Predicting Seasonal Influenza Hospitalizations Using an Ensemble Super Learner: A Simulation Study.

Accurate forecasts can inform response to outbreaks. Most efforts in influenza forecasting have focused on predicting influenza-like activity, with fewer on influenza-related hospitalizations. We conducted a simulation study to evaluate a super learner's predictions of 3 seasonal measures of influenza hospitalizations in the United States: peak hospitalization rate, peak hospitalization week, and cumulative hospitalization rate. We trained an ensemble machine learning algorithm on 15,000 simulated hospitalization curves and generated weekly predictions. We compared the performance of the ensemble (weighted combination of predictions from multiple prediction algorithms), the best-performing individual prediction algorithm, and a naive prediction (median of a simulated outcome distribution). Ensemble predictions performed similarly to the naive predictions early in the season but consistently improved as the season progressed for all prediction targets. The best-performing prediction algorithm in each week typically had similar predictive accuracy compared with the ensemble, but the specific prediction algorithm selected varied by week. An ensemble super learner improved predictions of influenza-related hospitalizations, relative to a naive prediction. Future work should examine the super learner's performance using additional empirical data on influenza-related predictors (e.g., influenza-like illness). The algorithm should also be tailored to produce prospective probabilistic forecasts of selected prediction targets.

Medically Attended Illness due to Respiratory Syncytial Virus Infection Among Infants Born in the United States Between 2016 and 2020.

BACKGROUND: Respiratory syncytial virus (RSV) is a leading cause of infant hospitalization in the United States. Preterm infants and those with select comorbidities are at highest risk of RSV-related complications. However, morbidity due to RSV infection is not confined to high-risk infants. We estimated the burden of medically attended (MA) RSV-associated lower respiratory tract infection (LRTI) among infants in the United States. METHODS: We analyzed commercial (MarketScan Commercial [MSC], Optum Clinformatics [OC]), and Medicaid (MarketScan Medicaid [MSM]) insurance claims data for infants born between April 2016 and February 2020. Using both specific and sensitive definitions of MA RSV LRTI, we estimated the burden of MA RSV LRTI during infants' first RSV season, stratified by gestational age, comorbidity status, and highest level of medical care associated with the MA RSV LRTI diagnosis. RESULTS: According to the specific definition 75.0% (MSC), 78.6% (MSM), and 79.6% (OC) of MA RSV LRTI events during infants' first RSV season occurred among term infants without known comorbidities. CONCLUSIONS: Term infants without known comorbidities account for up to 80% of the MA RSV LRTI burden in the United States during infants' first RSV season. Future prevention efforts should consider all infants.

Risk Analysis of Respiratory Syncytial Virus Among Infants in the United States by Birth Month.

BACKGROUND: Respiratory syncytial virus (RSV) is a major cause of morbidity and mortality among US infants. A child's calendar birth month determines their age at first exposure(s) to RSV. We estimated birth month-specific risk of medically attended (MA) RSV lower respiratory tract infection (LRTI) among infants during their first RSV season and first year of life (FYOL). METHODS: We analyzed infants born in the USA between July 2016 and February 2020 using three insurance claims databases (two commercial, one Medicaid). We classified infants' first MA RSV LRTI episode by the highest level of care incurred (outpatient, emergency department, or inpatient), employing specific and sensitive diagnostic coding algorithms to define index RSV diagnoses. In our main analysis, we focused on infants' first RSV season. In our secondary analysis, we compared the risk of MA RSV LRTI during infants' first RSV season to that of their FYOL. RESULTS: Infants born from May through September generally had the highest risk of first-season MA RSV LRTI-approximately 6-10% under the specific RSV index diagnosis definition and 16-26% under the sensitive. Infants born between October and December had the highest risk of RSV-related hospitalization during their first season. The proportion of MA RSV LRTI events classified as inpatient ranged from 9% to 54% (specific) and 5% to 33% (sensitive) across birth month and comorbidity group. Through the FYOL, the overall risk of MA RSV LRTI is comparable across birth months within each claims database (6-11% under the specific definition, 17-30% under the sensitive), with additional cases progressing to care at outpatient or ED settings. CONCLUSIONS: Our data support recent national recommendations for the use of nirsevimab in the USA. For infants born at the tail end of an RSV season who do not receive nirsevimab, a dose administered prior to the onset of their second RSV season could reduce the incidence of outpatient- and ED-related events.

Calculating risk and prevalence ratios and differences in R: developing intuition with a hands-on tutorial and code.

