Objective To analyze the evaluation results of health enterprises in Beijing City and their influencing factors. Methods A total of 40 enterprises that passed the health enterprise evaluation in Beijing from 2021 to 2022 were selected as the study subjects using the judgment sampling method. The total evaluation scores and influencing factors were analyzed based on the size, nature, industry classification, and establishment time of the enterprises. Results The total evaluation score of the 40 health enterprises was (553.1±18.4) points. Scores for the primary evaluation indicators, including management organization and system, occupational disease prevention measures, health environment, health promotion measures, prevention measures for occupational stress caused damage, and musculoskeletal injury prevention measures, were (94.0±4.8), (94.0±4.3), (94.5±3.8), (89.7±4.8), (89.6±4.6), and (91.4±5.9) points, respectively. The result of multiple linear regression analysis showed that, large enterprises had higher total evaluation scores than medium enterprises and micro and small enterprises after controlling for confounding factors such as industry classification and establishment time (all P<0.10). The total evaluation scores of private enterprises and foreign-funded enterprises were lower than that of state-owned or state-participated enterprises (all P<0.10). Conclusion Enterprise size and nature are influencing factors for the total evaluation score of health enterprises. It is necessary to enhance resource and policy support for medium-sized, micro and small enterprises and private enterprises to improve the development of the health enterprises.

Objective To analyze the prevalence status of cardiometabolic risk factor (CMRF) and its aggregation among workers engaged in new forms of employment. Methods A total of 5 429 new employment workers (including couriers, online food delivery workers, and ride hailing drivers) who underwent health medical examinations at a tertiary hospital in Beijing City were selected as the research subjects using the judgment sampling method. Data on waist circumference, blood pressure, blood glucose, and blood lipid levels were collected to analyze their CMRF [central obesity, elevated blood pressure, elevated blood glucose, elevated triglycerides, and reduced high-density lipoprotein cholesterol (HDL-C)] and their aggregation (with ≥ 2 of the above 5 risk factors) status. Results The detection rates of central obesity, elevated blood pressure, elevated blood glucose, elevated triglycerides, and reduced HDL-C were 61.2%, 38.2%, 29.5%, 40.9% and 22.6%, respectively. The detection rates of CMRF aggregation was 57.8%. The result of multivariable logistic regression analysis showed that male, age ≥45 years, smoking, overweight, and obesity were risk factors for CMRF aggregation (all P<0.05). Conclusion The detection rate of CMRF and its aggregation among workers with new forms of employment in Beijing City is relatively high. Targeted prevention and control efforts should be strengthened for high-risk populations, especially males, workers aged ≥45 years, smokers, and those who are overweight or obese.