VAX-MOM COVID-19: Increasing Maternal COVID-19 Vaccination: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for VAX-MOM COVID-19: Increasing Maternal COVID-19 Vaccination (NCT05570630), a randomized na study. The primary outcome is Rate of COVID-19 Vaccination (6 Month).

2. Clinical Question

The clinical rationale in the registry summary is that COVID-19 infection during pregnancy is associated with increased risk of pre-eclampsia, preterm birth and stillbirth, and higher rates of ICU admission and intubation than among people who are not pregnant. The summary also states that COVID-19 vaccination is recommended before and during pregnancy to decrease these risks. It reports that, as of June 2022, 71% of pregnant women were vaccinated, most prior to pregnancy, and that the rate was 58% among non-Hispanic Black women. These figures explain the motivation described in the registry; they are not outcomes of this randomized comparison.

3. Trial Design

VAX-MOM COVID-19 is a randomized, parallel-group trial with no masking reported. The registry record lists an enrollment of 6911 and identifies the sponsor as the University of Rochester. The trial is marked completed. Its start date is 2022-10-21, and its primary completion date is 2024-07-31. The phase is listed as NA.

The two arms were VAX-MOM COVID-19 Intervention and Standard of Care. The registry summary describes the project as a randomized controlled trial aimed at practice change in obstetricians' offices. However, the arm descriptions are empty, so the intervention content, how it was delivered, and the exact usual-care practices are not reported in the registry record. The randomization method, allocation concealment, unit of randomization, and details about participating practices are also not reported in the registry record. Although the summary refers to practice change, the record does not give enough detail to establish how assignment was implemented across clinics and patients.

The primary outcome is Rate of COVID-19 Vaccination (6 Month), with a timeframe of 6 months and a population description of 6 month cohort. It compares the average rate between intervention and control arms among vaccine-eligible patients at participating OB/GYN practice sites. Vaccination status was obtained from patient electronic health records. The secondary outcome is Rate of COVID-19 Vaccination (9 Month), assessed at 9 months.

For the 6-month outcome, the posted arm summaries include 2789 participants in the intervention arm and 1759 in Standard of Care. For the 9-month outcome, the respective posted arm counts are 4256 and 2655. These outcome-specific counts do not equal the overall enrollment, and the reasons for the differences are not reported in the registry record. The record does not provide a participant flow description or explain which patients contributed to each outcome cohort.

No masking was used according to the registry. The record does not state whether outcome extraction or analysis was blinded to assignment. It also does not report the original sample-size calculation or the trial's design assumptions. The planning illustrations based on observed effects are separate from the trial's own design planning.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Overall Study

Analysis population for the primary outcome, as defined in the registry: 6 month cohort

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

6. Primary Endpoint

The registry lists the primary outcome as Rate of COVID-19 Vaccination (6 Month), measured over 6 months.

The primary endpoint is Rate of COVID-19 Vaccination (6 Month). The registry describes it as the average rate of COVID-19 vaccination, compared between the intervention and control arms among vaccine-eligible patients at participating OB/GYN practice sites. The timeframe is 6 months, and the stated source for vaccination status is patient electronic health records. The posted outcome is represented as a count of participants with an event, rather than as a continuous measurement.

A vaccination endpoint is relevant to the intervention's stated aim: improving maternal COVID-19 vaccination through practice change in obstetricians' offices. A binary outcome also makes it possible to compare the proportion of participants with vaccination recorded in each arm. The risk difference expresses that contrast in absolute terms: it subtracts the comparator-arm event proportion from the intervention-arm event proportion. A positive estimate indicates a higher observed event proportion in the intervention arm; it does not by itself establish why the difference occurred or whether it is precise enough to distinguish from no difference.

The record says vaccination status came from electronic health records, but does not report how records were validated, how vaccination outside the participating health systems was handled, or how missing or uncertain status was classified. It also does not specify whether vaccination had to occur after assignment or how prior vaccination was treated in defining eligibility and the endpoint. Those details matter for interpreting what a recorded vaccination event represents. They are not reported in the registry record.

7. Results Posted in the Registry

• VAX-MOM COVID-19 Intervention: 9.0% (251/2789) versus 7.4% (130/1759) on control; risk difference 1.6 percentage points (95% CI -0.0 to 3.2), risk ratio 1.22 (0.99–1.49); unadjusted two-proportion z-test p = 0.056.

8. Secondary Efficacy Endpoints

The registry also posts results for secondary outcomes. For each outcome below, the endpoint type, the test applied to the posted summary statistics, the result and its interpretation are given in turn. Secondary endpoints are usually not adjusted for multiple testing, so they support rather than confirm the primary finding.

