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

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. Background

#### 1.2. COVID-19 Vaccine Hesitancy

## 2. Materials and Methods

#### 2.1. The Spatial Model Specification

#### 2.2. Spatial Weight Matrix

_{1}= W

_{2}= W

_{3}and simultaneous estimation of all parameters ($\beta ,\rho ,\theta ,$ and $\lambda $) is not possible. Different spatial models are possible by limiting the three spatial dependency parameters. The spatial lag model (S.L.M.) or spatial autoregressive model (S.A.R.) occurs when $\lambda $=$\theta $= 0, while the spatial error model (S.E.M.) ensues when $\rho $= $\theta $= 0, and when $\theta $= 0, the spatial autoregressive with spatial error (S.A.R.A.R.) model arises. For details on the potential of all estimable models, see the work by Anselin [31,32] and Burkey [43]. Generally, the spatial lag models (spatially lagged dependent variables or the spatial (autoregressive) lag model, which includes the lagged dependent variable (−$\rho {W}_{1}y))$ are suitable for use if there are strong connections among neighboring units. Therefore, the response variable depends on its neighbors through the weight matrix [44], meaning endogenous interaction effects are among the dependent variables. The model averages the adjacent values and accounts for autocorrelation using the weight matrix.

#### 2.3. The SARS-CoV-2 Hesitancy Outcome, Covariates Selection, and Data Analysis

#### 2.4. Model Selection

_{1}and W

_{2}must be equal (W

_{1}= W

_{3}= W), and they were constructed by allowing all counties to connect through a decaying distance (i.e., the influence and correlation of errors across counties decrease as the distance between neighboring counties increases). For simplicity, we set exogeneity effects through W

_{2}to only exist among neighboring counties by defining the connectivity distance threshold or the maximum length as between five or ten (arbitrarily set) neighboring counties. The distance threshold implied that only the closest neighbors would have similar observable and unobservable characteristics.

## 3. Results and Discussion

#### 3.1. Descriptive Statistics for the Response and Covariate Variables

#### 3.2. Spatial Regression Results

_{2}spatial weighting matrices. The Moran’s I test value was the slope of the line that best fits the relationship between neighboring SARS-CoV-2 vaccine hesitancy values and each county’s SARS-CoV-2 vaccine hesitancy in the dataset. The global Moran’s I statistic is an inferential statistic; the interpretation of the results is within the context of its null hypothesis. For the two spatial weighting matrices, the estimated Moran’s I statistics were 0.1869 and 0.18, which were not very large but statistically significant at confidence levels of more than 99. As the estimates were both statistically significant, we could reject the null hypothesis of spatial randomness. We must conclude that the SARS-CoV-2 vaccine hesitancy in the U.S. has substantial global spatial autocorrelation [70,71,75].

