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We measured severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in primary sewage sludge in the New Haven, Connecticut, USA, metropolitan area during the Coronavirus Disease 2019 (COVID-19) outbreak in Spring 2020. SARS-CoV-2 RNA was detected throughout the more than 10-week study and, when adjusted for time lags, tracked the rise and fall of cases seen in SARS-CoV-2 clinical test results and local COVID-19 hospital admissions. Relative to these indicators, SARS-CoV-2 RNA concentrations in sludge were 0–2 d ahead of SARS-CoV-2 positive test results by date of specimen collection, 0–2 d ahead of the percentage of positive tests by date of specimen collection, 1–4 d ahead of local hospital admissions and 6–8 d ahead of SARS-CoV-2 positive test results by reporting date. Our data show the utility of viral RNA monitoring in municipal wastewater for SARS-CoV-2 infection surveillance at a population-wide level. In communities facing a delay between specimen collection and the reporting of test results, immediate wastewater results can provide considerable advance notice of infection dynamics. Testing sewage for the novel coronavirus reveals epidemiological trends.

Upcoming vaccination efforts against typhoid fever require an assessment of the baseline burden of disease in countries at risk. There are no typhoid incidence data from most low- and middle-income countries (LMICs), so model-based estimates offer insights for decision-makers in the absence of readily available data. We developed a mixed-effects model fit to data from 32 population-based studies of typhoid incidence in 22 locations in 14 countries. We tested the contribution of economic and environmental indices for predicting typhoid incidence using a stochastic search variable selection algorithm. We performed out-of-sample validation to assess the predictive performance of the model. We estimated that 17.8 million cases of typhoid fever occur each year in LMICs (95% credible interval: 6.9-48.4 million). Central Africa was predicted to experience the highest incidence of typhoid, followed by select countries in Central, South, and Southeast Asia. Incidence typically peaked in the 2-4 year old age group. Models incorporating widely available economic and environmental indicators were found to describe incidence better than null models. Recent estimates of typhoid burden may under-estimate the number of cases and magnitude of uncertainty in typhoid incidence. Our analysis permits prediction of overall as well as age-specific incidence of typhoid fever in LMICs, and incorporates uncertainty around the model structure and estimates of the predictors. Future studies are needed to further validate and refine model predictions and better understand year-to-year variation in cases.

Background Surveillance for respiratory syncytial virus (RSV) likely captures just a fraction of the burden of disease. Understanding the burden of hospitalizations and disparities between populations can help to inform upcoming RSV vaccine programs and to improve surveillance. Methods We obtained monthly age-, ZIP code- and cause-specific hospitalizations in New York, New Jersey, and Washington from the US State Inpatient Databases (2005–2014). We estimated the incidence of respiratory hospitalizations attributable to RSV by age and by socioeconomic status using regression models. We compared the estimated incidence and the recorded incidence (based on ICD9-CM) of RSV hospitalizations to estimate the under-recorded ratio in different subpopulations. Results The estimated annual incidence of respiratory hospitalizations due to RSV was highest among infants < 1 year of age with low socioeconomic status (2800, 95% CrI [2600, 2900] per 100,000 person-years). We also estimated a considerable incidence in older adults (≥ 65 years of age), ranging from 130 to 960 per 100,000 person-years across different socioeconomic strata. The incidence of hospitalization recorded as being due to RSV represented a significant undercount, particularly in adults. Less than 5% of the estimated RSV hospitalizations were captured for those ≥ 65 years of age. Conclusions RSV causes a considerable burden of hospitalization in young children and in older adults in the US, with variation by socioeconomic group. Recorded diagnoses substantially underestimate the incidence of hospitalization due to RSV in older adults.

Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID-19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 404,214 COVID-19 deaths by the end of 2020, and that 28% of the US population had been infected. There was county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers.

