Estimation of the effective reproductive numberR_tis important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are usingR_tto assess the effectiveness of interventions and to inform policy. However, estimation ofR_tfrom available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation ofR_t, we recommend the approach of Cori and colleagues, which uses data from before timetand empirical estimates of the distribution of time between infections. Methods that require data from after timet, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resultingR_testimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems inR_testimation.

Background—: Evidence suggests that longer-term exposure to air pollutants over years confers higher risks of cardiovascular morbidity and mortality than shorter-term exposure. One explanation is that the cumulative adverse effects that develop over longer durations lead to the genesis of chronic disease. Preliminary epidemiological and clinical evidence suggests that air pollution may contribute to the development of hypertension and type 2 diabetes mellitus. Methods and Results—: We used Cox proportional hazards models to assess incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for incident hypertension and diabetes mellitus associated with exposure to fine particulate matter (PM _2.5 ) and nitrogen oxides in a cohort of black women living in Los Angeles. Pollutant levels were estimated at participants' residential addresses with land use regression models (nitrogen oxides) and interpolation from monitoring station measurements (PM _2.5 ). Over follow-up from 1995 to 2005, 531 incident cases of hypertension and 183 incident cases of diabetes mellitus occurred. When pollutants were analyzed separately, the IRR for hypertension for a 10-μg/m ³ increase in PM _2.5 was 1.48 (95% CI, 0.95–2.31), and the IRR for the interquartile range (12.4 parts per billion) of nitrogen oxides was 1.14 (95% CI, 1.03–1.25). The corresponding IRRs for diabetes mellitus were 1.63 (95% CI, 0.78–3.44) and 1.25 (95% CI, 1.07–1.46). When both pollutants were included in the same model, the IRRs for PM _2.5 were attenuated and the IRRs for nitrogen oxides were essentially unchanged for both outcomes. Conclusion—: Our results suggest that exposure to air pollutants, especially traffic-related pollutants, may increase the risk of type 2 diabetes mellitus and possibly of hypertension.

Background The United States was the second country to have a major outbreak of novel influenza A/H1N1 in what has become a new pandemic. Appropriate public health responses to this pandemic depend in part on early estimates of key epidemiological parameters of the virus in defined populations.Methods We use a likelihood‐based method to estimate the basic reproductive number (R₀) and serial interval using individual level U.S. data from the Centers for Disease Control and Prevention (CDC). We adjust for missing dates of illness and changes in case ascertainment. Using prior estimates for the serial interval we also estimate the reproductive number only.Results Using the raw CDC data, we estimate the reproductive number to be between 2·2 and 2·3 and the mean of the serial interval (μ) between 2·5 and 2·6 days. After adjustment for increased case ascertainment our estimates change to 1·7 to 1·8 forR₀and 2·2 to 2·3 days for μ. In a sensitivity analysis making use of previous estimates of the mean of the serial interval, both for this epidemic (μ = 1·91 days) and for seasonal influenza (μ = 3·6 days), we estimate the reproductive number at 1·5 to 3·1.Conclusions With adjustments for data imperfections we obtain useful estimates of key epidemiological parameters for the current influenza H1N1 outbreak in the United States. Estimates that adjust for suspected increases in reporting suggest that substantial reductions in the spread of this epidemic may be achievable with aggressive control measures, while sensitivity analyses suggest the possibility that even such measures would have limited effect in reducing total attack rates.

Objective To describe the effect of wartime military deployments on the behavior of young children in military families. Design Cross-sectional study. Setting Childcare centers on a large Marine base. Participants Parents and childcare providers of children aged 1½ to 5 years enrolled in on-base childcare centers. Main Exposure Parental deployment. Outcome Measures Mean externalizing, internalizing, and total symptom scores on the Child Behavior Checklist (CBCL) (1½-5 years) and the CBCL–Teacher Report Form (TRF) (1½-5 years). Results One hundred sixty-nine of 233 consenting families (73%) participated. Nonresponders did not differ from responders in their child's age or TRF scores. Fifty-five children (33%) had a deployed parent. Parents with children aged 3 years or older and a deployed spouse had significantly higher depression scores than those without a deployed spouse. There were no differences in the demographic characteristics between groups. After controlling for respondent's age, stress and depressive symptoms, deployed service member's rank, and total number of children in the home, we found an age by deployment interaction: children aged 3 years or older with a deployed parent had significantly higher CBCL externalizing and total scores (externalizing, 48.50 vs 43.31,P < .05; total, 47.71 vs 42.68,P < .05) and externalizing and total TRF scores (externalizing, 50.21 vs 45.62,P < .05; total, 48.54 vs 43.73,P < .05) compared with same-aged peers without a deployed parent. Conclusions This study is the first to show that children aged 3 years or older with a deployed parent exhibit increased behavioral symptoms compared with peers without a deployed parent after controlling for caregiver's stress and depressive symptoms.

Diagnosis of chronic hepatitis C virus (HCV) infection usually requires both a positive HCV antibody screen and confirmatory nucleic acid testing (NAT). This systematic review of 44 studies evaluat... Background: Diagnosis of chronic hepatitis C virus (HCV) infection requires both a positive HCV antibody screen and confirmatory nucleic acid testing (NAT). Testing for hepatitis C virus core antig...