and vaccine hesitancy perspectives. Our spatial regression (SARAR-het Durbin) shows that the number of primary care physicians is a significant predictor of vaccine uptake, and municipalities with higher access to the labour market have higher vaccination rates. We provide a historical perspective to demonstrate that the current health regime in Romania is a hybrid of internationalist and global health regimes, with socialist investments affecting labour participation, education, poverty, and vaccination rates. Our findings highlight the impact of regional disparities and partial privatization of the health system.

The Impact of Meso-Level Factors on SARS-CoV-2 Vaccine Early Hesitancy in the United States

The extant literature on the U.S. SARS-CoV-2 virus indicates that the vaccination campaign was lagging, insufficient, and uncoordinated. This study uses the spatial model to identify the drivers of vaccine hesitancy (in the middle of the pandemic), one of the critical steps in creating impactful and effective interventions to influence behavioral changes now and in the future. The applied technique accounted for observed and unobserved homogeneity and heterogeneity among counties. The results indicated that political and religious beliefs, quantified by Cook's political bipartisan index and the percentage of the population affiliated with the main Christian groups, were the main drivers of the SARS-CoV-2 vaccine hesitancy. The past vaccination experience and other variables determining the demand and supply of vaccines were also crucial in influencing hesitancy. The results imply that vaccination campaigns require engaging community leaders at all levels rather than depending on politicians alone and eliminating barriers to the supply and demand of vaccines at all levels. Coordination among religious and community leaders would build a practical institutional arrangement to facilitate (rather than frustrate) the vaccination drives.

Parents' Intentions, Concerns and Information Needs about COVID-19 Vaccination in New Jersey: A Qualitative Analysis

BACKGROUND

In 2019, the World Health Organization identified vaccine hesitancy as a top ten global health threat, which has been exacerbated by the COVID-19 pandemic. Despite local and nationwide public health efforts, adolescent COVID-19 vaccination uptake in the US remains low. This study explored parents' perceptions of the COVID-19 vaccine and factors influencing hesitancy to inform future outreach and education campaigns.

METHODS

We conducted two rounds of individual interviews via Zoom in May-September 2021 and January-February 2022, with parents of adolescents from the Greater Newark Area of New Jersey, a densely populated area with historically marginalized groups that had low COVID-19 vaccination uptake. Data collection and analysis was guided by the Increasing Vaccination Model and WHO Vaccine Hesitancy Matrix. Interview transcripts were double-coded and thematically analyzed in NVivo.

RESULTS

We interviewed 22 parents (17 in English, 5 in Spanish). Nearly half (45%) were Black and 41% were Hispanic. Over half (54%) were born outside of the US. Most of the parents described that their adolescents had received at least one dose of a COVID-19 vaccine. All but one parent had received the COVID-19 vaccine. Despite strong vaccination acceptance for themselves, parents remained hesitant about vaccinating their adolescents. They were mostly concerned about the safety and potential side effects due to the novelty of the vaccine. Parents sought information about the vaccines online, through healthcare providers and authorities, and at community spaces. Interpersonal communication exposed parents to misinformation, though some personal connections to severe COVID-19 illness motivated vaccination. Historical mistreatment by the healthcare system and politicization of the vaccine contributed to parents' mixed feelings about the trustworthiness of those involved with developing, promoting, and distributing COVID-19 vaccines.

CONCLUSIONS

We identified multilevel influences on COVID-19 vaccine-specific hesitancy among a racially/ethnically diverse sample of parents with adolescents that can inform future vaccination interventions. To increase vaccine confidence, future COVID booster campaigns and other vaccination efforts should disseminate information through trusted healthcare providers in clinical and also utilize community settings by addressing specific safety concerns and promoting vaccine effectiveness.

A spatiotemporal analysis of the social determinants of health for COVID-19

This research aims to uncover how the association between social determinants of health and COVID-19 cases and fatality rate have changed across time and space. To begin to understand these associations and show the benefits of analysing temporal and spatial variations in COVID-19, we utilized Geographically Weighted Regression (GWR). The results emphasize the advantages for using GWR in data with a spatial component, while showing the changing spatiotemporal magnitude of association between a given social determinant and cases or fatalities. While previous research has demonstrated the merits of GWR for spatial epidemiology, our study fills a gap in the literature, by examining a suite of variables across time to reveal how the pandemic unfolded across the US at a county-level spatial scale. The results speak to the importance of understanding the local effects that a social determinant may have on populations at the county level. From a public health perspective, these results can be used for an understanding of the disproportionate disease burden felt by different populations, while upholding and building upon trends observed in epidemiological literature.