Rate of COVID-19 Vaccination (9 Month)

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • VAX-MOM COVID-19 Intervention: 9.5% (404/4256) versus 6.8% (180/2655) on control; risk difference 2.7 percentage points (95% CI 1.4 to 4.0), risk ratio 1.40 (1.18–1.66); unadjusted two-proportion z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically higher for VAX-MOM COVID-19 Intervention than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The registry lists one secondary outcome: Rate of COVID-19 Vaccination (9 Month). It concerns vaccination status at a later timepoint than the primary outcome and uses the same general kind of event count. The posted result is directionally consistent with the primary comparison: the observed vaccination proportion is higher in the intervention arm for both outcomes. The secondary estimate is more clearly separated from zero in its posted confidence interval than the primary estimate.

That consistency can be informative because it suggests that the overall direction of the two posted comparisons is not contradictory. It does not, however, make the two results interchangeable or independent. They measure vaccination at different timepoints, use different outcome cohort counts, and may include overlapping participants. The registry does not report participant overlap or explain the cohort construction, so the extent of dependence between the outcomes is not reported in the registry record.

The secondary result should carry less confirmatory weight because it is not the designated primary endpoint and because the registry reports an unadjusted p-value without a multiplicity strategy. When several outcomes or timepoints are tested, the chance of observing an apparently strong result by chance can increase. Statistical methods can address this issue through prespecified testing sequences or adjustment procedures, but no such plan is reported in the registry record. The absence of a reported approach does not establish that none was used; it means the record does not permit evaluation of it.

The secondary result can therefore be considered alongside the primary outcome as contextual evidence about the pattern of recorded vaccination. It should not replace the primary result or be treated as a definitive confirmation on its own. The record does not report additional secondary analyses, subgroup analyses, or adjusted comparisons that would help clarify how stable the pattern is across participant groups or practice settings.

Secondary outcomes registered for this trial, for reference (only those above were recalculated):

• Rate of COVID-19 Vaccination (9 Month)

9. Statistical Interpretation

10. Statistical Methods Explained

A binary outcome records whether each participant experienced an event or did not. In this trial, the event is a participant counted as vaccinated for the relevant timepoint. The arm summaries give the number of participants with the event and the outcome-specific participant count. A rate or risk is the event count divided by the number assessed in that arm. For the 6-month endpoint, the registry also posts the arm proportions directly as 0.09 and 0.074. The comparison is presented as a risk difference, which is the intervention-arm risk minus the comparator-arm risk.

What does a risk difference describe?

The risk difference describes an absolute contrast on the outcome's risk scale. A value above zero means the observed event proportion is higher in the intervention arm; a value below zero means it is lower. A value of zero corresponds to equal proportions. The 6-month point estimate is 0.016. Its confidence interval reaches from -< 0.001 to 0.032, so the interval includes zero. The 9-month estimate is 0.027, with an interval from 0.014 to 0.040. These estimates are based on the posted arm summaries, and the registry does not report the detailed analysis method used to produce them.

How should a p-value be read?

A p-value is a measure of how compatible the observed data are with a specified null hypothesis under the assumptions of the statistical test. It is not the probability that the null hypothesis is true, and it does not quantify the probability that the intervention caused the observed result. The primary comparison has an unadjusted p-value of 0.056. The 9-month comparison has an unadjusted p-value of <0.001. These are unadjusted values, and no multiplicity adjustment is reported. Interpretation should consider the confidence interval and the study context as well as the p-value.

The registry gives the comparative metric as a risk difference, while also listing risk ratios and odds ratios. A risk ratio compares the event risk in one arm with the risk in the other. An odds ratio compares event odds, where odds are the event count relative to the non-event count. These relative measures can be useful for different purposes, but they do not convey the same scale as an absolute risk difference. The choice of measure affects how a contrast is communicated, not the underlying participant outcomes.

What does a confidence interval contribute?

A confidence interval represents a range of estimates compatible with the observed data under the calculation's assumptions. It communicates uncertainty around the point estimate. Its width reflects precision: narrower intervals indicate more precise estimates, while wider intervals leave a broader range of plausible values. The interval should not be read as a range that contains the true effect with certainty. It also does not account for concerns absent from the calculation, such as bias from missing data or an analysis that does not handle practice-level clustering appropriately.

For the primary estimate, the confidence interval includes zero, indicating that the posted data are compatible with a small negative difference, no difference, or a positive difference within the interval. The 9-month interval is entirely above zero, but its interpretation remains qualified by its secondary status and the absence of a reported multiplicity plan. Neither interval alone determines whether an effect is important in practice; the registry record does not state a threshold for a meaningful difference.