## 4. Discussion and Implications

## 5. Conclusion

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

## Appendix B

Variable | Average | Std. Dev. |
---|---|---|

Northeast | ||

Longitude | −75.095 | 2.795 |

Latitude | 41.825 | 1.429 |

Population density (people/km^{2}) | 4039.662 | 16,555.057 |

Metro–urban–rural continuum | ||

Rural counties (%) | 6.218 | |

Urban counties (%) | 27.979 | |

Metropolitan counties (%) | 65.803 | |

Cook’s political bipartisan index (P.V.I.) | 6.252 | 29.664 |

Adjusted social vulnerability index (S.V.I.) | 0.390 | 0.213 |

Socioeconomic status SV subindex | 0.372 | 0.192 |

Household characteristics S.V. subindex | 0.322 | 0.227 |

Racial and ethnic minority status S.V. subindex | 0.426 | 0.261 |

Housing and transportation S.V. subindex | 0.602 | 0.242 |

Surgo COVID-19 vaccine coverage (C.V.A.C.) index | 0.185 | 0.133 |

Historic under-vaccination subindex | 0.224 | 0.202 |

Resource-constrained healthcare system subindex | 0.237 | 0.190 |

Healthcare accessibility barriers subindex | 0.388 | 0.198 |

Irregular care-seeking behavior subindex | 0.216 | 0.149 |

COVID-19 death rate (per 100,000 people) | 184.0 | 83.73 |

Total adherence rate (per 1000 people) | 418.643 | 110.013 |

Evangelicals’ rate of adherence | 76.431 | 48.094 |

Historically black Protestants’ rate of adherence | 9.240 | 10.567 |

Mainline Protestants’ rate of adherence | 78.446 | 49.346 |

Catholics’ rate of adherence | 219.085 | 116.490 |

All other Christian faiths’ rate of adherence | 13.589 | 6.655 |

All other non-Christian faiths’ rate of adherence | 25.249 | 36.098 |

Midwest | ||

Longitude | −91.298 | 5.418 |

Latitude | 41.618 | 2.840 |

Population density (people/km^{2}) | 381.487 | 1082.475 |

Metro–urban–rural continuum | ||

Rural counties (%) | 33.220 | |

Urban counties (%) | 33.787 | |

Metropolitan counties (%) | 32.993 | |

Cook’s political bipartisan index (P.V.I.) | 34.310 | 23.839 |

Adjusted social vulnerability index (S.V.I.) | 0.346 | 0.234 |

Socioeconomic status SV subindex | 0.323 | 0.242 |

Household characteristics S.V. subindex | 0.397 | 0.251 |

Racial and ethnic minority status S.V. subindex | 0.322 | 0.218 |

Housing and transportation S.V. subindex | 0.413 | 0.267 |

Surgo COVID-19 vaccine coverage (C.V.A.C.) index | 0.356 | 0.245 |

Historic under-vaccination subindex | 0.442 | 0.331 |

Resource-constrained healthcare system subindex | 0.464 | 0.260 |

Healthcare accessibility barriers subindex | 0.355 | 0.250 |

Irregular care-seeking behavior subindex | 0.370 | 0.218 |

COVID-19 death rate (per 100,000 people) | 218.0 | 109.90 |

Total adherence rate (per 1000 people) | 474.859 | 180.632 |

Evangelicals’ rate of adherence | 182.634 | 105.243 |

Historically black Protestants’ rate of adherence | 16.569 | 21.989 |

Mainline Protestants’ rate of adherence | 132.545 | 143.136 |

Catholics’ rate of adherence | 137.341 | 104.056 |

All other Christian faiths’ rate of adherence | 14.007 | 11.463 |

