There are few studies on the relationship between sleep time and falls at present, and the research results are inconsistent.
To explore the relationship between sleeping time and falls in middle-aged and elderly residents in China, and to provide reference for preventing falls in this population.
The study based on the data from China Health and Retirement Longitudinal Survey (CHARLS) data in 2015. 18 181 subjects aged ≥45 years with complete key data were included. Collect the indicators of the middle-aged and old people in the past two years, such as falls and medical treatment, sleep time, demographic characteristics, behavior and lifestyle, health status, self-life satisfaction and self-rated health. The subjects were divided into 5 groups according to the sleep time per night: <5 h (2 945 cases) , 5-<6 h (2 755 cases) , 6-<7 h (reference group, 3 824 cases) , 7 to <8 hours (3 257 cases) and ≥8 hours (5 400 cases) . Logistic regression model was used to adjust different variables to gradually evaluate the relationship between sleep time and the occurrence of falls and medical treatment for falls in the past two years.
The average sleeping time of middle-aged and elderly people was (6.4±1.9) hours, the incidence of falls in the past two years was 17.01% (3 092/18 181) , and the rate of medical treatment for falls was 6.95% (1 264/18 181) . The incidence of falls in subjects with sleep time <5 h, 5-<6 h, 6-<7 h, 7-<8 h and≥8 h were 25.26% (744/2 945) , 19.82% (546/2 755) , 15.51% (593/3 824) , 14.03% (457/3 257) , 13.93% (752/5 400) , the incidence of falling to hospital was 10.97% (323/2 945) , 8.09% (223/2 755) , 6.04% (231/3 824) , 5.43% (177/3 257) and 5.74% (310/5 400) , which were significant differences demonstrated by the trend chi-square test, and the difference was statistically significant (P<0.05) . Compared with 6-<7 hours after adjusting factors such as age, gender, those with sleeping time ≥8 hours had a significantly lower risk of falling〔OR (95%CI) =0.88 (0.78, 0.99) 〕, and those with sleeping time <5 h and 5-<6 h had a significantly higher risk of falling〔OR (95%CI) <5 h=1.31 (1.16, 1.49) , OR (95%CI) 5-<6 h=1.14 (1.00, 1.30) 〕. The middle-aged and old people who slept less than 5 hours per night had a higher risk of falling and seeking medical treatment〔OR (95%CI) =1.30 (1.08, 1.56) 〕.
The incidence of falls in middle-aged and elderly people in China is relatively high, sleep time <6 hours per night will increase the risk of falls, and sleeping≥8 hours may reduce the risk of falls. Adjusting sleeping time of middle-aged and elderly people and strengthening lifestyle intervention can effectively reduce the incidence of falls in the middle-aged and elderly people.
Exploring the factors influencing healthy lifestyle in adolescents is beneficial to promoting the physical and mental health of adolescents, which is an important initiative to promote the achievement of the goals of Healthy China. Moreover, it is also an important means to achieve the goals of health poverty alleviation and targeted poverty alleviation projects in both left-behind and non-left-behind rural adolescents.
To investigate the longitudinal effect of cumulative ecological risk on healthy lifestyle and the mediating effect of future time perspective between them in rural left-behind and non-left-behind adolescents.
Rural left-behind and non-left-behind adolescents from 24 classes in 12 schools in Shijiazhuang, Hebei Province were selected from October 2020 to October 2021. Three surveys (the first, second and third were on cumulative ecological risk, future time perspective and healthy lifestyle, respectively) were conducted with them, and had 1 135, 1 102 and 1 053 responders, respectively. Person correlation analysis was used to analyze the correlation between cumulative ecological risk, future time perspective and healthy lifestyle. Structural equation model was used for mediation analysis, and Bootstrap sampling method was used to further assess the mediating effect of future time perspective.
