Distributions of self-treatment.

<p>Note: (a) self-treatment reasons. (b) self-treatment approaches. (c) self-treated diseases. (d): The reasons for not using insurance in self-treatment.</p

Characteristics of all the participants in the survey.

Characteristics of all the participants in the survey.</p

Descriptions of self-treatment for the middle-aged and elderly in Shanxi, China

<div><p>Objectives</p><p>Self-treatment is a widespread practice among patients with common symptoms and ailments; it is necessary to explore multiple aspects of it. Notably, there is little research into self-treatment among middle-aged and elderly people, who are more likely to fall ill. Our goals are to provide a comprehensive description of self-treatment and explore associated factors with insurance utilization and expenditures among the middle-aged and elderly populations in China.</p><p>Methods</p><p>A survey was conducted in July 2016 in Shanxi, China. A stratified sampling scheme was applied to achieve representativeness. A total of 972 subjects were surveyed. Descriptive statistics, t- and Chi-squared tests, multivariate logistic regression, and multivariate linear regression were utilized.</p><p>Results</p><p>In our study, 772 (79.4%) of the surveyed subjects self-treated during the previous twelve months. Among them, 253 (32.8%) used health insurance. Subjects’ characteristics were associated with insurance utilization and expenditures for self-treatment. Total cost was positively associated with insurance utilization. The subjects with a junior high education (<i>p</i>-value < 0.001, aOR = 0.049) and senior high education (<i>p</i>-value = 0.020, aOR = 0.146) had a lower probability of using insurance. For both total costs and out-of-pocket costs, subjects who were 51 to 60 years old had lower costs. The subjects who were seriously sick and had a primary school education, as well as enterprise occupations, had higher costs. Self-treatment times were also positively associated with costs. Finally, it was found that subjects who didn’t use insurance had lower total costs.</p><p>Conclusions</p><p>The prevalence of self-treatment was high (79.4%). Some characteristics were associated with insurance utilization and expenditures in self-treatment. Our results may be helpful for policy interventions, which are needed to further improve the effectiveness of health insurance in China.</p></div

Multivariate linear regression of expenditure (in RMB).

<p>Multivariate linear regression of expenditure (in RMB).</p

Survey locations map.

<p>Note: a. Map of China. The red area is Shanxi Province; b. Map of Shanxi Province. Taiyuan, Shuozhou, Jincheng, Jinzhong, Datong, Linfen, Lvliang, Yuncheng, and Xinzhou are presented. R V3.3.3 software, “maptools” and “ggplot2” packages were used to create this map. The relevant geographic data was downloaded from National Geomatics Center of China <a href="http://ngcc.cn/article//sjcg/mndxt/" target="_blank">http://ngcc.cn/article//sjcg/mndxt/</a> freely.</p

Logistic regression model for characteristics and insurance utilization (n = 772).

<p>Logistic regression model for characteristics and insurance utilization (n = 772).</p

Additional file 1 of The impact of COVID-19 continuous containment and mitigation strategy on the epidemic of vector-borne diseases in China

Additional file 1: Table S1. Dates of provinces implementing response to public health emergency. Table S2. List of five vector-borne diseases, insect vectors, and pathogens. Table S3. Changes in the average yearly incidence of vaccine-preventable, non-vaccine-preventable, viral, and non-viral diseases among five vector-borne diseases in 2020 were compared with the previous five years in China. Table S4. Pairwise comparisons of the average monthly incidence of five vector-borne diseases for January to April in 2015–2019, 2020 and 2021. Table S5. Changes in the average yearly mortality rates of the vaccine-preventable, non-vaccine-preventable, viral, and non-viral diseases among five vector-borne diseases in 2020 compared with the previous five years in China. Table S6. Pairwise comparisons of the average monthly mortality rates of five vector-borne diseases for January to April in 2015–2019, 2020 and 2021. Table S7. Changes in the yearly incidence of five vector-borne diseases in 2020 between observed and predicted values. Table S8. Changes in the average monthly incidence of five vector-borne diseases in the emergency response stage (January to April 2020) and the routine response stage (May to December 2020) between observed and predicted values. Table S9. Changes in the yearly mortality rates of five vector-borne diseases in 2020 between observed and predicted values. Table S10. Changes in the average monthly mortality rates of five vector-borne diseases in the emergency response stage (January to April 2020) and the routine response stage (May to December 2020) between observed and predicted values

Additional file 1 of Impact of the COVID-19 pandemic and the dynamic COVID-zero strategy on HIV incidence and mortality in China

Additional file 1: Supplementary Figure 1. Trends in the monthly incidence, mortality rates, and CFRs for HIV in China in 2015–2019, 2020, 2021, and 2022. Supplementary Figure 2. Scatter plot of the number of monthly reported HIV cases and the number of monthly reported COVID-19 cases in 2020–2022, China