All other non-Christian faiths’ rate of adherence | 3.898 | 11.725 |

South | ||

Longitude | −87.404 | 7.084 |

Latitude | 34.159 | 2.915 |

Population density (people/km^{2}) | 596.233 | 1749.380 |

Rural counties (%) | ||

Urban counties (%) | 24.240 | |

Metropolitan counties (%) | 32.268 | |

Cook’s political bipartisan index (P.V.I.) | 32.665 | 32.311 |

Adjusted social vulnerability index (S.V.I.) | 0.661 | 0.244 |

Socioeconomic status SV subindex | 0.669 | 0.238 |

Household characteristics S.V. subindex | 0.621 | 0.261 |

Racial and ethnic minority status S.V. subindex | 0.631 | 0.262 |

Housing and transportation S.V. subindex | 0.575 | 0.274 |

Surgo COVID-19 vaccine coverage (C.V.A.C.) index | 0.655 | 0.262 |

Historic under-vaccination subindex | 0.533 | 0.236 |

Resource-constrained healthcare system subindex | 0.618 | 0.267 |

Healthcare accessibility barriers subindex | 0.673 | 0.241 |

Irregular care-seeking behavior subindex | 0.574 | 0.289 |

COVID-19 death rate (per 100,000 people) | 239.30 | 107.10 |

Total adherence rate (per 1000 people) | 538.668 | 161.926 |

Evangelicals’ rate of adherence | 332.754 | 154.279 |

Historically black Protestants’ rate of adherence | 46.782 | 51.486 |

Mainline Protestants’ rate of adherence | 65.117 | 41.413 |

Catholics’ rate of adherence | 87.269 | 119.616 |

All other Christian faiths’ rate of adherence | 18.235 | 17.949 |

All other non-Christian faiths’ rate of adherence | 4.830 | 13.209 |

West | ||

Longitude | −117.605 | 12.811 |

Latitude | 42.867 | 7.729 |

Population density (people/km^{2}) | 550.857 | 2763.194 |

Metro–urban–rural continuum | ||

Rural counties (%) | 30.519 | |

Urban counties (%) | 28.896 | |

Metropolitan counties (%) | 40.584 | |

Cook’s political bipartisan index (P.V.I.) | 24.439 | 41.899 |

Adjusted social vulnerability index (S.V.I.) | 0.543 | 0.280 |

Socioeconomic status SV subindex | 0.724 | 0.600 |

Household characteristics S.V. subindex | 0.565 | 0.303 |

Racial and ethnic minority status S.V. subindex | 1.367 | 2.181 |

Housing and transportation S.V. subindex | 1.301 | 2.187 |

Surgo COVID-19 vaccine coverage index (C.V.A.C.) | 0.532 | 0.212 |

Historic under-vaccination subindex | 1.320 | 2.153 |

Resource-constrained healthcare system subindex | 0.385 | 0.244 |

Healthcare accessibility barriers subindex | 1.091 | 2.223 |

Irregular care-seeking behavior subindex | 1.460 | 2.146 |

COVID-19 death rate (per 100,000 people) | 140.60 | 115.2 |

Total adherence rate (per 1000 people) | 413.992 | 157.132 |

Evangelicals’ rate of adherence | 116.273 | 75.213 |

Historically black Protestants’ rate of adherence | 5.685 | 5.910 |

Mainline Protestants’ rate of adherence | 28.555 | 34.091 |

Catholics’ rate of adherence | 150.689 | 126.528 |

All other Christian faiths’ rate of adherence | 111.258 | 162.952 |

All other non-Christian faiths’ rate of adherence | 13.203 | 36.166 |

## Appendix C

## Appendix D

Spatial Weighting Matrix | Adjusted R Squared | Probability Values |
---|---|---|