Determinants of Covid-19 vaccination: Evidence from the US pulse survey

The Covid-19 disease is resurging across the United States and vaccine hesitancy remains a major obstacle to reaching the expected threshold for herd immunity. Using the nationally representative cross sectional Household Pulse Survey (HPS) Data published by the U.S. Census Bureau, this study identified demographic, socio-economic, and medical-psychological determinants of Covid-19 vaccination. Results revealed significant differences in Covid-19 vaccine uptake due to age, sex, sexual orientation, race or ethnicity, marital status, education, income, employment form, housing and living condition, physical illness, mental illness, Covid-19 illness, distrust of vaccines and beliefs about the efficacy of vaccines. Government policymakers need to be cognizant of these determinants of vaccine hesitancy when formulating policies to increase vaccine uptake and control the COVID-19 pandemic. The findings of this study suggest that segmented solutions to reach vulnerable groups like racial minorities and homeless people are needed to win the trust and optimize vaccine uptake.

Understanding the spatial heterogeneity of COVID-19 vaccination uptake in England

BACKGROUND

Mass vaccination has been a key strategy in effectively containing global COVID-19 pandemic that posed unprecedented social and economic challenges to many countries. However, vaccination rates vary across space and socio-economic factors, and are likely to depend on the accessibility to vaccination services, which is under-researched in literature. This study aims to empirically identify the spatially heterogeneous relationship between COVID-19 vaccination rates and socio-economic factors in England.

METHODS

We investigated the percentage of over-18 fully vaccinated people at the small-area level across England up to 18 November 2021. We used multiscale geographically weighted regression (MGWR) to model the spatially heterogeneous relationship between vaccination rates and socio-economic determinants, including ethnic, age, economic, and accessibility factors.

RESULTS

This study indicates that the selected MGWR model can explain 83.2% of the total variance of vaccination rates. The variables exhibiting a positive association with vaccination rates in most areas include proportion of population over 40, car ownership, average household income, and spatial accessibility to vaccination. In contrast, population under 40, less deprived population, and black or mixed ethnicity are negatively associated with the vaccination rates.

CONCLUSIONS

Our findings indicate the importance of improving the spatial accessibility to vaccinations in developing regions and among specific population groups in order to promote COVID-19 vaccination.

Exploring Determinants of HIV/AIDS Self-Testing Uptake in South Africa Using Generalised Linear Poisson and Geographically Weighted Poisson Regression

Increased HIV/AIDS testing is of paramount importance in controlling the HIV/AIDS pandemic and subsequently saving lives. Despite progress in HIV/AIDS testing programmes, most people are still reluctant to test and thus are still unaware of their status. Understanding the factors associated with uptake levels of HIV/AIDS self-testing requires knowledge of people's perceptions and attitudes, thus informing evidence-based decision making. Using the South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey of 2017 (SABSSM V), this study assessed the efficacy of Generalised Linear Poisson Regression (GLPR) and Geographically Weighted Poisson Regression (GWPR) in modelling the spatial dependence and non-stationary relationships of HIV/AIDS self-testing uptake and covariates. The models were calibrated at the district level across South Africa. Results showed a slightly better performance of GWPR (pseudo R² = 0.91 and AICc = 390) compared to GLPR (pseudo R² = 0.88 and AICc = 2552). Estimates of local intercepts derived from GWPR exhibited differences in HIV/AIDS self-testing uptake. Overall, the output of this study displays interesting findings on the levels of spatial heterogeneity of factors associated with HIV/AIDS self-testing uptake across South Africa, which calls for district-specific policies to increase awareness of the need for HIV/AIDS self-testing.

COVID-19 Vaccine Perceptions Survey for Real-Time Vaccine Outreach in Marin County, California

BACKGROUND

Understanding and addressing coronavirus disease 2019 (COVID-19) vaccine hesitancy is crucial to informing vaccination outreach strategies and achieving high vaccination coverage. Marin County, California, United States, has a history of vaccine hesitancy regarding childhood vaccinations required for school entry.

OBJECTIVES

We aimed to describe and address COVID-19 vaccine hesitancy in Marin County to inform outreach and messaging. Our objectives were to identify subgroups with high COVID-19 vaccine hesitancy early in distribution, better understand local concerns and feedback about the COVID-19 vaccine distribution process, and inform tailored vaccine messaging to increase vaccination confidence and coverage.

