Health education services utilization and its determinants among migrants: a cross-sectional study in urban-rural fringe areas of Beijing, China

Andersen health service utilization behavioral model, a well-validated theoretical framework, can predict determinants of health services utilization, take into consideration both individual and societal determinants [25]. In the original model, health education utilization is determined by three dynamics: predisposing, enabling, and need variances (PEN). Social demographic characteristics including sex, age, race, etc., can be divided into predisposing factors, which increase one’s needs for health education services. For example, a person with strong belief in health education services for effective disease prevention is more likely to seek health education. Enabling factors are individual, family and community resources support, and can facilitate or impede the use of health education services. Need factors represent both actual and self-perceived needs for health education services. Based on the previous research [26] and the evolution of Andersen’s health services utilization behavioral model [27], health behavior variances were also considered as the determinants of accessing to health care. So, in our study, health behavior variances served as a key dynamics parameter and were integrated to evaluate the predictions of a new model. Also we used a feedback loop to illustrate the relationship between health education behaviors (seek health education, and not seek health education) and other aspects. (See Fig. 1).

The study was undertaken as a part of Beijing Philosophy and Social Science Planning Project, a population-based cross-sectional survey on risk factors of health status for migrants. It was approved by the Ethical Committee of Capital Medical University, Beijing, China. Data were collected from a cross-sectional survey in urban-rural fringe areas of Beijing during 2016 to 2017. The parents or guardians are main decision-makers in public health services for children, thus those children under 18 were not included in this study. Written informed consent was obtained from each participant involved in this study. All participants’ information was anonymized and kept confidential.

A fieldwork survey of Public Health Service Utilization of Migrants Population in Beijing Urban-Rural Fringe Areas was performed from June 2016 to January 2017. All respondents were at least 18 years old, including inter-provincial migrants residing or working in the sampling regions (for no less than 6 months). Exclusion criteria consisted of the following: migrants who were not able to respond, those with mental health issues, and tourists in Beijing. The migrants dwell mainly in 5 (Chaoyang, Haidian, Fengtai, Daxing, Changping districts) out of 16 districts in Beijing. Five districts were divided into two types of region based on the number of migrants, including the region with more than 1 million migrants (Chaoyang, Haidian districts) and the region with 0.5 to 1 million migrants (Fengtai, Daxing, Changping districts). A sample of 1000 migrants was chosen from two of the five districts in Beijing by using three-stage stratified cluster random sampling, as follows: Stage One, one district was chosen from the first region (Haidian district), and the other district was chosen from the second region (Fengtai district). Stage Two, one street located in urban-rural fringe areas was chosen from each sampled district respectively according to the population size and social economic status. Stage Three, within each selected street, a list of residential communities (clusters) to allow for non-participation was compiled for migrants. Then two clusters were randomly selected from the list in each sampled street respectively. All migrants within the selected clusters were screened for eligibility and invited to participate in the study. The total number of 1000 migrants was recruited and investigated from four residential communities, with Haidian and Fengtai each 500 respectively. (See Fig. 2).

Data were collected via face-to-face cross-sectional surveys, and 921 migrants took part in the investigation. Finally, a total of 863 respondents were analyzed after excluding the data missing information on any of variables in the research. These samples can be considered representative of migrants population in Beijing as they presented similar distribution of age and gender status compared with those in the larger population, according to the report on China’s migrant population development in 2015 [28].

The structured questionnaire includes four parts of variances as follows: predisposing factors (sociodemographic characteristics), enabling factors (individual/family resources), need factors (general health status), health behavior factors (health promotion behaviors and health hazard behaviors) and health education seeking behavior (receiving health education or not).

In this survey, utilization and needs of health education were measured by responses of three questions:

The health education scheme of Basic Public Health Service Program includes five main forms, including receipt of “materials of health education”, “health education bulletin board”, “public health consultation”, “health knowledge lecture”, and “individualized health education” by population from CHSIs.

