Background County hospitals as the backbone of the China’s healthcare system are providing services for over 70% of the total population. However, the hospital management practice (HMP) and its links with quality of care, efficiency and finance in these hospitals are unknown. Methods We did two cross-sectional surveys of HMP in 2013 and 2015 among 101 county hospitals across rural China. Three managing roles (hospital director, director of medical affairs office and director of cardiology) and a cardiologist were invited to the surveys. A novel HMP rating scale, with 100 as full score, was used to measure the HMP in 17 indicators under four dimensions (target, operation, performance, and talent management) for each hospital. We analyzed the association of HMP score with variables on quality of care, efficiency and finance using linear mixed models with and without adjustment for potential confounders. Findings A total of 95 hospitals participated in at least one survey and were included in the analysis. The overall mean HMP score varied dramatically across the hospitals and 84% of them scored less than 60. The dimension mean HMP score was 38.6 (target), 56.4 (operation), 53.2 (performance) and 55.7 (talent), respectively. The pattern of indicator mean HMP score, however, was almost identical between hospitals with high and low overall HMP score, showing the same ‘strength’ (staff satisfaction, staff performance appraisal, ‘hard wares’, patient-centered services, etc.) and ‘weakness’ (target balance, target setting, continuous quality improvement, penalties on staff with dissatisfied performance, etc.). The associations of overall mean HMP score with quality of care and efficiency variables and cost per hospitalization was not statistically significant. The statistical significance in the association with hospital annual total income disappeared after adjusting for region, teaching status, number of competitors, number of staff and number of beds in use. Conclusion The HMP in Chinese county hospitals scores low in general and was not significantly associated with hospital care quality, efficiency and finance. The current healthcare reform in China should address the micro level issues in hospital management practices.

Background Online ride-hailing is a fast-developing new travel mode. However, tobacco control policies on its drivers remain underdeveloped. This study aims to reveal the status and determine the influencing factors of ride-hailing drivers’ smoking behaviour to provide a basis for the formulation of tobacco control policies. Methods We derived our cross-sectional data from an online survey of full-time ride-hailing drivers in China. We used a survey questionnaire to collect variables, including sociodemographic and work-related characteristics, health status, health behaviour, health literacy and smoking status. Finally, we analysed the influencing factors of current smoking by conducting chi-square test and multivariate logistic regression. Results A total of 8990 ride-hailing drivers have participated in the survey, in which 5024 were current smokers, accounting to 55.9%. Nearly one-third of smokers smoked in their cars (32.2%). The logistic regression analysis results were as follows: male drivers (OR = 0.519, 95% CI [0.416, 0.647]), central regions (OR = 1.172, 95% CI [1.049, 1.309]) and eastern regions (OR = 1.330, 95% CI [1.194, 1.480]), working at both daytime and night (OR = 1.287, 95% CI [1.164, 1.424]) and non-fixed time (OR = 0.847, 95% CI [0.718, 0.999]), ages of 35–54 years (OR = 0.585, 95% CI [0.408, 0.829]), current drinker (OR = 1.663, 95% CI [1.526, 1.813]), irregular eating habits (OR = 1.370, 95% CI [1.233, 1.523]), the number of days in a week of engaging in at least 10 min of moderate or vigorous exercise ≥3 (OR = 0.752, 95% CI [0.646, 0.875]), taking the initiative to acquire health knowledge occasionally (OR = 0.882, 95% CI [0.783, 0.992]) or frequently (OR = 0.675, 95% CI [0.591, 0.770]) and underweight (OR = 1.249, 95% CI [1.001, 1.559]) and overweight (OR = 0.846, 95% CI [0.775, 0.924]) have association with the prevalence of current smoking amongst online ride-hailing drivers. Conclusion The smoking rate of ride-hailing drivers was high. Sociodemographic and work-related characteristics and health-related factors affected their smoking behaviour. Psychological and behavioural interventions can promote smoking control management and encourage drivers to quit or limit smoking. Online car-hailing companies can also establish a complaint mechanism combined with personal credit.

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Background：Online ride-hailing is a fast-developing new travel mode, and tobacco control policies on it have not yet been improved. This study aims to reveal the smoking status and influencing factors of ride-hailing drivers, so as to provide a basis for the formulation of tobacco control policies.Methods：The cross-sectional data used in this study were derived from an online survey of full-time ride-hailing drivers in China. Questionnaires were employed to collect variables including sociodemographic and work-related characteristics, health status, health behavior, health literacy, and smoking status. Chi-Square test and multivariate logistic regression were used to analyze the influencing factors of current smoking.Results：A total of 8990 ride-hailing drivers were investigated, in which 5024 were current smokers, accounted to 55.9%. Current smokers (53.7% (2696/5024) v 44.2% (1752/3966); P<0.001) and drivers who smoked on the car (85.8% (1389/1618) v 38.4 (1307/3406); P<0.001) were more likely to allow passengers to smoke. Logistic regression analysis showed that men (OR=0.519, 95%CI (0.416, 0.647)), central regions (OR=1.172, 95%CI (1.049, 1.309)), eastern regions (OR=1.330, 95%CI (1.194, 1.480)), working at both daytime and night (OR=1.287, 95%CI (1.164, 1.424)), and working at non-fixed time (OR=0.847, 95%CI (0.718, 0.999)), 35-54 years old (OR=0.585, 95%CI (0.408, 0.829)), current drinker (OR=1.663, 95%CI (1.526, 1.813)), eating very irregularly (OR=1.370, 95%CI (1.233, 1.523)), the number of days a week of engaging in at least 10 minutes of moderate or vigorous exercise ≥ 3 (OR=0.752, 95%CI (0.646, 0.875)), taking the initiative to acquire health knowledge occasionally (OR=0.882, 95%CI (0.783, 0.992)) or frequently (OR=0.675, 95%CI (0.591, 0.770)) , underweight (OR=1.249, 95%CI (1.001, 1.559)) and overweight (OR=0.846, 95%CI (0.775, 0.924)) were associated with the prevalence of current smoking among online ride-hailing drivers (P<0.05). Conclusions：The smoking rate of ride-hailing drivers was high, and the social demographic and work-related characteristics, and health-related factors all affected their smoking behavior. Tobacco control measures targeted at online-hailing drivers should correct their cultural beliefs about smoking, increase their health literacy, guide them to exercise more and keep a regular schedule, as well as combine with drinking intervention and weight intervention.