The incidence of multidrug-resistant tuberculosis (MDR-TB) remains critically high in countries of the former Soviet Union, where >20% of new cases and >50% of previously treated cases have resistance to rifampin and isoniazid. Transmission of resistant strains, as opposed to resistance selected through inadequate treatment of drug-susceptible tuberculosis (TB), is the main driver of incident MDR-TB in these countries. We conducted a prospective, genomic analysis of all culture-positive TB cases diagnosed in 2018 and 2019 in the Republic of Moldova. We used phylogenetic methods to identify putative transmission clusters; spatial and demographic data were analyzed to further describe local transmission of Mycobacterium tuberculosis. Of 2,236 participants, 779 (36%) had MDR-TB, of whom 386 (50%) had never been treated previously for TB. Moreover, 92% of multidrug-resistant M. tuberculosis strains belonged to putative transmission clusters. Phylogenetic reconstruction identified 3 large clades that were comprised nearly uniformly of MDR-TB: 2 of these clades were of Beijing lineage, and 1 of Ural lineage, and each had additional distinct clade-specific second-line drug resistance mutations and geographic distributions. Spatial and temporal proximity between pairs of cases within a cluster was associated with greater genomic similarity. Our study lasted for only 2 years, a relatively short duration compared with the natural history of TB, and, thus, the ability to infer the full extent of transmission is limited. The MDR-TB epidemic in Moldova is associated with the local transmission of multiple M. tuberculosis strains, including distinct clades of highly drug-resistant M. tuberculosis with varying geographic distributions and drug resistance profiles. This study demonstrates the role of comprehensive genomic surveillance for understanding the transmission of M. tuberculosis and highlights the urgency of interventions to interrupt transmission of highly drug-resistant M. tuberculosis.

Recent years have been marked by an erosion of public trust in science, public health, and vaccines. Little is known about evidence-based strategies to restore trust. We first aimed to instill a feeling of vulnerability in participants by asking three questions about global development, which are commonly answered incorrectly. We then guided participants through a peer-reviewed study on the effect of COVID-19 vaccines on fertility and asked six easy questions. We hypothesized that people would be empowered to believe the conclusions of the study and with that, trust would increase. We found that the intervention increased trust in science (adjusted relative risk ratio (aRRR: 1.69, 95% confidence interval (CI): 1.30-2.20), public health (aRRR: 1.38, 95% CI: 1.08-1.77), and vaccines (aRRR: 1.38, 95% CI: 1.07-1.77). However, the intervention backfired (i.e., decreased trust) for trust in science (aRRR: 1.61, 95% CI: 1.23-2.12) overall, and specifically among people who were wrong and uncertain about their answers. The intervention both significantly increased and decreased beliefs that COVID-19 vaccines cause infertility. While this short intervention managed to increase trust in science, future iterations should be more responsive to baseline levels of trust and beliefs in false information.

The health impact of floods has not been well characterized. This study evaluated long-term associations between cause-specific mortality rates and county-level monthly flood days (excluding coastal floods caused by tropical storms) in the post-flood year in the contiguous U.S., using a triply robust approach incorporating propensity score, counterfactual estimation, and confounder adjustment. Death records came from the CDC National Center for Health Statistics (2001-2020) and floods came from the NOAA Storm Events Database (2000-2020). We found that one flood day was associated with 8.3 (95% CI: 2.5 to 14.1) excess all-cause deaths per 10 million individuals, 3.1 due to myocardial infarction, 2.4 due to respiratory diseases, and 5.9 due to external causes. From 2001 to 2020, 22,376 (95% CI: 6,758 to 37,993) all-cause deaths were attributable to floods. Our findings highlight the long-term health risks after floods, and a need for measures to reduce these risks. This paper characterizes the health impacts in the post-flood year in the United States. The researchers find elevated death risk from floods, primarily due to respiratory diseases, external causes, and specific circulatory diseases.

SARS-CoV-2 infection rates displayed strikingly organized patterns of temporal and spatial spread as new variants were introduced and subsequently transmitted within the United States. While these spatio-temporal “waves” of infection have been described previously, attempts to quantify the speed and extent of these waves have been limited. Here, we estimate and compare the wavefront speed and spatial expansion of the first two major infection waves in the United States, illustrating these dynamics through detailed visualizations. Our findings reveal that the origins of these waves coincide with large gatherings and the relaxation of masking mandates. Notably, we found that the second wave spread more rapidly than the first, possibly driven by multiple introduction events. These analyses highlight regional heterogeneity in epidemic dynamics and underscore the importance of localized public health measures in mitigating ongoing outbreaks.

