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Genetic differences between SARS-CoV-2 variants raise concerns about reinfection. Public health authorities monitored the incidence of suspected reinfection in Clark County, Nevada, USA, during March 2020–March 2022. Suspected reinfections, defined as a second positive PCR test collected >90 days after an initial positive test, were monitored through an electronic disease surveillance system. We calculated the proportion of all new cases per week that were suspected reinfections and rates per 1,000 previously infected persons by demographic groups. The rate of suspected reinfection remained <2.7% until December 2021, then increased to ≈11%, corresponding with local Omicron variant detection. Reinfection rates were higher among adults 18–50 years of age, women, and minority groups, especially persons identifying as American Indian/Alaska Native. Suspected reinfection became more common in Clark County after introduction of the Omicron variant, and some demographic groups are disproportionately affected. Public health surveillance could clarify the SARS-CoV-2 reinfection burden in communities.

Genome sequencing from wastewater enables accurate and cost-effective identification of SARS-CoV-2 variants. However, existing computational pipelines have limitations in detecting emerging variants not yet characterized in humans. Here, we present an unsupervised learning approach that clusters co-varying and time-evolving mutation patterns to identify SARS-CoV-2 variants. To build our model, we sequence 3659 wastewater samples collected over two years from urban and rural locations in Southern Nevada. We then develop a multivariate independent component analysis (ICA)-based pipeline to transform mutation frequencies into independent sources. These data-driven time-evolving and co-varying sources are compared to 8810 SARS-CoV-2 clinical genomes from Nevadans. Our method accurately detects the Delta variant in late 2021, Omicron variants in 2022, and emerging recombinant XBB variants in 2023. Our approach also reveals the spatial and temporal dynamics of variants in both urban and rural regions; achieves earlier detection of most variants compared to other computational tools; and uncovers unique co-varying mutation patterns not associated with any known variant. The multivariate nature of our pipeline boosts statistical power and supports accurate early detection of SARS-CoV-2 variants. This feature offers a unique opportunity to detect emerging variants and pathogens, even in the absence of clinical testing. Wastewater surveillance can help in pandemic or outbreak response. Here, the authors report an unsupervised learning approach to detect emerging SARS-CoV-2 variants from rural and urban wastewater showing it achieves earlier detection than existing methods and detects new variants without clinical testing data.

Background The Centers for Disease Control and Prevention (CDC)’s social vulnerability index (SVI) for exploring social and health disparities in the United States may not be suitable for assessing COVID-19 risk in specific communities and subpopulations. This study aims to develop the community vulnerability index (CVI) optimized for demographic-specific COVID-19 prevalence at the census tract level and apply it to Clark County, Nevada, which includes the vibrant Las Vegas metropolitan area. Methods We constructed the CVI using fifteen social condition variables from the CDC’s SVI along with eight additional community variables measuring inactive commuting, park deprivation, retail density, low-income homeowner or renter severe housing cost burden, housing inadequacy, segregation, and population density. Deploying weighted quantile sum (WQS) regression through a bootstrapping technique, the CVI was optimized by linking the 23 community variables to cumulative confirmed cases of COVID-19 from January 2020 to November 2021, excluding reinfections. This study resulted in whole-population and 13 demographic-specific CVIs representative of various age (0–4, 5–17, 18–24, 25–49, 50–64, and 65 +), race (White, Black, Hispanic, Asian/Pacific Islander, and others), and sex (male and female) groups. Results All WQS regressions revealed significant associations between the CVIs and corresponding COVID-19 prevalence. The most influential variables to the whole-population CVI included minority status, park deprivation, aged 17 and younger, inactive commuting, and housing inadequacy, which also contributed significantly to several CVIs corresponding to COVID-19 prevalence in subpopulations. Other influential community variables to the CVIs in general varied by subpopulation. The distributions of the subpopulation CVIs showed different levels of spatial disparities, with the largest disparities observed in female, White, and age 50–64 groups. Conclusions This study established a practical approach to optimize CVI for assessing COVID-19 risk. The incorporation of additional variables, specificity for subpopulations, and adaptability through the WQS regression collectively contribute to its value in informing evidence-based policy decisions and guiding targeted interventions to mitigate the impact of COVID-19 on vulnerable communities.