METHODS

The survey, which was administered from January 3 to May 10, 2021, queried demographics, vaccine acceptance, reasons for hesitancy, and reasons for acceptance. Open-ended questions were used for respondents to report additional reasons for hesitancy and for general feedback about the vaccine distribution process. We conducted quantitative and qualitative analyses stratified by COVID-19 vaccine acceptance to identify subgroups with high hesitancy. Results were shared weekly in real-time with leadership and key community partners working on vaccine outreach.

RESULTS

Among the 5,618 survey responses, there were differences in vaccine hesitancy by sociodemographic characteristics with the highest hesitancy reported among subgroups identifying as Black/African American and young adult, and within the lowest family income grouping. The most common reason for vaccine hesitancy was "uncertain about the side effects of the vaccine" (67.3% endorsement) and responses varied by race and ethnicity. Qualitative data revealed equity-related, vaccine distribution, and vaccine access themes that were not present in structured responses. Vaccine hesitancy survey results were paired with vaccination coverage and COVID-19 case data to inform tailored outreach strategies and priorities week-to-week.

CONCLUSIONS

Marin County had some of the highest COVID-19 vaccination rates in the United States during the pandemic and met equity goals aimed at ensuring vulnerable populations received vaccinations. Presenting real-time survey findings with leadership and key community partners informed a timely and tailored COVID-19 vaccine outreach and delivery strategy.

Using conditional inference to quantify interaction effects of socio-demographic covariates of US COVID-19 vaccine hesitancy

COVID-19 vaccine hesitancy has become a major issue in the U.S. as vaccine supply has outstripped demand and vaccination rates slow down. At least one recent global survey has sought to study the covariates of vaccine acceptance, but an inferential model that makes simultaneous use of several socio-demographic variables has been lacking. This study has two objectives. First, we quantify the associations between common socio-demographic variables (including, but not limited to, age, ethnicity, and income) and vaccine acceptance in the U.S. Second, we use a conditional inference tree to quantify and visualize the interaction and conditional effects of relevant socio-demographic variables, known to be important correlates of vaccine acceptance in the U.S., on vaccine acceptance. We conduct a retrospective analysis on a COVID-19 cross-sectional Gallup survey data administered to a representative sample of U.S.-based respondents. Our univariate regression results indicate that most socio-demographic variables, such as age, education, level of household income and education, have significant association with vaccine acceptance, although there are key points of disagreement with the global survey. Similarly, our conditional inference tree model shows that trust in the (former) Trump administration, age and ethnicity are the most important covariates for predicting vaccine hesitancy. Our model also highlights the interdependencies between these variables using a tree-like visualization.

Multiscale Dimensions of Spatial Process: COVID-19 Fully Vaccinated Rates in U.S. Counties

INTRODUCTION

This study aimed to examine the heterogeneity of the associations between social determinants and COVID-19 fully vaccinated rate.

METHODS

This study proposes 3 multiscale dimensions of spatial process, including level of influence (the percentage of population affected by a certain determinant across the entire area), scalability (the spatial process of a determinant into global, regional, and local process), and specificity (the determinant that has the strongest association with the fully vaccinated rate). The multiscale geographically weighted regression was applied to the COVID-19 fully vaccinated rates in U.S. counties (N=3,106) as of October 26, 2021, and the analyses were conducted in May 2022.

RESULTS

The results suggest the following: (1) Percentage of Republican votes in the 2020 presidential election is a primary influencer because 84% of the U.S. population lived in counties where this determinant is found the most dominant; (2) Demographic compositions (e.g., percentages of racial/ethnic minorities) play a larger role than socioeconomic conditions (e.g., unemployment) in shaping fully vaccinated rates; (3) The spatial process underlying fully vaccinated rates is largely local.

CONCLUSIONS

The findings challenge the 1-size-fits-all approach to designing interventions promoting COVID-19 vaccination and highlight the importance of a place-based perspective in ecological health research.

