Data collection
Research population
This was a cross-sectional study of a cluster sampling of managers selected from among the general managers in petroleum-producing enterprises in China. There are 31 provinces in mainland China, which can be divided into four parts — the north-east region, the western region, the middle region and the eastern region — according to the level of economic development and administrative divisions. We selected Guangdong, Tianjin to represent the eastern region and Heilongjiang to represent the north-east region. Sichuan, Yunnan, Inner Mongolia represented the western region in this study. The sampling method was based on a stratified purpose sampling approach, and therefore the survey of the managers in these provinces can well represent all petroleum managers in China. In each selected province, we used cluster sampling and enrolled all of the managers who took part in health management lectures specifically for petroleum enterprise managers.
From March 2017 to December 2018, the participants were gathered in a meeting room after the lecture and completed the questionnaires in 10 to 15 minutes. The integrity of the returned questionnaires was checked by our investigators in the field. Questionnaires with more than 30% missing data were excluded from the analysis.
Questionnaire
The questionnaire included SRH measurement scale (SRHMS) and general information questions. Unlike the single-item measures of SRH, such as “How do you rate your health today?”, SRHMS includes physical, mental and social subscales to measure the overall health status of the general population. SRHMS is consistent with the definition of health by the WHO: Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity[24]. Previous research has demonstrated that SRHMS is reliable, valid and sensitive and suitable for use in the Chinese population, so we selected SRHMS as a tool to measure self-rated health status in this research.
SRHMS consisted of 48 items which were divided into 9 dimensions: physical symptoms and organic functions (B1), daily physical activities (B2), physical mobility (B3), positive emotion (M1), psychosocial symptoms and negative emotions (M2), cognitive function (M3), role activity and social adaptability (S1), social resources and social contact (S2) and social support (S3). The nine dimensions were merged into physical health, mental health and social health subscales. The summarized scores of SHRMS and each subscale can be represented by SCZT, BZT, MZT and SZT, respectively.
A 10-cm horizontal line was used in the scale to rate each item. Raw scores ranged from 0 to 10 cm, including fractions of a centimeter. Every 48 items had a maximum possible score of 10 and a minimum possible score of 0. The maximum possible score was 440 because four items were not counted in the total score. In addition, items 4, 5, 7, 24, 25, 26, 27, 28, 29 and 30 were scored inversely, which means that a higher number indicates poorer status (i.e., 1.5 = 8.5, 9 = 1, and 2 = 8, etc.).
Demographic characteristics
Previous empirical researches[5, 25] reported that demographic factors such as gender, age, marital status and region affected health outcome. In this study, the demographic characteristics included age, gender and regions.
Age was divided into three groups: 30-40, 41-50 and 51-60 years. Region study was divided into east, north-east and west for a total of three groups.
Socio-economic status
The relationship between socio-economic status(SES)and SRH has been documented in many studies. The main indicators of SES were education, employment and income[12, 26]. Individuals with lower SES are more likely to have poorer SRH than those with high SES[12, 15, 25]. Objective measures were used to evaluate SES in this study. In this research, education was divided into three classes: junior college degree, Bachelor’s degree and Master’s and Doctoral degrees. We used managerial level as the indicator of employment, categorized into Primary, Medium and High. Income was captured as monthly income and was categorized into three classes [less than 10,000 (RMB), 10,001-15,000 (RMB) and more than 15,001 (RMB)] (Table 1).
Table 1 Variables used to construct the demographic characteristics and socio-economic status
|
Value 1
|
Value 2
|
Value 3
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Demographic status
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Age (years)
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30 - 40
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41 - 50
|
51 - 60
|
Gender
|
Male
|
Female
|
|
Regions
|
East
|
North-east
|
West
|
Socio-economic status
|
Education degree
|
junior college degree
|
Bachelor’s degree
|
Master’s and Doctoral degrees
|
Managerial level
|
Lower
|
Medium
|
Higher
|
Monthly income
|
≤ 10,000
|
10,001-15,000
|
≥ 15,001
|
Field work
All participants completed the questionnaire in the meeting room after lectures. During this time, the interviewer provided only necessary explanations of unclear questions.
Data
The raw score of each of the nine SRHMS dimensions was derived by summing the item scores and converting to a value for the dimension from 0 (worst possible health status measured by the questionnaire) to 100 (best possible health status). The raw score was then recalculated across the dimension as follows:
[Due to technical limitations, the formula could not be displayed here. Please see the supplementary files to access the formula.]
Statistical analysis was carried out with SPSS 25.0 for Windows. The statistical description of the demographic characteristics was performed by the use of frequencies, percentages, means and standard deviations.
We analyzed data by using both descriptive and analytical approaches. First, we examined the normality of the data (the scores of three subscales) and found that the score distributions were slightly negatively skewed. So, second, we converted each score of the three subscales to a binary variable. Third, we used univariate logistic regression analysis to indicate the determinants of SRH. Finally, we enrolled the variables showing association below P = .1 into a multivariate logistic regression model.
For the purposes of logistic regression analysis, the scores of the physical health subscale (BZT), the mental health subscale (MZT) and the social well-being subscale (SZT) were used, and, relative to the median scores, the research sample was divided into two groups: those who scored equal to or greater than the median (BZT, 80.38, n = 237; MZT, 71.73, n = 230; SZT, 73.26, n = 223) and those who scored below the median (BZT, 80.38, n = 180; MZT, 71.73, n = 187; SZT, 73.26, n = 194). Two-sided P values less than .05 were considered significant.
The data were analyzed by logistic regression separately for males and females and for different age groups, whereby each exploratory variable was entered separately into the univariate logistic regression. Poor SRH was used as the reference category. Results were presented as odds ratios (OR), and 95% confidence intervals (95% CI) were calculated separately for poor and normal SRH.
Ethical considerations
This study received approval from the Ethics Committee of the School of Public Health, Sun Yat-sen University. All participants gave their oral consent for the investigation. We kept the information about the participants confidential.