${W}_{1}={W}_{3}=W$ (maximum distance threshold = 0) | ||

$\mathrm{Binary}:W={d}_{ij}$ (binary) | 0.222 | 0.047 |

$\mathrm{Linear}:W=1-\left({d}_{ij}/dmax\right)$ | 0.690 | 0.047 |

$\mathrm{Concave}-\mathrm{up}:W=1-{({d}_{ij}/dmax)}^{1}$ | 0.690 | 0.047 |

$\mathrm{Concave}-\mathrm{up}:W=1-{({d}_{ij}/dmax)}^{2}$ | 0.342 | 0.047 |

$\mathrm{Concave}-\mathrm{up}:W=1-{({d}_{ij}/dmax)}^{3}$ | 0.272 | 0.047 |

$\mathrm{Concave}-\mathrm{up}:W=1-{({d}_{ij}/dmax)}^{4}$ | 0.261 | 0.047 |

$\mathrm{Concave}-\mathrm{up}:W=1-{({d}_{ij}/dmax)}^{5}$ | 0.245 | 0.047 |

$\mathrm{Concave}-\mathrm{down}:1/{d}_{ij}^{0.1}$ | 0.683 | 0.047 |

$\mathrm{Concave}-\mathrm{down}:1/{d}_{ij}^{0.3}$ | 0.839 | 0.047 |

$\mathrm{Concave}-\mathrm{down}:1/{d}_{ij}^{0.5}$ | 0.880 | 0.047 |

$\mathrm{Concave}-\mathrm{down}:1/{d}_{ij}^{0.7}$ | 0.900 | 0.047 |

$\mathrm{Concave}-\mathrm{down}:1/{d}_{ij}^{0.9}$ | 0.904 | 0.047 |

${W}_{2}$ (distance threshold: five neighbors (37.61 km)) | ||

$\mathrm{Binary}:W={d}_{ij}$ (binary) | 0.115 | 0.047 |

$\mathrm{Linear}:W=1-\left({d}_{ij}/dmax\right)$ | 0.719 | 0.047 |

$\mathrm{Concave}-\mathrm{up}:W=1-{({d}_{ij}/dmax)}^{1}$ | 0.719 | 0.047 |

$\mathrm{Concave}-\mathrm{up}:W=1-{({d}_{ij}/dmax)}^{2}$ | 0.393 | 0.047 |

$\mathrm{Concave}-\mathrm{up}:W=1-{({d}_{ij}/dmax)}^{3}$ | 0.282 | 0.047 |

$\mathrm{Concave}-\mathrm{up}:W=1-{({d}_{ij}/dmax)}^{4}$ | 0.228 | 0.047 |

$\mathrm{Concave}-\mathrm{up}:W=1-{({d}_{ij}/dmax)}^{5}$ | 0.216 | 0.047 |

$\mathrm{Concave}-\mathrm{down}:1/{d}_{ij}^{0.1}$ | 0.597 | 0.047 |

$\mathrm{Concave}-\mathrm{down}:1/{d}_{ij}^{0.3}$ | 0.814 | 0.047 |

$\mathrm{Concave}-\mathrm{down}:1/{d}_{ij}^{0.5}$ | 0.867 | 0.047 |

$\mathrm{Concave}-\mathrm{down}:1/{d}_{ij}^{0.7}$ | 0.895 | 0.047 |

$\mathrm{Concave}-\mathrm{down}:1/{d}_{ij}^{0.9}$ | 0.900 | 0.047 |

${W}_{2}$ (distance threshold: 10 neighbors (38.78)) | ||

$\mathrm{Binary}:W={d}_{ij}$ (binary) | 0.079 | 0.016 |

$\mathrm{Linear}:W=1-\left({d}_{ij}/dmax\right)$ | 0.707 | 0.016 |

$\mathrm{Concave}-\mathrm{up}:W=1-{({d}_{ij}/dmax)}^{1}$ | 0.707 | 0.016 |

$\mathrm{Concave}-\mathrm{up}:W=1-{({d}_{ij}/dmax)}^{2}$ | 0.370 | 0.016 |

$\mathrm{Concave}-\mathrm{up}:W=1-{({d}_{ij}/dmax)}^{3}$ | 0.262 | 0.016 |

$\mathrm{Concave}-\mathrm{up}:W=1-{({d}_{ij}/dmax)}^{4}$ | 0.214 | 0.016 |

$\mathrm{Concave}-\mathrm{up}:W=1-{({d}_{ij}/dmax)}^{5}$ | 0.210 | 0.016 |

$\mathrm{Concave}-\mathrm{down}:1/{d}_{ij}^{0.1}$ | 0.575 | 0.016 |

$\mathrm{Concave}-\mathrm{down}:1/{d}_{ij}^{0.3}$ | 0.796 | 0.016 |

$\mathrm{Concave}-\mathrm{down}:1/{d}_{ij}^{0.5}$ | 0.857 | 0.016 |

$\mathrm{Concave}-\mathrm{down}:1/{d}_{ij}^{0.7}$ | 0.891 | 0.016 |

$\mathrm{Concave}-\mathrm{down}:1/{d}_{ij}^{0.9}$ | 0.898 | 0.016 |

## Appendix E

Estimated Model | Metrics | |||
---|---|---|---|---|

${\mathit{R}}^{2}$ | RMSE | AIC | BIC | |

$\mathrm{SLX}:y=X\beta +{W}_{2}X\theta +\epsilon $ | 0.59 | 0.08 | −5761 | −5585 |

$\mathrm{Spatial}\text{}\mathrm{lag}:y=\rho Wy+X\beta +\epsilon $ | 0.56 | 0.08 | −5560 | −5460 |

$\mathrm{Spatial}\text{}\mathrm{error}:y=X\beta +\mu ,\mu =\lambda W\mu +\epsilon $ | 0.54 | 0.09 | −5381 | −5280 |

$Durbinerror:y=X\beta +{W}_{2}X\theta +\mu ,\mu =\lambda {W}_{2}\mu +\epsilon $ | 0.63 | 0.08 | −6035 | −5853 |