-Binary: 1 for received any form of health education at least once, 0 for did not receive.

The types of health education mainly consisted of “occupational disease prevention and therapy”, “child healthcare”, “antenatal, prenatal and postpartum healthcare”, “CD prevention and therapy”, “NCD prevention and therapy”, “adolescent healthcare”, “menopause healthcare” and “aged healthcare”, etc. Respondents should answer the question according to their utilization of health education.

The types of health education were same as in the question two. Respondents should answer the question according to their needs.

Quality-assurance measures for this survey include questionnaire evaluation, training investigators, and fieldwork supervision to monitor the survey procedure. It was reviewed, edited, and validated by experts from health administration and CHSIs prior to implementation. A trial survey covering 50 persons was implemented during June 6 to 11, 2016 for better understanding the questionnaire and the fieldwork procedures. Double entry and validation were adopted for all data using EpiData software (Version 3.1, EpiData Association, Odense, Denmark). Discrepancies, compared and analyzed from the two databases, would be clarified by reviewing the original data source.

A person was the unit of seeking health education at least once in the past year, rather than total number of times health education received. Chi-square independence test was used to analyze the differences in the categorical variables. Odds Ratios (ORs) and their 95% confidence intervals (CIs) were calculated using binary logistic regression analysis. In the first step, descriptive statistics and chi-square independence test were calculated, stratified by the categories of health education utilization (received and did not receive health education). In the second step, multivariate logistic regression analysis was performed to predict the potential major determinant factors in migrants health education utilization, and in possible confounding variables control. The full model consisted of all predisposing, enabling, health-related behaviors and the need factors were entered into the model. The full list of independent variables is summarized in the Table 1. All statistical analyses were performed using IBM Statistical Package for Social Science version 20.0 (SPSS Inc., Chicago IL, US) and all tests are two sided. The significance level for all analyses was set at P < 0.05.

In this study, 61.6% migrants desired to receive health education, while only 53.8% of them received it. Sample characteristics were presented in Table 2. A total of 863 inter-provincial migrants (males 383; females 480) in Beijing above 18 years old were investigated. The age of migrants varies from 18 to 76 years old, and average age was 36.6 ± 11.2 years old.

Descriptive statistics and chi-square independence test were used to describe the information and to analyze the influence factors of health education utilization by sociodemographic factors. Chi-square independence test showed that age, “Hukou” registration system, marital status, education level, plan to reside for a long time in Beijing, have at least one child in Beijing, employment status, housing source, average daily working time, do exercises, health knowledge, smoking, self-evaluation general health status are the major determinants affecting migrants to receive health education. (See Table 2).

Gender, “Hukou” registration system, education level, plan to reside for a long time in Beijing, have at least one child in Beijing, employment status, housing source, average daily working time, do exercises, acquire health knowledge are the major determinants affecting migrants to receive CD health education. “Hukou” registration system, housing source, average daily working time, do exercises are the major determinants affecting migrants to receive NCD health education. Age, “Hukou” registration system, marital status, education level, plan to reside for a long time in Beijing, have at least one child in Beijing, employment status, average daily working time, do exercises, acquire health knowledge, smoking, are the major determinants affecting migrants to receive occupational disease health education. (See Table 3) Table 4 shows that the top five types of health education received by male migrants were “occupational disease prevention and therapy” (19.1%), “child healthcare” (14.9%), “antenatal, prenatal and postpartum healthcare” (10.2%), “CD prevention and therapy” (9.9%), and “NCD prevention and therapy” (9.1%). However, the top five types of health education that male migrants wanted to receive were “NCD prevention and therapy” (58.2%), “CD prevention and therapy” (46.7%), “child healthcare” (44.9%), “aged healthcare”(42.0%) and “occupational disease prevention and therapy” (36.8%). The top five types of health education received by female migrants were “antenatal, prenatal and postpartum healthcare” (18.8%), “child healthcare” (16.5%), “CD prevention and therapy” (14.4%), and “NCD prevention and therapy” (12.7%), “occupational disease prevention and therapy” (11.9%). However, the top five types of health education that female migrants wanted to receive were “NCD prevention and therapy” (63.5%), “aged healthcare”(51.7%), “CD prevention and therapy” (50.6%), “child healthcare” (48.3%) and “adolescent healthcare” (40.2%).