COVID-19 Vaccine Hesitancy among U.S. Veterans Experiencing Homelessness in Transitional Housing

Little is known about COVID-19 vaccine hesitancy and acceptance among individuals experiencing homelessness, despite their higher risk for morbidity and mortality from SARS-CoV-2. This study examines COVID-19 vaccination attitudes and uptake among U.S. military Veterans experiencing homelessness enrolled in transitional housing programs funded by the U.S. Department of Veterans Affairs (VA). Telephone interviews were conducted with 20 Veterans in California, Florida, Iowa, Kentucky, and Massachusetts, USA (January-April 2021). A rapid analysis approach was used to identify and enumerate commonly occurring themes. Although 60% of interviewed Veterans either received the COVID-19 vaccine or were willing to do so, one-third expressed hesitancy to get vaccinated. COVID-19 vaccination attitudes (e.g., belief that the vaccines were inadequately tested), military experience, beliefs about influenza and other vaccines, and sources of information emerged as influential factors for COVID-19 vaccination uptake or hesitancy. Veterans in VA-funded homeless transitional housing programs are generally willing to be vaccinated. However, a substantial minority is reluctant to take the vaccine due to concerns about the COVID-19 vaccine and distrust of authority. Recommendations for increasing uptake include utilizing Veteran peers, homeless service providers, and healthcare providers as trusted messengers to improve confidence in the vaccine.

Community-level characteristics of COVID-19 vaccine hesitancy in England: A nationwide cross-sectional study

One year after the start of the COVID-19 vaccination programme in England, more than 43 million people older than 12 years old had received at least a first dose. Nevertheless, geographical differences persist, and vaccine hesitancy is still a major public health concern; understanding its determinants is crucial to managing the COVID-19 pandemic and preparing for future ones. In this cross-sectional population-based study we used cumulative data on the first dose of vaccine received by 01-01-2022 at Middle Super Output Area level in England. We used Bayesian hierarchical spatial models and investigated if the geographical differences in vaccination uptake can be explained by a range of community-level characteristics covering socio-demographics, political view, COVID-19 health risk awareness and targeting of high risk groups and accessibility. Deprivation is the covariate most strongly associated with vaccine uptake (Odds Ratio 0.55, 95%CI 0.54-0.57; most versus least deprived areas). The most ethnically diverse areas have a 38% (95%CI 36-40%) lower odds of vaccine uptake compared with those least diverse. Areas with the highest proportion of population between 12 and 24 years old had lower odds of vaccination (0.87, 95%CI 0.85-0.89). Finally increase in vaccine accessibility is associated with COVID-19 vaccine coverage (OR 1.07, 95%CI 1.03-1.12). Our results suggest that one year after the start of the vaccination programme, there is still evidence of inequalities in uptake, affecting particularly minorities and marginalised groups. Strategies including prioritising active outreach across communities and removing practical barriers and factors that make vaccines less accessible are needed to level up the differences.

Moving the Needle: Association Between a Vaccination Reward Lottery and COVID-19 Vaccination Uptake in Louisiana

OBJECTIVE

On June 17, 2021, Louisiana launched a lottery campaign to reward residents who received a COVID-19 vaccination. We investigated the association between the lottery and vaccination uptake by characteristics of parishes.

METHODS

We constructed an interrupted time series based on daily parish-level data on COVID-19 vaccinations to analyze the association with the lottery. We used recursive partitioning to separate vaccination uptake due to the Delta variant from vaccination uptake due to the lottery and limited our study period to May 25 through July 20, 2021. We performed subanalyses that grouped parishes by political affiliation, hesitancy toward COVID-19 vaccines, race and ethnicity, and socioeconomic status to detect heterogeneous responses to the lottery by these characteristics. We ran models separately for parishes in the top and bottom tertiles of each sociodemographic indicator and used a z test to check for differences.

RESULTS

The lottery was associated with an additional 1.03 (95% CI, 0.61-1.45; P < .001) first doses per parish per day. Comparing lottery impacts between top and bottom tertiles, we found significantly larger associations in parishes with lower vaccine hesitancy rates, higher percentage of Hispanic population, higher median annual household income, and more people with a college degree.

CONCLUSIONS

Results suggest that the lottery was associated with increased COVID-19 vaccination uptake in Louisiana. However, larger associations were observed in parishes with an already higher likelihood of accepting vaccines, which raises equity issues about the opportunity created by the lottery and its effectiveness as a long-term behavioral incentive.

Spatial analysis and modelling of depression relative to social vulnerability index across the United States

According to the Substance Abuse and Mental Health Services Administration, about 21 million adults in the US experience a major depressive episode. Depression is considered a primary risk factor for suicide. In the US, about 19.5% of adults are reported to be experiencing a depressive disorder, leading to over 45,000 deaths (14.0 deaths per 100,000) due to suicides. To our knowledge, no previous spatial analysis study of depression relative to the social vulnerability index has been performed across the nation. In this study, county-level depression prevalence and indicators were compiled. We analysed the geospatial distribution of depression prevalence based on ordinary least squares, geographically weighted regression, and multiscale geographically weighted regression models. Our findings indicated that the multiscale model could explain over 86% of the local variance of depression prevalence across the US based on per capita income, age 65 and older, belonging to a minority group (predominantly negative impacts), and disability (mainly positive effect). This study can provide valuable insights for public health professionals and policymakers to address depression disparities.

