Kimberly M. Shea

We assessed the impact of respiratory syncytial virus (RSV) preventive characteristics on the intentions of pregnant people and healthcare providers (HCPs) to protect infants with a maternal vaccine or monoclonal antibodies (mAbs). Pregnant people and HCPs who treated pregnant people and/or infants were recruited via convenience sample from a general research panel to complete a cross-sectional, web-based survey, including a discrete choice experiment (DCE) wherein respondents chose between hypothetical RSV preventive profiles varying on five attributes (effectiveness, preventive type [maternal vaccine vs. mAb], injection recipient/timing, type of medical visit required to receive the injection, and duration of protection during RSV season) and a no-preventive option. A best–worst scaling (BWS) exercise was included to explore the impact of additional attributes on preventive preferences. Data were collected between October and November 2022. Attribute-level preference weights and relative importance (RI) were estimated. Overall, 992 pregnant people and 310 HCPs participated. A preventive (vs. none) was chosen 89.2% (pregnant people) and 96.0% (HCPs) of the time (DCE). Effectiveness was most important to preventive choice for pregnant people (RI = 48.0%) and HCPs (RI = 41.7%); all else equal, pregnant people (RI = 5.5%) and HCPs (RI = 7.2%) preferred the maternal vaccine over mAbs, although preventive type had limited influence on choice. Longer protection, protection starting at birth or the beginning of RSV season, and use for both pre-term and full-term babies were ranked highest in importance (BWS). Pregnant people and HCPs strongly preferred a preventive to protect infants against RSV (vs. none), underscoring the need to incorporate RSV preventives into routine care.

Predictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models. We developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990–2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew’s Correlation Coefficient (MCC). For near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61. Our results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be improved by the inclusion of these models. Accurate and timely forecasts of dengue fever can help mitigate the impact of the disease. Currently, regression and time series models are frequently used to predict dengue cases and outbreaks. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) models offer an appealing alternative as they have a nonlinear framework and can be applied to high dimensional data. In this study, we compared the performance of ML algorithms with that of regression and time series models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance in 3 dengue-endemic regions. Model predictions were based upon local dengue surveillance (e.g., case counts), population, temporal, and weather data. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of the models. Our results identified 2 scenarios when ML models performed better than conventional models: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. This research suggests that ML models can be a beneficial tool for dengue early warning systems.

The objective of this study is to evaluate rates of all-cause pneumonia among “at-risk” and “high-risk” children and adults in Germany—in comparison with age-stratified healthy counterparts—during the period following the 2006 recommendation for universal immunization of infants with pneumococcal conjugate vaccine. Retrospective cohort design and healthcare claims information for 3.4 M persons in Germany (2009–2012) were employed. Study population was stratified by age and risk profile (healthy, “at-risk” [with chronic medical conditions], and “high-risk” [immunocompromised]). At-risk and high-risk conditions, as well as episodes of all-cause pneumonia, were identified via diagnosis, procedure, and drug codes. Rates of all-cause pneumonia were 1.7 (95 % CI 1.7-1.8) to 2.5 (2.4-2.5) times higher among children and adults with at-risk conditions versus healthy counterparts, and 1.8 (1.8-1.9) to 4.1 (4.0-4.2) times higher among children and adults with high-risk conditions. Rates of all-cause pneumonia among at-risk persons increased in a graded and monotonic fashion with increasing numbers of conditions (i.e., risk stacking). An increased risk for all-cause pneumonia in German children and adults with a spectrum of medical conditions persists in the era of widespread pneumococcal vaccination, and pneumonia risk in persons with ≥2 at-risk conditions is comparable or higher than those with high-risk conditions.

Background: Although asthma has recently been established as a risk factor for pneumococcal disease (PD), few studies have specifically evaluated this association in children. Methods: We conducted a nation-wide population-based cohort study of the effect of asthma on childhood PD among all singleton live births in Denmark from 1994 to 2007, before the introduction of the 7-valent pneumococcal conjugate vaccine. All data were abstracted from Danish medical registries. Because underlying comorbidity substantially increases the PD risk in children, standard methods were used to assess the evidence of biologic interaction between comorbidity and asthma on the risk of PD. Results: There were 2,253 cases of childhood PD among 888,655 children born in Denmark from 1994 to 2007. The adjusted incidence rate ratio of the effect of asthma on childhood PD was 2.2 (95% confidence interval [CI]: 2.0, 2.5). Age-stratified incidence rate ratios were 2.1 (95% CI: 1.8, 2.9) in children 6 months to <24 months, 4.1 (95% CI: 3.3, 5.1) in children 24 months to <60 months, and 2.3 (95% CI: 1.6, 3.2) in children ≥60 months. Evaluation of the biologic interaction between asthma and comorbidity in older children revealed that 55% (24 months to <60 months) to 73% (≥60 months) of cases among asthma-exposed children can be accounted for by the interaction between asthma and comorbidity. Conclusion: These results confirm that asthma is an important risk factor for PD in children and suggest that children with underlying comorbidities are more sensitive to the effect of asthma on PD than children without comorbidities.

Background. Although it is widely accepted that adults with immunocompromising conditions are at greatly increased risk of pneumococcal infection, the extent of risk among immunocompetent adults with chronic medical conditions is less certain, particularly in the current era of universal vaccination of children with pneumococcal conjugate vaccines.

We estimated the rate of reactivation tuberculosis (TB) in the United States, overall and by population subgroup, using data on TB cases and Mycobacterium tuberculosis isolate genotyping reported to the Centers for Disease Control and Prevention during 2006–2008. The rate of reactivation TB was defined as the number of non-genotypically clustered TB cases divided by the number of person-years at risk for reactivation due to prevalent latent TB infection (LTBI). LTBI was ascertained from tuberculin skin tests given during the 1999–2000 National Health and Nutrition Examination Survey. Clustering of TB cases was determined using TB genotyping data collected by the Centers for Disease Control and Prevention and analyzed via spatial scan statistic. Of the 39,920 TB cases reported during 2006–2008, 79.7% were attributed to reactivation. The overall rate of reactivation TB among persons with LTBI was estimated as 0.084 (95% confidence interval (CI): 0.083, 0.085) cases per 100 person-years. Rates among persons with and without human immunodeficiency virus coinfection were 1.82 (95% CI: 1.74, 1.89) and 0.073 (95% CI: 0.070, 0.075) cases per 100 person-years, respectively. The rate of reactivation TB among persons with LTBI was higher among foreign-born persons (0.098 cases/100 person-years; 95% CI: 0.096, 0.10) than among persons born in the United States (0.082 cases/100 person-years; 95% CI: 0.080, 0.083). Differences in rates of TB reactivation across subgroups support current recommendations for targeted testing and treatment of LTBI.

SUMMARY: This paper examined whether previously identified community-level factors (high proportion of crowded households and/or persons below the poverty level) remained associated with childhood pneumococcal carriage in the heptavalent pneumococcal conjugate vaccine (PCV7) era. Using logistic regression, individual factors were used to develop base models to which community-level factors were added to evaluate impact on pneumococcal carriage within two paediatric study cohorts from Massachusetts (urban Boston, outside Boston). Six years after introduction of universal childhood PCV7 vaccination, we found no consistent evidence that census tract characteristics (e.g. population size and density, age and race distribution, percent participating in group childcare, parental education, percent lacking in-unit plumbing, poverty, and community stability) affected odds of pneumococcal carriage when added to individual predictors (e.g. younger age, current respiratory tract infections, and attendance in group childcare). How community-level factors influence pneumococcal carriage continues to change in the era of increasing immunization coverage.