$\mathrm{Spatial}\text{}\mathrm{Durbin}\mathrm{one}:y=\rho {W}_{2}y+X\beta +{W}_{2}X\theta +\epsilon $ | 0.63 | 0.08 | −6050 | −5867 |

$\mathrm{Spatial}\text{}\mathrm{Durbin}\mathrm{two}:y=\rho Wy+X\beta +WX\theta +\epsilon $ | 0.63 | 0.08 | −6015 | −5832 |

$\mathrm{Kelejian}-\mathrm{Prucha}:y=\rho Wy+X\beta +\mu ,\text{}\mu =\lambda W\mu +\epsilon $ | 0.63 | 0.08 | −5909 | −5803 |

$\mathrm{Mansiki}:y=\rho Wy+X\beta +{W}_{2}X\theta +\mu ,\mu =\lambda W\mu +\epsilon $ | 0.63 | 0.09 | −5903 | −5797 |

## Appendix F

Variables | Estimate | Lower Interval | Upper Interval | |
---|---|---|---|---|

Intercept | 0.583 | 0.426 | 0.752 | *** |

Metro dummy variable | −0.012 | −0.019 | −0.004 | *** |

Cook’s bipartisan political index (P.V.I.) | 0.000 | 0.000 | 0.001 | *** |

Socioeconomic status vulnerability | 0.041 | 0.020 | 0.062 | *** |

Racial and ethnic minority status vulnerability | 0.010 | −0.014 | 0.034 | |

Housing and transportation vulnerability | 0.005 | −0.010 | 0.022 | |

Historic under-vaccination | −0.037 | −0.053 | −0.018 | *** |

Resource-constrained healthcare system | 0.031 | 0.016 | 0.044 | *** |

Healthcare accessibility barriers | 0.027 | 0.004 | 0.050 | ** |

Irregular care-seeking behavior | −0.045 | −0.070 | −0.025 | *** |

COVID-19 death rate (per 1,000,000 people) | 0.023 | 0.005 | 0.039 | *** |

Evangelicals’ rate of adherence | 0.004 | 0.001 | 0.007 | *** |

Black Protestants’ rate of adherence | 0.000 | −0.001 | 0.001 | |

Catholics’ rate of adherence | 0.000 | 0.000 | 0.000 | |

All other non-Christian faiths’ rate of adherence | −0.016 | −0.034 | 0.004 | |

Lag. rural dummy variable | −0.460 | −0.649 | −0.287 | *** |

Lag. Cook’s bipartisan political index (P.V.I.) | 0.004 | 0.001 | 0.007 | *** |

Lag. socioeconomic status vulnerability | 0.805 | 0.444 | 1.177 | *** |

Lag. racial and ethnic minority status | −0.446 | −0.643 | −0.250 | *** |

Lag. housing and transportation vulnerability | −0.466 | −0.802 | −0.115 | *** |

Lag. historic under-vaccination | −0.195 | −0.410 | 0.001 | ** |

Lag. resource-constrained healthcare system | −0.870 | −1.070 | −0.693 | *** |

Lag. healthcare accessibility barriers | 0.227 | −0.122 | 0.545 | |

Lag. irregular care-seeking behavior | 0.385 | 0.205 | 0.583 | *** |

Lag. COVID-19 death rate (per 1000,000 people) | 0.100 | −0.101 | 0.288 | |

Lag. evangelicals’ rate of adherence | −0.130 | −0.173 | −0.084 | *** |

Lag. black Protestants’ rate of adherence | 0.057 | 0.038 | 0.075 | *** |

Lag. mainline Protestants’ rate of adherence | −0.001 | −0.001 | 0.000 | *** |

Lag. Catholics’ rate of adherence | 0.108 | −0.182 | 0.400 | |

Spatial autocorrelation coefficient ($\rho $) | 0.993 | 0.958 | 0.999 | *** |

ML residual variance (sigma) | 0.077 | 0.076 | 0.080 |

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**Figure 2.**Stepwise covariate selection results. Different color dots: for clear numbers of covariates.