Finally, 1 053 cases who effectively responded to each of the three surveys were included for analysis, including 335 left-behind adolescents (31.81%) and 718 non-left-behind adolescents (68.19%) . The scores of annual cumulative ecological risk (October 2020) , future time perspective (April 2021) and healthy lifestyle (October 2021) were (2.78±1.45) , (53.40±13.80) and (150.72±24.67) , respectively for left-behind responders, and were (2.34±1.24) , (59.21±8.46) and (159.07±14.43) , respectively for non-left-behind responders. Correlation analysis showed that cumulative ecological risk was negatively correlated with both future time perspective and healthy lifestyle (P<0.05) , and future time perspective was negatively correlated with healthy lifestyle (P<0.05) . Forced regression indicated that the healthy lifestyle was inversely and longitudinally influenced by cumulative ecological risk in rural left-behind adolescents (β=-0.31, P<0.01) , showing a trend of sharp and positively accelerated decline. The healthy lifestyle was also inversely and longitudinally influenced by cumulative ecological risk in non-left-behind adolescents (β=-0.22, P<0.01) , demonstrating a relatively liner trend of mild decline. Mediating analysis using Bootstrap sampling revealed that future time perspective played a mediating role in the longitudinal effect produced by cumulative ecological risk on the healthy lifestyle in both rural left-behind and non-left-behind adolescents (P<0.01) .
The cumulative ecological risk may be a longitudinal predictor of the healthy lifestyle in left-behind and non-left-behind adolescents, and its predictive value was higher in the latter. Future time perspective was a mediator in the relationship between cumulative ecological risk and healthy lifestyle in both left-behind and non-left-behind adolescents.
Pediatricians often receive inconsistent reports from parents and teachers on children's behavior, but there are few available studies on comparing parents and teachers' evaluation of preschool children's behavior problems.
To investigate the prevalence of behavioral problems in preschoolers from Chongqing's main urban areas, and to explore the consistency and discrepancy between parents- and teachers-assessed behavioral problems in these children.
In June 2018, 2 698 preschoolers were sampled by cluster sampling from kindergartens in the main urban area of Chongqing. For understanding the prevalence of behavioral problems in them, their behaviors were rated by their parents using the Conners Parent Symptom Questionnaire (PSQ) , and by their teachers using the Conners Teacher Rating Scale (TRS) . And parents- and teachers-assessed results were compared.
The survey obtained a response rate of 98.59%, involving 2 660 children that were finally included. In accordance with parents' assessment, the prevalence of behavioral problems was 14.47% in preschool children (385/2 660) , and that in boys was higher than in girls〔17.91% (245/1 368) vs 10.84% (140/1 292) , P<0.001〕. According to teachers' assessment, the prevalence of behavioral problems was 4.44% in preschool children (118/2 660) , and that in boys was also higher than in girls〔6.29% (86/1 368) vs 2.48% (32/1 292) , P<0.001〕. Parents' assessment showed that boys had higher prevalence of learning problem, psychosomatic problems, and higher hyperactivity index, while teachers' assessment showed that boys had higher prevalence of conduct problems and higher hyperactivity index. In comparison with girls, boys had higher prevalence of behavior problems in in four factors of PSQ, namely learning problem, conduct problems, impulsivity-hyperactivity and hyperactivity index, and higher prevalence of behavior problems in all four factors of TRS. Spearman correlation analysis indicated that scores rated for boys by teachers in terms of conduct problems, inattention-passivity, and hyperactivity index in TRS were positively correlated with scores rated for them by parents in terms of conduct problems, learning problems, impulse-hyperactivity, and hyperactivity index in PSQ; that scores rated for boys by teachers in terms of hyperactivity problems in TRS were positively correlated with scores rated for them by parents in terms of conduct problems, impulse-hyperactivity, and hyperactivity index in PSQ (rs ranged from 0.056 to 0.113, P<0.05) ; that scores rated for boys by teachers in terms of hyperactivity problems in TRS were negative correlated with scores rated for them by parents in terms of anxiety problems (rs=-0.059, P<0.05) . In girls, the scores of each factor in TRS were not correlated with those in PSQ (P>0.05) .
The prevalence of behavior problems in preschoolers assessed by parents was higher than that assessed by teachers. Although there are some differences between the assessment by parents and teachers, there is a good consistency in their assessment of conduct problems and hyperactivity.
The cases of multiple chronic conditions are increasing yearly, yet their medication adherence is unsatisfactory though taking medication as prescribed is recognized as the most effective measure to manage chronic diseases. To improve the prevention and control of chronic diseases, it is crucial to identify the causes and influencing factors of non-compliance in multiple chronic conditions patients.
To classify the medication adherence and to identify the associated factors of each class of medication adherence in multiple chronic conditions patients.