Factors Associated with Delayed or Missed Second-Dose mRNA COVID-19 Vaccination among Persons >12 Years of Age, United States

To identify demographic factors associated with delaying or not receiving a second dose of the 2-dose primary mRNA COVID-19 vaccine series, we matched 323 million single Pfizer-BioNTech (https://www.pfizer.com) and Moderna (https://www.modernatx.com) COVID-19 vaccine administration records from 2021 and determined whether second doses were delayed or missed. We used 2 sets of logistic regression models to examine associated factors. Overall, 87.3% of recipients received a timely second dose (≤42 days between first and second dose), 3.4% received a delayed second dose (>42 days between first and second dose), and 9.4% missed the second dose. Persons more likely to have delayed or missed the second dose belonged to several racial/ethnic minority groups, were 18-39 years of age, lived in more socially vulnerable areas, and lived in regions other than the northeastern United States. Logistic regression models identified specific subgroups for providing outreach and encouragement to receive subsequent doses on time.

An optimized machine learning model for identifying socio-economic, demographic and health-related variables associated with low vaccination levels that vary across ZIP codes in California

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Machine learning was used to assess extensive and specific COVID-19 vaccination risk variables.

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Machine learning explained higher vaccination level variation than standard statistical tests.

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Twenty socioeconomic risk variables explained 72.90% variance in unvaccinated counts.

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Age groups, housing types, household income and education were among the most high-risk variables.

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ZIP Code assessments provided more specific, localized public health targets than counties.

There is an urgent need for an in-depth and systematic assessment of a wide range of predictive factors related to populations most at risk for delaying and refusing COVID-19 vaccination as cases of the disease surge across the United States. Many studies have assessed a limited number of general sociodemographic and health-related factors related to low vaccination rates. Machine learning methods were used to assess the association of 151 social and health-related risk factors derived from the American Community Survey 2019 and the Centers for Disease Control and Prevention (CDC) BRFSS with the response variables of vaccination rates and unvaccinated counts in 1,555 ZIP Codes in California. The performance of various analytical models was evaluated according to their ability to regress between predictive variables and vaccination levels. Machine learning modeling identified the Gradient Boosting Regressor (GBR) as the predictive model with a higher percentage of the explained variance than the variance identified through linear and generalized regression models. A set of 20 variables explained 72.90% of the variability of unvaccinated counts among ZIP Codes in California. ZIP Codes were shown to be a more meaningful geo-local unit of analysis than county-level assessments. Modeling vaccination rates was not as effective as modeling unvaccinated counts. The public health utility of this model provides for the analysis of state and local conditions related to COVID-19 vaccination use and future public health problems and pandemics.

Sparse spatially clustered coefficient model via adaptive regularization

Large spatial datasets with many spatial covariates have become ubiquitous in many fields in recent years. A question of interest is to identify which covariates are likely to influence a spatial response, and whether and how the effects of these covariates vary across space, including potential abrupt changes from region to region. To solve this question, a new efficient regularized spatially clustered coefficient (RSCC) regression approach is proposed, which could achieve variable selection and identify latent spatially heterogeneous covariate effects with clustered patterns simultaneously. By carefully designing the regularization term of RSCC as a chain graph guided fusion penalty plus a group lasso penalty, the RSCC model is computationally efficient for large spatial datasets while still achieving the theoretical guarantees for estimation. RSCC also adopts the idea of adaptive learning to allow for adaptive weights and adaptive graphs in its regularization terms and further improves the estimation performance. RSCC is applied to study the acceptance of COVID-19 vaccines using county-level data in the United States and discover the determinants of vaccination acceptance with varying effects across counties, revealing important within-state and across-state spatially clustered patterns of covariates effects.

