Alfonso Claudio Hernandez-Romieu

The representativeness and timeliness of sentinel surveillance for endemic and emerging arboviral and respiratory diseases in low-resource settings are understudied. We compared laboratory-confirmed epidemic dengue, non-epidemic dengue, Zika, chikungunya, and COVID-19 (pre-Omicron and Omicron periods) cases reported in Puerto Rico’s Sentinel Enhanced Dengue Surveillance System (SEDSS) with island-wide trends reported by the Department of Health’s passive disease surveillance system (PADSS). We plotted trends over time to assess representativeness and used lagged cross-correlations to determine whether SEDSS reporting preceded PADSS. SEDSS trends were representative of island-wide trends for all pathogens. SEDSS preceded reporting in PADSS by up to three, eight, and two weeks for epidemic dengue, Zika, and pre-Omicron COVID-19, respectively. Increasing case trends for chikungunya occurred at broadly similar times in both systems, while temporal concordance was lower for non-epidemic dengue. In Puerto Rico, sentinel surveillance was representative of island-wide trends and could provide early warning for dengue epidemics and emerging diseases, such as Zika and COVID-19.

Objective: Nursing home residents have been disproportionately impacted by respiratory virus-related morbidity and mortality due to inherent vulnerability and communal living environments. This study aims to identify nursing homes with higher infection rates during a period of intense SARS-CoV-2 transmission and explore facility-level characteristics potentially associated with infection surges. Design: A longitudinal k-means clustering approach followed by exploratory regression analyses. Setting: U.S. Nursing homes reporting to the Centers for Disease Control and Prevention’s National Healthcare Safety Network (NHSN). Methods: A longitudinal k-means method (kmlShape) classified the facilities based on their weekly SARS-CoV-2 incidence rate epidemic curve, identifying two categories (low vs high infection peak) based on the magnitude of infection peaks. A logistic regression model with bootstrapping was developed to assess facility characteristics associated with higher SARS-CoV-2 infection surges. Results: Among 11,990 nursing homes analyzed, 9,058 were classified as having a low infection peak, while 2,932 had a high infection peak. Nursing homes that are for-profit (OR = 1.570, 95% bootstrap confidence interval [BCI] 1.441–1.807), with high staff turnover (OR = 1.292, 95% BCI 1.154–1.451), or located in areas with higher social vulnerability (OR = 1.457, 95% BCI 1.239–1.880) were more likely to experience high infection peaks. Nursing homes with higher residents’ vaccination coverage (OR = .321, 95% BCI .248–.380) and located in urban areas were less likely to experience high infection peaks. Conclusions: The facility-level characteristics associated with lower SARS-CoV-2 infection peaks may indicate resiliency and help evaluate the capacity of nursing homes to endure stressors such as respiratory viruses and other communicable illnesses.

“Increasing Number and Volume of Cavitary Lesions on Chest Computed Tomography Are Associated With Prolonged Time to Culture Conversion in Pulmonary Tuberculosis” by Alfonso C. Hernandez-Romieu et al (2019 6(6): doi:10.1093/ofid/ofz232), The fourth affiliation (author: Destani Bizune) was published as “Epidemiology and Statistics Branch, Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia,” and is not correct. The affiliation should instead read as “Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia.”

Background: Cavitary lesions (CLs) primarily identified by chest x-ray (CXR) have been associated with worse clinical outcomes among patients with pulmonary tuberculosis (PTB). Chest computed tomography (CT), which has better resolution and increased sensitivity to detect lung abnormalities, has been understudied in PTB patients. We compared detection of CLs by CT and CXR and assessed their association with time to sputum culture conversion (tSCC). Methods: This was a retrospective cohort study of 141 PTB patients who underwent CT. We used multivariate Cox proportional hazards models to evaluate the association between CLs on CXR and the number and single largest volume of CLs on CT with tSCC. Results: Thirty (21%) and 75 (53%) patients had CLs on CXR and CT, respectively. CT detected cavities in an additional 44 patients (31%) compared with CXR. After multivariable adjustment, we observed a negative association between CLs and tSCC, with an adjusted hazard ratio (aHR) of 0.56 (95% confidence interval [CI], 0.32 to 0.97) for single CLs and 0.31 (95% CI, 0.16 to 0.60) for multiple CLs present on CT. Patients with a CL volume ≥25 mL had a prolonged tSCC (aHR, 0.39; 95% CI, 0.21 to 0.72). CLs on CXR were not associated with increased tSCC after multivariable adjustment. Conclusions: CT detected a larger number of cavities in patients with PTB relative to CXR. We observed an association between increasing number and volume of CLs on CT and delayed tSCC independent of sputum microscopy result. Our findings highlight a potential role for CT in the clinical and research setting as a tool to risk-stratify patients with PTB.