Table 5 shows that the main types of health education received by migrants in different age groups. “Occupational disease prevention and therapy” (30.9%), “CD prevention and therapy” (15.5%), and “adolescent healthcare” (18.2%) were received by migrants in the age group of 18 to 24 years old more than other groups. “Antenatal, prenatal and postpartum healthcare” (24.4%) and “child healthcare” (22.8%) were received by migrants in the age group of 25 to 34 years old more than other groups. “NCD prevention and therapy” (15.5%), “aged healthcare” (15.5%) and “menopause healthcare” (10.3%) were received by migrants in the age group of 55 years old and above more than other groups.

Table 6 shows that the types of health education desired to receive by migrants in different age groups. “Occupational disease prevention and therapy” (54.5%), “adolescent healthcare” (54.5%), and “antenatal, prenatal and postpartum healthcare” (50.9%) were desired to receive by migrants in the age group of 18 to 24 years old more than other groups. “NCD prevention and therapy” (37.6%), “aged healthcare” (81.0%) were wanted to receive by migrants in the age group of 55 years old and above more than other groups. “Child healthcare” (59.9%), “CD prevention and therapy” (53.8%), and “menopause healthcare” (45.2%) were wanted to receive by migrants respectively in the age groups of 25 to 34 years old, 35 to 44 years old, and 45 to 54 years old more than other groups.

Additionally, the top three pathways through which migrants want to acquire health information were television broadcasting (62.8%), internet (webpage and WeChat) (58.2%), and professionals and health managers (37.4%).

The findings indicate that all of the full models were able to distinguish between migrants with receiving health education and those without receiving health education, and all predictors were statistically significant at the P < 0.001 level (Model I, χ2 = 186.467, P < 0.001; Model II, χ2 = 49.367, P < 0.001; Model III, χ2 = 39.895, P < 0.001; Model IV, χ2 = 90.941, P < 0.001). In the model summary, Model I explained between 19.6% (Cox and Snell R square) and 26.3% (Nagelkerke R square) of the variance in health education utilization as a whole. Model II explained between 5.6% (Cox and Snell R square) and 10.7% (Nagelkerke R square) of the variance in CD health education utilization as a whole. Model III explained between 4.6% (Cox and Snell R square) and 9.1% (Nagelkerke R square) of the variance in NCD health education utilization of migrants as a whole. Model IV explained between 10.1% (Cox and Snell R square) and 17.7% (Nagelkerke R square) of the variance in occupational disease health education utilization as a whole. (See Table 7).

Table 8 predicts the determinant factors of health education utilization for migrants (Model I) by multivariate logistic regression. Model I shows that the migrants with 25–34, 35–44, 45–54, above 55 years old in the past year were at 0.559-times (OR = 0.559, 95%-CI: 0.327, 0.957), 0.302-times (OR = 0.302, 95%-CI: 0.168, 0.542), 0.278-times (OR = 0.278, 95%-CI: 0.149, 0.519) and 0.232-times (OR = 0.232, 95%-CI: 0.107, 0.505) lower chances of receiving health education respectively, compared with 18–24 years old group. Migrants with education of university or college and above, senior high school, and junior high school were at 4.423-times (OR = 4.423, 95%-CI: 2.229, 8.774), 3.545-times (OR = 3.545, 95%-CI: 1.814, 6.929), and 2.129-times (OR = 2.129, 95%-CI: 1.414, 3.973) higher chances of receiving health education respectively in the past year, compared with migrants with education of primary school or below. Migrants who have at least one child in Beijing (OR = 1.901, 95%-CI: 1.290, 2.800), do exercises (OR = 1.989, 95%-CI: 1.454, 2719), have chronic disease (OR = 1.565, 95%-CI: 1.035, 2.366) were more likely to receive health education. Plan to reside for a long time in Beijing (OR = 0.674, 95%-CI: 0.479, 0.946), smoking (OR = 0.605, 95%-CI: 0.406, 0.902), were less likely to receive health education. Additionally, the chances of receiving health education decreased 43.4% in poor health status (OR = 0.566, 95%-CI: 0.365, 0.877) and 41.9% in moderate health status (OR = 0.581, 95%-CI: 0.391, 0.862), compared with self-evaluated good health status.