Why can clustering matter?

Participants treated within the same practice may have more similar care and vaccination recording than participants in different practices. In general, such within-practice similarity is called clustering. If an analysis treats all participants as fully independent when outcomes are correlated within practices, standard errors may be too small and confidence intervals may be too narrow. This is a general statistical concern for practice-based trials, not a claim about how the VAX-MOM data were analyzed. The registry record does not report the randomization unit, the number of practices, or whether the analysis accounted for clustering.

Random allocation can help make comparison groups similar on average, but its protection depends on how allocation is carried out and on appropriate analysis. The registry identifies the design as randomized and parallel, but does not detail sequence generation, allocation concealment, or the unit of randomization. No adjusted model, covariate list, variance method, or approach to practice effects is reported in the registry record. Consequently, the posted estimates can be described, but their analytical construction cannot be fully evaluated from the record.

Outcome ascertainment through electronic health records can provide recorded vaccination information, but the record does not explain how records were searched or reconciled, whether vaccinations received elsewhere were captured, or how missing status was handled. A participant without a recorded event might be unvaccinated, might have been vaccinated outside the captured records, or might have incomplete information. Those possibilities are general measurement concerns; the registry does not say which, if any, occurred in this trial.

The registry lists no reported analyses, and therefore does not specify whether the comparison was intention-to-treat, per-protocol, or based on another analysis population. It also does not report a missing-data method or an adjusted analysis. In general, analysis-population choices and missing-outcome handling can affect estimates if follow-up or record availability differs between arms. They should not be inferred from the posted counts alone.

11. Confidence Intervals and Precision

Precision concerns how narrowly the data estimate the contrast, not whether the contrast is important or caused by the intervention. The primary 6-month risk difference is 0.016, and its 95% confidence interval runs from -< 0.001 to 0.032. This interval is compatible with estimates just below zero as well as positive differences extending to 0.032. Because zero lies within the interval, the result does not sharply distinguish a small increase from no difference. The point estimate alone gives a narrower impression than the uncertainty interval does.

The 9-month risk difference is 0.027, with a 95% confidence interval from 0.014 to 0.040. This interval is entirely positive and is narrower than the primary interval. It indicates more constrained uncertainty around the secondary estimate, under the assumptions used to calculate it. That apparent precision does not remove concerns about secondary-outcome multiplicity, possible clustering, missing status, or the undocumented analysis method.

Confidence intervals quantify sampling uncertainty under a statistical model; they do not automatically capture all sources of uncertainty. The registry does not report enough detail to assess whether the reported intervals account for the practice-based design, nor whether data missing from electronic records were handled in a way that could affect precision. The interval also does not say whether the difference is large enough to matter to patients or practices; no threshold for a meaningful change is reported in the registry record.

The same estimate can be expressed using a risk ratio or odds ratio, but these measures do not make the underlying evidence more precise. Interpretation should remain anchored in the outcome definition, arm counts, and uncertainty around the contrast. For the primary endpoint, the interval's inclusion of zero is central to understanding the uncertainty; for the secondary endpoint, its interval is positive but should be interpreted in the context of multiple outcomes and unadjusted reporting.

12. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The planning figures are participants per arm, not total events. They are illustrative calculations based on the observed effect, rather than the trial's original design calculation. The stated assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. Each scenario uses the stated fraction of the observed effect as the effect to detect. The trial's own original sample-size assumptions and justification are not reported in the registry record.

The 80% power column gives the number of participants per arm that the illustration estimates would be needed to have an 80% chance of detecting the specified effect, if the assumptions hold and the effect is real. The 90% power column gives the corresponding per-arm figure for a 90% chance. Power describes a design's capacity to detect an effect under assumptions; it is not a measure of the probability that a result is true. These figures are not observed power.

The scenarios labeled 100%, 75% and 50% of the observed effect use progressively smaller effects to detect. Smaller effects are harder to distinguish from random variation, so they require more participants per arm to reach the same power target. The figures are planning illustrations, not evidence that the trial was designed around these effects or that a future study would achieve the stated power. They also do not account for any design features absent from the stated calculation assumptions. In particular, the stated assumptions do not describe adjustment for practice-level clustering, loss to follow-up, or missing vaccination records. Such considerations would need to be addressed in a study-specific design calculation, and the registry does not report the original calculation for this trial.

13. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.074; treatment proportion p₁ = 0.090; average p̄ = 0.082.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.3879 = 0.7603.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.3877 = 0.3263.
4. n = (0.7603 + 0.3263)² / (0.0161)² = 4,560.3, rounded up to 4,561 participants per arm for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
p0 <- 0.0739056281978397                          # control proportion
p1 <- 0.0899964144854787                          # treatment proportion
n_per_arm <- function(power, frac = 1) {
  q1 <- p0 + (p1 - p0) * frac               # smaller effect = fraction of the observed difference
  pbar <- (p0 + q1) / 2
  z_b <- qnorm(power)
  ceiling(round((z_a * sqrt(2 * pbar * (1 - pbar)) + z_b * sqrt(p0 * (1 - p0) + q1 * (1 - q1)))^2 / (q1 - p0)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (unrounded n per arm)
cat("power.prop.test:", round(power.prop.test(p1 = p0, p2 = p1, power = 0.80)$n, 1), "\n")

# Simulation: how often does the z-test reject at the 80%-power sample size?
set.seed(5570630)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 4,561 (80% power), 6,105 (90% power)
 75% of observed effect: n per arm = 7,927 (80% power), 10,611 (90% power)
 50% of observed effect: n per arm = 17,426 (80% power), 23,327 (90% power)
power.prop.test: 4560.3 
Simulated power at n = 4,561 per arm: 0.803 (20000 replicates)

The simulation draws many pairs of event counts at the planned sample size and counts how often the two-proportion z-test rejects the null. It should land close to the target power; at very small required sample sizes it can fall below it, because the normal approximation is rough there.

14. Multiplicity, Missing Data and Analysis Populations

The registry reports a primary 6-month vaccination outcome and a 9-month secondary vaccination outcome. Both have comparative estimates and unadjusted p-values, but no multiplicity plan is reported. Multiplicity refers to the additional risk of false-positive findings when several hypotheses are tested. In general, a trial may address this through a prespecified hierarchy, adjustment, or another testing strategy. The registry record does not say whether such a strategy was specified or used, so the 9-month result should be interpreted as an unadjusted secondary finding rather than treated as a separate confirmatory test.

The record does not report whether missing outcome information occurred, how much was missing, or how missing vaccination status was handled. Since outcome status was obtained from electronic health records, incomplete capture could matter if vaccinations outside the records were not available or if record completeness differed between arms. These are possible measurement issues, not claims that missingness occurred. Without a participant flow account or missing-data description, the effect of such issues cannot be assessed.

The analysis population is not specified. The registry does not state whether participants were analyzed according to randomized assignment, whether eligibility was reassessed after assignment, or whether any participants were excluded from the posted comparisons. It also does not report whether analyses were adjusted for baseline characteristics, practice factors, or clustering. Those omissions limit assessment of whether the posted estimates preserve the advantages of randomization and whether their uncertainty reflects the trial's design.

The registry states that the study was randomized and parallel but does not report the randomization unit or method. The summary describes practice change in obstetricians' offices, making practice-level allocation or correlation a relevant design question, but the record does not establish how assignment occurred. It does not report a statistical analysis plan, interim analysis plan, or sample-size justification. These details are not reported in the registry record, and should not be inferred from the posted results.

15. What the Result Does, and Does Not, Mean

The posted results describe the proportions of participants with COVID-19 vaccination recorded in the two trial arms at the stated follow-up timepoints. At 6 months, the intervention arm has a higher point estimate than Standard of Care, but the confidence interval includes zero. At 9 months, the posted estimate is also higher in the intervention arm, with a confidence interval above zero. These are descriptions of the recorded outcomes and their statistical uncertainty, not medical recommendations.

The primary result does not establish that the intervention caused an increase. The study is identified as randomized, which can support causal interpretation when allocation, follow-up, and analysis are implemented appropriately. However, the registry does not report key implementation and analysis details, including the randomization unit, missing-data handling, analysis population, or treatment of practice-level clustering. The result should therefore be interpreted with those documentation limits in mind.

The outcome is vaccination status recorded in electronic health records, not infection, pre-eclampsia, preterm birth, stillbirth, ICU admission, or intubation. Although the registry summary discusses these outcomes as part of the clinical motivation, the posted comparisons do not show whether the trial changed them. They also do not establish vaccine safety or efficacy; those questions are outside the reported outcome comparisons.

The primary confidence interval permits a range of values from slightly below zero to a positive difference, so the estimate is not precise enough to rule out no difference at the stated confidence level. The secondary finding is more clearly positive in its posted interval, but remains subject to the limitations of an unadjusted secondary outcome. Neither the p-value nor the point estimate alone determines whether a difference is practically meaningful. The registry does not report a threshold for clinical or operational importance.