Type of Comparison | Hypothesis | Test Statistic | Adjusted p-Value |
---|---|---|---|

One-way analysis of variance | Equal mean | F value | Pr(>F) |

Regions (${\mu}_{i}-{\mu}_{j}=0)$ | 0.4125 | 213.8 | <0.001 *** |

Levene’s test for homogeneity of variance | Equal variance | F value | Pr(>F) |

Regions (${\sigma}_{i}-{\sigma}_{j}=0)$ | 0.1268 | 135 | <0.001 *** |

Tukey multiple comparisons of means | Mean difference | t value | Pr(>|t|) |

Midwest–Northeast | 0.10158 | 11.16 | <0.001 *** |

South–Northeast | 0.18620 | 21.05 | <0.001 *** |

West–Northeast | 0.11702 | 11.15 | <0.001 *** |

South–Midwest | 0.08462 | 16.89 | <0.001 *** |

West–Midwest | 0.01544 | 2.05 | 0.16 |

West–South | −0.06919 | −9.57 | <0.001 *** |

Variable | Average | Std. Dev. |
---|---|---|

Longitude | −91.33 | 12.675 |

Latitude | 38.195 | 5.391 |

Population density (number/km^{2}) | 769.226 | 483.00 |

Metro-urban-rural continuum | ||

Metropolitan counties (%) | 41.3 | |

Urban counties (%) | 32.07 | |

Rural counties (%) | 26.63 | |

Cook’s bipartisan political index (PVI) | 30.338 | 31.792 |

Adjusted social vulnerability index (SVI) | 0.524 | 0.283 |

Socioeconomic status vulnerability subindex | 0.54 | 0.347 |

Household characteristics vulnerability subindex | 0.519 | 0.285 |

Racial and ethnic minority status vulnerability subindex | 0.6 | 0.84 |

Housing and transportation vulnerability subindex | 0.608 | 0.831 |

Surgo COVID-19 vaccine coverage index (CVAC) | 0.508 | 0.291 |

Historic under-vaccination subindex | 0.573 | 0.829 |

Resource-constrained healthcare system subindex | 0.512 | 0.282 |

Healthcare accessibility barriers subindex | 0.596 | 0.825 |

Irregular care-seeking behavior subindex | 0.584 | 0.842 |

COVID-19 death rate (per 100,000 people) | 217.052 | 111.947 |

Total adherence rate (per 1000 people) | 494.435 | 171.3149 |

Evangelicals’ rate of adherence | 239.684 | 157.721 |

Historically black Protestants’ rate of adherence | 34.787 | 45.827 |

Mainline Protestants’ rate of adherence | 84.512 | 96.154 |

Catholics’ rate of adherence | 122.166 | 121.182 |

All other Christian faiths’ rate of adherence | 27.727 | 65.994 |

All other non-Christian faiths’ rate of adherence | 6.97 | 20.206 |

Variable | Estimate | Lower Interval | Upper Interval | |
---|---|---|---|---|

Direct | ||||

Irregular care-seeking behavior | −0.0404 | −0.0618 | −0.0191 | *** |

Historic under-vaccination | −0.0395 | −0.0556 | −0.0234 | *** |

Metro dummy variable | −0.0185 | −0.0260 | −0.0109 | *** |

All other non-Christian faiths’ rate of adherence | −0.0140 | −0.0334 | 0.0055 | |