Epidemiologic research questions often focus on evaluating binary outcomes, yet curricula and scientific literature do not always provide clear guidance or examples on selecting and calculating an appropriate measure of association in these scenarios. Reporting inappropriate measures may lead to misleading statistical conclusions. We present a hands-on tutorial that includes annotated code written in an open-source statistical programming language (R) showing readers how to apply, compare, and understand four methods used to estimate a risk or prevalence ratio (or difference), rather than presenting an odds ratio. We will provide guidance on when to use each method, discussing the strengths and limitations of each approach, and compare the results obtained across them. Ultimately, we aim to help trainees, public health researchers, and interdisciplinary professionals develop an intuition for these methods and empower them to implement and interpret these methods in their own research.

Correction: Improving the impact of HIV pre-exposure prophylaxis implementation in small urban centers among men who have sex with men: An agent-based modelling study.

[This corrects the article DOI: 10.1371/journal.pone.0199915.].

Examining neighborhood poverty-based disparities in HIV/STI prevalence: an analysis of Add Health data.

PURPOSE: The purpose of the study was to estimate the effect of exposure to neighborhood poverty in adolescence on HIV/STI prevalence in early adulthood. METHODS: Longitudinal data from three waves of the National Longitudinal Study of Adolescent to Adult Health were analyzed. The primary exposure was living in a high- versus medium/low-poverty neighborhood during wave I. The outcome was having a sexually transmitted infection (STI) or receiving a HIV/STI diagnosis in the past 12 months at wave III. Covariates included sociodemographic, behavioral, and mental health-related factors. Inverse probability weighted marginal structural models were used to estimate neighborhood poverty-based differences in HIV/STI prevalence. RESULTS: The analytic sample comprised 8232 National Longitudinal Study of Adolescent to Adult Health participants. Of these, 16% and 84% resided in high- and medium/low-poverty neighborhoods, respectively. Eleven percent currently had an STI or HIV/STI diagnosis within the prior 12 months. Accounting for measured potential sources of confounding and selection bias, the HIV/STI prevalence difference (95% confidence limits) for those who grew up in high- versus medium/low-poverty neighborhoods was 0.015 (-0.015, 0.045). CONCLUSIONS: Strong evidence for neighborhood poverty-based differences in HIV/STI prevalence was not observed. Researchers should continue to investigate the effect of neighborhood-level socioeconomic position measures and, if warranted, identify etiologically relevant exposure periods.

Improving the impact of HIV pre-exposure prophylaxis implementation in small urban centers among men who have sex with men: An agent-based modelling study.

OBJECTIVES: Identifying prescribing strategies that improve the efficiency of PrEP should increase its impact at the population level. This study identifies PrEP allocation criteria that most effectively reduce 10-year HIV incidence by 25%, in accordance with the US National HIV/AIDS Strategy's goal for the proportionate reduction in new diagnoses. METHODS: We used a discrete-time stochastic agent-based model to simulate several PrEP engagement strategies. The model represented MSM aged 15-74 in Rhode Island and was calibrated to statewide prevalence from 2009-2014. We simulated HIV transmission in the absence of PrEP and compared the following PrEP engagement scenarios: 1) allocation to the current patient population; 2) random allocation; 3) allocation to MSM with greater than 5 sexual partners in one year; 4) allocation to MSM with greater than 10 sexual partners in one year. For each scenario and coverage level we estimated the number and proportion of infections averted and the person-years on PrEP per averted infection. RESULTS: In 2014, HIV prevalence before PrEP implementation was between 4% and 5%. In the No PrEP scenario 826 new infections (95% simulation limits [SL]: 711, 955) occurred over 10 years, with an incidence rate of 3.51 per 1000 person-years (95% SL: 3.00, 4.08). Prevalence rose to 7.4% (95% SL: 6.7, 8.1). None of the PrEP scenarios reduced new HIV infections by 25% while covering less than 15% of the HIV-uninfected population. At 15% coverage, allocating PrEP to the current patient population, MSM with greater than 5 sexual partners in a year, and MSM with greater than 10 partners reduced new infections by at least 25%, requiring 161 (95% SL: 115, 289), 150 (95% SL: 107, 252), and 128 (95% SL: 100, 184) person-years on PrEP per averted infection, respectively. CONCLUSIONS: Engaging MSM with high numbers of sexual partners would improve the population-level impact and efficiency of PrEP in settings where PrEP coverage remains low. However, the sustained population-level PrEP coverage needed to reduce new infections by 25% is substantially higher than current levels of PrEP uptake.