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##### Survival plot

We estimate the survival function using the Kaplan-Meier estimator and represent this function visually using a Kaplan-Meier curve, showing the probability of not getting infected by SARS-CoV-2 at a certain time after onset of follow-up. The survival function is estimated for the control and intervention group.

The cumulative incidence of the event (SARS-CoV-2 infection) was additionally plotted.

##### Survival (time-to-event)

The probability of not getting infected by SARS-CoV-2 beyond a certain time after onset of follow-up (survival function, estimated using the Kaplan-Meier estimator) is reported for different periods.

Strata | Time | Number at risk | Cumulative sum of number of events | Cumulative sum of number censored | Survival | Std. error | Cumulative hazard | Std. error cumulative hazard
--- | --- | --- | --- | --- | --- | --- | --- | ---
Not fully vaccinated | 0 | 7757 | 0 | 0 | 1.0000 | 0.00000 | 0.0000 | 0.00000
Not fully vaccinated | 100 | 2632 | 66 | 5080 | 0.9849 | 0.00197 | 0.0152 | 0.00200
Not fully vaccinated | 200 | 884 | 199 | 6692 | 0.9089 | 0.00683 | 0.0954 | 0.00751
Not fully vaccinated | 300 | 597 | 312 | 6848 | 0.7669 | 0.01359 | 0.2651 | 0.01769
Not fully vaccinated | 400 | 475 | 333 | 6961 | 0.7398 | 0.01433 | 0.3009 | 0.01934
Not fully vaccinated | 500 | 93 | 335 | 7332 | 0.7312 | 0.01540 | 0.3126 | 0.02103
Fully vaccinated | 0 | 7757 | 0 | 0 | 1.0000 | 0.00000 | 0.0000 | 0.00000
Fully vaccinated | 100 | 2639 | 65 | 5073 | 0.9844 | 0.00202 | 0.0158 | 0.00205
Fully vaccinated | 200 | 863 | 242 | 6671 | 0.8821 | 0.00782 | 0.1254 | 0.00886
Fully vaccinated | 300 | 611 | 329 | 6817 | 0.7745 | 0.01282 | 0.2552 | 0.01653
Fully vaccinated | 400 | 495 | 344 | 6926 | 0.7553 | 0.01343 | 0.2803 | 0.01776
Fully vaccinated | 500 | 100 | 344 | 7317 | 0.7553 | 0.01343 | 0.2803 | 0.01776

##### Median survival time

The median survival time is the time corresponding to a probability of not obtaining a SARS-CoV-2 infection probability of 0.5. (if NA, the probability of not obtaining a SARS-CoV-2 infection did not drop below 50%)

Characteristic | Median survival (95% CI)
--- | ---
fully_vaccinated_bl |
FALSE | — (—, —)
TRUE | — (—, —)

##### Cox regression and estimation of the average treatment effect

A Cox regression model was built to examine the relationship between the distribution of the probability of not obtaining a SARS-CoV-2 infection (survival distribution) and completing a primary vaccination schedule (covariate). The Cox proportional hazards regression model was fitted with ‘fully_vaccinated_bl’ as a covariate and accounts for clustering within individuals (as one individual can be re-sampled as control).

A hazard ratio (HR) is computed for the covariate ‘fully_vaccinated_bl’. A hazard can be interpreted as the instantaneous rate of SARS-CoV-2 infections in individuals that are at risk for obtaining an infection (Cox proportional hazards regression assumes stable proportional hazards over time). A HR < 1 indicates reduced hazard of SARS-CoV-2 infection when having completed a primary vaccination schedule whereas a HR > 1 indicates an increased hazard of SARS-CoV-2 infection.

| Parameter estimate | SE coefficient | Robust SE coefficient | P-value | Hazard Ratio (HR) (95% CI for HR)
--- | --- | --- | --- | --- | ---
fully_vaccinated_blTRUE | 0.029 | 0.077 | 0.1 | 0.772 | 1.029 (0.828, 1.23)

The overall significance of the model is tested.

| Test statistic | Df | P-value
--- | --- | --- | ---
Likelihood ratio test | 0.14113991 | 1 | 0.7071504
Wald test | 0.08000000 | 1 | 0.7723077
Score (logrank) test | 0.14113660 | 1 | 0.7071537
Robust score test | 0.08457912 | 1 | 0.7711852

Proportional hazards during the study period might be unlikely. As such, the RMST and RMTL ratios are additionally calculated, providing an alternative estimate for the the Average Treatment Effect (ATE), without requiring the proportional hazards assumption to be met.

