In 2010, 13-valent pneumococcal conjugate vaccine (PCV13) was introduced in the US for prevention of invasive pneumococcal disease in children. Individual-level socioeconomic status (SES) is a potential confounder of the estimated effectiveness of PCV13 and is often controlled for in observational studies using zip code as a proxy. We assessed the utility of zip code matching for control of SES in a post-licensure evaluation of the effectiveness of PCV13 (calculated as [1-matched odds ratio]*100). We used a directed acyclic graph to identify subsets of confounders and collected SES variables from birth certificates, geo-coding, a parent interview, and follow-up with medical providers. Cases tended to be more affluent than eligible controls (for example, 48.3% of cases had private insurance vs. 44.6% of eligible controls), but less affluent than enrolled controls (52.9% of whom had private insurance). Control of confounding subsets, however, did not result in a meaningful change in estimated vaccine effectiveness (original estimate: 85.1%, 95% CI 74.8-91.9%; adjusted estimate: 82.5%, 95% CI 65.6-91.1%). In the context of a post-licensure vaccine effectiveness study, zip code appears to be an adequate, though not perfect, proxy for individual SES.

Objectives

External validity, or generalisability, is the measure of how well results from a study pertain to individuals in the target population. We assessed generalisability, with respect to socioeconomic status, of estimates from a matched case-control study of 13-valent pneumococcal conjugate vaccine effectiveness for the prevention of invasive pneumococcal disease in children in the USA.

Design

Matched case-control study.

Setting

Thirteen active surveillance sites for invasive pneumococcal disease in the USA.

Participants

Cases were identified from active surveillance and controls were age and zip code matched.

Outcome measures

Socioeconomic status was assessed at the individual level via parent interview (for enrolled individuals only) and birth certificate data (for both enrolled and unenrolled individuals) and at the neighbourhood level by geocoding to the census tract (for both enrolled and unenrolled individuals). Prediction models were used to determine if socioeconomic status was associated with enrolment.

Results

We enrolled 54.6% of 1211 eligible cases and found a trend toward enrolled cases being more affluent than unenrolled cases. Enrolled cases were slightly more likely to have private insurance at birth (p=0.08) and have mothers with at least some college education (p<0.01). Enrolled cases also tended to come from more affluent census tracts. Despite these differences, our best predictive model for enrolment yielded a concordance statistic of only 0.703, indicating mediocre predictive value. Variables retained in the final model were assessed for effect measure modification, and none were found to be significant modifiers of vaccine effectiveness.

Conclusions

We conclude that although enrolled cases are somewhat more affluent than unenrolled cases, our estimates are externally valid with respect to socioeconomic status. Our analysis provides evidence that this study design can yield valid estimates and the assessing generalisability of observational data is feasible, even when unenrolled individuals cannot be contacted.

Clinical trials of COVID-19 vaccines currently authorized for emergency use in the United States (Pfizer-BioNTech, Moderna, and Janssen [Johnson & Johnson]) indicate that these vaccines have high efficacy against symptomatic disease, including moderate to severe illness (1-3). In addition to clinical trials, real-world assessments of COVID-19 vaccine effectiveness are critical in guiding vaccine policy and building vaccine confidence, particularly among populations at higher risk for more severe illness from COVID-19, including older adults. To determine the real-world effectiveness of the three currently authorized COVID-19 vaccines among persons aged ≥65 years during February 1-April 30, 2021, data on 7,280 patients from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) were analyzed with vaccination coverage data from state immunization information systems (IISs) for the COVID-NET catchment area (approximately 4.8 million persons). Among adults aged 65-74 years, effectiveness of full vaccination in preventing COVID-19-associated hospitalization was 96% (95% confidence interval [CI] = 94%-98%) for Pfizer-BioNTech, 96% (95% CI = 95%-98%) for Moderna, and 84% (95% CI = 64%-93%) for Janssen vaccine products. Effectiveness of full vaccination in preventing COVID-19-associated hospitalization among adults aged ≥75 years was 91% (95% CI = 87%-94%) for Pfizer-BioNTech, 96% (95% CI = 93%-98%) for Moderna, and 85% (95% CI = 72%-92%) for Janssen vaccine products. COVID-19 vaccines currently authorized in the United States are highly effective in preventing COVID-19-associated hospitalizations in older adults. In light of real-world data demonstrating high effectiveness of COVID-19 vaccines among older adults, efforts to increase vaccination coverage in this age group are critical to reducing the risk for COVID-19-related hospitalization.