Table 9 predicts the determinants of CD, NCD, and occupational disease health education utilization for migrants (Model II, Model III, and Model IV) by multivariate logistic regression. Model II shows that the chances of receiving CD health education decreased 71.0% in average daily working time more than 8 h (OR = 0.290, 95%-CI: 0.113, 0.744), compared with migrants with average daily working time less than 8 h. Additionally, migrants who do exercises (OR = 2.204, 95%-CI: 1.423, 3.415), acquire health knowledge (OR = 1.954, 95%-CI: 1.236, 3.091) were more likely to receive CD health education. Model III indicates that divorced or widowed migrants were at 4.448-times (OR = 4.448, 95%-CI: 1.193, 16.584) higher chance of receiving NCD health education, compared with unmarried migrants. The chances of receiving NCD health education decreased 74.6% in average daily working time more than 8 h (OR = 0.254, 95%-CI: 0.098, 0.655), compared with migrants with average daily working time less than 8 h. Additionally, migrants who do exercises (OR = 2.436, 95%-CI: 1.555, 3.861), were more likely to receive NCD health education. Model IV indicates that the chances of receiving occupational disease health education decreased 58.3% in married (OR = 0.417, 95%-CI: 0.264, 0.657), and 68.5% in average daily working time more than 8 h (OR = 0.315, 95%-CI: 0.120, 0.827), compared with unmarried migrants and average daily working time less than 8 h respectively. Migrants who have formal work (OR = 2.001, 95%-CI: 1.245, 3.217), do exercises (OR = 1.827, 95%-CI: 1.222, 2.734), were more likely to receive occupational disease health education.

Previous researches indicated that the advantage of “healthy migrant effect” (first-generation migrants are often healthier with lower overall morbidity and mortality than local-born populations) will diminish dramatically, particularly in middle age [29, 30], together with demanding work schedules, poor working and living environment, insufficient health literacy, and negative attitudes toward the health preventive behaviors. Our research revealed that though many migrants were aware of significance of health education, and expressed a desire to gain access to health information for enhancing their well-being, yet low utilization rate lingered and only 34.5% migrants received health education in the past year. It was self-evident that most of the migrants investigated were middle-aged, and they had age-appropriate health education needs, such as antenatal, prenatal and postpartum healthcare, aged healthcare, and NCD prevention and therapy. Furthermore, with the evolution of migration model, migrants should take the responsibilities for caring for their child(ren) and parent(s), thus they have relatively high needs of child, adolescent, and aged healthcare [14]. It also seems strange that there were low rates of occupational disease health education utilization and needs actually, which are consistent with the previous study in Xi’an [20]. Furthermore, there might be a reason to explain the low needs of occupational disease health education among migrants. Different from acute occupational diseases (occupational allergic contact dermatitis, occupational poisoning) that always occur after a relatively brief exposure, the common chronic occupational diseases (pneumoconiosis, musculoskeletal disorders, psychological stress at work, occupational tumors) which occupy the majority of occupational diseases, only occur after prolonged exposure to relevant hazards [31, 32]. Migrants with low health literacy, unstable job, and limited knowledge of occupational hazards, would not pay enough attention to occupational diseases, even if chronic occupational diseases have occurred.