Arm | Measure | Estimate | SE | CI.lower | CI.upper
--- | --- | --- | --- | --- | ---
fully_vaccinated_bl==FALSE | RMST | 326.426 | 2.019 | 322.470 | 330.382
fully_vaccinated_bl==TRUE | RMST | 325.207 | 2.040 | 321.208 | 329.205
fully_vaccinated_bl==FALSE | RMTL | 38.574 | 2.019 | 34.618 | 42.530
fully_vaccinated_bl==TRUE | RMTL | 39.793 | 2.040 | 35.795 | 43.792

Measure | Estimate | CI.lower | CI.upper | p_value
--- | --- | --- | --- | ---
RMST (fully_vaccinated_bl==TRUE)-(fully_vaccinated_bl==FALSE) | -1.219 | -6.844 | 4.405 | 0.671
RMST (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 0.996 | 0.979 | 1.014 | 0.671
RMTL (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 1.032 | 0.894 | 1.191 | 0.671

##### Survival plot

We estimate the survival function using the Kaplan-Meier estimator and represent this function visually using a Kaplan-Meier curve, showing the probability of not getting infected by SARS-CoV-2 at a certain time after onset of follow-up. The survival function is estimated for the control and intervention group, within subsets defined by the vaccination schedule.

The cumulative incidence of the event (SARS-CoV-2 infection) was additionally plotted within subsets defined by the vaccination schedule.

##### Cox regression and estimation of the average treatment effect

A Cox regression model was built to examine the relationship between the distribution of the probability of not obtaining a SARS-CoV-2 infection (survival distribution) and completing a primary vaccination schedule (covariate), and whether this differs according to the administered vaccination schedule. A stratified Cox proportional hazards regression model was fitted with ‘fully_vaccinated_bl’ as a covariate, ‘vaccination_schedule_cd’ as a stratification factor, and accounting for clustering within individuals (as one individual can be re-sampled as control).

| Parameter estimate | SE coefficient | Robust SE coefficient | P-value | Hazard Ratio (HR) (95% CI for HR)
--- | --- | --- | --- | --- | ---
fully_vaccinated_blTRUE | 0.152 | 0.219 | 0.227 | 0.503 | 1.164 (0.646, 1.682)
fully_vaccinated_blTRUE:strata(vaccination_schedule_cd)BP-BP | -0.128 | 0.238 | 0.235 | 0.585 | 0.88 (0.474, 1.285)
fully_vaccinated_blTRUE:strata(vaccination_schedule_cd)JJ | -0.301 | 0.412 | 0.414 | 0.467 | 0.74 (0.139, 1.341)
fully_vaccinated_blTRUE:strata(vaccination_schedule_cd)MD-MD | -0.142 | 0.296 | 0.289 | 0.622 | 0.867 (0.377, 1.358)

The overall significance of the model is tested.

| Test statistic | Df | P-value
--- | --- | --- | ---
Likelihood ratio test | 0.7325156 | 4 | 0.9472594
Wald test | 0.6700000 | 4 | 0.9549868
Score (logrank) test | 0.7318399 | 4 | 0.9473451
Robust score test | 0.6771403 | 4 | 0.9541178

Proportional hazards during the study period might be unlikely. As such, the RMST and RMTL ratios are additionally calculated, providing an alternative estimate for the the Average Treatment Effect (ATE), without requiring the proportional hazards assumption to be met.