This investigation was conducted between July and September 2021 with a convenience sample of 267 inpatients from two tertiary A general hospitals of Henan Province using the Chinese version of Beliefs about Medicines (BMQ-C) , the Chinese version of 8-item Morisky Medication Adherence Scale (MMAS-8-C) , and the Medication Knowledge Scale (MKS) . Latent class analysis was used to classify the medication adherence. Demographic characteristics, medication use, medication knowledge and medication beliefs were compared by the class of medication adherence. Multiple Logistic regression was used to explore the associated factors of each class of medication adherence.
The medication adherence of the participants was divided into three latent classes, namely subjective poor medication adherence, overall poor medication adherence, and overall good medication adherence, and the prevalence of the three classes was 18.0%, 34.4% and 47.6%, respectively. The education level, occupational status after an illness, living situation, household monthly income per person, financial resources, prevalence of having pharmacist guidance, number of medications, frequency of taking medication, years of taking medication, the BMQ-C score, and MKS score in the participants differed significantly by the class of medication adherence (P<0.05) . By multiple Logistic regression analysis, compared with patients with subjective poor medication adherence, those with overall good medication adherence had higher prevalence of having pharmacist guidance, and higher average scores of BMQ-C and MKS, and lower prevalence of retirement due to illness and offspring's support as the only financial resource (P<0.05) . Compared with those with overall poor medication adherence, those with overall good medication adherence had higher prevalence of retirees, taking medication once a day, and having pharmacist guidance, as well as higher average scores of BMQ-C and MKS (P<0.05) .
The medication adherence in these multiple chronic conditions patients could be classified into three latent classes. More attention should be given to those who were retired due to illness or financially supported by their children, because they were prone to having poor medication adherence. Those who had lower frequency of medication use, medication guidance from a pharmacist, and higher levels of medication knowledge and beliefs were prone to having good medication adherence.
An era of expanded information capacity poses new challenges to data utilization capabilities of all practitioners. The true relationship between variables can only be revealed by identifying the type and characteristics of the data, then analyzed using the appropriate statistical analysis methods. Most available studies focuson the analysis of the rate of due/non-hospitalization, and there are few articles analyzing the factors influencing the number of hospitalizations using the countdata.
To fit an optimal model suitable for the annual number of hospitalizations of residents, and to analyze its influencing factors.
Data came from the health service survey database of the project "innovating payment system toimprove health benefits" carried out from July to August 2019. A total of 27 196 residents from four mountainous counties (Yanchi, Haiyuan, Pengyang and Xiji) in southern Ningxia were selected to attend a questionnaire survey by use of multistage stratified random sampling, and 22 427 (82.46%) of them with complete and clear key information who returned responsive questionnaires were included for analysis. Four models (Possion regression, negative binomial regression, zero-inflated Poisson regression and zero-inflated negative binomial regression) were established with annual number of hospitalizations as the dependent variable, and demographic factors (sex, age, marital status, education level and occupation) , household characteristics (household size, annual household income per capita, prevalence of poverty-stricken household/household living on minimum subsistence allowances) , social feature (distance to the nearest township health center) as independent variables, then the model with the best fitting performance was selected to explore the factors associated with the annual number of hospitalizations.
The percentage of residents with zero annual hospitalizations was 88.30% (19 802/22 427) . The negative binomial regression model was examined to have better fitting performance than the Possion regression model by discrete choice experiments (O=87.665, P<0.01) . The statistic value of Vuong's test was greater than 1.96, indicating that the data were zero-inflated. The zero-inflated negative binomial regression model had the smallest goodness of fit (18 331.87) measured by the Akaike information criterion. And the analysis using this model revealed that sex, education level, occupation, household size, annual household income per capita, an prevalence of poverty-stricken household/household living on minimum subsistence allowances were associated with annual number of hospitalizations (P<0.05) . And age, marital status, education level and occupation were associated with the zero-inflation in annual number of hospitalizations (P<0.05) .
The zero-inflated negative binomial regression model fitted the data of annual hospitalizations of the participants best. Being female, unemployed, or poverty-stricken household/household living on minimum subsistence allowances was associated with higher possibility of seeking hospitalization care, while higher education level, or a larger household size was associated with lower possibility of seeking hospitalization care. A higher potential demand for hospitalization care was found in 40-59-year-olds and the married, which could be partially lowered by improving education level and reducing physical labor intensity level.
