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Spatiotemporal analysis of high-resolution coronavirus disease (COVID-19) data can help health officials monitor disease spread and target interventions (1,2). Publicly available data have been used to detect COVID-19 spatiotemporal clusters [...]

RationaleObesity and underweight are contraindications to lung transplantation based on their associations with mortality in studies performed before implementation of the lung allocation score (LAS)-based organ allocation system in the United States Objectives: To determine the associations of body mass index (BMI) and plasma leptin levels with survival after lung transplantation.MethodsWe used multivariable-adjusted regression models to examine associations between BMI and 1-year mortality in 9,073 adults who underwent lung transplantation in the United States between May 2005 and June 2011, and plasma leptin and mortality in 599 Lung Transplant Outcomes Group study participants. We measured body fat and skeletal muscle mass using whole-body dual X-ray absorptiometry in 142 adult lung transplant candidates.Measurements and main resultsAdjusted mortality rates were similar among normal weight (BMI 18.5-24.9 kg/m(2)), overweight (BMI 25.0-29.9), and class I obese (BMI 30-34.9) transplant recipients. Underweight (BMI < 18.5) was associated with a 35% increased rate of death (95% confidence interval, 10-66%). Class II-III obesity (BMI ≥ 35 kg/m(2)) was associated with a nearly twofold increase in mortality (hazard ratio, 1.9; 95% confidence interval, 1.3-2.8). Higher leptin levels were associated with increased mortality after transplant surgery performed without cardiopulmonary bypass (P for interaction = 0.03). A BMI greater than or equal to 30 kg/m(2) was 26% sensitive and 97% specific for total body fat-defined obesity.ConclusionsA BMI of 30.0-34.9 kg/m(2) is not associated with 1-year mortality after lung transplantation in the LAS era, perhaps because of its low sensitivity for obesity. The association between leptin and mortality suggests the need to validate alternative methods to measure obesity in candidates for lung transplantation. A BMI greater than or equal to 30 kg/m(2) may no longer contraindicate lung transplantation.

The Bureau of Communicable Disease (BCD) at the New York City Department of Health and Mental Hygiene (DOHMH) monitors and investigates >70 reportable diseases among the city's 8.49 million residents. [...]

The article presents information on a study conducted on the efficacy of MRKAd5/HIV (Ad5) vaccines in controlling HIV infections. It is stated that Ad5 vaccination led to an increased risk of HIV infection. The study suggested that elevated risk for HIV infection resulted from vaccine antigen recognition in the absence of inflammatory context. MRKAd5/HIV preferentially induced a gene regulatory network that suppressed certain immune responses, and also impaired T-cell activation.

Staff at public health departments have few training materials to learn how to design and fine-tune systems to quickly detect acute, localized, community-acquired outbreaks of infectious diseases. Since 2014, the Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene has analyzed reportable communicable diseases daily using SaTScan. SaTScan is a free software that analyzes data using scan statistics, which can detect increasing disease activity without a priori specification of temporal period, geographic location, or size. The Bureau of Communicable Disease's systems have quickly detected outbreaks of salmonellosis, legionellosis, shigellosis, and COVID-19. This tutorial details system design considerations, including geographic and temporal data aggregation, study period length, inclusion criteria, whether to account for population size, network location file setup to account for natural boundaries, probability model (eg, space-time permutation), day-of-week effects, minimum and maximum spatial and temporal cluster sizes, secondary cluster reporting criteria, signaling criteria, and distinguishing new clusters versus ongoing clusters with additional events. We illustrate how to support health equity by minimizing analytic exclusions of patients with reportable diseases (eg, persons experiencing homelessness who are unsheltered) and accounting for purely spatial patterns, such as adjusting nonparametrically for areas with lower access to care and testing for reportable diseases. We describe how to fine-tune the system when the detected clusters are too large to be of interest or when signals of clusters are delayed, missed, too numerous, or false. We demonstrate low-code techniques for automating analyses and interpreting results through built-in features on the user interface (eg, patient line lists, temporal graphs, and dynamic maps), which became newly available with the July 2022 release of SaTScan version 10.1. This tutorial is the first comprehensive resource for health department staff to design and maintain a reportable communicable disease outbreak detection system using SaTScan to catalyze field investigations as well as develop intuition for interpreting results and fine-tuning the system. While our practical experience is limited to monitoring certain reportable diseases in a dense, urban area, we believe that most recommendations are generalizable to other jurisdictions in the United States and internationally. Additional analytic technical support for detecting outbreaks would benefit state, tribal, local, and territorial public health departments and the populations they serve., (©Alison Levin-Rector, Martin Kulldorff, Eric R Peterson, Scott Hostovich, Sharon K Greene. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 11.06.2024.)

In May 2019, the New York City Department of Health and Mental Hygiene (NYCDOHMH) detected an unusual cluster of five salmonellosis patients via automated spatiotemporal analysis of notifiable diseases using free SaTScan software (1). Within 1 day of cluster detection, graduate student interviewers determined that three of the patients had eaten prepared food from the same grocery store (establishment A) located inside the cluster area. NYCDOHMH initiated an investigation to identify additional cases, establish the cause, and provide control recommendations. Overall, 15 New York City (NYC) residents with laboratory-diagnosed salmonellosis who reported eating food from establishment A were identified. The most commonly consumed food item was chicken, reported by 10 patients. All 11 clinical isolates available were serotyped as Salmonella Blockley, sequenced, and analyzed by core genome multilocus sequence typing; isolates had a median difference of zero alleles. Environmental assessments revealed food not held at the proper temperature, food not cooled properly, and potential cross-contamination during chicken preparation. Elevated fecal coliform counts were found in two of four ready-to-eat food samples collected from establishment A, and Bacillus cereus was detected in three. The outbreak strain of Salmonella was isolated from one patient's leftover chicken. Establishing automated spatiotemporal cluster detection analyses for salmonellosis and other reportable diseases could aid in the detection of geographically focused, community-acquired outbreaks even before laboratory subtyping results become available., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.