Detection of asymptomatic or subclinical novel human coronavirus SARS-CoV-2 infection is critical for understanding the overall prevalence and infection potential of COVID-19. To estimate the cumulative prevalence of SARS-CoV-2 infection in China, we evaluated the host serologic response, measured by the levels of immunoglobulins M and G in 17,368 individuals, in the city of Wuhan, the epicenter of the COVID-19 pandemic in China, and geographic regions in the country, during the period from 9 March 2020 to 10 April 2020. In our cohorts, the seropositivity in Wuhan varied between 3.2% and 3.8% in different subcohorts. Seroposivity progressively decreased in other cities as the distance to the epicenter increased. Patients who visited a hospital for maintenance hemodialysis and healthcare workers also had a higher seroprevalence of 3.3% (51 of 1,542, 2.5-4.3%, 95% confidence interval (CI)) and 1.8% (81 of 4,384, 1.5-2.3%, 95% CI), respectively. More studies are needed to determine whether these results are generalizable to other populations and geographic locations, as well as to determine at what rate seroprevalence is increasing with the progress of the COVID-19 pandemic. Serologic surveillance has the potential to provide a more faithful cumulative viral attack rate for the first season of this novel SARS-CoV-2 infection.The novel human coronavirus SARS-CoV-2 is a highly contagious virus, and its disease, COVID-19, can lead to significant morbidity and mortality in a proportion of patients 1-3 . On 12 March 2020, the World Health Organization declared it a global pandemic 4 . As of 12 May 2020, there were more than 4.2 million confirmed infections globally in more than 180 countries with over 290,000 deaths (https://www.arcgis.com/apps/opsdashboard/index.html#/ bda7594740fd40299423467b48e9ecf6).Detection of SARS-CoV-2 in asymptomatic individuals 5,6 suggests that subclinical active infection might be an important contributor to this outbreak. Currently, reported cases of COVID-19 are mainly limited to symptomatic individuals, those having close

BackgroundType-specific high-risk HPV (hrHPV) infection is related to cervical carcinogenesis. The prevalence of hrHPV infection varies geographically, which might reflect the epidemiological characteristics of cervical cancer among different populations. To establish a foundation for HPV-based screening and vaccination programs in China, we investigated the most recent HPV prevalence and genotypic distributions in different female age groups and geographical regions in China.MethodsIn 2012, a total of 120,772 liquid-based cytological samples from women enrolled for population- or employee-based cervical screening in 37 Chinese cities were obtained by the Laboratory of Molecular Infectious Diseases of Guangzhou KingMed. A total of 111,131 samples were tested by Hybrid Capture II and the other 9,641 were genotyped using the Tellgenplex™ HPV DNA Assay.ResultsThe total positive rate for hrHPV was 21.07 %, which ranged from 18.42 % (Nanchang) to 31.94 % (Haikou) and varied by region. The regions of Nanchang, Changsha, Hangzhou, Chengdu, Fuzhou, Guangdong, and Guiyang could be considered the low prevalence regions. Age-specific prevalence showed a “two-peak” pattern, with the youngest age group (15–19 years) presenting the highest hrHPV infection rate (30.55 %), followed by a second peak for the 50–60-year-old group. Overall, the most prevalent genotypes were HPV16 (4.82 %) and HPV52 (4.52 %), followed by HPV58 (2.74 %). Two genotypes HPV6 (4.01 %) and HPV11 (2.29 %) were predominant in the low-risk HPV (lrHPV) type, while the mixed genotypes HPV16 + 52 and HPV52 + 58 were most common in women with multiple infections.ConclusionsThis study shows that HPV infection in China has increased to the level of an “HPV-heavy-burden” zone in certain regions, with prevalence varying significantly among different ages and regions. Data from this study represent the most current survey of the nationwide prevalence of HPV infection in China, and can serve as valuable reference to guide nationwide cervical cancer screening and HPV vaccination programs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-015-0998-5) contains supplementary material, which is available to authorized users.

Objective Nonmedical use of prescription opioids represents a national public health concern of growing importance. Mood and anxiety disorders are highly associated with nonmedical prescription opioid use. The authors examined longitudinal associations between nonmedical prescription opioid use and opioid disorder due to nonmedical opioid use with mood/anxiety disorders in a national sample, examining evidence for precipitation, self-medication and general shared vulnerability as pathways between disorders. Method Data were drawn from face-to-face surveys of 34,653 adult participants in Waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. Logistic regression models explored the temporal sequence and evidence for the hypothesized pathways. Results Baseline lifetime nonmedical prescription opioid use was associated with incidence of any mood disorder, major depressive disorder (MDD), bipolar disorder, any anxiety disorder, and generalized anxiety disorder (GAD in Wave 2, adjusted for baseline demographics, other substance use, and comorbid mood/anxiety disorders). Lifetime opioid disorder was not associated with any incident mood/anxiety disorders. All baseline lifetime mood disorders and GAD were associated with incident nonmedical prescription opioid use at follow-up, adjusted for demographics, comorbid mood/anxiety disorders, and other substance use. Baseline lifetime mood disorders, MDD, dysthymia, and panic disorder, were associated with incident opioid disorder due to nonmedical prescription opioid use at follow-up, adjusted for the same covariates. Conclusions These results suggest that preciptiation, self-medication as well as shared vulnerability are all viable pathways between nonmedical prescription opioid use and opioid disorder due to nonmedical opioid use with mood/anxiety disorders.

Background-While nonmedical use of opioids and psychiatric disorders are prevalent in the population, little is known about the temporal ordering between nonmedical opioid use and dependence and psychiatric disorders.Method-Data were gathered in a face-to-face survey of the United States conducted in the 2001-2002 (NESARC wave 1). Participants were household and group quarters residents aged 18 years and older (n=43,093). Cox proportional hazards models with time-dependent covariates were used to investigate potential pathways between lifetime nonmedical opioid use/dependence and psychiatric disorders.Results-Preexisting psychiatric disorders (mood disorders, major depressive disorder, bipolar I disorder, anxiety disorders, panic and generalized anxiety disorders) were associated with an increased risk of nonmedical opioid use, with hazard ratios ranging from 2.2[95% CI=1.6-3.1] (any anxiety disorder) to 3.1[95% CI= 2.4-2.4] (bipolar I disorder). Preexisting nonmedical opioid use was associated with an increased risk of onset of psychiatric disorders, with hazard ratios ranging from 2.8[95% CI=:2.2-3.6] (generalized anxiety disorder) to 3.6[95% CI=2.6-4.9] (bipolar I disorder), adjusted for demographics and other illegal drug use. Nonmedical use of opioids led to the development of dependence more often among individuals with preexisting psychiatric disorders, † Corresponding author: Silvia S. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access Author ManuscriptDrug Alcohol Depend. Author manuscript; available in PMC 2010 July 1. Published in final edited form as:Drug Alcohol Depend. Conclusions-Our findings support a general vulnerability to nonmedical opioid use and major psychopathologies, as well as evidence for a 'self-medication' model for dependence resulting from nonmedical opioid use with bipolar disorder and generalized anxiety disorder.