Background In 2017, Clark County, NV, implemented Public Health Vending Machines (PHVMs), an innovative approach to the dispensation of harm reduction supplies to persons who inject drugs (PWID), including sterile equipment and naloxone. Administrative data associated with PHVM operations can be valuable for understanding drug use behaviors among PWID. The current study examines the demographics and drug use profiles of PHVM registrants who completed the harm reduction survey between January 2021 to June 2023 with comparison to nation-wide trends. Methods All registration forms for PHVM services in Clark County, NV, between 1/1/2021-6/30/2022 with a completed harm reduction survey were included for analysis. Descriptive statistics were used to characterize differences in applicant demographics as well as self-reported injection and non-injection drug use, risk behaviors, and interest in harm reduction services. Logistic regression models tested the association between types of injection drug use and overdose and risk behaviors. Results A total of 637 PHVM applications with completed survey data were included for analysis. Respondents were an average of 36.1 ± 10.2 years old, 56.3% male sex, and 63.6% non-Hispanic White with 85.1% reporting injection drug use (IDU). Notably, greater proportions of respondents with histories of IDU also indicated non-injection drug administration, such as smoking and snorting. In the 3 months prior to registration, the majority of IDU respondents reported high risk drug use behaviors, including daily use, multiple injections per day, and opioid and stimulant co-use. Fentanyl was suspected in 62.1% of overdoses in the last 3 months. Compared to PWID using stimulants only, respondents with opioid and stimulant co-use had a higher likelihood of overdose (aOR 4.51; 95% CI 2.05, 11.1; p  < 0.001) and re-using injection supplies (aOR 2.14; 95% CI 1.33, 3.48; p  = 0.002). More opioid and stimulant co-use respondents were interested in treatment/detox and obtaining naloxone than those without co-use. Conclusions The demographics and drug use behaviors of the PHVM PWID are consistent with contemporaneous county and nation-wide. As the overdose crisis evolves, PHVM could be pivotal tools in the early detection of new risks to facilitate timely adaptation of harm reduction strategies to improve morbidity and mortality.

This study examines the pivotal role of COVID-19 testing in mitigating disease spread, particularly in underserved rural communities facing health care access challenges. The Southern Nevada Health District successfully implemented a vending program in Clark County, offering free COVID-19 antigen test kits. Strategically located based on health equity indices and featuring a user-friendly, multilingual registration process, these machines proved effective in reaching rural populations. The cost-effective model suggests potential adoption for broader public health interventions and services in other regions. ( Am J Public Health. 2024;114(S7):S558–S561. https://doi.org/10.2105/AJPH.2024.307718 )

Two rural settled Fulani villages, northeastern Nigeria, were surveyed for dietary practices and use of edible wild plants (n = 100 households). Commonly consumed species of edible wild barks, fruits, leaves, nuts, seeds, and tubers were analyzed for protein, fat, and carbohydrate and for minerals. Kuka bark (Adansonia digitata) given to infants to increase weight gain was high in fat, calcium, copper, iron, and zinc. Cediya (Ficus thonningii), dorowa (Parkia biglobosa) and zogale (Moringa oleifera) were good sources of protein and fat and excellent sources of calcium and iron or copper and zinc. Fruits, leaves, and nuts of aduwa (Balanites aegyptiaca) were widely used during the dry season and during drought. Edible wild species available during the wet season generally were inferior in energy and micronutrient mineral content compared to dry season plants. Fruits commonly eaten by children were poor sources of protein and minerals but rich in carbohydrate and fiber. Tsamiya seeds (Tamarindus indica) were good sources of zinc and used to make dawwa (porridge) commonly consumed during pregnancy. Kirya seeds (Prosopos africana) contained the highest zinc concentrations. Shiwaka leaves (Veronia colorate) to increase breastmilk production and to expel intestinal worms, were high in fiber, phosphorus, magnesium, manganese, and were adequate sources of calcium.