Overall, the results are best read as registry-posted comparisons of recorded vaccination in the observed cohorts. They provide information about the direction and uncertainty of those comparisons, but not a complete account of the intervention's implementation, the statistical analysis, or its effects on pregnancy and infection outcomes.

16. Why This Trial Matters Statistically

This trial addresses a statistical question that is directly tied to how care is organized: whether a practice-focused intervention is associated with a difference in recorded vaccination compared with Standard of Care. The registry describes an effort aimed at practice change in obstetricians' offices, rather than an intervention delivered only as an individual-level treatment. That setting makes design and analysis details especially important, since patients within the same practice may share processes that influence both vaccination and its documentation.

The primary and secondary results illustrate why an estimate should be read together with its uncertainty and endpoint status. The 6-month point estimate is positive, but its confidence interval includes zero. The 9-month estimate is also positive and its interval does not include zero, but it is a secondary result with an unadjusted p-value and no reported multiplicity strategy. A single threshold-based reading would miss these distinctions.

The trial also highlights the value of complete registry reporting. The record gives the outcome definitions, arm counts, estimates, and confidence intervals, but does not report the intervention components, the practice allocation structure, or the analysis plan. Without those details, readers cannot fully assess clustering, missing records, or how the randomized groups were compared. Reporting these methods would help distinguish statistical uncertainty from limitations that stem from incomplete documentation.

Finally, vaccination outcomes are not the same as downstream clinical outcomes. The registry summary motivates the trial with pregnancy and infection risks, while the posted endpoints assess vaccination status. Keeping that distinction clear prevents the observed endpoint comparisons from being interpreted as evidence about outcomes that were not reported.

17. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

Several trial-specific limitations affect interpretation. The intervention arm and comparator are named, but their descriptions are empty. As a result, the specific practice changes, staff activities, and patient-facing elements cannot be evaluated from the registry record. The record also does not report the number or characteristics of participating practices, the unit of randomization, or the procedures used to allocate them.

The primary endpoint relies on electronic health record vaccination status. The record does not explain how vaccination events outside the participating health systems were captured, how prior vaccinations were considered, how records were validated, or how uncertain status was classified. It also gives no account of missing data or participant flow. These omissions make it difficult to assess whether outcome recording was complete and comparable between arms.

The posted outcome counts differ from the overall enrollment, but the record does not explain the cohort construction or the reasons for the differences. Counts for the 6-month and 9-month outcomes also differ from one another. The registry does not state whether the cohorts overlap, whether different eligibility rules applied, or why participants were not included in each outcome denominator.

Although comparative estimates are posted, the registry lists no reported analyses and does not specify the statistical model, analysis population, covariate adjustment, or method for addressing practice-level correlation. The primary confidence interval includes zero, and the secondary comparison is unadjusted with no reported multiplicity strategy. The sample-size assumptions and justification for the original trial are not reported in the registry record.

The record reports zero entries for serious adverse events, other adverse events, and deaths in both arms, with zero participants at risk in those adverse-event fields. This does not provide a usable safety comparison or establish that no events occurred; the registry record does not explain the meaning of these entries. The reported endpoint comparisons concern vaccination status, not pregnancy or infection outcomes discussed in the trial rationale.

18. Related Tutorials

19. Related Statistical Calculators

20. Sources

• ClinicalTrials.gov: VAX-MOM COVID-19: Increasing Maternal COVID-19 Vaccination — NCT05570630.
• Registry record last updated 2025-09-04; status last verified 2025-08.
• Results first posted 2025-09-04.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

21. Record Summary

NCT05570630 is the completed, randomized, parallel-group VAX-MOM COVID-19 trial sponsored by the University of Rochester. The registry lists no masking and an enrollment of 6911. The start date is 2022-10-21, and the primary completion date is 2024-07-31. The intervention was VAX-MOM COVID-19 Intervention, compared with Standard of Care.

The primary endpoint was COVID-19 vaccination at 6 months among vaccine-eligible patients at participating OB/GYN practice sites, using electronic health record status. Its risk difference was 0.016, with a 95% confidence interval from -< 0.001 to 0.032 and an unadjusted p-value of 0.056. The secondary 9-month result had a risk difference of 0.027, with a 95% confidence interval from 0.014 to 0.040 and an unadjusted p-value of <0.001.

The registry does not report the intervention details, original sample-size justification, analysis plan, missing-data methods, or multiplicity approach. These omissions limit assessment of how the posted comparisons were constructed and how broadly they should be interpreted.