Pneumococcal conjugate vaccines (PCVs) protect against diseases caused by Streptococcus pneumoniae, such as meningitis, bacteremia, and pneumonia. It is challenging to estimate their population-level impact due to the lack of a perfect control population and the subtleness of signals when the endpoint—such as all-cause pneumonia—is nonspecific. Here we present a new approach for estimating the impact of PCVs: using least absolute shrinkage and selection operator (LASSO) regression to select variables in a synthetic control model to predict the counterfactual outcome for vaccine impact inference. We first used a simulation study based on hospitalization data from Mexico (2000–2013) to test the performance of LASSO and established methods, including the synthetic control model with Bayesian variable selection (SC). We found that LASSO achieved accurate and precise estimation, even in complex simulation scenarios where the association between the outcome and all control variables was noncausal. We then applied LASSO to real-world hospitalization data from Chile (2001–2012), Ecuador (2001–2012), Mexico (2000–2013), and the United States (1996–2005), and found that it yielded estimates of vaccine impact similar to SC. The LASSO method is accurate and easily implementable and can be applied to study the impact of PCVs and other vaccines.

Background: Pneumococcal conjugate vaccines (PCVs) offer indirect protection by reducing pneumococcal colonization in the vaccinated children and thus transmission. As higher-valency PCVs may trigger a weaker immune response, it is important to understand how differences in immunogenicity between PCVs translate to effectiveness against colonization. Methods: We estimated the serotype-specific relationship between the concentration of vaccine-induced serum immunoglobulin G (IgG) and protection against colonization using a hierarchical Bayesian model with the longitudinal data from a randomized controlled trial. We then combined these estimates with the summary-level immunogenicity data (geometric mean concentrations and 95% confidence intervals) from head-to-head clinical trials comparing 13-valent versus 7-valent PCV (PCV13 vs PCV7), 15-valent PCV (PCV15) versus PCV13, and 20-valent PCV (PCV20) versus PCV13 to infer the relative effectiveness of higher-valency PCVs against colonization. Results: The hierarchical Bayesian model predicted that the risk of colonization increased as serum IgG decreased, and the association differed by serotype. Our approach estimated higher-valency PCVs to have lower vaccine effectiveness against colonization with some serotypes: 14 and 23F across comparisons; 4 when comparing PCV13 with PCV7 and PCV20 with PCV13; 5, 6A, 6B 7F, 19A, and 19F when comparing PCV15 and PCV20 with PCV13; and 1, 9V, and 18C when comparing PCV20 with PCV13. Conclusions: These findings suggest that while new PCVs might provide sufficient protection against disease, protection against transmission might be somewhat reduced for some serotypes. The overall impact should be evaluated in the local context, and further monitoring is critical to evaluate the impact of these changes in the coming years.

Pertussis vaccination policy varies across Europe, not only in the type of vaccine—whole cell (wP) vs. acellular (aP1/2/3/5)—but also in the schedule and recommendation for parents. This study aims to investigate the determining factors for the type of vaccine, immunization schedule and maternal immunization recommendation. From March to May 2019, experts in national health agencies and major academic or research institutions from Denmark, France, Poland, Sweden and the UK were invited to a semi-structured interview. Thematic analysis was performed on the transcripts using a codebook formulated by three coders. Inter-coder agreement was assessed. Fifteen expert interviews were conducted. The identified driving factors for pertussis vaccine policy were classified into three domains: scientific factors, sociological factors, and pragmatic factors. The determining factors for the type of vaccine were prescriber’s preference, concern of adverse events following immunization (AEFI), effectiveness, and consideration of other vaccine components in combined vaccines. The determining factors for infant schedule were immunity response and the potential to improve coverage and timeliness. The determining factors for maternal immunization were infant mortality and public acceptability. To conclude, socio-political and pragmatic factors were, besides scientific factors, important in determining the pertussis vaccine type, schedule of childhood immunization and recommendations for parents.

Seroepidemiology has significant potential for uncovering the unreported burden of infectious diseases. However, for diseases without well-defined serological correlates of protection, natural immune boosting—whereby pathogen exposure triggers a detectable immune response without causing a transmissible infection—can complicate the interpretation of serosurveys. This issue is relevant to pertussis, a vaccine-preventable disease that remains a significant public health concern worldwide. Here, we aimed to evaluate the reliability of pertussis serosurveys using a transmission model that tracked the dynamics of pertussis infection, natural immune boosting, and seroprevalence. By fitting this model to seroprevalence data from the late whole-cell pertussis vaccine era in six European countries, we estimated that protection against infection conferred by natural infection or vaccination was variable but lasted, on average, for several decades. We then predicted the positive predictive value (PPV) of seropositivity in serosurveys among adults across twelve countries that broadly captured transmission patterns worldwide. Overall, we predicted a low PPV across multiple scenarios, especially in adults aged 20–39 years, where it typically dropped below 50%. Thus, although serosurveys are unquestionably useful for quantifying pertussis exposure levels, the common interpretation of seroprevalence as a measure of recent infections may lead to overestimating pertussis circulation and underestimating the impact of pertussis vaccines.

Background: While the pharmacy workforce is the third largest professional healthcare group worldwide, the pharmacy workforce landscape remains unclear in post-conflict areas in sub-Saharan Africa. Method: Key informants were selected for semi-structured interviews due to their role in providing pharmacy services in the selected country: the Central African Republic (CAR), the Democratic Republic of Congo (DRC), Ethiopia, and South Sudan. Transcripts from the interviews were anonymized, coded, and analyzed. Results: Nine participants were recruited (CAR: 2; DRC: 2; Ethiopia: 2; South Sudan: 3), and all except two were pharmacists. Conflict-specific challenges in pharmacy service delivery were identified as the following: unpredictable health needs and/or mismatched pharmaceutical supply, transport difficulties due to insecure roads, and shortage of pharmacy workforce due to brain drain or interrupted schooling. Barriers to health workforce retention and growth were identified to be brain drain as a result of suboptimal living and working conditions or remuneration, the perception of an unsafe work environment, and a career pathway or commitment duration that does not fit the diaspora or expatriate staff. Conclusion: To tackle the barriers of pharmacy health workforce retention and growth, policy solutions will be required and efforts that can bring about long-term improvement should be prioritized. This is essential to achieve universal health coverage and the targets of the sustainable development goals for conflict affected areas, as well as to “leave no one behind”.

Deciphering the properties of vaccines against an emerging pathogen is essential for optimizing immunization strategies. Early after vaccine roll-out, however, uncertainties about vaccine immunity raise the question of how much time is needed to estimate these properties, particularly the durability of vaccine protection. Here we designed a simulation study, based on a generic transmission model of vaccination, to simulate the impact of a breadth of vaccines with different mean (range: 10 months–2 years) and variability (coefficient of variation range: 50–100%) of the duration of protection. Focusing on the dynamics of SARS-CoV-2 in the year after start of mass immunization in Germany as a case study, we then assessed how confidently the duration of protection could be estimated under a range of epidemiological scenarios. We found that lower mean and higher heterogeneity facilitated estimation of the duration of vaccine protection. Across the vaccines tested, rapid waning and high heterogeneity permitted complete identification of the duration of protection; by contrast, slow waning and low heterogeneity allowed only estimation of the fraction of vaccinees with rapid loss of immunity. These findings suggest that limited epidemiological data can inform the duration of vaccine immunity. More generally, they highlight the need to carefully consider immunological heterogeneity when designing transmission models to evaluate vaccines.