Temporal variability and methodological differences in data normalization, among other factors, complicate effective trend analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) wastewater surveillance data and its alignment with coronavirus disease 2019 (COVID-19) clinical outcomes. As there is no consensus approach for these analyses yet, this study explored the use of piecewise linear trend analysis (joinpoint regression) to identify significant trends and trend turning points in SARS-CoV-2 RNA wastewater concentrations (normalized and non-normalized) and corresponding COVID-19 case rates in the greater Las Vegas metropolitan area (Nevada, USA) from mid-2020 to April 2023. The analysis period was stratified into three distinct phases based on temporal changes in testing protocols, vaccination availability, SARS-CoV-2 variant prevalence, and public health interventions. While other statistical methodologies may require fewer parameter specifications, joinpoint regression provided an interpretable framework for characterization and comparison of trends and trend turning points, revealing sewershed-specific variations in trend magnitude and timing that also aligned with known variant-driven waves. Week-level trend agreement corroborated previous findings demonstrating a close relationship between SARS-CoV-2 wastewater surveillance data and COVID-19 outcomes. These findings guide future applications of advanced statistical methodologies and support the continued integration of wastewater-based epidemiology as a complementary approach to traditional COVID-19 surveillance systems.

Community vulnerability is influenced by various determinants beyond socioeconomic status and plays a crucial role in COVID-19 disparities. This study aimed to develop and evaluate a novel community vulnerability index (CVI) related to temporal variations in COVID-19 incidence to provide insights into spatial disparities and inform targeted public health interventions in Clark County, Nevada. Utilizing data from the American Community Survey and other sources, 23 community measures were identified at the census tract level. The CVI was constructed using a lagged weighted quantile sum (LWQS) regression linking these measures to the monthly COVID-19 incidence from March 2020 to November 2021. The Besag–York–Mollié model subsequently evaluated the spatial association between the CVI and COVID-19 incidence, controlling for temporal and spatial autocorrelations. This study identified minority status, housing inadequacy, and inactive commuting as primary contributors to the CVI that consistently influenced COVID-19 vulnerability over time. The CVI demonstrated significant spatial disparities, with higher values found in northern Clark County and the northeastern Las Vegas metropolitan area. Spatial analyses revealed varying associations between COVID-19 incidence and the CVI across census tracts, with significant associations clustered in the northern and eastern regions of the Las Vegas metropolitan area. These findings advance our understanding of the complex interplay between community conditions and COVID-19. The CVI framework may be applied to other COVID-19 outcomes such as testing, vaccination, and hospitalization, offering a valuable tool for assessing and addressing community vulnerability.

Community mitigation activities (also referred to as nonpharmaceutical interventions) are actions that persons and communities can take to slow the spread of infectious diseases. Mitigation strategies include personal protective measures (e.g., handwashing, cough etiquette, and face coverings) that persons can use at home or while in community settings; social distancing (e.g., maintaining physical distance between persons in community settings and staying at home); and environmental surface cleaning at home and in community settings, such as schools or workplaces. Actions such as social distancing are especially critical when medical countermeasures such as vaccines or therapeutics are not available. Although voluntary adoption of social distancing by the public and community organizations is possible, public policy can enhance implementation. The CDC Community Mitigation Framework (1) recommends a phased approach to implementation at the community level, as evidence of community spread of disease increases or begins to decrease and according to severity. This report presents initial data from the metropolitan areas of San Francisco, California; Seattle, Washington; New Orleans, Louisiana; and New York City, New York* to describe the relationship between timing of public policy measures, community mobility (a proxy measure for social distancing), and temporal trends in reported coronavirus disease 2019 (COVID-19) cases. Community mobility in all four locations declined from February 26, 2020 to April 1, 2020, decreasing with each policy issued and as case counts increased. This report suggests that public policy measures are an important tool to support social distancing and provides some very early indications that these measures might help slow the spread of COVID-19.