Profile and dynamics of infectious diseases: a population-based observational study using multi-source big data.

BACKGROUND: The current surveillance system only focuses on notifiable infectious diseases in China. The arrival of the big-data era provides us a chance to elaborate on the full spectrum of infectious diseases. METHODS: In this population-based observational study, we used multiple health-related data extracted from the Shandong Multi-Center Healthcare Big Data Platform from January 2013 to June 2017 to estimate the incidence density and describe the epidemiological characteristics and dynamics of various infectious diseases in a population of 3,987,573 individuals in Shandong province, China. RESULTS: In total, 106,289 cases of 130 infectious diseases were diagnosed among the population, with an incidence density (ID) of 694.86 per 100,000 person-years. Besides 73,801 cases of 35 notifiable infectious diseases, 32,488 cases of 95 non-notifiable infectious diseases were identified. The overall ID continuously increased from 364.81 per 100,000 person-years in 2013 to 1071.80 per 100,000 person-years in 2017 (χ2 test for trend, P < 0.0001). Urban areas had a significantly higher ID than rural areas, with a relative risk of 1.25 (95% CI 1.23-1.27). Adolescents aged 10-19 years had the highest ID of varicella, women aged 20-39 years had significantly higher IDs of syphilis and trichomoniasis, and people aged ≥ 60 years had significantly higher IDs of zoster and viral conjunctivitis (all P < 0.05). CONCLUSIONS: Infectious diseases remain a substantial public health problem, and non-notifiable diseases should not be neglected. Multi-source-based big data are beneficial to better understand the profile and dynamics of infectious diseases.

Metabolomics and Lipidomics Profiling in Asymptomatic Severe Intracranial Arterial Stenosis: Results from a Population-Based Study.

No data are available on the serum metabolomics and lipidomics profiles of people with asymptomatic intracranial arterial stenosis. We explored the characteristic metabolites of individuals with asymptomatic severe intracranial arterial stenosis (asICAS) using untargeted serum metabolomics and lipidomics analyses based on ultra-high-performance liquid chromatography high-resolution mass spectrometry (UPLC-HRMS). This case-control study included 25 participants with asICAS and 25 age- and sex-matched controls free of asICAS, who were all diagnosed by using magnetic resonance angiography and derived from the same population-based study. Serum metabolomics and lipidomics profiles were determined using UPLC-HRMS, and possible biomarker metabolites were identified. Compared with the control group, the asICAS group showed higher levels of free choline, glycerophosphocholine, uracil, taurine, and four peptide molecules and lower levels of free fatty acids, hydroxydodecanedioic acid, hydroxy valeryl carnitine, hydroxytetradecanedioic acid, and two sphingomyelin molecules. The serum metabolomics and lipidomics profiles for people with asICAS are characterized by abnormal metabolism of sphingomyelin, taurine/hypotaurine, pyrimidine, and protein (peptide). The biological changes in asICAS may mainly involve taurine/hypotaurine, glycerophospholipid, and sphingolipid metabolism pathways. Biofunctional analysis indicated that these differential metabolites were correlated with metabolic diseases such as early myocardial injury, heart failure, and diabetes.

Lifespan Risk Exposure Measurement Instrument: a Feasible and Effective Tool for Life Course Epidemiology Research.

Life course epidemiology should practically illustrate how risk exposures and their dynamic changes influence the occurrence, development and prognosis of chronic diseases from early life to the elderly. This paper develops the lifespan risk exposure measurement instrument (LREMI) in the framework of retrospective study to collect lifestyle, diet, physical activity information across subjects'life courses from 18-years-old to current age. Through a pilot study, the result of the test-retest analysis demonstrated the reliability of LREMI. In Shandong Multicenter Cohort, the LREMI showed its feasibility, for it could measure the exposure spectrum on both individuals and population with different life experiences. Moreover, it had good differentiation ability for identifying cases versus controls in population-based case-control study. However, further studies should be conducted in an already available prospective cohort to ascertain that our results could match prospective data.

Epidemic and control of COVID-19 in Niger: quantitative analyses in a least developed country.

BACKGROUND: The COVID-19 pandemic is challenging the public health response system worldwide, especially in poverty-stricken, war-torn, and least developed countries (LDCs). METHODS: We characterized the epidemiological features and spread dynamics of COVID-19 in Niger, quantified the effective reproduction number (Rt ), evaluated the impact of public health control measures, and estimated the disease burden. RESULTS: As of 4 July 2020, COVID-19 has affected 29 communes of Niger with 1093 confirmed cases, among whom 741 (67.8%) were males. Of them 89 cases died, resulting in a case fatality rate (CFR) of 8.1%. Both attack rates and CFRs were increased with age (P < 0.0001). Health care workers accounted for 12.8% cases. Among the reported cases, 39.3% were isolated and treated at home, and 42.3% were asymptomatic. 74.6% cases were clustered in Niamey, the capital of Niger. The Rt fluctuated in correlation to control measures at different outbreak stages. After the authorities initiated the national response and implemented the strictest control measures, Rt quickly dropped to below the epidemic threshold (<1), and maintained low level afterward. The national disability-adjusted life years attributable to COVID-19 was 1267.38 years in total, of which years of life lost accounted for over 99.1%. CONCLUSIONS: Classic public health control measures such as prohibition of public gatherings, travelling ban, contact tracing, and isolation and quarantine at home, are proved to be effective to contain the outbreak in Niger, and provide guidance for controlling the ongoing COVID-19 pandemic in LDCs.

Impacts of transportation and meteorological factors on the transmission of COVID-19.

The ongoing pandemic of 2019 novel coronavirus disease (COVID-19) is challenging global public health response system. We aim to identify the risk factors for the transmission of COVID-19 using data on mainland China. We estimated attack rate (AR) at county level. Logistic regression was used to explore the role of transportation in the nationwide spread. Generalized additive model and stratified linear mixed-effects model were developed to identify the effects of multiple meteorological factors on local transmission. The ARs in affected counties ranged from 0.6 to 9750.4 per million persons, with a median of 8.8. The counties being intersected by railways, freeways, national highways or having airports had significantly higher risk for COVID-19 with adjusted odds ratios (ORs) of 1.40 (p = 0.001), 2.07 (p < 0.001), 1.31 (p = 0.04), and 1.70 (p < 0.001), respectively. The higher AR of COVID-19 was significantly associated with lower average temperature, moderate cumulative precipitation and higher wind speed. Significant pairwise interactions were found among above three meteorological factors with higher risk of COVID-19 under low temperature and moderate precipitation. Warm areas can also be in higher risk of the disease with the increasing wind speed. In conclusion, transportation and meteorological factors may play important roles in the transmission of COVID-19 in mainland China, and could be integrated in consideration by public health alarm systems to better prevent the disease.