Vaccination_schedule | Arm | Measure | Estimate | SE | CI.lower | CI.upper
--- | --- | --- | --- | --- | --- | ---
BP-BP | fully_vaccinated_bl==FALSE | RMST | 326.655 | 2.428 | 321.897 | 331.414
BP-BP | fully_vaccinated_bl==TRUE | RMST | 326.977 | 2.401 | 322.271 | 331.683
BP-BP | fully_vaccinated_bl==FALSE | RMTL | 38.345 | 2.428 | 33.586 | 43.103
BP-BP | fully_vaccinated_bl==TRUE | RMTL | 38.023 | 2.401 | 33.317 | 42.729
MD-MD | fully_vaccinated_bl==FALSE | RMST | 324.622 | 5.470 | 313.901 | 335.343
MD-MD | fully_vaccinated_bl==TRUE | RMST | 321.345 | 5.629 | 310.314 | 332.377
MD-MD | fully_vaccinated_bl==FALSE | RMTL | 40.378 | 5.470 | 29.657 | 51.099
MD-MD | fully_vaccinated_bl==TRUE | RMTL | 43.655 | 5.629 | 32.623 | 54.686
AZ-AZ | fully_vaccinated_bl==FALSE | RMST | 326.690 | 5.802 | 315.319 | 338.062
AZ-AZ | fully_vaccinated_bl==TRUE | RMST | 317.238 | 6.492 | 304.514 | 329.961
AZ-AZ | fully_vaccinated_bl==FALSE | RMTL | 38.310 | 5.802 | 26.938 | 49.681
AZ-AZ | fully_vaccinated_bl==TRUE | RMTL | 47.762 | 6.492 | 35.039 | 60.486
JJ | fully_vaccinated_bl==FALSE | RMST | 326.314 | 9.174 | 308.333 | 344.294
JJ | fully_vaccinated_bl==TRUE | RMST | 328.129 | 9.580 | 309.353 | 346.905
JJ | fully_vaccinated_bl==FALSE | RMTL | 38.686 | 9.174 | 20.706 | 56.667
JJ | fully_vaccinated_bl==TRUE | RMTL | 36.871 | 9.580 | 18.095 | 55.647

Vaccination_schedule | Measure | Estimate | CI.lower | CI.upper | p_value
--- | --- | --- | --- | --- | ---
BP-BP | RMST (fully_vaccinated_bl==TRUE)-(fully_vaccinated_bl==FALSE) | 0.322 | -6.371 | 7.014 | 0.925
BP-BP | RMST (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 1.001 | 0.981 | 1.022 | 0.925
BP-BP | RMTL (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 0.992 | 0.832 | 1.182 | 0.925
MD-MD | RMST (fully_vaccinated_bl==TRUE)-(fully_vaccinated_bl==FALSE) | -3.277 | -18.660 | 12.107 | 0.676
MD-MD | RMST (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 0.990 | 0.944 | 1.038 | 0.676
MD-MD | RMTL (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 1.081 | 0.749 | 1.560 | 0.677
AZ-AZ | RMST (fully_vaccinated_bl==TRUE)-(fully_vaccinated_bl==FALSE) | -9.453 | -26.518 | 7.612 | 0.278
AZ-AZ | RMST (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 0.971 | 0.921 | 1.024 | 0.279
AZ-AZ | RMTL (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 1.247 | 0.837 | 1.858 | 0.278
JJ | RMST (fully_vaccinated_bl==TRUE)-(fully_vaccinated_bl==FALSE) | 1.815 | -24.181 | 27.812 | 0.891
JJ | RMST (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 1.006 | 0.929 | 1.089 | 0.891
JJ | RMTL (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 0.953 | 0.478 | 1.899 | 0.891

##### Survival plot

We estimate the survival function using the Kaplan-Meier estimator and represent this function visually using a Kaplan-Meier curve, showing the probability of not getting infected by SARS-CoV-2 at a certain time after onset of follow-up. The survival function is estimated for the control and intervention group, within subsets defined by the NUTS3 residence area.

The cumulative incidence of the event (SARS-CoV-2 infection) was additionally plotted within subsets defined by the NUTS3 residence area.

