Challenges to estimating vaccine impact using hospitalization data.

Because the real-world impact of new vaccines cannot be known before they are implemented in national programs, post-implementation studies at the population level are critical. Studies based on analysis of hospitalization rates of vaccine-preventable outcomes are typically used for this purpose. However, estimates of vaccine impact based on hospitalization data are particularly prone to confounding, as hospitalization rates are tightly linked to changes in the quality, access and use of the healthcare system, which often occur simultaneously with introduction of new vaccines. Here we illustrate how changes in healthcare delivery coincident with vaccine introduction can influence estimates of vaccine impact, using as an example reductions in infant pneumonia hospitalizations after introduction of the 10-valent pneumococcal conjugate vaccine (PCV10) in Brazil. To this end, we explore the effect of changes in several metrics of quality and access to public healthcare on trends in hospitalization rates before (2008-09) and after (2011-12) PCV10 introduction in 2010. Changes in infant pneumonia hospitalization rates following vaccine introduction were significantly associated with concomitant changes in hospital capacity and the fraction of the population using public hospitals. Importantly, reduction of pneumonia hospitalization rates after PCV10 were also associated with the expansion of outpatient services in several Brazilian states, falling more sharply where primary care coverage and the number of health units offering basic and emergency care increased more. We show that adjustments for unrelated (non-vaccine) trends commonly employed by impact studies, such as use of single control outcomes, are not always sufficient for accurate impact assessment. We discuss several ways to identify and overcome such biases, including sensitivity analyses using different denominators to calculate hospitalizations rates and methods that track changes in the outpatient setting. Employing these practices can improve the accuracy of vaccine impact estimates, particularly in evolving healthcare settings typical of low- and middle-income countries

Impact of Pneumococcal Conjugate Vaccines on Pneumonia Hospitalizations in High- and Low-Income Subpopulations in Brazil.

BackgroundPneumococcal conjugate vaccines (PCVs) are being used worldwide. A key question is whether the impact of PCVs on pneumonia is similar in low- and high-income populations. However, most low-income countries, where the burden of disease is greatest, lack reliable data that can be used to evaluate the impact. Data from middle-income countries that have both low- and high-income subpopulations can provide a proxy measure for the impact of the vaccine in low-income countries.MethodsWe evaluated the impact of PCV10 on hospitalizations for all-cause pneumonia in Brazil, a middle-income country with localities that span a broad range of human development index (HDI) levels. We used complementary time series and spatiotemporal methods (synthetic controls and hierarchical Bayesian spatial regression) to test whether the decline in pneumonia hospitalizations associated with vaccine introduction varied across the socioeconomic spectrum.ResultsWe found that the declines in all-cause pneumonia hospitalizations in children and young and middle-aged adults did not vary substantially across low and high HDI subpopulations. Moreover, the estimated declines seen in infants and young adults were associated with higher levels of uptake of the vaccine at a local level.ConclusionsThese results suggest that PCVs have an important impact on hospitalizations for all-cause pneumonia in both low- and high-income populations

Forecasting temporal dynamics of cutaneous leishmaniasis in Northeast Brazil.

IntroductionCutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions.Methodology/principal findingsWe fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation.SignificanceThese outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets

Prediction of serotypes causing invasive pneumococcal disease in unvaccinated and vaccinated populations.

INTRODUCTION: Before the introduction of the heptavalent pneumococcal conjugate vaccine (Prevnar-7), the relative prevalence of serotypes of Streptococcus pneumoniae was fairly stable worldwide. We sought to develop a statistical tool to predict the relative frequency of different serotypes among disease isolates in the pre- and post-Prevnar-7 eras using the limited amount of data that is widely available. METHODS: We initially used pre-Prevnar-7 carriage prevalence and estimates of invasiveness derived from case-fatality data as predictors for the relative abundance of serotypes causing invasive pneumococcal disease during the pre- and post-Prevnar-7 eras, using negative binomial regression. We fit the model to pre-Prevnar-7 invasive pneumococcal disease data from England and Wales and used these data to (1) evaluate the performance of the model using several datasets and (2) evaluate the utility of the country-specific carriage data. We then fit an alternative model that used polysaccharide structure, a correlate of prevalence that does not require country-specific information and could be useful in determining the postvaccine population structure, as a predictor. RESULTS: Predictions from the initial model fit data from several pediatric populations in the pre-Prevnar-7 era. After the introduction of Prevnar-7, the model still had a good negative predictive value, though substantial unexplained variation remained. The alternative model had a good negative predictive value but poor positive predictive value. Both models demonstrate that the pneumococcal population follows a somewhat predictable pattern even after vaccination. CONCLUSIONS: This approach provides a preliminary framework to evaluate the potential patterns and impact of serotypes causing invasive pneumococcal disease