The impact of geo-environmental factors on global COVID-19 transmission: A review of evidence and methodology

Studies on Coronavirus Disease 2019 (COVID-19) transmission indicate that geo-environmental factors have played a significant role in the global pandemic. However, there has not been a systematic review on the impact of geo-environmental factors on global COVID-19 transmission in the context of geography. As such, we reviewed 49 well-chosen studies to reveal the impact of geo-environmental factors (including the natural environment and human activity) on global COVID-19 transmission, and to inform critical intervention strategies that could mitigate the worldwide effects of the pandemic. Existing studies frequently mention the impact of climate factors (e.g., temperature and humidity); in contrast, a more decisive influence can be achieved by human activity, including human mobility, health factors, and non-pharmaceutical interventions (NPIs). The above results exhibit distinct spatiotemporal heterogeneity. The related analytical methodology consists of sensitivity analysis, mathematical modeling, and risk analysis. For future studies, we recommend highlighting geo-environmental interactions, developing geographically statistical models for multiple waves of the pandemic, and investigating NPIs and care patterns. We also propose four implications for practice to combat global COVID-19 transmission.

GIS-based spatio-temporal analysis and modeling of COVID-19 incidence rates in Europe

The COVID-19 epidemic has emerged as one of the most severe public health crises worldwide, especially in Europe. Until early July 2021, reported infected cases exceeded 180 million, with almost 4 million associated deaths worldwide, almost a third of which are in continental Europe. We analyzed the spatio-temporal distribution of the disease incidence and mortality rates considering specific periods in this continent. Further, we applied Global Moran's I to examine the spatio-temporal distribution patterns of COVID-19 incidence rates and Getis-Ord Gi* hotspot analysis to represent high-risk areas of the disease. Additionally, we compiled a set of 40 demographic, socioeconomic, environmental, transportation, health, and behavioral indicators as potential explanatory variables to investigate the spatial variations of COVID-19 cumulative incidence rates (CIRs). Ordinary Least Squares (OLS), Spatial Lag model (SLM), Spatial Error Model (SLM), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) regression models were implemented to examine the spatial dependence and non-stationary relationships. Based on our findings, the spatio-temporal distribution pattern of COVID-19 CIRs was highly clustered and the most high-risk clusters of the disease were situated in central and western Europe. Moreover, poverty and the elderly population were selected as the most influential variables due to their significant relationship with COVID-19 CIRs. Considering the non-stationary relationship between variables, MGWR could describe almost 69% of COVID-19 CIRs variations in Europe. Since this spatio-temporal research is conducted on a continental scale, spatial information obtained from the models could provide general insights to authorities for further targeted policies.

Vaccine hesitancy in American healthcare workers during the COVID-19 vaccine roll out: an integrative review

OBJECTIVE

The purpose of this integrative review is to examine the literature on vaccine hesitancy among American healthcare workers during the COVID-19 vaccine rollout.

METHODS

A review of quantitative literature on acceptance, intention, refusal, or hesitation to accept the COVID-19 vaccine was conducted, searching in PubMed, Cumulative Index for Nursing and Allied Health Literature, PsycINFO, and Web of Science. Because of the immediacy of the topic, research letters were included in addition to articles. The 18 publications were appraised for quality using the Critical Appraisal Checklist for Cross-Sectional Studies by the Center for Evidence-Based Management.

RESULTS

Estimates of vaccine hesitancy among healthcare workers were similar to the general population. The literature indicates demographic characteristics associated with vaccine hesitancy, including being younger, female, Black, Hispanic, or Latinx. However, examination of the demographic data also points to gaps in the understanding and implications of those characteristics. The newness or perceived rush of vaccine development and implementation were the most cited sources for hesitancy.

CONCLUSION

The studies in this review give clear areas of need for translational research on dissemination and implementation relating to the correlational data, including in areas of comorbid, diasporic, and reproductive health concerns. However, with the gravity of the pandemic and quick arrival of the COVID-19 vaccine happening in the midst of an infodemic, adjunctive interventions could be warranted to combat hesitancy.

Contrasting Association Between COVID-19 Vaccine Hesitancy and Mental Health Status in India and Saudi Arabia-A Preliminary Evidence Collected During the Second Wave of COVID-19 Pandemic

Background

Vaccine hesitancy is a global public health threat. Understanding the role of psychological factors in vaccine hesitancy is often neglected and relatively less explored.

Aim and Objectives

To analyze the relationship between mental health and COVID-19 vaccine hesitancy before and after the advent of COVID-19 vaccines (AC19V) in the general population of India and Saudi Arabia (KSA) which vary in severity of the pandemic and vaccine mandates.

Materials and Methods

A total of 677 adult participants from India and KSA participated in this cross-sectional online web-based survey. Sociodemographic details and current COVID-19 status pertaining to infection and vaccination were collected. Depression, anxiety, post-traumatic stress disorder (PTSD) symptoms, and perceptive need for mental health support (MHS) were assessed before and after AC19V. A newly constructed and validated COVID19 vaccine hesitancy scale-12 (COVID19-VHS12) scale was used to evaluate the COVID-19 vaccine hesitancy.

