Seasonal and Monthly Patterns, Weekly Variations, and the Holiday Effect of Outpatient Visits for Type 2 Diabetes Mellitus Patients in China

Abstract

Objective: To explore the seasonal and monthly patterns, weekly variations, and the holiday effect of outpatient visits for type 2 diabetes mellitus patients, as well as the influence of gender, age, and insurance type on variations. Methods: Data were obtained from the Shandong medical insurance database, including all outpatients in 12 cities of Shandong province in China from 2015 to 2017. The seasonal index (St) was calculated in terms of seasons, months, and weeks by the moving average method. Results: A total of 904,488 patients received outpatient services during the study period. The seasonal indices of outpatient visits by type 2 diabetes patients were higher in autumn (108.36%) and spring (102.67%), while lower in winter (89.92%) and summer (99.04%), exhibiting an obvious seasonality. Gender and age had no effect on seasonal patterns. The month impacted the seasons patterns: January to February were the lowest and December the highest months of outpatient visits, complicating the seasonal patterns. We also identified a weekly pattern of outpatient visits. In addition, the outpatient visits for type 2 diabetes mellitus patients was also strongly affected by the Spring Festival, Lantern Festival, and National Day holiday periods. The type of medical insurance had a significant impact on outpatient visits. Conclusions: The outpatient visits for type 2 diabetes mellitus patients displayed seasonal patterns that were contradictory to the variations in blood glucose fluctuations found in previous studies and was also strongly affected by the holiday effect. The type of medical insurance impacted the pattern of outpatient visits.

Influencing Indicators and Spatial Variation of Diabetes Mellitus Prevalence in Shandong, China: A Framework for Using Data‐Driven and Spatial Methods

“…UEBMI patients had lower diabetes prevalence than IURMI patients. It may be subject to average individual characteristics in different groups (Huang et al., 2019). IURMI patients mainly consisted of rural residents, urban retired, unemployed, students, and children with lower education levels and poor physical conditions, which may increase the prevalence of diabetes.…”

“…Some studies have suggested that cold temperature could lead to elevating glycosylated hemoglobin levels and acute complications of diabetes (Hou et al., 2017; Huang et al., 2019). Also, the proportion of the secondary and third industry output has often been used in related diabetes research (Couchoud et al., 2011).…”

“…The medical insurance types consist of Urban Employed Basic Medical Insurance (UEBMI) and Integration of Urban and Rural Medical Insurance (IURMI), forming the basic medical insurance systems. Since there have been significantly different individual characteristics between these two types (Huang et al., 2019), it is worth exploring the patterns of diabetes by subgroups. China's basic medical insurance plans cover 95% of the population, thus the information from the medical insurance database can account for almost all the patients during the study period in each city.…”

“…The data including diabetes prevalence data across each county and the elevation data, and the codes crawling the POIs of medical facilities and obtaining remote sensing images that support the findings of this study are available at http://doi.org/10.6084/m9.figshare.14061104. Specific diabetes mellitus patients' health insurance records are available through Huang (2019). The meteorology data are available in the China Meteorological Data Network and other data can be obtained from the 2017 Shandong statistical yearbook (https://www.chinayearbooks.com/shandong-statistical-yearbook-2017.html).…”

Temporal Characteristics of Waterfronts in Wuhan City and People’s Behavioral Preferences Based on Social Media Data

“…Depending on the time to be analyzed, the time in the time series can be based on year, season, month, week, and so on. The seasonal index analysis method of the time series data can be adopted to estimate the seasonal indexes of each season, which can be used to analyze the temporal patterns of waterfront vibrancy (season, month, week, holiday, and weekend) [40].…”