Brian M. Gurbaxani

Background: Older individuals (≥65 years) are at greatest risk of severe influenza requiring hospitalization. Since 2009, influenza-associated hospitalization rates during A(H1N1)pdm09-predominant influenza seasons have been lower than during A(H3N2)-predominant seasons. Methods: Using laboratory-confirmed influenza hospitalization rates from US population–based surveillance, we investigated the relative A(H1N1)pdm09 to A(H3N2) hospitalization rate ratios by patient year of age and birth cohort from 2010 through 2025. Results: Results suggest partial protection against A(H1N1)pdm09 hospitalizations relative to A(H3N2) hospitalizations among patients born before 1945 (pre-1945 birth cohorts) and among individuals born during 1994 through 2009. Impact of partial protection against A(H1N1)pdm09-associated hospitalizations has diminished over time among older adults, contributing to elevated influenza hospitalization rates during the 2024–2025 influenza season. Conclusions: Our findings suggest that influenza A hospitalization rates may increase during future influenza seasons when influenza A(H3N2) and A(H1N1) viruses circulate or following the emergence of novel A(H1N1)pdm09-like viruses.

With over 100,000 patients on the kidney transplant waitlist in 2019, it is important to understand if and how the functional status of a patient may change while on the waitlist. Recorded both at registration and just prior to transplantation, the Karnofsky Performance Score measures a patient’s functional status and takes on values ranging from 0 to 100 in increments of 10. Using machine learning techniques, we built a gradient boosting regression model to predict a patient’s pre-transplant functional status based on information known at the time of waitlist registration. The model’s predictions result in an average root mean squared error of 12.99 based on 5 rolling origin cross validations and 12.94 in a separate out-of-time test. In comparison, predicting that the pre-transplant functional status remains the same as the status at registration, results in average root mean squared errors of 14.50 and 14.11 respectively. The analysis is based on 118,401 transplant records from 2007 to 2019. To the best of our knowledge, there has been no previously published research on building a model to predict kidney pre-transplant functional status. We also find that functional status at registration and total serum albumin, have the most impact in predicting the pre-transplant functional status.

We updated a published mathematical model of SARS-CoV-2 transmission with laboratory-derived source and wearer protection efficacy estimates for a variety of face masks to estimate their impact on COVID-19 incidence and related mortality in the United States. When used at already-observed population rates of 80% for those ≥65 years and 60% for those Article Summary Line Adapting a published SARS-CoV-2 transmission model together with updated, laboratory-derived source control and wearer protection efficacy estimates for a variety of face coverings as well as N95 respirators, we demonstrate that community masking as currently practiced has likely reduced cases and deaths and that this benefit can be increased with wider adoption of better performing masks.

The recently completed sequencing of the human genome heralds a new era in the study of social influences upon health. Because the interactions between genes and the environment are bidirectional, expertise in the areas of psychosocial processes and health behaviors will be necessary for elucidating how genes, behavior, and the environment interact to affect health outcomes. For investigators whose primary background is in social health research, the terminology used by geneticists may seem like a foreign language. To help navigate this foreign territory, the nature of genetic variation and gene action is presented in non‐technical terms using the serotonin transporter gene as an example because it is thought to influence sensitivity to the social environment. In addition, we describe several methodological pitfalls to be avoided when associating genetic variation with psychosocial and behavioral risk factors for poor health outcomes.