In the United States (US), incidence of early age of onset colorectal cancer (EOCRC, diagnosed <50 years of age) has been increasing. Using a Bayesian analytic approach, we evaluated the association between county-level ecological factors and survival among individuals with EOCRC and identified hotspot and coldspot counties with unexplained low and high survival, respectively. Principal component (PC) analysis was used to reduce dimensionality of 36 county-level social, behavioral, and preventive factors from the Centers for Disease Control and Prevention data. Survival information was derived from the Surveillance, Epidemiology, and End Results Program data from January 1, 2000 to December 31, 2019. The association between the identified PCs and survival was evaluated using multivariable spatial generalized linear mixed models. Counties with residual low and high survival (i.e., unexplained by the PCs) were classified as hotspots and coldspots, respectively. Four PCs were used to explain the spatial variability in 5-year survival among 75,215 individuals with EOCRC: PC1) poverty, chronic disease, health risk behaviors (β = -0.03, 95% credible interval (CrI): -0.04, -0.03); PC2) younger age, chronic disease-free, minority status (β = -0.01, 95% CrI: -0.02, 0.00); PC3) urban environment, preventive services (β = 0.02, 95% CrI: 0.00, 0.03); and PC4) older age (-0.04, 95% CrI: -0.06, -0.02). Among individuals with distant malignancies, the residual spatial variability remained high for two US counties: 1) Salt Lake County, UT residents experiencing 26.5% (95% CrI: 1.5%, 47.8%) lower odds of survival [hotspot], and 2) Riverside County, CA residents experiencing 37% (95% CrI: 7.97%, 78.8%) higher odds survival [coldspot] after adjustment for county-level factors. County-level ecological factors are strongly associated with survival among individuals with EOCRC. Yet there is some evidence of survival disparities among individuals with distant malignancies that remain unexplained by the included factors.

As extreme events such as drought and flood are projected to increase in frequency and intensity under climate change, there is still large missing evidence on how drought exposure potentially impacts mortality among young children. This study aimed to investigate the association between drought and risk of infant mortality in Africa, a region highly vulnerable to climate change that bears the heaviest share of the global burden. In this cross-sectional study, we obtained data on infant mortality in 34 African countries during 1992-2019 from the Demographic and Health Surveys program. We measured drought by the standardized precipitation evapotranspiration index at a timescale of 24 months and a spatial resolution of 10 × 10 km, which was further dichotomized into mild and severe drought. The association between drought exposure and infant mortality risk was estimated using Cox regression models allowing time-dependent covariates. We further examined whether the association varied for neonatal and post-neonatal mortality and whether there was a delayed association with drought exposure during pregnancy or infancy. The mean (standard deviation) number of months in which children experienced any drought during pregnancy and survival period (from birth through death before 1 year of age) was 4.6 (5.2) and 7.3 (7.4) among cases and non-cases, respectively. Compared to children who did not experience drought, we did not find evidence that any drought exposure was associated with an increased risk of infant mortality (hazard ratio [HR]: 1.02, 95% confidence interval [CI] [1.00, 1.04], p = 0.072). When stratified by drought severity, we found a statistically significant association with severe drought (HR: 1.04; 95% CI [1.01, 1.07], p = 0.015), but no significant association with mild drought (HR: 1.01; 95% CI [0.99, 1.03], p = 0.353), compared to non-exposure to any drought. However, when excluding drought exposure during pregnancy, the association with severe drought was found to be non-significant. In addition, an increased risk of neonatal mortality was associated with severe drought (HR: 1.05; 95% CI [1.01, 1.10], p = 0.019), but not with mild drought (HR: 0.99; 95% CI [0.96, 1.02], p = 0.657). Exposure to long-term severe drought was associated with increased infant mortality risk in Africa. Our findings urge more effective adaptation measures and alleviation strategies against the adverse impact of drought on child health.