Association between sleep-wake habits and use of health care services of middle-aged and elderly adults in China.

OBJECTIVE: To examine the relationship between sleep-wake habits and the use of health care services. RESULTS: The proportions of the participants who were "early to bed" and "late to bed" were 48.7% and 51.3%, respectively. In the full sample, compared with those who were early to bed and early to rise, participants who went to bed late were more likely to report physician visits (late to bed and early to rise: OR = 1.13, 95% CI: 1.08-1.19, late to bed and late to rise: OR = 1.27, 95% CI: 1.18-1.38, respectively). We found no significant association between sleep-wake habits and the number of hospitalization. CONCLUSIONS: Those middle-aged and elderly people who stayed up late and got up late are more likely to visit the doctors than those who went to bed early and got up early. METHODS: We obtained data from a cohort study of retired employees in China, and 36,601 (95.59%) involved in the present study. The participants were allocated into 4 sleep-wake habits groups: Early-bed/Early-rise, Early-bed/Late-rise, Late-bed/Early-rise, and Late-bed/Late-rise. We explored the association between sleep-wake habits with the number of physician visits and hospitalizations

Additional file 1 of Feasible intervention combinations for achieving a safe exit of the Zero-COVID policy in China and its determinants: an individual-based model study

Additional file 1

Series of observations of HFMD in Shenzhen.

<p>Series 1 shows the observations of the training set (from January 2008 to August 2012). Series 2 shows the observations of training set without the abnormal observation (AO). Series 3 shows the series 2 achieving stationary after one regular differencing and one seasonal differencing (d = 1, s = 12). Series 4 shows the validation set (from September 2012 to November 2012).</p

Exploring the potential of open big data from ticketing websites to characterize travel patterns within the Chinese high-speed rail system

<div><p>Big data have contributed to deepen our understanding in regards to many human systems, particularly human mobility patterns and the structure and functioning of transportation systems. Resonating the recent call for ‘open big data,’ big data from various sources on a range of scales have become increasingly accessible to the public. However, open big data relevant to travelers within public transit tools remain scarce, hindering any further in-depth study on human mobility patterns. Here, we explore ticketing-website derived data that are publically available but have been largely neglected. We demonstrate the power, potential and limitations of this open big data, using the Chinese high-speed rail (HSR) system as an example. Using an application programming interface, we automatically collected the data on the remaining tickets (RTD) for scheduled trains at the last second before departure in order to retrieve information on unused transit capacity, occupancy rate of trains, and passenger flux at stations. We show that this information is highly useful in characterizing the spatiotemporal patterns of traveling behaviors on the Chinese HSR, such as weekend traveling behavior, imbalanced commuting behavior, and station functionality. Our work facilitates the understanding of human traveling patterns along the Chinese HSR, and the functionality of the largest HSR system in the world. We expect our work to attract attention regarding this unique open big data source for the study of analogous transportation systems.</p></div

Time series of daily occupancy rates for the two intercity trains G9001 and G9002 between Langfang and Beijing during March 31—April 22, 2015.

<p>The horizontal color bands indicate mean occupancy rates with bandwidths indicating standard errors. The gray shaded areas indicate weekdays from Monday to Thursday.</p

Net passenger flux at high-speed railway stations between Nanjing and Shanghai from August 14 to September 3, 2016.

<p>Positive flux value represents outflow > inflow, and vice versa.</p

Between-station correlation matrix of passenger flux between Nanjing and Shanghai from August 14 to September 3, 2016.

<p>The Pearson’s correlation coefficients are shown.</p

Expected incidence cases and observations in the corresponding period from 2010 to 2013 in Shenzhen.

<p>*December in the previous year.</p><p>**We made the assumption that the expected cases in January and February were zero.</p

Retrieval of net passenger flux at stations using remaining ticket data.

<p>For a given scheduled train passing Station A, B and C sequentially, by enquiring into the numbers of remaining tickets for trips from Station A to B (n₁), and B to C (n₂), the net flux at station B can be calculated as their difference (Δn = n₁—n₂). Δn>0 represents outflow > inflow at station B, and vice versa.</p

Time series of passenger flux at stations between Nanjing and Shanghai from August 14 to September 3, 2016.

<p>Each color curve represents a single station. Positive flux value represents outflow > inflow, and vice versa.</p