The rate of health education utilization was higher in the groups of 18 to 24 and 25 to 34 years old than that in other three age groups, particularly in the first age group. The result was similar to a previous China-based study that migrants in the group of 25 to 34 years old have higher rate of health education utilization than other age groups, but the rate of migrants in age group of 18 to 24 years old was opposite [33]. There might be two factors for the differences. Firstly, the second-generation migrants with higher education level and relatively stable working condition, were born after 1980, had better health literacy and could acquire reliable health information from various sources, compared with the first-generation migrants [34]. Meanwhile, this research also indicated that migrants with high education level and regular exercises had a higher likelihood of receiving health education. The prevalence of health literacy was related to health knowledge, health decisions, health behaviors and health outcomes of the population [35‐37]. On the contrary, low education level, accompanied by low literacy and health awareness, pose difficulties and barriers in understanding complex health-related information, health practices and outcomes [36]. Secondly, selection bias would influence the results due to insufficient sample size of migrants in age group of 18 to 24 years old. In the future, migrants in this age group should be studied in terms of their health education utilization behaviors and influencing factors as a unique group.

Gaps exist between the needs and utilization of three types of health education for general migrants population investigated, including CD, NCD and occupational disease. Our study focuses on the determinations factors of three types of health education utilization.

For migrants, we observed that both average daily working time of enabling variables and do exercises of health behavior variables contributed significantly to the variances in three types of health education utilization. Migrants who worked more than 8 h daily and not to do exercises were less likely to use three types of health education than migrants with working time less than 8 h and do exercises. Put another way, the heavy workloads and poor health awareness for migrants reduce the opportunity to receive health information. Normally, people who do not exercise regularly are lacking of health awareness to access health education. Additionally, consistent with the previous research [33], migrants with formal work are more likely to receive occupational disease health education than those with informal work. In accordance with state regulations, employers have the responsibility to provide regular training, to educate their employees about occupational hazards, and to require them to strictly abide by safety rules. While, migrants are overwhelmingly employed in 3Ds jobs (dirty, dangerous and degrading) in worldwide, covering the service sector, production, construction and maintenance, transportation, which have more health-related risks and access less health education compared with other industries [22, 38‐40]. Meanwhile, the informal and temporary working status, long working time and situational stress cut down the needs of acquiring health information, especially on occupational disease for migrants.

Advances of information technology witness that smartphones and internet have become an integral part of our lives, and are widely used in health information research. The report of “Internet adoption, social media usage, and smartphone ownership rates in 37 countries across the world in 2017” from Pew research center revealed that the rates of internet use, smartphone ownership and social media use were 71, 68 and 60% respectively in China [41]. Different from traditional text-based health education tools, including brochures, leaflets, newspapers, web-based social media tools offer more convenient and effective methods of delivering health information [42]. Take the WeChat application as an example, as a free instant messaging application for smartphones, it plays an important part in modern lifestyles. WeChat can serve as a bridge between information technology and frequent multimedia messages to provide health support and management through the communication and transmission of voices, texts, pictures and videos over great distances [43]. Information related to the prevention and treatment of various diseases can be acquired and requested at any time via such applications [44, 45]. The increasing number of smartphones in China provides a mobile platform for delivering health education. As in our study, 58.2% migrants wanted to acquire health information via the internet. Therefore, mobile health interventions strategies have enormous potentials as an educational tool for behavioral change to further control the spread of epidemics, and development of the chronic disease for migrants.

The study has several limitations. Firstly, cross-sectional survey cannot be determined the time-effect and causality accurately compared with the cohort study, and cannot evaluate the effects of health education compared with the intervention study. Secondly, recall biases on self-report might underestimate the information on health education utilization. Thirdly, health education utilization behaviors were measured as a dichotomous variable (the received or did not receive of health education), rather than measured by the intensity of received health education. Finally, although the questionnaire was designed according to the previous theories and experiences, several significant potential determination factors may not be considered in the model, such as community resource factors. Future research is needed to explain the dynamic and cyclical causal relationships of Anderson’s health service utilization behavioral model by identifying more variables.