Artificial Intelligence-assisted Primary Care for RSV Prevention (AIPC-RSV) in China

A randomized controlled trial will be conducted to evaluate the effectiveness of an AI-assisted primary care intervention to improve RSV mAb uptake among infants in China, with additional health economic and implementation evaluation to assess its cost-effectiveness and potential for scale-up.

The sample size calculation is based on the primary outcome measure (RSV mAb uptake rate) and the main analysis method (comparison of rates between two groups). Based on literature review and survey, the current RSV mAb uptake rate among infants is estimated to be approximately 10%. We assume that the uptake rate could increase to 15% following the intervention. Using the formula for comparing two independent proportions, the minimum required sample size is calculated to be 683 participants in each group, with a significance level (α) of 0.05 and a statistical power (1-β) of 0.80. To account for potential loss to follow-up, assuming a 10% loss to follow-up rate, each group will include 750 participants, with a total sample size of 1,500.

A randomized controlled design will be employed across China. Eligible parents of infants who provide written informed consent will be randomly assigned in a 1:1 ratio to either the intervention group or the control group. The randomization sequence will be generated in advance by a statistician using a computer-generated random number method. At each study site, participants will be allocated sequentially according to their order of enrolment. Due to the nature of intervention, blinding of participants and on-site intervention implementers is not feasible. Local coordinators, outcome assessors, data managers, and statistical analysts will remain blinded to group allocation throughout the study to minimize assessment and analysis bias.

Building on the evaluation of intervention effectiveness, we will conduct a health economic evaluation to assess the economic value of the RSV AI-assisted primary care intervention. In addition, an implementation science study guided by the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, and Maintenance) will assess the real-world implementation of the RSV AI-assisted primary care intervention for RSV prevention.

Statistical Analysis

1. Primary Outcome Analysis An intention-to-treat (ITT) approach will be used as the primary analysis strategy, with all randomly assigned participants analyzed according to their original group allocation. Between-group comparisons will be performed using t-tests for continuous variables and chi-square tests for categorical variables.

   The primary outcome is RSV mAb uptake among the infants of study participants within one and three months after the intervention. A log-binomial regression model will be used to estimate the relative risk (RR) and its 95% confidence interval (95% CI), with CHCs included as a fixed effect and adjustment for prespecified covariates. If the log-binomial model fails to converge, a modified Poisson regression model will be used instead. The adjusted absolute risk difference and its 95% CI will also be estimated from the regression model to evaluate the public health significance of the intervention. Prespecified covariates will include region, infants (sex, age in months, and singleton status) and their parents (parenthood, age, education level, occupation, enrollment in employee basic medical insurance, and household income). Pre-specified subgroup analyses will be conducted by including subgroup variables and their interaction terms with the intervention group in the primary analysis model, to assess heterogeneity of intervention effects across participant characteristics. Prespecified subgroups will include region, infants (sex, age in months, and singleton status) and their parents (parenthood, age, education level, occupation, enrollment in employee basic medical insurance, and household income).

2. Secondary Outcomes Analysis Secondary outcomes include time to RSV mAb uptake among the infants of parents within three months following the intervention; parental perceived risk of RSV disease; parental RSV prevention literacy; parental confidence in RSV mAb; and willingness to use RSV mAb for their infants. Time-to-event analyses for time to RSV mAb uptake will be conducted using Kaplan-Meier methods and compared between groups with the log-rank test. Cox proportional hazards models will be used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). Perceived risk of RSV disease, RSV mAb confidence, and willingness to use RSV mAb will be analyzed as binary outcomes using the same log-binomial or modified Poisson regression models as for the primary outcome, accounting for CHCs effects. Results will be reported as relative risks (RRs) and 95% CIs. RSV prevention literacy will be analyzed as a continuous outcome using linear regression models, accounting for CHC effects. Intervention effects will be estimated after adjustment for prespecified covariates, with results reported as adjusted mean differences (aMDs) and 95% CIs. Prespecified subgroup analyses will be performed by including subgroup variables and their interaction terms with intervention assignment in the analysis models to assess heterogeneity of intervention effects across participant characteristics. Prespecified subgroups will include region, infants (sex, age in months, and singleton status) and their parents (parenthood, age, education level, occupation, enrollment in employee basic medical insurance, and household income).

3. Sensitivity Analysis A per-protocol (PP) analysis will be conducted as a sensitivity analysis to assess the robustness of the primary findings. The PP analysis will include intervention-group participants who complete the prescribed AI assisted primary care intervention, as well as participants in the control group. In addition, within the intervention group, intervention engagement will be assessed on the basis of AI assistant usage and physician recommendation. Participants will be categorized into high- and low-engagement groups. Differences in intervention effects between these engagement levels will then be compared to assess the association between intervention intensity and RSV mAb uptake.