Timothy L. Wiemken

BACKGROUND: Understanding the burden of community-acquired pneumonia (CAP) is critical to allocate resources for prevention, management, and research. The objectives of this study were to define incidence, epidemiology, and mortality of adult patients hospitalized with CAP in the city of Louisville, and to estimate burden of CAP in the US adult population. METHODS: This was a prospective population-based cohort study of adult residents in Louisville, Kentucky, from 1 June 2014 to 31 May 2016. Consecutive hospitalized patients with CAP were enrolled at all adult hospitals in Louisville. The annual population-based CAP incidence was calculated. Geospatial epidemiology was used to define ecological associations among CAP and income level, race, and age. Mortality was evaluated during hospitalization and at 30 days, 6 months, and 1 year after hospitalization. RESULTS: During the 2-year study, from a Louisville population of 587499 adults, 186384 hospitalizations occurred. A total of 7449 unique patients hospitalized with CAP were documented. The annual age-adjusted incidence was 649 patients hospitalized with CAP per 100000 adults (95% confidence interval, 628.2-669.8), corresponding to 1591825 annual adult CAP hospitalizations in the United States. Clusters of CAP cases were found in areas with low-income and black/African American populations. Mortality during hospitalization was 6.5%, corresponding to 102821 annual deaths in the United States. Mortality at 30 days, 6 months, and 1 year was 13.0%, 23.4%, and 30.6%, respectively. CONCLUSIONS: The estimated US burden of CAP is substantial, with >1.5 million unique adults being hospitalized annually, 100000 deaths occurring during hospitalization, and approximately 1 of 3 patients hospitalized with CAP dying within 1 year.

Machine learning approaches to modeling of epidemiologic data are becoming increasingly more prevalent in the literature. These methods have the potential to improve our understanding of health and opportunities for intervention, far beyond our past capabilities. This article provides a walkthrough for creating supervised machine learning models with current examples from the literature. From identifying an appropriate sample and selecting features through training, testing, and assessing performance, the end-to-end approach to machine learning can be a daunting task. We take the reader through each step in the process and discuss novel concepts in the area of machine learning, including identifying treatment effects and explaining the output from machine learning models.

The advent of PCR has improved the identification of viruses in patients with community-acquired pneumonia (CAP). Several studies have used PCR to establish the importance of viruses in the aetiology of CAP.We performed a systematic review and meta-analysis of the studies that reported the proportion of viral infection detected via PCR in patients with CAP. We excluded studies with paediatric populations. The primary outcome was the proportion of patients with viral infection. The secondary outcome was short-term mortality.Our review included 31 studies. Most obtained PCR via nasopharyngeal or oropharyngeal swab. The pooled proportion of patients with viral infection was 24.5% (95% CI 21.5-27.5%). In studies that obtained lower respiratory samples in >50% of patients, the proportion was 44.2% (95% CI 35.1-53.3%). The odds of death were higher in patients with dual bacterial and viral infection (OR 2.1, 95% CI 1.32-3.31).Viral infection is present in a high proportion of patients with CAP. The true proportion of viral infection is probably underestimated because of negative test results from nasopharyngeal or oropharyngeal swab PCR. There is increased mortality in patients with dual bacterial and viral infection.

Background: Following universal recommendation for use of 13-valent pneumococcal conjugate vaccine (PCV13) in US adults aged ≥65 years in September 2014, we conducted the first real-world evaluation of PCV13 vaccine effectiveness (VE) against hospitalized vaccine-type community-acquired pneumonia (CAP) in this population. Methods: Using a test-negative design, we identified cases and controls from a population-based surveillance study of adults in Louisville, Kentucky, who were hospitalized with CAP. We analyzed a subset of CAP patients enrolled 1 April 2015 through 30 April 2016 who were aged ≥65 years and consented to have their pneumococcal vaccination history confirmed by health insurance records. Cases were defined as hospitalized CAP patients with PCV13 serotypes identified via culture or serotype-specific urinary antigen detection assay. Remaining CAP patients served as test-negative controls. Results: Of 2034 CAP hospitalizations, we identified PCV13 serotypes in 68 (3.3%) participants (ie, cases), of whom 6 of 68 (8.8%) had a positive blood culture. Cases were less likely to be immunocompromised (29.4% vs 46.4%, P = .02) and overweight or obese (41.2% vs 58.6%, P = .01) compared to controls, but were otherwise similar. Cases were less likely to have received PCV13 than controls (3/68 [4.4%] vs 285/1966 [14.5%]; unadjusted VE, 72.8% [95% confidence interval, 12.8%-91.5%]). No confounding was observed during adjustment for patient characteristics, including immunocompromised status, body mass index, and history of influenza and pneumococcal polysaccharide vaccination (adjusted VE range, 71.1%-73.3%). Conclusions: Our study is the first to demonstrate real-world effectiveness of PCV13 against vaccine-type CAP in adults aged ≥65 years following introduction into a national immunization program.

Determining whether SARS-CoV-2 exhibits seasonality like other respiratory viruses is critical for public health planning. We evaluated whether COVID-19 rates follow a seasonal pattern using time series models. We used time series decomposition to extract the annual seasonal component of COVID-19 case, hospitalization, and mortality rates from March 2020 through December 2022 for the United States and Europe. Models were adjusted for a country-specific stringency index to account for confounding by various interventions. Despite year-round disease activity, we identified seasonal spikes in COVID-19 from approximately November through April for all outcomes and in all countries. Our results support employing annual preventative measures against SARS-CoV-2, such as administering seasonal booster vaccines in a similar timeframe as those in place for influenza. Whether certain high-risk individuals may need more than one COVID-19 vaccine booster dose each year will depend on factors like vaccine durability against severe illness and levels of year-round disease activity.