Results

Prevalence and levels of depression and anxiety symptoms decreased significantly in Saudis but not in Indians after AC19V. PTSD symptoms showed a significant reduction in both India and KSA. Anxiety symptoms were higher in KSA than India before AC19V while PTSD was higher in India before and after AC19V. Except for the place of residence and employment status, the subgroups of sociodemographic variables which were at higher risk of negative mental health before AC19V showed improvement in their mental health after AC19V. The prevalence of COVID-19 vaccine hesitancy in India and KSA was 50.8% (95% CI 45.73–55.89%) and 55.7% (95% CI 50.16–61.31%), respectively. A bidirectional association between vaccine hesitancy and mental health was observed in KSA but not in India. Higher vaccine hesitancy favored higher levels of depression, anxiety, and perceptive need for MHS and vice versa in KSA. None of the mental health parameters predicted vaccine hesitancy in India, while higher vaccine hesitancy increased the risk of anxiety.

Conclusion

Vaccine hesitancy has a negative impact on mental health and vice versa over and above the impact of sociodemographic factors and COVID-19 vaccination and infection status which shows variations between India and KSA.

Jabbing together? The complementarity between social capital, formal public health rules, and COVID-19 vaccine rates in the United States

COVID-19 vaccine rates provide a unique opportunity to explore vaccine hesitancy and potential interactions between social capital and individual, normative values, namely for public health and/or personal freedom. While economists and public health scholars realize the independent effects social capital and stringent public health rules have on prevalence and mortality rates, few recognize how these factors influence vaccination rates. We advance this literature with a novel framework to analyze these interactions. With county-level data on COVID-19 vaccinations, social capital, and measures of the values people have for personal freedom and public health, we find that vaccination rates depend on individual values, the level of social capital, and the interaction between the two. Social capital mediates the values people hold dear, which can influence vaccination rates in positive and negative ways. Our results are robust to the inclusion of relevant controls and under multiple specifications. These results suggest that individuals and the communities people enter into and exit out of play an important role in decisions to vaccinate, which are independent of formal, governmental public health measures.

Community-level characteristics of COVID-19 vaccine hesitancy in England: A nationwide cross-sectional study

One year after the start of the COVID-19 vaccination programme in England, more than 43 million people older than 12 years old had received at least a first dose. Nevertheless, geographical differences persist, and vaccine hesitancy is still a major public health concern; understanding its determinants is crucial to managing the COVID-19 pandemic and preparing for future ones. In this cross-sectional population-based study we used cumulative data on the first dose of vaccine received by 01-01-2022 at Middle Super Output Area level in England. We used Bayesian hierarchical spatial models and investigated if the geographical differences in vaccination uptake can be explained by a range of community-level characteristics covering socio-demographics, political view, COVID-19 health risk awareness and targeting of high risk groups and accessibility. Deprivation is the covariate most strongly associated with vaccine uptake (Odds Ratio 0.55, 95%CI 0.54-0.57; most versus least deprived areas). The most ethnically diverse areas have a 38% (95%CI 36-40%) lower odds of vaccine uptake compared with those least diverse. Areas with the highest proportion of population between 12 and 24 years old had lower odds of vaccination (0.87, 95%CI 0.85-0.89). Finally increase in vaccine accessibility is associated with higher COVID-19 uptake (OR 1.07, 95%CI 1.03-1.12). Our results suggest that one year after the start of the vaccination programme, there is still evidence of inequalities in uptake, affecting particularly minorities and marginalised groups. Strategies including prioritising active outreach across communities and removing practical barriers and factors that make vaccines less accessible are needed to level up the differences.

Impact of Socioeconomic Environment on Home Social Care Service Demand and Dependent Users

An aging population and rising life expectancy lead to an increased demand for social services to care for dependent users, among other factors. In Barcelona, home social care (HSC) services are a key agent in meeting this demand. However, demand is not evenly distributed among neighborhoods, and we hypothesized that this can be explained by the user’s social environment. In this work, we describe the user’s environment at a macroscopic level by the socioeconomic features of the neighborhood. This research aimed to gain a deeper understanding of the dependent user’s socioeconomic environment and service needs. We applied descriptive analytics techniques to explore possible patterns linking HSC demand and other features. These methods include principal components analysis (PCA) and hierarchical clustering. The main analysis was made from the obtained boxplots, after these techniques were applied. We found that economic and disability factors, through users’ mean net rent and degree of disability features, are related to the demand for home social care services. This relation is even clearer for the home-based social care services. These findings can be useful to distribute the services among areas by considering more features than the volume of users/population. Moreover, it can become helpful in future steps to develop a management tool to optimize HSC scheduling and staff assignment to improve the cost and quality of service. For future research, we believe that additional and more precise characteristics could provide deeper insights into HSC service demand.

Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms

The outbreak of coronavirus disease (COVID-19) has become one of the most challenging global concerns in recent years. Due to inadequate worldwide studies on spatio-temporal modeling of COVID-19, this research aims to examine the relative significance of potential explanatory variables (n = 75) concerning COVID-19 prevalence and mortality using multilayer perceptron artificial neural network topology. We utilized ten variable importance analysis methods to identify the relative importance of the explanatory variables. The main findings indicated that several variables were persistently among the most influential variables in all periods. Regarding COVID-19 prevalence, unemployment and population density were among the most influential variables with the highest importance scores. While for COVID-19 mortality, health-related variables such as diabetes prevalence and number of hospital beds were among the most significant variables. The obtained findings from this study might provide general insights for public health policymakers to monitor the spread of disease and support decision-making.

A Global Map of COVID-19 Vaccine Acceptance Rates per Country: An Updated Concise Narrative Review

The delay or refusal of vaccination, which defines vaccine hesitancy, is a major challenge to successful control of COVID-19 epidemic. The huge number of publications addressing COVID-19 vaccine hesitancy necessitates periodic review to provide a concise summary of COVID-19 vaccine acceptance rates worldwide. In the current narrative review, data on COVID-19 vaccine acceptance rates were retrieved from surveys in 114 countries/territories. In East and Southern Africa (n = 9), the highest COVID-19 vaccine acceptance rate was reported in Ethiopia (92%), while the lowest rate was reported in Zimbabwe (50%). In West/Central Africa (n = 13), the highest rate was reported in Niger (93%), while the lowest rate was reported in Cameroon (15%). In Asia and the Pacific (n = 16), the highest rates were reported in Nepal and Vietnam (97%), while the lowest rate was reported in Hong Kong (42%). In Eastern Europe/Central Asia (n = 7), the highest rates were reported in Montenegro (69%) and Kazakhstan (64%), while the lowest rate was reported in Russia (30%). In Latin America and the Caribbean (n = 20), the highest rate was reported in Mexico (88%), while the lowest rate was reported in Haiti (43%). In the Middle East/North Africa (MENA, n = 22), the highest rate was reported in Tunisia (92%), while the lowest rate was reported in Iraq (13%). In Western/Central Europe and North America (n = 27), the highest rates were reported in Canada (91%) and Norway (89%), while the lowest rates were reported in Cyprus and Portugal (35%). COVID-19 vaccine acceptance rates ≥60% were seen in 72/114 countries/territories, compared to 42 countries/territories with rates between 13% and 59%. The phenomenon of COVID-19 vaccine hesitancy appeared more pronounced in the MENA, Europe and Central Asia, and Western/Central Africa. More studies are recommended in Africa, Eastern Europe and Central Asia to address intentions of the general public to get COVID-19 vaccination.

Spatial Analysis of COVID-19 Vaccination: A Scoping Review

Spatial analysis of COVID-19 vaccination research is increasing in recent literature due to the availability of COVID-19 vaccination data that usually contain location components. However, to our knowledge, no previous study has provided a comprehensive review of this research area. Therefore, in this scoping review, we examined the breadth of spatial and spatiotemporal vaccination studies to summarize previous findings, highlight research gaps, and provide guidelines for future research. We performed this review according to the five-stage methodological framework developed by Arksey and O'Malley. We screened all articles published in PubMed/MEDLINE, Scopus, and Web of Science databases, as of 21 September 2021, that had employed at least one form of spatial analysis of COVID-19 vaccination. In total, 36 articles met the inclusion criteria and were organized into four main themes: disease surveillance (n = 35); risk analysis (n = 14); health access (n = 16); and community health profiling (n = 2). Our findings suggested that most studies utilized preliminary spatial analysis techniques, such as disease mapping, which might not lead to robust inferences. Moreover, few studies addressed data quality, modifiable areal unit problems, and spatial dependence, highlighting the need for more sophisticated spatial and spatiotemporal analysis techniques.