##### Cox regression and estimation of the average treatment effect

A Cox regression model was built to examine the relationship between the distribution of the probability of not obtaining a SARS-CoV-2 infection (survival distribution) and completing a primary vaccination schedule (covariate), and whether this differs according to the area of residence (NUTS3 level). A stratified Cox proportional hazards regression model was fitted with ‘fully_vaccinated_bl’ as a covariate, ‘residence_area_cd’ as a stratification factor, and accounting for clustering within individuals (as one individual can be re-sampled as control).

| Parameter estimate | SE coefficient | Robust SE coefficient | P-value | Hazard Ratio (HR) (95% CI for HR)
--- | --- | --- | --- | --- | ---
fully_vaccinated_blTRUE | 0.167 | 0.199 | 0.235 | 0.479 | 1.181 (0.636, 1.726)
fully_vaccinated_blTRUE:strata(residence_area_cd)732 | -0.048 | 0.283 | 0.343 | 0.888 | 0.953 (0.312, 1.594)
fully_vaccinated_blTRUE:strata(residence_area_cd)733 | -0.185 | 0.219 | 0.265 | 0.483 | 0.831 (0.4, 1.262)

The overall significance of the model is tested.

| Test statistic | Df | P-value
--- | --- | --- | ---
Likelihood ratio test | 1.0947216 | 3 | 0.7783485
Wald test | 0.7500000 | 3 | 0.8611512
Score (logrank) test | 1.0937299 | 3 | 0.7785879
Robust score test | 0.7764369 | 3 | 0.8550943

Proportional hazards during the study period might be unlikely. As such, the RMST and RMTL ratios are additionally calculated, providing an alternative estimate for the the Average Treatment Effect (ATE), without requiring the proportional hazards assumption to be met.

Residence_area | Arm | Measure | Estimate | SE | CI.lower | CI.upper
--- | --- | --- | --- | --- | --- | ---
733 | fully_vaccinated_bl==FALSE | RMST | 326.757 | 2.357 | 322.138 | 331.377
733 | fully_vaccinated_bl==TRUE | RMST | 326.919 | 2.371 | 322.273 | 331.565
733 | fully_vaccinated_bl==FALSE | RMTL | 38.243 | 2.357 | 33.623 | 42.862
733 | fully_vaccinated_bl==TRUE | RMTL | 38.081 | 2.371 | 33.435 | 42.727
731 | fully_vaccinated_bl==FALSE | RMST | 327.062 | 5.280 | 316.714 | 337.410
731 | fully_vaccinated_bl==TRUE | RMST | 320.789 | 5.565 | 309.882 | 331.696
731 | fully_vaccinated_bl==FALSE | RMTL | 37.938 | 5.280 | 27.590 | 48.286
731 | fully_vaccinated_bl==TRUE | RMTL | 44.211 | 5.565 | 33.304 | 55.118
732 | fully_vaccinated_bl==FALSE | RMST | 323.474 | 5.838 | 312.033 | 334.915
732 | fully_vaccinated_bl==TRUE | RMST | 321.090 | 5.662 | 309.993 | 332.187
732 | fully_vaccinated_bl==FALSE | RMTL | 41.526 | 5.838 | 30.085 | 52.967
732 | fully_vaccinated_bl==TRUE | RMTL | 43.910 | 5.662 | 32.813 | 55.007

Residence_area | Measure | Estimate | CI.lower | CI.upper | p_value
--- | --- | --- | --- | --- | ---
733 | RMST (fully_vaccinated_bl==TRUE)-(fully_vaccinated_bl==FALSE) | 0.161 | -6.390 | 6.713 | 0.961
733 | RMST (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 1.000 | 0.981 | 1.021 | 0.961
733 | RMTL (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 0.996 | 0.839 | 1.182 | 0.961
731 | RMST (fully_vaccinated_bl==TRUE)-(fully_vaccinated_bl==FALSE) | -6.273 | -21.307 | 8.762 | 0.414
731 | RMST (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 0.981 | 0.936 | 1.027 | 0.414
731 | RMTL (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 1.165 | 0.807 | 1.683 | 0.415
732 | RMST (fully_vaccinated_bl==TRUE)-(fully_vaccinated_bl==FALSE) | -2.384 | -18.323 | 13.555 | 0.769
732 | RMST (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 0.993 | 0.945 | 1.043 | 0.769
732 | RMTL (fully_vaccinated_bl==TRUE)/(fully_vaccinated_bl==FALSE) | 1.057 | 0.728 | 1.537 | 0.770