Decreased Anti-Inflammatory Responses to Vitamin D in Neonatal Neutrophils

Neutrophil activity is prolonged in newborns, suggesting decreased exposure and/or responses to immunosuppressive modulators, such as 1,25-hydroxyvitamin D3 (1,25-vit D3). We hypothesized that 1,25-vit D3 suppresses neutrophil activation and that this response is impaired in newborns. Consistent with this, 1,25-vit D3 decreased LPS-induced expression of macrophage inflammatory protein-1β and VEGF in adult, but not neonatal, neutrophils. Expression of vitamin D receptor (VDR) and 25-hydroxyvitamin D3-1α-hydroxylase was reduced in neonatal, relative to adult neutrophils. Moreover, 1,25-vit D3 induced VDR gene expression in activated adult, but not neonatal, neutrophils. 1,25-vit D3 also suppressed expression of cyclooxygenase-2 and induced expression of 5-lipoxygenase in LPS-exposed adult neutrophils, while neonatal cells were not affected. 1,25-vit D3 had no effect on respiratory burst in either adult or neonatal cells. Anti-inflammatory activity of vitamin D is impaired in neonatal neutrophils, and this may be due to decreased expression of VDR and 1α-hydroxylase. Insensitivity to 1,25-vit D3 may contribute to chronic inflammation in neonates

Direct and indirect mortality impacts of the COVID-19 pandemic in the United States, March 1, 2020 to January 1, 2022

Excess mortality studies provide crucial information regarding the health burden of pandemics and other large-scale events. Here, we used time series approaches to separate the direct contribution of SARS-CoV-2 infections on mortality from the indirect consequences of pandemic interventions and behavior changes in the United States. We estimated deaths occurring in excess of seasonal baselines stratified by state, age, week and cause (all causes, COVID-19 and respiratory diseases, Alzheimer’s disease, cancer, cerebrovascular disease, diabetes, heart disease, and external causes, including suicides, opioids, accidents) from March 1, 2020 to April 30, 2021. Our estimates of COVID-19 excess deaths were highly correlated with SARS-CoV-2 serology, lending support to our approach. Over the study period, we estimate an excess of 666,000 (95% Confidence Interval (CI) 556000, 774000) all-cause deaths, of which 90% could be attributed to the direct impact of SARS-CoV-2 infection, and 78% were reflected in official COVID-19 statistics. Mortality from all disease conditions rose during the pandemic, except for cancer. The largest direct impacts of the pandemic were seen in mortality from diabetes, Alzheimer’s, and heart diseases, and in age groups over 65 years. In contrast, the largest indirect consequences of the pandemic were seen in deaths from external causes, which increased by 45,300 (95% CI 30,800, 59,500) and were statistically linked to the intensity of non-pharmaceutical interventions. Within this category, increases were most pronounced in mortality from accidents and injuries, drug overdoses, and assaults and homicides, while the rate of death from suicides remained stable. Younger age groups suffered the brunt of these indirect effects. Overall, on a national scale, the largest consequences of the COVID-19 pandemic are attributable to the direct impact of SARS-CoV-2 infections; yet, the secondary impacts dominate among younger age groups, in periods of stricter interventions, and in mortality from external causes. Further research on the drivers of indirect mortality is warranted to optimize interventions in future pandemics

Estimating rates of carriage acquisition and clearance and competitive ability for pneumococcal serotypes in Kenya with a Markov transition model.

BACKGROUND: There are more than 90 serotypes of Streptococcus pneumoniae, with varying biologic and epidemiologic properties. Animal studies suggest that carriage induces an acquired immune response that reduces duration of colonization in a nonserotype-specific fashion. METHODS: We studied pneumococcal nasopharyngeal carriage longitudinally in Kenyan children 3-59 months of age, following up positive swabs at days 2, 4, 8, 16, and 32 and then monthly thereafter until 2 swabs were negative for the original serotype. As previously reported, 1868/2840 (66%) of children swabbed at baseline were positive. We estimated acquisition, clearance, and competition parameters for 27 serotypes using a Markov transition model. RESULTS: Point estimates of type-specific acquisition rates ranged from 0.00025/d (type 1) to 0.0031/d (type 19F). Point estimates of time to clearance (inverse of type-specific immune clearance rate) ranged from 28 days (type 20) to 124 days (type 6A). For the serotype most resistant to competition (type 19F), acquisition of other serotypes was 52% less likely (95% confidence interval = 37%-63%) than in an uncolonized host. Fitness components (carriage duration, acquisition rate, lack of susceptibility to competition) were positively correlated with each other and with baseline prevalence, and were associated with biologic properties previously shown to associate with serotype. Duration of carriage declined with age for most serotypes. CONCLUSIONS: Common S. pneumoniae serotypes appear superior in many dimensions of fitness. Differences in rate of immune clearance are attenuated as children age and become capable of more rapid clearance of the longest-lived serotypes. These findings provide information for comparison after introduction of pneumococcal conjugate vaccine