Housing and transportation vulnerability | −0.0013 | −0.0181 | 0.0155 | |

Catholics’ rate of adherence | −0.0001 | −0.0001 | 0.0000 | *** |

Cook’s bipartisan political index (P.V.I.) | 0.0004 | 0.0002 | 0.0006 | *** |

Black Protestants’ rate of adherence | 0.0011 | −0.0001 | 0.0023 | * |

Evangelicals’ rate of adherence | 0.0025 | −0.0005 | 0.0054 | |

Racial and ethnic minority status vulnerability | 0.0038 | −0.0199 | 0.0274 | |

Resource-constrained healthcare system | 0.0186 | 0.0032 | 0.0339 | ** |

COVID-19 death rate (per 1,000,000 people) | 0.0249 | 0.0079 | 0.0419 | *** |

Healthcare accessibility barriers | 0.0303 | 0.0086 | 0.0520 | *** |

Socioeconomic status vulnerability | 0.0529 | 0.0325 | 0.0733 | *** |

Indirect | ||||

Resource-constrained healthcare system | −20.1111 | −27.8318 | −12.3905 | *** |

Metro dummy variable | −11.2352 | −16.7978 | −5.6726 | *** |

Housing and transportation vulnerability | −10.8746 | −20.2472 | −1.5021 | ** |

Racial and ethnic minority status vulnerability | −10.4776 | −16.3187 | −4.6365 | *** |

Historic under-vaccination | −5.5636 | −10.8990 | −0.2282 | ** |

Evangelicals’ rate of adherence | −3.0024 | −4.5244 | −1.4804 | *** |

Catholics’ rate of adherence | −0.0227 | −0.0374 | −0.0080 | *** |

Cook’s bipartisan political index (P.V.I.) | 0.1118 | 0.0308 | 0.1928 | *** |

Black Protestants’ rate of adherence | 1.3705 | 0.7354 | 2.0057 | *** |

All other non-Christian faiths’ rate of adherence | 2.2432 | −4.8355 | 9.3220 | |

COVID-19 death rate (per 1,000,000 people) | 2.8096 | −2.4472 | 8.0664 | |

Healthcare accessibility barriers | 6.0689 | −2.4014 | 14.5393 | |

Irregular care-seeking behavior | 8.3187 | 3.1648 | 13.4726 | *** |

Socioeconomic status vulnerability | 20.0416 | 8.2052 | 31.8779 | *** |

Total | ||||

Resource-constrained healthcare system | −20.0926 | −27.8134 | −12.3717 | *** |

Metro dummy variable | −11.2537 | −16.8182 | −5.6892 | *** |

Housing and transportation vulnerability | −10.8759 | −20.2493 | −1.5025 | ** |

Racial and ethnic minority status vulnerability | −10.4739 | −16.3089 | −4.6388 | *** |

Historic under-vaccination | −5.6031 | −10.9311 | −0.2750 | ** |

Evangelicals’ rate of adherence | −2.9999 | −4.5221 | −1.4778 | *** |

Catholics’ rate of adherence | −0.0227 | −0.0374 | −0.0081 | *** |

Cook’s bipartisan political index (P.V.I.) | 0.1122 | 0.0312 | 0.1932 | *** |

Black Protestants’ rate of adherence | 1.3716 | 0.7362 | 2.0070 | *** |

All other non-Christian faiths’ rate of adherence | 2.2293 | −4.8477 | 9.3062 | |

COVID-19 death rate (per 1,000,000 people) | 2.8345 | −2.4210 | 8.0900 | |

Healthcare accessibility barriers | 6.0993 | −2.3699 | 14.5685 | |

Irregular care-seeking behavior | 8.2782 | 3.1311 | 13.4254 | *** |

Socioeconomic status vulnerability | 20.0945 | 8.2558 | 31.9331 | *** |

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**MDPI and ACS Style**

Kaliba, A.R.; Andrews, D.R.
The Impact of Meso-Level Factors on SARS-CoV-2 Vaccine Early Hesitancy in the United States. *Int. J. Environ. Res. Public Health* **2023**, *20*, 6313.
https://doi.org/10.3390/ijerph20136313

**AMA Style**

Kaliba AR, Andrews DR.
The Impact of Meso-Level Factors on SARS-CoV-2 Vaccine Early Hesitancy in the United States. *International Journal of Environmental Research and Public Health*. 2023; 20(13):6313.
https://doi.org/10.3390/ijerph20136313

**Chicago/Turabian Style**

Kaliba, Aloyce R., and Donald R. Andrews.
2023. "The Impact of Meso-Level Factors on SARS-CoV-2 Vaccine Early Hesitancy in the United States" *International Journal of Environmental Research and Public Health* 20, no. 13: 6313.
https://doi.org/10.3390/ijerph20136313